Tropospheric Water Vapor Variability and Linkage to Tropospheric - - PowerPoint PPT Presentation

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Tropospheric Water Vapor Variability and Linkage to Tropospheric - - PowerPoint PPT Presentation

Sep 29, 2023 168 likes •301 views

Tropospheric Water Vapor Variability and Linkage to Tropospheric Water Vapor Variability and Linkage to Large- -Scale Circulation Scale Circulation Large Hui Su and Jonathan H. Jiang Hui Su and Jonathan H. Jiang The AIRS and MLS Teams The

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SLIDE 1

Tropospheric Water Vapor Variability and Linkage to Tropospheric Water Vapor Variability and Linkage to Large Large-

  • Scale Circulation

Scale Circulation

Hui Su and Jonathan H. Jiang Hui Su and Jonathan H. Jiang

The AIRS and MLS Teams The AIRS and MLS Teams Jet Propulsion Laboratory, California Institute of Technology, P Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA asadena, CA Andrew Andrew Gettelman Gettelman National Center for Atmospheric Research, Boulder, CO National Center for Atmospheric Research, Boulder, CO Xianglei Huang Xianglei Huang Department of Atmospheric, Oceanic, and Space Sciences, Universi Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, MI ty of Michigan, Ann Arbor, MI

NASA Sounder Science Team Meeting, 4-7 May, 2009, Pasadena, CA

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SLIDE 2

Introduction Introduction

  • Water vapor is a dominant greenhouse gas and its variability is important

for global hydrological cycle and climate change predictions

  • On large-scale averages, column-integrated water vapor approximately

follows temperature changes via Clausius-Clapeyron equation assuming constant relative humidity

  • Water vapor is strongly coupled with convective processes: a two-way

interaction exists between water vapor and precipitation/clouds. A simple relation exists between water vapor path and precipitation

  • Questions we are interested in:
  • 1. How good is the “constant relative humidity” assumption on global and

regional spatial scales? In other words, how well does specific humidity track temperature change?

  • 2. How do the water vapor, precipitation, clouds and temperature relations vary

with height?

  • 3. How well do climate model simulations reproduce the water vapor

variabilities?

We will use AIRS and MLS water vapor measurements, combined with

  • ther satellite data to address these questions.
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SLIDE 3

Relationship of Water Vapor Path and Precipitation Relationship of Water Vapor Path and Precipitation

Precipitation binned on WVP

5 10 15 20 25 30 35 40 45 2 4 6 8 10 12 14

Precipitation (mm/day) Water Vapor Path (mm)

  • W. PAC
  • E. PAC

ATL IND

Column-integrated water vapor path (WVP) and CMAP precipitation obeys a similar relationship across the basins, as in earlier studies (Bretherton et al. 2004, Peters and Neelin 2006). Monthly data are used here. Number of Samples

5 10 15 20 25 30 35 40 45 2000 4000 6000 8000 10000 12000

Water Vapor Path (mm) Number of samples

  • W. PAC
  • E. PAC

ATL IND

Bretherton et al. (J. Clim, 2004)

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SLIDE 4

Vertical Structure of H Vertical Structure of H2

2O and Convection

O and Convection

Scatter plots and regressions of H2O and IWC

  • 4
  • 2

2 4 Log(IWC) 2 4 6 8 10 Log(H2O)

1000hPa 0.0844 700hPa 0.2647 500hPa 0.3598 300hPa 0.2662 200hPa 0.1697 100hPa

  • 0.0338

qv binned on P (log scale) (Western Pacific)

10 10

1

10

2

2 4 6 8 10 12 14 16 18

Precipitation (mm/day) AIRS Water Vapor (g/kg)

300mb 500mb 700mb 850mb 925mb 1000mb

Jan 2005,

30S-30N

AIRS H2O in black MLS H2O in blue

The increases of water vapor with convection (IWC, P) occurs at all vertical levels (except 100 hPa).

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SLIDE 5

Amplified UT Response to ENSO Amplified UT Response to ENSO

AIRS GFDL AM2

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SLIDE 6

Vertical Structure of ENSO Anomalies Vertical Structure of ENSO Anomalies

Water Vapor Anomaly DJF 06

100 hPa 215 hPa 500 hPa 850 hPa

Fractional Water Vapor Change (%) DJF 06 Temperature Anomaly (K) DJF 06

H2O (100 hPa, ppmv) DJF 06 – DJF 05 H2O (215 hPa, ppmv) DJF 06 – DJF 05 H2O Anom. (500 hPa, g/kg) DJF 06 H2O Anom. (850 hPa, g/kg) DJF 06

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SLIDE 7

Spatial Correlations Spatial Correlations

Water vapor and SST are positively correlated throughout the troposphere, with larger correlation in the lower troposphere and during ENSO years. Water vapor and temperature correlation is weak and has little inter-annual variability. Water vapor and precipitation are positively correlated, except near

  • surface. The largest correlation is in

the middle and upper troposphere.

Water Vapor − SST Water Vapor − Precipitation Water Vapor − Temperature

−0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 JAN JUL JAN JUL JAN JUL JAN JUL 2003 2004 2005 2006

200 300 400 500 600 700 800 900 1000 200 300 400 500 600 700 800 900 1000 200 300 400 500 600 700 800 900 1000

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SLIDE 8

Compare Model Simulations to Observations Compare Model Simulations to Observations

ECMWF QV

  • 100
  • 50

50 100 Verticle Velocity (hPa/day) 1000 800 600 400 200 Pressure (hPa) 0.0001 0.0010 0.0100 0.1000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

Aqua AIRS QV

  • 100
  • 50

50 100 Verticle Velocity (hPa/day) 1000 800 600 400 200 Pressure (hPa) 0.0001 0.0010 0.0100 0.1000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

GEOS5 QV

  • 100
  • 50

50 100 Verticle Velocity (hPa/day) 1000 800 600 400 200 Pressure (hPa) 0.0001 0.0010 0.0100 0.1000 test 0.00 0.29 0.83 2.40 6.93 20.00 QV (g/kg)

NCAR CAM QV

  • 100
  • 50

50 100 Verticle Velocity (hPa/day) 1000 800 600 400 200 Pressure (hPa) 0.0001 0.0010 0.0100 0.1000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

  • AIRS is drier above boundary layer than

ECWMF analysis and model simulations

  • Water vapor increases with increasing

upwelling, except at very large upwelling; CAM has maximum water vapor around

  • 50 hPa/day

GFDL AM2 QV

  • 100
  • 50

50 100 Verticle Velocity (hPa/day) 1000 800 600 400 200 Pressure (hPa) 0.0001 0.0010 0.0100 0.1000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

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SLIDE 9

Compare Model Simulations to Observations

ECMWF QV

16 18 20 22 24 26 28 30 NCEP SST (oC) 1000 800 600 400 200 Pressure (hPa) 0.001 0.010 0.100 1.000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

Aqua AIRS QV

16 18 20 22 24 26 28 30 NCEP SST (oC) 1000 800 600 400 200 Pressure (hPa) 0.001 0.010 0.100 1.000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

NCAR CAM QV

16 18 20 22 24 26 28 30 NCEP SST (oC) 1000 800 600 400 200 Pressure (hPa) 0.001 0.010 0.100 1.000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

GEOS5 QV

16 18 20 22 24 26 28 30 SST (oC) 1000 800 600 400 200 Pressure (hPa) 0.001 0.010 0.100 1.000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

GFDL AM2 QV

16 18 20 22 24 26 28 30 NCEP SST (oC) 1000 800 600 400 200 Pressure (hPa) 0.001 0.010 0.100 1.000 PDF 0.0 0.3 0.8 2.4 6.9 20.0 QV (g/kg)

  • AIRS is drier above boundary layer

than ECMWF and models

  • Water vapor increases monotonically

with increasing SST

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SLIDE 10

Does the water vapor simulation affect clouds? Does the water vapor simulation affect clouds?

CloudSat CWC

  • 100
  • 50

50 100 ω (hPa/day) 1000 800 600 400 200 Pressure (hPa)

0.001 0.010

CloudSat LWC

  • 100
  • 50

50 100 1000 800 600 400 200 Pressure (hPa)

0.001 0.010

CloudSat IWC

  • 100
  • 50

50 100 1000 800 600 400 200 Pressure (hPa)

0.001 0.010

GEOS5 IWC

  • 100
  • 50

50 100

0.001 0.010

GEOS5 LWC

  • 100
  • 50

50 100

0.001 0.010

GEOS5 CWC

  • 100
  • 50

50 100 ω (hPa/day)

0.001 0.010

CAM IWC

  • 100
  • 50

50 100

0.001 0.010

CAM LWC

  • 100
  • 50

50 100

0.001 0.010

CAM CWC

  • 100
  • 50

50 100 ω (hPa/day)

0.001 0.010

GFDL IWC

  • 100
  • 50

50 100

0.001 0.010 PDF

GFDL LWC

  • 100
  • 50

50 100

0.001 0.010 PDF

GFDL CWC

  • 100
  • 50

50 100 ω (hPa/day)

0.001 0.010 PDF

0.0 0.2 0.5 1.0 2.2 4.9 10.6 23.0 50.0 CLOUD WATER CONTENT (mg/m3)

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SLIDE 11

CloudSat IWC/LWC Sorted by SST CloudSat IWC/LWC Sorted by SST

CloudSat CWC

16 18 20 22 24 26 28 30 AMSR-E SST (oC) 1000 800 600 400 200 Pressure (hPa)

0.001 0.010 0.100

CloudSat LWC

16 18 20 22 24 26 28 30 1000 800 600 400 200 Pressure (hPa)

0.001 0.010 0.100

CloudSat IWC

16 18 20 22 24 26 28 30 1000 800 600 400 200 Pressure (hPa)

0.001 0.010 0.100

GEOS5 IWC

16 18 20 22 24 26 28 30

0.001 0.010 0.100

GEOS5 LWC

16 18 20 22 24 26 28 30

0.001 0.010 0.100

GEOS5 CWC

16 18 20 22 24 26 28 30 AMSR-E SST (oC)

0.001 0.010 0.100

CAM IWC

16 18 20 22 24 26 28 30

0.001 0.010 0.100

CAM LWC

16 18 20 22 24 26 28 30

0.001 0.010 0.100

CAM CWC

16 18 20 22 24 26 28 30 AMSR-E SST (oC)

0.001 0.010 0.100

GFDL IWC

16 18 20 22 24 26 28 30

0.001 0.010 0.100 PDF

GFDL LWC

16 18 20 22 24 26 28 30

0.001 0.010 0.100 PDF

GFDL CWC

16 18 20 22 24 26 28 30 AMSR-E SST (oC)

0.001 0.010 0.100 PDF

0.0 0.2 0.5 1.0 2.2 4.9 10.6 23.0 50.0 CLOUD WATER CONTENT (mg/m3)

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SLIDE 12

Summary Summary

  • The basin-scale water vapor path and precipitation relations

show some differences in different basins.

  • The interannual variation of water vapor has an amplified

response to ENSO in the UT.

  • Tropospheric water vapor has a stronger correlation with

precipitation than with tropospheric temperature, especially in the UT.

  • Model simulations of water vapor profile are better than

cloud water/ice profiles. But discrepancies still exist. AIRS shows a drier middle troposphere than ECMWF and climate models.