Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity

• Published in: Advances in Atmospheric Sciences
• Main Authors: Burrows, Susannah M., Dasgupta, Aritra, Reehl, Sarah, Bramer, Lisa, Ma, Po -Lun, Rasch, Philip J., Qian, Yun
• Language: unknown
• Published: 2022
• Subjects: 54 ENVIRONMENTAL SCIENCES; Southern Ocean
• Online Access: http://www.osti.gov/servlets/purl/1459662; https://www.osti.gov/biblio/1459662; https://doi.org/10.1007/s00376-018-7300-x

Evaluating a climate model’s fidelity (ability to simulate observed climate) is a critical step in establishing confidence in the model’s suitability for future climate projections, and in tuning climate model parameters. Model developers use their judgement in determining which trade-offs between different aspects of model fidelity are acceptable. However, little is known about the degree of consensus in these evaluations, and whether experts use the same criteria when different scientific objectives are defined. Here, we report on results from a broad community survey studying expert assessments of the relative importance of different output variables when evaluating a global atmospheric model’s mean climate. We find that experts adjust their ratings of variable importance in response to the scientific objective, for instance, scientists rate surface wind stress as significantly more important for Southern Ocean climate than for the water cycle in the Asian watershed. There is greater consensus on the importance of certain variables (e.g., shortwave cloud forcing) than others (e.g., aerosol optical depth). We find few differences in expert consensus between respondents with greater or less climate modeling experience, and no statistically significant differences between the responses of climate model developers and users. In conclusion, the concise variable lists and community ratings reported here provide baseline descriptive data on current expert understanding of certain aspects of model evaluation, and can serve as a starting point for further investigation, as well as developing more sophisticated evaluation and scoring criteria with respect to specific scientific objectives.