Ocean Dynamics | ScienceGate

AbstractWe consider the hydrostatic Boussinesq equations of global ocean dynamics, also known as the “primitive equations”, coupled to advection–diffusion equations for temperature and salt. The system of equations is closed by an equation of state that expresses density as a function of temperature, salinity and pressure. The equation of state TEOS-10, the official description of seawater and ice properties in marine science of the Intergovernmental Oceanographic Commission, is the most accurate equations of state with respect to ocean observation and rests on the firm theoretical foundation of the Gibbs formalism of thermodynamics. We study several specifications of the TEOS-10 equation of state that comply with the assumption underlying the primitive equations. These equations of state take the form of high-order polynomials or rational functions of temperature, salinity and pressure. The ocean primitive equations with a nonlinear equation of state describe richer dynamical phenomena than the system with a linear equation of state. We prove well-posedness for the ocean primitive equations with nonlinear thermodynamics in the Sobolev space $${{\mathcal {H}}^{1}}$$ H 1 . The proof rests upon the fundamental work of Cao and Titi (Ann. Math. 166:245–267, 2007) and also on the results of Kukavica and Ziane (Nonlinearity 20:2739–2753, 2007). Alternative and older nonlinear equations of state are also considered. Our results narrow the gap between the mathematical analysis of the ocean primitive equations and the equations underlying numerical ocean models used in ocean and climate science.

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Rapid observations of ocean dynamics and stratification along a steep island coast during Hurricane María

Science Advances ◽

10.1126/sciadv.abf1552 ◽

2021 ◽

Vol 7 (20) ◽

pp. eabf1552

Author(s):

Olivia M. Cheriton ◽

Curt D. Storlazzi ◽

Kurt J. Rosenberger ◽

Clark E. Sherman ◽

Wilford E. Schmidt

Keyword(s):

Puerto Rico ◽

Water Column ◽

High Frequency ◽

Coastal Communities ◽

Ocean Dynamics ◽

Sea Surface ◽

Hurricane Intensity ◽

Island Coast ◽

New Framework ◽

Insular Shelf

Hurricanes are extreme storms that affect coastal communities, but the linkages between hurricane forcing and ocean dynamics remain poorly understood. Here, we present full water column observations at unprecedented resolution from the southwest Puerto Rico insular shelf and slope during Hurricane María, representing a rare set of high-frequency, subsurface, oceanographic observations collected along an island margin during a hurricane. The shelf geometry and orientation relative to the storm acted to stabilize and strengthen stratification. This maintained elevated sea-surface temperatures (SSTs) throughout the storm and led to an estimated 65% greater potential hurricane intensity contribution at this site before eye passage. Coastal cooling did not occur until 11 hours after the eye passage. Our findings present a new framework for how hurricane interaction with insular island margins may generate baroclinic processes that maintain elevated SSTs, thus potentially providing increased energy for the storm.

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GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics

Journal of Climate ◽

10.1175/jcli-d-11-00560.1 ◽

2012 ◽

Vol 25 (19) ◽

pp. 6646-6665 ◽

Cited By ~ 620

Author(s):

John P. Dunne ◽

Jasmin G. John ◽

Alistair J. Adcroft ◽

Stephen M. Griffies ◽

Robert W. Hallberg ◽

...

Keyword(s):

Climate Changes ◽

Southern Oscillation ◽

Heat Content ◽

Ocean Dynamics ◽

Thermocline Depth ◽

Earth System ◽

System Models ◽

Model Version ◽

Earth System Models

Abstract The physical climate formulation and simulation characteristics of two new global coupled carbon–climate Earth System Models, ESM2M and ESM2G, are described. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous Climate Model version 2.1 (CM2.1) while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4p1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in El Niño–Southern Oscillation being overly strong in ESM2M and overly weak in ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to total heat content variability given its lack of long-term drift, gyre circulation, and ventilation in the North Pacific, tropical Atlantic, and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to surface circulation given its superior surface temperature, salinity, and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. The overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon–climate models.

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Dynamics of Interdecadal Climate Variability: A Historical Perspective*

Journal of Climate ◽

10.1175/2011jcli3980.1 ◽

2012 ◽

Vol 25 (6) ◽

pp. 1963-1995 ◽

Cited By ~ 158

Author(s):

Zhengyu Liu

Keyword(s):

Climate Variability ◽

Time Scale ◽

Ocean Dynamics ◽

Interdecadal Variability ◽

Driving Mechanism ◽

Stochastic Forcing ◽

The Pacific ◽

Ocean Atmosphere ◽

Almost All ◽

Interdecadal Climate Variability

Abstract The emerging interest in decadal climate prediction highlights the importance of understanding the mechanisms of decadal to interdecadal climate variability. The purpose of this paper is to provide a review of our understanding of interdecadal climate variability in the Pacific and Atlantic Oceans. In particular, the dynamics of interdecadal variability in both oceans will be discussed in a unified framework and in light of historical development. General mechanisms responsible for interdecadal variability, including the role of ocean dynamics, are reviewed first. A hierarchy of increasingly complex paradigms is used to explain variability. This hierarchy ranges from a simple red noise model to a complex stochastically driven coupled ocean–atmosphere mode. The review suggests that stochastic forcing is the major driving mechanism for almost all interdecadal variability, while ocean–atmosphere feedback plays a relatively minor role. Interdecadal variability can be generated independently in the tropics or extratropics, and in the Pacific or Atlantic. In the Pacific, decadal–interdecadal variability is associated with changes in the wind-driven upper-ocean circulation. In the North Atlantic, some of the multidecadal variability is associated with changes in the Atlantic meridional overturning circulation (AMOC). In both the Pacific and Atlantic, the time scale of interdecadal variability seems to be determined mainly by Rossby wave propagation in the extratropics; in the Atlantic, the time scale could also be determined by the advection of the returning branch of AMOC in the Atlantic. One significant advancement of the last two decades is the recognition of the stochastic forcing as the dominant generation mechanism for almost all interdecadal variability. Finally, outstanding issues regarding the cause of interdecadal climate variability are discussed. The mechanism that determines the time scale of each interdecadal mode remains one of the key issues not understood. It is suggested that much further understanding can be gained in the future by performing specifically designed sensitivity experiments in coupled ocean–atmosphere general circulation models, by further analysis of observations and cross-model comparisons, and by combining mechanistic studies with decadal prediction studies.

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