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

Abstract

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 ∼10°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).

Sensitivity of Surface Fluxes in the ECMWF Land Surface Model to the Remotely Sensed Leaf Area Index and Root Distribution: Evaluation with Tower Flux Data

“…The systematic underestimation of the Qle and overestimation of the Qh for CTR (see Figure 1) is opposite the findings of Martens et al (2020) [50], who compared ERA5 data against observations from 143 FLUXNET sites. Similar biases in CHTESSEL considering a subset of 51 FLUXNET stations were also found [51]. The differences of these studies compared to our results are likely associated with the different sampling of stations.…”

“…The proposed uniform root distribution with a single associated parameter, the maximum rooting depth, is also appealing for a parameter optimization. These could be further addressed along with the revision of land cover and vegetation recently proposed for CHTESSEL [65], which identified the necessity to calibrate vegetation related parameters [51]. Considering the coupled nature of the surface water and energy cycles and the relevance of land-atmosphere coupling, a calibration methodology considering multiple observational datasets, covering different water/energy components and temporal time-scales, should be favoured [66], also to possibly include some indirect information of poorly constrained parameters through variables coupled with them.…”

Monitoring Heat Extremes across Central Europe Using Land Surface Temperature Data Records from SEVIRI/MSG

“…Although acknowledging the relevance of reanalysis in providing a consistent dataset of atmospheric and surface variables, through the assimilation of observations from numerous sources, there is a growing interest in the development of high-quality observational datasets [34] that are independent and free of the necessary assumptions made in models. This is particularly the case for land surface variables, including temperature, moisture, or surface fluxes, as these are strongly influenced by the representation of surface characteristics in model land schemes (e.g., [35]).…”

Impact of Surface Roughness Changes on Surface Wind Speed over Western Europe: A Study with a Regional Climate Model

“…This pattern fits in well with the change of the patterns with height found at higher levels in the atmosphere (Figure 2). We note that the trends of SWS from ERA5 are not sensitive to the changes in surface roughness because land use and vegetation cover parameters are fixed over time and near surface wind variables over land are not directly assimilated (Fan et al, 2021;Nogueira et al, 2021). However, by comparing SWS trend from ERA5 reanalysis and ERA5 forecasts for lead times of 6 and 18 hours, we find that the magnitude of trend of SWS becomes slightly smaller for the forecast database and for a larger lead time (Figure not shown here).…”