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Volume 10, issue 3
Earth Syst. Sci. Data, 10, 1265-1279, 2018
https://doi.org/10.5194/essd-10-1265-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Sci. Data, 10, 1265-1279, 2018
https://doi.org/10.5194/essd-10-1265-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Jul 2018

13 Jul 2018

Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations

Gregory Duveiller1, Giovanni Forzieri1, Eddy Robertson2, Wei Li3, Goran Georgievski4, Peter Lawrence5, Andy Wiltshire2, Philippe Ciais3, Julia Pongratz4, Stephen Sitch6, Almut Arneth7, and Alessandro Cescatti1 Gregory Duveiller et al.
  • 1European Commission Joint Research Centre (JRC), Ispra, Italy
  • 2Met Office, Exeter, UK
  • 3Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France
  • 4Max-Planck Institut für Meteorologie, Hamburg, Germany
  • 5National Center for Atmospheric Research (NCAR), Boulder, USA
  • 6College of Life and Environmental Sciences, University of Exeter, Exeter, UK
  • 7Karlruher Institut für Technologie (KIT), Garmisch-Partenkirchen, Germany

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land–climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset consisting of both harmonized model simulation and remote sensing estimations is available at https://doi.org/10.5281/zenodo.1182145.

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Changing the vegetation cover of the Earth's surface can alter the local energy balance, which can result in a local warming or cooling depending on the specific vegetation transition, its timing and location, as well as on the background climate. While models can theoretically simulate these effects, their skill is not well documented across space and time. Here we provide a dedicated framework to evaluate such models against measurements derived from satellite observations.
Changing the vegetation cover of the Earth's surface can alter the local energy balance, which...
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