1. Ocean synthesis inter-comparison using OceanDIVA Alastair Gemmell Keith Haines Greg Smith Jon Blower Environmental Systems Science Centre University of Reading, UK DDDsDDDs http://www.resc.rdg.ac.uk Color-coded model-obs T misfits

2. Outline Methods – OceanDIVA – a Java web application to visualize and compare gridded model data, and in- situ point observations Methods – OceanDIVA – a Java web application to visualize and compare gridded model data, and in- situ point observations Geospatial representation of the data. Geospatial representation of the data. Statistical representation of the data Statistical representation of the data Conclusions Conclusions

3. OceanDIVA – Ocean Data Inter-comparison and Visualization Application Input data can be local to the web service or read in remotely via OPeNDAP protocol For this study: Used EN3 (ENACT/ENSEMBLES) observations dataset Compared a range of CLIVAR GSOP ocean syntheses accessible via OPeNDAP Analysed Sept ’04 (Sept ’01 for syntheses finishing before ‘04)

4. Geospatial representation of data in Google Earth

5. Minimum bin content = 1 Minimum bin content = 2Minimum bin content = 3 Probability Density Functions (PDFs) Covering the north Pacific. Model is Reading ¼ degree Binned data into bins of 10m by 0.2 o C Blues = bins with lower data density Reds = bins with higher data density Data density normalised to depth level

6. Probability Density Functions (PDFs) Covering the north Pacific. Model is Reading ¼ degree Depth Temperature Observed Depth Salinity Misfit Depth Misfit Salinity Misfit Temperature Misfit

7. Regional Variability This example: Reading ¼ degree model showing S(T) Pacific Obs.Misfit North South Trop. Atlantic Obs.Misfit

8. North Pacific z(T) across syntheses ObservationsECCO-JPLGFDLECMWF CERFACS 2001 ECCO-SIO 2001 SODAMERCATOR INGV 2001 GECCO 2001 Reading 1 o control Reading 1 o assim. WOA ‘05 ECCO-GODAE Reading 1/4 o control Reading 1/4 o assim. World Ocean Atlas ‘05

9. Bias v Standard Deviation North Pacific – z(T) – over T range 12-22 o C CERFACS ‘01 ECCO-GODAE ECCO-JPL ECCO-SIO ‘01 ECMWF GECCO ‘01 GFDL INGV ‘01 MERCATOR Reading 1 o control Reading 1 o assim. Reading ¼ o control Reading ¼ o assim. SODA WOA 2005 Misfit Mean (m) 50 0 25 Misfit Std. Dev. (m) 65

10. North Pacific S(T) across syntheses ObservationsECCO-JPLGFDLECMWF CERFACS 2001 ECCO-SIO 2001 SODAMERCATOR INGV 2001 GECCO 2001 Reading 1 o control Reading 1 o assim. WOA ‘05 ECCO-GODAE Reading 1/4 o control Reading 1/4 o assim. ECCO-GODAE

11. Bias v Standard Deviation North Pacific – S(T) – over T range 5-17 o C CERFACS ‘01 ECCO-GODAE ECCO-JPL ECCO-SIO ‘01 ECMWF GECCO ‘01 GFDL INGV ‘01 MERCATOR Reading 1 o control Reading 1 o assim. Reading ¼ o control Reading ¼ o assim. SODA WOA 2005 Misfit Mean (PSU) 0.14 0.0 0.05 Misfit Std. Dev. (PSU) 0.13

12. Bias v Standard Deviation North Pacific – S(T) – over T range 17-30 o C CERFACS ‘01 ECCO-GODAE ECCO-JPL ECCO-SIO ‘01 ECMWF GECCO ‘01 GFDL INGV ‘01 MERCATOR Reading 1 o control Reading 1 o assim. Reading ¼ o control Reading ¼ o assim. SODA WOA 2005 Misfit Mean (PSU) 0.08 0.0 0.04 Misfit Std. Dev. (PSU) 0.11

13. Conclusions OceanDIVA is a useful tool for visualizing data, and comparing model data with observations. OceanDIVA is a useful tool for visualizing data, and comparing model data with observations. Useful for validation in fields of Useful for validation in fields of Ocean reanalysesOcean reanalyses Operational oceanographyOperational oceanography Outputs shown which appear to reflect differences between synthesis techniques – e.g. methods of data assimilation. Outputs shown which appear to reflect differences between synthesis techniques – e.g. methods of data assimilation. E.g. mode waters, S(T) relationshipsE.g. mode waters, S(T) relationships Interesting Future work planned including Interesting Future work planned including different and longer time periodsdifferent and longer time periods using isopycnalsusing isopycnals more syntheses.more syntheses. Provided correct metadata and standards are used, there is the exciting prospect of increasing amounts of data available on OPeNDAP servers etc, leading to more collaborative work and comparisons being carried out. Provided correct metadata and standards are used, there is the exciting prospect of increasing amounts of data available on OPeNDAP servers etc, leading to more collaborative work and comparisons being carried out.