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E3SM Next Generation Development (NGD) of Atmospheric Physics

This overview outlines the goals and ongoing activities of the Next Generation Development (NGD) of Atmospheric Physics project. The focus is on convection and cloud microphysics, aerosols, atmospheric chemistry, and couplings to biogeochemical cycles and energy. Collaboration opportunities are identified and discussed.

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E3SM Next Generation Development (NGD) of Atmospheric Physics

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  1. E3SM Next Generation Development (NGD) of Atmospheric Physics DOE LAB Staff LLNL: Shaocheng Xie, Philip Cameron-Smith, Yuying Zhang, Chris Golaz, Qi Tang, a new hire PNNL: Hailong Wang, Jiwen Fan, Manish Shrivastava, Po-Lun Ma, Yun Qian, Kai Zhang, 2 Postdocs ANL: Yan Feng; BNL: Wuyin Lin \ Collaborators Vince Larson (UWM) Xiaohong Liu (UW) Michael Prather (UCI) Jadwiga (Yaga) Richter (NCAR) Joao Teixeira (UCLA) Guang Zhang (SCRIPPS) Shaocheng Xie Lawrence Livermore National Laboratory Work from LLNL is performed under the auspices of the US DOE by LLNL under Contract DE-AC52-07NA27344. LLNL-PRES-760689

  2. Overview: NGD-Atmospheric Physics

  3. Goals of the Breakout • Quick updates on ongoing activities • Increase cross-project awareness of activities. • Identify potential gaps, conflicting developments, and/or opportunities for collaboration. • Convection and cloud microphysics (30 min) • Aerosols - Atmospheric Chemistry - Couplings to BGC and Energy (60 min) (Susannah, Hailong, Philip, and Manish)

  4. New Developments Associated with Convection and Cloud Microphysics • Convection • CLUBB-SILHS for all types of clouds: Vince Larson (UWM) • EDMF for PBL+ShCu+DeepCu: Joao Teixeira (UCLA/JPL) • Coupling a stochastic convection with ZM, improving trigger, microphysics, and scale-awareness of convection scheme: Guang Zhang (Scripps/UCSD) • Improvement of E3SM Momentum Transport: Yaga Richter (NCAR) • Predicted Particle Properties (P3) for ice clouds (Jiwen Fan - PNNL)

  5. Discussion Items • Timelines • Data sharing • 30-year AMIP and coupled simulations with improved QBO • 0.8 – 4km WRF simulations with realistic MJO during YOTC • Observational data sets from CMDV-MCS • MCS tracking approach from CMDV-MCS • P3 • Impact on current convection parameterizations developments and collaborations • More benefit in high resolution model • Coordination with SCREAM • Should the assessment of different convection schemes based more on 25km model? • Other new features from CMDV-MCS (Jiwen’s talk) • Metrics for evaluating different convection schemes

  6. Timeline for convection and P3 tasks Complete most Implementations Decide on Convection 3-moment P3 Complete test of 3-moment P3 Initial test of CLUBB-SILHS Working on improving computational efficiency and accuracy Continue to evaluate and enhance the new features (Individual teams) Assessment of different convection options (Entire project) Implementation of EDMF Evaluate EDMF Test Stochastic scheme Address scale-awareness of ZM Implement convective/mesoscale momentum transport Evaluate the new features Dec 31, 2019 Dec 31, 2020 June 30, 2019 June 30, 2020 now

  7. J. Richter, M. Moncrieff NCAR Convection Permitting WRF Simulations Computational Domain WRF Simulation: dx = 4 km; Deep Convection explicit D1 (4km) WRF4KM TRMM D3 (.8km) D2 (.8km) 6-14 Apr 13-26 Apr D1: 4 km grid spacing with spectral nudging D2, D3: 0.8 km grid spacing without nudging Hovmoller Diagram of the 10oS-10oN-averaged hourly rain rate for April 2009

  8. Thoughts about adopting some CMDV-MCS work for NGD in cloud and convection • A new PDF vertical overlap treatment for CLUBB-SILHS • Implement P3 that allows for convective microphysics into ZM cumulus scheme • Evaluation approaches for comparing with high-resolution observations in MCS, cloud, and precipitation • The MCS tracking approach at ¼ degree (25 km) • Comparison with GPM and NEXRAD • RRM ¼ CONUS grid testbed 4. Newly developed observational datasets Jiwen Fan, PNNL

  9. 1. A new PDF vertical overlap treatment for CLUBB-SILHS Problems: PDF correlations between levels depend on layer separation, but correlation length scale z0 is the SAME FOR ALL variables • Developed a new parameterizations for correlation of distributions of microphysical variables at different levels • Began working with the E3SM code to implement the new overlap treatment Correlation Linear and power-law functions (correlation coefficient ~0.8) Ovchinnikov, M., S. Giangrande, V. E. Larson, A. Protat, and C. R. Williams, 2019: Dependence of vertical alignment of cloud and precipitation properties on their effective fall speeds, JGR, doi:10.1029/2018JD029346.

  10. 2. Implement P3 into ZM cumulus scheme • E3SM-P3 at 1 degree MG2 P3 Rain rate PDF (CONUS; Apr-Aug, 2011) • MMF-P3 (2 deg host grid and 4-km CRM) Rain rate PDF (SGP area; May 2011)

  11. 3. Evaluation approaches for comparing with high-resolution observations • The MCS tracking algorithm at ¼ degree (25 km) • FLEXTRKR (Feng et al. 2018): uses infrared brightness temperature (OLR) and precipitation only, both are commonly provided in global model outputs • The method is able to track MCSs identified by 4-km resolution satellite and radar data, and the MCS statistics agree well between the two sets of tracking 4-km Tracking 25-km Tracking Mean MCS precipitation during warm seasons MCS statistics comparison between two sets of trackings

  12. Uses of GPM and NEXRAD for evaluation Evaluated E3SM 1-deg for warm seasons in 2014-2016 with GPM and NEXRAD measurements (example processing: vertical smoothing, sub-column sampling, and update COSP radar simulator)

  13. RRM ¼ CONUS grid testbed Total and MCS rain rates during spring 2011 (Apr-Jun) OBS RRM 1/4 All MCS Rain rate PDF (CONUS; Apr-Aug, 2011) 1-deg • RRM ¼-deg improves rain rate PDF significantly. • RRM ¼-deg still greatly underestimates the MCS precipitation in spring, and fails to correctly simulate the diurnal pattern. • RRM ¼-deg overestimates non-MCS precipitation, but the phase of the diurnal is similar to OBS. Diurnal variation over the SGP region RRM 1/4

  14. 4. Newly developed observational datasets for cloud and precipitation • Processed ARM/NASA disdrometer data during MC3E (Joe) • High-resolution precipitation rate (every a few minutes) and hydrometeor(Joe) identifications from ARM polarimetric radar retrieved data • Vertical velocity retrieval from RWP (at least 5 yrs) and scanning radar (Die, Scott) • 13 years of 3-D MCS database from NEXRAD east of the Rocky Mountains (Zhe) • Global MCS database from GPM measurements (Jingyu, Jiwen, Bob). • Processed 1-yr of IWP/IWC retrievals from NEXRAD over the CONUS (Jingjing, Xiquan) All these data have been used in our E3SM or WRF evaluations under CMDV-MCS.

  15. Discussion Items • Timelines • Data sharing • 30-year AMIP and coupled simulations with improved QBO • 0.8 – 4km WRF simulations with realistic MJO during YOTC • Observational data sets from CMDV-MCS • MCS tracking approach from CMDV-MCS • P3 • Impact on current convection parameterizations developments and collaborations • More benefit in high resolution model • Coordination with SCREAM • Should the assessment of different convection schemes based more on 25km model? • Other new features from CMDV-MCS (Jiwen’s talk)

  16. Aerosols - Atmospheric Chemistry - Couplings to BGC and Energy Susannah Burrows, Hailong Wang, Philip Cameron-Smith, Manish Shrivastava Agenda • 10:30 – 10:35 Overview (Susannah Burrows) • 10:35 -  11:15 Short summaries - current status, gaps, future directions • Gas-phase chemistry in Phase II (Philip Cameron-Smith) • Nitrate, stratospheric aerosol, and couplings to chemistry (Hailong Wang) • Secondary organic aerosol (SOA) (Manish Shrivastava) • Dust aerosol couplings with surface moisture, land use, BGC (YanFeng) • Fire aerosol and VOC couplings with BGC (Li Xu) • Surface deposition of BC/dust, and unified radiative transfer in snow/ice (Cheng Deng, Adam Schneider) • New advection scheme (Andrew Bradley) • New chemistry solver (project lead: Joshua Fu, speaker TBD) • 11:15 - 11:30 General discussion: • Gaps in model capabilities • Evaluation / analysis needs • Intertask dependencies

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