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 Question 1. Explain how the causes of climate change (such as an increase in CO2 in the atmosphere) have led to this effect. You may like to present your information as a flow diagram.  Use Oxford Big Ideas 10 pp.64-65 and the following websites to help you complete.                   

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 Question 1. Explain how the causes of climate change (such as an increase in CO2 in the atmosphere) have led to this effect. You may like to present your information as a flow diagram.  Use Oxford Big Ideas 10 pp.64-65 and the following websites to help you complete.                   

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https://www.csiro.au/en/research/environmental-impacts/climate-change

: http://www.ipcc.ch/publications_and_data/ar4/syr/en/main.html

http://www.climatechange.gov.au/climate-change/climate-science/climate-change-impacts-australia

http://www.bbc.co.uk/schools/gcsebitesize/geography/climate_change/describing_climatic_trends_rev3.shtml

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Climate change and global warming - Climate change - AQA - GCSE Geography Revision - AQA - BBC Bitesize

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Learn about and revise climate change and its effects on the UK and the rest of the world with GCSE Bitesize Geography (AQA).

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Less snow and retreat of mountain glaciers

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Less snow and retreat of mountain glaciers

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2. Explain how the effect that you have selected may lead to other effects. You should consider both:

a impacts on the natural environment, and b impacts on people and communities.

3. Provide a map showing an aspect of the change you have selected. This could be a map showing how something has changed over time (for example, less sea ice in the Arctic) or the way in which one change leads to others (for example, fewer mountain glaciers means less water available for agriculture).

4.Use a variety of sources and be sure to submit a complete and properly cited bibliography. Also be sure to correctly in-text cite all facts and statistics and all visuals.

For example:

National Geographic Society (2022). Glacier. [online] education.nationalgeographic.org. Available at: https://education.nationalgeographic.org/resource/glacier/.

‌

https://education.nationalgeographic.org/resource/glacier/

https://climate.esa.int/en/

https://www.unep-wcmc.org/en

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For more than a century, the World Glacier Monitoring Service (WGMS) and its predecessor organizations have been compiling and disseminating standardized data on glacier fluctuations. Thereto, the WGMS annually collects glacier data through its scientific collaboration network that is active in more than 30 countries. More... In close collaboration with the U.S. National Snow and Ice Data Center (NSIDC) and the Global Land Ice Measurements from Space (GLIMS) initiative, the WGMS runs the Global Terrestrial Network for Glaciers (GTN-G) in support of the United Nations Framework Convention on Climate Change (UNFCCC). More...

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For more than a century, the World Glacier Monitoring Service (WGMS) and its predecessor organizations have been compiling and disseminating standardized data on glacier fluctuations. Thereto, the WGMS annually collects glacier data through its scientific collaboration network that is active in more than 30 countries. More... In close collaboration with the U.S.

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Increased number and severity of heat waves

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Increased number and severity of heat waves

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https://www.health.vic.gov.au/environmental-health/extreme-heat-and-heatwaves

https://www.noaa.gov/media-advisory/extreme-heat-media-resource-guide

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Key facts

Climate change is directly contributing to humanitarian emergencies from heatwaves, wildfires, floods, tropical storms and hurricanes and they are increasing in scale, frequency and intensity.

Research shows that 3.6 billion people already live in areas highly susceptible to climate change. Between 2030 and 2050, climate change is expected to cause approximately 250 000 additional deaths per year, from undernutrition, malaria, diarrhoea and heat stress alone.

The direct damage costs to health (excluding costs in health-determining sectors such as agriculture and water and sanitation) is estimated to be between US$ 2-4 billion per year by 2030.

Areas with weak health infrastructure - mostly in developing countries - will be the least able to cope without assistance to prepare and respond.

Reducing emissions of greenhouse gases through better transport, food and energy use choices can result in very large gains for health, particularly through reduced air pollution.

Overview

Climate change presents a fundamental threat to human health. It affects the physical environment as well as all aspects of both natural and human systems - including social and economic conditions and the functioning of health systems. It is therefore a threat multiplier, undermining and potentially reversing decades of health progress. As climatic conditions change, more frequent and intensifying weather and climate events are observed, including storms, extreme heat, floods, droughts and wildfires. These weather and climate hazards affect health both directly and indirectly, increasing the risk of deaths, noncommunicable diseases, the emergence and spread of infectious diseases, and health emergencies.

Climate change is also having an impact on our health workforce and infrastructure, reducing capacity to provide universal health coverage (UHC). More fundamentally, climate shocks and growing stresses such as changing temperature and precipitation patterns, drought, floods and rising sea levels degrade the environmental and social determinants of physical and mental health. All aspects of health are affected by climate change, from clean air, water and soil to food systems and livelihoods. Further delay in tackling climate change will increase health risks, undermine decades of improvements in global health, and contravene our collective commitments to ensure the human right to health for all.

Climate change impacts on health

The Intergovernmental Panel on Climate Change's (IPCC) Sixth Assessment Report (AR6) concluded that climate risks are appearing faster and will become more severe sooner than previously expected, and it will be harder to adapt with increased global heating.

It further reveals that 3.6 billion people already live in areas highly susceptible to climate change. Despite contributing minimally to global emissions, low-income countries and small island developing states (SIDS) endure the harshest health impacts. In vulnerable regions, the death rate from extreme weather events in the last decade was 15 times higher than in less vulnerable ones.

Climate change is impacting health in a myriad of ways, including by leading to death and illness from increasingly frequent extreme weather events, such as heatwaves, storms and floods, the disruption of food systems, increases in zoonoses and food-, water- and vector-borne diseases, and mental health issues. Furthermore, climate change is undermining many of the social determinants for good health, such as livelihoods, equality and access to health care and social support structures. These climate-sensitive health risks are disproportionately felt by the most vulnerable and disadvantaged, including women, children, ethnic minorities, poor communities, migrants or displaced persons, older populations, and those with underlying health conditions.
Figure: An overview of climate-sensitive health risks, their exposure pathways and vulnerability factors. Climate change impacts health both directly and indirectly, and is strongly mediated by environmental, social and public health determinants.
Although it is unequivocal that climate change affects human health, it remains challenging to accurately estimate the scale and impact of many climate-sensitive health risks. However, scientific advances progressively allow us to attribute an increase in morbidity and mortality to global warming, and more accurately determine the risks and scale of these health threats.

WHO data indicates 2 billion people lack safe drinking water and 600 million suffer from foodborne illnesses annually, with children under 5 bearing 30% of foodborne fatalities. Climate stressors heighten waterborne and foodborne disease risks. In 2020, 770 million faced hunger, predominantly in Africa and Asia. Climate change affects food availability, quality and diversity, exacerbating food and nutrition crises.

Temperature and precipitation changes enhance the spread of vector-borne diseases. Without preventive actions, deaths from such diseases, currently over 700 000 annually, may rise. Climate change induces both immediate mental health issues, like anxiety and post-traumatic stress, and long-term disorders due to factors like displacement and disrupted social cohesion.

Recent research attributes 37% of heat-related deaths to human-induced climate change. Heat-related deaths among those over 65 have risen by 70% in two decades. In 2020, 98 million more experienced food insecurity compared to the 1981-2010 average. The WHO conservatively projects 250 000 additional yearly deaths by the 2030s due to climate change impacts on diseases like malaria and coastal flooding. However, modelling challenges persist, especially around capturing risks like drought and migration pressures.

The climate crisis threatens to undo the last 50 years of progress in development, global health and poverty reduction, and to further widen existing health inequalities between and within populations. It severely jeopardizes the realization of UHC in various ways, including by compounding the existing burden of disease and by exacerbating existing barriers to accessing health services, often at the times when they are most needed. Over 930 million people - around 12% of the world's population - spend at least 10% of their household budget to pay for health care. With the poorest people largely uninsured, health shocks and stresses already currently push around 100 million people into poverty every year, with the impacts of climate change worsening this trend.

Climate change and equity

In the short- to medium-term, the health impacts of climate change will be determined mainly by the vulnerability of populations, their resilience to the current rate of climate change and the extent and pace of adaptation. In the longer-term, the effects will increasingly depend on the extent to which transformational action is taken now to reduce emissions and avoid the breaching of dangerous temperature thresholds and potential irreversible tipping points.

While no one is safe from these risks, the people whose health is being harmed first and worst by the climate crisis are the people who contribute least to its causes, and who are least able to protect themselves and their families against it: people in low-income and disadvantaged countries and communities.

Addressing climate change's health burden underscores the equity imperative: those most responsible for emissions should bear the highest mitigation and adaptation costs, emphasizing health equity and vulnerable group prioritization.

Need for urgent action

To avert catastrophic health impacts and prevent millions of climate change-related deaths, the world must limit temperature rise to 1.5°C. Past emissions have already made a certain level of global temperature rise and other changes to the climate inevitable. Global heating of even 1.5°C is not considered safe, however; every additional tenth of a degree of warming will take a serious toll on people's lives and health.

WHO response

WHO's response to these challenges centres around 3 main objectives:

Promote actions that both reduce carbon emissions and improve health: supporting a rapid and equitable transition to a clean energy economy; ensuring that health is central to climate change mitigation policy; accelerating mitigation actions that bring the greatest health gains; and mobilizing the strength of the health community to drive policy change and build public support.

Build better, more climate-resilient and environmentally sustainable health systems: ensuring core services, environmental sustainability and climate resilience as central components of UHC and primary health care (PHC); supporting health systems to leapfrog to cheaper, more reliable and cleaner solutions, while decarbonizing high-emitting health systems; and mainstreaming climate resilience and environmental sustainability into health service investments, including the capacity of the health workforce.

Protect health from the wide range of impacts of climate change: assessing health vulnerabilities and developing health plans; integrating climate risk and implementing climate-informed surveillance and response systems for key risks, such as extreme heat and infectious disease; supporting resilience and adaptation in health-determining sectors such as water and food; and closing the financing gap for health adaptation and resilience.

Leadership and Raising Awareness: WHO leads in emphasizing climate change's health implications, aiming to centralize health in climate policies, including through the UNFCCC. Partnering with major health agencies, health professionals and civil society, WHO strives to embed climate change in health priorities like UHC and target carbon neutrality by 2030.

Evidence and Monitoring: WHO, with its network of global experts, contributes global evidence summaries, provides assistance to nations in their assessments, and monitors progress. The emphasis is on deploying effective policies and enhancing access to knowledge and data.

Capacity Building and Country Support: Through WHO offices, support is given to ministries of health, focusing on collaboration across sectors, updated guidance, hands-on training, and support for project preparation and execution as well as for securing climate and health funding. WHO leads the Alliance for Transformative Action on Climate and Health (ATACH), bringing together a range of health and development partners, to support countries in achieving their commitments to climate-resilient and low carbon health systems.

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WHO fact sheet on climate change and health: provides key facts, patterns of infection, measuring health effects and WHO response.

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Gradual replacement of rainforest by savannah in areas such as South America

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Gradual replacement of rainforest by savannah in areas such as South America

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https://earthobservatory.nasa.gov/world-of-change/Deforestation-

https://www.globalforestwatch.org/

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Loss of biodiversity, particularly in tropical areas

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Loss of biodiversity, particularly in tropical areas

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https://www.globalforestwatch.org/

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Our work

There is great room for optimism, underpinned by clear evidence. The IUCN Red List of Threatened Species reveals that trends in species extinction risk would have been at least 20% worse in the absence of conservation action.

- IUCN Nature 2030 Programme

IUCN promotes strategies that integrate the sustainable management of land, water and biodiversity with human health and well-being. We provide tools like The IUCN Red List of Threatened Species, IUCN Red List of Ecosystems and the Integrated Biodiversity Assessment Tool to inform conservation projects, business, national and international policy. Our aim is to conserve and restore species and ecosystems, and secure the ecosystem services on which humanity depends

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There is great room for optimism, underpinned by clear evidence. The IUCN Red List of Threatened Species reveals that trends in species extinction risk would have been at least 20% worse in the absence of conservation action. - IUCN Nature 2030 Programme IUCN promotes strategies that integrate the sustainable management of land, water and biodiversity with human health and well-being.

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Increased risk of flash flooding in inland areas

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Increased risk of flash flooding in inland areas

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http://www.bom.gov.au/water/floods/floodWarningServices.shtml

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Severe Weather 101

Flood Basics

What is flooding?

Flooding is an overflowing of water onto land that is normally dry. Floods can happen during heavy rains, when ocean waves come on shore, when snow melts quickly, or when dams or levees break. Damaging flooding may happen with only a few inches of water, or it may cover a house to the rooftop. Floods can occur within minutes or over a long period, and may last days, weeks, or longer. Floods are the most common and widespread of all weather-related natural disasters.

Flash floods are the most dangerous kind of floods, because they combine the destructive power of a flood with incredible speed. Flash floods occur when heavy rainfall exceeds the ability of the ground to absorb it. They also occur when water fills normally dry creeks or streams or enough water accumulates for streams to overtop their banks, causing rapid rises of water in a short amount of time. They can happen within minutes of the causative rainfall, limiting the time available to warn and protect the public.

Where and when do floods occur?

Flooding occurs in every U.S. state and territory, and is a threat experienced anywhere in the world that receives rain. In the U.S. floods kill more people each year than tornadoes, hurricanes or lightning.

What areas are at risk from flash floods?

Densely populated areas are at a high risk for flash floods. The construction of buildings, highways, driveways, and parking lots increases runoff by reducing the amount of rain absorbed by the ground. This runoff increases the flash flood potential.

Sometimes, streams through cities and towns are routed underground into storm drains. During heavy rain, the storm drains can become overwhelmed or plugged by debris and flood the roads and buildings nearby.Low spots, such as underpasses, underground parking garages, basements, and low water crossings can become death traps.

Areas near rivers are at risk from floods. Embankments, known as levees, are often built along rivers and are used to prevent high water from flooding bordering land. In 1993, many levees failed along the Mississippi River, resulting in devastating floods. The city of New Orleans experienced massive devastating flooding days after Hurricane Katrina came onshore in 2005 due to the failure of levees designed to protect the city.

Dam failures can send a sudden destructive surge of water downstream. In 1889 a dam break upstream from Johnstown, Pennsylvania, released a 30-40 foot wall of water that killed 2200 people within minutes.

Mountains and steep hills produce rapid runoff, which causes streams to rise quickly. Rocks and shallow, clayey soils do not allow much water to infiltrate into the ground. Saturated soils can also lead to rapid flash flooding.Camping or recreating along streams or rivers can be a risk if there are thunderstorms in the area. A creek only 6 inches deep in mountainous areas can swell to a 10-foot deep raging river in less than an hour if a thunderstorm lingers over an area for an extended period of time. Sometimes the thunderstorms that produce the heavy rainfall may happen well upstream from the impacted area, making it harder to recognize a dangerous situation.

Very intense rainfall can produce flooding even on dry soil. In the West, mostcanyons, small streams and dry arroyos are not easily recognizable as a source of danger. The causative rainfall can occur upstream of the canyon, and hikers can be trapped by rapidly rising water. Floodwaters can carry fast-moving debris that pose significant risks to life.

Additional high-risk locations includerecent burn areas in mountains, and urban areas from pavement and roofs which enhance runoff.

Ice jams and snowmelt can help cause flash floods. A deep snowpack increases runoff produced by melting snow. Heavy spring rains falling on melting snowpack can produce flash flooding. Melting snowpack may also contribute to floods produced by ice jams on creeks and rivers. Thick layers of ice often form on streams and rivers during the winter. Melting snow and/or warm rain running into the streams may lift and break this ice, allowing large chunks of ice to jam against bridges or other structures. This causes the water to rapidly rise behind the ice jam. If the water is suddenly released, serious flash flooding could occur downstream. Huge chunks of ice can be pushed onto the shore and through houses and buildings.

National Weather Service Messaging Terminology

FLASH FLOOD WATCH or FLOOD WATCH

Flash flooding or flooding is possible within the designated watch area - be alert.

FLASH FLOOD WARNING or FLOOD WARNING

Flash flooding or flooding has been reported or is imminent - take necessary precautions at once! Get to higher ground!

Turn around, don't drown! Most fatalities in the US from flash flooding are from vehicles driving into flooded roadways.

URBAN and SMALL STREAM ADVISORY

Flooding of small streams, streets and low-lying areas, such as railroad underpasses and urban storm drains is occurring.

FLASH FLOOD STATEMENT or FLOOD STATEMENT

Follow-up information regarding a flash flood/flood event.

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Basic information about flooding, from the NOAA National Severe Storms Laboratory.

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More frequent coastal flooding and increased erosion from storms and sea level rise

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More frequent coastal flooding and increased erosion from storms and sea level rise

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https://www.dcceew.gov.au/climate-change/policy/adaptation/australias-coasts/national-coastal-risk-assessment

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This practice note provides guidance on:

managing coastal hazards

the decision-making process for assessing coastal hazard risk

planning for development in coastal areas.

Background

Victoria has over 2,500 km of coastline, including extensive:

sandy beach systems

rocky coasts

bays

inlets and estuaries

coastal lakes

floodplains.

These are dynamic environments shaped by natural coastal processes. Traditional Owners have cared for these diverse landscapes for countless generations.

Significant development has already occurred in many coastal areas. Population growth and the demand for coastal living create ongoing pressures. At times, coastal processes may have a negative impact on coastal values and uses. These processes include:

erosion

inundation

accretion

other physical/chemical processes.

What is a coastal hazard?

In this practice note, coastal hazard refers to coastal inundation and erosion and riverine flooding.

Coastal hazards may also include estuary dynamics, offshore sediment dynamics, saline groundwater intrusion.

Coastal hazard exposure occurs periodically across the coast and is projected to increase with changes in wave action, storm activity and sea level rise associated with climate change.

Coastal flooding

Coastal flooding can occur along all low-lying areas of the coast. Flooding in coastal areas can include the following elements.

Coastal inundation (inundation from the sea, mainly from storm surge and tides), including:

Storm-tide inundation: Temporary event-based inundation from the sea

Permanent inundation: Regular or persistent inundation by the regular tidal cycle.

Riverine flooding (flooding from catchment rainfall and drainage): Heavy rainfall produces surface run-off which flows into streams and rivers. When there is a large amount of run-off, water overflows the riverbanks on to adjacent low-lying land causing flooding. This is called riverine flooding and can occur in both rural and urban areas.

In coastal areas, riverine flooding may combine with coastal inundation from the sea, increasing the extent of coastal flooding. This is also referred to as 'co-incident flooding'. Rivers and drainage systems may become swollen, and water levels will rise until the inflows match the outflows into the sea. Flooding from the sea can also backup through the connecting rivers and drains.

The frequency, extent and magnitude of coastal flooding is likely to be altered by climate change over time including through the combined interactions with sea level rise, hightides, storm surges and other coastal processes.

The Land Subject to Inundation Overlay (LSIO) can identify areas affected by coastal flooding in planning schemes.

Coastal erosion

Coastal erosion is a naturally occurring process of sediment shifting away from localised areas of the coast (offshore or alongshore). Erosion can occur across sandy, rocky and tidal environments, and is influenced by a range of factors including coastal processes, climate change and land use.

Erosion can be either long term or short term.

Long-term coastal erosion:A trend of erosion extending over several years resulting in recession (retreat) of the shoreline position. This can be caused by a range of factors including changes in climate and sediment supply (where sediment is moved along the shore by wave action).

Short-term coastal erosion: Erosion that can occur over a short period of time as a result of extreme weather events (also termed 'storm bite'). This may result in a short-term eroding of the beach profile as sand is transported offshore/alongshore. In calmer conditions, wind and waves act to transport sand back onshore again to rebuild the beach and dunes. While this type of erosion can sometimes be significant, the average shoreline position remains similar over longer timeframes.

Other natural hazards

Other natural hazards, such as bushfire, acid sulfate soils, extreme heat, drought, subsidence and landslip, as well as issues such as water quality and groundwater changes also occur in the coastal zone and need to be considered in planning and building processes.

Climate change and coastal hazards

Climate change is likely to increase the intensity and extent of existing coastal hazards.

This means that for some parts of the Victorian coast climate change impacts are likely to exacerbate coastal erosion and flooding, potentially further increasing the impacts of these coastal hazards on existing and future coastal communities, development and the natural environment.

Planning should account for impacts such as sea level rise that is gradual and will occur over a long timeframe, as well as extreme weather events that can occur at any time and can significantly reshape the coastline, using best available technical data and advice.

Ministerial Direction No. 13 - Managing Coastal hazards and the Coastal Impacts of Climate Change sets out requirements for considering the impacts of climate change within coastal Victoria through the planning scheme amendment process.

Sea level rise planning benchmark

The Marine and Coastal Policy (Department of Environment, Land, Water and Planning, March 2020) sets out the policy and strategic direction for responding to coastal hazard risks in the context of climate change. It identifies the need to:
Plan for sea-level rise of not less than 0.8 metres by 2100, and allow for the combined effects of tides, storm surges, coastal processes and local conditions such as topography and geology when assessing risks and impacts associated with climate change.
The Marine and Coastal Policy is reflected in clause 13.01-2S (coastal inundation and erosion) of the planning scheme. The Policy enables planning for levels greater than 0.8 metres sea-level rise by 2100. Detailed local and regional coastal hazard assessments and advice from the relevant floodplain management authority support local variations.

Strategic planning and rezoning for urban purposes in coastal areas

It is important to avoid future development or intensification in coastal areas outside of existing settlements that are likely to be impacted by projected coastal hazards. Where development is appropriate, it should be sited and designed in a way that protects future communities and assets from coastal hazard risk.

A proposal to rezone land may be informed by an existing coastal hazard assessment, a coastal vulnerability assessment for that part of the coastline, or inundation advice from a floodplain manager. Guidelines on how to prepare a coastal hazard assessment are accessible on the DELWP Marine and Coasts website.

Strategic investigations should consider:

the Marine and Coastal Act 2018 guiding principles, including the precautionary principle

the values, uses and objectives for the area

the local geographic characteristics of the coastline such as ocean exposure (for example open coast or sheltered exposure) and land type (such as sandy, rocky, engineered)

the role of natural coastal processes and the need to allow for such processes to continue as a cost-effective form of coastal defence against climate change

the coastal hazard vulnerability and risk for all relevant site values and uses, including:

probability, magnitude, frequency and consequences

impacts on people, property, communities, infrastructure and environment

the intended use, design lifespan and value of future development, assessed against the relative risk exposure during that time

the adaptation options for managing coastal hazard set out in the Marine and Coastal Policy (Department of Environment, Land, Water and Planning, 2020) comprising: non-intervention, avoid, nature-based, accommodate, retreat and protect.

the critical need for coastal protection infrastructure (if the 'protect' pathway is required) and:

the type, location and cost of providing and maintaining such infrastructure throughout its intended lifespan

proposed actions when the design lifespan has been spent

the need for all-weather access during times of emergency for future communities

appropriate built form responses to avoid a projected permanent hazard event or withstand a temporary event such as the need for:

setbacks

particular materials

a particular sub-floor structure or floor level

the cumulative impact or any flow-on effects of future use and development and any necessary associated protection works to adjacent properties and the coastline

other identified geotechnical risks or natural hazards such as coastal acid sulfate soils, land subsidence, bushfire

any other issues relative to the orderly and proper management of use and development within coastal areas such as development within an identified settlement boundary, significant landscapes, native vegetation and cultural heritage

Traditional Owner rights and assertions as per the Marine and Coastal Policy (Department of Environment, Land, Water and Planning, 2020).

Strategic investigations should also consider other future management issues that may be relevant to ensure risk minimisation and effective long-term management of use and development.

Assessing development proposals

Floodplain manager referrals

The Water Act 1989 provides that a designated floodplain management authority has a responsibility to:

control development that may be proposed on land adjoining waterways

to develop and implement plans to take any action necessary to minimise flooding and flood damage

provide advice to local councils about flooding and controls on development proposals.

The Building Regulations 2018 also include flood-related responsibilities for a council, such as the setting of minimum floor levels in consultation with the relevant floodplain management authority and the assessment of the flood risk of a site.

A statutory referral to the floodplain management authority (relevant catchment management authority (CMA) or Melbourne Water) is currently required where the following apply:

Urban Floodway Zone (UFZ)

Land Subject to Inundation Overlay (LSIO)

Floodway Overlay (FO)

Special Building Overlay (SBO).

Their expert input can address issues such as facilitating the free passage of floodwaters and protecting buildings from floods and storm surges by establishing appropriate floor levels or mitigation measures.

Clause 65 of the planning scheme extends consideration of flood issues to all planning permit applications, regardless of whether the site is affected by a flood zone or overlay. Therefore, a responsible authority may also seek informal technical advice from a floodplain management authority on a planning permit application in coastal areas outside of these flood overlays. A council is encouraged to seek advice, where necessary, from the relevant floodplain management authority under section 52 of the Planning and Environment Act 1987.

The diagram at Figure 1 below outlines the kind of information a floodplain management authority uses to advise on finished floor levels in coastal areas (indicative only).

For further advice on managing coastal hazards in development proposals refer to:

Coastal vulnerability assessments

A coastal vulnerability assessment may assist a council to inform decision making outside an existing settlement or urban zoned area. This is usually undertaken by a qualified coastal engineer or coastal processes specialist, who can assist with understanding erosion rates and developing appropriate setbacks or protection works. In some instances, where local geology may be unknown or unstable, or where flooding from rivers and streams may also be an issue, advice can also be sought from a qualified hydrological or geotechnical expert.

Planning permit decisions should be made using the best available knowledge and information at the time. An assessment of coastal vulnerability is important when deciding whether a proposed use or development should be supported and should not be deferred for secondary consent. Section 173 agreements should also not be applied to individual properties to defer hazards being considered as part of any planning permit application.

Additional resources

For further information relevant to coastal adaptation planning see:

Coastal adaptation planning
The Coast and Planning in Victoria
The Marine and Coasts at marineandcoasts.vic.gov.au

The Marine and Coastal Act 2018 at legislation.vic.gov.au

The Marine and Coastal Policy (Department of Environment, Land, Water and Planning, March 2020)

The Marine and Coastal Strategy (Department of Environment, Land, Water and Planning, May 2022)

Victoria's Marine and Coastal Reforms - Final Transition Plan (Department of Environment, Land, Water and Planning, 2018) at marineandcoasts.vic.gov.au.

The Port Phillip Bay Coastal Hazard Assessment at marineandcoasts.vic.gov.au

Victoria's Resilient Coast - Adapting for 2100+ at marineandcoasts.vic.gov.au

Local Coastal Hazard Assessments (LCHAs)

Local Coastal Hazard Assessments (LCHAs) - LCHAs are available for Port Fairy, Bellarine -Corio Bay, Gippsland Lakes and Ninety Mile Beach, Western Port and the Inverloch region at marineandcoasts.vic.gov.au. (In some areas where a completed coastal hazard assessment is not yet available, CMAs and councils may apply local information appropriate for future planning and decision-making).

Victorian Government's climate change program

Climate Action at climatechange.vic.gov.au

Victorian Marine and Coastal Council at marineandcoasts.vic.gov.au

Built Environment Climate Change Adaptation Action Plan 2022-2026 (Department of Environment, Land, Water and Planning, 2022)

Sea level rise impacts along the Victorian coast

Victorian Coastal Inundation Dataset (DELWP) at discover.data.vic.gov.au

Victorian Coastal Hazard Guide (Department of Sustainability and Environment, 2012)

Erosion processes data resources along the Victorian coast

Victorian Coastal Monitoring Program at marineandcoasts.vic.gov.au

Glenelg-Hopkins Catchment Management Authority
(03) 5571 2526

Postal Address
PO Box 502
Hamilton VIC 3300

Hamilton office
79 French Street
Hamilton VIC 3300

Corangamite Catchment Management Authority
1800 002 262

Postal Address
PO Box 159
Colac VIC 3250

Colac office
64 Dennis Street
Colac VIC 3250

Geelong office
54A Corio Street
Geelong VIC 3220

Melbourne Water
Melbourne Water is the floodplain management authority for the whole of the Port Phillip and Westernport region
131 722

Postal Address
PO Box 4342
Melbourne 3001

Corporate office
990 La Trobe Street
Docklands 3008

West Gippsland Catchment Management Authority
1300 094 262

Postal Address
PO Box 1374
Traralgon VIC 3844

Traralgon office
16 Hotham Street
Traralgon VIC 3844

Leongatha office
Corner Young and Bair Streets
Leongatha VIC 3953

East Gippsland Catchment Management Authority
(03) 5152 0600

Postal Address
PO Box 1012
Bairnsdale VIC 3875

East Gippsland office
574 Main Street
Bairnsdale VIC 3875

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PPN53: Managing coastal hazards and the coastal impacts of climate change

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Provides guidance on managing coastal hazards, the decision-making process for assessing coastal hazard risk and planning for development in coastal areas.

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Loss of food production due to changing rainfall patterns

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Loss of food production due to changing rainfall patterns

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According to the World Resources Institute, climate change is projected to have net adverse impacts on crop yields, with up to 50 percent less yields given a 3-degree Celsius warmer world.

The Challenge

There will be 9.7 billion people in the world by 2050. Feeding them will require increasing current food production by up to 98 percent.

This is not the only challenge. Climate change is an increasing threat to weather-dependent agriculture as we see more erratic rainfall patterns and more frequent floods and droughts. In addition, our current methods of food production contribute a quarter of global greenhouse gas emissions, further exacerbating climate change.

Agriculture also has a significant impact on the environment as it consumes large amounts of precious natural resources like fresh water which are depleting rapidly. Moreover, we cannot expand the land under cultivation to increase food production without causing further deforestation.

About 84 percent of the world's farmers are smallholders and produce 30-34 percent of the world's food. Yet, they contend with challenges such as low productivity, poor efficiency, and the effects of climate change mentioned. Increasing their productivity is essential for eliminating global poverty.

To ensure food security for our future generations, agriculture needs become more sustainable - environmentally, economically, and socially - through applying technology, digital and innovation.

Blockchain is an emerging technology that can help solve the three major issues in food supply chains: trust, transparency, and cost.

The Global Centre is engaged in exploring the existing and emerging technologies that can help food systems become more sustainable and resilient, sharing knowledge and building capacity to help UNDP's field offices, policymakers and other stakeholders to understand these technologies and their applications - including the opportunities and challenges they present and building multi-stakeholder partnerships to support their large-scale adoption, especially in developing countries to benefit smallholder farmers.

Based in Singapore, the Global Centre is ideally located to leverage the innovations in agriculture in the Asia-Pacific region as well as benefit from the vibrant agri- and food-tech ecosystem emerging in Singapore.

Key Initiatives

In November 2019, we launched the Cultiv@te programme, a global innovation initiative that aims to find bright minds and creative entrepreneurs, startups and R&D teams from around the world to solve challenges in agriculture.

▶️ Watch a short clip of the programme:

After a call for applications over 15 weeks, we selected 31 teams to work with 11 countries to pilot these technologies and innovations on the ground.

Read more about these 31 solutions here.

We are now connecting the teams to the UNDP field offices and other local stakeholders that they work with, including governments, research agencies, and other international organisations. We're planning to pilot these solutions in 2021 and eventually scale them to impact the lives of farmers and help achieve the SDGs.

Blockchain for Food Traceability

Blockchain for food traceability is gaining momentum in the agri-food sector globally. Yet, there is little understanding among ecosystem stakeholders and governments about what blockchain is, how it works and how it can be applied to the supply chain. Our aim is to make this information accessible and easy to understand for all.

To achieve this, we are designing a case study and concept note that will explain how blockchain technologies work and its benefits to food traceability for different actors in the supply chain. This will include a guide explaining how blockchain can be applied to the food value chain, including examples and case studies where its application has been successful.

We believe that blockchain technology will be transformational for a more efficient and transparent food system and can significantly contribute to international trade, improve food safety, and protect the rights of producers and consumers around the world.

he farms of today are increasingly becoming digitalised. Digital farms are often said to have improved profitability and sustainability, but how much these technologies can help assist smallholder farmers and whether they are economically viable are unanswered.

Digital farming broadly encompasses technologies to assist producers in farming, most commonly known as precision agriculture technologies. We intend to design a report that will demonstrate the utility and viability of adopting digital farming technologies (focused on precision agriculture). This report will include detailed analyses of these technologies, their applications, outcomes and impact and feasibility of use.

Through this document, we hope to provide governments and policymakers with a detailed breakdown of quantifiable benefits of various digital farming technologies and inform them of the best methods through which these technologies should be transferred and applied.

The Digital Feed, in partnership with SGInnovate, is a multi-episode series which will feature developments and trends across various stages of food value chains - from food production and supply to consumption and food science.

Subscribe to our YouTube channel to be updated on the latest episodes. For more information about the series and each episode, visit our page here.

The podcast version of these episodes is also available on Spotify. Listen to the latest episode here ⬇️

Partner With Us

UNDP depends on multi-stakeholder collaborations to perform its role of an "integrator" for achieving the Sustainable Development Goals (SDGs). To carry out the Global Centre's mandate of promoting technology and innovation for sustainable agriculture, we look forward to partnering with diverse actors:

Governments seeking technology-based solutions to their agricultural challenges.

Investors, philanthropies, and financial institutions seeking to invest in high-impact solutions for building sustainable and resilient food and agricultural systems.

Technology providers including startups and industry leaders with impactful solutions for revolutionising food and agricultural systems.

Reach out to us if you are interested in partnering.

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According to the World Resources Institute, climate change is projected to have net adverse impacts on crop yields, with up to 50 percent less yields given a 3-degree Celsius warmer world. There will be 9.7 billion people in the world by 2050. Feeding them will require increasing current food production by up to 98 percent.

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Increase in tropical cyclone activity and severity

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Increase in tropical cyclone activity and severity

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http://www.bom.gov.au/cyclone/tropical-cyclone-knowledge-centre/understanding/categories/

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Coordinating Lead Authors:

Sonia I. Seneviratne (Switzerland), Xuebin Zhang (Canada)

Lead Authors:

Muhammad Adnan (Pakistan), Wafae Badi (Morocco), Claudine Dereczynski (Brazil), Alejandro Di Luca (Australia/Canada/Argentina), Subimal Ghosh (India), Iskhaq Iskandar (Indonesia), James Kossin (United States of America), Sophie Lewis (Australia), Friederike Otto (United Kingdom/Germany), Izidine Pinto (South Africa/Mozambique), Masaki Satoh (Japan), Sergio M. Vicente-Serrano (Spain), Michael Wehner (United States of America), Botao Zhou (China)

Contrib uting Authors:

Mathias Hauser (Switzerland), Megan Kirchmeier-Young (Canada/United States of America), Lisa V. Alexander (Australia), Richard P. Allan (United Kingdom), Mansour Almazroui (Saudi Arabia), Lincoln M. Alvez (Brazil), Margot Bador (France, Australia/France), Rondrotiana Barimalala (South Africa/Madagascar), Richard A. Betts (United Kingdom), Suzana J. Camargo (United States of America/Brazil, United States of America), Pep G. Canadell (Australia), Erika Coppola (Italy), Markus G. Donat (Spain/Germany, Australia), Hervé Douville (France), Robert J. H. Dunn (United Kingdom/Germany,United Kingdom), Erich Fischer (Switzerland), Hayley J. Fowler (United Kingdom), Nathan P. Gillett (Canada), Peter Greve (Austria/Germany), Michael Grose (Australia), Lukas Gudmundsson (Switzerland/Germany, Iceland), José Manuel Guttiérez (Spain), Lofti Halimi (Algeria), Zhenyu Han (China), Kevin Hennessy (Australia), Richard G. Jones (United Kingdom), Yeon-Hee Kim (Republic of Korea), Thomas Knutson (United States of America), June-Yi Lee (Republic of Korea), Chao Li (China), Georges-Noel T. Longandjo (South Africa/Democratic Republic of the Congo), Kathleen L. McInnes (Australia), Tim R. McVicar (Australia), Malte Meinshausen (Australia/Germany), Seung-Ki Min (Republic of Korea), Ryan S. Padron Flasher (Switzerland/Ecuador, United States of America), Christina M. Patricola (United States of America), Roshanka Ranasinghe (The Netherlands/Sri Lanka, Australia), Johan Reyns (The Netherlands/Belgium), Joeri Rogelj (United Kingdom/Belgium), Alex C. Ruane (United States of America), Daniel Ruiz Carrascal (United States of America/Colombia), Bjørn H. Samset (Norway), Jonathan Spinoni (Italy), Qiaohong Sun (Canada/China), Ying Sun (China), Mouhamadou Bamba Sylla (Rwanda/Senegal), Claudia Tebaldi (United States of America), Laurent Terray (France), Wim Thiery (Belgium), Jessica Tierney (United States of America), Maarten K. van Aalst (The Netherlands), Bart van den Hurk (The Netherlands), Robert Vautard (France), Wen Wang (China), Seth Westra (Australia), Jakob Zscheischler (Germany)

R eview Editors:

Johnny Chan (China), Asgeir Sorteberg (Norway), Carolina Vera (Argentina)

Chap ter Scientists:

Mathias Hauser (Switzerland), Megan Kirchmeier-Young (Canada/United States of America), Hui Wan (Canada)

This Chapter should be cited as:

Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513-1766, doi: 10.1017/9781009157896.013 .

This chapter assesses changes in weather and climate extremes on regional and global scales, including observed changes and their attribution, as well as projected changes. The extremes considered include temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, storms (including tropical cyclones), as well as compound events (multivariate and concurrent extremes). The assessment focuses on land regions excluding Antarctica. Changes in marine extremes are addressed in Chapter 9 and Cross-Chapter Box 9.1. Assessments of past changes and their drivers are from 1950 onward, unless indicated otherwise. Projections for changes in extremes are presented for different levels of global warming, supplemented with information for the conversion to emissions scenario-based projections (Cross-Chapter Box 11.1 and Table 4.2). Since the IPCC Fifth Assessment Report (AR5), there have been important new developments and knowledge advances on changes in weather and climate extremes, in particular regarding human influence on individual extreme events, on changes in droughts, tropical cyclones, and compound events, and on projections at different global warming levels (1.5°C-4°C). These, together with new evidence at regional scales, provide a stronger basis and more regional information for the AR6 assessment on weather and climate extremes.

It is an established fact that human-induced greenhouse gas emissions have led to an increased frequency and/or intensity of some weather and climate extremes since pre-industrial time, in particular for temperature extremes. Evidence of observed changes in extremes and their attribution to human influence (including greenhouse gas and aerosol emissions and land-use changes) has strengthened since AR5, in particular for extreme precipitation, droughts, tropical cyclones and compound extremes (including dry/hot events and fire weather). Some recent hot extreme events would have been extremely unlikely to occur without human influence on the climate system. {11.2, 11.3, 11.4, 11.6, 11.7, 11.8}

Regional changes in the intensity and frequency of climate extremes generally scale with global warming. New evidence strengthens the conclusion from the IPCC Special Report on Global Warming of 1.5°C (SR1.5) that even relatively small incremental increases in global warming (+0.5°C) cause statistically significant changes in extremes on the global scale and for large regions ( high confidence). In particular, this is the case for temperature extremes ( very likely ), the intensification of heavy precipitation ( high confidence) including that associated with tropical cyclones ( medium confidence), and the worsening of droughts in some regions ( high confidence). The occurrence of extreme events unprecedented in the observed record will rise with increasing global warming, even at 1.5°C of global warming. Projected percentage changes in frequency are higher for the rarer extreme events ( high confidence). {11.1, 11.2, 11.3, 11.4, 11.6, 11.9, Cross-Chapter Box 11.1}

Since AR5, the confidence about past and future changes in weather and climate extremes has increased due to better physical understanding of processes, an increasing proportion of the scientific literature combining different lines of evidence, and improved accessibility to different types of climate models ( high confidence). There have been improvements in some observation-based datasets, including reanalysis data ( high confidence). Climate models can reproduce the sign (direction) of changes in temperature extremes observed globally and in most regions, although the magnitude of the trends may differ ( high confidence). Models are able to capture the large-scale spatial distribution of precipitation extremes over land ( high confidence). The intensity and frequency of extreme precipitation simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models are similar to those simulated by CMIP5 models ( high confidence). Higher horizontal model resolution improves the spatial representation of some extreme events (e.g., heavy precipitation events), in particular in regions with highly varying topography ( high confidence). {11.2, 11.3, 11.4}

The frequency and intensity of hot extremes (including heatwaves) have increased, and those of cold extremes have decreased on the global scale since 1950 ( virtually certain). This also applies at regional scale, with more than 80% of AR6 regions showing similar changes assessed to be at least likely . In a few regions, limited evidence (data or literature) prevents the reliable estimation of trends. {11.3, 11.9}

Human-induced greenhouse gas forcing is the main driver of the observed changes in hot and cold extremes on the global scale ( virtually certain ) and on most continents ( very likely ). The effect of enhanced greenhouse gas concentrations on extreme temperatures is moderated or amplified at the regional scale by regional processes such as soil moisture or snow/ice-albedo feedbacks, by regional forcing from land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. Changes in anthropogenic aerosol concentrations have likely affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA ( medium confidence). Urbanization has likely exacerbated changes in temperature extremes in cities, in particular for nighttime extremes. {11.1, 11.2, 11.3}

The frequency and intensity of hot extremes will continue to increase and those of cold extremes will continue to decrease, at global and continental scales and in nearly all inhabited regions with increasing global warming levels. This will be the case even if global warming is stabilized at 1.5°C. Relative to present-day conditions, changes in the intensity of extremes would be at least double at 2°C, and quadruple at 3°C of global warming, compared to changes at 1.5°C of global warming. The number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves will increase over most land areas ( virtually certain). In most regions, future changes in the intensity of temperature extremes will very likely be proportional to changes in global warming, and up to two to three times larger ( high confidence). The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions and in the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( high confidence). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( high confidence). The frequency of hot temperature extreme events will very likely increase nonlinearly with increasing global warming, with larger percentage increases for rarer events. {11.2, 11.3, 11.9; Table 11.1; Figure 11.3}

The frequency and intensity of heavy precipitation events have likely increased at the global scale over a majority of land regions with good observational coverage. Heavy precipitation has likely increased on the continental scale over three continents: North America, Europe, and Asia. Regional increases in the frequency and/or intensity of heavy precipitation have been observed with at least medium confidence for nearly half of AR6 regions, including WSAF, ESAF, WSB, SAS, ESB, RFE, WCA, ECA, TIB, EAS, SEA, NAU, NEU, EEU, GIC, WCE, SES, CNA, and ENA. {11.4, 11.9}

Human influence, in particular greenhouse gas emissions, is likely the main driver of the observed global-scale intensification of heavy precipitation over land regions. It is likely that human-induced climate change has contributed to the observed intensification of heavy precipitation at the continental scale in North America, Europe and Asia. Evidence of a human influence on heavy precipitation has emerged in some regions ( high confidence). {11.4, 11.9, Table 11.1}

Heavy precipitation will generally become more frequent and more intense with additional global warming. At a global warming level of 4°C relative to the pre-industrial level, very rare (e.g., one in 10 or more years) heavy precipitation events would become more frequent and more intense than in the recent past, on the global scale ( virtually certain ) and in all continents and AR6 regions. The increase in frequency and intensity is extremely likely for most continents and very likely for most AR6 regions. At the global scale, the intensification of heavy precipitation will follow the rate of increase in the maximum amount of moisture that the atmosphere can hold as it warms ( high confidence), of about 7% per 1°C of global warming. The increase in the frequency of heavy precipitation events will be non-linear with more warming and will be higher for rarer events ( high confidence), with a likely doubling and tripling in the frequency of 10-year and 50-year events, respectively, compared to the recent past at 4°C of global warming. Increases in the intensity of extreme precipitation at regional scales will vary, depending on the amount of regional warming, changes in atmospheric circulation and storm dynamics ( high confidence). {11.4, Box 11.1}

The projected increase in the intensity of extreme precipitation translates to an increase in the frequency and magnitude of pluvial floods - surface water and flash floods - ( high confidence), as pluvial flooding results from precipitation intensity exceeding the capacity of natural and artificial drainage systems. {11.4}

River Floods

Significant trends in peak streamflow have been observed in some regions over the past decades ( high confidence). The seasonality of river floods has changed in cold regions where snow-melt is involved, with an earlier occurrence of peak streamflow ( high conf idence). {11.5}

Global hydrological models project a larger fraction of land areas to be affected by an increase in river floods than by a decrease in river floods ( medium confidence). Regional changes in river floods are more uncertain than changes in pluvial floods because complex hydrological processes and forcings, including land cover change and human water management, are involved. {11.5}

Droughts

Different drought types exist, and they are associated with different impacts and respond differently to increasing greenhouse gas concentrations. Precipitation deficits and changes in evapotranspiration govern net water availability. A lack of sufficient soil moisture, sometimes amplified by increased atmospheric evaporative demand, results in agricultural and ecological drought. Lack of runoff and surface water result in hydrological drought. {11.6}

Human-induced climate change has contributed to increases in a gricultural and ecological droughts in some regions due to evapotranspiration increases ( medium confidence). Increases in evapotranspiration have been driven by increases in atmospheric evaporative demand induced by increased temperature, decreased relative humidity and increased net radiation ( high confidence). Trends in precipitation are not a main driver in affecting global-scale trends in drought ( medium confidence), but have induced increases in meteorological droughts in a few AR6 regions (NES: high confidence; WAF, CAF, ESAF, SAM, SWS, SSA, SAS: medium confidence). Increasing trends in agricultural and ecological droughts have been observed on all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, WNA, NES: medium confidence), but decreases only in one AR6 region (NAU: medium confidence). Increasing trends in hydrological droughts have been observed in a few AR6 regions (MED: high confidence; WAF, EAS, SAU: medium confidence). Regional-scale attribution shows that human-induced climate change has contributed to increased agricultural and ecological droughts (MED, WNA), and increased hydrological drought (MED) in some regions ( medium confidence). {11.6, 11.9}

More regions are affected by increases in agricultural and ecological droughts with increasing global warming ( high confidence). Several regions will be affected by more severe agricultural and ecological droughts even if global warming is stabilised at 2°C, including MED, WSAF, SAM and SSA ( high confidence), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, WCE, NCA, WNA and CNA ( medium confidence). Some regions are also projected to be affected by more severe agricultural and ecological droughts at 1.5°C (MED, WSAF, ESAF, SAU, NSA, SAM, SSA, CNA, medium confidence). At 4°C of global warming, about 50% of all inhabited AR6 regions would be affected by increases in agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF, MDG: medium confidence or higher), and only two regions (NEAF, SAS) would experience decreases in agricultural and ecological drought ( medium confidence). There is high confidence that the projected increases in agricultural and ecological droughts are strongly affected by evapotranspiration increases associated with enhanced atmospheric evaporative demand. Several regions are projected to be more strongly affected by hydrological droughts with increasing global warming (at 4°C of global warming: NEU, WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, MDG: medium confidence or higher). There is low confidence that effects of enhanced atmospheric carbon dioxide (CO ₂) concentrations on plant water-use efficiency alleviate extreme agricultural and ecological droughts in conditions characterized by limited soil moisture and enhanced atmospheric evaporative demand. There is also low confidence that these effects will substantially reduce global plant transpiration and the severity of hydrological droughts. There is high confidence that the land carbon sink will become less efficient due to soil moisture limitations and associated drought conditions in some regions in higher-emissions scenarios, in particular under global warming levels above 4°C. {11.6, 11.9, Cross-Chapter Box 5.1}

The average and maximum rain rates associated with tropical cyclones (TCs), extratropical cyclones and atmospheric rivers across the globe, and severe convective storms in some regions, increase in a warming world ( high confidence) . Available event attribution studies of observed strong TCs provide medium confidence for a human contribution to extreme TC rainfall. Peak TC rain rates increase with local warming at least at the rate of mean water vapour increase over oceans (about 7% per 1°C of warming) and in some cases exceeding this rate due to increased low-level moisture convergence caused by increases in TC wind intensity ( medium confidence). {11.7, 11.4, Box 11.1}

It is likely that the global proportion of Category 3-5 tropical cyclone instances has increased over the past four decades. The average location where TCs reach their peak wind intensity has very likely migrated poleward in the western North Pacific Ocean since the 1940s, and TC translation speed has likely slowed over the conterminous USA since 1900. Evidence of similar trends in other regions is not robust. The global frequency of TC rapid intensification events has likely increased over the past four decades. None of these changes can be explained by natural variability alone ( medium confidence).

The proportion of intense TCs, average peak TC wind speeds, and peak wind speeds of the most intense TCs will increase on the global scale with increasing global warming ( high confidence). The total global frequency of TC formation will decrease or remain unchanged with increasing global warming ( medium confide nce). {11.7.1}

There is low confidence in past changes of maximum wind speeds and other measures of dynamical intensity of extratropical cyclones. Future wind speed changes are expected to be small, although poleward shifts in the storm tracks could lead to substantial changes in extreme wind speeds in some regions ( medium confidence). There is low confidence in past trends in characteristics of severe convective storms, such as hail and severe winds, beyond an increase in precipitation rates. The frequency of spring severe convective storms is projected to increase in the USA, leading to a lengthening of the severe convective storm season ( medium confidence); evidence in other regions is limited. {11.7.2, 11.7.3}.

The probability of compound events has likely increased in the past due to human-induced climate change and will likely continue to increase with further global warming. Concurrent heatwaves and droughts have become more frequent, and this trend will continue with higher global warming ( high confidence). Fire weather conditions (compound hot, dry and windy events) have become more probable in some regions ( medium confidence) and there is high confidence that they will become more frequent in some regions at higher levels of global warming. The probability of compound flooding (storm surge, extreme rainfall and/or river flow) has increased in some locations ( medium confidence), and will continue to increase due to sea level rise and increases in heavy precipitation, including changes in precipitation intensity associated with tropical cyclones ( high confidence). The land area affected by concurrent extremes has increased ( high confidence). Concurrent extreme events at different locations, but possibly affecting similar sectors (e.g., critical crop-producing areas for global food supply) in different regions, will become more frequent with increasing global warming, in particular above 2°C of global warming ( high confidence). {11.8, Box 11.2, Box 11.4}.

The future occurrence of low-likelihood, high-impact events linked to climate extremes is generally associated with low confidence, but cannot be excluded, especially at global warming levels above 4°C. Compound events, including concurrent extremes, are a factor increasing the probability of low-likelihood, high-impact events ( high confidence). With increasing global warming, some compound events with low likelihood in past and current climates will become more frequent, and there is a higher chance of occurrence of historically unprecedented events and surprises ( high confidence). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1°C of global warming) can be perceived as surprises because of the pace of global warming ( high confidence). {Box 11.2}

This chapter provides assessments of changes in weather and climate extremes (collectively referred to as extremes) framed in terms of the relevance to the Working Group II (WGII) assessment. It assesses observed changes in extremes, their attribution to causes, and future projections, at three global warming levels: 1.5°C, 2°C, and 4°C. This chapter is also one of the four 'regional chapters' of the WGI Report (along with Chapters 10 and 12 and the Atlas). Consequently, while it encompasses assessments of changes in extremes at global and continental scales to provide a large-scale context, it also addresses changes in extremes at regional scales.

Extremes are climatic impact-drivers (Annex VII: Glossary, see Chapter 12 for a comprehensive assessment). The IPCC risk framework (Chapter 1) articulates clearly that the exposure and vulnerability to climatic impact-drivers, such as extremes, modulate the risk of adverse impacts of these drivers, and that adaptation which reduces exposure and vulnerability will increase resilience, resulting in a reduction in impacts. Nonetheless, changes in extremes lead to changes in impacts as a direct consequence of changes in their magnitude and frequency, and also through their influence on exposure and resilience.

Figure 11.1 | Visual guide to Chapter 11.

Building on the SREX report and AR5, this Report defines an extreme weather event as 'an event that is rare at a particular place and time of year', and an extreme climate event as 'a pattern of extreme weather that persists for some time, such as a season' (see Glossary). The definitions of 'rare' are wide ranging, depending on applications. Some studies consider an event as an extreme if it is unprecedented; other studies consider events that occur several times a year as moderate extreme events. Rarity of an event with a fixed magnitude also changes under human-induced climate change, making events that are unprecedented so far rather probable under present conditions, but unique in the observational record - and thus often considered as 'surprises' (see Box 11.2).

Various approaches are used to define extremes. These are generally based on the determination of relative (e.g., 90 percentile) or absolute (e.g., 35°C for a hot day) thresholds above which conditions are considered extremes. Changes in extremes can be examined from two perspectives, either focusing on changes in frequency of given extremes, or on changes in their intensity. These considerations in the definition of extremes are further addressed in Section 11.2.1.

The types of extremes assessed in this chapter include temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, and storms. The drought assessment addresses meteorological droughts, agricultural and ecological droughts, and hydrological droughts (see Glossary). The storms assessment addresses tropical cyclones, extratropical cyclones, and severe convective storms. This chapter also assesses changes in compound events - that is, multivariate or concurrent extreme events - because of their relevance to impacts as well as the emergence of new literature on the subject. Most of the considered extremes were also assessed in SREX and AR5. Compound events were not assessed in depth in past IPCC reports (SREX Chapter 3; Section 11.8 of this Report). Marine-related extremes such as marine heatwaves and extreme sea level, are assessed in Section 9.6.4 and Box 9.2 of this Report.

Extremes and related phenomena are of various spatial and temporal scales. Tornadoes have a spatial scale as small as less than 100 metres and a temporal scale as short as a few minutes. In contrast, a drought can last for multiple years, affecting vast regions. The level of complexity of the involved processes differs from one type of extreme to another, affecting our capability to detect, attribute and project changes in weather and climate extremes. Temperature and precipitation extremes studied in the literature are often based on extremes derived from daily values. Studies of events on longer time scales for temperature or precipitation, or on sub-daily extremes, are scarcer, which generally limits the assessment for such events. Nevertheless, extremes on time scales different from daily are assessed for temperature extremes and heavy precipitation, when possible (Sections 11.3 and 11.4). Droughts and tropical cyclones are treated as phenomena in general in the assessment, not limited by their extreme forms, because these phenomena are relevant to impacts (Sections 11.6 and 11.7). Both precipitation and wind extremes associated with storms are considered.

Multiple concomitant extremes can lead to stronger impacts than those resulting from the same extremes had they happened in isolation. For this reason, the occurrence of multiple extremes that are multivariate and/or concurrent and/or happening in succession, also called 'compound events' (SREX Chapter 3), are assessed in this chapter based on emerging literature on this topic ( Section 11.8). Box 11.2 also provides an assessment on low-likelihood, high-impact scenarios associated with extremes.

The assessment of projected future changes in extremes is presented as function of different global warming levels ( Section 11.2.4 and Cross-Chapter Box 11.1). This provides traceability and comparison to the SR1.5 assessment ( Hoegh-Guldberg et al., 2018, hereafter referred to as SR1.5 Chapter 3). Also, this is useful for decision makers as actionable information, as much of the mitigation policy discussion and adaptation planning can be tied to the level of global warming. For example, regional changes in extremes, and thus their impacts, can be linked to global mitigation efforts. There is also the advantage of separating uncertainty in future projections due to regional responses as a function of global warming levels from other factors such as differences in global climate sensitivity and emissions scenarios (Cross-Chapter Box 11.1). Information is also provided on the translation between information provided at global warming levels and for single emissions scenarios (Cross-Chapter Box 11.1). This facilitates easier comparison with the AR5 assessment and with some analyses provided in other chapters as function of emissions scenarios.

A global-scale synthesis of this chapter's assessments is provided in Section 11.1.7. In particular, Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes, and projected changes in extremes, respectively, at different global warming levels (1.5°C, 2°C, and 4°C). Tables on regional-scale assessments for changes in temperature extremes, heavy precipitation and droughts, are provided in Section 11.9.

The SREX, AR5, and SR1.5 assessed that there is evidence from observations that some extremes have changed since the mid-20th century, that some of the changes are a result of anthropogenic influences, and that some observed changes are projected to continue into the future. Additionally, other changes are projected to emerge from natural climate variability under enhanced global warming (SREX Chapter 3; AR5 Chapter 10).

Figure 11.2 | Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature (green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue). Global and land mean temperature anomalies are relative to their 1850-1900 means and are based on the multi-product mean annual time series assessed in Section 2.3.1.1.3 (see text for references). TXx and TNn anomalies are relative to their respective 1961-1990 means and are based on the HadEX3 dataset (Dunn et al., 2020) using values for grid boxes with at least 90% temporal completeness over 1961-2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Changes in annual maximum one-day precipitation (Rx1day) are proportional to mean global surface temperature changes, at about 7% increase per 1°C of warming, that is, following the Clausius-Clapeyron relation (Box 11.1), both in observations ( Westra et al., 2013) and in future projections ( Kharin et al., 2013) at the global scale. Extreme short-duration precipitation in North America also scales with global surface temperature ( Prein et al., 2016b; C. Li et al., 2019a). At the local and regional scales, changes in extremes are also strongly modulated and controlled by regional forcings and feedback mechanisms ( Section 11.1.6), whereby some regional forcings, for example, associated with changes in land cover and land use or aerosol emissions, can have non-local or some (non-homogeneous) global-scale effects. In general, there is high confidence in changes in extremes due to global-scale thermodynamic processes (i.e., global warming, mean moistening of the air) as the processes are well understood, while the confidence in those related to dynamic processes or regional and local forcing, including regional and local thermodynamic processes, is much lower due to multiple factors (see the following subsection and Box 11.1).

Figure 11.3 | Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socio-economic Pathway (SSP) forci ng scena ri os, SSP1 -1.9 , SSP1 -2.6 , SSP2 -4.5, SSP3-7. 0, and SSP5-8.5. Changes in TXx and GSAT are relative to the 1850-1900 baseline, and changes in GSAT are expressed as global warminglevel. (a) Individual models from the CMIP6 ensemble (grey), the multi-model median under three selected SSPs (colours), and the multi-modelmedian (black); (b) to (l) Multi-model median for the pooled data for individual AR6 regions. Numbers in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2.

Box 11.1 | Thermodynamic and Dynamic Changes in Extremes Across Scales

Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities (thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and dynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing to changes in climate extremes (e.g., Shepherd, 2014).

Tempe rature extremes

Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations show that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due to decreases in surface albedo (see Section 11.3.1). In some mid-latitude areas, temperature increases are amplified by the higher atmospheric evaporative demand ( Fu and Feng, 2014; Vicente-Serrano et al., 2020a) that results in a drying of soils in some regions ( Section 11.6), leading to increased sensible heat fluxes (soil-moisture-temperature feedback, see Sections 11.1.6 and 11.3.1 for more background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced atmospheric carbon dioxide (CO ₂) concentrations that can reduce the overall transpiration, and thus also enhance temperature in projections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6).

Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather patterns (e.g., (Cross-Chapter Box 10.1; Suarez-Gutierrez et al., 2020a). For example, a robust thermodynamic effect of polar amplification is a weakened north-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes because of the reduction of cold air advection ( Holmes et al., 2015; Schneider et al., 2015; Gross et al., 2020). Much less robust is the dynamic effect of polar amplification ( Section 7.4.4.1) and the reduced low-altitude meridional temperature gradient that has been linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes Francis and Vavrus, 2012; Coumou et al. , 2015, 2018; Mann et al., 2017).

Precipi tation extremes

Box 11.1, Figure 1: Multi-model Coupled Model Intercomparison Project Phase 5 (CMIP5) mean fractional changes (in % per degree of warming). (a) changes in annual maximum precipitation (Rx1day); (b) changes in Rx1day due to the thermodynamic contribution; and (c) changes in Rx1day due to the dynamic contribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period 1950-2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models (n=22) agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box (Atlas 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl et al. (2017). Figure adapted from Pfahl et al. (2017), originally published inNature Climate Change/Springer Nature. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Box 11.1

Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra latent heat released from the thermodynamic increases in moisture(, 2018; Lackmann, 2013; Willison et al. , 2013; Marciano et al. , 2015; Nie et al. Molnar et al., 2015; Nie et al., 2018), although weakening effects have also been found in mid-latitude cyclones (e.g., Kirshbaum et al., 2017). Additionally, the increase in latent heat can also suppress convection at larger scales due to atmospheric stabilization ( Nie et al., 2018; Mizuta and Endo, 2020). The extra latent heat released within storms has been shown to increase precipitation extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., Tandon et al., 2018; Kendon et al., 2019). As these dynamic effects result from feedback processes within storms where convective processes are crucial, their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, in current climate models (i.e., Kendon et al. , 2014; Westra et al. , 2014; Ban et al. , 2015; Meredith et al. , 2015; Prein et al. , 2015; Nie et al., 2018).

Droughts

Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect droughts by increasing atmospheric evaporative demand ( Martin, 2018; Gebremeskel Haile et al., 2020; Vicente-Serrano et al., 2020a) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through changes in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight ( Section 11.6). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing land-ocean warming contrast, vegetation feedbacks and responses to rising CO ₂ concentrations, and dynamic shifts in the location of the wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability ( Byrne and O'Gorman, 2015; Kumar et al., 2015; Allan et al., 2020).

In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur ( high confidence). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low confidence in how dynamic changes affect the location and magnitude of extreme events in a warming climate.

Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes in extremes, and projected changes in extremes, respectively, at different levels of global warming. This synthesis assessment focuses on the assessed range of observed and projected changes. In this chapter, the assessed likely range in a projection typically corresponds to the 90% range of the multi-model ensemble spread to take into account other sources of uncertainty, unless stated otherwise. Some low-likelihood, high-impact scenarios that can be of high relevance are addressed in Box 11.2.

Building on the assessments from Tables 11.1 and 11.2, Figure 11.5 provides a synthesis on the level of confidence in the attribution and projection of changes in extremes. In the case where the signal in the observations is still relatively weak but the physical processes underlying the changes in extremes in response to human forcing are well understood, confidence in the projections would be higher than in the attribution because of strengthening in the signal with warming. But, when the observed signal is already strong and when observational evidence is consistent with model simulated responses, confidence in the projection may be lower than that in attribution if certain physical processes could be expected to behave differently in a much warmer world and under much higher greenhouse gas forcing, and in particular if such a behaviour is poorly understood.

Figure 11.5 | confidence and likelihood of past changes and projected future changes at 2°C of global warming on the global scale. The information in this figure is based on Tables 11.1 and 11.2.

Further synthesis for regional assessments are provided in Figure 11.4 (event attribution), Figure 11.6 (projected change in hot temperature extremes) and Figure 11.7 (projected changes in precipitation extremes). A synthesis on regional assessments for observed, attributed and projected changes in extremes is provided in Section 11.9 for all AR6 reference regions (see Section 1.4.5 and Figures 1.18 and Atlas.2 for definitions of AR6 regions).

Figure 11.6 | Projected changes in the frequency of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 18 50-1900 baseline. Extreme temperatures are defined as the maximum daily temperatures that were exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850-1900 base period. Results are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the 'whiskers' extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from Li et al. (2021). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Figure 11.7 | Projected changes in the frequency of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 1850 -19 0 0 baseline. Extreme precipitation is defined as the annual maximum daily precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850-1900 base period. Results are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the 'whiskers' extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from Li et al. (2021). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Table 11.1 | Synthesis table on observed changes in extremes and contribution by human influence. Note that observed changes in marine extremes are assessed in Cross-Chapter Box 9.1.

Warmer and/or more frequent hot days and nights over most land areas

Virtually certain on global scale {11.3}

Extremely likely main contributor on global scale {11.3}

Continental-scale evidence:

North America, Europe, Australasia, Asia: Very likely

Central and South America: High confidence

Africa: Medium confidence

{11.3, 11.9}

Continental-scale evidence:

Asia, Australasia, Europe, North America: Very likely

Central and South America: High confidence

Africa: Medium confidence

{11.3, 11.9}

Warmer and/or fewer cold days and nights over most land areas

Warm spells/heatwaves: increases in frequency or intensity over most land areas

Cold spells/cold waves: decreases in frequency or intensity over most land areas

Heavy precipitation events: increase in the frequency, intensity, and/or amount of heavy precipitation

Likely on global scale, over majority of land regions with good observational coverage {11.3}

Likely main contributor to the observed intensification of heavy precipitation in land regions on global scale.

{11.3}

Continental-scale evidence:

Asia, Europe, North America: Likely

Africa, Australasia, Central and South America: Low confidence

{11.3, 11.9}

Continental-scale evidence:

Asia, Europe, North America: Likely

Africa, Australasia, Central and South America: Low confidence

{11.3, 11.9}

Increases in agricultural and ecological drought events

Medium confidence some regions {11.6, 11.9}

Increasing trends in agricultural and ecological droughts have been observed in AR6 regions on all continents ( medium confidence) {11.6, 11.9}

Severe convective storms (tornadoes, hail, rainfall, wind, lightning)

Low confidence in past trends in hail and winds and tornado activity due to short length of high-quality data records. {11.7}

Increase in compound events

Likely increase in the probability of compound events.

Likely that human-induced climate change has increased the probability of compound events.

High confidence that human influence has increased the frequency of concurrent heatwaves and droughts.

Medium confidence that human influence has increased fire weather occurrence in some regions.

Low confidence that human influence has contributed to changes in compound events leading to flooding.

{11.8}

High confidence that concurrent heatwaves and droughts are becoming more frequent under enhanced greenhouse gas forcing at global scale.

Medium confidence that fire weather, i.e. compound hot, dry and windy events, have become more frequent in some regions.

Medium confidence that compound flooding risk has increased in some locations.

{11.8}

Warmer and/or more frequent hot days and nights over most land areas

Virtually certain on global scale

Virtually certain on global scale

Virtually certain on global scale

Virtually certain on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( high confidence){11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( high confidence){11.3}

Continental-scale projections:

Virtually certain: Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

Virtually certain on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( high confidence){11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( high confidence){11.3}

Continental-scale projections:

Virtually certain: Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

Extremely likely on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( high confidence){11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( high confidence){11.3}

Continental-scale projections:

Extremely likely : Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

Warmer and/or fewer cold days and nights over most land areas

Warm spells/heatwaves; increases in frequency or intensity over most land areas

Cold spells/cold waves: decreases in frequency or intensity over most land areas

Heavy precipitation events: increase in the frequency, intensity, and/or amount of heavy precipitation

High confidence that increases take place in most land regions{11.4}

Likely that increases take place in most land regions{11.4}

Very likely that increases take place in most land regions{11.4}

Virtually certain: Africa, Asia, North America

Extremely likely : Central and South America, Europe

Very likely Australasia

{11.4, 11.9}

Extremely likely : Asia, North America

Very likely : Africa, Europe

Likely: Australasia, Central and South America

{11.4, 11.9}

Very likely : Asia, North America

Likely: Africa, Europe

High confidence: Central and South America

Medium confidence: Australasia

{11.4, 11.9}

Agricultural and ecological droughts: increases in intensity and/or duration of drought events

More regions affected by increases in agricultural and ecological droughts compared to observed changes ( high confidence). {11.6, 11.9}

More regions affected by increases in agricultural and ecological droughts than at 1.5°C of global warming ( high confidence). {11.6, 11.9}

More regions affected by increases in agricultural and ecological droughts than at 2°C of global warming ( very likely) . {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in several regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in several regions. ( high confidence)

{11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in some regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in some regions. ( high confidence)

{11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in some regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in some regions. ( high confidence)

{11.6, 11.9}

Increase in precipitation associated with tropical cyclones (TCs)

High confidence in a projected increase of TC rain rates at the global scale with amedian projected increase due to human emissions of about 11%. {11.7}

High confidence in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 14%. {11.7}

High confidence in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 28%. {11.7}

Medium confidence that rain rates will increase in every basin. {11.7}

Medium confidence that rain rates will increase in every basin. {11.7}

Medium confidence that rain rates will increase in every basin. {11.7}

Increase in mean TC lifetime-maximum wind speed (intensity)

Increase in likelihood that a TC will reach major TC intensity (Category 4-5)

High confidence for an increase in the proportion of TCs that reach the strongest (Category 4-5) levels. The median projected increase in this proportion is about 10%.

{11.7}

Severe convective storms

High confidence that the average and maximum rain rates associated with severe convective storms increase in some regions, including the USA. High confidence that convective available potential energy (CAPE) increases in response to global warming in the tropics and subtropics, suggesting more favourable environments for severe convective storms. Medium confidence that the frequency of spring severe convective storms is projected to increase in the USA, leading to a lengthening of the severe convective storm season. {11.7}

Increase in compound events (frequency, intensity)

Likely that probability of compound events will continue to increase with global warming.

High confidence that concurrent heatwaves and droughts will continue to increase under higher levels of global warming, with higher frequency/intensity with every additional 0.5°C of global warming.

High confidence that fire weather, (i.e. compound hot, dry and windy events), will become more frequent in some regions at higher levels of global warming.

High confidence that compound flooding at the coastal zone will increase under higher levels of global warming. {11.8}

Box 11.2 | Changes in Low-likelihood, High-impact Extremes

The SREX (Chapter 3) assigned low confidence to changes in low-likelihood, high-impact (LLHI) events (termed 'low-probability high-impact scenarios'). Such events are often not anticipated and thus sometimes referred to as 'surprises'. There are several types of LLHI events. Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result from tipping points in the climate system ( Section 1.4.4.3), such as the shutdown of the Atlantic thermohaline circulation (SROCC Chapter 6; Collins et al., 2019) or the drydown of the Amazonian rainforest (SR1.5 Chapter 3, Hoegh-Guldberg et al., 2018; Drijfhout et al., 2015), or from uncertainties in climate processes, including climate feedbacks, that may enhance or damp extremes either related to global or regional climate responses ( Seneviratne et al., 2018a; Sutton, 2018). The low confidence does not by itself exclude the possibility of such events occuring, rather it indicates a poor state of knowledge. Such outcomes, while improbable, could be associated with very high impacts, and are thus highly relevant from a risk perspective (see Section 1.4.3 and Box 11.4; Sutton, 2018, 2019). Alternatively, high impacts can occur when different extremes occur at the same time, or in short succession at the same location, or in several regions with shared vulnerability (e.g., food-basket regions Gaupp et al., 2019). These 'compound events' are assessed in Section 11.8, and Box 11.4 provides a case study example.

Difficulties persist in determining the likelihood of occurrence and time frame of potential tipping points and LLHI events. However, new literature has emerged on unanticipated and LLHI events. There are some events that are sufficiently rare that they have not been observed in meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate system - see examples below and in , 2015; , 2018; McCollum et al. (2020). The rare nature of such events and the limited availability of relevant data makes it difficult to estimate their occurrence probability and thus gives little evidence on whether to include such hypothetical events in planning decisions and risk assessments. The estimation of such potential surprises is often limited to events that have historical analogues (including before the instrumental records began, Wetter et al., 2014), albeit the magnitude of the event may differ. Additionally, there is also a limitation of available resources to exhaust all plausible trajectories of the climate system. As a result, there will still be events that cannot be anticipated. These events can be surprises to many in that the events have not been experienced, although their occurrence could be inferred by statistical means or physical modelling approaches ( Chen et al., 2017; van Oldenborgh et al., 2017; Harrington and Otto, 2018a). Another approach focusing on the estimation of low-probability events and of events whose likelihood of occurrence is unknown consists in using physical climate models to create a physically self-consistent storyline of plausible extreme events and assessing their impacts and driving factors in past ( Section 11.2.3) or future conditions ( Section 11.2.4) (Hazeleger et al. et al. Shepherd, 2016; Zappa and Shepherd, 2017; Cheng , 2018; Shepherd et al. et al. , 2020; Wehrli et al., 2020). Sutton, 2018; Schaller

In many parts of the world, observational data are limited to 50-60 years. This means that the chance to observe an extreme event at a particular location that occurs once in several hundred or more years is small. Thus, when a very extreme event occurs, it becomes a surprise to many ( Bao et al., 2017; McCollum et al., 2020), and very rare events are often associated with high impacts(van Oldenborgh et al. , 2017; Philip et al. , 2018b; Tozer et al. , 2020). Attributing and projecting very rare events in a particular location by assessing their likelihood of occurrence within the same larger region and climate thus provides another way to make quantitative assessments regarding events that are extremely rare locally. Some examples of such events include:

Hurricane Harvey, that made landfall in Houston, TX in August 2017 (Section 11.7.1.4.)

The 2010-2011 extreme floods in Queensland, Australia (Christidis et al., 2013a)

The 2018 concurrent heatwaves across the Northern Hemisphere (Box 11.4)

Tropical Cyclone Idai in Mozambique (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)

The California fires in 2018 and 2019

The 2019-2020 Australia fires (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)

One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and that the framework of reference for adaptation is continuously moving. As an example, the concurrent heatwaves that occurred across the Northern Hemisphere in the summer of 2018 were considered very unusual and were unprecedented given the total area that was concurrently affected(Drouard et al. , 2019; Kornhuber et al. , 2019; Toreti et al. , 2019; Vogel et al. , 2019); however, the probability of this event under 1°C global warming was found to be about 16% ( Vogel et al., 2019), which is not particularly low. Similarly, the 2013 summer temperature over eastern China was the hottest on record at the time, but it had an estimated recurrence interval of about four years in the climate of 2013 ( Sun et al., 2014). Furthermore, when other aspects of the risk, vulnerability, and exposure are historically high or have recently increased (see WGII, Chapter 16, Section 16.4), relatively moderate extremes can have very high impacts ( Otto et al., 2015b; Philip et al., 2018b). As warming continues, the climate moves further away from its historical state we are familiar with, resulting in an increased likelihood of unprecedented events and surprises. This is particularly the case under high global warming levels - for example, the climate of the late 21st century under high-emissions scenarios, above 4°C of global warming (Cross-Chapter Box 11.1).

Another factor highlighted in Section 11.8 and Box 11.4 making events high-impact and difficult to anticipate is that several locations under moderate warming levels could be affected simultaneously, or very repeatedly by different types of extremes (Mora et al., 2018; Gaupp et al., 2019; Vogel et al., 2019). Box 11.4 shows that concurrent events at different locations, which can lead to major impacts across the world, can also result from the combination of anomalous circulation or natural variability (e.g., El Niño-Southern Oscillation) patterns with amplification of resulting responses to human-induced global warming. Also multivariate extremes at single locations pose specific challenges to anticipation ( Section 11.8), with low likelihoods in the current climate but the probability of occurrence of such compound events strongly increasing with increasing global warming levels ( Vogel et al., 2020a). Therefore, in order to estimate whether, and at what level of global warming, very high impacts arising from extremes would occur, the spatial extent of extremes and the potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight increasing evidence that temperature extremes, higher intensity precipitation accompanying tropical cyclones, and compound events such as dry/hot conditions conducive to wildfire or storm surges resulting from sea level rise and heavy precipitation events, pose widespread threats to societies already at relatively low warming levels. Studies have already shown that the probability for some recent extreme events is so small in the undisturbed world that these events were extremely unlikely to occur without human influence ( Section 11.2.4). Box 11.2, Table 1, provides examples of projected changes in LLHI extremes (single extremes, compound events) of potential relevance for impact and adaptation assessments showing that today's very rare events can become commonplace in a warmer future.

Box 11.2, Table 1 | Examples of changes in low-likelihood, high-impact extreme conditions (single extremes, compound events) at different global warming levels.

Number of 1-5 day duration extreme floods with 1% of probability under present-day warming (+1°C) (H. Ali et al., 2019) Indian subcontinent

Up to 3 in individual locations

Up to 5 in individual locations

2-6 in most locations

Up to 12 in individual locations (4°C)

Increase in days exposed to dangerous extreme heat - measured in Health Heat Index (HHI) (Q. Sun et al., 2019) global land

Not assessed, baseline is 1981-2000

1.6 times higher risk of experiencing heat >40.6

2.3 times higher risk of experiencing heat >40.6

Around 80% of land area exposed to dangerous heat, tropical regions 1/3 of the year (4°C)

Increase in regional mean fire season length (Q. Sun et al., 2019; Xu et al., 2020) global land

Not assessed, baseline is 1981-2000

6.2 days

9.5 days

About 50 days (4°C)

In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with low confidence, but cannot be excluded, especially at global warming levels above 4°C. Compound events, including concurrent extremes, are a factor increasing the probability of LLHI events ( high confidence). With increasing global warming, some compound events with low likelihood in past and current climate will become more frequent, and there is a higher chance of historically unprecedented events and surprises ( high confidence). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1°C of global warming) can be perceived as surprises because of the pace of global warming ( high confidence).

This section provides an assessment of observational data and methods used in the analysis and attribution of climate change specific to weather and climate extremes. It also introduces some concepts used in presenting future projections of extremes. Later sections (Sections 11.3-11.8) also provide additional assessments on relevant observational datasets and model validation specific to the type of extremes to be assessed. General background on climate modelling is provided in Chapters 4 and 10.

In the literature, an event is generally considered extreme if the value of a variable exceeds (or lies below) a threshold. The thresholds have been defined in different ways, leading to differences in the meaning of extremes that may share the same name. For example, two sets of metrics for the frequency of hot/warm days have been used in the literature. One set counts the number of days when maximum daily temperature is above a relative threshold defined as the 90th or higher percentile of maximum daily temperature for the calendar day over a base period. An event based on such a definition can occur at any time of the year, and the impact of such an event would differ depending on the season. The other set counts the number of days in which maximum daily temperature is above an absolute threshold such as 35°C, because exceeding this temperature can sometimes cause health impacts (however, these impacts may depend on location and whether ecosystems and the population are adapted to such temperatures). While both types of hot extreme indices have been used to analyse changes in the frequency of hot/warm events, they represent different events that occur at different times of the year, possibly affected by different types of processes and mechanisms, and possibly also associated with different impacts.

Changes in extremes have also been examined from two perspectives: changes in the frequency for a given magnitude of extremes; or changes in the magnitude for a particular return period (frequency). Changes in the probability of extremes (e.g., temperature extremes) depend on the rarity of the extreme event that is assessed, with a larger change in probability associated with a rarer event (e.g., Kharin et al., 2018). However, changes in the magnitude represented by the return levels of the extreme events may not be as sensitive to the rarity of the event. While the answers to the two different questions are related, their relevance may differ for distinct audiences. Conclusions regarding the respective contribution of greenhouse gas forcing to changes in magnitude versus frequency of extremes may also differ ( Otto et al., 2012). Correspondingly, the sensitivity of changes in extremes to increasing global warming is also dependent on the definition of the considered extremes. In the case of temperature extremes, changes in magnitude have been shown to often depend linearly on global surface temperature ( Seneviratne et al., 2016; Wartenburger et al., 2017), while changes in frequency tend to be nonlinear and can, for example, be exponential for increasing global warming levels ( Fischer and Knutti, 2015; Kharin et al., 2018). When similar damage occurs once a fixed threshold is exceeded, it is more important to ask a question regarding changes in the frequency. But when the exceedance of this fixed threshold becomes a normal occurrence in the future, this can lead to a saturation in the change of probability ( Harrington and Otto, 2018a). Also, if the impact of an event increases with the intensity of the event, it would be more relevant to examine changes in the magnitude. Finally, adaptation to climate change might change the relevant thresholds over time, although such aspects are still rarely integrated in the assessment of projected changes in extremes. Framing is considered when forming the assessments of this Chapter, including how extremes are defined and how the questions are asked in the literature .

Studies of past and future changes in weather and climate extremes, and in the mean state of the climate, use the same original sources of weather and climate observations, including in situ observations, remotely sensed data, and derived data products such as reanalyses. Sections 2.3 and 10.2 assess various aspects of these data sources and data products from the perspective of their general use, and in the analysis of changes in the mean state of the climate in particular. Building on these previous chapters, this subsection highlights particular aspects that are related to extremes and are most relevant to the assessment of this Chapter. The SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 2, Hartmann et al., 2013) addressed critical issues regarding the quality and availability of observed data and their relevance for the assessment of changes in extremes.

Extreme weather and climate events occur on time scales of hours (e.g., convective storms that produce heavy precipitation) to days (e.g., tropical cyclones, heatwaves), to seasons and years (e.g., droughts). A robust determination of long-term changes in these events can have different requirements for the spatial and temporal scales and sample size of the data. In general, it is more difficult to determine long-term changes for events of fairly large temporal duration, such as 'megadroughts' that last several years or longer (e.g., Ault et al., 2014), because of the limitations of the observational sample size. Literature that studies changes in extreme precipitation and temperature often uses indices representing specifics of extremes that are derived from daily precipitation and temperature values. Station-based indices would have the same issues as those for the mean climate regarding the quality, availability, and homogeneity of the data. For the purpose of constructing regional information and/or for comparison with model outputs, such as model evaluation, and detection and attribution, these station-based indices are often interpolated onto regular grids. Two different approaches, involving two different orders of operation, have been used in producing such gridded datasets.

Extreme indices computed from various reanalysis data products have been used in some studies, but reanalysis extreme statistics have not been rigorously compared to observations ( Donat et al., 2016a).

Box 11.3 | Extremes in Paleoclimate Archives Compared to Instrumental Records

Examining extremes in pre-instrumental information can help to put events occurring in the instrumental record (referred to as 'observed') in a longer-term context. This box focuses on extremes in the Common Era (CE, the last 2000 years), because there is generally higher confidence in pre-instrumental information gathered from the more recent archives from the Common Era than from earlier evidence. It addresses evidence of extreme events in paleoreconstructions, documentary evidence (such as grape harvest data, religious documents, newspapers, and logbooks) and model-based analyses, and whether observed extremes have or have not been exceeded in the Common Era. This box provides overviews of: (i) AR5 assessments; (ii) types of evidence assessed here; evidence of: (iii) droughts; (iv) temperature extremes; (v) paleofloods; and (vi) paleotempests; and (vii) a summary.

( Chapter 5 of AR5 ( Masson-Delmotte et al., 2013) concluded with high confidence that droughts of greater magnitude and of longer duration than those observed in the instrumental period occurred in many regions during the preceding millennium. There was high confidence in evidence that floods during the past five centuries in northern and Central Europe, the western Mediterranean region, and eastern Asia were of a greater magnitude than those observed instrumentally, and medium confidence in evidence that floods in the Near East, India and Central North America were comparable to modern observed floods. While AR5 assessed 20th century summer temperatures compared to those reconstructed in the Common Era, it did not assess shorter duration temperature extremes.

Evidence of shorter duration extreme event types, such as floods and tropical storms, is further restricted by the comparatively low chronological controls and temporal resolution (e.g., monthly, seasonal, yearly, multiple years) of most archives compared to the events (e.g., minutes to days). Natural archives may be sensitive only to intense environmental disturbances, and so only sporadically record short-duration or small spatial-scale extremes. Interpreting sedimentary records as evidence of past short-duration extremes is also complex and requires a clear understanding of natural processes (Wilhelm et al. , 2019). For example, paleoflood reconstructions of flood recurrence and intensity produced from geological evidence (e.g., river and lake sediments), speleothems ( Denniston and Luetscher, 2017), botanical evidence (e.g., flood damage to trees, or tree ring reconstructions), and floral and faunal evidence (e.g., diatom fossil assemblages) require understanding of sediment sources and flood mechanisms. Pre-instrumental records of tropical storm intensity and frequency (also called paleotempest records) derived from overwash deposits of coastal lake and marsh sediments are difficult to interpret. Many factors have an impact on whether disturbances are deposited in archives ( Muller et al., 2017) and deposits may provide sporadic and incomplete preservation histories (e.g., Tamura et al., 2018).

Overall, the most complete pre-instrumental evidence of extremes occurs for long-duration, large spatial-scale extremes, such as for multi-year meteorological droughts or seasonal- and regional-scale temperature extremes. Additionally, more precise insights into recent extremes emerge where multiple studies have been undertaken, compared to the confidence in extremes reported at single sites or in single studies, which may not necessarily be representative of large-scale changes, or for reconstructions that synthesize multiple proxies over large areas (e.g., drought atlases). Multiproxy synthesis products combine paleoclimate temperature reconstructions and cover sub-continental- to hemispheric-scale regions to provide continuous records of the Common Era(e.g., Ahmed et al. , 2013; Neukom et al. , 2014 fo r temperature).

There is high confidence in the occurrence of long-duration and severe drought events during the Common Era for many locations, although their severity compared to recent drought events differs between locations and the lengths of reconstruction provided. Recent observed drought extremes in some regions - such as the eastern Mediterranean Levant ( Cook et al., 2016a), California in the USA( Cook et al., 2014b; Griffin and Anchukaitis, 2014), and in the Andes (Garreaud et al. , 2017; Domínguez-Castro et al. , 2018) - do not have precedents within the multi-century periods reconstructed in these studies, in terms of duration and/or severity. In some regions (in south-western North America( Asmerom et al., 2013; Cook et al., 2015), the Great Plains region ( Cook et al., 2004), the Middle East ( Kaniewski et al., 2012), and China ( Gou et al., 2015)), recent drought extremes may have been exceeded in the Common Era. In further locations, there is conflicting evidence for the severity of pre-instrumental droughts compared to observed extremes, depending on the length of the reconstruction and the seasonal perspective provided(see Cook et al. , 2016c; Freund et al. , 2017 for Australia). There can also be differing conclusions for the severity, or even the occurrence, of specific individual pre-instrumental droughts when different evidence is compared (e.g., Wetter et al., 2014; Büntgen et al., 2015).

There is medium confidence that the magnitudes of large-scale, seasonal-scale extreme high temperatures in observed records exceed those reconstructed over the Common Era in some locations, such as Central Europe. In one example, multiple studies have examined the unusualness of present-day European summer temperature records in a long-term context, particularly in comparison to the exceptionally warm year of 1540 CE in Central Europe. Several studies indicate that recent extreme summers (2003 and 2010) in Europe have been unusually warm in the context of the last 500 years ( Barriopedro et al., 2011; Wetter and Pfister, 2013; Wetter et al., 2014; Orth et al., 2016b), or longer ( Luterbacher et al., 2016). Others studies show that summer temperatures in Central Europe in 1540 were warmer than the present-day (1966-2015) mean, but note that it is difficult to assess whether or not the 1540 summer was warmer than observed record extreme temperatures ( Orth et al., 2016b).

There is high confidence that the magnitude of floods over the Common Era exceeded observed records in some locations, including Central Europe and eastern Asia. Recent literature supports the AR5 assessments of floods ( Masson-Delmotte et al., 2013). For example, high temporally resolved records provide evidence of Common Era floods exceeding the probable maximum flood levels in the Upper Colorado River, USA ( Greenbaum et al., 2014) and peak discharges that are double gauge levels along the middle Yellow River, China ( Liu et al., 2014). Further studies demonstrate pre-instrumental or early instrumental differences in flood frequency compared to the instrumental period, including reconstructions of high and low flood frequency in the European Alps (e.g., Swierczynski et al., 2013; Amann et al., 2015) and Himalayas ( Ballesteros Cánovas et al., 2017). The combination of extreme historical flood episodes determined from documentary evidence also increases confidence in the determination of flood frequency and magnitude, compared to using geomorphological archives alone ( Kjeldsen et al., 2014). In regions, such as Europe and China, that have rich historical flood documents, there is strong evidence of high-magnitude flood events over pre-instrumental periods (Kjeldsen et al., 2014; Benito et al., 2015; Macdonald and Sangster, 2017). A key feature of paleoflood records is variability in flood recurrence at centennial timescales ( Wilhelm et al., 2019), although constraining climate-flood relationships remains challenging. Pre-instrumental floods often occurred in considerably different contexts in terms of land use, irrigation, and infrastructure, and may not provide direct insight into modern river systems, which further prevents long-term assessments of flood changes being made based on these sources.

There is medium confidence that periods of both more and less tropical cyclone activity (frequency or intensity) than observed occurred over the Common Era in many regions. Paleotempest studies cover a limited number of locations that are predominantly coastal, and hence provide information on specific locations that cannot be extrapolated basin-wide (see Muller et al., 2017). In some locations, such as the Gulf of Mexico and the New England, USA, coast, similarly intense storms to those observed recently have occurred multiple times over centennial timescales ( Donnelly et al., 2001; Bregy et al., 2018). Further research focused on the frequency of tropical storm activity. Extreme storms occurred considerably more frequently in particular periods of the Common Era, compared to the instrumental period in north-east Queensland, Australia ( Nott et al., 2009; Haig et al., 2014), and the Gulf Coast (e.g., Brandon et al., 2013; Lin et al., 2014).

The probability of finding an unprecedented extreme event increases with a longer length of past record-keeping, in the absence of longer-term trends. Thus, as a record is extended to the past based on paleoreconstruction, there is a higher chance of very rare extreme events having occurred at some time prior to instrumental records. Such an occurrence is not, in itself, evidence of a change, or lack of a change, in the magnitude or the likelihood of extremes in the past or in the instrumental period at regional and local scales. Yet, the systematic collection of paleoclimate records over wide areas may provide evidence of changes in extremes. In one study, extended evidence of the last millennium from observational data and paleoclimate reconstructions using tree rings indicates that human activities affected the worldwide occurrence of droughts as early as the beginning of the 20th century ( Marvel et al., 2019).

The most important quantity used to characterize past and future climate change is global warming relative to its pre-industrial level. Changes in global warming are linked quasi-linearly to global cumulative carbon dioxide (CO ₂) emissions (IPCC, 2013), and for their part, changes in regional climate, including many types of extremes, scale quasi-linearly with changes in global warming, often independently of the underlying emissions scenarios (SR1.5 Chapter 3; Seneviratne et al., 2016; Matthews et al., 2017; Wartenburger et al., 2017; Kharin et al., 2018; Y. Sun et al., 2018a; Tebaldi and Knutti, 2018; Beusch et al., 2020; Li et al., 2021). In addition, the use of global warming levels in the context of global policy documents - in particular the 2015 Paris Agreement ( UNFCCC, 2016) implies that information on changes in the climate system, and specifically extremes, as a function of global warming are of particular policy relevance. Cross-Chapter Box 11.1 provides an overview on the translation between information at global warming levels (GWLs) and scenarios.

The SR1.5 (Chapter 3) assessed different climate responses at +1.5°C of global warming, including transient climate responses, short-term stabilization responses, and long-term equilibrium stabilization responses, and their implications for future projections of different extremes. Indeed, the temporal dimension - that is, when the given GWL occurs - also matters for projections, in particular beyond the 21st century, and for some climate variables related to components of the climate system associated with large inertia (e.g., sea level rise and associated extremes). Nonetheless, for assessments focused on conditions within the next decades, and for the main extremes considered in this chapter, derived projections are relatively insensitive to details of climate scenarios and can be well-estimated based on transient simulations (Cross-Chapter Box 11.1; see also SR1.5).

To some extent, analyses as functions of GWLs replace the time axis with a global surface temperature axis. Nonetheless, information on the timing of given changes in extremes is obviously also relevant. (For information on the time frame at which given GWLs are reached, see Cross-Chapter Box 11.1 and Section 4.6). Figure 11.5 provides a synthesis of attributed and projected changes in extremes as function of GWLs (see also Figures. 11.3, 11.6 and 11.7 for regional analyses).

Cross-Chapter Box 11.1 | Translating Between Regional Information at Global Warming Levels Versus Scenario s for End Users

Contributors: Erich Fischer (Switzerland), Mathias Hauser (Switzerland), Sonia I. Seneviratne (Switzerland), Richard Betts (United Kingdom), José M. Gutiérrez (Spain), Richard G. Jones (United Kingdom), June-Yi Lee (Republic of Korea), Malte Meinshausen (Australia/Germany), Friederike Otto (United Kingdom/Germany), Izidine Pinto (Mozambique), Roshanka Ranasinghe (The Netherlands/Sri Lanka/Australia), Joeri Rogelj (Germany/Belgium), Bjørn Samset (Norway), Claudia Tebaldi (United States of America), Laurent Terray (France)

Background

Traditionally, projections of climate variables are summarized and communicated as function of time and emissions scenarios. Recently, quantifying global and regional climate at specific global warming levels (GWLs) has become widespread, motivated by the inclusion of explicit GWLs in the long-term temperature goal of the Paris Agreement ( Section 1.6.2). GWLs, expressed as changes in global surface temperature relative to the 1850-1900 period (see Cross-Chapter Box 2.3), are used in SR1.5 and in the assessment of Reasons for Concerns in the WGII reports (see also Cross-Chapter Box 12.1). Cross-Chapter Box 11.1, Figure 1 illustrates how the assessment of the climate response at GWLs relates to the uncertainty in scenarios regarding the timing of the respective GWLs, as well as to the uncertainty in the associated regional climate responses, including extremes and other climatic impact-drivers (CIDs). For many (but not all) climate variables and CIDs, the response pattern for a given GWL is consistent across different scenarios (Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-term means (e.g., 20-year averages) compared to the pre-industrial period, are commonly used in the literature, and were also underlying main assessments of SR1.5 (Chapter 3).

Cross-Chapter Box 11.1, Figure 1 | Schematic representation of relationship between emissions scenarios, global warming levels (GWLs), regional climate responses, and impacts. The illustration shows the implied uncertainty problem associated with differentiating between 1.5°C, 2°C, and other GWLs. Focusing on GWLs raises questions associated with emissions pathways to get to these temperatures (scenarios), as well as regional climate responses and the associated impacts at the corresponding GWL (the impacts question). Adapted from James et al. (2017) and Rogelj (2013) under the terms of the Creative Commons Attribution licence.

Numerous studies have compared the regional response to anthropogenic forcing at GWLs in annual and seasonal mean values and extremes of different climate and impact variables across different multi-model ensembles and/or different scenarios (e.g., Frieler et al. , 2012; Schewe et al. , 2014; Herger et al. , 2015; Schleussner et al. , 2016; Seneviratne et al. , 2016; Wartenburger et al. , 2017; Betts et al. , 2018; et al. , 2019; Tebaldi et al. , 2020; see Sections 4.6.1, 8.5.3, 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrass et al. , 2015; Seneviratne et al. , 2016; Wartenburger et al. , 2017; Pendergrass et al., 2015; Wartenburger et al., 2017). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at a given GWL are smaller than model uncertainty and internal variability in many regions ( Dosio and Fischer, 2018; Samset Herger et al., 2015). The response pattern is further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h). Seneviratne and Hauser, 2020). The consistency tends to be higher for temperature-related variables than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions scenarios than for low-emissions scenarios (e.g., for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 scenario:

Cross-Chapter Box 11.1, Figure 2 | (a-c) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean precipitation change at 2°C global warming level (GWL) (20-year mean) in three different Shared Socio-economic Pathway (SSP) scenarios relative to 1850-1900. All models reaching the corresponding GWL in the corresponding scenario are averaged. The number of models averaged across is shown at the top right of the panel. The maps for the other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown) are consistent. (d-f) Same as (a-c) but for annual mean temperature. (g) Annual mean temperature change at 2°C in CMIP6 models with high warming rate reaching the GWL in the corresponding scenario before the earliest year of the assessed very likely range ( Section 4.3.4). (h) Climate response at 2°C GWL across all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not shown in (g). The close agreement of (g) and (h) demonstrates that the mean temperature response at 2°C is not sensitive to the rate of warming, and thereby the global mean surface air temperature (GSAT) warming of the respective models in 2081-2100. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on the sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1.

The limited scenario dependence of the GWL-based response for many variables implies that the regional response to emissions scenarios can be split in almost independent contributions of: (i) the transient global warming response to scenarios (see Chapter 4); and (ii) the regional response as function of a given GWL, which has also been referred to as 'regional climate sensitivity' ( Seneviratne and Hauser, 2020). This property has also been used to develop regionally resolved emulators for global climate models, using global surface temperature as input ( Beusch et al., 2020; Tebaldi et al., 2020). Analyses of the CMIP6 and CMIP5 multi-model ensembles shows that the GWL-based responses are very similar for temperature and precipitation extremes across the ensembles ( Seneviratne and Hauser, 2020; Wehner, 2020; Li et al. , 2021). This is despite their difference in global warming response (Chapter 4), confirming a substantial decoupling between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the GWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty induced by scenario, global mean model response and internal variability (Cross-Chapter Box, Figure 1).

Mapping between GWL- and scenario-based responses in model analyses

To map scenario-based climate projections into changes at specific GWLs, first, all individual Earth system model (ESM) simulations that reach a certain GWL are identified. Second, the climate response patterns at the respective GWL are calculated using an approach termed here 'GWL-sampling' - sometimes also referred to as epoch analysis, time shift, or time sampling approach - taking into account all models and scenarios (Cross-Chapter Box, Figure 3). Note that the range of years when a given GWL is reached in the CMIP6 ensemble is different from the AR6 assessed range of projected global surface temperature ( Section 4.3.4; Table 4.5). The latter further takes into account different lines of evidence, including the assessed observed warming between pre-industrial and present day, information from observational constraints on CMIP6, and emulators using the assessed transient climate response (TCR) and equilibrium climate sensitivity (ECS) ranges ( Section 4.3.4). Hence the Chapter 4 assessed range (Table 4.5) is the reference to determine when a given GWL is likely reached under given scenarios, while the mapping between scenarios/time frames and GWLs is used to assess the respective regional responses happening at these time frames (which also allows accounting for the global surface temperature assessment, rather than using scenarios analyses directly from CMIP6 output).

Cross-Chapter Box11.1, Figure 3 | Illustration of the AR6 global warming level (GWL) sampling approach to derive the timing and the response at a given GWL for the case of Coupled Model Intercomparison Project Phase 6 (CMIP6) data. For the mapping of scenarios/time slices into GWLs for CMIP6, please refer to Table 4.2. Respective numbers for the CMIP5 multi-model experiment are provided in Chapter 11 Supplementary Material (11.SM.1). Note that the time frames used to derive the GWL time slices can also include a different number of years (e.g., 30 years for some analyses).

Mapping between GWL- and scenario-based responses for literature

A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based information is required instead, an approximated mapping of the multi-model mean can be derived based on the known GWL in the given experiments for a particular time period. As a rough approximation, CMIP6 multi-model mean projections for the near-term (2021-2040) correspond to changes at about 1.5°C, and projections for the high-end scenario (SSP5-8.5) for the long-term (2081-2100) correspond to about 4°C-5°C of global warming(see Table 4.2 for changes in the CMIP6 ensemble and the Chapter 11 Supplementary Material (11.SM.1) and Hauser (2021) for details on other time periods and CMIP5). These approximated changes are used for some of the GWL-based assessments provided in the Chapter 11 regional tables ( Section 11.9 and Table 11.3) when literature based on scenario projections is used to assess estimated changes at given GWLs.

GWLs ver sus scenarios

The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways are consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached ( Section 4.3.4), and to assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for variables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land management changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium states. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need to account for the uncertainty in GSAT changes or climate-carbon feedbacks.

Forcing dependence of the GWL response is found for global mean precipitation ( Section 8.4.3), but less for regional patterns of mean precipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the cryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence (Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with a substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent for sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach a given GWL ( Section 9.6.3.4).

In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example in the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in the response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state when the land-sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context of projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables are less dependent on the pathway than for projections beyond 2100 ( Section 9.6.3.4).

Key conclusions on assessment s based on GWLs

GWL-based projections can inform society and policymakers on how climate would change under GWLs consistent with the aims of the Paris Agreement (stabilization at 1.5°C/well below 2°C), as well as on the consequences of missing these aims and reaching GWLs of 3°C or 4°C by the end of the century. The AR6 assessment shows that every bit of global warming matters and that changes in global warming of 0.5°C lead to statistically significant changes in mean climate and climate extremes on global scale and for large regions (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6 and 11.9, Figures 11.8 and 11.9, Atlas and Interactive Atlas), as also assessed in IPCC SR1.5.

This section assesses changes in temperature extremes at global, continental and regional scales. The main focus is on the changes in the magnitude and frequency of moderate extreme temperatures (those that occur several times a year) to very extreme temperatures (those that occur once in 10 or more years) of time scales from a day to a season, though there is a strong emphasis on the daily scale where literature is most concentrated.

The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that greenhouse gas forcing is the dominant factor for the increases in the intensity, frequency, and duration of warm extremes and the decrease in those of cold extremes. This general global-scale warming is modulated by large-scale atmospheric circulation patterns, as well as by feedbacks such as soil moisture-evapotranspiration-temperature and snow/ice-albedo-temperature feedbacks, and local forcings such as land-use change or changes in aerosol concentrations at the regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). Therefore, changes in temperature extremes at regional and local scales can have heterogeneous spatial distributions. Changes in the magnitudes (or intensities) of extreme temperatures are often larger than changes in global surface temperature, because of larger warming on land than on the ocean surface ( Section 2.3.1.1), and because of feedbacks, though they are of similar magnitude to changes in the local mean temperature (Figure 11.2).

In summary, greenhouse gas forcing is the dominant driver leading to the warming of temperature extremes. At regional scales, changes in temperature extremes are modulated by changes in large-scale patterns and modes of variability, feedbacks including soil-moisture-evapotranspiration-temperature or snow/ice-albedo-temperature feedbacks, and local and regional forcings such as land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. This leads to heterogeneity in regional changes and their associated uncertainties ( high confidence). Changes in anthropogenic aerosol concentrations have likely affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA ( medium confidence). Urbanization has likely exacerbated the effects of global warming in cities, in particular for nighttime temperature extremes.

The SREX (IPCC, 2012) reported a very likely decrease in the number of cold days and nights and increase in the number of warm days and nights at the global scale. Confidence in trends was assessed as regionally variable ( low to medium confidence) due to either a lack of observations or varying signals in sub-regions.

Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional-scale studies have examined trends in temperature extremes using different metrics that are based on daily temperatures, such as the Commission for Climatology/World Climate Research Program/Commission for Oceanography and Marine Meteorology joint Expert Team on Climate Change Detection and Indices (ETCCDI) indices ( Dunn et al., 2020). The additional observational records, along with a stronger warming signal, show very clearly that changes observed at the time of AR5 (IPCC, 2014) continued, providing strengthened evidence of an increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes. While the magnitude of the observed trends in temperature-related extremes varies depending on the region, spatial and temporal scales, and metric assessed, evidence of a warming effect is overwhelming, robust, and consistent. In particular, an increase in the intensity and frequency of hot extremes is almost always associated with an increase in the hottest temperatures and in the number of heatwave days. It is also the case for changes (decreases) in cold extremes. For this reason, and to simplify the presentation, the phrase 'increase in the intensity and frequency of hot extremes' is used to represent, collectively, an increase in the magnitude of extreme day and/or night temperatures, in the number of warm days and/or nights, and in the number of heatwave days. Changes in cold extremes are assessed similarly.

Figure 11.9 | Linear trends over 1960-2018 for three temperature extreme indices: (a) the annual maximum daily maximum temperature (TXx), (b) the annual minimum daily minimum temperature (TNn), and (c) the annual number of days when daily maximum temperature exceeds its 90th percentile from a base period of 1961-1990 (TX90p); based on the HadEX3 dataset (Dunn et al. , 2020). Linear trends are calculated only for grid points with at least 66% of the annual values over the period and which extend to at least 2009. Areas without sufficient data are shown in grey. No overlay indicates regions where the trends are significant at the p = 0.1 level. Crosses indicate regions where trends are not significant. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

In Africa (Table 11.4), while it is difficult to assess changes in temperature extremes in parts of the continent because of a lack of data, evidence of an increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes is clear and robust in regions where data are available. These include an increase in the frequency of warm days and nights and a decrease in the frequency of cold days and nights with high confidence ( Donat et al., 2013a, 2014b; Kruger and Sekele, 2013; Chaney et al., 2014; Filahi et al., 2016; Moron et al., 2016; Ringard et al., 2016; Barry et al., 2018; Gebrechorkos et al., 2018) and an increase in heatwaves ( Russo et al., 2016; Ceccherini et al., 2017). The increase in TNn is more notable than in TXx (Figure 11.9). Cold spells occasionally strike subtropical areas, but are likely to have decreased in frequency ( Barry et al., 2018). The frequency of cold events has likely decreased in South Africa ( Song et al., 2014; Kruger and Nxumalo, 2017), North Africa ( Filahi et al., 2016; Driouech et al., 2021), and the Sahara ( Donat et al., 2016a). Over the whole continent, there is medium confidence in an increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes; it is likely thatsimilar changes have also occurred in areas with poor data coverage, as warming is widespread and as projected future changes are similar over all regions ( Section 11.3.5).

In Australasia (Table 11.10), there is very robust evidence for very likely increases in the number of warm days and warm nights and decreases in the number of cold days and cold nights since 1950 ( Lewis and King, 2015; Jakob and Walland, 2016; Alexander and Arblaster, 2017). The increase in extreme minimum temperatures occurs in all seasons over most of Australia and typically exceeds the increase in extreme maximum temperatures (X.L. Wang et al., 2013b; Jakob and Walland, 2016). However, some parts of Southern Australia have shown stable or increased numbers of frost days since the 1980s ( Dittus et al., 2014) (see also Section 11.3.4). Similar positive trends in extreme minimum and maximum temperatures have been observed in New Zealand, in particular in the autumn and winter seasons, although they generally show higher spatial variability ( Caloiero, 2017). In the tropical Western Pacific region, spatially coherent warming trends in maximum and minimum temperature extremes have been reported for the period 1951-2011 ( Whan et al., 2014; McGree et al., 2019).

In Central and South America (Table 11.13), there is high confidence that observed hot extremes (TN90p, TX90p) have increased, and cold extremes (TN10p, TX10p) have decreased over recent decades, though trends vary among different extremes types, datasets, and regions ( Skansi et al., 2013; Dittus et al., 2016; Rusticucci et al., 2017; Meseguer-Ruiz et al., 2018; Salvador and de Brito, 2018; Dereczynski et al., 2020; Dunn et al., 2020; Olmo et al., 2020). An increase in the intensity and frequency of heatwave events was also observed between 1961 and 2014 in an area covering most of South America ( Ceccherini et al., 2016; Geirinhas et al., 2018). However, there is medium confidence that warm extremes (TXx and TX90p) have decreased in the last decades over the central region of South-Eastern South America (SES) during austral summer ( Tencer and Rusticucci, 2012; Skansi et al., 2013; Rusticucci et al., 2017; Wu and Polvani, 2017). There is medium confidence that TNn extremes are warming faster than TXx extremes, with the largest warming rates observed over North-East Brazil (NEB) and Northern South America (NSA) for cold nights ( Skansi et al., 2013).

Trends in some measures of heatwaves are also observed at the global scale. Globally averaged heatwave intensity, heatwave duration, and the number of heatwave days have significantly increased from 1950-2011 ( Perkins, 2015). There are some regional differences in trends in characteristics of heatwaves, with significant increases reported in Europe ( Russo et al., 2015; Forzieri et al., 2016; Sánchez-Benítez et al., 2020) and Australia (CSIRO and BOM, 2016; Alexander and Arblaster, 2017). In Africa, there is medium confidence that heatwaves, regardless of the definition, have been becoming more frequent, longer-lasting, and hotter over more than three decades ( Fontaine et al., 2013; Mouhamed et al., 2013; Ceccherini et al., 2016, 2017; Forzieri et al., 2016; Moron et al., 2016; Russo et al., 2016). The majority of heatwave characteristics examined in China between 1961 and 2014 show increases in heatwave days, consistent with warming ( You et al., 2017; Xie et al., 2020). Increases in the frequency and duration of heatwaves are also observed in Mongolia ( Erdenebat and Sato, 2016) and India ( Ratnam et al., 2016; Rohini et al., 2016). In the UK, the lengths of short heatwaves have increased since the 1970s, while the lengths of long heatwaves (more than 10 days) have decreased over some stations in the south-east of England (M. Sanderson et al., 2017). In Central and South America, there are increases in the frequency of heatwaves ( Barros et al., 2015; Bitencourt et al., 2016; Ceccherini et al., 2016; Piticar, 2018), although decreases in Excess Heat Factor (EHF), which is a metric for heatwave intensity, are observed in South America in data derived from HadGHCND ( Cavanaugh and Shen, 2015).

In summary, it is virtually certain that there has been an increase in the number of warm days and nights and a decrease in the number of cold days and nights on the global scale since 1950. Both the coldest extremes and hottest extremes display increasing temperatures. It is very likely that these changes have also occurred at the regional scale in Europe, Australasia, Asia, and North America. It is virtually certain that there has been increases in the intensity and duration of heatwaves and in the number of heatwave days at the global scale. These trends likely occur in Europe, Asia, and Australia. There is medium confidence in similar changes in temperature extremes in Africa and high confidence in South America; the lower confidence is due to reduced data availability and fewer studies. Annual minimum temperatures on land have increased about three times more than global surface temperature since the 1960s, with particularly strong warming in the Arctic ( hi gh confidence).

There have also been many extreme event attribution studies that examined short-duration temperature extremes, including daily temperatures, temperature indices, and heatwave metrics. Examples of these events from different regions are summarized in various annual Explaining Extreme Events supplements of the Bulletin of the American Meteorological Society ( Peterson et al., 2012, 2013a; Herring et al., 2014, 2015, 2016, 2018, 2019, 2020), including a number of approaches to examine extreme events (described in Easterling et al., 2016; Stott et al., 2016; Otto, 2017). Several studies of recent events from 2016 onwards have determined an infinite risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that the occurrence probability for such events is close to zero in model simulations without anthropogenic influences (see Herring et al., 2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019). Though it is difficult to accurately estimate the lower bound of the uncertainty range of the FAR in these cases ( Paciorek et al., 2018), the fact that those events are so far outside the envelop of the models with only natural forcing indicates that it is extremely unlikely for those events to occur without human influence.

Different event attribution studies can produce a wide range of changes in the probability of event occurrence because of different framing. The temperature event definition itself plays a crucial role in the attributable signal ( Fischer and Knutti, 2015; Kirchmeier‐Young et al., 2019). Large-scale, longer-duration events tend to have notably larger attributable risk ratios ( Angélil et al., 2014, 2018; Uhe et al., 2016; Harrington, 2017; Kirchmeier‐Young et al., 2019), as natural variability is smaller. While uncertainty in the best estimates of the risk ratios may be large, their lower bounds can be quite insensitive to uncertainties in observations or model descriptions, thus increasing confidence in conservative attribution statements ( Jeon et al., 2016).

The relative strength of anthropogenic influences on temperature extremes is regionally variable, in part due to differences in changes in atmospheric circulation, land-surface feedbacks, and other external drivers such as aerosols. For example, in the Mediterranean and over western Europe, risk ratios on the order of 100 have been found ( Kew et al., 2019; Vautard et al., 2020), whereas in the USA, changes are much less pronounced. This is probably a reflection of the land-surface feedback enhanced extreme 1930s temperatures that reduce the rarity of recent extremes, in addition to the definition of the events and framing of attribution analyses (e.g., spatial and temporal scales considered). Local forcing may mask or enhance the warming effect of greenhouse gases. In India, short-lived aerosols or an increase in irrigation may be masking the warming effect of greenhouse gases ( Wehner et al., 2018c). Irrigation and crop intensification have been shown to lead to a cooling in some regions, in particular in North America, Europe, and India ( high confidence) (N.D. Mueller et al., 2016; Thiery et al., 2017, 2020; Chen and Dirmeyer, 2019). Deforestation has contributed about one third of the total warming of hot extremes in some mid-latitude regions since pre-industrial times ( Lejeune et al., 2018). Despite all of these differences, and larger uncertainties at the regional scale, nearly all studies demonstrated that human influence has contributed to an increase in the frequency or intensity of hot extremes and to a decrease in the frequency or intensity of cold extremes.

In summary, long-term changes in various aspects of long- and short-duration extreme temperatures, including intensity, frequency, and duration have been detected in observations and attributed to human influence at global and continental scales. It is extremely likely that human influence is the main contributor to the observed increase in the intensity and frequency of hot extremes and the observed decrease in the intensity and frequency of cold extremes on the global scale. It is very likely that this applies on continental scales as well. Some specific recent hot extreme events would have been extremely unlikely to occur without human influence on the climate system. Changes in aerosol concentrations have affected trends in hot extremes in some regions, with the presence of aerosols leading to attenuated warming, in particular from 1950 to 1980. Crop intensification, irrigation and no-till farming have attenuated increases in summer hot extremes in some regions, such as Central North America ( medi um confidence).

The AR5 (Chapter12, Collins et al., 2013) concluded that it is virtually certain there will be more frequent hot extremes and fewer cold extremes at the global scale and over most land areas in a future warmer climate, and it is very likely that heatwaves will occur with a higher frequency and longer duration.The SR1.5 (Chapter 3, Hoegh-Guldberg et al., 2018) assessment on projected changes in hot extremes at 1.5°C and 2°C global warming is consistent with the AR5 assessment, concluding that it is very likely a global warming of 2°C, when compared with a 1.5°C warming, would lead to more frequent and more intense hot extremes on land, as well as to longer warm spells, affecting many densely inhabited regions. The SR1.5 also assessed it is very likely that the strongest increases in the frequency of hot extremes are projected for the rarest events, while cold extremes will become less intense and less frequent, and cold spells will be shorter.

New studies since AR5 and SR1.5 confirm these assessments. New literature since AR5 includes projections of temperature-related extremes in relation to changes in mean temperatures, projections based on CMIP6 simulations, projections based on stabilized global warming levels, and the use of new metrics. Constraints for the projected changes in hot extremes were also provided ( Borodina et al., 2017b; Sippel et al., 2017b; Vogel et al., 2017). Overall, projected changes in the magnitude of extreme temperatures over land are larger than changes in global mean temperature, over mid-latitude land regions in particular (Figures 11.3, 11.11; Fischer et al., 2014; Seneviratne et al., 2016; B.M. Sanderson et al., 2017; Wehner et al., 2018b; Di Luca et al., 2020b). Large warming in hot and cold extremes will occur, even at the 1.5°C GWL (Figure 11.11). At this level, widespread significant changes at the grid-box level occur for different temperature indices ( Aerenson et al., 2018). In agreement with CMIP5 projections, CMIP6 simulations show that a 0.5°C increment in global warming will significantly increase the intensity and frequency of hot extremes, and decrease the intensity and frequency of cold extremes on the global scale (Figures 11.6, 11.8 and 11.12). It takes less than half of a degree for the changes in TXx to emerge above the level of natural variability (Figure 11.8) and the 66% ranges of the land medians of the 10-year or 50-year TXx events do not overlap between 1.0°C and 1.5°C in the CMIP6 multi-model ensemble simulations(Figure 11.6, Li et al., 2021).

Figure 11.12 | Projected changes in the intensity of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 185 0-1900 baseline. Extreme temperature events are defined as the daily maximum temperatures (TXx) that were exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850-1900 base period. Results are shown for the global land. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the intensity changes across the multi-model ensemble, and the 'whiskers' extend to the 90% uncertainty range. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from Li et al. (2021). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Tebaldi and Wehner (2018) showed that, at the middle of the 21st century, 66% of the land surface area would experience the present-day 20-year return values of TXx and the running three-day average of the daily maximum temperature every other year, on average, under the Representative Concentration Pathway 8.5 (RCP8.5) scenario, as opposed to only 34% under RCP4.5. By the end of the century, these area fractions increase to 92% and 62%, respectively. Such nonlinearities in the characteristics of future regional extremes are shown, for instance, for Europe ( Dosio and Fischer, 2018; Spinoni et al., 2018b; Lionello and Scarascia, 2020), Asia ( Guo et al., 2017; Harrington and Otto, 2018b; King et al., 2018), and Australia ( Lewis et al., 2017a) under various global warming thresholds. The nonlinear increase in fixed-threshold indices (e.g., based on a percentile for a given reference period, or on an absolute threshold) as a function of global warming is consistent with a linear warming of the absolute temperature of the temperature extremes (e.g., Whan et al., 2015). Compared to the historical climate, warming will result in strong increases in heatwave area, duration and magnitude ( Vogel et al., 2020b). These changes are mostly due to the increase in mean seasonal temperature, rather than changes in temperature variability, though the latter can have an effect in some regions ( Brown, 2020; Di Luca et al., 2020b; Suarez-Gutierrez et al., 2020a).

In all continental regions, including Africa (Table 11.4), Asia (Table 11.7), Australasia (Table 11.10), Central and South America (Table 11.13), Europe (Table 11.16), North America (Table 11.19) and at the continental scale, it is very likely that the intensity and frequency of hot extremes will increase and the intensity and frequency of cold extremes will decrease compared with the 1995-2014 baseline, even under 1.5°C global warming. Those changes are virtually certain to occur under 4°C global warming. At the regional scale, and for almost all AR6 regions, it is likely that the intensity and frequency of hot extremes will increase and the intensity and frequency of cold extremes will decrease compared with the 1995-2014 baseline, even under 1.5°C global warming. Those changes are virtually certain to occur under 4°C global warming. Exceptions include lower confidence in the projected decrease in the intensity and frequency of cold extremes compared with the 1995-2014 baseline under 1.5°C of global warming ( medium confidence) and 4°C of global warming ( very likely) in Northern Central America, Central North America, and Western North America.

In Africa (Table 11.4), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations (Giorgi et al., 2014; Engelbrecht et al., 2015; Lelieveld et al., 2016; Russo et al., 2016; Dosio, 2017; Bathiany et al., 2018; Mba et al., 2018; Nangombe et al., 2018; Weber et al., 2018; Kruger et al., 2019; Coppola et al., 2021b; Li et al., 2021). Cold spells are projected to decrease under all RCPs, and even at low warming levels in Western and Central Africa ( Diedhiou et al., 2018). The number of cold days is projected to decrease in East Africa ( Ongoma et al., 2018b).

In summary, it is virtually certain that further increases in the intensity and frequency of hot extremes, and decreases in the intensity and frequency of cold extremes, will occur throughout the 21st century and around the world. It is virtually certain that the number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves compared to 1995-2014 will increase over most land areas. In most regions, changes in the magnitude of temperature extremes are proportional to global warming levels ( high confidence). The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, at about 1.5 times to twice the rate of global warming ( high confidence). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( high confidence). The probability of temperature extremes generally increases nonlinearly with increasing global warming levels ( high confidence). Confidence in assessments depends on the spatial and temporal scales of the extreme in question, with high confidence in projections of temperature-related extremes at global and continental scales for daily to seasonal scales. There is high confidence that, on land, the magnitude of temperature extremes increases more strongly than global mean temperature.

This section assesses changes in heavy precipitation at global and regional scales. The main focus is on extreme precipitation at a daily scale where literature is most concentrated, though extremes of shorter (sub-daily) and longer (five-day or more) durations are also assessed to the extent the literature allows.

The SREX (Chapter 3, Seneviratne et al., 2012) assessed changes in heavy precipitation in the context of the effects of thermodynamic and dynamic changes. Box 11.1 assesses thermodynamic and dynamic changes in a warming world to aid the understanding of changes in observations and projections in some extremes and the sources of uncertainties (see also Section 8.2.3.2). In general, warming increases the atmospheric water-holding capacity following the Clausius-Clapeyron (C-C) relation. This thermodynamic effect results in an increase in extreme precipitation at a similar rate at the global scale. On a regional scale, changes in extreme precipitation are further modulated by dynamic changes (Box 11.1).

Decreases in atmospheric aerosols results in warming and thus an increase in extreme precipitation ( Samset et al., 2018; Sillmann et al., 2019). Changes in atmospheric aerosols also result in dynamic changes such as in tropical cyclones ( Takahashi et al., 2017; Strong et al., 2018). Uncertainty in the projections of future aerosol emissions results in additional uncertainty in the heavy precipitation projections of the 21st century ( Lin et al., 2016).

There has been new evidence of the effect of local land-use and land-cover change on heavy precipitation. There is a growing set of literature linking increases in heavy precipitation in urban centres to urbanization ( Argüeso et al., 2016; Y. Zhang et al., 2019b). Urbanization intensifies extreme precipitation, especially in the afternoon and early evening, over the urban area and its downwind region ( medium confidence) (Box 10.3). There are four possible mechanisms: (i) increases in atmospheric moisture due to horizontal convergence of air associated with the urban heat island effect ( Shastri et al., 2015; Argüeso et al., 2016); (ii) increases in condensation due to urban aerosol emissions ( Han et al., 2011; Sarangi et al., 2017); (iii) aerosol pollution that impacts cloud microphysics (Box 8.1; Schmid and Niyogi, 2017); and (iv) urban structures that impede atmospheric motion (Shepherd, 2013; Ganeshan and Murtugudde, 2015; Paul et al., 2018). Other local forcing, including reservoirs ( Woldemichael et al., 2012), irrigation ( Devanand et al., 2019), or large-scale land-use and land-cover change ( Odoulami et al., 2019), can also affect local extreme precipitation.

Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (IPCC, 2014 Chapter 2) concluded it was likely that the number of heavy precipitation events over land had increased in more regions than it had decreased, though there were wide regional and seasonal variations, and trends in many locations were not statistically significant. This assessment has been strengthened with multiple studies finding robust evidence of the intensification of extreme precipitation at global and continental scales, regardless of spatial and temporal coverage of observations and the methods of data processing and analysis.

The average annual maximum precipitation amount in a day (Rx1day) has significantly increased since the mid-20th century over land ( Du et al., 2019; Dunn et al., 2020) and in the humid and dry regions of the globe ( Dunn et al., 2020). The percentage of observing stations with statistically significant increases in Rx1day is larger than expected by chance, while the percentage of stations with statistically significant decreases is smaller than expected by chance, over the global land as a whole and over North America, Europe, and Asia (Figure 11.13; Sun et al., 2021) and over global monsoon regions ( Zhang and Zhou, 2019) where data coverage is relatively good. The addition of the past decade of observational data shows a more robust increase in Rx1day over the global land region ( Sun et al., 2021). Light, moderate, and heavy daily precipitation has all intensified in a gridded daily precipitation dataset ( Contractor et al., 2020a). Daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions ( high confidence) ( Section 8.3.1.3). The probability of precipitation exceeding 50 mm/day increased during 1961-2018 ( Benestad et al., 2019). The globally averaged annual fraction of precipitation from days in the top 5% (R95pTOT) has also significantly increased ( Dunn et al., 2020). The increase in the magnitude of Rx1day in the 20th century is estimated to be at a rate consistent with C-C scaling with respect to global mean temperature ( Fischer and Knutti, 2016; Sun et al., 2021). Studies on past changes in extreme precipitation of durations longer than a day are more limited, though there are some studies examining long-term trends in annual maximum five-day precipitation (Rx5day). On global and continental scales, long-term changes in Rx5day are similar to those of Rx1day in many aspects (Zhang and Zhou 2019; Sun et al., 2021). As discussed below, at the regional scale, changes in Rx5day are also similar to those of Rx1day where there are analyses of changes in both Rx1day and Rx5day.

Figure 11.13 | Signs and significance of the observed trends in annual maximum daily precipitation (Rx1day) during 1950-2018 at 8345 stations with suficient data. (a) Percentage of stations with statistically significant trends in Rx1day; green dots show positive trends and brown dots negative trends. Box and 'whisker' plots indicate the expected percentage of stations with significant trends due to chance estimated from 1000 bootstrap realizations under a no-trend null hypothesis. The boxes mark the median, 25th percentile, and 75th percentile. The upper and lower whiskers show the 97.5th and the 2.5th percentiles, respectively. Maps of stations with positive (b) and negative (c) trends. The light colour indicates stations with non-significant trends, and the dark colour stations with significant trends. Significance is determined by a two-tailed test conducted at the 5% level. Adapted from Sun et al. (2021). Figure copyright © American Meteorological Society (used with permission). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Overall, there is a lack of systematic analysis of long-term trends in sub-daily extreme precipitation at the global scale. Often, sub-daily precipitation data have only sporadic spatial coverage and are of limited length. Additionally, the available data records are far shorter than needed for a robust quantification of past changes in sub-daily extreme precipitation ( C. Li et al., 2019a). Despite these limitations, there are studies in regions of almost all continents that generally indicate intensification of sub-daily extreme precipitation, although there remains low confidence in an overall increase at the global scale. Studies include an increase in extreme sub-daily rainfall in summer over South Africa ( Sen Roy and Rouault, 2013), annually in Australia ( Guerreiro et al., 2018b), over 23 urban locations in India ( Ali and Mishra, 2018), in Peninsular Malaysia ( Syafrina et al., 2015), and in eastern China in the summer season during 1971-2013 ( Xiao et al., 2016). In some regions in Italy ( Arnone et al., 2013; Libertino et al., 2019) and in the USA during 1950-2011 ( Barbero et al., 2017), there is also an increase. In general, an increase in sub-daily heavy precipitation results in an increase in pluvial floods over smaller watersheds ( Ghausi and Ghosh, 2020).

In Africa (Table 11.5), evidence shows an increase in extreme daily precipitation for the late half of the 20th century over the continent where data are available; there is a larger percentage of stations showing significant increases in extreme daily precipitation than decreases ( Sun et al., 2021). There are increases in different metrics relevant to extreme precipitation in various regions of the continent ( Chaney et al., 2014; Harrison et al., 2019; Dunn et al., 2020; Sun et al., 2021). There is an increase in extreme precipitation events in Southern Africa (Weldon and Reason, 2014; Kruger et al., 2019) and a general increase in heavy precipitation over East Africa, the Greater Horn of Africa ( Omondi et al., 2014). Over sub-Saharan Africa, increases in the frequency and intensity of extreme precipitation have been observed over the well-gauged areas during 1950-2013; however, this covers only 15% of the total area of sub-Saharan Africa ( Harrison et al., 2019). There is medium confidence about the increase in extreme precipitation for some regions where observations are more abundant, but for Africa as whole, there is low confidence because of a general lack of continent-wide systematic analysis, the sporadic nature of available precipitation data over the continent, and spatially non-homogenous trends in places where dataare available (Donat et al., 2014a; Mathbout et al., 2018b; Alexander et al., 2019; Funk et al., 2020).

In Australasia (Table 11.11), available evidence has not shown an increase or a decrease in heavy precipitation over Australasia as a whole ( medium confidence), but heavy precipitation tends to increase over Northern Australia (particularly the north-west) and decrease over the eastern and southernregions (e.g., Jakob and Walland, 2016; Guerreiro et al., 2018b; Dey et al., 2019b; Dunn et al., 2020; Sun et al., 2021). Available studies that used long-term observations since the mid-20th century showed nearly as many stations with an increase as those with a decrease in heavy precipitation ( Jakob and Walland, 2016) or slightly more stations with a decrease than with an increase in Rx1day and Rx5day ( Sun et al., 2021), or strong differences in Rx1day trends with increases over Northern Australia and Central Australia in general, but mostly decreases over Southern Australia and Eastern Australia ( Dunn et al., 2020). Over New Zealand, decreases are observed for moderate-heavy precipitation events, but there are no significant trends for very heavy events (more than 64 mm in a day) for the period 1951-2012. The number of stations with an increase in very wet days is similar to that with a decrease during 1960-2019 (MfE and Stats NZ, 2020). Overall, there is low confidence in trends in the frequency of heavy rain days, with mostly decreases over New Zealand (Harringtonand Renwick, 2014; Caloiero, 2015).

In Central and South America (Table 11.14), evidence shows an increase in extreme precipitation, but in general there is low confidence; while continent-wide analyses produced wetting trends are not robust. Rx1day increased at more stations than it decreased in South America between 1950 and 2018 ( Sun et al., 2021). Over the period 1950-2010, both Rx5day and R99p increased over large regions of South America, including North-Western South America, Northern South America, and South-Eastern South America ( Skansi et al., 2013). There are large regional differences. A decrease in daily extreme precipitation is observed in north-eastern Brazil (Skansi et al., 2013; Bezerra et al., 2018; Dereczynski et al., 2020). Trends in extreme precipitation indices were not statistically significant over the period 1947-2012 within the São Francisco River basin in the Brazilian semi-arid region ( Bezerra et al., 2018). An increase in extreme rainfall is observed in the Amazon with medium confidence ( Skansi et al., 2013) and in South-Eastern South America with high confidence ( Skansi et al., 2013; Valverde and Marengo, 2014; Barros et al., 2015; Ávila et al., 2016; Wu and Polvani, 2017; Lovino et al., 2018; Dereczynski et al., 2020). Among all sub-regions, South-Eastern South America shows the highest rate of increase for rainfall extremes, followed by the Amazon ( Skansi et al., 2013). Increases in the intensity of heavy daily rainfall events have been observed in the southern Pacific and in the Titicaca basin ( Skansi et al., 2013; Huerta and Lavado‐Casimiro, 2021). In Southern Central America, trends in annual precipitation are generally not significant, although small (but significant) increases are found in Guatemala, El Salvador, and Panama ( Hidalgo et al., 2017). Small positive trends were found in multiple extreme precipitation indices over the Caribbean region over a short time period (1986-2010) ( Stephenson et al., 2014; McLean et al., 2015).

In Europe (Table 11.17), there is robust evidence that the magnitude and intensity of extreme precipitation has very likely increased since the 1950s. There is a significant increase in Rx1day and Rx5day during 1950-2018 in Europe as a whole ( Sun et al., 2021, also Figure 11.13). The number of stations with increases far exceeds those with decreases in the frequency of daily rainfall exceeding its 90th or 95th percentile in century-long series ( Cioffi et al., 2015). The five-, 10-, and 20-year events of one-day and five-day precipitation during 1951-1960 became more common since the 1950s ( van den Besselaar et al., 2013). There can be large discrepancies among studies and regions and seasons ( Croitoru et al., 2013; Willems, 2013; Casanueva et al., 2014; Roth et al., 2014; Fischer et al., 2015); evidence for increasing extreme precipitation is more frequently observed for summer and winter, but not in other seasons ( Madsen et al., 2014; Helama et al., 2018). An increase is observed in central Europe ( Volosciuk et al., 2016; Zeder and Fischer, 2020), and in Romania ( Croitoru et al., 2016). Trends in the Mediterranean region are in general not spatially consistent ( Reale and Lionello, 2013), with decreases in the western Mediterranean and some increases in the eastern Mediterranean ( Rajczak et al., 2013; Casanueva et al., 2014; de Lima et al., 2015; Gajić-Čapka et al., 2015; Sunyer et al., 2015; Pedron et al., 2017; Serrano-Notivoli et al., 2018; Ribes et al., 2019). In the Netherlands, the total precipitation contributed from extremes higher than the 99th percentile doubles per 1°C increase in warming ( Myhre et al., 2019), though extreme rainfall trends in Northern Europe may differ in different seasons ( Irannezhad et al., 2017).

In Small Islands, there is a lack of evidence showing changes in heavy precipitation overall. There were increases in extreme precipitation in Tobago from 1985-2015 ( Stephenson et al., 2014; Dookie et al., 2019) and decreases in south-western French Polynesia and the southern subtropics ( low confidence) (Table 11.5; Atlas.10). Extreme precipitation leading to flooding in the Small Islands has been attributed in part to tropical cyclones, as well as being influenced by ENSO (Box 11.5; Khouakhi et al., 2016; Hoegh-Guldberg et al., 2018).

In summary, the frequency and intensity of heavy precipitation have likely increased at the global scale over a majority of land regions with good observational coverage. Since 1950, the annual maximum amount of precipitation falling in a day, or over five consecutive days, has likely increased over land regions with sufficient observational coverage for assessment, with increases in more regions than there are decreases. Heavy precipitation has likely increased on the continental scale over three continents (North America, Europe, and Asia) where observational data are more abundant. There is very low confidence about changes in sub-daily extreme precipitation due to the limited number of studies and available data.

Figure 11.14 | Multi-model mean bias in annual maximum daily precipitation (Rx1day, %) for the period 1979-2014. Calculated as the difference between the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean and the average of available observational or reanalysis products including (a) ERA5, (b) HadEX3, and (c) REGEN. Bias is expressed as the percent error relative to the long-term mean of the respective observational data products. Brown indicates that models are too dry, while green indicates that they are too wet. Areas without sufficient observational data are shown in grey. Adapted from Wehner et al. (2020) under the terms of the Creative Commons Attribution licence. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

In general, there is high confidence that historical simulations by CMIP5 and CMIP6 models of similar horizontal resolutions are interchangeable in their performance in simulating the observed climatology of extreme precipitation.

Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10, IPCC, 2014) concluded with medium confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. These assessments were based on the evidence of anthropogenic influence on aspects of the global hydrological cycle, in particular, the human contribution to the warming-induced observed increase in atmospheric moisture that leads to an increase in heavy precipitation, and limited evidence of anthropogenic influence on extreme precipitation of durations of one and five days.

Since AR5 there has been new and robust evidence and improved understanding of human influence on extreme precipitation. In particular, detection and attribution analyses have provided consistent and robust evidence of human influence on extreme precipitation of one- and five-day durations at global to continental scales. The observed increases in Rx1day and Rx5day over the Northern Hemisphere land area during 1951-2005 can be attributed to the effect of combined anthropogenic forcing, including greenhouse gases and anthropogenic aerosols, as simulated by CMIP5 models and the rate of intensification with regard to warming is consistent with C-C scaling ( Zhang et al., 2013). This is confirmed to be robust when an additional nine years of observational data and the CMIP6 model simulations were used (Cross-Chapter Box 3.2, Figure 1; Paik et al., 2020). The influence of greenhouse gases is attributed as the dominant contributor to the observed intensification. The global average of Rx1day in the observations is consistent with simulations by both CMIP5 and CMIP6 models under anthropogenic forcing, but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The observed increase in the fraction of annual total precipitation falling into the top fifth or top first percentiles of daily precipitation can also be attributed to human influence at the global scale ( Dong et al., 2021). The CMIP5 models were able to capture the fraction of land experiencing a strong intensification of heavy precipitation during 1960-2010 under anthropogenic forcing, but not in unforced simulations ( Fischer et al., 2014). But the models underestimated the observed trends ( Borodina et al., 2017a). Human influence also significantly contributed to the historical changes in record-breaking one-day precipitation ( Shiogama et al., 2016). There is also limited evidence of the influences of natural forcing. Substantial reductions in Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation intensity over the global summer monsoon regions occurred during 1957-2000 after explosive volcanic eruptions ( Paik and Min, 2018). The reduction in post-volcanic eruption extreme precipitation in the simulations is closely linked to the decrease in mean precipitation, for which both thermodynamic effects (moisture reduction due to surface cooling) and dynamic effects (monsoon circulation weakening) play important roles.

There has been new evidence of human influence on extreme precipitation at continental scales, including the detection of the combined effect of greenhouse gases and aerosol forcing on Rx1day and Rx5day over North America, Eurasia, and mid-latitude land regions ( Zhang et al., 2013) and of greenhouse gas forcing in Rx1day and Rx5day in the mid-to-high latitudes, western and eastern Eurasia, and the global dry regions ( Paik et al., 2020). These findings are corroborated by the detection of human influence in the fraction of extreme precipitation in the total precipitation over Asia, Europe, and North America ( Dong et al., 2021). Human influence was found to have contributed to the increase in frequency and intensity of regional precipitation extremes in North America during 1961-2010, based on optimal fingerprinting and event attribution approaches ( Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) found the observed latitudinal increase in extreme precipitation over Europe to be consistent with model-simulated responses to anthropogenic forcing.

In summary, most of the observed intensification of heavy precipitation over land regions is likely due to anthropogenic influence, for which greenhouse gases emissions are the main contributor. New and robust evidence since AR5 includes attribution to human influence of the observed increases in annual maximum one-day and five-day precipitation and in the fraction of annual precipitation falling in heavy events. The evidence since AR5 also includes a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, both of which can only be explained when anthropogenic greenhouse gas forcing is considered. Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are more abundant ( high confidence) (North America, Europe and Asia). On the spatial scale of AR6 regions, there is limited evidence of human influence on extreme precipitation, but new evidence is emerging; in particular, studies attributing individual heavy precipitation events found that human influence was a significant driver of the events, particularly in the winter season.

The AR5 concluded it is very likely that extreme precipitation events will be more frequent and more intense over most of the mid-latitude land masses and wet tropics in a warmer world ( Collins et al., 2013). Post-AR5 studies provide more and robust evidence to support the previous assessments. These include an observed increase in extreme precipitation ( Section 11.4.3) and human causes of past changes ( Section 11.4.4), as well as projections based on either GCM and/or RCM simulations. The CMIP5 models project that the rate of increase in Rx1day with warming is independent of the forcing scenario ( Section 8.5.3.1; Pendergrass et al., 2015) or forcing mechanism ( Sillmann et al., 2017a). This is confirmed in CMIP6 simulations ( Sillmann et al., 2019; Li et al., 2021). In particular, for extreme precipitation that occurs once a year or less frequently, the magnitudes of the rates of change per 1°C change in global mean temperature are similar, regardless of whether the temperature change is caused by increases in carbon dioxide (CO ₂), methane (CH ₄), solar forcing, or sulphate (SO ₄) ( Sillmann et al., 2019). In some models - CESM1 in particular - the extreme precipitation response to warming may follow a quadratic relation ( Pendergrass et al., 2019). Figure 11.15 shows changes in the 10- and 50-year return values of Rx1day at different warming levels as simulated by the CMIP6 models. The median value of the scaling over land, across all Shared Socio-economic Pathway (SSP) scenarios and all models, is close to 7% per 1°C of warming for the 50-year return value of Rx1day. It is just slightly smaller for the 10- and 50-year return values of Rx5day ( Li et al., 2021). The 90% ranges of the multimodel ensemble changes across all land grid boxes in the 50-year return values for Rx1day and Rx5day do not overlap between 1.5°C and 2°C warming levels ( Li et al., 2021), indicating that a small increment such as 0.5°C in global warming can result in a significant increase in extreme precipitation. Projected long-period Rx1day return value changes are larger than changes in mean Rx1day and with larger relative changes for more rare events ( Pendergrass, 2018; Mizuta and Endo, 2020; Wehner, 2020). The rate of change of moderate extreme precipitation may depend more on the forcing agent, similar to the mean precipitation response to warming ( Lin et al., 2016, 2018). Thus, there is high confidence that extreme precipitation that occurs once a year or less frequently increases proportionally to the amount of surface warming, and the rate of change in precipitation is not dependent on the underlying forcing agents of warming.

The spatial patterns of the projected changes across different warming levels are quite similar, as shown in Figure 11.16, and confirmed by near-linear scaling between extreme precipitation and global warming levels at regional scales ( Seneviratne and Hauser, 2020). Internal variability modulates changes in heavy rainfall ( Wood and Ludwig, 2020), resulting in different changes in different regions ( Seneviratne and Hauser, 2020). Extreme precipitation nearly always increases across land areas with larger increases at higher global warming levels, except in very few regions, such as Southern Europe around the Mediterranean Basin at low warming levels (Table 11.17). The very likely ranges of the multi-model ensemble changes across all land grid boxes in the 50-year return values for Rx1day and Rx5day between 1.5°C and 1°C warming levels are above zero for all continents except Europe, with the lower bound of the likely range above zero over Europe ( Li et al., 2021). Decreases in extreme precipitation are confined mostly to subtropical ocean areas and are highly correlated to decreases in mean precipitation due to storm track shifts. These subtropical decreases can extend to nearby land areas in individual realizations.

In Africa (Table 11.5), extreme precipitation will likely increase under warming levels of 2°C or below (compared to pre-industrial values) and very likely increase at higher warming levels. Simulations by CMIP5, CMIP6 and CORDEX regional models project an increase in daily extreme precipitation between 1.5°C and 2.0°C warming levels. The pattern of change in heavy precipitation under different scenarios or warming levels is similar with larger increases for higher warming levels (e.g., Nikulin et al., 2018; Li et al., 2021). With increases in warming, extreme precipitation is projected to increase in the majority of land regions in Africa ( Mtongori et al., 2016; Pfahl et al., 2017; Diedhiou et al., 2018; Dunning et al., 2018; Akinyemi and Abiodun, 2019; Giorgi et al., 2019). Over Southern Africa, heavy precipitation will likely increase by the end of the 21st century under RCP 8.5 ( Dosio, 2016; Pinto et al., 2016; Abiodun et al., 2017; Dosio et al., 2019). However, heavy rainfall amounts are projected to decrease over western South Africa ( Pinto et al., 2018) as a result of a projected decrease in the frequency of the prevailing westerly winds south of the continent that translates into fewer cold fronts and closed mid-latitudes cyclones ( Engelbrecht et al., 2009; Pinto et al., 2018). Heavy precipitation will likely increase by the end of the century under RCP8.5 in West Africa ( Diallo et al., 2016; Dosio, 2016; Sylla et al., 2016; Abiodun et al., 2017; Akinsanola and Zhou, 2019; Dosio et al., 2019) and is projected to increase ( high confidence) in Central Africa ( Fotso-Nguemo et al., 2018, 2019; Sonkoué et al., 2019) and eastern Africa ( Thiery et al., 2016; Ongoma et al., 2018a). In north-east and central east Africa, extreme precipitation intensity is projected to increase across CMIP5, CMIP6 and CORDEX-CORE ( high confidence) in most areas annually ( Coppola et al., 2021a), but the trends differ from season to season in all future scenarios ( Dosio et al., 2019). In northern Africa, there is low confidence in the projected changes in heavy precipitation, either due to a lack of agreement among studies on the sign of changes ( Sillmann et al., 2013a; Giorgi et al., 2014) or due to insufficient evidence.

In Australasia (Table 11.11), most CMIP5 models project an increase in Rx1day under RCP4.5 and RCP8.5 scenarios for the late 21st century (CSIRO and BOM, 2015; Alexander and Arblaster, 2017; Grose et al., 2020) and the CMIP6 multi-model median projects an increase in the 10- and 50-year return values of Rx1day and Rx5day at a rate between 5% and 6% per 1°C of near-surface global mean warming (Figure 11.7; Li et al., 2021). Yet, there is large uncertainty in the increase because projected changes in dynamic processes lead to a decrease in Rx1day that can offset the thermodynamic increase over a large portion of the region (Box 11.1, Figure 1; Pfahl et al., 2017). Projected changes in moderate extreme precipitation (the 99th percentile of daily precipitation) by RCMs under RCP8.5 for 2070-2099 are mixed, with more regions showing decreases than increases ( Evans et al., 2021). It is likely that daily rainfall extremes such as Rx1day will increase at the continental scale for global warming levels at or above 3°C. Daily rainfall extremes are projected to increase at the 2.0°C global warming level ( medium confidence), and there is low confidence in changes at the 1.5°C. Projected changes show important regional differences with very likely increases over Northern Australia ( Alexander and Arblaster, 2017; Herold et al., 2018; Grose et al., 2020) and New Zealand ( MfE, 2018) where projected dynamic contributions are small (Box 11.1 Figure 1; Pfahl et al., 2017) and medium confidence on increases over central, eastern, and Southern Australia where dynamic contributions are substantial and can affect local phenomena (CSIRO and BOM, 2015; Pepler et al., 2016; Bell et al., 2019; Dowdy et al., 2019).

In Central and South America (Table 11.14), extreme precipitation will likely increase at global warming levels of 2°C and below, but very likely increase at higher warming levels for the region as whole. A larger increase in global surface temperature leads to a larger increase in extreme precipitation, independent of emissions scenarios ( Li et al., 2021). But there are regional differences in the projection, and projected changes for more moderate extreme precipitation are also more uncertain. Extreme precipitation, represented by the number of days with daily precipitation exceeding 50 mm and the annual fraction of precipitation falling during days with the top 10% daily precipitation amount, is projected to increase on the eastern coast of Southern Central America, but to decrease along the Pacific coasts of El Salvador and Guatemala ( Imbach et al., 2018). Chouet al. (2014b) and Giorgi et al. (2014) projected an increase in extreme precipitation over South-Eastern South America and the Amazon. Projected changes in moderate extreme precipitation represented by the 99th percentile of daily precipitation by different models under different emissions scenarios, even at high warming levels, are mixed: increases are projected for all regions by the CORDEX-CORE and CMIP5 simulations, while increases for some regions and decreases for other regions are projected by CMIP6 simulations ( Coppola et al., 2021a). Extreme precipitation is projected to increase in the La Plata basin ( Cavalcanti et al., 2015; Carril et al., 2016). Taylor et al. (2018) projected a decrease in days with intense rainfall in the Caribbean under 2°C global warming by the 2050s under RCP4.5 relative to 1971-2000.

In North America (Table 11.20), the intensity and frequency of extreme precipitation will likely increase at the global warming levels of 2°C and below, and very likely increase at higher warming levels. An increase is projected by CMIP6 model simulations ( Li et al., 2021) and by previous model generations (Wu,2015; Easterling et al., 2017; Innocenti et al., 2019), as well as by RCMs (Coppola et al., 2021a). Projections of extreme precipitation over the southern portion of the continent and over Mexico are more uncertain, with decreases possible ( Sillmann et al., 2013b; Alexandru, 2018; Coppola et al., 2021a).

In summary, heavy precipitation will generally become more frequent and more intense with additional global warming. At global warming levels of 4°C relative to the pre-industrial, very rare (e.g., one in 10 or more years) heavy precipitation events would become more frequent and more intense than in the recent past, on the global scale ( virtually certain), and in all continents and AR6 regions: The increase in frequency and intensity is extremely likely for most continents and very likely for most AR6 regions. The likelihood is lower at lower global warming levels and for less-rare heavy precipitation events. At the global scale, the intensification of heavy precipitation will follow the rate of increase in the maximum amount of moisture that the atmosphere can hold as it warms ( high confidence), of about 7% per 1°C of global warming. The increase in the frequency of heavy precipitation events will be non-linear with more warming and will be higher for rarer events ( high confidence), with 10- and 50-year events to be approximately double and triple, respectively, at the 4°C warming level. Increases in the intensity of extreme precipitation events at regional scales will depend on the amount of regional warming as well as changes in atmospheric circulation and storm dynamics leading to regional differences in the rate of heavy precipitation changes ( hi gh confidence).

Floods are the inundation of normally dry land, and are classified into types (e.g., pluvial floods, flash floods, river floods, groundwater floods, surge floods, coastal floods) depending on the space and time scales and the major factors and processes involved ( Section 8.2.3.2; Nied et al., 2014; Aerts et al., 2018). Flooded area is difficult to measure or quantify and, for this reason, many of the existing studies on changes in floods focus on streamflow. Thus, this section assesses changes in flow as a proxy for river floods, in addition to some types of flash floods. Pluvial and urban floods - types of flash floods resulting from the precipitation intensity exceeding the capacity of natural and artificial drainage systems - are directly linked to extreme precipitation. Because of this link, changes in extreme precipitation are the main proxy for inferring changes in pluvial and urban floods (see also Section 12.4), assuming there is no additional change in the surface condition. Changes in these types of floods are not assessed in this section, but can be inferred from the assessment of changes in heavy precipitation in Section 11.4. Coastal floods due to extreme sea levels and flood changes at regional scales are assessed in Section 12.4.

Since AR5, the number of studies on understanding how floods may have changed, and will change in the future, has substantially increased. Floods are a complex interplay of hydrology, climate, and human management, and the relative importance of these factors varies for different flood types and regions.

In regions with a seasonal snow cover, snowmelt is the main cause of extreme river flooding over large areas ( Pall et al., 2019). Extensive snowmelt combined with heavy and/or long-duration precipitation can cause significant floods (D. Li et al., 2019; Krug et al., 2020). Changes in floods in these regions can be uncertain because of the compounding and competing effects of the responses of snow and rain to warming that affect snowpack size: warming results in an increase in precipitation, but also a reduction in the time period of snowfall accumulation ( Teufel et al., 2019). An increase in atmospheric CO ₂ enhances water-use efficiency by plants ( Roderick et al., 2015; Milly and Dunne, 2016; Swann et al., 2016; Swann, 2018); this could reduce evapotranspiration and contribute to the maintenance of soil moisture and streamflow levels under enhanced atmospheric CO ₂ concentrations ( Yang et al., 2019). This mechanism would suggest an increase in the magnitude of some floods in the future ( Kooperman et al., 2018). But this effect is uncertain as an increase in leaf area index, and vegetation coverage could also result in overall larger water consumption ( Mátyás and Sun, 2014; Mankin et al., 2019; Teuling et al., 2019), and there are also other CO ₂ -related mechanisms that come into play (Cross-Chapter Box 5.1).

Various factors, such as extreme precipitation ( Cho et al., 2016; Archer and Fowler, 2018), glacier lake outbursts ( Schneider et al., 2014; Schwanghart et al., 2016), or dam breaks ( Biscarini et al., 2016) can cause flash floods. Very intense rainfall, along with a high fraction of impervious surfaces can result in flash floods in urban areas ( Hettiarachchi et al., 2018). Because of this direct connection, changes in very intense precipitation can translate to changes in urban flood potential ( Rosenzweig et al., 2018), though there can be a spectrum of urban flood responses to this flood potential ( Smith et al., 2013), as many factors, such as the overland flow rate and the design of urban ( Falconer et al., 2009) and storm water drainage systems ( Maksimović et al., 2009), can play an important role. Nevertheless, changes in extreme precipitation are the main proxy for inferring changes in some types of flash floods, (which are addressed in Section 12.4), given the relation between extreme precipitation and pluvial floods, the very limited literature on urban and pluvial floods (e.g., Skougaard Kaspersen et al., 2017), and limitations of existing methodologies for assessing changes in floods ( Archer et al., 2016).

The SREX ( Seneviratne et al., 2012) assessed low confidence for observed changes in the magnitude or frequency of floods at the global scale. This assessment was confirmed by AR5 ( Hartmann et al., 2013). The SR1.5 ( Hoegh-Guldberg et al., 2018) found increases in flood frequency and extreme streamflow in some regions, but decreases in other regions. While the number of studies on flood trends has increased since AR5, and there were also new analyses after the release of SR1.5 ( Berghuijs et al., 2017; Blöschl et al., 2019; Gudmundsson et al., 2019), hydrological literature on observed flood changes is heterogeneous, focusing at regional and sub-regional basin scales, making it difficult to synthesize at the global and sometimes regional scales. The vast majority of studies focus on river floods using streamflow as a proxy, with limited attention to urban floods. Streamflow measurements are not evenly distributed over space, with gaps in spatial coverage, and their coverage in many regions of Africa, South America, and parts of Asia is poor (e.g., Do et al., 2017), leading to difficulties in detecting long-term changes in floods ( Slater and Villarini, 2017). See also Section 8.3.1.5.

Peak flow trends are characterized by high regional variability and lack overall statistical significance of a decrease or an increase over the globe as a whole.

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Chapter 11: Weather and Climate Extreme Events in a Changing Climate

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Decline of ice sheets in the Arctic and Greenland regions

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Decline of ice sheets in the Arctic and Greenland regions

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https://www.climate.gov/news-features/understanding-climate/2023-arctic-report-card-image-highlights#:~:text=The%20average%20surface%20air%20temperature,during%20the%20last%2017%20years.

https://nsidc.org/ice-sheets-today

https://www.theguardian.com/australia-news/2024/jan/21/antarctica-melting-ice-shelf-sea-cover-professor-matt-king

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Climate Change: Vital Signs of the Planet

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Data from NASA's GRACE and GRACE Follow-On satellites show that the land ice sheets in both Antarctica (upper chart) and Greenland (lower chart) have been losing mass since 2002.

The GRACE mission ended in June 2017. The GRACE Follow-On mission began collecting data in June 2018 and is continuing to monitor both ice sheets. This record includes new data-processing methods and is continually updated as more numbers come in, with a delay of up to two months.

This is important because the ice sheets of Greenland and Antarctica store about two-thirds of all the fresh water on Earth. They are losing ice due to the ongoing warming of Earth's surface and ocean. Meltwater coming from these ice sheets is responsible for about one-third of the global average rise in sea level since 1993.

Note: You now need to create an Earthdata account to access NASA's ice sheet data. Register here for free. Once logged in, click "HTTP" under the charts on this page to access the data.

Missions That Observe Land Ice
Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) NASA's IceBridge

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Vital Signs of the Planet: Global Climate Change and Global Warming. Current news and data streams about global warming and climate change from NASA.

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Increase in frequency and severity of bushfires

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Increase in frequency and severity of bushfires

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https://earthobservatory.nasa.gov/global-maps/MOD14A1_M_FIRE

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"When the weather is hot, humidity (moisture in the atmosphere) is low and there's been little recent rain, vegetation dries out and becomes more flammable. Periods of wet weather can encourage vegetation growth, increasing the amount of fuel available-and future bushfire risk, if dry weather follows.

Strong, gusty winds help fan flames and will cause a fire to spread faster across the landscape, reducing the time you have to prepare.

Above the fire, strong winds can carry hot embers long distances. These can start 'spot' fires many kilometres ahead of the main fire front.

A change in wind direction can bring a dangerous period of bushfire activity. This is often seen as a trough or cold front (also known as a cool change) shifting the direction of the wind, altering the course of the fire and broadening the fire front.

Lightning, produced by thunderstorms, can ignite bushfires. Large fires can also create their own thunderstorms. These are known as pyrocumulonimbus, and are caused by the heat of the fire forcing the smoke and hot air above it to rise to the point where it cools and condenses into water droplets, eventually forming large clouds."

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Bushfire | Australian Disaster Resilience Knowledge Hub

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A bushfire is an unplanned vegetation fire. It is a generic term that includes grass fires, forest fires and scrub fires. Bushfires are a natural, essential and complex part of the Australian environment and have been for thousands of years. Bushfires can significantly impact on lives, property and the environment.

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Less water available in dry areas such as northern Africa

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Less water available in dry areas such as northern Africa

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https://water.org/our-impact/where-we-work/kenya/

https://www.oxfam.org/en/press-releases/one-five-persons-dont-have-enough-water-drought-stricken-east-africa
https://arabcenterdc.org/resource/confronting-water-scarcity-in-north-africa/
https://www.globalcitizen.org/en/content/water-scarcity-in-africa-explainer-what-to-know/

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The 2030 Agenda for Sustainable Development commits UN member states to take bold and transformative steps to 'shift the world onto a sustainable and resilient path', 'realize the human rights of all', 'end poverty in all its forms', and ensure 'no one will be left behind'. Since 2000, billions of people have gained access to drinking water, sanitation and hygiene services, but many countries still have a long way to go to fully realize the Sustainable Development Goal (SDG) ambition to achieve 'universal' access 'for all'. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) report Progress on household drinking water, sanitation and hygiene assesses current rates of progress and the prospects for achieving SDG targets by 2030.

Five years into the SDGs, the world is not on track to achieve SDG targets 6.1 and 6.2. Achieving universal coverage by 2030 will require a quadrupling of current rates of progress in safely managed drinking water services, safely managed sanitation services, and basic hygiene. Least Developed Countries have the furthest to go and it will be especially challenging to accelerate progress in fragile contexts. Many more countries are facing challenges in extending services to rural areas and to poor and vulnerable populations who are most at risk of being left behind.
Download the report Download the data
1 in 4 people or 2 billion people around the world lack safe drinking water

In 2020, 74 per cent of the global population used safely managed drinking water services. National estimates were available for 138 countries and four out of eight SDG regions, representing 45 per cent of the global population. Coverage was lower in rural areas (60 per cent) than in urban areas (86 per cent), which were home to two out of three of the 5.8 billion people using safely managed services. By 2020 a total of 84 countries had achieved universal (>99 per cent) coverage of at least basic drinking water services.

Nearly half of the global population or 3.6 billion people lack safe sanitation

In 2020, 54 per cent of the global population (4.2 billion people) used safely managed sanitation services. National estimates were available for 120 countries and seven out of eight SDG regions, representing 81 per cent of the global population. Coverage was higher in urban areas (62 per cent) than in rural areas (44 per cent), and two thirds of the population using safely managed services lived in urban areas. Two thirds of those who still lacked even basic services lived in rural areas, nearly half lived in sub-Saharan Africa.

494 million people around the world still practise open defecation

The number of people practising open defecation globally decreased by 245 million between 2015 and 2020, with countries in Central and South Asia accounting for four out of five (196 million). Eastern and South Eastern Asia and Latin America and the Caribbean recorded reductions of 24 million and 10 million respectively. While most regions are on track to eliminate open defecation by 2030, sub-Saharan Africa is progressing too slowly and open defecation rates in Oceania have increased.

3 out of 10 people or 2.3 billion people around the world lack basic hygiene services

In 2020, 71 per cent of the global population (5.5 billion) had a basic handwashing facility with soap and water available at home. Another 21 per cent (1.6 billion) had handwashing facilities which lacked water or soap at the time of the survey, and 9 per cent (670 million) had no handwashing facility at all. Handwashing estimates were available for only four out of eight SDG regions and for 79 countries, representing 50 per cent of the global population, but few data were available for high-income countries.

Disaggregated data reveal huge disparities in service between and within countries

The SDG targets have an explicit focus on reducing inequalities but national, regional and global averages often mask significant inequalities in service levels between and within countries. For example, while three out of four people worldwide used safely managed drinking water services in 2020, regional coverage ranged from 96 per cent in Europe and Northern America to just 30 per cent in sub-Saharan Africa. Inequalities were even more pronounced among the 21 countries in sub-Saharan Africa, with national estimates ranging from 94 per cent in Reunion to just 6 per cent in Chad. The national average for Chad also masks a big gap in coverage between rural areas and urban areas, which were nine times more likely to have safely managed drinking water in 2020.

The Chad 2019 Multiple Indicator Cluster Survey (MICS) enables further analysis of sub-national inequalities in accessibility on premises, availability when needed, and drinking water quality (free from contamination). Disparities between the richest and poorest were relatively small for availability and quality but much larger for accessibility, with 42 per cent of the richest and just 4 per cent of the poorest having improved sources accessible on premises. There were also large disparities in accessibility between sub-national regions, ranging from 69 per cent on premises in Borkou to just 2 per cent in Lac.

Achieving SDG targets in fragile contexts will require a dramatic acceleration in current rates of progress

Fragility poses a major threat to the achievement of the SDGs. Fragile contexts were home to 23 per cent of the world's population (1.8 billion people) and more than three quarters of those living in extreme poverty in 2020. People living in fragile contexts often have much lower WASH service levels and will need to be a major focus of efforts to accelerate progress towards the 2030 SDG targets.

Over the first five years of the SDG period, basic drinking water coverage in fragile contexts has increased from 71 per cent in 2015 to 74 per cent in 2020. However, achieving universal access to basic drinking water will require a 4x increase in current rates of progress, while achieving universal access to safely managed drinking water will require a 23x increase. Fragile contexts have reduced open defecation from 16 per cent in 2015 to 13 per cent in 2020, but rates of progress will need to double to achieve elimination by 2030. Achieving universal access to basic sanitation will require a 7x increase, and achieving universal access to safely managed services will require a 9x increase. Coverage of basic hygiene services has increased from 43 per cent in 2015 to 48 per cent in 2020, but at current rates of progress only 58 per cent of the population living in fragile contexts will have access by 2030. Achieving the SDG target for hygiene in fragile contexts will require a 5x increase in current rates of progress.

New data show that many women and girls do not have the services they need for menstrual health

SDG target 6.2 calls for 'special attention to the needs of women and girls' and WASH programmes are increasingly monitoring menstrual health related needs. The JMP has expanded its global database to include emerging national data on menstrual health. New indicators related to menstrual health and associated WASH service needs have been progressively included in household survey questionnaires for women and girls age 15 to 49, and can be grouped as follows:

Awareness of menstruation before menarche (first menstruation).

Use of menstrual materials to capture and contain menstrual blood, such as pads, cloths, tampons or cups. These can also be grouped into single-use and reusable materials.

Access to a private place to wash and change while at home.

Participation in activities during menstruation, such as school, work and social activities.

National data on the above menstrual health indicators have been collected from 42 countries. In many countries a significant proportion of women and girls do not have the services they need for menstrual health and there are often substantial disparities between population sub-groups, particularly between sub-national regions and for women and girls with and without disabilities.
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Progress on household drinking water, sanitation and hygiene, 2000-2020: Five years into the SDGs - UNICEF DATA

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The 2030 Agenda for Sustainable Development commits UN member states to take bold and transformative steps to 'shift the world onto a sustainable and resilient path', 'realize the human rights of all', 'end poverty in all its forms', and ensure 'no one will be left behind'.

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https://www.bas.ac.uk/science/our-research/topics/climate-climate-change/

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This page contains archived content and is no longer being updated. At the time of publication, it represented the best available science.

Relatively few people have seen Antarctica. A myth until the early 1800s, the ice-covered southern continent is difficult to get to and hostile to most life. But now, the MODIS Rapid Response System at NASA's Goddard Space Flight Center is producing daily photo-like images of Antarctica. The images are valuable to scientists studying everything from ice to penguins, as well as to the crews of ships navigating through the southern ice pack.

The Moderate Resolution Imaging Spectroradiometer ( MODIS) on NASA's Aqua satellite captured this composite image on January 27, 2009. Few features are visible on the ice-covered landscape. The surface appears rough where the Transantarctic Mountains curve in a shallow "s" from the shore of the Ross Sea to the Ronne Ice Shelf. The Polar Plateau in the center of the continent is smooth, shaded only by the faint shadow cast by clouds. The Weddell Sea is textured with chunks of sea ice.

Antarctica was enjoying summer when this image was taken. During the summer, sunlight shines on the continent constantly. Winter is dark. For this reason, MODIS can only image Antarctica during the summer. Flying on both the Aqua and Terra satellites, MODIS orbits the Earth from north to south, passing over Antarctica many times every day. This mosaic image of Antarctica was created from data collected on various overpasses throughout the day. Each overpass is a pie-shaped wedge in the image.

More information about the daily mosaic is available from the MODIS Rapid Response System's Antarctica Project Page.
NASA image courtesy the MODIS Rapid Response Team at NASA GSFC. Caption by Holli Riebeek.

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Relatively few people have seen Antarctica. But now, the MODIS Rapid Response System is producing daily photo-like images of Antarctica.

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Coral bleaching due to ocean acidification

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Coral bleaching due to ocean acidification

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https://www.climate.gov/news-features/featured-images/no-safe-haven-coral-combined-impacts-warming-and-ocean-acidification
https://www.uq.edu.au/news/article/2023/12/coral-reefs-peril-record-breaking-ocean-heat

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Changes in animal behaviour such as earlier migration and egg-laying for birds

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Changes in animal behaviour such as earlier migration and egg-laying for birds

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https://www.abc.net.au/news/2023-11-12/nt-whales-migration-delayed-timor-leste/103076920?utm_campaign=abc_news_web&utm_content=link&utm_medium=content_shared&utm_source=abc_news_web
https://www.nationalgeographic.com/environment/article/paid-content-migration-and-movement-in-the-anthropocene
https://www.worldwildlife.org/magazine/issues/fall-2015/articles/animals-affected-by-climate-change
https://www.weforum.org/agenda/2023/10/climate-crisis-impacting-animal-migration/