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Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA, highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihood of daily maximum temperatures. For instance, over 56 % of the Earth's total land area, all three framework methods agree that maximum temperatures greater than the preindustrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally, over 52 % of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of preindustrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health, and other ecosystem/human system impact studies.

It sounds straightforward. As the Earth warms due to the increased concentration of greenhouse gases in the atmosphere, global temperatures rise and so heatwaves become warmer as well. This means that a fixed temperature threshold is passed more often: the probability of extreme heat increases. However, land use changes, vegetation change, irrigation, air pollution, and other changes also drive local and regional trends in heatwaves. Sometimes they enhance heatwave intensity, but they can also counteract the effects of climate change, and in some regions, the mechanisms that impact on trends in heatwaves have not yet been fully identified. Climate models simulate heatwaves and the increased intensity and probability of extreme heat reasonably well on large scales. However, changes in annual daily maximum temperatures do not follow global warming over some regions, including the Eastern United States and parts of Asia, reflecting the influence of local drivers as well as natural variability. Also, temperature variability is unrealistic in many models, and can fail standard quality checks. Therefore, reliable attribution and projection of change in heatwaves remain a major scientific challenge in many regions, particularly where the moisture budget is not well simulated, and where land surface changes, changes in short-lived forcers, and soil moisture interactions are important., Earth's Future, 10 (6), ISSN:2328-4277

Around October 12, 2019, torrential rainfall from Typhoon Hagibis caused large-scale flooding in a large area around the metropole region of Tokyo leading to large-scale destruction including losses of lives, livelihoods, and economic losses of well over $10 bn US dollars. In this paper we use a multi-method probabilistic event attribution framework to assess the role of human-induced climate change in the heavy rainfall event responsible for a large proportion of the damages. Combining different observational datasets and various climate model simulations, we find an increase in the likelihood of such an event to occur of 15–150%. We use this assessment and the calculated fraction of attributable risk (FAR) to further estimate the economic costs attributable to anthropogenic climate change based on the insured economic losses. Our conservative estimate is that ~$4bn of the damages due to the extreme heavy rainfall associated with Typhoon Hagibis are due to human-induced climate change.

The question of whether, how, and to what extent climate change is affecting health is central to many climate and health studies. We describe a set of formal methods, termed detection and attribution, used by climatologists to determine whether a climate trend or extreme event has changed and to estimate the extent to which climate change influenced that change. We discuss events where changing weather patterns were attributed to climate change and extend these analyses to include health impacts from heat waves in 2018 and 2019 in Europe and Japan, and we show how such impact attribution could be applied to melting ice roads in the Arctic. Documenting the causal chain from emissions of greenhouse gases to observed human health outcomes is important input into risk assessments that prioritize health system preparedness and response interventions and into financial investments and communication about potential risk to policy makers and to the public.

Citizens in many countries are now experiencing record-smashing heatwaves that were intensified due to anthropogenic climate change. Whether today’s most impactful heatwaves could have occurred in a pre-industrial climate, traditionally a central focus of attribution research, is fast becoming an obsolete question. The next frontier for attribution science is to inform adaptation decision-making in the face of unprecedented future heat.

In the immediate aftermath of an extreme weather or climate-related event, the question is invariably asked whether and to what extent it was influenced by anthropogenic climate change. As a trusted source of weather information, National Meteorological Services (NMSs) in particular are facing this question and given their status as government services they are expected to answer, leading to calls for operationalising event attribution studies. Under the umbrella of Copernicus, the European climate service provider, a team of scientists and several national Met Services started a pilot project, following established protocols (van Oldenborgh et al. 2021; Philip et al. 2020) to test whether the task of attributing individual weather extremes can now be taken over by an operational service for the simpler extremes (cold and hot extremes, large-scale heavy rainfall).

Slides presented at the 102 Annual American Meteorological Society Meeting, as part of the session "Major Weather Events and Impacts of 2021" (paper6.3- It's Getting Hot in Here: Real-Time Climate Fingerprints Applied to the 2021 Extreme Heat Season) For more information, please reach out to Daniel Gilford at dgilford@climatecentral.org. Presentation Abstract: Extreme heat was observed and experienced across large portions of the United States in 2021, including during notable record-breaking events in the Pacific Northwest, the Southwest, and along the East coast. The contiguous US experienced its hottest June on record, and excess heat related deaths stretched into the thousands. While more frequent and intense periods of extreme heat are expected consequences of anthropogenic climate change, rapidly and continuously assessing the degree to which human emissions of greenhouse gases increase the likelihood of a specific event remains a challenging technical process. In this study we introduce the Realtime Climate attribution framework and illustrate its application through an analysis of observed 2021 extreme heat events. The framework implements one model-based and two observation-based approaches to produce three distinct attribution assessments, including best estimates and uncertainties. The framework is designed to be flexible across a range of variables and scales, computationally lightweight, and adaptable for impact studies. Using a suite of global climate models, observed global mean temperatures, and local observed daily temperatures, we quantify the extent to which human-driven climate change made 2021 maximum and minimum daily temperature extremes more likely across the United States. Results confirm the continued and growing influence of human-driven climate change in local weather extremes. For instance, we find that the record-breaking high temperatures in June near Phoenix, AZ, were at least 3.25 times more likely because of human activity. Through this framework, we are building the capacity to produce attribution estimates while an event is unfolding. Furthermore, the ability to estimate attribution levels continuously will enhance studies of extreme heat impacts on human health, along with other socioeconomic or influences.

2015 saw the strongest El Niño event in the historical record, resulting in extreme drought conditions in Brazil. As drought conditions may also lead to greater fire danger, this study uses the 2015 fire in Brazil as a case study to examine whether and to what extent human-induced climate change has contributed to the fire weather conditions in the Cerrado and the southern Amazonia transitional forests known as the Arc of deforestation. Our results show that anthropogenic climate change is indeed a driver of meteorological conditions conducive to strong fire weather in these two regions, measured by fire weather index (FWI), especially on shorter timescales of daily and weekly. The anthropogenic climate change signal of FWI on short timescales corresponds to a similar order of increase in the FWI sub-indices (initial spread index and fine fuel moisture code) that can rapidly change due to the influence of the instantaneous weather conditions. For both regions the changes in fire weather in response to anthropogenic climate change are dominated by the combination of temperature and relative humidity responses. High FWI is more likely to occur under El Niño conditions, less likely under La Niña conditions, although the impacts of El Niño vs La Niña conditions are not symmetric when compared with El Niño Southern Oscillation neutral states. To summarize, both human-induced climate change and the presence of El Niño increased the likelihood of occurrence for the strong fire weather condition in 2015. Our results suggest that local and regional adaptation measures, such as improved drought monitoring and warning systems, could help with effective planning of fire prevention, firefighting actions, and disaster preparedness.

Southern Madagascar recently experienced a severe food security crisis, made significantly worse by well below average rainfall from July 2019 to June 2021. This exceptional drought has affected a region with high pre-existing levels of vulnerability to food insecurity (subsistence agriculture and pastoralism in the region is rain-fed only), while impacts have been compounded further by COVID-19 restrictions and pest infestations. The rainy seasons of both 2019/20 and 2020/21 saw just 60% of normal rainfall across the Grand South region and was estimated as a 1-in-135 year dry event, only surpassed in severity by the devastating drought of 1990–92. Based on a combination of observations and climate modelling, the likelihood of experiencing such poor rains in the region was not significantly increased due to human-caused climate change: while the observations and models combine to indicate a small shift toward more droughts like the 2019–2021 event as a consequence of climate change, these trends remain overwhelmed by natural variability. This result is consistent with previous research, with the Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report concluding that any perceptible changes in drought will only emerge in this region if global mean temperatures exceed 2 °C above pre-industrial levels.

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific northwest areas of the U.S. and Canada, leading to spikes in sudden deaths, and sharp increases in hospital visits for heat-related illnesses and emergency calls. Here we present a multi-model, multi-method attribution analysis to investigate to what extent human-induced climate change has influenced the probability and intensity of extreme heatwaves in this region. Based on observations and modeling, the occurrence of a heatwave with maximum daily temperatures (TXx) as observed in the area 45° N–52° N, 119° W–123° W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lie far outside the range of historically observed temperatures. This makes it hard to quantify with confidence how rare the event was. In the most realistic statistical analysis, which uses the assumption that the heatwave was a very low probability event that was not caused by new nonlinearities, the event is estimated to be about a 1 in 1000 year event in today’s climate. With this assumption and combining the results from the analysis of climate models and weather observations, an event, defined as daily maximum temperatures (TXx) in the heatwave region, as rare as 1 in a 1000 years would have been at least 150 times rarer without human-induced climate change. Also, this heatwave was about 2 °C hotter than a 1 in 1000-year heatwave that at the beginning of the industrial revolution would have been (when global mean temperatures were 1.2 °C cooler than today). Looking into the future, in a world with 2 °C of global warming (0.8 °C warmer than today), a 1000-year event would be another degree hotter. It would occur roughly every 5 to 10 years in such global warming conditions. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being, and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future.

Climate-induced food production shocks, like droughts, can cause food shortages and price spikes, leading to food insecurity. In 2007, a synchronous crop failure in Lesotho and South Africa—Lesotho’s sole trading partner—led to a period of severe food insecurity in Lesotho. Here, we use extreme event attribution to assess the role of climate change in exacerbating this drought, going on to evaluate sensitivity of synchronous crop failures to climate change and its implications for food security in Lesotho. Climate change was found to be a critical driver that led to the 2007 crisis in Lesotho, aggravating an ongoing decline in food production in the country. We show how a fragile agricultural system in combination with a large trade-dependency on a climatically connected trading partner can lead to a nonlinear response to climate change, which is essential information for building a climate-resilient food-supply system now and in the future.

The Paris Agreement recognises that climate change and extreme weather events alter human mobility patterns. Even under an optimistic scenario, an increase in global temperature of 1.5 °C above pre-industrial levels is likely to increase the numbers of people vulnerable to climatic stress while limiting the capacity for effective climate change adaptation. Under a less optimistic scenario the disruption is expected to be worse. Yet, the impacts of different warming scenarios, including observed warming, on human mobility remain insufficiently understood and discussed in policymaking. A synthesis of empirical evidence from the IPCC Special Report on Global Warming of 1.5 °C and subsequently published literature on climate change effects in African drylands highlight implications of warming scenarios on human mobility in this region, where climatic change is a defining feature of local communities and predominantly agricultural economies. Adverse effects of climate change on African dryland communities may not necessarily cause mass migration but are likely to alter human mobility from more voluntary towards more forced displacement or entrapment. A systems approach can be used as a starting point to address the urgent challenges of climate-related human mobility.

Extreme weather damage databases report no significant heatwave impacts in sub-Saharan Africa since 1900, yet the region has experienced a number of heatwaves and will be affected disproportionately by them under climate change. Addressing this reporting discrepancy is crucial to assess the impacts of future extreme heat there.

Addressing questions of loss and damage from climate change in courts is limited by many scientific, legal and political challenges. However, modifying existing extreme event attribution frameworks to resolve the evolution of the impacts of climate change over time will improve our understanding of the largest scientific uncertainties.

Lawsuits concerning the impacts of climate change make causal claims about the effect of defendants’ greenhouse gas (GHG) emissions on plaintiffs and have proliferated around the world. Plaintiffs have sought, inter alia, compensation for climate-related losses and to compel governments to reduce their GHG emissions. So far, most of these claims have been unsuccessful. Here we assess the scientific and legal bases for establishing causation and evaluate judicial treatment of scientific evidence in 73 lawsuits. We find that the evidence submitted and referenced in these cases lags considerably behind the state of the art in climate science, impeding causation claims. We conclude that greater appreciation and exploitation of existing methodologies in attribution science could address obstacles to causation and improve the prospects of litigation as a route to compensation for losses, regulatory action and emission reductions by defendants seeking to limit legal liability. Legal cases to force governments to reduce emissions or to pursue compensation for climate change-related losses are increasing. The scientific evidence used in such cases is found to be lagging behind state-of-the-art climate science; using up-to-date methodologies could improve causation claims.

Over the first half of 2020, Siberia experienced the warmest period from January to June since records began and on the 20th of June the weather station at Verkhoyansk reported 38 °C, the highest daily maximum temperature recorded north of the Arctic Circle. We present a multi-model, multi-method analysis on how anthropogenic climate change affected the probability of these events occurring using both observational datasets and a large collection of climate models, including state-of-the-art higher-resolution simulations designed for attribution and many from the latest generation of coupled ocean-atmosphere models, CMIP6. Conscious that the impacts of heatwaves can span large differences in spatial and temporal scales, we focus on two measures of the extreme Siberian heat of 2020: January to June mean temperatures over a large Siberian region and maximum daily temperatures in the vicinity of the town of Verkhoyansk. We show that human-induced climate change has dramatically increased the probability of occurrence and magnitude of extremes in both of these (with lower confidence for the probability for Verkhoyansk) and that without human influence the temperatures widely experienced in Siberia in the first half of 2020 would have been practically impossible. Supplementary Information The online version contains supplementary material available at 10.1007/s10584-021-03052-w.

Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categories of increasingly complex climate change risk that focus on interactions among the multiple drivers of risk, as well as among multiple risks. A significant innovation is recognizing that risks can arise both from potential impacts due to climate change and from responses to climate change. This approach encourages thinking that traverses sectoral and regional boundaries and links physical and socio-economic drivers of risk. Advancing climate change risk assessment in these ways is essential for more informed decision making that reduces negative climate change impacts. © 2021 The Authors

The science of extreme event attribution (EEA)—which connects specific extreme weather events with anthropogenic climate change—could prove useful for engaging the public about climate change. However, there is limited empirical research examining EEA as a climate change communication tool. To help fill this gap, we conducted focus groups with members of the U.K. public to explore benefits and challenges of utilizing EEA results in climate change advocacy messages. Testing a range of verbal and visual approaches for communicating EEA, we found that EEA 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. Communication challenges include adequately capturing nuances around extreme weather risks, vulnerability, adaptation, and disaster risk reduction; expressing scientific uncertainty without undermining accessibility of key findings; and difficulties interpreting mathematical aspects of EEA results. On the basis of our findings, we provide recommendations to help address these challenges when communicating EEA results beyond the climate science community. We conclude that EEA can help catalyze important dialogues about the links between extreme weather and human-driven climate change.

Compound events lead to substantial risks to societies around the globe. As climate change is increasingly exacerbating the intensity and frequency of many hazards in vulnerable regions, ex situ responses to climate change including human mobility and displacement are starkly moving into the spotlight. Whilst proactive migration is often used as an adaptation response to the impact of climate and weather events, reactive migration following unprecedented climatic shocks is often involuntarily and can seriously disrupt livelihoods and undermine human security. The extent to which human mobility (here, measured by internal displacement) can be attributed to extreme weather and compound events and in turn, whether and to what extent extreme weather events and consequently human mobility can be attributed to anthropogenic climate change, has been largely unexplored. Applying a framework based on probabilistic event attribution (PEA) of extreme weather events, we investigate, for the first time, human mobility responses attributed to anthropogenic climate change along a causal chain from anthropogenic climate change and changing frequencies and intensities of extreme weather and climate events to human mobility outcomes. We use the April 2020 extreme precipitation which lead to flooding and associated displacement in Somalia as a feasibility study to present the state of the art of this method. Our attribution model investigates two locations: First, we attribute extreme precipitation at the origin region of the extreme event to then attribute the resulting flood event in the displacement impact region. Event though the analysis shows no attributable link to anthropogenic climate change, our method advances the field of climate impact research regarding statistical approaches, model development and evaluation. For our feasibility study, we also find that sparsity of climate observations reveal one of many reasons for a lack of a climate change signal, which suggests an application of our model to other climate event contexts is needed to further test our method.

Extreme and impactful weather events of the recent past provide a vital but under-utilised data source for understanding present and future climate risks. Extreme event attribution (EEA) enables us to quantify the influence of anthropogenic climate change (ACC) on a given event in a way that can be tailored to stakeholder needs, thereby enhancing the potential utility of studying past events. Here we set out a framework for systematically recording key details of high-impact events on a national scale (using the UK and Puerto Rico as examples), combining recent advances in event attribution with the risk framework. These ‘inventories’ inherently provide useful information depending on a user’s interest. For example, as a compilation of the impacts of ACC, we find that in the UK since 2000, at least 1500 excess deaths are directly attributable to human-induced climate change, while in Puerto Rico the increased intensity of Hurricane Maria alone led to the deaths of up to 3670 people. We also explore how inventories form a foundation for further analysis, learning from past events. This involves identifying the most damaging hazards and crucially also vulnerabilities and exposure characteristics over time. To build a risk assessment for heat-related mortality in the UK we focus on a vulnerable group, elderly urban populations, and project changes in the hazard and exposure within the same framework. Without improved preparedness, the risk to this group is likely to increase by ~50% by 2028 and ~150% by 2043. In addition, the framework allows the exploration of the likelihood of otherwise unprecedented events, or 'Black Swans’. Finally, not only does it aid disaster preparedness and adaptation at local and national scales, such inventories also provide a new source of evidence for global stocktakes on adaptation and loss and damage such as mandated by the Paris Climate Agreement.

In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits. Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate. We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit., Earth System Dynamics, 12 (1), ISSN:2190-4987, ISSN:2190-4979

The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.

The question of whether, how, and to what extent climate change is affecting health is central to many climate and health studies. We describe a set of formal methods, termed detection and attribution, used by climatologists to determine whether a climate trend or extreme event has changed and to estimate the extent to which climate change influenced that change. We discuss events where changing weather patterns were attributed to climate change and extend these analyses to include health impacts from heat waves in 2018 and 2019 in Europe and Japan, and we show how such impact attribution could be applied to melting ice roads in the Arctic. Documenting the causal chain from emissions of greenhouse gases to observed human health outcomes is important input into risk assessments that prioritize health system preparedness and response interventions and into financial investments and communication about potential risk to policy makers and to the public.

A strict application of legal tests to find the cause of an event has always been a challenge for a coherent causal analysis. This is again the case for making causal statements in the climate change context, although we are witnessing unprecedented impacts of a changing climate at a global scale. While probabilistic event attribution provides information linking greenhouse gas emission levels to observable characteristics of extreme weather and climate related events, the conventional understanding of causation in law provides a very limited response to this scientific knowledge. We offer a new matrix for developing causal explanations based on formal logic, for a coherent analysis that is compatible with climate science and law. This matrix causally explains the relation between emissions, the increase in global mean surface temperature, the general increase in frequency and severity of climate related events and, most significantly, between emissions and individual climate related events.

The subtropical (south of 15deg.S) southern Africa experienced one of the most severe droughts in the record - accompanied with an exceptional heat wave – during the austral spring (October through December – the first half of the main rainy season) of 2015. The observed surface hydro-meteorological conditions led to substantial socio-economic impacts in the region - with mostly semi-arid climate and high spatial-temporal variability - where drought is the principal type of widespread natural disaster. More specifically, very low precipitation - compounded with very high surface air temperature (SAT) - caused low runoff, water shortages and restrictions, reduced electricity generation, and considerable loss of crops and livestock prompting Botswana, Namibia, Lesotho, Malawi, Swaziland and Zimbabwe to declare national drought emergencies. Every extreme event is the result of a combination of external drivers, natural (solar forcing and volcanos), and anthropogenic (carbon dioxide emissions, land use, etc.), and internal variability. The risk-based or probabilistic event attribution assesses to what extent anthropogenic forcing modifies the probability and magnitude, and hence the risk of an extreme event or a class of events to occur (i.e. to identify “the sharp edge” of climate change). This study utilises multiple long-term observations (CRU TS 4.03, GPCC v2018, NOAA PREC/L, etc.), and AGCM and CGCM historical simulations (12 models in total spread across CMIP3, CMIP5 and CMIP6 generations) to estimate risk indicators such as probability (risk) ratio (RR) and intensity change for the OND 2015 drought with respect to the beginning of the 20th century or pre-industrial conditions. Our multi-method approach indicates significant influence of climate change in total OND precipitation, e.g. RR = 1.48 (with 95% CI: 1.20, 1.85), and precipitation-temperature (mean OND SAT) ratio fields over the subtropical southern Africa, but uncertainty of risk indicators can be substantial. The crucial elements of atmospheric circulation and teleconnections (such as Angola Low and ENSO influence) associated with this extreme event are analysed and elaborated using the latest NOAA-CIRES-DOE 20th Century Reanalysis version 3.