T. Lee9, I. Ascione7, R. Gudgel4, I. Ishikawa10 A Real-time Ocean Reanalyses Intercomparison 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 3Meteorological Research 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

A Real-Time Ocean Reanalyses Intercomparison Project (Motived by TPOS2020 Workshop in Jan 2014) The operational ocean reanalyses from 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 ratio supports 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

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

Operational Ocean Reanalyses (for Temperature Intercomparison at NCEP) Product Forcing Ocean Model Data Assim. Method Ocean Observations Analysis Period NCEP GODAS (NGODAS) NCEP-R2 1°x1/3° MOM3 3DVAR T/SST 1979-present Behringer and Xue 2014 NCEP CFSR (NCFSR) Coupled DA 0.5°x1/4° MOM4   Saha et al. 2010 GFDL (ECDA) 1ox1/3° MOM4 EnKF T/S/SST Zhang et al. 2007 BOM (PEODAS) ERA40 to 2002; NCEP-R2 thereafter 1°x2° MOM2 1970-present Yin et al. 2011 ECMWF (ORAS4) ERA40 to 1988; ERAi thereafter 1°x1/3° NEMO3 SLA/T/S/SST/SIC Balmaseda et al. 2013 JMA (MOVE-G2) JRA55 corr + CORE Bulk 1ox0.5° MRI.COM3 SLA/T/S/SST Toyoda et al. 2013 NASA (MERRA Ocean) MERRA + Bulk EnOI Vernieres et al. 2012 UK MET (GloSea5) ERAi + 1/4°x1/4° NEMO3.2 1993-present Waters et al. 2015 MERCATOR (GLORYS2V3) ERAi corr + 1/4°x1/4° NEMO3.1 EnKF+ Lellouche et al. 2013 from Yan Xue

NRMSE Difference from EM Root-Mean-Square Difference (RMSD) with TAO/TRITON (measuring the fit to the buoy temperature data in upper 300m)   RMSE (oC) NRMSE (%) NRMSE Difference from EM EM NCEP GODAS JMA ECMWF GFDL NASA BOM MET MERCATOR CFSR EEPac 0.25 20 7 10 5 14 13 -3 9 19 WEPac 24 11 2 15 16 NEPac 0.33 37 1 17 25 18 -11 23 NWPac 0.29 6 12 SPac 0.21 3 8 27 -2 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

Normalized RMSD (%) with Ensemble Mean in 1993-2014 (measuring the fit to EM temperature in upper 300m) Good agreement in the equatorial belt and at buoy sites, poor agreement in the north-eastern Pacific near 5N and 10 degree poleward Normalized RMSD (RMSD divided by STD of EM) in upper 300m from Yan Xue

Impacts of TPOS Data on Ensemble Spread of Total Temp. Date Counts 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

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. Large spread before 1985, TAO helped to reduce the spread. Spread increased during the 1997/98 El Nino, and Argo data helped to reduce the ensemble spread substantially around 2004, but the spread of anomalous temperature has an upward trend and peaked during the 2015/16 El Nino, indicating big El Nino increases the ensemble spread and also there are bias in climatology that impacts anomaly spread Impacts of Argo data Impacts of clim. biases from Yan Xue

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

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

from Yan Xue

Summary 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. from Yan Xue

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