|Title||Impact of a satellite-derived Leaf Area Index monthly climatology in a global Numerical Weather Prediction model|
|Publication Type||Technical memorandum|
|Secondary Title||ECMWF Technical Memoranda|
|Authors||Boussetta, S, Balsamo, G, Beljaars, A, Kral, T, Jarlan, L|
The Leaf Area Index (LAI), defined as the one-sided green leaf area per unit ground area, is used in many Numerical Weather Prediction (NWP) models as an indicator of the vegetation development state which is of paramount importance to characterise land evaporation, photosynthesis and carbon uptake processes. The LAI is often simply represented by look-up tables, dependent on the vegetation type and seasons. However, global LAI datasets derived from remote sensing observations have more recently become available. These products are based on sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or the Moderate Resolution Imaging Spectro-radiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically three days and with a spatial resolution of the order of 1 km. We examine the meteorological impact of satellite-derived LAI products on near-surface air temperature and humidity, which comes both from the leaves stomata transpiration and from the intercepted water on the leaves surface, re-evaporating into the atmosphere. Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the Integrated Forecasting System of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface (screen-level) air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needle-leaf forests, crops and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have positive impact on the model climate particularly during the boreal spring where the LAI climatology has a large seasonal cycle.