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Atmosphere
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Regional Climate Modeling
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Regional Climate Modeling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 22760

Special Issue Editor

Dr. Tomonori Sato

E-Mail Website
Guest Editor
Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan
Interests: mesoscale convective system; atmosphere-land interaction; dynamical downscaling; orographically-induced circulation

Special Issue Information

Dear Colleagues,

Regional climate modeling has been widely adopted as a methodology to study regional climate details at horizontal scales, ranging from sub-kilometer to thousands of kilometers. With the advantages of higher resolutions and sophisticated physics schemes, regional climate models can replicate mesoscale meteorological processes more realistically than those in forcing dataset, such as general circulation models (GCMs) or reanalysis datasets. This value-added will help investigate physical mechanisms behind regional climate systems across worldwide, which are typically emphasized by convective systems, and/or underlying surface conditions, such as topographies, land-sea contrasts, vegetation patterns, snow covers, urban landscapes, and sea surface temperature and sea ice inhomogeneities. Recently, due to increasing social demands for impact assessment of climate change, regional climate models have provided high-resolution climate scenarios that are used as the input of assessment models. Furthermore, the utilization of regional climate models has been expanded to weather applications, including renewable energy forecast and risk assessment of the weather-related disasters.

This Special Issue calls for contributions that meet themes as below. We also welcome submissions that will deepen our understanding in regional climate modeling.

1. Regional climate model experiments aiming to understand mesoscale meteorological processes, particularly those studying mesoscale precipitation systems, orographic effects on weather and climate, atmosphere–land interaction, and atmosphere-ocean interaction. Influences of lateral and surface boundary conditions and atmospheric internal processes on regional climate. Interactions of synoptic and mesoscale weather systems over various time scales.

2. Proposal and/or practice of technical improvements in running and evaluating regional climate models, including development of new physics scheme, new experimental design of dynamical downscaling that can reduce computational cost or error, methodology to generate ensemble members for statistical analysis of low-frequency events, bias correction method that retains physical consistency, and new methodology for model evaluation.

3. Regional climate studies for detection and attribution of historical, ongoing, and future climate changes. Regional climate modeling with sensitivity experiments aiming to diagnose physical mechanism of climate change. Practice of dynamical downscaling experiments for generating future climate scenarios at regional scale.

4. Application of regional climate modeling toward the solution of regional environmental issues, including the assessment and prediction skill of solar and wind energy resources and diagnosis of the risk for natural disasters.

Dr. Tomonori Sato
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dynamical downscaling
  • regional climate change
  • atmosphere-land interaction
  • atmosphere-ocean interaction
  • mesoscale meteorological modeling

Published Papers (5 papers)

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Research

19 pages, 155111 KiB  
Article
Sensitivity to Convective Schemes on Precipitation Simulated by the Regional Climate Model MAR over Belgium (1987–2017)
by Sébastien Doutreloup, Coraline Wyard, Charles Amory, Christoph Kittel, Michel Erpicum and Xavier Fettweis
Atmosphere 2019, 10(1), 34; https://doi.org/10.3390/atmos10010034 - 17 Jan 2019
Cited by 16 | Viewed by 4119
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Regional Climate Modeling)
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22 pages, 7032 KiB  
Article
Effects of Meteorology Nudging in Regional Hydroclimatic Simulations of the Eastern Mediterranean
by George Zittis, Adriana Bruggeman, Panos Hadjinicolaou, Corrado Camera and Jos Lelieveld
Atmosphere 2018, 9(12), 470; https://doi.org/10.3390/atmos9120470 - 30 Nov 2018
Cited by 3 | Viewed by 3748
Abstract
In this study, we investigated the effects of grid and spectral nudging in regional hydroclimatic simulations over the Eastern Mediterranean climate change hot-spot. We performed year-long simulations for the hydrological year October 2001–September 2002 using the Weather Research and Forecasting (WRF) model at [...] Read more.
In this study, we investigated the effects of grid and spectral nudging in regional hydroclimatic simulations over the Eastern Mediterranean climate change hot-spot. We performed year-long simulations for the hydrological year October 2001–September 2002 using the Weather Research and Forecasting (WRF) model at 12-km resolution, driven by the ERA-Interim reanalyses. Six grid and three spectral nudging options were tested using a number of model configurations. Due to the large uncertainty of regional observations, we compared the model with various satellite- and station-based meteorological datasets. The effect of nudging was tested for mean weather conditions and precipitation characteristics and extremes. For certain parts of the study domain, WRF was found to reproduce both aspects of rainfall over the Eastern Mediterranean reasonably well. Our findings highlighted that, for the WRF modeling system, nudging is critical for the simulation of rainfall; however, the application of interior constraint methods was found to have different impacts on various locations and climatic regimes. For the hyperarid parts of the domain, nudging did not improve the simulation of precipitation amounts (about 20% additional drying was introduced), while it added much value for the wetter rainfall regimes of the Eastern Mediterranean (corrections of about 30%). Improvements in the simulated precipitation were mostly introduced by spectral nudging; however, this option required significant computational resources. For these ERA-Interim-driven simulations, grid nudging that involves specific humidity within the planetary boundary layer is not recommended for the simulation of precipitation since it introduces dry biases up to 75–80%. Full article
(This article belongs to the Special Issue Regional Climate Modeling)
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21 pages, 5289 KiB  
Article
Origin of Warm SST Bias over the Atlantic Cold Tongue in the Coupled Climate Model FGOALS-g2
by Yanyan Shi, Wenyu Huang, Bin Wang, Zifan Yang, Xinsheng He and Tianpei Qiu
Atmosphere 2018, 9(7), 275; https://doi.org/10.3390/atmos9070275 - 18 Jul 2018
Cited by 5 | Viewed by 4068
Abstract
Most of the coupled models contain a strong warm bias in sea surface temperature (SST) over the Atlantic Cold Tongue (ACT) region (10° S–3° N, 20° W–10° E) during June–August (JJA) and September–November (SON). In this study, the origins of the ACT SST [...] Read more.
Most of the coupled models contain a strong warm bias in sea surface temperature (SST) over the Atlantic Cold Tongue (ACT) region (10° S–3° N, 20° W–10° E) during June–August (JJA) and September–November (SON). In this study, the origins of the ACT SST bias and their relative contributions to the bias are explored by conducting a set of sensitivity experiments, which are based on an ocean-ice model, and by ignoring the nonlinear effects of each origin. The origins for the warm bias over the ACT in the coupled climate model during JJA are estimated as follows: westerly wind bias along the equator (5° S–5° N) during March–May (MAM; contributes approximately 32.6% of the warm bias), northerly bias over the southern tropical Atlantic (25° S–3° N, 40° W–20° E) during MAM and JJA (21.4%), bias in the surface specific humidity and surface air temperature (11.9%), and downward shortwave radiation bias (6.5%). The origins of the ACT bias during SON are as follows: northerly bias over the southern tropical Atlantic during SON (31.2%), bias in the surface specific humidity and surface air temperature (27.9%), downward shortwave radiation bias (17.4%), and zonal wind bias (13.4%). Note that these contribution ratios of these origins may be model-dependent. In addition, the local and non-local effects of the zonal wind bias are explored explicitly, while those of all the other biases are examined implicitly. Therefore, a better-performing atmospheric component is crucial when simulating zonal winds during MAM along the equator (5° S–5° N) and meridional winds during MAM, JJA, and SON over the southern tropical Atlantic, which will alleviate the warm bias over the ACT region in the coupled climate model. Full article
(This article belongs to the Special Issue Regional Climate Modeling)
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23 pages, 14787 KiB  
Article
Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR
by Coraline Wyard, Sébastien Doutreloup, Alexandre Belleflamme, Martin Wild and Xavier Fettweis
Atmosphere 2018, 9(7), 262; https://doi.org/10.3390/atmos9070262 - 13 Jul 2018
Cited by 12 | Viewed by 5532
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Regional Climate Modeling)
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22 pages, 5959 KiB  
Article
Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China
by Peng Liu, Xiaobin Qiu, Yi Yang, Yuanyuan Ma and Shuanglong Jin
Atmosphere 2018, 9(3), 101; https://doi.org/10.3390/atmos9030101 - 11 Mar 2018
Cited by 8 | Viewed by 3851
Abstract
This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two [...] Read more.
This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF) land surface data (OLD) and accurate dynamically accordant MODIS data (NEW). Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT), spectral nudging (SN), and re-initialization (Re) experiments. The Weather Research and Forecasting (WRF) model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF) data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error). A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident. Full article
(This article belongs to the Special Issue Regional Climate Modeling)
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