A reproducible uncertainty quantification framework for ocean heat content with space-time dependent local conditional simulations
Date:
Abstract: Estimating the global ocean heat content (OHC) with reliable uncertainties is critical for understanding the evolution of Earth’s climate, as most excess energy accumulated in the climate system by the Earth Energy Imbalance is stored in the ocean. Argo floats provide a rich 20-year dataset of temperature measurements across the global ocean, but its large size and nonstationarity make reliably accounting for the space-time dependence in OHC uncertainties both computationally and statistically challenging. Here, we propose addressing these challenges using an improved OHC uncertainty quantification method. This method is based on space-time dependent locally stationary conditional simulations from a Gaussian process model. We implement this method as part of a modularized open-source software framework that facilitates reproducibility and code reuse. Using this framework, we present 2004-2022 estimates and uncertainties for OHC and related quantities, both on a global and regional scale. In addition, we include the latest estimates and uncertainties from the bivariate Gaussian process extension that also incorporates the vertical dependence across pressure levels.