Abstract
Abstract. Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.
Highlights
Land surface temperature (LST) plays a central role in the land–atmosphere energy, water, and carbon exchanges
We used the LSA-SAF satellite product to evaluate the summer LST over Iberia simulated by two land surface models (LSMs) – CHTESSEL and SURFEX – during the 2004–2015 period
The results show a large cold bias of the JJA LSTmax simulated by CHTESSEL, reaching magnitudes larger than 10 ◦C over wide portions of central and southwestern Iberia
Summary
Land surface temperature (LST) plays a central role in the land–atmosphere energy, water, and carbon exchanges. We consider satellite-based LST estimates derived from the outgoing thermal infrared radiation (TIR) measured at the top of the atmosphere (TOA). This spectral band (corresponding to the 8–13 μm range) is appropriate as it presents relatively weak atmospheric attenuation under clear-sky conditions and includes the peak of the Earth’s spectral radiance (Li et al, 2013; Ermida et al, 2019). LST estimates derived from TIR are limited to clear-sky observation, representing a significant limitation to its coverage (e.g., Trigo et al, 2011; Li et al, 2013; Ermida et al, 2019). This method has the main advantage of allowing LST estimates under all-weather conditions, MW LST estimates have usually lower spatial resolution and lower accuracy values, typically in the 4–6 K range (e.g., Aires et al, 2001; Prigent et al, 2016; Duan et al, 2017), when compared with TIR LST, generally within 1–4 K (e.g. Trigo et al, 2011; Göttsche et al, 2016; Ermida et al, 2019; Martins et al, 2019)
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