Three current thrusts in development of climate model diagnostics are overviewed, with an eye to how efforts can be coordinated to close some key gaps.
First, process-oriented diagnostics (PODs) seek hypothesis-based relationships among variables to yield process-based observation-model comparisons—but in practice the next step to specific revisions of model parameterizations can remain challenging. Directions for more actionable process information are advocated based on experience from the NOAA Model Diagnostics Task Force (MDTF), including close involvement of model developers with diagnostics teams and inclusion of parameter perturbation experiments in POD development.
Second, a collaboration between the MDTF and the Department of Energy (DOE) Coordinated Model Evaluation Capability (CMEC) has initiated common standards for metrics and diagnostics to help unify community diagnostics efforts. Directions for these Earth System Metrics and Diagnostics Standards (EMDS) will be outlined.
Third, feature identification of phenomena such as mesoscale convective systems, low pressure systems, fronts and atmospheric rivers is a prominent direction in diagnosing processes leading to extreme precipitation. Results from a pair of DOE precipitation-phenomena projects point to the importance of coordinated feature identification, as individual tracking algorithms often each capture the same precipitation events. For instance, a majority of extreme (99th percentile of intensity) midlatitude precipitation events identified as atmospheric rivers are also identified as frontal, with a substantial fraction also associated with mesoscale convective systems by individual trackers. These separate identifications are not wrong, but simply prone to the proverbial elephant-in-the-dark attribution of a complex underlying phenomenon to the separate aspects they each encounter. Directions for a coordinated workflow for feature identification and attribution are delineated.