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CMDV (CM)4 Project - University of Washington contribution. Final report

Technical Report ·
DOI:https://doi.org/10.2172/1716599· OSTI ID:1716599
 [1]
  1. Univ. of Washington, Seattle, WA (United States). Dept. of Atmospheric Sciences

The original goal of this project was to diagnose and improve CLUBB, the turbulence and cloud fraction parameterization used in DOE’s E3SM model. For this purpose, we planned to use large-eddy simulation (LES) and ARM observations from the NE Pacific Ocean and the SGP site, in coordination with ARM’s LASSO program. It was determined that for these cloud regimes, many of the turbulent ‘moment closures’ which underlie CLUBB’s mathematical formulation are inconsistent with LES, which is an appropriate benchmark for testing this. The research assistant found that these closures could be more accurately formulated using machine learning using the LES as a training dataset. However the resulting parameterization proved to quickly drift away from physical plausibility. A novel machine-learning based boundary layer parameterization called MARBLE based on matching the time evolution of a parameterized cloud-topped boundary layer to reanalysis was then developed and published.

Research Organization:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
DOE Contract Number:
SC0016433
OSTI ID:
1716599
Report Number(s):
DOE-UWASH-16433-1
Resource Relation:
Related Information: McGibbon, J., and Bretherton, C. S., 2019: Machine-Assisted Reanalysis Boundary Layer Emulation (MARBLE). Single-column emulation of reanalysis of the Northeast Pacific marine boundary layer. Geophys. Res. Lett., 46, https://doi.org/10.1029/2019MS001647.Monteiro, J. M., McGibbon, J., and Caballero, R., 2018: sympl (v. 0.4.0) and climt (v. 0.15.3) – towards a flexible framework for building model hierarchies in Python, Geosci. Model Dev., 11, 3781-3794, https://doi.org/10.5194/gmd-11-3781-2018.
Country of Publication:
United States
Language:
English

References (2)

sympl (v. 0.4.0) and climt (v. 0.15.3) – towards a flexible framework for building model hierarchies in Python journal January 2018
Evolution of the Double‐ITCZ Bias Through CESM2 Development journal July 2019