ALBERT

Description: Much of modern climate science fails to consider millennium-scale processes, many of which may prove to be important for predicting the climate trajectory in the shorter term.

Description: Dynamic vegetation models simulate global vegetation in terms of fractional coverages of a few plant functional types (PFTs). Although these models often share the same concept, they differ with respect to the number and kind of PFTs, complicating the comparability of simulated vegetation distributions. Pollen-based reconstructions are initially only available in form of time-series of individual taxa that are not distinguished in the models. Thus, to evaluate simulated vegetation distributions, the modelling results and pollen-based reconstructions have to be converted into a comparable format. The classical approach is the method of biomisation, but hitherto, PFT-based biomisation methods were only available for individual models. We introduce and evaluate a simple, universally applicable technique to harmonize PFT-distributions by assigning them into nine mega-biomes that follow the definitions commonly used for vegetation reconstructions. The method works well for all state-of the art dynamic vegetation models, independent of the spatial resolution or the complexity of the models. Large biome belts (such as tropical forest) are well represented, but regionally confined biomes (warm-mixed forest, Savanna) are only partly captured. Overall, the PFT-based biomisation is able to keep up with the conventional biomisation approach of forcing biome models (here: BIOME1) with the background climate states. The new method has, however, the advantage that it allows a more direct comparison and evaluation of the vegetation distributions simulated by Earth System Models. Thereby, the new method provides a powerful tool for the evaluation of Earth System Models in general.

Description: 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.