Explore the vast range of titles available.

Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large ensembles, we show that mean precipitation trends exclusively modulate the future occurrence of compound hot–dry events over land. This occurs because local warming will be large enough that future droughts will always coincide with at least moderately hot extremes, even in a 2 °C warmer world. By contrast, precipitation trends are often weak and equivocal in sign, depending on the model, region and internal climate variability. Therefore, constraining regional precipitation trends will also constrain future compound hot–dry events. These results help to assess future frequencies of other compound extremes characterized by strongly different trends in the drivers.Co-occurring hot and dry extremes are predicted to increase with global warming. Changes in precipitation will modulate the extent of these changes, highlighting the importance of understanding regional precipitation trends to prepare society and minimize impacts.

Extreme event attribution answers the question of whether and by how much anthropogenic climate change has contributed to the occurrence or magnitude of an extreme weather event. It is also used to link extreme event impacts to climate change. Impacts, however, are often related to multiple compounding climate drivers. Because extreme event attribution typically focuses on univariate assessments, these assessments might only provide a partial answer to the question of anthropogenic influence to a high-impact event. We present a theoretical extension to classical extreme event attribution for certain types of compound events. Based on synthetic data, we illustrate how the bivariate fraction of attributable risk (FAR) differs from the univariate FAR depending on the extremeness of the event as well as the trends in and dependence between the contributing variables. Overall, the bivariate FAR is similar in magnitude or smaller than the univariate FAR if the trend in the second variable is comparably weak and the dependence between both variables is moderate or high, a typical situation for temporally co-occurring heat waves and droughts. If both variables have similarly large trends or the dependence between both variables is weak, bivariate FARs are larger and are likely to provide a more adequate quantification of the anthropogenic influence. Using multiple climate model large ensembles, we apply the framework to two case studies, a recent sequence of hot and dry years in the Western Cape region of South Africa and two spatially co-occurring droughts in crop-producing regions in South Africa and Lesotho.

Attribution studies have identified a robust anthropogenic fingerprint in increased 21 st century wildfire risk. However, the risks associated with individual aspects of anthropogenic aerosol and greenhouse gases (GHG) emissions, biomass burning and land use/land cover change remain unknown. Here, we use new climate model large ensembles isolating these influences to show that GHG-driven increases in extreme fire weather conditions have been balanced by aerosol-driven cooling throughout the 20th century. This compensation is projected to disappear due to future reductions in aerosol emissions, causing unprecedented increases in extreme fire weather risk in the 21st century as GHGs continue to rise. Changes to temperature and relative humidity drive the largest shifts in extreme fire weather conditions; this is particularly apparent over the Amazon, where GHGs cause a seven-fold increase by 2080. Our results allow increased understanding of the interacting roles of anthropogenic stressors in altering the regional expression of future wildfire risk. Human emissions are thought to have caused an increase in wildfire risk, but how different emission sources contribute is less well known. Here, the authors show that the increase due to greenhouse gas emissions was balanced by aerosol-driven cooling, an effect that is projected to disappear during the 21st century.

Time of emergence of anthropogenic climate change is a crucial metric in risk assessments surrounding future climate predictions. However, internal climate variability impairs the ability to make accurate statements about when climate change emerges from a background reference state. None of the existing efforts to explore uncertainties in time of emergence has explicitly explored the role of internal atmospheric circulation variability. Here a dynamical adjustment method based on constructed circulation analogs is used to provide new estimates of time of emergence of anthropogenic warming over North America and Europe from both a local and spatially aggregated perspective. After removing the effects of internal atmospheric circulation variability, the emergence of anthropogenic warming occurs on average two decades earlier in winter and one decade earlier in summer over North America and Europe. Dynamical adjustment increases the percentage of land area over which warming has emerged by about 30% and 15% in winter (10% and 5% in summer) over North America and Europe, respectively. Using a large ensemble of simulations with a climate model, evidence is provided that thermodynamic factors related to variations in snow cover, sea ice, and soil moisture are important drivers of the remaining uncertainty in time of emergence. Model biases in variability lead to an underestimation (13%–22% over North America and <5% over Europe) of the land fraction emerged by 2010 in summer, indicating that the forced warming signal emerges earlier in observations than suggested by models. The results herein illustrate opportunities for future detection and attribution studies to improve physical understanding by explicitly accounting for internal atmospheric circulation variability.

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction.Flash droughts, which develop over the course of weeks, are difficult to forecast given the current state of subseasonal-to-seasonal prediction. This Perspective offers operational and research definitions, places them in the broader context of climate and suggests avenues for future research.

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks. The authors show that robust analyses of high-impact compound weather and climate events require many samples. Thus, they argue that large ensemble climate model simulations should be used to provide the best available information on climate risks.

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.

Understanding changes in precipitation variability is essential for a complete explanation of the hydrologic cycle’s response to warming and its impacts. While changes in mean and extreme precipitation have been studied intensively, precipitation variability has received less attention, despite its theoretical and practical importance. Here, we show that precipitation variability in most climate models increases over a majority of global land area in response to warming (66% of land has a robust increase in variability of seasonal-mean precipitation). Comparing recent decades to RCP8.5 projections for the end of the 21 st century, we find that in the global, multi-model mean, precipitation variability increases 3–4% K −1 globally, 4–5% K −1 over land and 2–4% K −1 over ocean, and is remarkably robust on a range of timescales from daily to decadal. Precipitation variability increases by at least as much as mean precipitation and less than moisture and extreme precipitation for most models, regions, and timescales. We interpret this as being related to an increase in moisture which is partially mitigated by weakening circulation. We show that changes in observed daily variability in station data are consistent with increased variability.

On short (15-year) to mid-term (30-year) time-scales how the Earth's surface temperature evolves can be dominated by internal variability as demonstrated by the global-warming pause or 'hiatus'. In this study, we use six single model initial-condition large ensembles (SMILEs) and the Coupled Model Intercomparison Project 5 (CMIP5) to visualise the role of internal variability in controlling possible observable surface temperature trends in the short-term and mid-term projections from 2019 onwards. We confirm that in the short-term, surface temperature trend projections are dominated by internal variability, with little influence of structural model differences or warming pathway. Additionally we demonstrate that this result is independent of the model-dependent estimate of the magnitude of internal variability. Indeed, and perhaps counter intuitively, in all models a lack of warming, or even a cooling trend could be observed at all individual points on the globe, even under the largest greenhouse gas emissions. The near-equivalence of all six SMILEs and CMIP5 demonstrates the robustness of this result to the choice of models used. On the mid-term time-scale, we confirm that structural model differences and scenario uncertainties play a larger role in controlling surface temperature trend projections than they did on the shorter time-scale. In addition we show that whether internal variability still dominates, or whether model uncertainties and internal variability are a similar magnitude, depends on the estimate of internal variability, which differs between the SMILEs. Finally we show that even out to thirty years large parts of the globe (or most of the globe in MPI-GE and CMIP5) could still experience no-warming due to internal variability.

Anthropogenic changes in the variability of precipitation stand to impact both natural and human systems in profound ways. Precipitation variability encompasses not only extremes like droughts and floods, but also the spectrum of precipitation which populates the times between these extremes. Understanding the changes in precipitation variability alongside changes in mean and extreme precipitation is essential in unraveling the hydrological cycle’s response to warming. We use a suite of state-of-the-art climate models, with each model consisting of a single-model initial-condition large ensemble (SMILE), yielding at least 15 individual realizations of equally likely evolutions of future climate state for each climate model. The SMILE framework allows for increased precision in estimating the evolving distribution of precipitation, allowing for forced changes in precipitation variability to be compared across climate models. We show that the scaling rates of precipitation variability, the relation between the rise in global temperature and changes in precipitation variability, are markedly robust across timescales from interannual to decadal. Over mid- and high latitudes, it is very likely that precipitation is increasing across the entire spectrum from means to extremes, as is precipitation variability across all timescales, and seasonally these changes can be amplified. Model or structural uncertainty is a prevailing uncertainty especially over the Tropics and Subtropics. We uncover that model-based estimates of historical interannual precipitation variability are sensitive to the number of ensemble members used, with ‘small’ initial-condition ensembles (of less than 30 members) systematically underestimating precipitation variability, highlighting the utility of the SMILE framework for the representation of the full precipitation distribution.