Ten-year seasonal climate reforecasts over South America using the Eta Regional Climate Model

Ten-year seasonal climate reforecasts over South America are obtained using the Eta Regional Climate Model at 40 km resolution, driven by the large-scale forcing from the global atmospheric model of the Center for Weather Forecasts and Climate Studies. The objective of this work is to evaluate these...

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Veröffentlicht in:Anais da Academia Brasileira de Ciências 2020-01, Vol.92 (3), p.e20181242-e20181242, Article 20181242
Hauptverfasser: Chou, Sin Chan, Dereczynski, Claudine, Gomes, Jorge Luis, Pesquero, Jose Fernando, De Avila, Ana Maria H., Resende, Nicole C., Alves, Luis Felipe, Ruiz-Cardenas, Ramiro, De Souza, Carlos Renato, Bustamante, Josiane Ferreira F.
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Sprache:eng
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Zusammenfassung:Ten-year seasonal climate reforecasts over South America are obtained using the Eta Regional Climate Model at 40 km resolution, driven by the large-scale forcing from the global atmospheric model of the Center for Weather Forecasts and Climate Studies. The objective of this work is to evaluate these regional reforecasts. The dataset is comprised of four-month seasonal forecasts performed on a monthly basis between 2001 and 2010. An ensemble of five members is constructed from five slightly different initial conditions to partially reduce the uncertainty in the seasonal forecasts. The seasonal mean precipitation and 2-meter temperature forecasts are compared with the observations. The comparison shows that, in general, forecasted precipitation is underestimated in the central part of the continent in the austral summer, whereas the forecasted 2 meter temperature is underestimated in most parts of the continent and throughout the year. Skill scores show higher skill in the northern part of the continent and lower skill in the southern part of the continent, but mixed skill signs are seen in the central part of the continent. During the El Nino and La Nifia seasons, the forecast skill scores clearly increase. The downscaling of the Eta model seasonal forecasts provides added value over the driver global model forecasts, especially during rainy periods.
ISSN:0001-3765
1678-2690
1678-2690
DOI:10.1590/0001-3765202020181242