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Derived Properties of Warm Marine Low Clouds over the Southern Ocean: Precipitation Susceptibility and Sensitivity to

Environmental Parameters

Jay MaceTrevor FergusonStephanie AveySteve Cooper

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The Southern Ocean is one of the cloudiest regions on Earth…

… and primarily populated by geometrically thin layers based in the boundary layer.

Methodology:

1. Using WS climatology provided at GISS, collect A-Train hydrometeor layer statistics as a function of WS along the CloudSat Track

2. Aggregate the RL-GEOPROF data for that weather state over a period of time to characterize the layer properties

3. Examine regional similarities in hydrometeor layer distributions within a WS.

Use 3-years of A-Train data (2007,8,9) across the Pacific Basin

WS3: Anvils and Isolated Convection

WS 5: Polar and High Latitudes

Dominated by low level clouds of moderate optical thickness – often with cirrus above.

North:

South:

WS 5: Polar and High Latitudes

The Inversion Algorithm

• Assumption: Bimodal PSD– cloud mode and precipitation modes

• Input: CloudSat Z Profile, 94 GHz Tb (New Product!), MODIS 0.55, 1.6, 2.1 um reflectances

• Output: Cloud LWC, re, Nd, and Precip LWC, re, Nd in each range bin.

• Prior data from RICO and MASE

Forward Models:

Radar Forward Model: Posselt and Mace (2013). Mie backscatter and extinction. Direct integration of modified gamma PSD’s. Accounts for air and droplet attenuation.

MODIS reflectances simulated using Radiant 2.0 eigenmatrix solver (Christi and Gabriel, 2004)

Microwave: Kummerow et al. (1993) with modifications by Lebsock. W-Band ocean emissivity developed by Greg Elsaesser at CSU.

The A-Train Observations:

• Cloudsat CPR is sensitive to precip when precip is present but cloud otherwise• MODIS Vis and NIR Reflectances sensitive to Cloud properties primarily but precip

contributes• Passive Microwave (from Cloudsat Noise - New!) responds to water path – mostly

cloud but precip contributes

Together these measurements have independent (but tangled) information on the cloud and precipitation coexisting within a profile.

Southern Ocean Attenuation Comparison:Observed by Cloudsat – calculated from retrieved microphysics

Examples: Warm Shallow Cumulus in the SE Pacific20070106, orbit 03691

NonPrecipitating Case94 GHz Tb: 246 KMODIS 550 nm: 0.62MODIS 1.6 um: 0.48MODIS 2.1 um: 0.31

Cloud Number: 80 cm-1 ±±60%

Cloud LWC: 0.2 g m-3

±70%Cloud re: 10 um ±30%

Precipitating Case94 GHz Tb: 259 KMODIS 550 nm: 0.63MODIS 1.6 um: 0.45MODIS 2.1 um: 0.23

Cloud Number: 80 cm-1 ±±120%

Cloud LWC: 0.3 g m-3

±130%Cloud re: 20 um ±180%

Precip Rate: 80 mm/day ±40%

Examine the Co-Dependence of Albedo and Precipitation Susceptibility in Warm Shallow Clouds of the Southern Ocean

Periods Considered: Winter, 2008, ~11,000 Profiles Summer, 2007, ~32,000 Profiles

Co-Dependence of Albedo and Precipitation Susceptibility: Seasonal Comparison

Summer Winter

# Profiles: 11140# Profiles: 32389

Summer Microphysics Stats

• Ncld=90 cm-3

• LWP=120 g m-2

• Prate=22 mm day-1

• Albedo=0.4• Prate> 1mm/day: 68%• Prate> 10 mm/day: 20%

Winter Microphysics Stats

• Ncld=9 cm-3

• LWP=98 g m-2


• Albedo=0.2• Prate> 1mm/day: 89%• Prate> 10 mm/day: 32%

Consistency with Feingold et al., (2013)Feingold et al. (2013) show that• Cloud environments dominated by autoconversion are sensitive to Nd • Cloud environments dominated by accretion are less sensitive to Nd• S0 maximum increases for autoconversion dominated environments• Accretion dominated environments transition rapidly to lower S0

Lower Nd, Transitions to accretion dominated regime at lower S0 for LWP > 250 g/m2

Higher Nd, remains in autoconversion dominated regime with higher S0 and no obvious transition

Summer Winter

Summary

• Atrain provides unique information regarding the relationships between warm cloud properties and light precipitation properties

• Information is distributed between radar, passive microwave, solar reflectance.• Uncertainties in cloud properties are substantial in the presence of precipitation

when cloud property retrievals rely more on passive measurements.

• Southern Ocean clouds and precipitation show large seasonal amplitudes in cloud and precipitation properties

• Results consistent with seasonal oscillations in sulfate aerosol and sensitivity to sea salt aerosol due to surface winds

• Results suggest • 1st indirect effect (albedo susceptibility) remains active in summer and winter• 2nd aerosol indirect effect (precip susceptibility) diminishes in winter consistent

with Feingold et al. modeling work.

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“If you look deeply, you can see the clouds in the rain …” Thich Naht Hanh, Zen Buddhist Monk

Left Profile: • More LWC information. • Cloud droplets are bigger

relative to precip, so radar contributes to information

Right Profile: • Less LWC information.

• Cloud droplets are smaller relative to precip and passive data contributes more than radar.

Graciosa Island

re=25 umN=15 cm-3

re=110 umN=0.2 cm-3

re=15 umN=20 cm-3

re=110 umN=0.08 cm-3

NonPrecipitating Case Precipitating Case

Examples: Warm Shallow Cumulus in the SE Pacific

Cloud Droplet Number



LWC Info Content

From Klein et al., (2013)

The ubiquitous low cloud cover over the Southern Ocean remains a challenge for climate model simulations



Cloud Liquid Water



Cloud Effective Radius



LWC Sensitivity

Note the magnitudes

Note the relative contributions

Summer

Winter

SummerLow Wind

SummerHigh Wind

WinterLow Wind

WinterHigh Wind

SummerLow Wind

WinterLow Wind

SummerHigh Wind

WinterHigh Wind

Knowledge of precipitation processes within dynamical environment are central to understanding ….

Advanced cloud parameterizations (Weber and Quas, 2012) attempt to represent subgrid statistics of precip processes with little knowledge of what actually exists in nature (Morrison and Gettelemen, 2008)

Our Goal: Build on Lebsock et al. to derive simultaneous cloud-precip property statistics within dynamical regimes of the Southern Ocean.

Examine the Co-Dependence of Albedo and Precipitation Susceptibility in Warm Shallow Clouds of the Southern Ocean

Albedo Susceptibility (Platnick and Twomey, 1994):

SA=dA/dlnN

Painemal and Minnis, 2012 Sorooshian et al, 2009

Precipitation Susceptibility (Feingold and Stevens, 2009):

SA=dlnP/dlnN

Co-Dependence of Albedo and Precipitation Susceptibility: Summer – Low Wind vs. High Wind

Low Wind Tercile (<5 m/s) High Wind Tercile (>12 m/s)


Summer Low Wind Microphysics Stats

• Ncld=99 cm-3

• LWP=110 g m-2


• Albedo=0.39• Prate> 1 mm/day: 64%• Prate> 10 mm/day: 17%

Summer High Wind Microphysics Stats

• Ncld=133 cm-3

• LWP=125 g m-2



Co-Dependence of Albedo and Precipitation Susceptibility: Winter - Low Wind vs. High Wind

Low Wind Tercile High Wind Tercile


Winter Low Wind Microphysics Stats

• Ncld=7 cm-3

• LWP=86 g m-2



Winter High Wind Microphysics Stats

• Ncld=13 cm-3

• LWP=107 g m-2



So, What is going on?

• Sulfate aerosol decreases significantly from Summer to Winter

Ayers and Gras (1991)Cape Grim (41 S)

Kloster et al (2005)Amsterdam Is. (37 S)

• Sea salt aerosol increases with wind speed

O’Dowd et al., 1997

Feingold et al., 2013: Is the precipitation process dominated by autoconversion (Nd dependent) or accretion? LES suggest that S diminishes with decreasing Nd at a given LWP.