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Jong-Seong Kug
and
Yoo-Geun Ham

Abstract

Observational studies hypothesized that Indian Ocean (IO) feedback plays a role in leading to a fast transition of El Niño. When El Niño accompanies IO warming, IO warming induces the equatorial easterlies over the western Pacific (WP), leading to a rapid termination of El Niño via an oceanic adjust process. In this study, this IO feedback is reinvestigated using the Coupled Model Intercomparison Project phase 3 (CMIP3) coupled GCM simulations. It is found that most of the climate models mimic this IO feedback reasonably, supporting the observational hypothesis. However, most climate models tend to underestimate the strength of the IO feedback, which means the phase transition of ENSO due to the IO feedback is less effective than the observed one. Furthermore, there is great intermodel diversity in simulating the strength of the IO feedback. It is shown that the strength of the IO feedback is related to the precipitation responses to El Niño and IO SST forcings over the warm-pool regions. Moreover, the authors suggest that the distribution of climatological precipitation is one important component in controlling the strength of the IO feedback.

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Sunyong Kim
and
Jong-Seong Kug

Abstract

A significant negative relationship is found between the summer mean north Indian Ocean sea surface temperature (SST) and East Asian surface temperature anomalies. However, the relationship is distinctively different for each month and shows a time-lagged relation rather than a simultaneous one. The north Indian Ocean warming in June is responsible for significant cold anomalies over the region of the Korean Peninsula and Japan (KJ region) that peak in July, exhibiting a 1-month leading role. The SST increase is closely associated with enhanced convective activity in the region in June, but the relationship between SST and resultant precipitation is substantially weakened afterward. This dependency of the precipitation sensitivity on SST anomalies is related to the climatological evolution of SST. The relatively low background SST due to the strengthening of southwesterly monsoons in the late summer can weaken the sensitivity of the precipitation to SST anomalies, yet the background SST in June is strong enough to maintain an increased sensitivity of precipitation. Thus, the Indian Ocean warming in June effectively drives atmospheric Kelvin waves that propagate into the equatorial western Pacific. In the western North Pacific (WNP), the resultant Kelvin wave–induced Ekman divergence triggers suppressed convection and anticyclonic anomalies. The WNP suppressed convection and anticyclonic anomalies move slowly northeastward until they are located near 20°N through the local air–sea interaction, and act as a source of the Pacific–Japan teleconnection. This teleconnection pathway brings clod surface anomalies to the KJ region due to the cyclonic circulation that causes the radiative and horizontal advection.

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Jong-Seong Kug
and
In-Sik Kang

Abstract

A feedback process of the Indian Ocean SST on ENSO is investigated by using observed data and atmospheric GCM. It is suggested that warming in the Indian Ocean produces an easterly wind stress anomaly over Indonesia and the western edge of the Pacific during the mature phase of El Niño. The anomalous easterly wind in the western Pacific during El Niño helps a rapid termination of El Niño and a fast transition to La Niña by generating upwelling Kelvin waves. Thus, warming in the Indian Ocean, which is a part of the El Niño signal, operates as a negative feedback mechanism to ENSO. This Indian Ocean feedback appears to operate mostly for relatively strong El Niños and results in a La Niña one year after the mature phase of the El Niño. This 1-yr period of phase transition implies a possible role of Indian Ocean–ENSO coupling in the biennial tendency of the ENSO. Atmospheric GCM experiments show that Indian Ocean SST forcing is mostly responsible for the easterly wind anomalies in the western Pacific.

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Woo Geun Cheon
and
Jong-Seong Kug

Abstract

In the framework of a sea ice–ocean general circulation model coupled to an energy balance atmospheric model, an intensity oscillation of Southern Hemisphere (SH) westerly winds affects the global ocean circulation via not only the buoyancy-driven teleconnection (BDT) mode but also the Ekman-driven teleconnection (EDT) mode. The BDT mode is activated by the SH air–sea ice–ocean interactions such as polynyas and oceanic convection. The ensuing variation in the Antarctic meridional overturning circulation (MOC) that is indicative of the Antarctic Bottom Water (AABW) formation exerts a significant influence on the abyssal circulation of the globe, particularly the Pacific. This controls the bipolar seesaw balance between deep and bottom waters at the equator. The EDT mode controlled by northward Ekman transport under the oscillating SH westerly winds generates a signal that propagates northward along the upper ocean and passes through the equator. The variation in the western boundary current (WBC) is much stronger in the North Atlantic than in the North Pacific, which appears to be associated with the relatively strong and persistent Mindanao Current (i.e., the southward flowing WBC of the North Pacific tropical gyre). The North Atlantic Deep Water (NADW) formation is controlled by salt advected northward by the North Atlantic WBC.

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Geon-Il Kim
and
Jong-Seong Kug

Abstract

On the basis of 32 long-term simulations with state-of-the-art coupled GCMs, we investigate the relationship between tropical Pacific decadal variability (TPDV) and El Niño–Southern Oscillation (ENSO). The first empirical orthogonal function (EOF) mode for the 11-yr moving sea surface temperatures (SSTs) in the coupled models is commonly characterized by El Niño–like decadal variability with Bjerknes air–sea interaction. However, the second EOF mode can be separated into two groups, such that 1) some models have a zonal dipole SST pattern and 2) other models are characterized by a meridional dipole pattern. We found that models with the zonal dipole pattern in the second mode tend to simulate strong ENSO amplitude and asymmetry in comparison with those of the other models. Also, the residual patterns, which are defined as the summation of El Niño and La Niña SST composite anomalies, are very similar to the decadal dipole pattern, which suggests that ENSO residuals can cause the dipole decadal variability. It is found that decadal modulation of ENSO variability in these models strongly depends on the phase of the dipole decadal variability. The decadal changes in ENSO residual correspond well with the decadal changes in the dipole pattern, and the nonlinear dynamic heating terms by ENSO anomalies are well matched with the decadal dipole pattern.

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Yoo-Geun Ham
and
Jong-Seong Kug

Abstract

In this study, a new methodology is developed to improve the climate simulation of state-of-the-art coupled global climate models (GCMs), by a postprocessing based on the intermodel diversity. Based on the close connection between the interannual variability and climatological states, the distinctive relation between the intermodel diversity of the interannual variability and that of the basic state is found. Based on this relation, the simulated interannual variabilities can be improved, by correcting their climatological bias. To test this methodology, the dominant intermodel difference in precipitation responses during El Niño–Southern Oscillation (ENSO) is investigated, and its relationship with climatological state. It is found that the dominant intermodel diversity of the ENSO precipitation in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is associated with the zonal shift of the positive precipitation center during El Niño. This dominant intermodel difference is significantly correlated with the basic states. The models with wetter (dryer) climatology than the climatology of the multimodel ensemble (MME) over the central Pacific tend to shift positive ENSO precipitation anomalies to the east (west). Based on the model’s systematic errors in atmospheric ENSO response and bias, the models with better climatological state tend to simulate more realistic atmospheric ENSO responses.

Therefore, the statistical method to correct the ENSO response mostly improves the ENSO response. After the statistical correction, simulating quality of the MME ENSO precipitation is distinctively improved. These results provide a possibility that the present methodology can be also applied to improving climate projection and seasonal climate prediction.

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Geon-Il Kim
and
Jong-Seong Kug

Abstract

Based on two long-term simulations using state-of-the-art coupled global climate models, we examined the physical processes that control the decadal modulation of El Niño–Southern Oscillation (ENSO) amplitude. To identify the contributions of various feedback processes to the ENSO amplitude, we used the Bjerknes stability (BJ) index, which aims to quantify the main ENSO feedbacks from a linear perspective. To start, we demonstrated that the time-varying BJ index is highly correlated with the decadal changes in the standard deviation of the ENSO index, suggesting that it provides a good representation of the decadal modulation of the ENSO amplitude. We further revealed that this phenomenon can be attributed mainly to thermocline feedback changes, particularly changes in the oceanic response to zonal wind stress. In addition, two critical features of the background state were found to contribute significantly to changes in the equatorial thermocline feedback: 1) the subtropical–tropical cells and 2) ocean stratification. It was suggested that weak (strong) background subtropical meridional overturning circulation partly contributes to regulating the narrower (wider) meridional scales of the sea surface temperature and the associated zonal wind stress anomalies. The more stratified the ocean, the stronger ocean responses to a given wind stress forcing, which affects the ENSO amplitude.

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Jong-Seong Kug
,
In-Sik Kang
, and
Jong-Ghap Jhun

Abstract

To improve forecasting skills in the western Pacific sea surface temperature (SST), the authors utilized and modified an intermediate El Niño prediction model. The original model does not have the major SST thermodynamics for western Pacific SST variability, so it cannot simulate interannual variation in the western Pacific correctly. Therefore, the authors have introduced some modifications, such as heat flux and vertical mixing, into the dynamical model in order to capture SST thermodynamics more realistically. The modified model has better forecast skill than the original one, not only for the western Pacific but also for the eastern-central Pacific. The model has predictive skill up to 6-months lead time as judged by a correlation exceeding 0.5.

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Jong-Seong Kug
,
June-Yi Lee
, and
In-Sik Kang

Abstract

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

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Jong-Seong Kug
,
June-Yi Lee
, and
In-Sik Kang

Abstract

Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.

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