1. Introduction to CMUG assessments, SST and plans for phase 2 Roger Saunders www.cci-cmug.org 4 th Integration Meeting

2. CMUG Assessments of CCI CDRs Why does CMUG assess CCI datasets? Provide an independent view of the datasets and associated uncertainties Study consistency between ECVs Demonstrate applications for climate modelling to accelerate use by the climate/reanalysis communities

3. Options for assessing CDRs Data used to assess CDRsAdvantagesDrawbacks Climate Model (single, ensemble)Spatially and temporally complete Model has uncertainties Not all variables available Re-analyses CMF Tool.Spatially and temporally complete Analysis has uncertainties Not all variables available PrecursorsComparing like with likeSome precursors may have large uncertainties Independent satellite or in situ measurements Different ‘view’ of atmosphere/surface May have much larger uncertainty than CDR, need to include representativity errors Related observations (surface and TOA fluxes, temperature, water vapour) Assures consistency with other model variables May not be spatially or temporally complete

4. CMUG assessments in phase 1 Methodology used for assessment of ECVs Assessment of precursors (see CMUG D3.1 report series ) Assessment of CCI CDRs (see CMUG D3.1 report series ) Climate Model (single, ensemble) O 3( (IASI), Land Cover (GlobCover), SSH (AVISO), Cloud (ISCCP), Fire (GFEDv3) O 3, Land Cover, SM, SSH, Cloud Re-analysesSST (HadISST), O 3 (ERA)O 3, SM, Aerosols, GHG, SSH Precursor datasetsOC, SSH, SST, O 3 Independent satellite or in situ measurements SST (ARC)SST, O 3, OC Related observations (surface and TOA fluxes, temperature, water vapour) Fire (GFEDv3 )SM AssimilationGlobColorOC

5. CMUG assessments of ECVs Sea Surface Temperature www.cci-cmug.org

6. Data issues found and fixed Data gaps –Day: western Pacific and off western coast of India (only ATSR- 1 [after failure of 3.7 μm] and ATSR-2), –Night: south-western Atlantic and first 7 months of 1993. Abrupt change in the number of retrievals during night at ~2 o S and ~8 o N. Fill values for depth SST and uncertainties (huge numbers for 1993, 2000 and 2002 [AATSR]). Days without data, e.g. 29 th February for the leap years.

7. Assessment of CCI SST Main validation against drifting buoys Comparison with precursor ARC dataset Retrieval methods –CCI: optimal estimation for ATSR-2 and AATSR, but ATSR-1 as ARC. –ARC: coefficients from radiative transfer simulations. Different spatial resolution –CCI: 0.05 deg –ARC: 0.1 deg Same cloud screening method –minor changes due to updated versions of radiative transfer and NWP models.

8. Bias: ATSRs-drifting buoys Larger bias in the tropics during day (water vapour ?) and in the mid-latitudes and over Indian and western Pacific oceans during night (systematic errors ?). CCI ARC night day

9. Bias: Hovmoller diagrams AATSR performs better than ATSR-1/2. Oscillation with a warmer bias during the boreal winter months in mid-latitudes. Surprising difference between CCI and ARC for ATSR-1, given the same retrieval method. CCI ARC night day

10. Robust standard deviation CCI ARC night day

11. 3-way error analysis Use TMI SST for validation of ATSR-2 in the zone [-40, 40] o N for the first time. Practically, ATSR-2 and AATSR have the same performance with standard deviation of error 0.18 K for ARC and 0.23 K for CCI. Drifting buoys showing an improvement of their random error with time, while also AMSR-E has better performance than TMI. Method which provides the random error given that the three datasets (buoys, ATSRs and MWs [TMI and AMSR-E]) are uncorrelated.

12. Uncertainty assessment ARC’s uncertainty assignment is in general better than CCI’s uncertainty, although not for all uncertainty values or validation criteria. CCI ARC night day No CCI uncertainty < 0.1 K.

13. Conclusions on assessment of SST CCI 2ch biases in SST significantly higher than for ARC 3ch bias in SST slightly higher than ARC Uncertainties of CCI product suggest they are reasonable but less matchups for nightime cases than for ARC Feedback suggests AVHRR SST dataset is an improvement over the pathfinder SST dataset. This is good news to extend the time series back before 1995.

14. CMUG Phase 2 www.cci-cmug.org 4 th Integration Meeting

15. Task 1: Meeting the evolving needs of the climate community Task 2: Providing integrated view of CCI & feedback to ESA and CCI teams Task 3: Assessing consistency and quality of CCI products across ECVs Task 4: Exploiting CCI products in MIP experiments Task 5: Adaptation of community climate evaluation tools for CCI needs Task 6: Coordination and Outreach Task 7: Interface to the European Climate Service CMUG will assess both end of phase 1 products and mid-term phase 2 products CMUG Outlook – Phase 2 NEW

16. Cross-ECV consistency SST SLClSiceOCAeroGHGLVFireOzoneGlaciICSM SST xxXXx x Sea level x x x Clouds x xXxxXxX Sea ice Xxx X x x Ocean col X xx x Aerosol x X XxXx GHG x x xX Landcover x x x x x Fire x xxX x x Ozone x xX Glaciers x X x Ice Sheets X SoilM xx Strong Weaker

17. CMUG phase 2 - Core Proposal Cross-ECV dataset assessments Cross-Assessment of marine ECVs (SST, OC, SSH, SI)(Met Office) Integrated assessment of the aerosol, GHG, and ozone datasets (ECMWF) Integrated exploitation of CCI terrestrial ECVs (LC, Fire, SM) (MPI-M/IPSL) Cross-Assessment of ECVs from sea-ice with atmospheric ECVs (MPI-M/DLR) Cross-Assessment of Aerosols, Cloud and Radiation CCI ECVs (DLR) Cross assessment of clouds, radn, aerosol, GHG, s moisture, SST (SMHI/MF) Exploiting CCI products in CMIP like experiments Assessing CCI datasets as boundary conditions in CMIP5-like atmosphere simulations (MF/IPSL) Adaption of community climate evaluation tools Benchmarking models with ESA CCI data in the era of CMIP6 (DLR/MPI-M) Development of community climate dataset evaluation tools (ECMWF) Interface to climate services

18. Discussion After Coffee Phase 1 Outcomes: What additional value / problems compared to the CMUG results were identified in climate research groups of the different ECV teams? What were major limiting factors in data usage identified by the CRG's? Phase 2 and CCI-2 plans How will phase-2 address limitations of phase-1 data identified by CMUG/CRG's ? What should be the new variables/products in a potential CCI-2? (e.g. Snow, Lakes, Salinity, Albedo, …….)