Upgrading Land-Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium-Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to similar to 10 degrees C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land-surface and coupled land-atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state-of-the-art EOs-the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root-mean-squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re-calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).

Automated summary

This study found that limitations in land cover and vegetation seasonality representation in the ECMWF model led to large biases in simulated daily maximum land surface temperature. By updating the model with state-of-the-art Earth Observations data and a clumping parameterization method, the biases were reduced, with no impact on daily minimum land surface temperature. The study also emphasized regional heterogeneities in atmospheric sensitivity to land cover and vegetation changes, calling for model parameter recalibration and assumption revision.