1. Page 1 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Key 13: The future observing system in the UTLS Author: W.A. Lahoz Data Assimilation Research Centre, University of Reading RG6 6BB, UK

2. Page 2 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Among the maxims on Lord Naoshige’s wall there was this one: “Matters of concern should be treated lightly.” Master Ittei commented, “Matters of small concern should be treated seriously.” Among one’s affairs there should not be more than two or three matters of what one could call great concern. If these are deliberated upon during ordinary times, they can be understood. Thinking about things previously and then handling them lightly when the time comes is what this is all about. To face an event and solve it lightly is difficult if you are not resolved beforehand, and there will always be uncertainty in hitting your mark. However, if the foundation is laid previously, you can think of the saying, “Matters of great concern should be treated lightly,” as your own basis for action. Prepare well… Hagakure, The Book of the Samurai

3. Page 3 COST/ESF School: UTLS, Cargese, 3-15 October 2005 The importance of the UTLS region: NWP; climate; monitoring; understanding atmosphere (obs, models) What information we require from the UTLS: Geophysical parameters, coverage, data transmission & resolution How can we provide this information: What is the current global observing system and how it should evolve How can DA help to provide this information & quantify value of global observing system components? Topics:

4. Page 4 COST/ESF School: UTLS, Cargese, 3-15 October 2005 The importance of the UTLS region

5. Page 5 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Observation types used by Met Office for NWP UTLS

6. Page 6 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Courtesy IGACO UTLS

7. Page 7 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Importance of UTLS  Radiative-dynamics-chemistry feedbacks associated with strat O 3 & relevant to studies of climate change & attribution (WMO 1999)  Important role UTLS water vapour plays in atmos radiative budget (SPARC 2000)  Need realistic representation of the STE & between tropics & extra-tropics in strat -> key role in the distribution of strat O 3 (WMO 1999) -> radiative budget  ALSO: Quantitative evidence knowledge of the strat state may help predict the tropospheric state at time-scales of 10-45 days (Charlton et al. 2003) -> strat—trop connections

8. Page 8 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Importance of water vapour Radiation: Dominant GHG in atmosphere Radiative forcing from water vapour Dynamics: Diagnostic of atmospheric circulation Transport & distribution of tracers Chemistry: Source of OH; PSCs; HOx cycles Ozone loss via PSCs & HOx

9. Page 9 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Recommendations from SPARC assessment on UT/S H2O  Quantify & understand differences between sensors: - importance of high resolution in situ data for trop/strat transport  Strong validation programmes: - previous lack in UT  Continuity of measurements to determine long-term changes especially stratospheric H2O (what is the trend?)  Monitor UTH to determine long-term variations. - Need complementary observations  Process studies of UTH & convection. - Joint measurements of H2O, cloud microphysical properties & tracers with signature of “age of air”  More observations in tropical tropopause region (15-20 km) (in situ & remote sensing) needed to improve understanding of STE  Monitor stratospheric H2O (CH4 measurements desirable). Overlap of future satellites with current instruments  Theoretical work to understand observations

10. Page 10 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Integrated Global Atmospheric Chemistry Observations (IGACO) / Integrated Global Observing Strategy (IGOS) -> identified four grand challenges in atmospheric chemistry: Tropospheric air quality: O 3, CO,… Oxidation efficiency of the atmosphere: O 3, CO, Stratospheric chemistry and ozone depletion: O 3, H 2 O,… Chemistry-climate interactions: CO 2, O 3, H 2 O,… Increased recognition of importance of chemistry Atmospheric chemistry: Role of UTLS

11. Page 11 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Importance of ozone Recognition of key role of stratospheric O 3 in determining temperature distribution & circulation of atmosphere -> Incorporation of photochemical schemes of varying complexities into climate models:  Coupled climate/chemistry models (e.g. Austin 2002)  CTMs for study of ozone loss (e.g. Khattatov et al. 2003)  Cariolle scheme in NWP systems (ECMWF; Struthers et al. 2002) SPARC CCMVal initiative: evaluate Chemistry-climate models

12. Page 12 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Paucity of observations of key species (H 2 O, O 3 ): time and space; coverage Model shortcomings: parametrizations, e.g., convection Coupling dynamics/radiation/chemistry: how to couple? how to include aerosols? Many processes require high temporal & spatial resolution: observations & models; higher resolution DA (balance?) Lack of global observations of stratospheric winds in the current operational meteorological system We have no good current estimates of state of the tropical stratosphere Challenges in UTLS

13. Page 13 COST/ESF School: UTLS, Cargese, 3-15 October 2005 What information we require from the UTLS

14. Page 14 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Courtesy IGACO 2004 Chemical variables Dynamical (and other) variables

15. Page 15 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Based on IGACO Group 1: O 3, H 2 O, CO 2, CO, NO 2, BrO, ClO, HCl, N 2 O, CFCs, ClONO 2 & aerosol optical properties. Reasonably comprehensive set of global observations for both troposphere & stratosphere using sparse number of LEOs, g-based networks & aircraft measurements. Good atmospheric modelling capabilities. Good network of g-based & satellite observations that only require maintenance & some gaps to be filled. Routine aircraft observations but not yet comprehensive enough. DA in good shape. Observation requirements

16. Page 16 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Courtesy IGACO 2004 Target/threshold (1) Hours (NWP); (2) days-weeks (O3 loss,…); (3) months (climate research)

17. Page 17 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Based on IGACO Group 2: CH 4, HCHO, VOCs, SO 2, HNO 3, OClO, NO, CH 3 Br, the halons, and j(NO 2 ) and j(O 1 D). All current satellites are in experimental “demonstration” mode & only have limited lifetime. Some g-based in situ measurements. Except for CH 4, global network sparse. Next 10 years need to be spent developing instrumentation & putting monitoring infrastructure in place. Observation requirements

18. Page 18 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Courtesy IGACO 2004 **: in situ measurements

19. Page 19 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Courtesy IGACO 2004Aerosol requirements

20. Page 20 COST/ESF School: UTLS, Cargese, 3-15 October 2005 How can we provide this information

21. Page 21 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Ground-based data

22. Page 22 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Geostationary satellite orbit courtesy NASDA: GEO High temporal resolution-> Diurnal variability Now-casting Quasi-polar satellite orbits courtesy www.planetearthsci.com: LEO www.planetearthsci.com High spatial resolution & global coverage-> NRT information for initializing NWP models

23. Page 23 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Recent developments to take account of  Satellite data (Research) NASA: EOS-Terra, EOS-Aqua, EOS-Aura ESA: ERS-2, Envisat, GMES Sentinels (esp. 4-5) NASDA: ADEOS-1,-2, GOSAT ESA/CSA: ODIN  Future satellite data (Operational): e.g. METOP, MSG  Synergy between research & operational satellite data

24. Page 24 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Study & monitoring of atmospheric composition & transcontinental pollution, a minimum set of requirements can be identified: Provision of height-resolved observations of key parameters in the stratosphere and UTLS: O 3 and H 2 O. Provision of tropospheric column observations of key parameters: O 3, CO 2, CO, CH 4. Provision of information appropriate for estimating sources and sinks of key parameters: CO 2, CO, CH 4. Provision of dynamical information: pressure, temperature, winds. High benefit/cost ratio for observation platforms. Some key data requirements

25. Page 25 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Difficult to find observing platforms that satisfy all these minimum data requirements. GEOs; LEOs. Importance of synergy with other missions (operational & research). A synergy similar to A-train would enhance the platforms considered and could make them more attractive. Combine with in situ networks. High spatial & temporal resolution + global Need to evaluate in a quantitative way. A recommendation would be OSSEs; they are already used by ESA to evaluate future missions. Role of DA. See DA 12 Multi-disciplinary task: involve all actors in mission (instrument teams, modellers, theoreticians…) Considerations for GOS

26. Page 26 COST/ESF School: UTLS, Cargese, 3-15 October 2005 1.Limb/nadir geometries-> stratosphere/troposphere 2.Different instruments/species/frequencies (ozone, water vapour) -> cal-val/robustness/extend domain 3.Model/observations evaluation (using DA) ->cal-val 4.Dynamics/chemistry (partition effects; improved assimilation; unobserved species) 5.Operational/research (chemistry feedbacks; use all data) 6.Geostationary/polar satellites (use all data) 7.In situ + satellite (good resolution + global coverage) Synergies:

27. Page 27 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Operational/research synergy: Already happening at a number of met agencies  ECMWF: operational use of GOME total ozone data (April 2002 – June 2003), MIPAS data (Sep 2003 – April 2004) for ozone and SCIAMACHY total ozone data (Sep 2004 - )  Met Office (with U. Reading/DARC): assimilation of research satellite data with operational data, ozone + temperature (UARS MLS & GOME + operational; MIPAS + operational)  Météo-France (with CERFACS): development of a coupled NWP/chemistry assimilation system Also BIRA-IASB/MSC

28. Page 28 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Likely outcomes from operational/research data synergy:  Operational use of research satellite data: ozone (already assimilated at ECMWF), stratospheric H 2 O  Limb/nadir synergy: combine advantages from each geometry  Satellite constellations: operational/research satellites  Assimilation of limb radiances by research/operational groups. Development of fast & accurate RT models. Progress more advanced for IR radiances than UV/Vis  Chemical forecasting & tropospheric pollution forecasting  Coupled dynamics/chemistry DA systems (GCM/CTM)

29. Page 29 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Relatively good horizontal resolution Relatively poor vertical resolution Relatively poor horizontal resolution Relatively good vertical resolution Combine the advantages of these geometries -> synergy Used by met agencies Used by research groups Courtesy NATO ASI 2003

30. Page 30 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Example of limb/nadir synergy: UARS MLS ERS-2 GOME Courtesy UARS MLS web-site & ESA web-site

31. Page 31 COST/ESF School: UTLS, Cargese, 3-15 October 2005 The Earth Observing System AM Constellation Landsat-7 EO-1 SAC-C Terra 27 min 12 min 1 min

32. Page 32 COST/ESF School: UTLS, Cargese, 3-15 October 2005 How can data assimilation help?

33. Page 33 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Information on Earth System (observations - Truth) discrete in space & time Further progress: quantification -> observational “information gaps” need to be filled in (see DA 11) Models (understanding) of how information varies between discrete set of observations Observations and models have errors Information

34. Page 34 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Filling in “information” gaps requires observational & model information: How can we combine in an objective way, information from observations with information from a model of evolving system, taking account of errors in observations and model? Framework of data assimilation encompasses multiple techniques from estimation & control theories that can be used to address this question (NATO ASI 2003). DA tells us how to use an objective model to interpolate in space & time information from observations, taking due account of observation & model errors

35. Page 35 COST/ESF School: UTLS, Cargese, 3-15 October 2005 1. Observations (truth): satellite, ground-based, aircraft, sondes,… 2. Models (understanding): GCMs, CTMs, coupled GCM/CTM 3. Errors: observations (random, bias, representativeness) 4. Errors: models (“background”: B, “model”: Q) 5. Algorithms: variational (3d- & 4d-var), sequential (KF & variants), ensembles 6. Assimilation cycle: quality control, initialization, analysis, forecast Ingredients of DA:

36. Page 36 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Need to take account of recent atmospheric model developments (also increases in computing power): Increases in resolution: horizontal: T511 at ECMWF; vertical in UTLS Top of atmospheric models extended upwards Improve forecasting & long-term capability Extend range of validity of forecasts; novel geophysical parameters More consistent & realistic climate models Confront & evaluate forecast & climate models (done at NWP centres) ALSO many obstacles to be removed (e.g. access to large EO archives & metadata, common formats)

37. Page 37 COST/ESF School: UTLS, Cargese, 3-15 October 2005 & of recent developments in DA: GCM: 1. incorporation of “novel” atmospheric species (ozone) 2. extensions of simple photochemical parametrizations (Cariolle) 3. incorporation of novel observation geometries (limb) 4. improvements in error characterization of model 5. radiance assimilation CTM: 1. extension of models to include novel chemical species (e.g. CFCs) 2. improvements in heterogeneous chemistry 3. incorporation of aerosols (troposphere & stratosphere) 4. improvements in error characterization of model 5. radiance assimilation

38. Page 38 COST/ESF School: UTLS, Cargese, 3-15 October 2005 NWP: UV-forecasting; air quality Radiance assimilation code: temperature, ozone Monitoring Constraints on other chemical species Test chemical theories Tracer information Specific example: Why ozone DA?

39. Page 39 COST/ESF School: UTLS, Cargese, 3-15 October 2005 BUT: Challenges in DA: Bias models/DA systems -> inappropriate increments? Assimilation of water vapour in stratosphere/tropopause region Assimilation of “novel” geophysical parameters (e.g. ozone, stratospheric winds) into NWP systems Synergy from measurement geometries Coupled dynamics/chemistry in data assimilation Limb radiance assimilation Assimilation of novel photochemical species (e.g. CFC-11, CFC-12, ClONO2) Aerosol assimilation (stratosphere & troposphere) Tropospheric chemistry Novel retrieval methods (e.g. tomography) Data management

40. Page 40 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Biases in DA? Position of parcels after 50 days; parcels launched from the tropics; Schoeberl et al. 2003 CTM forced by a DA system CTM forced by a GCM

41. Page 41 COST/ESF School: UTLS, Cargese, 3-15 October 2005 What does UTLS GOS require? Global, ht-resolved meas of several key chemical species: H2O, O3: High vertical resolution: ~1 km or better; horizontal resolution ~100 km In situ measurements of several key chemical species: H2O, O3: V. high vertical & horizontal resolution (~100’s metres) (BUT not global) Global tropospheric columns of several key species: CO, CH4, CO2, O3 Global, ht-resolved meas of wind: High vertical resolution: ~2 km or better; 2 wind components (unless use DA) Continuity of measurements -> Radiative budget; dynamics information; chemical distributions -> Sources & sinks of pollution/transcontinental transport Observational requirements for CO2: Houweling et al. (2004) -> Dynamics & transport -> Heritage; monitoring

42. Page 42 COST/ESF School: UTLS, Cargese, 3-15 October 2005  DA has an important role to play in setting up GOS for UTLS  Benefits:  Climate studies – better models & simulations  Monitoring – better observations (quality, coverage)  NWP – better use of observations, better models  OSSEs – quantification of future observations (see DA 12) - note methodology & caveats -> impact on society: health, compliance with treaties, information for policy makers,… BUT: models and observations are important ingredients! Note CAPACITY study: http://www.knmi.nl/capacity/workshop.htmlhttp://www.knmi.nl/capacity/workshop.html Looked at development of operational atmospheric chemistry missions Conclusions

43. Page 43 COST/ESF School: UTLS, Cargese, 3-15 October 2005 Global Earth Observing system for 2008-2010: An artist’s view – need a scientific view!