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...
Published in: | Monthly Weather Review |
---|---|
Main Authors: | , , , |
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 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1810293217736785920 |