An estimation of the land-atmosphere coupling strength in South America using the Global Land Data Assimilation System

Publicado en International Journal of Climatology , v. 35(14):4151-4166 
Autores

Spennemann, P.C. and Saulo, A.C.

Año de publicación 2015
DOI https://doi.org/10.1002/joc.4274
Afiliaciones
  • Centro de Investigaciones del Mar y la Atmósfera (CONICET‐UBA), UMI IFAECI/CNRS, Buenos Aires, Argentina
  • Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
Programa

CRN3

Proyecto CRN3035
Keywords

Abstract

The aim of this study is to identify regions of strong land surface-atmosphere coupling for the austral summer over South America. To accomplish this, a statistical methodology is applied to estimate the interactions of soil moisture with evapotranspiration and precipitation derived from the Global Land Data Assimilation System (GLDAS) dataset. Possible impacts of El Niño Southern Oscillation (ENSO) on the coupling strength are also examined. Particular emphasis is set over two sub‐regions of interest: Southeastern South America (SESA) and the continental part of the South Atlantic Convergence Zone (SACZ). Positive and significant soil moisture-precipitation feedbacks are found over parts of SACZ and in the southern part of South America. Instead, significant negative feedback is found over SESA. The influence of ENSO over the soil moisture-precipitation coupling strength signal is evident over tropical regions. Plausible physical mechanisms involved in the land surface-atmosphere interactions, the influence of ENSO and that of precipitation persistence over extratropical regions on the results, are discussed. The implications of this analysis on monthly to seasonal forecast are also examined. Despite that this methodology cannot be used to establish a precise causal-effect relationship, this study gives a valuable first order approximation of land surface-atmosphere interactions over South America that complements pre‐existing work.