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ATTRIBUTION | August 4. 2022. 16:30

In the early 2000s, a new field of climate-science research emerged that began to explore the human fingerprint on extreme weather, such as floods, heatwaves, droughts and storms.

Known as "extreme event attribution", the field has gained momentum, not only in the science world, but also in the media and public imagination. These studies have the power to link the seemingly abstract concept of climate change with personal and tangible experiences of the weather.

Scientists have published more than 400 peer-reviewed studies looking at weather extremes around the world, from wildfires in the US and heatwaves in India and Pakistan to typhoons in Asia and record-breaking rainfall in the UK. The result is mounting evidence that human activity is raising the risk of some types of extreme weather, especially those linked to heat.

To track how the evidence on this fast-moving topic is stacking up, Carbon Brief has mapped - to the best of our knowledge - every extreme-weather attribution study published to date.

Carbon Brief's analysis reveals:

71% of the 504 extreme weather events and trends included in the map were found to be made more likely or more severe by human-caused climate change.

9% of events or trends were made less likely or less severe by climate change, meaning 80% of all events experienced some human impact. The remaining 20% of events and trends showed no discernible human influence or were inconclusive.

Of the 152 extreme heat events that have been assessed by scientists, 93% found that climate change made the event or trend more likely or more severe.

For the 126 rainfall or flooding events studied, 56% found human activity had made the event more likely or more severe. For the 81 drought events studied, it's 68%.

First published in July 2017, this article is the fifth annual update (see endnote) to incorporate new studies. The aim is that it serves as a tracker for the evolving field of "extreme event attribution".

Using the map

The map above shows 504 extreme weather events and trends across the globe for which scientists have carried out attribution studies. The different symbols show the type of extreme weather; for example, a heatwave, flood or drought. The colours indicate whether the attribution study found a link to human-caused climate change (red), no link (blue) or was inconclusive (grey).

How to use our map of attribution studies.

Use the plus and minus buttons in the top-left corner, or double click anywhere, to zoom in on any part of the world. Click on a symbol to reveal more information, including a quote from the original paper to summarise the findings and a link to the online version.

The filter on the left allows users to select a specific type of weather event to look at or, for example, only those found to be influenced by climate change.

The filter can also be used to highlight extreme events from a particular year. (Note: earlier versions of this map classified events by the year that the study or analysis was published.) To isolate studies that assess the changing trends of weather extremes, click the "trend" box in the filter.

The software used to make the map currently only works with a Web Mercator projection (as used by virtually all major online map providers). It is worth noting that this - like all map projections - offers a somewhat distorted view of the world.

It is important to note that the weather events scientists have studied so far are not randomly chosen. They can be high-profile events, such as Hurricane Harvey, or simply the events that occurred nearest to scientific research centres. (More on this below.)

The map includes three different types of studies. The circles and hexagons on the map indicate papers published in peer-reviewed journals. The different shapes refer to whether the study considers an individual extreme event (circles), such as a wildfire or storm, or whether it analyses longer-term trends in extreme weather (hexagons), such as the change in frequency of flooding or marine heatwaves over time.

The third shape - triangles - indicate rapid attribution studies. These are quickfire assessments of the climate change contribution to extreme weather events, published online shortly after an event concludes. (More on this below.)

Finally, it is worth noting that some of the icon locations are approximate - particularly for studies that cover large regions. For example, global studies can be found grouped together in the middle of the Atlantic Ocean.

Extreme weather types

The events and trends shown on the map are covered by 431 individual scientific papers or rapid studies. Where a single study covers multiple events or locations, these have been separated out into individual entries on the map.

Combining the evidence over the past 20 years, the literature is heavily dominated by studies of extreme heat (30%), rainfall or flooding (25%) and drought (16%). Together, these make up more than two-thirds of all published studies (71%). The full list is available in this Google sheet.

As the chart below shows, the number of extreme events studied has grown substantially over the past 10-15 years. Note that formal studies typically follow a year or so after the event itself as the writing and peer-review process for journal papers can take many months.

The majority of studies included here have been published in the annual "Explaining extreme events" special issues of the Bulletin of the American Meteorological Society ( BAMS). Each bumper volume typically contains around 15-30 peer-reviewed studies of events from the previous year. Other studies have been found through the Climate Signals database and online searches through journals. This update includes studies published up to the end of May 2022.

(Note: The map currently only includes studies published in English.)

Specific types of event can be displayed in the chart below by clicking on the category names at the top.

Number of attribution studies by extreme weather event type and year. Note: the total number of events dipped in 2017 because the Bulletin of the American Meteorological Society special report for that year was published in early 2018 rather than late 2017. Chart by Carbon Brief using Highcharts.

Most of the categories of extreme weather are self-explanatory, but "storms" and "oceans" require a bit of explanation.

For this latest iteration of the map, a new category of "compound" extreme events has also been added. This includes, for example, a 2021 study that found climate change had contributed to the "high likelihood" of combined dry and hot events in recent decades over most of China.

A man wades through a flooded road in Madagascar, after tropical cyclone Batsirai, Feb 2022. Credit: Xinhua / Alamy Stock Photo.

Such studies show that attribution studies are increasingly considering the impacts of extremes, rather than focusing purely on the weather event.

This shift towards impacts "is quite significant", says Prof Peter Stott, who leads the climate monitoring and attribution team at the Met Office Hadley Centre and has been a co-editor of the BAMS reports since they began in 2012. He tells Carbon Brief:

"Impacts are hard to do because you have to establish a significant link between the meteorology and the impact in question. As editors, we've been trying to encourage more studies on impacts because it's the impacts rather than the meteorology per se that tends to motivate these types of study - and if we only have the attribution on the meteorological event then we only have an indirect link to the relevant impact."

Attribution of climate impacts could even be used in the courts, one 2021 study explained. The authors wrote a Carbon Brief guest post explaining how attribution science can be "translated into legal causality". They wrote:

"Attribution can bridge the gap identified by judges between a general understanding that human-induced climate change has many negative impacts and providing concrete evidence of the role of climate change at a specific location for a specific extreme event that already has led or will lead to damages."

Human influence on extreme weather

Of the attribution studies included here, scientists found that human-caused climate change has altered the likelihood or severity of an extreme weather event in 80% of cases studied (71% made more severe or likely and 9% made less so).

In Carbon Brief's first edition of this analysis in 2017, 68% of events were found to have a human impact (with 63% made more severe or likely and 6% less so).

While these figures are not representative of all extreme weather events - attribution studies have only been conducted on a relatively small number - previous research has taken this broader view. For example, a 2015 study estimated the fraction of all globally occurring heat and heavy rainfall extremes that was attributable to warming. The authors found that around 75% of "moderate daily hot extremes over land" and 18% of "moderate daily precipitation extremes over land" were attributable to the observed temperature increase since pre-industrial times. These fractions are expected to increase with further warming, the authors noted.

Note that events are classified here as having an human impact if climate change is found to have influenced at least one aspect of that event. For example, a study of the 2011 East Africa drought found that climate change contributed to the failure of the "long rains" in early 2011, but that the lack of "short rains" in late 2010 was down to the climate phenomenon La Niña. This event is, thus, designated as having a human impact.

For the majority of events affected by climate change, the balance has shifted in the same direction. That is, rising temperatures made the event in question more severe or more likely to occur. These events are represented by the red in the chart below. Clicking on the red "slice" reveals that heatwaves account for 40% of such events, rainfall or flooding for 20%, and droughts for 15%. Return to the original chart, and do the same with the other slices to see the proportion of different weather types in each category.

Pie chart showing the proportion of extreme events/studies that were found to have been made more likely/severe by climate change (red segment), less likely/severe (orange), had no link identified (blue) and were inconclusive or lacked sufficient data (grey). Clicking on a segment reveals the makeup of different types of extremes within that category. Chart by Carbon Brief using Highcharts.

In 11% of extreme weather events and trends studied, scientists found no discernible influence from human activity. These are coloured blue in the map and the chart above. For a further 9%, the observational data or modelling techniques used in the study were insufficient to reach a reliable conclusion (shown as grey in the map and pie chart).

In 9% of studied weather events and trends, scientists found climate change had made the event less likely or less severe (pale orange in the chart above).

Interestingly, a 2020 study analysed the way links between climate change and the California drought were portrayed in US media. It finds that the links were "covered widely in both local and national news", but notes:

"However, legitimate differences in the methods underpinning the attribution studies performed by different researchers often resulted in a frame of scientific uncertainty or disagreement in the media coverage."

As the case of California's drought shows, it is often necessary to dig deeper to understand the full picture. The rest of this article looks at the evidence for the three most-studied types of extreme weather - heatwaves, heavy rain and floods, and droughts - as well as some of the main issues in event attribution, and where the field as a whole is heading.

Heatwaves

The attribution map includes studies of 152 extreme heat events, of which 142 (93%) have been made more likely or more severe because of climate change. No studies have found a heatwave that has been made less severe by climate change, while studies of two events (1%) identified no influence and a further eight (5%) were inconclusive.

A volunteer pours water on a pedestrian during a heatwave in southern Pakistan, May 2022. Credit: Xinhua / Alamy Stock Photo.

The studies on extreme heat that did not find a role for climate change were an analysis of the Russian heatwave in 2010 and a rapid attribution study of the all-time high temperatures recorded in Rajasthan, India in May 2016. For the latter, the authors suggested that "the lack of a detectable trend may be due to the masking effect of aerosols on global warming and increased use of irrigation".

A particularly well-studied region for heatwaves in the literature is Australia, which accounts for 10% of the heat-related events included here. And climate change was found to play a role in all but one of the 15 Australian heat events studied. It is worth noting for that one event, however, that although the study (pdf, p145) was inconclusive for the city of Melbourne in south-east Australia, the authors did detect a human influence on extreme heat up the coast in Adelaide.

This raises a few important points. First, finding that climate change contributed to an event is not the same as saying it "caused" that event. Attribution is about working out if the likelihood or magnitude of a particular event happening now is different from what it would be in a world that was not warming.

A useful analogy - as explained in the first BAMS report in 2012 - is of a baseball player who starts taking steroids. If the player begins hitting 20% more home runs than before, it would not be possible to say for sure whether a particular home run is because of the steroids or the player's spontaneous skill. But it is possible to say how the steroids have altered the likelihood that the player hits a home run, by comparing their current and historical performances. As the report put it:

"Given that steroids have resulted in a 20% increased chance that any particular swing of the player's bat results in a home run, you would be able to make an attribution statement that, all other things being equal, steroid use had increased the probability of that particular occurrence by 20%."

Another important point is that in cases where attribution science finds that climate change is making a given type of extreme weather more likely, it does not necessarily follow that the chance of experiencing that kind of weather gets incrementally higher each year. Natural variability means that there will still be ups and downs in the strength and frequency of extreme events.

Finally, there is usually a level of confidence attached to attribution results. So, while two studies might both find a role for human influence in a given weather event, the signal may be stronger for one than the other. For the purposes of this analysis, the attribution map does not distinguish between high- and low-confidence results, but users can click through to each study for more details.

Heavy rain and flooding

Of the 126 rainfall or flooding events included in the attribution map, 71 (56%) found human activity had made the event more likely or more severe - a far smaller proportion than for heat-related studies. Nineteen studies (15%) found that climate change had made the whole event less likely to occur. Of the remaining heavy rainfall events, studies of 24 (19%) found no evidence of a link to climate change, while 12 (10%) were inconclusive.

That there is a more divided set of results for extreme rainfall than for heatwaves could suggest several things. In some cases, limited data might make it difficult to detect a clear "signal" of climate change above the "noise" of weather considered normal for a particular region. In other cases, an inconclusive result could reflect the fact that rainfall or flooding events are inherently more complex than heatwaves, with many ways for natural variability to play a role. Human factors, such as land use and drainage, also play a part in whether heavy rain leads to flooding.

Volunteers help move sandbags to protect a home from rising flood waters near Ottawa Canada, during 2019 floods. Credit: Colin Clarke / Alamy Stock Photo.

Take the UK, for example. While one study found climate change had increased the risk of floods in England and Wales in Autumn 2000 by at least 20% (and even up to 90%), another found little influence on summer rainfall in 2012 (pdf, p36).

This raises another important point. When it comes to interpreting the results of event attribution studies, it matters what the question is. For example, a 2013 study asked whether recent wet summers in north-western Europe were a response to retreating Arctic sea ice (pdf, p32). The answer from the study was "no". But, as a foreword from that year's BAMs report explains:

"Given the numerous ways climate change could influence precipitation in this region, a 'no' result for the role of Arctic sea ice should not be interpreted as an absence of any role at all for climate change."

The paper notes that, in a chaotic weather system, the complex dynamics of the atmosphere mean the size and path of a storm or heavy rainfall event has a large element of chance. This can make it tricky to identify where climate change fits in, potentially underestimating its influence.

Therefore, rather than analysing the weather patterns that bring a storm to an area, the authors argue that scientists should be looking at how the impact of that storm has been boosted by temperature changes - known as "thermodynamic" effects. Higher temperatures mean warmer seas, higher sea levels and more moisture evaporating into the atmosphere. These are changes that scientists can be more confident in, the authors wrote, and so should be the focus for attribution studies - rather than looking at changes to circulation patterns in the atmosphere.

For example, the paper reexamines an earlier study (pdf, p15) that suggested climate change had reduced the chances of the five-day heavy rainfall event that hit north-east Colorado in September 2013. Trenberth and colleagues argue that while climate change might not have made the specific weather system that brought the rain more likely, it will have contributed to the sheer volume of moisture in the atmosphere.

Chart shows the number of studies for each type of extreme event that fall within each category of human influence: More severe/likely (red), less severe/likely (yellow), no influence (blue) and inconclusive (grey). Chart by Carbon Brief using Highcharts.

While attribution studies of heatwaves are generally more straightforward than storms - as they focus on thermodynamic influences - the type of question they are asking is still important. The Russian heatwave in 2010 is a good example of this. One study looking at the severity of the event did not find a role for climate change. Yet another one, which did find an influence, looked at the likelihood of the event.

This apparent contradiction is tackled by a third study that reconciles the other two. It explains that "the same event can be both mostly internally-generated [i.e. by natural variability] in terms of magnitude and mostly externally-driven [i.e. by human-caused climate change] in terms of occurrence probability".

Otto, who is lead author of the third study, tells Carbon Brief:

"The studies, thus, only appear to be contradictory, but are, in fact, complementary."

It is also important to stress that the absence of evidence for a link to climate change is not the same as evidence of absence. In other words, it does not necessarily mean there was no human influence, just that a particular analysis did not find one. This is why a single study should never be considered the final word on how climate change influences a given type of extreme weather.

Drought

Of the 81 drought events and trends considered here, climate change was found to have increased the severity or likelihood of 55 (68%). Of the remainder, the likelihood or severity was reduced for one event (1%), while no discernible link with human activity was found for 15 (19%) and 10 (12%) were inconclusive.

This mixed bag of results reflects the inherent complexity of droughts. And, again, the specific question matters. Conclusions about the role of climate change in a specific drought could depend on whether a study looks at temperature, rainfall or soil moisture, for example.

As the 2015 BAMS report explains:

"Drought continues to be an event type where the results require significant context, and easy answers often remain elusive because of the many meteorological, hydrological, and societal drivers that combine to cause drought."

(For more on the different ways that droughts can be categorised, see this Carbon Brief guest post from 2018.)

Geographical reach

While much has been achieved in the field of extreme event attribution in a short space of time, scientists are constantly looking for ways to tailor their work to suit the people who might use it.

One major goal since the early days of the field has been to expand extreme event attribution to cover a larger and more diverse geographical area.

Where in the world scientists can carry out attribution studies - and for what kind of events - will always be limited by the quality and availability of observed data and appropriate models. The attribution map highlights, for example, that there are relatively few studies of extreme weather in Africa and South America.

In another example, scientists hoping to analyse Super Typhoon Mangkhut - which hit the Philippines in September 2018 - were unable to in part because of "very poor quality" observed data in publicly available datasets and a lack of models.

At the moment, there is also a heavy leaning towards weather events that are local to the modelling groups, or that have a particular scientific interest. Otto explains:

"For example, scientists often do attribution studies because an event occurs on their doorstep. The UK, California and Boulder [in Colorado] are, therefore, studied much more than other parts of the world, but that does not necessarily make them places particularly impacted by climate change."

This means that while the studies carried out so far are indicative of the role climate change is playing in extreme weather around the world, they should not be considered representative of all types of extreme weather everywhere, says Otto. She tells Carbon Brief:

"[The studies so far] are part of a picture, but we don't know what's on the missing puzzle pieces. And, crucially, we don't know how many pieces are missing."

For example, Otto recently penned a Carbon Brief guest post on how the lack of monitoring of heatwaves in Africa means they are a "forgotten impact" of climate change.

Real-time extreme weather attribution

As well as expanding the science to cover different types of weather and more of the world, scientists are getting faster at turning the handle on extreme event attribution studies - sometimes crunching the numbers just days after an event has occurred.

The rapid studies included here are all produced by the World Weather Attribution (WWA) initiative, described earlier, or the UK Met Office.

An example of analysis performed by the latter includes their review of the UK's weather in 2020, which was published by Carbon Brief. This showed that climate change increased the likelihood of the UK's warm year by approximately a factor of 50.

While the WWA individual rapid assessments are not individually peer-reviewed, they are conducted using methods that have been through the peer-review process. As the 2014 BAMS report explains:

"Much like other routine analysis, such as an operational seasonal forecast, statements made about heat events using these methods do not necessarily need to go through the peer-reviewed literature to be considered credible."

By conducting the analysis in the immediate aftermath of a weather event, these rapid studies provide almost-real-time information on the influence of climate change, rather than having to wait many months for a formal study.

(In some cases, these rapid assessments are later published in peer-reviewed journals. In these instances, the formal study is included in the attribution map, rather than the initial analysis. In some cases, this means earlier rapid assessments are removed from the Carbon Brief map in order to add in the relevant peer-reviewed paper once it is published.)

The Trans Canada highway remains partially submerged by flood water after rainstorms lashed the western Canadian province of British Columbia in November 2021. Credit: Reuters / Alamy Stock Photo.

This collaboration between the UK Met Office, German weather service (DWD), Meteo France, Dutch weather service (KNMI) and the University of Oxford will "demonstrate how attribution of extreme weather events can be carried out operationally by national meteorological services", Prof Stott tells Carbon Brief:

"In this project we are developing and testing the protocols we will need to enable rapid and reliable assessments of the extent to which recent extreme weather events have been made more or less intense or more or less likely by climate change. We started at the beginning of the year and so far we have tested our procedures on an analysis of the 2018 heatwave in Europe."

The progress to date has "demonstrated the importance of international collaboration for developing new techniques and processes", says Stott. But "national meteorological services across Europe will continue to have a particular remit to deliver advice on weather and climate in their own countries", he adds:

"At the Met Office in the UK we are developing our operational attribution capability to help inform the public, policymakers and journalists about the extent to which damaging extreme weather events like the floods in Yorkshire [in 2019] have been affected by climate change."

An evolving science

As the science of extreme event attribution has matured and become more nuanced, so has the choice of terminology around extreme weather and climate change.

While some attest that all extreme weather must be affected by a world that is warming, this warrants some caution. As the first BAMS report in 2012 noted:

"While it has been argued that in the Anthropocene, all extreme weather or climate events that occur are altered by human influence on climate...this does not mean that climate change can be blamed for every extreme weather or climate event. After all, there has always been extreme weather."

But while it would be premature to suggest that any single study is the last word, it is clear that - in many cases - the science can do better than that. Similarly, scientific thinking has clearly moved on from the unsatisfactory statement that it is not possible to attribute any individual weather event to climate change. Indeed, as mentioned earlier, there have been some studies that have concluded that an event - or aspect of an event - would have been impossible without climate change.

Russian Federal Agency for Forestry works to put out a forest fire in Basly, Russia in August 2020. Credit: Reuters / Alamy Stock Photo.

Ultimately, there are no blanket rules in attribution. Scientists need to examine the circumstances of each individual weather event - or a longer pattern of events for trend studies. It is only by combining evidence from all around the world that they can begin to draw broader conclusions.

Attribution studies, therefore, rely heavily on the quality and availability of observational data and climate model simulations. In a short paper for the journal Weather, Dr Otto says that "the models used for attribution need to be able to reliably estimate the likelihoods of the types of events being attributed".

As discussed earlier, attribution studies of heatwaves tend to be more straightforward because of their focus on thermodynamic effects, rather than atmospheric circulation. Yet, Otto tells Carbon Brief that recent studies suggest models overestimate the year-to-year variability of heat extremes in some parts of the world, and thus underestimate the trend and the role of climate change.

In a rapid attribution study of the western Europe summer heatwave in 2019, for example, Otto and her colleagues found that, for the month of June, the models "show about 50% smaller trends than observations in this part of Europe and much higher year-to-year variability than the observations". Similarly, a study of the 2019-20 Australian bushfires noted that "models underestimate the observed trend in heat" and so the "real increase could be much higher".

These findings emphasise how important it is to analyse models and observations together, Otto says:

"This made me realise just how important attribution is for the scientific community - and everyone using climate science - at bringing observations and models together in a very concrete real-world test case.

Attribution can, therefore, be used to help scientists "identify where the models are doing well and for what they are not in a much more direct way than the classical skill assessment of climate projections does", adds Otto.

Forecasted attribution

One attempt to move attribution science forward was the very first "advance forecasted" attribution analysis, which quantified the impact of climate change on the size, rainfall, and intensity of Hurricane Florence before it made landfall in North Carolina in September 2018.

The analysis ran two sets of short-term forecasts for the hurricane: one as the climate is today and the other in a simulated world without human-caused climate change. The researchers concluded at the time:

"We find that rainfall will be significantly increased by over 50% in the heaviest precipitating parts of the storm. This increase is substantially larger than expected from thermodynamic considerations alone. We further find that the storm will remain at a high category on the Saffir-Simpson scale for a longer duration and that the storm is approximately 80km in diameter larger at landfall because of the human interference in the climate system."

The analysis received a mixed reaction. Prof Stott told Carbon Brief that it was "quite a cool idea", but would be dependent on being able to forecast such events reliably. Dr Kevin Trenberth, distinguished senior scientist at the National Center for Atmospheric Research, described it as "a bit of a disaster". He told Carbon Brief that the quality of the forecast was questionable for the assessment:

"The forecasts made were not good: the intensity of the forecast storm at landfall was category 4 or 5 as I recall, instead of category 2. And so the statements made were based on quite flawed assumptions: namely, that they had a good forecast."

A key requirement for a reliable attribution study is for models to accurately replicate the extreme event, Trenberth added, but "obviously one can not assess the goodness of the forecast if one does this in advance".

Houses in North Carolina sit in floodwater caused by Hurricane Florence. Credit: Reuters / Alamy Stock Photo.

The authors subsequently published a paper in Science Advances that "reviews the forecasted attribution with the benefit of hindsight". The findings show that climate change increased rainfall amounts "associated with the forecasted storm's core" by around 5%, and contributed to Hurricane Florence being "about 9km larger in mean maximum diameter (or a 1.6% increase in storm area) due to climate change".

The authors acknowledged that the "quantitative aspects of our forecasted attribution statements fall outside broad confidence intervals of our hindcasted statements and are quite different from the hindcasted best estimates". In short, the results are quite a way off what they forecasted.

However, the authors also said they have identified what went wrong with their forecasted analysis. Problems with the way their "without climate change" model runs were set up created a larger contrast against their real-world simulations. The results thus suggested that climate change would have a bigger impact than it actually did.

Nonetheless, the study did identify a quantifiable impact of climate change on Hurricane Florence, adding to the evidence from studies by other author groups, the researchers concluded:

"As the climate continues to warm, it is expected that extreme tropical cyclone precipitation events and resulting inland flooding will become yet more frequent."

In addition, a 2021 study of the record Australian heat event of October 2015 noted the potential of their methods "to provide attribution statements for forecast events within an outlook period". This will "allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services", the authors wrote.

On the topic of forecasts, a 2021 study showed how it was possible to use a weather forecast model for attribution. The researchers, who penned a Carbon Brief guest post about their work, tested their methods using the European heatwave of February 2019 - an event their model successfully predicted:

"We find that the direct impact of the extra carbon dioxide (CO2) that humans have pumped into the atmosphere made the event 42% more likely for the British Isles and at least 100% (two times) more likely for France."

Their work "so far represents just the first few steps towards an operational forecast-based attribution system", they noted.

Finally, as well as casting forwards, attribution can also look back in time. A 2020 study on the US " Dust Bowl " heat and drought events of the 1930s takes an unconventional approach of looking at how the past event "would behave" with present-day levels of greenhouse gases.

The researchers find that "the return period of a 1-in-100-year heatwave summer (as observed in 1936) would be reduced to about 1-in-40 years" in today's climate.

Carbon Brief will continue to add new extreme event attribution studies to the map and update the accompanying analysis every year. Please get in touch with any suggestions of attribution studies that could be included.

This is the fifth iteration of Carbon Brief's attribution map. This is the fourth iteration of Carbon Brief's attribution map. The first, second and third and fourth editions, published in July 2017, March 2019 and April 2020, respectively, are still available at the archived links included here.

Original interactive map by Rosamund Pearce for Carbon Brief. Revised and updated by Tom Prater and Joe Goodman.

Please note: International borders are determined by the mapping software, not Carbon Brief.

Update: This article was updated on 05/08/2022 to include a mention of a 2015 attribution study of all global heat and heavy rainfall extremes.

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Mapped: How climate change affects extreme weather around the world

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In the early 2000s, a new field of climate science research emerged that began to explore the human fingerprint on extreme weather, such as floods, heatwaves, droughts and storms.

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Interview with  Mark Hertsgaard, environment correspondent of The Nation magazine

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Interview with  Mark Hertsgaard, environment correspondent of The Nation magazine

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Climate change's role in the extreme weather around the world

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Science News Article on the Global Impact of Climate Change in 2022

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Science News Article on the Global Impact of Climate Change in 2022

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Extreme weather in 2022 showed the global impact of climate change

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Heat waves, floods, wildfires and drought around the world were exacerbated by Earth’s changing climate.

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Ten Major Global weather events due to Climate Change in 2021

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Ten Major Global weather events due to Climate Change in 2021

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The year 2021 made an indelible mark in the annals of weather history. Not only did it feature the most extreme heat wave in history - the late June heat wave over western North America that smashed all-time records by unprecedented margins - it also had four weather mega-disasters costing over $20 billion each, tied with 2017 for the most such disasters on record.

Figure 1 (updated 1/13/2022). Time series of global weather mega-disasters costing at least $20 billion (adjusted to 2021 dollars). Data for the U.S. is from NOAA; data for the rest of the world is from the international disaster database, EMDAT. There has been a concerning rise in these mega-disasters in recent years, as evidenced by the blue linear trend line, and 2021 had the most on record: four. Note that the database from insurance broker Aon has at least one $20+ billion-dollar event not in the EMDAT database: monsoon flooding in China that cost $35 billion in 2020.

Figure 2 (Updated 1/13/2022). Global weather mega-disasters costing at least $20 billion (adjusted to 2021 dollars) from 1980-2021, as of January 2022. Data for the U.S. is from NOAA; data for the rest of the world is from the international disaster database, EMDAT. Note that the database from insurance broker Aon has at least one $20+ billion-dollar event not in the EMDAT database: monsoon flooding in China that cost $35 billion in 2020. Aon also has a much higher cost for the 2021 European floods: $43 billion. Also note that a January 10, 2022, update from NOAA listed two more U.S. disasters that had crossed the $20 billion threshold, thanks to inflation adjustments. These disasters were not shown in the original versions of Figure 1 or Figure 2, but are shown here.

A total of eight extreme weather events were ranked in the top ten; in addition, there were two concerning climate change discoveries that may presage serious future challenges. Below is a list of the top-10 weather and climate change events of 2021, as rated by the impacts on humans and/or meteorological significance.

1. The most extreme heat wave in world history

Never in the century-plus history of world weather observation have so many all-time heat records fallen by such a large margin than in the historic late-June 2021 heat wave in western North America. The intense heat wave was the second-deadliest weather disaster of the year, with 1,037 deaths: 808 in western Canada and 229 in the northwestern U.S. The only deadlier weather disaster of 2021 was summer monsoon flooding in India that claimed 1,292 lives, according to insurance broker Aon.

Two examples of the insane extremity of the heat wave:

* Canada broke its all-time national temperature record on three consecutive days at Lytton, British Columbia, which topped out at a stunning 49.6°C (121°F) on June 29 - a day before the town burned down in a ferocious wildfire fed by the extreme heat. The old Canadian heat record was 8°F cooler, 45.0°C (113°F) on July 5, 1937.

* Quillayute, Washington, broke its all-time high by a truly astonishing 11°F, after hitting 110°F on June 29 (old record: 99°F on August 9, 1981). Quillayute is located near the lush Hoh Rain Forest on the Olympic Peninsula, just three miles from the Pacific Ocean, and receives an average of 100 inches of precipitation per year.

"This was the most anomalous regional extreme heat event to occur anywhere on Earth since temperature records began. Nothing can compare," said weather historian Christopher Burt, author of the book Extreme Weather, in an email. Pointing to Lytton, Canada, he added, "There has never been a national heat record in a country with an extensive period of record and a multitude of observation sites that was beaten by 7°F to 8°F." International weather records researcher Maximiliano Herrera (@extremetemps) agreed. "What we are seeing now is totally unprecedented worldwide," said Herrera, who tweeted on June 30, "It's an endless waterfall of records being smashed."

According to Herrera, more all-time heat records were broken by at least five degrees Celsius (9°F) during the heatwave than in the previous 85-plus years of world weather recordkeeping, going back to July 1936, when the hottest summer in U.S. history brought the previous most extreme heatwave in world history. It's worth noting that the record North American heat of the 1930s, including 1936, was largely connected to the Dust Bowl, in which the effects of a multiyear drought were amplified by over-plowed, denuded soil across the Great Plains - an example of human-induced climate change itself, albeit temporary.

A rapid-response study from the World Weather Attribution program found that the daily high temperatures observed in a study area encompassing much of western Oregon, Washington, and British Columbia during June 2021 would have been "virtually impossible without human-caused climate change." The study estimated it was roughly a 1-in-1000-year event in today's climate, but in a world with 2 degrees Celsius of global warming (0.8 degree Celsius warmer than today, which, at current emission levels, would be reached as early as the 2040s), an event like this could occur roughly every five to 10 years.

2. Hurricane Ida: fifth-costliest weather disaster in world history ($65 billion)

Hurricane Ida made landfall at Port Fourchon, Louisiana, on August 29 as a category 4 storm with 150 mph winds. Ida moved up the U.S. East Coast and unleashed a devastating flood event over much of the mid-Atlantic and Northeast. Aon estimated Ida's damages at $65 billion, making it the fifth-most expensive weather disaster in world history. NOAA's estimate was $75 billion.

3. European summer floods: costliest weather disaster in European history ($43 billion)

Europe's deadliest flood since 1985 struck western Germany and eastern Belgium July 12-18, when a stalled low-pressure system dumped torrential rains that killed 240 people and caused $43 billion in damage, according to Aon (note that EM-DAT had lower damages: $22 billion). The flood ranks as the costliest weather disaster in European history.

A rapid-response study from the World Weather Attribution program found that the likelihood of such an extreme one-day rainfall event has increased by a factor between 1.2 and 9 because of human-caused global warming.

The 6" rainfall totals over just a few hours that led to ravaging floods in west Germany may not seem that extreme by U.S. standards, but they were enormous in a landscape accustomed to more gentle rains. @CC_Yale @DrJeffMasters https://t.co/fqV714dFr8
- Bob Henson (@bhensonweather) July 21, 2021

4. Flooding in China: third-costliest weather disaster in Asian history ($30 billion)

An extreme rainfall event of nearly unimaginable intensity hit Zhengzhou, China, on July 20, which recorded an astonishing 644.6 mm (25.38 inches) of rain in 24 hours. This is literally more than a year's worth of rain: Its average annual precipitation (1981-2010 climatology) is only 640.9 mm (25.23 inches).

Flooding in China during the June-through-September rainy season killed 347 people, damaged or destroyed 1.4 million homes and businesses, and did $30 billion in damage, according to Aon and EM-DAT. EM-DAT ranks that total as the third-most expensive non-U.S. weather disaster since accurate records began in 1990 (adjusted for inflation), behind 1998 flooding in China ($48 billion) and 2011 flooding in Thailand ($47 billion).

In a September 2020 study published in the Bulletin of the American Meteorological Society, " Each 0.5°C of Warming Increases Annual Flood Losses in China by More than US$60 Billion," researchers found that annual average flood losses in China during the period 1984-2018 were $19.2 billion (2015 dollars), which was 0.5% of China's GDP. Annual flood losses increased to $25.3 billion annually during the period 2006-2018. The study authors predicted that each additional 0.5 degree Celsius of global warming will increase China flood losses by $60 billion per year.

5. February cold wave in central U.S.: second-costliest winter weather disaster in world history ($23 billion)

A disastrous winter weather onslaught over the central U.S. brought heavy snow, freezing rain, and severe cold to Texas and surrounding states February 12-20, killing 246 people and causing $23 billion in damage. One result: the most expensive winter weather disaster in U.S. history (previous record: $10.1 billion in 2021 dollars from the 1993 "Storm of the Century" in the eastern U.S.). Globally, the only costlier winter weather disaster was a $26 billion event in 2008 in China.

Extreme cold has become less common as a result of global warming, so it's reasonable to expect that disasters of this nature are growing less likely. As documented by meteorologist Guy Walton, record high maximum temperatures outpaced record low minimum temperatures by a ratio of nearly three to one in the U.S. in 2021.

Climate scientist Judah Cohen led a 2021 study demonstrating that the 2021 Texas freeze was a result of a stratospheric polar vortex disruption where it stretched like a rubber band or taffy. For the months of October through February, these stretched polar vortex events have roughly doubled since 1980. The increase has been attributed to reduced Arctic sea ice and increased snowfall across Siberia during the fall months - largely from human-caused climate change.

6. July 2021: Earth's warmest month in recorded history

July 2021 was Earth's hottest July since global record-keeping began in 1880, 0.93 degrees Celsius (1.67°F) above the 20th-century average, NOAA's National Centers for Environmental Information reported. Since July is also the hottest month of the seasonal cycle, that meant that July 2021 was "more likely than not the warmest month on record for the globe since 1880," NOAA said. July 2021 was just 0.01 degree Celsius hotter than July of 2016, 2019, and 2020, so these months can be considered to be in a statistical tie for Earth's hottest month on record.

The record July warmth was particularly remarkable since there was a moderate La Niña event in the Eastern Pacific that ended in May 2021. La Niña events typically cause global cooling of about 0.1 degree Celsius; the peak cooling occurs five months after the La Niña peak, on average. July 2021 temperatures would have been even warmer had a La Niña event not occurred earlier in the year.

During the month, Death Valley National Park's Furnace Creek Visitor Center in California (U.S.) hit an astonishing 130.0 degrees Fahrenheit (54.4°C) on July 9, beating the previous all-time world record for hottest reliably measured temperature of 129.9 degrees Fahrenheit (54.4°C), set at the same location on August 16, 2020. As explained in our post on the record, the official world record remains 134 degrees Fahrenheit at Furnace Creek in 1913, but this record has been strongly disputed as invalid.

7. Danger signs: a key Atlantic Ocean current system is near collapse

The climate over the past few thousand years has been unusually stable, helping bring about the rise of modern civilization. However, ice core studies reveal that the "normal" climate for Earth is one of frequent extreme jumps - like a light switch flicking on and off. So it is incorrect to think that global warming will lead to a slow and steady increase in temperature that humans can readily adapt to. Global warming could push the climate system past a threshold where a sudden, irreversible climate shift would occur.

That outcome would most likely happen if the increased precipitation and glacial meltwater from global warming flood the North Atlantic with enough fresh water to slow down or even halt the Atlantic Meridional Overturning Circulation (AMOC), which transports warm, salty water from the tropics to the North Atlantic and sends cold water to the south along the ocean floor. The mighty Gulf Stream current forms the portion of the AMOC that runs along the U.S. East Coast. If the AMOC were to shut down, the Gulf Stream would no longer pump warm, tropical water to the North Atlantic. Average temperatures would cool in Europe and North America by three degrees Celsius (5°F) or more in just a few years - not enough to trigger a full-fledged ice age, but enough cooling to bring snows in June and killing frosts in July and August to New England and northern Europe, such as occurred in the famed 1816 "year without a summer". In addition, shifts in the jet stream pattern would bring about severe droughts and damaging floods in regions unaccustomed to such events, greatly straining global food and water supplies.

A study published in August 2021 looked at eight independent measures of the AMOC, and found that all eight showed early warning signs that the ocean current system may be nearing collapse. "The AMOC may have evolved from relatively stable conditions to a point close to a critical transition," the authors wrote.

8. A wild 2021 Atlantic hurricane season

The 2021 Atlantic hurricane season generated an extraordinary 21 named storms (third highest on record), seven hurricanes, four major hurricanes, and an accumulated cyclone energy (ACE) of 145. Those numbers compare with the 1991-2020 averages for an entire season of 14.4 named storms, 7.2 hurricanes, 3.2 major hurricanes, and an ACE index of 123. As documented by Brian McNoldy, Senior Research Associate at University of Miami's Rosenstiel School of Marine and Atmospheric Science, 2021 marked the sixth consecutive year with an ACE index above 129: "this has never happened before, not during the satellite era, not since records began in 1851. This sustained level of tropical cyclone activity in the Atlantic is unprecedented even for four years, let alone six!"

Eight named storms made landfall in the contiguous U.S. in 2021, ranking as the third highest on record, behind 2020 (11) and 1916 (nine). The two-year period 2020-2021 had a truly astonishing 19 landfalls in the contiguous U.S., six times the average for a two-year period, and beating the previous two-year landfall record of 15, set in 2004-2005. From 1950 through 2020, the U.S. averaged just three landfalling tropical storms (with one a hurricane) per year.

9. Two unprecedented December U.S. tornado and severe weather outbreaks

A week of stunning record warmth in the U.S. Midwest in mid-December led to two unprecedented severe weather outbreaks. The first outbreak, on December 10, was the nation's deadliest and most damaging on record for any December, with at least 90 fatalities and a record 69 confirmed tornadoes, chiefly across western and central Kentucky. Preliminary insured damage estimates are $3-$5 billion, and total economic damages will be much higher.

Then, on December 15, the record set just five days previously was smashed, as a massive cyclone whipped across the central U.S., spawning EF2 tornadoes as far north as Minnesota and Wisconsin - an outbreak unprecedented for December in its northward extent and in the total number of twisters confirmed (a total of 100, one of the largest one-day outbreaks on record for any time of year).

10. Bad news from a key glacier in Antarctica

Scientists from the International Thwaites Glacier Collaboration in December announced their discovery of cracks and fissures in the floating ice shelf buttressing West Antarctica's Thwaites Glacier. They predicted that the ice shelf could fracture in as little as five years, allowing for accelerated melting of the Thwaites Glacier, which currently contributes four percent of annual global sea level rise.

In a worst-case scenario, fracturing of the ice shelf would allow part of Thwaites Glacier to triple in speed, increasing the glacier's contribution to global sea level rise to 5% in the short term. In the long term, loss of the ice shelf buttressing the Thwaites Glacier could lead to its rapid disintegration over a period of decades or centuries, resulting in the loss of the entire West Antarctic Ice Sheet, and an increase in global sea levels of about 10 feet. However, the speed with which that disintegration might occur is highly uncertain.
Bob Henson contributed to this post. Editor's note: This post was updated on Jan. 11 to include a newer damage assessment from NOAA for Hurricane Ida ($75 billion instead of $65 billion), and to update Figure 2 with numbers from EM-DAT instead of Aon. Website visitors can comment on "Eye on the Storm" posts (see below). Please read our Comments Policy prior to posting. (See all EOTS posts here. Sign up to receive notices of new postings here.)

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The top 10 global weather and climate change events of 2021 " Yale Climate Connections

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The most extreme heat wave in modern history, a record four $20 billion-plus weather disasters, and the hottest month on record globally highlighted a remarkable year in weather.

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Global Climate Change and the Impact on Children's Health 

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Global Climate Change and the Impact on Children's Health 

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Climate Change Increases the spread of Influenza Epidemic 

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Climate Change Increases the spread of Influenza Epidemic 

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Qi Liu Zhe-Min Tan Jie Sun Yayi Hou Congbin Fu Zhaohua Wu 5,1,2,3, 5,1, ², ⁴, 6,1,3 and 6,2

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Environmental Research Letters , Volume 15 , Number 4 Citation Qi Liu DOI 10.1088/1748-9326/ab70bc et al 2020 Environ. Res. Lett. 15 044004

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¹ School of Atmospheric Sciences, Nanjing University, Nanjing 210023, People's Republic of China

² Department of Earth, Ocean, and Atmospheric Science & Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee FL 32306, United States of America

³ Institute for Climate and Global Change Research, Nanjing University, Nanjing 210023, People's Republic of China

⁴ The State Key Laboratory of Pharmaceutical Biotechnology, Division of Immunology, Medical School, Nanjing University, Nanjing 210023, People's Republic of China

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⁵ These authors contributed equally to this work.

⁶ Authors to whom any correspondence should be addressed.

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1748-9326/15/4/044004

Abstract

It is believed that the continuing change in the Earth's climate will affect the viral activity and transmission of influenza over the coming decades. However, a consensus of the severity of the risk of an influenza epidemic in a warming climate has not been reached. It was previously reported that the warmer winter can reduce influenza epidemic caused mortality, but this relation cannot explain the deadly influenza epidemic in many countries over northern mid-latitudes in the winter of 2017-2018, one of the warmest winters in recent decades. Here, we reveal that the widely spread 2017-2018 influenza epidemic can be attributed to the abnormally strong rapid weather variability. We demonstrate, from historical data, that the large rapid weather variability in autumn can precondition the deadly influenza epidemic in the subsequent months in highly populated northern mid-latitudes; and the influenza epidemic season of 2017-2018 was a typical case. We further show that climate model projections reach a consensus that the rapid weather variability in autumn will continue to strengthen in some regions of northern mid-latitudes in a warming climate, implying that the risk of an influenza epidemic may increase 20% to 50% in some highly populated regions in the later 21st century.

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Influenza epidemics tend to occur more frequently from October to May, peaking in January and February over the highly populated northern mid-latitudes [ 1, 2]. This boreal winter half of a year is often referred to as influenza season. The seasonality of influenza suggests a potential tie to the seasonality of weather and climate [ 3, 4]. However, a consensus of the severity of the risk of an influenza epidemic in a warming climate has not been reached [ 5- 7]. Previous studies have suggested that low surface air temperature and humidity in winter constitute a favorable climatic environment for the survival and transmission of the influenza virus [ 8- 10] and, therefore, the continuing fast warming of the Earth's climate in winter can depreciate the favorable climatic environment for the survival and transmission of the influenza virus and reduce future influenza epidemic risk [ 5]. However, this relation cannot explain the deadly influenza epidemic in many countries over northern mid-latitudes in the winter of 2017-2018 [ 11, 12], one of the warmest winters in recent decades.

In general, the transmission of the influenza virus and the spread of human influenza-like disease (ILI) depends on many factors. One of them is the survival and reproductivity of the influenza virus in different ambient conditions. Previous studies have shown that the reproductivity and survival length of the virus in colder and less moist air is larger and, thereby, the transmission of the influenza virus is more effective in winter seasons when both the moisture level and temperature are low. This disease dynamic has been confirmed in experiments with guinea pigs [ 9, 13, 14], and the relation was even incorporated into models that fit the past data and make predictions of the strength of the incoming flu season [ 15- 17]. It should be noted that becoming infected with the influenza virus also depends on the strength of the human immune system [ 18], as evidenced by the use of influenza vaccines. Seasonal fluctuations in human immunity could also play a role in the seasonality of influenza epidemics [ 19]. In such a sense, understanding the variability and change in any climatic aspect that can affect the human immune system will help more accurately estimate the relation between the continuing climate change and future influenza epidemic risk. It is also noted that the seasonality association is not only with ILI but also with other diseases [ 20].

2.1. Meteorological data

Daily maximum surface air temperatures of Europe and the continental United States for the period January 1, 1997 to February 28, 2018 are derived from the Global Historical Climatology Network ( ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/), covering a total number of 7729 days. The data are all the meteorological station-observed daily maximum temperatures. Many individual stations contain non-observed days. In this study, only the daily temperature time series of individual stations that satisfy the following conditions are analyzed: (1) the station-observed temperature time series has no more than 80 missing days for any individual winter half year (from August 1 to February 28); and (2) fewer than 750 days of missing data over the study period (1997-2018). There are 559 stations satisfying these two conditions in the United States and 547 in Europe. The available temporal domain of Italy is from 1999 to 2018. The spatial interpolation using the Matlab function griddata is then applied to fill in missing data for any individual days for all meteorological stations. The homogenized data is then averaged to obtain state-wise (United States) or country-wise (Europe) averaged daily maximum temperatures. The Global Historical Climatology Network contains relatively fewer numbers of stations over China. Therefore, the station-observed daily maximum temperature over China is from the quality-controlled [ 21] observations assembled by the National Meteorological Information Center of the China Meteorological Administration ( http://data.cma.cn/). There are 654 stations over China that satisfy the above two conditions.

Daily near-surface mean absolute humidity is from reanalysis provided by the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR reanalysis) [ 22]. This dataset is a spatially gridded one that has a fixed zonal resolution of 1.875 degrees of longitude and a varying Gaussian-shaped meridional resolution with its average close to 1.875 degrees of latitude. The data covers the whole global domain starting from January 1, 1948. In this study, we analyze data covering the same temporal span, from January 1, 1997 to February 28, 2018, to obtain state-wise (United States) or country-wise (Europe and China) averaged absolute humidity fields. The reanalysis can be downloaded from: https://esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html.

The simulated and projected daily surface maximum temperatures are from the CMIP5 [ 23] for the periods Jan. 1950-Dec. 2005 and Jan. 2006-Dec. 2100, respectively. The projected future climate data selected are from two future emission scenarios: the RCP4.5 and RCP8.5. Seven widely acclaimed Earth system models from different countries are selected, with their names, institutes, and horizontal resolution listed in supplementary table S1 (available online at stacks.iop.org/ERL/15/044004/mmedia). The simulated and projected data can be downloaded from: https://esgf-node.llnl.gov/projects/cmip5/.

2.2. Influenza-like illness and influenza morbidity (ILI/IM) data

Due to the different settings in the influenza datasets (supplementary table S2), the analyzed data from the United States of America (USA) are the ILI, defined as the percentage of patients with ILI among all patients. For European countries and mainland China, the analyzed datasets contain the weekly percentage of confirmed influenza patients among all tested patient samples, which is referred to as influenza morbidity (IM) in this study. Due to the varying spatiotemporal resolutions of different datasets, interpolation or summation methods are used to obtain the same spatiotemporal resolutions if necessary.

Weekly IM data over Europe and China are obtained from the Influenza Laboratory Surveillance Information of the World Health Organization. Weekly IM data from34 countries are analyzed in this study, with source laboratories and available temporal domains listed in supplementary table S2. The data can be downloaded from: http://apps.who.int/flumart/Default?ReportNo=14.

The ILI data are from the US Center for Disease Control and Prevention. The dataset has weekly temporal resolution and includes three different subsets: (1) the state-wise reported percentage of ILI patients covering the period from the 40th week of 2010 to the present; (2) the standard federal region-wise data covering the period from the 40th week of 1997 to the present; and (3) the whole United States averaged data covering the period from the 40th week of 1997 to the present. The data can be downloaded from: https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html. The reason for using ILI data rather than IM data over the United States is due to the inconsistency in IM data. According to the US Center for Disease Control and Prevention, the methods for collecting IM data changed after 2015-2016, while those for collecting ILI data remained the same for 1997 to 2018.

2.3. Definition of rapid weather variability

It has been proposed that sudden large changes in temperature can impair the human immune system and trigger immune evasion [ 24- 29]. The possible mechanism is that the human thermoregulation of immune defense is less adjustable to the sudden large change in temperature [ 26, 27] and less resistant to various diseases [ 24, 25]. Recently, studies showed that the sudden large change in temperature tends to cause high respiratory mortality [ 24- 27, 30] and to impact influenza seasonality [ 31]. It is, in fact, based on this pathological mechanism that we suspect that frequent fluctuating weather, as an additional factor, may play a significant role in influenza epidemics.

To quantify the sudden large change in temperature, here we introduce a climatic quantity called rapid weather variability (RWV) for any given temporal location: the total number of consecutive days with surface air temperature differences [ 32] larger than 3 K over a three-week period ending at that temporal location (see supplementary table S1). The procedure for quantifying RWV is shown in supplementary figure S1; the number of absolute differences of consecutive-day surface air temperature (figure S1(b)) is first calculated from the raw daily surface air temperature time series (figure S1(a)), and the number of days with differences larger than 3 K over a three-week sliding window is obtained (figure S1(c)). The selection of a temporal window length of three weeks allows us to retain sufficient variability of the RWV and reduce the effect of randomness. It is important to note that the threshold of 3 K is not an arbitrary selection; rather, it is based on the sensitivity of the human immune system to temperature variability [ 26]. Both positive and negative changes in temperature of more than 3 K would remarkably increase the respiratory mortality [ 26]. It is noted that the key results presented later in this paper are not sensitive to the selection of window size for defining RWV as long as it is between two weeks to four weeks (see supplementary text and supplementary figures S2-S3).

2.4. The lagged correlations between weather variables and ILI/IM

In this study, we explore the impact of RWV on ILI/IM. Since the former cannot be changed by the latter, we anticipate an either simultaneous or delayed response of ILI/IM to RWV. To characterize this relation, lagged correlations of various delays are calculated for the weekly anomalous weather variable V i,j and the weekly anomalous value N i,j of ILI/IM of the same region, where the subscripts i and j represent the ordered year and ordered week of a year, respectively. The lagged correlation between V i,j and N i,j is defined as

where i s is the starting influenza season, and i e is the ending influenza season. In this study, i s corresponds to the 1997-1998 influenza season and i e corresponds to the 2017-2018 influenza season, but with the 2009-2010 influenza season excluded as the ILI/IM in that season was dominated by swine influenza that has abnormal spreading dynamics [ 33- 35]. In addition, the Monte Carlo test was used to examine the statistical significance of lagged temporal correlation. The statistical significances of different countries for the correlation coefficients of ±0.4, ±0.45, ±0.5, and ±0.55 are listed in supplementary table S3.

3.1. Rapid weather variability preconditions influenza epidemic

Figure 1 presents various relations between autumn RWV and ILI/IM. For the four northern mid-latitude countries/regions, i.e. the United States, mainland China, Italy, and France, which have relatively longer ILI/IM data, a common feature that emerged is that the autumn RWV appears to have a long-lasting effect on the influenza epidemic strength in the subsequent months (coherent reddish blocks in the correlation maps displayed in the left column panels of figure 1; statistical significances are shown in supplementary table S3), implying that the RWV has preconditioned the occurrence of the influenza epidemic, although the influenza peaking times were different for all these regions. The case in figure 1(b) in which the January RWV is correlated with influenza mortality in the previous November was largely caused by a few individual influenza seasons, such as the 2010-2011 winter, 2013-2014 winter, and 2015-2016 winter (supplementary figure S8(b)). With a small sample, such a spurious relation can exist, although it is unreal. Another feature common to all four regions is that the peak strength of ILI/IM, P increases with the strength of the RWV in autumn. The normalized change rates,

To further confirm the lasting effect of autumn RWV on the later winter ILI/IM peaking strength, figure 2 presents the spatial patterns of RWV and ILI/IM of the 2017-2018 influenza season for the United States and a large portion of Europe. The influenza epidemic season of 2017-2018 was one of the severest influenza seasons in the United States and Europe [ 11, 12], causing up to 4064 mortalities a week in the United States alone. The spatial patterns of RWV in mid-October to mid-November of 2017 for the United States and in September of 2017 for Europe share similar spatial structures of their corresponding peak ILI/IM in January and February of 2018, with spatial correlations between RWV in autumn and ILI/IM of 0.32 for the United States and 0.33 for Europe, and both exceeding a 95 percent confidence level against a null hypothesis of random spatial distribution. However, the simultaneous spatial correlations between RWV and peak ILI/IM are much smaller, with values of −0.06 in the United States and −0.23 in Europe, and are not statistically significant.

Results from the above analysis suggest a mechanism of ILI/IM temporal evolution: in later autumn, the intensified RWV contributes to the increase in influenza patients. When the mass of patients reaches a critical level in a densely populated region, the direct contacts between influenza patients and healthy persons increase and the rate of persons being infected with the influenza virus reaches a level greater than the rate of influenza recovery, leading to a fast increase in influenza patients and a severe influenza season. The 2017-2018 influenza season of the United States appears to confirm the hidden operation of such a mechanism, although it may not be the most dominant one. Since an influenza epidemic's peak season is mostly in the second half of winter and RWV in autumn contributes significantly to building critical patient levels in densely populated regions, the strength of RWV during autumn may serve as a valuable predictor of the severity of the consequent influenza season, thus facilitating earlier preparation and prevention.

The statistical relationships between winter-time temperature/humidity and influenza epidemics in many highly populated regions of northern mid-latitudes over the past two decades was also reexamined. However, the identified relations between winter temperature/humidity and influenza epidemics are less robust (see supplementary text and supplementary figures S5-S10).

3.2. Changing risk of influenza in a warming climate

Over the past century, the Earth's climate has been changing at an unprecedented pace [ 36, 37], especially in the highly populated northern mid-latitudes. This climate change is not limited only to surface temperature; rather, it also includes changes in other climate variables, such as the mid-latitude synoptic variability [ 23, 38]. Since the change in RWV can cause large changes in the ILI/IM, as revealed above, understanding the spatial patterns of future RWV can help us determine the severity of future ILI/IM threats.

The above identified relation between RWV and ILI/IM provides a chance to estimate the changing risk of the influenza epidemic in a warming climate. Figure 3 presents the triweekly RWV changes for two three-decade spans, 2020-2049 and 2070-2099, based on the diagnoses of seven climate system model (supplementary table S1) outputs from the Representative Concentration Pathway (RCP) 8.5 emission scenario of the Coupled Model Intercomparison Project Phase 5 (CMIP5) [ 39]. CMIP5 was used to project future climates in the Intergovernmental Panel on Climate Change's fifth assessment report. The displayed RWV changes are the anomalies of the means of triweekly autumn RWV of the above selected two temporal spans with respect to the historical three-decade mean RWV for the temporal span 1970-1999. While the detailed spatial patterns of RWV changes in these seven models are not exactly the same, a common feature emerges: many regions of northern mid-latitudes will have an RWV anomaly increase of more than 0.5 over the next three decades and more than 1 in the last three decades of the 21st century. Most of these models projected that highly populated Europe will have the largest RWV increase, with triweekly RWV increases larger than 1 over the next three decades and more than 2 in the last three decades of the 21st century. This large increase in RWV projects that some regions of Europe will have the risk of IM increase by more than 50% if solely following the statistically robust RWV-IM relation presented in figure 1. However, this number may be an overestimation since RWV is not the only cause of IM. All model-projected RWV changes in the United States show an increase only in its highly populated northeast region with a relatively smaller value. The models also projected that the large majority of China will also have RWV increases by up to 1 by the end of the 21st century, implying that IM will increase by more than 20%. In summary, the increasing RWV is likely to increase the risk of IM in future, although a reliable quantification is out of our reach.

The validity of the above projection of the risk of ILI/IM is highly dependent on the accuracy of the projection of RWV. Is this projected RWV physically justified? The historical data shows that the noticeable land warming after the industrial revolution started first in the land neighboring the Arctic and the subtropical northern hemisphere [ 40]. These two bands intensified and generated an anomalous temperature gradient that led to the increased anomalous heat transport from polar/subpolar regions and subtropical regions to northern mid-latitude regions in the autumn season. By the end of the 21st century, the maximum warming was at about 50 °N (figure 4), leading to the increased meridional temperature gradient north to that latitude [ 40]. The increased meridional temperature gradient provided a more favorable environment for the synoptic system to develop [ 41- 43], explaining the intensification of RWV. This maximum warming latitude is still shifting southward as the globe continues to warm, see figure 3 of Ji et al [ 40]. The climate projection based on the above-mentioned model outputs show that this zone of increased meridional temperature gradient is further south, with its southern edge located at about 45 °N. Thus, the increased RWV in climate model projection is consistent with previous understandings and can be anticipated. It is noted that, for the Asian region, the zone of the intensified meridional temperature gradient locates south to the zonally averaged one, while for the United States and Europe, this zone locates north to the zonally averaged one.

The similar spatial pattern changes in RWV and zonally averaged temperatures are also seen in the projections under a more moderate emission scenario, RCP4.5. Supplementary figure S11 displays the triweekly autumn RWV changes in seven climate system models for a more moderate emission scenario, RCP4.5. The spatial patterns of RWV increase under RCP4.5 and resemble those of RCP8.5 (figure 3), but with reduced amplitude. Under the RCP4.5 emission scenario, many regions of northern mid-latitudes will have an RWV anomaly increase more than 0.3 over the next three decades, and more than 0.4 in the last three decades of the 21st century. Under the RCP4.5 scenario, the IM over Europe and China may increase by more than 30% and 15%, respectively, in the last three decades of the 21st century. In addition, model projections under the RCP4.5 emission scenario are characterized by maximum warming over northern mid-latitudes (supplementary figure S12).

It has been long recognized that climate change will affect human health and put billions of people at increased risk [ 41, 43, 44]. Previous studies focused more on the disease dynamics, and directly observed temperature and humidity affecting the influenza epidemic. Our study, based on statistical analysis, shows that RWV also played a significant role in changing the strength of the influenza epidemic in the past. In a warming climate, the RWV will intensify and the influenza epidemic risk can increase by up to 50% in some northern mid-latitude regions.

It is noted that our identified relation between ILI and RWV is based on the limited data of ILI. The small number of samples may cause bias in the calculation, especially when the ILI data are highly non-Gaussian. Neverthless, this identified relation may contribute to earlier preparation and prevention of influenza epidemics. Since an influenza epidemic's peak season is mostly in the second half of winter and autumn RWV contributes significantly to building critical patient levels, the strength of autumn RWV may serve as a valuable predictor of an influenza epidemic in the subsequent months.

Finally, it is also noted that the data we analyzed are mostly regional averaged over large spatial domains and age-undistinguished; thereby, the identified statistical relation may contain unknown degrees of bias [ 45, 46]. In figure S4, we presented evidence that the identified RWV-ILI relationship is more robust for preschool children and the elderly. We anticipate that, with fine categorized data potentially available in future, more accurate relationships may be revealed.

Q L and C F are supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change; Q L is also supported by financial support from the program of China Scholarships Council (201706190153); Z-M T is supported by the Natural Science Foundation of China (41461164008); and Z W and J S are supported by the US National Science Foundation grant (AGS-1723300). The authors declare no competing financial interests.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Changing rapid weather variability increases influenza epidemic risk in a warming climate

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Qi Liu Zhe-Min Tan Jie Sun Yayi Hou Congbin Fu Zhaohua Wu 5,1,2,3, 5,1, 2, 4, 6,1,3 and 6,2 * Environmental Research Letters , Volume 15 , Number 4 Citation Qi Liu DOI 10.1088/1748-9326/ab70bc et al 2020 Environ. Res. Lett.

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2021 International Climate Change Reports Assess the Earth's Climate

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Two international reports evaluate environmental indicators to capture the current state of climate change.

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Public understanding of Climate related sea level rise 

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Public understanding of Climate related sea level rise 

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Rebecca K. Priestley,

Zoë Heine,

Taciano L. Milfont

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Abstract

Sea-level rise resulting from climate change is impacting coasts around the planet. There is strong scientific consensus about the amount of sea-level rise to 2050 (0.24-0.32 m) and a range of projections to 2100, which vary depending on the approach used and the mitigation measures taken to reduce carbon emissions. Despite this strong scientific consensus regarding the reality of climate change-related sea-level rise, and the associated need to engage publics in adaptation and mitigation efforts, there is a lack of empirical evidence regarding people's understanding of the issue. Here we investigate public understanding of the amount, rate and causes of sea-level rise. Data from a representative sample of New Zealand adults showed a suprising tendency for the public to overestimate the scientifically plausible amount of sea-level rise by 2100 and to identify melting sea ice as its primary causal mechanism. These findings will be valuable for scientists communicating about sea-level rise, communicators seeking to engage publics on the issue of sea-level rise, and media reporting on sea-level rise.

Citation: Priestley RK, Heine Z, Milfont TL (2021) Public understanding of climate change-related sea-level rise. PLoS ONE 16(7): e0254348. https://doi.org/10.1371/journal.pone.0254348

Editor: Riccardo Riva, Technische Universiteit Delft, NETHERLANDS

Received: November 24, 2020; Accepted: June 25, 2021; Published: July 9, 2021

Copyright: © 2021 Priestley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The full sea-level rise survey, as well as overall descriptive results produced by Horizon Research, and the raw data, can be found in the Open Science Framework: https://osf.io/vcjuf/.

Funding: RKP and ZH: Supported by New Zealand Ministry of Business, Innovation, and Employment Grant through Victoria University of Wellington (RTUV1705), NZ SeaRise Programme administered by the Antarctic Research Centre. LM: Victoria University of Wellington sabbatical (222992). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Between 1902 and 2015, global sea level rose by 16 cm [ 1]. In recent decades sea-level rise has been accelerating, due to increasing rates of ice loss from the Greenland and Antarctic ice sheets. The majority of the world's large cities are located on the coasts, and by 2050 one billion people could be at risk of coastal inundation or high-tide flooding [ 1]. While public understanding of climate change has been well canvassed in recent years [ 2- 5], public understanding of sea-level rise is relatively unexplored.

Studies conducted in the United States and the United Kingdom show the public does not feel well informed about, and has difficulty understanding, sea-level rise associated with climate change [ 6, 7]. Moreover, a review of international sea-level rise communication [ 8] noted sea-level rise has suffered from low media attention and salience as a public issue. This lack of public understanding and salience as an issue is despite the fact that sea-level rise is already causing increased coastal flooding [ 9], and there is relatively low uncertainty about the amount of sea-level rise that will occur to 2050, which is projected to be between 0.24 and 0.32 m [ 10]. (Sea-level rise is always counted with respect to a reference date: in this paper we have adopted the baseline used in reports by the IPCC [ 11] and the New Zealand Ministry for the Environment [ 12]. Projections in these reports are relative to mean sea level averaged over the two-decade period 1986-2005.) Moreover, present day extreme water-level events will become commonplace within the next few decades [ 9] and sea-level rise of 0.39 m (0.26-0.53) by 2100 is likely even under the lowest emissions pathway (RCP2.6) [ 1]. While mitigation efforts are still important, adaptation to this coastal change is essential and urgent, and will require significant financial investments [ 9, 13]. Support from publics-as voters, taxpayers, coastal residents, and coastal business owners-is thus essential. This paper uses New Zealand as a case study to gauge the public's current understandings of sea-level rise.

New Zealand is an island nation in the South Pacific, stretching 1,500 km between latitudes 34° and 47° south. The country has 18,000 km of coastline with no point on land more than 130 km from the sea [ 14], and it has a population of five million people [ 15], most of whom live in coastal cities or districts. Most New Zealanders believe climate change is occurring, is anthropogenic, and is a serious concern [ 3, 16]. Notably, stories about sea-level rise in the mainstream news media are largely consistent with the scientific consensus and reports issued by the Intergovernmental Panel on Climate Change (IPCC) [ 17].

In a 2015 report, the New Zealand Parliamentary Commissioner for the Environment noted, "It is certain that the sea is rising and will continue to do so for centuries to come. But much is uncertain-how rapidly it will rise, how different coastal areas will be affected, and how we should prepare" [ 18, p5]. The NZ SeaRise Programme aims to address this uncertainty with a 5-year (2018-2023) government-funded research initiative [ 19]. Its goal is to produce a set of location specific sea-level rise projections for New Zealand's coastline, taking into account global and regional sea-level rise projections and new knowledge of local vertical land movements.

This study is part of the public engagement workstream of the NZ SeaRise Programme. Too often, development of outreach activities is dominated by intuitive approaches and personal or institutional motivations [ 20]. Relevant literature, though, advises that designing a public engagement campaign involves first understanding your audience and determining what they already know [ 21, 22]. Survey data is one form of evidence that can be used to inform the design of public enagement programmes on sea-level rise [ 23]. To gauge current understanding of sea-level rise, we therefore conducted a pilot study followed by a representative survey to identify what New Zealanders knew about the amount, rate and causal mechanisms of sea-level rise. The survey was initiated with an awareness that while media coverage in New Zealand tends to focus on IPCC projections and government reports, there is also voice given to climate change deniers and to people who warn of catastrophic sea-level rise [ 17]. Results of the survey will inform NZ SeaRise Programme researchers who are planning, or undertaking, efforts to engage publics with the issue of sea-level rise. Rather than starting with assumptions about what publics know and understand, researchers can use the survey results to approximate the knowledge and perspectives of the publics they are seeking to engage with.

Regarding the rate and timing of future sea-level rise, projections from the Fifth IPCC Assessment Report (AR5) put likely mean global sea-level rise by 2100 at between 0.26 to 0.98 m [ 11]. In 2017, the New Zealand Ministry for the Environment published projections for New Zealand of 0.46-1.05 m of sea-level rise by 2100, depending on how quickly global carbon emissions are reduced [ 12]. These projections did factor in a modest Antarctic ice melt contribution that was not assessed in the AR5; however, a subsequent study using a structured expert judgment approach that evaluated all the latest projections [ 24] found that global sea-level rise could exceed 2 m by 2100 under a low-probability, high-emissions scenario. The 2019 IPCC Special Report on the Oceans and Cryosphere in a Changing Climate [ 1], which reviewed more recent studies [ 25- 27] than did AR5, assessed likely global sea-level rise by 2100 as ranging from 0.29 m to 1.10 m under the different IPCC scenarios. In the same report, thermal expansion, melting of the Greenland ice sheet, and melting of land-based glaciers were identified as the main contributors to sea-level rise to date, with greater contributions from melting of the Antarctic ice sheets for the rest of the century and beyond [ 10].

To understand the public's knowledge about the amount, rate and causal mechanisms of sea-level rise, we commissioned a national survey. To reflect the New Zealand population, survey responses were weighted by age, sex, highest education, ethnicity and region using census data. The percentage results shown below are thus weighted to be representive of the New Zealand population.

Method

Data were collected from a representative sample of 1100 New Zealanders aged 18 and over conducted by Horizon Research in September 2019. Participants, who were part of the company's research panel, completed 15 multichoice questions plus demographic questions online in their own time. In the information provided at the start of the survey, participants were advised that by continuing the survey they were giving consent for their responses to be used in this research, and were assured that it was an anonymous survey and they would not be identifiable. The research was approved by the Human Ethics Committee at Victoria University of Wellington in August 2019 (reference number 0000027157).

The majority of respondents were male (50.5%), within the 45-54 years age bracket (22.7%), with a household income between $70,001 and $100,000 per year (17.9%), employed (69.5%), and indicated a personal income between $30,001 and $50,000 per year (19.6%), an undergraduate (Bachelor) degree as the highest qualification (29.1%), Auckland Council as their local government (32.8%), and NZ European/Pakeha as their self-identified ethnicity (64.2%).

Prior to the representative survey, a pilot online survey, comprising 27 multi-choice and short answer questions plus demographic questions, was conducted in July 2019 and received 665 respondents, with respondents aged between 18 and 87, most of whom (58%) were female.

The pilot survey was useful for informing the final representative survey and helping us to select the 15 most critical questions (scaling down the initial survey was necessary because of the financial constraints involved in using a company to collect data). We acknowledge Akerlof's points that "some fraction of the public has no prior opinion on questions asked in surveys, but feels impelled to provide an answer" [ 23, p. 423] and that "reported beliefs and attitudes may either be randomly chosen by the respondent or selected based on cues from the question format" [ 23, p. 423]. We excluded 'I don't know' answers in many of the response options, though, because we wanted participants to indicate their view, or best approximate, even if they didn't have full knowledge of the topic, and also because 'I don't know' response options can yield unwanted methodological issues [ 28].

The S1 File provides more detail about survey design and findings from other questions included in the survey ( S1 File -representative and pilot surveys). The full survey, raw data, as well as overall descriptive results produced by Horizon Research, can be obtained in the Open Science Framework: https://osf.io/vcjuf/.

Public overestimates sea-level rise possible by 2100

We first examined New Zealanders' understanding of current sea-level rise. As noted, average global sea level has risen by more than 16 cm since 1900 [ 1]. Respondents were thus asked to identify, from options ranging from 'not at all' to 'more than 40 cm', how much they thought global sea level has risen since 1900. More than a quarter of respondents answered '10-20 cm' (26.9%), a figure that is in line with current understanding of sea-level rise to date. But a larger proportion (38.2%) of respondents either underestimated the amount of sea-level rise, selecting 'not at all' (11.2%) or '5-10 cm' (27%), or overestimated (35%) the amount of sea-level rise from 1900 to date, selecting '20-30 cm' (13.4%), '30-40 cm' (8.4%) or 'more than 40 cm' (13.2%).

We then examined knowledge of sea-level rise projections to 2100 (see Fig 1A). While a minority (6.8%) of respondents said that sea level was not projected to rise by 2100, nearly 75% of respondents selected options that were in line with scientifically-plausible projections, selecting 'up to 40 cm' (28.6%), 'up to 1 m' (30.9%), and 'up to 2 m' (14.9%). But a large group of respondents (18.9%) overestimated sea-level rise projections to 2100, selecting 'up to 5 m' (10.7%) or 'more than 5 m' (8.2%).

(a): Respondents were asked 'How much do you think global sea level is projected to rise by 2100?' (b): Respondents were asked 'How much could global average sea level rise under 'a scientifically credible worst-case scenario'?.

To further explore the amount of sea-level rise respondents thought was possible by 2100, we asked how much global sea level could rise by 2100 under 'a scientifically credible worst-case scenario' (see Fig 1B). While a similar small number of respondents said that sea-level rise was not going to happen at all (7%), and 22.5% of respondents underestimated the worst-case scenario by selecting 'up to 1 metre', this time only 33.1% of respondents gave an answer broadly in line with current science, answering '1 m or more' (16.7%) or '2 m or more' (16.4%). Another 37.4% of respondents substantially overestimated sea-level rise by 2100, selecting '5 m or more' (18%), '8 m or more' (7.3%), '12 m or more' (5.3%) or '15 m or more' (6.8%).

To assess what respondents thought was physically possible, we then asked what they thought would be the 'maximum amount of sea-level rise' that could occur if 'all of the glaciers and ice sheets and ice caps on planet Earth melted', and what was the fastest time period over which this could occur. The largest group (38.8%) of respondents underestimated the maximum amount of sea-level rise possible, selecting 'about 30 m'. The next largest group (22.3%) chose a figure broadly in line with current estimates of 66.07 m [ 29], selecting 'about 60 m'. The remaining 38.9% of respondents overestimated total maximum sea-level rise, selecting 'about 120 m' (17%), 'about 240 m' (10.9%) and 'more than 500 m' (11%).

In selecting the 'fastest period of time' over which their perceived maximum amount of sea-level rise could occur, respondents overestimated the speed at which all the planet's ice could melt. While scientists suggest that such melting could take place under a sustained warming climate over a period of thousands of years [ 30, 31], the majority of respondents overestimated how fast the planet's ice could melt, selecting 'decades' (33.1%) or 'centuries' (39%). A smaller group (11.6%) of respondents answered in line with the science by selecting 'thousands of years'. The remaining respondents selected 'tens of thousands' (3.2%) or 'hundreds of thousands' (2.7%) of years, or acknowledged that 'I really don't know' (10.4%).

Public identified melting sea ice as causing sea-level rise

Respondents were also asked to identify and rank the major causes of sea-level rise from a ten-item list that included melting ice sheets, melting land-based glaciers, melting sea ice, thermal expansion, and land subsidence. No definitions of these phrases were provided as our intention was to gauge people's understanding of sea-level rise from existing sources of information, such as the media. While climate scientists and the media make efforts when communicating with publics to not identify melting of Arctic and Antarctic sea ice (which is widely covered in the media) as causing sea-level rise, 28.7% of respondents identified this as their top ranked cause of sea-level rise (see Fig 2A). Encouragingly, other respondents' top ranked causal mechanisms that do contribute to sea-level rise, selecting melting ice sheets (24%), thermal expansion of the oceans (7%), and melting of land-based glaciers (4.2%).

Respondents were asked to identify and rank the major contributors to sea-level rise. In (a) we show respondents' top ranked cause of sea-level rise (causes selected by fewer than 2% of respondents are not shown). In (b) we show the sum of the top three causes identified by respondents (causes ranked by fewer than 10% of respondents are not shown).

When we grouped respondents' top three rankings for causes of sea-level rise ( Fig 2B), melting ice sheets (62.3%) was first, followed by melting sea ice (59.8%), melting of land-based glaciers (44.1%), thermal expansion of the oceans (23.6%) and land subsidence in some areas (14.2%). There were also large proportions of respondents ranking some other mechanisms that do not impact on sea-level rise, such as 'increased slash and sediment runoff due to deforestation' (14.3% ranked it in their top three), 'dumping of rubbish/waste into the ocean' (14%), 'increased rainfall in the tropics' (9.9%) and 'submarine landslides' (7.5%).

Notably, 25.9% of respondents also ranked 'something else' as one of the top three causes of sea-level rise. When asked to specify what these causes might be, more than half (54%) were unsure or gave no answer, while 23.5% attributed sea-level rise broadly to climate change, global warming or human activities, and 16.5% gave answers such as 'solar activity', 'earthquakes', or 'storms', suggesting these respondents believe natural processes are responsible for sea-level rise.

Challenges of projecting and communicating sea-level rise

Projecting sea-level rise is complicated [ 32]. Different climate, mountain glacier and polar ice sheet models produce different results. This is especially the case for Antarctic ice sheet models, which can range from projecting a net gain in mass to a significant loss. Thus, the IPCC approach is to take the mean of ensembles of model experiments, that all use broadly similar boundary conditions and climate forcings. Moreover, glacier melting, vertical land movement, gravitational effects, and ocean dynamics all contribute to sea-level rise being different in different locations; and calculations must be made for a number of different scenarios representing different mitigation pathways [ 33]. Sea-level rise projections are therefore usually scenario dependent and framed in terms of a probablistic range, rather than a single figure, to reflect the uncertainty in the projections.

Communicating about sea-level rise, given the uncertainties inherent in projections, is also complicated [ 8]. IPCC reports, which assess and interpret all the latest science, contain jargon and caveats about 'uncertainty ranges' with evidence that publics misunderstand verbal probability expressions, known as calibrated uncertainty language [ 34], such as 'likely' and 'unlikely' [ 35]. Even the IPCC summaries for policymakers have been criticised for their formal structure and use of jargon [ 36]. This is a complex challenge for science communicators. As noted by Seethhaler et al, communicating about uncertainty is "one of the greatest challenges in science communication, one that raises ethical issues including, but not constrained to, finding a middle ground in prognostication between false assurances and doomsday scenarios" [ 37, p. 381].

Media interpretations of the science adds to the problem. While the New Zealand media gives extensive coverage to IPCC projections, it also gives voice to more extreme and catastrophic views from local and international scholars. For example, in 2016 New Zealand media reported on research that projected several metres of sea-level rise by 2100 [ 38- 40], and in 2017 there was significant media coverage when a philosophy professor warned that sea-level rise could reach 5 m or more by 2100 [ 41- 43]. These stories, though, also included comments from scientists who communicated the IPCC projections and from politicians who underestimated the importance of sea-level rise as an issue. Such indiscriminate media coverage, and attempts at journalistic balance, draw the uncertain nature of sea-level rise projections to the public's attention and detract from the certainties that exist out to 2050. This could therefore be contributing to public confusion on the issue. While our pilot study revealed that the majority of respondents (68%; n = 518) felt somewhat or pretty well informed about sea-level rise, only 38% (n = 519) felt somewhat or extremely clear about the rate/timing/extent of sea-level rise to 2100.

Some of the most extreme projections selected by our respondents go beyond the 5 m of sea-level rise by 2100 that some media have reported. For example, the 6.8% of respondents who thought sea-level rise could reach '15 m or more' by 2100 and the 33.1% who believed that all the planet's ice could melt over a period of 'decades,' selected options that are unprecedented in the geological record and defy physical laws around how fast ice can melt, even under extreme temperature forcing [ 44, 45].

Beyond the more descriptive analysis of the answers, we also examined the extent to which overestimation was associated with level of concern about sea-level rise. Results from t-tests confirmed that respondents who were more likely to overestimate the amount of sea-level rise and how fast this would occur were significantly more likely to express greater concern about sea-level rise to 2100 in New Zealand, and globally, compared to their counterparts (i.e., t > 5, p < .001; Cohen's d > .50; see Section 1.4 of the S1 File for detail).

When assessing the severity of environmental problems for geographically distant places, individuals tend to rate the severity of these problems as worse 'there' than 'here' [ 46- 48]. Hence, we also examined whether such spatial bias would be observed in relation to concern for sea-level rise. The spatial bias effect was operationalized as the difference between ratings of national concern and global concern (i.e., average Aotearoa New Zealand minus average global). A difference score equal to zero represents judgement of no nation-global distinction, while values other than zero represent biased comparative judgements. Values above zero represent the expected spatial bias effect with global sea-level rise judged as more concerning than national sea-level rise, and values below zero represent an opposite effect. For most respondents (78.8%) a biased judgement was absent (i.e., the difference score was zero) but results from a one-sample t-test indicate the mean difference score of .08 ( SD = .59) was statistically different from zero, t(1034) = 4.28, p < .001, Cohen's d = .14. A spatial bias effect was thus observed for sea-level rise: respondents judge global sea-level rise as more concerning than national sea-level rise.

The public's association of melting sea ice with sea-level rise, as well as its selection as the top-ranked cause of sea-level rise, provides further evidence of public confusion on this issue, which may be due to the significant media coverage given to melting sea ice in the Arctic. It is possible, though, that some respondents might have been influenced by science that says the flow of ice sheets can be modulated by the presence or absence of sea ice [ 49- 51].

Research into how publics understand sea-level rise is still an emerging field [ 8] and there are few international studies with which to compare our results. However, we note that our findings that respondents tend to overestimate the amount of sea-level rise possible, contrast with a 2019 Australian study showing that New South Wales coastal users and businesses tended to underestimate the amount and rate of sea-level rise along the NSW coast with 45% of respondents predicting a rise of 1-25 cms in the next 20-50 years [ 52]. Similarly to our results a 2016 American study found that individuals were concerned by the impacts of sea-level rise but were uncertain about its timing [ 53]. This survey did not ask respondents to estimate the projected amount of sea-level rise, but asked "When do you believe the effects of sea-level rise will significantly impact the county, if ever?" Finally, a 2016 study of residents of New Jersey following Hurricane Sandy showed that while participants believed sea-level rise was occurring, they did not view sea-level rise as a current threat to themselves [ 54]. This survey did not ask about the projected amount of sea-level rise or its timing, but asked participants whether they believed sea-level rise was occurring and whether it was affecting them. These studies and our own show that there is plenty of space to develop more in-depth understanding of how the public perceives the threat of sea-level rise.

The present research revealed the unexpected finding that one key audience for public engagement with sea-level rise is the concerned public who overestimate sea-level rise by 2100 as being catastrophic (and scientifically implausible). While there are obvious dangers associated with a public that underestimates or minimises the dangers of sea-level rise-such as not taking adaptation measures, or not supporting governments committed to mitigation and adaptation-there are also dangers associated with a public that overestimates sea-level rise. A focus on extreme (and often unsound) projections of sea-level rise can result in public anxiety and feelings of helplessness, rather than motivation to take action to mitigate and adapt [ 55, 56]. Nevertheless, the present research shows that publics are aware of, and concerned about, 21 st century sea-level rise, which is already impacting on coastal dwellers and infrastructure. Any public engagement efforts will therefore need to be more focused and nuanced than raising awareness of the issue, and could include, for example, provision of ongoing and credible sea-level rise information that is accessible to a wide range of publics and decision-makers. With the knowledge that overestimating can lead to failure to act, communicating with this segment of the audience could focus on providing scientifically plausible projections of sea-level rise and communicating that both adaptation and mitigation-to avoid the most extreme sea-level rise scenarios-are necessary.

Conclusion

As noted by Akerlof [ 23] in relation to a project focused on community adaptation to sea-level rise, 'communication programs and public consultation by governments can benefit from the use of survey data to support evidence-based decision-making' (p 406). Overall, our survey findings indicate that New Zealanders have a tendency to overestimate the amount of sea-level rise possible by 2100, with those more likely to overestimate future projections tending to be most concerned about sea-level rise. They also have a mistaken association of sea-level rise with melting of sea ice, and judge global sea-level rise projections as higher, and more concerning, than national sea-level rise projections.

Criticisms of the 'deficit model' of science communication show that encouraging action on an issue-such as sea-level rise-is not as simple as ensuring that publics are fully informed [ 57]. However, it is essential that publics have access to information that can inform their decision making, particularly with an issue such as sea-level rise that has widespread consequences for coastal livelihoods. If publics have a better understanding of the scale and rate of sea-level rise projected over this century and beyond, and the adaptation measures necessary to respond to this, there is evidence they will be more willing to also adopt mitigation measures [ 58]. Hence, having publics informed about sea-level rise and its impact will enable them to be involved in decisions about both adaptation and mitigation strategies; that is, to prepare for the 0.24 to 0.32 m of sea-level rise we know is coming by 2050, as well as to reduce greenhouse gas emissions to avoid the higher sea levels possible by 2100 and beyond. The inclusion of informed publics in these decisions (and discussions) is an essential part of democratic decision making [ 59].

Acknowledgments

The authors wish to thank NZ SeaRise Programme researchers Nick Golledge, Judy Lawrence, Richard Levy, and Tim Naish for reviewing drafts of this manuscript.

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Public understanding of climate change-related sea-level rise

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Sea-level rise resulting from climate change is impacting coasts around the planet. There is strong scientific consensus about the amount of sea-level rise to 2050 (0.24-0.32 m) and a range of projections to 2100, which vary depending on the approach used and the mitigation measures taken to reduce carbon emissions.