Historical ocean reanalyses combine ocean general circulation models with data assimilation schemes that ingest rescued observations. They can be used as a tool to investigate long-term changes in the ocean climate. However, large uncertainties, due to the poorly developed atmospheric and oceanic observing networks in early periods, still remain. Thus, detailed studies to assess the uncertainty and its time dependency are required to quantify the feasibility of historical ocean reanalyses for climate change assessment. In this work, we estimate the ocean heat content variability from a set of ocean reanalyses that cover the period 1900-2010. The ocean reanalyses include realizations forced by two different atmospheric reanalyses (20CRv2 and ERA-20C), combined with different data assimilation strategies, in the attempt to evaluate the relative weight of the atmospheric forcing and observation uncertainties on the resulting ocean heat content estimates. Results suggest that even when observing networks are poor, the observations are able to shape the upper ocean heat content variability, in terms of long-term trends and reproduction of individual warming/cooling events related to volcanic eruptions. The assimilation of in-situ profiles has an effect even on the sea surface temperature variability and is able to constrain the top 700m heat content since the 1950s with respect to the uncertainty borne by the atmospheric forcing. The vertical propagation of the upper ocean observational information is however slow (with typically decadal time scale). Consequently, the total column heat content is constrained by observations only in the latest two decades. We conclude that upper ocean heat content diagnostics from historical ocean reanalyses bear the climate change signature and may be considered for long-term studies when complemented by proper uncertainty estimation.

Quantifying the effects of observational constraints and uncertainty in atmospheric forcing on historical ocean reanalyses

Yang Chunxue;Storto Andrea;
2019

Abstract

Historical ocean reanalyses combine ocean general circulation models with data assimilation schemes that ingest rescued observations. They can be used as a tool to investigate long-term changes in the ocean climate. However, large uncertainties, due to the poorly developed atmospheric and oceanic observing networks in early periods, still remain. Thus, detailed studies to assess the uncertainty and its time dependency are required to quantify the feasibility of historical ocean reanalyses for climate change assessment. In this work, we estimate the ocean heat content variability from a set of ocean reanalyses that cover the period 1900-2010. The ocean reanalyses include realizations forced by two different atmospheric reanalyses (20CRv2 and ERA-20C), combined with different data assimilation strategies, in the attempt to evaluate the relative weight of the atmospheric forcing and observation uncertainties on the resulting ocean heat content estimates. Results suggest that even when observing networks are poor, the observations are able to shape the upper ocean heat content variability, in terms of long-term trends and reproduction of individual warming/cooling events related to volcanic eruptions. The assimilation of in-situ profiles has an effect even on the sea surface temperature variability and is able to constrain the top 700m heat content since the 1950s with respect to the uncertainty borne by the atmospheric forcing. The vertical propagation of the upper ocean observational information is however slow (with typically decadal time scale). Consequently, the total column heat content is constrained by observations only in the latest two decades. We conclude that upper ocean heat content diagnostics from historical ocean reanalyses bear the climate change signature and may be considered for long-term studies when complemented by proper uncertainty estimation.
2019
Istituto di Scienze Marine - ISMAR
historical reanalysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/377679
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