Surface air temperature variability in global climate models

TL;DR

This study analyzes surface air temperature variability in climate models and reanalysis datasets, revealing that low-lying over-land regions show the strongest correlation between mean and variability, influenced by heat capacity differences.

Contribution

It identifies geographic regions with the strongest mean-variability correlation and links this to the effective heat capacity of the atmosphere in climate models.

Findings

Low-lying over-land regions have the strongest temperature variability correlation.

Differences in atmospheric heat capacity influence temperature response.

Results are consistent across CMIP5 and reanalysis datasets.

Abstract

New results from the Coupled Model Inter-comparison Project phase 5 (CMIP5) and multiple global reanalysis datasets are used to investigate the relationship between the mean and standard deviation in the surface air temperature. A combination of a land-sea mask and orographic filter were used to investigate the geographic region with the strongest correlation and in all cases this was found to be for low-lying over-land locations. This result is consistent with the expectation that differences in the effective heat capacity of the atmosphere are an important factor in determining the surface air temperature response to forcing.