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How well must climate models agree with observations?

Dirk Notz1

  • 1Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany dirk.notz@mpimet.mpg.de.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|September 9, 2015
PubMed
Summary

Evaluating climate models requires more than comparing them to observations. Factors like internal variability and model tuning must be considered for accurate climate simulation assessment.

Keywords:
Earth System Modelsevaluationsea ice

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Area of Science:

  • Climate science
  • Earth system science

Background:

  • Climate model simulations are crucial tools for understanding Earth's climate system.
  • Assessing the fidelity of climate models is essential for reliable climate projections.
  • Standard evaluation metrics may not fully capture model performance due to various influencing factors.

Purpose of the Study:

  • To discuss the limitations of solely relying on observational agreement for climate model evaluation.
  • To highlight the importance of considering factors beyond direct comparison with observations.
  • To examine the impact of limiting factors on model evaluation metrics using sea ice as a case study.

Main Methods:

  • General discussion of factors influencing climate model evaluation.
  • Analysis of internal variability, model tuning, observational uncertainty, and forcing uncertainty.
  • Case study using sea ice metrics (area, volume) to illustrate limitations.

Main Results:

  • Agreement with observations alone is insufficient to infer climate model usefulness.
  • Factors such as internal variability, model tuning, and observational uncertainty significantly impact model evaluation.
  • Standard sea ice metrics provide limited insight into individual model shortcomings.

Conclusions:

  • A comprehensive approach is necessary for robust climate model evaluation.
  • The purpose of model use must guide the selection and interpretation of evaluation metrics.
  • Further research is needed to develop more informative metrics for assessing climate model performance.