Global Sea Surface Temperature Prediction Using a Multimodel Ensemble

JONG-SEONG KUG

Climate Environment System Research Center, Seoul National University, Seoul, South Korea

JUNE-YI LEE

International Pacific Research Center, SOEST, University of Hawaii at Manoa, Honolulu, Hawaii

IN-SIK KANG

Climate Environment System Research Center, Seoul National University, Seoul, South Korea

(Manuscript received 2 August 2006, in final form 16 December 2006)

ABSTRACT

In a tier-two seasonal prediction system, prior to AGCM integration, global SSTs should first be pre-dicted as a boundary condition to the AGCM. In this study, a global SST prediction system has beendeveloped as a part of the tier-two seasonal prediction system. This system uses predictions from fourmodels—one dynamic, two statistical, and persistence—and a simple composite ensemble method is appliedto these models. The simple composite ensemble prediction system has predictive skill over most of theglobal oceans for up to a 6-month forecast lead time. The simple ensemble method is also compared withother more sophisticated ensemble methods. The simple composite method has forecast skill comparable tothe other ensemble methods over the ENSO region and significantly better skill outside the ENSO region.

1. Introduction

As scientific and economic interests in seasonal cli-mate prediction and predictability have increased con-siderably in recent years, a need for global SST pre-diction for use in global forecasts for seasonal to inter-annual climate variability studies has emerged. In thetier-two seasonal prediction approach, SST forecastsfor the global oceans are first produced, which thenserve as the lower boundary condition for an atmo-spheric GCM (AGCM; Bengtsson et al. 1993). Thus,the seasonal predictive skill of a tier-two prediction sys-tem has a strong dependence on the skill in predictingglobal SSTs, in which a memory of the climate systemresides. It is well known that tropical SSTs play an im-portant role in global atmospheric circulation. How-ever, recent studies report that climate variability inmany local areas is also related to regional ocean SSTs

(Goddard and Graham 1999; Meehl and Arblaster2002; Behera et al. 2005). Therefore, there is also aneed for an a priori indication of the expected seasonalvariability of regional SST prediction.

In spite of the need for global SST prediction, littleresearch effort has been devoted to predicting SSTs forocean areas other than the tropical Pacific (e.g., Land-man and Mason 2001). In this study, we have developeda global SST prediction system, which employs a mul-timodel ensemble system. The multimodel ensemblesystem consists of a dynamical El Niño predictionmodel, two statistical models, and SST predictions us-ing persistence. It is well established that a multimodelensemble technique can give a better prediction thanindividual models by reducing systematic and randomerror variances (Krishnamurti et al. 2000; Palmer et al.2000; Yoo and Kang 2005). To obtain an ensemble pre-diction from the relatively short prediction period of1980–2003, a simple ensemble method has been appliedin this study. In section 2, we introduce four SST pre-diction models, and the ensemble method. The modelperformance is described in section 3. Section 4 gives abrief summary and discussion.

Corresponding author address: Dr. In-Sik Kang, School ofEarth and Environmental Sciences, Seoul National University,Seoul 151-742, South Korea.E-mail: kang@climate.snu.ac.kr

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DOI: 10.1175/MWR3458.1

© 2007 American Meteorological Society

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2. Model descriptions

The SST data used are the observed monthly meanfrom the Improved Extended Reconstructed Sea Sur-face Temperature version 2 (ERSST V2) dataset(Smith and Reynolds 2004) created by the National Cli-matic Data Center (NCDC). This dataset uses monthlyand 2° spatial superobservations, which are defined asindividual observations averaged onto a 2° grid. In thisstudy, the SST data were interpolated to a 5° latitude �5° longitude grid to focus on spatially large-scale SSTvariation. To obtain a final SST prediction, four SSTprediction models were utilized. They consist of onedynamical model, two statistical models, and predictionusing persistence. Using these four different SST pre-dictions, the final SST prediction is made by a simpleensemble method. Descriptions for each predictionmodel and the ensemble method are described in sec-tions 2a–d.

a. Dynamical El Niño prediction model

The dynamical El Niño prediction model used in thisstudy is based on the intermediate ocean and statisticalatmosphere coupled model developed by Kang andKug (2000). The atmospheric model is a statisticalmodel that is based on the singular value decomposi-tion (SVD) of the observed wind stress and SST. Thepresent model uses only the first two SVD modes. Theocean model is a modified version of the Lamont model(Zebiak and Cane 1987). The primary change is in sub-surface temperature parameterization. Also, latent heatflux, shortwave radiation, and vertical mixing werenewly parameterized to consider western Pacific SSTvariability (Kug et al. 2005).

The initial conditions of the ocean model were ob-tained by spinning up the wind stress, made by combin-ing the National Centers for Environmental Prediction(NCEP) wind stress and the model-produced windstress (Kug et al. 2001). In addition, the initial SST wasadopted from the observed SST. For details of themodel the reader is referred to Kang and Kug (2000)and Kug et al. (2005). Note that the model domain isconfined to the tropical Pacific basin (20°S–20°N,130°E–85°W) so it only produces SST predictions overthis region.

b. Lagged linear regression (LLR) model

The model is a pointwise statistical model based on alagged linear regression (LLR) developed by Kug et al.(2004). The predictor of the model is monthly Niño-3SST and the predictand is the SST at each grid pointover the global ocean. Unlike traditional statistical

models, the model uses not only observational data butalso data previously forecasted by the dynamical ElNiño prediction model as a predictor. In addition, theoptimal lag is automatically selected by a hindcast pro-cess. For details of the model the reader is referred toKug et al. (2004). Although the previous study onlyfocused on Indian Ocean SST prediction, we extendedthe prediction domain into the global oceans in thisstudy.

c. Pattern Projection Model (PPM)

In this study, we have developed a new statisticalmodel based on a pattern projection method. A PPM isa type of pointwise regression model. The predictor ofthe model is a SST pattern in a certain domain and thepredictand is the SST at each grid point. The main ideaof the model is to produce SST predictions by project-ing the predictor field onto the covariance pattern be-tween the large-scale predictor field and the one-pointpredictand. The model equation is as follows:

SSTi�tf � � �Pi�tf �, where

� �

1T �

t

T

SST�t�Pi�t�

1T �

t

T

Pi2�t�

,

Pi�t� � �x,y

Di

cov�x, y���x, y, t � lag�, with

cov�x, y� �1T �

t

T

SST�t���x, y, t � lag�, �1�

where x, y, and t denote longitude, latitude, and timegrid, respectively. Here Pi indicates a time series pro-jected by the covariance pattern between predictand,SST(t), and predictor field, �(x, y, t � lag), in a certaindomain, Di. The SSTi is a prediction corresponding tothe domain Di and � is a regression coefficient of theprojected time series, Pi, on the predictand SST duringa training period, T. In this study, we adopted the train-ing period as the last 25 yr from the date of the predic-tion, tf. For example, when we try to forecast the SST inJuly 1982, the training is performed using 25 cases fromJuly 1957 to July 1981. Here lag is the lag time betweenthe predictand and the predictand field. We checkedand found that the results are insensitive to the selec-tion of the training period when it is larger than 20 yr.The lag was changed from 1 to 15 months. The selectionof maximum lag (e.g., 15 month) does not affect thepredictive skill, in particular for short-term forecast

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lead times. Note that the lag has to be larger than theforecast lead time.

In this statistical prediction, selection of the predictordomain and the lag play a crucial role in the predictiveskill. To obtain a better forecast skill, the best predictordomain is automatically sought by a model procedure.To choose the best location and domain size for thepredictor field, hindcast experiments are repeated for alarge number of cases of predictor domain of flexiblesize and location during the training period. The do-main size is varied from the minimum domain size (i.e.,10° latitude � 80° longitude) to the maximum domainsize (i.e., the entire domain of the global oceans). Theselection of the minimum domain size is arbitrary inthis study; however too small a domain can cause theoverfitting problem in the statistical model. This flex-ible domain is moved from east to west and south tonorth. Following the above strategy, at every grid, atotal of 1024 cases are tested to produce SST predic-tions for all available lags. For each domain case andlag, a preliminary SST prediction is produced, and itsforecast skill is calculated by cross validation during thetraining period. Among the large number of prelimi-nary predictions, some predictions, having the highesthindcast skill, are selected, and the final prediction isobtained by simply averaging them.

How can we determine the predictions to select froma large number of cases? We classified the cases intonine groups by their hindcast skills during the trainingperiod. The groups were classified according to the sig-nificance level of the correlation skill of the hindcast.The criteria for the groupings are the 99.9%, 99%,97%, 95%, 93%, 91%, 89%, 87%, and 85% significancelevels. The classifying criterion for the grouping issomewhat arbitrary. However, the skill of the simpleensemble model did not change much when we usedthe correlation skill of the hindcast PPM as the criteriafor the grouping. In the statistical model, only predic-tions in the first group (99% significant level) areused for the final prediction. When there is no case inthe first group, the second group predictions are usedfor the final prediction. Similarly, if the nth group doesnot have any preliminary predictions, the (n 1)thgroup predictions are used. As a result, the best-skillcases found during the training period are selected forthe final prediction. If there is no prediction in anygroup, the model predicts the climatological value asthe final prediction. Note that the whole predictionstrategy is stratified by calendar month.

d. Ensemble procedure

Given the four SST predictions made by the dynami-cal El Niño model, LLR, PPM, and persistence, impor-

tant questions arise such as what is the highest level ofSST forecast skill achievable with the four differentpredictions and how can we get the best forecast skill?To address these questions, several ensemble methodswere applied to obtain the final ensemble prediction.Among them, we adopted the simple ensemble com-posite method, in which the final ensemble predictionwas obtained by simply averaging all the available pre-dictions with even weighting. Therefore, the four pre-dictions are averaged over the tropical Pacific, and theLLR, PPM, and persistence predictions are averagedoutside of the tropical Pacific because the dynamical ElNiño prediction model predicts only for the tropicalPacific region. Hereafter, we refer to the simple en-semble method as SC. Though other ensemble tech-niques were tried for ensemble SST prediction, thesimple ensemble method was superior to the otherones. We will compare the skills of different ensemblepredictions in section 3.

3. Predictions

Four different predictions of monthly SST over theglobe were produced for 24 yr from January 1980 toDecember 2003 using the four models described in sec-tion 2. Cross-validated forecast skills of each modelwere calculated using a total of 288 samples and com-pared with each other. Then, the final multimodel en-semble prediction of SST over the entire globe wasproduced and its forecast skill was evaluated.

Figure 1 shows the skill using correlation as the mea-sure of the SC prediction with 3-, 6-, and 9-month leadtimes. For comparison, the differences from the skill ofprediction using persistence are also displayed. For a3-month lead time, the ensemble predictions have highcorrelation skills, which are 0.7, over the ENSO re-gion. However, the prediction using persistence has arelatively low correlation skill compared to that of theensemble prediction. Not only over the ENSO regionbut also over other oceans, the ensemble predictionbeats persistence even over a short-term lead. In par-ticular, the SST prediction is significantly improved byusing the ensemble system over the Indian Ocean andthe western Pacific, where even a small SST changeplays a crucial role in the large-scale atmospheric cir-culation.

For a 6-month lead forecast, the SST over the ENSOregion is still quite predictable with the ensemble pre-diction system maintaining a correlation at around 0.7.In addition, the SST predictions over the Indian Oceanand western Pacific also have skill. By contrast, persis-tence does not have skill over most of the global ocean.The correlation differences between ensemble predic-

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tion and prediction using persistence are significant atthe 95% confidence level over the tropical Pacific, theIndian Ocean, the Kuroshio extension region in theNorth Pacific, and the Atlantic Ocean in the NorthernHemisphere. The significant test was carried out by thebootstrap approach (Efron and Tibshirani 1993). Notethat the correlation differences keep getting larger asthe forecast lead time gets longer (Fig. 1c). Over theoceans in the Southern Hemisphere, the simple en-semble prediction method does not beat persistenceover most lead times, indicating that the simple en-semble prediction should not be used in these areas.

In addition to the skill shown in the correlations,ensemble prediction also has a smaller root-mean-square error (RMSE) than persistence prediction. Fig-ure 2 shows the RMSE differences between the simpleensemble prediction method and persistence. To con-sider regional differences in SST standard deviation,normalized RMSE differences are also displayed. Thenormalized RMSE is obtained by dividing the RMSEdifference by the SST standard deviation at each gridpoint. The results show the simple ensemble predictionmethod is significantly better than persistence using thismeasure of skill. In particular, for a 9-month lead time,

FIG. 1. (left) Correlation coefficients between observed SSTs and forecasts by the simple ensemble prediction system with (a) 3-, (b)6-, and (c) 9-month lead times. (right) The correlation difference between the simple ensemble method and persistence for the samelead times; light (dark) shading indicates confidence levels greater than 95% (99%). Blue shading indicates that the difference isnegatively significant.

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the RMSE is reduced to about 30% of the SST standarddeviation over the tropical Pacific, the Indian Ocean,and the northern Atlantic Ocean. The RMS differenceis significant at the 99% confidence level over most leadtimes and over most global oceans (not shown). It isinteresting that the improvement in the RMSE isclearer than that in the correlation. We even found thatthe RMSE is improved over some regions where thecorrelation is degraded. This is because the multimodelensemble method is more effective in reducing theRMSE than in improving the correlation.

Figure 3 shows the correlation skill of the 6-monthlead time forecast used to target a season in order toshow the seasonal dependency of the prediction skill. It

is found that the skill of the simple ensemble predictionmodel has a strong seasonal dependency. For theENSO region, the ensemble model has the best skill forJanuary SSTs and the worst skill for July SSTs. This isrelated to the so-called spring barrier, consistent withprevious studies (Barnston and Ropelewski 1992; Latifet al. 1994; Balmaseda et al. 1995; Xue et al. 1994; Chenet al. 1997). Over the Indian Ocean, the skill is de-graded over the boreal autumn season, when the IndianOcean dipole model is active. This was also reported byKug et al. (2004). Though there is strong seasonal skilldependence, it seems the simple ensemble predictionmodel has predictive skill up to a 6-month lead timeover most of the seasons and tropical oceans.

FIG. 2. (left) RMSEs and (right) normalized RMSEs of SSTs predicted using the simple ensemble prediction system with (a) 3-,(b) 6-, and (c) 9-month lead times.

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To compare the skill of the multimodel ensembleprediction with each of its component models, threeSST indices were defined and used to evaluate predic-tive skill. The indices are the Niño-3.4 SST, the westernPacific (WP) SST, and the Indian Ocean (IO) SST in-dices whose definitions are listed in Table 1. Figure 4shows the correlation skills in predicting the three SSTindices for the period 1980–2003 using the individualmodels and the multimodel ensemble scheme. ForNiño-3.4 SST prediction, the El Niño prediction modeland the LLR have relatively higher skill than the PPMand persistence. Because the LLR use the Niño-3 SSTpredicted by the El Niño model as the predictor, thetwo models have a similar skill over the central andeastern Pacific. When the ensemble method is used, theskill improves and is better than that of each model. Inaddition, the improvement is greater as the forecastlead time increases. The correlation of the ensembleprediction method is 0.6 for up to a 12-month leadtime.

For the WP SST, each model has a relatively lowerskill compared to prediction of the Niño-3.4 SST. ThePPM has the best skill among the four model predic-tions but its skill is lower than that of the ensemblemodel. It seems that ensemble prediction has a predic-tive skill for up to a 6-month lead time for the WP SST,as judged by a correlation of 0.6. For the IO SST, only

three SST predictions are available because the El Niñomodel does not produce SST predictions outside of thetropical Pacific. The LLR has fairly high skill and isbetter than the other predictions since the LLR wasoriginally developed to predict in that region. Unlikeother models, the LLR is superior to the ensemble pre-diction for long lead times. This implies that the simpleensemble method may not be sufficient when the skillsof its component models are too diverse.

So far, the skill of the simple ensemble predictionmodel (SC) has been investigated. To show the superi-ority of the SC, two other traditional ensemble methodswere applied to the same data. One designated best one(B1) chooses the best model in the cross-validation pe-riod at each grid point and adopts the prediction of thebest model as its final prediction. The other was theweighted ensemble (WE) method, which averages thepredictions with different weighed coefficients. Theweighted coefficients are obtained by the SVD method

TABLE 1. List of regions used for the definition of the SSTindices.

Niño-3.4 SST WP SST IO SST

5°S–5°N, 5°–15°N, 10°S–10°N,170°–120°W 130°–160°E 40°–110°E

FIG. 3. Correlation coefficients between the observed SST and the 6-month lead time forecast by the simple ensemble predictionsystem for (a) January, (b) April, (c) July, and (d) October SST.

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to minimize the RMSE in the cross-validation method(Yun et al. 2003).

Figure 5 shows the correlation coefficients of the B1and WE ensemble methods. In addition, the signifi-cance of the correlation difference between the SC andthe two other methods is shaded with areas of positiveshading indicating that the SC is superior. Compared tothe B1 and the WE, the SC is significantly better overmost of the global oceans except in the ENSO regionfor 3- and 6-month lead time forecasts. In particular,the superiority of the SC is significant at the 99% con-fidence level over the northern Indian Ocean, northernsubtropical Atlantic Ocean, and the eastern coastal sec-tion of the tropical Pacific. For the equatorial centralPacific, the skill of the SC is lower than that of B1 andWE, but the difference is not large. Therefore, thesimple ensemble method, the SC, was chosen to pro-duce the final SST predictions in this study.

4. Summary and discussion

In a tier-two seasonal prediction system, prior toAGCM integration global SSTs should first be pre-dicted as a boundary condition to the AGCM. In thisstudy, a global SST prediction system was developed asa part of the two-tiered seasonal prediction system. Thesystem consists of four different SST prediction modelsand a simple ensemble model (SC), which uses the out-

puts of the four models to produce the final SST mul-timodel ensemble prediction. This prediction systemhas predictive skill for up to a 6-month lead time overmost of the global ocean. In addition, the skill of theensemble prediction is better than that of persistenceand of any of the four single models over most oceansand for most lead times.

In this study, three different multimodel ensemblemethods were compared. Interestingly outside of theENSO region, the simple ensemble method is superiorto the more sophisticated WE, which is known as askillful method for multimodel ensemble prediction inmedium-range forecasting (Krishnamurti et al. 2000).Recently, using idealized seasonal prediction experi-ments Yoo and Kang (2005) showed that the SCmethod is superior to the WE method for the limiteddata sample used in the study. It is clear that the WEwill give the best skill for the fitting period. However, ifthe weighting coefficients for different predictions arenot stable in the cross-validation process, predictionskill is seriously degraded. In particular, because thepresent study used only a 24-yr data sample, the stabil-ity problem for the weighted coefficients can cause aserious degradation of the prediction skill.

Though the SC method is an optimal method for arelatively short time data sample, it seems the SCmethod is problematic when the skills of its differentcomponent models are too diverse. In these situations,

FIG. 4. Correlation skill of the simple ensemble prediction model and each of its component models for (a) theNiño-3.4 SST, (b) the WP SST, and (c) the Indian Ocean SST indices.

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the skill of the SC can be degraded compared to that ofthe B1 model. For example, over the Indian Ocean, thesingle model, the LLR, has better skill than the SC forlonger forecast lead times as shown in Fig. 4. In thesecases, if the worst model were excluded from the en-semble process, the predictive skill of the simple en-semble model might be improved. To examine this, weapplied the ensemble method removing the worstmodel using certain thresholds to the data used in thisstudy. We found that the skill is slightly improved com-pared to the SC though the difference is not that sig-nificant. The skill difference between these two meth-ods depends on the region. For example, over the In-dian Ocean and equatorial central Pacific where theskills of the component models are relatively diverse,the skill of the ensemble model after removing theworst model is improved. However, over other regionsthe skill is even degraded. If many models are used forensemble prediction, this problem could be more sig-nificant. Therefore, an optimal ensemble techniqueshould be developed for considering the skill distribu-tion of multimodels in future work.

Acknowledgments. The authors appreciate the help-ful comments from two anonymous reviewers. This re-search was partly supported by the Korea Meteorologi-cal Administration Research and Development Pro-gram under Grant CATER_2006-4206. J.-S. Kug wassupported by the SRC program of the Korean Scienceand Engineering Foundation.

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