A dynamically consistent reconstruction of ocean temperature

TitleA dynamically consistent reconstruction of ocean temperature
Publication TypeJournal Article
Year of Publication2017
AuthorsShen S.SP, Behm G.P, Song Y.T, Qu T.D
JournalJournal of Atmospheric and Oceanic Technology
Volume34
Pagination1061-1082
Date Published2017/05
Type of ArticleArticle
ISBN Number0739-0572
Accession NumberWOS:000402077000007
Keywordscirculation; earths energy imbalance; heat-content; modeling-system; pacific; sea-level; southern-ocean; sst data; surface; uncertainty
Abstract

This paper provides a spectral optimal gridding (SOG) method to make a dynamically consistent reconstruction of water temperature for the global ocean at different depth levels. The dynamical consistency is achieved by using the basis of empirical orthogonal functions (EOFs) derived from NASA Jet Propulsion Laboratory (JPL) non-Boussinesq ocean general circulation model (OGCM) output at 1/4 degrees resolution from 1958 to 2013. A convenient singular value decomposition (SVD) method is used to calculate the EOFs, in order to enable efficient computing for a fine spatial grid globally. These EOFs are used as explainable variables and are regressed against the sparsely distributed in situ ocean temperature data at 33 standard depth levels. The observed data are aggregated onto a 18 latitude-longitude grid at each level from the surface to the 5500-m layer for the period 1950-2014. Three representative temperature reconstruction examples are presented and validated: two 10-m-layer (i.e., the second layer from the surface) reconstructions for January 2008 and January 1998, which are compared with independent sea surface temperature (SST) observations; and one 100-m-layer reconstruction for January 1998, which shows a strong cold anomaly El Nino signal in the western tropical Pacific up to -5 degrees C from 150 degrees E to 140 degrees W. The SOG reconstruction can accurately locate the El Nino signal region in different ocean layers. The SOG reconstruction method is shown reliable and yields satisfactory accuracy even with sparse data. Validation and error analysis indicate that no systematic biases exist in the observed and reconstructed data.

DOI10.1175/jtech-d-16-0133.1
Short TitleJ. Atmos. Ocean. Technol.
Student Publication: 
No