Process-oriented Diagnostics in CMIP6 Models and Beyond


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.

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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Michael Notaro
Jenna Jorns
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Laura Briley


Credible modeling, tools, and guidance, regarding the changing Laurentian Great Lakes and the climatic impacts, are needed by local decision-makers to inform their management and planning. The present study addresses this need through a model evaluation study of the representation of lake–atmosphere interactions and resulting lake-effect snowfall in the Great Lakes region. Analysis focuses on an extensive ensemble of 74 historical simulations generated by 23 high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP). The model assessment addresses the modeling treatment of the Great Lakes, the spatial distribution and seasonality of climatological snowfall, the seasonal cycle of lake-surface temperatures and overlake turbulent fluxes, and the lake-effect ratio between upwind and downwind precipitation. A deeper understanding of model performance and biases is achieved by partitioning results between HighResMIP GCMs that are 1) coupled to 1D lake models versus GCMs that exclude lake models, 2) between prescribed-ocean model configurations versus fully coupled configurations, and 3) between deep Lake Superior versus relatively shallow Lake Erie. While the HighResMIP GCMs represent the Great Lakes by a spectrum of approaches that include land grid cells, ocean grid cells (with lake surface temperature and ice cover boundary conditions provided by the Met Office Hadley Center Sea Ice and Sea Surface Temperature Dataset), and 1D lake models, the current investigation demonstrates that none of these rudimentary approaches adequately represent the complex nature of seasonal lake temperature and ice cover evolution and its impact on lake–atmosphere interactions and lake-effect precipitation in the Great Lakes region.

Significance Statement

The purpose of this study is to evaluate the capability of high-resolution global climate models to simulate lake–atmosphere interactions and lake-effect snowfall in the Great Lakes region, given the critical influence of the lakes on regional climate and vast societal and environmental impacts of lake-effect snowfall. It is determined that the models inadequately represent lake temperatures and ice cover, often leading to insufficient annual snowfall in the lake-effect zones. More advanced, three-dimensional lake models need to be coupled to climate models to support greater credibility in regional lake and climate simulations and future climate projections.

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