Earth system models (esms) suffer from biases in cloud properties. These biases can be partially mitigated by tuning free parameters associated with microphysics or other cloud- related…

This is a $513,248 research grant awarded by the National Aeronautics and Space Administration (NASA) Modeling, Analysis and Prediction (MAP) program to the University Corporation for Atmospheric Research (UCAR), doing business as the National Center for Atmospheric Research (NCAR). The grant is focused on improving the representation of warm cloud microphysics in Earth system models, such as the GISS Model E, GEOS, and CESM. The research aims to better constrain model parameters using a combination of bottom-up (process-level) and top-down (satellite observation) approaches, as well as develop an emulator-based framework to facilitate future model tuning and parameter optimization. The research outputs, including model data, observational data, and the parameter optimization code, will be made available to the broader scientific community. This grant does not have a set-aside designation and is part of UCAR's broader portfolio of federal research contracts and grants supporting atmospheric and Earth system science.

Earth system models (esms) suffer from biases in cloud properties. These biases can be partially mitigated by tuning free parameters associated with microphysics or other cloud- related parameterizations. Different modeling centers apply different tuning approaches to find optimal parameter values, from heuristic approaches to more systematic methods using perturbed parameter ensembles (ppes). A common tuning framework across multiple models could better leverage bottom-up process-level parameterization advances and global observational datasets for model constraint. A consistent top down framework for constraining model parameters could also help quantify uncertainty at the process level across models, which for many cloud-related parameterizations is likely large but remains mostly unquantified. In particular, for cloud microphysics uncertainties exist at all levels of scheme complexity and scale. The main objective of the proposed research is to improve process-level and global constraint of warm microphysics in giss model e, geos, and cesm. Best-estimates of micro- physical parameterization uncertainty will be informed by both bottom-up (process-level) constraints and top-down (global satellite observational) constraints. An emulator-based PPE approach will also facilitate constraint and tuning of these models when new parameterization developments are tested in the future (for all parameterizations, not just microphysics). Model data, observational data for model constraint (including uncertainty estimates), and the code for the PPE-based framework will be made available to the wider community. A multi-model PPE framework will also provide a better community assessment of structural uncertainty in our ability to represent the climate system and its future trajectory. This proposal is highly relevant to the NASA modeling, analysis and prediction (map) program solicitation. The work here is central to the clouds in the earth system theme, and on clouds in earth system models, including process representations of low stratiform clouds that strongly impact climate sensitivity.