Finding forced trends in oceanic oxygen. Issue 2 (29th February 2016)

• Record Type: Journal Article
• Authors: Long, Matthew C.; Deutsch, Curtis; Ito, Taka
• Is Part Of: Global biogeochemical cycles. Volume 30:Issue 2(2016:Feb.)
• Page Start: 381
• Page End: 397
• Publication Date: 2016-02-29
• Subjects: climate variability -- dissolve oxygen -- ocean biogeochemistry; Biogeochemical cycles -- Periodicals; Electronic journals; 577.1405
• DOI: 10.1002/2015GB005310
• Languages: English
• ISSNs: 0886-6236

Abstract

Abstract: Anthropogenically forced trends in oceanic dissolved oxygen are evaluated in Earth system models in the context of natural variability. A large ensemble of a single Earth system model is used to clearly identify the forced component of change in interior oxygen distributions and to evaluate the magnitude of this signal relative to noise generated by internal climate variability. The time of emergence of forced trends is quantified on the basis of anomalies in oxygen concentrations and trends. We find that the forced signal should already be evident in the southern Indian Ocean and parts of the eastern tropical Pacific and Atlantic basins; widespread detection of forced deoxygenation is possible by 2030–2040. In addition to considering spatially discrete metrics of detection, we evaluate the similarity of the spatial structures associated with natural variability and the forced trend. Outside of the subtropics, these patterns are not wholly distinct on the isopycnal surfaces considered, and therefore, this approach does not provide significantly advanced detection. Our results clearly demonstrate the strong impact of natural climate variability on interior oxygen distributions, providing an important context for interpreting observations. Key Points: Natural variability drives strong fluctuations in dissolved oxygen within the ocean interior Natural variability challenges detection of forced trends in dissolved oxygen Time of emergence of forced trends is quantified based on state, trends, and spatial patterns