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
Property
GCOS (2006) statement
CCI survey 2010
Accuracy
0.25 K
0.1 K on 100 km scales
Stability
0.1 K / decade
Random uncertainty --
0.1 K
Spatial resolution
1 km
0.1o (<1 km)
Temporal resolution
3 hourly
Daily (3 hourly)
Uncertainty information
Total uncertainty in every cell.
Error covariance information.
Quality information
Simple: probability of “bad”
SST meaning
Skin and depth required
Independence
Preferred by 60%

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

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

8. Pathfinder v5 NLSST
1 year Metop-A > 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: /j.rse
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. Development logic for AVHRR optimal estimate retrieval (“OE2”)
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
PATHFINDER
ARC
CCI SST
Sensors
AVHRR
ATSR
AVHRR + ATSR
Tied to
Drifting buoys
Independent
Homogenized No
Yes
Accounting for diurnal effects
Meets GCOS accuracy (0.25 K)
Meets ARC target accuracy (0.1 K)
Mostly
Yes/mostly
Retrieval method
Coefficients
Optimal
Meets GCOS stability ?
Likely
Stability quantified
Clearly defined SST
SST-skin & depth
Stable during strat. aerosol
Quantified uncertainties
Spatial resolution
4 km
0.1o
1 km to 0.05o

31. EXTERNAL INVOLVEMENT IN SST CCI
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. THANK YOU FOR YOUR ATTENTION. QUESTIONS?
The SST CCI: scientific approaches
THANK YOU FOR YOUR ATTENTION. QUESTIONS?