Q&A: The evolving science of ‘extreme weather attribution’

Different types of impact attribution study. Adapted from graphic in Carlson et al

1) Trend-to-trend impact attribution

The first method, called “trend-to-trend” impact attribution, assesses long-term trends in both the climate system and in “health outcomes”. This approach was used in a 2021 study on heat-related mortality around the world, which received extensive media attention.

The authors used data from 732 locations in 43 countries to identify relationships between temperature and mortality in different locations, known as “exposure-response functions”. This allowed them to estimate how many people would die in a given location, if temperatures reach a certain level.

The authors used these relationships to calculate heat-related mortality over 1991-2018 for each location under two scenarios – one with and one without human-caused climate change. The study concluded that 37 per cent of “warm-season heat-related deaths” can be attributed to human-caused climate change.

2) Event-to-event attribution

The second type of study is known as “event-to-event” attribution. In one study using this method, the authors used data on observed mortality rates to determine how many people died in Switzerland during the unusually warm summer of 2022.

They calculated how much climate change contributed to warming during that summer. They then then ran a model to calculate the “hypothetical heat-related burden” that would have been seen during the summer without the warming influence of climate change. 

Using this method, they estimate that 60 per cent of the 623 heat-related deaths “could have been avoided in absence of human-induced climate change”.

3) Risk-based event attribution

“Risk-based” event impact attribution – which is demonstrated in a more recent study on the 2003 European heatwave – is the third type of impact attribution. This method combines probabilistic event attribution with resulting health outcomes.

When the paper was published, its lead author, Prof Dann Mitchell – a professor of climate science at the University of Bristol – explained the method to Carbon Brief:

“We have a statistical relationship between the number of additional deaths per degree of warming. This is specific to a certain city and changes a lot between cities. We use climate simulations to calculate the heat in 2003, and in 2003 without human influences. Then we compare the simulations, along with the observations.”

They find, for example, that in the summer of 2003, anthropogenic climate change increased the risk of heat-related mortality in London by around 20 per cent. This means that out of the estimated 315 deaths in London during the heatwave, 64 were due to climate change.

4) Fractional attribution

In the final method, known as “fractional” attribution, the authors combine the results of two independent numbers – an estimation of the total damages caused by an extreme weather event, and a calculation of the proportion of the risk from an extreme weather event for which anthropogenic climate change is responsible, known as the “fraction of attributable risk” (FAR).

The authors of one study used this method to estimate the economic damages linked to Hurricane Harvey.

The authors calculate that “fraction of attributable risk” for the rainfall from Harvey was around three-quarters – meaning that climate change was responsible for three-quarters of the intense rainfall.

Separately, the authors find that according to best estimates, the hurricane caused damages of around US$90 billion. From this, the authors conclude that US$67 billion of the damages caused by the Hurricane’s intense rainfall can be attributed to climate change.

A study on the 2010 Russian heatwave also used this method. The authors found that the heatwave was responsible for more than 55,000 deaths (pdf), and found an 80 per cent chance that the extreme heat would not have occurred without climate warming. The study concludes that almost 45,000 of the deaths were attributable to human-caused climate change.

However, the fractional attribution method has received criticism. One paper argues that the method “inflates the impacts associated with anthropogenic climate change”, because it “incorrectly assumes” that the event has no impact unless it exceeds the threshold defined by the researchers.

Some of the authors of the Hurricane Harvey paper later wrote a paper advising caution in interpreting the results of FAR studies. They say:

“The fraction of attributable risk (FAR) method, useful in extreme weather attribution research, has a very specific interpretation concerning a class of events, and there is potential to misinterpret results from weather event analyses as being applicable to specific events and their impact outcomes…FAR is not generally appropriate when estimating the magnitude of the anthropogenic signal behind a specific impact.”

Expanding scope

Impact attribution is continuing to expand in scope. For example, studies are now being conducted to assess the impact of climate change on disease transmission.

In 2020, scientists quantified the influence of climate change on specific episodes of extreme ice loss from glaciers for the first time. They found that human-caused climate change made the extreme “mass loss” seen in glaciers in the Southern Alps, New Zealand, in 2018 at least 10 times more likely. 

Scientists have also linked climate change to ecosystem shifts. One study focusing on temperature finds that the “extremely early cherry tree flowering” seen in Kyoto in 2021 was made 15 times more likely due to climate change. 

Others go even further, linking weather extremes to societal impacts. For example, a 2021 study published in Scientific Reports says:

“By combining an extreme event attribution analysis with a probabilistic model of food production and prices, we find that climate change increased the likelihood of the 2007 co-occurring drought in South Africa and Lesotho, aggravating the food crisis in Lesotho.”

Meanwhile, Imperial College London’s Grantham Institute is working on an initiative to publish rapid impact attribution studies about extreme weather events around the world. Similar to WWA studies, these rapid studies will not be peer reviewed individually, but will be based on a peer-reviewed methodology.

Dr Emily Theokritoff – a research associate at Grantham, who is working on the initiative, tells Carbon Brief that it will be launched “in the near future”. She adds:

“The aim is to recharge the field, start a conversation about climate losses and damages, and help people understand how climate change is making life more dangerous and more expensive.”

How do scientists attribute ‘unprecedented’ events?

An attribution method known as the “storyline approach” or “conditional attribution” has become increasingly popular over the past decade – despite initially causing controversy in the attribution community.

In this approach, researchers first select an extreme weather event, such as a specific heatwave, storm or drought. They then identify the physical components, such as sea surface temperature, soil moisture and atmospheric dynamics, that led to the event unfolding in the way it did. This series of events is called a “storyline”. 

The authors then use models to simulate this “storyline” in two different worlds – one in the world as we know it and one in a counterfactual world – for example, with a different sea surface temperature or CO2 level. By comparing the model runs, the researchers can draw conclusions about how much climate change influenced that event.

The storyline approach is useful for explaining the influence of climate change on the physical processes that contributed to the event. It can also be used to explore in detail how this event would have played out in a warmer (future) or cooler (pre-industrial) climate. 

One study describes the storyline approach as an “autopsy”, explaining that it “gives an account of the causes of the extreme event”.

Prof Ted Shepherd, a researcher at the University of Reading, was one of the earliest advocates of the storyline attribution approach. At the EGU general assembly in Vienna in April 2024, Shepherd provided the opening talk in a session on storyline attribution. 

He told the packed conference room that the storyline approach was born out of the need for a “forensic” approach to attribution, rather than a “yes/no” approach. He emphasised that extreme weather events have “multiple causes” and that the storyline approach allows researchers to dissect each of these components.

Dr Linda van Garderen is a postdoctoral researcher at Utrecht University and has carried out multiple studies using the storyline method. She tells Carbon Brief that, while traditional attribution typically investigates probability, the storyline approach analyses intensity. 

For example, she led an attribution study using the storyline method which concluded that the 2003 European and 2010 Russian heatwaves would have been 2.5-4°C cooler in a world without climate change.

She adds that it can make communication easier, telling Carbon Brief that “probabilities can be challenging to interpret in practical daily life, whereas the intensity framing of storyline studies is more intuitive and can make attribution studies easier to understand”.

Dr Nicholas Leach is a researcher at the University of Oxford who has conducted multiple studies using the storyline approach. He tells Carbon Brief that probabilistic attribution often produces “false negatives”, wrongly concluding that climate change did not influence an event. 

This is because climate models have “biases and uncertainties” which can lead to “noise” – particularly when it comes to dynamical features such as atmospheric circulation patterns. Probabilistic attribution methods often end up losing the signal of climate change in this noise, he explains.

The storyline approach is able to avoid these issues more easily, he says. He explains that by focusing on the dynamics of one specific event, rather than a “broad class of events”, storyline studies can eliminate some of this noise, making it more straightforward to identify a signal, he says.

Conversely, others have critiqued the storyline method for producing false positives, which  wrongly claim that climate change influenced an extreme weather event.

The storyline approach has also been praised for its ability to attribute “unprecedented” events. In the EGU session on the storyline method, many presentations explored how the storyline method could be used to attribute “statistically impossible” extremes.

Leach explains that when a completely unprecedented extreme event occurs, statistical models often indicate that the event “shouldn’t have happened”. When running a probabilistic analysis using these models, Leach explains: “You end up with the present probability being zero and past probability being zero, so you can’t say a lot.”

He points to the Pacific north-west heatwave of 2021 as an example of this. This event was one of the most extreme regional heat events ever recorded globally, breaking some local high temperature records by more than 6°C.

WWA conducted a rapid attribution study on the heatwave, using its probabilistic attribution method. The heatwave was “so extreme” that the observed temperatures “lie far outside the range” of historical observations, the researchers said. 

Their assessment suggests that the heatwave was around a one-in-1,000-year event in today’s climate and was made at least 150-times more likely because of climate change.

Leach and his colleagues used the storyline method to attribute the same heatwave. The methods of this study will be discussed more in the following section.

Leach explains that using the storyline approach, he was able to consider the physics of the event, including an atmospheric river that coincided with the “heat dome” that was a key feature of the event. This helped him to represent the event well in his models. The study concluded that the heatwave was 1.3°C hotter and eight times more likely as a result of climate change. 

Many experts tell Carbon Brief there was initially tension in the attribution community between probabilistic and storyline advocates when the latter was first introduced. However, as the storyline method has become more mainstream, criticism has abated and many scientists are now publishing research using both techniques.

Van Garderen tells Carbon Brief that storyline attribution is “adding to the attribution toolbox”, rather than attempting to replace existing methods. She emphasises that probability-based and storyline attribution answer different questions, and that both are important. 

How can weather forecasts be used in attribution studies?

Forecast attribution is the most recent major addition to the attribution toolbox. This method uses weather forecasts instead of climate models to carry out attribution studies. Many experts describe this method as sitting part-way between probabilistic and storyline attribution.

One benefit of using forecasts, rather than climate models, is that their higher resolution allows them to simulate extreme weather events in more detail. By using forecasts, scientists can also attribute events that have not yet happened.

The first use of “advance forecasted” attribution analysis (pdf) 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 authors, in essence, carried out the probabilistic attribution method, using two sets of short-term forecasts for the hurricane rather than large-scale climate models. The analysis received a mixed reaction. Stott told Carbon Brief at the time that it was “quite a cool idea”, but was highly dependent on being able to forecast such events reliably.

Dr Kevin Trenberth, distinguished senior scientist at the National Center for Atmospheric Research, told Carbon Brief in 2019 that the study was “a bit of a disaster”, explaining that the quality of the forecast was questionable for the assessment.

The authors subsequently published a paper in Science Advances reviewing their study “with the benefit of hindsight”. The authors acknowledged that the results are quite a way off what they forecasted. However, they also claimed to have identified what went wrong with their forecasted analysis.

Problems with the “without climate change” model runs created a larger contrast against their real-world simulations, meaning the analysis overestimated the impact of climate change on the event, they said.

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

This research team has since published more forecast-based attribution studies on hurricanes. One study used hindcasts – forecasts that start from the past and then run forward into the present – to analyse the 2020 hurricane season. The team then ran a series of “counterfactual” hindcasts over the same period, without the influence of human warming from sea surface temperatures.

They found that warmer waters increased three-hour rainfall rates and three-day accumulated rainfall for tropical storms by 10 per cent and 5 per cent, respectively, over the 2020 season.

Meanwhile, a 2021 study by a different team showed how it was possible to use traditional weather forecasts for attribution. The researchers, who penned a Carbon Brief guest post about their work, found that the European heatwave of February 2019 was 42 per cent more likely for the British Isles and at least 100 per cent more likely for France.

To conduct their study, the authors used a weather forecast model – also known as a “numerical weather prediction” model (NWP).

They explain that a NWP typically runs at a higher resolution than a climate model, meaning that it has more, smaller grid cells. This allows it to simulate processes that a climate model cannot and makes them “more suitable for studying the most extreme events than conventional climate models,” the authors argue.

More recently, Leach and his team carried out a forecast attribution study on the record-breaking Pacific north-west heatwave of 2021, years after the event took place.

The authors defined 29 June 2021 as the start of the event, as this is when the maximum temperature of the heatwave was recorded. They then ran their forecasts using a range of “lead times” – the number of days before the event starts that the model simulation is initialised.

The shortest lead time in this study was three days, meaning the scientists began running the model using the weather conditions recorded on 26 June 2021. The short lead time meant that they could tailor the model very closely to the weather conditions at this time and simulated the event itself very accurately. 

By comparison, the longest lead times used in this study were 2-4 months. This means that the models were initialised in spring and, by the time they simulated the June heatwave, their simulation did not closely resemble the events that actually unfolded. 

Leach tells Carbon Brief that by lengthening the lead time of the weather forecast, they can effectively “shift the dial” from storyline to probabilistic attribution. He explains:

“If you’re using a forecast that’s initialised really near to your event, then you’re kind of going down that storyline approach, by saying, ‘I want what my model is stimulating to look really similar to the event I’m interested in’…

“The further back [in time] you go, the closer you get to the more probabilistic style of statements that are more unconditioned.”

This combination of storyline and probabilistic attribution allows the authors to draw conclusions both about how climate change affected the intensity and the likelihood of the heatwave. The authors estimate that the heatwave was 1.3°C more intense and eight times more likely as a result of climate change.

More recently, Climate Central has produced a tool that uses temperature forecasts over the US over the coming days to calculate a “climate shift index”. This index gives the ratio of how common the forecasted temperature is in today’s climate, compared to how likely it would be in a world without climate change.

The index runs from five to minus five. A result of zero indicates that climate change has no detectable influence, an index of five means that climate change made the temperature at least five times more likely and an index of minus five means that climate change made the temperature at least five times less likely.

The tool can be used for attribution. For example, recent analysis by the group used the index to quantify how climate change has influenced the number of uncomfortably hot nights. It concluded:

“Due to human-caused climate change, 2.4 billion people experienced an average of at least two additional weeks per year where nighttime temperatures exceeded 25°C. Over one billion people experienced an average of at least two additional weeks per year of nights above 20°C and 18°C.”

What are the applications of attribution science?

One often-touted application of attribution studies is to raise awareness about the role of climate change in extreme weather events. However, there are limited studies about how effective this is.

One study presents the results of focus group interviews with UK scientists, who were not working on climate change, in which participants were given attribution statements. The study concludes:

“Extreme event attribution shows significant promise for climate change communication because of its ability to connect novel, attention-grabbing and event-specific scientific information to personal experiences and observations of extreme events.”

However, the study identified a range of challenges, including “adequately capturing nuances”, “expressing scientific uncertainty without undermining accessibility of key findings” and difficulties interpreting mathematical aspects of the results.

In another experiment, researchers informed nearly 4,000 adults in the US that climate change had made the July 2023 heatwave in the US at least five times more likely. The team also shared information from Climate Central’s climate shift index. According to the study, both approaches “increased the belief that climate change made the July 2023 heatwave more likely and is making heatwaves in general more likely as well”.

Meanwhile, as the science of extreme weather attribution becomes more established, lawyers, governments and civil society are finding more uses for this evolving field. 

For example, attribution is starting to play an important role in courts. In 2017, two lawyers wrote a Carbon Brief guest post stating “we expect that attribution science will provide crucial evidence that will help courts determine liability for climate change related harm”.

Four years later, the authors of a study on “climate litigation” 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.”

In 2024, around 2,000 Swiss women used an attribution study, alongside other evidence, to win a landmark case in the European Court of Human Rights. The women, mostly in their 70s, said that their age and gender made them particularly vulnerable to heatwaves linked to climate change. The court ruled that Switzerland’s efforts to meet its emissions targets had been “woefully inadequate”.

The 2024 European Geosciences Union conference in Vienna dedicated an entire session to climate change and litigation. Prof Wim Thiery – a scientist who was involved in many conference sessions on climate change and litigation – tells Carbon Brief that attribution science is particularly important for supporting “reparation cases”, in which vulnerable countries or communities seek compensation for the damages caused by climate change.

He adds Carbon Brief that seeing the “direct and tangible impact” of an attribution study in a court case “motivates climate scientists in engaging in this community”.

(Other types of science are also important in court cases related to climate change, he added. For example, “source attribution” identifies the relative contribution of different sectors and entities – such as companies or governments – to climate change.)

Dr Rupert Stuart-Smith, a research associate in climate science and the law at the University of Oxford’s Sustainable Law Programme, adds:

“We’re seeing a new evolution whereby communities are increasingly looking at impact-relevant variables. Think about inundated areas, lake levels, heatwave mortalities. These are the new target variables of attribution science. This is a new frontier and we are seeing that those studies are directly usable in court cases.”

He tells Carbon Brief that some cases “have sought to hold high-emitting corporations – such as fossil fuel or agricultural companies – liable for the costs of climate change impacts”. He continues:

“In cases like these, claimants typically need to show that climate change is causing specific harms affecting them and courts may leverage attribution or climate projections to adjudicate these claims. Impact attribution is particularly relevant in this context.”

Dr Delta Merner is a lead scientist at the science hub for climate litigation. She tells Carbon Brief that “enhanced source attribution for companies and countries” will be “critical” for holding major emitters accountable. She adds:

“This is an urgent time for the field of attribution science, which is uniquely capable of providing robust, actionable evidence to inform decision-making and drive accountability.”

Meanwhile, many countries’s national weather services are working on “operational attribution” – the regular production of rapid attribution assessments.

Stott tells Carbon Brief that the UK Met Office is operationalising attribution studies. For example, on 2 January 2024, it announced that 2023 was the second-warmest year on record for the UK, with an average temperature of 9.97°C.

New methods are also being developed. For example, groups, such as the “eXtreme events: Artificial Intelligence for Detection and Attribution” (XAIDA) team, are researching the use of machine learning and artificial intelligence for attribution studies.

One recent attribution study uses a machine-learning approach to create “dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature”. The authors estimate that the south-central North American heatwave of 2023 was 1.18-1.42°C warmer because of global warming.

The authors conclude:

“Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.”

Other scientists are using a method called UNSEEN, which involves running models thousands of times to increase the size of the datasets used to make it easier to derive accurate probabilities from highly variable extremes.

What are the next steps for attribution research?

The experts that Carbon Brief spoke to for this article have high hopes for the future of attribution science. For example, Stott says:

“Attribution science has great potential to improve the resilience of societies to future climate change, can help monitor progress towards the Paris goals of keeping global warming to well below 2°C and can motivate progress in driving down emissions towards net-zero by the middle of this century.”

However, despite the progress made over the past two decades, there are still challenges to overcome. One of the key barriers in attribution science is a lack of high-quality observational data in low-income countries.

To carry out an attribution study, researchers need a Iong, high-quality dataset of observations from the area being studied. However, inadequate funding or political instability means that many developing countries do not have sufficient weather station data.