Northeast Atlantic and North Sea Storminess as Simulated by a Regional Climate Model during 1958–2001 and Comparison with Observations

2005 ◽  
Vol 18 (3) ◽  
pp. 465-479 ◽  
Author(s):  
Ralf Weisse ◽  
Hans von Storch ◽  
Frauke Feser
Keyword(s):  
Regional Climate Model ◽  
North Sea ◽  
North Atlantic ◽  
Climate Model ◽  
Regional Climate ◽  
Near Surface ◽  
Severe Storms ◽  
Northeast Atlantic ◽  
The North ◽  
The North Sea

Abstract An analysis of the storm climate of the northeast Atlantic and the North Sea as simulated by a regional climate model for the past 44 yr is presented. The model simulates the period 1958–2001 driven by the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Comparison with observations shows that the model is capable of reproducing impact-related storm indices such as the number of severe and moderate storms per year or the total number of storms and upper intra-annual percentiles of near-surface wind speed. The indices describe both the year-to-year variability of the frequency, as well as changes in the average intensity of storm events. Analysis of these indices reveals that the average number of storms per year has increased near the exit of the North Atlantic storm track and over the southern North Sea since the beginning of the simulation period (1958), but the increase has attenuated later over the North Sea and the average number of storms per year has been decreasing over the northeast Atlantic since about 1990–95. The frequency of the most severe storms follows a similar pattern over the northeast North Atlantic while too few severe storms occurred in other areas of the model domain, preventing a statistical analysis for these areas.

scholarly journals Future projections of the climate and surface mass balance of Svalbard with the regional climate model MAR

2015 ◽  
Vol 9 (1) ◽  
pp. 115-140 ◽  
Author(s):  
C. Lang ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Regional Climate Model ◽  
Mass Balance ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Mass Balance ◽  
Near Surface ◽  
Future Projections ◽  
Surface Mass ◽  
Ice Caps ◽  
The North

Abstract. We have performed future projections of the climate and surface mass balance (SMB) of Svalbard with the MAR regional climate model forced by the MIROC5 global model, following the RCP8.5 scenario at a spatial resolution of 10 km. MAR predicts a similar evolution of increasing surface melt everywhere in Svalbard followed by a sudden acceleration of the melt around 2050, with a larger melt increase in the south compared to the north of the archipelago and the ice caps. This melt acceleration around 2050 is mainly driven by the albedo-melt feedback associated with the expansion of the ablation/bare ice zone. This effect is dampened in part as the solar radiation itself is projected to decrease due to cloudiness increase. The near-surface temperature is projected to increase more in winter than in summer as the temperature is already close to 0 °C in summer. The model also projects a strong winter west-to-east temperature gradient, related to the large decrease of sea ice cover around Svalbard. At the end of the century (2070–2099 mean), SMB is projected to be negative over the entire Svalbard and, by 2085, all glaciated regions of Svalbard are predicted to undergo net ablation, meaning that, under the RCP8.5 scenario, all the glaciers and ice caps are predicted to start their irreversible retreat before the end of the 21st century.

Applying machine learning for drought prediction using a large ensemble of climate simulations

2021 ◽  
Author(s):  
Elizaveta Felsche ◽  
Ralf Ludwig
Keyword(s):  
Machine Learning ◽  
Regional Climate Model ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
North Atlantic Oscillation Index ◽  
Climate Model ◽  
Regional Climate ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<p>There is strong scientific and social interest to understand the factors leading to extreme events in order to improve the management of risks associated with hazards like droughts. Recent events like the summer 2018 drought in Germany already had severe und unexpected impacts, e.g. forest fires and crop failures; in order to increase preparedness robust prediction tools are  urgently required. In this study, machine learning methods are applied to predict the occurrence of a drought with lead times of one to three months. The approach takes into account a list of thirty atmospheric and soil variables<strong> </strong>as predictor input parameters from a single regional climate model initial condition large ensemble (CRCM5-LE). The data was produced the context of the ClimEx project by Ouranos with the Canadian Regional Climate Model (CRCM5) driven by 50 members of the Canadian Earth System Model (CanESM2) for the Bavarian and Quebec domains.</p><p>Drought occurrence was defined using the Standardized Precipitation Index. The training and test datasets were chosen from the current climatology (1955-2005) for the Munich and Lisbon subdomain within the CRCM5-LE. The best performing machine learning algorithms managed to obtain a correct classification of drought or no drought for a lead time of one month for around 60 % of the events of each class for the both domains. Explainable AI methods like feature importance and shapley values were applied to gain a better understanding of the trained algorithms. Physical variables like the North Atlantic Oscillation Index and air pressure one month before the event proved to be of high importance for the prediction. The study showed that better accuracies can be obtained for the Lisbon domain, due to the stronger influence of the North Atlantic Oscillation Index on Portugal’s climate.</p>

scholarly journals Sensitivity to Convective Schemes on Precipitation Simulated by the Regional Climate Model MAR over Belgium (1987–2017)

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 34 ◽  
Author(s):  
Sébastien Doutreloup ◽  
Coraline Wyard ◽  
Charles Amory ◽  
Christoph Kittel ◽  
Michel Erpicum ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Precipitation Amount ◽  
Regional Model ◽  
Convective Precipitation ◽  
Mean Annual Precipitation ◽  
The North ◽  
Simulated Precipitation ◽  
The North Sea

The aim of this study is to assess the sensitivity of convective precipitation modelled by the regional climate model MAR (Modèle Atmosphérique Régional) over 1987–2017 to four newly implemented convective schemes: the Bechtold scheme coming from the MESO-NH regional model and the Betts-Miller-Janjić, Kain-Fritsch and modified Tiedtke schemes coming from the WRF regional model. MAR version 3.9 is used here at a resolution of 10 km over a domain covering Belgium using the ERA-Interim reanalysis as forcing. The simulated precipitation is compared against SYNOP and E-OBS gridded precipitation data. Trends in total and convective precipitation over 1987–2017 are discussed. None of the MAR experiments compares better with observations than the others and they all show the same trends in (extreme) precipitation. Over the period 1987–2017, MAR suggests a significant increase in the mean annual precipitation amount over the North Sea but a significant decrease over High Belgium.

scholarly journals Future climate and surface mass balance of Svalbard glaciers in an RCP8.5 climate scenario: a study with the regional climate model MAR forced by MIROC5

2015 ◽  
Vol 9 (3) ◽  
pp. 945-956 ◽  
Author(s):  
C. Lang ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Regional Climate Model ◽  
Mass Balance ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Scenario ◽  
Surface Mass Balance ◽  
Future Projection ◽  
Near Surface ◽  
Surface Mass ◽  
The North

Abstract. We have performed a future projection of the climate and surface mass balance (SMB) of Svalbard with the MAR (Modèle Atmosphérique Régional) regional climate model forced by MIROC5 (Model for Interdisciplinary Research on Climate), following the RCP8.5 scenario at a spatial resolution of 10 km. MAR predicts a similar evolution of increasing surface melt everywhere in Svalbard followed by a sudden acceleration of melt around 2050, with a larger melt increase in the south compared to the north of the archipelago. This melt acceleration around 2050 is mainly driven by the albedo–melt feedback associated with the expansion of the ablation/bare ice zone. This effect is dampened in part as the solar radiation itself is projected to decrease due to a cloudiness increase. The near-surface temperature is projected to increase more in winter than in summer as the temperature is already close to 0 °C in summer. The model also projects a stronger winter west-to-east temperature gradient, related to the large decrease of sea ice cover around Svalbard. By 2085, SMB is projected to become negative over all of Svalbard's glaciated regions, leading to the rapid degradation of the firn layer.

scholarly journals Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe

2020 ◽  
Vol 11 (3) ◽  
pp. 617-640 ◽  
Author(s):  
Andrea Böhnisch ◽  
Ralf Ludwig ◽  
Martin Leduc
Keyword(s):  
Regional Climate Model ◽  
North Atlantic ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Single Model ◽  
Central European ◽  
The North ◽  
The North Atlantic Oscillation

Abstract. Central European weather and climate are closely related to atmospheric mass advection triggered by the North Atlantic Oscillation (NAO), which is a relevant index for quantifying internal climate variability on multi-annual timescales. It remains unclear, however, how large-scale circulation variability affects local climate characteristics when downscaled using a regional climate model. In this study, 50 members of a single-model initial-condition large ensemble (LE) of a nested regional climate model are analyzed for a NAO–climate relationship. The overall goal of the study is to assess whether the range of NAO internal variability is represented consistently between the driving global climate model (GCM; the Canadian Earth System Model version 2 – CanESM2) and the nested regional climate model (RCM; the Canadian Regional Climate Model version 5 – CRCM5). Responses of mean surface air temperature and total precipitation to changes in the NAO index value are examined in a central European domain in both CanESM2-LE and CRCM5-LE via Pearson correlation coefficients and the change per unit index change for historical (1981–2010) and future (2070–2099) winters. Results show that statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions. NAO flow pattern reproductions in the CanESM2-LE trigger responses in the high-resolution CRCM5-LE that are comparable to reanalysis data. NAO–response relationships weaken in the future period, but their inter-member spread shows no significant change. The results stress the value of single-model ensembles for the evaluation of internal variability by pointing out the large differences of NAO–response relationships among individual members. They also strengthen the validity of the nested ensemble for further impact modeling using RCM data only, since important large-scale teleconnections present in the driving data propagate properly to the fine-scale dynamics in the RCM.

Temperature, Salinity and Plankton in the Eastern North Atlantic and Coastal Waters of Britain, 1957.: I. The Characterisation and Distribution of Surface Waters

1963 ◽  
Vol 20 (3) ◽  
pp. 789-826 ◽  
Author(s):  
B. McK. Bary
Keyword(s):  
North Sea ◽  
North Atlantic ◽  
Coastal Waters ◽  
Water Bodies ◽  
English Channel ◽  
Oceanic Water ◽  
Climatic Fluctuations ◽  
The North ◽  
The North Sea ◽  
Eastern North Atlantic

Monthly temperature-salinity diagrams for 1957 have demonstrated that three surface oceanic "water bodies" were consistently present in the eastern North Atlantic; two are regarded as modified North Atlantic Central water which give rise to the third by mixing. As well in the oceanic areas, large and small, high or low salinity patches of water were common. Effects of seasonal climatic fluctuations differed in the several oceanic water bodies. In coastal waters, differences in properties and in seasonal and annual cycles of the properties distinguish the waters from the North Sea, English Channel and the western entrance to the Channel.The geographic distributions of the oceanic waters are consistent with "northern" and "southern" water bodies mixing to form a "transitional" water. Within this distribution there are short-term changes in boundaries and long-term (seasonal) changes in size of the water bodies.Water in the western approaches to the English Channel appeared to be influenced chiefly by the mixed, oceanic transitional water; oceanic influences in the North Sea appear to have been from northern and transitional waters.

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

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

<p>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.</p><p>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.</p>

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