An experimental coupled forecast system at the National Meteorological Center: Some early results

Coupled Model Project, National Meteorological Center, NOAA/NWS, Washington, D.C. 20233, USA
Tellus (Impact Factor: 1.76). 07/1994; 46(4):398 - 418. DOI: 10.1034/j.1600-0870.1994.t01-3-00006.x


An experimental coupled ocean-atmosphere model forecast system has been implemented at the National Meteorological Center (NMC) for routine multi-season climate forecasts. The ocean initial conditions for the forecasts are provided by an ocean data assimilation system which uses a basin model of the Pacific Ocean integrated with a four-dimensional variational data assimilation system. Pacific basin ocean reanalyses for the period 1982–1992 provided both the initial conditions and the verification fields for the coupled model forecasts. The coupled model consists of a modified T40 version of NMC's operational medium range forecast model coupled to a Pacific basin ocean general circulation model. Hindcast experiments starting on the first of every month from October 1983–1993 have been made to establish the skill of this system in forecasting sea surface temperature variability in the tropical Pacific with lead times of up to several seasons. The system consistently out performs persistence forecasts even at short forecast lead times, and is able to forecast most of the sea surface temperature variability that occurred in the tropical Pacific during 1984–1993.

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    • "The TAO/TRITON measurements were essential for the development of ENSO theory (Neelin et al. 1998) and ENSO predictions (Latif et al. 1998). The implementation of the TAO/TRITON array stimulated a rapid development of operational seasonal prediction (Ji et al. 1994; Stockdale et al. 1998) and ocean data assimilation systems (ODAS) (e.g., Behringer et al. 1998; Alves et al. 2004; Zhang et al. 2007; Yin et al. 2011; Xue et al. 2011; Xue et al. 2012; Balmaseda et al. 2013). The value of the TAO/TRITION data for monitoring ENSO and improving ENSO forecast skill has been extensively documented in literature (Rosati et al. 1997; Ji et al. 1998; Alves et al. 2004; Sun et al. 2007; Balmaseda and Anderson 2009; Stockdale et al. 2011; Xue et al. 2013). "
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    ABSTRACT: and objective analyses. Without assimilation of in situ observations , both GODAS and ECDA had large mean biases and RMSD in all variables. Assimilation of all in situ data significantly reduced mean biases and RMSD in all variables except zonal current at the equator. For GODAS, the mooring data is critical in constraining temperature in the eastern and northwestern tropical Pacific, while for ECDA both the mooring and Argo data is needed in constraining temperature in the western tropical Pacific. The Argo data is critical in constraining temperature in off-equatorial regions for both GODAS and ECDA. For constraining salinity, sea surface height and surface current analysis, the influence of Argo data was more pronounced. In addition, the salinity data from the TRITON buoys played an important role in constraining salinity in the western Pacific. GODAS was more sensitive to withholding Argo data in off-equatorial regions than ECDA because it relied on local observations to correct model biases and there were few XBT profiles in those regions. The results suggest that multiple ocean data assimilation systems should be used to assess sensitivity of ocean analyses to changes in the distribution of ocean observations to get more robust results that can guide the design of future tropical Pacific observing systems.
    Climate Dynamics 08/2015; DOI:10.1007/s00382-015-2743-6 · 4.67 Impact Factor
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    • "ENSO in the tropical Pacific is the most important climate variation on seasonal-to-interannual time scales, which can have profound effects on weather and climate worldwide. Since its first dynamic prediction conducted around three decades ago (Cane et al. 1986), the ability of dynamical models to predict ENSO has improved significantly (e.g., Ji et al. 1994; Chen et al. 1995; Kirtman et al. 1997; Latif et al. 1998; Zhang et al. 2003; Jin et al. 2008; Xue et al. 2013; Zhu et al. 2012, 2013c, 2014). Operational seasonal ENSO prediction is now routinely conducted using coupled general circulation models (CGCMs) in many major climate centers worldwide. "
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    ABSTRACT: This study examines El Niño–Southern Oscillation (ENSO) prediction in Project Minerva, a recent collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The focus is primarily on the impact of the atmospheric horizontal resolution on ENSO prediction, but the effect from different ensemble sizes is also discussed. Particularly, three sets of 7-month hindcasts performed with ECMWF prediction system are compared, starting from 1 May (1 November) during 1982–2011 (1982–2010): spectral T319 atmospheric resolution with 15 ensembles, spectral T639 with 15 ensembles, and spectral T319 with 51 ensembles. The analysis herein shows that simply increasing either ensemble size from 15 to 51 or atmospheric horizontal resolution from T319 to T639 does not necessarily lead to major improvement in the ENSO prediction skill with current climate models. For deterministic prediction skill metrics, the three sets of predictions do not produce a significant difference in either anomaly correlation or root-mean-square error (RMSE). For probabilistic metrics, the increased atmospheric horizontal resolution generates larger ensemble spread, and thus increases the ratio between the intraensemble spread and RMSE. However, there is little change in the categorical distributions of predicted SST anomalies, and consequently there is little difference among the three sets of hindcasts in terms of probabilistic metrics or prediction reliability.
    Journal of Climate 03/2015; 28(5):2080-2095. DOI:10.1175/JCLI-D-14-00302.1 · 4.44 Impact Factor
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    • "Although ENSO originates and develops primarily in the tropical Pacific, it can have profound global effects. Over the past few decades, the ability of dynamical models to predict ENSO has improved significantly [e.g., Cane et al., 1986; Ji et al., 1994; Chen et al., 1995; Kirtman et al., 1997; Latif et al., 1998; Schneider et al., 1999; Kirtman et al., 2002; Zhang et al., 2003; Jin et al., 2008; Wang et al., 2010; Zhu et al., 2012]. Operational seasonal ENSO prediction is now routinely done using coupled general circulation models (CGCMs) in many major climate centers worldwide. "
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    ABSTRACT: In this study, the impact of ocean initial conditions (OIC) on the prediction skill in the tropical Pacific Ocean is examined. Four sets of OIC are used to initialize the 12-month hindcasts of the tropical climate from 1979 to 2007, using the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model at the National Centers for Environmental Predictions (NCEP). These OICs are chosen from four ocean analyses produced by the NCEP and the European Center for Medium Range Weather Forecasts (ECMWF). For each hindcast starting from a given OIC, four ensemble members are generated with different atmosphere and land initial states. The predictive skill in the tropical Pacific Ocean is assessed based on the ensemble mean hindcasts from each individual as well as multiple oceanic analyses. To reduce the climate drift from various oceanic analyses, an anomaly initialization strategy is used for all hindcasts. The results indicate that there exists a substantial spread in the sea surface temperature (SST) prediction skill with different ocean analyses. Specifically, the ENSO prediction skill in terms of the anomaly correlation of Niño-3.4 index can differ by as much as 0.1-0.2 at lead times longer than 2 months. The ensemble mean of the predictions initialized from all four ocean analyses gives prediction skill equivalent to the best one derived from the individual ocean analysis. It is suggested that more accurate OIC can improve the ENSO prediction skill and an ensemble ocean initialization has the potential of enhancing the skill at the present stage.
    Geophysical Research Letters 05/2012; 39(L09602). DOI:10.1029/2012GL051503 · 4.20 Impact Factor
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