A Zonally Averaged, Coupled Ocean-Atmosphere Model for Paleoclimate Studies

In this paper, we show how oracle-based optimization can be used effectively for the calibration of an intermidiate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle-based optimization tool, Proximal-ACCPM. The oracle is a procedure which finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a 'best fit' estimate over ten times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available.

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“…On the other hand C-GOLDSTEIN is an o.o.m. slower than the reduced dimensionality Bern 2.5-D model (Stocker et al, 1992).…”

Section: The Modelmentioning

confidence: 96%

Oracle-based optimization applied to climate model calibration

Beltrán

Edwards

Haurie

et al. 2005

Environ Model Assess

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“…All but three of the models are primitive equation models (Bryan, 1969). The exceptions are MPIM, which neglects non-linear terms in the advection equation (Large-scale Geostrophic Model) (MaierReimer, 1993), AWI, which uses an adjoint technique to derive the circulation from hydrographic and geochemical data (temperature, salinity, alkalinity, phosphate, dissolved inorganic carbon, oxygen, silicate and nitrate) (Schlitzer, 1999), and PIUB where momentum equations are balances between coriolis forces, horizontal pressure gradients and zonal wind stress (Stocker et al, 1992). The PIUB model is also a zonally averaged basin model.…”

Section: Description Of the Circulation Modelsmentioning

confidence: 99%

Evaluation of ocean model ventilation with CFC-11: comparison of 13 global ocean models

et al. 2002

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We compared the 13 models participating in the Ocean Carbon Model Intercomparison Project (OCMIP) with regards to their skill in matching observed distributions of CFC-11. This analysis characterizes the abilities of these models to ventilate the ocean on timescales relevant for anthropogenic CO uptake. We found a large range in the modeled global inventory (AE30%), mainly due to differences in ventilation from the high latitudes. In the Southern Ocean, models differ particularly in the longitudinal distribution of the CFC uptake in the intermediate water, whereas the latitudinal distribution is mainly controlled by the subgrid-scale parameterization. Models with isopycnal diffusion and eddy-induced velocity parameterization produce more realistic intermediate water ventilation. Deep and bottom water ventilation also varies substantially between the models. Models coupled to a sea-ice model systematically provide more realistic AABW formation source region; however these same models also largely overestimate AABW ventilation if no specific parameterization of brine rejection during sea-ice formation is included. In the North Pacific Ocean, all models exhibit a systematic large underestimation of the CFC uptake in the thermocline of the subtropical gyre, while no systematic difference toward the observations is found in the subpolar gyre. In the North Atlantic Ocean, the CFC uptake is globally underestimated in subsurface. In the deep ocean, all but the adjoint model, failed to produce the two recently ventilated branches observed in the North Atlantic Deep Water (NADW). Furthermore, simulated transport in the Deep Western Boundary Current (DWBC) is too sluggish in all but the isopycnal model, where it is too rapid. Ó

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“…On the other hand, EMICs are computationally efficient enough to allow for long-term climate simulations over several tens of thousands of years or a broad range of sensitivity experiments over several millennia. Currently, there are several EMICs in operation such as: two-dimensional, zonally averaged ocean models coupled to a simple atmospheric module [57] [58] or geostrophic two-dimensional [59] or statisticaldynamical [60] atmospheric modules; three-dimensional models with a statistical-dynamical atmospheric and oceanic modules [61]; reduced-form comprehensive models [62] and those that involve an energy-moisture balance model coupled to an OGCM and a sea-ice model [63]. Results from experiments that were part of the contribution of several EMICs to the 5th Assessment Report of IPCC Working Group 1 are described in [64] [65].…”

Section: Latest Advances By Using Statistical-dynamical Modelsmentioning

confidence: 99%

Studies of Climate Change with Statistical-Dynamical Models: A Review

Franchito¹,

Rao²

2015

AJCC

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The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is no simple way to reconcile them. So, simple climate models, like statisticaldynamical models (SDMs), appear to be useful in this context. This kind of models is essentially mechanistic, being directed towards understanding the dependence of a particular mechanism on the other parameters of the problem. In this paper, the utility of SDMs for studies of climate change is discussed in some detail. We show that these models are an indispensable part of hierarchy of climate models.

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A Zonally Averaged, Coupled Ocean-Atmosphere Model for Paleoclimate Studies

Cited by 262 publications

References 42 publications

Oracle-based optimization applied to climate model calibration

Oracle-based optimization applied to climate model calibration

Evaluation of ocean model ventilation with CFC-11: comparison of 13 global ocean models

Studies of Climate Change with Statistical-Dynamical Models: A Review

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