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Climate change is expected to alter European floods and associated economic losses in various ways. Here we investigate the impact of precipitation change on European average winter and summer financial losses due to flooding under a 1.5 °C warming scenario (reflecting a projected climate in the year 2115 according to RCP2.6)and for a counterfactual current-climate scenario where the climate has evolved without anthropogenic influence (reflecting a climate corresponding to pre-industrial conditions). Climate scenarios were generated with the Community Atmospheric Model (CAM)version 5. For each scenario, we derive a set of weights that when applied to the current climate's precipitation results in a climatology that approximates that of the scenario. We apply the weights to annual losses from a well-calibrated (to the current climate)flood loss model that spans 50,000 years and re-compute the average annual loss to assess the impact of precipitation changes induced by anthropogenic climate change. The method relies on a large stochastic set of physically based flood model simulations and allows quick assessment of potential loss changes due to change in precipitation based on two statistics, namely total precipitation, and total precipitation of very wet days (defined here as the total precipitation of days above the 95th percentile of daily precipitation). We compute the statistics with the raw CAM precipitation and bias-corrected precipitation. Our results show that for both raw and bias-corrected statistics i)average flood loss in Europe generally tend to increase in winter and decrease in summer for the future scenario, and consistent with that change we also show that ii)average flood losses have increased (decreased)for winter (summer)from pre-industrial conditions to the current day. The magnitude of the change varies among scenarios and statistics chosen.

Several damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher‐category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal‐to‐noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east‐to‐west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region. [ABSTRACT FROM AUTHOR]

The Colorado floods of September 2013 caused severe damage and fatalities, and resulted from prolonged heavy rainfall unusual for that time of year – both in its record-breaking amounts and associated weather systems. We investigate the possible role of anthropogenic climate change in this extreme event. The unusual hydrometeorology of the event, however, challenges standard frameworks for attributing extreme events to anthropogenic climate change, because they typically struggle to simulate and connect the large-scale meteorology associated with local weather processes. Therefore we instead employ a part dynamical modelling- part observational- based event attribution approach, which simulates regional Colorado rainfall conditional on boundary conditions prescribed from the observed synoptic-scale meteorology in September 2013 – and assumes these conditions would have been similar in the absence of anthropogenic forcing. Using this ‘conditional event attribution’ approach we find that our regional climate model simulations indicate that anthropogenic drivers increased the magnitude of heavy northeast Colorado rainfall for the wet week in September 2013 by 30%, with the occurrence probability of a week at least that wet increasing by at least a factor of 1.3. By comparing the convective and large-scale components of rainfall, we find that this increase resulted in part from the additional moisture-carrying capacity of a warmer atmosphere – allowing more intense local convective rainfall that induced a dynamical positive feedback in the existing larger scale moisture flow – and also in part from additional moisture transport associated with larger scale circulation change. Our approach precludes assessment of changes in the frequency of the observed synoptic meteorological conditions themselves, and thus does not assess the effect of anthropogenic climate drivers on the statistics of heavy Colorado rainfall events. However, tailoring analysis tools to diagnose particular aspects of localized extreme weather events, conditional on the observed large-scale meteorology, can prove useful for diagnosing the physical effects of anthropogenic climate change on severe weather events – especially given large uncertainties in assessments of anthropogenic driven changes in atmospheric circulation.

Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing1. Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events (2) such as the UK floods of October and November2000. Occurring during the wettest autumn in England and Wales since records began in 1766 (3,4),these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at 1.3 billion £ (refs 5, 6). Although the flooding was deemed a 'wakeup call' to the impacts of climate change at the time (7), such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail (8,9) to account fully for the complex hydrometeorology (4,10) associated with flooding. Here we present a multi-step, physically based 'probabilistic event attribution' framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing (11,12,) we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth-century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%., Recent widespread UK floods--such as in spring 1998, autumn 2000, winter 2003 and summer 2007--have prompted debate as to whether these particular events are attributable to anthropogenic climate change (6,7,13-15). [...]

Rain-on-snow (ROS) events are multivariate hydrometeorological phenomena requiring a combination of rain and snowpack, with complex processes occurring on and within the snowpack. Impacts include floods and landslides, and rain may freeze within the snowpack or on bare ground, potentially affecting vegetation, wildlife, and permafrost. ROS events occur mainly in high-latitude and mountainous areas, where sparse observational networks hinder accurate quantification – as does a scale mismatch between coarse (50-100 km) resolution re-analysis products and localised events. A recent study (Pall et al., submitted; available at: https://eartharxiv.org/k72ej/) used a high (1km) resolution observational data set for mainland Norway to construct a ROS climatology for recent decades. Its main result was that, compared to 1961-1990, ROS events in the 1981-2010 period decrease most in the southwest in winter, southeast in spring, and north in summer (consistent with less snow cover in a warming climate), and increase most in the southwest, central mountains, and north in winter-spring (consistent with increased precipitation and/or more snow falling as rain in a warming climate). Here we will investigate this result further, by using a high-resolution climate model to simulate a Norwegian ROS climatology for a future period (2070-2100; under the IPCC RCP8.5), as well as first evaluating model simulations against the aforementioned observed climatology (1981-2010). A particular focus will be on investigating changes in ROS events that result in large damaging floods such as in Oppland county in May 2013. This work forms a contribution to the LATICE (Land-ATmosphere Interactions in Cold Environments) project at the University of Oslo. [ABSTRACT FROM AUTHOR]