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Stochastic Multiscale Climate Models

Coupling an implicit low-resolution model to an explicit high-resolution ocean model

In climate models it will not be possible to capture all relevant processes through a higher resolution or better process description. Ocean models currently use already near eddy-resolving horizontal resolutions (e.g. 0.1°) but many important processes such as upper ocean turbulence and sub-mesoscale eddies, are not adequately captured at this resolution.

To overcome this problem one needs to exploit the property that high-frequency components in the flow get into statistical equilibrium much faster than low-frequency components and, moreover, are locally determined by low-frequency components. This can be accomplished by coupling an implicit low-resolution model to an explicit high-resolution ocean model. One runs the high-resolution model alternatingly with the low-resolution model, for a short and long time period, respectively.

In fact, we will run an instance of the high-resolution model for each grid cell of the low-resolution model, using initial and boundary values computed at low resolution. This leads to an embarrassingly parallelizable set of high-resolution models.

Hence, very suitable for Exascale architectures. For each low-resolution grid cell, the statistics (mean, variance) resulting from these computations will be used to define a stochastic term (state-dependent) in the low-resolution model that parametrizes the behavior of the high-resolution model.

This process is repeated until the model gets into statistical equilibrium. For the coupling of the models, we will extend the eScience tool OMUSE, developed by NLeSC in a recent project, to one which can deal with one low-resolution model that can interact with many high-resolution models.

Participating organisations

DLR
Netherlands eScience Center
University of Groningen
Utrecht University

Team

Ben van Werkhoven
Ben van Werkhoven
Senior eScience Research Engineer
Netherlands eScience Center
FW
Fred Wubs
Principal investigator
Inti Pelupessy
Inti Pelupessy
eScience Research Engineer
Netherlands eScience Center
MC
Maria Chertova
eScience Research Engineer
Netherlands eScience Center
MV
Merijn Verstraaten
eScience Research Engineer
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
eScience Coordinator
Netherlands eScience Center

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Related tools

OMUSE

OM

A Python environment to interface and couple oceanographic and other earth system model codes.

Updated 5 months ago
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