Future Changes in European Severe Convection Environments in a Regional Climate Model Ensemble

The occurrence of environmental conditions favorable for severe convective storms was assessed in an ensemble of 14 regional climate models covering Europe and the Mediterranean with a horizontal grid spacing of 0.44°. These conditions included the collocated presence of latent instability and strong deep-layer (surface to 500 hPa) wind shear, which is conducive to the severe and well-organized convective storms. The occurrence of precipitation in the models was used as a proxy for convective initiation. Two climate scenarios (RCP4.5 and RCP8.5) were investigated by comparing two future periods (2021–50 and 2071–2100) to a historical period (1971–2000) for each of these scenarios. The ensemble simulates a robust increase (change larger than twice the ensemble sample standard deviation) in the frequency of occurrence of unstable environments (lifted index ≤ −2) across central and south-central Europe in the RCP8.5 scenario in the late twenty-first century. This increase coincides with the increase in lower-tropospheric moisture. Smaller, less robust changes were found until midcentury in the RCP8.5 scenario and in the RCP4.5 scenario. Changes in the frequency of situations with strong (≥15 m s−1) deep-layer shear were found to be small and not robust, except across far northern Europe, where a decrease in shear is projected. By the end of the century, the simultaneous occurrence of latent instability, strong deep-layer shear, and model precipitation is simulated to increase by up to 100% across central and eastern Europe in the RCP8.5 and by 30%–50% in the RCP4.5 scenario. Until midcentury, increases in the 10%–25% range are forecast for most regions. A large intermodel variability is present in the ensemble and is primarily due to the uncertainties in the frequency of the occurrence of unstable environments.

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The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble

Journal of Climate ◽

10.1175/jcli-d-21-0061.1 ◽

2021 ◽

pp. 1-56

Keyword(s):

Global Warming ◽

Pacific Northwest ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Wrf Model ◽

Regional Climate Models ◽

Winter Precipitation ◽

Historical Period ◽

Continental Interior

This paper describes the downscaling of an ensemble of twelve GCMs using the WRF model at 12-km grid spacing over the period 1970-2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP 8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. April 1 snowpack declines are large over the lower to middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCM’s producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the 21st century.

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Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-0004.1 ◽

2017 ◽

Vol 98 (1) ◽

pp. 79-93 ◽

Cited By ~ 121

Author(s):

Elizabeth J. Kendon ◽

Nikolina Ban ◽

Nigel M. Roberts ◽

Hayley J. Fowler ◽

Malcolm J. Roberts ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Global Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Projections ◽

Convective Storms ◽

Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

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Analysis of EURO-CORDEX sub-daily rainfall simulations and derived event characteristics

10.5194/egusphere-egu21-2119 ◽

2021 ◽

Author(s):

Paola Nanni ◽

David J. Peres ◽

Rosaria E. Musumeci ◽

Antonino Cancelliere

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Rainfall Event ◽

Historical Period ◽

High Temporal Resolution ◽

Hydrological Hazards ◽

The Future

Climate change is a phenomenon that is claimed to be responsible for a significant alteration of the precipitation regime in different regions worldwide and for the induced potential changes on related hydrological hazards. In particular, some consensus has raised about the fact that climate changes can induce a shift to shorter but more intense rainfall events, causing an intensification of urban and flash flooding hazards. &#160;Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted. For this reason, it is of key importance to ascertain the quality of regional climate models (RCMs), especially with reference to their ability to reproduce the main climatological regimes with respect to an historical period. To this end, several studies have focused on the analysis of annual or monthly data, while few studies do exist that analyze the sub-daily data that are made available by the regional climate projection initiatives. In this study, with reference to specific locations in eastern Sicily (Italy), we first evaluate historical simulations of precipitation data from selected RCMs belonging to the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) with high temporal resolution (three-hourly), in order to understand how they compare to fine-resolution observations. In particular, we investigate the ability to reproduce rainfall event characteristics, as well as annual maxima precipitation at different durations. With reference to rainfall event characteristics, we specifically focus on duration, intensity, and inter-arrival time between events. Annual maxima are analyzed at sub-daily durations. We then analyze the future simulations according to different Representative concentration scenarios. The proposed analysis highlights the differences between the different RCMs, supporting the selection of the most suitable climate model for assessing the impacts in the considered locations, and to understand what trends for intense precipitation are to be expected in the future.

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Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041992 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1992

Author(s):

Weijia Qian ◽

Howard H. Chang

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Warm Season ◽

Climate Science ◽

Regional Climate Models ◽

Health Impact ◽

Quantile Mapping ◽

Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

Hydrology and Earth System Sciences ◽

10.5194/hess-22-673-2018 ◽

2018 ◽

Vol 22 (1) ◽

pp. 673-687 ◽

Cited By ~ 10

Author(s):

Antoine Colmet-Daage ◽

Emilia Sanchez-Gomez ◽

Sophie Ricci ◽

Cécile Llovel ◽

Valérie Borrell Estupina ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Extreme Precipitation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Historical Period ◽

Mountainous Regions ◽

Extreme Precipitation Events ◽

Precipitation Events

Abstract. The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

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Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

10.5194/egusphere-egu21-4892 ◽

2021 ◽

Author(s):

Jeremy Carter ◽

Amber Leeson ◽

Andrew Orr ◽

Christoph Kittel ◽

Melchior van Wessem

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate Change ◽

Near Surface ◽

Virtual Lab ◽

Model Variability ◽

The Antarctic

Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.

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Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽

10.3390/atmos9070262 ◽

2018 ◽

Vol 9 (7) ◽

pp. 262 ◽

Cited By ~ 4

Author(s):

Coraline Wyard ◽

Sébastien Doutreloup ◽

Alexandre Belleflamme ◽

Martin Wild ◽

Xavier Fettweis

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Radiative Flux ◽

Added Value ◽

Historical Trends ◽

The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

10.21203/rs.3.rs-725819/v1 ◽

2021 ◽

Author(s):

Antoine Doury ◽

Samuel Somot ◽

Sébastien Gadat ◽

Aurélien Ribes ◽

Lola Corre

Keyword(s):

Climate Change ◽

Network Architecture ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate ◽

Near Surface ◽

The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

10.5194/egusphere-egu21-12054 ◽

2021 ◽

Author(s):

Gaby S. Langendijk ◽

Diana Rechid ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Climate Data ◽

Urban Rural

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2&#176;C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

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A statistical framework for evaluating EURO-CORDEX simulations and derived drought characteristics

10.5194/egusphere-egu21-2330 ◽

2021 ◽

Author(s):

David J. Peres ◽

Alfonso Senatore ◽

Paola Nanni ◽

Antonino Cancelliere ◽

Giuseppe Mendicino ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Circulation Model ◽

Regional Climate Models ◽

Monitoring Networks ◽

Global Circulation Model ◽

Drought Characteristics ◽

Precipitation And Temperature

Regional climate models (RCMs) are commonly used for assessing, at proper spatial resolutions, future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic observed climate (temperature and precipitation). We specifically focus on the models&#8217; performance in reproducing drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices. The density of the measurements is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the models based on the combination of the HadGEM2 global circulation model (GCM) with the CLM-Community RCMs are the most skillful in reproducing precipitation and temperature variability as well as drought characteristics. Nevertheless, the ranking of the models may slightly change depending on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, aiding on the selection of the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables.

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