Do we understand tropospheric δd remote sensing products? Example for the MUSICA dataset

Transcription

1 Do we understand tropospheric δd remote sensing products? Example for the MUSICA dataset M. Schneider, Y. Gonzalez, A. Wiegele, E. Christner, S. Barthlott, F. Hase, T. Blumenstock, S. Dohe, M. Kiel, C. Dyroff, O. Garcia, E. Sepúlveda slide 1. Overview of tropospheric δd remote sensing datasets 2 2. The project MUSICA 6 3. Theory of δd remote sensing data Empirical validation of the isotopologues added value Further issues for discussion: 32 (1) column-integrated δd (XδD) (2) near infrared retrievals (TCCON XδD) (3) δd profiles from space (under-constrained retrievals?) 6. Summary 4 Acknowledgement: Large part of this work has been made within the project MUSICA. MUSICA is funded by the European Research Council under the European Community's Seventh Framework Programme (FP7/27-213) / ERC Grant agreement n

2 1. Overview of tropospheric δd remote sensing data sets 2

3 Chapter 1 δd remote sensing from ground NDACC / FTIR (MUSICA): e.g., Schneider et al. (26; 21; 212) Pros: - theoretically well characterised (MUSICA) - empirical characterisation is ongoing (MUSICA) - dataset starts in the mid 199s Cons: - still not available via a web-database (dissemination via the NDACC database/webpage is planned for 214) TCCON / FTIR: Risi et al. (212) Pros: - easily available via the TCCON database/webpage Cons: - limited theoretical data characterisation - theoretically low sensitivity for δd (in the infrared the HDO lines are rather weak) 3

4 Chapter 1 Tropospheric δd remote sensing from space AURA / TES: e.g., Worden et al. (26; 27; 212) Pros: - some profile information? - theoretical data characterisation Cons: - TES has a limited coverage METOP / IASI (KIT IMK-ASF, MUSICA): Schneider and Hase (211) Pros: - large efforts in theoretical + empirical characterisation (MUSICA) - IASI has a high potential! Cons: - still not available via a web-database (dissemination is planed for 214) - so far reduced dataset (only for some selected sites) METOP / IASI (ULB): Lacour et al. (212) Pros: - large dataset with ongoing improvements in data characterisation - IASI has a high potential! Cons: - theoretically reduced sensitivity? SCIAMACHY and GOSAT: Frankenberg et al. (29; 213); Boesch et al. (213) Pros: - sensitivity down to the surface Cons: - reduced sensitivity (weak HDO lines)? - data characterisation could be improved 4

5 Chapter 1 Stratospheric (+ UTLS) δd remote sensing from space MIPAS: SMR: ACE: 5

6 2. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) 6

7 Chapter 2 ERC project MUSICA: MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water + Mexico (1) ground-based FTIR Addis (2) space-based IASI (3) in-situ, surface + aircraft MUSICA integrates ground- & space-based remote sensing, and in-situ observations 7

8 Chapter 2 MUSICA NDACC/FTIR dataset, large-term + high quality precipitable H 2 O [mm 1 Eureka, 8 N Ny Alesund, 79 N Bremen, 53 N Karlsruhe, 49 N Jungfraujoch, 47 N Izana, 28 N Wollongong, 35 S Arrival Heights, 78 S 1.1 Kiruna, 68 N Lauder, 45 S column integrated D [ / Data products so far available for ten stations, occasionally dating back to the mid 199s. Bremen, 53 N Jungfraujoch, 47 N Eureka, 8 N Ny Alesund, 79 N Kiruna, 68 N Karlsruhe, 49 N Izana, 28 N Wollongong, 35 S Lauder, 45 S Arrival Heights, 78 S Schneider et al., AMT 212 8

9 Chapter 2 MUSICA IASI dataset, large scale picture August 1, Longitude [ D [ / August 11, 211 Latitude [ August 12, Data products so far available for the period for observations close to the MUSICA FTIR/NDACC sites Kiruna, Karlsruhe, and Izana. Izana Longitude [ Schneider and Hase, ACP, 211 and Wiegele et al., in preparation

10 Chapter 2 MUSICA surface in-situ reference sites KIT-IMK, Karlsruhe, Germany: reference site for the continental boundary layer 49 N 8 E 11 m a.s.l. adopted from Google Maps 1

11 Chapter 2 MUSICA surface in-situ reference sites Izaña (CIAI-AEMET), Tenerife, Spain: reference sites for the lower free troposphere 28 N, 16 W Izana: 237 m a.s.l. Teide: 355 m a.s.l. adopted from Google Maps 11

12 Chapter 2 MUSICA surface in-situ reference data sets Karlsruhe (11 m a.s.l.) Tenerife (237 m a.s.l.) Tenerife (355 m a.s.l.) H2O [ppmv 1 H2O [ppmv 1 5 H2O [ppmv D [ / D [ / -2-4 D [ / Christner et al., in preparation Gonzalez et al., in preparation Data products so far available for: (1) Tenerife (subtropical free troposphere): March September 212 and since March 213 (2) Karlsruhe (continental boundary layer): January 212 May

13 Chapter 2 MUSICA aircraft in-situ reference data set (ISOWAT instrument) ISOWAT: Dyroff et al., 21 altitude [km End of July 213: 6 profiles, 1 m 68 m a.s.l H2O [ppmv -4-2 D [ / Dyroff et al., in preparation 13

14 3. Theory of δd remote sensing data 14

15 Chapter 3 Principles of passive atmospheric remote sensing - We measure radiation spectra (direct solar light, thermal radiation from the Earth s surface). - Different atmospheric states can lead to very similar spectra (we face an ill-posed problem). - We cannot determine a unique solution, but the radiation measurement enables us to estimate the most likely solution (for the given measurement). - The spectra are inverted by an optimal estimation method. Minimise the cost function: Rodgers (2) measurement information + a priori information the result depends on a realistic a priori description (x a and S a )

16 Chapter 3 The proper description of the water isotopologue state The interesting parameters are H2O and (HDO/H2O). we need an optimal estimation for H2O and (HDO/H2O) and not for H2O and HDO!!! However: Jacobians of HDO/H2O are very difficult to calculate! Trick: (1) Transfer the problem on a logarithmic scale (2) Transformation from the {ln[h2o} and {ln[hdo} states to the {(ln[h2o + ln[hdo)/2} and {(ln[hdo - ln[h2o)} states Trans. Matrix: The {(ln[h2o + ln[hdo)/2} state is a good proxy for humidity The {(ln[hdo - ln[h2o)} state is a good proxy for δd Schneider et al., AMT

17 Chapter 3 The realistic apriori statistics S a : covariance for the {ln[h2o, ln[hdo} state S a : covariance for the {(ln[h2o + ln[hdo)/2, (ln[hdo - ln[h2o)} state S a S a Schneider et al., AMT 212 An atmospheric remote sensing retrieval of water isotopologues must consider the significant cross correlations between H2O and HDO!!! 17

18 Chapter 3 Analytic description of the data characteristics For an analytic characterisation we can use the isotopologue proxy states : {ln[h2o, ln[hdo} and {(ln[h2o + ln[hdo)/2, (ln[hdo - ln[h2o)} H2O and δd averaging kernels: be careful with a potential cross dependency! Uncertainty estimations: Schneider et al., AMT 212 The characterisation and error analysis can be made in analogy to Rodgers (2). We can theoretically describe the combined H2O / δd product! 18

19 Chapter 3 Theoretical characterisation of the remote sensing data Example for MUSICA NDACC / FTIR averaging kernels (remote sensing from ground): x = {ln[h2o, ln[hdo)} x = {.5*(ln[H2O + ln[hdo), (ln[h2o -ln[hdo} A A = PAP -1 A = CPAP -1 The MUSICA NDACC / FTIR data offer some profile information. We can distinguish between lower and middle/upper troposphere. Schneider et al., AMT

20 Chapter 3 Theoretical characterisation of the remote sensing data Example for MUSICA METOP / IASI averaging kernels (remote sensing from space): x = {ln[h2o, ln[hdo)} x = {.5*(ln[H2O + ln[hdo), (ln[h2o -ln[hdo} A A = PAP -1 A = CPAP -1 The MUSICA METOP / IASI data offers no profile information. The sensitivity is typically limited to the middle troposphere. Wiegele et al., in preparation we need the {humidity, δd} - proxy state in order to fully understand the sensitivity of the remote sensing system. MUSICA provides a good example for the complexity of the data product! 2

21 Chapter 3 Theoretical characterisation of the remote sensing data Example for MUSICA uncertainty estimates: (1) Precision altitude [km MUSICA IASI NOI GNT LTT MTT UTT STT EMI SWA GNA CL1 CL2 x-dep. on Hum. MUSICA NDACC / FTIR IASI: 3 (for H 2 O: 1-3%) FTIR: 25 (for H 2 O: 1-2%) error, sqrt(diag(s e )) [ / (2) Bias IASI: 1 (for H 2 O:.5-1%) FTIR: 1 (for H 2 O: 5-1%) the {humidity, δd} - proxy state enables us to perform a comprehensive analytic error estimation in analogy to Rodgers (2) 21

22 4. Empirical validation of the isotopologues added value 22

23 Chapter 4 Empirical validation of the isotopologues added value Question: what is the added value of the isotopologues? The MUSICA surface Picarro in situ data (measured at Karlsruhe and Izana): KA + IZ KA -1 Rayleigh distillation processes are a dominating source of δd variations -2 D [/ -2 D [/ KA + IZ IZ H2O [ppmv 1 H2O [ppmv The MUSICA aircraft / ISOWAT in-situ data (measured at -7 km, over the subtropical ocean): Flight Flight Flight Flight Flight 1373 Flight all flights D [/ H2O [ppmv 1 H2O [ppmv 1 H2O [ppmv 1 H2O [ppmv 1 H2O [ppmv 1 H2O [ppmv 23

24 Chapter 4 Empirical validation of the isotopologues added value Question: what is the added value of the isotopologues? Global linear relation between ln[h2o and δd obtained at different sites and different altitudes): Picarro data all data (ISOWAT + Picarro) regression for all data (ISOWAT + Picarro) -2 D [/ D [/ -2 ISOWAT data all data (ISOWAT + Picarro) regression for all data (ISOWAT + Picarro) H2O [ppmv 1 1 H2O [ppmv R2 =.793 (!) about 8% of the δd signal is linearly correlated to ln[h2o, i.e., adds no value to the H2O measurement. We are interested in the remaining 2% (the added value )! 24

25 Chapter 4 Empirical validation of the isotopologues added value There is a globally valid and significant linear correlation between ln[h2o and δd. This has to be considered when validating isotopologue data. δd correlation plots mainly reflect the δd signal that can easily be predicted from the H2O signal! δd correlation plots DO NOT validate the added information of the isotopologues! D FTIR (11 m asl) [ / -1-2 Karlsruhe, boundary layer -2-1 D Picarro (11 m asl) [ / D FTIR (237 m asl) [ / Izana, free troposphere D Picarros (at 237 m m asl) [ / D IASI [ Izana, 5 km D FTIR [ These plots provide no validation for the added value!!! Instead: we should validate δd-versus-h2o plots ( two dimensional validation) 25

26 Chapter 4 Empirical validation of the isotopologue s added value Picarro surface in-situ (continental boundary layer) MUSICA NDACC / FTIR Picarro L212-i, Karlsruhe (11 m asl) D [ / -1-2 all (18 days) high D (1 days) all (18 days) low D (5 days) Filter: - afternoon (15-17 UT) - coincidences to FTIR H 2 O [ppmv H 2 O [ppmv FTIR (NDACC / MUSICA), Karlsruhe (11 m asl) D [ / -1-2 all coinc. to high Picarro D all coinc. to -4 low Picarro D 1 1 Filter: - afternoon (13-19 UT) - DOFs > coincidences to Picarro H 2 O [ppmv H 2 O [ppmv 26

27 Chapter 4 Empirical validation of the isotopologue s added value Picarro surface in-situ (lower free troposphere) MUSICA NDACC / FTIR Picarro L212-i, Izana (237 m asl) D [ / all (114 days) high D (4 days) all (114 days) low D (43 days) 1 1 Filter: - early morning (6-8 UT) - coincidences to FTIR H 2 O [ppmv H 2 O [ppmv FTIR (NDACC / MUSICA), Izana (237 m asl) D [ / all coinc. to high Picarro D all coinc. to low Picarro D 1 1 Filter: - early morning (before 1 UT) - DOFs > coincidences to Picarro H 2 O [ppmv H 2 O [ppmv 27

28 Chapter 4 Empirical validation of the isotopologue s added value Aircraft / ISOWAT in-situ MUSICA NDACC / FTIR 6 5 ISOWAT (FTIR grid) Overview: MUSICA NDACC / FTIR s added value at 56 m asl: altitude [km altitude [km ISOWAT (smoothed with FTIR kernel) D [ / ISOWAT (at 56 m asl) NDACC /FTIR (at 56 m asl) 2 6 FTIR altitude [km D [ / H2O [ppmv D (difference to apriori) [ / 28

29 FTIR (smoothed with IASI avk) Chapter 4 Added value in space-based remote sensing data: NDACC / FTIR MUSICA METOP / IASI D [permil Izana upper 1% lower 1% all data -15 Karlsruhe Kiruna D [permil FTIR IASI D [permil vmr H 2 O [ppmv vmr H 2 O [ppmv vmr H 2 O [ppmv Wiegele et al., in preparation 29

30 Chapter 4 Direct comparison of in-situ and space-based remote sensing data: Aircraft / ISOWAT MUSICA METOP / IASI 29 IASI D [ / ISOWAT D (averaged around 5 km) [ / Longitude [ 29-2 IASI D [ / ISOWAT D (averaged around 5 km) [ / Longitude [ 3

31 Chapter 4 Direct comparison of in-situ and space-based remote sensing data: Aircraft / ISOWAT MUSICA METOP / IASI Validation of the added value : ISOWAT (averaged around 5 km) MUSICA IASI (at 5 km) -2-2 D [ / D [ / 1 2 H2O [ppmv 1 2 H2O [ppmv 31

32 5. Further issues for discussion 32

33 Chapter 5 Discussion (1): column-integrated δd (XδD) Question: Do column-integrated isotopologue data represent near surface processes? Analysis with the six ISOWAT profiles (21 st 31 st July 213): 7 6 ISOWAT profiles ISOWAT (integration 5-5 m asl) ISOWAT (integration 5-7 m asl) altitude [km D [ / FT BL X D [ / XH2O [ppmv XH2O [ppmv On a day-to-day time scale the column-integrated data seem to be no proxy for near surface processes. 33

34 Chapter 5 Discussion (1): column-integrated δd (XδD) Question: Do column-integrated isotopologue data represent near surface processes? Analysis with the NDACC / FTIR data (Karlsruhe + Izana, Jan. 212 Sept. 213): X D [ / FTIR (NDACC / MUSICA), Karlsruhe (11 m asl) 1 all high at surface (Picarro) all low at surface (Picarro) X D [ / FTIR (NDACC / MUSICA), Izana (237 m asl) 1 all high at surface (Picarro) all low at surface (Picarro) XH 2 O [ppmv XH 2 O [ppmv XH 2 O [ppmv XH 2 O [ppmv On longer time scales (e.g., seasonal cycles) the column-integrated data might partly serve as a proxy for near surface processes. 34

35 Chapter 5 Discussion (2): near infrared remote sensing (TCCON XδD) In the near infrared the H2O signatures are strong, but the HDO signatures are rather weak! There are some concerns: - reduced sensitivity for δd - different sensitivities for H2O and HDO might cause a significant crossdependency of δd on H2O TCCON retrieval strategy: - daily update of H2O and δd apriori profiles - Subsequent scaling of prescribed H2O and HDO profiles Apriori is variable! Examples for Central Europe (Karlsruhe): 15 1 summer: 211/7/15 winter: 212/1/ /7/15 212/1/15 H2O apriori profile NCEP analysis altitude [km 5 altitude [km 5 δd apriori profile very simplified model: δd =.695 * ln[h2o H2O [ppmv -4-2 D [ / 35

36 Chapter 5 Discussion (2): near infrared remote sensing (TCCON XδD) Analysis of TCCON XδD signals, example Karlsruhe: X D [ / -2-4 X D MUSICA X D + 1 / MUSICA X D [ / -2-4 diagonal X D [ / -2-4 X D apriori model X D from apriori model [ / -2-4 Good 1:1 -agreement of XδD with (1/f)*AXδD: X D [ / -2-4 X D (1/f)*AX D (1/f)*AX D [ / X D [ / f: retrieved H2O scaling factor (f = XH2O/AXH2O) AXH2O, AXδD: apriori The strong correlation of XδD with (XH2O/AXH2O)*AXδD is likely due to a cross dependency of the TCCON s XδD on TCCON s XH2O product. TCCON s XδD product reflects NCEP H2O uncertainties! 36

37 Chapter 5 Discussion (2): near infrared remote sensing (TCCON XδD) Added value in TCCON XδD? Example Karlsruhe: MUSICA NDACC / FTIR, Karlsruhe (11 m asl) -1-1 X D [ / -2-4 all high D -2-4 all low D TCCON / FTIR, Karlsruhe (11 m asl) -1-1 X D [ / -2-4 all for high MUSICA X D -2-4 all for low MUSICA X D XH 2 O [ppmv XH 2 O [ppmv Conclusion: There is added value in the TCCON XδD product, but the issue of cross dependency makes an interpretation of the data rather difficult. A better theoretical characterisation of the TCCON XδD product is needed! 37

38 IASI kernels Wiegele et al., in preparation IASI kernels Chapter 5 Discussion (3): δd profiles from space? D [permil vmr H 2 O [ppmv vmr H 2 O [ppmv High and low δd signals as defined by coinciding MUSICA NDACC / FTIR measurements Coinciding IASI signals (MUSICA standard constraint) D [permil altitude [km altitude [km vmr H 2 O [ppmv vmr H 2 O [ppmv Coinciding IASI signals (loose constraint) D [permil altitude [km altitude [km vmr H 2 O [ppmv vmr H 2 O [ppmv

39 Chapter 5 Discussion: δd profiles from space? The δd signal ( added value signal) is really small! Measuring it is a real challenge for space-based remote sensing! (1) We need to work with large spectral windows (many H2O and HDO lines with different strengths) in order to get enough spectral signal. (2) We must be careful with our constraint: under-constraining the problem produces a noisy signal of doubtful scientific value. 39

40 6. Summary 4

41 Chapter 6 Summary 1) Different remote sensing datasets are available, but some are poorly characterised! 2) The concept of isotopologue proxy states allows a characterisation with standard methods (with the methods described in Rodgers, 2). 3) Validation should be performed on the H2O δd surface: two dimensional validation problem! δd correlation plots provide a rather limited picture. 4) The added value has been demonstrated for the MUSICA dataset (NDACC / FTIR and METOP / IASI). 5) Further issues for discussion: - Scientific information in XδD? - Interpretation of the near infrared δd data (TCCON, SCIAMACHY, GOSAT)? - Space-based algorithms can be tuned for profile sensitivity (by reducing the constraints). However, then the measured spectra are likely over-interpreted, i.e., the data uncertainty increases. Question: where is the gain in information? KEY QUESTIONS: - A characterisation of the datasets is needed (theoretical + empirical): To what extent do the isotopologue datasets add information to the apriori and/or to the H2O data? - Small uncertainty / high vertical + horizontal resolution / global / real time: All at once is not possible! Where are the priorities for science? - Outlook: Would an operational IASI / METOP isotopologue product be a breakthrough? 41

42 EXTRA SLIDES 42

43 Outlook: NDACC and TCCON δd retrievals? The good quality of NDACC H 2 O and δd products has been extensively documented. NDACC spectra cover the middle infrared (75 cm cm -1 ), example for main absorption signatures at 265 cm cm -1 : H 2 16 O HD 16 O CH 4 43

44 Outlook: NDACC and TCCON δd retrievals? For TCCON δd products there is so far no similar quality documentation! TCCON spectra cover the near infrared (>38 cm -1 ). Near infrared region with strongest HD 16 O absorption signatures (4 425 cm -1 ): H 2 16 O HD 16 O CH 4 the TCCON HD 16 O signature is very weak and TCCON δd data might be significantly affected by the actual H 2 O and CH 4 amounts. Further studies are needed! 44

45 MUSICA reference sites Izaña (CIAI-AEMET): - NDACC / MUSICA FTIR station: 2-3 times per week H 2 O and δd mid-infrared remote sensing profiles. -TCCON FTIR station: 1-2 times per week near infrared H 2 O remote sensing profiles. - Picarro L212-i analyzer for isotopic H 2 O: continuous free tropospheric in-situ δd and δ 18 O (vapour + precipitation). - Winter and summer aircraft campaigns applying homemade ISOWAT instrument: insitu H 2 O, δd, and δ 18 O profiles. - WMO radiosonde station #618: twice daily Vaisala RS92 H 2 O in-situ profiles. -Meteorological Research Observatory: continuous free tropospheric in-situ T, P, RH, U, V, etc. 45

46 MUSICA reference sites KIT-IMK (IMK-ASF, Karlsruhe): - NDACC / MUSICA FTIR station: 2-3 times per week H 2 O and δd mid-infrared remote sensing profiles. -TCCON FTIR station: 1-2 per times week near infrared H 2 O remote sensing profiles. - Picarro L212-i analyzer for isotopic H 2 O: boundary layer in-situ δd and δ 18 O. - Homemade ISOWAT analyzer for isotopic H 2 O: planed Picarro/ISOWAT intercomparison study. - Tall tower (2 m): in-situ T, P, RH, U, V, etc. profile measurements. 46

47 Empirical validation of the isotopologue s added value Surface in-situ (237 m m a.s.l.) NDACC / FTIR Picarro L212-i ( m asl) D [ / all (4 days) high D (21 days) all (4 days) low D (9 days) 1 1 Filter: - early morning (6-8 UT) - coincidences between the two Picarros - coincidences to FTIR H 2 O [ppmv H 2 O [ppmv FTIR (NDACC / MUSICA), Izana (237 m asl) D [ / all coinc. to high Picarro D all coinc. to low Picarro D 1 1 Filter: - early morning (before 1 UT) - DOFs > coincidences to Picarro H 2 O [ppmv H 2 O [ppmv 47

48 Empirical validation of the isotopologue s added value 237 m a.s.l.: aircraft / ISOWAT in-situ NDACC / FTIR ISOWAT (at 237 m asl) Picarro 212i (237 m asl) Picarro 212i (355 m asl) D [ / D [ / D [ / H2O [ppmv 1 1 H2O [ppmv 1 1 H2O [ppmv D [ / -1-2 NDACC / FTIR (at 237 m asl) H2O [ppmv 48