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What:    Facing the urgent challenge of extreme weather and climate-related events, our societal frameworks for “resilience” and “adaptation” are proving to be insufficient. This paper introduces the “Exploring Unprecedented Extremes” workshop, which was convened to elucidate the research gap in light of such challenges. The workshop tackled a broad spectrum of issues, ranging from assessing out-of-sample climatic events that defy traditional modeling approaches to enhancing the communication of risks and likelihoods associated with such unprecedented events.When:    21–23 November 2023Where:    Ouranos, Montréal, Canada

Landslides are complex phenomena that cause important impacts in vulnerable areas, including the destruction of infrastructure, environmental damage, and loss of life. The occurrence of landslide events is often triggered by rainfall episodes, single and intense ones or multiple ones occurring in sequence, i.e., clustered in time. Landslide prediction is typically obtained via process-based or empirical thresholds. Here, we develop a new approach that uses information on the temporal clustering of rainfall to detect landslide events and compare it with the use of classical empirical rainfall thresholds. In addition, we evaluate the performance of the two approaches combined together as a case study in the region of Lisbon in Portugal. We consider a dataset that categorizes landslides into shallow and deep events and a review of empirical rainfall thresholds that makes a good benchmark for testing our novel method. We show that the new approach based on temporal clustering overall has a good power of detecting landslide events but has a skill comparable with the classic rainfall threshold method. While there is no clear outperformance of one method, the novel clustering-based method has a higher sensitivity despite a lower precision than the threshold-based method. For all approaches, the potential detection is better for deep landslides than for shallow ones. The results of this study could help to improve the prediction of rainfall-triggered landslides.

Floods regularly cause substantial damage worldwide. Changing flood characteristics, e.g., due to climate change, pose challenges to flood risk management. The spatial extent of floods is an important indicator of potential impacts, as consequences of widespread floods are particularly difficult to mitigate. The highly uneven station distribution in space and time, however, limits the ability to quantify flood characteristics and, in particular, changes in flood extents over large regions. Here, we use observation-driven routed runoff simulations over the last 70 years in Europe from a state-of-the-art hydrological model (the mesoscale Hydrologic Model – mHM) to identify large spatiotemporally connected flood events. Our identified spatiotemporal flood events compare well against an independent flood impact database. We find that flood extents increase by 11.3 % on average across Europe. This increase occurs over most of Europe, except for parts of eastern and southwestern Europe. Over northern Europe, the increase in flood extent is mainly driven by the overall increase in flood magnitude caused by increasing precipitation and snowmelt. In contrast, the increasing trend in flood extent over central Europe can be attributed to an increase in the spatial extent of heavy precipitation. Overall, our study illustrates the opportunities to combine long-term consistent regional runoff simulations with a spatiotemporal flood detection algorithm to identify large-scale trends in key flood characteristics and their drivers. The detected change in flood extent should be considered in risk assessments as it may challenge flood control and water resource management.

Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires ( + 940 % by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves ( + 900 % ) and river floods and wildfires ( + 250 % ). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.

As extreme event attribution (EEA) matures, explaining the impacts of extreme events has risen to be a key focus for attribution scientists. Studies of this type usually assess the contribution of anthropogenic climate change to observed impacts. Other scientific communities have developed tools to assess how human activities influence impacts of extreme weather events on ecosystems and societies. For example, the disaster risk reduction (DRR) community analyses how the structure of human societies affects exposure, vulnerability, and ultimately the impacts of extreme weather events, with less attention to the role of anthropogenic climate change. In this perspective, we argue that adapting current practice in EEA to also consider other causal factors in attribution of extreme weather impacts would provide richer and more comprehensive insight into the causes of disasters. To this end, we propose a framework for EEA that would generate a more complete picture of human influences on impacts and bridge the gap between the EEA and DRR communities. We provide illustrations for five case studies: the 2021-2022 Kenyan drought; the 2013-2015 marine heatwave in the northeast Pacific; the 2017 forest fires in Portugal; Acqua Alta (flooding) events in Venice and evaluation of the efficiency of the Experimental Electromechanical Module (MoSE), an ensemble of mobile barriers that can be activated to mitigate the influx of seawater in the city; and California droughts and the Forecast Informed Reservoir Operations (FIRO) system as an adaptation strategy

Compound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, by analyzing the event both on oceans and landmasses via state-of-the-art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, and is most pronounced under a high-emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development.

Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires (+940 % by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves (+900 %) and river floods and wildfires (+250 %). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.

Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires (+940 % by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves (+900 %) and river floods and wildfires (+250 %). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

Previous studies indicate atmospheric instability, total column water vapor, and horizontal moisture transport as major drivers of precipitation extremes, however little is known about how the combination of these drivers affects precipitation extremes across the world. Here, using daily data from the ERA-5 reanalysis spanning the period 1981–2020, we identified the combinations of extreme values for these three major drivers that enhance the probability of daily precipitation extremes on a global scale. Our findings show that extreme daily precipitation is practically impossible without any of these drivers being extreme. Atmospheric instability is the primary driver of precipitation extremes, meaning that, among the three cases of the drivers being extreme in isolation, extreme atmospheric instability is associated with the highest average probability of extreme precipitation over landmasses (29% during December–February, 32% during June–August). When considering the combination of two drivers being simultaneously extreme, joint extremes of atmospheric instability and total column water vapor (and non-extreme horizontal moisture transport) lead to the highest probability of extreme precipitation (69% during December–February, 70% during June–August), which is similar to the probability under three drivers in extreme conditions (67% and 72%). Our results point to a latitudinal variation of the combination that leads to the highest probability of extreme precipitation. In subtropics, the case of the three extreme drivers dominates, whereas in extratropical regions, the dominant combination is that of the joint extremes of atmospheric instability and total column water vapor (and non-extreme horizontal moisture transport). By providing information on the most important drivers of precipitation extremes worldwide, these results can serve as a basis for evaluating precipitation extremes in climate models and understanding projected changes, which is vital for developin

Extreme wildfires continue to be a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely to be highly impacted by climate change. To facilitate appropriate risk mitigation, it is imperative to identify the main drivers of extreme wildfires and assess their spatiotemporal trends, with a view to understanding the impacts of the changing climate on fire activity. To this end, we analyze the monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020 and identify high fire activity during this period in eastern Europe, Algeria, Italy, and Portugal. We build an extreme quantile regression model with a high-dimensional predictor set describing meteorological conditions, land-cover usage, and orography, for the domain. To model the complex relationships between the predictor variables and wildfires, we make use of a hybrid statistical deep learning framework that allows us to disentangle the effects of vapor pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that while VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects wildfire spread. Furthermore, to gain insights into the effect of climate trends on wildfires in the near future, we focus on the extreme wildfires in August 2001 and perturb VPD and temperature according to their observed trends. We find that, on average over Europe, trends in temperature (median over Europe: +0.04 K yr−1) lead to a relative increase of 17.1% and 1.6% in the expected frequency and severity, respectively, of wildfires in August 2001; similar analyses using VPD (median over Europe: +4.82 Pa yr−1) give respective increases of 1.2% and 3.6%. Our analysis finds evidence suggesting that global warming can lead to spatially nonu

Long-duration, sub-seasonal dry spells in combination with high temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 (coupled model intercomparison project 5) GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with a daily precipitation of less than 1 mm. CMIP5 models tend to underestimate the persistence of dry spells in northern Europe, while a large variability exists between model estimates in central and southern Europe, where models have contrasting biases. Throughout Europe, we also find a large spread between models in their representation of temperature extremes during dry spells. In central and southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Our results indicate that this variability in model estimates is due to model differences and not internal variability. At latitudes between 50–60∘ N, the differences in the representation of persistent dry spells are strongly related to the representation of persistent anticyclonic systems, such as atmospheric blocking and subtropical ridges. Furthermore, models simulating a higher frequency of anticyclonic systems than ERA5 also simulate temperatures in dry spells that are between 1.4, and 2.8 K warmer than models with a lower frequency in these areas. Overall, there is a large spread between CMIP5 models in their representation of long-duration dry and hot events that is due to errors in the representation of large-scale anticyclonic

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.

Gross primary production is the basis of global carbon uptake. Gross primary production losses are often related to hydroclimatic extremes such as droughts and heatwaves, but the trend of such losses driven by hydroclimatic extremes remains unclear. Using observationally-constrained and process-based model data from 1982-2016, we show that drought-heat events, drought-cold events, droughts and heatwaves are the dominant drivers of gross primary production loss. Losses associated with these drivers increase in northern midlatitude ecosystem but decrease in pantropical ecosystems, thereby contributing to around 70% of the variability in total gross primary production losses. These asymmetric trends are caused by an increase in the magnitude of gross primary production losses in northern midlatitudes and by a decrease in the frequency of gross primary production loss events in pantropical ecosystems. Our results suggest that the pantropics may have become less vulnerable to hydroclimatic variability over recent decades whereas gross primary production losses and hydroclimatic extremes in northern midlatitudes have become more closely entangled.

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors., Plain Language Summary: Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high-impact events is crucial for an accurate assessment of climate-related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still limited. In practice, studying compound events is a challenging task, which often requires interaction between experts across multiple disciplines. Recently, compound events were divided into four types to aid the framing of research on this topic, but guidelines on how to study these four types are missing. Here, we take a pragmatic approach and—focusing on case studies of different compound event types—illustrate how to address specific research questions that could be of interest to users. The results allow identifying recommendations for compound event analyses. Furthermore, through the case studies, we highlight the relevance that compounding effects have for the occurrence of landslides, flooding, vegetation impacts, and crop failures. The guidelines emerged from this work will assist the development of compound event analysis across disciplines and sectors.

Landslides are a major natural hazard, but uncertainties about their occurrence in a warmer climate are substantial. The relative role of rainfall, soil moisture, and land-use changes and the importance of climate change mitigation are not well understood. Here, we develop an event storyline approach to address these issues, considering an observed event in Austria with some 3000 landslides as a showcase. We simulate the event using a convection permitting regional climate model and a statistical landslide model at present and a range of plausible future climate and land use conditions. Depending on the changes of rainfall and soil moisture, the area affected during a 2009-type event could grow by 45% at 4 K global warming, although a slight reduction is also possible. Such growth could be reduced to less than 10% by limiting global warming according to the Paris agreement. Anticipated land-use changes towards a climate-resilient forest would fully compensate for such a limited increase in hazard.

Compound wind and precipitation extremes (CWPEs) can severely impact multiple sectors and regions, often causing critical infrastructure failure and fatalities, especially in the Indo-Pacific region, which is a hotspot for CWPEs of various synoptic origins. Results show that the northwestern Pacific Ocean and its coasts have experienced the most frequent, strongest, and longest-lasting CWPEs in summer in recent decades, which are induced by cyclones. Landfalling atmospheric rivers are one of the main drivers for frequent occurrences of CWPEs in central and western China and the northwestern Indo-China Peninsula in both boreal summer and winter. The frequency of CWPEs over southern China exhibits significant decreasing trends in contrast to increasing trends in equatorial tropical areas. Moreover, the magnitude and pattern for the observed changes in the frequency of CWPEs result primarily from the variations in the dependence between univariate extremes, when evaluating the Indo-Pacific as a whole.