Key 13: The future observing system in the UTLS

1. Key 13: The future observing system in the UTLS Author: W.A. Lahoz Data Assimilation Research Centre, University of Reading RG6 6BB, UK

2. 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. Topics: • 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?

4. The importance of the UTLS region

5. Observation types used by Met Office for NWP UTLS

6. UTLS Courtesy IGACO

7. Importance of UTLS • Radiative-dynamics-chemistry feedbacks associated with strat O3 & 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 O3 (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. 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. 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 Recommendations from SPARC assessment on UT/S H2O

10. Atmospheric chemistry: Integrated Global Atmospheric Chemistry Observations (IGACO) / Integrated Global Observing Strategy (IGOS) -> identified four grand challenges in atmospheric chemistry: • Tropospheric air quality: O3, CO,… • Oxidation efficiency of the atmosphere: O3, CO, • Stratospheric chemistry and ozone depletion: O3, H2O,… • Chemistry-climate interactions: CO2, O3, H2O,… Increased recognition of importance of chemistry Role of UTLS

11. Importance of ozone Recognition of key role of stratospheric O3 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. Challenges in UTLS • Paucity of observations of key species (H2O, O3): 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

13. What information we require from the UTLS

14. Chemical variables Courtesy IGACO 2004 Dynamical (and other) variables

15. Observation requirements Based on IGACO Group 1: O3, H2O, CO2, CO, NO2, BrO, ClO, HCl, N2O, CFCs, ClONO2 & 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.

16. Target/threshold (1) Hours (NWP); (2) days-weeks (O3 loss,…); (3) months (climate research) Courtesy IGACO 2004

17. Observation requirements Based on IGACO Group 2: CH4, HCHO, VOCs, SO2, HNO3, OClO, NO, CH3Br, the halons, and j(NO2) and j(O1D). • All current satellites are in experimental “demonstration” mode & only have limited lifetime. • Some g-based in situ measurements. • Except for CH4, global network sparse. • Next 10 years need to be spent developing instrumentation & putting monitoring infrastructure in place.

18. Courtesy IGACO 2004 **: in situ measurements

19. Courtesy IGACO 2004 Aerosol requirements

20. How can we provide this information

21. Ground-based data

22. Geostationary satellite orbit • courtesy NASDA: GEO • High temporal resolution-> • Diurnal variability • Now-casting • Quasi-polar satellite orbits courtesy www.planetearthsci.com: LEO • High spatial resolution & global coverage-> • NRT information for initializing NWP models

23. 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. Some key data requirements 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: O3 and H2O. • Provision of tropospheric column observations of key parameters: O3, CO2, CO, CH4. • Provision of information appropriate for estimating sources and sinks of key parameters: CO2, CO, CH4. • Provision of dynamical information: pressure, temperature, winds. • High benefit/cost ratio for observation platforms.

25. Considerations for GOS • 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…)

26. Synergies: • Limb/nadir geometries-> stratosphere/troposphere • Different instruments/species/frequencies (ozone, water vapour) -> cal-val/robustness/extend domain • Model/observations evaluation (using DA) ->cal-val • Dynamics/chemistry (partition effects; improved assimilation; unobserved species) • Operational/research (chemistry feedbacks; use all data) • Geostationary/polar satellites (use all data) • In situ + satellite (good resolution + global coverage)

27. 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. Likely outcomes from operational/research data synergy: • Operational use of research satellite data: ozone (already assimilated at ECMWF), stratospheric H2O • 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. Relatively poor horizontal resolution Relatively good vertical resolution Relatively good horizontal resolution Relatively poor vertical resolution Used by met agencies Used by research groups Combine the advantages of these geometries -> synergy Courtesy NATO ASI 2003

30. Example of limb/nadir synergy: ERS-2 GOME UARS MLS Courtesy UARS MLS web-site & ESA web-site

31. SAC-C EO-1 Terra Landsat-7 12 min 1 min 27 min 10:03 10:15 10:02 10:42 The Earth Observing System AM Constellation

32. How can data assimilation help?

33. Information • 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

34. 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. Ingredients of DA: • Observations (truth): satellite, ground-based, aircraft, sondes,… • Models (understanding): GCMs, CTMs, coupled GCM/CTM • Errors: observations (random, bias, representativeness) • Errors: models (“background”: B, “model”: Q) • Algorithms: variational (3d- & 4d-var), sequential (KF & variants), ensembles • Assimilation cycle: quality control, initialization, analysis, forecast

36. 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. GCM: incorporation of “novel” atmospheric species (ozone) extensions of simple photochemical parametrizations (Cariolle) incorporation of novel observation geometries (limb) improvements in error characterization of model radiance assimilation CTM: extension of models to include novel chemical species (e.g. CFCs) improvements in heterogeneous chemistry incorporation of aerosols (troposphere & stratosphere) improvements in error characterization of model radiance assimilation & of recent developments in DA:

38. Specific example: Why ozone DA? • NWP: UV-forecasting; air quality • Radiance assimilation code: temperature, ozone • Monitoring • Constraints on other chemical species • Test chemical theories • Tracer information

39. 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 BUT: Challenges in DA:

40. Biases in DA? CTM forced by a DA system CTM forced by a GCM Position of parcels after 50 days; parcels launched from the tropics; Schoeberl et al. 2003

41. 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 What does UTLS GOS require?

42. Conclusions • 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.html Looked at development of operational atmospheric chemistry missions

43. Global Earth Observing system for 2008-2010: An artist’s view – need a scientific view!