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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.

Tropical rainforests in Africa are one of the most under-researched regions in the world, but research in the Amazonian rainforest suggests potential vulnerability to climate change. Using the large ensemble of Atmosphere-only general circulation model (AGCM) simulations within the weather@home project, statistics of precipitation in the dry season of the Congo Basin rainforest are analysed. By validating the model simulation against observations, we could identify a good model performance for the June, July, August (JJA) dry season, but this result does need to be taken with caution as observed data are of poor quality. Additional validation methods have been used to investigate the applicability of probabilistic event attribution analysis from large model ensembles to a tropical region, in this case the Congo Basin. These methods corroborate the confidence in the model, leading us to believe the attribution result to be robust. That is, that there are no significant changes in the risk of low precipitation extremes during this dry season (JJA) precipitation in the Congo Basin. Results for the December, January, February dry season are less clear. The study highlights that attribution analysis has the potential to provide valuable scientific evidence of recent or anticipated climatological changes, especially in regions with sparse observational data and unclear projections of future changes. However, the strong influence of sea surface temperature teleconnection patterns on tropical precipitation provides more challenges in the set up of attribution studies than midlatitude rainfall.

Since the UNFCCC Paris Agreement came into force after 2015 international climate policy rests on three pillars: mitigation, adaptation and loss and damage. However, while there are clear agreed‐upon metrics to measure emissions, base mitigation goals against and hold countries and companies accountable to, the evidence base for the impacts of climate change to inform adaptation and loss and damage is very different. There are no agreed‐upon metrics, nor are there guidelines or criteria to delineate the impacts of climate change from other drivers of losses and damages. This imbalance is reflected in the lack of ability to set and enforce goals. With a new body of scientific evidence introduced in the IPCC, we argue that this can change. Especially with an increasing number of climate litigation cases being recognised as a legitimate root to justice, and thus being given due consideration in courts, the imbalance in evidence could change and put adaptation and loss and damage on more equal footing with mitigation. [ABSTRACT FROM AUTHOR]

This article discusses the need for climate restoration law, which goes beyond simply protecting the climate in its current state and focuses on restoring it to a healthier state. The current approach to climate legislation primarily focuses on reducing greenhouse gas emissions and setting targets for mitigation, with less attention and funding given to adaptation. However, there are challenges in translating scientific knowledge into legal provisions, such as the lack of mathematical precision in carbon budget calculations and the limitations of temperature thresholds. The article argues for a shift in climate law towards incorporating scientific evidence more fully and considering the long-term effects of climate change, as well as justice and equity implications. The authors also highlight the role of climate litigation and the need for collective and long-term solutions that value the natural world. The article concludes by emphasizing the importance of science-informed climate laws in improving living conditions for present and future generations. [Extracted from the article]

Climate litigation has become a strategic tool to push for climate justice, including compensation for losses caused by climate change. Many cases rely on the establishment of a causal relationship between the defendants' emission of greenhouse gases (GHG) and the plaintiffs' losses. All decided cases seeking compensation for a concrete climate related impact have been unsuccessful (thus far). Legal scholars as well as social and natural scientists have looked at individual cases and evidence of these unsuccessful claims, aiming to identify legal and scientific hurdles. Based on previous research where we analysed specific cases, we step back from a case‐specific analysis in this article and identify the social context in which law and science operate and intersect. We assert that without a general understanding of the urgency of climate change and the scientifically proven fact that climate change impacts the present, and that it is possible to attribute individual losses to human‐caused climate change, the fate and future of climate litigation focusing on losses and damages will continue to encounter major obstacles in courts. This is despite the increasingly sophisticated strategies of litigants; the positive outcome of some strategic litigation and improvements in the field of climate science, all of which would be expected to sway for a successful future of the fight against climate change. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Heatwaves are arguably the most deadly weather phenomena. As the Earth warms due to higher concentrations of greenhouse gases, one would expect heatwaves to become worse as well, killing even more people unless they are better protected against the heat. However, it turns out that the world is not so simple and that many other factors also influence heatwaves. Land use changes, irrigation, air pollution, and other changes also drive trends in heatwaves. Some of these cause much larger trends while some have counteracted the climate change‐driven trends up to now. In some regions, the causes of high trends have not yet been identified. Current generation climate models often do not simulate all these mechanisms correctly so will have to be improved before we can more confidently trust their description of past trends and projections of future trends in heatwaves. Key Points: The IPCC AR6 WG1 states the "frequency and intensity of hot extremes have increased"The IPCC notes that the effect of increased greenhouse gas on high temperatures is moderated or amplified at local scales by other factorsConfident quantitative attribution statements of the human influence on heatwaves are limited by our understanding of these local processes [ABSTRACT FROM AUTHOR]

Abstract. 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. [ABSTRACT FROM AUTHOR]

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 examiningEEAas 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The science of event attribution has emerged to routinely answer the question whether and to what extent human-induced climate change altered the likelihood and intensity of recently observed extreme weather events. In Europe a pilot program to operationalize the method started in November 2019, highlighting the demand for timely information on the role of climate change when it is needed most: in the direct aftermath of an extreme event. Independent of whether studies are provided operationally or as academic studies, the necessity of good observational data and well-verified climate models imply most attributions are currently made for highly developed countries only. Current attribution assessments therefore provide very little information about those events and regions where the largest damages and socio-economic losses are incurred. Arguably, these larger damages signify a much greater need for information on how the likelihood and intensity of such high-impact events have been changing and are likely to change in a warmer world. In short, why do we not focus event attribution research efforts on the whole world, and particularly events in the developing world? The reasons are not just societal and political but also scientific. We simply cannot attribute these events in the same probabilistic framework employed in most studies today. We outline six focus areas to lessen these barriers, but we will not overcome them in the near future. [ABSTRACT FROM AUTHOR]

The early twentieth-century warming (EW; 1910–45) and the mid-twentieth-century cooling (MC; 1950–80) have been linked to both internal variability of the climate system and changes in external radiative forcing. The degree to which either of the two factors contributed to EW and MC, or both, is still debated. Using a two-box impulse response model, we demonstrate that multidecadal ocean variability was unlikely to be the driver of observed changes in global mean surface temperature (GMST) after AD 1850. Instead, virtually all (97%–98%) of the global low-frequency variability (>30 years) can be explained by external forcing. We find similarly high percentages of explained variance for interhemispheric and land–ocean temperature evolution. Three key aspects are identified that underpin the conclusion of this new study: inhomogeneous anthropogenic aerosol forcing (AER), biases in the instrumental sea surface temperature (SST) datasets, and inadequate representation of the response to varying forcing factors. Once the spatially heterogeneous nature of AER is accounted for, the MC period is reconcilable with external drivers. SST biases and imprecise forcing responses explain the putative disagreement between models and observations during the EW period. As a consequence, Atlantic multidecadal variability (AMV) is found to be primarily controlled by external forcing too. Future attribution studies should account for these important factors when discriminating between externally forced and internally generated influences on climate. We argue that AMV must not be used as a regressor and suggest a revised AMV index instead [the North Atlantic Variability Index (NAVI)]. Our associated best estimate for the transient climate response (TCR) is 1.57 K (±0.70 at the 5%–95% confidence level). [ABSTRACT FROM AUTHOR]

Several major storms pounded western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analysed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend in the frequency of strong winds associated with such storms. High-resolution regional climate models, on the other hand, show no trend up to now and a small increase in storminess in future due to climate change. This shows that factors other than climate change, which are not in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering the Netherlands and in previous studies. This observed trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two mentioned. However, all simulations indicate that global warming could lead to a marginal increase (0 %–20 %) in the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. This excludes other factors, such as surface roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are correctly simulated by climate models, we cannot give credible projections of future storminess over land in Europe. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Increasing likelihoods of extreme weather events is the most noticeable and damaging manifestation of anthropogenic climate change. In the aftermath of an extreme event, policy makers are often called upon to make timely and sensitive decisions about rebuilding and managing present and future risks. Information regarding whether, where and how present-day and future risks are changing is needed to adequately inform these decisions. But, this information is often not available and when it is, it is often not presented in a systematic way. Here, we demonstrate a seamless approach to the science of extreme event attribution and future risk assessment by using the same set of model ensembles to provide such information on past, present and future hazard risks in four case studies on different types of events. Given the current relevance, we focus on estimating the change in future hazard risk under 1.5 °C and 2 °C of global mean temperature rise. We find that this approach not only addresses important decision-making gaps, but also improves the robustness of future risk assessment and attribution statements alike. [ABSTRACT FROM AUTHOR]

Abstract: In the last decade, climate mitigation policy has galvanized around staying below specified thresholds of global mean temperature, with an understanding that exceeding these thresholds may result in dangerous interference of the climate system. United Nations Framework Convention on Climate Change texts have developed thresholds in which the aim is to limit warming to well below 2 °C of warming above preindustrial levels, with an additional aspirational target of 1.5 °C. However, denoting a specific threshold of global mean temperatures as a target for avoiding damaging climate impacts implicitly obscures potentially significant regional variations in the magnitude of these projected impacts. This study introduces a simple framework to quantify the magnitude of this heterogeneity in changing climate hazards at 1.5 °C of warming, using case studies of emergent increases in temperature and rainfall extremes. For example, we find that up to double the amount of global warming (3.0 °C) is needed before people in high‐income countries experience the same relative changes in extreme heat that low‐income nations should anticipate after only 1.5 °C of warming. By mapping how much warming is needed in one location to match the impacts of a fixed temperature threshold in another location, this “temperature of equivalence” index is a flexible and easy‐to‐understand communication tool, with the potential to inform where targeted support for adaptation projects should be prioritized in a warming world. [ABSTRACT FROM AUTHOR]

Several major storms pounded Western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analyzed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend of the frequency of strong winds associated with such storms. High-resolution regional climate models on the other hand show no trend up to now and a small increase in the future due to climate change. This shows that that factors other than climate change, which are not represented (well) in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering The Netherlands and in previous studies. This trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two studied. However, all simulations indicate that global warming could lead to a marginal increase (0-20%) of the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. However, this excludes other factors, such as roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are simulated well by climate models they cannot give credible projections of future storminess over land in Europe. [ABSTRACT FROM AUTHOR]

Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes in such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows one to generate a very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of the weather@home system (weather@home 2) with a higher-resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid-scale land-surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM temperature biases are overall reduced, in particular the warm bias over eastern Europe, but large biases remain. Precipitation is improved over the Alps in summer, with mixed changes in other regions and seasons. The model is shown to represent the main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales. [ABSTRACT FROM AUTHOR]

Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes of such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows to generate very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of weather@home system (weather@home 2) with a higher resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid scale land surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 km to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM biases are overall reduced, in particular the warm and dry bias over eastern Europe, but large biases remain. The model is shown to represent main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales. [ABSTRACT FROM AUTHOR]

The article explores reasons behind the heavy precipitation recorded in upper Danube and Elbe basins in May to June 2013, when some places were receiving monthly precipitation amount within a day or two. Topics discussed include the results of experiments on simulating 2013 under current climate conditions and on representing weather conditions in 25 different years without the influence of human activity.

The article reports that flash flood in Krymsk, southwest Russia, which is attributed to climate change, shows convection-resolving simulations that implies the increasing warming of sea surface temperature in the Black Sea over the past few decades which has resulted to a sudden amplification of convective precipitation. Other topics tackled include dynamical changes in atmospheric circulation over the Black Sea and the importance of conducting attribution studies of individual weather events.

The United Nations Framework Convention on Climate Change (UNFCCC) has established the Warsaw International Mechanism (WIM) to deal with loss and damage associated with climate change impacts, including extreme events, in developing countries. It is not yet known whether events will need to be attributed to anthropogenic climate change to be considered under the WIM. Attribution is possible for some extreme events - a climate model assessment can estimate how greenhouse gas emissions have affected the likelihood of their occurrence. Dialogue between scientists and stakeholders is required to establish whether, and how, this science could play a role in the WIM. [ABSTRACT FROM AUTHOR]