1. 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 Sciences University of Colorado, Boulder CO With Derek Brown (CU-Boulder), Joe Galewsky (UNM), John Worden (JPL), Kevin Bowman (JPL)

2. Objectives Global observations of HDO and H 2 O 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

3. Reminder of isotope physics Two simple isotope models… Condensation Vapor becomes depleted as heavy removed preferentially H O H liquid (e.g., ocean) vapor (e.g., atmosphere) Evaporation Returns to isotopic composition of the (ocean/land) source.  Ratio of HDO to H 2 O Measured as a difference from ocean water. Conditions under which condensation occurs is different from the conditions when evaporation occurs H O D

4.  D climatology (850-500 hPa) December 2004 – March 2008 Helliker and Noone in press, Noone, et al., in prep.

6. Averaging kernel diagonal December-January-FebruaryJune-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

7. Dehydration Drying (mixing by eddies/weather) Emmanuel and Pierhumbert, 1999 Isotopic depletion Isotopes conserved Debate in community – both impact climate change. Isotopes differentiate the effects of “dynamics” versus “microphysics” Isotopic depletion Subtropical water source

8. What controls relative humidity?  D when RH is high minus  D when RH is low (i.e., correlation, or slope” d(  D)/d(RH) Comparison with theoretical expectations (“hypotheses”) provides a measure of which processes control water vapor abundance Noone, in review Noone, J Climate, in review

9. Processes: define box budgets Noone, J, Climate, in review Ei/E given by Craig and Gordon (1965) (Fick’s law) Pi/P assume fractionation against qi/q (Rayleigh-like) Reversible moist adiabatic Condensation Mixing/hydration Pseudoadiabatic

10. 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) (Noone, in review) Framework for interpreting HDO

11. PDFs of TES observations W Pacific values where f>1, i.e., rain evaporation/exchange important S./C. Pacific reversible adiabatic control (closed-system) N. Pacific irreversible (Rayleigh)

12. Cloud/rainfall efficiency (JJA) (Preliminary, adapted from Brown et al., in prep) 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 (  ) %

13. Land, ocean, sea ice (-40/-80 ‰, or ~ -110 ‰) Humidity, disequilibrium. PBL Precipitation,  ‰  Free troposphere  ‰ S  ‰ Liquid retained in subtropics Schematic of findings: 40S-40N Vapor recycled by rain evaporation Polar troposphere  ‰ Isentropic mixing Dry downdrafts f < 1 f > 1 for convection E P

14. HAVAIKI 2008 Hawaii atmospheric vapor isotope “k” intercomparison Objectives 1.Test laser spectrometers JPL, Picarro, Los Gatos Research 2.Provide validation opportunity for TES and IASI 3.Science objectives Understand hydrology of dry zones PIs: David Noone (U. Colorado) and Joe Galewsky (U. New Mexico) 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

15. http://cires.colorado.edu/ science/features/vapor/ LGR WVIAPicarro IWVAJPL TWI Vacuum flasks Cryogenic traps Inlet

16. Noone et al., in prep, TES Special Observations: Transects and Step & Stares Continuation proposal being drafted: Synergy with ongoing TES and ongoing missions

17. 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: 1.remoistening “super-Rayleigh” in region of strong convection 2.slow/reversible “sub-Rayleigh” in dry parts of the atmosphere Fall just short of full partitioning due to lack of constraint (typically also use 18 O, 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

18. Challenges and ongoing Validation of TES HDO remains an issue Lack 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 ( 18 O 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)