GenClim

Climate Impacts. Anthropogenic climate change poses severe risks to natural and human systems, making the assessment of its impacts an urgent task of fundamental importance for our society. Societies and ecosystems are particularly sensitive to weather-related extreme events, such as heatwaves, droughts, floods, and storms, which are projected to become more frequent and intense under ongoing global warming. Recently, empirical evidence has been found that temperature and precipitation extremes also have strong impacts on economic development. In addition, dynamical climate impact models have been developed to represent our process understanding of the biophysical response to weather fluctuations, e.g., in terms of crop yields, vegetation growth, runoff and river discharge, water quality, or fire. Projecting Future Climates. Numerical Earth system models (ESMs) are our primary tool for making projections about possible future climate conditions by simulating relevant physical processes of the different Earth system components and their interactions, given anthropogenic forcing scenarios, e.g. in terms of greenhouse gas emissions and land-use change. Uncertainties in the ESM formulation, natural variability, or external forcing are assessed using ensemble simulations with perturbed parameters and initial conditions, as well as different Shared Socioeconomic Pathway (SSP) scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Global ESM simulations, however, require vast computational resources. To make global ESM ensemble simulations computationally feasible, the horizontal spatial resolution of ESMs is currently limited to around 100 kilometers. Key processes, such as precipitation, involve smaller spatial scales, or are otherwise computationally too expensive to be modelled explicitly in the ESMs. Instead, these processes are parameterized, i.e., formulated as semi-empirical functions of the resolved variables. Such approximations can lead to systematic errors (biases) in the simulations. Examples are a misrepresentation of extreme events, large-scale mean errors, such as the double-peaked Intertropical Convergence Zone bias, or unrealistic spatial patterns, e.g., overly smooth fields that lack the fine-grained spatial variability that is crucial for reliable impact assessments.