Response of Humidity and Clouds to Tropical Deep Convection Mark - PowerPoint PPT Presentation

Response of Humidity and Clouds to Tropical Deep Convection Mark Zelinka University of Washington Dept. of Atmospheric Sciences NASA Sounder Science Team Meeting 14 October 2009

Acknowledgements • Dennis Hartmann (advisor) – Chris Bretherton – Rob Wood – Qiang Fu • Funding: NASA Earth and Space Science Fellowship • Reference: Zelinka, M.D. and D.L. Hartmann, 2009: Response of Humidity and Clouds to Tropical Deep Convection. J. Climate , 22 , 2389-2404. (Please see me for reprints.)

Motivation • “Confidence in modeled water vapor feedback is dependent upon understanding of the physical processes important for controlling upper-tropospheric humidity, and confidence in their representation in GCMs.” • “Difficulties in observing water vapor in the upper troposphere have long hampered both observational and modeling studies, and significant limitations remain in coverage and reliability of observational humidity data sets.” • “Understanding processes determining the distribution and variability in RH is therefore central to understanding of the water vapor – lapse rate feedback.” – IPCC AR 4, Ch. 8

Tropical average RH (radiosondes) Reduction in OLR due to adding 3% RH Spencer and Braswell 1997

Methodology • Investigate the evolution of moisture, high clouds, vertical motion, and clear sky OLR in association with tropical deep convection • Composite over many deep convective events (defined as RRs ≥ the 90 th percentile) – Center composite region on high rain rate pixel – Composite same rain rate-centric region in time RR ≥ 90 th percentile Lat. Lon. Time -9 -12 -6 -3 0 +3 +6 +9 +12 Hour

Data • AIRS, AMSR-E, MODIS, & CERES on board Aqua satellite • TMPA precipitation • NCEP/NCAR Reanalysis ω • N. edge of Western Pacific Warm Pool (5°N-15°N, 120E°-160°E) • Jan 2003 - Dec 2005

AIRS L2 Data • Cloud-cleared radiances – Uses several adjacent fields of view to retrieve atmospheric quantities (temp, water vapor, etc.) in clear-sky portions of scene • Retrievals OK in up to 80% cloud cover • Retrievals removed where RH liq > 100%

MODIS L2 Joint Product • High clouds segregated by cloud top temperature and cloud optical thickness (daytime data only, 5 km resolution) Net Cloud Forcing (W m -2 ) • CTT < 245 K Cloud Top Temperature (K) • Thin cirrus: τ < 4 • Anvil: 4 ≤ τ < 32 • Convective core: τ ≥ 32 Visible Optical Depth

TRMM Multisatellite Precipitation Analysis (TMPA) • Uses information from a suite of measurements: – TRMM TMI – SSM/I – AMSR-E – AMSU-B – GOES (window channel IR) – Rain gauge data (calibration) • 3 hourly resolution (0000, 0300, …, 2100 UTC ) – AQUA overpasses: 1:30 AM and 1:30 PM local time – AQUA observations at many time lags from TMPA RR observation  use 3 hour bins

Results… RR ≥ 90 th percentile Lat. Lon. Time -12 -9 +3 +6 +12 -6 -3 0 +9 Hour

Composite Averages -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 RR (mm/hr) 0.5 1 1.5 2 2.5 WVP (mm) 42 44 46 48 50 52 54 56 58 60 OLR CLR (W/m 2 ) 282 284 286 288 290 292 294 296 298 C. Core Cloud Fraction (%) 5 10 15 20 25 30 35 40 Anvil Cloud Fraction (%) 10 15 20 25 30 35 40 45 50 55 Cirrus Cloud Fraction (%) 4 6 8 10 12 14 16 18 20 22

Composite Anomalies -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 RR (mm/hr) 0 0.5 1 1.5 WVP (mm) -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 OLR CLR (W/m 2 ) -5 -4 -3 -2 -1 0 1 2 C. Core Cloud Fraction (%) -5 0 5 10 15 20 25 Anvil Cloud Fraction (%) -5 0 5 10 15 Cirrus Cloud Fraction (%) -10 -8 -6 -4 -2 0 2

Composite RH Anomalies (%) -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 200-150 hPa 250-200 hPa 300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 1000-925 hPa -8 -6 -4 -2 0 2 4 6 8 10

Average Vertical Velocity (negative = UP) Vertical Velocity Anomaly (negative = UP) Gamache & Houze (1983) RH Anomaly Vertical Motion

Spatially-Averaged RH Anomalies (%) Lag-height regression of RH vs. RR (Mapes et al. 2009) Yikes! KWAJEX radiosonde GFDL AM2 Model

Maximum Anomalies RH UT C.Core Anvil % or –hPa/day Thin ω UT

Maximum Anomalies as Functions of Spatially-Averaged RR Upper Tropospheric Upper Tropospheric Vertical Velocity Relative Humidity (%) (hPa day -1 , sign reversed) RR 1 RR 2 RR 3 RR 4 all

Composite Anomalies -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 SST (K) -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 SWCF (W m -2 ) -30 -20 -10 0 10 20 30 LWCF (W m -2 ) -30 -20 -10 0 10 20 30 NetCF (W m -2 ) -15 -10 -5 0 5 10 15

Conclusions (1 of 2) • Clear sky OLR is most sensitive to upper tropospheric humidity fluctuations • Deep convection is the main source of upper tropospheric humidity • Composite AIRS measurements nicely capture the evolution of humidity at high vertical resolution – Moist at low levels prior to convection – Moist at upper levels afterwards, peaking at hours +9 to +12 – Moist signature spreads horizontally following convection – Greater spatially-averaged RR  Larger, more persistent moist region • Clear sky OLR remains reduced for several hours following deep convective event

Conclusions (2 of 2) • Composite vertical motion field nicely shows transition from low level upward motion to upper level upward motion over the course of convection • Convective core clouds peak in phase with RR • Anvil cloud fractions peak 3 hours following peak RR • High SSTs precede convection, but the development of high thick clouds with large negative shortwave cloud forcing result in strong cooling of the ocean surface during and following deep convection  this framework can be used to investigate short-term energy variations of ocean and atmosphere

Thank you!

Cumulative Fraction of Total Rain Cumulative Fraction Rain rate (mm/hr)

Cumulative Fraction of Total Rain Cumulative Fraction 57% falls in events with RR ≥ 1.6 mm/hr Rain rate (mm/hr)

RH Anomalies (%): 1 st RR quartile -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 200-150 hPa 250-200 hPa 300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 1000-925 hPa

RH Anomalies (%): 2 nd RR quartile -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 200-150 hPa 250-200 hPa 300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 1000-925 hPa

RH Anomalies (%): 3 rd RR quartile -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 200-150 hPa 250-200 hPa 300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 1000-925 hPa

RH Anomalies (%): 4 th RR quartile -24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24 200-150 hPa 250-200 hPa 300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 1000-925 hPa

Spencer and Braswell 1997

ERBE data provided by Marc Michelsen

OLR Sensitivity to Water Vapor Perturbations All Sky Clear Sky W m -2 K -1 per 100 hPa Soden et al. (2008)

Evolution of Clouds and Humidity in Association with Tropical Deep Convection Mark Zelinka University of Washington Dept. of Atmospheric Sciences American Geophysical Union Fall Meeting 12 December 2007