Michael Buchwitz, Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany

1. GHG-CCI CCI Integration Meeting, ECMWF, 14-16 March 2011 Essential Climate Variable (ECV) Greenhouse Gases (GHG) Michael Buchwitz, Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany and the GHG-CCI team v3

2. Outline • GHG-CCI reminder & remarks • User requirements • Product specification • Integrated perspective / across-ECV consistency • How to deal with uncertainties ? • ECMWF data needs • Round Robin Evaluation Protocol (RREP) • Summary and outlook 2

3. ECV GHG ? CO2 and CH4 are the two most important anthropogenic greenhouse gases and increasing concentrations result in global warming. • Reliable climate prediction requires a good understanding of the natural and anthropogenic (surface) sources and sinks of CO2 and CH4. • Important questions are, for example • Where are they ? • How strong are they ? • How do they respond to a changing climate ? • A better understanding requires appropriate global observations and (inverse) modelling. ECV GHG: Global distribution of atmospheric Greenhouse Gases, as Methane and Carbon Dioxide, of sufficient quality to estimate regional sources and sinks.

4. Ingredients needed to achieve this Calibration (L 0-1) Global satellite observations Upper layer CO2 & CH4 Calibrated radiances Global information on near-surface CO2 & CH4 SCIAMACHY/ENVISAT TANSO/GOSAT AIRS, IASI, TES, MIPAS, SCIA/occ, ACE-FTS, … Retrieval (L 1-2) Atmospheric GHG distributions Global observations Reference observations Validation ? CO2 CH4 CO2 ? Inverse modelling, CCDAS Improved information on GHG sources & sinks CH4 4

5. Note that ... • … GHG-CCI data products are relatively new and the accuracy requirements are challenging • -> GHG-CCI differs compared to most other CCI projects as focus is on required fundamental research: • 2 year Round Robin Phase • No Task 5 (System Spec) • Relaxed schedule, eg, URDv1, DARDv1, PSDv1 due month 6 = Feb 2011 • … 5

6. GHG-CCI schedule Sep 2010: Kick-off Mar 2011 (= month 7): CCI Integration Meeting Delivered in time = month 6 = Feb 2011 6

7. Algorithms & Data Products I For comparison Core Products GHG-CCI ECV Core Algos. (ECAs) ECV: baseline; ECV: alternative;cmp:comparisononly Algorithms & data products for additional constraints and comparison (ACAs): LMD: AIRS CO2, IASI CO2&CH4, ACE-FTS CO2, …, KIT: MIPAS CH4, IUP: SCIA-occultation CH4, … FCDR: DLR: SCIA L1 improvements (GHG-cci specific; in coop. with SQWG), … 7

8. Algorithms & Data Products II • ECV Core Algorithms (ECAs) -> Core data products: • CO2_SCI_BESD (IUP) • CO2_SCI_WFMD (IUP) • CO2_GOS_OCFP (ULE) • CO2_GOS_SRFP (SRON) • CH4_SCI_WFMD (IUP) • CH4_GOS_OCFP (ULE) • CH4_SCI_IMAP (SRON) • CH4_GOS_SRFP (SRON) • CH4_GOS_SRPR (SRON) • Additional Constraints Algorithms (ACAs) -> Additional constraints data products: • CO2_AIR_NLIS (LMD), CO2_IAS_NLIS (LMD), CO2_ACE_CLSR (LMD), CH4_IAS_NLIS (LMD), CH4_MIP_IMK (KIT), CH4_SCI_ONPD (IUP) Plus external data products for comparison: GOSAT CO2 and CH4 from NIES and NASA-ACOS, NASA TES CO2, ESA MIPAS CH4, ... 8

9. Algorithms & Data Products III • Under study: • Identification of • reason(s) for differences • Example: • Time sampling issue July 2009: • - TCCON biased towards end of month • GOS_OCFP retrievals spread over entire month • > sampling bias 9

10. Algorithms & Data Products IV XCO2: Lamont, USA Under study: Identification of reason(s) for differences 10

11. Algorithms & Data Products V XCH4: Lamont, USA Under study: Identification of reason(s) for differences 11

12. Algorithms & Data Products VI XCH4: Wollongong, Australia Under study: Identification of reason(s) for differences 12

13. User Requirements I • URDv1 delivered as planned • Publicly available from http://www.esa-ghg-cci.org/ • Approach: • Focus on XCO2 and XCH4 from SCIAMACHY and GOSAT (= ECAs) • Explicit formulation of requirements only if not a given characteristic of these existing instruments • Requirements based on peer-reviewed publications, discussion with users (represented by GHG-CCI CRG), GCOS-107, CMUG requirements, ... • Does NOT specify a future mission (such as CarbonSat) ! 13

14. User Requirements II • Missing requirements ?: • No major ones identified so far but can be added later as updates are planned • Requirements consistent with GHG-CCI CRG ?: • Yes – URDv1 has been approved by CRG • Consistent with GCOS and CMUG ? • Consistent yes, but not identical, eg, • certain URDv1 requirements are stricter (eg accuracy) • important threshold requirements cannot be met (eg 6 h observing cycle) as not possible with existing instruments (although the data are useful = threshold) 14

15. User Requirements III 15

16. Product Specification I • PSDv1 delivered as planned • Publicly available from http://www.esa-ghg-cci.org/ • Approach: • Focus on ECAs (ie XCO2 and XCH4 from SCIAMACHY and GOSAT) but ACAs also covered • Most of the products under development; at present different format / content for each algorithm • Product spec therefore still preliminary • GHG-CCI ECAs products: • NetCDF • Common parameters (for all) • Plus algo specific parameters 16

17. Product Specification II • Note: Detailed content still under discussion: Probably nothing will be removed but likely more will be added (eg entire state vector) • Will the users use the proposed products for their application ? • Yes • They are already using them. Eg, SCIAMACHY XCH4 has been used to constrain methane emissions (eg Bergamaschi et al., 2007) and this data product is assimilated at ECMWF as part of MACC-GHG and planned to be used within MACC-2 • SCIAMACHY and GOSAT XCO2 and XCH4 are core satellite input data products to be used by MACC-GHG; this is also true for ACAs, eg, AIRS CO2, IASI CH4, ... 17

18. Integrated Perspective • GHG-CCI benefits from other CCI projects incl. CMUG • Strengthens link to climate modelling user community via CMUG • Establishes link to ECMWF -> access to state-of-the-art met data • Gives access to other data sets including experts know-how (eg aerosols) • Use of common standards and definitions -> facilitates the international recognition of the data products, helps to make the various data products consistent, ... • ... • Across-ECV consistency: • Risk of across-ECV inconsistencies GHG-CCI <-> Other-CCI ? • Unlikely if common standards and definitions are used. • Auxiliary data: Unlikely a source for inconsistencies: Key is that the most appropriate input data for accurate XCO2 and XCH4 retrieval from SCIAMACHY and GOSAT are used. They have to best match the spatio-temporal resolution and sampling of these sensors. Unlikely that eg a met data set which is optimum for other ECVs (such as SST) is also the optimum for GHG-CCI 18

19. How to deal with uncertainties ? I • Major challenges: • To generate data products with the required accuracy and to find out how accurate they actually are • To define reliable error bars • To establish and report error correlations as also requested by the users • Approach (ongoing ...): • Detailed analysis of simulated retrievals (using radiative transfer modelling and known = true atmosphere/surface conditions) -> Very useful but says little about potential issues with real data • Comparison with accurate remote sensing reference data (primarily TCCON) -> Very useful but limited due to sparse spatial coverage of TCCON • Comparison with other less optimal measurements (eg NDACC) -> helps to overcome limitations of TCCON • Comparisons using models (esp if matched to highly accurate in-situ data) • Study of correlations of differences to reference data with other data sets (eg, aerosols, cirrus clouds, etc) • ... • -> Will be documented in “Algorithm Inter-comparison and Error Characterization & Analysis Report” (AIECAR); v0 due Aug 2011 19

20. How to deal with uncertainties ? II Typical example: New SCIAMACHY XCO2 algorithm BESD: Reuter et al., AMT, 2010 Reuter et al., JGR, 2011 20

21. ECMWF data needs • Described in DARD • DARDv1 delivered as planned • Publicly available from http://www.esa-ghg-cci.org/ • ECMWF data needs: • ERA Interim (analysis (every 6 hrs) and forecast (every 3 hrs); N128 Gaussian, ~0.7o) • 2002 – 2012 • At all model levels: T, spec. hum. • At surface: suf. press., geopotential • At 10 m: U and V wind • Same for operational archive but only 2010 for sensitivity analysis to study effect of x2 higher spatial resolution • Download: Option: By UK partner ULE for distribution within GHG-cci team 21

22. Round Robin Evaluation Protocol (RREP) • Described in GHG-CCI RREP (see left) • Version 1 (approved by CGR) placed on GHG-CCI website (public) • Approach: • Several quantitative criteria and have been defined which characterise the quality of a given data product (mainly relative to TCCON; e.g., annual and seasonal biases, standard deviation, correlation coefficients, amount of data, etc.) • CRG will decide on best algorithm using this information + additional information (maps, time series, other comparisons) + expert judgment (incl. own analysis) • If products differ but best product cannot be unambiguously identified an option is to recommend an “ensemble approach” (as done for climate models) 22

23. Summary and Outlook • General: • Note that GHG-CCI is in “research mode” • Progress so far: • Progress as planned • Major documents delivered: URDv1, DARDv1, PSDv1 • Regular document updates are foreseen • 1st version of “Round Robin Evaluation Protocol” (RREP) • Initial draft of other documents generated defining responsibilities and due dates: ATBD & AIECAR (stand alone error characterization document), both due month 12 (= Aug 2011) • Next steps: • Ongoing: Algo improvements, error analysis, data processing, analysis of resulting data products, inter-comparisons, ... • PM2 planned as splinter meeting at EGU, Vienna, Thursday 7 Apr 2011, 15:30 – 19:00, room SM5 (CMUG has been invited) • Documents: PVP (due month 9), ... 23

24. Thank you very much for your attention ! 24