As Europe transitions to renewable energy sources, its electricity system becomes increasingly dependent on weather conditions. This reliance introduces new challenges for energy security, as periods of high electricity demand may coincide with low renewable energy production (energy droughts). Weather conditions driving such energy droughts transcend national boundaries, potentially causing widespread energy stress across Europe. Existing studies have explored such energy droughts, but they have primarily focused on moderate cases or utilized historical reanalysis data, which limits the ability to assess the full range of variability and extreme impacts from different meteorological conditions.
This study addresses these limitations by using 1600 years of simulated meteorological data to model country-level renewable electricity production (solar PV, wind power, and run-of-river hydropower) and demand across Europe. We identify clusters of countries that frequently experience simultaneous 7-day energy droughts and analyze how different large-scale weather regimes influence these co-occurrence patterns. Our results show that the North Atlantic Oscillation (NAO) negative phase and Blocking weather regimes pose the highest risk of co-occurring energy droughts, though their impacts vary by region. For example, northern Europe faces a doubling of drought risk under NAO negative, while Iberia sees reduced risk. Blocking has the opposite effect on these regions. Despite the potential for international electricity transmission to mitigate energy stress in certain areas, our results suggest that energy droughts often span large areas. This highlights the need for robust backup solutions to address renewable energy intermittency.

The shift toward renewable energy as part of Europe's climate-neutral strategy increases the energy system's reliance on weather conditions. This study explores the impacts of changes in climate variability and extremes on Europe's renewable electricity systems, affecting reliability. It uses a large ensemble approach integrating 1600 years of climate data under present-day (PD) and +2 °C warming scenarios into a modeling framework for wind, solar, and hydropower production alongside electricity demand. The study assesses changes in mean states, variability, and extremes, identifying rare, high-impact events, e.g., energy droughts and multi-year low electricity production. The results reveal notable regional and seasonal variations in energy system dynamics under future warming scenarios. In the Nordic region, increased winter runoff leads to higher hydropower availability, reducing residual loads and shortening energy drought durations. In contrast, Iberia faces growing challenges with extended summer cooling demands, exacerbated by reduced wind and hydropower availability. Importantly, the analysis shows that changes in extremes differ significantly from mean trends, with deviations up to −20% (overestimation) or +4% (underestimation) in the most severe scenarios. Decadal variability analysis underscores the critical influence of natural climate modes like the Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation on energy production and demand. In the PD ensemble, the AMV shows strong correlations with energy variables (0.93 for mean demand anomalies and >0.73 for wind power). However, the +2 °C warming scenario reduces the statistical significance of these correlations. This study highlights the importance of explicitly analyzing extremes, as mean trends alone may misrepresent (changes in) system risks. By explicitly accounting for both natural variability and climate change, it provides insights into extreme compound events, giving a foundation for robust, adaptive strategies to ensure energy system reliability in a changing climate.

We present a novel approach to studying the regime behavior of the Euro-Atlantic summer circulation by analyzing its spatiotemporal preferred trajectories. Our approach is inspired by the dynamical system concept of unstable periodic orbits (UPOs) and motivated by the potential practical utility in modeling the circulation as a sequence of short-lived but well-defined trajectories. Here, we identify the dominant spatiotemporal preferred trajectories in a large ensemble (2000-yr) of simulated present-day climate data with a multistage clustering algorithm. Unlike conventional regime definitions based solely on spatial patterns, our method also explicitly takes into account the temporal trajectory through phase space. We find that 13 spatiotemporal clusters together capture over 80% of the circulation dynamics over the Euro-Atlantic domain. We distinguish between clusters with a quasistationary tendency and clusters with a transient tendency. Markov transition probabilities between the clusters reveal that the circulation tends to alternate between quasistationary and transient episodes, instead of transitioning between quasistationary clusters directly. We show that traversing the phase space between quasistationary blocking and southerly shifted zonal jet states takes at least 10–15 days and that trajectories tend to stay close to these states in phase space for prolonged periods. Taken together, our results demonstrate that the spatiotemporal clusters capture a diverse range of well-known circulation regimes while also revealing more nuanced behavioral characteristics of the Euro-Atlantic summer circulation.

Realistic projections of future wildfires need to account for both the stochastic nature of climate and the randomness of individual fire events. Here we adopt a probabilistic approach to predict current and future fire probabilities using a large ensemble of 1,600 modelled years representing different stochastic realisations of the climate during a modern reference period (2000–2009) and a future characterised by an additional 2°C global warming. This allows us to characterise the distribution of fire years for the contiguous United States, including extreme years when the number of fires or the length of the fire season exceeded those seen in the short observational record. We show that spread in the distribution of fire years in the reference period is higher in areas with a high mean number of fires, but that there is variation in this relationship with regions of proportionally higher variability in the Great Plains and southwestern United States. The principal drivers of variability in simulated fire years are related either to interannual variability in fuel production or atmospheric moisture controls on fuel drying, but there are distinct geographic patterns in which each of these is the dominant control. The ensemble also shows considerable spread in fire season length, with regions such as the southwestern United States being vulnerable to very long fire seasons in extreme fire years. The mean number of fires increases with an additional 2°C warming, but the spread of the distribution increases even more across three quarters of the contiguous United States. Warming has a strong effect on the likelihood of less fire-prone regions of the northern United States to experience extreme fire years. It also has a strong amplifying effect on annual fire occurrence and fire season length in already fire-prone regions of the western United States. The area in which fuel availability is the dominant control on fire occurrence increases substantially with warming. These analyses demonstrate the importance of taking account of the stochasticity of both climate and fire in characterising wildfire regimes, and the utility of large climate ensembles for making projections of the likelihood of extreme years or extreme fire seasons under future climate change.

We see unprecedented weather causing widespread impacts across the world. In this perspective, we provide an overview of methods that help anticipate unprecedented weather hazards that can contribute to stop being surprised. We then discuss disaster management and climate adaptation practices, their gaps, and how the methods to anticipate unprecedented weather may help build resilience. We stimulate thinking about transformative adaptation as a foundation for long-term resilience to unprecedented weather, supported by incremental adaptation through upgrading existing infrastructure, and reactive adaptation through short-term early action and disaster response. Because in the end, we should take responsibility to build resilience rather than being surprised by unprecedented weather.

Multi-year droughts (MYDs) are severe natural hazards that have become more common due to climate change. Given their significant societal impact compared to droughts of shorter duration, it is crucial to better understand the drivers of MYDs. Using reanalysis data, this study provides a historical overview of MYDs in California, Western Europe, India, central Argentina, South Africa, and southeast Australia. For each region, the characteristics and drivers of the multi-year droughts are given and compared to those of normal droughts (NDs). Additionally, we investigated the potential for longer-term memory of droughts. Our findings reveal that MYD occurrence and duration vary significantly per region, with relatively larger differences in duration between MYDs and NDs observed in California, Argentina, and Australia. Regions with distinctive seasonality in their precipitation climatology tend to experience faster drought onsets compared to regions with climatologically steady precipitation. Our analysis shows that MYDs and NDs often start with similar conditions but diverge over time, with larger potential evapotranspiration values for most regions, and additional lower precipitation rates for Argentina and India. Longer-term memory is present in Argentina, Australia, and South Africa, which might provide avenues for the predictability of MYDs in these regions. Teleconnections influenced by oceans and land are expected to play a significant role here, while in other regions MYD occurrence may be more subject to chance. These findings can aid in decision-making on water management, preceding and during droughts.

Coastal settlements, facing increasing flood risk from tropical cyclones (TCs) under climate change, need local and detailed climate information for effective adaptation. Analysis of historical events and their impacts provides such information. This study uses storylines to evaluate adaptation strategies, focusing on cyclone Idai’s impact on Beira, Mozambique, under different climate conditions and tidal cycles. A storyline of Idai under 3°C warming increases flood impacts by 1.8 times, while aligning Idai with spring tides amplifies these by 21 times. Combining both conditions increases impacts beyond 37 times. An adaptation strategy combining flood protection and accommodation measures reduces impacts by maximum 83%, while a seawall strategy reduces these by 10%. By offering localized, detailed information, storylines can be used to measure the effectiveness of adaptation strategies against extreme events, evaluating their robustness across different scenarios, and quantifying residual impacts, complementing traditional climate risk assessments for informed decision-making.

Meteorological drought may lead to water shortages, which has negative impacts on water-dependent sectors. Whilst there is a wealth of studies on changing drought intensity or frequency due to climate change, much less is known regarding potential shifts in the timing of drought. The purpose of this study is to analyze the timing of the drought season in the Netherlands and climatic changes therein, with a special focus on the onset of the drought season. Based on an analysis of meteorological observations in the Netherlands over the period 1965–2023, we conclude that the Dutch meteorological drought season has extended forward in time. On average, the drought season starts 16 d earlier in the period 1994–2023 compared to 1965–1993. This is mostly the result of an increase in potential evapotranspiration, while the amount of precipitation does not show a clear change at the start of the growing season. Using three climate model ensembles, we show that a forced climate change signal exists, but that natural variability also plays a role. Following this assessment of trends in meteorological variables, we analyze the consequences for the operational monitoring of meteorological drought. In the Netherlands, this is done by means of the 'precipitation deficit'-indicator, based on a fixed-in-time starting point (1 April) of the drought season. The combination of this fixed starting point and the observed earlier onset of the drought season, means that in some years the indicator underestimates drought intensity, and that climatic trends are underestimated. We therefore advocate for an update of the operational drought indicator, such that meteorological drought occurring before 1 April will not be missed.

In July 2021, a cut-off low-pressure system brought extreme precipitation to Western Europe. Record daily rainfall totals led to flooding that caused loss of life and substantial damage to infrastructure. Climate change can amplify rainfall extremes via thermodynamic processes, but the role of dynamical changes is uncertain. We assess how the dynamics involved in this particular event are changing using flow analogues. Using past and present periods in reanalyses and large ensemble climate model data of the present-day climate and 2°C warmer climate, we find that the best flow analogues become more similar to the cut-off low-pressure system observed over Western Europe in 2021. This may imply that extreme rain events will occur more frequently in the future. Moreover, the magnitude of the analogue lows has deepened, and the associated air masses contain more precipitable water. Simulations of future climate show similar events of the future could lead to intense rainfall further east than in the current climate, due to a shift of the pattern. Such unprecedented events can have large consequences for society, we need to mitigate and adapt to reduce future impacts.

As Europe’s renewable energy capacities expand, electricity systems face increased risks of energy droughts—periods of low production coinciding with high demand. We evaluate characteristics of electricity variability due to weather variations by calculating 1,600 years of daily production and demand. Focusing on five European countries—chosen for their energy mix including hydropower—we find that energy droughts result from processes that cause (temporally) compounding impacts in the energy and meteorological system. These can turn what might have been short-term droughts into prolonged high unmet energy demand. For instance, low reservoir inflows in spring quadruple the chance of prolonged energy droughts: reduced snowpack and rainfall lower hydro availability but also dry out subsoils, increasing the chance of heatwaves and therewith extending the energy problems into summer. We identify and quantify three compounding energy/climate conditions and the associated characteristics and risks of multi-year energy droughts, crucial for informing future energy system design.

We propose an index to quantify and analyse the impact of climatological variability on the energy system at different timescales. We define the Climatological Renewable Energy Deviation Index (CREDI) as the cumulative anomaly of a renewable resource with respect to its climate over a specific time period of interest. For this we introduce the smooth, yet physical, hourly rolling window climatology that captures the expected hourly to yearly behaviour of renewable resources. We analyse the presented index at decadal, annual and (sub-)seasonal timescales for a sample region and discuss scientific and practical implications. CREDI is meant as an analytical tool for researchers and stakeholders to help them quantify, understand, and explain, the impact of energy-meteorological variability on future energy system. Improved understanding translates to better assessments of how renewable resources, and the associated risks for energy security, may fare in current and future climatological settings. The practical use of the index is in resource planning. For example transmission system operators may be able to adjust short-term planning to reduce adequacy issues before they occur or combine the index with storyline event selection for improved assessments of climate change related risks.

This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry-trending and wet-trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM-RCM initial-condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society.

High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm's intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.

Recent impactful hydrometeorological events, on both the extreme wet and dry side of the spectrum, remind policymakers and citizens that climate change is a reality and that a shift in water management solutions is required. A selection of policy-shaping events in the Netherlands shows that both floods and droughts have occurred historically and continue to occur, causing significant impacts and challenges for water resources management. For decades, water management in the Netherlands has focused on implementing flood prevention policies, mostly prompted by specific events. The occurrence of droughts did not lead to comparable significant transitions in water management. The bias toward adaptation measures on the wet part of the spectrum (i.e., floods), increases vulnerability to dry extremes (i.e., droughts) as experienced in 2018–2020 and 2022. A required long-term, integral vision to rethink the existing water management system is challenging as droughts and floods act on different time scales. Furthermore, there is a fierce competition for land use and water use functions. ‘Transformation pathways’, applied across the full flood–drought spectrum, could provide a valuable framework in the development toward a sustainable management of water resources, involving stakeholders for just and equitable transitions and translating long-term visions into pathways for action.

Alongside mean increases in poleward moisture transport (PMT) to the Arctic, most climate models also project a linear increase in the interannual variability (IAV) with future warming. It is still uncertain to what extent atmospheric rivers (ARs) contribute to the projected IAV increase of PMT. We analyzed large-ensemble climate simulations to (a) explore the link between PMT and ARs in the present-day (PD) and in two warmer climates (+2 and +3°C compared to pre-industrial global mean temperature), (b) assess the dynamic contribution to changes in future ARs, and (c) analyze the effect of ARs on Arctic climate on interannual timescales. We find that the share of AR-related PMT (ARPMT) to PMT increases from 42% in the PD to 53% in the +3°C climate. Our results show that the mean increases in AR-frequency and intensity are mainly caused by higher atmospheric moisture levels, while dynamic variability regulates regional ARs on an interannual basis. Notably, the amount of ARs reaching the Arctic in any given region and season strongly depends on the regional jet stream position and speed southwest of this region. This suggests that future changes in dynamics may significantly amplify or dampen the regionally consistent moisture-induced increase in ARs in a warmer climate. Our results further support previous findings that positive ARPMT anomalies are profoundly linked to increased surface air temperature and precipitation, especially in the colder seasons, and have a predominantly negative effect on sea ice.

Large-ensemble modelling has become an increasingly popular approach to studying the mean climate and the climate system’s internal variability in response to external forcing. Here we present the Royal Netherlands Meteorological Institute (KNMI) Large Ensemble Time Slice (KNMI–LENTIS): a new large ensemble produced with the re-tuned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 years each: a present-day time slice and a +2 K warmer future time slice relative to the present day. The initial conditions for the ensemble members are generated with a combination of micro- and macro-perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160 × 10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time slice approach makes it possible to study extreme events on sub-daily timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI–LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The large ensemble is particularly geared towards research in the land–atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI–LENTIS are advised to make in-depth comparisons with observational or reanalysis data, especially if their studies focus on ocean processes, on locations in the Southern Hemisphere, or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI–LENTIS for extreme- and compound-event research and for climate-impact modelling.

The UK has experienced recurring hydrological droughts in the past and their frequency and severity are predicted to increase with climate change. However, quantifying the risks of extreme droughts is challenging given the short observational record, the multivariate nature of droughts and large internal variability of the climate system. We use EC-Earth time-slice large ensembles, which consist of 2000 years of data each for present-day, 2°C and 3°C conditions relative to pre-industrial, to drive hydrological models of river catchments in Great Britain (GB) to obtain a large set of plausible droughts. Since future warming is certain, the uncertainty in drought is mainly associated with uncertainty in precipitation. Estimates of unprecedented extremes show that the chance of a summer month in a given year drier than the observed driest summer (1995) is projected to increase with future warming (from 9% in the present-day (PD) to 18% in a 3°C warmer world (3C) for southeast England). For winter, the chance of a dry winter month drier than the observed driest winter (1991–92) slightly decreases (from 10% - PD to 8% − 3C for southeast England) but the chance of the driest winter does not change significantly with future warming. We add value to these probabilistic estimates by sampling for physical climate storylines of drought sequences characterised by dry spring-summers, autumn-winters and consecutive dry winters. Dry spring-summers are estimated to become drier with future warming primarily driven by reduced precipitation in summer. Dry autumn-winters may become wetter mainly driven by the general trend of more precipitation in winter although drought conditions triggered by moderate autumn–winter precipitation deficits may worsen given the higher likelihood of being followed by a dry summer. Similarly, drought impacts of consecutive dry winters, a particular risk for slow-responding catchments in the English Lowlands, may worsen with future warming as the intervening summer is projected to become hotter and drier. These storylines can be used to stress-test hydrological systems and inform decision-making.

While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.

The frequency of heatwaves, droughts and their co-occurrence vary greatly in simulations of different climate models. Since these extremes are expected to become more frequent with climate change, it is important to understand how vegetation models respond to different climatologies in heatwave and drought occurrence. In previous work, six climate scenarios featuring different drought-heat signatures have been developed to investigate how single versus compound extremes affect vegetation and carbon dynamics. Here, we use these scenarios to force six dynamic global vegetation models to investigate model agreement in vegetation and carbon cycle response to these scenarios. We find that global responses to different drought-heat signatures vary considerably across models. Models agree that frequent compound hot-dry events lead to a reduction in tree cover and vegetation carbon stocks. However, models show opposite responses in vegetation changes for the scenario with no extremes. We find a strong relationship between the frequency of concurrent hot-dry conditions and the total carbon pool, suggesting a reduction of the natural land carbon sink for increasing occurrence of hot-dry events. The effect of frequent compound hot and dry extremes is larger than the sum of the effects when only one extreme occurs, highlighting the importance of studying compound events. Our results demonstrate that uncertainties in the representation of compound hot-dry event occurrence in climate models propagate to uncertainties in the simulation of vegetation distribution and carbon pools. Therefore, to reduce uncertainties in future carbon cycle projections, the representation of compound events in climate models needs to be improved.

The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3. In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: (1) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal climate variability, and (2) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability. Therefore, single members of the ensemble provide only limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three prediction pairs. This analysis shows that there is AV in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value.

Climate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constraints. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight on the occurrence of hydrological extremes as they offer order of magnitude more realizations of future weather. At the same time recent developments in Machine Learning (ML) in hydrology create great opportunities to study current and upcoming problems in a new way, including and combining large amounts of data. In this study, we combined LE together with a local, observation based ML model framework with the goal to see if and how these aspects can be combined and to simulate, assess and produce estimates of hydrological extremes under different warming levels for local scales. For this, first a new post-processing approach was developed that allowed us to use LE simulation data for local applications. The simulation results of discharge extreme events under different warming levels were assessed in terms of frequency, duration and intensity and number of events at national, regional and local scales. Clear seasonal cycles with increased low flow frequency were observed for summer and autumn months as well as increased high flow periods for early spring. For both extreme events, the 3C warmer climate scenario showed the highest percentages. Regional differences were seen in terms of shifts and range. These trends were further refined into location specific results. The shifts and trends observed between the different scenarios were due to a change in climate variability. In this study we show that by combining the wealth of information from LE and the speed and local relevance of ML models we can advance the state-of-the-art when it comes to modeling hydrological extremes under different climate change scenarios for national, regional and local scale assessments providing relevant information for water management in terms of long term planning.

Three consecutive dry summers in western Europe (2018–2019–2020) had widespread negative impacts on society and ecosystems, and started societal debate on (changing) drought vulnerability and adaptation measures. We investigate the occurrence of multi-year droughts in the Rhine basin, with a focus on event probability in the present and in future warmer climates. Additionally, we investigate the temporally compounding physical drivers of multi-year drought events. A combination of multiple reanalysis datasets and multi-model large ensemble climate model simulations was used to provide a robust analysis of the statistics and physical processes of these rare events. We identify two types of multi-year drought events (consecutive meteorological summer droughts and long-duration hydrological droughts), and show that these occur on average about twice in a 30 year period in the present climate, though natural variability is large (zero to five events can occur in a single 30 year period). Projected decreases in summer precipitation and increases in atmospheric evaporative demand, lead to a doubling of event probability at 1 $deg;C additional global warming relative to present-day and an increase in the average length of events. Consecutive meteorological summer droughts are forced by two, seemingly independent, summers of lower than normal precipitation and higher than normal evaporative demand. The soil moisture response to this temporally compound meteorological forcing has a clear multi-year imprint, resulting in a relatively larger reduction of soil moisture content in the second year of drought, and potentially more severe drought impacts. Long-duration hydrological droughts start with a severe summer drought followed by lingering meteorologically dry conditions. This limits and slows down the hydrological recovery of soil moisture content, leading to long-lasting drought conditions. This initial exploration provides avenues for further investigation of multi-year drought hazard and vulnerability in the region, which is advised given the projected trends and vulnerability of society and ecosystems.

Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios.

Regional climate projections indicate that European summer precipitation may change considerably in the future. Southern Europe can expect substantial drying while Northern Europe could actually become wetter. Model spread and internal variability in these projections are large, however, and unravelling the processes that underlie the changes is essential to get more confidence in these projections. Large-scale circulation change is one of the contributors to model spread. In this paper we quantify the role of future large-scale circulation changes to summer precipitation change, using a 16-member single-model ensemble obtained with the regional climate model RACMO2, forced by the global climate model EC-Earth2.3 and the RCP8.5 emission scenario. Using the method of circulation analogues three contributions to the future precipitation change are distinguished. The first is the precipitation change occurring without circulation change (referred to as the thermodynamic term). This contribution is characterised by a marked drying-to-wetting gradient as one moves north from the Mediterranean. The second contribution measures the effects of changes in the mean circulation. It has a very different spatial pattern and is closely related to the development of a region of high pressure (attaining its maximum west of Ireland) and the associated anti-cyclonic circulation response. For a large area east of Ireland including parts of western Europe, it is the major contributor to the overall drying signal, locally explaining more than 90% of the ensemble-mean change. In regions where the patterns overlap, the signal-to-noise ratio of the total change is either enhanced or reduced depending on their relative signs. Although the second term is expected to be particularly model dependent, the high-pressure region west of Ireland also appears in CMIP5 and CMIP6 ensemble-mean projections. The third contribution records the effects of changes in the circulation variability. This term has the smallest net contribution, but a relatively large uncertainty. The analogues are very good in partitioning the ensemble-mean precipitation change, but describe only up to 40% of the ensemble-spread. This demonstrates that other precipitation-drivers (SST, spring soil moisture etc.) will generally strongly influence trends in single climate realisations. This also re-emphasises the need for large ensembles or using alternative methods like the Pseudo Global Warming approach where signal to noise ratios are higher. Nevertheless, identifying the change mechanisms helps to understand the future uncertainties and differences between models.

With the need to reduce greenhouse gas emissions, the coming decades will see a transition of Europe’s power system, currently mainly based on fossil fuels towards a higher share of renewable sources. Increasing effects of fluctuations in electricity production and demand as a result of meteorological variability might cause compound events with unforeseen impacts. We constructed and validated a modeling framework to examine such extreme impact events on the European power system. This framework includes six modules: i) a reservoir hydropower inflow and ii) dispatch module; iii) a run-of-river hydropower production module; iv) a wind energy production module; v) a photovoltaic solar energy production model; and vi) an electricity demand module. Based on ERA5 reanalysis input data and present-day capacity distributions, we computed electricity production and demand for a set of European countries in the period 2015–2021 and compared results to observed data. The model captures the variability and extremes of wind, photovoltaic and run-of-river production well, with correlations between modelled and observed data for most countries of more than 0.87, 0.68 and 0.65 respectively. The hydropower dispatch module also functions well, with correlations up to 0.82, but struggles to capture reservoir inflows and operating procedures of some countries. A case study into the meteorological drivers of extreme events in Sweden and Spain showed that the meteorological conditions during extreme events selected by the model and extracted from observational data are similar, giving confidence in the application of the modeling framework for (future changes in) extreme event analysis.

Climate change poses complex impacts on the global wheat supply and demand chain. The impacts of climate change on average wheat yields are reasonably well studied, but its effects on yield variability and the associated economic consequences are poorly understood. Here, we show that future global wheat prices will exhibit steeper spikes at 2°C global warming (6.2% increase in the 95th percentile of global consumer price anomalies) despite a 1.7% increase in production given that CO2 fertilization benefits crops. Such economic stresses could be abated by trade liberalization with lower prices. However, on the supply side, trade liberalization has contrasting effects: the profitability of farmers in advanced economies can be maintained or even raised, but this will inevitably cause economic losses and inequalities for farmers in less-developed, wheat-importing countries. Agricultural trade liberalization accompanied by protection policies in developing countries would be beneficial for global food security in the threat of climate change.

Energy system models underpin decisions by energy system planners and operators. Energy system modeling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modeling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modeling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modeling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision-making.

Extreme climate events, such as droughts and heatwaves, can have large impacts on the environment. Disentangling their individual and combined effects is a difficult task, due to the challenges associated with generating controlled environments to study differences in their impacts. One approach to this problem is creating artificial climate forcing with varying magnitude of univariate and compound extremes, which can be applied to process-based impact models. Here, we propose and describe a set of six 100-year long climate scenarios with varying drought-heat signatures that are derived from climate model simulations whose mean climate is comparable to present-day climate conditions. The changes in extremes are most notable in the 3 months in which vegetation activity is highest and where arguably hot and dry extremes may have the largest impacts. Besides a control scenario representing natural variability (Control), one scenario has neither heat nor drought extremes (Noextremes), one has univariate extremes but no compound extremes (Nocompound), one has only heat extremes but few droughts (Hot), one has only droughts but few heatwaves (Dry), and one has many compound heat and drought extremes (Hotdry). These scenarios differ only moderately in their global mean climate (about 0.3°C in temperature and 6% in precipitation) and do not contain any long-term trends. The data are provided on a daily timescale over land (except Antarctica and parts of Greenland) on a regular 1° × 1° grid. These scenarios were constructed primarily to investigate the impact of varying drought-heat signatures on vegetation and the terrestrial carbon cycle. However, we believe that they may also prove useful to study the differential impacts of droughts and heatwaves in other areas, such as the occurrence of wildfires or crop failure. The data described here can be found on zenodo (https://doi.org/10.5281/zenodo.4385445, Tschumi et al., 2020).

There is considerable uncertainty surrounding future changes in tropical cyclone (TC) frequency and intensity, particularly at local scales. This uncertainty complicates risk assessments and implementation of risk mitigation strategies. We present a novel approach to overcome this problem, using the statistical model STORM to generate 10,000 years of synthetic TCs under past (1980–2017) and future climate (SSP585; 2015–2050) conditions from an ensemble of four high-resolution climate models. We then derive high-resolution (10-km) wind speed return period maps up to 1000 years to assess local-scale changes in wind speed probabilities. Our results indicate that the probability of intense TCs, on average, more than doubles in all regions except for the Bay of Bengal and the Gulf of Mexico. Our unique and innovative methodology enables globally consistent comparison of TC risk in both time and space and can be easily adapted to accommodate alternative climate scenarios and time periods.

Modeling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extreme value theory is often used to model spatial extremes. However, most commonly used approaches require strong assumptions and are either too simplistic or over-parameterized. From a machine learning perspective, generative adversarial networks (GANs) are a powerful tool to model dependencies in high-dimensional spaces. Yet in the standard setting, GANs do not well represent dependencies in the extremes. Here we combine GANs with extreme value theory (evtGAN) to model spatial dependencies in summer maxima of temperature and winter maxima in precipitation over a large part of western Europe. We use data from a stationary 2000-year climate model simulation to validate the approach and explore its sensitivity to small sample sizes. Our results show that evtGAN outperforms classical GANs and standard statistical approaches to model spatial extremes. Already with about 50 years of data, which corresponds to commonly available climate records, we obtain reasonably good performance. In general, dependencies between temperature extremes are better captured than dependencies between precipitation extremes due to the high spatial coherence in temperature fields. Our approach can be applied to other climate variables and can be used to emulate climate models when running very long simulations to determine dependencies in the extremes is deemed infeasible.

The frequency and severity of droughts and heatwaves are projected to increase under global warming. However, the differential impacts of climate extremes on the terrestrial biosphere and anthropogenic CO2 sink remain poorly understood. In this study, we analyse the effects of six hypothetical climate scenarios with differing drought-heat signatures, sampled from a long stationary climate model simulation, on vegetation distribution and land carbon dynamics, as modelled by a dynamic global vegetation model (LPX-Bern v1.4). The six forcing scenarios consist of a Control scenario representing a natural climate, a Noextremes scenario featuring few droughts and heatwaves, a Nocompound scenario which allows univariate hot or dry extremes but no co-occurring extremes, a Hot scenario with frequent heatwaves, a Dry scenario with frequent droughts, and a Hotdry scenario featuring frequent concurrent hot and dry extremes. We find that a climate with no extreme events increases tree coverage by up to 10 % compared to the Control scenario and also increases ecosystem productivity as well as the terrestrial carbon pools. A climate with many heatwaves leads to an overall increase in tree coverage primarily in higher latitudes, while the ecosystem productivity remains similar to the Control scenario. In the Dry and even more so in the Hotdry scenario, tree cover and ecosystem productivity are reduced by up to −4 % compared to the Control scenario. Regionally, this value can be much larger, for example up to −80 % in mid-western USA or up to −50 % in mid-Eurasia for Hotdry tree ecosystem productivity. Depending on the vegetation type, the effects of the Hotdry scenario are stronger than the effects of the Hot and Dry scenarios combined, illustrating the importance of correctly simulating compound extremes for future impact assessment. Overall, our study illustrates how factorial model experiments can be employed to disentangle the effects of single and compound extremes.

Large-ensemble climate model simulations can provide deeper understanding of the characteristics and causes of extreme events than historical observations, due to their larger sample size. However, adequate evaluation of simulated 'unseen' events that are more extreme than those seen in historical records is complicated by observational uncertainties and natural variability. Consequently, conventional evaluation and correction methods cannot determine whether simulations outside observed variability are correct for the right physical reasons. Here, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We illustrate these steps for a 2000 year Amazon monthly flood ensemble simulated by the global climate model EC-Earth and global hydrological model PCR-GLOBWB. EC-Earth and PCR-GLOBWB are adequate for large-scale catchments like the Amazon, and have simulated 'unseen' monthly floods far outside observed variability. We find that the realism of these simulations cannot be statistically explained. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. We conclude that there is high sensitivity of simulations outside observed variability to the bias correction method, and that physical credibility checks are crucial to understanding what is driving the simulated extreme events. Understanding the driving mechanisms of unseen events may guide future research by uncovering key climate model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers.

Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3 $deg;C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot–dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events.

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.

In the aftermath of observed extreme weather events, questions arise on the role of climate change in such events and what future events might look like. We present a method for the development of physical storylines of future events comparable to a chosen observed event, to answer some of these questions. A storyline approach, focusing on physical processes and plausibility rather than probability, improves risk awareness through its relation with our memory of the observed event and contributes to decision making processes through their user focus. The method is showcased by means of a proof-of-concept for the 2018 drought in western Europe. We create analogues of the observed event based on large ensemble climate model simulations representing 2~\degree C and 3~\degree C global warming scenarios, and discuss how event severity, event drivers and physical processes are influenced by climate change. We show that future Rhine basin meteorological summer droughts like 2018 will be more severe. Decreased precipitation and increased potential evapotranspiration, caused by higher temperatures and increased incoming solar radiation, lead to higher precipitation deficits and lower plant available soil moisture. Possibly, changes in atmospheric circulation contribute to increased spring drought, amplifying the most severe summer drought events. The spatial extent of the most severe drought impacts increases substantially. The noted changes can partly be explained by changes in mean climate, but for many variables, changes in the relative event severity on top of these mean changes contribute as well.

Persistent hot–dry or cold–wet summer weather can have significant impacts on agriculture, health, and the environment. For northwestern Europe, these weather regimes are typically linked to, respectively, blocked or zonal jet stream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e., fully or almost intransitive) for European summer circulation, capitalizing on a newly available very large ensemble (2000 years) of present-day climate data in the fully coupled global climate model EC-Earth. Using self-organizing maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers that are characterized by distinctly different trajectories through phase space, and that prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

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.

Hydrological extremes affect societies and ecosystems around the world in many ways, stressing the need to make reliable predictions using hydrological models. However, several hydrological models can be selected to simulate extreme events. A difference in hydrological model structure results in a spread in the simulation of extreme runoff events. We investigated the impact of different model structures on the magnitude and timing of simulated extreme high- and low-flow events, by combining two state-of-the-art approaches; a modular modelling framework (FUSE) and large ensemble meteorological simulations. This combination of methods created the opportunity to isolate the impact of specific hydrological process formulations at long return periods without relying on statistical models. We showed that the impact of hydrological model structure was larger for the simulation of low-flow compared to high-flow events and varied between the four evaluated climate zones. In cold and temperate climate zones, the magnitude and timing of extreme runoff events were significantly affected by different parameter sets and hydrological process formulations, such as evaporation. The impact of hydrological model structures on extreme runoff events was smaller in the arid and tropical climate zones. This novel combination of approaches provided insights into the importance of specific hydrological processes formulations in different climate zones, which can support adequate model selection for the simulation of extreme runoff events.

Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.

Natural disasters in High Mountain Asia (HMA) are largely induced by precipitation and temperatures extremes. Precipitation extremes will change due to global warming, but these low frequency events are difficult to analyse using (short) observed time series. In this study we analysed large 2000 year ensembles of present day climate and of a 2 °C and 3 °C warmer world produced with the EC‐EARTH model. We performed a regional assessment of climate indicators related to temperature and precipitation (positive degree days, accumulated precipitation, (pre‐ and post‐) monsoon precipitation), their sensitivity to temperature change and the change in return periods of extreme temperature and precipitation in a 2 and 3 °C warmer climate.
In general, the 2 °C warmer world shows a homogeneous response of changes in climate indicators and return periods, while distinct differences between regions are present in a 3 °C warmer world and changes no longer follow a general trend. This non‐linear effect can indicate the presence of a tipping point in the climate system. The most affected regions are located in monsoon‐dominated regions, where precipitation amounts, positive degree days, extreme temperature, extreme precipitation and compound events are projected to increase the most. Largest changes in climate indicators are found in East Himalaya, followed by the Hindu Kush and West and Central Himalaya regions. Western regions will experience drier summers and wetter winters, while monsoon dominated regions drier winters and wetter summers and Northern regions a wetter climate year round. We also found that precipitation increases in HMA in a 3 °C warmer world are substantially larger (13%) compared to the global average (5.9%). Additionally, the increase in weather extremes will exacerbate natural hazards with large possible impacts for mountain communities. The results of this study could provide important guidance for formulating climate change adaptation strategies in HMA.

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

The frequency of climate extremes will change in response to shifts in both mean climate and climate variability. These individual contributions, and thus the fundamental mechanisms behind changes in climate extremes, remain largely unknown. Here we apply the probability ratio concept in large-ensemble climate simulations to attribute changes in extreme events to either changes in mean climate or climate variability. We show that increased occurrence of monthly high-temperature events is governed by a warming mean climate. In contrast, future changes in monthly heavy-precipitation events depend to a considerable degree on trends in climate variability. Spatial variations are substantial however, highlighting the relevance of regional processes. The contributions of mean and variability to the probability ratio are largely independent of event threshold, magnitude of warming and climate model. Hence projections of temperature extremes are more robust than those of precipitation extremes, since the mean climate is better understood than climate variability.

Extreme summer temperatures can cause severe societal impacts. Early warnings can aid societal preparedness, but reliable forecasts for extreme temperatures at subseasonal-to-seasonal (S2S) timescales are still missing. Earlier work showed that specific sea surface temperature (SST) patterns over the northern Pacific are precursors of high temperature events in the eastern United States, which might provide skillful forecasts at long-leads (~50 days). However, the verification was based on a single skill metric and a probabilistic forecast was missing. Here, we introduce a novel algorithm that objectively extracts robust precursors from SST linked to a binary target variable. When applied to reanalysis (ERA-5) and climate model data (EC-Earth), we identify robust precursors with the clearest links over the North-Pacific. Different precursors are tested as input for a statistical model to forecast high temperature events. Using multiple skill metrics for verification, we show that daily high temperature events have no predictive skill at long leads. By systematically testing the influence of temporal and spatial aggregation, we find that noise in the target timeseries is an important bottleneck for predicting extreme events on S2S timescales. We show that skill can be increased by a combination of (1) aggregating spatially and/or temporally, (2) lowering the threshold of the target events to increase the base-rate, or (3) add additional variables containing predictive information (soil-moisture). Exploiting these skill-enhancing factors, we obtain forecast skill for moderate heatwaves (i.e. 2 or more hot days closely clustered together in time) up to 50 days lead-time.

Over the last few years, methods have been developed to answer questions on the effect of global warming on recent extreme events. Many “event attribution” studies have now been performed, a sizeable fraction even within a few weeks of the event, to increase the usefulness of the results. In doing these analyses, it has become apparent that the attribution itself is only one step of an extended process that leads from the observation of an extreme event to a successfully communicated attribution statement. In this paper we detail the protocol that was developed by the World Weather Attribution group over the course of the last 4 years and about two dozen rapid and slow attribution studies covering warm, cold, wet, dry, and stormy extremes. It starts from the choice of which events to analyse and proceeds with the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis and ends with the communication procedures. This article documents this protocol. It is hoped that our protocol will be useful in designing future event attribution studies and as a starting point of a protocol for an operational attribution service.

The South Pacific Convergence Zone (SPCZ) is a diagonal band of intense rainfall and deep atmospheric convection extending from the equator to the subtropical South Pacific. Displacement of the SPCZ causes variability in rainfall, tropical-cyclone activity and sea level that affects South Pacific island populations and surrounding ecosystems. In this Review, we synthesize recent advances in understanding the physical mechanisms responsible for the SPCZ location and orientation, its interactions with the principal drivers of tropical climate variability, regional and global effects of the SPCZ and its response to anthropogenic climate change. Emerging insight is beginning to provide a coherent description of the character and variability of the SPCZ over synoptic, intraseasonal, interannual and longer timescales. For example, the diagonal orientation of the SPCZ and its natural variability are both the result of a subtle chain of interactions between the tropical and extratropical atmosphere, forced and modulated by the underlying sea surface temperature gradients. However, persistent biases in, and deficiencies of, existing models limit confidence in future projections. Improved climate models and new methods for regional modelling might better constrain future SPCZ projections, aiding climate change adaptation and planning among vulnerable South Pacific communities.

The summer of 2018 was characterized by high temperatures and low precipitation values in the Netherlands. The drought negatively impacted different sectors, resulting in an estimated damage of 450 to 2080 million Euros. Strong regional differences were observed in the precipitation shortfall across the country, with highest deficits in the southern and eastern regions. This raised two questions: (i) have increasing global temperatures contributed to changes in meteorological and agricultural droughts as severe or worse as in 2018? And (ii) are trends in these types of droughts different for coastal and inland regions? In this paper we show that there is no trend in summer drought (Apr-Sep) near the coast. However, a trend in agricultural drought is observed for the inland region where water supply is mainly dependent on local precipitation. This trend is driven by strong trends in temperature and global radiation rather than a trend in precipitation, resulting in an overall trend in potential evapotranspiration. Climate model analyses confirm that this trend in agricultural drought can at least in part be attributed to global climate change.

Heatwaves and extreme temperatures during summer (April-May) in India have profound implications on public health, mortality, water availability, and productivity of labourers. However, how the frequency of the hottest summers in observed record (1951-2015) will change under the warming climate in India is not well explored. Using observations from India Meteorological Department (IMD), we show that mean maximum summer temperature has increased significantly in three (Arid, monsoon, and Savannah) out of five major climatic regions of India during 1951-2015. We identify the hottest summer in the observed record in the five climatic regions in India. The arid, cold, and temperate regions experienced the hottest summer in 2010 while monsoon and Savannah regions witnessed the hottest summer in 1979 and 1973, respectively. Based on simulations from the Climate of 20th Century Plus (C20C+) Detection and Attribution project, we show that the regional hottest summer of 2010 can be attributed to the anthropogenic warming. We then use simulations of a large (2000 year) ensemble of the EC-Earth model to estimate the exceedance probability of the observed hottest summer in the present climate, 2 and 3°C warming worlds in India. The exceedance probability of the observed hottest summers shows a rise of more than seven and twenty-fold in the 2 and 3°C warming world, 26 respectively compared to the present climate. The projected increases in the frequency of the hot summers and associated heatwave days will pose great societal challenges in the future in India.

The investigation of risk due to weather and climate events is an example of policy relevant science. Risk is the result of complex interactions between the physical environment (geophysical events or conditions, including but not limited to weather and climate events) and societal factors (vulnerability and exposure). The societal impact of two similar meteorological events at different times or different locations may therefore vary widely. Despite the complex relation between meteorological conditions and impacts most meteorological research is focused on the occurrence or severity of extreme meteorological events, and climate impact research often undersamples climatological natural variability. Here we argue that an approach of ensemble climate-impact modelling is required to adequately investigate the relationship between meteorology and extreme impact events. We demonstrate that extreme weather conditions do not always lead to extreme impacts; in contrast, extreme impacts may result from (coinciding) moderate weather conditions. Explicit modelling of climate impacts, using the complete distribution of weather realisations, is thus necessary to ensure that the most extreme impact events are identified. The approach allows for the investigation of high-impact meteorological conditions and provides higher accuracy for consequent estimates of risk.

The Arctic region is projected to experience amplified warming as well as strongly increasing precipitation rates. Equally important to trends in the mean climate are changes in interannual variability, but changes in precipitation fluctuations are highly uncertain and the associated processes are unknown. Here, we use various state-of-the-art global climate model simulations to show that interannual variability of Arctic precipitation will likely increase markedly (up to 40% over the 21st century), especially in summer. This can be attributed to increased poleward atmospheric moisture transport variability associated with enhanced moisture content, possibly modulated by atmospheric dynamics. Because both the means and variability of Arctic precipitation will increase, years/seasons with excessive precipitation will occur more often, as will the associated impacts.

Flooding is a function of hydrologic, climatologic, and land use characteristics. However, the relative contribution of these factors to flood risk over the long-term is uncertain. In response to this knowledge gap, this study quantifies how urbanization and climatological trends influenced flooding in the greater Houston region during Hurricane Harvey. The region—characterized by extreme precipitation events, low topographic relief, and clay-dominated soils—is naturally flood prone, but it is also one of the fastest growing urban areas in the United States. This rapid growth has contributed to increased runoff volumes and rates in areas where anthropogenic climate changes has also been shown to be contributing to extreme precipitation. To disentangle the relative contributions of urban development and climatic changes on flooding during Hurricane Harvey, we simulate catchment response using a spatially-distributed hydrologic model under 1900 and 2017 conditions. This approach provides insight into how timing, volume, and peak discharge in response to Harvey-like events have evolved over more than a century. Results suggest that over the past century, urban development and climate change have had a large impact on peak discharge at stream gauges in the Houston region, where development alone has increased peak discharges by 54% (±28%) and climate change has increased peak discharge by about 20% (±3%). When combined, urban development and climate change nearly doubled peak discharge (84% ±35%) in the Houston area during Harvey compared to a similar event in 1900, suggesting that land use change has magnified the effects of climate change on catchment response. The findings support a precautionary approach to flood risk management that explicitly considers how current land use decisions may impact future conditions under varying climate trends, particularly in low-lying coastal cities.

Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~200 km, ~50 km and ~25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2×CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime—mean and extreme—wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the 'Scandinavian Blocking' and 'North Atlantic Oscillation negative' regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 1.5 and 2.0, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-)seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential.

Observations show that reduced regional sea-ice cover is coincident with cold mid-latitude winters on interannual timescales. However, it remains unclear whether these observed links are causal, and model experiments suggest that they might not be. Here we apply two independent approaches to infer causality from observations and climate models and to reconcile these sources of data. Models capture the observed correlations between reduced sea ice and cold mid-latitude winters, but only when reduced sea ice coincides with anomalous heat transfer from the atmosphere to the ocean, implying that the atmosphere is driving the loss. Causal inference from the physics-based approach is corroborated by a lead–lag analysis, showing that circulation-driven temperature anomalies precede, but do not follow, reduced sea ice. Furthermore, no mid-latitude cooling is found in modelling experiments with imposed future sea-ice loss. Our results show robust support for anomalous atmospheric circulation simultaneously driving cold mid-latitude winters and mild Arctic conditions, and reduced sea ice having a minimal influence on severe mid-latitude winters.

To mitigate climate change a renewable energy transition is needed. Existing power systems will need to be redesigned to balance variable renewable energy production with variable energy demand. We investigate the meteorological sensitivity of a highly-renewable European energy system using large ensemble simulations from two global climate models. Based on 3 x 2000 years of simulated weather conditions, daily wind and solar energy yields, and energy demand are calculated. From this data, 1-, 7- and 14-days events of extreme low renewable energy production and extreme high energy shortfall are selected. Energy shortfall is defined as the residual load, i.e. demand minus renewable production. 1-day low energy production days are characterised by large-scale high pressure systems over central Europe, with lower than normal wind speeds. These events typically occur in winter when solar energy is limited due to short day lengths. Situations of atmospheric blocking lead to long lasting periods of low energy production, such 7- and 14-days low production events peak late summer. High energy shortfall events occur due to comparable high pressure systems though now combined with below normal temperatures, driving up energy demand. In contrast to the low energy production events, 1-, 7- and 14-days high shortfall events all occur mid-winter, locked to the coldest months of the year. A spatial redistribution of wind turbines and solar panels cannot prevent these high-impact events, options to import renewable energy from remote locations during these events are limited. Projected changes due to climate change are substantially smaller than interannual variability. Future power systems with large penetration of variable renewable energy must be designed with these events in mind.

The assessment of return periods of extreme hydrological events often relies on statistical analysis using generalized extreme value (GEV) distributions. Here we compare the traditional GEV approach with a novel large ensemble approach to determine the added value of a direct, empirical distribution‐based estimate of extreme hydrological events. Using the global climate and hydrological models EC‐Earth and PCR‐GLOBWB, we simulate 2,000 years of global hydrology for a present‐day and 2 °C warmer climate. We show that the GEV method has inherent limitations in estimating changes in hydrological extremes, especially for compound hydrological events. The large ensemble method does not suffer from these limitations and quantifies the impacts of climate change with greater precision. The explicit simulation of extreme events enables better hydrological process understanding. We conclude that future studies focusing on the impact of climatic changes on hydrological extremes should use large ensemble techniques to properly account for these rare hydrological events.

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 °C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed.
In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95 % confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 °C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: we find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.

Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model’s 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.

Unprecedented high-intensity flooding induced by extreme precipitation was reported over Chennai in India during November–December of 2015, which led to extensive damage to human life and property. It is of utmost importance to determine the odds of occurrence of such extreme floods in the future, and the related climate phenomena, for planning and mitigation purposes. Here, a suite of simulations from GFDL high-resolution coupled climate models are used to investigate the odds of occurrence of extreme floods induced by extreme precipitation over Chennai and the role of radiative forcing and/or large-scale SST forcing in enhancing the probability of such events in the future. The climate of twentieth-century experiments with large ensembles suggest that the radiative forcing may not enhance the probability of extreme floods over Chennai. Doubling of CO2 experiments also fails to show evidence for an increase of such events in a global warming scenario. Further, this study explores the role of SST forcing from the Indian and Pacific Oceans on the odds of occurrence of Chennai-like floods. Neither El Nino nor La Nina enhances the probability of extreme floods over Chennai. However, a warm Bay of Bengal tends to increase the odds of occurrence of extreme Chennai-like floods. In order to trigger a Chennai like-flood, a conducive weather event, such as a tropical depression over the Bay of Bengal with strong transport of moisture from a moist atmosphere over the warm Bay, is necessary for the intense precipitation.

On 4–6 December 2015, storm Desmond caused very heavy rainfall in Northern England and Southern Scotland which led to widespread flooding. A week after the event we provided an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing Northern England and Southern Scotland using data and methods available immediately after the event occurred. The analysis was based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agreed that the effect of climate change was positive, making precipitation events like this about 40% more likely, with a provisional 2.5%–97.5% confidence interval of 5%–80%. Here we revisit the assessment using more station data, an additional monthly event definition, a second global climate model and regional model simulations of winter 2015/16. The overall result of the analysis is similar to the real-time analysis with a best estimate of a 59% increase in event frequency, but a larger confidence interval that does include no change. It is important to highlight that the observational data in the additional monthly analysis does not only represent the rainfall associated with storm Desmond but also that of storms Eve and Frank occurring towards the end of the month.

During August 25-30, 2017, Hurricane Harvey stalled over Texas and caused particularly extreme precipitation over Houston and the surrounding area on August 26-28. This resulted in extensive flooding with over 80 fatalities and large economic costs. It was an extremely rare event: the return period of the highest observed three-day precipitation amount, 1043.4 mm/3dy at Baytown, is more than 9,000 years (97.5% one-sided confidence interval) and return periods exceeded 1,000 yr (750 mm/3dy) over a large area in the current climate. Observations since 1880 over the region show a clear positive trend in the intensity of extreme precipitation of between 12% and 22%, roughly two times the increase of the moisture holding capacity of the atmosphere expected for 1°C warming according to the Clausius-Clapeyron (CC) relation. This would indicate that the moisture flux was increased by both the moisture content and stronger winds or updrafts driven by the heat of condensation of the moisture. We also analysed extreme rainfall in the Houston area in three ensembles of 25 km resolution models. The first also shows 2xCC scaling, the second 1xCC scaling and the third did not have a realistic representation of extreme rainfall on the Gulf Coast. Extrapolating these results to the 2017 event, we conclude that global warming made the precipitation about 15% (8% to 19%) more intense, or equivalently made such an event three (1.5 to 5) times more likely. This analysis makes clear that extreme rainfall events along the Gulf Coast are on the rise. And while fortifying Houston to fully withstand the impact of an event as extreme as Hurricane Harvey may not be economically feasible, it is critical that information regarding the increasing risk of extreme rainfall events in general should be part of the discussion about future improvements to Houston's flood protection system.

Sea and land breeze circulations driven by surface temperature differences between land and sea often evolve into gravity currents with sharp fronts. Along narrow peninsulas, islands and enclosed seas, sea/land breeze fronts from opposing shorelines may converge and collide and may initiate deep convection and heavy precipitation. Here we investigate the collision of two sea breeze gravity current fronts in an analogue laboratory setting. We examine these collisions by means of ‘lock-exchange’ experiments in a rectangular channel. The effects of differences in gravity current density and height are studied. Upon collision, a sharp front separating the two currents develops. For symmetric collisions (the same current densities and heights) this front is vertical and stationary. For asymmetric collisions (density differences, similar heights) the front is tilted, changes shape in time and propagates in the same direction as the heavier current before the collision. Both symmetric and asymmetric collisions lead to upward displacement of fluid from the gravity currents and mixing along the plane of contact. The amount of mixing along the collision front decreases with asymmetry. Height differences impact post-collision horizontal propagation: there is significant propagation in the same direction as the higher current before collision, independent of density differences. Collisions of two gravity current fronts force sustained ascending motions which increase the potential for deep convection. From our experiments we conclude that this potential is larger in stationary collision fronts from symmetric sea breeze collisions than in propagating collision fronts from asymmetric sea breeze collisions.

A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations.
The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29-31° N, 85-95° W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (CI): 450-1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11-110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.

Climate change has been shown to impact the mean climate state and climate extremes. Though climate extremes have the potential to disrupt society, extreme conditions are rare by definition. In contrast, mild weather occurs frequently and many human activities are built around it. We provide a global analysis of mild weather based on simple criteria and explore changes in response to radiative forcing. We find a slight global mean decrease in the annual number of mild days projected both in the near future (-4 days per year, 2016-2035) and at the end of this century (-10 days per year, 2081-2100). Projected seasonal and regional redistributions of mild days are substantially greater. These changes are larger than the interannual variability of mild weather caused by El Nino-Southern Oscillation. Finally, we show an observed global decrease in the recent past, and that observed regional changes in mild weather resemble projections.

Precipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2°x2° grid cells (typical resolution in the CMIP5 archive) to 0.25°x0.25° (tropical cyclone permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities, and seasonal timing. In response to 2xCO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of approximately 3%-4% K-1. However, projected regional patterns of changes in extremes are dependent on model resolution. For example, the highest-resolution models show increased precipitation rates during extreme events in the hurricane season in the U.S. Southeast; this increase is not found in the low-resolution model. These results emphasize that, for the study of extreme precipitation there is a minimum model resolution that is needed to capture the weather phenomena generating the extremes. Finally, the observed record and historical model experiments were used to investigate changes in the recent past. In part because of large intrinsic variability, no evidence was found for changes in extreme precipitation attributable to climate change in the available observed record.

Science, in our case the climate and geosciences, is increasingly interdisciplinary. Scientists must therefore communicate across disciplinary boundaries. For this communication to be successful, scientists must write clearly and concisely, yet the historically poor standard of scientific writing does not seem to be improving. Scientific writing must improve, and the key to long-term improvement lies with the early-career scientist (ECS). Many interventions exist for an ECS to improve their writing, like style guides and courses. However, momentum is often difficult to maintain after these interventions are completed. Continuity is key to improving writing.
This paper introduces the ClimateSnack project, which aims to motivate ECSs to develop and continue to improve their writing and communication skills. The project adopts a peer-learning framework where ECSs voluntarily form writing groups at different institutes around the world. The group members learn, discuss, and improve their writing skills together.
Several ClimateSnack writing groups have formed. This paper examines why some of the groups have flourished and others have dissolved. We identify the challenges involved in making a writing group successful and effective, notably the leadership of self-organized groups, and both individual and institutional time management. Within some of the groups, peer learning clearly offers a powerful tool to improve writing as well as bringing other benefits, including improved general communication skills and increased confidence.

The South Pacific Convergence Zone (SPCZ) is a northwest-southeast oriented precipitation band over the South Pacific Ocean. Latent heat release from condensation leads to substantial diabatic heating, which has potentially large impacts on local and global climate. The influence of this diabatic heating within the SPCZ is investigated using the Intermediate General Circulation Model (IGCM4).
Precipitation in the SPCZ has been shown to be triggered by transient Rossby waves that originate in the Australian subtropical jet and are refracted towards the equatorial eastern Pacific. A Rossby wave triggers a SPCZ 'convective event', with associated diabatic heat release and vortex stretching. Consequently, the Rossby wave is dissipated in the SPCZ region. These features are simulated well in a control integration of IGCM4.
In an experiment, convective heating is prescribed to its 'climatological' value in the SPCZ region during the Rossby wave 'events' and dynamic forcing from Rossby waves is decoupled from the usual thermodynamic response. In this experiment Rossby waves over the SPCZ region are not dissipated, confirming the vortex stretching mechanism from previous studies. Furthermore, the change in Rossby wave propagation has an impact on momentum transport. Overall, the effect of the Rossby wave-induced convection in the SPCZ is to decrease the strength of the Pacific subtropical jet and the equatorial eastern Pacific upper-tropospheric westerlies, by about 2–6 m s−1.
Following these changes to the basic state, two potential feedbacks in the SPCZ and larger Pacific climate system are suggested: increased SPCZ convection due to the enhancement of negative zonal stretching deformation in the SPCZ region and decreased equatorward refraction of Rossby waves into the westerly duct leading to less SPCZ 'events'. As the convective events in the SPCZ have a significant impact on Pacific mean climate, it is crucial that the SPCZ is represented correctly in climate models.

During austral summer, the majority of precipitation over the Pacific Ocean is concentrated in the South Pacific Convergence Zone (SPCZ). The surface boundary conditions required to support the diagonally (northwest–southeast) oriented SPCZ are determined through a series of experiments with an atmospheric general circulation model. Continental configuration and orography do not have a significant influence on SPCZ orientation and strength. The key necessary boundary condition is the zonally asymmetric component of the sea surface temperature (SST) distribution. This leads to a strong subtropical anticyclone over the southeast Pacific that, on its western flank, transports warm moist air from the equator into the SPCZ region. This moisture then intensifies (diagonal) bands of convection that are initiated by regions of ascent and reduced static stability ahead of the cyclonic vorticity in Rossby waves that are refracted toward the westerly duct over the equatorial Pacific. The climatological SPCZ is comprised of the superposition of these diagonal bands of convection. When the zonally asymmetric SST component is reduced or removed, the subtropical anticyclone and its associated moisture source is weakened. Despite the presence of Rossby waves, significant moist convection is no longer triggered; the SPCZ disappears. The diagonal SPCZ is robust to large changes (up to +/-6 °C) in absolute SST (i.e. where the SST asymmetry is preserved). Extreme cooling (change [-6 °C) results in a weaker and more zonal SPCZ, due to decreasing atmospheric temperature, moisture content and convective available potential energy.

The South Pacific Convergence Zone (SPCZ) and South Atlantic Convergence Zone (SACZ) are diagonal bands of precipitation that extend from the Equator southeastward into the Southern Hemisphere over the western Pacific and Atlantic Oceans, respectively. With mean precipitation rates over 5 mm day−1, they are a major component of the tropical and global climate in austral summer. However, their basic formation mechanism is not fully understood. Here, a conceptual framework for the diagonal convergence zones is developed, based on calculations of the vorticity budget from reanalysis and Rossby wave theory.
Wave trains propagate eastward along the Southern Hemisphere subtropical jet, with initially quasi-circular vorticity centres. In the zonally sheared environment on the equatorward flank of the jet, these vorticity centres become elongated and develop a northwest–southeast tilt. Ray-tracing diagnostics in a non-divergent, barotropic Rossby wave framework then explain the observed equatorward propagation of these diagonal vorticity structures toward the westerly ducts over the equatorial Pacific and Atlantic. The baroclinic component of these circulations leads to destabilisation and ascent ahead of the cyclonic vorticity anomaly in the wave, triggering deep convection because of the high sea surface temperatures in this region. Latent heat release then forces additional ascent and strong upper-tropospheric divergence, with an associated anticyclonic vorticity tendency. A vorticity budget shows that this cancels out the advective cyclonic vorticity tendency in the wave train over the SPCZ, and dissipates the wave within a day. The mean SPCZ is consequently comprised of the sum of these pulses of diagonal bands of precipitation.
Similar mechanisms also operate in the SACZ. However, the vorticity anomalies in the wave trains are stronger, and the precipitation and negative feedback from the divergence and anticyclonic vorticity tendency are weaker, resulting in continued propagation of the wave and a more diffuse diagonal convergence zone.

The IGCM4 (Intermediate Global Circulation Model version 4) is a global spectral primitive equation climate model whose predecessors have extensively been used in areas such as climate research, process modelling and atmospheric dynamics. The IGCM4's niche and utility lies in its speed and flexibility allied with the complexity of a primitive equation climate model. Moist processes such as clouds, evaporation, atmospheric radiation and soil moisture are simulated in the model, though in a simplified manner compared to state-of-the-art global circulation models (GCMs). IGCM4 is a parallelised model, enabling both very long integrations to be conducted and the effects of higher resolutions to be explored. It has also undergone changes such as alterations to the cloud and surface processes and the addition of gravity wave drag. These changes have resulted in a significant improvement to the IGCM's representation of the mean climate as well as its representation of stratospheric processes such as sudden stratospheric warmings. The IGCM4's physical changes and climatology are described in this paper.

EC-Earth, a new Earth system model based on the operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF), is presented. The performance of version 2.2 (V2.2) of the model is compared to observations, reanalysis data and other coupled atmosphere–ocean-sea ice models. The large-scale physical characteristics of the atmosphere, ocean and sea ice are well simulated. When compared to other coupled models with similar complexity, the model performs well in simulating tropospheric fields and dynamic variables, and performs less in simulating surface temperature and fluxes. The surface temperatures are too cold, with the exception of the Southern Ocean region and parts of the Northern Hemisphere extratropics. The main patterns of interannual climate variability are well represented. Experiments with enhanced CO2 concentrations show well-known responses of Arctic amplification, land-sea contrasts, tropospheric warming and stratospheric cooling. The global climate sensitivity of the current version of EC-Earth is slightly less than 1 K/(W m-2). An intensification of the hydrological cycle is found and strong regional changes in precipitation, affecting monsoon characteristics. The results show that a coupled model based on an operational seasonal prediction system can be used for climate studies, supporting emerging seamless prediction strategies.