Abstract
We investigate the dependency of projected regional changes in surface air temperature (SAT) and precipitation on the model biases, resolution and global temperature sensitivity in two global climate model (GCM) ensembles. End of twenty-first century changes under high end scenarios normalized in units of per degree of global warming (PDGW) are examined for CMIP5 (RCP8.5) and CMIP6 (SSP5-8.5) ensembles of comparable size over 26 sub-continental scale regions, for December–January–February (DJF) and June–July–August (JJA). A brief analysis is also carried out for the scenario SSP3-7.0, which shows results essentially in line with the SSP5-8.5 ones. We find that the average regional change patterns are very similar between the CMIP5 and CMIP6 ensembles, both for SAT and precipitation, with spatial correlations exceeding 0.84. Also similar are the regional bias patterns over most regions analyzed, suggesting that these two generations of models still share some common systematic errors. A statistically significant relationship between projected regional changes and biases is found in 27% of regional cases for both SAT and precipitation; between regional changes and model resolution in 2% of cases for SAT and 12% of cases for precipitation; and between regional changes and global temperature sensitivity in 19% of cases for SAT and 14% of cases for precipitation. Therefore, we assess that the GCM resolution does not appear to be a significant factor in affecting the sub-continental scale projected changes, at least for the resolution range in the CMIP5 and CMIP6 models, while global temperature sensitivity and especially model biases play a more important role. These dependencies are not always consistent between the CMIP5 and CMIP6 ensembles. Overall, in our assessment the CMIP6 ensemble does not appear to provide substantially different, and presumably improved, regional surface climate change information compared to CMIP5 despite the use of more comprehensive models and somewhat higher resolution.
Highlights
Coupled global climate models (GCMs) are the primary tools we have today to carry out projections of future climate change based on different greenhouse gas (GHG)concentration pathways
Precipitation produced by ensembles of CMIP5 and CMIP6 projections, and in particular we investigate the effect of model biases, resolution and global temperature sensitivity (GTS) on the regional changes
We focus on 26 regions of sub-continental size (Figure 1) and on an end of century time slice of high end scenarios (RCP85 for CMIP5 and SSP585 for CMIP6), since our analysis is carried out on the basis of changes expressed in PDGW, the conclusions are to a first order scalable to other scenarios and time slices (e.g. Tebaldi and Arblaster 2014; Osborn et al 2018)
Summary
Coupled global climate models (GCMs) are the primary tools we have today to carry out projections of future climate change based on different greenhouse gas (GHG). We analyze CMIP5 and CMIP6 ensembles to investigate eventual differences in behavior between these successive generations of models, and focus on an end of 21st century time slice for the most extreme scenarios, RCP8.5 for CMIP5 and SSP585 for CMIP6 (Moss et al 2010) This choice of scenario is done in order to maximize the signal-to-noise ratio, the analysis is carried out in terms of change per degree of global warming, which, on the one hand removes the direct dependency of the regional change magnitudes on the GTS, and on the other hand it can be extended to a first order approximation to other scenarios and time slices through the above mentioned pattern scaling property of GCM projections.
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