WG2: Connecting climate impact transmission chain models with biophysical climate impact data

Leaders: Dr Elisa Delpiazzo & Dr Johanna Hedlund

WG2 brings together scientists from a wide range of communities that model impact transmission chains, e.g. trade, supply chain, financial, economic and migration modellers. By bringing these groups together, the focus of WG2 is 1) to enable a common understanding of the different methods, model types (process-based or empirical) and tools to simulate trade patterns, supply chains, financial markets and human displacement and 2) to assess how these systems are affected by and can be managed to respond to cascading climate impacts.

These models include more sectoral models (e.g. only trading a few, specific but globally relevant crops, e.g. wheat in the TWIST model) as well as Partial and General Computational Equilibrium Models (e.g. MAgPIE, ICES), material flow and/or input-output models (Trase, IOTA, FABIO) and agent-based models (ACCLIMATE) as well as financial models including effects on investments and investor portfolios, the insurance industry (e.g. CLIMADA) and econometric/spatial interaction models for migration. By explaining and exchanging these existing models in between different modelling groups, WG2 will foster the development of methods and tools to link impact transmission chain models with the biophysical impact model simulations of WG1.

WG2 will foster the exchange of and the quality control of software and code developed to conduct cascading climate impact assessments and deals with issues relevant to uncertainty assessment in impact transmission chain models such as the consideration of (implicit) economic assumptions built into trade models. The COST networking tools in WG2 will support researchers to get acquainted with the main impact transmission chain models and model comparison techniques to foster the development of impact transmission chain models and their contribution to impact model comparisons in countries where such efforts are currently underrepresented.