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This work integrates machine learning into an atmosphericparameterization to target uncertain mixing processes while maintaininginterpretable, predictive, and well-established physical equations. Weadopt an eddy-diffusivity mass-flux (EDMF) parameterization for theunified modeling of various convective and turbulent regimes. To avoiddrift and instability that plague offline-trained machine learningparameterizations that are subsequently coupled with climate models, weframe learning as an inverse problem: Data-driven models are embeddedwithin the EDMF parameterization and trained online using output fromlarge-eddy simulations (LES) forced with GCM-simulated large-scaleconditions in the Pacific. Rather than optimizing subgrid-scaletendencies, our framework directly targets climate variables ofinterest, such as the vertical profiles of entropy and liquid waterpath. Specifically, we use ensemble Kalman inversion to simultaneouslycalibrate both the EDMF parameters and the parameters governingdata-driven lateral mixing rates. The calibrated parameterizationoutperforms existing EDMF schemes, particularly in tropical andsubtropical locations of the present climate, and maintains highfidelity in simulating shallow cumulus and stratocumulus regimes underincreased sea surface temperatures from AMIP4K experiments. The resultsshowcase the advantage of physically-constraining data-driven models anddirectly targeting relevant variables through online learning to buildrobust and stable machine learning parameterizations.
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Connection Between Mass Flux Transport and Eddy Diffusivity in Convective Atmospheric Boundary Layers
Abstract Turbulence parameterizations for convective boundary layer in coarse‐scale atmospheric models usually consider a combination of the eddy‐diffusive transport and a non‐local transport, typically in the form of a mass flux term, such as the widely adopted eddy‐diffusivity mass‐flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large‐eddy simulations, here, we show that a Taylor series expansion of the updraft and downdraft mass‐flux transport can be used to approximate the eddy‐diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy‐diffusivity and mass‐flux transport theories in convective conditions, which also quantifies departure from the Monin‐Obukhov similarity (MOS) in the surface layer. This study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on MOS.
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- PAR ID:
- 10373111
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 48
- Issue:
- 8
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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