Preprints
https://doi.org/10.5194/gmd-2021-251
https://doi.org/10.5194/gmd-2021-251

Submitted as: methods for assessment of models 27 Sep 2021

Submitted as: methods for assessment of models | 27 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

Earth System Model Parameter Adjustment Using a Green's Functions Approach

Ehud Strobach1,2, Andrea Molod2, Donifan Barahona2, Atanas Trayanov2,3, Dimitris Menemenlis5, and Gael Forget4 Ehud Strobach et al.
  • 1Agricultural Research Organization, Rishon Lezion, Israel
  • 2Goddard Space Flight Center, Greenbelt, MD, US
  • 3Science Systems and Applications Inc., Greenbelt, MD, US
  • 4Massachusetts Institute of Technology, Cambridge, MA, US
  • 5Jet Propulsion Laboratory,California Institute of Technology, Pasadena, CA, US

Abstract. We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth System Model (ESM). This estimation approach had previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea-ice, or carbon-cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere-land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea-ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carry out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These control parameters can be used to adjust sea-ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We define eight observational targets: sea-ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

Ehud Strobach et al.

Status: open (until 22 Nov 2021)

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Ehud Strobach et al.

Ehud Strobach et al.

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Short summary
The green's functions methodology offers a systematic, easy to implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. In this paper, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.