Evolution of CESM Cloud Microphysics: Parameterization of Unified Microphysics Across Scales (PUMAS) - Community Earth System Model
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Evolution of CESM Cloud Microphysics:
Parameterization of Unified Microphysics
Across Scales (PUMAS)
A. Gettelman, H. Morrison (NCAR/MMM), K. Thayer-Calder
(NCAR/CGD), T. Eidhammer, G. Thompson (NCAR/RAL), D. Barahona
(NASA/GSFC), C. Chen, C. McCluskey (NCAR/CGD), D. J. Gagne, J. Dennis
(NCAR/CISL), C. Bardeen (NCAR/ACOM), R. Forbes (ECMWF)
Cloud Microphysics Kills!
• Clouds are responsible for most
severe weather
• Tornadoes, Thunderstorms, Hail,
Tropical Cyclones
• Critical processes depend on cloud
microphysics (Thunderstorms, Hail,
even Tornadoes)
Outline • What is cloud microphysics • CAM Microphysics Evolution: PUMAS • Some current work • Unified Ice • CAM6 Perturbed Parameter Ensemble (PPE) • MG3 in IFS (ECMWF: Starting with OpenIFS SCM) • Summary
What is Cloud Microphysics?
Community Atmosphere Model (CAM6)
Finite Volume
Dynamics
4-Mode Surface Fluxes
Liu, Ghan et al Precipitation
Crystal/Drop
A, qv, ql, qi, qr qs,
Activation Radiation rei, rel, rer, reg
Mass,
Number Conc Aerosols Deep Convection
CCN,IN Zhang-
2 Moment Microphysics Sub-Step McFarlane
Clouds (Al),
Morrison & Gettelman Condensate (qv, ql)
Ice supersaturation
Cloud fraction & Unified Turbulence
Prognostic 2-moment Precip Condensate:
T, Adeep, Ash
CLUBB
A = cloud fraction, q=H2O, re=effective radius (size), T=temperature
(i)ce, (l)iquid, (v)apor, (r)ain, (s)now
Still need Microphysics Regardless of scale…
What if you ran CAM at 7km? 2km? 1km?
Non-Hydrostatic
Dynamics
Surface Fluxes
Precipitation
A, qc, qi, qg, qr, qv
rei, rel, rer, reg
Radiation
Mass,
Number Conc Aerosols
CCN,IN
Microphysics
Clouds (Al),
Condensate (qc, qi, qg, qr)
Clouds & Condensate: Unified Turbulence
T, PDF(Cld)
CLUBB
A = cloud fraction, q=H2O, re=effective radius (size), T=temperature
(i)ce, (l)iquid, (v)apor , (g)raupel (rimed ice)
Types of Microphysical Schemes
Lagrangian or Follow Lagrangian trajectories of “super-droplets”
Represent individual drop interactions with each other
“Superdroplet’
Represent the number of particles in each size ‘bin’
‘Explicit’ or Bin Number and/or mass by bin for each ’class’ of
hydrometeor. Computationally expensive, but ‘direct’
Predict the total mass
One Moment (Bulk) (Usually) represent the size distribution with a function (fixed shape, size)
Mass for different ‘Classes’ (Liquid, Ice, Precip)
Computationally efficient
Two Moment (Bulk) Predict mass and number giving more flexibility
Represent the size distribution with a function (fixed or variable shape)
Distribution function for different ‘Classes’ (Liquid, Ice, Precip)
CAM Microphysics Evolution • CAM4: 1 moment (Rasch & Krisjansson 1998) • CAM5: 2 moment (MG: Morrison & Gettelman 2008) • CAM6: 2 moment+ Prognostic Precip (MG2: Gettelman & Morrison 2015) • Convective microphysics available (Song and Zhang 2011) • CAM6.2: 2 moment + Rimed Ice (MG3: Gettelman et al 2019) [On CAM Trunk] • CAM6.2: Pulling microphysics from a separate repository (PUMAS) • Other efforts • MG2 + Unified Ice (Eidhammer et al 2016) • Optimization and GPU directives added (CISL) • MG2 or MG3 currently being used/available in versions of: • CAM5/6 derivative modes (e.g. E3SM & NorESM, others), GISS (Ackerman), GEOS (Barahona) • Ported to CCPP as part of NOAA testing • Also ported to ECMWF-IFS for comparisons (current work) • Made available to Climate Modeling Alliance (CliMA: Caltech effort)
PUMAS PUMAS = Parameterization of Unified Microphysics Across Scales • State of the art bulk 2 moment microphysics for all scales of modeling • LES to Mesoscale/Regional to GCM for Weather/Climate • Scalable and efficient for different models • Served from an external github repository: more modern software • Include unit tests • Interfaces for different models • Enable broader contributions • Incorporate performance improvements and GPU directives • Based on MG3. • Not just M & G anymore! • There are 5 NCAR labs + outside collaborators on the title slide
Current PUMAS Status
• GitHub repository established (https://github.com/ESCOMP/PUMAS)
• Pulled into CAM as an external (micro_mg3_0.F90, micro_mg_utils.F90)
• Developing unit tests for code
• New workflow facilitates development across model systems
• Will work to have sample interfaces, CCPP port, etc.
• Average CAM user doesn’t really have to care about this
• Science efforts
• Unified Ice
• Machine Learning
• Efficiency (Vectorization & GPU directives)
• Independent aerosol formulations (separate lightweight aerosols)
• Ice Nucleation
• Convert a version to CCPP interface
Unified Ice • Similar to Predicted Particle Properties (P3: Morrison and Milbrant) for combining ice and snow. Single category of ice. Not rimed yet. • Based in Eidhammer et al (2016) • Putting it together with a separate rimed ice variable (in MG3) • Have it running globally. Trying to understand how to balance the climate with a new ice/snow class combined. (Non-trivial)
Unified Ice results
LWCRE
MG2 Control
PUMAS-Unified Ice
SWCRE
• Unified ice code has smaller sizes than MG2 snow. Less ice mass, and lower net Cloud
Radiative Forcing
• Still working on if this can be adjusted (‘tuned’) to match observations
• New code eliminates many uncertain parameters, e.g. ice autoconversion
• This has not been used in a global model except by Eidhammer et al 2016, with similar
issues in CAM5
• Embarking on a Perturbed Parameter Ensemble (PPE) experimentCAM6 Perturbed Parameter Ensemble (PPE) • Goal: understand sensitivity of CAM6 to parameter changes. Also help tune new parameterizations • Method: develop a list of parameters and ranges, do a Latin hypercube sampling of the parameters (3-5x as many sets as parameters). Run experiments and then develop an ‘emulator’ from the output. • Have some CPU time to do this. Starting now with SCAM tests • Will make ALL output available to the community. • Intent is to modify CAM6 physics: microphysics, aerosols, deep convection, CLUBB
Parameters
• Have a list, let me know if
interested
• Is your favorite parameter here?
• Hope to start SCAM testing in a
month or so
• Simulations will be 1˚ standard
CAM6, AMIP and/or nudged
• Will also look at forcing (PI
Aerosols) and feebacks (SST+4K)MG3 in the ECMWF IFS (SCM) With Richard Forbes, ECMWF MICRE: 10 of 90 days over Macquarie Island (56˚S): Jan-Mar 2017 Cloud Fraction
MG3 in the ECMWF IFS (SCM) MICRE: 10 of 90 days over Macquarie Island (56˚S): Jan-Mar 2017 Liquid (Supercooled)
MG3 in the ECMWF IFS (SCM) MICRE: 10 of 90 days over Macquarie Island (56˚S): Jan-Mar 2017 Total Ice = Ice + Snow
Other work (I know about) • S. Ocean Cloud Studies (supercooled liquid) • Machine learning the warm rain process • Optimization and GPU Port of MG2 microphysics (CISL, Dennis Group) • CCPP version of MG3 from NOAA • Ice nucleation, especially in mixed phase (McCluskey) • CARMA sectional aerosols/cloud modeling with CAM
Summary • MG2 and 3 (graupel) are currently available in CAM6 • Microphysics is now pulled from an external repository • That means need to manage_externals to see all the code. • Goal is developing flexible options in the microphysics • Use in different models • Microphysics across scales (LES or certainly mesoscale à ESM) • Different complexity available • Explore different science goals/approaches • CAM6 Perturbed Parameter Ensemble (PPE) experiment starting now
Southern Ocean Cloud Studies (SOCRATES)
CAM6 simulations along flight tracks in the S. Ocean (Jan-Feb 2018)
• Model size distributions compared to observations.
• CAM6 does very well (this is a 100km global GCM v. in-situ aircraft)
• Not enough supercooled liquid water (distribution not peaked enough)
• Too much warm rain
• Using this to play with the functional form of size distributions
• Work ‘recommends’ we switch Autoconversion to Seifert & Behang (2001)
Gettelman et al 2020 (Submitted to JGR)Machine Learning
Can we do the warm rain process better?
Replace autoconversion, accretion and self collection
Use a stochastic collection kernel:
1. Break distributions of cloud and rain into bins
2. Run stochastic collection kernel from a bin microphysics code
3. Use altered distributions to estimate AUTO+ACCRE tendency
Results:
Similar climate, but different process rate tendencies
Rain formation process looks more like a bin model…different precip structure
Same ACI as control model
Different S. Ocean cloud feedbacks!
So try to emulate it with a neural network. Mostly works: with Guardrails
Control Bin (Emulated)
Control =significant rain for all Re
Bin = Little rain for ReMachine Learning: Forcing and Feedback ACI Is unchanged with Stochastic Collection Cloud feedback reduced with Stochastic Collection Conclusion: Autoconversion/Accretion may not be the issue! Reduced DCldFraction and +FICE in present day
MG3 in the ECMWF IFS (SCM): 90 day summary MICRE: 10 of 90 days over Macquarie Island (56˚S): Jan-Mar 2017 Total Ice = Ice + Snow
MG3 in the ECMWF IFS (SCM): 90 day summary MICRE: 10 of 90 days over Macquarie Island (56˚S): Jan-Mar 2017 Liquid
MG3 in the ECMWF IFS (SCM): 90 day summary
MICRE: 10 of 90 days over Macquarie Island (56˚S): Jan-Mar 2017
Radiative Fluxes
(wm/2) MG3 refIFS SFC SW SFC LW
SFC SW Down 178 157
SFC LW Down 288 296
TOA SW 168 148
TOA LW -223 -204
TOA SW TOA LW
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