Estimating temperature variability and trends from a combination of seismic and in situ data

Estimating the large-scale variability and trends in subsurface ocean temperatures is limited by sparse in situ observations inadequate for resolving mesoscale eddies. Travel times of seismically generated sound waves, sensitive to path-integrated temperature, provide complementary integral constraints. We here use earthquakes along the Japan Trench and receivers at Wake Island to sample the Kuroshio Extension region in the Northwest Pacific. We develop a Gaussian process framework, optimized via maximum likelihood, to estimate temperature anomalies and uncertainties from this seismic data and to combine it with in situ data from Argo profiles and shipboard data. This framework shows seismic measurements are quantitatively consistent with in situ data and substantially reduce uncertainties in large-scale variability and trends. Relative to their prior, error variances of area-mean temperature fluctuations due to mesoscale eddies from 2008 to 2021 are reduced by 30% by the in situ data, 39% by the seismic data, and 50% by the combination. For pathmean estimates, the combined reduction is 83% in error variances, compared to 45% from in situ data alone. The data show a steady subsurface warming of 11.8 ± 5.0 mK yr −1 (2σ uncertainty) from 2008 to 2021 and no substantial trend between 1997 and 2008. Supplementary Material File (sot_and_argo_proceedings_a.pdf) - Download - 4.06 MB Information & Authors Information Version history Peer review timeline Published Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences Version of Record14 May 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Funding Information Metrics & Citations Metrics Article Usage 205views 177downloads Citations Download citation Shirui Peng, Jörn Callies. Estimating temperature variability and trends from a combination of seismic and in situ data. Authorea. 28 April 2025. DOI: https://doi.org/10.22541/au.174585697.72529577/v1 DOI: https://doi.org/10.22541/au.174585697.72529577/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.