1. Warm rain variability and its association with cloud mesoscale structure and cloudiness transitions Robert Wood, University of Washington with help and data from Louise Leahy (UW), Matt Lebsock (JPL), Irina Sandu (ECMWF) Robert Wood, University of Washington with help and data from Louise Leahy (UW), Matt Lebsock (JPL), Irina Sandu (ECMWF) Photo: Mingxi Zhang

2. A paradigm of strongly precipitating open cells and weakly precipitating closed cells? Observations from VOCALS, 2008

3. Canonical modes of mesoscale variability in subtropical and tropical marine low clouds No mesoscale cellular convection Closed mesoscale cellular convection Open mesoscale cellular convection Cellular but disorganized Wood and Hartmann (2006), J. Climate

4. SE PacificNE Pacific Frequency of occurrence of different mesoscale modes

5. Stratocumulus to trade cumulus transition

6. Decoupling and Sc to Cu transition: “Deepening-warming mechanism” Well-mixed MBL deepens by entraining FT air which is positively buoyant. Higher SST  increases LHF  stronger buoyancy flux  more TKE  more entrainment Insufficient LW cooling to allow entrained air to mix down, so pools near MBL top Surface moisture source cut off  cloud breakup q t [g kg -1 ]  L [K] Wyant et al. (1997, JAS)

7. Data CloudSat: new warm rain retrievals from Matt Lebsock (JPL) – column maximum precipitation rate for clouds with tops < 3 km derived from blend of Z-R and attenuation-based (2C- Precip) retrievals CALIPSO: low cloud fractional coverage (high cloud cleared) and cloud top height MODIS: Vis/NIR liquid water path retrievals to determine cloud mesoscale morphology for 256x256 km daytime subscenes using trained neural network (Wood and Hartmann 2006). – Where CloudSat swath passes through the MODIS scene we store precipitation statistics

8. CloudSat column maximum precipitation rate from low clouds (z top <3 km) Precipitation rates maximize in the Sc to Cu transition regions

9. Low cloud fraction (CALIPSO) Does drizzle formation play a role in Sc to Cu transiton? Regions of max drizzle Trajectories from Sandu et al. (2010, ACP)

10. Mean cloud top height for low clouds (CALIPSO, z top < 3 km) Regions of max drizzle Precipitation maximizes in regions of maximum low cloud top height

11. COSMIC GPS-RO boundary layer depth Using refractivity gradient. Primarily detects hydrolapse, but also thermal inversion at higher latitudes

12. Frequency of occurrence of closed and open cells (MODIS, Annual 2008) Open cell frequency is well correlated with precipitation rate from low clouds Precipitation rate (z top <3 km) Precipitation and mesoscale cellularity

13. Frequency of occurrence of closed and open cells (MODIS, Annual 2008) Open cell frequency is well correlated with precipitation rate from low clouds Precipitation and mesoscale cellularity

14. Cell type frequency and CloudSat column max precipitation rate along Sc-Cu transition trajectories Closed cells Open cells Rising precipitation rate coincides with decrease in closed cells and increase in open cells Open cells appear to lag precipitation Trajectories courtesy of Irina Sandu (MPI, now ECMWF) NE Pacific

15. Cell type frequency and CloudSat column max precipitation rate along Sc-Cu transition trajectories Closed cells Open cells Rising precipitation rate coincides with decrease in closed cells and increase in open cells Open cells appear to lag precipitation SE Pacific shows double max in precipitation and open cell frequency Open cells Closed cells SE Pacific Trajectories courtesy of Irina Sandu (MPI, now ECMWF)

16. Distribution of mean precipitation rate for open and closed cells Differences in distributions of mean precipitation rates in open and closed cells Open cells appear to have a narrower distribution of precipitation rates, consistent with robust self-organized system discussed by Feingold Heaviest precipitation rates appear to occur in closed cells Closed cells Open cells

17. Precipitation and stratocumulus to trade cumulus transition Precipitation maxima Mean cloud top height Height of thickest clouds <1 km 3 km

18. Nature of precipitation along Sc-Cu trajectory Initial increase in precipitation explained by increase in precipitation frequency Precipitation rate continues to rise after frequency peaks and begins to fall Heavier but less frequency precipitation in trade Cu regions dBZ histogram

19. How can drizzle cause decoupling? Results from EPIC/VOCALS cruises (Bretherton et al. 2004) Entrainment warming

20. Does drizzle help decouple the MBL? Strongly drizzling cases tend to be decoupled However, decoupling primarily related to layer thickness between inversion and LCL Difficult to separate deepening- warming impacts on decoupling from effects due to drizzle Jones et al. (2011, ACPD) z i – z LCL [m]

21. Bretherton and Wyant minimal model Mixed layer model: dry and moist static energy constant with height Longwave cooling drives mixing and TKE production in the Sc- topped boundary layer (STBL). Assume surface fluxes do not impact TKE Entrainment depends upon buoyancy flux, which is strongly related to surface LHF, so warmer SST  more entrainment Entrainment brings in buoyant air from the FT making it difficult to mix to surface leading to decoupling Can express decoupling criterion using ratio of LHF to radiative flux divergence  F across the MBL: – LHF/  F > [A h/z i ] -1 for decoupling, where h is cloud thickness (z i – LCL), and z i is the MBL depth, and A is the entrainment efficiency (  1) Drizzle not accounted for

22. Modified Bretherton and Wyant minimal model Incorporate drizzle into mixed layer model Decoupling criterion is 3-way balance between  F, LHF, and precipitation rate at cloud base R cb and fraction f evap evaporating. For decoupling: A [h/z i ] LHF +  L v R cb (1+1.4f evap ) >  F f evap  0.75 is a reasonable parameterization of drizzle evaporation Given that LCL is typically 600 m over ocean (Betts argument), h  z i  600, we can estimate: z i =1.0 km: 0.4 LHF + 2.0 L v R cb >  F z i =1.5 km: 0.6 LHF + 2.0 L v R cb >  F z i =2.0 km: 0.7 LHF + 2.0 L v R cb >  F This means that 1 mm day -1 of cloudbase precipitation flux exerts as much “decoupling potential” as 80-140 W m -2 of LHF

23. Summary Precipitation rates from subtropical/tropical low clouds maximize in the transition regions between Sc and trade Cu Maximal rates correspond with transition from closed to open mesoscale cellular convection Frequency of occurrence of open cells occurs slightly downstream of maxima in precipitation rate. Heaviest precipitation rates tend to be found in deep closed cell cases prior to breakup Rates in these regions are  1 mm day -1, sufficient to compete with surface latent heat fluxes as a driver of decoupling and Sc-Cu transition

25. Precipitation and cloud optical depth SE Pacific 70-110 o W, 40 o S-0 o S dBZ Precip L.Heat [mm day -1 ] [W m -2 ] -15 0.15 5 -7.5 0.7 20 0 2.0 60

26. Diurnal cycle of precipitation from low clouds (z top <3km) NIGHT [1:30am] DAY [1:30pm] NIGHT - DAY EPIC Cruise, SE Pacific

27. “Background” cloud droplet concentration critical for determining aerosol indirect effects Quaas et al., AEROCOM indirect effects intercomparison, Atmos. Chem. Phys., 2009 Low N d background  strong Twomey effect High N d background  weaker Twomey effect  A  ln(N perturbed /N unpertubed ) LAND OCEAN

28. C-130 flight path (grey) Cloud base (lidar-derived) LCL (“well-mixed cloud base”) Radar reflectivity (drizzle proxy) We use vertical profiles and subcloud level legs