A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI‐ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low‐level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO₂ over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO₂ forcing, which nonetheless can be represented by a simple two‐layer model.

Grasslands occupy almost half of the world's land area. Soil organic carbon (SOC) is a key indicator of soil fertility and grassland productivity. Increasing SOC stocks (so‐called SOC sequestration) improves soil fertility and contributes to climate change mitigation by binding atmospheric carbon dioxide (CO₂). Grasslands constitute about 70% of all agricultural land, but their potential for SOC sequestration is largely unknown. This review paper quantitatively summarizes observation‐based studies on the SOC sequestration potential of grasslands in six East African countries (Burundi, Ethiopia, Kenya, Rwanda, Tanzania and Uganda) and seeks to identify knowledge gaps related to SOC sequestration potential in the region. In the studies reviewed, SOC stocks in grasslands range from 3 to 93 Mg C/ha in the upper 0.3 m of the soil profile, while SOC sequestration rate ranges from 0.1 to 3.1 Mg C ha^‐1 year^‐1 under different management strategies. Grazing management is reported to have a considerable impact on SOC sequestration rates, and grassland regeneration and protection are recommended as options to stimulate SOC sequestration. However, a very limited number of relevant studies are available (n = 23) and there is a need for fundamental information on SOC sequestration potential in the region. The effectiveness of potential incentive mechanisms, such as payments for environmental services, to foster uptake of SOC‐enhancing practices should also be assessed.

Global model estimates of soil carbon changes from past land-use changes remain uncertain. We develop an approach for evaluating dynamic global vegetation models (DGVMs) against existing observational meta-analyses of soil carbon changes following land-use change. Using the DGVM JSBACH, we perform idealized simulations where the entire globe is covered by one vegetation type, which then undergoes a land-use change to another vegetation type. We select the grid cells that represent the climatic conditions of the meta-analyses and compare the mean simulated soil carbon changes to the meta-analyses. Our simulated results show model agreement with the observational data on the direction of changes in soil carbon for some land-use changes, although the model simulated a generally smaller magnitude of changes. The conversion of crop to forest resulted in soil carbon gain of 10 % compared to a gain of 42 % in the data, whereas the forest-to-crop change resulted in a simulated loss of −15 % compared to −40 %. The model and the observational data disagreed for the conversion of crop to grasslands. The model estimated a small soil carbon loss (−4 %), while observational data indicate a 38 % gain in soil carbon for the same land-use change. These model deviations from the observations are substantially reduced by explicitly accounting for crop harvesting and ignoring burning in grasslands in the model. We conclude that our idealized simulation approach provides an appropriate framework for evaluating DGVMs against meta-analyses and that this evaluation helps to identify the causes of deviation of simulated soil carbon changes from the meta-analyses.

Historical changes in soil carbon associated with land-use change (LUC) result mainly from the changes in the quantity of litter inputs to the soil and the turnover of carbon in soils. We use a factor separation technique to assess how the input-driven and turnover-driven controls, as well as their synergies, have contributed to historical changes in soil carbon associated with LUC. We apply this approach to equilibrium simulations of present-day and pre-industrial land use performed using the dynamic global vegetation model JSBACH. Our results show that both the input-driven and turnover-driven changes generally contribute to a gain in soil carbon in afforested regions and a loss in deforested regions. However, in regions where grasslands have been converted to croplands, we find an input-driven loss that is partly offset by a turnover-driven gain, which stems from a decrease in the fire-related carbon losses. Omitting land management through crop and wood harvest substantially reduces the global losses through the input-driven changes. Our study thus suggests that the dominating control of soil carbon losses is via the input-driven changes, which are more directly accessible to human management than the turnover-driven ones.