O verview of Grell -Freitas Cumulus Parameterization

1. Current Developmental Activity on the Grell-Freitas Cumulus Parameterization Including the Addition of Number Concentrations and Storm Motion Hannah C. Barnes1,3, Georg A. Grell1, Saulo R. Freitas4,5, Haiqin Li1,3, Keren Rosado1,6, Gregory Thompson2 15 July 2019, UK Met Office, Exeter, UK 1NOAA/Earth Systems Research Laboratory, 2National Center of Atmospheric Research, 3 Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder, 4Universities Space Research Association, 5NASA/GSFC Global Modeling and Assimilation Branch, 6NOAA Center for Atmospheric Science at Howard University

2. Overview of Grell-Freitas Cumulus Parameterization

3. Grell-Freitas Convective Parameterization Scale-aware/Aerosol-aware (Grell and Freitas, 2014, ACP); (Freitas et al. 2018, JAMES) • Stochastic approach adapted from the Grell-Devenyi (2002) scheme, but changed to include temporal and spatial perturbation patterns • Scale awareness through Arakawa approach (2011) • Aerosol awareness is implemented with empirical assumptions based on a paper by Jiang and Feingold, also using a combination of memory and scavenging based on Lee and Feingold • Many additions since 2014 paper: mixed phase physics impacts, momentum transport, memory impacts, Bechtold diurnal cycle method, PDF for vertical mass flux, cloud water detrainment profiles

4. Recent New Implementations into GF (since ACP paper) • Momentum transport (as in SAS and/or ECMWF) • Additional closure for deep convection: Diurnal cycle effect (Bechtold 2014) • PDF approach for normalized mass flux profiles was implemented • Originally to fit LES modeling for shallow convection • Represents deep plumes in grid box • Allows easy application of mass conserving stochastic perturbation of vertical heating and moistening profiles • Provides smooth vertical profiles • Third type of plumes (congestus type convection) • Changed cloud water detrainment treatment • Stochastic part now coupled to Stochastic Parameter Perturbation (SPP), and Stochastic Kinetic Energy Backscatter (SKEBS) approach (J. Berner ) • Mixed phase physics and coupling to double moment microphysics Versions now used in WRF, FIM, GFS, GEOS-5, will be operational in RAP

5. TWP-ICE Single Column Model verse Observations SCM model results for normalized mass flux PDF, deep, shallow, and downdraft mass fluxes From “The Estimation of Convective Mass Flux from Radar Reflectivities” (JAMC, Kumar et al. 2016) Mass Flux (kg /m2 / s) (downdraft) Deep Mid (updraft) Deep Mid Shallow 16 14 Radar derived Vertical wind profiler derived 12 10 Height (km) 8 6 4 2 0.02 0 -0.02 -0.01 0.01 Mass flux profiles in GF similar to observations.

6. Recent New Implementations into GF (since ACP paper) • Momentum transport (as in SAS and/or ECMWF) • Additional closure for deep convection: Diurnal cycle effect (Bechtold 2014) • PDF approach for normalized mass flux profiles was implemented • Originally to fit LES modeling for shallow convection • Represents deep plumes in grid box • Allows easy application of mass conserving stochastic perturbation of vertical heating and moistening profiles • Provides smooth vertical profiles • Third type of plumes (congestus type convection) • Changed cloud water detrainment treatment • Stochastic part now coupled to Stochastic Parameter Perturbation (SPP), and Stochastic Kinetic Energy Backscatter (SKEBS) approach (J. Berner ) • Mixed phase physics and coupling to double moment microphysics Versions now used in WRF, FIM, GFS, GEOS-5, will be operational in RAP For more details about updates to GF see Haiqin Li’s poster on Thursday (Poster 410)

7. Number Concentrations In Grell-Freitas

8. Number Concentrations in GF: Motivation Total 12hr Accumulated Precipitation • Model performance is improved when double-moment microphysical parameterizations are used • Potential problem at coarse resolution: • Cumulus parameterizations are single-moment • Creates artificial modification of the particle size distribution that is feed into the microphysics scheme • Can impact model performance Grell et al., 2018

9. Number Concentrations in GF: Methodology • Develop a simple, inexpensive, diagnostic method to output cloud water and cloud ice number concentrations from GF • Cloud water approximation based on: • Cloud water mixing ratio from GF • Water-friendly aerosol characteristics • Cloud ice approximate based on: • Cloud ice mixing ratio from GF • Ice size – temperature relationship • Methodology made to be consistent with the Aerosol-Aware Thompson Microphysical Parameterization

10. Difference CFADs (Contour Frequency by Altitude Diagrams) Number Concentrations in GF: Mixing Ratio Number Concentration 0.002 0.004 16 km 16 km 12 12 Cloud Water CCPP-FV3 Results Frequency Frequency 0 0 • Generally: • Small mixing ratios/ number concentrations increase • Large mixing ratios/ number concentrations decrease • Changes in cloud ice greater than changes in cloud water 8 8 4 4 -0.002 -0.004 2e-4 5e-4 (kg kg-1) 0 4e-4 1e-4 3e-4 1.2e8 0.8e8 1.6e8 (kg-1) 0.4e8 0 Height (km) 0.01 0.08 16 km 16 km 12 12 Cloud Ice Frequency 0 Frequency 0 8 8 4 4 -0.08 -0.01 4e5 (kg-1) 0 2e5 1e5 3e5 2e-6 4e-6 0 10e-6 (kg kg-1) 8e-6 6e-6

11. Number Concentrations in GF: CCPP-FV3 Results Zonally Averaged Changes (Modified – Original) 90 90 Explicit Precipitation (mm) Implicit Precipitation (mm) 60 60 30 30 0 0 • Tropics • Magnitude of change often largest here • Precipitation trends highly variable • Shortwave flux and brightness temperatures increase • Mid-latitudes • Implicit (GF) precipitation increase • Explicit precipitation decrease • Shortwave flux and brightness temperatures decrease 30 30 60 60 -90 -90 -0.05 0 0.10 -0.10 0.05 -0.02 0 0.02 0.04 -0.04 Latitude Downward Shortwave Flux (Wm-1) Brightness Temperature (K) 90 90 60 60 30 30 0 0 30 30 60 60 -90 -90 6 2 4 0 -4 -2 -6 -0.5 0 -1 0.5 1

12. Number Concentrations in GF: CCPP-FV3 Results Error Profile Bias Profile Northern Hemisphere 120h Temperature 120hr Temperature Differences [Modified – Original] (K) 1000 hPa 100 hPa 850 hPa 1.2 1.2 150 200 300 400 0 0 500 700 850 1000 -1.2 2.0 2.6 1.8 2.4 2.2 -1.2 100 hPa 500 hPa 700 hPa 0.8 1 150 200 300 400 0 0 500 700 850 1000 0 0.2 -0.6 -0.4 -0.2 0.4 -1 -0.8 Modified Original Slightly improves low- and mid-level temperature errors.

13. Storm Motion InGrell-Freitas

14. Mesoscale Convective Systems Storm Motion in GF:Motivation https://www.eoas.ubc.ca/courses/atsc113/flying/met_concepts/04-met_concepts/04a-Tstorm_types/index-mcs.html Squall Lines • Downdrafts are one mechanism that can foster convective propagation and organization • GF already simulates downdrafts • This work tries to use the downdrafts represented in GF to foster storm propagation Basic GF Plume Wakimoto et al., 2006 Cold pools Zhe et al., 2015 http://cires1.colorado.edu/science/groups/pielke/classes/at730/AWangFinal.pdf

15. Storm Motion in GF: WRF Methodology • Introduces a term to represent downdraft mass flux • Downdraft mass flux = (total cloud base mass flux) * (ratio of downdraft to updraft cloud base mass flux) * (normalized downdraft profile) • Classified as an advective scalar Time Step 2 • Select one value from each advected downdraft mass flux column • Represents advection from Time Step 1 • Currently use the 850 – 650 hPa average • Calculate the total cloud base mass flux • Add selected downdraft mass flux to the total cloud base mass flux Time Step 1 • Calculate downdraft mass flux after the deep GF scheme is completed • Advect the downdraft mass flux (3D) • Advance to next time step

16. Storm Motion in GF: Results Maps of Precipitation and Winds at Advection Level Blue: No Advection, Black: Advection, Red: Advection – No Advection F02 F06 Winds at advection level have an extra component in the direction of advection

17. Storm Motion in GF: Results Difference Maps Shading: Advection – No Advection, Green: No Advection, Black: Advection 1 1 2m Potential Temperature Difference (K) Surface Pressure Difference (hPa) 0.5 0.5 0 0 -0.5 -0.5 -1 -1 The simulation with advection has a stronger cold pool and larger surface pressure perturbations.

18. Conclusions • Overview of the Grell-Freitas Cumulus Parameterization (GF) • Stochastic, scale-aware, and aerosol aware (Grell and Freitas, 2014; Freitas et al., 2018) • Since the scheme was published developmental activities include • Modifications to stochastic approaches and detrainment • Adding momentum transport, diurnal cycle closure, and a PDF approach to mass flux • Haiqin Li’s poster Thursday (410) • Highlight two ongoing developmental activities • Address particle size distribution differences in cumulus and microphysics schemes • Add diagnostic number concentrations of cloud water and cloud ice to GF • Distribution of cloud water and ice shift to smaller mixing ratios and lower number concentrations • Impacts precipitation, shortwave radiation, and temperature errors • Enable convection to propagation through the advection of downdraft mass flux • Simulations with advection have enhanced winds in the direction of advection • Increases pressure anomalies and strength of cold pool Hannah.barnes@noaa.gov