Recent changes in extreme floods across multiple continents

Analyses of trends in observed floods often focus on relatively frequent events, whereas changes in rare floods are only studied for a small number of locations that have exceptionally long observational records. Understanding changes in rare floods is especially relevant as these events are often most damaging and influence the design of major structures. Here, we provide an assessment of changes in the largest flood events (similar to 0.033 annual exceedance probability) observed during the period 1980-2009 for 1744 catchments located in Australia, Brazil, Europe and the United States. The occurrence of rare floods in spatial aggregate shows strong temporal variability and peaked around 1995. During the 30 year period, there are overall increases in both the frequency and magnitude of extreme floods. These increases are strongest in Europe and the United States, and weakest in Brazil and Australia. Physical causes of the reported short-term variability and longer-term changes in extreme floods currently remain elusive, because the key drivers vary between catchments. Nonetheless, this approach provides the basis for a more spatially representative assessment of changes in extreme flood occurrence

Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe

For sectors like agriculture, hydrology and ecology, increasing interannual variability (IAV) can have larger impacts than changes in the mean state, whereas decreasing IAV in winter implies that the coldest seasons warm more than the mean. IAV is difficult to reliably quantify in single realizations of climate (observations and single-model realizations) as they are too short, and represent a combination of external forcing and IAV. Single-model initial-condition large ensembles (SMILEs) are powerful tools to overcome this problem, as they provide many realizations of past and future climate and thus a larger sample size to robustly evaluate and quantify changes in IAV. We use three SMILE-based regional climate models (CanESM-CRCM, ECEARTH-RACMO and CESM-CCLM) to investigate downscaled changes in IAV of summer and winter temperature and precipitation, the number of heat waves, and the maximum length of dry periods over Europe. An evaluation against the observational data set E-OBS reveals that all models reproduce observational IAV reasonably well, although both under- and overestimation of observational IAV occur in all models in a few cases. We further demonstrate that SMILEs are essential to robustly quantify changes in IAV since some individual realizations show significant IAV changes, whereas others do not. Thus, a large sample size, i.e., information from all members of SMILEs, is needed to robustly quantify the significance of IAV changes. Projected IAV changes in temperature over Europe are in line with existing literature: increasing variability in summer and stable to decreasing variability in winter. Here, we further show that summer and winter precipitation, as well as the two summer extreme indicators mostly also show these seasonal changes.ISSN:2190-4987ISSN:2190-497

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—‘noise’, intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM–GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in highresolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability

Recent changes in extreme floods across multiple continents

Analyses of trends in observed floods often focus on relatively frequent events, whereas changes in rare floods are only studied for a small number of locations that have exceptionally long observational records. Understanding changes in rare floods is especially relevant as these events are often most damaging and influence the design of major structures. Here, we provide an assessment of changes in the largest flood events (~0.033 annual exceedance probability) observed during the period 1980−2009 for 1744 catchments located in Australia, Brazil, Europe and the United States. The occurrence of rare floods in spatial aggregate shows strong temporal variability and peaked around 1995. During the 30 year period, there are overall increases in both the frequency and magnitude of extreme floods. These increases are strongest in Europe and the United States, and weakest in Brazil and Australia. Physical causes of the reported short-term variability and longer-term changes in extreme floods currently remain elusive, because the key drivers vary between catchments. Nonetheless, this approach provides the basis for a more spatially representative assessment of changes in extreme flood occurrence.ISSN:1748-9326ISSN:1748-931

The 2018 west-central European drought projected in a warmer climate:How much drier can it get?

Projections of changes in extreme droughts under future climate conditions are associated with large uncertainties, owing to the complex genesis of droughts and large model uncertainty in the atmospheric dynamics. In this study we investigate the impact of global warming on soil moisture drought severity in west-central Europe by employing pseudo global warming (PGW) experiments, which project the 1980-2020 period in a globally warmer world. The future analogues of present-day drought episodes allow for investigation of changes in drought severity conditional on the historic day-To-day evolution of the atmospheric circulation. The 2018 west-central European drought is the most severe drought in the 1980-2020 reference period in this region. Under 1.5, 2 and 3g C global warming, this drought episode experiences strongly enhanced summer temperatures but a fairly modest soil moisture drying response compared to the change in climatology. This is primarily because evaporation is already strongly moisture-constrained during present-day conditions, limiting the increase in evaporation and thus the modulation of the temperature response under PGW. Increasing precipitation in winter, spring and autumn limits or prevents an earlier drought onset and duration. Nevertheless, the drought severity, defined as the cumulative soil moisture deficit volume, increases considerably, with 20ĝ€¯% to 39ĝ€¯% under 2g C warming. The extreme drought frequency in the 1980-2020 period strongly increases under 2g C warming. Several years without noticeable droughts under present-day conditions show very strong drying and warming. This results in an increase in 2003-like drought occurrences, compounding with local summer temperature increases considerably above 2g C. Even without taking into account a (potentially large) dynamical response to climate change, drought risk in west-central Europe is strongly enhanced under global warming. Owing to increases in drought frequency, severity and compounding heat, a reduction in recovery times between drought episodes is expected to occur. Our physical climate storyline provides evidence complementing conventional large-ensemble approaches and is intended to contribute to the formulation of effective adaptation strategies.</p

Validation of a Rapid Attribution of the May/June 2016 Flood-Inducing Precipitation in France to Climate Change

International audienceThe extreme precipitation that resulted in historic flooding in central-northern France began 26 May 2016 and was linked to a large cutoff low. The floods caused some casualties and over a billion euros in damage. To objectively answer the question of whether anthropogenic climate change played a role, a near-real-time “rapid” attribution analysis was performed, using well-established event attribution methods, best available observational data, and as many climate simulations as possible within that time frame. This study confirms the results of the rapid attribution study. We estimate how anthropogenic climate change has affected the likelihood of exceedance of the observed amount of 3-day precipitation in April–June for the Seine and Loire basins. We find that the observed precipitation in the Seine basin was very rare, with a return period of hundreds of years. It was less rare on the Loire—roughly 1 in 20 years. We evaluated five climate model ensembles for 3-day basin-averaged precipitation extremes in April–June. The four ensembles that simulated the statistics agree well. Combining the results reduces the uncertainty and indicates that the probability of such rainfall has increased over the last century by about a factor of 2.2 (>1.4) on the Seine and 1.9 (>1.5) on the Loire due to anthropogenic emissions. These numbers are virtually the same as those in the near-real-time attribution study by van Oldenborgh et al. Together with the evaluation of the attribution of Storm Desmond by Otto et al., this shows that, for these types of events, near-real-time attribution studies are now possible

Ecosystem adaptation to climate change: The sensitivity of hydrological predictions to time-dynamic model parameters

Future hydrological behavior in a changing world is typically predicted based on models that are calibrated on past observations, disregarding that hydrological systems and, therefore, model parameters may change as well. In reality, hydrological systems experience almost continuous change over a wide spectrum of temporal and spatial scales. In particular, there is growing evidence that vegetation adapts to changing climatic conditions by adjusting its root zone storage capacity, which is the key parameter of any terrestrial hydrological system. In addition, other species may become dominant, both under natural and anthropogenic influence. In this study, we test the sensitivity of hydrological model predictions to changes in vegetation parameters that reflect ecosystem adaptation to climate and potential land use changes. We propose a top-down approach, which directly uses projected climate data to estimate how vegetation adapts its root zone storage capacity at the catchment scale in response to changes in the magnitude and seasonality of hydro-climatic variables. Additionally, long-term water balance characteristics of different dominant ecosystems are used to predict the hydrological behavior of potential future land use change in a space-for-time exchange. We hypothesize that changes in the predicted hydrological response as a result of 2gK global warming are more pronounced when explicitly considering changes in the subsurface system properties induced by vegetation adaptation to changing environmental conditions. We test our hypothesis in the Meuse basin in four scenarios designed to predict the hydrological response to 2gK global warming in comparison to current-day conditions, using a process-based hydrological model with (a) a stationary system, i.e., no assumed changes in the root zone storage capacity of vegetation and historical land use, (b) an adapted root zone storage capacity in response to a changing climate but with historical land use and (c,gd) an adapted root zone storage capacity considering two hypothetical changes in land use. We found that the larger root zone storage capacities (+34g%) in response to a more pronounced climatic seasonality with warmer summers under 2gK global warming result in strong seasonal changes in the hydrological response. More specifically, streamflow and groundwater storage are up to -15g% and -10g% lower in autumn, respectively, due to an up to +14g% higher summer evaporation in the non-stationary scenarios compared to the stationary benchmark scenario. By integrating a time-dynamic representation of changing vegetation properties in hydrological models, we make a potential step towards more reliable hydrological predictions under change

Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters

Future hydrological behavior in a changing world is typically predicted based on models that are calibrated on past observations, disregarding that hydrological systems and, therefore, model parameters may change as well. In reality, hydrological systems experience almost continuous change over a wide spectrum of temporal and spatial scales. In particular, there is growing evidence that vegetation adapts to changing climatic conditions by adjusting its root zone storage capacity, which is the key parameter of any terrestrial hydrological system. In addition, other species may become dominant, both under natural and anthropogenic influence. In this study, we test the sensitivity of hydrological model predictions to changes in vegetation parameters that reflect ecosystem adaptation to climate and potential land use changes. We propose a top-down approach, which directly uses projected climate data to estimate how vegetation adapts its root zone storage capacity at the catchment scale in response to changes in the magnitude and seasonality of hydro-climatic variables. Additionally, long-term water balance characteristics of different dominant ecosystems are used to predict the hydrological behavior of potential future land use change in a space-for-time exchange. We hypothesize that changes in the predicted hydrological response as a result of 2 K global warming are more pronounced when explicitly considering changes in the subsurface system properties induced by vegetation adaptation to changing environmental conditions. We test our hypothesis in the Meuse basin in four scenarios designed to predict the hydrological response to 2 K global warming in comparison to current-day conditions, using a process-based hydrological model with (a) a stationary system, i.e., no assumed changes in the root zone storage capacity of vegetation and historical land use, (b) an adapted root zone storage capacity in response to a changing climate but with historical land use and (c, d) an adapted root zone storage capacity considering two hypothetical changes in land use. We found that the larger root zone storage capacities (+34 %) in response to a more pronounced climatic seasonality with warmer summers under 2 K global warming result in strong seasonal changes in the hydrological response. More specifically, streamflow and groundwater storage are up to −15 % and −10 % lower in autumn, respectively, due to an up to +14 % higher summer evaporation in the non-stationary scenarios compared to the stationary benchmark scenario. By integrating a time-dynamic representation of changing vegetation properties in hydrological models, we make a potential step towards more reliable hydrological predictions under change.Water Resource