Seminar: Melissa Ruiz-Vasquez
Institutsseminar
- Date: Jun 13, 2024
- Time: 02:00 PM (Local Time Germany)
- Speaker: Melissa Ruiz-Vasquez
- Room: (online only)
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 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use/land cover datasets in these models are often outdated.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 European Centre for
Medium-range Weather Forecast (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 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.