C.A.M.Brennikmeijer, S.Assonov, S.Gromov MPI for Chemistry, Mainz

1. What CARIBIC sees flying overthe South Asian Monsoonthrough the prism of CO2 isotopesand Monte-Carlo Boxing C.A.M.Brennikmeijer, S.Assonov, S.Gromov MPI for Chemistry, Mainz AEA Environment Laboratories, Seibersdorf

2. The Monte-Carlo nano-“how-to” • Largestatistics – simulate a lot! • quantitative trial and error may yield qualitative picture • MC can be used in “forward” and “reverse” modes • forward: how do results depend on parameter distributions? • reverse: which parameters lead to desired results? • MECCA has the MC simulation mode • it is “forward” only • out-of-box only probing uncertainties in reaction rates • no out-of-box facility for uncertainties in initial conditions/parameters • serial execution and output Source: Wikipedia

3. The advanced MC engine in MESSy • This is work in progress • designed to be applicable with any sub-model • implemented for CAABA/MECCA • applied to CARIBIC observations on CO2 isotope in S. Asian Monsoon • Features • multiple tries within one run (until sufficient statistics is gathered) • generation of any number of normally/uniformly distributed parameters/boundary conditions • multiple hit targets (e.g. observed samples or experiment conditions) • multiple sets of parameters (e.g. hitting desired MRs or reactivities) • common & hit-target-wise output of desired parameters/results into netCDFs • single/parallel (using MESSy’s mo_mpi) execution, master PE and restart • highly scalable with CAABA

4. Application: What CARIBIC sees over theSouth Asian Monsoon? • Flights 244-245, August 2008, Frankfurt ↔ Chennai, 28 samples • We focus on samples #10-19: • Beautiful latitudinal gradients in CH4, CO, O3 and δ18O(CO2), => mutual correlation! • Tight tracer-tracer relationship: CO2 and δ13C(CO2)

5. The observations – CARIBIC isotope CO2 • Isotope CO2: • Mixing relationship in δ13C (“Keeling-plot”) • No Keeling for δ18O, but clear latitudinal gradient? • > Isotope ratios present different signals • N.B.: Opposite is seen for δ13C and δ18O in CO! CO2 uptake → CO2 respiration by vegetation ← by veg./soils (plants like (“breath” out CO2 12C over 13C) with diff. 18O/16O) NET FLUXES GROSS FLUXES

6. What do we want to infer? • Gross regional CO2 fluxes • δ18O of local precipitation • Terrestrial 13C fractionation (BL air ↔ biosphere CO2 exchange dynamics, how plants take up CO2), seen in δ13C(CO2) and Keeling-plot • How “dirty” is the air in the Monsoon BL (e.g. O3, CO, CH4, SF6 mixing ratios) • Any better estimate on the emission ratios (e.g. w.r.t. CO) characteristic for the region/season? • How much of this air is seen by CARIBIC in the FT/UT (quantify mixing/entrainment/transport)?

7. And how can we infer? • Using sensible assumptions on the history of air sampled

8. And how can we infer? • Using sensible assumptions on the history of air sampled

9. And how can we infer? • Using sensible assumptions on the history of air sampled

10. And how can we infer? • Using sensible assumptions on the history of air sampled

11. And how can we infer? • Sensible assumptions on the history of air sampled

12. Difficulties • We cannot use a simpler model • It likely underparameterises the formulation (does not grasp important relationships, e.g. CO2 re-diffusion from stomata) • It never has an analytical solution • Our sample size is too small (too large?) and variable • Conditions (i.e. mixing proportions of HL, LL, BL air) change with sample • Parameter space is large • Solutions? • Use the Monte-Carlo approach • Try to “hit” every sample • Analyse in detail a selected sample with MC again

13. The Most Sensible Model? • Compartmental kinetic exchange in CAABA/MECCA • with a very much simplified CO2 exchange with the biosphere • Hit sets: • Trace gases (TG): CO2, CO, O3, CH4, SF6 • Isotope CO2 (IC):CO2 + 13C + 18O • Known values come from CARIBIC and/or surface stations • Unknowns(*) are probed in the MC • Total parameters • 16 normally distributed • 12 uniformly distributed • Huge parameter space

14. “Hit sets” • Used to select different successful realization scenarios

15. Results – mixing • “Hitting” trace gases • Allows inferring air mixing proportions => Largest BL air fraction in Sample #11 (colour denotes sample no.) • Using fixed proportions • Reduces MC parameter space • Allows obtaining much moreprecise distributions (results)

16. Results – BL trace gases • BL air composition derived from the sample #11 • Distinct MRs predicted • Allows estimating emission fluxes/ratios

17. Results – BL isotope CO2 • BL air composition derived from sample #11 • Trace gases predict only isotopic mixing of CO2 (‘t4’ set, left) • “Hitting” isotope ratios allows to get results sensitive to biosphere-exchange processes (‘ii’ set, right)

18. Results – BL CO2 fluxes • Relative (to advection through BL) CO2 fluxes from sample #11 • Lifetime of a CO2 molecule in uptake is 3-4x shorter than in advection • In-diffusion flux is 90-100x, retro-diffusion is 50-60x => fixation at 40x • Respiration is slow (0.2x) ?

19. Recap • MESSy framework has an advanced MC engine • simulates chemistry in a box (or many boxes) with MECCA • applicable to any base (box) model • It allows efficiently scanning parameter space • highly scalable => huge space can be explored • multi-target => different cases in the same space • Successful application to CARIBIC observations allows to do “remote-sensing” of the Asian Monsoon BL air properties • air mixing proportions, trace gas mixing ratios • exchange with the biosphere in the BL • Application to chamber studies is exciting! • inferring reaction rates for known/proposed pathways? • quantifying any parameter (i.e. insolation, KIE, deposition)

20. Thank you!

21. Performance: CO2 isotopes (IC) set Try-hit statistic Sample hit “inefficiency”

22. Performance: trace gases (TG) set Try-hit statistic Sample hit “inefficiency”

23. What does the record of δ13C of air CO2 show? Credit: S. Assonov Mace Head, NOAA data. CO2 concentration varies due to CO2 sources/sinks. Seasonality: on local and global scale d13C co-varies with CO2. Sources/sinks have often very similar d13C signatures, e.g. photosynthesis &respiration. Difference in d13C-slopes mean variable source/sink balances. d13C observations help constraining CO2 fluxes. d13C(CO2) reflects NET CO2 fluxes.

24. Carbon cycle: Fluxes are bidirectional Credit: S. Assonov fromICCP 2007.

25. δ18O and CO2 exchange with leaves and soil Credit: S. Assonov Yakir et al., 2000 Bowen et al., 2003 CO2 isotopically equilibrates with water: This is exchange : d18O is not a source/sink signature; it could be NO concentration change. Different water pools (soils and leaves) have different d18O(H2O), resulting in different d18O(CO2). d18O of water in soils and leavesdepends on precipitation’ d18O, temp. & rel.humidity. Ciais et al., 1997 d18O-precipitation maps are based on extrapolation of observational data.

26. Processes affecting the δ18O signal in CO2Credit: S. Assonov Processes & parameters forming d18O(CO2) signal : • Precipitation d18O pattern • Soil invasion • Respiration • Leaf CO2 exchange • Temperature • Evapotranspiration / air relative humidity + Transport. d18O(CO2) reflects GROSS fluxes, needs deeper understanding.

27. What (else) can we infer? • How much of Monsoon air is seen by CARIBIC in the FT/UT (quantify mixing/entrainment/transport)?

28. Performance: IC • The isotope CO2 (IC) probe-hit statistic

29. Performance: TG • The trace-gas (TG) probe-hit statistic

30. Some results, IC, sample #16

31. Some results • Intra-sample distribution of parameters (IC)

32. Some results • Intra-sample distribution of parameters (TG)