GSOP Meeting in Qingdao, China on Sep 18, 2016

1. A Real-time Ocean ReanalysesIntercomparison Project(in the context of TPOS and ENSO monitoring) Y. Xue1, C. Wen1, A. Kumar1, M. Balmaseda2, Y. Fujii3, O. Alves6, M. Martin7, X. Yang4, G. Vernieres5, C. Desportes8, T. Lee9, I. Ascione7, R. Gudgel4, I. Ishikawa10 1Climate Prediction Center, NCEP/NWS/NOAA, College Park, Maryland, USA 2European Center for Medium-Range Weather Forecasts, Reading, UK 3MeteorologicalResearch Institute, Japan Meteorological Agency, Tsukuba, Japan 4Geophysical Fluid Dynamics Laboratory, NOAA/OAR, Princeton, NJ, USA 5Goddard Space Flight Center, NASA, Greenbelt, MD, USA 6Bureau of Meteorology, Melbourne, Australia 7Met Office, Exeter, Devon, United Kingdom 8Mercator Ocean, France 9Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA 10Japan Meteorological Agency, Tokyo, Japan GSOP Meeting in Qingdao, China on Sep 18, 2016

2. A Real-Time Ocean Reanalyses Intercomparison Project (Motived by TPOS2020 Workshop in Jan 2014) • The operational ocean reanalysesfrom ORA-IP are used to extend ORA-IP into near real-time • The ensemble mean among ocean reanalyses provides a more reliable estimate of climate signal, while the ensemble spread provides an estimate of climate noise • The real-time estimation of signal-to-noise ratiosupports ENSO monitoring and prediction • Large departure from the ensemble mean indicates potential problem in individual products • Regions of large ensemble spread highlights where ocean data assimilation systems need improvements • Monitoring the ensemble spread helps discern the impacts of TPOS data on analysis uncertainty and provide support for the TPOS2020 Project • Temperature intercomparison is led by NCEP and salinity intercomparison led by BOM from Yan Xue

3. Temperature Intercomparison Led by NCEP Salinity Intercomparison Led by BOM http://www.cpc.ncep.noaa.gov/products/GODAS/multiora_body.html http://poama.bom.gov.au/project/salt_19812010/ from Yan Xue

4. Operational Ocean Reanalyses (for Temperature Intercomparison at NCEP) from Yan Xue

5. Root-Mean-Square Difference (RMSD) with TAO/TRITON (measuring the fit to the buoy temperature data in upper 300m) • RMSE of ensemble mean (EM) averaged in upper 300m is about 0.2-0.3oC. • Normalized RMSE (NRMSE, RMSE divided by STD of TAO temp. anomaly) is about 20-25% except it is 37% in NEPac (170W-90W, 5N-8N) • MET has smaller NRMSE than that of the ensemble mean (EM) due to strong fit to data • EM is superior to individual ORAs in the fit to the buoy data EEPac: 170W-90W, 2S/0/2N WEPac: 120E-180W, 2S/0/2N NEPac: 170W-90W, 5N/8N NWPac: 120E-180W, 5N/8N Spac: 120E-90W, 5S/8S from Yan Xue

6. Normalized RMSD (%) with Ensemble Mean in 1993-2014 (measuring the fit to EM temperature in upper 300m) from Yan Xue Normalized RMSD (RMSD divided by STD of EM) in upper 300m

7. Impacts of TPOS Data on Ensemble Spread of Total Temp. Date Counts Spread Pre-TAO/TRITON 1985-1993 TAO/TRITON 1994-2003 Argo 2004-2011 (Left column) The ensemble spread of temperature anomaly averaged in the upper 300m in (a) from 1985 to 1993, (b) from 1994 to 2003, and (c) from 2004 to 2011, along with (right column) the associated data counts (number of daily temperature profiles in each 1x1 degree box). from Yan Xue

8. Impacts of TPOS Data on Ensemble Spread of Total and Anom. Temp. in 8S-8N Impacts of TAO data loss TAO Argo XBT Spread of total temp. Spread of anom. temp. Impacts of Argo data Impacts of clim. biases from Yan Xue

9. D20 Anomaly in 2012-2016 Signal, Noise, Signal-to-Noise Ratio, Data Counts from Yan Xue

10. Normalized RMSD HC300 with Ensemble Mean in 1993-2014 (measuring the fit to EM HC300) from Yan Xue Climate signal is discernable in tropical Pacific and Indian Ocean, eastern North Pacific

11. from Yan Xue

12. An ensemble of nine (seven) operational ORAs for 1993-present (1979-present) has been collected at NCEP to assess signal (ensemble mean) and noise (ensemble spread) in upper ocean temperature analysis in real-time; • The real-time ensemble ocean monitoring products have been used in support of ENSO monitoring and prediction; • Despite the constraints by TPOS data, uncertainties in ORAs are still large in the northwestern tropical Pacific, in the SPCZ region, as well as in the central and northeastern tropical Pacific; • The analysis uncertainty shows a strong flow-dependency, which increased substantially during big El Ninos. This highlights the need for sustained TPOS on reducing the analysis uncertainty; • The current data assimilation systems tend to constrain the solution very locally near the buoy sites, potentially damaging the larger-scale dynamical consistency. There is an urgent need to improve data assimilation systems so that they can optimize the observation information from TPOS and contribute to improved ENSO prediction. • Climate signal in upper 300m ocean heat content is only discernable by the ensemble ORAs in the tropical Pacific, tropical Indian Ocean and eastern North Pacific. Summary from Yan Xue

13. Normalized RMSD (%) with Ensemble Mean in 1993-2014