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Title: Understanding Precipitation Bias Sensitivities in E3SM-Multi-Scale Modeling Framework From a Dilution Framework

Abstract

We investigate a set of Energy Exascale Earth System Model Multi-scale modeling framework (MMF) (E3SM-MMF) simulations that vary the dimensionality and momentum transport configurations of the embedded cloud-resolving models (CRMs), including unusually ambitious 3D configurations. Issues endemic to all MMF simulations include too much Intertropical Convergence Zone rainfall and too little over the Amazon. Systematic MMF improvements include more on-equatorial rainfall across the Warm Pool. Interesting sensitivities to the CRM domain are found in the regional time-mean precipitation pattern over the tropics. The 2D E3SM-MMF produces an unrealistically rainy region over the northwestern tropical Pacific; this is reduced in computationally ambitious 3D configurations that use 1,024 embedded CRM grid columns per host cell. Trajectory analysis indicates that these regional improvements are associated with desirably fewer tropical cyclones and less extreme precipitation rates. To understand why and how the representation of precipitation improved in 3D, we propose a framework that dilution is stronger in 3D. This viewpoint is supported by multiple indirect lines of evidence, including a delayed moisture-precipitation pickup, smaller precipitation efficiency, and amplified convective mass flux profiles and more high clouds. We also demonstrate that the effects of varying embedded CRM dimensionality and momentum transport on precipitation can bemore » identified during the first few simulated days, providing an opportunity for rapid model tuning without high computational cost. Meanwhile the results imply that other less computationally intensive ways to enhance dilution within MMF CRMs may also be strategic tuning targets.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [3]
  1. University of California, Irvine, CA (United States)
  2. University of California, Irvine, CA (United States); NVIDIA Research, Santa Clara, CA (United States)
  3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); National Science Foundation (NSF); National Oceanic and Atmospheric Administration (NOAA)
OSTI Identifier:
2203624
Report Number(s):
LLNL-JRNL-855413
Journal ID: ISSN 1942-2466; 1084037
Grant/Contract Number:  
AC52-07NA27344; SC0023368; ACI-1548562; ACI-1928147; NA18NWS4620043B
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Volume: 15; Journal Issue: 4; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Liu, Nana, Pritchard, Michael S., Jenney, Andrea M., and Hannah, Walter M. Understanding Precipitation Bias Sensitivities in E3SM-Multi-Scale Modeling Framework From a Dilution Framework. United States: N. p., 2023. Web. doi:10.1029/2022ms003460.
Liu, Nana, Pritchard, Michael S., Jenney, Andrea M., & Hannah, Walter M. Understanding Precipitation Bias Sensitivities in E3SM-Multi-Scale Modeling Framework From a Dilution Framework. United States. https://doi.org/10.1029/2022ms003460
Liu, Nana, Pritchard, Michael S., Jenney, Andrea M., and Hannah, Walter M. Fri . "Understanding Precipitation Bias Sensitivities in E3SM-Multi-Scale Modeling Framework From a Dilution Framework". United States. https://doi.org/10.1029/2022ms003460. https://www.osti.gov/servlets/purl/2203624.
@article{osti_2203624,
title = {Understanding Precipitation Bias Sensitivities in E3SM-Multi-Scale Modeling Framework From a Dilution Framework},
author = {Liu, Nana and Pritchard, Michael S. and Jenney, Andrea M. and Hannah, Walter M.},
abstractNote = {We investigate a set of Energy Exascale Earth System Model Multi-scale modeling framework (MMF) (E3SM-MMF) simulations that vary the dimensionality and momentum transport configurations of the embedded cloud-resolving models (CRMs), including unusually ambitious 3D configurations. Issues endemic to all MMF simulations include too much Intertropical Convergence Zone rainfall and too little over the Amazon. Systematic MMF improvements include more on-equatorial rainfall across the Warm Pool. Interesting sensitivities to the CRM domain are found in the regional time-mean precipitation pattern over the tropics. The 2D E3SM-MMF produces an unrealistically rainy region over the northwestern tropical Pacific; this is reduced in computationally ambitious 3D configurations that use 1,024 embedded CRM grid columns per host cell. Trajectory analysis indicates that these regional improvements are associated with desirably fewer tropical cyclones and less extreme precipitation rates. To understand why and how the representation of precipitation improved in 3D, we propose a framework that dilution is stronger in 3D. This viewpoint is supported by multiple indirect lines of evidence, including a delayed moisture-precipitation pickup, smaller precipitation efficiency, and amplified convective mass flux profiles and more high clouds. We also demonstrate that the effects of varying embedded CRM dimensionality and momentum transport on precipitation can be identified during the first few simulated days, providing an opportunity for rapid model tuning without high computational cost. Meanwhile the results imply that other less computationally intensive ways to enhance dilution within MMF CRMs may also be strategic tuning targets.},
doi = {10.1029/2022ms003460},
journal = {Journal of Advances in Modeling Earth Systems},
number = 4,
volume = 15,
place = {United States},
year = {Fri Mar 31 00:00:00 EDT 2023},
month = {Fri Mar 31 00:00:00 EDT 2023}
}

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