Abstract
Vegetation plays a fundamental role in modulating the exchange of
water, energy, and carbon fluxes between the land and the atmosphere.
These exchanges are modelled by Land Surface Models (LSMs), which are an
essential part of numerical weather prediction and data assimilation.
However, most current LSMs implemented specifically in weather
forecasting systems use climatological vegetation indices, and land
use/land cover datasets in these models are often outdated. In this
study, we update land surface data in the ECMWF land surface modelling
system ECLand using Earth observation-based time varying leaf area index
and land use/land cover data, and evaluate the impact of vegetation
dynamics on model performance. The performance of the simulated latent
heat flux and soil moisture is then evaluated against global gridded
observation-based datasets. Updating the vegetation information does not
always yield better model performances because the model’s parameters
are adapted to the previously employed land surface information.
Therefore we recalibrate key soil and vegetation-related parameters at
individual grid cells to adjust the model parameterizations to the new
land surface information. This substantially improves model performance
and demonstrates the benefits of updated vegetation information.
Interestingly, we find that a regional parameter calibration outperforms
a globally uniform adjustment of parameters, indicating that parameters
should sufficiently reflect spatial variability in the land surface. Our
results highlight that newly available Earth-observation products of
vegetation dynamics and land cover changes can improve land surface
model performances, which in turn can contribute to more accurate
weather forecasts.