906 Seamless Enhancement of Climate Prediction over Land by Increasing the Model Sensitivity to Vegetation Variability

Thursday, 14 January 2016
Hall D/E ( New Orleans Ernest N. Morial Convention Center)
Andrea Alessandri, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Santa Maria di Galeria - Rome, Italy; and F. Catalano, M. De Felice, F. J. Doblas-Reyes, B. J. J. M. van den Hurk, P. Miller, S. Boussetta, and G. Balsamo

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate by only changing the vegetation physiological resistance to evapotranspiration. This coupling has been reported to have a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales as a function of leaf-canopy growth, phenology and senescence, and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new effective fractional-coverage parameterization, spanning from centennial (20th Century) simulations and predictions from short term to decadal time scales (5-years), we show for the first time a significant seamless enhancement across scales of climate simulation and prediction over land by including land-vegetation processes. Particularly large seamless effects are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover corrects the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant seamless improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North America Great Plains, Nordeste Brazil and South East Asia, mainly related to improved skill in the surface evapotranspiration.
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