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Isotopic constraints on moist processes over the tropics in NASA GISS ModelE2

Isotopic constraints on moist processes over the tropics in NASA GISS ModelE2. Robert Field, Daehyun Kim, Gavin Schmidt, John Worden, Allegra LeGrande, Max Kelley. JPL. Motivation. Climat e projections with ModelE form a major part of the GISS contribution to IPCC.

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Isotopic constraints on moist processes over the tropics in NASA GISS ModelE2

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  1. Isotopic constraints on moist processes over the tropics in NASA GISS ModelE2 Robert Field, Daehyun Kim, Gavin Schmidt, John Worden, Allegra LeGrande, Max Kelley JPL

  2. Motivation Climate projections with ModelE form a major part of the GISS contribution to IPCC. Are measurements of the isotopic composition of water vapor useful for model evaluation? Can we use them to identify compensation errors in the parameterizations? What physical processes might they be constraining, especially those that are hard to measure?

  3. Moisture in lower free troposphere ERA-I ModelE

  4. Model performance across different metrics Ocean Land Pattern correlation

  5. Aura TES HDO/H2O Simulator Varying TES retrieval quality and vertical sensitivity must be taken into account. Retrieval quality (%) Height of peak HDO retrieval sensitivity (hPa) See: Field, R.D., C. Risi, G.A. Schmidt, J. Worden, A. Voulgarakis, A.N. LeGrande, A.H. Sobel, R.J. Healy, A Tropospheric Emission Spectrometer HDO/H2O retrieval simulator for climate models, Atmospheric Chemistry and Physics, 12, doi:10.5194/acp-12-10485-2012, 10485-10504, 2012.

  6. Effects of TES operator on modeled δD fields Raw model δD (‰) Change to raw model δD after applying ‘standard’ TES operator. Only works for prescribed meteorology. Change to raw model δD after applying new TES operator. Works for arbitrary, free-running model configurations.

  7. δD over lower free troposphere TES ModelE with operator

  8. Model performance across different metrics Ocean Land Pattern correlation

  9. Model performance across different metrics Ocean Land Pattern correlation

  10. q as a constraint on convective recycling Ocean Land Pattern correlation Convective recycling ratio

  11. δD as a constraint on convective recycling Ocean Land Pattern correlation Convective recycling ratio

  12. Key points • Isotopic measurements provide a stronger constraint than moisture amount or precipitation, especially over the ocean. • They can provide guidance on the fidelity of hard-to-measure processes within the model. • Next up: • Mechanistic constraints on model variability: MJO & ENSO • Land surface fluxes: evaporation / transpiration partitioning • Observational error

  13. Convective moisture recycling • Ratio of re-evaporated to total convective condensate • Not well constrained

  14. q and δD over lower free troposphere qERA-I δDTES HDO concentrations are expressed relative to an ocean water standard, in units of permil (‰).

  15. Capturing variation in vertical sensitivity Obs. More complicated categorizations

  16. Precipitation GPCP ModelE

  17. TES vs. ModelE δD δDTES δDRaw- δDTES δDRetrAK - δDTES δDCatAK - δDTES

  18. 16O 18O 2H 1H 1H 1H 0.20% 0.03% 99.73% 16O 1H 1H Stable isotopes (isotopologues) of water The heavy isotopes of water evaporate less readily and condense preferentially. There are now sufficient HDO measurements in the troposphere against which to evaluate models. We are currently working with HDO/H2O retrievals from the Tropospheric Emission Spectrometer (TES).

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