2009, Brovkin, V., Petoukhov, V., Claussen, M., Bauer, E., Archer, D., Jaeger, C.

We use a coupled climate-carbon cycle model of intermediate complexity to investigate scenarios of stratospheric sulfur injections as a measure to compensate for CO2-induced global warming. The baseline scenario includes the burning of 5,000 GtC of fossil fuels. A full compensation of CO2-induced warming requires a load of about 13 MtS in the stratosphere at the peak of atmospheric CO2 concentration. Keeping global warming below 2°C reduces this load to 9 MtS. Compensation of CO 2 forcing by stratospheric aerosols leads to a global reduction in precipitation, warmer winters in the high northern latitudes and cooler summers over northern hemisphere landmasses. The average surface ocean pH decreases by 0.7, reducing the calcifying ability of marine organisms. Because of the millennial persistence of the fossil fuel CO2 in the atmosphere, high levels of stratospheric aerosol loading would have to continue for thousands of years until CO2 was removed from the atmosphere. A termination of stratospheric aerosol loading results in abrupt global warming of up to 5°C within several decades, a vulnerability of the Earth system to technological failure. © 2008 The Author(s).

2004, Böhm, U., Kücken, M., Hauffe, D., Gerstengarbe, E.-W., Werner, P.C., Flechsig, M., Keuler, K., Block, A., Ahrens, W., Nocke, T.

We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.

2006, Knopf, B., Flechsig, M., Zickfeld, K.

Parameter uncertainty analysis of climate models has become a standard approach for model validation and testing their sensitivity. Here we present a novel approach that allows one to estimate the robustness of a bifurcation point in a multi-parameter space. In this study we investigate a box model of the Indian summer monsoon that exhibits a saddle-node bifurcation against those parameters that govern the heat balance of the system. The bifurcation brings about a change from a wet summer monsoon regime to a regime that is characterised by low precipitation. To analyse the robustness of the bifurcation point itself and its location in parameter space, we perform a multi-parameter uncertainty analysis by applying qualitative, Monte Carlo and deterministic methods that are provided by a multi-run simulation environment. Our results show that the occurrence of the bifurcation point is robust over a wide range of parameter values. The position of the bifurcation, however, is found to be sensitive on these specific parameter choices.