scholarly journals Validation of land surface models using satellite-derived surface temperature

2001 ◽  
Vol 106 (D17) ◽  
pp. 20085-20099 ◽  
Author(s):  
Joshua Rhoads ◽  
Ralph Dubayah ◽  
Dennis Lettenmaier ◽  
Greg O'Donnell ◽  
Venkat Lakshmi
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Land Surface Models ◽  
Surface Models

scholarly journals Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia

2020 ◽  
Author(s):  
Miguel Nogueira ◽  
Clément Albergel ◽  
Souhail Boussetta ◽  
Frederico Johannsen ◽  
Isabel F Trigo ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Added Value ◽  
Vegetation Coverage ◽  
Daily Maximum ◽  
Cold Bias ◽  
Land Surface Models ◽  
Surface Models

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 Forecasting (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 modelling platform (SURFEX-ISBA) for the 2004–2015 period. The results show that the daily maximum LST simulated by CHTESSEL over Iberia is 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 is 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 includes a clumping approach that introduces seasonality to the vegetation cover. The results show 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 guide 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 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 show 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 provide hints into 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 demonstrates the added value in using multiple earth observation products for constraining and improving weather and climate simulations.

scholarly journals Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

2021 ◽  
Author(s):  
Sandy P. Harrison ◽  
Wolfgang Cramer ◽  
Oskar Franklin ◽  
Iain Colin Prentice ◽  
Han Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Models ◽  
Surface Models ◽  
Evolutionary Optimality
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