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

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

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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

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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

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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.)

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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

  • f the water vapor – lapse rate feedback.”

– IPCC AR 4, Ch. 8

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Spencer and Braswell 1997

Tropical average RH (radiosondes)

Reduction in OLR due to adding 3% RH

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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 90th percentile)

– Center composite region on high rain rate pixel – Composite same rain rate-centric region in time

Lat. Lon. RR ≥ 90th percentile Hour +9 +12

  • 12
  • 9
  • 6
  • 3

+3 +6 Time

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SLIDE 7

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
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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

RHliq > 100%

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MODIS L2 Joint Product

  • High clouds segregated by cloud top

temperature and cloud optical thickness (daytime data only, 5 km resolution)

  • CTT < 245 K
  • Thin cirrus: τ < 4
  • Anvil: 4 ≤ τ < 32
  • Convective core:

τ ≥ 32

Cloud Top Temperature (K) Net Cloud Forcing (W m-2) Visible Optical Depth

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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

  • bservation  use 3 hour bins
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Lat. Lon. RR ≥ 90th percentile Hour +9 +12

  • 12
  • 9
  • 6
  • 3

+3 +6 Time

Results…

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SLIDE 12

RR (mm/hr) OLRCLR (W/m2)

  • C. Core Cloud

Fraction (%) Anvil Cloud Fraction (%) Cirrus Cloud Fraction (%) WVP (mm)

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24

Composite Averages

4 6 8 10 12 14 16 18 20 22 10 15 20 25 30 35 40 45 50 55 5 10 15 20 25 30 35 40 282 284 286 288 290 292 294 296 298 42 44 46 48 50 52 54 56 58 60 0.5 1 1.5 2 2.5

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RR (mm/hr) OLRCLR (W/m2)

  • C. Core Cloud

Fraction (%) Anvil Cloud Fraction (%) Cirrus Cloud Fraction (%) WVP (mm)

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24

Composite Anomalies

0 0.5 1 1.5

  • 1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
  • 5 -4 -3 -2 -1 0 1 2
  • 5 0 5 10 15 20 25
  • 5 0 5 10 15
  • 10 -8 -6 -4 -2 0 2
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300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24

250-200 hPa 1000-925 hPa 200-150 hPa

Composite RH Anomalies (%)

  • 8 -6 -4 -2 0 2 4 6 8 10
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Average Vertical Velocity (negative = UP) Vertical Velocity Anomaly (negative = UP) RH Anomaly Gamache & Houze (1983) Vertical Motion

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Spatially-Averaged RH Anomalies (%)

Lag-height regression of RH vs. RR (Mapes et al. 2009)

KWAJEX radiosonde GFDL AM2 Model Yikes!

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RHUT C.Core Anvil Thin ωUT

Maximum Anomalies % or –hPa/day

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Upper Tropospheric Vertical Velocity (hPa day-1, sign reversed) Upper Tropospheric Relative Humidity (%)

RR 1 RR 2 RR 3 RR 4 all

Maximum Anomalies as Functions of Spatially-Averaged RR

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SST (K) NetCF (W m-2) LWCF (W m-2) SWCF (W m-2)

  • 15 -10 -5 0 5 10 15
  • 30 -20 -10 0 10 20 30
  • 30 -20 -10 0 10 20 30
  • 0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1
  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21

+24

Composite Anomalies

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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
  • f 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

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  • 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

Conclusions (2 of 2)

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Thank you!

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

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

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300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24

250-200 hPa 1000-925 hPa 200-150 hPa

RH Anomalies (%): 1st RR quartile

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300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 250-200 hPa 1000-925 hPa 200-150 hPa

RH Anomalies (%): 2nd RR quartile

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24
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300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 250-200 hPa 1000-925 hPa 200-150 hPa

RH Anomalies (%): 3rd RR quartile

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24
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300-250 hPa 400-300 hPa 500-400 hPa 600-500 hPa 700-600 hPa 850-700 hPa 925-850 hPa 250-200 hPa 1000-925 hPa 200-150 hPa

RH Anomalies (%): 4th RR quartile

  • 24 -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 +24
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Spencer and Braswell 1997

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ERBE data provided by Marc Michelsen

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Soden et al. (2008)

OLR Sensitivity to Water Vapor Perturbations

All Sky Clear Sky W m-2 K-1 per 100 hPa

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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