Real-time Ocean Reanalyses Intercomparison Project for Quantifying Impacts of Tropical Pacific Observing Systems on Constraining Ocean Reanalyses for ENSO Y. Xue 1, C. Wen 1, A. Kumar 1, M. Balmaseda 2, Y. Fujii 3, G. Vecchi 4, G. Vernieres 5, O. Alves 6, M. Martin 7, F. Hernandez 8, T. Lee 9, D. Legler 10 1 NCEP/NOAA, USA 2 ECMWF 3 JMA, Japan 4 GFDL/NOAA, USA 5 GSFC/NASA, USA 6 BOM, Australia 7 UK Met Office, England 8 MERCATOR, France 9 JPL/NASA, USA 10 CPO/NOAA, USA The 40 th CDPW, Denver, October 26-29, 2015

What is Operational Ocean Reanalysis? Ocean Model Reanalysis Surface Fluxes Data Assimilation Scheme (optimally combine model solutions and ocean observations) Ocean Observations MooringsAltimeter SST Argo XBT - Initialization for dynamical seasonal predictions - Providing historical context for ENSO variability - Real-time operational ENSO monitoring

NCEP Global Ocean Data Assimilation System (GODAS) (Ocean-alone, Implemented in 2003) Global Ocean Data Assimilation System (GODAS) Climate Forecast System (CFSv1) SST XBT Moorings Altimeter Argo R2 Surface Fluxes SST Anomaly Forecast Forecasters Official ENSO Forecast Official Probabilistic Surface Temperature & Rainfall Forecasts Seasonal Forecasts for North America CCA, CA Markov CCA, OCN MR, ENSO Ocean Initial Conditions IRI

Ocean Monitoring Products Based on GODAS (A partnership between CPC and COD/CPO to deliver climate relevant products to the society) Synthesis of global ocean observations by NCEP’s Global Ocean Data Assimilation System (GODAS) Monthly Ocean Briefing since May 2007 Products used widely by operational climate prediction centers, researchers, fishery managers, news media, program managers, teachers and students Contact: Yan Xue, NOAA/CPC Synthesis of Ocean Observations

- Reached 300,000 hits per month in early 2014 and The big jump in Feb 2015 is related to the onset of El Nino Sep 2010 Aug 2015

NCEP Climate Forecast System Reanalysis (CFSR) (Partially Coupled System, Implemented in 2011) Atmosphere Data Assimilation System (T382L64 GSI) Ocean Data Assimilation System (MOM4 GODAS) CFS Forecast Ocean Initial Conditions for CFSv 2

7 Black: All data Red: TAO/TRITON Blue: XBT Green: Argo Tropical Pacific Observing Systems 8S-8N Jun 2012 Nov 2014 TAO Argo What are influences of missing TAO data on uncertainties in ocean reanalyses?

 Extend CLIVAR-GSOP/GODAE OceanView Ocean Reanalyses Intercomparison Project (ORA-IP) into real-time  Deliver ensemble ocean monitoring products in real time  Quantify uncertainties in the ocean state estimation for the purpose of ENSO monitoring and prediction  Understand how variations in observing systems influence uncertainties in ocean reanalyses  Provide support for the TPOS 2020 project on the design of the future tropical Pacific observing system  Asses how NCEP ocean reanalyses compare with other state-of-art ocean reanalyses Yan Xue Climate Prediction Center8 Real-time Ocean Reanalyses Intercomparison Project (Motived by TPOS Workshop in Jan. 2014)

NameMethod & Forcings In Situ Data Altimetry Data ResolutionPeriodVintageReference NCEP (GODAS) 3D-VAR T,/SSTNO (Yes since 2007) 1°x 1° (1/3° near Eq)1979- present 2003Behringer and Xue (2004 ECMWF (S4) OI T/S/SSTYes1°x1° (1/3° near Eq)1959- present 2011Balmaseda et al. (2012) JMA3D-VAR T/S/SSTYes1°x1° (1/3° near Eq)1979- present 2015Usui et al. (2006) GFDL (ECDA) EnKF coupled T/S/SSTNo 1°x 1° (1/3° near Eq)1970- present 2010Zhang et al. (2009) NASAEnOI Partially coupled T/S/SSTYes1/2°x 1/2° (1/4° near Eq) present 2011Rienecker at al. (2011) BOM (PEODAS) EnKFT/S/SSTNo2°x 1.5 ° (1/2° near Eq.)1980- preesnt 2009Yin et al. (2010) NCEP (CFSR) 3D-VAR Coupled T/SSTNo1/2°x 1/2° (1/4° near Eq) present 2010Xue et al. (2011) UK Met (GLOSEA5) 3DVART/S/SSTYes1/4°x 1/4°1993- present ??Waters et al. (2014) MERCATOR (GLORYS2) KF-SEEKT/S/SSTYes1/4°x 1/4°1993- present ?? Operational Ocean Reanalyses 7 products from 1979-present 9 products from 1993-present

 Evaluate against tropical mooring array data  Evaluate against the ensemble mean, regarded as representative of the “truth”  Identify outliers  Link temporal variations of ensemble spread with temporal variations of data counts  Understand how variations in observing systems influence uncertainties in ocean reanalyses Real-time monitoring of climate signal and noise Yan Xue Climate Prediction Center10 Analysis Method

RMSE ( o C) NRMSE (%) NRMSE Difference from EM (%) EM NCEP GODAS JMA ECMWF GFDL NASA BOM MET MERC ATOR NCEP CFSR EEPac WEPac NEPac NWPac SPac RMSE with TAO/TRITON in 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 -RMSE of ensemble mean (EM) averaged in upper 300m is about o C. -Normalized RMSE (NRMSE, RMSE divided by STD of TAO) is about 20-25% except it is 38% in NEPac (170W-90W, 5N-8N) -UK MET has smaller NRMSE than that of EM due to strong fit to data -ECMWF fits to the mooring data best, NCEP CFSR the worst.

RMSD with Ensemble Mean in UK MET has largest departure from the ensemble mean, particularly in the western Pacific -GODAS, GFDL, MERCATOR and CFSR have largest departure from the ensemble mean in off-equatorial regions -JMA, ECMWF and NASA agree with the ensemble mean the best

-UK MET has too strong fit to observations and largest RMSD in the western Pacific -GODAS and CFSR have largest departure in the eastern Pacific -ECMWF and JMA agree with the ensemble mean the best RMSD with Ensemble Mean at Eq. in

(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). Pre-TAO/TRITON TAO/TRITON and pre-Argo TAO/TRITON and Argo Spread Date Counts

(top panel) The number of daily temperature profiles from TAO/TRITON (red line), TAO/TRITON/Argo/XBT (blue line), (middle panel) the standard deviation of temperature anomaly of the ensemble mean of seven products (solid line) and nine products (dash line), and (bottom panel) the ensemble spread averaged in the upper 300m based on seven products (solid line) and nine products (dash line) for the eastern equatorial Pacific (EEPac, 160 o W-90 o W, 2 o S-2 o N). Large spread in 2012/13 TAO data loss in 2012/13 TAO

(based on climatology)

(based on Climatology) In development

Sep 2015 Sep 1997 Sep 1982

Sep 2015 Sep 1997 Sep 1982

Summary  An ensemble of nine (seven) operational ORAs for 1993-present (1979-present) has been collected 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;  TAO/TRITON array significantly reduces analysis uncertainty in the equatorial Pacific; Argo data reduces analysis uncertainty in off- equatorial regions;  TAO/TRITON data constrains analysis spread in the equatorial belt. when there was a significant TAO data loss in , the spread among analyses increased significantly;  Despite of uncertainties in ocean reanalyses, the ensemble mean of multiple ocean reanalyses provides the best estimation of the state of ocean and can be used to derive climate indicators. The ensemble spread provides uncertainties in our estimation;  Analysis and monitoring will be expanded beyond the tropical Pacific in the future.

Sep 2015 Sep 1997 Sep 1982

RMSD with Ensemble Mean in

Air-Sea Coupling NINO3.4 Heat Budget Oceanic Kelvin Waves Monthly Ocean Briefing Plots o n ENSO