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"Apples and Oranges": On comparing simulated historic near‐surface temperature changes with observations
Author(s) -
Jones Gareth S.
Publication year - 2020
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3871
Subject(s) - climatology , surface air temperature , sea surface temperature , climate model , environmental science , climate change , meteorology , atmospheric sciences , geology , geography , oceanography
Simulated historic near‐surface air temperature variations are often compared with observations of land air temperatures blended with sea surface temperatures. This study investigates claims that this is not a “true like‐with‐like” comparison, which may cause small biases in simulated twentieth century temperature changes, with implications for different climate attribution and projection studies. A more appropriate analysis, it is claimed, should use simulated sea surface temperatures blended with land air temperatures; an apparent discrepancy with observed trends is then reduced. As the temperature of the uppermost level in a model's ocean is used to represent simulated sea surface temperatures, that models have inconsistent ways of representing land, and that simulations have differing sea ice coverages, the claim of an idealised analysis approach is challenged. An examination of Coupled Model Intercomparison Project simulations, compared with an observational dataset of near‐surface temperatures, suggests there is a bias in simulated historic trends when upper‐ocean temperatures are used instead of marine air temperatures, but this bias is small compared to other model and observational uncertainties and the impact of analysis choices. The results indicate that it is generally appropriate to use global near‐surface air temperature diagnostics to compare simulated historic climate change with observed temperature changes. Alternative model diagnostics are not necessarily superior to those used in standard approaches, and the emphasis of model and observational discrepancies may be based on overconfident reasoning.

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