Tom Akkermans

The Regional Climate Impact of a Realistic Future Deforestation Scenario in the Congo Basin

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 0.78C. 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.

Validation and comparison of two soil-vegetation-atmosphere transfer models for tropical Africa

This study aims to compare and validate two soil-vegetation-atmosphere-transfer (SVAT) schemes: TERRA-ML and the Community Land Model (CLM). Both SVAT schemes are run in standalone mode (decoupled from an atmospheric model) and forced with meteorological in-situ measurements obtained at several tropical African sites. Model performance is quantified by comparing simulated sensible and latent heat fluxes with eddy-covariance measurements. Our analysis indicates that the Community Land Model corresponds more closely to the micrometeorological observations, reflecting the advantages of the higher model complexity and physical realism. Deficiencies in TERRA-ML are addressed and its performance is improved: (1) adjusting input data (root depth) to region-specific values (tropical evergreen forest) resolves dry-season underestimation of evapotranspiration; (2) adjusting the leaf area index and albedo (depending on hard-coded model constants) resolves overestimations of both latent and sensible heat fluxes; and (3) an unrealistic flux partitioning caused by overestimated superficial water contents is reduced by adjusting the hydraulic conductivity parameterization. CLM is by default more versatile in its global application on different vegetation types and climates. On the other hand, with its lower degree of complexity, TERRA-ML is much less computationally demanding, which leads to faster calculation times in a coupled climate simulation.

Long-term evaluation of COSMO forecasting using combined observational data of the GOP period

Data of two years of observations (2007-2008) from the General Observation Period (GOP) are used to evaluate forecasts of the operational COSMO model applications (COSMO-DE and COSMO-EU) of the German Weather Service (DWD). As part of the German Priority Programme on Quantitative Precipitation Forecasting (PQP), the GOP gathered a comprehensive data set from existing instrumentation not used in routine verification and corresponding model output. In this paper we focus on the water cycle variables: integrated water vapor (IWV), cloud base height (CBH) and precipitation. In addition brightness temperatures (BT) from satellite observations are included. The biases in IWV and BT 6.2 μm data are small for COSMO-DE and COSMO-EU. CBH data show a larger bias with a maximum in the summer season. The largest biases have been found in the precipitation and BT 10.8 μm data. The latter can probably be explained by deficiencies in modelled clouds in the upper troposphere. A classification into different weather condition types gives some additional insight into model deficits. For northerly/north-westerly (maritime) flows model forecasts are too dry (cold) and for southerly (continental) flows too humid (warm).

Quantifying successional land cover after clearing of tropical rainforest along forest frontiers in the Congo Basin

State-of-the-art impact-modeling studies in environmental and climatological sciences require detailed future deforestation scenarios that allow forest to be replaced by a mosaic of multiple successional land-cover types, rather than the simple conversion of forest to a single land-cover type, such as bare soil or cropland. Therefore, not only the amount and location of forest removal has to be known (as is typically provided by scenarios), but also knowledge about the successional land-cover types and their relative areal proportions is needed. The main objective of this study was to identify these successional land-cover types and quantify their areal proportions in regions deforested during the past 37 years around the city of Kisangani, D.R. Congo. The fallow vegetation continuum was categorized in different stages, adapted from existing classifications. Ground-truth points describing the present-day vegetation were obtained during a field campaign and used for supervised and validated land-cover classification of these categories, using the Landsat image of 2012. Areal proportions of successional land-cover types were then derived from the resulting land-cover map. The second objective of this study was to relate these areal proportions to time since deforestation, which is expected to influence fallow landscapes. Landsat images of 1975, 1990, and 2001 were analyzed. Present-day mature tree fallow is less abundant on areas deforested during 1975–1990. The relative areal proportions were used to refine a deforestation scenario and apply it to existing data-sets of LAI and canopy height (CH). Assuming a simple conversion of forest to cropland, the deforestation scenario projected a reduction of grid-cell-averaged CH from 25.5 to 7.5 m (within deforested cells), whereas the refined scenarios that we propose show more subtle changes, with a reduced CH of 13 m. This illustrates the importance of taking successional land cover correctly into account in environmental and climatological modeling studies.