4.4 Article

Analyzing Dynamical Downscaling over the Tropical South America Using RegCM4

期刊

PURE AND APPLIED GEOPHYSICS
卷 179, 期 10, 页码 3859-3874

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SPRINGER BASEL AG
DOI: 10.1007/s00024-022-03153-2

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Dynamical downscaling; RegCM4; HadGEM2-ES; Tropical South America

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The Regional Climate Model version 4 (RegCM4) driven by the general circulation model HadGEM2-ES from CMIP5 was used to evaluate the 1986-2005 climate simulation over tropical South America. Results show that while RegCM4 has drier biases, it provides a more realistic spatial distribution of rainfall intensity, especially in the AMZ Basin.
We use the Regional Climate Model version 4 (RegCM4) driven by the general circulation model HadGEM2-ES from Coupled Model Intercomparison Project 5 (CMIP5) to evaluate 1986-2005 climate simulation over tropical South America (SA). The simulated seasonal spatial pattern and annual cycle (using statistical metrics) of precipitation and temperature are compared with observational datasets (CRU, GPCP) and reanalysis (ERA5). The results show that, while RegCM4 has drier biases, HadGEM2-ES simulates higher rainfall intensity, providing a more realistic spatial distribution in some regions of tropical SA, specifically in the AMZ Basin. On the other hand, RegCM4 reduces the warm (cold) bias of HadGEM2-ES over the Andes Mountains (NEB region). Both models represent spatial patterns of pressure, humidity and atmospheric circulation similar to reanalysis. The annual cycle of precipitation in AMZ Basin are better captured by HadGEM-ES than by RegCM4, especially during the austral summer. In NEB region, the annual cycle of RegCM4 mainly follows the driving field. Both RegCM4 and HadGEM2-ES exhibit similar performance in terms of variation in annual cycle of temperature over the both subregions. In general, the results highlight the RegCM4's ability to reproduce climatic patterns during the austral summer season, in a domain which presents a great diversity of climatic behavior. Despite some persistent biases, the regional model added value to the global model, especially for the temperature over regions of complex topography.

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