AIMES Lightning Talk Contribution 19 May 2022 | Past, present and future societal risk arising from global change: a collaborative approach
Michael Neil Evans 1, Felix Riede 2, Blas Valero-Garcés 3, Cornelia B Krug 4, Markus Reichstein 5, Marie-France Loutre 6
University of Maryland, United States of America1, Aarhus University, Denmark2, Spanish National Research Council, Spain3, University of Zurich, Switzerland4, Max-Planck-Institute for Biogeochemistry, Germany5, Past Global Changes, Switzerland6
As global change accelerates, the risk of low probability but extreme cost/benefit events may be changing. At the same time, growing at-risk infrastructure and population are exacerbating the threat they pose to lives and livelihoods. Research on past climate and environmental change has produced a rich spectrum of data and model results that provide a multivariate and long-term perspective needed to understand climate variability and its varied impacts on past human societies and the environment. While it is clear that this picture has relevance to understanding future risk, information from (paleo)environmental research is not often integrated into risk modeling used on a practical level by insurers or municipalities, and (paleo)climate and -environmental research is not often designed with this aim in mind. We here envision a partnership between natural scientists, social scientists, risk modelers, and managers to improve the estimates of risks associated with climate change by incorporating insights from earth system observations, modeling of natural and social systems, risk modeling and management, and risk exposure reduction practice. This work is necessary to address the impacts of changes in the probabilities of extreme storm, fire, drought, flood and ecosystem stress events, and tipping points between mean states. These impact assessments might in turn inform financial exposure estimates and municipal planning, and explore ways in which people and organizations might modify their behavior in response to changes in risk perception, not least across temporal horizons beyond electoral cycles and prevalent medium-term scenarios (i.e. beyond 2100). We illustrate a collaborative framework for doing so, using an idealized but nonlinear and coupled natural, socioeconomic and risk model.