Model derived uncertainties in deep ocean temperature trends between 1990-2010

Type Article
Date 2019-02
Language English
Author(s) Garry F. K.1, 2, McDonagh E. L.3, Blaker A. T.3, Roberts C. D.4, Desbruyères DamienORCID3, Frajka-Williams E.1, 3, King B. A.3
Affiliation(s) 1 : Ocean and Earth Science, University of Southampton; National Oceanography Centre; Southampton, UK
2 : College of Life and Environmental Sciences; University of Exeter, Streatham Campus; Exeter ,UK
3 : National Oceanography Centre; Southampton, UK
4 : Met Office Hadley Centre; FitzRoy Road Exeter, UK
Source Journal Of Geophysical Research-oceans (2169-9275) (American Geophysical Union (AGU)), 2019-02 , Vol. 124 , N. 2 , P. 1155-1169
DOI 10.1029/2018JC014225
WOS© Times Cited 10
Keyword(s) deep oceans, temperature trends, ocean heat content, decadal variability, ocean modeling, observational uncertainties
Abstract

We construct a novel framework to investigate the uncertainties and biases associated with estimates of deep ocean temperature change from hydrographic sections, and demonstrate this framework in an eddy‐permitting ocean model. Biases in estimates from observations arise due to sparse spatial coverage (few sections in a basin), low frequency of occupations (typically 5‐10 years apart), mismatches between the time period of interest and span of occupations, and from seasonal biases relating to the practicalities of sampling during certain times of year. Between the years 1990 and 2010, the modeled global abyssal ocean biases are small, although regionally some biases (expressed as a heat flux into the 4000 ‐ 6000 m layer) can be up to 0.05 W m−2. In this model, biases in the heat flux into the deep 2000 ‐ 4000 m layer, due to either temporal or spatial sampling uncertainties, are typically much larger and can be over 0.1 W m−2 across an ocean. Overall, 82% of the warming trend deeper than 2000 m is captured by hydrographic section‐style sampling in the model. At 2000 m, only half the model global warming trend is obtained from observational‐style sampling, with large biases in the Atlantic, Southern and Indian Oceans. Biases due to different sources of uncertainty can have opposing signs and differ in relative importance both regionally and with depth, revealing the importance of reducing temporal and spatial uncertainties in future deep ocean observing design.

Plain Language Summary

In recent decades, deep (below 2000 m) ocean temperature trends have been measured when scientific research vessels repeat the same lines across an ocean basin. Repeats typically happen once or twice a decade and there are only a few repeated lines across each basin. The sparsity of data in both space and time will result in errors in the multidecadal temperature trends calculated from this data. Here, we use a state‐of‐the‐art ocean model to show how trends calculated from observational‐style sampling compare to trends calculated using all model data. For the period 1990‐2010, we estimate the error that may exist in observed deep ocean trend estimates. Overall, around 80% of the below 2000 m warming trend was captured by observational‐style sampling in the model, so deep ocean warming in recent decades may have been underestimated. However, our results are based on only one model simulation. The largest sources of sampling error are found in the Atlantic, Southern and Indian Oceans. For each basin, we reveal whether limited sampling in time or space contributes most error to the temperature trend estimate, and therefore in which regions temperature trend estimates would benefit from additional deep ocean sampling.

Full Text
File Pages Size Access
Publisher's official version 40 2 MB Open access
Supporting information 13 MB Open access
Top of the page

How to cite 

Garry F. K., McDonagh E. L., Blaker A. T., Roberts C. D., Desbruyères Damien, Frajka-Williams E., King B. A. (2019). Model derived uncertainties in deep ocean temperature trends between 1990-2010. Journal Of Geophysical Research-oceans, 124(2), 1155-1169. Publisher's official version : https://doi.org/10.1029/2018JC014225 , Open Access version : https://archimer.ifremer.fr/doc/00479/59021/