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An Open Framework for Process-Oriented Diagnostics of Global Models

Problem addressed and rationale: There is a need to identify targeted improvements to the fidelity of models for the Earth System and its variability. Process-oriented diagnostics characterize a physical process in a manner related directly to mechanisms essential to its simulation, and thus provide valuable guidance for model improvement. An organizational framework that integrates such diagnostic development projects aids accessibility by modelers.

Work Summary: The proposed Type 1 team will expand an open framework to entrain process- oriented diagnostics developed by multiple research teams into the development stream of the modeling centers. Building on work by the previous Type 1 team project, it will coordinate Type 2 individual projects through an Application Programming Interface (API) for process-oriented diagnostics. Modules under this protocol will compare any development model version to observations, while leveraging analysis of the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble to place these diagnostics in a multi-model context. The CMIP6 ensemble will be used in the framework to aid the model developer in identification of poorly represented physical pathways. The API will permit comparison of multiple model runs from CMIP6 models or perturbation/ensemble runs of individual models. The lead PI team maintains consistency with the previous Type 1 team while expanding representation from the Geophysical Fluid Dynamics Laboratory (GFDL) model development and diagnostics teams and from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to leverage community data standards and enhance coordination of metrics and diagnostics development across agencies. A task force will be created, modeled on the current Model Diagnostics Task Force, which will emphasize proactively reaching out to PIs of Type 2 proposals funded under this MAPP call. A key ingredient in ensuring that diagnostics are useful to the development teams is feedback from these teams and from other groups. Task Force members will be invited to present their diagnostic development plans early, to coordinate with expansion of the API. The interaction will promote common standards and tools, fostering diagnostics modules that are well targeted and implemented for ease of coordination both within the Task Force and with national and international efforts. Self-documentation and community data and metadata protocols will be included in the API. The task force will also coordinate synthetic publications. The Type 1 Team will also develop tools and additional process-based diagnostics in key areas complementing Type 2 proposals, including tools to assist modelers in navigating trade-offs among multiple observational constraints. Diagnostics for basin-scale heat uptake and sea level change will be standardized. Diagnostics for feedback mechanisms in regional hydroclimate extremes including cloud feedbacks will be developed, complemented by parameter-perturbation experiments with the GFDL model that will be made available to the Type 2 teams. Diagnostics will be brought into the framework for processes affecting temperature and precipitation distribution tails, including advanced convective diagnostics and moist-static energy diagnostics.

Relevance to competition: This proposal directly addresses the call for the “Modeling, Analysis, Predictions, and Projections (MAPP) Competition 2: Addressing Key Issues in CMIP6-era Earth System Models” by developing a Type 1 core team to lead integration of projects on process oriented diagnostics. It proposes a code and data sharing framework that facilitates integration of these into the development path of modeling centers, scientific development of new process- oriented diagnostics, and protocols to engage and synthesize the efforts of Type 2 projects in model evaluation, as well as plans for the dissemination of this information. It addresses NOAA’s long-term climate goals by strengthening foundational capabilities, combining observations with modeling and prediction, and communication of scientific understanding.

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