1. Derived Properties of Warm Marine Low Clouds over the Southern Ocean: Precipitation Susceptibility and Sensitivity to Environmental Parameters Jay Mace Trevor Ferguson Stephanie Avey Steve Cooper Photo Credit: patternsofnature.files.wordpress.com

2. e.f. The Southern Ocean is one of the cloudiest regions on Earth… … and primarily populated by geometrically thin layers based in the boundary layer.

3. 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

4. WS 5: Polar and High Latitudes Dominated by low level clouds of moderate optical thickness – often with cirrus above.

5. North: South: WS 5: Polar and High Latitudes

6. 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.

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

8. Examples: Warm Shallow Cumulus in the SE Pacific 20070106, orbit 03691 NonPrecipitating Case 94 GHz Tb: 246 K MODIS 550 nm: 0.62 MODIS 1.6 um: 0.48 MODIS 2.1 um: 0.31 Cloud Number: 80 cm -1 ±±60% Cloud LWC: 0.2 g m -3 ±70% Cloud r e : 10 um ±30% Precipitating Case 94 GHz Tb: 259 K MODIS 550 nm: 0.63 MODIS 1.6 um: 0.45 MODIS 2.1 um: 0.23 Cloud Number: 80 cm -1 ±±120% Cloud LWC: 0.3 g m -3 ±130% Cloud r e : 20 um ±180% Precip Rate: 80 mm/day ±40%

11. 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

12. Co-Dependence of Albedo and Precipitation Susceptibility: Seasonal Comparison Summer Winter # Profiles: 11140 # Profiles: 32389 Summer Microphysics Stats N cld =90 cm -3 LWP=120 g m -2 P rate =22 mm day -1 Albedo=0.4 P rate > 1mm/day: 68% P rate > 10 mm/day: 20% Winter Microphysics Stats N cld =9 cm -3 LWP=98 g m -2 P rate =23 mm day -1 Albedo=0.2 P rate > 1mm/day: 89% P rate > 10 mm/day: 32%

13. 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 S 0 for LWP > 250 g/m2 Higher Nd, remains in autoconversion dominated regime with higher S 0 and no obvious transition Summer Winter

14. 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 1 st indirect effect (albedo susceptibility) remains active in summer and winter 2 nd aerosol indirect effect (precip susceptibility) diminishes in winter consistent with Feingold et al. modeling work. Photo Credit: patternsofnature.files.wordpress.com

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

16. 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.

17. Graciosa Island

18. r e =25 um N=15 cm -3 r e =110 um N=0.2 cm -3 r e =15 um N=20 cm -3 r e =110 um N=0.08 cm -3

19. NonPrecipitating CasePrecipitating Case Examples: Warm Shallow Cumulus in the SE Pacific Cloud Droplet Number

20. NonPrecipitating CasePrecipitating Case Examples: Warm Shallow Cumulus in the SE Pacific LWC Info Content

21. From Klein et al., (2013) The ubiquitous low cloud cover over the Southern Ocean remains a challenge for climate model simulations

22. NonPrecipitating CasePrecipitating Case Examples: Warm Shallow Cumulus in the SE Pacific Cloud Liquid Water

23. NonPrecipitating CasePrecipitating Case Examples: Warm Shallow Cumulus in the SE Pacific Cloud Effective Radius

24. NonPrecipitating CasePrecipitating Case Examples: Warm Shallow Cumulus in the SE Pacific LWC Sensitivity Note the magnitudes Note the relative contributions

25. Summer Winter

26. Summer Low Wind Summer High Wind

27. Winter Low Wind Winter High Wind

28. Summer Low Wind Winter Low Wind

29. Summer High Wind Winter High Wind

30. 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.

31. Examine the Co-Dependence of Albedo and Precipitation Susceptibility in Warm Shallow Clouds of the Southern Ocean Albedo Susceptibility (Platnick and Twomey, 1994): S A =dA/dlnN Painemal and Minnis, 2012 Sorooshian et al, 2009 Precipitation Susceptibility (Feingold and Stevens, 2009): S A =dlnP/dlnN

32. 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) # Profiles: 10796 Summer Low Wind Microphysics Stats N cld =99 cm -3 LWP=110 g m -2 P rate =18 mm day -1 Albedo=0.39 P rate > 1 mm/day: 64% P rate > 10 mm/day: 17% Summer High Wind Microphysics Stats N cld =133 cm -3 LWP=125 g m -2 P rate =23 mm day -1 Albedo=0.40 P rate > 1 mm/day: 70% P rate > 10 mm/day: 22%

33. Co-Dependence of Albedo and Precipitation Susceptibility: Winter - Low Wind vs. High Wind Low Wind TercileHigh Wind Tercile # Profiles: 3715 Winter Low Wind Microphysics Stats N cld =7 cm -3 LWP=86 g m -2 P rate =15 mm day -1 Albedo=0.18 P rate > 1 mm/day: 88% P rate > 10 mm/day: 28% Winter High Wind Microphysics Stats N cld =13 cm -3 LWP=107 g m -2 P rate =37 mm day -1 Albedo=0.21 P rate > 1 mm/day: 90% P rate > 10 mm/day: 39%

34. 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.