How is precipitation from low clouds important for climate?

Transcription

1 How is precipitation from low clouds important for climate? CloudSat estimated precipitation rate from low clouds Low clouds over the SE Pacific, Nov 11 th 2014 Robert Wood, University of Washington With Isabel McCoy, Andreas Muhlbauer, Ryan Eastman, Daniel Grosvenor (U. Washington); Matt Lebsock (JPL)

2 Messages Precipitation is an critical component of low cloud systems, particularly over the ocean, which influences cloud mesoscale morphology, dynamics and albedo Low cloud precipitation also removes aerosols extremely efficiently, helping to set the marine background aerosol

3 Prevalence of drizzle from low clouds DAY NIGHT Fraction of low clouds (1-4 km tops) with Z max > -15 dbz Leon et al., JGR (2008) Azores, NE Atlantic Cloud frequency Precip. frequency Rémillard et al. (J. Climate, 2012)

4 CloudSat column maximum precipitation rate from warm, low clouds (z top <3 km) Precipitation rates maximize in the Sc to Cu transition regions CloudSat-derived cloud base precipitation rate [mm day -1 ]

5 Precipitation and pockets of open cells Strikingly different precipitation structure in POCs compared with overcast stratocumulus Sandra Yuter (see Waliser et al. 2013) Precipitation generates cold outflows and local convergence that maintains open cell structure Feingold et al., Nature, 2010 Open cells Closed cells

6 Precipitation and the mesoscale morphology of low clouds Cloudbase precip rate [mm day -1 ] Closed cells Frequency Radar reflectivity, Z [dbz] Open cells Wood and Hartmann (2006); Muhlbauer et al. (2014)

7 Precipitation and mesoscale morphology Frequency of occurrence of closed and open cells (MODIS, Annual 2008) Open cell frequency associated with high precipitation rate from low clouds Precipitation rate (z top <3 km) fraction

8 Lagrangian framework for Sc to Cu transition T=24 hr (24 hour trajectories) Treat anomalies of cloud cover as a red noise process rr[ff cc tt + TT, ff cc (tt)] = ee TT/ττ where ττ is the Lagrangian decorrelation timescale. Slope of initial (t = 0) vs final (t = T = 24 hr) anomalies (right) provides the value of r, then f c anomaly at t = 24 hr [%] Note: linear slope linear decay process (mean rate of decay of initial signal does not depend on the amplitude) Zero slope (r = 0 τ = 0) ττ = TT/ ln rr 20 hours f c anomaly at t = 0 [%] We can explore how the cloud cover change depends upon various factors Eastman et al. (2015)

9 Lagrangian cloud cover changes Examine 24 hr cloud cover changes for 60,000 PBL trajectories over subtropical Eastern oceans PBL (cloud top) height most important for determining breakup Sparse/light precipitation does not make a difference [top panel] For shallow PBLs, more frequent/heavier precipitation appears to suppress cloudiness decreases; opposite might be the case for deeper clouds [bottom panel] Cloud cover change [compared with climatological transition] Non-precipitating Frequency of precipitation Non-precipitating Frequency of precipitation Precipitating Precipitating Eastman et al. (2015)

10 Extreme coupling between drizzle and CCN Southeast Pacific Drizzle causes cloud morphology transitions and depletes aerosols Wood et al. (2008)

11 Extreme coupling between drizzle and CCN Aerosols and cloud macrophysical properties are strongly coupled in Pockets of Open Cells (POCs): Drizzle strongly depletes aerosols and causes cloud morphological transitions in LES (Wang et al. 2011, Berner et al. 2011, 2013): Aerosol depletion Overcast Sc Open cells days LES modeling based on VOCALS RF06 case: Berner et al., Atmos. Chem. Phys. Disc. (2013)

12 Precipitation is primary driver of geographical variability in mean N d away from coasts Model reproduces significant amount of variance in N d over oceans implications for interpretation of AOD vs r e relationships Model (fixed aerosol sources) Wood et al. (J. Geophys. Res. 2006, 2012)

13 Steady state CCN/N d model prediction Southeastern Pacific (10-30 o S, o W) MODIS observed Model predicted (N FT =125 cm -3 ) seasonality driven by precip only Free-tropospheric CCN Steady state CCN conc in MBL NN = Surface CCN flux NN FFFF + FF 0 ww ee 1 + SS precip Non dimensional precip sink Entrainment rate Adapted from Wood et al. (2012) Steady state CCN/N d budget shows skill in predicting SE Pacific N d assuming seasonally invariant FT aerosols. Application to other regions challenging Unknown FT CCN seasonality constraints Problems with mixed phase precipitation

14 Background (minimum imposed) cloud droplet concentration influences aerosol indirect effects LAND OCEAN Forcing [W m -2 ] A ln(n perturbed /N unpertubed ) Low N d background strong Twomey effect High N d background weaker Twomey effect Quaas et al., AEROCOM (Atmos. Chem. Phys., 2009) Hoose et al. (GRL, 2009)

15 Take-home points Precipitation from low clouds over oceans is common and of sufficient magnitude to exert first order effects on PBL dynamics, structure, cloud mesoscale morphology, cloud cover and albedo Lagrangian framework can unravel cause and effect Light precipitation from low clouds exerts major control on CCN and cloud droplet concentration Drives geographical variation of the annual mean N d away from coastal zones Drives N d seasonal cycle in some regions (e.g. SE Pacific, possibly S. Ocean)

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17 What controls CCN and cloud microphysical variability in the marine boundary layer? A simple CCN budget for the PBL ENTRAINMENT SURF. SOURCE PRECIP. SINK Assume nucleation/secondary processes unimportant Dry deposition is negligible (Georgi 1990) Sea-spray formulation (e.g. Clarke et al. 2006) Ignore advection Precipitation sink primarily from accretion process Equivalency of CCN and cloud drop conc. N d Wood et al. (2012, J. Geophys. Res.)

18 Steady-state CCN budget FREE TROPOSPHERIC CCN SEA-SPRAY PRODUCTION PRECIP. SINK Concentration relaxes to FT concentration N FT + wind speed dependent surface contribution dependent upon subsidence rate (Dz i ) Precipitation sink controlled by precipitation rate at cloud base P CB. Use expression from Wood (2006).

19 Precipitation important in controlling gradient in N d Assume constant FT aerosol concentration Precipitation from CloudSat estimates from Lebsock and L Ecuyer (2011) Observed surface winds Model N d gradients mostly driven by precipitation sinks Wood et al. (J. Geophys. Res. 2012)

20 Precipitation is primary driver of geographical variability in mean N d away from coasts Model reproduces significant amount of variance in N d over oceans implications for interpretation of AOD vs r e relationships Model (fixed FT aerosol) Wood et al. (J. Geophys. Res. 2012)

21 Does the precipitation sink drive seasonality in N d? Most subtropical stratocumulus regions exhibit significant seasonality in cloud droplet concentration that is anticorrelated with precipitation rate Precipitation Droplet conc. r [N d, R cb ] = r [N d, R cb ] = -0.84

22 Steady state CCN/N d model prediction Southeastern Pacific (10-30 o S, o W) MODIS observed Model predicted (N FT =125 cm -3 ) seasonality driven by precip only Free-tropospheric CCN Steady state CCN conc in MBL NN = Surface CCN flux NN FFFF + FF 0 ww ee 1 + SS precip Non dimensional precip sink Entrainment rate Adapted from Wood et al. (2012) Steady state CCN/N d budget shows skill in predicting SE Pacific N d assuming seasonally invariant FT aerosols. Application to other regions challenging Unknown FT CCN seasonality constraints Problems with mixed phase precipitation

23 Steady state CCN/N d model prediction Southeastern Pacific (10-30 o S, o W) MODIS observed Model predicted (N FT =125 cm -3 ) seasonality driven by precip only Free-tropospheric CCN Steady state CCN conc in MBL NN = Surface CCN flux NN FFFF + FF 0 ww ee 1 + SS precip Non dimensional precip sink Entrainment rate Adapted from Wood et al. (2012) Steady state CCN/N d budget shows skill in predicting SE Pacific N d assuming seasonally invariant FT aerosols. Application to other regions challenging Unknown FT CCN seasonality constraints Problems with mixed phase precipitation

24 Strong seasonal cycle of aerosols and cloud droplet concentration N d over the Southern Ocean Marked annual cycle of N d in low clouds over Southern Ocean Summer N d maximum hypothesized to be biogenic (DMS, organics) In situ and satellite observations consistent Summertime albedo enhancement (Twomey) of 25% Figure by R. Wood, SOCRATES White Paper (2014)

25 CloudSat precipitation (2c-precip-column) Wintertime maximum for low cloud precipitation likely, but annual cycle not hugely strong (range: mm day -1 ) Outstanding retrieval problems over Southern Ocean Cold-topped clouds, radar echoes below 800 m altitude contaminated by ground clutter

26 Lagrangian framework Treat anomalies of cloud droplet concentration N d as a red noise process rr[nn dd tt + TT, NN dd (tt)] = ee TT/ττ where ττ is the Lagrangian decorrelation timescale. Slope of initial (t = 0) vs final (t = T = 24 hr) anomalies (right) provides the value of r, then ττ = TT/ ln rr 30 hours ττ for low cloud cover is much shorter ( hours). N d anomaly at t = 24 hr [cm -3 ] Note: linear slope linear decay process (mean rate of decay of initial signal does not depend on the amplitude) Clouds therefore decorrelate faster than the aerosol they are forming on T=24 hr (24 hour trajectories) Zero slope (r = 0 τ = 0) N d anomaly at t = 0 [cm -3 ] Eastman et al. (2015)

27 Timescale for precipitation removal (Wood 2006, J. Geophys. Res.) ττ cccccccc = NN = zz ii KKKPP CCCC 2/P CB CALIOP Cloud top height (Muhlbauer et al. 2014) Values: K = 2.25 m 2 kg -1 (Wood 2006); z i = 1500 m; h = 350 m (typical values) ττ cccccccc 2 days for P CB = 1 mm day Cloud top height [km]

28 Take-home points Light precipitation from low clouds exerts major control on CCN and cloud droplet concentration Drives geographical variation of the annual mean N d away from coastal zones Drives N d seasonal cycle in some regions (e.g. SE Pacific, possibly S. Ocean) Lagrangian decorrelation timescale for N d ( 30 hours) is substantially longer than for cloud cover Clouds decorrelate faster than the underlying aerosol that they ingest Decorrelation timescale comparable with timescale for precipitation removal but next steps will apply Lagrangian framework analysis to explore connection between precipitation and N d

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30 Additional slides

31 Correcting solar zenith angle biases in MODIS-derived N d Grosvenor and Wood (Atmos. Chem. Phys. 2014)

32 Open cells drizzle harder, but more intermittently Muhlbauer et al. (2014)

33 What factors control the magnitude and uncertainty of the global first aerosol indirect effect? Ghan et al. (J.Geophys. Res., 2013)

34 Natural emissions contribute half of AIE uncertainty (a) Seasonal cycle; (b) contribution from natural, anthropogenic emissions and aerosol processes; (c) uncertainty ranges from different perturbed parameters Volcanic and DMS produced SO 2 are major natural sources of uncertainty Anthropogenic SO2 key anth. Source Aerosol processes are not major sources of uncertainty in this analysis Carslaw et al. (Nature, 2013)

35 Does the precipitation sink drive seasonality in N d? Steady state CCN/N d budget shows skill in predicting SE Pacific N d (Wood et al. 2012) Application to other regions challenging Poor FT CCN constraints Aforementioned issues with warm rain estimates MODIS/CloudSat observations Free-tropospheric CCN Steady state CCN conc in MBL NN = Surface CCN flux NN FFFF + FF 0 ww ee 1 + SS precip Non dimensional precip sink Entrainment rate Adapted from Wood et al. (2012) Key result is that S precip is 1-2 for mean drizzle rate of 1 mm day -1