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Impact of updating vegetation information on land surface model performance
  • +7
  • Melissa Ruiz-Vásquez,
  • Sungmin O,
  • Gabriele Arduini,
  • Souhail Boussetta,
  • Alexander Brenning,
  • Ana Bastos,
  • Sujan Koirala,
  • Gianpaolo Balsamo,
  • Markus Reichstein,
  • Rene Orth
Melissa Ruiz-Vásquez
Max-Planck Institute for Biogeochemistry

Corresponding Author:[email protected]

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Sungmin O
Ewha Womans University
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Gabriele Arduini
European Centre for Medium-Range Weather Forecasts
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Souhail Boussetta
ECMWF
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Alexander Brenning
Friedrich Schiller University
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Ana Bastos
Max Planck Institute for Biogeochemistry
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Sujan Koirala
Max Planck Institute for Biogeochemistry
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Gianpaolo Balsamo
ECMWF Model Division/Physics
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Markus Reichstein
Max Planck Institute fur Biogeochemistry
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Rene Orth
Max Planck Institute for Biogeochemistry
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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forecasting systems use climatological vegetation indices, and land use/land cover datasets in these models are often outdated.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.
18 Apr 2023Submitted to ESS Open Archive
18 Apr 2023Published in ESS Open Archive