Prediction of NINO3 SST anomaly in a hybrid coupled model

Prediction of NINO3 SST anomaly in a hybrid coupled model

with a piggy-back data assimilation initialization

contributed by Joyce E. Meyerson, Hui Su and J. David Neelin

Department of Atmospheric Science University of California, Los Angeles, California

A hybrid coupled model (HCM), similar to the one used in Syu et al. (1995), Waliser et el. (1994) and Blanke et al. (1997), is used to predict the NINO3 SST anomaly (SSTA). The atmospheric model is estimated from observations using a singular value decomposition (SVD) technique. The model contains the first seven SVD modes of the covariance matrix calculated from the time series of pairs of observed monthly mean Reynolds SST anomalies and Florida State University (FSU) subjective pseudo-stress anomalies over a 19-year period from January, 1970 through December, 1988. Atmospheric adjustment is parameterized by a simple 60-day spin-up time scale (Syu and Neelin 2000a). Heat flux is parameterized according to Oberhuber's (1988) formulation using climatological data, with the negative feedback on SST estimated following Seager et al. (1988). The OGCM is a version of the GFDL Modular Ocean Model (Pacanowski, Dixon and Rosati, 1991, personal communication) for the Pacific basin. The vertical resolution is 27 levels, with 10 levels in the upper 100 meters. A Richardson-number-dependent vertical mixing scheme is combined with a surface mixed layer parameterization (see Syu and Neelin 2000a for model details).

The HCM has a reasonable simulation of ENSO in spatial and temporal features, with ENSO periods of 3 to 4 years. Model performance in "retroactive real-time forecasts" (hindcasts hereafter) from 1980-1992 has been shown in the September 1997 issue of the Experimental Long-Lead Forecast Bulletin, with further analysis in Syu and Neelin (2000b).The ocean climatology used in all hindcast/forecast experiments is specified to be the averaged model SST, forced by the FSU subjective wind stress product over 1978 to 1993 without modification by the data assimilation scheme. The climatological wind stress used in the hindcast/forecast experiments is also specified to be the average of the FSU subjective wind stress over the same period (1978-1993). The forecast results after 1994 are verified against the observations from Reynolds' (1988) SST data set after applying optimum interpolation method as described in Reynolds and Smith (1994).

The initialization scheme makes use of both the wind information (FSU converted wind stress) and the ocean model data assimilation product from the Climate Prediction Center (CPC) (Ji et al., 1995). In addition to the specified FSU wind stress forcing, the CPC reanalyzed anomalous ocean temperature field is "injected" into the ocean model (27 layers) every month since 1980 up to the start of the hindcast (injection scheme hereafter). Because our ocean model (GFDL MOM) is in a version reasonably close to that used by CPC, approximate consistency is assumed in injecting the CPC reanalyzed data. To make distinction between this procedure and raw-data injection, we refer to it as a "piggy-back" data assimilation scheme, because it makes use of the effort from an CPC data assimilation product. The "piggy-back" data assimilation scheme gives a substantial improvement in hindcast skill (see the Sep. 1997 issue and Syu and Neelin 2000b), and thus appears to be a viable, economical forecast method.

In March 2002, Florida State University (FSU) changed to an objectively analyzed pseudo-stress product (Bourassa et al., 2001). A new initialization scheme was therefore implemented (see June 2002 issue of ELLFB). However, forecasts using the new objective FSU pseudo-stress product are subject to the caveat that they have not performed up to expectations during March 2002-present and are still being evaluated. Figures 1 and 2 present the NINO3 index for forecasts from 1993 to present overlaid onto the March 2002 published results which utilized the subjective FSU wind product. Observations through August 2003 are used.

Figure 1 shows NINO3 SSTAs for observations (3-month running average, thick black curves) and forecasts (gray curves) at 3-, 6- and 9-month lead (previously published results dashed gray curves). Averages of each lead month based on forecast verification over the 1980-1992 time span are removed before plotting the curves. Vertical bars represent plus and minus one RMS error, over the same forecast verification time span. NINO3 SST anomaly forecasts for 3-, 6-, and 9-month lead show the model continues to produce cooler temperatures than observations since March 2002 when the new FSU pseudo-stress was introduced. The cooling seen in the 3-, 6-, and 9-month forecast starting in March 2003 changes to forecasts of normal conditions beginning late in 2003.

Figure 2 shows the latest two forecast results (starting from October and November, 2002, respectively, for 12 months, dark dotted line), with the mean over the forecast verification time span (1980-1992) removed. The observations (solid line), model initialization run (dark dashed line) and the previously published results (light dashed line) since 1993 are also displayed. Both forecasts initiated with July and August observations indicate that roughly normal temperatures can be expected through summer of 2004.

References:

Blanke, B., J. D. Neelin, and D. Gutzler, 1997: Estimating the effect of stochastic wind stress forcing on ENSO irregularity. J. Climate, 10, 1473-1486.

Ji, M., A. Leetmaa, and J. Derber, 1995: An ocean analysis system for seasonal to interannual climate studies. Mon. Wea. Rev., 123, 460-481.

Oberhuber, J. M., 1988: An atlas based on the COADS data set: the budgets of heat, buoyancy and turbulent kinetic energy at the surface of the global ocean. Max-Planck-Institut f"ur Meteorologie Report No. 15, Bundesstrasse 55, D-2000, Hamburg 13, FRG.

Reynolds, R. W., 1988: A real-time global sea surface temperature analysis. J. Climate, 1, 75-86.

Reynolds, R. W. and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929-948.

Seager, R., S. E. Zebiak, and M. A. Cane, 1988: A model of the tropical Pacific sea surface temperature climatology. J. Geophys. Res., 93, 1265-1280.

Syu H.-H., J. D. Neelin, and D. Gutzler, 1995: Seasonal and interannual variability in a hybrid coupled GCM. J. Clim., 8, 2121-2143.

Syu, H.-H., and J. D. Neelin, 2000a: ENSO in a hybrid coupled model. Part I: sensitivity to physical parameterizations. Climate Dynamics, 16, 19-34.

Syu, H.-H., and J. D. Neelin, 2000b: ENSO in a hybrid coupled model: Part II: prediction with piggyback data assimilation. Climate Dynamics, 16, 35-48.

Waliser, D. E., B. Blanke, J. D. Neelin, and C. Gautier, 1994: Shortwave feedbacks and El Nino-Southern Oscillation: Forced ocean and coupled ocean-atmosphere experiments. J. Geophys, Res., 99, 25109-25125.

Figure Captions:

Fig. 1. The forecasts of NINO3 SST anomalies from 1993 to present using the new FSU objective wind product (solid gray curve) and 1993 to February 2002 using the old FSU subjective wind product (dashed gray curve). The initialization includes Reynolds SST data and CPC data in both cases. The latest forecast starts from August 2002. The mean for each lead month over the forecast verification time span (1980-92) is removed before plotting. Vertical bars represent plus and minus one RMS error over the same forecast verification time span. Shown for (a) 3-month, (b) 6-month and (c) 9-month lead.

Fig. 2. The latest two forecasts (dotted lines) of NINO3 SST anomalies up to 12 lead months starting from July and August 2002. Observations (black solid line) and model initialization run (black dashed line) from 1993 to present are also shown. The mean for each lead month is removed as in Fig. 1. Vertical bars indicate the same plus and minus one RMS error used in Fig. 1. The previously published model initialization run from 1993 to February 2002 which employed the FSU subjective wind product is shown for comparison (gray dashed line).