Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

Chengcheng Shen; Honghua Shi; Yongzhi Liu; Fen Li; Dewen Ding

doi:10.1007/s00343-016-5068-3

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Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

Updated：2023-05-16

- Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment
- Journal of Oceanology and Limnology Vol. 34, Issue 4, Pages: 683-696(2016)
- Affiliations：
  
  1. College of Environmental Science and Engineering Ocean University of China
  2. The First Institute of Oceanography State Oceanic Administration
  3. School of Mathematical Sciences Ocean University of China
- Author bio：
- Funds：
- DOI：10.1007/s00343-016-5068-3
  CLC：
- Online First：10 December 2015，
  
  Published：2016
- Accepted：
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Shen, C., Shi, H., Liu, Y. et al. Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment., Chin. J. Ocean. Limnol. 34, 683–696 (2016). https://doi.org/10.1007/s00343-016-5068-3
DOI：

Shen, C., Shi, H., Liu, Y. et al. Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment., Chin. J. Ocean. Limnol. 34, 683–696 (2016). https://doi.org/10.1007/s00343-016-5068-3 DOI：

摘要

Abstract

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill

and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO)

which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO

which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally

by focusing on the degree of impact of various factors on model skill

model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however

improvement in model skill still lacks global treatments and its assessment is not integrated. Thus

the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions

limiting their application. Therefore

a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

关键词

Keywords

references

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