On the use of probabilistic network models to assess spatially compound (rare) events in a warmer world

The combination of multiple climate drivers and/or hazards that contribute to societal or environmental risk are the so-called compound weather and climate events. These compound events can be the result of a combination of factors over different dimensions: temporal, spatial, multi-variable, etc. In particular, spatially compound events take place when hazards in multiple connected locations cause an aggregated impact. 

In current research we apply Probabilistic Network Models (PNMs) to (1) extract and characterize most essential spatial dependencies of compound events resulting from concurrent temperature and precipitation hazards, either in the same location or spatially connected, which can be relevant for agriculture. Furthermore, (2) the same PNMs are used to propagate evidence of different levels of observed and projected global warming to assess the possible evolution of compound events in a changing climate. 

We will discuss the challenge of (1) modeling rare events and (2) constraining (network) models.