Land-Use and Climate, IDentification of robust impacts

Acronym
LUCID
Name
Land-Use and Climate, IDentification of robust impacts
Description
The LUCID project is focused on the biogeophysical effects of LULCC on climate. Pitman et al. (2009) investigated the climatic effect of land cover changes from the preindustrial period to present-day using several AGCMs. The models simulated substantial changes in latent and sensible heat fluxes, albedo, and near-surface air temperature over the regions with considerable LULCC, although the magnitude of those LULCC-induced changes differed considerably among the models.
The fifth Coupled Model Intercomparison Project (CMIP5) is a coordinated effort of more than 20 climate modeling groups from around the world to improve our understanding of climate change (Taylor et al. 2012). Integrated Assessment Model (IAM) groups provided the CMIP5 community with four representative concentration pathways (RCPs) of greenhouse gases and aerosols, and the associated land use and land cover changes through the 21st century. The set of RCP scenarios envelopes different scenarios of future land-use changes, which satisfy the demand for food, biofuels and afforestation (or reforestation) to mitigate CO2-induced climate changes.
To isolate the effect of land-use changes on climate, several CMIP5 modeling groups performed additional LUCID-CMIP5 simulations without anthropogenic land-use changes from 2006 to 2100. The differences between simulations with and without land-use changes reveal climatic effects of LULCC on global and regional scales. The biogeophysical effects and changes in the land carbon storage due to LULCC are focused on two RCP simulations driven by prescribed CO2 concentrations. These simulations allow quantifying the climatic effect of changes in land cover in comparison to those caused by changes in fossil fuel emissions for the RCP scenarios. The inter-model comparison provides a quantitative assessment of the uncertainty in climatic effects of land-cover changes due to differences in model parameterizations (Brovkin et al., 2013).

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