Decadal variability of the North Atlantic in an ocean general circulation model

Abstract The dominant pattern of atmospheric variability in the North Atlantic sector is the North Atlantic Oscillation (NAO). Since the 1970s the NAO has been well characterized by a trend toward its positive phase. Recent atmospheric general circulation model studies have linked this trend to a progressive warming of the Indian Ocean. Unfortunately, a clear mechanism responsible for the change of the NAO could not be given. This study provides further details of the NAO response to Indian Ocean sea surface temperature (SST) anomalies. This is done by conducting experiments with a coupled ocean–atmosphere general circulation model (OAGCM). The authors develop a hypothesis of how the Indian Ocean impacts the NAO.

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Pathways of the North Pacific Intermediate Water identified through the tangent linear and adjoint models of an ocean general circulation model

Journal of Geophysical Research Oceans ◽

10.1002/jgrc.20094 ◽

2013 ◽

Vol 118 (4) ◽

pp. 2035-2051 ◽

Cited By ~ 14

Author(s):

Yosuke Fujii ◽

Toshiya Nakano ◽

Norihisa Usui ◽

Satoshi Matsumoto ◽

Hiroyuki Tsujino ◽

...

Keyword(s):

General Circulation Model ◽

North Pacific ◽

General Circulation ◽

Circulation Model ◽

Ocean General Circulation Model ◽

Intermediate Water ◽

North Pacific Intermediate Water ◽

The North ◽

Ocean General Circulation ◽

The North Pacific

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Global Decadal Upper-Ocean Heat Content as Viewed in Nine Analyses

Journal of Climate ◽

10.1175/2008jcli2489.1 ◽

2008 ◽

Vol 21 (22) ◽

pp. 6015-6035 ◽

Cited By ~ 55

Author(s):

James A. Carton ◽

Anthony Santorelli

Keyword(s):

Data Assimilation ◽

North Atlantic ◽

General Circulation ◽

Heat Content ◽

Circulation Model ◽

Ocean General Circulation Model ◽

Global Ocean ◽

Warm Bias ◽

The North ◽

Ocean General Circulation

Abstract This paper examines nine analyses of global ocean 0-/700-m temperature and heat content during the 43-yr period of warming, 1960–2002. Among the analyses are two that are independent of any numerical model, six that rely on sequential data assimilation, including an ocean general circulation model, and one that uses four-dimensional variational data assimilation (4DVAR), including an ocean general circulation model and its adjoint. Most analyses show gradual warming of the global ocean with an ensemble trend of 0.77 × 108 J m−2 (10 yr)−1 (=0.24 W m−2) as the result of rapid warming in the early 1970s and again beginning around 1990. One proposed explanation for these variations is the effect of volcanic eruptions in 1963 and 1982. Examination of this hypothesis suggests that while there is an oceanic signal, it is insufficient to explain the observed heat content variations. A second potential cause of decadal variations in global heat content is the uncorrelated contribution of heat content variations in individual ocean basins. The subtropical North Atlantic is warming at twice the global average, with accelerated warming in the 1960s and again beginning in the late 1980s and extending through the end of the record. The Barents Sea region of the Arctic Ocean and the Gulf of Mexico have also warmed, while the western subpolar North Atlantic has cooled. Heat content variability in the North Pacific differs significantly from the North Atlantic. There the spatial and temporal patterns are consistent with the decadal variability previously identified through observational and modeling studies examining SST and surface winds. In the Southern Hemisphere large heat content anomalies are evident, and while there is substantial disagreement among analyses on average the band of latitudes at 30°–60°S contribute significantly to the global warming trend. Thus, the uncorrelated contributions of heat content variations in the individual basins are a major contributor to global heat content variations. A third potential contributor to global heat content variations is the effect of time-dependent bias in the set of historical observations. This last possibility is examined by comparing the analyses to the unbiased salinity–temperature–depth dataset and finding a very substantial warm bias in all analyses in the 1970s relative to the latter decades. This warm bias may well explain the rapid increase in analysis heat content in the early 1970s, but not the more recent increase, which began in the early 1990s. Finally, this study provides information about the similarities and differences between analyses that are independent of a model and those that use sequential assimilation and 4DVAR. The comparisons provide considerable encouragement for the use of the sequential analyses for climate research despite the presence of erroneous variability (also present in the no-model analyses) resulting from instrument bias. The strengths and weaknesses of each analysis need to be considered for a given application.

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