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

Abstract

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 fi ve members is constructed from fi ve 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 Niño and La Niña 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.

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

“…Other studies also verified that global climate models (GCMs) underestimate air temperature prediction in SA [41,42,50,51]. For example, Kim et al [50] analyzed the ECMWF-SEAS4 and Climate Forecast System version 2 (CFSv2) hindcasts and observed a systematic cold bias of both models in SA.…”

“…The seasonal anomaly skill score evaluates the precipitation and temperature predictions from the ECMWF-SEAS5 hindcasts. These skill scores consist of mean temporal correlations between predicted and observed trimonthly anomalies [42], considering the climatological mean (1993-2016) from the ECMWF-SEAS5 and CPC datasets. Given that the samples are composed of 24 pairs of values (1993-2016) for each season, a two-tailed Student's t-test with 95% confidence (α = 0.05) and 22 degrees of freedom provides the critical Pearson value of ≈0.40, indicating that correlations equal to or above this threshold are statistically significant [43].…”

Topography-based local spherical Voronoi grid refinement on classical and moist shallow-water finite-volume models

“…Furthermore, Figueroa et al (2016) show that both the Brazilian Global Atmospheric Model (BAM) and the Global Forecast System (GFS) produce dry or wet biases in the Amazon Basin, over the Andes and in the Brazilian southeast. This bias is also observed in other models (Silva et al, 2011;Chou et al, 2020). Therefore, aiming to improve the precipitation forecast in those regions, it is desirable to better represent the L. F. Santos and P. S. Peixoto: Locally refined spherical Voronoi grids on a moist shallow-water model shape of the Andes Range in the employed model.…”

Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters

“…These conditions drive the regional model domain, which in turn provides a smaller scale climate forecasts that is dynamically downscaling [2,3]. The Eta Regional Climate Model outputs used in this study are derived from ten-year seasonal reforecasts [28], which have been shown to add value over the driver coarse global model forecasts, especially during the rainy seasons. The evaluation was based on the temporal correlation between forecasts and observations of precipitation seasonal anomaly.…”

“…This is the rainy season over most of South America. Further details on the construction of this data set can be found in [28].…”