2018
DOI: 10.3390/atmos9070262
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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

Abstract: Abstract:The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (E g↓ ) 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 E g↓ , and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizo… Show more

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Cited by 12 publications
(17 citation statements)
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“…The coarse resolution of ERA-20C (∼ 125 km) does not allow for a correct description of the atmospheric variables over mountainous areas, which justifies the use of a regional model with higher resolution and advanced physical parameterizations. MAR has already been successfully tested with the ERA-20C reanalysis as boundary conditions over Europe (Wyard et al, 2017(Wyard et al, , 2018. After a spin-up of 2 years, the model in general and the surface component in particular are sup- applied with a resolution of 7 km and laterally forced with ERA-20C.…”
Section: Mar Configurationmentioning
confidence: 99%
See 1 more Smart Citation
“…The coarse resolution of ERA-20C (∼ 125 km) does not allow for a correct description of the atmospheric variables over mountainous areas, which justifies the use of a regional model with higher resolution and advanced physical parameterizations. MAR has already been successfully tested with the ERA-20C reanalysis as boundary conditions over Europe (Wyard et al, 2017(Wyard et al, , 2018. After a spin-up of 2 years, the model in general and the surface component in particular are sup- applied with a resolution of 7 km and laterally forced with ERA-20C.…”
Section: Mar Configurationmentioning
confidence: 99%
“…However, their coarse resolution precludes accurate simulations of small-scale processes typical of mountainous areas, such as those inducing the spatial heterogeneity of precipitation and snow cover. It is, therefore, difficult to study the Alpine climate variability with GCMs (Zubler et al, 2016). Due to their finer resolution (∼ 25 to 1 km) and their more detailed parametrization for physical processes (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…This is probably due to a slight underestimation (10-20%) of the cloud cover in the MAR version used in this study, which has been largely reduced in the latest version of the MAR (MAR v3.9, see e.g. Wyard et al 2018).…”
Section: Evaluation Of the Mar Surface Scheme In Terms Of Smb And Sebmentioning
confidence: 90%
“…It is a hydrostatic primitive equation model initially developed for Polar regions [22] such as the Greenland ice sheet (e.g., [23,24]) or the Antarctic region (e.g., [25,26]). However, MAR has successfully been adapted for Western European temperate regions [27][28][29][30][31] and has also been chosen to be part of the European branch of the international COordinated Regional climate Downscaling EXperiment (EURO-CORDEX) project thanks to the Belgian CORDEX.be project [32]. For more details about the physical parameterization of the MAR model used here, we refer to [19].…”
Section: Models and Methodsmentioning
confidence: 99%