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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