The SST CCI: Scientific Approaches

1. The SST CCI:Scientific Approaches

2. The SST CCI: Scientific Approaches OUTLINE

3. What are we aiming for in a satellite SST CDR? • What do current techniques give? • What will we try in SST CCI? • External involvement in SST CCI

4. The SST CCI: scientific approaches WHAT ARE WE AIMING FOR?

5. Requirements for SST CDR

6. Independence • Two meanings of independence • Retrievals not tied to in situ observations • Information for SST in retrieval near 100%

7. The SST CCI: scientific approaches WHAT DO CURRENT TECHNIQUES GIVE?

8. Pathfinder v5 NLSST 1 year Metop-A >200000 drifter night-time matches Single pixel Located at buoy MAD time 1h20

9. Derive coefficients and bias BTs, y Least squares regression MD SSTs, x Coefficients, a Map Predicted SST, ,given y and a

10. Regional annual biases

11. “Random” uncertainty

12. Dependence on prior Algorithm Sensitivity to true SST, x Fraction of information from prior

13. Imperfect sensitivity to SST Change in NLSST for a 1 K change in SST

14. Stability • Zero mean bias against drifting buoy sample • Prior error depends on mean of matches • Stability could depend on buoy distribution • Needs to be assessed

15. Issues with NLSST for CDR • Empirically tied to drifting buoys • Neither skin nor depth SST • Not independent • Dependence of bias on evolving match-up? • Biases and “random” errors exceed user requirements • Dependence: (5% to 60%) of result supplied by implicit prior

16. How to improve on NLSST? • Use 3.7 um when available • Improves on bias, precision and prior dependence • But introduces day-night inconsistencies • Banding of coefficients • Latitude, TCWV • Bias correction by simulation • Le Borgne, 2011, doi:10.1016/j.rse.2010.08.004 • Optimal estimation

17. ATSR Reprocessing for Climate >15 years global coverage, 0.1 deg Accuracy < 0.1 K Stability of 0.05 K per decade Both skin and depth SSTs Diurnal cycle removed Comprehensive error characterization Independent of other records

18. Radiative transfer modeling and inverse theory Probabilistic, physically based Physical models of skin and stratification 18

19. ARC SST mean v. drifters • N2 (b) N3 • (c) D2 (d) D3

20. ARC SST RSD v. drifters

21. ARC dependence on prior • N2 (b) N3 • (c) D2 (d) D3

22. ARC stability (provisional) Global oceans (data gaps filled) Provisional homogeneity ATSR2/AATSR Trend uncertainty magnitude displayed relative to end of time-series

23. The SST CCI: scientific approaches WHAT WILL WE TRY NEXT?

24. Bringing AVHRR and ATSR together Tie AVHRR to ATSR instead of buoys • Basis for independence, traceable to physics of radiative transfer Not merely adjusting AVHRR SST bias to ATSR Use common Optimal Estimation retrieval for IR • Overcome information deficit in single view • Meet 0.1 K bias target • Information content / prior dependence known

25. (Sub) System for Long-term CCI SST

26. Multi-sensor match-up data set Development logic for AVHRR optimal estimate retrieval (“OE2”)

27. Mean diurnal cycle

28. AVHRR orbit drift

29. AVHRR orbit drift

30. Characteristics of Long Term CCI SST

31. The SST CCI: scientific approaches EXTERNAL INVOLVEMENT IN SST CCI

32. Ways to get involved Augment Multi-sensor Match-up Dataset • Talk to us now! Algorithm selection round robin • August 2011 to November 2011 Climate Data Research Package • January 2013

33. The SST CCI: scientific approaches THANK YOU FOR YOUR ATTENTION.QUESTIONS?