Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-3975-2020
https://doi.org/10.5194/gmd-13-3975-2020
Model evaluation paper
 | 
03 Sep 2020
Model evaluation paper |  | 03 Sep 2020

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

Miguel Nogueira, Clément Albergel, Souhail Boussetta, Frederico Johannsen, Isabel F. Trigo, Sofia L. Ermida, João P. A. Martins, and Emanuel Dutra

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Cited articles

Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M.: A new neural network approach including first-guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature and emissivities over land from satellite microwave observations, J. Geophys. Res., 106, 887–907, 2001. 
Albergel, C., Dutra, E., Bonan, B., Zheng, Y., Munier, S., Balsamo, G., Rosnay, P. D., Sabater, J. M., and Calvet, J.: Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation, Remote Sensing, 11, 520, https://doi.org/10.3390/rs11050520, 2019. 
Alessandri, A., Catalano, F., De Felice, M., Van Den Hurk, B., Doblas Reyes, F., Boussetta, S., Balsamo, G., and Miller, P. A.: Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth, Clim. Dynam., 49, 1215–1237, 2017. 
Balsamo, G., Viterbo, P., Beljaars, A., van den Hurk, B., Hirschi, M., Betts, A. K., and Scipal, K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, 2009. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
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Short summary
We used earth observations to evaluate and improve the representation of land surface temperature (LST) and vegetation coverage over Iberia in CHTESSEL and SURFEX land surface models. We demonstrate the added value of updating the vegetation types and fractions together with the representation of vegetation coverage seasonality. Results show a large reduction in daily maximum LST systematic error during warm months, with neutral impacts in other seasons.