Assessing the abilities of CMIP5 models to represent the seasonal cycle of surface ocean pCO(2)
| Type | Article | ||||||||
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| Date | 2015-07-03 | ||||||||
| Language | English | ||||||||
| Author(s) | Pilcher Darren J.1, Brody Sarah R.2, Johnson Leah3, Bronselaer Benjamin4 | ||||||||
| Affiliation(s) | 1 : Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI 53706 USA. 2 : Duke Univ, Div Earth & Ocean Sci, Durham, NC USA. 3 : Univ Washington, Sch Oceanog, Seattle, WA 98195 USA. 4 : Univ Oxford, Dept Phys, Oxford, England. |
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| Source | Journal Of Geophysical Research-oceans (0148-0027) (Amer Geophysical Union), 2015-07-03 , Vol. 120 , N. 7 , P. 4625-4637 | ||||||||
| DOI | 10.1002/2015JC010759 | ||||||||
| WOS© Times Cited | 10 | ||||||||
| Abstract | The ability of Earth System Models to accurately simulate the seasonal cycle of the partial pressure of CO2 in surface water (pCO(2)(SW)) has important implications for projecting future ocean carbon uptake. Here we develop objective model skill score metrics and assess the abilities of 18 CMIP5 models to simulate the seasonal mean, amplitude, and timing of pCO(2)(SW) in biogeographically defined ocean biomes. The models perform well at simulating the monthly timing of the seasonal minimum and maximum of pCO(2)(SW), but perform somewhat worse at simulating the seasonal mean values, particularly in polar and equatorial regions. The results also illustrate that a single "best'' model can be difficult to determine, despite an analysis restricted to the seasonality of a single variable. Nonetheless, groups of models tend to perform better than others, with significant regional differences. This suggests that particular models may be better suited for particular regions, though we find no evidence for model tuning. Timing and amplitude skill scores display a weak positive correlation with observational data density, while the seasonal mean scores display a weak negative correlation. Thus, additional mapped pCO(2)(SW) data may not directly increase model skill scores; however, improved knowledge of the dominant mechanisms may improve model skill. Lastly, we find skill score variability due to internal model variability to be much lower than variability within the CMIP5 intermodel spread, suggesting that mechanistic model differences are primarily responsible for differences in model skill scores. | ||||||||
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