Data Assimilation Meetings at Reading

Date Meeting type Speakers
16 March 2015
GU01, 12PM
Invited speaker
Met departmental seminar
Craig Bishop (Naval Research Laboratory)
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, we know that several factors undermine this assumption – shared treatment of processes between models, shared observed data for evaluation and even shared model code. Here, we use a perfect model approach to test whether a previously proposed Ensemble Dependence Transformation (EDT) can improve 21st century CMIP projections. In these tests, where 21st 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 per cent 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 21st century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16 per cent (0.2C for B1), regional average temperatures by as much as 2.6C (RCP85) and regional average annual rainfall by as much as 410mm (RCP60). In some regions, however, the effect is minimal. We also find the EDT causes changes to temperature projections that differ in sign for different emissions scenarios, and speculate that these differences may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.

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