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Open AccessFeature PaperArticle

A Lagrangian Ocean Model for Climate Studies

Department of Geology and Geophysics, Yale University, New Haven, CT 06511, USA
Climate 2019, 7(3), 41; https://doi.org/10.3390/cli7030041
Received: 1 February 2019 / Revised: 7 March 2019 / Accepted: 9 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue Climate and Atmospheric Dynamics and Predictability)
Most weather and climate models simulate circulations by numerically approximating a complex system of partial differential equations that describe fluid flow. These models also typically use one of a few standard methods to parameterize the effects of smaller-scale circulations such as convective plumes. This paper discusses the continued development of a radically different modeling approach. Rather than solving partial differential equations, the author’s Lagrangian models predict the motions of individual fluid parcels using ordinary differential equations. They also use a unique convective parameterization, in which the vertical positions of fluid parcels are rearranged to remove convective instability. Previously, a global atmospheric model and basin-scale ocean models were developed with this approach. In the present study, components of these models are combined to create a new global Lagrangian ocean model (GLOM), which will soon be coupled to a Lagrangian atmospheric model. The first simulations conducted with the GLOM examine the contribution of interior tracer mixing to ocean circulation, stratification, and water mass distributions, and they highlight several special model capabilities: (1) simulating ocean circulations without numerical diffusion of tracers; (2) modeling deep convective transports at low resolution; and (3) identifying the formation location of ocean water masses and water pathways. View Full-Text
Keywords: Lagrangian numerical method; ocean modeling; ocean mixing Lagrangian numerical method; ocean modeling; ocean mixing
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Haertel, P. A Lagrangian Ocean Model for Climate Studies. Climate 2019, 7, 41.

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