S. Boussetta

Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model

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 characterize land evaporation, photosynthesis, and carbon-uptake processes. LAI is often simply represented by lookup 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 Spectroradiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically 3 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 stomatal transpiration of leaves and from the intercepted water on the surface of leaves, 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 needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.

On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model

ABSTRACT The impact of lakes in numerical weather prediction is investigated in a set of global simulations performed with the ECMWF Integrated Forecasting System (IFS). A Fresh shallow-water Lake model (FLake) is introduced allowing the coupling of both resolved and subgrid lakes (those that occupy less than 50% of a grid-box) to the IFS atmospheric model. Global fields for the lake ancillary conditions (namely lake cover and lake depth), as well as initial conditions for the lake physical state, have been derived to initialise the forecast experiments. The procedure for initialising the lake variables is described and verified with particular emphasis on the importance of surface water temperature and freezing conditions. The response of short-range near surface temperature to the representation of lakes is examined in a set of forecast experiments covering one full year. It is shown that the impact of subgrid lakes is beneficial, reducing forecast error over the Northern territories of Canada and over Scandinavia particularly in spring and summer seasons. This is mainly attributed to the lake thermal effect, which delays the temperature response to seasonal radiation forcing.

Comparison of model land skin temperature with remotely sensed estimates and assessment of surface‐atmosphere coupling

The coupling between land surface and the atmosphere is a key feature in Earth System Modeling for exploiting the predictability of slowly evolving geophysical variables (e.g., soil moisture or vegetation state), and for correctly representing rapid variations within the diurnal cycle, particularly relevant in data assimilation applications. In this study, land surface temperature (LST) estimated from Meteosat Second Generation (MSG) is used to assess the European Centre for Medium-Range Weather Forecasts (ECMWF) skin temperature, which can be interpreted as a radiative temperature of the model surface. It is shown that the ECMWF model tends to slightly overestimate skin temperature during nighttime and underestimate daytime values. Such underestimation of daily amplitudes is particularly pronounced in (semiarid) arid regions, suggesting a misrepresentation of surface energy fluxes in those areas. The LST estimated from MSG is used to evaluate the impact of changes in some of the ECMWF model surface parameters. The introduction of more realistic model vegetation is shown to have a positive but limited impact on skin temperature: long integration leads to an equilibrium state where changes in the latent heat flux and soil moisture availability compensate each other. Revised surface roughness lengths for heat and momentum, however, lead to overall positive impact on daytime skin temperature, mostly due to a reduction of sensible heat flux. This is particularly relevant in nonvegetated areas, unaffected by model vegetation. The reduction of skin conductivity, a parameter which controls the heat transfer to ground by diffusion, is shown to further improve the model skin temperature.

Satellite Data Assimilation: Application to the Water and Carbon Cycles

Abstract: Land surface processes control the water and energy fluxes between the continental surfaces and the atmosphere, as well as the interaction of the water and carbon cycles. They are complex and characterized by strong heterogeneities both spatially (orography, land use, soil types, etc.) and temporally (variability of diurnal and seasonal cycles). Due to the complexity and the large range of spatial and temporal scales involved in these processes, land surface data assimilation has not yet reached levels of maturity, which are found in the areas of atmospheric (notably, in weather forecast) and oceanographic data assimilation. Nevertheless, considerable progress has been made in recent years in the development of the data assimilation on continental surfaces, in conjunction with the expansion of spatial observation of the Earth.