- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
The impacts from anthropogenic climate change are directly felt through extremes. The existing research skills in assessing future changes in impacts from climate extremes is however still limited. Despite the fact that a multitude of climate simulations is now available that allows the analysis of such climatic events, available approaches have not yet sufficiently analysed the complex and dynamic aspects that are relevant to estimate what climate extremes mean for society in terms of impacts and damages. Machine Learning (ML) algorithms have the ability to model multivariate and nonlinear relationships, with possibilities for non-parametric regression and classification, and are therefore well-suited to model highly complex relations between climate extremes and their impacts.
In this presentation, I will highlight some recent ML applications, focussing on monetary damages from floods and windstorms. For these extremes, ML models are built using observational datasets of extremes and their impacts. Here I will also address the sample selection bias, which occurs between observed moderate impact events, and more extreme events sampled in current observed and projected future data. This can be addressed by adjusting weighting for such variable values, as is demonstrated for extreme windstorm events.
Another application focusses on health outcomes, in this case the occurrence of myocardial infarctions (MI). Several ML algorithms are tested to better predict MI events under changing environmental and demographic conditions, using data from the city of Augsburg (Germany) between 1998 and 2015. Multivariable predictors include weather (air temperature, relative humidity), air pollution (particulate matter, nitrogen oxide, nitrogen dioxide, sulphur dioxide, and ozone), surrounding vegetation, as well as demographic data.
Finally, I will suggest some further applications that could be developed for predicting climate impacts as well as impacts from policy planning and adaptation.