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

A. Alessandri, F. Catalano, M. De Felice, B.J.J.M. van den Hurk, F. Doblas Reyes, S. Boussetta, G. Balsamo, P.A. Miller

    Research output: Contribution to JournalArticleAcademicpeer-review

    114 Downloads (Pure)

    Abstract

    Abstract 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 multiscale
    enhancement of vegetation impacts in climate simulation
    and prediction over land. Particularly large effects at
    multiple time scales are shown over boreal winter middleto-
    high latitudes over Canada, West US, Eastern Europe,
    Russia and eastern Siberia due to the implemented timevarying
    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.
    Original languageEnglish
    Pages (from-to)1215-1237
    Number of pages23
    JournalClimate Dynamics
    Volume49
    Issue number4
    Early online date5 Oct 2016
    DOIs
    Publication statusPublished - Aug 2017

    Fingerprint

    Dive into the research topics of 'Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth'. Together they form a unique fingerprint.

    Cite this