Seasonal extrema of sea surface temperature in CMIP6 models
CMIP6 model sea surface temperature (SST) seasonal extrema averaged over 1981–2010 are assessed against the World Ocean Atlas (WOA18) observational climatology. We propose a mask to identify and exclude regions of large differences between three commonly used climatologies (WOA18, WOCE-Argo Global H...
| Published in: | Ocean Science |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://ueaeprints.uea.ac.uk/id/eprint/85466/ https://ueaeprints.uea.ac.uk/id/eprint/85466/1/os_18_839_2022.pdf https://doi.org/10.5194/os-18-839-2022 |
| Summary: | CMIP6 model sea surface temperature (SST) seasonal extrema averaged over 1981–2010 are assessed against the World Ocean Atlas (WOA18) observational climatology. We propose a mask to identify and exclude regions of large differences between three commonly used climatologies (WOA18, WOCE-Argo Global Hydrographic climatology (WAGHC) and the Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST)). The biases in SST seasonal extrema are largely consistent with the annual mean SST biases. However, the amplitude and spatial pattern of SST bias vary seasonally in the 20 CMIP6 models assessed. Large seasonal variations in the SST bias occur in eastern boundary upwelling regions, polar regions, the North Pacific and the eastern equatorial Atlantic. These results demonstrate the importance of evaluating model performance not simply against annual mean properties. Models with greater vertical resolution in their ocean component typically demonstrate better representation of SST extrema, particularly seasonal maximum SST. No significant relationship of SST seasonal extrema with horizontal ocean model resolution is found. |
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