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Title: Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework

Abstract

Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so-called “natural variability” impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass-size (m-D) relationship (m=aDb) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m-D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m-D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ-IWP relationship variability is nonexistent using the deterministic m-D ensemble. Additional sensitivity tests are performed in which the fallspeed-size relationship (V=cDd) is stochastically varied, resultingmore » in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small-amplitude noise added to the potential temperature field.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [4]; ORCiD logo [5];  [6]
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  1. Univ. of Utah, Salt Lake City, UT (United States)
  2. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  3. Univ. of Utah, Salt Lake City, UT (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Cooperative Inst. for Mesoscale Meteorological Studies. Norman, OK (United States)
  5. Cooperative Inst. for Mesoscale Meteorological Studies, Norman, OK (United States); Univ. of Oklahoma, Norman, OK (United States)
  6. Environment and Climate Change Canada, Dorval, QB (Canada)
Publication Date:
Research Org.:
Univ. of Oklahoma, Norman, OK (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1594793
Grant/Contract Number:  
SC0016476; AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Volume: 11; Journal Issue: 11; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ice microphysics; mesoscale convective systems; stochastic physics; parameterization development; model-observation comparison; cloud radiative forcing

Citation Formats

Stanford, McKenna W., Morrison, Hugh, Varble, Adam, Berner, Judith, Wu, Wei, McFarquhar, Greg, and Milbrandt, Jason. Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework. United States: N. p., 2019. Web. doi:10.1029/2019MS001730.
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Stanford, McKenna W., Morrison, Hugh, Varble, Adam, Berner, Judith, Wu, Wei, McFarquhar, Greg, & Milbrandt, Jason. Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework. United States. doi:https://doi.org/10.1029/2019MS001730
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Stanford, McKenna W., Morrison, Hugh, Varble, Adam, Berner, Judith, Wu, Wei, McFarquhar, Greg, and Milbrandt, Jason. Mon . "Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework". United States. doi:https://doi.org/10.1029/2019MS001730. https://www.osti.gov/servlets/purl/1594793.
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@article{osti_1594793,
title = {Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework},
author = {Stanford, McKenna W. and Morrison, Hugh and Varble, Adam and Berner, Judith and Wu, Wei and McFarquhar, Greg and Milbrandt, Jason},
abstractNote = {Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so-called “natural variability” impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass-size (m-D) relationship (m=aDb) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m-D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m-D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ-IWP relationship variability is nonexistent using the deterministic m-D ensemble. Additional sensitivity tests are performed in which the fallspeed-size relationship (V=cDd) is stochastically varied, resulting in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small-amplitude noise added to the potential temperature field.},
doi = {10.1029/2019MS001730},
journal = {Journal of Advances in Modeling Earth Systems},
number = 11,
volume = 11,
place = {United States},
year = {2019},
month = {8}
}
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DOI:  https://doi.org/10.1029/2019MS001730
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