Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a perfect model'' approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample observations,'' the mean-square difference between the transformed ensemble mean and observations'' is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the observations'' much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2 degrees C for B1 scenario), regional average temperatures by as much as 2.6 degrees C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.