Logo Logo
Help
Contact
Switch Language to German
Duveiller, Gregory; Forzieri, Giovanni; Robertson, Eddy; Li, Wei; Georgievski, Goran; Lawrence, Peter; Wiltshire, Andy; Ciais, Philippe; Pongratz, Julia ORCID: 0000-0003-0372-3960; Sitch, Stephen; Arneth, Almut; Cescatti, Alessandro (2018): Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations. In: Earth System Science Data, Vol. 10, No. 3: pp. 1265-1279
[img]
Preview
4MB

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.