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This paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the "east plus west" sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983–94, when station density was relatively high, and 1998–2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3, TRMM 3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983–94 in the east plus west sector is on the order of 20 mm month−1 for GPCC V7 and 20–30 mm month−1 for the other products. The compares with 40–60 mm month−1 for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30–40 mm month−1 for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998–2010), when the station network is sparse, than during the period 1983–94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30–40 mm month−1 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites. [ABSTRACT FROM AUTHOR]

The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models. [ABSTRACT FROM AUTHOR]

The demand for agricultural land in the Congo basin is expected to yield substantial deforestation over the coming decades. Although several studies exist on the climatological impact of deforestation in the Congo basin, deforestation scenarios that are implemented in climate models are generally crude. This study aims to refine current impact assessments by removing the primary forest according to an existing spatially explicit scenario, and replacing it by successional vegetation typically observed for the Congo basin. This is done within the Consortium for Small-Scale Modeling (COSMO) model in climate mode (COSMO-CLM), a regional climate model at 25-km grid spacing coupled to a state-of-the-art soil-vegetation-atmosphere transfer scheme (Community Land Model). An evaluation of the model shows good performance compared to in situ and satellite observations. Model integrations indicate that the deforestation, expected for the middle of the twenty-first century, induces a warming of about 0.7°C. This is about half the greenhouse gas-induced surface warming in this region, given an intermediate emission scenario (A1B) with COSMO-CLM driven by the ECHAM5 global climate model. This shows the necessity of taking into account deforestation to obtain realistic future climate projections. The deforestation-induced warming can be attributed to reduced evaporation, but this effect is mitigated by increased albedo and increased sensible heat loss to the atmosphere. Precipitation is also affected: as a consequence of surface warming resulting from deforestation, a regional heat low develops over the rain forest region. Resulting low-level convergence causes a redistribution of moisture in the boundary layer and a stabilization of the atmospheric column, thereby reducing convection intensity and hence precipitation by 5%-10% in the region of the heat low. [ABSTRACT FROM AUTHOR]