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Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

Alessandri, Andrea (author)
Italian National Agency for New Technologies, Energy and Sustainable Economic Development
Catalano, Franco (author)
Italian National Agency for New Technologies, Energy and Sustainable Economic Development
De Felice, M (author)
Italian National Agency for New Technologies, Energy and Sustainable Economic Development
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Van Den Hurk, Bart (author)
Royal Netherlands Meteorological Institute
Doblas Reyes, F.J. (author)
Catalan Institute of Climate Sciences
Boussetta, Souhail (author)
European Centre for Medium-range Weather Forecasts
Balsamo, G (author)
European Centre for Medium-range Weather Forecasts
Miller, Paul (author)
Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science
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 (creator_code:org_t)
2016-10-05
2017
English.
In: Climate Dynamics. - : Springer Science and Business Media LLC. - 1432-0894 .- 0930-7575. ; 49:4, s. 1215-1237
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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 basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only 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 in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness 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 variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales 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 tends to correct 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 improvements of the prediction of 2 m 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 American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)

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