The Effects of the Surface Conditions on Extreme Events: Regional Model Simulations using Ecosystem Functional Types

ERNESTO H. BERBERY; DOMINGO ALCARAZ-SEGURA; SEUNG-JAE LEE; OMAR V. MÜLLER

Barcelona

Workshop; WCRP Workshop on Drought Predictability and Prediction in a Changing Climate; 2011

World Climate Research Program

Sea surface temperature (SST) anomalies are recognized as the main driver of extremes, with land surface conditions modulating droughts or persistent wet spells as a result of regional feedbacks. However, the role of regional feedbacks is not fully understood, as many current atmospheric models have simplified land surface schemes that employ fixed land-cover maps or vegetation types along with tables of their corresponding biophysical properties. Using a fixed land-cover classification does not account for interannual changes of those properties due to, for example, excess or deficit of precipitation. This representation of vegetation functioning may result in delayed responses in the atmospheric signal, and reduces the ability of models to represent rapid changes including land-use shifts, floods, or droughts.In the same manner as plant species can be grouped into plant functional types, terrestrial ecosystems can be grouped into Ecosystem Functional Types (EFTs). EFTs are groups of ecosystems that share functional characteristics in relation to the amount and timing of the exchanges of matter and energy between the biota and the physical environment. In other words, EFTs are homogeneous patches of the land surface that exchange mass and energy with the atmosphere in a common way. Since EFTs can be defined from NDVI fields on an annual basis, the year-to-year variability of the surface conditions can thus be identified. In this work, we examine the use of Ecosystem Functional Types as an alternative to Land Cover Types in simulations of South American climate.WRF/Noah model simulations were carried out for austral spring in South America to evaluate the sensitivity of climate to the changing surface conditions as estimated from the EFTs. A dry year and a wet year, respectively having low and high Net Primary Production, were chosen to investigate the interannual variability of land-atmosphere interactions and their effect on regional climate. The first results suggest that the use of EFTs helps reduce the model biases both in temperature and precipitation leading to a better representation of seasonally anomalous periods. Long term simulations were then performed to study the evolution of the severe Argentine drought that began in late 2007 and lasted until early 2009. As in the case of the seasonal anomalies, the more realistic representation of the surface tends to improve the simulations of the drought.