Impact of Updating Vegetation Information on Land Surface Model Performance

• Published in: Journal of geophysical research. Atmospheres Vol. 128; no. 21
• Main Authors: Ruiz‐Vásquez, Melissa, O, Sungmin, Arduini, Gabriele, Boussetta, Souhail, Brenning, Alexander, Bastos, Ana, Koirala, Sujan, Balsamo, Gianpaolo, Reichstein, Markus, Orth, René
• Format: Journal Article
• Language: English
• Published: 16.11.2023
• Subjects: Earth observations; land surface model performance; parameter recalibration; vegetation dynamics
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1029/2023JD039076

Vegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modeled 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forecasting systems use climatological vegetation indices, and land use/land cover data sets in these models are often outdated.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 modeling 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 data sets. 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. Plain Language Summary The accuracy of weather forecasts relies critically on the accurate modeling of the exchange of water and energy between the land surface and the atmosphere. The latent heat flux and the soil moisture are two important land surface variables in this exchange through the net balances of water and energy. The accurate simulation of these variables is challenging in most land surface models specifically used for numerical weather prediction due to (a) outdated land surface cover information and/or (b) neglecting the role of short‐term anomalies in vegetation functioning, for example, related to droughts. This study quantifies the benefits of including up‐to‐date land use/land cover information and an explicit consideration of the current vegetation state on the prediction of latent heat flux and soil moisture. We find that model simulation performance can only benefit from updated land surface information through further adjustments to key soil and vegetation related parameters in the model. Overall, we demonstrate that the new Earth observation data sets can help to improve land surface model performance, which then contributes to more accurate weather forecasts. Key Points We find a substantial impact on the European Centre for Medium‐range Weather Forecast land surface modeling system simulated latent heat flux and soil moisture after updating land surface information A regional calibration of land surface related parameters yields substantial better agreement between model simulations and observations Our results highlight the importance of representing vegetation dynamics and land cover changes in land surface models