Events

For a better understanding of the Earth system we need a stronger integration of observations and (mechanistic) models. Classical model-data integration approaches start with a model structure and try to estimate states or parameters via data assimilation and inverse modelling, respectively. Sometimes, several model structures are employed and evaluated, e.g. in Bayesian model averaging, but still parametric model structures are assumed.Recently, Reichstein et al. (2019) proposed a fusion of machine learning and mechanistic modelling approaches into so-called hybrid modelling. Ideally, this combines scientific consistency with the versatility of data driven approaches and is expected to allow for better predictions and better understanding of the system, e.g. by inferring unobserved variables. This talk will elaborateon developments of this concept and illustrate its promise but also challenges with examples on biosphere-atmosphere exchange, and carbon and water cycles from the ecosystem to the global scale. [more]

This session is organized by the Knowledge Action Network on Emergent Risks and Extreme Events (Risk KAN) to discuss climate change related systemic risks in the context of sustainable development and societal resilience. Systemic risk refers to the potential for adverse consequences that can spread within and across interconnected systems and sectors via movements of people, goods, resources, capital, and information within and across countries, even continents to eventually lead to existential impacts and systems collapse. Climate change is projected to lead to increasing extreme events and natural hazard, which, when interacting with socio-economic drivers, will increase systemic climate-related risk. Globalization contributes to systemic risk through dependencies within and across social systems affecting people worldwide. This session will address the most relevant topics regarding climate related systemic risk and present how systemic risk assessments may inform transformational and sustainable adaptation. [more]