Process-oriented Diagnostics in CMIP6 Models and Beyond

Description:

There is growing community interest in moving beyond typical model evaluation metrics to process-oriented diagnostics. These diagnostics better constrain poorly-represented physics components in climate models, provide actionable feedback to model developers, and are expected to play a key role in advancing the next-generation climate and earth system models.

The scope of this collection encompasses studies developing new process-oriented diagnostics—and the underlying understanding of climate system processes—as well as those applying existing diagnostics to climate models. Of particular interest are applications to models participating in the Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models but the scope is open to diagnostics of models beyond CMIP6, including higher-resolution models.

The special collection solicits studies from all realms of the climate system, and therefore spans several American Meteorological Society (AMS) journals. The special collection is organized by members of the NOAA Model Diagnostics Force (MDTF). The collection contains contributions from current task force members as well as community-wide contributions.

Organizers:
J David Neelin, University of California, Los Angeles
John Krasting, Geophysical Fluid Dynamics Laboratory
Fiaz Ahmed, University of California, Los Angeles
Allison Wing, Florida State University
Eric Maloney, Colorado State University

Process-oriented Diagnostics in CMIP6 Models and Beyond

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J. David Neelin
,
John P. Krasting
,
Aparna Radhakrishnan
,
Jessica Liptak
,
Thomas Jackson
,
Yi Ming
,
Wenhao Dong
,
Andrew Gettelman
,
Danielle R. Coleman
,
Eric D. Maloney
,
Allison A. Wing
,
Yi-Hung Kuo
,
Fiaz Ahmed
,
Paul Ullrich
,
Cecilia M. Bitz
,
Richard B. Neale
,
Ana Ordonez
, and
Elizabeth A. Maroon

Abstract

Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.

Open access