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Advances in the field of extreme event attribution allow to estimate how anthropogenic global warming affects the odds of individual climate disasters, such as river floods. Extreme event attribution typically uses precipitation as proxy for flooding. However, hydrological processes and antecedent conditions make the relation between precipitation and floods highly nonlinear. In addition, hydrology acknowledges that changes in floods can be strongly driven by changes in land‐cover and by other human interventions in the hydrological system, such as irrigation and construction of dams. These drivers can either amplify, dampen or outweigh the effect of climate change on local flood occurrence. Neglecting these processes and drivers can lead to incorrect flood attribution. Including flooding explicitly, that is, using data and models of hydrology and hydrodynamics that can represent the relevant hydrological processes, will lead to more robust event attribution, and will account for the role of other drivers beyond climate change. Existing attempts are incomplete. We argue that the existing probabilistic framework for extreme event attribution can be extended to explicitly include floods for near‐natural cases, where flood occurrence was unlikely to be influenced by land‐cover change and human hydrological interventions. However, for the many cases where this assumption is not valid, a multi‐driver framework for conditional event attribution needs to be established. Explicit flood attribution will have to grapple with uncertainties from lack of observations and compounding from the many processes involved. Further, it requires collaboration between climatologists and hydrologists, and promises to better address the needs of flood risk management. This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangePaleoclimates and Current Trends > Detection and AttributionAssessing Impacts of Climate Change > Observed Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Extreme event attribution is an active area of scientific research, but public attribution of extreme events to climate change is not well understood – despite its importance to climate change communication and policy. We surveyed a representative sample of the U.S. population (n = 1071) to measure the public’s confidence in attributing five event types to climate change – wildfire, heat, rainfall/flooding, tornadoes, and hurricanes. Our respondents had the highest confidence in attributing wildfires and extreme heat to climate change, and the lowest confidence for hurricanes and tornadoes. Respondent characteristics, such as education level, age, race/ethnicity, political affiliation, and self-reported extreme event impacts, were linked to attribution confidence. Overall, those reporting negative impacts from extreme events had higher levels of attribution confidence. While Republicans on average had lower levels of attribution confidence, we found that self-reported negative event impacts had a moderating effect on attribution confidence among Republicans. Republicans who were more negatively impacted by extreme events had higher levels of attribution confidence compared to Republicans who were less impacted. We also compared the public’s attribution confidence to scientific assessments, developing a measure of attribution alignment. We found that respondents aligned with scientific event attribution for an average of 2 out of 5 extreme event types. While respondent characteristics were less consistently related to attribution alignment overall, Democrats on average had lower alignment. Our study suggests that the public is connecting climate change to extreme weather and making distinctions in attribution levels, but politics and experiences with extreme weather matter. We recommend that scientists and climate change communicators reflect this discernment in discourses about extreme events, climate change, and policy. [ABSTRACT FROM AUTHOR]

The field of extreme event attribution (EEA) has rapidly developed over the last two decades. Various methods have been developed and implemented, physical modelling capabilities have generally improved, the field of impact attribution has emerged, and assessments serve as a popular communication tool for conveying how climate change is influencing weather and climate events in the lived experience. However, a number of non-trivial challenges still remain that must be addressed by the community to secure further advancement of the field whilst ensuring scientific rigour and the appropriate use of attribution findings by stakeholders and associated applications. As part of a concept series commissioned by the World Climate Research Programme, this article discusses contemporary developments and challenges over six key domains relevant to EEA, and provides recommendations of where focus in the EEA field should be concentrated over the coming decade. These six domains are: (1) observations in the context of EEA; (2) extreme event definitions; (3) statistical methods; (4) physical modelling methods; (5) impact attribution; and (6) communication. Broadly, recommendations call for increased EEA assessments and capacity building, particularly for more vulnerable regions; contemporary guidelines for assessing the suitability of physical climate models; establishing best-practice methodologies for EEA on compound and record-shattering extremes; co-ordinated interdisciplinary engagement to develop scaffolding for impact attribution assessments and their suitability for use in broader applications; and increased and ongoing investment in EEA communication. To address these recommendations requires significant developments in multiple fields that either underpin (e.g., observations and monitoring; climate modelling) or are closely related to (e.g., compound and record-shattering events; climate impacts) EEA, as well as working consistently with experts outside of attribution and climate science more generally. However, if approached with investment, dedication, and coordination, tackling these challenges over the next decade will ensure robust EEA analysis, with tangible benefits to the broader global community.

In July 2022, regions with elevations exceeding 5 000 m on the inner Tibetan Plateau (TP) witnessed a record-breaking heatwave. But how the atmospheric circulation and soil moisture play roles in the occurrence and maintenance of the heatwave in such high elevation climate sensitive region remains unknown. Here, by using the flow analogue method, we find that negative soil moisture anomalies explain more than half of the extreme high temperature during the heatwave, while atmospheric circulation explains less than half. The high soil moisture-temperature coupling metric and the increased correlation between latent heat flux and soil moisture during heatwave revealed strong land-atmosphere feedback in the Qiangtang Plateau which has amplified the heatwave. Analyses of numerical experiments confirm that the presence of interaction between soil moisture and the atmosphere has increased the intensity of hot extreme event under the same atmospheric circulation conditions. Under the warming background, land-atmosphere coupling leads to a faster increase in extreme high temperatures compared to the global mean warming rate, and it is twice as fast as the increase in extreme high temperatures without coupling. We highlight the increased probability of extreme high temperature over the TP in the future due to soil moisture feedback and the results are hoped to inform policymakers in making decisions for climate adaptation activities.

In the last decade, the detection and attribution science that links climate change to extreme weather and climate events has emerged as a growing field of research with an increasing body of literature. This paper overviews the methods for extreme event attribution (EEA) and discusses the new insights that EEA provides for infrastructure adaptation. We found that EEA can inform stakeholders about current climate risk, support vulnerability-based and hazard-based adaptations, assist in the development of cost-effective adaptation strategies, and enhance justice and equity in the allocation of adaptation resources. As engineering practice shifts from a retrospective approach to a proactive, forward-looking risk management strategy, EEA can be used together with climate projections to enhance the comprehensiveness of decision making, including planning and preparing for unprecedented extreme events. Additionally, attribution assessment can be more useful for adaptation planning when the exposure and vulnerability of communities to past events are analyzed, and future changes in the probability of extreme events are evaluated. Given large uncertainties inherent in event attribution and climate projections, future research should examine the sensitivity of engineering design to climate model uncertainties, and adapt engineering practice, including building codes, to uncertain future conditions. While this study focuses on adaptation planning, EEA can also be a useful tool for informing and enhancing decisions related to climate mitigation.

Statistical methods are proposed to select homogeneous regions when analyzing spatial block maxima data, such as in extreme event attribution studies. Here, homogeneitity refers to the fact that marginal model parameters are the same at different locations from the region. The methods are based on classical hypothesis testing using Wald-type test statistics, with critical values obtained from suitable parametric bootstrap procedures and corrected for multiplicity. A large-scale Monte Carlo simulation study finds that the methods are able to accurately identify homogeneous locations, and that pooling the selected locations improves the accuracy of subsequent statistical analyses. The approach is illustrated with a case study on precipitation extremes in Western Europe. The methods are implemented in an R package that allows for easy application in future extreme event attribution studies. [ABSTRACT FROM AUTHOR]

Greece is characterized by a significant warming trend in recent decades, accompanied by increasing frequency, intensity, and duration of heat waves (HWs). A particularly devastating HW that affected the country was the late July/early August 2021 event (JA2021HW), which lasted for 9 days (July 28–August 5). Focusing on the hottest day of the event (August 3), the main characteristics of JA2021HW are presented in the current study, using model reanalysis data and up to 11‐year observations derived from the dense network of ground‐based weather stations operated by the Meteo Unit at the National Observatory of Athens (NOA). This analysis highlights the severity of JA2021HW, especially in the central and southernmost regions of Greece. Most importantly, the impact of the direct effect of anthropogenic greenhouse gases to the examined extreme event, in terms of intensity and probability of occurrence, is examined by employing a regional 31‐member ensemble (ENS) modeling approach based on Weather Research and Forecasting (WRF) model, which is operationally used by NOA/Meteo. Firstly, WRF is validated under 7‐day lead‐time ENS simulations with current‐state greenhouse gas (GHG) concentrations (GHG_2021 ENS), showing a robust model performance in replicating the JA2021HW's magnitude on August 3rd. Then, 7‐day lead‐time ENS simulations with the GHG concentrations reduced to the pre‐industrial (1854) levels (GHG_1854 ENS) are performed and compared to the GHG_2021 ENS experiment. The results reveal a contribution of the immediate anthropogenic warming due to the increased GHG concentrations to the JA2021HW intensity in West and South continental and insular Greece, which can be important in the framework of the human health impacts of extreme temperatures. For the event's occurrence probability, no robust evidence of any change could be derived. These statements are partially constrained by the fact that only the direct GHG effect on the timescale of a few days was examined. [ABSTRACT FROM AUTHOR]

Attribution of high-impact weather events to anthropogenic climate change is important for disentangling long-term trends from natural variability and estimating potential future impacts. Up to this point, most attribution studies have focused on univariate drivers, despite the fact that many impacts are related to multiple compounding weather and climate drivers. For instance, co-occurring climate extremes in neighbouring regions can lead to very large combined impacts. Yet, attribution of spatially compounding events with different hazards poses a great challenge. Here, we present a comprehensive framework for compound event attribution to disentangle the effects of natural variability and anthropogenic climate change on the event. Taking the 2020 spatially compounding heavy precipitation and heatwave event in China as a showcase, we find that the respective dynamic and thermodynamic contributions to the intensity of this event are 51% (35–67%) and 39% (18–59%), and anthropogenic climate change has increased the occurrence probability of similar events at least 10-fold. We estimate that compared to the current climate, such events will become 10 times and 14 times more likely until the middle and end of the 21st century, respectively, under a high-emissions scenario. This increase in likelihood can be substantially reduced (to seven times more likely) under a low-emissions scenario. Our study demonstrates the effect of anthropogenic climate change on high-impact compound extreme events and highlights the urgent need to reduce greenhouse gas emissions.

In June 2021, the Pacific Northwest experienced a heatwave that broke all previous records. Estimated return levels based on observations up to the year before the event suggested that reaching such high temperatures is not possible in today's climate. We here assess the suitability of the prevalent statistical approach by analyzing extreme temperature events in climate model large ensemble and synthetic extreme value data. We demonstrate that the method is subject to biases, as high return levels are generally underestimated and, correspondingly, the return period of low‐likelihood heatwave events is overestimated, if the underlying extreme value distribution is derived from a short historical record. These biases have even increased in recent decades due to the emergence of a pronounced climate change signal. Furthermore, if the analysis is triggered by an extreme event, the implicit selection bias affects the likelihood assessment depending on whether the event is included in the modeling. Plain Language Summary: In June 2021, the Pacific Northwest experienced a record‐breaking heatwave event. Based on historical data, the scientific community has applied statistical models to understand how likely this event was to occur. However, due to the record‐shattering nature of this particular heatwave, the model suggested that reaching such high temperatures should not have been possible. In this study, we evaluate the accuracy of these statistical models in describing the occurrence probability of extreme events. We find that the current models tend to underestimate the occurrence probability and that the bias has become more pronounced in recent years due to climate change. Finally, we assess how the way extreme events are included in the model can also affect the accuracy of estimates. Key Points: Standard return period estimates of temperature extremes are systematically overestimated in short records under non‐stationary conditionsThe small‐sample bias in maximum likelihood estimates is found both for extremes in climate model data and in synthetic data experimentsFuture analysis should account for the statistical implications of the selection bias if the analysis is triggered by an extreme event [ABSTRACT FROM AUTHOR]

Weather and climate phenomena have outsized impacts on society when they are particularly extreme. Extreme Event Attribution (EEA) seeks to quantify the extent to which extreme weather and climate phenomena are the result of anthropogenic climate change (ACC), and thus it has implications for many pertinent climate change discussions, including those on potential legal claims of loss and damages and calculations of the social cost of carbon. The Fraction of Attributable Risk (FAR) is one metric that is used to quantify the proportion of an extreme weather or climate “event” associated with ACC. The FAR is typically applied to changes in the likelihood of exceeding some geophysical value chosen, post hoc, to represent the “event” (e.g., i.e., rainfall amounts, flood depths, drought measures, temperature values, etc.). The FAR has further been used to estimate the fraction of observed impacts (e.g., lives lost or economic damage) that can be associated with ACC by multiplying realized impacts by the FAR (IFAR = Impact×FAR). Here, we illustrate with a few stylized examples that this IFAR calculation only produces reliably useful results when the weather or climate phenomena in question can be easily conceived of as a discrete binary “event” (i.e., the entirety of the event either occurs or it does not). We show that the IFAR calculation can produce misleading results when the weather or climate phenomena in question are on a continuum, and ACC can be thought of as altering the intensity of the geophysical value that is used in the eventhood definition. Specifically, we show that the IFAR calculation inflates the impacts associated with ACC in these circumstances because it inaccurately assumes that there would have been zero impact had the geophysical value chosen to define eventhood not been exceeded. We illustrate that for weather and climate phenomena on a continuum (e.g., floods, droughts, temperatures, etc.), a clearer way of conceptualizing the impacts associated with ACC is to compare the expected value of the impact between the ACC and preindustrial conditions across the full continuum. [ABSTRACT FROM AUTHOR]

Abstract In June 2021, the Pacific Northwest experienced a heatwave that broke all previous records. Estimated return levels based on observations up to the year before the event suggested that reaching such high temperatures is not possible in today's climate. We here assess the suitability of the prevalent statistical approach by analyzing extreme temperature events in climate model large ensemble and synthetic extreme value data. We demonstrate that the method is subject to biases, as high return levels are generally underestimated and, correspondingly, the return period of low‐likelihood heatwave events is overestimated, if the underlying extreme value distribution is derived from a short historical record. These biases have even increased in recent decades due to the emergence of a pronounced climate change signal. Furthermore, if the analysis is triggered by an extreme event, the implicit selection bias affects the likelihood assessment depending on whether the event is included in the modeling.

Extreme event attribution aims at evaluating the impact of climate change on specific extreme events. In this work, we present an attribution methodology for severe extratropical cyclones, and test it on storm Alex. Alex was an explosive extratropical cyclone that affected Southern France and Northern Italy at the beginning of October 2020. The methodology exploits mathematical properties of circulation analogues, and identifies changes in physical and statistical properties. We first divide 6-hourly ERA5 data into two periods: a counterfactual period (1950–1984) and a factual period (1986–2021). We then identify the 30 cyclones in each period whose sea-level pressure maps are closest to Alex's map by selecting those with the lowest Euclidean distance from Alex. We term these "analogues" of Alex. We find that analogues in the factual period are more persistent than in the counterfactual period, which may favour severe impacts resulting from persistent strong winds and heavy precipitation, as was the case for Alex. This effect is compounded by the doubling in accumulated daily precipitation detected in Northern Italy between the counterfactual and factual analogues. In the factual period, the analogues display an increase in the eddy kinetic energy in their growth phase, with poleward-shifted backward tracks. We also identify a seasonal shift of the analogues, from spring to autumn. Finally, the analogues in the factual period are closer to Alex than in the counterfactual period. These changes collectively point to high-impact storms like Alex having become more common in a changing climate. [ABSTRACT FROM AUTHOR]

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ∘ C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region. [ABSTRACT FROM AUTHOR]

Two extreme heatwaves hit Western Europe in the summer of 2019, with historical records broken by more than a degree in many locations, and significant societal impacts, including excess mortality of several thousand people. The extent to which human influence has played a role in the occurrence of these events has been of large interest to scientists, media and decision makers. However, the outstanding nature of these events poses challenges for physical and statistical modeling. Using an unprecedented number of climate model ensembles and statistical extreme value modeling, we demonstrate that these short and intense events would have had extremely small odds in the absence of human-induced climate change, and equivalently frequent events would have been 1.5 °C to 3 °C colder. For instance, in France and in The Netherlands, the July 3-day heatwave has a 50-150-year return period in the current climate and a return period of more than 1000 years without human forcing. The increase in the intensities is larger than the global warming by a factor 2 to 3. Finally, we note that the observed trends are much larger than those in current climate models.

In September 2021, southern China witnessed an extreme high-temperature and high-humidity event. The average regional wet bulb globe temperature (WBGT) anomaly (relative to 1961–1990 mean) in 110.0°–120.0°E, 27.5°–32.5°N region was the highest on record at 3.28 °C and exceeded three times the observed standard deviation. To investigate the underlying causes, we examine the effects of anthropogenic forcings and anomalous circulation patterns on this event using the multi-model ensembles from the Coupled Model Intercomparison Project Phase 6. Results indicate that 2021-like events would happen extremely rarely without anthropogenic warming (would not occur in counterfactual world simulations) and have become a 1-in-16-year event in the factual world. For the threshold of the second most extreme year, the occurrence probability of extreme WBGT events increases approximately 50 times due to the impact of anthropogenic forcings. The effect of anthropogenic warming under similar atmosphere circulation increases the probability of extreme WBGT events by 13–60 times, and that of corresponding circulation patterns under the same anthropogenic warming increases the probability by 1.3–1.8 times.

Changes in regional climate variability serve as the initial indicators of positive climate feedback mechanisms, which are expected to intensify as greenhouse gas (GHG) emissions unfold. Therefore, it is crucial to examine the extent to which GHG-induced changes in regional climate variability, in isolation from changes in mean climate, contribute to the frequency of extreme weather events. In this study, I build upon the concept of the fraction of attributable risk (FAR) by introducing the fraction of preventable risk (FPR), allowing for the assessment of GHG forcing’s contribution to risk reduction in scenarios of decreasing risk extremes. Results derived from four global climate models indicate that while the predominant factor amplifying the frequency of hot extremes is the mean warming, with a 18-fold increase in probability and an attributable risk fraction of 0.96 to GHG forcing, changes in regional climate variability have already modified the probability of extremes. In South Asia, for instance, the 12-fold increase in hot extremes resulting from mean warming is compounded by an additional ∼3 times, solely attributed to the increased temperature variability. Conversely, during winter in the Arctic, the 10-fold increase in the frequency of hot extremes due to mean warming is offset by a ∼2.5-fold decrease resulting from diminished variability, with a preventable risk fraction of −0.55 to GHG forcing. Concerning heavy-precipitation events, in certain regions, GHG-induced changes in precipitation variability carry greater significance than changes in the mean. For example, in the West African summer monsoon region GHG forcing is amplifying the risk of extreme monsoon precipitation by ∼4 times. This amplified risk of potential flooding arises from increases in both mean precipitation and variability. Separating attribution metrics into mean and variability components offers valuable insights into region-specific mechanisms affecting extreme event frequency.

In boreal summer (July–August) 2022, an unprecedented marine heatwave (MHW) occurred in the northwest Pacific Ocean (NWP), while a record-breaking terrestrial heatwave (THW) hit the Yangtze River Basin (YRB). The temperature anomalies caused by this compound MHW-THW event exceeded climatology by 2.5 standard deviations (SDs), breaking the historical record for nearly 100 years, with severe impacts on the ecosystems and social economy. To investigate the underlying causes, we explored the potential roles of anthropogenic forcing, atmospheric circulation, and ‘triple-dip’ La Niña on this compound event using the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations. Results indicate that the 2022-like compound MHW-THW event was extremely unlikely to happen without anthropogenic warming, and that such extreme heatwaves were governed by the climatic mean temperature rather than changes in temperature variability. Notably, the anticyclone circulation patterns and associated high-pressure systems (i.e. western North Pacific subtropical high (WNPSH) and South Asian high (SAH)) increase the probability of a 2022-like MHW-THW event by 3.7 times. However, the La Niña phase has no significant effect on the occurrence probability of such events. We further estimate that the 2022-like MHW-THW event will become 7.5 and 11.4 times more likely under the SSP3-7.0 scenario by the middle and end of the 21st century, respectively. This study demonstrates the contribution of anthropogenic climate change and natural variability to the occurrence of compound MHW-THW events and highlights the urgent need to build mitigation strategies for compound MHW-THW events.

The changing probabilities of extreme climate and weather events, in terms of frequency, intensity, spatial extent, duration, and timing is one of the most noticeable and damaging manifestations of human-induced climate change. During the March-April-May (MAM) rainfall season of 2012, 2016 and 2018, Kenya experienced high rainfall that caused both widespread and localised flooding, resulting in human and livestock deaths, destruction of infrastructure and property, bursting of riverbanks, submerging of farmlands and emergence of isolated cases of water-borne diseases. Here, we aim to quantify how the magnitude of heavy rainfall during these seasons may have been altered by human-induced climate change. We undertake a probabilistic attribution analysis using three different approaches utilising two observational datasets and two independent climate model experiment set-ups. We analyse three different seasonal heavy rainfall indices, maximum consecutive 5-day, 10-day, and 20-day rainfall, to compare the magnitude of maxima recorded in MAM 2012, 2016 and 2018 with the magnitude of maxima in a pre-industrial climate (with little or no anthropogenic influence). We find a shift towards intensification of extreme rainfall in today's climate, although these increases are not in all cases statistically distinguishable from our estimates of magnitudes in the preindustrial climate. Although we find no significant anthropogenic climate change influence, the intensification of extreme rainfall amid the observed drying trend and the projected increases in rainfall in the MAM season in Kenya, leave the already vulnerable societies with uncertainties about how to prepare for a changing climate. This study, therefore, provides a basis for an in-depth assessment of current and future trends of extreme rainfall in East Africa in adapting to changing climate risks for sustainable development in the already vulnerable and less resilient society.

Both climate scientists and non-scientists (laypeople) attribute extreme weather events to various influences. Laypeople’s attributions for these events are important as these attributions likely influence their views and actions about climate change and extreme events. Research has examined laypeople’s attribution scepticism about climate change in general; however, few climate scientists are familiar with the processes underpinning laypeople’s attributions for individual extreme events. Understanding these lay attributions is important for scientists to communicate their findings to the public. Following a brief summary of the way climate scientists calculate attributions for extreme weather events, we focus on cognitive and motivational processes that underlie laypeople’s attributions for specific events. These include a tendency to prefer single-cause rather than multiple-cause explanations, a discounting of whether possible causes covary with extreme events, a preference for sufficient causes over probabilities, applying prevailing causal narratives, and the influence of motivational factors. For climate scientists and communicators who wish to inform the public about the role of climate change in extreme weather events, these patterns suggest several strategies to explain scientists’ attributions for these events and enhance public engagement with climate change. These strategies include showing more explicitly that extreme weather events reflect multiple causal influences, that climate change is a mechanism that covaries with these events and increases the probability and intensity of many of these events, that human emissions contributing to climate change are controllable, and that misleading communications about weather attributions reflect motivated interests rather than good evidence. [ABSTRACT FROM AUTHOR]

The study analyses observed and numerical simulations of daily maximum and minimum temperature from 1920 onwards and to investigate the unprecedented heat event that occurred in 21–23 August 2023 in Barcelona. The historical changes in the intensity of such events, their expected future changes under scenarios of +1.5 °C, +2 °C, and + 3 °C, and the future exposure of populations to such kind of events are examined using the flow analogues approach. The findings indicate a significant increase in observed temperatures for similar heatwaves to those occurred in August 2023. The study also emphasises the impact of global warming on the intensification of heat events over the impact of urbanization. Additionally, after examining the role of natural variability in temperature changes, we concluded that global warming is the primary factor driving the increase in heatwave intensity. In terms of the frequency of such events, we found that extreme heat events, such as the August 2023 heatwave, will become 2 and 5 times more likely with a global summer warming of 2 °C and 3 °C, respectively. This will expose a large portion of the population to dangerous heat levels highlighting the importance of limiting global warming to 1.5 °C to mitigate the impacts on urban populations. [Display omitted] • Global warming escalates urban heatwave risks, especially in Mediterranean. • Increasing intensity of extreme heat events like the one in August 2023 in Barcelona. • Urban growth and natural variability contribute little to the observed warming. • Future scenarios show rising frequency and exposure to extreme heat events. • Urgent need for climate strategies to limit temperature rise to 1.5 °C. [ABSTRACT FROM AUTHOR]

In summer 2019, several countries in Europe experienced unprecedented heatwaves. Two extreme event attribution (EEA) studies, which assess the role of climate change in extreme weather events, were published at roughly the same time as the heatwaves were taking place (June/August 2019). Building on a prior study of online news media coverage of the heatwaves, this study surveyed journalists from major news outlets in France, Germany, the Netherlands and the UK. Based on the responses of 42 journalists, we found a relative lack of knowledge about EEA studies but a high level of importance assigned to writing about the link between the heatwaves and climate change (e.g., likelihood or intensity); a relatively low number of specialist reporters vs. general reporters covering the heatwaves; a strong reliance on scientific experts as sources; no inclusion of climate change deniers; stronger role perceptions as educators than advocates; relatively little time and resource constraints on their reporting; and an overall tendency for the journalists to report more about climate change. The findings provide new insights into journalism practice and climate journalism in terms of the peculiarities and contextual factors that can influence coverage of extreme weather events and climate change. [ABSTRACT FROM AUTHOR]

During the winters of 2013-2014 and 2014-2015, anomalously warm temperatures in western North America and anomalously cool temperatures in eastern North America resulted in substantial human and environmental impacts. Motivated by the impacts of these concurrent temperature extremes and the intrinsic atmospheric linkage between weather conditions in the western and eastern United States, we investigate the occurrence of concurrent "warm-West/cool-East" surface temperature anomalies, which we call the "North American winter temperature dipole." We find that, historically, warm-West/cool-East dipole conditions have been associated with anomalous mid-tropospheric ridging over western North America and downstream troughing over eastern North America. We also find that the occurrence and severity of warm-West/cool-East events have increased significantly between 1980 and 2015, driven largely by an increase in the frequency with which high-amplitude "ridge-trough" wave patterns result in simultaneous severe temperature conditions in both the West and East. Using a large single-model ensemble of climate simulations, we show that the observed positive trend in the warm-West/cool-East events is attributable to historical anthropogenic emissions including greenhouse gases, but that the co-occurrence of extreme western warmth and eastern cold will likely decrease in the future as winter temperatures warm dramatically across the continent, thereby reducing the occurrence of severely cold conditions in the East. Although our analysis is focused on one particular region, our analysis framework is generally transferable to the physical conditions shaping different types of extreme events around the globe.

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model-based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The "storyline" attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event thatwas predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019. [ABSTRACT FROM AUTHOR]

We apply climate attribution techniques to sea surface temperature time series from five regional North Pacific ecosystems to track the growth in human influence on ocean temperatures over the past seven decades (1950–2022). Using Bayesian estimates of the Fraction of Attributable Risk (FAR) and Risk Ratio (RR) derived from 23 global climate models, we show that human influence on regional ocean temperatures could first be detected in the 1970s and grew until 2014–2020 temperatures showed overwhelming evidence of human contribution. For the entire North Pacific, FAR and RR values show that temperatures have reached levels that were likely impossible in the preindustrial climate, indicating that the question of attribution is already obsolete at the basin scale. Regional results indicate the strongest evidence for human influence in the northernmost ecosystems (Eastern Bering Sea and Gulf of Alaska), though all regions showed FAR values > 0.98 for at least one year. Extreme regional SST values that were expected every 1000–10 000 years in the preindustrial climate are expected every 5–40 years in the current climate. We use the Gulf of Alaska sockeye salmon fishery to show how attribution time series may be used to contextualize the impacts of human-induced ocean warming on ecosystem services. We link negative warming effects on sockeye fishery catches to increasing human influence on regional temperatures (increasing FAR values), and we find that sockeye salmon migrating to sea in years with the strongest evidence for human effects on temperature (FAR ⩾ 0.98) produce catches 1.4 standard deviations below the long-term log mean. Attribution time series may be helpful indicators for better defining the human role in observed climate change impacts, and may thus help researchers, managers, and stakeholders to better understand and plan for the effects of climate change.

A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.

In March 2022, large parts over the north Indian plains including the breadbasket region, and southern Pakistan began experiencing prolonged heat, which continued into May. The event was exacerbated due to prevailing dry conditions in the region, resulting in devastating consequences for public health and agriculture. Using event attribution methods, we analyse the role of human-induced climate change in altering the chances of such an event. To capture the extent of the impacts, we choose March–April average of daily maximum temperature over the most affected region in India and Pakistan as the variable. In observations, the 2022 event has a return period of ∼1-in-100 years. For each of the climate models, we then calculate the change in probability and intensity of a 1-in-100 year event between the actual and counterfactual worlds for quantifying the role of climate change. We estimate that human-caused climate change made this heatwave about 1 °C hotter and 30 times more likely in the current, 2022 climate, as compared to the 1.2 °C cooler, pre-industrial climate. Under a future global warming of 2 °C above pre-industrial levels, heatwaves like this are expected to become even more common (2–20 times more likely) and hotter (by 0 °C–1.5 °C) compared to now. Stronger and frequent heat waves in the future will impact vulnerable groups as conditions in some regions exceed limits for human survivability. Therefore, mitigation is essential for avoiding loss of lives and livelihood. Heat Action Plans have proved effective to help reduce heat-related mortality in both countries.

In a recent very influential court case, Juliana v. United States, climate scientist Kevin Trenberth used the “storyline” approach to extreme event attribution to argue that greenhouse warming had affected and will affect extreme events in their regions to such an extent that the plaintiffs already had been or will be harmed. The storyline approach to attribution is deterministic rather than probabilistic, taking certain factors as contingent and assessing the role of climate change conditional on those factors. The US Government’s opposing expert witness argued that Trenberth had failed to make his case because “all his conclusions of the injuries to Plaintiffs suffer from the same failure to connect his conditional approach to Plaintiffs’ local circumstances.” The issue is whether it is possible to make statements about individual events based on general knowledge. A similar question is sometimes debated within the climate science community. We argue here that proceeding from the general to the specific is a process of deduction and is an entirely legitimate form of scientific reasoning. We further argue that it is well aligned with the concept of legal evidence, much more so than the more usual inductive form of scientific reasoning, which proceeds from the specific to the general. This has implications for how attribution science can be used to support climate change litigation. “The question is”, said Alice, “whether you can make words mean different things.” “The question is”, said Humpty Dumpty, “which is to be master — that’s all.” (Lewis Carroll, Alice’s Adventures in Wonderland). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Using a new modelling methodology known as "extreme event attribution," or EEA, climate scientists can now connect extreme weather to anthropogenic forcings. This paper seeks to uncover the significance of EEA for the epistemology of climate change, nature, and culture in the Anthropocene. First, we examine how EEA is emblematic of a larger turn in climate modelling, one that seeks to deploy anthropogenic climate change as an explanatory tool for an increasing number of socio‐natural phenomena. While some theorists have argued that the Anthropocene heralds the end of the nature–culture divide, we argue that EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation: a divide which we call "partitioned causality." Second, we demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts. Finally, however, interviews with EEA scientists, journalists, and policymakers on the 2011–2017 California drought reveal that EEA remains a nascent scientific framework, one marked by epistemic slippage and divergent results. Thus, it serves as a powerful example of how emergent attempts to "domesticate" climate often become caught up in socio‐political conflicts around who – or what – has the power to shape discourses of climate change in the Anthropocene. This paper seeks to uncover the significance of extreme event attribution (EEA) for the epistemology of climate change, nature, and culture in the Anthropocene. We argue that, contrary to popular theories of the Anthropocene and its role in erasing the nature–culture divide, EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation – a divide we call "partitioned causality." We demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts and influence. [ABSTRACT FROM AUTHOR]

The spring of 2018 was the hottest on record since 1951 over eastern China based on station observations, being 2.5°C higher than the 1961–90 mean and with more than 900 stations reaching the record spring mean temperature. This event exerted serious impacts in the region on agriculture, plant phenology, electricity transmission systems, and human health. In this paper, the contributions of human-induced climate change and anomalous anticyclonic circulation to this event are investigated using the newly homogenized observations and updated Met Office Hadley Centre system for attribution of extreme events, as well as CanESM2 (Second Generation Canadian Earth System Model) simulations. Results indicate that both anthropogenic influences and anomalous anticyclonic circulation played significant roles in increasing the probability of the 2018 hottest spring. Quantitative estimates of the probability ratio show that anthropogenic forcing may have increased the chance of this event by ten-fold, while the anomalous circulation increased it by approximately two-fold. The persistent anomalous anticyclonic circulation located on the north side of China blocked the air with lower temperature from high latitudes into eastern China. Without anthropogenic forcing or without the anomalous circulation in northern China, the occurrence probability of the extreme warm spring is significantly reduced. [ABSTRACT FROM AUTHOR]

High‐impact poor air quality events, such as Beijing's so‐called "Airpocalypse" in January 2013, demonstrate that short‐lived poor air quality events can have significant effects on health and economic vitality. Poor air quality events result from the combination of the emission of pollutants and meteorological conditions favorable to their accumulation, which include limited scavenging, dispersion, and ventilation. The unprecedented nature of events such as the 2013 Airpocalypse, in conjunction with our nonstationary climate, motivate an assessment of whether climate change has altered the meteorological conditions conducive to poor winter air quality in Beijing. Using three indices designed to quantify the meteorological conditions that support poor air quality and drawing on the attribution methods of Diffenbaugh et al. (2017, https://doi.org/10.1073/pnas.1618082114), we assess (i) the contribution of observed trends to the magnitude of events, (ii) the contribution of observed trends to the probability of events, (iii) the return interval of events in the observational record, preindustrial model‐simulated climate and historical model‐simulated climate, (iv) the probability of the observed trend in the preindustrial and historical model‐simulated climates, and (v) the relative influences of anthropogenic forcing and natural variability on the observed trend. We find that anthropogenic influence has had a small effect on the probability of the January 2013 event in all three indices but has increased the probability of a long‐term positive trend in two out of three indices. This work provides a framework for both further understanding the role of climate change in air quality and expanding the scope of event attribution. Key Points: Extreme air quality conditions present in Beijing during January 2013 represented by three indices of air quality meteorologyNatural variability found to have a larger influence on extreme air quality events than anthropogenic forcingAnthropogenic forcing drove long‐term positive trends in two out of three indices, despite small effect on individual events [ABSTRACT FROM AUTHOR]

The exceptional severity of recent climate extremes has raised the question of whether some events would have been impossible in the absence of global warming. This question is critical for climate adaptation, but is challenging to answer given the length and non-stationarity of the observational record. The large single-model ensemble climate simulations archived in the Coupled Model Intercomparison Project (CMIP6) offer a unique opportunity to explore whether the hottest temperatures of the current climate are more extreme than any that could have occurred in the absence of human forcings. We first analyze the one CMIP6 model that has daily data archived for large ensembles in both the historical all-forcings and historical natural forcings experiments. We find that, for large areas of the world, the maximum daily-, seasonal- and annual-scale thresholds of the large single-model ensemble with natural and human forcings (‘all-forcings’) are never reached in the large single-model ensemble with only natural forcings. However, we also identify widespread areas—notably in the northern hemisphere mid-latitudes—where the hottest thresholds of the all-forcings ensemble are frequently exceeded in the absence of human forcings. Further analysis suggests that human forcings other than greenhouse gases (GHGs) are a primary cause of this discrepancy. For example, when comparing the late and early periods of the CMIP6 historical all-forcings experiment, other large single-model ensembles exhibit similar muting of extremely warm years over northern-hemisphere mid-latitude regions. However, under GHG-only forcing, all years in the recent period are hotter than the hottest early-period year over most of the globe. These results suggest that, although the hottest possible events in the current climate may have been virtually impossible in the absence of historical GHG emissions, other non-GHG anthropogenic forcings have muted the emergence of previously impossible events.

Two extreme heatwaves hit Western Europe in the summer of 2019, with historical records broken by more than a degree in many locations, and significant societal impacts, including excess mortality of several thousand people. The extent to which human influence has played a role in the occurrence of these events has been of large interest to scientists, media and decision makers. However, the outstanding nature of these events poses challenges for physical and statistical modeling. Using an unprecedented number of climate model ensembles and statistical extreme value modeling, we demonstrate that these short and intense events would have had extremely small odds in the absence of human-induced climate change, and equivalently frequent events would have been 1.5 °C to 3 °C colder. For instance, in France and in The Netherlands, the July 3-day heatwave has a 50–150-year return period in the current climate and a return period of more than 1000 years without human forcing. The increase in the intensities is larger than the global warming by a factor 2 to 3. Finally, we note that the observed trends are much larger than those in current climate models.

In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5 °C and 2 °C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.

Based on skill estimates from hindcasts made over the last couple of decades, recent studies have suggested that considerable success has been achieved in forecasting winter climate anomalies over the Euro-Atlantic area using current-generation dynamical forecast models. However, previous-generation models had shown that forecasts of winter climate anomalies in the 1960s and 1970s were less successful than forecasts of the 1980s and 1990s. Given that the more recent decades have been dominated by the North Atlantic Oscillation (NAO) in its positive phase, it is important to know whether the performance of current models would be similarly skilful when tested over periods of a predominantly negative NAO. To this end, a new ensemble of atmospheric seasonal hindcasts covering the period 1900-2009 has been created, providing a unique tool to explore many aspects of atmospheric seasonal climate prediction. In this study we focus on two of these: multi-decadal variability in predicting the winter NAO, and the potential value of the long seasonal hindcast datasets for the emerging science of probabilistic event attribution. The existence of relatively low skill levels during the period 1950s-1970s has been confirmed in the new dataset. The skill of the NAO forecasts is larger, however, in earlier and later periods. Whilst these inter-decadal differences in skill are, by themselves, only marginally statistically significant, the variations in skill strongly co-vary with statistics of the general circulation itself suggesting that such differences are indeed physically based. The mid-century period of low forecast skill coincides with a negative NAO phase but the relationship between the NAO phase/amplitude and forecast skill is more complex than linear. Finally, we show how seasonal forecast reliability can be of importance for increasing confidence in statements of causes of extreme weather and climate events, including effects of anthropogenic climate change. [ABSTRACT FROM AUTHOR]

The impacts of weather and climate-related disasters are increasing, and climate change can exacerbate many disasters. Effectively communicating climate risk and integrating science into policy requires scientists and stakeholders to work together. But dialogue between scientists and policymakers can be challenging given the inherently multidimensional nature of the issues at stake when managing climate risks. Building on the growing use of serious games to create dialogue between stakeholders, we present a new game for policymakers called Climate Attribution Under Loss and Damage: Risking, Observing, Negotiating (CAULDRON). CAULDRON aims to communicate understanding of the science attributing extreme events to climate change in a memorable and compelling way, and create space for dialogue around policy decisions addressing changing risks and loss and damage from climate change. We describe the process of developing CAULDRON, and draw on observations of players and their feedback to demonstrate its potential to facilitate the interpretation of probabilistic climate information and the understanding of its relevance to informing policy. Scientists looking to engage with stakeholders can learn valuable lessons in adopting similar innovative approaches. The suitability of games depends on the policy context but, if used appropriately, experiential learning can drive coproduced understanding and meaningful dialogue. [ABSTRACT FROM AUTHOR]

The influence of climate change on mid-latitudes atmospheric circulation is still very uncertain. The large internal variability makes it difficult to extract any statistically significant signal regarding the evolution of the circulation. Here we propose a methodology to calculate dynamical trends tailored to the circulation of specific days by computing the evolution of the distances between the circulation of the day of interest and the other days of the time series. We compute these dynamical trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heat-waves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single model (CESM), in order to account for different sources of uncertainty. While we find a positive trend for most models for 2003, we cannot conclude for 2010 since the models disagree on the trend estimates.

May 2016 was the third wettest May on record since 1961 over central eastern China based on station observations, with total monthly rainfall 40% more than the climatological mean for 1961–2013. Accompanying disasters such as waterlogging, landslides and debris flow struck part of the lower reaches of the Yangtze River. Causal influence of anthropogenic forcings on this event is investigated using the newly updated Met Office Hadley Centre system for attribution of extreme weather and climate events. Results indicate that there is a significant increase in May 2016 rainfall in model simulations relative to the climatological period, but this increase is largely attributable to natural variability. El Niño years have been found to be correlated with extreme rainfall in the Yangtze River region in previous studies—the strong El Niño of 2015–2016 may account for the extreme precipitation event in 2016. However, on smaller spatial scales we find that anthropogenic forcing has likely played a role in increasing the risk of extreme rainfall to the north of the Yangtze and decreasing it to the south.

On 21–25 July 2017 a record-breaking heatwave occurred in Central Eastern China, affecting nearly half of the national population and causing severe impacts on public health, agriculture and infrastructure. Here, we compare attribution results from two UK Met Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50 km HadRM3P). Within HadGEM3-GA6 July 2017-like heatwaves were unequaled in the ensemble representing the world without human influences. Such heatwaves became approximately a 1 in 50 year event and increased by a factor of 4.8 (5%–95% range of 3.1 to 8.0) in weather@home as a result of human activity. Considering the risk ratio (RR) for the full range of return periods shows a discrepancy at all return times between the two model results. Within weather@home a range of different counterfactual sea surface temperature (SST) patterns were used, whereas HadGEM3-GA6 used a single estimate. The global mean difference in SST (between factual and counterfactual simulations) is shown to be related to the generalised extreme value (GEV) location parameter and consequently the RR, especially for return periods of less than 50 years. It is suggested that a suitable range of SST patterns are used for future attribution studies to ensure that this source of uncertainty is represented within the simulations and subsequent attribution results. It is shown that the risk change between factual and counterfactual simulations is not purely a simple shift in the distribution (i.e. change in GEV location parameter). For return periods greater than 50 years, the GEV shape parameter is found to strongly influence the RR determined with the GEV scale parameter affecting only the most severe events.