Water is warmer than air, so why do we use sea surface temperature to estimate global temperature?

Abstract: In the study of global climate, ocean temperature estimates use sea surface temperature (SST) anomalies instead of marine atmospheric temperature (MAT) anomalies. A key question is to ask what biases result from this choice. In this talk we employ hierarchical statistical models to investigate spatial-temporal differences between SST and MAT anomalies in the tropical Pacific. The analysis uses observations from the Tropical Atmosphere Ocean (TAO) buoy network. We use a spatio-temporal modeling approach that accounts for missing data in the observation network, and allows for full uncertainty quantification. We also compare our results to another analysis that uses data from the European Center for Medium Range Weather Forecasting fifth generation reanalysis product (ERA5). We find no indication of bias or trend in replaciing MAT by SST in calculating global temperature anomalies.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.