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Activity Number: 249 - The Climate Program at SAMSI
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #304655
Title: Statistics for Ocean Heat Content Estimation with Argo Profiling Floats
Author(s): Mikael Kuusela* and Donata Giglio and Anirban Mondal and Michael Stein
Companies: Carnegie Mellon University and University of Colorado Boulder and Case Western Reserve University and University of Chicago
Keywords: spatio-temporal statistics; local stationarity; trend estimation; oceanography; climate change; climatology

Argo floats measure seawater temperature and salinity in the upper 2000 meters of the ocean. These floats are uniquely capable of measuring the heat content of the global ocean, a quantity that is of central importance for understanding changes in the Earth's climate system. But providing detailed spatio-temporal estimates of the heat content is statistically challenging due to the irregular and complex structure of Argo data. We have previously demonstrated (Kuusela and Stein, 2018) that locally stationary Gaussian process regression leads to improved and computationally efficient interpolation of Argo data. Here we build upon those findings to produce improved Argo-based global ocean heat content estimates. We study the sensitivity of these estimates to the underlying statistical assumptions and present results indicating that the magnitude of the overall warming trend may depend on the modeling of the climatological time trend in the mean field estimate. We also investigate the benefits of including time in the interpolation and propose a method for uncertainty quantification that yields appropriate spatial correlations without the need for a global covariance model.

Authors who are presenting talks have a * after their name.

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