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Unveiling the dominant control of the systematic cooling bias in CMIP6 models: quantification and corrective strategies

Zhang, Jie ORCID: https://orcid.org/0000-0002-8925-1011; Furtado, Kalli; Turnock, Steven T. ORCID: https://orcid.org/0000-0002-0036-4627; Lu, Yixiong ORCID: https://orcid.org/0000-0001-9823-9367; Wu, Tongwen ORCID: https://orcid.org/0000-0001-5187-9121; Zhang, Fang; Xin, Xiaoge; Liu, Yuyun. 2026 Unveiling the dominant control of the systematic cooling bias in CMIP6 models: quantification and corrective strategies. Atmospheric Chemistry and Physics, 26 (3). 2175-2189. 10.5194/acp-26-2175-2026

Abstract
Including sophisticated aerosol schemes in the models of the sixth Coupled Model Inter-comparison Project (CMIP6) has not improved historical climate simulations. In particular, the models underestimate the surface air temperature anomaly (SATa) when anthropogenic sulfur emissions increased in 1960–1990, making the reliability of the CMIP6 projections questionable. This cooling bias is largely attributable to the unreasonable simulated atmospheric sulfate burden changes. Sulfate burden anomaly are closely linked to both sulfate and SO2 deposition processes. Intensified sulfate deposition directly reduces atmospheric sulfate loading, while enhanced SO2 deposition limits precursor availability for sulfate formation by oxidation. These deposition processes regulate sulfate concentrations directly and indirectly. The systematically underestimated sulfate turnover time in CMIP6 models suggests that refining SO2 deposition process rather than sulfate deposition would be a more scientific approach for model improvement. This is supported by two post-CMIP6 models that show better SATa reproduction after improving the SO2 deposition parameterizations. Strong correlations between sulfate burden anomaly and SATa persist before, during, and after the 1960–1990 period. Such temporal consistency confirms the dominant role of sulfate-related physical processes across all examined time intervals.
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Programmes:
NOC Mission Networks > Mission Network - Climate
Research Groups > Ocean Modelling
NOC Research Groups 2025 > Ocean Modelling
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