ACCP Aerosols, Clouds, Convection, and Precipitation Study

ACCP suborbital component – Aerosol

Presentation to EarthCare validation workshop, May 26, 2021
Jens Redemann (OU), Felix Seidel (JPL), Rich Moore (NASA Langley)
Walt Petersen (NASA Marshall), Jay Mace (UofUtah)

Thanks to Silke Gross, Rich Ferrare and the Sub-Orbital Working Group (SOWG) Members
Jennifer Comstock, Andrew Dessler, Silke Gross, Andrew Heymsfield, Jose Jimenez, Pedro Campuzano-Jost, Ralph
Kahn, Pierre Kirstetter Mark Kulie, Zen Mariani, James Mather, Allison McComiskey, Greg McFarquhar, RichardMoore, Joe Munchak, Steve Nesbitt, Sebastian Schmidt, Martin Wirth, Mengistu Wolde, Rob Wood

For info: jredemann@ou.edu

Role and Status of Suborbital Activities
Background

Suborbital activities are an integral
part of ACCP science and have tofulfill multiple purposes:• Provide science that is best, better, or
only done from sub-orbital vantagepoint• provide priors for/utility toalgorithms• provide synergies for Cal/val• Ideally bridge gaps between PoR
and/or launch scheduleStatus••••Two workshops conducted during Observable Study
Workshop 1 (March 2020): Scope of suborbital
Workshop 2 (March 2021): Implementation approaches

Given scope of ACCP science and complexity of instruments,
initial suborbital budget makes synergies essential
2

Broad Spectrum of Implementations Possible

3

ndScope of the 2 SOWG Workshop

Workshop 2

Objectives: Seeking Community input on the suborbital implementation
concepts that address the science identified in Workshop 1• Because…
• There is vastly more suborbital science that could be done than what ispossible (given anticipated budget)• ACCP has specific science objectives that require Suborbital to achieve(what suborbital prioritizes must be traceable back to the SATM)• Not all decisions have yet been made regarding orbital assets• We need a spectrum of implementation concepts for each science theme

ACCP Aerosol, Clouds, Convection, and Precipitation Study4

A spectrum of implementation concepts
1. Explore Existing data sets (surface and airborne):$•$$

what are they and how can they contribute to ACCP orbital science?

2. Existing and planned surface measurement capabilities ACCP can leverage• Surface-based Partnerships: E.g., ARM Permanent and Mobile, AERONET, EARLINET,MPLNET, ship campaigns with piggyback deployments of SOWG assets (ODP program?),NOAA MRMS operational radar network products and similar international efforts/datastreams etc.
• ACCP-led surface-based Deployment of measurement suites with existing mobile
instrumentation$$$

3. Participate/Partner in airborne campaigns (i.e., EVS, interagency, international)$$$$

4. Major Deployments: Multiple, single, or systematic airborne campaigns

ACCP Aerosol, Clouds, Convection, and Precipitation Study5

Science Themes and Modules• Three science themes with highly synergistic process modules and a Systematic aerosol sampling module• Space-time resolved and in situ measurements, airborne and/or surface-based platform accessible

Themes →Cloud & Aerosol processes

Cf.
individualmodule files

Implementation
Prior Field DataLong Term
Ground Basedin situ andRemoteSensing

ACCP Aerosol, Clouds, Convection, and Precipitation Study

Workshop 2
Multi orfocusedsingle Aircraft
Campaign

SystematicAircraft
Measurement

Ship-BasedRemoteSensing

Non-traditional(UAS, Balloon…)
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Modules →Themes ↓Convection andHigh Clouds

SOWG Science Driver Overview: Motivation
Jens Redemann and Jay Mace

Convective process coupling
and the environment

Convective core detrainment andanvil growth- character andprocess

Low Clouds/ACI

Cloud & Aerosol
lifecycle andradiativeprocesses

Precipitation Initiationin Shallow Cumulus

Open-Closed Cell Transitionin marine Stratocumulus

Vertically resolved aerosol

Impact of convection onaerosol redistribution andremoval.

.

effects on cloud formation

Credits: NASA/Luke Ziemba

https://www.arm.gov/news-events/docs/sgp/lecture11-mccomiskey-aerosolradiative-forcing.pdf

Anvil to cirrus evolution,lifecycle, and feedbacks

Ice Precipitation
Processes in Cold
Marine Boundary
Layer Clouds
Influence of PBL processes
on aerosol attribution andvertical redistribution.

https://www.e-education.psu.edu/meteo300/node/711

Traceability to ACCP SATMESD Focus Areas:• Coupling of the water and energy cycles• Extending and improving weather and air quality forecasts• Reducing climate uncertainty and informing societal response

O1,O5

Goal 1: Reduce the uncertainty in low- and high-cloud climate feedbacks by advancing our ability topredict the properties of low and high cloudsO1: Low Cloud Feedbacks

Determine the sensitivity of boundary layer bulk/microphysical cloud physical and radiative properties to large-scale and local
environmental factors including thermodynamic and dynamic properties.

Goal 4: Reduce uncertainty in key processes that link aerosols to weather, climate and air quality
related impacts.

O5: Aerosol Attribution and Air-Quality

Quantify optical and microphysical aerosol properties in the PBL and free troposphere to improve process understanding, estimates ofaerosol emissions, speciation, and predictions of near-surface particulate concentrations.
Enhanced: Characterize variations in vertical profiles of optical and microphysical properties over space and time in terms of 3D transport,
spatially resolved emission sources and residual production and loss terms.

Influence of PBL processes
on aerosol attribution andvertical redistribution.

https://www.e-education.psu.edu/meteo300/node/711

Vertically resolved aerosol
effects on cloud formation.

O6: Aerosol Processing, Wet Removal, Vertical Redistribution

O6,O8

Characterize the processing and wet removal and vertical redistribution of aerosols by clouds and light and moderate precipitation (< 5mm/hr) and heavy precipitation (> 5 mm/hr).

Goal 5: Reduce the uncertainty in Direct and Indirect aerosol radiative forcing of the climate system.

O7: Aerosol Direct Effect and Absorption

Reduce uncertainties in estimates of: 1) global mean clear and all-sky shortwave direct radiative effects (DRE) to ±1.2 W/m2 at TOA and theanthropogenic fraction, 2) regional TOA and surface DRE, and 3) Quantify the impacts of absorbing aerosol on atmospheric stability.
Quantify the impact of absorbing aerosols on vertically resolved aerosol radiative heating rates and DRE commensurate with theuncertainties in global mean at TOA and surface.

O8: Aerosol Indirect Effect

Provide measurements to constrain process level understanding of aerosol-warm/cold and mixed- phase cloud interactions to improve
estimates of aerosol indirect radiative forcing.
Minimum Enhanced N/ACredits: NASA/Luke Ziemba

O6,O7,O8

Impact of convection on
aerosol redistribution andremoval.

https://www.arm.gov/news-events/docs/sgp/lecture11-mccomiskey-aerosol-radiative-forcing.pdf

Large differences inaerosol vertical
distributionbetween AeroCom
models for various
aerosol species.

Impact of convection on aerosol
redistribution and removal.

https://www.arm.gov/news-events/docs/sgp/lecture11-mccomiskey-aerosol-radiative-forcing.pdf

Kipling et al., 2016

• Single-model “process” simulations
produce a spread in type-specificaerosol vertical distribution• Features in some models not
reproduced by single-model
simulations → additional
differences between models (e.g. ,convective transport, in-cloudscavenging).

MultiAircraft
Campaigns

FocusedSingle
AircraftSingle-model
sensitivity of aerosol
vertical distribution tovarious model
processes

Why Sub-Orbital:

Orbital may provide:• Snapshot of convective stove-pipes• Aerosol loading/extinction insurrounding regions
Orbital does not provide (well):• Chemically-speciated aerosol loading• Cloud microphysics• Thermodynamic/Dynamic
environment• Time evolution of convective storms• Tracking of outflow after convective
processing

Sub-Orbital focus:

Ground Based
Mobile Radar& aerosol insitua) Aerosol profile measurements at high spatial andtemporal resolutions. incl. chemical speciationb) Measurements of aerosol sinks and related processes(dry & wet deposition/scavenging)c) Lagrangian measurements of diffusion and convectionof particles, as well as the changes those particles
undergo (e.g., ‘aging’, coating, particle growth)Existing data sets: Major (mostly airborne) Aerosol IOPs since 1996
ARCTAS, 2008
ARISE, 2014
PODEX, 2013
ACEPOL, 2017
CARES, 2010
ADAM, 2003
EVE, 2004

TARFOX, 1996
TCAP, 2012
AIOP, 2003
ALIVE, 2005

ACE-ENA, 2017

SEAC4RS/DC3, 2013
FIREX-AQ, 2019
PRIDE, 2000

INTEX-A, 2004
NAAMES, 2015-2017

HALO, 2018
ACTIVATE, 2020-22
CLAMS, 2001

DISCOVER-AQ, 2011-14
INTEX-B/MILAGRO, 2006

NARVAL-II, 2016
EUREC4A, 2020

ACE-Asia, 2001

LACE1998, 1998
EUCAARI, 2008
SAMUM, 2006&08
ACE-2, 1997

AMMA, 2006

UAE2, 2004

KORUS-AQ, 2019

ATom, 2016-18
CAMP2EX, 2019

VOCALS, 2008
SAMBAA, 2012

ORACLES, 2016-18
CLARIFY, 2017
AEROCLO-sA, 2017
LASIC 2016-17

SAFARI, 2000

Colors indicate aerosol type: Biomass Burning, Seasalt, Pollution, Dust

SOCRATES, 2018

Bold font: most comprehensive

Existing data sets: Major (mostly airborne) Aerosol IOPs since 1996
(i.e., very comprehensive and/or state of the art in situ and/or HSRL + polarimeter)

ARCTAS, 2008
ARISE, 2014
PODEX, 2013
ACEPOL, 2017

TCAP, 2012 NAAMES, 2015-2017

SEAC4RS/DC3, 2013
FIREX-AQ, 2019

ACE-ENA, 2017
HALO, 2018
ACTIVATE, 2020-22

EUCAARI, 2008
SAMUM, 2006&08
KORUS-AQ, 2019DISCOVER-AQ, 2011-14
INTEX-B/MILAGRO, 2006

NARVAL-II, 2016
EUREC4A, 2020

ATom, 2016-18
CAMP2EX, 2019

VOCALS, 2008

ORACLES, 2016-18
CLARIFY, 2017
AEROCLO-sA, 2017
LASIC 2016-17

Colors indicate aerosol type: Biomass Burning, Seasalt, Pollution, Dust

SOCRATES, 2018

Bold font: most comprehensive

Existing data sets: Major (mostly airborne) Aerosol IOPs since 1996
(i.e., very comprehensive and/or state of the art in situ and/or HSRL + polarimeter)

ARCTAS, 2008
ARISE, 2014
PODEX, 2013
ACEPOL, 2017

TCAP, 2012 NAAMES, 2015-2017

SEAC4RS/DC3, 2013
FIREX-AQ, 2019

ACE-ENA, 2017
HALO, 2018
ACTIVATE, 2020-22

EUCAARI, 2008
SAMUM, 2006&08
KORUS-AQ, 2019DISCOVER-AQ, 2011-14
INTEX-B/MILAGRO, 2006

NARVAL-II, 2016
EUREC4A, 2020

ATom, 2016-18
CAMP2EX, 2019

VOCALS, 2008

ORACLES, 2016-18
CLARIFY, 2017
AEROCLO-sA, 2017
LASIC 2016-17

SOCRATES, 2018

Blue highlight: Aerosol-cloud interaction focus
Colors indicate aerosol type: Biomass Burning, Seasalt, Pollution, Dust Bold font: most comprehensive

Existing data sets: Major (mostly airborne) Aerosol IOPs since 1996
(i.e., very comprehensive and/or state of the art in situ and/or HSRL + polarimeter)

A few thoughts:• Good distribution (geographically) of biomassburning missions• Relatively few dust missions, no US• Relatively few sea-salt missions (inlet?)• Some comprehensive missions with possibly
outdated instrumentation (e.g., ARCTAS,
INTEX-B, VOCALS, AMMA)• Few recent missions focused on multi-species
regimes (e.g., polluted dust from Asia)• ACI campaigns predominantly focused onmarine clouds

Colors indicate aerosol type: Biomass Burning, Seasalt, Pollution, Dust

Bold font: most comprehensive

Workshop 2 - Summary• Extensive list of NASA, NOAA, NSF, DOE, and Int’l Partners airborne campaigns• evolved from exploratory studies in the 1990s, 2000s to more systematic, statistically-driven in 2010s• statistically-driven studies (e.g., EVSs) are particularly relevant for ACCP
• datasets remain underutilized; need to leverage these first before spending $$$ on new observations• Extensive list of ground networks and observational campaigns• Opportunities to augment existing networks with additional ACCP-relevant instruments• Leverage temporary or mobile ground assets by placing along orbit tracks and during airborne campaigns.
ARM mobile facility is a particularly useful asset.
• Need smaller campaigns with airborne remote sensors on small aircraft. Logistically easier and cheaper.
ideal for L1/L2 science data validation• List of upcoming airborne campaigns in the 2022-2025 timeframe, also next round of EVS• Many ideas on campaign concepts - some sound like potential Earth Venture Suborbital,2024-2029 (EVS) proposals.
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Workshop 2 – Summary of L1/L2 validation needs
Level 1
Observable/Measurement

Level 2Derived Parameters / Retrievals

A. Total attenuated backscatter (profile)

A. Liquid water path

B. Molecular att. backscatter (profile;HSRL)
C. Total volume depolarization ratio
D. Polarized radiance(vis/IR/submm/MW)
E. Radar reflectivity profile
F. Radar Doppler velocity profile

Program of Record
Data Inputs
A. Temperature, Pressure, Humidity

profiles (model evaluation)
B. Large-scale and cloud-scale horizontal
and vertical winds (model evaluation)
C. Water vapor mixing ratio profiles
D. Precursor gas concentrations

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B. Liquid water content / ice water content

profilesC. Cloud albedo
D. Cloud optical depth
E. Cloud fraction
F. Cloud droplet effective radius
G. Volumetric cloud fraction
H. Precipitation rate
I. In-cloud vertical velocity
J. Aerosol optical depth
K. Aerosol absorption AOD
L. Aerosol backscatter coef. (HSRL)
M. Aerosol extinction coef. (HSRL)
N. Aerosol absorption coef. (spectral?)O. Aerosol effective radius
P. Aerosol fine mode extinction coef.
Q. Lidar ratio (aerosol extinction:backscatter ratio)
R. Aerosol asymmetry parameter
S. Aerosol-cloud feature mask (should includeaerosol layer height)
T. PBL height
U. Aerosol number concentration
V. CCN=f(RHwater), IN=f(RHice, T)
W. Aerosol size distribution
X. Aerosol mass extinction efficiency
Y. PM2.5, PM10
Z. Aerosol type classification
AA.Water vapor mixing ratio and Humidity
BB.Precipitation particle mean size profile
CC. Ice density profile

Algorithm Assumptions andProcess Priors
A. Shape parameter of the gamma distribution for

precip. Retrieval
B. Multi-modal characterization of ice PSD (singlecrystals vs. aggregates)
C. Aerosol mass absorption, extinction efficiencies
depend ona. size distribution
b. refractive index
c. Shaped. mixing state
D. Cloud adiabaticity and entrainment importantfor microphysical retrievals
E. Relationships between satellite observables andCCN, IN spectra
F. Sub-grid-scale variability (both for models andsatellites)a. Sub-grid cloud fraction for wet removalparameterizations
b. Non-uniform beam filling factor for radars
G. Density-fall speed relationships for ice
H. Ice aspect ratio and orientation distribution as afunction of environmental factors andprecipitation type (convective/stratiform)

Potential Strategies / Approaches● Remote Sensing Payload○ Precipitation and cloud radar○ High spectral resolution lidar○ Polarimeter○ Microwave+sub-mm radiometer○ Differential absorption lidar (clear air water vapor)○ Differential absorption radar (in cloud water vapor)● In Situ Payload○ Total water content○ Ice water content○ Particle size distribution○ Temperature/humidity/pressure○ 3D wind○ Orthogonal ice particle imaging probes● Airborne (one, or preferably, two planes)○ Satellite orbit under-flights○ Intensive process and survey focused field campaigns○ Validate with in situ the advanced airborne sensors● Surface-based profiling remote sensing networks● Surface-based in situ sensor networks● Tethered balloons● Connection to geostationary satellites (TEMPO, GEMS,SENTINEL)● Need to incorporate / account for the retrieval
algorithms needed to translate the Level 1 data products
into the Level 2 data products○ verify algorithm assumptions, parameterization
inputs○ good example is precipitation retrievalsSummary• SubOrbital is integral to ACCP science - traceable to Science & Applications
Traceability Matrix (SATM), focusing on augmenting and supplementing• Diversity of ACCP science: a spectrum of implementation strategies from groundbased to multi-aircraft.
• Emphasis is on strong intra-agency, inter-agency, and international partnerships• Science and Implementation strategies modularized so that ACCP SubOrbital can
1. Respond quickly
2. Develop long-term planning for implementation in Phase A• Aerosol and Clouds/Convection/Prcp SubOrbital activities will be highly synergistic
and address science as prioritized by broader community
16• Remaining slide are backup

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Aerosol Ground-based networks(remote sensing)
AERONET:a federation of groundbased remote sensing
aerosol networks
established by NASA andPHOTONS (Univ. of Lille
1, CNES, and CNRS-INSU)

SKYNET:

Ground-based radiation
network dedicated toaerosol-cloud-solar
radiation interaction
research

GALION (GAW Aerosol LIdar Observing Network)
MPLNET:a federated network of Micro-Pulse
Lidar (MPL) systems designed tomeasure aerosol and cloud verticalstructure, and boundary layerheights

EARLINET:established to create aquantitative,comprehensive, andstatistically significant
database for the horizontal,vertical, and temporal
distribution of aerosols on acontinental scaleAerosol Ground-based networks(in situ)
NOAA/ESRL Federated Aerosol
Network (NFAN):

monitors surface in-situ aerosol opticalproperties at field sites around the world
Andrews et al., 2019

AirNow: Air Quality data portal-One-stop source for air quality data
-CONUS & global; Multiple data sources-Long-term support; will be expanded

ACTRIS (Aerosols, Clouds, and Trace gases

Research Infrastructure Network)
ACSM (Aerosol Chemical Speciation Monitor)
EMEP (European Measurement andEvaluation Programme)
Laj et al., 2020

IMPROVE

visibility monitoring network,measuring speciated PM2.5
composition

WMO GAW(Global Atmosphere Watch)

By: EPA, NOAA, NASA, CDC, National Parks, US Forest
Service, Nat. Assoc. Clean Air Agencies, Environ. Canada

Various smaller and/or regional networks:
CAPMoN (Canadian Air and Precipitation
Monitoring Network) EANET (Acid Deposition
Monitoring Network in East Asia)
KRAQNb (Korea Air Quality Network)