The sensitivity of climate to the loss of the Congo basin rainforest through changes in land cover properties is examined using a regional climate model. The complete removal of the Congo basin rainforest results in a dipole rainfall anomaly pattern, characterized by a decrease (∼−42%) in rainfall over the western Congo and an increase (∼10%) in the basin's eastern part. Three further experiments systematically examine the individual response to the changes in albedo, surface roughness, and evapotranspiration efficiency that accompany deforestation. The increased albedo (∼0.05) caused by the Congo basin rainforest clearance results in cooler and drier climate conditions over the entire basin. The drying is accompanied with a reduction in available surface energy. Reducing evapotranspiration efficiency or roughness length produces similar positive air temperature anomaly patterns. The decreased evapotranspiration efficiency leads to a dipole response in rainfall, similar to that resulting from a reduced surface roughness following Congo basin rainforest clearance. This precipitation anomaly pattern is strongly linked to the change in low-level water vapor transport, the influence of the Rift valley highlands, and the spatial pattern of water recycling activity. The climate responds linearly to the separate albedo, surface roughness, and evapotranspiration efficiency changes, which can be summed to produce a close approximation to the impact of the full deforestation experiment. It is suggested that the widely contrasting climate responses to deforestation in the literature could be partly due to the relative magnitude of change of the radiative and nonradiative parameterizations in their respective land surface schemes.

This paper investigates the performance of 10 Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998–2008 and performed over a common model grid spacing 0.44° ( ∼50 km). Multiple observational data sets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC‐WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC‐WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not generally outperform individual RCMs realization as it is reported in several previous studies.

A new deforestation and land-use change scenario generator model (FOREST-SAGE) is presented that is designed to interface directly with dynamic vegetation models used in latest generation earth system models. The model requires a regional-scale scenario for aggregate land-use change that may be time-dependent, provided by observational studies or by regional land-use change/economic models for future projections. These land-use categories of the observations/economic model are first translated into equivalent plant function types used by the particular vegetation model, and then FOREST-SAGE disaggregates the regional-scale scenario to the local grid-scale of the earth system model using a set of risk-rules based on factors such as proximity to transport networks, distance weighted population density, forest fragmentation and presence of protected areas and logging concessions. These rules presently focus on the conversion of forest to agriculture and pasture use, but could be generalized to other land use change conversions. After introducing the model, an evaluation of its performance is shown for the land-cover changes that have occurred in the Central African Basin from 2001–2010 using retrievals from MODerate Resolution Imaging Spectroradiometer Vegetation Continuous Field data. The model is able to broadly reproduce the spatial patterns of forest cover change observed by MODIS, and the use of the local-scale risk factors enables FOREST-SAGE to improve land use change patterns considerably relative to benchmark scenarios used in the latest Coupled Model Intercomparison Project integrations. The uncertainty to the various risk factors is investigated using an ensemble of investigations, and it is shown that the model is sensitive to the population density, forest fragmentation and reforestation factors specified.