1. 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

2. 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

3. Temperature Intercomparison Led by NCEP Salinity Intercomparison
Led by BOM
from Yan Xue

4. 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

5. 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 oC.
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: W-90W, 2S/0/2N
WEPac: E-180W, 2S/0/2N
NEPac: W-90W, 5N/8N
NWPac: E-180W, 5N/8N
Spac: E-90W, 5S/8S
from Yan Xue

6. 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

7. Impacts of TPOS Data on Ensemble Spread of Total Temp.
Date Counts
Pre-TAO/TRITON
TAO/TRITON
Argo
(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.
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

9. Signal, Noise, Signal-to-Noise Ratio, Data Counts
D20 Anomaly in
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. 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

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