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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 work has shown that (sub-)seasonal variability in tropical Pacific convection, closely linked to the El Niño–Southern Oscillation (ENSO), relates to summertime circulation over the Euro-Atlantic. The teleconnection is non-stationary, probably due to long-term changes in both the tropical Pacific and extra-tropical Atlantic. It also appears imperfectly captured by numerical models. A dipole in west Pacific sea surface temperatures (SSTs) was found to be the best predictor of errors in numerical sub-seasonal forecasts of European temperature. In this diagnostic study we use reanalysis data to further investigate the teleconnection pathway and the processes behind its non-stationarity. We show that SST gradients associated with the dipole represent a combination of ENSO variability and west Pacific warming, and have become stronger since 1980. Associated patterns of suppressed and enhanced tropical heating are followed by quasi-stationary waves that linger for multiple weeks. Situations with La Niña-like gradients are followed by high-pressure centres over eastern Europe and Russia, three to six weeks later. Inverted situations are followed by high pressure over western Europe, three to six weeks later. The latter situation is conditional on a strong meridional tripole in north Atlantic SSTs and a co-located jet stream. Overall, the sub-seasonal pathway diagnosed in this study connects to patterns detected on seasonal scales, and confirms earlier findings that the summertime connectivity between the Pacific and Europe has shifted in recent decades.

Much of the forecast skill in the mid-latitudes on seasonal timescales originates from deep convection in the tropical belt. For boreal summer, such tropical–extratropical teleconnections are less well understood compared to winter. Here we validate the representation of boreal summer tropical–extratropical teleconnections in a general circulation model in comparison with observational data. To characterise variability between tropical convective activity and mid-latitude circulation, we identify the South Asian monsoon (SAM)–circumglobal teleconnection (CGT) pattern and the western North Pacific summer monsoon (WNPSM)–North Pacific high (NPH) pairs as the leading modes of tropical–extratropical coupled variability in both reanalysis (ERA5) and seasonal forecast (SEAS5) data. We calculate causal maps based on the Peter and Clark momentary conditional independence (PCMCI) causal discovery algorithm, which identifies causal links in a 2D field, to show the causal effect of each of these patterns on circulation and convection in the Northern Hemisphere. The spatial patterns and signs of the causal links in SEAS5 closely resemble those seen in ERA5, independent of the initialisation date of SEAS5. By performing a subsampling experiment (over time), we analyse the strengths of causal links in SEAS5 and show that they are qualitatively weaker than those in ERA5. We identify those regions for which SEAS5 data well reproduce ERA5 values, e.g. the southeastern USA, and highlight those where the bias is more prominent, e.g. North Africa and in general tropical regions. We demonstrate that different El Niño–Southern Oscillation phases have only a marginal effect on the strength of these links. Finally, we discuss the potential role of model mean-state biases in explaining differences between SEAS5 and ERA5 causal links.

As the leading climate mode of wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Eurasian cryosphere as a possible predictor for the wintertime NAO. However, missing correlation between snow cover and wintertime NAO in climate model experiments and strong non-stationarity of this link in reanalysis data are questioning the causality of this relationship. Here we use the large ensemble of Atmospheric Seasonal Forecasts of the 20th Century (ASF-20C) with the European Centre for Medium-Range Weather Forecasts model, focusing on the winter season. Besides the main 110-year ensemble of 51 members, we investigate a second, perturbed ensemble of 21 members where initial (November) land conditions over the Northern Hemisphere are swapped from neighboring years. The Eurasian snow–NAO linkage is examined in terms of a longitudinal snow depth dipole across Eurasia. Subsampling the perturbed forecast ensemble and contrasting members with high and low initial snow dipole conditions, we found that their composite difference indicates more negative NAO states in the following winter (DJF) after positive west-to-east snow depth gradients at the beginning of November. Surface and atmospheric forecast anomalies through the troposphere and stratosphere associated with the anomalous positive snow dipole consist of colder early winter surface temperatures over eastern Eurasia, an enhanced Ural ridge and increased vertical energy fluxes into the stratosphere, with a subsequent negative NAO-like signature in the troposphere. We thus confirm the existence of a causal connection between autumn snow patterns and subsequent winter circulation in the ASF-20C forecasting system.

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 ∘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.

Abstract Summer 2010 saw two simultaneous extremes linked by an atmospheric wave train: a record-breaking heatwave in Russia and severe floods in Pakistan. Here, we study this wave event using a large ensemble climate model experiment. First, we show that the circulation in 2010 reflected a recurrent wave train connecting the heatwave and flooding events. Second, we show that the occurrence of the wave train is favored by three drivers: (1) 2010 sea surface temperature anomalies increase the probability of this wave train by a factor 2-to-4 relative to the model’s climatology, (2) early-summer soil moisture deficit in Russia not only increases the probability of local heatwaves, but also enhances rainfall extremes over Pakistan by forcing an atmospheric wave response, and (3) high-latitude land warming favors wave-train occurrence and therefore rainfall and heat extremes. These findings highlight the complexity and synergistic interactions between different drivers, reconciling some seemingly contradictory results from previous studies.

The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a managed water system in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present an impact-focused multivariate statistical framework to model the dependence between these flooding drivers and the resulting return periods of inland water levels. This framework is applied to the same managed water system using the aforementioned large ensemble. Composite analysis is used to guide the selection of suitable predictors and to obtain an impact function that optimally describes the relationship between high inland water levels (the impact) and the explanatory predictors. This is complex due to the high degree of human management affecting the dynamics of the water level. Training the impact function with subsets of data uniformly distributed along the range of water levels plays a major role in obtaining an unbiased performance. The dependence structure between the defined predictors is modelled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. Regarding the return levels, this is quantified by a root-mean-square deviation of 0.02 m. The proposed framework is able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. Even though the framework has only been applied and validated in one study area, it shows great potential to be transferred to other areas. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50-year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020).

Tropical convective activity represents a source of predictability for mid-latitude weather in the Northern Hemisphere. In winter, the El Niño–Southern Oscillation (ENSO) is the dominant source of predictability in the tropics and extratropics, but its role in summer is much less pronounced and the exact teleconnection pathways are not well understood. Here, we assess how tropical convection interacts with mid-latitude summer circulation at different intra-seasonal timescales and how ENSO affects these interactions. First, we apply maximum covariance analysis (MCA) between tropical convective activity and mid-latitude geopotential height fields to identify the dominant modes of interaction. The first MCA mode connects the South Asian monsoon with the mid-latitude circumglobal teleconnection pattern. The second MCA mode connects the western North Pacific summer monsoon in the tropics with a wave-5 pattern centred over the North Pacific High in the mid-latitudes. We show that the MCA patterns are fairly insensitive to the selected intra-seasonal timescale from weekly to 4-weekly data. To study the potential causal interdependencies between these modes and with other atmospheric fields, we apply the causal discovery method PCMCI at different timescales. PCMCI extends standard correlation analysis by removing the confounding effects of autocorrelation, indirect links and common drivers. In general, there is a two-way causal interaction between the tropics and mid-latitudes, but the strength and sometimes sign of the causal link are timescale dependent. We introduce causal maps that show the regionally specific causal effect from each MCA mode. Those maps confirm the dominant patterns of interaction and in addition highlight specific mid-latitude regions that are most strongly connected to tropical convection. In general, the identified causal teleconnection patterns are only mildly affected by ENSO and the tropical mid-latitude linkages remain similar. Still, La Niña strengthens the South Asian monsoon generating a stronger response in the mid-latitudes, while during El Niño years the Pacific pattern is reinforced. This study paves the way for process-based validation of boreal summer teleconnections in (sub-)seasonal forecast models and climate models and therefore works towards improved sub-seasonal predictions and climate projections.

The alternation of active and break phases in Indian summer monsoon (ISM) rainfall at intraseasonal timescales characterizes each ISM season. Both tropical and mid-latitude drivers influence this intraseasonal ISM variability. The circumglobal teleconnection observed in boreal summer drives intraseasonal variability across the mid-latitudes, and a two-way interaction between the ISM and the circumglobal teleconnection pattern has been hypothesized. We use causal discovery algorithms to test the ISM circumglobal teleconnection hypothesis in a causal framework. A robust causal link from the circumglobal teleconnection pattern and the North Atlantic region to ISM rainfall is identified, and we estimate the normalized causal effect (CE) of this link to be about 0.2 (a 1 standard deviation shift in the circumglobal teleconnection causes a 0.2 standard deviation shift in the ISM rainfall 1 week later). The ISM rainfall feeds back on the circumglobal teleconnection pattern, however weakly. Moreover, we identify a negative feedback between strong updraft located over India and the Bay of Bengal and the ISM rainfall acting at a biweekly timescale, with enhanced ISM rainfall following strong updraft by 1 week. This mechanism is possibly related to the boreal summer intraseasonal oscillation. The updraft has the strongest CE of 0.5, while the Madden–Julian oscillation variability has a CE of 0.2–0.3. Our results show that most of the ISM variability on weekly timescales comes from these tropical drivers, though the mid-latitude teleconnection also exerts a substantial influence. Identifying these local and remote drivers paves the way for improved subseasonal forecasts.

In a warmer climate, it is expected that precipitation intensities will increase, and form a considerable risk of high-impact precipitation extremes. This study applies three methods to transform a historic extreme precipitation event in the Netherlands to a similar event in a future warmer climate, thus compiling a future weather scenario. The first method uses an observation-based non-linear relation between the hourly-observed summer precipitation and the antecedent dew-point temperature (the Pi–Td relation). The second method simulates the same event by using the convective-permitting numerical weather model (NWP) model HARMONIE, for both present-day and future warmer conditions. The third method is similar to the first method, but applies a simple linear delta transformation to the historic data by using indicators from The Royal Netherlands Meteorological Institute (KNMI)'14 climate scenarios. A comparison of the three methods shows comparable intensity changes, ranging from below the Clausius–Clapeyron (CC) scaling to a 3 times CC increase per degree of warming. In the NWP model, the position of the events is somewhat different; due to small wind and convection changes, the intensity changes somewhat differ with time, but the total spatial area covered by heavy precipitation does not change with the temperature increase. The Pi–Td method is simple and time efficient compared to numerical models. The outcome can be used directly for hydrological and climatological studies and for impact analysis, such as flood-risk assessments.

Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer timescales can also contribute to flood generation. In this study, we follow a pragmatic approach to analyse the hydro-meteorological pre-conditions of 501 historical damaging floods from 1980 to 2010 in sub-Saharan Africa. These are separated into (a) weather timescale (0–6 days) and (b) seasonal timescale conditions (up to 6 months) before the event. The 7-day precipitation preceding a flood event (PRE7) and the standardized precipitation evapotranspiration index (SPEI) are analysed for the two timescale domains, respectively. Results indicate that high PRE7 does not always generate floods by itself. Seasonal SPEIs, which are not directly correlated with PRE7, exhibit positive (wet) values prior to most flood events across different averaging times, indicating a relationship with flooding. This paper provides evidence that bringing together weather and seasonal conditions can lead to improved flood risk preparedness.

In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).

Disaster risk reduction efforts traditionally focus on long-term preventative measures or post-disaster response. Outside of these, there are many short-term actions, such as evacuation, that can be implemented in the period of time between a warning and a potential disaster to reduce the risk of impacts. However, this precious window of opportunity is regularly overlooked in the case of climate and weather forecasts, which can indicate heightened risk of disaster but are rarely used to initiate preventative action. Barriers range from the protracted debate over the best strategy for intervention to the inherent uncomfortableness on the part of donors to invest in a situation that will likely arise but is not certain. In general, it is unclear what levels of forecast probability and magnitude are "worth" reacting to. Here, we propose a novel forecast-based financing system to automatically trigger action based on climate forecasts or observations. The system matches threshold forecast probabilities with appropriate actions, disburses required funding when threshold forecasts are issued, and develops standard operating procedures that contain the mandate to act when these threshold forecasts are issued. We detail the methods that can be used to establish such a system, and provide illustrations from several pilot cases. Ultimately, such a system can be scaled up in disaster-prone areas worldwide to improve effectiveness at reducing the risk of disaster.

Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

The feedback between soil moisture and precipitation has long been a topic of interest due to its potential for improving weather and seasonal forecasts. The generally proposed mechanism assumes a control of soil moisture on precipitation via the partitioning of the surface turbulent heat fluxes, as assessed via the evaporative fraction (EF), i.e., the ratio of latent heat to the sum of latent and sensible heat, in particular under convective conditions. Our study investigates the poorly understood link between EF and precipitation by relating the before-noon EF to the frequency of afternoon precipitation over the contiguous US, through statistical analyses of multiple EF and precipitation data sets. We analyze remote-sensing data products (Global Land Evaporation: the Amsterdam Methodology (GLEAM) for EF, and radar precipitation from the NEXt generation weather RADar system (NEXRAD)), FLUXNET station data, and the North American Regional Reanalysis (NARR). Data sets agree on a region of positive relationship between EF and precipitation occurrence in the southwestern US. However, a region of strong positive relationship over the eastern US in NARR cannot be confirmed with observation-derived estimates (GLEAM, NEXRAD and FLUXNET). The GLEAM–NEXRAD data set combination indicates a region of positive EF–precipitation relationship in the central US. These disagreements emphasize large uncertainties in the EF data. Further analyses highlight that much of these EF–precipitation relationships could be explained by precipitation persistence alone, and it is unclear whether EF has an additional role in triggering afternoon precipitation. This also highlights the difficulties in isolating a land impact on precipitation. Regional analyses point to contrasting mechanisms over different regions. Over the eastern US, our analyses suggest that the EF–precipitation relationship in NARR is either atmospherically controlled (from precipitation persistence and potential evaporation) or driven by vegetation interception rather than soil moisture. Although this aligns well with the high forest cover and the wet regime of that region, the role of interception evaporation is likely overestimated because of low nighttime evaporation in NARR. Over the central and southwestern US, the EF–precipitation relationship is additionally linked to soil moisture variations, owing to the soil-moisture-limited climate regime.

The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.

The skill of the land surface model HTESSEL is assessed to reproduce evaporation in response to land surface characteristics and atmospheric forcing, both being spatially variable. Evaporation estimates for the 2005 growing season are inferred from satellite observations of the Western part of Hungary and compared to model outcomes. Atmospheric forcings are obtained from a hindcast run with the Regional Climate Model RACMO2. Although HTESSEL slightly underpredicts the seasonal evaporative fraction as compared to satellite estimates, the mean, 10th and 90th percentile of this variable are of the same magnitude as the satellite observations. The initial water as stored in the soil and snow layer does not have a significant effect on the statistical properties of the evaporative fraction. However, the spatial distribution of the initial soil and snow water significantly affects the spatial distribution of the calculated evaporative fraction and the models ability to reproduce evaporation correctly in low precipitation areas in the considered region. HTESSEL performs weaker in dryer areas. In Western Hungary these areas are situated in the Danube valley, which is partly covered by irrigated cropland and which also may be affected by shallow groundwater. Incorporating (lateral) groundwater flow and irrigation, processes that are not included now, may improve HTESSELs ability to predict evaporation correctly. Evaluation of the model skills using other test areas and larger evaluation periods is needed to confirm the results. Based on earlier sensitivity analysis, the effect of a number of modifications to HTESSEL has been assessed. A more physically based reduction function for dry soils has been introduced, the soil depth is made variable and the effect of swallow groundwater included. However, the combined modification does not lead to a significantly improved performance of HTESSEL.

Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This study presents an approach for identifying multi-hazard events during the past 123 years (1900-2023) using the EM-DAT global disaster database. Leveraging the 'associated hazard' information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19 % of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59 % of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Beyond the starvation of millions of livestock, close to 23 million individuals in the region are currently facing high food insecurity in Kenya, Somalia and Ethiopia alone. The severity of these impacts calls for the urgent upscaling and optimisation of early action for droughts. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action triggered by forecasts is seldom addressed. This study investigates the potential for early action for droughts by using seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system for the March-April-May (MAM) and October-November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEV max  = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits persist (PEV max  = 0.2). Outside of Kenya, MAM forecasts have limited value. The existence of opportunities for early action during the OND season in Kenya and Somalia is demonstrated by high PEV values, with some regions recording PEV max values close to 0.8. To illustrate the practical value of this research, we point to a dilemma that a pastoralist in the Kenyan drylands faces when deciding whether to adopt early livestock destocking. This study underscores the importance to determine the value of early actions for forecast users with different action characteristics, and to disseminate this value alongside the standard forecasts themselves. This allows users to trigger effective actions before drought impacts develop., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters., Supplementary Information: The online version contains supplementary material available at 10.1007/s41885-023-00126-0., Competing Interests: Competing InterestsThe authors declare no competing interests., (© The Author(s) 2023.)

Conventional green roofs have often been criticised for their limited water buffer capacity during extreme rainfall events and for their susceptibility to droughts when additional irrigation is unavailable. One solution to these challenges is to create an extra blue water retention layer underneath the green layer. Blue-green roofs allow more stormwater to be stored, and the reservoir can act as a water source for the green layer throughout capillary rises. An automated valve regulates the water level of the system. It can be opened to drain water when extreme precipitation is expected. Therefore, the water buffer capacity of the system during extreme rainfall events can be maximised by integrating precipitation forecasts as triggers for the operation of the valve. However, the added value of this forecast-based operation is yet unknown. Accordingly, in this study, we design and evaluate a hydrological blue-green roof model that utilises precipitation forecasts. We test its performance to capture (extreme) precipitation and to increase evapotranspiration and evaporative cooling under a variety of precipitation forecast-based decision rules. We show that blue-green roofs can capture between 70 % and 97 % of extreme precipitation (>20 mm/h) when set to anticipate ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). This capture ratio is considerably higher than that of a conventional green roof without extra water retention (12 %) or that of a blue-green roof that does not use forecast information (i.e., valve always closed; 59 %). Moreover, blue-green roofs allow for high evapotranspiration rates relative to potential evapotranspiration on hot summer days (around 70 %), which is higher than from conventional green roofs (30 %). This serves to underscore the higher capacity of blue-green roofs to reduce heat stress. Using the city of Amsterdam as a case study, we show the high upscaling potential of the concept: on average, potentially suitable flat roofs cover 13.3 % of the total area of the catchments that are susceptible to pluvial flood risk. If the 90th percentile of the ECMWF forecast is used, an 84 % rainfall capture ratio can translate into capturing 11 % of rainfall in flood-prone urban catchments in Amsterdam., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

COVID-19 has revealed how challenging it is to manage global, systemic and compounding crises. Like COVID-19, climate change impacts, and maladaptive responses to them, have potential to disrupt societies at multiple scales via networks of trade, finance, mobility and communication, and to impact hardest on the most vulnerable. However, these complex systems can also facilitate resilience if managed effectively. This review aims to distil lessons related to the transboundary management of systemic risks from the COVID-19 experience, to inform climate change policy and resilience building. Evidence from diverse fields is synthesised to illustrate the nature of systemic risks and our evolving understanding of resilience. We describe research methods that aim to capture systemic complexity to inform better management practices and increase resilience to crises. Finally, we recommend specific, practical actions for improving transboundary climate risk management and resilience building. These include mapping the direct, cross-border and cross-sectoral impacts of potential climate extremes, adopting adaptive risk management strategies that embrace heterogenous decision-making and uncertainty, and taking a broader approach to resilience which elevates human wellbeing, including societal and ecological resilience., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Flood risk can be reduced at various stages of the disaster management cycle. Traditionally, permanent infrastructure is used for flood prevention, while residual risk is managed with emergency measures that are triggered by forecasts. Advances in flood forecasting hold promise for a more prominent role to forecast-based measures. In this study, we present a methodology that compares permanent with forecast-based flood-prevention measures. On the basis of this methodology, we demonstrate how operational decision-makers can select between acting against frequent low-impact, and rare high-impact events. Through a hypothetical example, we describe a number of decision scenarios using flood risk indicators for Chikwawa, Malawi, and modelled and forecasted discharge data from 1997 to 2018. The results indicate that the choice between permanent and temporary measures is affected by the cost of measures, climatological flood risk, and forecast ability to produce accurate flood warnings. Temporary measures are likely to be more cost-effective than permanent measures when the probability of flooding is low. Furthermore, a combination of the two types of measures can be the most cost-effective solution, particularly when the forecast is more skillful in capturing low-frequency events. Finally, we show that action against frequent low-impact events could more cost-effective than action against rare high-impact ones. We conclude that forecast-based measures could be used as an alternative to some of the permanent measures rather than being used only to cover the residual risk, and thus, should be taken into consideration when identifying the optimal flood risk strategy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The net greenhouse gas benefits of wind turbines compared to their fossil energy counterparts depend on location-specific wind climatology and the turbines' technological characteristics. Assessing the environmental impact of individual wind parks requires a universal but location-dependent method. Here, the greenhouse gas payback time for 4161 wind turbine locations in northwestern Europe was determined as a function of (i) turbine size and (ii) spatial and temporal variability in wind speed. A high-resolution wind atlas (hourly wind speed data between 1979 and 2013 on a 2.5 by 2.5 km grid) was combined with a regression model predicting the wind turbines' life cycle greenhouse gas emissions from turbine size. The greenhouse gas payback time of wind turbines in northwestern Europe varied between 1.8 and 22.5 months, averaging 5.3 months. The spatiotemporal variability in wind climatology has a particularly large influence on the payback time, while the variability in turbine size is of lesser importance. Applying lower-resolution wind speed data (daily on a 30 by 30 km grid) approximated the high-resolution results. These findings imply that forecasting location-specific greenhouse gas payback times of wind turbines globally is well within reach with the availability of a high-resolution wind climatology in combination with technological information.

A new set of climate change scenarios for 2050 for the Netherlands was produced recently. The scenarios span a wide range of possible future climate conditions, and include climate variables that are of interest to a broad user community. The scenario values are constructed by combining output from an ensemble of recent General Climate Model (GCM) simulations, Regional Climate Model (RCM) output, meteorological observations and a touch of expert judgment. For temperature, precipitation, potential evaporation and wind four scenarios are constructed, encompassing ranges of both global mean temperature rise in 2050 and the strength of the response of the dominant atmospheric circulation in the area of interest to global warming. For this particular area, wintertime precipitation is seen to increase between 3.5 and 7% per degree global warming, but mean summertime precipitation shows opposite signs depending on the assumed response of the circulation regime. Annual maximum daily mean wind speed shows small changes compared to the observed (natural) variability of this variable. Sea level rise in the North Sea in 2100 ranges between 35 and 85 cm. Preliminary assessment of the impact of the new scenarios on water management and coastal defence policies indicate that particularly dry summer scenarios and increased intensity of extreme daily precipitation deserves additional attention in the near future.

Simulations with regional climate models (RCMs), carried out for the Rhine basin, have been analyzed in the context of implications of the possible future discharge of the Rhine river. In a first analysis, the runoff generated by the RCMs is compared to observations, in order to detect the way the RCMs treat anomalies in precipitation in their land surface component. A second analysis is devoted to the frequency distribution of area averaged precipitation, and the impact of selection of various driving global climate models.