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by Nicolai Farcas

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Right now, the effects of climate change are already being felt by people across Africa. Evidence shows that the change in temperature has affected the health, livelihoods, food productivity, water availability, and overall security of the African people.

According to the Climate Change Vulnerability Index for 2015, seven of the ten countries most at risk from climate change are in Africa .

Africa has seen a decrease in rainfall over large parts of the Sahel and Southern Africa, and an increase in parts of Central Africa. Over the past 25 years, the number of weather-related disasters, such as floods and droughts, has doubled, resulting in Africa having a higher mortality rate from droughts than any other region.

1. Impacts on weather patterns
Flooding
Flooding is the most prevalent disaster in North Africa, the second most common in East, South and Central Africa, and the third most common in West Africa (AWDR, 2006).

In North Africa, the 2001 disastrous flood in northern Algeria resulted in about 800 deaths and economic loss of about $400 million. In Mozambique, the 2000 flood (worsened by two cyclones) caused 800 deaths, affected almost 2 million people of which about 1 million needed food, 329,000 people were displaced and agricultural production land was destroyed (AWDR, 2006).
Drought
Between July 2011 and mid-2012, a severe drought affected the entire East Africa region and was said to be "the worst drought in 60 years."

2. Impacts on Water Supply and Quality

Observable effects of climate change on water resources in Africa include: flooding, drought, change in distribution of rainfall, drying-up of rivers, melting of glaciers and the receding of bodies of water.
West Africa
Entire economies suffer when the water levels of Africa's huge rivers drop. Ghana, for example, has become totally reliant on the hydro-electric output of the Akosombo dam on the river Volta. Mali is dependent on the river Niger for food, water and transport. However, great stretches of the river is now facing environmental devastation as a result of pollution. In Nigeria, half the population has no access to clean water.
Mount Kilimanjaro Glaciers
The gradual yet dramatic disappearance of the glaciers on Mount Kilimanjaro is a result of climate change (IPCC, 2001). The glaciers act as a water tower and several rivers are now drying up. It is estimated that 82% of the ice that capped the mountain, when it was first recorded in 1912, is now gone. (IPCC, 2001)

Image © EarthKAM

3. Impacts on Agriculture and Food

Across Africa the landscape is changing. Droughts, heat stress and flooding have led to a reduction in crop yields and livestock productivity.

East Africa is facing the worst food crisis in the 21 st century. According to Oxfam, 12 million people in Ethiopia, Kenya and Somalia are in dire need of food. Rainfall has been below average with 2010/2011 being the driest year since 1950/1951, a serious problem for a continent almost entirely dependent on rain for its agriculture.

Image © wphna.org

4. Impacts on Human Health

Climate-sensitive diseases and health impacts can be high in poor countries that have minimal resources to treat and prevent illness. Examples of climate related health impacts include:

Frequent and severe heat stress linked to sustained increases in temperature

The reduction in air quality that often accompanies a heat wave can lead to breathing problems and worsen respiratory diseases.

Impacts of climate change on agriculture and other food systems increases rates of malnutrition and contributes to poverty - "With one in four people still undernourished in sub-Saharan Africa, climate change impacts make it even more difficult for governments across the region to improve food security and help reduce tensions."

The spread of Malaria may increase in areas projected to receive more precipitation and flooding. Increases in rainfall and temperature can cause spreading of dengue fever

5. Impacts on Shelter

Severe flooding and intense droughts has led to the destruction of many homes, shelters and villages across Africa. Conflicts over resources also exacerbate these impacts and, in turn, contribute to the ongoing migration within and between countries in Africa.

Extreme events displace large amounts of people, especially those who are unable to respond and rebuild after disasters, due to lack of resources.

"South Sudan refugees residing in a UN camp are living in knee-deep, sewage-contaminated floodwater, forcing some families to sleep standing up so they can hold their children out of the water."

Image © AlJazeera

6. Impacts on Vulnerable Population

Women, children and the elderly are more vulnerable to climate change impacts across Africa. Women labourers often experience additional duties as caregivers and as well as from societal responses to climate change after extreme weather events (eg, male migration).

The water scarcity places an additional burden on African women, who walk hours and sometimes even days, to fetch it. (IPCC, 2014)

Children and the elderly face graver risks due to susceptibility to infectious diseases, such as Malaria, limited mobility and reduced intake of food. The elderly face physical danger and even death due to droughts, heat stress and wildfires. Children often die from starvation, malnutrition, diarrheal diseases and flooding. (IPCC, 2014)

Image © UNAMID

7. Impacts on National Security

Climate change impacts have the potential to exacerbate national security issues and increase the number of international conflicts. Conflicts often occur over the use of already limited natural resources, fertile ground and water. Access to consistent and dependable sources of water is greatly valued in many African regions. However, changes in the timing and intensity of rainfall have threatened water availability and are causing conflicts over this limited resource (IPCC, 2014).
"A United Nations report predicts that access to water may be the single biggest cause of conflict and war in Africa in the next 25 years. Such wars are most likely to be in countries where rivers or lakes are shared by more than one country."

The changes in precipitation and temperature are already affecting crop yields in Sub-Saharan Africa. This has resulted in food shortages, that have triggered cross border migration and intraregional conflicts, which has sparked political instability in Nigeria for example.

8. Impacts on ecosystems

Image © WWF-US

Climate change has already led to changes in freshwater and marine ecosystems in eastern and southern Africa, and terrestrial ecosystems in southern and western Africa. The extreme weather events have demonstrated the vulnerability of some of South Africa's ecosystems. The migration patterns, geographic range and seasonal activity of many terrestrial and marine species have shifted in response to climate change. The abundance and interaction among species has also changed (IPCC, 2014).

Despite the fact that the African continent has contributed the least to anthropogenic factors causing climate change, Africa is the worst hit.

But you can help Africa fight - join the climate movement today. Get involved here.

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8 ways climate change is already affecting Africa

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Right now, the effects of climate change are already being felt by people across Africa. Evidence shows that the change in temperature has affected the health, livelihoods, food productivity, water availability, and overall security of the African people. According to the Climate Change Vulnerability Index for 2015, seven of the ten countries most at risk from climate change are in Africa .

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What the World Would Look Like if All the Ice Melted

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What the World Would Look Like if All the Ice Melted

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Sunny Fan

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This story appears in the September 2013 issue ofNational Geographic magazine.

The maps here show the world as it is now, with only one difference: All the ice on land has melted and drained into the sea, raising it 216 feet and creating new shorelines for our continents and inland seas.

There are more than five million cubic miles of ice on Earth, and some scientists say it would take more than 5,000 years to melt it all. If we continue adding carbon to the atmosphere, we'll very likely create an ice-free planet, with an average temperature of perhaps 80 degrees Fahrenheit instead of the current 58.

North America

The entire Atlantic seaboard would vanish, along with Florida and the Gulf Coast. In California, San Francisco's hills would become a cluster of islands and the Central Valley a giant bay. The Gulf of California would stretch north past the latitude of San Diego-not that there'd be a San Diego.

South America

The Amazon Basin in the north and the Paraguay River Basin in the south would become Atlantic inlets, wiping out Buenos Aires, coastal Uruguay, and most of Paraguay. Mountainous stretches would survive along the Caribbean coast and in Central America.

Compared with other continents, Africa would lose less of its land to the ultimate sea-level catastrophe, but Earth's rising heat might make much of it uninhabitable. In Egypt, Alexandria and Cairo will be swamped by the intruding Mediterranean.

London? A memory. Venice? Reclaimed by the Adriatic Sea. Thousands of years from now, in this catastrophic scenario, the Netherlands will have long since surrendered to the sea, and most of Denmark will be gone too. Meanwhile, the Mediterranean's expanding waters will also have swelled the Black and Caspian Seas.

Land now inhabited by 600 million Chinese would flood, as would all of Bangladesh, population 160 million, and much of coastal India. The inundation of the Mekong Delta would leave Cambodia's Cardamom Mountains stranded as an island.

Predominantly desert, the continent would gain a new inland sea-but it would lose much of the narrow coastal strip where four out of five Australians now live.

Antarctica

East Antarctica: The East Antarctica ice sheet is so large-it contains four-fifths of all the ice on Earth-that it might seem unmeltable. It survived earlier warm periods intact. Lately it seems to be thickening slightly-because of global warming. The warmer atmosphere holds more water vapor, which falls as snow on East Antarctica. But even this behemoth is unlikely to survive a return to an Eocene Climate.

West Antarctica: Like the Greenland ice sheet, the West Antarctic one was apparently much smaller during earlier warm periods. It's vulnerable because most of it sits on bedrock that's below sea level.The warming ocean is melting the floating ice sheet itself from below, causing it to collapse. Since 1992 it has averaged a net loss of 65 million metric tons of ice a year.

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What the World Would Look Like if All the Ice Melted

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This story appears in the September 2013 issue ofNational Geographic magazine. The maps here show the world as it is now, with only one difference: All the ice on land has melted and drained into the sea, raising it 216 feet and creating new shorelines for our continents and inland seas.

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Gerrico Adaya

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Coral reefs are highly vulnerable to climate change and the impacts will be far reaching.

Coral reefs are complex structures built mainly from the calcium carbonate (limestone) skeletons laid down by hard corals. These reef-building corals are highly vulnerable to rising sea temperatures and ocean acidification. Slowed growth and loss of hard corals will reduce essential habitat for many other reef creatures.

Reef structures themselves will also begin to crumble if reef growth does not keep pace with erosion by animals and storms.

Coral reefs comprise only six per cent of the area of the Great Barrier Reef, yet they provide critical habitat and food for many species in the ecosystem. Healthy coral reefs are also the essential foundation for reef-based tourism and fishing.

They are vitally connected to other Great Barrier Reef habitats including mangroves and salt marshes, seagrass meadows, estuaries, and open water environments. Reefs also act as barriers, protecting inshore habitats and human communities from large waves and storm surges.

Rising sea temperature

Hard corals are highly susceptible to coral bleaching caused by higher-than-normal sea temperatures. Coral bleaching is expected to occur more often and with greater severity in the future, making it difficult for corals to recover between bleaching events. As a result, the abundance of living corals on reefs is likely to decline in coming decades.

Some coral types, such as staghorn corals, are especially sensitive to bleaching, and these will be the most seriously affected. Coral communities will increasingly be dominated by types that are more tolerant to temperature stress.

Large, fleshy seaweeds (macroalgae), which compete with corals for space on the reef, will also benefit from rising temperatures and coral bleaching. Scientists have shown that degrading reefs can be rapidly overgrown by these macroalgae, which in turn impede coral recovery. Reefs dominated by macroalgae and bleaching-resistant corals have less three-dimensional structure than healthy coral reefs. Such reefs provide fewer shelters and refuges for the many animals that rely on the reef for their habitat.

Ocean acidification

Coral reefs are also highly vulnerable to ocean acidification. Hard corals and many other organisms that contribute to reef building, such as coralline algae, make their skeletons from calcium carbonate (limestone).

The rate of skeleton formation, known as calcification, will slow if waters become more acidic and the skeletons of these animals and plants will be weaker. Reefs are continually worn down by storms, and creatures that eat, burrow or dissolve their way through limestone.

For a healthy reef to be maintained, the growth of corals and encrusting algae has to at least keep pace with this erosion. Continuing ocean acidification will ultimately contribute to coral loss, and a weakening and collapse of limestone reef structures.

Extreme weather events

The Great Barrier Reef has adapted to cope with the impacts of cyclones and severe storms. However, many scientists predict that intense cyclones (such as cyclone Hamish and cyclone Yasi) will occur more often due to climate change. Reef recovery from such severe storms is slow, because fewer corals survive to recolonise affected areas. An increase in severe cyclones could therefore contribute to the degradation of reefs structures already weakened by coral bleaching and ocean acidification.

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Coral reefs are highly vulnerable to climate change and the impacts will be far reaching. Coral reefs are complex structures built mainly from the calcium carbonate (limestone) skeletons laid down by hard corals. These reef-building corals are highly vulnerable to rising sea temperatures and ocean acidification.

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Giant pandas threatened by climate change

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Giant pandas threatened by climate change

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Meagan Lee

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Giant pandas could be left hungry and struggling to survive by global warming, scientists have warned.

A new study predicts that climate change is set to wipe out much of the bamboo on which the bears rely for food.

Prime panda habitat in China could be completely lost by the end of the century, say the researchers.

Human development adds to the threat by blocking the bears' access to places where bamboo is less affected by rising temperatures, they point out.

"We will need proactive actions to protect the current giant panda habitats," said lead researcher Mao-Ning Tuanmu, from Yale University in the US.

"We need time to look at areas that might become panda habitat in the future, and to think now about maintaining connectivity of areas of good panda habitat and habitat for other species."

The research, published in the journal Nature Climate Change, focused on the Qinling Mountains in Shaanxi province, which is home to around 275 wild pandas. The animals make up around 17% of the entire wild giant panda population.

Qinling pandas have been isolated for thousands of years due to a long history of human habitation around the mountain range. Their restricted range makes them particularly vulnerable to the loss of food resources.

Bamboo, which carpets the forest floor where the pandas live, is the sole item in the bears' diet and also provides essential food and shelter for other animals.

The plant's unusual reproductive cycle limits its ability to adapt to climate change. One species studied by the scientists only flowers and reproduces every 30-35 years.

Tuanmu's team assessed how three dominant bamboo species were likely to fare in the Qinling Mountains as the climate warmed.

Even the most optimistic forecasts predicted major bamboo die-offs by the turn of the century.

Conservation efforts should now aim to protect areas that have a better chance of supplying pandas with food, despite climate change, said the scientists. Natural "bridges" could also be created to help the pandas escape from a bamboo famine.

Co-author Jianguo Liu, from Michigan State University in the US, said: "Understanding impacts of climate change is an important way for science to assist in making good decisions.

"Looking at the climate impact on the bamboo can help us prepare for the challenges that the panda will likely face in the future."

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Giant pandas threatened by climate change

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Giant pandas could be left hungry and struggling to survive by global warming, scientists have warned. A new study predicts that climate change is set to wipe out much of the bamboo on which the bears rely for food. Prime panda habitat in China could be completely lost by the end of the century, say the researchers.

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NASA links port-city sea levels to regional ice melt 

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NASA links port-city sea levels to regional ice melt 

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Ashley Chung

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Climate Change: Vital Signs of the Planet

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A new NASA tool links changes in sea level in 293 global port cities to specific regions of melting land ice, such as southern Greenland and the Antarctic Peninsula. It is intended to help coastal planners prepare for rising seas in the decades to come.

All coastal cities will see some impacts of global sea level rise. But the new tool shows that, for example, New York City is more strongly affected by melting ice in northeastern Greenland than in southwestern Greenland; while Sydney has a greater risk from the rapidly melting Antarctic Peninsula than from East Antarctica.

A paper describing the new tool, titled "Should coastal planners have concern over where land ice is melting?," was recently published in the journal Science Advances. The research team is Eric Larour, Erik Ivins and Surendra Adhikari of NASA's Jet Propulsion Laboratory in Pasadena, California.

Melting ice and rising ocean temperatures contribute about evenly to global sea level rise today. Individual cities are also affected by local conditions such as land sinking. Other Web-based resources such as the U.S. Climate Resilience Toolkit address some of these issues, but the new NASA tool is the only resource to match specific melting ice locations with their effects on the world's ports.

Water from melted ice on land doesn't spread evenly across the world's oceans because of a gravitational push-pull between ice and ocean. As a melting glacier or ice sheet dwindles, it loses mass, causing its local gravitational pull on nearby ocean water to diminish. Seawater that had been pulled toward the ice by the force of gravity flows away - in other words, sea level drops in the vicinity of a melting glacier but rises farther away. When this spatial pattern can be attributed to a given glacier or ice sheet, it is known as a sea level fingerprint.

To calculate this and other influences on sea level such as Earth's rotation, Larour and his colleagues used a dynamic mathematical formula called the adjoint method, which is used in seismic and meteorological studies. The method enables fast computation of the sensitivity of a model's output to its inputs - in this case, the sensitivity of sea level to ice melting. They used the method with JPL's well-tested computer model of ice sheet melting, the Ice Sheet System Model, to develop their new tool, called Gradient Fingerprint Mapping.

Users of the tool need no specialized training or extreme computer power; they simply download it, input data or projections of ice loss, and let it evolve the shifting ice and water patterns forward into the future. The result: a detailed profile of the sensitivity of sea level at any of these cities to changes in ice anywhere in the world.

Calculations of sea level fingerprints have been made in previous studies but tended to be cumbersome and spatially coarse, Larour said. The new tool provides an overall mechanism for rapidly computing high-resolution results using a variety of potential data sets.

Gradient Fingerprint Mapping is not dependent on a particular climate change scenario, Larour said. "You can apply the method to any type of melting scenario that you want." That means it will retain its utility as improved projections of ice loss become available in the future.

The computations show that the specific location of mass loss in Greenland is crucial, as it greatly affects the local sea level predictions for many major coastal cities in North America and Europe. The spatial details of Antarctic melting are important for areas south of the equator in South America, Africa and South Asia.

Among some intriguing results, Larour said, are those for New York, London and Oslo. Greenland's northeastern ice stream was shown to have an outsized effect on New York's local sea level, but the island's southern glaciers had little influence. London was more strongly affected by Greenland's northwestern and western glaciers. And Norway is so close to Greenland, the island's gravitational fingerprint is contributing to sea level decrease in Oslo.

The authors note that ocean dynamics can accelerate or offset the changes in sea level from gravitational fingerprints - particularly in New York, where the contribution of melting ice to accelerated sea level rise is minor compared to other sources.

"This is really a new capability," Larour said. "Now a coastal planner can understand and see how the melting or growing of a given ice sheet could be detrimental or beneficial to a specific location."

Access the tool at:
https://vesl.jpl.nasa.gov/research/sea-level/slr-gfm
News media contact

Alan Buis
Jet Propulsion Laboratory, Pasadena, California
818-354-0474
Alan.Buis@jpl.nasa.gov

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NASA links port-city sea levels to regional ice melt - Climate Change: Vital Signs of the Planet

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A new NASA tool links changes in sea level in 293 global port cities to specific regions of melting land ice, such as southern Greenland and the Antarctic Peninsula. It is intended to help coastal planners prepare for rising seas in the decades to come.

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Are emperor penguins marching to extinction?

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Are emperor penguins marching to extinction?

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Ella Fontes

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The emperor penguin - caring parent, extreme survivor and unwitting movie-star - could be marching to extinction by the turn of the next century. In its Antarctic home, the penguins frequently have to deal with prolonged bouts of starvation, frosty temperatures of -40 degrees Celsius, and biting polar winds that blow at 90 miles per hour. And yet this icy environment that so brutally tests the penguins' endurance is also critical to their survival. This is a species that depends on sea ice for breeding and feeding.

So what will happen to the emperor penguin as Antarctica's sea ice shrinks, as it assuredly will in the face of a warming globe? Stephanie Jenouvrier from the Woods Hole Oceanographic Institution have tried to answer that question by combining the forty years of census data on a specific emperor colony with the latest models from the Intergovernmental Panel on Climate Change (IPCC).

The results are not encouraging. They suggest that the number of emperors in a large colony at Terre Adelie (where March of the Penguins was filmed) will fall from about 6,000 breeding pairs at the moment to a mere 400 by 2100. There's even a one in three chance that the population will drop by 95%- a level described as "quasi-extinction", when the population is so small that it's unlikely to sustain itself.

Image copyright of Samuel Blanc

The fates of the emperor penguin are tied to the extent of the sea ice in a complicated way. Colonies live far inland and adults make long foraging trips to the ocean. If the ice cover is too extensive, the penguins burn more energy on their travels to the water and aren't able to provide as much food for their chicks. Then again, their fishy prey depend on small crustaceans called krill and these fall in number when the sea ice breaks up. The resulting lack of food and the possibility that the ice they roost on may break up, means that adult survival is jeopardised if there's not enough ice around.

This tenuous relationship was made dramatically clear in the 1970s when shifting winds brought warm subtropical air to Antarctica and created unusually warm winters. In Terre Adelie, the ice cover decreased by 11%, and the local emperor population halved.

To predict the fate of the penguins, Jenouvrier used 10 different climate models from the IPCC that forecasted the extent of the sea ice and the frequency of warm spells in Antarctica (like that seen in Terre Adelie) until the end of the century. She combined these with information on the entire life cycle of the emperors and the health of the Terre Adelie colony over 60 years.

The climate models took a "business as usual" outlook, where the world of the future fuels its rapid economic growth through the continued use of fossil fuels, as well as some renewable sources. According to these projections, levels of carbon dioxide will double from 360 parts per million in preindustrial times to 720 ppm in 2100. While the details differed, all of them predicted a greater frequency of warm spells and a shrinking of the sea ice.

For each of the 10 models, Jenouvrier created 1,000 separate population projections for the emperors. Averaged out, these simulations painted a picture of a population that remained fairly stable between 2000 and 2006 (which matched the actual census data) and would start to fall soon after. The emperors' decline will speed up after 2018 and eventually fall to a low of 400 breeding pairs by 2100 - just 7% of their current numbers.

Of course, that's an average pattern - there was naturally a lot of variation within the team's 10,000 simulations, but every single one of them had the penguin population as either quasi-extinct or declining strongly by 2100.

Accurate predictions, or unnecessary scaremongering? Jenouvrier certainly puts a strong case for the former. She argues that, if anything, her estimates are too optimistic. Her models assume that future warm spells in Antarctica will be no more severe than the 1970s event that so badly slashed the penguin population. However, the IPCC predicts that the warm years of the future will be warmer than those of today, so taking that into account would only increase the emperors' risk of quasi-extinction.

For the emperor penguin, there are only a few options. They could make small adjustments to the life cycle to compensate for their shifting environment, but they may not be able to do so fast enough. Other seabirds at Terre Adelie have shifted their breeding and egg-laying timings to adapt to the changing climate, but the penguins haven't. It's possible that their long generation times make them particularly bad at responding to new evolutionary pressures.

The alternative is to move. The northernmost part of the Antarctic Peninsula, where the effects of global warming have been felt most strongly, is not an option; there, only a few breeding pairs of emperors eke out an existence today. The southernmost populations in the Ross sea are stable for the moment, and have benefited from a recent glut of sea ice. For now, it's a penguin sanctuary, but even this steadfast home may shrink away as the world warms.

Reference: PNAS in press DOI: 10.1073/pnas.0806638106

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Are emperor penguins marching to extinction?

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The emperor penguin - caring parent, extreme survivor and unwitting movie-star - could be marching to extinction by the turn of the next century. In its Antarctic home, the penguins frequently have to deal with prolonged bouts of starvation, frosty temperatures of -40 degrees Celsius, and biting polar winds that blow at 90 miles per hour.

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Four decades and counting: New NASA instrument continues measuring solar energy input to Earth

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Four decades and counting: New NASA instrument continues measuring solar energy input to Earth

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Jordan Bahmani

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Climate Change: Vital Signs of the Planet

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We live on a solar-powered planet. As we wake up in the morning, the Sun peeks over the horizon to shed light on us, blanket us with warmth and provide cues to start our day. At the same time, our Sun's energy drives our planet's ocean currents, seasons, weather and climate. Without the Sun, life on Earth would not exist.

For nearly 40 years, NASA has been measuring how much sunshine powers our home planet. This December, NASA is launching an instrument to the International Space Station to continue monitoring the Sun's energy input to the Earth system. The Total and Spectral solar Irradiance Sensor (TSIS-1) will precisely measure what scientists call "total solar irradiance." These data will give us a better understanding of Earth's primary energy supply and help improve models simulating Earth's climate.

This composite shows the Sun's total solar irradiance since 1978 as observed from nine previous satellites. These observations are important to help scientists know precisely how much the Sun's energy changes and how that affects Earth. Credit: NASA.

"You can look at the Earth and Sun connection as a simple energy balance. If you have more energy absorbed by the Earth than leaving it, its temperature increases and vice versa," said Peter Pilewskie, TSIS-1 lead scientist at the Laboratory for Atmospheric Physics (LASP) in Boulder, Colorado. Under NASA's direction, LASP is providing and distributing the instrument's measurements to the scientific community. "We're measuring all the radiant energy that is coming to Earth."

But it's not so simple: the Sun's output energy is not constant. Over the course of about 11 years, our Sun cycles from a relatively quiet state to a peak in intense solar activity - like explosions of light and solar material - called a solar maximum. In subsequent years the Sun returns to a quiet state and the cycle starts over again. The Sun has fewer sunspots - dark areas that are often the source of increased solar activity - and stops producing so many explosions, going through a period called the solar minimum. Over the course of one solar cycle (one 11-year period), the Sun's emitted energy varies on average at about 0.1 percent. That may not sound like a lot, but the Sun emits a large amount of energy - 1,361 watts per square meter. Even fluctuations at just a tenth of a percent can affect Earth.

In addition to those 11-year changes, entire solar cycles can vary from decade to decade. Scientists have observed unusually quiet magnetic activity from the Sun for the past two decades with previous satellites. During the last prolonged solar minimum in 2008-2009, our Sun was as quiet it has been observed since 1978. Scientists expect the Sun to enter a solar minimum within the next three years, and TSIS-1 will be primed to take measurements of the next minimum.

In terms of climate change research, scientists need to understand the balance between energy coming in from the Sun and energy radiating out from Earth, as modulated by Earth's surface and atmosphere. Measurements from TSIS, the Total and Spectral Solar Irradiance Sensor, will help our understanding of the Earth-Sun connection and improve climate models. Credit: NASA/Michael Starobin. Download this video from the Scientific Visualization Studio.

"We don't know what the next solar cycle is going to bring, but we've had a couple of solar cycles that have been weaker than we've had in quite a while so who knows. It's a pretty exciting time to be studying the Sun," said Dong Wu, the TSIS-1 project scientist at NASA's Goddard Space Flight Center in Greenbelt, Maryland. Goddard is responsible for the overall development and operation of TSIS-1 on the International Space Station.

TSIS-1 data are particularly important for helping scientists understand the causes of total solar irradiance fluctuations and how they are connected with the Sun's behavior over decades or centuries. Today, scientists have neither enough data nor the forecasting skill to predict whether total solar irradiance has any long-term trend, said Doug Rabin, deputy project scientist at Goddard. TSIS-1 will continue a data sequence that is vital to answering that question.

These data are also important for understanding Earth's climate through models. Scientists use computer models to interpret changes in the Sun's energy input. If less solar energy is available, scientists can gauge how that will affect Earth's atmosphere, oceans, weather and seasons by using computer simulations. The input from the Sun is just one of many factors scientists used to model Earth's climate. Earth's climate is also affected by other factors such as greenhouse gases, clouds scattering light and small particles in the atmosphere called aerosols - all of which are taken into account in comprehensive climate models.

TSIS-1 will study the total amount of solar radiation emitted by the Sun using the Total Irradiance Monitor, one of two sensors on the instrument. The second sensor, called the Spectral Irradiance Monitor, will measure how the Sun's energy is distributed over the ultraviolet, visible and infrared regions of light. TSIS-1 spectral irradiance measurements of the Sun's ultraviolet radiation are critical to understanding the ozone layer - Earth's natural sunscreen that protects life from harmful radiation.

"Knowing the Sun's behavior and knowing how Earth's atmosphere responds to the Sun is even more important now because of all the different factors that affect climate change. We need to understand how all of these interact on Earth's system," said Pilewskie.

For more information, visit:

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national aeronautics and space administration

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Four decades and counting: New NASA instrument continues measuring solar energy input to Earth

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We live on a solar-powered planet. As we wake up in the morning, the Sun peeks over the horizon to shed light on us, blanket us with warmth and provide cues to start our day. At the same time, our Sun's energy drives our planet's ocean currents, seasons, weather and climate.

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How are Penguin's affected by Climate Change

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How are Penguin's affected by Climate Change

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Global climate change, greenhouse gases, and global temperature increasing

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Global climate change, greenhouse gases, and global temperature increasing

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Cooper Ambre

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Human activities have increased carbon-dioxide emissions, driving up temperatures. Extreme weather and melting polar ice are among the possible effects.

What is climate change?

The Earth's average temperature is about 15C but has been much higher and lower in the past.

There are natural fluctuations in the climate but scientists say temperatures are now rising faster than at many other times.

This is linked to the greenhouse effect, which describes how the Earth's atmosphere traps some of the Sun's energy.

Solar energy radiating back to space from the Earth's surface is absorbed by greenhouse gases and re-emitted in all directions.

This heats both the lower atmosphere and the surface of the planet. Without this effect, the Earth would be about 30C colder and hostile to life.

Scientists believe we are adding to the natural greenhouse effect, with gases released from industry and agriculture trapping more energy and increasing the temperature.

This is known as climate change or global warming.

What are greenhouse gases?

The greenhouse gas with the greatest impact on warming is water vapour. But it remains in the atmosphere for only a few days.

Carbon dioxide (CO2), however, persists for much longer. It would take hundreds of years for a return to pre-industrial levels and only so much can be soaked up by natural reservoirs such as the oceans.

Most man-made emissions of CO2 come from burning fossil fuels. When carbon-absorbing forests are cut down and left to rot, or burned, that stored carbon is released, contributing to global warming.

Climate change: Where we are in seven charts

Five things the 1.5C report taught us

Since the industrial revolution began in about 1750, CO2 levels have risen more than 30%. The concentration of CO2 in the atmosphere is higher than at any time in at least 800,000 years.

Other greenhouse gases such as methane and nitrous oxide are also released through human activities but they are less abundant than carbon dioxide.

What is the evidence for warming?

The world is about one degree Celsius warmer than before widespread industrialisation, the World Meteorological Organization (WMO) says.

The 20 warmest years on record all occurred in the past 22 years, with 2015-18 making up the top four.

Across the globe, the average sea level increased by 3.6mm per year between 2005 and 2015.

Most of this change was because water increases in volume as it heats up.

However, melting ice is now thought to be the main reason for rising sea levels. Most glaciers in temperate regions of the world are retreating.

And satellite records show a dramatic decline in Arctic sea-ice since 1979. The Greenland Ice Sheet has experienced record melting in recent years.

Satellite data also shows the West Antarctic Ice Sheet is losing mass. A recent study indicated East Antarctica may also have started to lose mass.

The effects of a changing climate can also be seen in vegetation and land animals. These include earlier flowering and fruiting times for plants and changes in the territories of animals.

How much will temperatures rise in future?

The change in the global surface temperature between 1850 and the end of the 21st Century is likely to exceed 1.5C, most simulations suggest.

The WMO says that if the current warming trend continues, temperatures could rise 3-5C by the end of this century.

Temperature rises of 2C had long been regarded as the gateway to dangerous warming. More recently, scientists and policymakers have argued that limiting temperature rises to 1.5C is safer.

An Intergovernmental Panel on Climate Change (IPCC) report in 2018 suggested that keeping to the 1.5C target would require " rapid, far-reaching and unprecedented changes in all aspects of society".

The UN is leading a political effort to stabilise greenhouse-gas emissions. China emits more CO2 than any other country. It is followed by the US and the European Union member states, although emissions per person are much greater there.

But even if we now cut greenhouse-gas emissions dramatically, scientists say the effects will continue. Large bodies of water and ice can take hundreds of years to respond to changes in temperature. And it takes CO2 decades to be removed from the atmosphere.

How will climate change affect us?

There is uncertainty about how great the impact of a changing climate will be.

It could cause freshwater shortages, dramatically alter our ability to produce food, and increase the number of deaths from floods, storms and heatwaves. This is because climate change is expected to increase the frequency of extreme weather events - though linking any single event to global warming is complicated.

As the world warms, more water evaporates, leading to more moisture in the air. This means many areas will experience more intense rainfall - and in some places snowfall. But the risk of drought in inland areas during hot summers will increase. More flooding is expected from storms and rising sea levels. But there are likely to be very strong regional variations in these patterns.

Vietnam's children and the fear of climate change

Would you give up beef to help the planet?

Poorer countries, which are least equipped to deal with rapid change, could suffer the most.

Plant and animal extinctions are predicted as habitats change faster than species can adapt. And the World Health Organization (WHO) has warned that the health of millions could be threatened by increases in malaria, water-borne disease and malnutrition.

As more CO2 is released into the atmosphere, uptake of the gas by the oceans increases, causing the water to become more acidic. This could pose major problems for coral reefs.

Global warming will cause further changes that are likely to create further heating. This includes the release of large quantities of methane as permafrost - frozen soil found mainly at high latitudes - melts.

Responding to climate change will be one of the biggest challenges we face this century.

Climate-related words and phrases explained

How much warmer is your city?

What is your diet's carbon footprint?

Where we are in seven charts

What questions do you have about changes in our climate?
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Human activities have increased carbon-dioxide emissions, driving up temperatures. Extreme weather and melting polar ice are among the possible effects. The Earth's average temperature is about 15C but has been much higher and lower in the past. There are natural fluctuations in the climate but scientists say temperatures are now rising faster than at many other times.

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Climate change's effect on Whales and Dolphins

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Climate change's effect on Whales and Dolphins

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Jake Conlon

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Light and warmth on planet Earth comes almost exclusively from the sun. What happens to this arriving solar radiation - including how much of it is bounced back into space - depends on a number of things, including the composition of gases in the atmosphere and how reflective the Earth's surface is (for example, polar ice is more reflective than ploughed fields).

Most importantly it is planet Earth's atmosphere that has moderated the effects of solar radiation to maintain the conditions that we now enjoy, and which has also allowed life on earth to evolve and be maintained here for many millions of years. The greenhouse effect is in fact a natural phenomenon caused by the atmosphere and which helps to keep the Earth warm (you might like to think of the atmosphere as a blanket around the Earth, keeping a lot of the heat in). However, in the last centuries we have significantly changed the atmosphere, putting extra amounts of what have become known as 'greenhouse gases' into it (the blanket has become thicker and the temperature underneath is becoming dangerously hot).

The main greenhouse gas being pumped out by humankind is carbon dioxide and it comes from the burning of fossil fuels (coal, oil and gas) which we use to power our factories, warm and light our homes and run our cars. The increasing amounts of greenhouse gases mean the atmosphere is retaining more heat and so it is that the planet is warming up. At the same time this increased warmth is melting the polar ice cover - so there is less shiny ice to reflect light away and this too is increasing the warming effect. In addition vast areas of rainforest are being destroyed and this too is releasing carbon dioxide into the atmosphere.

Climate change, driven by the greenhouse effect, is a fundamental threat to whales, dolphins and porpoises. There is unequivocal evidence that climate change is happening and that human activities are contributing to it and the climate is changing fast: so fast that some whale and dolphin populations may be unable to adapt. Yet at the same time, whales themselves play a key part in helping to combat this threat through their role in the marine ecosystem.

Climate change is expected to affect whales, dolphins and porpoises mainly through the loss of habitat (given the distinct temperature-linked ranges of most species), changes in prey availability, potential increased competition from range expansions of other species. The potential impacts include changes in abundance, distribution, timing and range of migration, prey abundance and distribution, and reproductive success and ultimately survival.

It is unclear to what extent whales, dolphins and porpoises will be able to adapt to the rate of climate change predicted, in some cases such range shifts will not be possible. For example, the northern Indian Ocean is fringed by land, limiting the ability of species to move northwards into cooler habitat as waters warm.

There are some indications that some cetaceans are already being significantly affected by climate change and to protect them and indeed to protect all other living things, including ourselves, we need to act now.

The extent and the duration of temperature increases and the associated effects of climate change around the world will depend on how swiftly and effectively emissions of greenhouse gases can be restricted and reduced.

WDC facilitates research on the impacts of climate change, leading work to raise awareness of the impacts on whales and dolphins. Working with other groups, we campaign for international action to find solutions and reduce the impacts of threats.
Find out more about how the continued recovery of whale populations can play a major role in combating climate change in our new report: Whales - their future is our future.
How whales support the marine ecosystem

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Light and warmth on planet Earth comes almost exclusively from the sun. What happens to this arriving solar radiation - including how much of it is bounced back into space - depends on a number of things, including the composition of gases in the atmosphere and how reflective the Earth's surface is (for example, polar ice is more reflective than ploughed fields).

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Right now, the effects of climate change are already being felt by people across Africa. Evidence shows that the change in temperature has affected the health, livelihoods, food productivity, water availability, and overall security of the African people.

According to the Climate Change Vulnerability Index for 2015, seven of the ten countries most at risk from climate change are in Africa .

Africa has seen a decrease in rainfall over large parts of the Sahel and Southern Africa, and an increase in parts of Central Africa. Over the past 25 years, the number of weather-related disasters, such as floods and droughts, has doubled, resulting in Africa having a higher mortality rate from droughts than any other region.

1. Impacts on weather patterns
Flooding
Flooding is the most prevalent disaster in North Africa, the second most common in East, South and Central Africa, and the third most common in West Africa (AWDR, 2006).

In North Africa, the 2001 disastrous flood in northern Algeria resulted in about 800 deaths and economic loss of about $400 million. In Mozambique, the 2000 flood (worsened by two cyclones) caused 800 deaths, affected almost 2 million people of which about 1 million needed food, 329,000 people were displaced and agricultural production land was destroyed (AWDR, 2006).
Drought
Between July 2011 and mid-2012, a severe drought affected the entire East Africa region and was said to be "the worst drought in 60 years."

2. Impacts on Water Supply and Quality

Observable effects of climate change on water resources in Africa include: flooding, drought, change in distribution of rainfall, drying-up of rivers, melting of glaciers and the receding of bodies of water.
West Africa
Entire economies suffer when the water levels of Africa's huge rivers drop. Ghana, for example, has become totally reliant on the hydro-electric output of the Akosombo dam on the river Volta. Mali is dependent on the river Niger for food, water and transport. However, great stretches of the river is now facing environmental devastation as a result of pollution. In Nigeria, half the population has no access to clean water.
Mount Kilimanjaro Glaciers
The gradual yet dramatic disappearance of the glaciers on Mount Kilimanjaro is a result of climate change (IPCC, 2001). The glaciers act as a water tower and several rivers are now drying up. It is estimated that 82% of the ice that capped the mountain, when it was first recorded in 1912, is now gone. (IPCC, 2001)

Image © EarthKAM

3. Impacts on Agriculture and Food

Across Africa the landscape is changing. Droughts, heat stress and flooding have led to a reduction in crop yields and livestock productivity.

East Africa is facing the worst food crisis in the 21 st century. According to Oxfam, 12 million people in Ethiopia, Kenya and Somalia are in dire need of food. Rainfall has been below average with 2010/2011 being the driest year since 1950/1951, a serious problem for a continent almost entirely dependent on rain for its agriculture.

Image © wphna.org

4. Impacts on Human Health

Climate-sensitive diseases and health impacts can be high in poor countries that have minimal resources to treat and prevent illness. Examples of climate related health impacts include:

Frequent and severe heat stress linked to sustained increases in temperature

The reduction in air quality that often accompanies a heat wave can lead to breathing problems and worsen respiratory diseases.

Impacts of climate change on agriculture and other food systems increases rates of malnutrition and contributes to poverty - "With one in four people still undernourished in sub-Saharan Africa, climate change impacts make it even more difficult for governments across the region to improve food security and help reduce tensions."

The spread of Malaria may increase in areas projected to receive more precipitation and flooding. Increases in rainfall and temperature can cause spreading of dengue fever

5. Impacts on Shelter

Severe flooding and intense droughts has led to the destruction of many homes, shelters and villages across Africa. Conflicts over resources also exacerbate these impacts and, in turn, contribute to the ongoing migration within and between countries in Africa.

Extreme events displace large amounts of people, especially those who are unable to respond and rebuild after disasters, due to lack of resources.

"South Sudan refugees residing in a UN camp are living in knee-deep, sewage-contaminated floodwater, forcing some families to sleep standing up so they can hold their children out of the water."

Image © AlJazeera

6. Impacts on Vulnerable Population

Women, children and the elderly are more vulnerable to climate change impacts across Africa. Women labourers often experience additional duties as caregivers and as well as from societal responses to climate change after extreme weather events (eg, male migration).

The water scarcity places an additional burden on African women, who walk hours and sometimes even days, to fetch it. (IPCC, 2014)

Children and the elderly face graver risks due to susceptibility to infectious diseases, such as Malaria, limited mobility and reduced intake of food. The elderly face physical danger and even death due to droughts, heat stress and wildfires. Children often die from starvation, malnutrition, diarrheal diseases and flooding. (IPCC, 2014)

Image © UNAMID

7. Impacts on National Security

Climate change impacts have the potential to exacerbate national security issues and increase the number of international conflicts. Conflicts often occur over the use of already limited natural resources, fertile ground and water. Access to consistent and dependable sources of water is greatly valued in many African regions. However, changes in the timing and intensity of rainfall have threatened water availability and are causing conflicts over this limited resource (IPCC, 2014).
"A United Nations report predicts that access to water may be the single biggest cause of conflict and war in Africa in the next 25 years. Such wars are most likely to be in countries where rivers or lakes are shared by more than one country."

The changes in precipitation and temperature are already affecting crop yields in Sub-Saharan Africa. This has resulted in food shortages, that have triggered cross border migration and intraregional conflicts, which has sparked political instability in Nigeria for example.

8. Impacts on ecosystems

Image © WWF-US

Climate change has already led to changes in freshwater and marine ecosystems in eastern and southern Africa, and terrestrial ecosystems in southern and western Africa. The extreme weather events have demonstrated the vulnerability of some of South Africa's ecosystems. The migration patterns, geographic range and seasonal activity of many terrestrial and marine species have shifted in response to climate change. The abundance and interaction among species has also changed (IPCC, 2014).

Despite the fact that the African continent has contributed the least to anthropogenic factors causing climate change, Africa is the worst hit.

But you can help Africa fight - join the climate movement today. Get involved here.

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8 ways climate change is already affecting Africa

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Right now, the effects of climate change are already being felt by people across Africa. Evidence shows that the change in temperature has affected the health, livelihoods, food productivity, water availability, and overall security of the African people. According to the Climate Change Vulnerability Index for 2015, seven of the ten countries most at risk from climate change are in Africa .

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Arctic’s Rapid Sea Ice Loss Threatens Wildlife

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Arctic’s Rapid Sea Ice Loss Threatens Wildlife

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Nico Cabral-Omana

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By Tim Radford, Climate News Network

LONDON - The loss of Arctic sea ice is bad news for the Pacific walrus, and for polar bears: the walrus has further to swim to dig for clams on the sea floor, and the polar bear has less chance of catching seals. But the real problems begin at the base of the food chain.

Since the end of the last century, more than two million square kilometers of sea ice have disappeared, and the loss of summer ice is accelerating. Researchers call this "a stunning loss of habitat for sea ice algae and sub-ice plankton which together account for 57 percent of the total annual primary production in the Arctic Ocean."

The Arctic fox is one of the species likely to be affected by genetic isolation as the ice melts.
Credit: Jeff Kerby, Eric Post lab, Penn State University

Eric Post, a biologist at Penn State University in the U.S., and colleagues report in that the seasonal timing of the ice algae bloom is critical to the successful reproduction of the zooplankton copepods (small crustaceans) that graze on the algae, and the bloom in phytoplankton that follows the normal seasonal retreat of the sea ice delivers food for the copepod offspring.

These two "pulses" in productivity in the Arctic are the basis of the food web for all the higher creatures. The copepod explosion provides breakfast, lunch and supper for whales, seabirds, small fish, crustaceans and so on.

But the fact that the total area under ice each summer has been shrinking at 86,000 square kilometers a year, and the annual ice melt season has been extended by at least 20 days, could have profound consequences for the entire ecosystem, and for the big, warm-blooded creatures at the top of the food chain.

Hybrid polar bears

"Our team set out to explore the domino effects of sea ice loss on marine mammals, as well as on land-dwelling species living adjacent to the ice," said Post. The ice shelf is habitat for large mammals: as the ice thins and dwindles so does the habitat, and therefore the hunting opportunities.

Walrus forage for food in shallow waters, and use the sea ice to rest, and to dive from, between foraging. As the ice melts and retreats from the shoreline the ice edge moves over deeper waters, and the walrus community increasingly has to crowd together on the shoreline rather than the ice, making the spread of infection easier, and increasing the dangers of trampling for the young.

As the ice retreats, polar bears have less access to seal populations - and supplies of blubber - and they too have begun to mingle populations: there have been increasing reports of hybridization with grizzly bears.

As the ice melts, walrus' crowd together on the shoreline, making the spread of infection easier, and increasing the dangers of trampling for the young.
Credit: Joel Garlich-Miller/U.S. Fish and Wildlife

Because sea ice in Arctic Canada is decreasing, there is a greater likelihood of contact between eastern and western Arctic seal populations, and a greater chance that phocine distemper virus will spread from the eastern seals to the western population.

Sea ice loss could affect the populations of Arctic fox and wolf - because the loss of ice will increase genetic isolation - and the warming of the land surfaces in the Arctic is beginning to alter the vegetation, with consequences for caribou and other grazers.

Cooling mechanism falters

Another important consequence of sea ice loss is a change in the Arctic albedo - the measure of radiation that normally bounces off the ice and back into space, keeping the Arctic cold.

In Nature Climate Change Aku Riihelä of the Finnish Meteorological Institute in Helsinki and colleagues report that they looked at satellite data from 1989 to 2002, during the summer months, for both open water and sea ice regions of the Arctic.

They found that all albedo trends show a significant reduction, which is another way of saying that more sunlight is being absorbed into the land and ocean of the Arctic - which will accelerate the rate of warming and the loss of further ice.

The consequences of this for the whole Arctic ecosystem will be considerable, warns Professor Post. As the seas open, they become accessible to shipping traffic and drilling rigs, and any increase in this could affect both terrestrial and marine creatures, among them the bowhead whales and the Pacific walrus.

"By viewing sea ice as essential habitat and a substrate for important species interactions, rather than as a lifeless blank surface, its loss as a result of warming becomes a rather stunning prospect," he says.

Tim Radford is a reporter for Climate News Network. Climate News Network is a news service led by four veteran British environmental reporters and broadcasters. It delivers news and commentary about climate change for free to media outlets worldwide.

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Arctic's Rapid Sea Ice Loss Threatens Wildlife

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By Tim Radford, Climate News Network LONDON - The loss of Arctic sea ice is bad news for the Pacific walrus, and for polar bears: the walrus has further to swim to dig for clams on the sea floor, and the polar bear has less chance of catching seals.

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Climate change threatens to wipe out Tiger population

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Climate change threatens to wipe out Tiger population

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Joseph Schiefer

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WASHINGTON, DC, January 20, 2010 - One of the world's largest tiger populations could disappear by the end of this century as rising sea levels caused by climate change destroy their habitat along the coast of Bangladesh in an area known as the Sundarbans, according to a new World Wildlife Fund-led study published in the journal Climatic Change.

Tigers are among the world's most threatened species, with only an estimated 3,200 remaining in the wild. WWF officials said the threats facing these Royal Bengal tigers and other iconic species around the world highlight the need for urgent international action to reduce greenhouse gas emissions.

"If we don't take steps to address the impacts of climate change on the Sundarbans, the only way its tigers will survive this century is with scuba gear," said Colby Loucks, WWF's deputy director of conservation science and the lead author of the study Sea Level Rise and Tigers: Predicted Impacts to Bangladesh's Sundarbans Mangroves. "Tigers are a highly adaptable species, thriving from the snowy forests of Russia to the tropical forests of Indonesia. The projected sea level rise in the Sundarbans will likely outpace the tiger's ability to adapt."

According to the study, an expected sea level rise of 11 inches above 2000 levels may cause the remaining tiger habitat in the Sundarbans to decline by 96 percent, pushing the total population to fewer than 20 breeding tigers. Unless immediate action is taken, the Sundarbans, its wildlife and the natural resources that sustain millions of people may disappear within 50 to 90 years, the study said.

"The mangrove forest of the Bengal tiger now joins the sea-ice of the polar bear as one of the habitats most immediately threatened as global temperatures rise during the course of this century," said Keya Chatterjee, acting director of WWF's climate change program. "To avert an ecological catastrophe on a much larger scale, we must sharply reduce greenhouse gas emissions and prepare for the impacts of climate change we fail to avoid. In 2010, the Chinese Year of the Tiger, there is no better time for the US to pass domestic climate legislation and to reach an effective international agreement."

The Sundarbans, a UNESCO World Heritage Site shared by India and Bangladesh at the mouth of the Ganges River, is the world's largest single block of mangrove forest. Mangroves are found at the inter-tidal region between land and sea, and not only serve as breeding grounds for fish but help protect coastal regions from natural disasters such as cyclones, storm surges and wind damage.

Providing the habitat for between 250 and 400 tigers, the Sundarbans is also home to more than 50 reptile species, 120 commercial fish species, 300 bird species and 45 mammal species. While their exact numbers are unclear, the tigers living in the Sundarbans of India and Bangladesh may represent as many as 10 percent of all the remaining wild tigers on Earth.

Using the rates of sea level rise projected by the Intergovernmental Panel on Climate Change in its Fourth Assessment Report (2007), the study authors said an 11-inch sea level rise may be realized around 2070, at which point tigers will be unlikely to survive in the Sundarbans. However, recent research suggests that the seas may rise even more swiftly than what was predicted in the 2007 IPCC assessment.

In addition to climate change, the Sundarbans tigers, like other tiger populations around the world already face tremendous threats from poaching and habitat loss. Tiger ranges have decreased by 40 percent over the past decade, and tigers today occupy less than seven percent of their original range. Scientists fear that accelerating deforestation and rampant poaching could push some tiger populations to the same fate as their now-extinct Javan and Balinese relatives in other parts of Asia. Tigers are poached for their highly prized skins and body parts, which are used in traditional Chinese medicine. The 2010 Year of the Tiger will mark an important year for conservation efforts to save wild tigers, with WWF continuing to play a vital role in implementing bold new strategies to save this magnificent Asian big cat.

Recommendations in the study include:

Locally, governments and natural resource managers should take immediate steps to conserve and expand mangroves while preventing poaching and retaliatory killing of tigers.

Regionally, neighboring countries should increase sediment delivery and freshwater flows to the coastal region to support agriculture and replenishment of the land;

Globally, governments should take stronger action to limit greenhouse gas emissions;

"It's disheartening to imagine that the Sundarbans - which means 'beautiful forest' in Bengali - could be gone this century, along with its tigers," Loucks said. "We very much hope that in this, the Year of the Tiger, the world will focus on curtailing the immediate threats to these magnificent creatures and preparing for the long-term impacts of climate change."
Learn more:
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Editors Notes:

1. Photos and video of tigers, and maps showing the projections of sea level rise in the Sundarbans are available upon request.

2. The article appears in the journal Climatic Change, an interdisciplinary, international journal devoted to the description, causes and implications of climate change. The authors of the article are:

Colby Loucks and Shannon Barber-Meyer, World Wildlife Fund-US

Md. Abdullah Abraham Hossain, Bana Bhaban Forest Department, Bangladesh and the Institute for Environment and Development at Universiti Kebangsaan, Malaysia

Adam Barlow, Zoological Society of London

Ruhul Mohaiman, The World Fish Center, Bangladesh

4. Previous analyses of the impact of sea level rise on the Sundarbans have used relatively coarse data. Loucks and his colleagues used a new high resolution digital elevation model with eight estimates of sea level rise to predict the impact on tiger habitat and population size in the Bangladeshi Sundarbans. By importing over 80,000 Global Positioning System (GPS) elevation points they were able to come up with the most accurate predictions to date of the loss of tiger habitat due to sea level rise.

5. World Wildlife Fund is running a Year of the Tiger campaign to coincide with the Chinese New Year and culminating in a Global Tiger Summit in Vladivostok, Russia. Find out more at: www.worldwildlife.org/tigers

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Climate Change Threatens to Wipe Out One of World's Largest Tiger Populations this Century | Press Releases | WWF

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One of the world's largest tiger populations could disappear by the end of this century as rising sea levels caused by climate change destroy their habitat along the coast of Bangladesh in an area known as the Sundarbans, according to a new World Wildlife Fund-led study published in the journal Climatic Change.

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500-year floods could strike NYC every five years, climate study says

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500-year floods could strike NYC every five years, climate study says

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http://www.cnn.com/2017/10/27/us/climate-change-new-york-city-floods-study/index.html

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Makaihla Taylor

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How does climate change? And what happens to environments and ecosystems when it does? In this episode of Crash Course Kids, Sabrina shows us the effects that climate change can have and how one small change in an ecosystem can throw everything off. Watch More Crash Course Kids: https://www.youtube.com/user/crashcoursekids ///Standards Used in This Video/// 5-LS2-1.

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Climate Change made Harvey and Irma Worse

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Climate Change made Harvey and Irma Worse

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Rebecca Wei

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climate change made harvey and irma worse - cnn

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Yes, climate change made Harvey and Irma worse

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Scientists say the effects of climate change, from rising sea levels to warmer oceans, made the hurricanes more destructive than they would have been in previous decades.

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4 ways Polar Bears Are Dealing With Climate Change.

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4 ways Polar Bears Are Dealing With Climate Change.

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Alison Zorn

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It's a tough time to be a polar bear.

The Arctic predators-which depend on constantly diminishing amounts of sea ice to catch marine mammals such as seals-are declining in number, and fast.

There are fewer than 25,000 polar bears left in the wild, according to the nonprofit organization Polar Bears International. Near the southern Beaufort Sea (map), for instance, the population has dropped about 40 percent between 2001 to 2010, from 1,500 to 900 bears.

And as their habitat shrinks, they've have been acting strangely. On Wednesday, five bears surrounded a team of scientists at a weather station in Russia,trapping the people inside.

On the same day, elsewhere in the Arctic, scientists representing Canada, Denmark, Norway, Russia, and the United States announced a ten-year plan to prevent polar bears from going extinct. Actions include preserving polar bear habitat and working with policymakers and the public to address climate change.

Also this week, President Obama became the first sitting president to visit the U.S. Arctic, traveling through several Alaska cities to highlight threats posed by global warming.

But combating climate change doesn't happen overnight, and the lack of food and habitat means polar bears are getting creative to stay alive. ( Read our Polar Bear Watch blog.)

Here are four strategies polar bears are using to weather our changing planet.

Putting Leftovers on Ice

When a research team inSvalbard, a group of islands in the Norwegian Arctic, recently observed a starving polar bear eating a dolphin and burying the leftovers in the snow to save for later, it was surprising on a lot of levels.

For one thing, polar bears generally prefer to eat seals. What's more, scientists had never observed the predators eating this species of dolphin. And though other bear species, such as grizzlies, store food for later, sticking leftovers into what amounts to a refrigerator is unusual behavior for polar bears.

Here's what likely happened: The dolphins swam into Svalbard while it was ice-free-also unusual, since that area is normally frozen year-round. When ice packed into the area later in the year, the dolphins were trapped and fell prey to the polar bear.

These ice entrapments are a short-term boon for the bears, giving them an easy source of fatty food they can just scoop out of a hole in the ice.

But Peter Ewins, leader of Arctic conservation for the conservation group WWF, says it doesn't mean Norwegian polar bears will be just fine.

"In the long term, the populations of these species of food for the polar bears are going to decline," he says. "So it's not going to be a persisting source of high fat for the polar bears."

Surf and Turf on the Menu

Each year, snow geese come to Canada to breed. Females typically lay four eggs to a clutch. While they were once endangered in the United States, the species has rebounded-which is lucky for polar bears.

In a 2011 study published in the journal Oikos, vertebrate zoologist Robert Rockwell found that these eggs provide a backup food source for the polar bears. Downing one of these eggs is like "eating a stick of butter," Rockwell said in an interview at the time. (Also see " Polar Bears Turning to Goose Eggs to Survive Warming?")

While Rockwell estimates that 88 snow goose eggs are about the caloric equivalent of a seal, WWF's Ewins likens it to eating granola once a day for the summer, and expecting it to sustain you for the next six months.

"A few specialist polar bears might be able to eke out a living on a mixture of seaweed, and fish, and whale carcasses that happen to be around" -and, clearly, goose eggs-"but eating those kinds of things doesn't sustain anything like the current population levels," Ewins says.

Karyn Rode, a wildlife biologist with the U.S. Geological Survey in Anchorage, Alaska, agrees a few lucky bears could benefit from foods such as goose eggs.

But Rode, leader of an April study in the journal Frontiers in Ecology and the Environment on the topic, cautions these opportunistic meals won't help the species as a whole survive.

Walking Hibernation? Not So Much

Rising global temperatures and Arctic ice's natural variability have led to longer annual ice melts in the summer, and incomplete re-freezes in the winter. This makes it harder for the polar bears to find food, since they depend on ice sheets to hunt seals that also use them for rearing pups.

"We're seeing a greater proportion of bears come on shore in the summertime during the sea ice minimum, and they're spending longer times there," Rode says.

Some researchers had thought that the polar bears used an active hibernation strategy to conserve energy during lean summer months, when some of them swim ashore.

But a study published in July in the journal Sciencecalls that theory into question: The activity trackers the researchers put on the polar bears revealed that the bears didn't actually slow their metabolic rates beyond the natural slowing caused by starvation.

Polar bears are "very opportunistic eaters," Rode says. "They'll eat anything, and sometimes to their detriment."

That includes the food and waste that people store in their towns. (See " As Sea Ice Shrinks, Can Polar Bears Survive on Land?")

In 2014, the town of Arviat in Nunavut Territory, Canada, had to cancel regular trick- or-treating and hold Halloween indoors due to concerns that children wandering outside would be at risk of a polar bear attack.

To reduce polar bear's temptations in Arviat, WWF has been handing out bear-proof metal bins to store food. The nonprofit also hired a polar bear monitor, who uses spotlights and bear bangers-a type of instrument that makes a loud noise-to discourage the bears.

Ewins says that polar bears are going to have to keep adapting as they forage for food.

Until climate change is a little more under control, "it's going to be a few more decades of these problems for animals that are dependent on ice," he says.

"We're trying to help polar bears and local people survive through that transition."
Ker Than contributed reporting.

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4 Ways Polar Bears Are Dealing With Climate Change

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It's a tough time to be a polar bear. The Arctic predators-which depend on constantly diminishing amounts of sea ice to catch marine mammals such as seals-are declining in number, and fast. There are fewer than 25,000 polar bears left in the wild, according to the nonprofit organization Polar Bears International.

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Michael Ji

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Climate Change: Vital Signs of the Planet

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Our Trajectory

Earth Will Continue to Warm and the Effects Will Be Profound

The potential future effects of global climate change include more frequent wildfires, longer periods of drought in some regions, and an increase in the wind intensity and rainfall from tropical cyclones. Credit: left - Mike McMillan/USFS, center - Tomas Castelazo / Wikimedia Commons / CC BY-SA 4.0, right - NASA.

Global climate change is not a future problem. Changes to Earth's climate driven by increased human emissions of heat-trapping greenhouse gases are already having widespread effects on the environment: glaciers and ice sheets are shrinking, river and lake ice is breaking up earlier, plant and animal geographic ranges are shifting, and plants and trees are blooming sooner.

Effects that scientists had long predicted would result from global climate change are now occurring, such as sea ice loss, accelerated sea level rise, and longer, more intense heat waves.

" The magnitude and rate of climate change and associated risks depend strongly on near-term mitigation and adaptation actions, and projected adverse impacts and related losses and damages escalate with every increment of global warming."

- Intergovernmental Panel on Climate Change

Some changes (such as droughts, wildfires, and extreme rainfall) are happening faster than scientists previously assessed. In fact, according to the Intergovernmental Panel on Climate Change (IPCC) - the United Nations body established to assess the science related to climate change - modern humans have never before seen the observed changes in our global climate, and some of these changes are irreversible over the next hundreds to thousands of years.

Scientists have high confidence that global temperatures will continue to rise for many decades, mainly due to greenhouse gases produced by human activities.

A Degree of Difference

So, the Earth's average temperature has increased about 2 degrees Fahrenheit during the 20th century. What's the big deal?

Read more

The IPCC's Sixth Assessment report, published in 2021, found that human emissions of heat-trapping gases have already warmed the climate by nearly 2 degrees Fahrenheit (1.1 degrees Celsius) since 1850-1900. The global average temperature is expected to reach or exceed 1.5 degrees C (about 3 degrees F) within the next few decades. These changes will affect all regions of Earth.

What's the difference between climate change and global warming?

"Global warming" refers to the long-term warming of the planet. "Climate change" encompasses global warming, but refers to the broader range of changes that are happening to our planet, including rising sea levels; shrinking mountain glaciers; accelerating ice melt in Greenland, Antarctica and the Arctic; and shifts in flower/plant blooming times.

detailed answer

The severity of effects caused by climate change will depend on the path of future human activities. More greenhouse gas emissions will lead to more climate extremes and widespread damaging effects across our planet. However, those future effects depend on the total amount of carbon dioxide we emit. So, if we can reduce emissions, we may avoid some of the worst effects.

" The scientific evidence is unequivocal: climate change is a threat to human wellbeing and the health of the planet. Any further delay in concerted global action will miss the brief, rapidly closing window to secure a liveable future." ²

- Intergovernmental Panel on Climate Change

Future Effects in the U.S.

U.S. Regional Effects

U.S. Regional Effects

Climate change is bringing different types of challenges to each region of the country. Some of the current and future impacts are summarized below. These findings are from the Third and Fourth National Climate Assessment Reports, released by the U.S. Global Change Research Program.

Is it too late to prevent climate change?

Humans have caused major climate changes to happen already, and we have set in motion more changes still. However, if we stopped emitting greenhouse gases today, the rise in global temperatures would begin to flatten within a few years. Temperatures would then plateau but remain well-elevated for many, many centuries.

detailed answer

Northeast. Heat waves, heavy downpours, and sea level rise pose increasing challenges to many aspects of life in the Northeast. Infrastructure, agriculture, fisheries, and ecosystems will be increasingly compromised. Farmers can explore new crop options, but these adaptations are not cost- or risk-free. Moreover, adaptive capacity, which varies throughout the region, could be overwhelmed by a changing climate. Many states and cities are beginning to incorporate climate change into their planning.

Northwest. Changes in the timing of peak flows in rivers and streams are reducing water supplies and worsening competing demands for water. Sea level rise, erosion, flooding, risks to infrastructure, and increasing ocean acidity pose major threats. Increasing wildfire incidence and severity, heat waves, insect outbreaks, and tree diseases are causing widespread forest die-off.

Southeast. Sea level rise poses widespread and continuing threats to the region's economy and environment. Extreme heat will affect health, energy, agriculture, and more. Decreased water availability will have economic and environmental impacts.

Midwest. Extreme heat, heavy downpours, and flooding will affect infrastructure, health, agriculture, forestry, transportation, air and water quality, and more. Climate change will also worsen a range of risks to the Great Lakes.

Southwest. Climate change has caused increased heat, drought, and insect outbreaks. In turn, these changes have made wildfires more numerous and severe. The warming climate has also caused a decline in water supplies, reduced agricultural yields, and triggered heat-related health impacts in cities. In coastal areas, flooding and erosion are additional concerns.

References
References
Header image shows cracked mud stretches across sections of the Panamint Valley. Credit: NPS/Kurt Moses; A degree of concern thermometer image credit: Pixabay/geralt

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intergovernmental panel on climate change

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Vital Signs of the Planet: Global Climate Change and Global Warming. Long-term effects of global climate change in the United States.

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Climate Change Impacts on World's Smallest Rabbit

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Climate Change Impacts on World's Smallest Rabbit

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Shannon Zhu

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Authors

OLA M. JOHANNESSEN,

Corresponding author

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Geophysical Institute, University of Bergen, Norway

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LENNART BENGTSSON,

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Max Planck Institute for Meteorology, Hamburg, Germany

3. Environmental Systems Science Centre, University of Reading, UK

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MARTIN W. MILES,

1. Bjerknes Centre for Climate Research, Bergen, Norway

2. Environmental Systems Analysis Research Center, Boulder, Colorado, USA

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VLADIMIR A. SEMENOV,

1. Max Planck Institute for Meteorology, Hamburg, Germany

2. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia

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*Corresponding author.
e-mail: ola.johannessen@nersc.no

ABSTRACT

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic's response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of century- and multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere-ice-ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

1. Introduction

A consensus result from different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should be enhanced in the Arctic ( Räisänen, 2001). The Intergovernmental Panel on Climate Change ( IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ∼3-4°C during the next 50 yr, or more than twice the global mean.

Recent overviews of results from observational studies of atmospheric and climate-sensitive variables (e.g. sea ice, snow cover, river discharge, glaciers and permafrost) have concluded that, taken together, a reasonably coherent portrait of recent change in the northern high latitudes is apparent ( Serreze et al., 2000; Moritz et al., 2002; Peterson et al., 2002). However, it remains open to debate whether the warming in recent decades is an enhanced greenhouse-warming signal or natural decadal and multidecadal variability ( Polyakov and Johnson, 2000; Polyakov et al., 2002), e.g. as possibly expressed by the arctic warming observed in the 1920s and 1930s followed by cooling until the 1960s (e.g. Kelly et al., 1982). The uncertainties are exacerbated by a lack of homogeneous, century-scale instrumental data sets (see Moritz et al., 2002, whose fig. 2a includes no temperature data for the central Arctic) needed to resolve the inherent time-scales of variability in the Arctic ( Venegas and Mysak, 2000), a region characterized by high variability.

From these concerns, two overarching questions are as follows. (1) To what degree are the gradually changing atmosphere-ice-ocean conditions in the Arctic a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse gas (GHG) forcing. (2) To what degree may anthropogenic forcing induce the arctic sea-ice cover to decrease or even disappear in this century? In order to study these questions, we analyse a new set of pertinent multidecadal to century-scale data-surface air temperature (SAT), sea-ice extent, sea-ice area and sea-ice thickness-in combination with global coupled atmosphere-ice-ocean climate model simulations using the ECHAM4 model of the Max Planck Institute for Meteorology ( Roeckner et al., 1999) and the HadCM3 model of the UK Meteorological Office ( Gordon et al., 2000). ECHAM4 and HadCM3 are state-of-the-art versions of two of the models demonstrated-in an intercomparison of climate-change scenario output from 19 coupled models ( Räisänen, 2001)-to be among those most representative of the 19-model mean temperature change.

2. Surface air temperature: observed and modelled

Statistical analyses of global SAT data sets have indicated substantial fluctuations in the extratropical Northern Hemisphere on decadal to multidecadal time-scales (e.g. Schlesinger and Ramankutty, 1994; Hansen et al., 1999; Jones et al., 1999). In the high latitudes, differences in spatial-temporal coverage have led to some discrepancies concerning temperature variability trends in the last century (e.g. Jones et al., 1999; Przybylak, 2000; Polyakov et al., 2002). The global gridded SAT data set ( Jones et al., 1999) used most extensively for studies of climate variability has major gaps in the northern high latitudes, in particular over the ice-covered Arctic Ocean and some surrounding land areas. Here, we analyse for the first time a unique century-long SAT data set focused on the high latitudes of the Northern Hemisphere. The data set is provided through the Arctic and Antarctic Research Institute (AARI), St Petersburg, Russia, produced through a project within the programme International Association for the Promotion of Cooperation with Scientists from the Former Soviet Union ( Alekseev et al., 1999). The input data are daily temperatures from 1486 meteorological stations in the Northern Hemisphere, including land- and drifting-stations from the Arctic. A gridded data set (5° lat. × 10° long.) based on these data has been developed from several sources. First, monthly-mean SAT anomaly maps were produced at the USSR's Main Geophysical Observatory for the period 1891-1969 and at the Hydrometeorological Research Center for the period 1970-1976. Secondly, the maps were visually analysed and interpolated into a gridded data set, as described and evaluated previously ( Vinnikov, 1977). Thirdly, the data set was extended in the same manner for the period 1977-1986, presented as SAT with reduction to common mean values and taking into the account the moist adiabatic temperature gradient (0.6° per 100 m) as in Alekseev and Svyaschennikov (1991). Fourthly, the data set was continued at the Hydrometeorological Research Center from 1986-1995, through visual interpolation and reading from monthly-mean SAT maps. Since 1995, the monthly-mean SATs have been produced at AARI through monthly averaging of gridded daily SAT from the European Centre for Medium-Range Weather Forecasting (ECMWF).

The reliability of the new SAT data set for climate analyses is evidenced through statistical comparison with the National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) re-analysis data ( Kalnay et al., 1996) and the Jones et al. (1999) data set, tested here over common areas and time periods. The quantitative agreement between interannual variations in annual mean SAT north of 55°N (excluding the Greenland area 20-70°W) from NCAR/NCEP and our data set is from 1955-1990. The agreement between the Jones data and our data set from a large arctic-subarctic test area (between 52.5°N-67.5°N and 2.5°W-62.5°E) from 1930-1990 is nearly identical, with a correlation coefficient and a mean difference ∼0.15°C. Therefore, the SAT data set put forth here is considered a reliable alternative to the standard data sets and has the advantage of improved coverage in the Arctic and extending over the last century.

Figure 1a shows the time evolution of the zonally averaged anomalies in annual mean SAT from 30-90°N. Two characteristic warming events stand out, the first from the mid 1920s to about 1940 and the second starting about 1980 and still ongoing. Here, we show that the early twentieth-century warming was largely confined to north of 60°N, whereas the latter warming encompasses the whole Earth ( Jones et al., 1999) but is none the less significantly enhanced in the Arctic ( Fig. 1a).

The early twentieth-century warming trend in the Arctic was nearly as large as the warming trend for the last 20 yr, such that some researchers (e.g. Polyakov et al., 2002) regard them to be part and parcel of the same natural low-frequency oscillation. However, our spatial comparison of these periods reveals key differences in their patterns ( Fig. 2). The 20-yr SAT trends for the 1920s-1930s warming period ( Figs 2a and b) and the subsequent cooling period ( Figs 2c and d) have remarkably similar patterns, thus suggesting similar underlying processes. In the winter half-year, the high-latitude warming ( Fig. 2a) and cooling ( Fig. 2c) patterns are organized symmetrically around the pole, while in the summer half-year, the warming ( Fig. 2b) and cooling ( Fig. 2d) appear to reflect the positions of the latitudinal quasi-stationary wave structure, predominantly wavenumber three and four. However, the warming trend for the last 20 yr is more widespread and has a markedly different pattern from the earlier periods in both winter ( Fig. 2e) and summer ( Fig. 2f). Both the 1920-39 and 1980-99 warming are most pronounced during winter for the high Arctic. In addition, in the latter period, there is pronounced warming in the Eurasian midlatitudes, especially in summer.

A recent modelling study ( Delworth and Knutson, 2000) has suggested that the 1920s-1930s warming anomaly was due to natural processes, insofar as models are capable of simulating such anomalies as due to internal chaotic processes of the climate system. Here, a similar high-latitude anomaly, although less extreme and of a shorter duration, is found in a 300-yr control run (without increasing anthropogenic forcings) with the ECHAM4 model-100 yr are shown in Fig. 1d. This anomaly occurred after 150 yr of integration and lasted for some 15 yr. This simulation without increasing anthropogenic forcing is able to produce an anomaly similar to the observed high-latitude warming in the 1920s-1930s. Therefore, we strongly support the contention of Delworth and Knutson (2000) that this high-latitude warming event represents primarily natural variability within the climate system, rather than being caused primarily by external forcings, whether solar forcing alone ( Thejll and Lassen, 2000) or a combination of increasing solar irradiance, increasing anthropogenic trace gases, and decreasing volcanic aerosols, as suggested from an analysis of 400 yr of temperature proxy data from the Arctic ( Overpeck et al., 1997). Changes in solar forcing have been suggested (e.g. Lean and Rind, 1998) as the cause of the warming. It has attracted considerable interest because of the apparent similarity between the assumed solar variability and the SAT trend of the Northern Hemisphere extratropics ( Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993). Reconstructions of solar variability have been used in modelling studies ( Cubasch et al., 1997; Cubasch and Voss, 2000; Stott et al., 2001) providing results broadly consistent with long-term estimated or observed temperature trends. This is not surprising because multidecadal and longer trends in solar forcing generate a response in global average modelled temperatures on similar time-scales as the anticipated variation in solar forcing ( Cubasch et al., 1997). Rigorous testing of the assumption of solar forcing requires reliable observations; however, these data exist only for the past two decades ( Lean and Rind, 1998), such that it remains only a hypothesis. The anthropogenic forcing in the 1920s-1930s was far too weak to generate the observed warming-the change in the GHG forcing in the early decades of the twentieth century was only ∼20% of the present ( Roeckner et al., 1999). The most plausible explanation in this case is the low-frequency, multidecadal oscillation related to North Atlantic Ocean circulation ( Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Polyakov and Johnson, 2000).

In contrast, no comprehensive numerical-model integrations have produced the present global warm anomaly ( Fig. 1a) without including observed anthropogenic forcing. Figure 1b shows the ECHAM4 model simulation with anthropogenic GHG forcing. The patterns compare well with the last two decades of observed warming, although the modelled warming occurs slightly earlier and also encompasses lower latitudes than observed ( Fig. 1a). The patterns from a simulation including GHGs and sulfate aerosols (GSD) ( Fig. 1c) show that, although the recent midlatitude warming is underestimated, the high-latitude enhancement is in agreement with the observations and other modelling results ( Räisänen, 2001). Therefore, anthropogenic forcing is the dominant cause of the recent pronounced warming in the Arctic.

What then are the possible mechanisms by which anthropogenic forcing could bring about high-latitude warming, given that the recent trend in arctic SAT is greater than the direct radiative effect of GHGs? First, as has been demonstrated by Bengtsson (1999) and Hansen et al. (1999), the large-scale spatial pattern of forcing and the pattern of response to forcing are practically uncorrelated, which stresses the key role of advective atmosphere-ocean processes in bringing about regional climate change; see also Schneider et al. (2003). A possible mechanism is suggested by recent findings from observations and modelling experiments ( Hoerling et al., 2001; Lin et al., 2002). Hoerling et al. (2001) show that the observed long-term warming trend (up to 0.2°C per decade) in the tropical oceans-anthropogenically forced ( Levitus et al., 2000; Reichert et al., 2002)-during the last 50 yr has generated, through enhanced convective activity, an intensification of the midlatitude tropospheric westerlies, and consequently because of geostrophy in the Atlantic sector, an enhanced positive North Atlantic Oscillation/Arctic Oscillation (NAO/AO) pattern ( Hoerling et al., 2001). Because the NAO and arctic temperature are linked-the interannual variations in the NAO index ( Hurrell, 1995) and SAT from 60-90°N are found here to be significantly correlated ( when using data since the mid twentieth century)-it follows that an NAO enhancement, linked to anthropogenic warming of the tropical oceans, can explain a substantial portion of the warming trend in the Arctic. It is unlikely that this was a mechanism for the early twentieth-century warming phase, because of the small anthropogenic GHG effect at the time and because no positive correlation between arctic SAT and the NAO before 1950 is found; in fact, here we find that the correlation is negative ().

Secondly, an ice-albedo feedback may enhance the arctic warming, as new ECHAM4 sensitivity experiments have demonstrated a robust relationship between sea ice and air temperature ( Bengtsson et al., 2004), in which advective mechanisms-in particular, enhanced wind-driven oceanic heat inflow into the Barents Sea-may have a predominant role.

3. Sea ice: observations

3.1. Sea-ice concentration and derived parameters

Sea-ice concentration (percent ice area per unit area) and derived parameters such as ice extent (the area within the ice-ocean margin defined as 15% ice concentration) and ice area (extent minus the open-water area) can be reliably retrieved from satellite passive-microwave sensor measurements, which are available continuously since 1978, thus among the longest satellite-retrieved geophysical records. Satellite data have shown that the winter maximum ice area ( Fig. 3a) is typically about while the summer minimum ice area ( Fig. 3a) is about . These data, here updated from Johannessen et al. (1995, 1999) and Bjørgo et al. (1997) through March 2003, indicate a decrease of (∼7.4%) in the Northern Hemisphere annual sea-ice area, 1978-2003 ( Fig. 3b). During this period, the decreases have been larger in summer (see also Chapman and Walsh, 1993; Parkinson et al., 1999); here, we calculate a (14%) decrease in September versus (5%) in March (1978-2003). This seasonal difference has resulted in a 7-9% per decade reduction in the area of thicker, multiyear (MY) ice (ice that has survived at least one summer melt) over the last two decades ( Johannessen et al., 1999; Comiso, 2002). It is noteworthy that our updated time series through September 2002 indicate that the record minimum summer ice cover recently reported ( Serreze et al., 2003) is indeed unprecedented in the nearly quarter-century satellite record ( Fig. 3a)-less than in area, which occurred from anomalous warm southerly winds in spring followed by low SLP and high SAT in the Arctic in summer ( Serreze et al., 2003). Note also the large positive anomaly in 1996, which occurred in the summer after an extreme temporary reversal in the NAO in the winter 1995/96.

The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figs 4a and 4b. The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figs 4c and d. During this period, the decreases in winter have been most pronounced (as large as ∼50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ∼30-50% in the Siberian marginal seas. These summer patterns are in agreement with an independent analysis of ice-cover minima from 1978-1998 (see fig. 2c in Comiso, 2002).

The decreases in recent decades, which are also partially due to circulation-driven ice export through the Fram Strait between Greenland and Svalbard ( Vinje, 2001), have coincided with a positive trend in the NAO, with unusually high index values in the late 1980s and 1990s. During this period, the variability of ice motion and ice export through the Fram Strait was correlated strongly with the NAO; for the ice area flux ( Kwok and Rothrock, 1999) and for the ice volume flux ( Hilmer and Jung, 2000), although the relationship was insignificant before the mid 1970s ( Hilmer and Jung, 2000). Deser et al. (2000) analysed a 40-yr gridded data set (1958-97) to determine the association between arctic sea ice, SAT and SLP, concluding that the multidecadal trends in the NAO/AO in the past three decades have been 'imprinted upon the distribution of Arctic sea ice', with the first principal component of sea-ice concentration significantly correlated with the NAO index, recently cause-and-effect modelled by Hu et al. (2002). None the less, our calculations and those of Deser et al. (2000) indicate that, even in recent decades, only about one third of the variability in arctic total ice extent and MY ice area ( Johannessen et al., 1999) is explained by the NAO index, implying that other factors including enhanced radiative forcing and ice-albedo feedbacks ( Björk and Söderkvist, 2002; Bengtsson et al., 2003) must be invoked to explain the variability.

A pronounced reduction in the Northern Hemisphere sea-ice extent, such as seen in the satellite record of the last two decades, is scarcely apparent in the early twentieth-century warm period, at least in the two most widely-used century-long sea-ice data sets ( Chapman and Walsh, 1993; Rayner et al., 1996). To ascertain whether this has a physical explanation or is due to acknowledged data deficiencies before the 1950s ( Chapman and Walsh, 1993; Vinnikov et al., 1999), we have analysed these data together with a new century-long 'Zakharov' data set ( Zakharov, 1997; Alekseev et al., 2000), which includes hitherto under-recognized Russian data. The data set comprises sea-ice extent (regardless of ice concentration within the ice-ocean margin) for ∼77% ( km of the area of the Arctic Ocean. This region occupies the perennially ice-covered central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi marginal seas, leaving out only the eastern Chukchi and Beaufort seas and the Canadian Arctic straits and bays.

In the Atlantic-European sector (Greenland and Barents seas), the sea-ice extents from 1900 to the late 1950s are known only from spring and summer (April to August). It has not previously been possible to gain understanding about the annual ice cover variability in these decades. An attempt has been made to obtain these missing mean annual data. Towards this end, the actual year-round data on the ice extent from 1959-1988 were used to derive an equation to calculate the annual average ice extent in the region based on its mean value in April to August : . The correlation between and is . By means of this equation, the missing annual ice-extent means from 1900-1958 were calculated. The main problem of any reconstruction is errors inevitable in the procedures of data series reconstruction. This problem can be resolved by comparing the actual and calculated annual ice extent averages, in this case for the period from 1959, and by calculating the root-mean-square (rms) error of the reconstruction. The error , and as the rms deviation of the initial actual series , the ratio . Thus, the methodological error of the reconstruction comprises only one third of the rms deviation of the actual series, indicating a sufficiently high reliability of the reconstruction method used.

Data on the ice edge are available for August from the mid 1920s in the Siberian sector (Kara, Laptev, and East Siberian seas). From the 1930s, with the development of shipping along the Northern Sea Route, knowledge of the position of these boundaries was extended to two adjacent months (July and September); however, the accuracy of the ice data from that time cannot be considered high, because observations were not carried out systematically. From the mid 1930s, aviation was used to collect ice information and from the mid 1940s became the main source of sea-ice information in the Siberian Arctic. The observations attain a regular character covering (with some exceptions) the entire navigation season. An attempt has been made to obtain these missing mean annual data, using incomplete data from 1924-1946 to reconstruct the conditions of those years. With this aim, using the 1946-1999 series, the equation: was derived, where is the mean sea-ice extent in the Siberian seas in June-October, and is the ice area in the Siberian seas in August. The correlation between and is . The ratio again suggests a sufficiently high reconstruction quality based on the knowledge of ice extent only in August.

Figure 5 shows time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard 'Walsh' data set ( Chapman and Walsh, 1993), in comparison with the zonal annual mean SAT between 70-90°N since 1900. In contrast to the Walsh data, these new data indicate a substantially reduced () ice cover in the 1920s-1930s warming period. Note that the correspondence between the subsequent cooling into the mid 1960s and increasing sea ice is seen from the late 1950s; the ice data from WWII and the early post-war years are inadequate. Note also that the apparent differences in trends in the two sea-ice time series during the last 25 yr are due to relatively large reductions in the Beaufort Sea and Chukchi seas ( Fig. 4)-areas not included in the Zakharov data set. The correlations between the SAT and the Zakharov and Walsh sea-ice extents are maximum at 0 lag, and 0.3, respectively. This indicates that the interannual variability in the arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, although the r-value may partially reflect feedback processes from the ice cover to the atmosphere, e.g. ECHAM4-model sensitivity experiments (see Bengtsson et al., 2004) have demonstrated a strong SAT response-particularly in the Atlantic-European sector-to model-imposed changes in sea ice.

3.2. Sea-ice thickness

The variability of ice thickness is relatively poorly known, due primarily to spatial-temporal sampling deficiencies in data from submarines carrying upward-looking sonar ( Wadhams, 1997). An analysis ( Rothrock et al., 1999) of data from the summers 1958, 1976, 1993, 1996 and 1997 found that between the 1950s/1970s and the 1990s, the mean ice thickness decreased from 3.1 to 1.8 m. The 1.3-m decrease-if representative-corresponds to an ∼40% reduction over the three to four decades ( Rothrock et al., 1999). However, analyses of sonar data alone from different transects, years and seasons yield a range of estimates, from a comparable decrease ( Wadhams and Davis, 2000) to no significant change ( Winsor, 2001) to an intermediate estimate ( Tucker et al., 2001). An analysis of submarine ( Rothrock et al., 1999, 2003) and modelled ( Holloway and Sau, 2002) ice thickness over the same common time period has demonstrated that ice motion and high interannual variability could mislead inference of trends from sonar transect data, e.g. 12% ( Holloway and Sau, 2002) versus the 40% decrease reported by Rothrock et al. (1999). Thus, the available sonar data alone remain inadequate to produce a reliable, long-term climatology of arctic ice thickness variability.

Here, for the first time, we put forth a unique 20-yr time series of monthly, area-averaged ice thickness derived from field-based measurements of surface elastic-gravity waves from Russian North Polar drifting stations 1970-91, when regular measurements of ice surface vibrations were made in the central Arctic Ocean ( Nagurnyi et al., 1994). Long elastic-gravity waves (on the order of 1 km) in the sea-ice cover arise from the interaction with ocean swells. These elastic-gravity waves can propagate for hundreds to thousands of kilometres before dampening out. Based on a linear theory of free vibrations of the sea-ice cover, the measured wavelength, wave period and direction are then related to thickness through a wave-energy dispersion relation. The ice thicknesses determined from different propagation directions are averaged to provide a basin-wide mean thickness estimate ( Nagurnyi et al., 1994, 1999). The values compare well to those observed in the regional ice cover ( Romanov, 1995) and their interannual variability correlates well with modelled arctic ice volume (see fig. 1 in Hilmer and Lemke, 2000). The thickness estimates have also been found to correlate strongly (∼0.88) to the satellite-derived area of the perennial, MY ice cover in winter ( Johannessen et al., 1999), suggesting moreover that the decreases found in MY ice area represent a mass balance change rather than merely a peripheral effect.

Figure 6a shows the 20-yr time series of area-averaged ice thickness, 1970-91, from which trends for winter and summer are derived ( Fig. 6b). The mean thickness estimates are ∼2.9-3 m in winter and ∼2.5-2.6 in summer (i.e. seasonal cycle ∼40 cm). The linear trend of anomalies from 1971-90 indicates a decrease of only ∼10 cm (less than 4%) over 20 yr. This is comparable with some observational and modelling analyses (e.g. Holloway and Sau, 2002), but is much less than the 1950s/1970s to 1990s sonar data analysis ( Rothrock et al., 1999) upon which the IPCC based its statement that the arctic ice thickness has been reduced 40% during summer in recent decades. The large variability inherent in the arctic sea-ice-climate system, coupled with the problem of obtaining ice thickness data, renders the evaluation of ice thickness trends from the available observational data an open question. None the less, it is notable that our ECHAM4 coupled model experiments (not shown) with the IPCC IS92 emission scenarios for greenhouse and related gases indicate that: (1) our 20 yr of ice thickness data are consistent with the modelled results 1970-90, and (2) substantial decreases in modelled ice thickness commence only in the last two decades of the twentieth century. The ice thickness from both observations and models is presently ∼2.5-3 m in summer, whereas our model results indicate less than 1 m at the end of this century for the remaining ice cover.

4. Sea-ice extent: modelled

The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses ( Vinnikov et al., 1999; Johannessen et al., 2001), which predicted a reduction of ∼15% in the Northern Hemisphere mean ice extent to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, for the first time, both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing are analysed to 2100, using ECHAM-4 and HadCM3 model predictions including different IPCC emissions scenarios.

The observed versus ECHAM4-modelled trends in Northern Hemisphere winter and summer sea-ice extent in the twentieth century are similar ( Fig. 7). Our ECHAM4-model run-using an IPCC IS92 emission scenario similar to IPCC Special Report on Emissions Scenarios (SRES) scenario B2-predicts the decreases to continue such that the summer ice cover may be reduced by ∼80% at the end of the twenty-first century ( Fig. 7, lower). This is much greater than the winter ( Fig. 7, upper) or annual means modelled previously ( Vinnikov et al., 1999; Johannessen et al., 2001) and is comparable to recent projections for the summer ( Gregory et al., 2002).

The spatial distributions of the ECHAM4-modelled sea-ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Fig. 8. In order to test the robustness of our ECHAM4 estimates, we have used a different coupled atmosphere-ocean model, the HadCM3, which is an improvement upon the HadCM2 model used in a previous sea-ice study ( Vinnikov et al., 1999). Furthermore, we use two different SRES scenarios, A2 and B2, which are 'medium-high' and 'medium-low' scenarios, respectively. Results from the B2 experiments are shown in Fig. 9, which depicts decadal averages of winter ( Figs 9a and b) and summer ( Figs 9c and d) sea-ice concentrations in 2001-2010 and 2081-2090. The ECHAM4 ( Fig. 8) and HadCM3 ( Fig. 9) results support each other, both predicting moderate reductions in winter and drastic reduction in the summer ice extent. The spatial distributions of the ECHAM4 and HadCM3 modelled summer ice cover in late century ( Figs 8d and 9d) indicate essentially ice-free arctic marginal seas except north of Greenland and the Canadian Arctic Archipelago, although there are some differences in the modelled spatial patterns.

5. Conclusions and implications

The results of our observational and modelling analysis lead to the following conclusions. First, we theorize that the Arctic warming in the 1920s-1930s and the subsequent cooling until about 1970 are due to natural fluctuations internal to the climate system. Secondly, we believe there are strong indications that neither the warming trend nor the decrease of ice extent and volume over the last two decades can be explained by natural processes alone. Thirdly, the state-of-the-art ECHAM4 and HadCM3 coupled climate models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century.

A range of potential consequences of arctic warming and a shrinking ice cover can be hypothesized, as follows.

(i) Reductions in albedo and increased open water would have significant effects on energy balances and atmospheric and oceanic circulation in the high latitudes.

(ii) Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity for CO absorption, could become a new and important sink of atmospheric CO (Anderson and Kaltin, 2001,).

(iii) Broad changes in the marine ecosystem, e.g. changes in plankton in the North Atlantic due to less ice and greater inflow of melt water (Beaugrand et al., 2002), could have a negative impact on arctic and subarctic marine biodiversity. However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route north of Siberia (Ragner, 2000).

(iv) Changes in the pathways and spreading of melt water and in the stratification in the Nordic Seas, and the effects of reduced deep-water formation in the Greenland Sea on the global thermohaline circulation (Rahmsdorf, 1999; Alekseev et al., 2001), could greatly alter the climate of the northern latitudes.

6. Acknowledgments

This research has been supported under the European Union's 5th Framework Programme project 'Arctic Ice Cover Simulation Experiment (AICSEX)' and the International Association for the Promotion of Cooperation with Scientists from the Independent States of the Former Soviet Union (INTAS), as well as the Research Council of Norway's projects 'Role of Arctic Sea-Ice-Atmosphere Processes (ROLARC)' and 'Marine Climate and Ecosystems in the Seasonal Ice Zone (MACESIZ)'. We also acknowledge the valuable assistance of Cathrine Myrmehl and Anne-Mette Olsen of the Nansen Environmental and Remote Sensing Center.

Ancillary

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Authors

OLA M. JOHANNESSEN,

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1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Geophysical Institute, University of Bergen, Norway

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LENNART BENGTSSON,

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Max Planck Institute for Meteorology, Hamburg, Germany

3. Environmental Systems Science Centre, University of Reading, UK

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MARTIN W. MILES,

1. Bjerknes Centre for Climate Research, Bergen, Norway

2. Environmental Systems Analysis Research Center, Boulder, Colorado, USA

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VLADIMIR A. SEMENOV,

1. Max Planck Institute for Meteorology, Hamburg, Germany

2. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia

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*Corresponding author.
e-mail: ola.johannessen@nersc.no

ABSTRACT

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic's response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of century- and multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere-ice-ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

1. Introduction

A consensus result from different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should be enhanced in the Arctic ( Räisänen, 2001). The Intergovernmental Panel on Climate Change ( IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ∼3-4°C during the next 50 yr, or more than twice the global mean.

Recent overviews of results from observational studies of atmospheric and climate-sensitive variables (e.g. sea ice, snow cover, river discharge, glaciers and permafrost) have concluded that, taken together, a reasonably coherent portrait of recent change in the northern high latitudes is apparent ( Serreze et al., 2000; Moritz et al., 2002; Peterson et al., 2002). However, it remains open to debate whether the warming in recent decades is an enhanced greenhouse-warming signal or natural decadal and multidecadal variability ( Polyakov and Johnson, 2000; Polyakov et al., 2002), e.g. as possibly expressed by the arctic warming observed in the 1920s and 1930s followed by cooling until the 1960s (e.g. Kelly et al., 1982). The uncertainties are exacerbated by a lack of homogeneous, century-scale instrumental data sets (see Moritz et al., 2002, whose fig. 2a includes no temperature data for the central Arctic) needed to resolve the inherent time-scales of variability in the Arctic ( Venegas and Mysak, 2000), a region characterized by high variability.

From these concerns, two overarching questions are as follows. (1) To what degree are the gradually changing atmosphere-ice-ocean conditions in the Arctic a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse gas (GHG) forcing. (2) To what degree may anthropogenic forcing induce the arctic sea-ice cover to decrease or even disappear in this century? In order to study these questions, we analyse a new set of pertinent multidecadal to century-scale data-surface air temperature (SAT), sea-ice extent, sea-ice area and sea-ice thickness-in combination with global coupled atmosphere-ice-ocean climate model simulations using the ECHAM4 model of the Max Planck Institute for Meteorology ( Roeckner et al., 1999) and the HadCM3 model of the UK Meteorological Office ( Gordon et al., 2000). ECHAM4 and HadCM3 are state-of-the-art versions of two of the models demonstrated-in an intercomparison of climate-change scenario output from 19 coupled models ( Räisänen, 2001)-to be among those most representative of the 19-model mean temperature change.

2. Surface air temperature: observed and modelled

Statistical analyses of global SAT data sets have indicated substantial fluctuations in the extratropical Northern Hemisphere on decadal to multidecadal time-scales (e.g. Schlesinger and Ramankutty, 1994; Hansen et al., 1999; Jones et al., 1999). In the high latitudes, differences in spatial-temporal coverage have led to some discrepancies concerning temperature variability trends in the last century (e.g. Jones et al., 1999; Przybylak, 2000; Polyakov et al., 2002). The global gridded SAT data set ( Jones et al., 1999) used most extensively for studies of climate variability has major gaps in the northern high latitudes, in particular over the ice-covered Arctic Ocean and some surrounding land areas. Here, we analyse for the first time a unique century-long SAT data set focused on the high latitudes of the Northern Hemisphere. The data set is provided through the Arctic and Antarctic Research Institute (AARI), St Petersburg, Russia, produced through a project within the programme International Association for the Promotion of Cooperation with Scientists from the Former Soviet Union ( Alekseev et al., 1999). The input data are daily temperatures from 1486 meteorological stations in the Northern Hemisphere, including land- and drifting-stations from the Arctic. A gridded data set (5° lat. × 10° long.) based on these data has been developed from several sources. First, monthly-mean SAT anomaly maps were produced at the USSR's Main Geophysical Observatory for the period 1891-1969 and at the Hydrometeorological Research Center for the period 1970-1976. Secondly, the maps were visually analysed and interpolated into a gridded data set, as described and evaluated previously ( Vinnikov, 1977). Thirdly, the data set was extended in the same manner for the period 1977-1986, presented as SAT with reduction to common mean values and taking into the account the moist adiabatic temperature gradient (0.6° per 100 m) as in Alekseev and Svyaschennikov (1991). Fourthly, the data set was continued at the Hydrometeorological Research Center from 1986-1995, through visual interpolation and reading from monthly-mean SAT maps. Since 1995, the monthly-mean SATs have been produced at AARI through monthly averaging of gridded daily SAT from the European Centre for Medium-Range Weather Forecasting (ECMWF).

The reliability of the new SAT data set for climate analyses is evidenced through statistical comparison with the National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) re-analysis data ( Kalnay et al., 1996) and the Jones et al. (1999) data set, tested here over common areas and time periods. The quantitative agreement between interannual variations in annual mean SAT north of 55°N (excluding the Greenland area 20-70°W) from NCAR/NCEP and our data set is from 1955-1990. The agreement between the Jones data and our data set from a large arctic-subarctic test area (between 52.5°N-67.5°N and 2.5°W-62.5°E) from 1930-1990 is nearly identical, with a correlation coefficient and a mean difference ∼0.15°C. Therefore, the SAT data set put forth here is considered a reliable alternative to the standard data sets and has the advantage of improved coverage in the Arctic and extending over the last century.

Figure 1a shows the time evolution of the zonally averaged anomalies in annual mean SAT from 30-90°N. Two characteristic warming events stand out, the first from the mid 1920s to about 1940 and the second starting about 1980 and still ongoing. Here, we show that the early twentieth-century warming was largely confined to north of 60°N, whereas the latter warming encompasses the whole Earth ( Jones et al., 1999) but is none the less significantly enhanced in the Arctic ( Fig. 1a).

The early twentieth-century warming trend in the Arctic was nearly as large as the warming trend for the last 20 yr, such that some researchers (e.g. Polyakov et al., 2002) regard them to be part and parcel of the same natural low-frequency oscillation. However, our spatial comparison of these periods reveals key differences in their patterns ( Fig. 2). The 20-yr SAT trends for the 1920s-1930s warming period ( Figs 2a and b) and the subsequent cooling period ( Figs 2c and d) have remarkably similar patterns, thus suggesting similar underlying processes. In the winter half-year, the high-latitude warming ( Fig. 2a) and cooling ( Fig. 2c) patterns are organized symmetrically around the pole, while in the summer half-year, the warming ( Fig. 2b) and cooling ( Fig. 2d) appear to reflect the positions of the latitudinal quasi-stationary wave structure, predominantly wavenumber three and four. However, the warming trend for the last 20 yr is more widespread and has a markedly different pattern from the earlier periods in both winter ( Fig. 2e) and summer ( Fig. 2f). Both the 1920-39 and 1980-99 warming are most pronounced during winter for the high Arctic. In addition, in the latter period, there is pronounced warming in the Eurasian midlatitudes, especially in summer.

A recent modelling study ( Delworth and Knutson, 2000) has suggested that the 1920s-1930s warming anomaly was due to natural processes, insofar as models are capable of simulating such anomalies as due to internal chaotic processes of the climate system. Here, a similar high-latitude anomaly, although less extreme and of a shorter duration, is found in a 300-yr control run (without increasing anthropogenic forcings) with the ECHAM4 model-100 yr are shown in Fig. 1d. This anomaly occurred after 150 yr of integration and lasted for some 15 yr. This simulation without increasing anthropogenic forcing is able to produce an anomaly similar to the observed high-latitude warming in the 1920s-1930s. Therefore, we strongly support the contention of Delworth and Knutson (2000) that this high-latitude warming event represents primarily natural variability within the climate system, rather than being caused primarily by external forcings, whether solar forcing alone ( Thejll and Lassen, 2000) or a combination of increasing solar irradiance, increasing anthropogenic trace gases, and decreasing volcanic aerosols, as suggested from an analysis of 400 yr of temperature proxy data from the Arctic ( Overpeck et al., 1997). Changes in solar forcing have been suggested (e.g. Lean and Rind, 1998) as the cause of the warming. It has attracted considerable interest because of the apparent similarity between the assumed solar variability and the SAT trend of the Northern Hemisphere extratropics ( Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993). Reconstructions of solar variability have been used in modelling studies ( Cubasch et al., 1997; Cubasch and Voss, 2000; Stott et al., 2001) providing results broadly consistent with long-term estimated or observed temperature trends. This is not surprising because multidecadal and longer trends in solar forcing generate a response in global average modelled temperatures on similar time-scales as the anticipated variation in solar forcing ( Cubasch et al., 1997). Rigorous testing of the assumption of solar forcing requires reliable observations; however, these data exist only for the past two decades ( Lean and Rind, 1998), such that it remains only a hypothesis. The anthropogenic forcing in the 1920s-1930s was far too weak to generate the observed warming-the change in the GHG forcing in the early decades of the twentieth century was only ∼20% of the present ( Roeckner et al., 1999). The most plausible explanation in this case is the low-frequency, multidecadal oscillation related to North Atlantic Ocean circulation ( Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Polyakov and Johnson, 2000).

In contrast, no comprehensive numerical-model integrations have produced the present global warm anomaly ( Fig. 1a) without including observed anthropogenic forcing. Figure 1b shows the ECHAM4 model simulation with anthropogenic GHG forcing. The patterns compare well with the last two decades of observed warming, although the modelled warming occurs slightly earlier and also encompasses lower latitudes than observed ( Fig. 1a). The patterns from a simulation including GHGs and sulfate aerosols (GSD) ( Fig. 1c) show that, although the recent midlatitude warming is underestimated, the high-latitude enhancement is in agreement with the observations and other modelling results ( Räisänen, 2001). Therefore, anthropogenic forcing is the dominant cause of the recent pronounced warming in the Arctic.

What then are the possible mechanisms by which anthropogenic forcing could bring about high-latitude warming, given that the recent trend in arctic SAT is greater than the direct radiative effect of GHGs? First, as has been demonstrated by Bengtsson (1999) and Hansen et al. (1999), the large-scale spatial pattern of forcing and the pattern of response to forcing are practically uncorrelated, which stresses the key role of advective atmosphere-ocean processes in bringing about regional climate change; see also Schneider et al. (2003). A possible mechanism is suggested by recent findings from observations and modelling experiments ( Hoerling et al., 2001; Lin et al., 2002). Hoerling et al. (2001) show that the observed long-term warming trend (up to 0.2°C per decade) in the tropical oceans-anthropogenically forced ( Levitus et al., 2000; Reichert et al., 2002)-during the last 50 yr has generated, through enhanced convective activity, an intensification of the midlatitude tropospheric westerlies, and consequently because of geostrophy in the Atlantic sector, an enhanced positive North Atlantic Oscillation/Arctic Oscillation (NAO/AO) pattern ( Hoerling et al., 2001). Because the NAO and arctic temperature are linked-the interannual variations in the NAO index ( Hurrell, 1995) and SAT from 60-90°N are found here to be significantly correlated ( when using data since the mid twentieth century)-it follows that an NAO enhancement, linked to anthropogenic warming of the tropical oceans, can explain a substantial portion of the warming trend in the Arctic. It is unlikely that this was a mechanism for the early twentieth-century warming phase, because of the small anthropogenic GHG effect at the time and because no positive correlation between arctic SAT and the NAO before 1950 is found; in fact, here we find that the correlation is negative ().

Secondly, an ice-albedo feedback may enhance the arctic warming, as new ECHAM4 sensitivity experiments have demonstrated a robust relationship between sea ice and air temperature ( Bengtsson et al., 2004), in which advective mechanisms-in particular, enhanced wind-driven oceanic heat inflow into the Barents Sea-may have a predominant role.

3. Sea ice: observations

3.1. Sea-ice concentration and derived parameters

Sea-ice concentration (percent ice area per unit area) and derived parameters such as ice extent (the area within the ice-ocean margin defined as 15% ice concentration) and ice area (extent minus the open-water area) can be reliably retrieved from satellite passive-microwave sensor measurements, which are available continuously since 1978, thus among the longest satellite-retrieved geophysical records. Satellite data have shown that the winter maximum ice area ( Fig. 3a) is typically about while the summer minimum ice area ( Fig. 3a) is about . These data, here updated from Johannessen et al. (1995, 1999) and Bjørgo et al. (1997) through March 2003, indicate a decrease of (∼7.4%) in the Northern Hemisphere annual sea-ice area, 1978-2003 ( Fig. 3b). During this period, the decreases have been larger in summer (see also Chapman and Walsh, 1993; Parkinson et al., 1999); here, we calculate a (14%) decrease in September versus (5%) in March (1978-2003). This seasonal difference has resulted in a 7-9% per decade reduction in the area of thicker, multiyear (MY) ice (ice that has survived at least one summer melt) over the last two decades ( Johannessen et al., 1999; Comiso, 2002). It is noteworthy that our updated time series through September 2002 indicate that the record minimum summer ice cover recently reported ( Serreze et al., 2003) is indeed unprecedented in the nearly quarter-century satellite record ( Fig. 3a)-less than in area, which occurred from anomalous warm southerly winds in spring followed by low SLP and high SAT in the Arctic in summer ( Serreze et al., 2003). Note also the large positive anomaly in 1996, which occurred in the summer after an extreme temporary reversal in the NAO in the winter 1995/96.

The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figs 4a and 4b. The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figs 4c and d. During this period, the decreases in winter have been most pronounced (as large as ∼50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ∼30-50% in the Siberian marginal seas. These summer patterns are in agreement with an independent analysis of ice-cover minima from 1978-1998 (see fig. 2c in Comiso, 2002).

The decreases in recent decades, which are also partially due to circulation-driven ice export through the Fram Strait between Greenland and Svalbard ( Vinje, 2001), have coincided with a positive trend in the NAO, with unusually high index values in the late 1980s and 1990s. During this period, the variability of ice motion and ice export through the Fram Strait was correlated strongly with the NAO; for the ice area flux ( Kwok and Rothrock, 1999) and for the ice volume flux ( Hilmer and Jung, 2000), although the relationship was insignificant before the mid 1970s ( Hilmer and Jung, 2000). Deser et al. (2000) analysed a 40-yr gridded data set (1958-97) to determine the association between arctic sea ice, SAT and SLP, concluding that the multidecadal trends in the NAO/AO in the past three decades have been 'imprinted upon the distribution of Arctic sea ice', with the first principal component of sea-ice concentration significantly correlated with the NAO index, recently cause-and-effect modelled by Hu et al. (2002). None the less, our calculations and those of Deser et al. (2000) indicate that, even in recent decades, only about one third of the variability in arctic total ice extent and MY ice area ( Johannessen et al., 1999) is explained by the NAO index, implying that other factors including enhanced radiative forcing and ice-albedo feedbacks ( Björk and Söderkvist, 2002; Bengtsson et al., 2003) must be invoked to explain the variability.

A pronounced reduction in the Northern Hemisphere sea-ice extent, such as seen in the satellite record of the last two decades, is scarcely apparent in the early twentieth-century warm period, at least in the two most widely-used century-long sea-ice data sets ( Chapman and Walsh, 1993; Rayner et al., 1996). To ascertain whether this has a physical explanation or is due to acknowledged data deficiencies before the 1950s ( Chapman and Walsh, 1993; Vinnikov et al., 1999), we have analysed these data together with a new century-long 'Zakharov' data set ( Zakharov, 1997; Alekseev et al., 2000), which includes hitherto under-recognized Russian data. The data set comprises sea-ice extent (regardless of ice concentration within the ice-ocean margin) for ∼77% ( km of the area of the Arctic Ocean. This region occupies the perennially ice-covered central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi marginal seas, leaving out only the eastern Chukchi and Beaufort seas and the Canadian Arctic straits and bays.

In the Atlantic-European sector (Greenland and Barents seas), the sea-ice extents from 1900 to the late 1950s are known only from spring and summer (April to August). It has not previously been possible to gain understanding about the annual ice cover variability in these decades. An attempt has been made to obtain these missing mean annual data. Towards this end, the actual year-round data on the ice extent from 1959-1988 were used to derive an equation to calculate the annual average ice extent in the region based on its mean value in April to August : . The correlation between and is . By means of this equation, the missing annual ice-extent means from 1900-1958 were calculated. The main problem of any reconstruction is errors inevitable in the procedures of data series reconstruction. This problem can be resolved by comparing the actual and calculated annual ice extent averages, in this case for the period from 1959, and by calculating the root-mean-square (rms) error of the reconstruction. The error , and as the rms deviation of the initial actual series , the ratio . Thus, the methodological error of the reconstruction comprises only one third of the rms deviation of the actual series, indicating a sufficiently high reliability of the reconstruction method used.

Data on the ice edge are available for August from the mid 1920s in the Siberian sector (Kara, Laptev, and East Siberian seas). From the 1930s, with the development of shipping along the Northern Sea Route, knowledge of the position of these boundaries was extended to two adjacent months (July and September); however, the accuracy of the ice data from that time cannot be considered high, because observations were not carried out systematically. From the mid 1930s, aviation was used to collect ice information and from the mid 1940s became the main source of sea-ice information in the Siberian Arctic. The observations attain a regular character covering (with some exceptions) the entire navigation season. An attempt has been made to obtain these missing mean annual data, using incomplete data from 1924-1946 to reconstruct the conditions of those years. With this aim, using the 1946-1999 series, the equation: was derived, where is the mean sea-ice extent in the Siberian seas in June-October, and is the ice area in the Siberian seas in August. The correlation between and is . The ratio again suggests a sufficiently high reconstruction quality based on the knowledge of ice extent only in August.

Figure 5 shows time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard 'Walsh' data set ( Chapman and Walsh, 1993), in comparison with the zonal annual mean SAT between 70-90°N since 1900. In contrast to the Walsh data, these new data indicate a substantially reduced () ice cover in the 1920s-1930s warming period. Note that the correspondence between the subsequent cooling into the mid 1960s and increasing sea ice is seen from the late 1950s; the ice data from WWII and the early post-war years are inadequate. Note also that the apparent differences in trends in the two sea-ice time series during the last 25 yr are due to relatively large reductions in the Beaufort Sea and Chukchi seas ( Fig. 4)-areas not included in the Zakharov data set. The correlations between the SAT and the Zakharov and Walsh sea-ice extents are maximum at 0 lag, and 0.3, respectively. This indicates that the interannual variability in the arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, although the r-value may partially reflect feedback processes from the ice cover to the atmosphere, e.g. ECHAM4-model sensitivity experiments (see Bengtsson et al., 2004) have demonstrated a strong SAT response-particularly in the Atlantic-European sector-to model-imposed changes in sea ice.

3.2. Sea-ice thickness

The variability of ice thickness is relatively poorly known, due primarily to spatial-temporal sampling deficiencies in data from submarines carrying upward-looking sonar ( Wadhams, 1997). An analysis ( Rothrock et al., 1999) of data from the summers 1958, 1976, 1993, 1996 and 1997 found that between the 1950s/1970s and the 1990s, the mean ice thickness decreased from 3.1 to 1.8 m. The 1.3-m decrease-if representative-corresponds to an ∼40% reduction over the three to four decades ( Rothrock et al., 1999). However, analyses of sonar data alone from different transects, years and seasons yield a range of estimates, from a comparable decrease ( Wadhams and Davis, 2000) to no significant change ( Winsor, 2001) to an intermediate estimate ( Tucker et al., 2001). An analysis of submarine ( Rothrock et al., 1999, 2003) and modelled ( Holloway and Sau, 2002) ice thickness over the same common time period has demonstrated that ice motion and high interannual variability could mislead inference of trends from sonar transect data, e.g. 12% ( Holloway and Sau, 2002) versus the 40% decrease reported by Rothrock et al. (1999). Thus, the available sonar data alone remain inadequate to produce a reliable, long-term climatology of arctic ice thickness variability.

Here, for the first time, we put forth a unique 20-yr time series of monthly, area-averaged ice thickness derived from field-based measurements of surface elastic-gravity waves from Russian North Polar drifting stations 1970-91, when regular measurements of ice surface vibrations were made in the central Arctic Ocean ( Nagurnyi et al., 1994). Long elastic-gravity waves (on the order of 1 km) in the sea-ice cover arise from the interaction with ocean swells. These elastic-gravity waves can propagate for hundreds to thousands of kilometres before dampening out. Based on a linear theory of free vibrations of the sea-ice cover, the measured wavelength, wave period and direction are then related to thickness through a wave-energy dispersion relation. The ice thicknesses determined from different propagation directions are averaged to provide a basin-wide mean thickness estimate ( Nagurnyi et al., 1994, 1999). The values compare well to those observed in the regional ice cover ( Romanov, 1995) and their interannual variability correlates well with modelled arctic ice volume (see fig. 1 in Hilmer and Lemke, 2000). The thickness estimates have also been found to correlate strongly (∼0.88) to the satellite-derived area of the perennial, MY ice cover in winter ( Johannessen et al., 1999), suggesting moreover that the decreases found in MY ice area represent a mass balance change rather than merely a peripheral effect.

Figure 6a shows the 20-yr time series of area-averaged ice thickness, 1970-91, from which trends for winter and summer are derived ( Fig. 6b). The mean thickness estimates are ∼2.9-3 m in winter and ∼2.5-2.6 in summer (i.e. seasonal cycle ∼40 cm). The linear trend of anomalies from 1971-90 indicates a decrease of only ∼10 cm (less than 4%) over 20 yr. This is comparable with some observational and modelling analyses (e.g. Holloway and Sau, 2002), but is much less than the 1950s/1970s to 1990s sonar data analysis ( Rothrock et al., 1999) upon which the IPCC based its statement that the arctic ice thickness has been reduced 40% during summer in recent decades. The large variability inherent in the arctic sea-ice-climate system, coupled with the problem of obtaining ice thickness data, renders the evaluation of ice thickness trends from the available observational data an open question. None the less, it is notable that our ECHAM4 coupled model experiments (not shown) with the IPCC IS92 emission scenarios for greenhouse and related gases indicate that: (1) our 20 yr of ice thickness data are consistent with the modelled results 1970-90, and (2) substantial decreases in modelled ice thickness commence only in the last two decades of the twentieth century. The ice thickness from both observations and models is presently ∼2.5-3 m in summer, whereas our model results indicate less than 1 m at the end of this century for the remaining ice cover.

4. Sea-ice extent: modelled

The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses ( Vinnikov et al., 1999; Johannessen et al., 2001), which predicted a reduction of ∼15% in the Northern Hemisphere mean ice extent to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, for the first time, both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing are analysed to 2100, using ECHAM-4 and HadCM3 model predictions including different IPCC emissions scenarios.

The observed versus ECHAM4-modelled trends in Northern Hemisphere winter and summer sea-ice extent in the twentieth century are similar ( Fig. 7). Our ECHAM4-model run-using an IPCC IS92 emission scenario similar to IPCC Special Report on Emissions Scenarios (SRES) scenario B2-predicts the decreases to continue such that the summer ice cover may be reduced by ∼80% at the end of the twenty-first century ( Fig. 7, lower). This is much greater than the winter ( Fig. 7, upper) or annual means modelled previously ( Vinnikov et al., 1999; Johannessen et al., 2001) and is comparable to recent projections for the summer ( Gregory et al., 2002).

The spatial distributions of the ECHAM4-modelled sea-ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Fig. 8. In order to test the robustness of our ECHAM4 estimates, we have used a different coupled atmosphere-ocean model, the HadCM3, which is an improvement upon the HadCM2 model used in a previous sea-ice study ( Vinnikov et al., 1999). Furthermore, we use two different SRES scenarios, A2 and B2, which are 'medium-high' and 'medium-low' scenarios, respectively. Results from the B2 experiments are shown in Fig. 9, which depicts decadal averages of winter ( Figs 9a and b) and summer ( Figs 9c and d) sea-ice concentrations in 2001-2010 and 2081-2090. The ECHAM4 ( Fig. 8) and HadCM3 ( Fig. 9) results support each other, both predicting moderate reductions in winter and drastic reduction in the summer ice extent. The spatial distributions of the ECHAM4 and HadCM3 modelled summer ice cover in late century ( Figs 8d and 9d) indicate essentially ice-free arctic marginal seas except north of Greenland and the Canadian Arctic Archipelago, although there are some differences in the modelled spatial patterns.

5. Conclusions and implications

The results of our observational and modelling analysis lead to the following conclusions. First, we theorize that the Arctic warming in the 1920s-1930s and the subsequent cooling until about 1970 are due to natural fluctuations internal to the climate system. Secondly, we believe there are strong indications that neither the warming trend nor the decrease of ice extent and volume over the last two decades can be explained by natural processes alone. Thirdly, the state-of-the-art ECHAM4 and HadCM3 coupled climate models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century.

A range of potential consequences of arctic warming and a shrinking ice cover can be hypothesized, as follows.

(i) Reductions in albedo and increased open water would have significant effects on energy balances and atmospheric and oceanic circulation in the high latitudes.

(ii) Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity for CO absorption, could become a new and important sink of atmospheric CO (Anderson and Kaltin, 2001,).

(iii) Broad changes in the marine ecosystem, e.g. changes in plankton in the North Atlantic due to less ice and greater inflow of melt water (Beaugrand et al., 2002), could have a negative impact on arctic and subarctic marine biodiversity. However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route north of Siberia (Ragner, 2000).

(iv) Changes in the pathways and spreading of melt water and in the stratification in the Nordic Seas, and the effects of reduced deep-water formation in the Greenland Sea on the global thermohaline circulation (Rahmsdorf, 1999; Alekseev et al., 2001), could greatly alter the climate of the northern latitudes.

6. Acknowledgments

This research has been supported under the European Union's 5th Framework Programme project 'Arctic Ice Cover Simulation Experiment (AICSEX)' and the International Association for the Promotion of Cooperation with Scientists from the Independent States of the Former Soviet Union (INTAS), as well as the Research Council of Norway's projects 'Role of Arctic Sea-Ice-Atmosphere Processes (ROLARC)' and 'Marine Climate and Ecosystems in the Seasonal Ice Zone (MACESIZ)'. We also acknowledge the valuable assistance of Cathrine Myrmehl and Anne-Mette Olsen of the Nansen Environmental and Remote Sensing Center.

Ancillary

References

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Authors

OLA M. JOHANNESSEN,

Corresponding author

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Geophysical Institute, University of Bergen, Norway

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LENNART BENGTSSON,

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Max Planck Institute for Meteorology, Hamburg, Germany

3. Environmental Systems Science Centre, University of Reading, UK

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MARTIN W. MILES,

1. Bjerknes Centre for Climate Research, Bergen, Norway

2. Environmental Systems Analysis Research Center, Boulder, Colorado, USA

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VLADIMIR A. SEMENOV,

1. Max Planck Institute for Meteorology, Hamburg, Germany

2. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia

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*Corresponding author.
e-mail: ola.johannessen@nersc.no

ABSTRACT

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic's response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of century- and multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere-ice-ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

1. Introduction

A consensus result from different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should be enhanced in the Arctic ( Räisänen, 2001). The Intergovernmental Panel on Climate Change ( IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ∼3-4°C during the next 50 yr, or more than twice the global mean.

Recent overviews of results from observational studies of atmospheric and climate-sensitive variables (e.g. sea ice, snow cover, river discharge, glaciers and permafrost) have concluded that, taken together, a reasonably coherent portrait of recent change in the northern high latitudes is apparent ( Serreze et al., 2000; Moritz et al., 2002; Peterson et al., 2002). However, it remains open to debate whether the warming in recent decades is an enhanced greenhouse-warming signal or natural decadal and multidecadal variability ( Polyakov and Johnson, 2000; Polyakov et al., 2002), e.g. as possibly expressed by the arctic warming observed in the 1920s and 1930s followed by cooling until the 1960s (e.g. Kelly et al., 1982). The uncertainties are exacerbated by a lack of homogeneous, century-scale instrumental data sets (see Moritz et al., 2002, whose fig. 2a includes no temperature data for the central Arctic) needed to resolve the inherent time-scales of variability in the Arctic ( Venegas and Mysak, 2000), a region characterized by high variability.

From these concerns, two overarching questions are as follows. (1) To what degree are the gradually changing atmosphere-ice-ocean conditions in the Arctic a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse gas (GHG) forcing. (2) To what degree may anthropogenic forcing induce the arctic sea-ice cover to decrease or even disappear in this century? In order to study these questions, we analyse a new set of pertinent multidecadal to century-scale data-surface air temperature (SAT), sea-ice extent, sea-ice area and sea-ice thickness-in combination with global coupled atmosphere-ice-ocean climate model simulations using the ECHAM4 model of the Max Planck Institute for Meteorology ( Roeckner et al., 1999) and the HadCM3 model of the UK Meteorological Office ( Gordon et al., 2000). ECHAM4 and HadCM3 are state-of-the-art versions of two of the models demonstrated-in an intercomparison of climate-change scenario output from 19 coupled models ( Räisänen, 2001)-to be among those most representative of the 19-model mean temperature change.

2. Surface air temperature: observed and modelled

Statistical analyses of global SAT data sets have indicated substantial fluctuations in the extratropical Northern Hemisphere on decadal to multidecadal time-scales (e.g. Schlesinger and Ramankutty, 1994; Hansen et al., 1999; Jones et al., 1999). In the high latitudes, differences in spatial-temporal coverage have led to some discrepancies concerning temperature variability trends in the last century (e.g. Jones et al., 1999; Przybylak, 2000; Polyakov et al., 2002). The global gridded SAT data set ( Jones et al., 1999) used most extensively for studies of climate variability has major gaps in the northern high latitudes, in particular over the ice-covered Arctic Ocean and some surrounding land areas. Here, we analyse for the first time a unique century-long SAT data set focused on the high latitudes of the Northern Hemisphere. The data set is provided through the Arctic and Antarctic Research Institute (AARI), St Petersburg, Russia, produced through a project within the programme International Association for the Promotion of Cooperation with Scientists from the Former Soviet Union ( Alekseev et al., 1999). The input data are daily temperatures from 1486 meteorological stations in the Northern Hemisphere, including land- and drifting-stations from the Arctic. A gridded data set (5° lat. × 10° long.) based on these data has been developed from several sources. First, monthly-mean SAT anomaly maps were produced at the USSR's Main Geophysical Observatory for the period 1891-1969 and at the Hydrometeorological Research Center for the period 1970-1976. Secondly, the maps were visually analysed and interpolated into a gridded data set, as described and evaluated previously ( Vinnikov, 1977). Thirdly, the data set was extended in the same manner for the period 1977-1986, presented as SAT with reduction to common mean values and taking into the account the moist adiabatic temperature gradient (0.6° per 100 m) as in Alekseev and Svyaschennikov (1991). Fourthly, the data set was continued at the Hydrometeorological Research Center from 1986-1995, through visual interpolation and reading from monthly-mean SAT maps. Since 1995, the monthly-mean SATs have been produced at AARI through monthly averaging of gridded daily SAT from the European Centre for Medium-Range Weather Forecasting (ECMWF).

The reliability of the new SAT data set for climate analyses is evidenced through statistical comparison with the National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) re-analysis data ( Kalnay et al., 1996) and the Jones et al. (1999) data set, tested here over common areas and time periods. The quantitative agreement between interannual variations in annual mean SAT north of 55°N (excluding the Greenland area 20-70°W) from NCAR/NCEP and our data set is from 1955-1990. The agreement between the Jones data and our data set from a large arctic-subarctic test area (between 52.5°N-67.5°N and 2.5°W-62.5°E) from 1930-1990 is nearly identical, with a correlation coefficient and a mean difference ∼0.15°C. Therefore, the SAT data set put forth here is considered a reliable alternative to the standard data sets and has the advantage of improved coverage in the Arctic and extending over the last century.

Figure 1a shows the time evolution of the zonally averaged anomalies in annual mean SAT from 30-90°N. Two characteristic warming events stand out, the first from the mid 1920s to about 1940 and the second starting about 1980 and still ongoing. Here, we show that the early twentieth-century warming was largely confined to north of 60°N, whereas the latter warming encompasses the whole Earth ( Jones et al., 1999) but is none the less significantly enhanced in the Arctic ( Fig. 1a).

The early twentieth-century warming trend in the Arctic was nearly as large as the warming trend for the last 20 yr, such that some researchers (e.g. Polyakov et al., 2002) regard them to be part and parcel of the same natural low-frequency oscillation. However, our spatial comparison of these periods reveals key differences in their patterns ( Fig. 2). The 20-yr SAT trends for the 1920s-1930s warming period ( Figs 2a and b) and the subsequent cooling period ( Figs 2c and d) have remarkably similar patterns, thus suggesting similar underlying processes. In the winter half-year, the high-latitude warming ( Fig. 2a) and cooling ( Fig. 2c) patterns are organized symmetrically around the pole, while in the summer half-year, the warming ( Fig. 2b) and cooling ( Fig. 2d) appear to reflect the positions of the latitudinal quasi-stationary wave structure, predominantly wavenumber three and four. However, the warming trend for the last 20 yr is more widespread and has a markedly different pattern from the earlier periods in both winter ( Fig. 2e) and summer ( Fig. 2f). Both the 1920-39 and 1980-99 warming are most pronounced during winter for the high Arctic. In addition, in the latter period, there is pronounced warming in the Eurasian midlatitudes, especially in summer.

A recent modelling study ( Delworth and Knutson, 2000) has suggested that the 1920s-1930s warming anomaly was due to natural processes, insofar as models are capable of simulating such anomalies as due to internal chaotic processes of the climate system. Here, a similar high-latitude anomaly, although less extreme and of a shorter duration, is found in a 300-yr control run (without increasing anthropogenic forcings) with the ECHAM4 model-100 yr are shown in Fig. 1d. This anomaly occurred after 150 yr of integration and lasted for some 15 yr. This simulation without increasing anthropogenic forcing is able to produce an anomaly similar to the observed high-latitude warming in the 1920s-1930s. Therefore, we strongly support the contention of Delworth and Knutson (2000) that this high-latitude warming event represents primarily natural variability within the climate system, rather than being caused primarily by external forcings, whether solar forcing alone ( Thejll and Lassen, 2000) or a combination of increasing solar irradiance, increasing anthropogenic trace gases, and decreasing volcanic aerosols, as suggested from an analysis of 400 yr of temperature proxy data from the Arctic ( Overpeck et al., 1997). Changes in solar forcing have been suggested (e.g. Lean and Rind, 1998) as the cause of the warming. It has attracted considerable interest because of the apparent similarity between the assumed solar variability and the SAT trend of the Northern Hemisphere extratropics ( Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993). Reconstructions of solar variability have been used in modelling studies ( Cubasch et al., 1997; Cubasch and Voss, 2000; Stott et al., 2001) providing results broadly consistent with long-term estimated or observed temperature trends. This is not surprising because multidecadal and longer trends in solar forcing generate a response in global average modelled temperatures on similar time-scales as the anticipated variation in solar forcing ( Cubasch et al., 1997). Rigorous testing of the assumption of solar forcing requires reliable observations; however, these data exist only for the past two decades ( Lean and Rind, 1998), such that it remains only a hypothesis. The anthropogenic forcing in the 1920s-1930s was far too weak to generate the observed warming-the change in the GHG forcing in the early decades of the twentieth century was only ∼20% of the present ( Roeckner et al., 1999). The most plausible explanation in this case is the low-frequency, multidecadal oscillation related to North Atlantic Ocean circulation ( Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Polyakov and Johnson, 2000).

In contrast, no comprehensive numerical-model integrations have produced the present global warm anomaly ( Fig. 1a) without including observed anthropogenic forcing. Figure 1b shows the ECHAM4 model simulation with anthropogenic GHG forcing. The patterns compare well with the last two decades of observed warming, although the modelled warming occurs slightly earlier and also encompasses lower latitudes than observed ( Fig. 1a). The patterns from a simulation including GHGs and sulfate aerosols (GSD) ( Fig. 1c) show that, although the recent midlatitude warming is underestimated, the high-latitude enhancement is in agreement with the observations and other modelling results ( Räisänen, 2001). Therefore, anthropogenic forcing is the dominant cause of the recent pronounced warming in the Arctic.

What then are the possible mechanisms by which anthropogenic forcing could bring about high-latitude warming, given that the recent trend in arctic SAT is greater than the direct radiative effect of GHGs? First, as has been demonstrated by Bengtsson (1999) and Hansen et al. (1999), the large-scale spatial pattern of forcing and the pattern of response to forcing are practically uncorrelated, which stresses the key role of advective atmosphere-ocean processes in bringing about regional climate change; see also Schneider et al. (2003). A possible mechanism is suggested by recent findings from observations and modelling experiments ( Hoerling et al., 2001; Lin et al., 2002). Hoerling et al. (2001) show that the observed long-term warming trend (up to 0.2°C per decade) in the tropical oceans-anthropogenically forced ( Levitus et al., 2000; Reichert et al., 2002)-during the last 50 yr has generated, through enhanced convective activity, an intensification of the midlatitude tropospheric westerlies, and consequently because of geostrophy in the Atlantic sector, an enhanced positive North Atlantic Oscillation/Arctic Oscillation (NAO/AO) pattern ( Hoerling et al., 2001). Because the NAO and arctic temperature are linked-the interannual variations in the NAO index ( Hurrell, 1995) and SAT from 60-90°N are found here to be significantly correlated ( when using data since the mid twentieth century)-it follows that an NAO enhancement, linked to anthropogenic warming of the tropical oceans, can explain a substantial portion of the warming trend in the Arctic. It is unlikely that this was a mechanism for the early twentieth-century warming phase, because of the small anthropogenic GHG effect at the time and because no positive correlation between arctic SAT and the NAO before 1950 is found; in fact, here we find that the correlation is negative ().

Secondly, an ice-albedo feedback may enhance the arctic warming, as new ECHAM4 sensitivity experiments have demonstrated a robust relationship between sea ice and air temperature ( Bengtsson et al., 2004), in which advective mechanisms-in particular, enhanced wind-driven oceanic heat inflow into the Barents Sea-may have a predominant role.

3. Sea ice: observations

3.1. Sea-ice concentration and derived parameters

Sea-ice concentration (percent ice area per unit area) and derived parameters such as ice extent (the area within the ice-ocean margin defined as 15% ice concentration) and ice area (extent minus the open-water area) can be reliably retrieved from satellite passive-microwave sensor measurements, which are available continuously since 1978, thus among the longest satellite-retrieved geophysical records. Satellite data have shown that the winter maximum ice area ( Fig. 3a) is typically about while the summer minimum ice area ( Fig. 3a) is about . These data, here updated from Johannessen et al. (1995, 1999) and Bjørgo et al. (1997) through March 2003, indicate a decrease of (∼7.4%) in the Northern Hemisphere annual sea-ice area, 1978-2003 ( Fig. 3b). During this period, the decreases have been larger in summer (see also Chapman and Walsh, 1993; Parkinson et al., 1999); here, we calculate a (14%) decrease in September versus (5%) in March (1978-2003). This seasonal difference has resulted in a 7-9% per decade reduction in the area of thicker, multiyear (MY) ice (ice that has survived at least one summer melt) over the last two decades ( Johannessen et al., 1999; Comiso, 2002). It is noteworthy that our updated time series through September 2002 indicate that the record minimum summer ice cover recently reported ( Serreze et al., 2003) is indeed unprecedented in the nearly quarter-century satellite record ( Fig. 3a)-less than in area, which occurred from anomalous warm southerly winds in spring followed by low SLP and high SAT in the Arctic in summer ( Serreze et al., 2003). Note also the large positive anomaly in 1996, which occurred in the summer after an extreme temporary reversal in the NAO in the winter 1995/96.

The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figs 4a and 4b. The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figs 4c and d. During this period, the decreases in winter have been most pronounced (as large as ∼50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ∼30-50% in the Siberian marginal seas. These summer patterns are in agreement with an independent analysis of ice-cover minima from 1978-1998 (see fig. 2c in Comiso, 2002).

The decreases in recent decades, which are also partially due to circulation-driven ice export through the Fram Strait between Greenland and Svalbard ( Vinje, 2001), have coincided with a positive trend in the NAO, with unusually high index values in the late 1980s and 1990s. During this period, the variability of ice motion and ice export through the Fram Strait was correlated strongly with the NAO; for the ice area flux ( Kwok and Rothrock, 1999) and for the ice volume flux ( Hilmer and Jung, 2000), although the relationship was insignificant before the mid 1970s ( Hilmer and Jung, 2000). Deser et al. (2000) analysed a 40-yr gridded data set (1958-97) to determine the association between arctic sea ice, SAT and SLP, concluding that the multidecadal trends in the NAO/AO in the past three decades have been 'imprinted upon the distribution of Arctic sea ice', with the first principal component of sea-ice concentration significantly correlated with the NAO index, recently cause-and-effect modelled by Hu et al. (2002). None the less, our calculations and those of Deser et al. (2000) indicate that, even in recent decades, only about one third of the variability in arctic total ice extent and MY ice area ( Johannessen et al., 1999) is explained by the NAO index, implying that other factors including enhanced radiative forcing and ice-albedo feedbacks ( Björk and Söderkvist, 2002; Bengtsson et al., 2003) must be invoked to explain the variability.

A pronounced reduction in the Northern Hemisphere sea-ice extent, such as seen in the satellite record of the last two decades, is scarcely apparent in the early twentieth-century warm period, at least in the two most widely-used century-long sea-ice data sets ( Chapman and Walsh, 1993; Rayner et al., 1996). To ascertain whether this has a physical explanation or is due to acknowledged data deficiencies before the 1950s ( Chapman and Walsh, 1993; Vinnikov et al., 1999), we have analysed these data together with a new century-long 'Zakharov' data set ( Zakharov, 1997; Alekseev et al., 2000), which includes hitherto under-recognized Russian data. The data set comprises sea-ice extent (regardless of ice concentration within the ice-ocean margin) for ∼77% ( km of the area of the Arctic Ocean. This region occupies the perennially ice-covered central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi marginal seas, leaving out only the eastern Chukchi and Beaufort seas and the Canadian Arctic straits and bays.

In the Atlantic-European sector (Greenland and Barents seas), the sea-ice extents from 1900 to the late 1950s are known only from spring and summer (April to August). It has not previously been possible to gain understanding about the annual ice cover variability in these decades. An attempt has been made to obtain these missing mean annual data. Towards this end, the actual year-round data on the ice extent from 1959-1988 were used to derive an equation to calculate the annual average ice extent in the region based on its mean value in April to August : . The correlation between and is . By means of this equation, the missing annual ice-extent means from 1900-1958 were calculated. The main problem of any reconstruction is errors inevitable in the procedures of data series reconstruction. This problem can be resolved by comparing the actual and calculated annual ice extent averages, in this case for the period from 1959, and by calculating the root-mean-square (rms) error of the reconstruction. The error , and as the rms deviation of the initial actual series , the ratio . Thus, the methodological error of the reconstruction comprises only one third of the rms deviation of the actual series, indicating a sufficiently high reliability of the reconstruction method used.

Data on the ice edge are available for August from the mid 1920s in the Siberian sector (Kara, Laptev, and East Siberian seas). From the 1930s, with the development of shipping along the Northern Sea Route, knowledge of the position of these boundaries was extended to two adjacent months (July and September); however, the accuracy of the ice data from that time cannot be considered high, because observations were not carried out systematically. From the mid 1930s, aviation was used to collect ice information and from the mid 1940s became the main source of sea-ice information in the Siberian Arctic. The observations attain a regular character covering (with some exceptions) the entire navigation season. An attempt has been made to obtain these missing mean annual data, using incomplete data from 1924-1946 to reconstruct the conditions of those years. With this aim, using the 1946-1999 series, the equation: was derived, where is the mean sea-ice extent in the Siberian seas in June-October, and is the ice area in the Siberian seas in August. The correlation between and is . The ratio again suggests a sufficiently high reconstruction quality based on the knowledge of ice extent only in August.

Figure 5 shows time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard 'Walsh' data set ( Chapman and Walsh, 1993), in comparison with the zonal annual mean SAT between 70-90°N since 1900. In contrast to the Walsh data, these new data indicate a substantially reduced () ice cover in the 1920s-1930s warming period. Note that the correspondence between the subsequent cooling into the mid 1960s and increasing sea ice is seen from the late 1950s; the ice data from WWII and the early post-war years are inadequate. Note also that the apparent differences in trends in the two sea-ice time series during the last 25 yr are due to relatively large reductions in the Beaufort Sea and Chukchi seas ( Fig. 4)-areas not included in the Zakharov data set. The correlations between the SAT and the Zakharov and Walsh sea-ice extents are maximum at 0 lag, and 0.3, respectively. This indicates that the interannual variability in the arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, although the r-value may partially reflect feedback processes from the ice cover to the atmosphere, e.g. ECHAM4-model sensitivity experiments (see Bengtsson et al., 2004) have demonstrated a strong SAT response-particularly in the Atlantic-European sector-to model-imposed changes in sea ice.

3.2. Sea-ice thickness

The variability of ice thickness is relatively poorly known, due primarily to spatial-temporal sampling deficiencies in data from submarines carrying upward-looking sonar ( Wadhams, 1997). An analysis ( Rothrock et al., 1999) of data from the summers 1958, 1976, 1993, 1996 and 1997 found that between the 1950s/1970s and the 1990s, the mean ice thickness decreased from 3.1 to 1.8 m. The 1.3-m decrease-if representative-corresponds to an ∼40% reduction over the three to four decades ( Rothrock et al., 1999). However, analyses of sonar data alone from different transects, years and seasons yield a range of estimates, from a comparable decrease ( Wadhams and Davis, 2000) to no significant change ( Winsor, 2001) to an intermediate estimate ( Tucker et al., 2001). An analysis of submarine ( Rothrock et al., 1999, 2003) and modelled ( Holloway and Sau, 2002) ice thickness over the same common time period has demonstrated that ice motion and high interannual variability could mislead inference of trends from sonar transect data, e.g. 12% ( Holloway and Sau, 2002) versus the 40% decrease reported by Rothrock et al. (1999). Thus, the available sonar data alone remain inadequate to produce a reliable, long-term climatology of arctic ice thickness variability.

Here, for the first time, we put forth a unique 20-yr time series of monthly, area-averaged ice thickness derived from field-based measurements of surface elastic-gravity waves from Russian North Polar drifting stations 1970-91, when regular measurements of ice surface vibrations were made in the central Arctic Ocean ( Nagurnyi et al., 1994). Long elastic-gravity waves (on the order of 1 km) in the sea-ice cover arise from the interaction with ocean swells. These elastic-gravity waves can propagate for hundreds to thousands of kilometres before dampening out. Based on a linear theory of free vibrations of the sea-ice cover, the measured wavelength, wave period and direction are then related to thickness through a wave-energy dispersion relation. The ice thicknesses determined from different propagation directions are averaged to provide a basin-wide mean thickness estimate ( Nagurnyi et al., 1994, 1999). The values compare well to those observed in the regional ice cover ( Romanov, 1995) and their interannual variability correlates well with modelled arctic ice volume (see fig. 1 in Hilmer and Lemke, 2000). The thickness estimates have also been found to correlate strongly (∼0.88) to the satellite-derived area of the perennial, MY ice cover in winter ( Johannessen et al., 1999), suggesting moreover that the decreases found in MY ice area represent a mass balance change rather than merely a peripheral effect.

Figure 6a shows the 20-yr time series of area-averaged ice thickness, 1970-91, from which trends for winter and summer are derived ( Fig. 6b). The mean thickness estimates are ∼2.9-3 m in winter and ∼2.5-2.6 in summer (i.e. seasonal cycle ∼40 cm). The linear trend of anomalies from 1971-90 indicates a decrease of only ∼10 cm (less than 4%) over 20 yr. This is comparable with some observational and modelling analyses (e.g. Holloway and Sau, 2002), but is much less than the 1950s/1970s to 1990s sonar data analysis ( Rothrock et al., 1999) upon which the IPCC based its statement that the arctic ice thickness has been reduced 40% during summer in recent decades. The large variability inherent in the arctic sea-ice-climate system, coupled with the problem of obtaining ice thickness data, renders the evaluation of ice thickness trends from the available observational data an open question. None the less, it is notable that our ECHAM4 coupled model experiments (not shown) with the IPCC IS92 emission scenarios for greenhouse and related gases indicate that: (1) our 20 yr of ice thickness data are consistent with the modelled results 1970-90, and (2) substantial decreases in modelled ice thickness commence only in the last two decades of the twentieth century. The ice thickness from both observations and models is presently ∼2.5-3 m in summer, whereas our model results indicate less than 1 m at the end of this century for the remaining ice cover.

4. Sea-ice extent: modelled

The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses ( Vinnikov et al., 1999; Johannessen et al., 2001), which predicted a reduction of ∼15% in the Northern Hemisphere mean ice extent to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, for the first time, both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing are analysed to 2100, using ECHAM-4 and HadCM3 model predictions including different IPCC emissions scenarios.

The observed versus ECHAM4-modelled trends in Northern Hemisphere winter and summer sea-ice extent in the twentieth century are similar ( Fig. 7). Our ECHAM4-model run-using an IPCC IS92 emission scenario similar to IPCC Special Report on Emissions Scenarios (SRES) scenario B2-predicts the decreases to continue such that the summer ice cover may be reduced by ∼80% at the end of the twenty-first century ( Fig. 7, lower). This is much greater than the winter ( Fig. 7, upper) or annual means modelled previously ( Vinnikov et al., 1999; Johannessen et al., 2001) and is comparable to recent projections for the summer ( Gregory et al., 2002).

The spatial distributions of the ECHAM4-modelled sea-ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Fig. 8. In order to test the robustness of our ECHAM4 estimates, we have used a different coupled atmosphere-ocean model, the HadCM3, which is an improvement upon the HadCM2 model used in a previous sea-ice study ( Vinnikov et al., 1999). Furthermore, we use two different SRES scenarios, A2 and B2, which are 'medium-high' and 'medium-low' scenarios, respectively. Results from the B2 experiments are shown in Fig. 9, which depicts decadal averages of winter ( Figs 9a and b) and summer ( Figs 9c and d) sea-ice concentrations in 2001-2010 and 2081-2090. The ECHAM4 ( Fig. 8) and HadCM3 ( Fig. 9) results support each other, both predicting moderate reductions in winter and drastic reduction in the summer ice extent. The spatial distributions of the ECHAM4 and HadCM3 modelled summer ice cover in late century ( Figs 8d and 9d) indicate essentially ice-free arctic marginal seas except north of Greenland and the Canadian Arctic Archipelago, although there are some differences in the modelled spatial patterns.

5. Conclusions and implications

The results of our observational and modelling analysis lead to the following conclusions. First, we theorize that the Arctic warming in the 1920s-1930s and the subsequent cooling until about 1970 are due to natural fluctuations internal to the climate system. Secondly, we believe there are strong indications that neither the warming trend nor the decrease of ice extent and volume over the last two decades can be explained by natural processes alone. Thirdly, the state-of-the-art ECHAM4 and HadCM3 coupled climate models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century.

A range of potential consequences of arctic warming and a shrinking ice cover can be hypothesized, as follows.

(i) Reductions in albedo and increased open water would have significant effects on energy balances and atmospheric and oceanic circulation in the high latitudes.

(ii) Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity for CO absorption, could become a new and important sink of atmospheric CO (Anderson and Kaltin, 2001,).

(iii) Broad changes in the marine ecosystem, e.g. changes in plankton in the North Atlantic due to less ice and greater inflow of melt water (Beaugrand et al., 2002), could have a negative impact on arctic and subarctic marine biodiversity. However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route north of Siberia (Ragner, 2000).

(iv) Changes in the pathways and spreading of melt water and in the stratification in the Nordic Seas, and the effects of reduced deep-water formation in the Greenland Sea on the global thermohaline circulation (Rahmsdorf, 1999; Alekseev et al., 2001), could greatly alter the climate of the northern latitudes.

6. Acknowledgments

This research has been supported under the European Union's 5th Framework Programme project 'Arctic Ice Cover Simulation Experiment (AICSEX)' and the International Association for the Promotion of Cooperation with Scientists from the Independent States of the Former Soviet Union (INTAS), as well as the Research Council of Norway's projects 'Role of Arctic Sea-Ice-Atmosphere Processes (ROLARC)' and 'Marine Climate and Ecosystems in the Seasonal Ice Zone (MACESIZ)'. We also acknowledge the valuable assistance of Cathrine Myrmehl and Anne-Mette Olsen of the Nansen Environmental and Remote Sensing Center.

Ancillary

References

Alekseev, G. V. and Svyaschennikov, P. N. 1991. 159 pp (in Russian). Natural Variability of Climate Characteristics in Northern Polar Region and Northern Hemisphere. St Petersburg , Russia, Gidrometeoizdat ,

Alekseev, G. V., Aleksandrov, Ye. I., Bekriayev, R. V., Svyaschennikov, P. N. and Harlanienkova, N. Ya. 1999. Surface air temperature from meteorological data. In: Detection and Modelling of Greenhouse Warming in Arctic and sub-Arctic, INTAS Grant 97-1277 Technical Report on Task 1, Arctic and Antarctic Research Institute , St Petersburg , Russia .

Alekseev, G. V., Zakharov, V. F. and Radionov, V. F. 2000. In: 141 pp (in Russian). Problems of Hydrometeorology and Environment at the Start of the XX Century. Proc. Int. Theor. Conf. , St Petersburg , Russia, Gidrometeoizdat ,

Beaugrand, G., Reid, P. C., Ibañez, F., Lindley, J. A. and Edwards, M. 2002. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692-1694.

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Bengtsson, L., Semenov, V. and Johannessen, O. M. 2004. The early 20th century warming in the Arctic-a possible mechanism. J. Climate in press.

Björk, G. and Söderkvist, J. 2002. Dependence of the Arctic Ocean thickness distribution on the poleward energy flux in the atmosphere. J. Geophys. Res. 107, 10.1029/2000JC000723.

Comiso, J. 2002. A rapidly declining perennial ice cover in the Arctic. Geophys. Res. Lett. 29, 1956 (doi: DOI: 10.1029/2002GL015650).

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Johannessen, O. M., Shalina, E., Kuzmina, S., Miles, M. W. and Nagurnyi, A. 2001. Shrinking of the Arctic cover over the last decades. In: W. L.Smith, and Y. M.Timofeyev). Deepak Publishing, Hampton , USA , 1007-1011. Proc. Int. Radiation Symp., St Petersburg , Russia , 24-29 July 2000 (eds

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Corresponding author Nansen Environmental and Remote Sensing Center, Bergen, Norway Geophysical Institute, University of Bergen, Norway Search for more papers by this author Nansen Environmental and Remote Sensing Center, Bergen, Norway Max Planck Institute for Meteorology, Hamburg, Germany Environmental Systems Science Centre, University of Reading, UK Search for more papers by this author Bjerknes Centre for Climate Research, Bergen, Norway Environmental Systems Analysis Research Center, Boulder, Colorado, USA Search for more papers by this author Max Planck Institute for Meteorology, Hamburg, Germany Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia Search for more papers by this author *Corresponding author.

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The Victorian humorist and poet Thomas Hood took a dim view of the penultimate month of the year: "No fruits, no flowers, no leaves, no birds! - November!"

If he meant no bird song, then, when he was writing, during the cooler climatic interlude known as the Little Ice Age, he would have been spot-on.

Apart from the robin, which unusually among our songbirds holds an autumn territory and so sings throughout the season, the parks and gardens around Hood's north London home would have been silent in these dark days of late autumn.

But that certainly isn't true now. Thanks to the effects of climate change, combined with the " urban-heat-island effect" - in which cities are several degrees warmer than the surrounding countryside - London is now a hotspot for unseasonal birdsong. Visit any of the capital's parks, especially at dawn when the sound of commuter traffic has yet to reach its peak, and you'll hear a wide range of songbirds.

Listen out for the impossibly loud song of the wren - with the trill my fellow writer Dominic Couzens calls "the twiddle in the middle" - as this tiny bird signals its presence from deep inside hedgerows and shrubberies. Joining the chorus are the great tit, which sings its "tea-cher, tea-cher" song from a prominent twig, the scolding, frantic sound of the blue tit, and the rather monotonous song of the dunnock.

For something a little more exotic, pay a visit to the WWT's London Wetland Centre or the RSPB's Rainham Marshes reserve, where you can hear the explosive sound of a relatively recent colonist, Cetti's warbler, more or less all year round.

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The Victorian humorist and poet Thomas Hood took a dim view of the penultimate month of the year: "No fruits, no flowers, no leaves, no birds! - November!" If he meant no bird song, then, when he was writing, during the cooler climatic interlude known as the Little Ice Age, he would have been spot-on.

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Holmberg, Brooke

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Human Nature - Conservation International Blog

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Editor's note: August 19 is National Honeybee Day.
Did you eat an apple today or drink coffee this morning? Thank a bee.

About one out of every three bites of food is made possible by bees and other pollinators - in the United States alone, honeybees pollinate $15 billion worth of crops each year. But the world's honeybees are in steep decline, with 10 million hives disappearing just in the past three years. Not having honey is the least of our problems.
1. Habitat loss
While scientists aren't clear on exactly what factors are behind bees' decline, what is known is that climate change is also making life harder for bees. Here's how:
2. Shifting temperatures
Climate change is causing habitat loss as bees fail to migrate to cooler areas and establish new hives. A recent study on bumblebee migrations found that bee territories have shrunk by nearly 200 miles in North America and Europe. In South Africa, CI is keeping important pollinator habitat intact and helping to ensure future food security there.
3. Disease
As average monthly temperatures rise, flowers bloom earlier in the spring, creating a potential mismatch in seasonal timing between when flowers produce pollen and when bees are ready to feed on that pollen. Even a small mismatch of three to six days could negatively affect bees' health, making them less likely to reproduce and less resistant to predators and parasites.

Honeybees are susceptible to parasites such as Varroa mites and the gut parasite Nosema ceranae, and environmental stresses may increase infections. Scientists first discovered the Nosema ceranae in the early 1990s in Asian honeybees. It has since spread to Europe and the U.S., causing shorter lifespans and colony collapse. A recent study found that lower temperatures were associated with lower prevalence of the parasite, indicating that higher temperatures as a result of climate change could result in more bees infected with Nosema ceranae.
Leah Duran is a staff writer for CI. Want to read more stories like this? Further reading for email updates. Donate to Conservation International.
What can you do? Experts suggest starting with the bees and other pollinators in your own backyard: Plant a pollinator-friendly garden designed to maximize blooming for most of the year. In urban areas, porch and window planters can provide important food sources for bees.

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Editor's note: August 19 is National Honeybee Day. Did you eat an apple today or drink coffee this morning? Thank a bee. About one out of every three bites of food is made possible by bees and other pollinators - in the United States alone, honeybees pollinate $15 billion worth of crops each year.

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Next Victim of Global Warming: The beaches

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Reagan Pantoja

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Until May 1, when turtle nesting season forced them to stop, they were also pumping hundreds of thousands of cubic yards of sand onto eroded beaches. Florida has relied on this approach for decades, but after the past few storm seasons, there has been an increase in applications for sea wall permits, many from Mr. Tomasello's clients. "If you have a house or a condo that's threatened, it's really the only alternative," he said.

Maintaining eroding beaches with artificial infusions of sand is difficult and costly, and as sea levels rise, it may become economically impractical or even impossible. "The combination of sea walls and rising sea level will accelerate the rate of land loss in front of those sea walls," said Peter Howd, an oceanographer who conducts shoreline research for the United States Geological Survey in St. Petersburg. "So people with a sea wall and a beach in front of it will end up with just a sea wall."

Many people "want to disagree" that global warming is a threat to the coast, said Daniel Trescott, a planner on the staff of the Southwest Florida Regional Planning Council, one of 11 such boards in the state. "But the first place you see these impacts is on the beach."

The council is participating in a federal program to map areas that are vulnerable to rising sea levels, identify crucial infrastructure there and assess how much will probably end up protected by armor. Mr. Trescott said he hoped that the effort would put sea levels "on the radar, to start addressing how we are going to respond to this rise."

Elsewhere, scientists are studying data from ancient sediment formations to predict how the barrier islands that form most of the East and Gulf Coasts will respond to rising seas. "As scientists, and especially as federal agency scientists, it's our responsibility to think long term," said S. Jeffress Williams, a coastal geologist at the United States Geological Survey in Woods Hole, Mass., who is organizing a session on sea level rise at a meeting of coastal scientists next year. "What are the cumulative impacts we can expect over the next 50 to 100 years?"

Dr. Williams pointed to a plan by the Army Corps of Engineers for beach maintenance on the South Shore of Long Island, from Fire Island to Montauk Point. The project relies on historical rates of sea level rise, measured by an array of instruments in many locations, rather than on predictions of future acceleration, said Cliff Jones, a project manager for the corps. That is typical of the corps, he said, "to take advantage of what history has shown, as opposed to what might be predicted."

It is an understandable approach, Dr. Williams said, but "it is going to build up false expectations."

As with climate change and other environmental problems that develop imperceptibly, it is hard for people to see rising sea levels as a threat.

"It's a slow process," Dr. Howd said. "It's not something that is visible right now or next week or a year from now."

And the remedies are not attractive, to say the least. Few coastal residents want to see their towns walled off and surrounded by water. And few want to elevate their houses by 20 feet or more, as flooding experts are beginning to recommend in some coastal areas. The approach favored by many scientists, a gradual retreat from the coast, is a perennial nonstarter among real estate interests and their political allies.

"Socioeconomically, politically, it's an ugly mess," Dr. Howd said.
The Rising Tide
Since the Industrial Revolution, and particularly in the last 50 years, the burning of fossil fuels has been sending heat-trapping gases into the atmosphere. In the last century, global sea levels have risen by about eight inches.

Some of the rise - scientists argue over how much - is because of natural variation, like changes in atmospheric pressures and winds over the Southern Ocean. But much of it results from warming; as water warms, it expands, occupying more space.

Also, warming melts inland glaciers and ice sheets, sending torrents of fresh water into the oceans. In 2001, the Intergovernmental Panel on Climate Change, convened by the United Nations, said that the rise in sea levels was accelerating. Their mid-range projection for 2100 was a rise of just under 20 inches from a 1990 baseline, partly because of this melting. Evidence reported since then suggests that the rise may be even faster. (If ice sheets on Greenland and Antarctica melt significantly, seas will rise by 20 feet or more, but few scientists expect that to happen in this century.)

Even a rise of one or two feet would allow "the waves, the tides, the storms to impact parts of the beaches and marshes and estuaries that they don't reach right now," Dr. Howd said.

How much depends on the geography of the area. For example, much of Florida is so low that a one-foot rise in sea level would send water 100 feet inland. Dr. Howd, 49, who lives in St. Petersburg, said that is one reason he does not expect to retire there. His house is about 600 feet from the beach, but only about 6 feet above sea level.

Undeveloped beaches and wetlands deal with encroaching seas by shifting to higher elevations inland. The barrier islands migrate, in effect, as storms send sand washing over them from ocean to lagoon. The ocean side erodes, but sand piles up on the back side.

"The beach is attempting to find an elevation at which it can maintain its shape," said Laura J. Moore, a geologist who is working with Dr. Williams at Woods Hole while on sabbatical from Oberlin College.

Dr. Moore is using data on past barrier island migration to predict how coastal barriers will fare as the sea level rise accelerates. For any stretch of coast, she said, the answer depends on a number of factors, including geology, wave patterns and sand supply, whether natural or artificial. But in general, the faster the sea level rises, the faster the barrier islands will have to retreat.
The Problem With Walls
At present rates of sea level rise, Dr. Moore said, the computer model she is using "suggests the barriers can maintain themselves, if they are allowed to migrate." But if a sea wall or other infrastructure is in the way, the island is pinned down. Sand that would wash over is blocked as the island erodes. In time, rising water meets the wall and drowns the beach. Meanwhile, storm waves scour the wall's base and erode the underwater beach slope. "Eventually the sea wall collapses because the situation is so extreme."

Now the island is free to move, but it may be too late, she said. If water continues to rise, the island may just disintegrate.

That is already happening on the islands that form the Outer Banks of North Carolina, according to Stan Riggs and Dorothea V. Ames, geologists at East Carolina University. The islands have been pinned down for 70 years by an artificial dune - a wall, in effect - built during the Depression by the Civilian Conservation Corps.

In their book "Drowning the North Carolina Coast" (North Carolina Sea Grant, 2003), they say perennial washouts of the road connecting the islands show that "large segments of the Outer Banks are already collapsing." If even the 2001 estimates from the United Nations panel are correct, they say, there will be "major land loss."

A recent study led by Michael Oppenheimer, a climate researcher at the Woodrow Wilson School of Public and International Affairs at Princeton, said a sea level rise of two to four feet would be devastating in New Jersey. For example, the study found that Cape May would "attenuate, change in composition and potentially disappear."

What does this mean for people whose houses or businesses are on the beach? "They may be difficult to maintain," Dr. Moore said. "We have to start thinking about that."
Holding the Line
There are those who doubt that things will be so bad. One of them is Robert G. Dean of the University of Florida, one of the nation's leading coastal engineers. Like many coastal scientists, he attributes much of the beach erosion Florida has experienced to coastal inlets, most of them artificial, that interfere with the natural flow of sand. And he does not foresee the kind of sea level rise predicted by the United Nations panel.

If sea levels rise in the 21st century at the same rate they did in the 20th, Dr. Dean said - "and if I had to, I'd bet on that"- his fellow engineers "would be able to hold 99 percent of the Florida shoreline."

If sea levels follow the panel's midrange estimate of a nearly 20-inch rise, "then I'd back off that 99 percent to say 70-80-85 percent," he said.

Finding replacement sand for eroding beaches would be troublesome - it is already hard to find - but, he said, "they will import sand or bring it from inland or maybe manufacture it" by grinding up stone, as he said was done years ago in Monaco. "People will be creative."

Of course, it will be expensive. Many Florida beaches exist now only because they are artificially maintained, and by some estimates at least 20 percent of the state's coast is armored, a percentage certain to rise. Like other states, Florida has setback requirements and other regulations that in theory limit sea wall construction, but owners of buildings in imminent danger are often given emergency permission to protect them. And once walls are in place, they are rarely removed.

The result is ever more stretches of beach that must be artificially replenished. According to Mr. Trescott of the Southwest Florida Regional Planning Council, government agencies have spent "about half a billion dollars" on beach building in Florida since the 2004 hurricane season. That's a bargain, according to many state and local officials, who point out that beaches pump $40 billion or more into Florida's economy each year.

But it is far from clear that replenishing them will be affordable or even possible in an era of rising seas. "You are probably conservatively talking about hundreds of millions of cubic meters of sand, and for the most part that sand is not available," Dr. Williams said. Counting on that approach "is the wrong thing to do unless all that is sorted out," he said.

Even Dr. Dean believes that a rise of three feet or more will mean trouble.

"I don't believe that's going to happen," he said, pointing out that people who study sea level rise "may have somewhat of a vested interest in keeping the project alive." But if it did happen, he conceded, "that would not be good at all."
Waiting for Sand
Volusia County, on the east coast of Florida, was hit hard by the hurricanes of 2004. "In some areas we lost 6 or 7 feet of beach grade, and the dune system cut back in some areas as much as 20 to 25 feet," said Jennifer Winters, who manages turtle habitats for the county.

Repairs began at once, with property owners winning emergency permission to install sandbags, corrugated vinyl bulkheads, sea walls and other devices, and work was still under way this spring.

At East 27th Street Park in New Smyrna Beach, workers replaced a fallen sea wall and filled in behind it. Beginning in January, a pipeline disgorged a slurry of water and sand, dredged from the Ponce de Leon Inlet nearly five miles away, onto the beach. According to David May, an engineer with Subaqueous Services, a dredging contractor based in Orlando that did the work, 743,000 cubic yards of additional sand would be pumped onto a five-mile stretch of beach, at a cost of about $14 million.

People who lived on the beach "kept complaining to me about their property, and I said, 'hold on, sand is coming,' " Ms. Winters recalled recently as she drove a county truck along the beach, passing houses and condominium developments armored with concrete, vinyl sheet piling, piles of rock, sandbags or even wooden planks.

She made the drive at low tide; at high tide the water would reach the walls in many areas, leaving no dry beach. "You cannot even walk the beach at high tide," she said.

That is typical of armored beaches, researchers who study them say, and it is one of the reasons environmentalists worry about the armor-and-fill approach.

In research in Santa Barbara, Calif., that was reported in the winter issue of the journal Shore and Beach, Jenifer E. Dugan and David M. Hubbard of the University of California, Santa Barbara, found that on armored beaches, there was much less accumulation of seaweed and other drift material, far fewer insects and crustaceans that feed on or in this wrack, fewer intertidal species like sand crabs and clams, and fewer species of birds.

Their conclusion? "The combination of rising sea levels predicted by climate change models and the increasing extent of coastal armoring will accelerate beach loss and increase ecological consequences for sandy beach communities and shorebirds in many regions."

The dredging process itself inflicts environmental damage by altering currents, changing the way sand moves in the water or making water murky, according to Ken Lindeman, a senior scientist with the advocacy group Environmental Defense. So-called borrow sites where sand is dredged (Dr. Lindeman calls them underwater "craters") may not recover their plant and animal biodiversity, he said, and little is known about the effects of artificial beach building on habitats near shore, where, he said, "juvenile fish develop before migrating offshore and where the greatest impact of coastal construction is."

"The assumption is made that the fish just swim away," but fish settle in these areas as larvae, he said. "They are not adapted to swimming away."

Dr. Lindeman pointed out that advocates for anglers were beginning to talk about these problems. And surfers are starting to speak up when dredging projects threaten to disrupt wave patterns they favor. "Surfers, divers, fishermen are trying to get answers to excellent questions," he said.
Does the Government Know?
James Titus, an Environmental Protection Agency project manager for sea level rise who is leading an agency mapping effort, wrote an essay for a law review a few years ago in which he argued that the nation needed to make decisions on whether or how wetlands and beaches should be allowed to migrate inland. Otherwise, he wrote, government policy is saying, in effect, that "wetlands and beaches are important resources that must be preserved for the duration of this generation, but whether they survive for the next 50 to 100 years is not our problem."

Mr. Titus titled his essay, published in the Golden Gate Law Review in 2000, "Does the U.S. Government Realize That the Sea Is Rising?" It was accompanied by a disclaimer noting that it did not represent the views of the E.P.A.

Reached by telephone, Mr. Titus said he was no longer allowed to discuss such issues publicly and referred questions to the agency's press office, which would not allow him to speak about it on the record. Instead, requests for on-the-record information were referred to Bill Wehrum, the agency's acting assistant administrator for air and radiation.

"The administration's strategy for dealing with climate change is to continue to put significant resources into understanding climate change," Mr. Wehrum said. "The goal is to develop information that will be useful for local planners. This is about looking at coastal areas and assessing how those areas are used and then helping people with the question of how much protection they might want to provide for those areas if sea level continues to rise."

In general, Mr. Wehrum said, it seemed quite likely that people would want to protect developed areas and might be willing to let undeveloped areas like wildlife refuges or coastal farms migrate.

Meanwhile, though, people like Ms. Winters and Dr. Williams watch as, one by one, people make decisions that will collectively have big implications for beaches.

"The levee failures and flooding of Katrina were no surprise to geologists who studied the area," Dr. Williams said, and damage from future coastal storms will not surprise them either. But he said, "I don't think we have the political will at the administration level" to confront the issue.

"We're rebuilding bigger and better," Dr. Williams said. "We see that in every storm."

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Until May 1, when turtle nesting season forced them to stop, they were also pumping hundreds of thousands of cubic yards of sand onto eroded beaches. Florida has relied on this approach for decades, but after the past few storm seasons, there has been an increase in applications for sea wall permits, many from Mr. Tomasello's clients.

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By Eduardo Lujan

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Human activities cause ozone depletion and global warming

Ozone (O ₃) depletion does not cause global warming, but both of these environmental problems have a common cause: human activities that release pollutants into the atmosphere altering it.

Global warming is caused primarily by putting too much carbon dioxide into the atmosphere when coal, oil, and natural gas are burned to generate electricity or to run our cars.

Carbon dioxide spreads around the planet like a blanket, and is one of the main gases responsible for the absorption of infrared radiation (felt as heat), which comprises the bulk of solar energy.

Ozone depletion occurs when chlorofluorocarbons (CFCs) and halons-gases formerly found in aerosol spray cans and refrigerants-are released into the atmosphere (see details below).

Ozone sits in the upper atmosphere and absorbs ultraviolet radiaton, another type of solar energy that's harmful to humans, animals and plants. CFCs and halons cause chemical reactions that break down ozone molecules, reducing ozone's ultraviolet radiation-absorbing capacity.

How ozone works

The sun emits electromagnetic radiation at different wavelengths, meaning energy at different intensities. The atmosphere acts like a multi-layer shield that protects Earth from dangerous solar radiation.

Ozone is found in two different parts of our atmosphere. Ground level or "bad" ozone is a human health irritant and component of smog. It is found in the lower atmosphere (troposphere) and has nothing to do with the "ozone hole."

High level or "good" ozone occurs in the stratosphere and accounts for the vast majority of atmospheric ozone.

The stratospheric ozone layer absorbs ultraviolet (UV) radiation, preventing dangerous UV rays from hitting Earth's surface and harming living organisms. UV rays cannot be seen or felt, but they are very powerful and change the chemical structure of molecules.

UV radiation plays a small role in global warming because its quantity is not enough to cause the excess heat trapped in the atmosphere. UV radiation represents a small percentage of the energy from the sun, and is not highly absorbed or scattered in the atmosphere-especially when compared with other wavelengths, like infrared. But, ozone depletion is also concerning because it directly impacts the health of humans, and other living organisms.

The ozone hole

The term 'ozone hole' refers to the depletion of the protective ozone layer in the upper atmosphere (stratosphere) over Earth's polar regions. People, plants, and animals living under the ozone hole are harmed by the solar radiation now reaching the Earth's surface-where it causes health problems, from eye damage to skin cancer.

Stratospheric ozone is constantly produced by the action of the sun's ultraviolet radiation on oxygen molecules (known as photochemical reactions). Although ozone is created primarily at tropical latitudes, large-scale air circulation patterns in the lower stratosphere move ozone toward the poles, where its concentration builds up.

In addition to this global motion, strong winter polar vortices are also important to concentrating ozone at the poles. During the continuously dark polar winter, the air inside the polar vortices becomes extremely cold, a necessary condition for polar stratospheric cloud formation.

Polar stratospheric clouds create the conditions for drastic ozone destruction, providing a surface for chlorine to change into ozone-destroying form. They generally last until the sun comes up in the spring.

In the 1980s, scientists discovered that the ozone layer was thinning in the lower stratosphere, with particularly dramatic ozone loss-known as the "ozone hole"-in the Antarctic spring (September and October).

Scientists also discovered that the thinning in the ozone layer was caused by increasing concentrations of ozone-depleting chemicals - chlorofluorocarbons or CFCs (compounds with chlorine and/or fluorine attached to carbon) and to a lesser extent halons (similar compounds with bromine or iodine). These chemicals can remain in the atmosphere for decades to over a century.

At the poles, CFCs attach to ice particles in clouds. When the sun comes out again in the polar spring, the ice particles melt, releasing the ozone-depleting molecules from the ice particle surfaces.

Once released, these ozone-destroying molecules do their dirty work, breaking apart the molecular bonds in UV radiation-absorbing ozone.

The Montreal Protocol

CFCs were commonly found in refrigerants, solvents, propellants, and foam-blowing agents before the Montreal Protocol was agreed on in the 1980s- an international commitment to phase out ozone-depleting chemicals that was universally ratified by all countries that participate in the UN.

The Montreal Protocol set an important precedent but more needs to be done. It appears unlikely that the decrease in ozone-depleting substances alone will lead to the recovery of the stratospheric ozone layer to its pre-1980 concentration levels because of the competing and uncertain effects of further climate change.

In a worrying development, after an extremely cold winter in early 2011, for the first time the ozone reduction in the Arctic was comparable to that in the Antarctic.

Stratospheric ozone also has natural processes that remove it from the atmosphere. Tiny sulfate particles ( aerosols) blasted into the stratosphere by the volcanic eruption of Mount Pinatubo in 1991 caused measurable decreases in ozone for several years following the eruptions.

Does global warming have an impact on the stratospheric ozone layer?

Since the 1960s, there has been a trend of increasing warming of the lower atmosphere and a cooling of the upper atmosphere. This warming-cooling dynamic creates conditions that lead to ozone loss.

Observations show that as greenhouse gases increase and result in heating in the lower atmosphere (troposphere), a cooling is occurring in the upper atmosphere (stratosphere). Largely because heat from Earth's surface that normally would convey through the troposphere and stratosphere, and eventually escape to space, is now being trapped (or confined to the troposphere).

The increasing temperatures at the Earth's surface and decreasing temperatures in higher parts of the atmosphere can be partly explained using the blanket analogy.

Carbon dioxide and other heat-trapping gases rise into the atmosphere and spread around the globe, like a blanket wrapping Earth. This blanket warms the surface of the Earth and protects it from the cold air above it.

The increased concentrations of heat-trapping gases make the blanket uncomfortably thicker. Wrapped now in a thicker blanket, Earth's surface warms up, heats the blanket itself, and traps more heat in the lower atmosphere.

The blanket also prevents heat from moving from the lower atmosphere to the stratosphere, cooling down the stratosphere as a result.

In other words, heat-trapping gases contribute to creating the cooling conditions in the atmosphere that lead to ozone depletion. Greenhouse gases absorb heat at relatively low altitudes and warm the surface--but they have the opposite effect in higher altitudes because they prevent heat from rising.

In a cooler stratosphere, ozone loss creates a cooling effect that results in further ozone depletion. UV radiation releases heat into the stratosphere when it reacts with ozone. With less ozone there is less heat released, amplifying the cooling in the lower stratosphere, and enhancing the formation of ozone-depleting polar stratospheric clouds, especially near the South Pole.

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Is There a Connection Between the Ozone Hole and Global Warming?

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Ozone (O 3) depletion does not cause global warming, but both of these environmental problems have a common cause: human activities that release pollutants into the atmosphere altering it. Global warming is caused primarily by putting too much carbon dioxide into the atmosphere when coal, oil, and natural gas are burned to generate electricity or to run our cars.

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By Elizabeth Sado

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Information About Sea Turtles: Threats from Climate Change

The Problem: Because sea turtles use both marine and terrestrial habitats during their life cycles, the affects of climate change are likely to have a devastating impact on these endangered species. A rise in the sea level will impact sea turtle nesting beaches. Sea turtles' memories are "imprinted" with a magnetic map of the sandy beach where they hatch. This gives them the unique ability to return to that same site decades later to repeat their ancient nesting ritual. With melting polar ice caps and rising sea levels, these beaches are beginning to disappear. The direct impacts of sea level rise include losing beaches, ecologically productive wetlands and barrier islands.

An increase in nesting beach temperatures will also have an impact on sea turtles. Because sea turtles are reptiles, they rely on the temperature of the sand in which the eggs incubate to determine the gender of the hatchling in a nest. Typically, the eggs in the lower, cooler, part of the nest will become males, while the eggs in the upper, warmer, part of the nest will become females. With increasing nest temperatures, scientists predict that there will be more female than male hatchlings, creating a significant threat to genetic diversity. Warmer ocean temperatures are also likely to negatively impact food resources for sea turtles, and virtually all marine species. Coral reefs, which are an important food source for sea turtles, are in great danger. Almost half of the coral reef ecosystems in the U.S. are in poor or fair condition. As a result of rising temperatures, coral reefs are suffering from a "bleaching" effect that kills off parts of the reef.

Species Affected: All species of sea turtles are affected by climate change.

The Solution: While there is much debate about climate change and actions that can impact temperature, there are simple steps people can take to make a difference.
* Purchase Energy-Star qualified appliances;
* Replace incandescent light bulbs with energy-efficient fluorescents;
* Reduce, reuse, and recycle all plastic, glass and paper waste;
* Use energy-saving methods of transportation;
* Tell a friend about the problems the sea turtles and Earth are facing; knowledge is power and collective influence can force change;
* Plant vegetation along beaches to provide better nesting habitat;
* Look for alternative sources of energy, like solar or wind;
* Write to law makers to encourage more efficient use of energy.

Case Study: Since 2005, the Caribbean region has lost 50 percent of its corals, largely because of rising sea temperatures. From 1997 to 1998 alone, mass bleaching is estimated to have caused serious mortality to 16 percent of the world's coral reefs. While strong signs of recovery have been observed in some locations, many will take decades to fully recover. In terms of sea turtle gender-ratio, the beaches at Playa Grande on Costa Rica's Pacific coast are already producing nests that are 70 percent to 90 percent female, depending on the year. At Junquillal Beach on the Pacific coast, where it is often too hot for eggs to hatch at all, scientists have begun moving eggs to nurseries - essentially holes dug to a certain depth on cooler areas of the beach. When the hatchlings emerge, rangers chaperone them from the nest to the water, protecting them against human and animal predators alike.

Related links:
* Learn more about climate change
* Changed reproductive schedule of eastern Pacific leatherback turtles Dermochelys coriacea following the 1997-98 El Nino to La Nina transition
* Climate Change and Sea Turtles
* Climate turns up heat on sea turtles
* Sea turtles
* Heat Could Be Stifling Turtles' Swimming Abilities In Australia
* Florida's Sea Turtles Besieged
* Investigating the potential impacts of climate change on a marine turtle population

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Information About Sea Turtles: Threats from Climate Change - Sea Turtle Conservancy

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The Problem: Because sea turtles use both marine and terrestrial habitats during their life cycles, the affects of climate change are likely to have a devastating impact on these endangered species. A rise in the sea level will impact sea turtle nesting beaches.

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Forty percent. That's the stunning population loss for polar bears in the southern Beaufort Sea. The news comes from a new study linking the dramatic decline in this polar bear subpopulation in northeast Alaska and Canada to a loss of sea ice due to climate change.

How does climate change affect polar bears so dramatically? Polar bears rely on sea ice to access the seals that are their primary source of food as well as to rest and breed. With less sea ice every year, polar bears and many other ice-dependent creatures are at risk.

Today's study, published in Ecological Applications, analyzed data on polar bears in northeast Alaska and the Northwest Territories and documented a 40 percent population loss between 2001-2010 from 1,500 to 900 bears.
An urgent need to address climate change
Climate change is the main threat facing polar bears. But we also know the effects are being seen around the world. Now is the time we must speak up and demand global action.

"This is a clear warning sign of the impact a warming Arctic has on ice-dependent species like the polar bear," said Dr. Pete Ewins, WWF's Senior Species Officer in Canada. "Given this subpopulation is at the edge of the range, it's no surprise to see this happening so soon."

Added Margaret Williams, Managing Director of WWF's Arctic Program, "Here are concrete numbers to show us that the impacts of climate change are happening now. We need to change course if we want to stop further habitat loss and ensure resilient wildlife populations, both in the Arctic and around the world."

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Polar bear population decline a wake up call for climate change action

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Forty percent. That's the stunning population loss for polar bears in the southern Beaufort Sea. The news comes from a new study linking the dramatic decline in this polar bear subpopulation in northeast Alaska and Canada to a loss of sea ice due to climate change.

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Mateen Khosravan

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Climate change brings with it an increase in malnutrition, mental health conditions, infectious disease spread and even death

Climate change is often thought of in terms of its effects on our physical environment: melting icecaps, rising sea levels, heat-waves and storms. But increasing evidence shows that the human impact - and in particular the impact on human health - will be a major challenge for scientists, politicians and ordinary people in years to come.

The precise extent of the impact is difficult to quantify exactly because there are so many different factors at play. But one thing is certain: climate change is having an effect, and as the planet warms up, that effect is only going to increase.

Direct and indirect consequences

The first major health impact of climate change is the rise in rates of mortality and diseases caused by extreme weather events. These include floods, droughts, tsunamis, heat-waves and other disasters which kill thousands of people in both the developed and developing worlds. Over 280,000 people were killed by the Asian tsunami of 2004, for example, while the 2003 European heat-wave killed 15,000 people in France alone.

Disasters like these make the headlines, but the indirect health consequences of climate change are just as important. Higher temperatures pose major health risks to older people and raise the likelihood that those who work outside - such as farmers and builders - will suffer from heat exhaustion and heat stroke. Many killer diseases, including malaria and cholera, increase as temperature and rainfall increase. The mosquitos that carry the malaria virus, for example, thrive in hot and humid conditions - weather which climate change is likely to make more common.

Malnutrition and mental health

A third impact on human health is yet more indirect, and comes as a result of climate change's effect on human society and economic development. Experts now think that climate change is raising rates of malnutrition and mental health, for example. The connection may not be obvious, so let's take each issue in turn.

In the developing world, malnutrition is rising because crops are failing, and that's happening because of extreme weather. Cycles of drought and flood in West Africa are making it harder and harder for subsistence farmers to grow enough food to feed their families. And when the rain does come, it washes the topsoil away, degrading the land, so it becomes even more difficult to cultivate crops in the future. As a result, people go hungry and children in particular suffer from malnourishment.

The impact of climate change on mental health is a relatively new field of enquiry, but it should not be underestimated. People who have survived droughts, floods, tropical storms and similar extreme weather events often lose their homes and their families. As a result, they can experience post-traumatic stress disorder, severe depression and other mental health problems. In developing countries, where the impacts of climate change are at their most severe, there is less access to mental health services, so symptoms go untreated and unchecked.

Benefits?

There are possible health benefits of climate change. Older people living in more temperate countries may be less at risk of dying from the cold during harsh winters, and farmers may find agricultural yields improve as a result of a longer summer.

However, the benefits are likely to accrue for small numbers of people in more affluent countries. The losers from climate change are expected to be the poor and vulnerable in the developing world.

References

Further reading

The factors behind climate change are complex, dynamic and interlinked

Are you interested in studying climate change? There are many different scientific disciplines that analyse the subject

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'Big Picture' is a free and impartial educational resource for biology teachers and students exploring the innovations and implications of cutting-edge science. Our articles, videos, animations, infographics and lesson ideas set out to explain biomedical science and connect it with its ethical and social challenges.

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How Climate Change Impacts  Adélie penguins

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How Climate Change Impacts  Adélie penguins

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By Mateus Behrend

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Climate change may shrink Adélie penguin range by end of century - NASA Science

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Rising temperatures in Antarctica have caused a population decline in a penguin species that breeds across the continent.

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By Nivedita Rethnakar

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Landscape sculptor. Climate driver. Life supporter. Water is the most important molecule on our planet.

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By Allison Leigh

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When people talk about sea otters, the words "cute," "cuddly," and "curious" often come to mind. But now you can add another descriptor to that list - climate change fighters.

Along the West Coast these aquatic acrobats have become a surprising vanguard in the fight to strike at the root cause of climate change trends. And their heartwarming tale illustrates a much larger story about the ways wildlife conservation can also combat climate change.

Here's how otters do it.

Climate change is the result of a buildup of heat-trapping gases in our atmosphere. One of the most significant contributors to this phenomenon is carbon dioxide (CO2), a gas emitted by coal-burning power plants and automobiles, among other things.

Kelp forests, where otters hang out, are some of the most efficient absorbers of carbon dioxide known. Like any land-based forest, kelp forests sequester carbon dioxide from the atmosphere through photosynthesis, transforming it into the energy they need to build their leafy structure.

But these kelp forests are at risk from sea urchins: small, spiky marine animals that love munching on kelp. With no predators around, sea urchin populations can multiply, forming herds that sweep across the ocean floor devouring entire stands of kelp.

Fortunately, sea otters have an appetite for urchins. The otters help keep urchin populations in check, allowing kelp to flourish and capture more CO2. According to a recent study, otter-supported kelp forests can absorb up to 12 times more CO2 from the atmosphere than if they were just left to the urchins.

For a time, otters were not around to eat urchins. Sea otters were nearly hunted to extinction during the fur trade in the 18th and 19th centuries. But through conservation efforts, sea otters have been restored to much of their historic range in North America.

Sea otters play a small role in mitigating global climate change, but their impact points to a larger lesson: wildlife conservation can save vegetation needed to reduce CO2. Whether it is pumas, jaguars, and eagles in the forests of Venezuela that keep understory browsers in check, grey wolves keeping willow-munching elk on their toes in Yellowstone National Park, or otters guarding kelp along the coast, these non-human neighbors can foster plant growth that leads to more carbon capture.

And with sea otters, the effort to mitigate climate change through top predators has never been so adorable.

Sea Otters v. Climate Change

Author

Michael James Werner

Michael Werner is an award-winning independent filmmaker, photographer and writer. His work has been featured in/by: The PBS NewsHour, HBO Films, The Associated Press, Earthfix, Oregon Field Guide, KCTS-9 Seattle, Voice of America TV, The World Channel, the U.S. Olympic Committee and the Cannes International Film Festival. In addition he is a former faculty member at the University of Oregon School of Journalism and holds a master's degree in narrative journalism. In 2010 he spent five weeks exploring the Democratic Republic of the Congo for a documentary project and developed an appreciation for the taste of curried caterpillars.

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When people talk about sea otters, the words "cute," "cuddly," and "curious" often come to mind. But now you can add another descriptor to that list - climate change fighters. Along the West Coast these aquatic acrobats have become a surprising vanguard in the fight to strike at the root cause of climate change trends.

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Climate Change Affect on Natural Disasters

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Climate Change Affect on Natural Disasters

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By Jack Howell

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Climate change and disasters

Climate change does have an impact on a number of different kinds of events - including hurricanes (or tropical storms to give them their universal name). But the relationship isn't simple. It seems that climate change will increase the number of the most powerful tropical storms. But not necessarily the total number of storms. Tropical storms may also carry more water as the planet warms, making flooding worse.

The impact of a warming planet on hurricanes is also regional - not everywhere is affected in the same way. There is often a desire to label a particular event as being caused by climate change.

However, this is difficult. The simplest way to express the relationship in ordinary language is to say that climate change is altering the patterns of some disasters.

A useful analogy is health issues that are caused by multiple lifestyle factors. It isn't possible to say that someone's heart attack is caused by eating bacon. But a lifetime of eating a lot of bacon, combined with other factors, made a heart attack more likely.

Climate change now Vs. climate change in the future

An important distinction is whether we are talking about the role of climate change in a current specific disaster. Or whether we are talking about the potential role of unchecked climate change in future disasters. Existing evidence points to climate change altering patterns of some disasters at the moment.

For example, scientists are able to connect increasing global temperatures with increased levels of drought in East Africa using existing historical data. But scientists are also able to make predictions about how a warming planet will also alter patterns of disasters in the future using predictive computer models.

These are two very different things. These predictions are based on particular scenarios about how much we decide to reduce our emissions by. When thinking about the role climate change plays in disasters it is important to distinguish between what is happening now, and what will happen in the future.

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We know climate is going to create disaster displacement - but how?

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Climate change does have an impact on a number of different kinds of events - including hurricanes (or tropical storms to give them their universal name). But the relationship isn't simple. It seems that climate change will increase the number of the most powerful tropical storms. But not necessarily the total number of storms. Tropical storms may also carry more water as the planet warms, making flooding worse.

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Climate Changes Affect on Mountain Gorillas

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Climate Changes Affect on Mountain Gorillas

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By: Andrew Crisostomo

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When you live on the top of a mountain, you don't have many places to run if the environment of that mountain habitat changes. Look at the American pika, for example. These tiny, rabbit-like mammals have evolved to live in cold, high-elevation habitats and die if exposed to temperatures above 25 degrees Celsius. Unfortunately climate change is warming their mountain habitats, pushing the pikas into higher and higher elevations where they have less space to live and fewer plants to eat. Eventually, they may run out of room entirely.

Will the same thing happen to the iconic, critically endangered mountain gorillas ( Gorilla beringei beringei) of central Africa? In 2011 research by the United Nations' Food and Agriculture Organization predicted that the vegetation in mountain gorilla habitat will be dramatically altered by climate change. Warming temperatures will push certain plant species further up the mountains leaving the gorillas, which spend most of their days foraging, with fewer food sources. According to some climate change models, the current habitats for mountain gorillas in Uganda, Rwanda, and the Democratic Republic of Congo could become completely unsuitable for the massive primates for the year 2090.

But now new research published Sept. 23 in indicates that mountain gorillas may be able to avoid the worst effects of climate change. In part, the research suggests, gorillas may be able to adapt to changes in vegetation. That alone may not be enough, though. According to the researchers- from the University of California, Davis; Environmental Planning Institute; International Gorilla Conservation Programme; and EcoAdapt-humans living near the gorillas may need to adapt as well.

So what makes mountain gorillas different from other mountain species such as the pika? For one thing, mountain gorillas did not specifically evolve in their current habitats. Instead they escaped into these regions, climbing high into the mountains to avoid humans who encroached upon their territories. Mountain gorillas can live in the colder temperatures of higher elevations because they have long, thick fur, but they do not rely upon any specific vegetation and pick their food from among dozens of plant species based on availability and nutritional content. This suggests that even if plant species shift further up the mountain or disappear entirely, the gorillas may just move on to different vegetation.

But in the process mountain gorillas may need to expand their current territory beyond the confines of Virunga National Park and Bwindi Impenetrable National Park, the two protected areas set up to preserve the primates. That won't be easy. The two parks are surrounded by people and agriculture. The parks could be expanded, the researchers write, but even a five kilometer shift in their borders would be expensive and require convincing entire human settlements to move. Although much of that territory is already heavily deforested and unsuitable for gorillas, the authors identify several sites in the three countries that could conceivably work as expansions to the existing parks.

Another option would involve relocating some mountain gorillas to entirely new territories. This poses a lot of risk. Relocated gorillas could be exposed to new diseases or predators. They could also have a negative effect on the species in their new habitat. Meanwhile, there is no current research that indicates mountain gorillas would be able to adapt to completely new habitats. And then there's the economic aspect: the communities around Virunga and Bwindi parks rely upon eco-tourism from mountain gorillas, so would moving some of the animals hurt the economies of those regions?

No matter what happens in the future, mountain gorillas face many threats today. Their habitat continues to shrink, they face poachers and violence in the war-torn region, and they continue to catch diseases from humans. Most recently a British company announced plans to drill for oil in Virunga National Park, which could further imperil the rare primates. But at the same time a lot of people and organizations have done good work for mountain gorillas, often putting their lives on the line to protect them.

The mountain gorilla population has increased from just 620 in 1989 to 880 today. That's already a victory, but this new research reminds us that many more challenges remain.
by Lukas Vermeer via Flickr. Used under Creative Commons license Previously in Extinction Countdown:

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How Will Climate Change Affect Mountain Gorillas?

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When you live on the top of a mountain, you don't have many places to run if the environment of that mountain habitat changes. Look at the American pika, for example. These tiny, rabbit-like mammals have evolved to live in cold, high-elevation habitats and die if exposed to temperatures above 25 degrees Celsius.

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Authors

OLA M. JOHANNESSEN,

Corresponding author

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Geophysical Institute, University of Bergen, Norway

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LENNART BENGTSSON,

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Max Planck Institute for Meteorology, Hamburg, Germany

3. Environmental Systems Science Centre, University of Reading, UK

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MARTIN W. MILES,

1. Bjerknes Centre for Climate Research, Bergen, Norway

2. Environmental Systems Analysis Research Center, Boulder, Colorado, USA

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VLADIMIR A. SEMENOV,

1. Max Planck Institute for Meteorology, Hamburg, Germany

2. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia

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*Corresponding author.
e-mail: ola.johannessen@nersc.no

ABSTRACT

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic's response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of century- and multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere-ice-ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

1. Introduction

A consensus result from different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should be enhanced in the Arctic ( Räisänen, 2001). The Intergovernmental Panel on Climate Change ( IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ∼3-4°C during the next 50 yr, or more than twice the global mean.

Recent overviews of results from observational studies of atmospheric and climate-sensitive variables (e.g. sea ice, snow cover, river discharge, glaciers and permafrost) have concluded that, taken together, a reasonably coherent portrait of recent change in the northern high latitudes is apparent ( Serreze et al., 2000; Moritz et al., 2002; Peterson et al., 2002). However, it remains open to debate whether the warming in recent decades is an enhanced greenhouse-warming signal or natural decadal and multidecadal variability ( Polyakov and Johnson, 2000; Polyakov et al., 2002), e.g. as possibly expressed by the arctic warming observed in the 1920s and 1930s followed by cooling until the 1960s (e.g. Kelly et al., 1982). The uncertainties are exacerbated by a lack of homogeneous, century-scale instrumental data sets (see Moritz et al., 2002, whose fig. 2a includes no temperature data for the central Arctic) needed to resolve the inherent time-scales of variability in the Arctic ( Venegas and Mysak, 2000), a region characterized by high variability.

From these concerns, two overarching questions are as follows. (1) To what degree are the gradually changing atmosphere-ice-ocean conditions in the Arctic a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse gas (GHG) forcing. (2) To what degree may anthropogenic forcing induce the arctic sea-ice cover to decrease or even disappear in this century? In order to study these questions, we analyse a new set of pertinent multidecadal to century-scale data-surface air temperature (SAT), sea-ice extent, sea-ice area and sea-ice thickness-in combination with global coupled atmosphere-ice-ocean climate model simulations using the ECHAM4 model of the Max Planck Institute for Meteorology ( Roeckner et al., 1999) and the HadCM3 model of the UK Meteorological Office ( Gordon et al., 2000). ECHAM4 and HadCM3 are state-of-the-art versions of two of the models demonstrated-in an intercomparison of climate-change scenario output from 19 coupled models ( Räisänen, 2001)-to be among those most representative of the 19-model mean temperature change.

2. Surface air temperature: observed and modelled

Statistical analyses of global SAT data sets have indicated substantial fluctuations in the extratropical Northern Hemisphere on decadal to multidecadal time-scales (e.g. Schlesinger and Ramankutty, 1994; Hansen et al., 1999; Jones et al., 1999). In the high latitudes, differences in spatial-temporal coverage have led to some discrepancies concerning temperature variability trends in the last century (e.g. Jones et al., 1999; Przybylak, 2000; Polyakov et al., 2002). The global gridded SAT data set ( Jones et al., 1999) used most extensively for studies of climate variability has major gaps in the northern high latitudes, in particular over the ice-covered Arctic Ocean and some surrounding land areas. Here, we analyse for the first time a unique century-long SAT data set focused on the high latitudes of the Northern Hemisphere. The data set is provided through the Arctic and Antarctic Research Institute (AARI), St Petersburg, Russia, produced through a project within the programme International Association for the Promotion of Cooperation with Scientists from the Former Soviet Union ( Alekseev et al., 1999). The input data are daily temperatures from 1486 meteorological stations in the Northern Hemisphere, including land- and drifting-stations from the Arctic. A gridded data set (5° lat. × 10° long.) based on these data has been developed from several sources. First, monthly-mean SAT anomaly maps were produced at the USSR's Main Geophysical Observatory for the period 1891-1969 and at the Hydrometeorological Research Center for the period 1970-1976. Secondly, the maps were visually analysed and interpolated into a gridded data set, as described and evaluated previously ( Vinnikov, 1977). Thirdly, the data set was extended in the same manner for the period 1977-1986, presented as SAT with reduction to common mean values and taking into the account the moist adiabatic temperature gradient (0.6° per 100 m) as in Alekseev and Svyaschennikov (1991). Fourthly, the data set was continued at the Hydrometeorological Research Center from 1986-1995, through visual interpolation and reading from monthly-mean SAT maps. Since 1995, the monthly-mean SATs have been produced at AARI through monthly averaging of gridded daily SAT from the European Centre for Medium-Range Weather Forecasting (ECMWF).

The reliability of the new SAT data set for climate analyses is evidenced through statistical comparison with the National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) re-analysis data ( Kalnay et al., 1996) and the Jones et al. (1999) data set, tested here over common areas and time periods. The quantitative agreement between interannual variations in annual mean SAT north of 55°N (excluding the Greenland area 20-70°W) from NCAR/NCEP and our data set is from 1955-1990. The agreement between the Jones data and our data set from a large arctic-subarctic test area (between 52.5°N-67.5°N and 2.5°W-62.5°E) from 1930-1990 is nearly identical, with a correlation coefficient and a mean difference ∼0.15°C. Therefore, the SAT data set put forth here is considered a reliable alternative to the standard data sets and has the advantage of improved coverage in the Arctic and extending over the last century.

Figure 1a shows the time evolution of the zonally averaged anomalies in annual mean SAT from 30-90°N. Two characteristic warming events stand out, the first from the mid 1920s to about 1940 and the second starting about 1980 and still ongoing. Here, we show that the early twentieth-century warming was largely confined to north of 60°N, whereas the latter warming encompasses the whole Earth ( Jones et al., 1999) but is none the less significantly enhanced in the Arctic ( Fig. 1a).

The early twentieth-century warming trend in the Arctic was nearly as large as the warming trend for the last 20 yr, such that some researchers (e.g. Polyakov et al., 2002) regard them to be part and parcel of the same natural low-frequency oscillation. However, our spatial comparison of these periods reveals key differences in their patterns ( Fig. 2). The 20-yr SAT trends for the 1920s-1930s warming period ( Figs 2a and b) and the subsequent cooling period ( Figs 2c and d) have remarkably similar patterns, thus suggesting similar underlying processes. In the winter half-year, the high-latitude warming ( Fig. 2a) and cooling ( Fig. 2c) patterns are organized symmetrically around the pole, while in the summer half-year, the warming ( Fig. 2b) and cooling ( Fig. 2d) appear to reflect the positions of the latitudinal quasi-stationary wave structure, predominantly wavenumber three and four. However, the warming trend for the last 20 yr is more widespread and has a markedly different pattern from the earlier periods in both winter ( Fig. 2e) and summer ( Fig. 2f). Both the 1920-39 and 1980-99 warming are most pronounced during winter for the high Arctic. In addition, in the latter period, there is pronounced warming in the Eurasian midlatitudes, especially in summer.

A recent modelling study ( Delworth and Knutson, 2000) has suggested that the 1920s-1930s warming anomaly was due to natural processes, insofar as models are capable of simulating such anomalies as due to internal chaotic processes of the climate system. Here, a similar high-latitude anomaly, although less extreme and of a shorter duration, is found in a 300-yr control run (without increasing anthropogenic forcings) with the ECHAM4 model-100 yr are shown in Fig. 1d. This anomaly occurred after 150 yr of integration and lasted for some 15 yr. This simulation without increasing anthropogenic forcing is able to produce an anomaly similar to the observed high-latitude warming in the 1920s-1930s. Therefore, we strongly support the contention of Delworth and Knutson (2000) that this high-latitude warming event represents primarily natural variability within the climate system, rather than being caused primarily by external forcings, whether solar forcing alone ( Thejll and Lassen, 2000) or a combination of increasing solar irradiance, increasing anthropogenic trace gases, and decreasing volcanic aerosols, as suggested from an analysis of 400 yr of temperature proxy data from the Arctic ( Overpeck et al., 1997). Changes in solar forcing have been suggested (e.g. Lean and Rind, 1998) as the cause of the warming. It has attracted considerable interest because of the apparent similarity between the assumed solar variability and the SAT trend of the Northern Hemisphere extratropics ( Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993). Reconstructions of solar variability have been used in modelling studies ( Cubasch et al., 1997; Cubasch and Voss, 2000; Stott et al., 2001) providing results broadly consistent with long-term estimated or observed temperature trends. This is not surprising because multidecadal and longer trends in solar forcing generate a response in global average modelled temperatures on similar time-scales as the anticipated variation in solar forcing ( Cubasch et al., 1997). Rigorous testing of the assumption of solar forcing requires reliable observations; however, these data exist only for the past two decades ( Lean and Rind, 1998), such that it remains only a hypothesis. The anthropogenic forcing in the 1920s-1930s was far too weak to generate the observed warming-the change in the GHG forcing in the early decades of the twentieth century was only ∼20% of the present ( Roeckner et al., 1999). The most plausible explanation in this case is the low-frequency, multidecadal oscillation related to North Atlantic Ocean circulation ( Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Polyakov and Johnson, 2000).

In contrast, no comprehensive numerical-model integrations have produced the present global warm anomaly ( Fig. 1a) without including observed anthropogenic forcing. Figure 1b shows the ECHAM4 model simulation with anthropogenic GHG forcing. The patterns compare well with the last two decades of observed warming, although the modelled warming occurs slightly earlier and also encompasses lower latitudes than observed ( Fig. 1a). The patterns from a simulation including GHGs and sulfate aerosols (GSD) ( Fig. 1c) show that, although the recent midlatitude warming is underestimated, the high-latitude enhancement is in agreement with the observations and other modelling results ( Räisänen, 2001). Therefore, anthropogenic forcing is the dominant cause of the recent pronounced warming in the Arctic.

What then are the possible mechanisms by which anthropogenic forcing could bring about high-latitude warming, given that the recent trend in arctic SAT is greater than the direct radiative effect of GHGs? First, as has been demonstrated by Bengtsson (1999) and Hansen et al. (1999), the large-scale spatial pattern of forcing and the pattern of response to forcing are practically uncorrelated, which stresses the key role of advective atmosphere-ocean processes in bringing about regional climate change; see also Schneider et al. (2003). A possible mechanism is suggested by recent findings from observations and modelling experiments ( Hoerling et al., 2001; Lin et al., 2002). Hoerling et al. (2001) show that the observed long-term warming trend (up to 0.2°C per decade) in the tropical oceans-anthropogenically forced ( Levitus et al., 2000; Reichert et al., 2002)-during the last 50 yr has generated, through enhanced convective activity, an intensification of the midlatitude tropospheric westerlies, and consequently because of geostrophy in the Atlantic sector, an enhanced positive North Atlantic Oscillation/Arctic Oscillation (NAO/AO) pattern ( Hoerling et al., 2001). Because the NAO and arctic temperature are linked-the interannual variations in the NAO index ( Hurrell, 1995) and SAT from 60-90°N are found here to be significantly correlated ( when using data since the mid twentieth century)-it follows that an NAO enhancement, linked to anthropogenic warming of the tropical oceans, can explain a substantial portion of the warming trend in the Arctic. It is unlikely that this was a mechanism for the early twentieth-century warming phase, because of the small anthropogenic GHG effect at the time and because no positive correlation between arctic SAT and the NAO before 1950 is found; in fact, here we find that the correlation is negative ().

Secondly, an ice-albedo feedback may enhance the arctic warming, as new ECHAM4 sensitivity experiments have demonstrated a robust relationship between sea ice and air temperature ( Bengtsson et al., 2004), in which advective mechanisms-in particular, enhanced wind-driven oceanic heat inflow into the Barents Sea-may have a predominant role.

3. Sea ice: observations

3.1. Sea-ice concentration and derived parameters

Sea-ice concentration (percent ice area per unit area) and derived parameters such as ice extent (the area within the ice-ocean margin defined as 15% ice concentration) and ice area (extent minus the open-water area) can be reliably retrieved from satellite passive-microwave sensor measurements, which are available continuously since 1978, thus among the longest satellite-retrieved geophysical records. Satellite data have shown that the winter maximum ice area ( Fig. 3a) is typically about while the summer minimum ice area ( Fig. 3a) is about . These data, here updated from Johannessen et al. (1995, 1999) and Bjørgo et al. (1997) through March 2003, indicate a decrease of (∼7.4%) in the Northern Hemisphere annual sea-ice area, 1978-2003 ( Fig. 3b). During this period, the decreases have been larger in summer (see also Chapman and Walsh, 1993; Parkinson et al., 1999); here, we calculate a (14%) decrease in September versus (5%) in March (1978-2003). This seasonal difference has resulted in a 7-9% per decade reduction in the area of thicker, multiyear (MY) ice (ice that has survived at least one summer melt) over the last two decades ( Johannessen et al., 1999; Comiso, 2002). It is noteworthy that our updated time series through September 2002 indicate that the record minimum summer ice cover recently reported ( Serreze et al., 2003) is indeed unprecedented in the nearly quarter-century satellite record ( Fig. 3a)-less than in area, which occurred from anomalous warm southerly winds in spring followed by low SLP and high SAT in the Arctic in summer ( Serreze et al., 2003). Note also the large positive anomaly in 1996, which occurred in the summer after an extreme temporary reversal in the NAO in the winter 1995/96.

The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figs 4a and 4b. The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figs 4c and d. During this period, the decreases in winter have been most pronounced (as large as ∼50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ∼30-50% in the Siberian marginal seas. These summer patterns are in agreement with an independent analysis of ice-cover minima from 1978-1998 (see fig. 2c in Comiso, 2002).

The decreases in recent decades, which are also partially due to circulation-driven ice export through the Fram Strait between Greenland and Svalbard ( Vinje, 2001), have coincided with a positive trend in the NAO, with unusually high index values in the late 1980s and 1990s. During this period, the variability of ice motion and ice export through the Fram Strait was correlated strongly with the NAO; for the ice area flux ( Kwok and Rothrock, 1999) and for the ice volume flux ( Hilmer and Jung, 2000), although the relationship was insignificant before the mid 1970s ( Hilmer and Jung, 2000). Deser et al. (2000) analysed a 40-yr gridded data set (1958-97) to determine the association between arctic sea ice, SAT and SLP, concluding that the multidecadal trends in the NAO/AO in the past three decades have been 'imprinted upon the distribution of Arctic sea ice', with the first principal component of sea-ice concentration significantly correlated with the NAO index, recently cause-and-effect modelled by Hu et al. (2002). None the less, our calculations and those of Deser et al. (2000) indicate that, even in recent decades, only about one third of the variability in arctic total ice extent and MY ice area ( Johannessen et al., 1999) is explained by the NAO index, implying that other factors including enhanced radiative forcing and ice-albedo feedbacks ( Björk and Söderkvist, 2002; Bengtsson et al., 2003) must be invoked to explain the variability.

A pronounced reduction in the Northern Hemisphere sea-ice extent, such as seen in the satellite record of the last two decades, is scarcely apparent in the early twentieth-century warm period, at least in the two most widely-used century-long sea-ice data sets ( Chapman and Walsh, 1993; Rayner et al., 1996). To ascertain whether this has a physical explanation or is due to acknowledged data deficiencies before the 1950s ( Chapman and Walsh, 1993; Vinnikov et al., 1999), we have analysed these data together with a new century-long 'Zakharov' data set ( Zakharov, 1997; Alekseev et al., 2000), which includes hitherto under-recognized Russian data. The data set comprises sea-ice extent (regardless of ice concentration within the ice-ocean margin) for ∼77% ( km of the area of the Arctic Ocean. This region occupies the perennially ice-covered central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi marginal seas, leaving out only the eastern Chukchi and Beaufort seas and the Canadian Arctic straits and bays.

In the Atlantic-European sector (Greenland and Barents seas), the sea-ice extents from 1900 to the late 1950s are known only from spring and summer (April to August). It has not previously been possible to gain understanding about the annual ice cover variability in these decades. An attempt has been made to obtain these missing mean annual data. Towards this end, the actual year-round data on the ice extent from 1959-1988 were used to derive an equation to calculate the annual average ice extent in the region based on its mean value in April to August : . The correlation between and is . By means of this equation, the missing annual ice-extent means from 1900-1958 were calculated. The main problem of any reconstruction is errors inevitable in the procedures of data series reconstruction. This problem can be resolved by comparing the actual and calculated annual ice extent averages, in this case for the period from 1959, and by calculating the root-mean-square (rms) error of the reconstruction. The error , and as the rms deviation of the initial actual series , the ratio . Thus, the methodological error of the reconstruction comprises only one third of the rms deviation of the actual series, indicating a sufficiently high reliability of the reconstruction method used.

Data on the ice edge are available for August from the mid 1920s in the Siberian sector (Kara, Laptev, and East Siberian seas). From the 1930s, with the development of shipping along the Northern Sea Route, knowledge of the position of these boundaries was extended to two adjacent months (July and September); however, the accuracy of the ice data from that time cannot be considered high, because observations were not carried out systematically. From the mid 1930s, aviation was used to collect ice information and from the mid 1940s became the main source of sea-ice information in the Siberian Arctic. The observations attain a regular character covering (with some exceptions) the entire navigation season. An attempt has been made to obtain these missing mean annual data, using incomplete data from 1924-1946 to reconstruct the conditions of those years. With this aim, using the 1946-1999 series, the equation: was derived, where is the mean sea-ice extent in the Siberian seas in June-October, and is the ice area in the Siberian seas in August. The correlation between and is . The ratio again suggests a sufficiently high reconstruction quality based on the knowledge of ice extent only in August.

Figure 5 shows time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard 'Walsh' data set ( Chapman and Walsh, 1993), in comparison with the zonal annual mean SAT between 70-90°N since 1900. In contrast to the Walsh data, these new data indicate a substantially reduced () ice cover in the 1920s-1930s warming period. Note that the correspondence between the subsequent cooling into the mid 1960s and increasing sea ice is seen from the late 1950s; the ice data from WWII and the early post-war years are inadequate. Note also that the apparent differences in trends in the two sea-ice time series during the last 25 yr are due to relatively large reductions in the Beaufort Sea and Chukchi seas ( Fig. 4)-areas not included in the Zakharov data set. The correlations between the SAT and the Zakharov and Walsh sea-ice extents are maximum at 0 lag, and 0.3, respectively. This indicates that the interannual variability in the arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, although the r-value may partially reflect feedback processes from the ice cover to the atmosphere, e.g. ECHAM4-model sensitivity experiments (see Bengtsson et al., 2004) have demonstrated a strong SAT response-particularly in the Atlantic-European sector-to model-imposed changes in sea ice.

3.2. Sea-ice thickness

The variability of ice thickness is relatively poorly known, due primarily to spatial-temporal sampling deficiencies in data from submarines carrying upward-looking sonar ( Wadhams, 1997). An analysis ( Rothrock et al., 1999) of data from the summers 1958, 1976, 1993, 1996 and 1997 found that between the 1950s/1970s and the 1990s, the mean ice thickness decreased from 3.1 to 1.8 m. The 1.3-m decrease-if representative-corresponds to an ∼40% reduction over the three to four decades ( Rothrock et al., 1999). However, analyses of sonar data alone from different transects, years and seasons yield a range of estimates, from a comparable decrease ( Wadhams and Davis, 2000) to no significant change ( Winsor, 2001) to an intermediate estimate ( Tucker et al., 2001). An analysis of submarine ( Rothrock et al., 1999, 2003) and modelled ( Holloway and Sau, 2002) ice thickness over the same common time period has demonstrated that ice motion and high interannual variability could mislead inference of trends from sonar transect data, e.g. 12% ( Holloway and Sau, 2002) versus the 40% decrease reported by Rothrock et al. (1999). Thus, the available sonar data alone remain inadequate to produce a reliable, long-term climatology of arctic ice thickness variability.

Here, for the first time, we put forth a unique 20-yr time series of monthly, area-averaged ice thickness derived from field-based measurements of surface elastic-gravity waves from Russian North Polar drifting stations 1970-91, when regular measurements of ice surface vibrations were made in the central Arctic Ocean ( Nagurnyi et al., 1994). Long elastic-gravity waves (on the order of 1 km) in the sea-ice cover arise from the interaction with ocean swells. These elastic-gravity waves can propagate for hundreds to thousands of kilometres before dampening out. Based on a linear theory of free vibrations of the sea-ice cover, the measured wavelength, wave period and direction are then related to thickness through a wave-energy dispersion relation. The ice thicknesses determined from different propagation directions are averaged to provide a basin-wide mean thickness estimate ( Nagurnyi et al., 1994, 1999). The values compare well to those observed in the regional ice cover ( Romanov, 1995) and their interannual variability correlates well with modelled arctic ice volume (see fig. 1 in Hilmer and Lemke, 2000). The thickness estimates have also been found to correlate strongly (∼0.88) to the satellite-derived area of the perennial, MY ice cover in winter ( Johannessen et al., 1999), suggesting moreover that the decreases found in MY ice area represent a mass balance change rather than merely a peripheral effect.

Figure 6a shows the 20-yr time series of area-averaged ice thickness, 1970-91, from which trends for winter and summer are derived ( Fig. 6b). The mean thickness estimates are ∼2.9-3 m in winter and ∼2.5-2.6 in summer (i.e. seasonal cycle ∼40 cm). The linear trend of anomalies from 1971-90 indicates a decrease of only ∼10 cm (less than 4%) over 20 yr. This is comparable with some observational and modelling analyses (e.g. Holloway and Sau, 2002), but is much less than the 1950s/1970s to 1990s sonar data analysis ( Rothrock et al., 1999) upon which the IPCC based its statement that the arctic ice thickness has been reduced 40% during summer in recent decades. The large variability inherent in the arctic sea-ice-climate system, coupled with the problem of obtaining ice thickness data, renders the evaluation of ice thickness trends from the available observational data an open question. None the less, it is notable that our ECHAM4 coupled model experiments (not shown) with the IPCC IS92 emission scenarios for greenhouse and related gases indicate that: (1) our 20 yr of ice thickness data are consistent with the modelled results 1970-90, and (2) substantial decreases in modelled ice thickness commence only in the last two decades of the twentieth century. The ice thickness from both observations and models is presently ∼2.5-3 m in summer, whereas our model results indicate less than 1 m at the end of this century for the remaining ice cover.

4. Sea-ice extent: modelled

The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses ( Vinnikov et al., 1999; Johannessen et al., 2001), which predicted a reduction of ∼15% in the Northern Hemisphere mean ice extent to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, for the first time, both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing are analysed to 2100, using ECHAM-4 and HadCM3 model predictions including different IPCC emissions scenarios.

The observed versus ECHAM4-modelled trends in Northern Hemisphere winter and summer sea-ice extent in the twentieth century are similar ( Fig. 7). Our ECHAM4-model run-using an IPCC IS92 emission scenario similar to IPCC Special Report on Emissions Scenarios (SRES) scenario B2-predicts the decreases to continue such that the summer ice cover may be reduced by ∼80% at the end of the twenty-first century ( Fig. 7, lower). This is much greater than the winter ( Fig. 7, upper) or annual means modelled previously ( Vinnikov et al., 1999; Johannessen et al., 2001) and is comparable to recent projections for the summer ( Gregory et al., 2002).

The spatial distributions of the ECHAM4-modelled sea-ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Fig. 8. In order to test the robustness of our ECHAM4 estimates, we have used a different coupled atmosphere-ocean model, the HadCM3, which is an improvement upon the HadCM2 model used in a previous sea-ice study ( Vinnikov et al., 1999). Furthermore, we use two different SRES scenarios, A2 and B2, which are 'medium-high' and 'medium-low' scenarios, respectively. Results from the B2 experiments are shown in Fig. 9, which depicts decadal averages of winter ( Figs 9a and b) and summer ( Figs 9c and d) sea-ice concentrations in 2001-2010 and 2081-2090. The ECHAM4 ( Fig. 8) and HadCM3 ( Fig. 9) results support each other, both predicting moderate reductions in winter and drastic reduction in the summer ice extent. The spatial distributions of the ECHAM4 and HadCM3 modelled summer ice cover in late century ( Figs 8d and 9d) indicate essentially ice-free arctic marginal seas except north of Greenland and the Canadian Arctic Archipelago, although there are some differences in the modelled spatial patterns.

5. Conclusions and implications

The results of our observational and modelling analysis lead to the following conclusions. First, we theorize that the Arctic warming in the 1920s-1930s and the subsequent cooling until about 1970 are due to natural fluctuations internal to the climate system. Secondly, we believe there are strong indications that neither the warming trend nor the decrease of ice extent and volume over the last two decades can be explained by natural processes alone. Thirdly, the state-of-the-art ECHAM4 and HadCM3 coupled climate models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century.

A range of potential consequences of arctic warming and a shrinking ice cover can be hypothesized, as follows.

(i) Reductions in albedo and increased open water would have significant effects on energy balances and atmospheric and oceanic circulation in the high latitudes.

(ii) Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity for CO absorption, could become a new and important sink of atmospheric CO (Anderson and Kaltin, 2001,).

(iii) Broad changes in the marine ecosystem, e.g. changes in plankton in the North Atlantic due to less ice and greater inflow of melt water (Beaugrand et al., 2002), could have a negative impact on arctic and subarctic marine biodiversity. However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route north of Siberia (Ragner, 2000).

(iv) Changes in the pathways and spreading of melt water and in the stratification in the Nordic Seas, and the effects of reduced deep-water formation in the Greenland Sea on the global thermohaline circulation (Rahmsdorf, 1999; Alekseev et al., 2001), could greatly alter the climate of the northern latitudes.

6. Acknowledgments

This research has been supported under the European Union's 5th Framework Programme project 'Arctic Ice Cover Simulation Experiment (AICSEX)' and the International Association for the Promotion of Cooperation with Scientists from the Independent States of the Former Soviet Union (INTAS), as well as the Research Council of Norway's projects 'Role of Arctic Sea-Ice-Atmosphere Processes (ROLARC)' and 'Marine Climate and Ecosystems in the Seasonal Ice Zone (MACESIZ)'. We also acknowledge the valuable assistance of Cathrine Myrmehl and Anne-Mette Olsen of the Nansen Environmental and Remote Sensing Center.

Ancillary

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Authors

OLA M. JOHANNESSEN,

Corresponding author

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Geophysical Institute, University of Bergen, Norway

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LENNART BENGTSSON,

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Max Planck Institute for Meteorology, Hamburg, Germany

3. Environmental Systems Science Centre, University of Reading, UK

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MARTIN W. MILES,

1. Bjerknes Centre for Climate Research, Bergen, Norway

2. Environmental Systems Analysis Research Center, Boulder, Colorado, USA

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VLADIMIR A. SEMENOV,

1. Max Planck Institute for Meteorology, Hamburg, Germany

2. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia

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*Corresponding author.
e-mail: ola.johannessen@nersc.no

ABSTRACT

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic's response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of century- and multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere-ice-ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

1. Introduction

A consensus result from different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should be enhanced in the Arctic ( Räisänen, 2001). The Intergovernmental Panel on Climate Change ( IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ∼3-4°C during the next 50 yr, or more than twice the global mean.

Recent overviews of results from observational studies of atmospheric and climate-sensitive variables (e.g. sea ice, snow cover, river discharge, glaciers and permafrost) have concluded that, taken together, a reasonably coherent portrait of recent change in the northern high latitudes is apparent ( Serreze et al., 2000; Moritz et al., 2002; Peterson et al., 2002). However, it remains open to debate whether the warming in recent decades is an enhanced greenhouse-warming signal or natural decadal and multidecadal variability ( Polyakov and Johnson, 2000; Polyakov et al., 2002), e.g. as possibly expressed by the arctic warming observed in the 1920s and 1930s followed by cooling until the 1960s (e.g. Kelly et al., 1982). The uncertainties are exacerbated by a lack of homogeneous, century-scale instrumental data sets (see Moritz et al., 2002, whose fig. 2a includes no temperature data for the central Arctic) needed to resolve the inherent time-scales of variability in the Arctic ( Venegas and Mysak, 2000), a region characterized by high variability.

From these concerns, two overarching questions are as follows. (1) To what degree are the gradually changing atmosphere-ice-ocean conditions in the Arctic a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse gas (GHG) forcing. (2) To what degree may anthropogenic forcing induce the arctic sea-ice cover to decrease or even disappear in this century? In order to study these questions, we analyse a new set of pertinent multidecadal to century-scale data-surface air temperature (SAT), sea-ice extent, sea-ice area and sea-ice thickness-in combination with global coupled atmosphere-ice-ocean climate model simulations using the ECHAM4 model of the Max Planck Institute for Meteorology ( Roeckner et al., 1999) and the HadCM3 model of the UK Meteorological Office ( Gordon et al., 2000). ECHAM4 and HadCM3 are state-of-the-art versions of two of the models demonstrated-in an intercomparison of climate-change scenario output from 19 coupled models ( Räisänen, 2001)-to be among those most representative of the 19-model mean temperature change.

2. Surface air temperature: observed and modelled

Statistical analyses of global SAT data sets have indicated substantial fluctuations in the extratropical Northern Hemisphere on decadal to multidecadal time-scales (e.g. Schlesinger and Ramankutty, 1994; Hansen et al., 1999; Jones et al., 1999). In the high latitudes, differences in spatial-temporal coverage have led to some discrepancies concerning temperature variability trends in the last century (e.g. Jones et al., 1999; Przybylak, 2000; Polyakov et al., 2002). The global gridded SAT data set ( Jones et al., 1999) used most extensively for studies of climate variability has major gaps in the northern high latitudes, in particular over the ice-covered Arctic Ocean and some surrounding land areas. Here, we analyse for the first time a unique century-long SAT data set focused on the high latitudes of the Northern Hemisphere. The data set is provided through the Arctic and Antarctic Research Institute (AARI), St Petersburg, Russia, produced through a project within the programme International Association for the Promotion of Cooperation with Scientists from the Former Soviet Union ( Alekseev et al., 1999). The input data are daily temperatures from 1486 meteorological stations in the Northern Hemisphere, including land- and drifting-stations from the Arctic. A gridded data set (5° lat. × 10° long.) based on these data has been developed from several sources. First, monthly-mean SAT anomaly maps were produced at the USSR's Main Geophysical Observatory for the period 1891-1969 and at the Hydrometeorological Research Center for the period 1970-1976. Secondly, the maps were visually analysed and interpolated into a gridded data set, as described and evaluated previously ( Vinnikov, 1977). Thirdly, the data set was extended in the same manner for the period 1977-1986, presented as SAT with reduction to common mean values and taking into the account the moist adiabatic temperature gradient (0.6° per 100 m) as in Alekseev and Svyaschennikov (1991). Fourthly, the data set was continued at the Hydrometeorological Research Center from 1986-1995, through visual interpolation and reading from monthly-mean SAT maps. Since 1995, the monthly-mean SATs have been produced at AARI through monthly averaging of gridded daily SAT from the European Centre for Medium-Range Weather Forecasting (ECMWF).

The reliability of the new SAT data set for climate analyses is evidenced through statistical comparison with the National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) re-analysis data ( Kalnay et al., 1996) and the Jones et al. (1999) data set, tested here over common areas and time periods. The quantitative agreement between interannual variations in annual mean SAT north of 55°N (excluding the Greenland area 20-70°W) from NCAR/NCEP and our data set is from 1955-1990. The agreement between the Jones data and our data set from a large arctic-subarctic test area (between 52.5°N-67.5°N and 2.5°W-62.5°E) from 1930-1990 is nearly identical, with a correlation coefficient and a mean difference ∼0.15°C. Therefore, the SAT data set put forth here is considered a reliable alternative to the standard data sets and has the advantage of improved coverage in the Arctic and extending over the last century.

Figure 1a shows the time evolution of the zonally averaged anomalies in annual mean SAT from 30-90°N. Two characteristic warming events stand out, the first from the mid 1920s to about 1940 and the second starting about 1980 and still ongoing. Here, we show that the early twentieth-century warming was largely confined to north of 60°N, whereas the latter warming encompasses the whole Earth ( Jones et al., 1999) but is none the less significantly enhanced in the Arctic ( Fig. 1a).

The early twentieth-century warming trend in the Arctic was nearly as large as the warming trend for the last 20 yr, such that some researchers (e.g. Polyakov et al., 2002) regard them to be part and parcel of the same natural low-frequency oscillation. However, our spatial comparison of these periods reveals key differences in their patterns ( Fig. 2). The 20-yr SAT trends for the 1920s-1930s warming period ( Figs 2a and b) and the subsequent cooling period ( Figs 2c and d) have remarkably similar patterns, thus suggesting similar underlying processes. In the winter half-year, the high-latitude warming ( Fig. 2a) and cooling ( Fig. 2c) patterns are organized symmetrically around the pole, while in the summer half-year, the warming ( Fig. 2b) and cooling ( Fig. 2d) appear to reflect the positions of the latitudinal quasi-stationary wave structure, predominantly wavenumber three and four. However, the warming trend for the last 20 yr is more widespread and has a markedly different pattern from the earlier periods in both winter ( Fig. 2e) and summer ( Fig. 2f). Both the 1920-39 and 1980-99 warming are most pronounced during winter for the high Arctic. In addition, in the latter period, there is pronounced warming in the Eurasian midlatitudes, especially in summer.

A recent modelling study ( Delworth and Knutson, 2000) has suggested that the 1920s-1930s warming anomaly was due to natural processes, insofar as models are capable of simulating such anomalies as due to internal chaotic processes of the climate system. Here, a similar high-latitude anomaly, although less extreme and of a shorter duration, is found in a 300-yr control run (without increasing anthropogenic forcings) with the ECHAM4 model-100 yr are shown in Fig. 1d. This anomaly occurred after 150 yr of integration and lasted for some 15 yr. This simulation without increasing anthropogenic forcing is able to produce an anomaly similar to the observed high-latitude warming in the 1920s-1930s. Therefore, we strongly support the contention of Delworth and Knutson (2000) that this high-latitude warming event represents primarily natural variability within the climate system, rather than being caused primarily by external forcings, whether solar forcing alone ( Thejll and Lassen, 2000) or a combination of increasing solar irradiance, increasing anthropogenic trace gases, and decreasing volcanic aerosols, as suggested from an analysis of 400 yr of temperature proxy data from the Arctic ( Overpeck et al., 1997). Changes in solar forcing have been suggested (e.g. Lean and Rind, 1998) as the cause of the warming. It has attracted considerable interest because of the apparent similarity between the assumed solar variability and the SAT trend of the Northern Hemisphere extratropics ( Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993). Reconstructions of solar variability have been used in modelling studies ( Cubasch et al., 1997; Cubasch and Voss, 2000; Stott et al., 2001) providing results broadly consistent with long-term estimated or observed temperature trends. This is not surprising because multidecadal and longer trends in solar forcing generate a response in global average modelled temperatures on similar time-scales as the anticipated variation in solar forcing ( Cubasch et al., 1997). Rigorous testing of the assumption of solar forcing requires reliable observations; however, these data exist only for the past two decades ( Lean and Rind, 1998), such that it remains only a hypothesis. The anthropogenic forcing in the 1920s-1930s was far too weak to generate the observed warming-the change in the GHG forcing in the early decades of the twentieth century was only ∼20% of the present ( Roeckner et al., 1999). The most plausible explanation in this case is the low-frequency, multidecadal oscillation related to North Atlantic Ocean circulation ( Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Polyakov and Johnson, 2000).

In contrast, no comprehensive numerical-model integrations have produced the present global warm anomaly ( Fig. 1a) without including observed anthropogenic forcing. Figure 1b shows the ECHAM4 model simulation with anthropogenic GHG forcing. The patterns compare well with the last two decades of observed warming, although the modelled warming occurs slightly earlier and also encompasses lower latitudes than observed ( Fig. 1a). The patterns from a simulation including GHGs and sulfate aerosols (GSD) ( Fig. 1c) show that, although the recent midlatitude warming is underestimated, the high-latitude enhancement is in agreement with the observations and other modelling results ( Räisänen, 2001). Therefore, anthropogenic forcing is the dominant cause of the recent pronounced warming in the Arctic.

What then are the possible mechanisms by which anthropogenic forcing could bring about high-latitude warming, given that the recent trend in arctic SAT is greater than the direct radiative effect of GHGs? First, as has been demonstrated by Bengtsson (1999) and Hansen et al. (1999), the large-scale spatial pattern of forcing and the pattern of response to forcing are practically uncorrelated, which stresses the key role of advective atmosphere-ocean processes in bringing about regional climate change; see also Schneider et al. (2003). A possible mechanism is suggested by recent findings from observations and modelling experiments ( Hoerling et al., 2001; Lin et al., 2002). Hoerling et al. (2001) show that the observed long-term warming trend (up to 0.2°C per decade) in the tropical oceans-anthropogenically forced ( Levitus et al., 2000; Reichert et al., 2002)-during the last 50 yr has generated, through enhanced convective activity, an intensification of the midlatitude tropospheric westerlies, and consequently because of geostrophy in the Atlantic sector, an enhanced positive North Atlantic Oscillation/Arctic Oscillation (NAO/AO) pattern ( Hoerling et al., 2001). Because the NAO and arctic temperature are linked-the interannual variations in the NAO index ( Hurrell, 1995) and SAT from 60-90°N are found here to be significantly correlated ( when using data since the mid twentieth century)-it follows that an NAO enhancement, linked to anthropogenic warming of the tropical oceans, can explain a substantial portion of the warming trend in the Arctic. It is unlikely that this was a mechanism for the early twentieth-century warming phase, because of the small anthropogenic GHG effect at the time and because no positive correlation between arctic SAT and the NAO before 1950 is found; in fact, here we find that the correlation is negative ().

Secondly, an ice-albedo feedback may enhance the arctic warming, as new ECHAM4 sensitivity experiments have demonstrated a robust relationship between sea ice and air temperature ( Bengtsson et al., 2004), in which advective mechanisms-in particular, enhanced wind-driven oceanic heat inflow into the Barents Sea-may have a predominant role.

3. Sea ice: observations

3.1. Sea-ice concentration and derived parameters

Sea-ice concentration (percent ice area per unit area) and derived parameters such as ice extent (the area within the ice-ocean margin defined as 15% ice concentration) and ice area (extent minus the open-water area) can be reliably retrieved from satellite passive-microwave sensor measurements, which are available continuously since 1978, thus among the longest satellite-retrieved geophysical records. Satellite data have shown that the winter maximum ice area ( Fig. 3a) is typically about while the summer minimum ice area ( Fig. 3a) is about . These data, here updated from Johannessen et al. (1995, 1999) and Bjørgo et al. (1997) through March 2003, indicate a decrease of (∼7.4%) in the Northern Hemisphere annual sea-ice area, 1978-2003 ( Fig. 3b). During this period, the decreases have been larger in summer (see also Chapman and Walsh, 1993; Parkinson et al., 1999); here, we calculate a (14%) decrease in September versus (5%) in March (1978-2003). This seasonal difference has resulted in a 7-9% per decade reduction in the area of thicker, multiyear (MY) ice (ice that has survived at least one summer melt) over the last two decades ( Johannessen et al., 1999; Comiso, 2002). It is noteworthy that our updated time series through September 2002 indicate that the record minimum summer ice cover recently reported ( Serreze et al., 2003) is indeed unprecedented in the nearly quarter-century satellite record ( Fig. 3a)-less than in area, which occurred from anomalous warm southerly winds in spring followed by low SLP and high SAT in the Arctic in summer ( Serreze et al., 2003). Note also the large positive anomaly in 1996, which occurred in the summer after an extreme temporary reversal in the NAO in the winter 1995/96.

The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figs 4a and 4b. The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figs 4c and d. During this period, the decreases in winter have been most pronounced (as large as ∼50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ∼30-50% in the Siberian marginal seas. These summer patterns are in agreement with an independent analysis of ice-cover minima from 1978-1998 (see fig. 2c in Comiso, 2002).

The decreases in recent decades, which are also partially due to circulation-driven ice export through the Fram Strait between Greenland and Svalbard ( Vinje, 2001), have coincided with a positive trend in the NAO, with unusually high index values in the late 1980s and 1990s. During this period, the variability of ice motion and ice export through the Fram Strait was correlated strongly with the NAO; for the ice area flux ( Kwok and Rothrock, 1999) and for the ice volume flux ( Hilmer and Jung, 2000), although the relationship was insignificant before the mid 1970s ( Hilmer and Jung, 2000). Deser et al. (2000) analysed a 40-yr gridded data set (1958-97) to determine the association between arctic sea ice, SAT and SLP, concluding that the multidecadal trends in the NAO/AO in the past three decades have been 'imprinted upon the distribution of Arctic sea ice', with the first principal component of sea-ice concentration significantly correlated with the NAO index, recently cause-and-effect modelled by Hu et al. (2002). None the less, our calculations and those of Deser et al. (2000) indicate that, even in recent decades, only about one third of the variability in arctic total ice extent and MY ice area ( Johannessen et al., 1999) is explained by the NAO index, implying that other factors including enhanced radiative forcing and ice-albedo feedbacks ( Björk and Söderkvist, 2002; Bengtsson et al., 2003) must be invoked to explain the variability.

A pronounced reduction in the Northern Hemisphere sea-ice extent, such as seen in the satellite record of the last two decades, is scarcely apparent in the early twentieth-century warm period, at least in the two most widely-used century-long sea-ice data sets ( Chapman and Walsh, 1993; Rayner et al., 1996). To ascertain whether this has a physical explanation or is due to acknowledged data deficiencies before the 1950s ( Chapman and Walsh, 1993; Vinnikov et al., 1999), we have analysed these data together with a new century-long 'Zakharov' data set ( Zakharov, 1997; Alekseev et al., 2000), which includes hitherto under-recognized Russian data. The data set comprises sea-ice extent (regardless of ice concentration within the ice-ocean margin) for ∼77% ( km of the area of the Arctic Ocean. This region occupies the perennially ice-covered central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi marginal seas, leaving out only the eastern Chukchi and Beaufort seas and the Canadian Arctic straits and bays.

In the Atlantic-European sector (Greenland and Barents seas), the sea-ice extents from 1900 to the late 1950s are known only from spring and summer (April to August). It has not previously been possible to gain understanding about the annual ice cover variability in these decades. An attempt has been made to obtain these missing mean annual data. Towards this end, the actual year-round data on the ice extent from 1959-1988 were used to derive an equation to calculate the annual average ice extent in the region based on its mean value in April to August : . The correlation between and is . By means of this equation, the missing annual ice-extent means from 1900-1958 were calculated. The main problem of any reconstruction is errors inevitable in the procedures of data series reconstruction. This problem can be resolved by comparing the actual and calculated annual ice extent averages, in this case for the period from 1959, and by calculating the root-mean-square (rms) error of the reconstruction. The error , and as the rms deviation of the initial actual series , the ratio . Thus, the methodological error of the reconstruction comprises only one third of the rms deviation of the actual series, indicating a sufficiently high reliability of the reconstruction method used.

Data on the ice edge are available for August from the mid 1920s in the Siberian sector (Kara, Laptev, and East Siberian seas). From the 1930s, with the development of shipping along the Northern Sea Route, knowledge of the position of these boundaries was extended to two adjacent months (July and September); however, the accuracy of the ice data from that time cannot be considered high, because observations were not carried out systematically. From the mid 1930s, aviation was used to collect ice information and from the mid 1940s became the main source of sea-ice information in the Siberian Arctic. The observations attain a regular character covering (with some exceptions) the entire navigation season. An attempt has been made to obtain these missing mean annual data, using incomplete data from 1924-1946 to reconstruct the conditions of those years. With this aim, using the 1946-1999 series, the equation: was derived, where is the mean sea-ice extent in the Siberian seas in June-October, and is the ice area in the Siberian seas in August. The correlation between and is . The ratio again suggests a sufficiently high reconstruction quality based on the knowledge of ice extent only in August.

Figure 5 shows time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard 'Walsh' data set ( Chapman and Walsh, 1993), in comparison with the zonal annual mean SAT between 70-90°N since 1900. In contrast to the Walsh data, these new data indicate a substantially reduced () ice cover in the 1920s-1930s warming period. Note that the correspondence between the subsequent cooling into the mid 1960s and increasing sea ice is seen from the late 1950s; the ice data from WWII and the early post-war years are inadequate. Note also that the apparent differences in trends in the two sea-ice time series during the last 25 yr are due to relatively large reductions in the Beaufort Sea and Chukchi seas ( Fig. 4)-areas not included in the Zakharov data set. The correlations between the SAT and the Zakharov and Walsh sea-ice extents are maximum at 0 lag, and 0.3, respectively. This indicates that the interannual variability in the arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, although the r-value may partially reflect feedback processes from the ice cover to the atmosphere, e.g. ECHAM4-model sensitivity experiments (see Bengtsson et al., 2004) have demonstrated a strong SAT response-particularly in the Atlantic-European sector-to model-imposed changes in sea ice.

3.2. Sea-ice thickness

The variability of ice thickness is relatively poorly known, due primarily to spatial-temporal sampling deficiencies in data from submarines carrying upward-looking sonar ( Wadhams, 1997). An analysis ( Rothrock et al., 1999) of data from the summers 1958, 1976, 1993, 1996 and 1997 found that between the 1950s/1970s and the 1990s, the mean ice thickness decreased from 3.1 to 1.8 m. The 1.3-m decrease-if representative-corresponds to an ∼40% reduction over the three to four decades ( Rothrock et al., 1999). However, analyses of sonar data alone from different transects, years and seasons yield a range of estimates, from a comparable decrease ( Wadhams and Davis, 2000) to no significant change ( Winsor, 2001) to an intermediate estimate ( Tucker et al., 2001). An analysis of submarine ( Rothrock et al., 1999, 2003) and modelled ( Holloway and Sau, 2002) ice thickness over the same common time period has demonstrated that ice motion and high interannual variability could mislead inference of trends from sonar transect data, e.g. 12% ( Holloway and Sau, 2002) versus the 40% decrease reported by Rothrock et al. (1999). Thus, the available sonar data alone remain inadequate to produce a reliable, long-term climatology of arctic ice thickness variability.

Here, for the first time, we put forth a unique 20-yr time series of monthly, area-averaged ice thickness derived from field-based measurements of surface elastic-gravity waves from Russian North Polar drifting stations 1970-91, when regular measurements of ice surface vibrations were made in the central Arctic Ocean ( Nagurnyi et al., 1994). Long elastic-gravity waves (on the order of 1 km) in the sea-ice cover arise from the interaction with ocean swells. These elastic-gravity waves can propagate for hundreds to thousands of kilometres before dampening out. Based on a linear theory of free vibrations of the sea-ice cover, the measured wavelength, wave period and direction are then related to thickness through a wave-energy dispersion relation. The ice thicknesses determined from different propagation directions are averaged to provide a basin-wide mean thickness estimate ( Nagurnyi et al., 1994, 1999). The values compare well to those observed in the regional ice cover ( Romanov, 1995) and their interannual variability correlates well with modelled arctic ice volume (see fig. 1 in Hilmer and Lemke, 2000). The thickness estimates have also been found to correlate strongly (∼0.88) to the satellite-derived area of the perennial, MY ice cover in winter ( Johannessen et al., 1999), suggesting moreover that the decreases found in MY ice area represent a mass balance change rather than merely a peripheral effect.

Figure 6a shows the 20-yr time series of area-averaged ice thickness, 1970-91, from which trends for winter and summer are derived ( Fig. 6b). The mean thickness estimates are ∼2.9-3 m in winter and ∼2.5-2.6 in summer (i.e. seasonal cycle ∼40 cm). The linear trend of anomalies from 1971-90 indicates a decrease of only ∼10 cm (less than 4%) over 20 yr. This is comparable with some observational and modelling analyses (e.g. Holloway and Sau, 2002), but is much less than the 1950s/1970s to 1990s sonar data analysis ( Rothrock et al., 1999) upon which the IPCC based its statement that the arctic ice thickness has been reduced 40% during summer in recent decades. The large variability inherent in the arctic sea-ice-climate system, coupled with the problem of obtaining ice thickness data, renders the evaluation of ice thickness trends from the available observational data an open question. None the less, it is notable that our ECHAM4 coupled model experiments (not shown) with the IPCC IS92 emission scenarios for greenhouse and related gases indicate that: (1) our 20 yr of ice thickness data are consistent with the modelled results 1970-90, and (2) substantial decreases in modelled ice thickness commence only in the last two decades of the twentieth century. The ice thickness from both observations and models is presently ∼2.5-3 m in summer, whereas our model results indicate less than 1 m at the end of this century for the remaining ice cover.

4. Sea-ice extent: modelled

The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses ( Vinnikov et al., 1999; Johannessen et al., 2001), which predicted a reduction of ∼15% in the Northern Hemisphere mean ice extent to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, for the first time, both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing are analysed to 2100, using ECHAM-4 and HadCM3 model predictions including different IPCC emissions scenarios.

The observed versus ECHAM4-modelled trends in Northern Hemisphere winter and summer sea-ice extent in the twentieth century are similar ( Fig. 7). Our ECHAM4-model run-using an IPCC IS92 emission scenario similar to IPCC Special Report on Emissions Scenarios (SRES) scenario B2-predicts the decreases to continue such that the summer ice cover may be reduced by ∼80% at the end of the twenty-first century ( Fig. 7, lower). This is much greater than the winter ( Fig. 7, upper) or annual means modelled previously ( Vinnikov et al., 1999; Johannessen et al., 2001) and is comparable to recent projections for the summer ( Gregory et al., 2002).

The spatial distributions of the ECHAM4-modelled sea-ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Fig. 8. In order to test the robustness of our ECHAM4 estimates, we have used a different coupled atmosphere-ocean model, the HadCM3, which is an improvement upon the HadCM2 model used in a previous sea-ice study ( Vinnikov et al., 1999). Furthermore, we use two different SRES scenarios, A2 and B2, which are 'medium-high' and 'medium-low' scenarios, respectively. Results from the B2 experiments are shown in Fig. 9, which depicts decadal averages of winter ( Figs 9a and b) and summer ( Figs 9c and d) sea-ice concentrations in 2001-2010 and 2081-2090. The ECHAM4 ( Fig. 8) and HadCM3 ( Fig. 9) results support each other, both predicting moderate reductions in winter and drastic reduction in the summer ice extent. The spatial distributions of the ECHAM4 and HadCM3 modelled summer ice cover in late century ( Figs 8d and 9d) indicate essentially ice-free arctic marginal seas except north of Greenland and the Canadian Arctic Archipelago, although there are some differences in the modelled spatial patterns.

5. Conclusions and implications

The results of our observational and modelling analysis lead to the following conclusions. First, we theorize that the Arctic warming in the 1920s-1930s and the subsequent cooling until about 1970 are due to natural fluctuations internal to the climate system. Secondly, we believe there are strong indications that neither the warming trend nor the decrease of ice extent and volume over the last two decades can be explained by natural processes alone. Thirdly, the state-of-the-art ECHAM4 and HadCM3 coupled climate models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century.

A range of potential consequences of arctic warming and a shrinking ice cover can be hypothesized, as follows.

(i) Reductions in albedo and increased open water would have significant effects on energy balances and atmospheric and oceanic circulation in the high latitudes.

(ii) Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity for CO absorption, could become a new and important sink of atmospheric CO (Anderson and Kaltin, 2001,).

(iii) Broad changes in the marine ecosystem, e.g. changes in plankton in the North Atlantic due to less ice and greater inflow of melt water (Beaugrand et al., 2002), could have a negative impact on arctic and subarctic marine biodiversity. However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route north of Siberia (Ragner, 2000).

(iv) Changes in the pathways and spreading of melt water and in the stratification in the Nordic Seas, and the effects of reduced deep-water formation in the Greenland Sea on the global thermohaline circulation (Rahmsdorf, 1999; Alekseev et al., 2001), could greatly alter the climate of the northern latitudes.

6. Acknowledgments

This research has been supported under the European Union's 5th Framework Programme project 'Arctic Ice Cover Simulation Experiment (AICSEX)' and the International Association for the Promotion of Cooperation with Scientists from the Independent States of the Former Soviet Union (INTAS), as well as the Research Council of Norway's projects 'Role of Arctic Sea-Ice-Atmosphere Processes (ROLARC)' and 'Marine Climate and Ecosystems in the Seasonal Ice Zone (MACESIZ)'. We also acknowledge the valuable assistance of Cathrine Myrmehl and Anne-Mette Olsen of the Nansen Environmental and Remote Sensing Center.

Ancillary

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Authors

OLA M. JOHANNESSEN,

Corresponding author

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Geophysical Institute, University of Bergen, Norway

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LENNART BENGTSSON,

1. Nansen Environmental and Remote Sensing Center, Bergen, Norway

2. Max Planck Institute for Meteorology, Hamburg, Germany

3. Environmental Systems Science Centre, University of Reading, UK

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MARTIN W. MILES,

1. Bjerknes Centre for Climate Research, Bergen, Norway

2. Environmental Systems Analysis Research Center, Boulder, Colorado, USA

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VLADIMIR A. SEMENOV,

1. Max Planck Institute for Meteorology, Hamburg, Germany

2. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia

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*Corresponding author.
e-mail: ola.johannessen@nersc.no

ABSTRACT

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic's response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of century- and multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere-ice-ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

1. Introduction

A consensus result from different coupled atmosphere-ice-ocean climate models is that greenhouse global warming should be enhanced in the Arctic ( Räisänen, 2001). The Intergovernmental Panel on Climate Change ( IPCC, 2001) states that the winter warming of northern high-latitude regions by the end of the century will be at least 40% greater than the global mean, based on a number of models and emissions scenarios, while the warming predicted for the central Arctic is ∼3-4°C during the next 50 yr, or more than twice the global mean.

Recent overviews of results from observational studies of atmospheric and climate-sensitive variables (e.g. sea ice, snow cover, river discharge, glaciers and permafrost) have concluded that, taken together, a reasonably coherent portrait of recent change in the northern high latitudes is apparent ( Serreze et al., 2000; Moritz et al., 2002; Peterson et al., 2002). However, it remains open to debate whether the warming in recent decades is an enhanced greenhouse-warming signal or natural decadal and multidecadal variability ( Polyakov and Johnson, 2000; Polyakov et al., 2002), e.g. as possibly expressed by the arctic warming observed in the 1920s and 1930s followed by cooling until the 1960s (e.g. Kelly et al., 1982). The uncertainties are exacerbated by a lack of homogeneous, century-scale instrumental data sets (see Moritz et al., 2002, whose fig. 2a includes no temperature data for the central Arctic) needed to resolve the inherent time-scales of variability in the Arctic ( Venegas and Mysak, 2000), a region characterized by high variability.

From these concerns, two overarching questions are as follows. (1) To what degree are the gradually changing atmosphere-ice-ocean conditions in the Arctic a consequence of natural climate processes and/or external factors such as anthropogenic greenhouse gas (GHG) forcing. (2) To what degree may anthropogenic forcing induce the arctic sea-ice cover to decrease or even disappear in this century? In order to study these questions, we analyse a new set of pertinent multidecadal to century-scale data-surface air temperature (SAT), sea-ice extent, sea-ice area and sea-ice thickness-in combination with global coupled atmosphere-ice-ocean climate model simulations using the ECHAM4 model of the Max Planck Institute for Meteorology ( Roeckner et al., 1999) and the HadCM3 model of the UK Meteorological Office ( Gordon et al., 2000). ECHAM4 and HadCM3 are state-of-the-art versions of two of the models demonstrated-in an intercomparison of climate-change scenario output from 19 coupled models ( Räisänen, 2001)-to be among those most representative of the 19-model mean temperature change.

2. Surface air temperature: observed and modelled

Statistical analyses of global SAT data sets have indicated substantial fluctuations in the extratropical Northern Hemisphere on decadal to multidecadal time-scales (e.g. Schlesinger and Ramankutty, 1994; Hansen et al., 1999; Jones et al., 1999). In the high latitudes, differences in spatial-temporal coverage have led to some discrepancies concerning temperature variability trends in the last century (e.g. Jones et al., 1999; Przybylak, 2000; Polyakov et al., 2002). The global gridded SAT data set ( Jones et al., 1999) used most extensively for studies of climate variability has major gaps in the northern high latitudes, in particular over the ice-covered Arctic Ocean and some surrounding land areas. Here, we analyse for the first time a unique century-long SAT data set focused on the high latitudes of the Northern Hemisphere. The data set is provided through the Arctic and Antarctic Research Institute (AARI), St Petersburg, Russia, produced through a project within the programme International Association for the Promotion of Cooperation with Scientists from the Former Soviet Union ( Alekseev et al., 1999). The input data are daily temperatures from 1486 meteorological stations in the Northern Hemisphere, including land- and drifting-stations from the Arctic. A gridded data set (5° lat. × 10° long.) based on these data has been developed from several sources. First, monthly-mean SAT anomaly maps were produced at the USSR's Main Geophysical Observatory for the period 1891-1969 and at the Hydrometeorological Research Center for the period 1970-1976. Secondly, the maps were visually analysed and interpolated into a gridded data set, as described and evaluated previously ( Vinnikov, 1977). Thirdly, the data set was extended in the same manner for the period 1977-1986, presented as SAT with reduction to common mean values and taking into the account the moist adiabatic temperature gradient (0.6° per 100 m) as in Alekseev and Svyaschennikov (1991). Fourthly, the data set was continued at the Hydrometeorological Research Center from 1986-1995, through visual interpolation and reading from monthly-mean SAT maps. Since 1995, the monthly-mean SATs have been produced at AARI through monthly averaging of gridded daily SAT from the European Centre for Medium-Range Weather Forecasting (ECMWF).

The reliability of the new SAT data set for climate analyses is evidenced through statistical comparison with the National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) re-analysis data ( Kalnay et al., 1996) and the Jones et al. (1999) data set, tested here over common areas and time periods. The quantitative agreement between interannual variations in annual mean SAT north of 55°N (excluding the Greenland area 20-70°W) from NCAR/NCEP and our data set is from 1955-1990. The agreement between the Jones data and our data set from a large arctic-subarctic test area (between 52.5°N-67.5°N and 2.5°W-62.5°E) from 1930-1990 is nearly identical, with a correlation coefficient and a mean difference ∼0.15°C. Therefore, the SAT data set put forth here is considered a reliable alternative to the standard data sets and has the advantage of improved coverage in the Arctic and extending over the last century.

Figure 1a shows the time evolution of the zonally averaged anomalies in annual mean SAT from 30-90°N. Two characteristic warming events stand out, the first from the mid 1920s to about 1940 and the second starting about 1980 and still ongoing. Here, we show that the early twentieth-century warming was largely confined to north of 60°N, whereas the latter warming encompasses the whole Earth ( Jones et al., 1999) but is none the less significantly enhanced in the Arctic ( Fig. 1a).

The early twentieth-century warming trend in the Arctic was nearly as large as the warming trend for the last 20 yr, such that some researchers (e.g. Polyakov et al., 2002) regard them to be part and parcel of the same natural low-frequency oscillation. However, our spatial comparison of these periods reveals key differences in their patterns ( Fig. 2). The 20-yr SAT trends for the 1920s-1930s warming period ( Figs 2a and b) and the subsequent cooling period ( Figs 2c and d) have remarkably similar patterns, thus suggesting similar underlying processes. In the winter half-year, the high-latitude warming ( Fig. 2a) and cooling ( Fig. 2c) patterns are organized symmetrically around the pole, while in the summer half-year, the warming ( Fig. 2b) and cooling ( Fig. 2d) appear to reflect the positions of the latitudinal quasi-stationary wave structure, predominantly wavenumber three and four. However, the warming trend for the last 20 yr is more widespread and has a markedly different pattern from the earlier periods in both winter ( Fig. 2e) and summer ( Fig. 2f). Both the 1920-39 and 1980-99 warming are most pronounced during winter for the high Arctic. In addition, in the latter period, there is pronounced warming in the Eurasian midlatitudes, especially in summer.

A recent modelling study ( Delworth and Knutson, 2000) has suggested that the 1920s-1930s warming anomaly was due to natural processes, insofar as models are capable of simulating such anomalies as due to internal chaotic processes of the climate system. Here, a similar high-latitude anomaly, although less extreme and of a shorter duration, is found in a 300-yr control run (without increasing anthropogenic forcings) with the ECHAM4 model-100 yr are shown in Fig. 1d. This anomaly occurred after 150 yr of integration and lasted for some 15 yr. This simulation without increasing anthropogenic forcing is able to produce an anomaly similar to the observed high-latitude warming in the 1920s-1930s. Therefore, we strongly support the contention of Delworth and Knutson (2000) that this high-latitude warming event represents primarily natural variability within the climate system, rather than being caused primarily by external forcings, whether solar forcing alone ( Thejll and Lassen, 2000) or a combination of increasing solar irradiance, increasing anthropogenic trace gases, and decreasing volcanic aerosols, as suggested from an analysis of 400 yr of temperature proxy data from the Arctic ( Overpeck et al., 1997). Changes in solar forcing have been suggested (e.g. Lean and Rind, 1998) as the cause of the warming. It has attracted considerable interest because of the apparent similarity between the assumed solar variability and the SAT trend of the Northern Hemisphere extratropics ( Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993). Reconstructions of solar variability have been used in modelling studies ( Cubasch et al., 1997; Cubasch and Voss, 2000; Stott et al., 2001) providing results broadly consistent with long-term estimated or observed temperature trends. This is not surprising because multidecadal and longer trends in solar forcing generate a response in global average modelled temperatures on similar time-scales as the anticipated variation in solar forcing ( Cubasch et al., 1997). Rigorous testing of the assumption of solar forcing requires reliable observations; however, these data exist only for the past two decades ( Lean and Rind, 1998), such that it remains only a hypothesis. The anthropogenic forcing in the 1920s-1930s was far too weak to generate the observed warming-the change in the GHG forcing in the early decades of the twentieth century was only ∼20% of the present ( Roeckner et al., 1999). The most plausible explanation in this case is the low-frequency, multidecadal oscillation related to North Atlantic Ocean circulation ( Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Polyakov and Johnson, 2000).

In contrast, no comprehensive numerical-model integrations have produced the present global warm anomaly ( Fig. 1a) without including observed anthropogenic forcing. Figure 1b shows the ECHAM4 model simulation with anthropogenic GHG forcing. The patterns compare well with the last two decades of observed warming, although the modelled warming occurs slightly earlier and also encompasses lower latitudes than observed ( Fig. 1a). The patterns from a simulation including GHGs and sulfate aerosols (GSD) ( Fig. 1c) show that, although the recent midlatitude warming is underestimated, the high-latitude enhancement is in agreement with the observations and other modelling results ( Räisänen, 2001). Therefore, anthropogenic forcing is the dominant cause of the recent pronounced warming in the Arctic.

What then are the possible mechanisms by which anthropogenic forcing could bring about high-latitude warming, given that the recent trend in arctic SAT is greater than the direct radiative effect of GHGs? First, as has been demonstrated by Bengtsson (1999) and Hansen et al. (1999), the large-scale spatial pattern of forcing and the pattern of response to forcing are practically uncorrelated, which stresses the key role of advective atmosphere-ocean processes in bringing about regional climate change; see also Schneider et al. (2003). A possible mechanism is suggested by recent findings from observations and modelling experiments ( Hoerling et al., 2001; Lin et al., 2002). Hoerling et al. (2001) show that the observed long-term warming trend (up to 0.2°C per decade) in the tropical oceans-anthropogenically forced ( Levitus et al., 2000; Reichert et al., 2002)-during the last 50 yr has generated, through enhanced convective activity, an intensification of the midlatitude tropospheric westerlies, and consequently because of geostrophy in the Atlantic sector, an enhanced positive North Atlantic Oscillation/Arctic Oscillation (NAO/AO) pattern ( Hoerling et al., 2001). Because the NAO and arctic temperature are linked-the interannual variations in the NAO index ( Hurrell, 1995) and SAT from 60-90°N are found here to be significantly correlated ( when using data since the mid twentieth century)-it follows that an NAO enhancement, linked to anthropogenic warming of the tropical oceans, can explain a substantial portion of the warming trend in the Arctic. It is unlikely that this was a mechanism for the early twentieth-century warming phase, because of the small anthropogenic GHG effect at the time and because no positive correlation between arctic SAT and the NAO before 1950 is found; in fact, here we find that the correlation is negative ().

Secondly, an ice-albedo feedback may enhance the arctic warming, as new ECHAM4 sensitivity experiments have demonstrated a robust relationship between sea ice and air temperature ( Bengtsson et al., 2004), in which advective mechanisms-in particular, enhanced wind-driven oceanic heat inflow into the Barents Sea-may have a predominant role.

3. Sea ice: observations

3.1. Sea-ice concentration and derived parameters

Sea-ice concentration (percent ice area per unit area) and derived parameters such as ice extent (the area within the ice-ocean margin defined as 15% ice concentration) and ice area (extent minus the open-water area) can be reliably retrieved from satellite passive-microwave sensor measurements, which are available continuously since 1978, thus among the longest satellite-retrieved geophysical records. Satellite data have shown that the winter maximum ice area ( Fig. 3a) is typically about while the summer minimum ice area ( Fig. 3a) is about . These data, here updated from Johannessen et al. (1995, 1999) and Bjørgo et al. (1997) through March 2003, indicate a decrease of (∼7.4%) in the Northern Hemisphere annual sea-ice area, 1978-2003 ( Fig. 3b). During this period, the decreases have been larger in summer (see also Chapman and Walsh, 1993; Parkinson et al., 1999); here, we calculate a (14%) decrease in September versus (5%) in March (1978-2003). This seasonal difference has resulted in a 7-9% per decade reduction in the area of thicker, multiyear (MY) ice (ice that has survived at least one summer melt) over the last two decades ( Johannessen et al., 1999; Comiso, 2002). It is noteworthy that our updated time series through September 2002 indicate that the record minimum summer ice cover recently reported ( Serreze et al., 2003) is indeed unprecedented in the nearly quarter-century satellite record ( Fig. 3a)-less than in area, which occurred from anomalous warm southerly winds in spring followed by low SLP and high SAT in the Arctic in summer ( Serreze et al., 2003). Note also the large positive anomaly in 1996, which occurred in the summer after an extreme temporary reversal in the NAO in the winter 1995/96.

The spatial patterns of the mean winter (March) and summer (September) sea-ice cover 1978-2002 are shown in Figs 4a and 4b. The winter and summer trends (linear regressions) in sea-ice concentration from 1978-2002 are indicated in Figs 4c and d. During this period, the decreases in winter have been most pronounced (as large as ∼50%) in the Barents and Greenland seas, whereas the summer decreases have been greater than 50% in some areas of the Beaufort and Chukchi seas, and as large as ∼30-50% in the Siberian marginal seas. These summer patterns are in agreement with an independent analysis of ice-cover minima from 1978-1998 (see fig. 2c in Comiso, 2002).

The decreases in recent decades, which are also partially due to circulation-driven ice export through the Fram Strait between Greenland and Svalbard ( Vinje, 2001), have coincided with a positive trend in the NAO, with unusually high index values in the late 1980s and 1990s. During this period, the variability of ice motion and ice export through the Fram Strait was correlated strongly with the NAO; for the ice area flux ( Kwok and Rothrock, 1999) and for the ice volume flux ( Hilmer and Jung, 2000), although the relationship was insignificant before the mid 1970s ( Hilmer and Jung, 2000). Deser et al. (2000) analysed a 40-yr gridded data set (1958-97) to determine the association between arctic sea ice, SAT and SLP, concluding that the multidecadal trends in the NAO/AO in the past three decades have been 'imprinted upon the distribution of Arctic sea ice', with the first principal component of sea-ice concentration significantly correlated with the NAO index, recently cause-and-effect modelled by Hu et al. (2002). None the less, our calculations and those of Deser et al. (2000) indicate that, even in recent decades, only about one third of the variability in arctic total ice extent and MY ice area ( Johannessen et al., 1999) is explained by the NAO index, implying that other factors including enhanced radiative forcing and ice-albedo feedbacks ( Björk and Söderkvist, 2002; Bengtsson et al., 2003) must be invoked to explain the variability.

A pronounced reduction in the Northern Hemisphere sea-ice extent, such as seen in the satellite record of the last two decades, is scarcely apparent in the early twentieth-century warm period, at least in the two most widely-used century-long sea-ice data sets ( Chapman and Walsh, 1993; Rayner et al., 1996). To ascertain whether this has a physical explanation or is due to acknowledged data deficiencies before the 1950s ( Chapman and Walsh, 1993; Vinnikov et al., 1999), we have analysed these data together with a new century-long 'Zakharov' data set ( Zakharov, 1997; Alekseev et al., 2000), which includes hitherto under-recognized Russian data. The data set comprises sea-ice extent (regardless of ice concentration within the ice-ocean margin) for ∼77% ( km of the area of the Arctic Ocean. This region occupies the perennially ice-covered central Arctic Ocean and the Greenland, Barents, Kara, Laptev, East Siberian and western Chukchi marginal seas, leaving out only the eastern Chukchi and Beaufort seas and the Canadian Arctic straits and bays.

In the Atlantic-European sector (Greenland and Barents seas), the sea-ice extents from 1900 to the late 1950s are known only from spring and summer (April to August). It has not previously been possible to gain understanding about the annual ice cover variability in these decades. An attempt has been made to obtain these missing mean annual data. Towards this end, the actual year-round data on the ice extent from 1959-1988 were used to derive an equation to calculate the annual average ice extent in the region based on its mean value in April to August : . The correlation between and is . By means of this equation, the missing annual ice-extent means from 1900-1958 were calculated. The main problem of any reconstruction is errors inevitable in the procedures of data series reconstruction. This problem can be resolved by comparing the actual and calculated annual ice extent averages, in this case for the period from 1959, and by calculating the root-mean-square (rms) error of the reconstruction. The error , and as the rms deviation of the initial actual series , the ratio . Thus, the methodological error of the reconstruction comprises only one third of the rms deviation of the actual series, indicating a sufficiently high reliability of the reconstruction method used.

Data on the ice edge are available for August from the mid 1920s in the Siberian sector (Kara, Laptev, and East Siberian seas). From the 1930s, with the development of shipping along the Northern Sea Route, knowledge of the position of these boundaries was extended to two adjacent months (July and September); however, the accuracy of the ice data from that time cannot be considered high, because observations were not carried out systematically. From the mid 1930s, aviation was used to collect ice information and from the mid 1940s became the main source of sea-ice information in the Siberian Arctic. The observations attain a regular character covering (with some exceptions) the entire navigation season. An attempt has been made to obtain these missing mean annual data, using incomplete data from 1924-1946 to reconstruct the conditions of those years. With this aim, using the 1946-1999 series, the equation: was derived, where is the mean sea-ice extent in the Siberian seas in June-October, and is the ice area in the Siberian seas in August. The correlation between and is . The ratio again suggests a sufficiently high reconstruction quality based on the knowledge of ice extent only in August.

Figure 5 shows time series of annual sea-ice extent based on these data and annual sea-ice extent from the standard 'Walsh' data set ( Chapman and Walsh, 1993), in comparison with the zonal annual mean SAT between 70-90°N since 1900. In contrast to the Walsh data, these new data indicate a substantially reduced () ice cover in the 1920s-1930s warming period. Note that the correspondence between the subsequent cooling into the mid 1960s and increasing sea ice is seen from the late 1950s; the ice data from WWII and the early post-war years are inadequate. Note also that the apparent differences in trends in the two sea-ice time series during the last 25 yr are due to relatively large reductions in the Beaufort Sea and Chukchi seas ( Fig. 4)-areas not included in the Zakharov data set. The correlations between the SAT and the Zakharov and Walsh sea-ice extents are maximum at 0 lag, and 0.3, respectively. This indicates that the interannual variability in the arctic sea-ice extent in the last century was coupled to the high-latitude SAT variability to a large degree, although the r-value may partially reflect feedback processes from the ice cover to the atmosphere, e.g. ECHAM4-model sensitivity experiments (see Bengtsson et al., 2004) have demonstrated a strong SAT response-particularly in the Atlantic-European sector-to model-imposed changes in sea ice.

3.2. Sea-ice thickness

The variability of ice thickness is relatively poorly known, due primarily to spatial-temporal sampling deficiencies in data from submarines carrying upward-looking sonar ( Wadhams, 1997). An analysis ( Rothrock et al., 1999) of data from the summers 1958, 1976, 1993, 1996 and 1997 found that between the 1950s/1970s and the 1990s, the mean ice thickness decreased from 3.1 to 1.8 m. The 1.3-m decrease-if representative-corresponds to an ∼40% reduction over the three to four decades ( Rothrock et al., 1999). However, analyses of sonar data alone from different transects, years and seasons yield a range of estimates, from a comparable decrease ( Wadhams and Davis, 2000) to no significant change ( Winsor, 2001) to an intermediate estimate ( Tucker et al., 2001). An analysis of submarine ( Rothrock et al., 1999, 2003) and modelled ( Holloway and Sau, 2002) ice thickness over the same common time period has demonstrated that ice motion and high interannual variability could mislead inference of trends from sonar transect data, e.g. 12% ( Holloway and Sau, 2002) versus the 40% decrease reported by Rothrock et al. (1999). Thus, the available sonar data alone remain inadequate to produce a reliable, long-term climatology of arctic ice thickness variability.

Here, for the first time, we put forth a unique 20-yr time series of monthly, area-averaged ice thickness derived from field-based measurements of surface elastic-gravity waves from Russian North Polar drifting stations 1970-91, when regular measurements of ice surface vibrations were made in the central Arctic Ocean ( Nagurnyi et al., 1994). Long elastic-gravity waves (on the order of 1 km) in the sea-ice cover arise from the interaction with ocean swells. These elastic-gravity waves can propagate for hundreds to thousands of kilometres before dampening out. Based on a linear theory of free vibrations of the sea-ice cover, the measured wavelength, wave period and direction are then related to thickness through a wave-energy dispersion relation. The ice thicknesses determined from different propagation directions are averaged to provide a basin-wide mean thickness estimate ( Nagurnyi et al., 1994, 1999). The values compare well to those observed in the regional ice cover ( Romanov, 1995) and their interannual variability correlates well with modelled arctic ice volume (see fig. 1 in Hilmer and Lemke, 2000). The thickness estimates have also been found to correlate strongly (∼0.88) to the satellite-derived area of the perennial, MY ice cover in winter ( Johannessen et al., 1999), suggesting moreover that the decreases found in MY ice area represent a mass balance change rather than merely a peripheral effect.

Figure 6a shows the 20-yr time series of area-averaged ice thickness, 1970-91, from which trends for winter and summer are derived ( Fig. 6b). The mean thickness estimates are ∼2.9-3 m in winter and ∼2.5-2.6 in summer (i.e. seasonal cycle ∼40 cm). The linear trend of anomalies from 1971-90 indicates a decrease of only ∼10 cm (less than 4%) over 20 yr. This is comparable with some observational and modelling analyses (e.g. Holloway and Sau, 2002), but is much less than the 1950s/1970s to 1990s sonar data analysis ( Rothrock et al., 1999) upon which the IPCC based its statement that the arctic ice thickness has been reduced 40% during summer in recent decades. The large variability inherent in the arctic sea-ice-climate system, coupled with the problem of obtaining ice thickness data, renders the evaluation of ice thickness trends from the available observational data an open question. None the less, it is notable that our ECHAM4 coupled model experiments (not shown) with the IPCC IS92 emission scenarios for greenhouse and related gases indicate that: (1) our 20 yr of ice thickness data are consistent with the modelled results 1970-90, and (2) substantial decreases in modelled ice thickness commence only in the last two decades of the twentieth century. The ice thickness from both observations and models is presently ∼2.5-3 m in summer, whereas our model results indicate less than 1 m at the end of this century for the remaining ice cover.

4. Sea-ice extent: modelled

The variability of annual sea-ice extent has been modelled and compared to observations in previous analyses ( Vinnikov et al., 1999; Johannessen et al., 2001), which predicted a reduction of ∼15% in the Northern Hemisphere mean ice extent to 2050. However, potentially large and important spatial and seasonal aspects were not considered. Here, for the first time, both the spatial and seasonal variability of the ice cover and its modelled response to anthropogenic forcing are analysed to 2100, using ECHAM-4 and HadCM3 model predictions including different IPCC emissions scenarios.

The observed versus ECHAM4-modelled trends in Northern Hemisphere winter and summer sea-ice extent in the twentieth century are similar ( Fig. 7). Our ECHAM4-model run-using an IPCC IS92 emission scenario similar to IPCC Special Report on Emissions Scenarios (SRES) scenario B2-predicts the decreases to continue such that the summer ice cover may be reduced by ∼80% at the end of the twenty-first century ( Fig. 7, lower). This is much greater than the winter ( Fig. 7, upper) or annual means modelled previously ( Vinnikov et al., 1999; Johannessen et al., 2001) and is comparable to recent projections for the summer ( Gregory et al., 2002).

The spatial distributions of the ECHAM4-modelled sea-ice cover for the present decade (2001-2010) and towards the end of the century (2081-2090) are indicated in Fig. 8. In order to test the robustness of our ECHAM4 estimates, we have used a different coupled atmosphere-ocean model, the HadCM3, which is an improvement upon the HadCM2 model used in a previous sea-ice study ( Vinnikov et al., 1999). Furthermore, we use two different SRES scenarios, A2 and B2, which are 'medium-high' and 'medium-low' scenarios, respectively. Results from the B2 experiments are shown in Fig. 9, which depicts decadal averages of winter ( Figs 9a and b) and summer ( Figs 9c and d) sea-ice concentrations in 2001-2010 and 2081-2090. The ECHAM4 ( Fig. 8) and HadCM3 ( Fig. 9) results support each other, both predicting moderate reductions in winter and drastic reduction in the summer ice extent. The spatial distributions of the ECHAM4 and HadCM3 modelled summer ice cover in late century ( Figs 8d and 9d) indicate essentially ice-free arctic marginal seas except north of Greenland and the Canadian Arctic Archipelago, although there are some differences in the modelled spatial patterns.

5. Conclusions and implications

The results of our observational and modelling analysis lead to the following conclusions. First, we theorize that the Arctic warming in the 1920s-1930s and the subsequent cooling until about 1970 are due to natural fluctuations internal to the climate system. Secondly, we believe there are strong indications that neither the warming trend nor the decrease of ice extent and volume over the last two decades can be explained by natural processes alone. Thirdly, the state-of-the-art ECHAM4 and HadCM3 coupled climate models both predict a dramatic decrease of the ice cover, which could result in a nearly ice-free Arctic Ocean during summer at the end of this century.

A range of potential consequences of arctic warming and a shrinking ice cover can be hypothesized, as follows.

(i) Reductions in albedo and increased open water would have significant effects on energy balances and atmospheric and oceanic circulation in the high latitudes.

(ii) Exposure of vast areas of the Arctic Ocean with cold open water, which has a high capacity for CO absorption, could become a new and important sink of atmospheric CO (Anderson and Kaltin, 2001,).

(iii) Broad changes in the marine ecosystem, e.g. changes in plankton in the North Atlantic due to less ice and greater inflow of melt water (Beaugrand et al., 2002), could have a negative impact on arctic and subarctic marine biodiversity. However, there would be a larger area for potential fisheries, as well as increased offshore activities and marine transportation, including the Northern Sea Route north of Siberia (Ragner, 2000).

(iv) Changes in the pathways and spreading of melt water and in the stratification in the Nordic Seas, and the effects of reduced deep-water formation in the Greenland Sea on the global thermohaline circulation (Rahmsdorf, 1999; Alekseev et al., 2001), could greatly alter the climate of the northern latitudes.

6. Acknowledgments

This research has been supported under the European Union's 5th Framework Programme project 'Arctic Ice Cover Simulation Experiment (AICSEX)' and the International Association for the Promotion of Cooperation with Scientists from the Independent States of the Former Soviet Union (INTAS), as well as the Research Council of Norway's projects 'Role of Arctic Sea-Ice-Atmosphere Processes (ROLARC)' and 'Marine Climate and Ecosystems in the Seasonal Ice Zone (MACESIZ)'. We also acknowledge the valuable assistance of Cathrine Myrmehl and Anne-Mette Olsen of the Nansen Environmental and Remote Sensing Center.

Ancillary

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Corresponding author Nansen Environmental and Remote Sensing Center, Bergen, Norway Geophysical Institute, University of Bergen, Norway Search for more papers by this author Nansen Environmental and Remote Sensing Center, Bergen, Norway Max Planck Institute for Meteorology, Hamburg, Germany Environmental Systems Science Centre, University of Reading, UK Search for more papers by this author Bjerknes Centre for Climate Research, Bergen, Norway Environmental Systems Analysis Research Center, Boulder, Colorado, USA Search for more papers by this author Max Planck Institute for Meteorology, Hamburg, Germany Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia Search for more papers by this author *Corresponding author.