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

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

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

4. 4 Daily, 1/4 ° grid Daily, 1/4 ° grid Based on satellite and in situ data Based on satellite and in situ data o AVHRR-only (from 1981) o AVHRR+AMSR (from 2002) 3 Gridded Products 3 Gridded Products o Full SST o Random and sampling error variance o Bias error variance. 01. 02.03.04.05. 06.07 1 2 3 4 5 6 7 X 10 -3 OISST with AVHRR 17 & 18 May 15, 2010 Bias error Random and sampling error

5. 5 SourceERSSTOISST Random Small due to filtering and smoothing Lumped with sampling Sampling Low (decadal) frequency wrt model output High frequency wrt detrended OI Expected OI increment variance reduced by observations used Bias Methodology changes (e.g., bucket types, engine intake) Ship adjusted to buoys from 1970’s Depends on expected variance for each mode, and which modes are used for bias correction per satellite dataset Error estimation requires a reference or expected value. Analysis steps defined how error is computed.

6. 6 AMSR reduces sampling error but not bias AVHRR+AMSR AVHRR-only 1 2 3 4 5 6 7 X 10 -3.01. 02.03.04.05. 06.07

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

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

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

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

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

12. 12 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). 95% CI shown by MetOffice, while also showing departure from normal Global warming skepticism needs to be addressed Most recent 3 decades progressively warmer

13. 13 Emphasis on use of multiple indicators and ensemble presentations Emphasis on use of multiple indicators and ensemble presentations Avoids problem of deciding whether error estimates are equivalent Avoids problem of deciding whether error estimates are equivalent

14. 14 http://www.cpc.noaa.gov/products/ Index=Sum of SST anomalies in specific region 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?

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

16. 16 NCDC SST analysis products are distributed with error fields NCDC SST analysis products are distributed with error fields For operational climate assessments, errors not used routinely 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 Producers need to give recommendations and help develop protocols

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

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

21. 21 Sampling and Random error 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) Bias error (systematic to measurement methodology or analysis method)  Depends on number of EOT modes used, and number of data sources

22. 22 Random Random  Assumed very small due to smoothing Sampling 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 Bias  Pre-1940: night air temp comparison  assumed to be constant after 1940

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