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On the dependency of GCM-based regional surface climate change projections on model biases, resolution and climate sensitivity

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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.

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Acknowledgements

The CMIP5 data can be found at http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. The CMIP6 data can be found at https://esgf-node.llnl.gov/search/cmip6/.

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  1. Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

    Filippo Giorgi & Francesca Raffaele

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  1. Filippo Giorgi
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  2. Francesca Raffaele
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Correspondence to Filippo Giorgi.

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Giorgi, F., Raffaele, F. On the dependency of GCM-based regional surface climate change projections on model biases, resolution and climate sensitivity. Clim Dyn 58, 2843–2862 (2022). https://doi.org/10.1007/s00382-021-06037-8

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  • DOI: https://doi.org/10.1007/s00382-021-06037-8

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