Outline

1. Outline • Motivation • SST analysis products at NCDC • Extended Reconstruction SST (ERSST) v.3b • Daily Optimum Interpolation SST (OISST) v.2 • Error computations • Use of SST for Operational Climate activities • Warming trend • El Nino • Modelling (not this talk)

2. Can SST error estimates be improved? What are operational uses of SST for climate applications? What are the uses of SST ? Are SST error fields used in their operations? Why is there minimal use?

3. Extended Reconstruction SST v.3b • Provides historical context for modern measurements • Produced monthly on 2° grid from 1854 to present • Input: in situ data only • NetCDF 3 Gridded Products • Full SST • SST anomaly (wrt1971-2000 climatology) • Total error variance http://www.ncdc.noaa.gov/oa/climate/research/sst/ersstv3.php

4. OISST with AVHRR 17 & 18 May 15, 2010 Daily Optimal Interpolation SST v.2 Random and sampling error 1 2 3 4 5 6 7 X 10 -3 Bias error . 01 . 02 .03 .04 .05 . 06 .07 • Daily, 1/4° grid • Based on satellite and in situ data • AVHRR-only (from 1981) • AVHRR+AMSR (from 2002) • 3 Gridded Products • Full SST • Random and sampling error variance • Bias error variance

5. Analysed SST Error Error estimation requires a reference or expected value. Analysis steps defined how error is computed.

6. Internal use for product improvement AVHRR+AMSR AVHRR-only 1 2 3 4 5 6 7 X 10 -3 .01 . 02 .03 .04 .05 . 06 .07 AMSR reduces sampling error but not bias

7. Long Term Warming trends 1941) After 1940s, the average trend is ~0.1°C/decade Global errors must be < 0.5°C/100 yrs or 0.05°C/10 yrs Regional error requirements can differ

8. Focus on 2 operational users: CMB and CPC NCDC Climate Services Division: Climate Monitoring Branch Remote Sensing & Applications Division MergedLandOcean Temperature Monthly State of the Climate Assessment Monthly ERSST Daily OISST (AVHRR-only) Yearly State of the Climate Report Daily OISST (AVHRR&AMSR) NCEP Climate Prediction Center Global Data Assimilation System El Nino Monthly Discussion Seasonal Ocean Forecast Weekly OISST (AVHRR-only)

9. Monthly State of Climate Report Emphasis on departure from normal SST is merged with Land temperatures for global map. Gridpoints with high error are masked out.

10. Monthly State of Climate • Values with high errors excluded from averages • With global warming, all recent years are above normal • Thus, emphasis on rankings

11. Rankings • Allow comparison among months and between years, rather than wrt normal • “Tie” reflects rounding off NOT significance testing • Errors could also be used to define 95% CI http://www.ncdc.noaa.gov/sotc/?report=global

12. Annual State of Climate Report Most recent 3 decades progressively warmer Global warming skepticism needs to be addressed 95% CI shown by MetOffice, while also showing departure from normal Fig. 2.3. 95% confidence range of decadal average temperatures for the HadCRUT3 temperature analysis (see Brohan et al. 2006 for the error model derivation).

13. Annual State of climate report • Emphasis on use of multiple indicators and ensemble presentations • Avoids problem of deciding whether error estimates are equivalent

14. Index=Sum of SST anomalies in specific region El Nino Among teleconnection phenomena, El Nino is the only one with an SST-based index CPC uses weekly OISST for diagnostics and forecast; ERSST for historical context Concern: public confused why 2 SST products? http://www.cpc.noaa.gov/products/

15. El Nino Index • SST products will not be exactly the same • 95% CI indicates OISST is better for forecasting Daily OISST Monthly ERSST http://www.cpc.noaa.gov/products/

16. Summary • NCDC SST analysis products are distributed with error fields • For operational climate assessments, errors not used routinely • Error fields used only when producer is involved • Users need time and resources to evaluate and incorporate into routine operations • Effective communication to public a major issue • Producers need to give recommendations and help develop protocols

18. Monthly State of Climate Report • WMO has requested presentation be changed to terciles (above, below and normal) to relate more easily to forecasts • But what is normal?

19. State of Global Ocean

20. Error • RANDOM ERROR: caused by natural variations when measurement is repeated • SAMPLING ERROR: if SST estimate affected by distribution and density • BIAS error: if SST obs offset from true value by measurement method or analysis

21. OISST Errors • Sampling and Random error • Equal to OI analysis increment SD if there are no obs • Reduced proportionally by (OI wt*source error) • SNR ratio= 1.94 ship; 0.5 buoy&AVHRR; 0.35 AMSR • Bias error (systematic to measurement methodology or analysis method) • Depends on number of EOT modes used, and number of data sources

22. ERSST Errors • Random • Assumed very small due to smoothing • Sampling • LF : based on annual sampling over 5X5 grid; damping ; truth=low-pass filtereds CGCM SST anomalies 1861-2000 • HF: mode variance and how many modes resolved; truth= detrended variance of OI anomalies v2 (1982-2005) • Bias • Pre-1940: night air temp comparison • assumed to be constant after 1940

23. Decadal Averages Every year of 2000s warmer than 1990s average. 1990s: warmest decade (at the time). 1980s: warmest decade (at the time). Every year of 1990s warmer than 1980s average.