Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm

Improvement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for...

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Published in:Monthly Weather Review
Main Authors: Sumata, Hiroshi, Kauker, Frank, Karcher, Michael, Gerdes, Rüdiger
Format: Article in Journal/Newspaper
Language:unknown
Published: AMER METEOROLOGICAL SOC 2019
Subjects:
Online Access:https://epic.awi.de/id/eprint/49781/
https://journals.ametsoc.org/doi/full/10.1175/MWR-D-18-0360.1
https://hdl.handle.net/10013/epic.0ca56b92-99be-426c-80a7-c29923f9b724
id ftawi:oai:epic.awi.de:49781
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spelling ftawi:oai:epic.awi.de:49781 2024-09-15T17:51:20+00:00 Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm Sumata, Hiroshi Kauker, Frank Karcher, Michael Gerdes, Rüdiger 2019-02-19 https://epic.awi.de/id/eprint/49781/ https://journals.ametsoc.org/doi/full/10.1175/MWR-D-18-0360.1 https://hdl.handle.net/10013/epic.0ca56b92-99be-426c-80a7-c29923f9b724 unknown AMER METEOROLOGICAL SOC Sumata, H. orcid:0000-0002-2832-2875 , Kauker, F. orcid:0000-0002-7976-3005 , Karcher, M. orcid:0000-0002-9587-811X and Gerdes, R. (2019) Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm , Monthly Weather Review . doi:10.1175/MWR-D-18-0360.1 <https://doi.org/10.1175/MWR-D-18-0360.1> , hdl:10013/epic.0ca56b92-99be-426c-80a7-c29923f9b724 EPIC3Monthly Weather Review, AMER METEOROLOGICAL SOC, ISSN: 0027-0644 Article isiRev 2019 ftawi https://doi.org/10.1175/MWR-D-18-0360.1 2024-06-24T04:22:11Z Improvement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for a coupled sea ice– oceanmodel based on a genetic algorithm. To take the interrelation of dynamic and thermodynamicmodel parameters into account, the system is set up to optimize 15 model parameters simultaneously. The optimization is minimizing a cost function composed of the model–observation misfit of three sea ice quantities (concentration, drift, and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990–2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013–16). The similarity of the final values of the cost function and the resulting sea ice fields from a set of 11 independent optimizations suggest that the obtained sea ice fields are close to the best possible achievable by the current model setup, which allows us to identify limitations of the model formulation. The optimized parameters are applied for a simulation with a higher-resolution model to examine a portability of the parameters. The result shows good portability, while at the same time, it shows the importance of the oceanic conditions for the portability. Article in Journal/Newspaper Arctic North Atlantic Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Monthly Weather Review 147 6 1899 1926
institution Open Polar
collection Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
op_collection_id ftawi
language unknown
description Improvement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for a coupled sea ice– oceanmodel based on a genetic algorithm. To take the interrelation of dynamic and thermodynamicmodel parameters into account, the system is set up to optimize 15 model parameters simultaneously. The optimization is minimizing a cost function composed of the model–observation misfit of three sea ice quantities (concentration, drift, and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990–2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013–16). The similarity of the final values of the cost function and the resulting sea ice fields from a set of 11 independent optimizations suggest that the obtained sea ice fields are close to the best possible achievable by the current model setup, which allows us to identify limitations of the model formulation. The optimized parameters are applied for a simulation with a higher-resolution model to examine a portability of the parameters. The result shows good portability, while at the same time, it shows the importance of the oceanic conditions for the portability.
format Article in Journal/Newspaper
author Sumata, Hiroshi
Kauker, Frank
Karcher, Michael
Gerdes, Rüdiger
spellingShingle Sumata, Hiroshi
Kauker, Frank
Karcher, Michael
Gerdes, Rüdiger
Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm
author_facet Sumata, Hiroshi
Kauker, Frank
Karcher, Michael
Gerdes, Rüdiger
author_sort Sumata, Hiroshi
title Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm
title_short Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm
title_full Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm
title_fullStr Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm
title_full_unstemmed Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm
title_sort simultaneous parameter optimization of an arctic sea ice–ocean model by a genetic algorithm
publisher AMER METEOROLOGICAL SOC
publishDate 2019
url https://epic.awi.de/id/eprint/49781/
https://journals.ametsoc.org/doi/full/10.1175/MWR-D-18-0360.1
https://hdl.handle.net/10013/epic.0ca56b92-99be-426c-80a7-c29923f9b724
genre Arctic
North Atlantic
Sea ice
genre_facet Arctic
North Atlantic
Sea ice
op_source EPIC3Monthly Weather Review, AMER METEOROLOGICAL SOC, ISSN: 0027-0644
op_relation Sumata, H. orcid:0000-0002-2832-2875 , Kauker, F. orcid:0000-0002-7976-3005 , Karcher, M. orcid:0000-0002-9587-811X and Gerdes, R. (2019) Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm , Monthly Weather Review . doi:10.1175/MWR-D-18-0360.1 <https://doi.org/10.1175/MWR-D-18-0360.1> , hdl:10013/epic.0ca56b92-99be-426c-80a7-c29923f9b724
op_doi https://doi.org/10.1175/MWR-D-18-0360.1
container_title Monthly Weather Review
container_volume 147
container_issue 6
container_start_page 1899
op_container_end_page 1926
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