Abstract
The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.
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