Multi-year ocean thermal variability

Numerous indicators show that multi-annual and longer oceanic baroclinic variability retains a complicated spatial structure out to decades and longer. With time-averaging, the sub-basin scales connected to abyssal topography and meteorological structures emerge in the fields. Here, using 26-years o...

Full description

Bibliographic Details
Main Author: Carl Wunsch
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Tellus: Series A, Dynamic Meteorology and Oceanography
Subjects:
Online Access:http://dx.doi.org/10.1080/16000870.2020.1824485
id doaj-09cbe074915a4a32859c5fdc1643e62d
record_format Article
spelling doaj-09cbe074915a4a32859c5fdc1643e62d2021-02-18T10:31:40ZengTaylor & Francis GroupTellus: Series A, Dynamic Meteorology and Oceanography1600-08702020-01-0172111510.1080/16000870.2020.18244851824485Multi-year ocean thermal variabilityCarl Wunsch0Department of Earth and Planetary Sciences, Harvard UniversityNumerous indicators show that multi-annual and longer oceanic baroclinic variability retains a complicated spatial structure out to decades and longer. With time-averaging, the sub-basin scales connected to abyssal topography and meteorological structures emerge in the fields. Here, using 26-years of an oceanic state estimate (ECCO), an attempt is made to extract simpler patterns from the vertical average (whole water column) annual mean temperature anomalies and, separately, the vertical structures at each horizontal position. Singular vectors (SVs)/empirical orthogonal functions (EOFs) successfully simplify vertical and horizontal fields, but principal observation patterns (POPs) do not do so. About 3 horizontal spatial patterns account for more than 95% of the interannual and longer variances. A breakdown of the purely vertical structure at each grid point leads in contrast to an intricate variability with depth. Results have implications both for future sampling strategies, and for estimates, e.g. of the accuracy of any mean oceanic scalar.http://dx.doi.org/10.1080/16000870.2020.1824485ocean variabilityocean temperature
collection DOAJ
language English
format Article
sources DOAJ
author Carl Wunsch
spellingShingle Carl Wunsch
Multi-year ocean thermal variability
Tellus: Series A, Dynamic Meteorology and Oceanography
ocean variability
ocean temperature
author_facet Carl Wunsch
author_sort Carl Wunsch
title Multi-year ocean thermal variability
title_short Multi-year ocean thermal variability
title_full Multi-year ocean thermal variability
title_fullStr Multi-year ocean thermal variability
title_full_unstemmed Multi-year ocean thermal variability
title_sort multi-year ocean thermal variability
publisher Taylor & Francis Group
series Tellus: Series A, Dynamic Meteorology and Oceanography
issn 1600-0870
publishDate 2020-01-01
description Numerous indicators show that multi-annual and longer oceanic baroclinic variability retains a complicated spatial structure out to decades and longer. With time-averaging, the sub-basin scales connected to abyssal topography and meteorological structures emerge in the fields. Here, using 26-years of an oceanic state estimate (ECCO), an attempt is made to extract simpler patterns from the vertical average (whole water column) annual mean temperature anomalies and, separately, the vertical structures at each horizontal position. Singular vectors (SVs)/empirical orthogonal functions (EOFs) successfully simplify vertical and horizontal fields, but principal observation patterns (POPs) do not do so. About 3 horizontal spatial patterns account for more than 95% of the interannual and longer variances. A breakdown of the purely vertical structure at each grid point leads in contrast to an intricate variability with depth. Results have implications both for future sampling strategies, and for estimates, e.g. of the accuracy of any mean oceanic scalar.
topic ocean variability
ocean temperature
url http://dx.doi.org/10.1080/16000870.2020.1824485
work_keys_str_mv AT carlwunsch multiyearoceanthermalvariability
_version_ 1724263512142774272