Validation of model internal variability for partitioning climate projection uncertainty in Large Ensembles and CMIP5/6

Abstract:

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenario has historically relied on assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as SMILEs allow a more robust separation between sources of uncertainty. Here, we quantify and partition uncertainty in temperature and precipitation projections using multiple SMILEs as well as the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We illustrate the importance and challenge of validating internal variability in models and review new tools to aid that effort. We further investigate whether absolute and relative importance of uncertainty sources have changed between CMIP5 and CMIP6. Examples of model uncertainty being dependent on the generation of emissions scenario are discussed in the context of how to best compare results from CMIP5 and CMIP6.