Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
CL3.1.7 | Constraining and sub-selecting multi-model ensembles
Constraining and sub-selecting multi-model ensembles
Convener: Lukas Brunner | Co-convener: Said QasmiECSECS
Using all available models from multi-model ensembles such as CMIP6 is often not feasible for regional climate modelling or impact studies and does not necessarily provide the best possible representation of climate or its changes. In recent years many approaches have, therefore, been developed to weight, filter, constrain or sub-select models, often based on historical observations. The aim being to produce climate information that is as realistic as possible, tailored to the downstream application, and provides reliable assessments of change. These methods cover a range of spatial scales from global to local and temporal scales from near term predictions to long term projections. Constraints have been based on climatological means, dynamical features, or the representation of changes at different spatio-temporal scales.

This session welcomes contributions focusing on, but not limited to, weighting, filtering, constraining, or sub-selecting multi-model ensembles. This includes:
- work on global emergent constraints, regional weighting and filtering approaches, as well as qualitative and quantitative model sub-selection
- methodological as well as application oriented studies including, for example, pseudo-observation approaches to test the reliability of constraints, machine learning approaches for constraining future projections, and climate impact studies
- constraints based on model performance, model independence, and model spread in a range of variables or derived quantities

Contributions evaluating or selecting CMIP6 models for the latest iteration of CORDEX (the Coordinated Regional Climate Downscaling Experiment) are particularly encouraged as this session also aims to to connect the global and regional climate modelling communities.