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

2. 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 Outline 2

3. ECV GHG: Global distribution of atmospheric Greenhouse Gases, as Methane and Carbon Dioxide, of sufficient quality to estimate regional sources and sinks. ECV GHG: Global distribution of atmospheric Greenhouse Gases, as Methane and Carbon Dioxide, of sufficient quality to estimate regional sources and sinks. ECV GHG ? Reliable climate prediction requires a good understanding of the natural and anthropogenic (surface) sources and sinks of CO 2 and CH 4. 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. CO 2 and CH 4 are the two most important anthropogenic greenhouse gases and increasing concentrations result in global warming.

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

5. … 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 … Note that... 5

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

7. IUP Univ. Leicester SRON NIESNASA BESDWFMDOCO- FP FSI- WFMD IMAP (CO 2 PROXY) SRON- FP Operational GOSAT ACOS SCIA XCO 2 ECV SCIA XCH 4 ECV GOSAT XCO 2 ECV cmp GOSAT XCH 4 ECV cmp ECV: baseline; ECV: alternative; cmp: comparison only Algorithms & data products for additional constraints and comparison (ACAs): LMD: AIRS CO 2, IASI CO 2 &CH 4, ACE-FTS CO 2, …, KIT: MIPAS CH 4, IUP: SCIA-occultation CH 4, … FCDR: DLR: SCIA L1 improvements (GHG-cci specific; in coop. with SQWG), … GHG-CCI ECV Core Algos. (ECAs) Core Products Algorithms & Data Products I 7 For comparison

8. 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) 8 Algorithms & Data Products II Plus external data products for comparison: GOSAT CO2 and CH4 from NIES and NASA-ACOS, NASA TES CO2, ESA MIPAS CH4,...

9. 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

10. 10 Algorithms & Data Products IV XCO 2 : Lamont, USA Under study: Identification of reason(s) for differences

11. 11 Algorithms & Data Products V XCH 4 : Lamont, USA Under study: Identification of reason(s) for differences

12. 12 Algorithms & Data Products VI XCH 4 : Wollongong, Australia Under study: Identification of reason(s) for differences

13. User Requirements I 13 URDv1 delivered as planned Publicly available from http://www.esa- ghg-cci.org/http://www.esa- ghg-cci.org/ Approach: Focus on XCO 2 and XCH 4 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) !

14. 14 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) User Requirements II

15. 15 User Requirements III

16. Product Specification I 16 PSDv1 delivered as planned Publicly available from http://www.esa-ghg-cci.org/ http://www.esa-ghg-cci.org/ Approach: Focus on ECAs (ie XCO 2 and XCH 4 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

17. 17 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 XCH 4 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 XCO 2 and XCH 4 are core satellite input data products to be used by MACC-GHG; this is also true for ACAs, eg, AIRS CO 2, IASI CH 4,... Product Specification II

18. 18 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 XCO 2 and XCH 4 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 Integrated Perspective

19. 19 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 How to deal with uncertainties ? I

20. 20 How to deal with uncertainties ? II Typical example: New SCIAMACHY XCO 2 algorithm BESD: Reuter et al., AMT, 2010 Reuter et al., JGR, 2011

21. ECMWF data needs 21 Described in DARD DARDv1 delivered as planned Publicly available from http://www.esa- ghg-cci.org/http://www.esa- ghg-cci.org/ ECMWF data needs: ERA Interim (analysis (every 6 hrs) and forecast (every 3 hrs); N128 Gaussian, ~0.7 o ) 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

22. Round Robin Evaluation Protocol (RREP) 22 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)

23. 23 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 1 st 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),... Summary and Outlook

24. 24 Thank you very much for your attention !