The use of HDO observations for understanding processes controlling the water vapor feedback

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The use of HDO observations for understanding
processes controlling the water vapor feedback

• David Noone
• Dept. Atmospheric and Oceanic Sciences and
  Cooperative Institute for Research in
  Environmental SciencesUniversity of Colorado,
  Boulder CO

With Derek Brown (CU-Boulder), Joe Galewsky
(UNM), John Worden (JPL), Kevin Bowman (JPL)
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Objectives

• Global observations of HDO and H2O in the
  mid/lower troposphere
• Use these to constrain water budget
• Two observables gives more information than one
  (isotopes tell about the processes)
• Specifically
• Why are subtropics dry? (How does this change?)
• Identify sources of water(Especially recycling
  of rainwater though re-evaporation)
• Characterize type of cloud processes (in the
  tropics)
• Characterize type of sink (remoistening in region
  of convection, reversible adiabatic cloud
  processes, efficiency with which water is lost
  from the atmosphere)
• Information from isotopes is on exchange processes

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Reminder of isotope physics
Ratio of HDO to H2O Measured as a difference from
ocean water.
a
vapor(e.g., atmosphere)
liquid (e.g., ocean)
Two simple isotope models Condensation Vapor
becomes depleted as heavy removed preferentially
Evaporation Returns to isotopic composition of
the (ocean/land) source.
Conditions under which condensation occurs is
different from the conditions when evaporation
occurs
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dD climatology (850-500 hPa)
December 2004 March 2008
Helliker and Noone in press, Noone, et al., in
prep.
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Averaging kernel diagonal
December-January-February
June-July-August
800-500 hPa layer has adequate sensitivity. (DOFs
0.5 1.2) Unwise to look in upper
troposphere/boundary layer Tropics/subtropics
most reliable
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Dehydration
Drying (mixing by eddies/weather)
Isotopic depletion
Isotopes conserved
Dehydration
Isotopic depletion
Subtropical water source
Debate in community both impact climate
change. Isotopes differentiate the effects of
dynamics versus microphysics
Emmanuel and Pierhumbert, 1999
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What controls relative humidity?
dD when RH is high minus dD when RH is low(i.e.,
correlation, or slope d(dD)/d(RH)
Comparison with theoretical expectations
(hypotheses) provides a measure of which
processes control water vapor abundance
Noone, J Climate, in review
Noone, in review
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Processes define box budgets
Reversible moist adiabatic
Pseudoadiabatic
Ei/E given by Craig and Gordon (1965)
(Ficks law) Pi/P assume fractionation against
qi/q (Rayleigh-like)
Noone, J, Climate, in review
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Framework for interpreting HDO
(Noone, in review)
Very powerful analytic tool since constrains
system Two things to worry about 1) What is
source composition? (end members, balance of
sources) 2) What is slope? (rainfall efficiency,
type of cloud)
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PDFs of TES observations
W Pacific values where fgt1, i.e., rain
evaporation/exchange important S./C. Pacific
reversible adiabatic control (closed-system) N.
Pacific irreversible (Rayleigh)
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Cloud/rainfall efficiency (JJA)

Measure of how many times irreversible latent
heating occurs i.e., The fraction of the water
is removed from 850-500 hPa layer Derived
directly from knowing the isotopic fractionation
(a)
(Preliminary, adapted from Brown et al., in prep)
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Schematic of findings 40S-40N
Polar troposphere -400
Liquid retained in subtropics
f lt 1
Isentropic mixing
Free troposphere -220/-180
f gt 1 for convection
Dry downdrafts
E
PBL
Vapor recycled by rain evaporation
P
S -170/-80
Humidity, disequilibrium.
Land, ocean, sea ice (-40/-80 , or -110 )
Precipitation, (-40/-80 )
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HAVAIKI 2008Hawaii atmospheric vapor isotope k
intercomparison
PIs David Noone (U. Colorado) and Joe Galewsky
(U. New Mexico)

• Objectives
• Test laser spectrometersJPL, Picarro, Los Gatos
  Research
• Provide validation opportunity for TES and IASI
• Science objectivesUnderstand hydrology of dry
  zones

University of Colorado PI David Noone Adriana
Bailey Derek Brown Darin Toohey NASA JPL Lance
Christensen Chris Webster John Worden
University of New Mexico PI Joe Galewsky Zach
Sharp John Hurley Leah Johnson Mel Strong NOAA
Mauna Loa Obs John Barnes
Los Gatos Research Feng Dong Doug Baer Manish
Gupta Picarro Eric Crosson Priya Gupta Aaron van
Pelt
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Cryogenic traps
Vacuum flasks
http//cires.colorado.edu/science/features/vapor/

LGR WVIA
Picarro IWVA
JPL TWI
Inlet
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TES Special Observations Transects and Step
Stares
Continuation proposal being drafted Synergy
with ongoing TES and ongoing missions
Noone et al., in prep,
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Conclusions

• TES HDO has allowed rethinking of atmospheric
  hydrology more integrative (but tentative)
• Eddy transport processes at the edge of the
  subtropics important(moist air poleward, dry air
  equatorward)
• Captured the cyclic nature of the hydrologic
  cycle, not just state
• Subtropics balance between
• dehydration via mixing (although, not clear if
  this is from high latitude or high altitude)
• Moistening via (reversible) adiabatic processes
• Rainfall evaporation/exchange important in
  convective regions (within the subtropical
  mixing barrier)
• Undergraduate textbooks are misleading low
  humidity NOT caused by subsidence.
• Cloud processes quantified two opposing
  processes
• remoistening super-Rayleigh in region of strong
  convection
• slow/reversible sub-Rayleigh in dry parts of
  the atmosphere
• Fall just short of full partitioning due to lack
  of constraint (typically also use 18O, which we
  can not retrieve from TES)
• Demonstrate relevance of precise long-term
  measurements of atmospheric water measurements in
  key regions (Mauna Loa, Darwin, Amazon, ) to
  assess the response to climate forcing and that
  these compliment ongoing remote sensing

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Challenges and ongoing

• Validation of TES HDO remains an issueLack of
  validation data (John Worden will speak to this)
• Without this, one should be skeptical of science
  outcomes!
• Processes studies are limited by lack of vertical
  information (especially the boundary layer)
• Similarly, other science with upper troposphere
  sensitivity (18O must be on wish list for future
  missions)
• Modeling (GCMs) reached greater maturity (now 10
  models have isotopes). (Jeonghoon Lee/Kei
  Yoshimura)
• Real opportunities for TES HDO to constrain
  understanding of water cycle (Derek Brown/Kevin
  Bowman)

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