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Quality control for community-based sea-ice model development.

Andrew F Roberts1, Elizabeth C Hunke2, Richard Allard3

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PubMed
Summary
This summary is machine-generated.

The CICE Consortium developed quality control methods for sea-ice models, ensuring code integrity and facilitating community participation. These efficient statistical methods objectively assess changes in sea-ice thickness simulations.

Keywords:
CICEIcepackearth system modellingsea-ice forecasting

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Area of Science:

  • * Climate Science
  • * Computational Physics
  • * Oceanography

Background:

  • * The sea-ice model development community requires robust quality control (QC) procedures.
  • * The CICE Consortium aims to foster open, collaborative development of the Los Alamos Sea Ice Model (CICE).
  • * Ensuring the integrity of numerical codes' physical representations is crucial for reliable climate modeling.

Purpose of the Study:

  • * To devise and implement efficient QC methods for the CICE model.
  • * To maintain the integrity of physical representations within CICE's numerical codes.
  • * To enable broad scientific community participation in the Consortium's open software development.

Main Methods:

  • * Formulation of QC methods using statistical properties of sea-ice thickness from five CICE configurations.
  • * Efficient testing for significant changes in model results.
  • * Grading modifications into four categories: bit-for-bit amendments to significant upgrades.
  • * Assessment using criteria accounting for autocorrelation in sea-ice time series and a quadratic skill metric for hemispheric changes.

Main Results:

  • * Developed computationally efficient QC methods for sea-ice model development.
  • * Established objective criteria for assessing new physical representations and code functionality.
  • * Demonstrated the utility of statistical properties and skill metrics for evaluating model changes.
  • * Categorized code modifications to manage their impact on model output.

Conclusions:

  • * The CICE Consortium's QC procedures effectively maintain code integrity.
  • * These methods facilitate objective assessment of model changes, supporting community contributions.
  • * The developed metrics provide reliable guidance for evaluating new physical representations and code functionality in sea-ice models.