Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution
When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (E...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2011-05-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-4433/2/2/129/ |
| _version_ | 1818438147753639936 |
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| author | Arne Spekat Wolfgang Enke Frank Kreienkamp Sonja Baumgart |
| author_facet | Arne Spekat Wolfgang Enke Frank Kreienkamp Sonja Baumgart |
| author_sort | Arne Spekat |
| collection | DOAJ |
| description | When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade. |
| first_indexed | 2024-12-14T17:35:57Z |
| format | Article |
| id | doaj.art-8591e4e3bf324cc1be3848cf5b62f5dc |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-12-14T17:35:57Z |
| publishDate | 2011-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-8591e4e3bf324cc1be3848cf5b62f5dc2022-12-21T22:52:58ZengMDPI AGAtmosphere2073-44332011-05-012212914510.3390/atmos2020129Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s ResolutionArne SpekatWolfgang EnkeFrank KreienkampSonja BaumgartWhen assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade.http://www.mdpi.com/2073-4433/2/2/129/climate modellingregional climate changedownscalingmulti-approach ensembleempirical statistical downscaling |
| spellingShingle | Arne Spekat Wolfgang Enke Frank Kreienkamp Sonja Baumgart Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution Atmosphere climate modelling regional climate change downscaling multi-approach ensemble empirical statistical downscaling |
| title | Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution |
| title_full | Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution |
| title_fullStr | Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution |
| title_full_unstemmed | Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution |
| title_short | Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution |
| title_sort | climate signals on the regional scale derived with a statistical method relevance of the driving model s resolution |
| topic | climate modelling regional climate change downscaling multi-approach ensemble empirical statistical downscaling |
| url | http://www.mdpi.com/2073-4433/2/2/129/ |
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