Calibrated Physics Ensembles and Climate OSSEs: Tools for Constraining Uncertainty and Quantifying the Societal Value of New Observations

Recently, satellite observations were successfully used to constrain and quantify uncertainty in the NASA GISS ModelE Earth system model; the development and successful application of this machine-learning accelerated Bayesian parameter estimation approach has been mirrored by similar developments at Earth system modeling centers worldwide. The result of this enterprise yields what we label a "CPE": a Calibrated Physics Ensemble. It is differentiated from Perturbed Parameter Ensembles in that it contains only observationally plausible parameter and model configurations. We comment on the previous CPE, as well as the next generation of ModelE CPEs, being prepared for CMIP7. We also present a critical application of the CPE methodology towards quantifying observational information content in a method analogous to an Observing System Simulation Experiment (OSSE). In contrast to traditional data-assimilation based OSSEs, our approach quantifies the uncertainties most relevant for climatic projections and impact assessments: model physics uncertainties. We demonstrate a proof of concept focusing on the value of reduced uncertainty in PBL water vapor retrievals, toward supporting design for a future NASA Planetary Boundary Layer satellite mission currently in incubation.