Soil moisture forecasts potential to predict rain anomalies over land-atmosphere ?hot spots? in South America

P. SPENNEMANN; C. SAULO

Montreal

Conferencia; WWOSC; 2014

WMO

Land surface variables (e.g. soil moisture) play an important role in the energy balance between the land surface and the atmosphere as well as in the evolution of the hydrological cycle. A better representation of the ?slowly? varying variables (e.g. SST, soil moisture) in numerical models is essential to improve monthly and seasonal forecasts. The goal of this work is to evaluate monthly and seasonal soil moisture and rainfall retrospective forecasts (up to 4 months) in the Climate Forecast System (CFS, v2) over South America compared against the GLDAS (Global Land Data Assimilation System) for 1982-2010 summer periods. Previous work show that GLDAS Version 2 provides good estimates of soil moisture variability over southern South America and is suitable to disclose areas with strong land-atmosphere coupling ?identified following Notaro (2008) methodology- . Our analysis focuses over two sub-regions of interest: Southeastern South America and the South Atlantic Convergence Zone. Monthly forecasts show similar soil moisture variability patterns to those obtained with the GLDAS, with error increase (both systematic and non-systematic) with forecast length. These errors are related with the uncertainty in the initial condition and also with errors in the representation of soil moisture model driver, mainly rainfall. The comparison of forecasted and GLDAS coupling strength further contributes to understand the impact of soil moisture errors on rainfall forecast skill, and shows that soil moisture anomalies may be an adequate tool to anticipate rainfall anomalies one month in advance.