Next Article in Journal
Impact of Material on Response and Calibration of Particle Number Systems
Previous Article in Journal
A Numerically Sensitive Study of Two Continuous Heavy-Pollution Episodes in the Southern Sichuan Basin of China
Previous Article in Special Issue
Dimensionality Reduction by Similarity Distance-Based Hypergraph Embedding
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 13
Issue 11
10.3390/atmos13111772
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble

by
Eung-Sup Kim
1,
Vladimir N. Kryjov
2 and
Joong-Bae Ahn
1,*
1
Department of Atmospheric Sciences, Pusan National University, Busan 46241, Korea
2
Research Institute for Basic Sciences, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(11), 1772; https://doi.org/10.3390/atmos13111772
Submission received: 30 September 2022 / Revised: 22 October 2022 / Accepted: 26 October 2022 / Published: 27 October 2022
(This article belongs to the Special Issue Climate Modeling and Dynamics)
Browse Figures
Versions Notes

Abstract

:
The predictability of the wintertime Western Pacific (WP) pattern is evaluated based on seasonal predictions from five models participating in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) between the observed and MME-predicted WP indices was 0.61 (0.37–0.54 for individual models) for the entire series. However, when only three Super El Niño (SEN) years (Niño3.4 ≥ 2.0) out of the 40-year series were excluded, the TCC dropped down to 0.54 (0.27–0.42). During the SEN years, the WP was strongly affected by the SEN-excited anomalies via the PNA. In observations from non-SEN years, the WP pattern was strongly related to the dipole pattern in Northwestern Pacific SST (TCC = 0.8), for the description of which we suggested a Northwestern Pacific (NWP) index, and it was significantly weakly related to the ENSO and IOD, whereas in the model simulations, the main role was played by the ENSO (TCC = 0.6). The NWP index was well predictable in MME (TCC = 0.73) and individual models (0.56–0.71). We showed that the prediction of the WP index polarity is reliable when both predicted WP and NWP anomalies are significant and indicate the same WP sign that has implications for the seasonal forecasting.

1. Introduction

The Western Pacific (WP) pattern is one of the teleconnections that occur prominently in the mid-latitudes of the Northern Hemisphere and is one of the main patterns that influence the Northern Hemisphere climate variability in winter. The WP is a dipole pattern that comprises a north–south seesaw in the middle troposphere geopotential height between the sub-polar and subtropical latitudes of the western North Pacific [1,2,3].
The WP pattern is related to the north-south shifts of the Asian-Pacific jet [4,5,6,7], Pacific storm track modulation [7], Rossby wave breaking in the North Pacific [5], and sea ice conditions in the Bering and Okhotsk seas [8]. Research on the relationship between the WP and the Pacific-North American (PNA) pattern and the El Niño-Southern Oscillation (ENSO) is being actively conducted. The WP pattern tends to appear stronger at the same polarity as the PNA, and it tends to appear stronger and longer during El Niño [9,10]. They have suggested that the ENSO affects the WP by exciting the PNA by furthering its expansion westward. Meanwhile, Garfinkel [11,12] and Dai and Tan [9] documented that, during the La Niña winters, the polarity of the WP tends to be similar to that of the El Niño winters, although weaker. Meanwhile, according to Tanaka et al. [6], the WP pattern can sustain itself through efficient energy transformation in the climatological-field without external forcing, including remote influences from the tropics.
The WP pattern affects the variability of the East Asia Winter Monsoon (EAWM). According to previous studies, the WP pattern has a significant effect on temperature in East Asia [13]. It was found that the WP pattern also affects the extremely low temperature in East Asia. A study found that the WP pattern had an effect on the record cold waves in North America in the winter of 2013/2014 [14]. Cheung et al. [15] reported that cold waves in Hong Kong were related to the WP-like pattern.
The coupled general circulation model (CGCM), which simulates the interactions among different earth sub-systems, such as atmosphere, ocean, sea-ice, and land surface, is an ultimate and viable tool for seasonal prediction [16,17,18]. Nowadays, most operational climate prediction centers produce global long-range forecasts using a multi-model ensemble (MME) consisting of several CGCMs. Widely available are the Copernicus Climate Change Service (C3S) of the European Center for Medium-Range Weather Forecasts (ECMWF) [18], MME long-range forecasts of the World Meteorological Organization (WMO) Lead Center for Long-Range Forecast Multi-Model Ensemble (LC-LRFMME) [19], the North American Multi-Model Ensemble (NMME) of North American modeling centers [20], and the MME seasonal forecast of the Asian-Pacific Economic Cooperation (APEC) Climate Center (APCC) [21]. Although seasonal forecast studies using these systems are being actively conducted, most studies focused on the predictability of global and regional fields of temperature and precipitation; for example, in [21,22,23,24], the predictability of hemispheric and regional circulation were mostly confined to the predictability of the AO and NAO [25,26,27,28,29]. As mentioned earlier, the WP pattern has a significant effect on the winter climate in East Asia and also affects the extreme temperature phenomenon. For this reason, it is necessary to continuously predict and monitor the WP pattern, but there are few studies on this. Particularly, Lee and Oh [30] studied the ability of the APCC model set (which is out of date now) to reproduce the WP-like mode of variability, but without the assessment of its predictions. Therefore, we intended to evaluate and improve the predictability of the WP pattern by using the global seasonal forecast data of the APCC MME seasonal forecast system.

2. Data and Methods

2.1. Data

This study utilized seasonal predictions from five models participating in the APCC MME seasonal prediction project. Currently, more than 15 institutions provide their seasonal forecasts for the APCC. However, only predictions from CGCMs with a sufficient length of hindcast series were selected for this study. A detailed description of the models is given in Table 1 and Figure 1. We used one month lead seasonal predictions for 40 winters (December–January–February, DJF) from 1982/1983 to 2021/2022, with model integration being initialized in early November at the latest. The parameters used are geopotential height at 500 hPa (Z500), temperature at 2 m (T2m), and sea surface temperature (SST); all the model predictions were interpolated to a 2.5° × 2.5° grid.
Reanalysis data were used as observation for the prediction skill assessment. The fields of Z500 is those from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data (0.25° × 0.25°) [36]. The SST data is NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) V2 (OISSTv2) data (1.0° × 1.0°) [37]. All observation data were interpolated to the model grid.

2.2. Methods

This study was based on ensemble mean model predictions combined into a multi-model ensemble (MME) without weighting using a simple composite method (SCM). Prediction skill was assessed with the temporal correlation coefficient (TCC) and spatial anomaly correlation coefficient (ACC). The significance of the correlation coefficients was assessed by Student’s t-statistic in two tailed tests. The significance of the difference between correlation coefficients was assessed with the use of Fisher’s z-transformation [38]. For the analysis of spatial patterns of anomalies, we performed the Principal Components Analysis (PCA) on a matrix of covariances between grid-point series weighted by the square root of cosine of latitude.
There are several definitions of the WP index [1,3,39,40], with two of them being mostly in use nowadays (Table 2). The original WP index suggested by Wallace and Gutzler [3], WG81 hereafter, is defined as a difference between normalized Z500 anomalies at two points over the northern and subtropical North Pacific, with the positive WP index polarity corresponding to the positive (negative) anomaly at the northern (southern) point. The WP index from NOAA/Climate Prediction Center (CPC) [1] is based on a rotated PCA of the Z500 anomalies over the northern extratropics. The widely accepted polarity of these indices is opposite to that of the WG81’s index. In our study, we followed the polarity of the CPC’s WP index, opposite to that of the WG81’s original WP index. That is, the positive polarity of the WP index corresponds to the negative (positive) Z500 anomaly over the northern (subtropical) North Pacific.
The WP pattern is a meridional dipole in the western North Pacific (Figure 2a,b). The northern node of the WP pattern spans from North Eastern Siberia to Alaska centered on the Kamchatka Peninsula, and the southern node spans from the Eastern China Sea to the Central Pacific cantered at latitude 25 °N. Seasonal model predictions are usually spatially shifted compared with observed patterns, e.g., [41,42], that make inappropriate definitions of an index based on two fixed grid-points for our study. Thus, in this study, we estimated and used a simplified WP index appropriate for a model prediction assessment. We selected the areas of the 99% significant correlations common for the two WP indices (green boxes in Figure 2) and defined the WP index as the normalized difference between the normalized area-weighted Z500 average anomalies in the southern and northern rectangular areas:
WP = [Z500 (20.0–35.0 °N, 130.0–180.0 °E) − Z500 (52.5–67.5 °N, 140.0–175 °E)]
It should be noted that the estimated WP index is not a newly defined one; it is rather a composite of the WP indices suggested in the previous studies adjusted to the model predictions.
The composite WP pattern strongly correlates with the named above indices (Figure 2c), with TCC within 0.87 to 0.97 (Table 3), as well as its spatial correlation pattern closely resembling those corresponding to the previously defined WP indices (Figure 2), with the ACC within 0.95 to 0.99.

3. Results and Discussion

3.1. Current Status of WP Prediction

The TCC between WP indices from the observations and from MME (WP_MME) estimated over the whole 40-year series (Figure 3a) was 0.61, which is significant at the 99% confidence level and implies a good predictability of the WP index. The sign of the WP index was predicted correctly for 27 winters (67.5%) which exceeds the skill of random guessing at the 95% confidence level. However, visually noticeable is that mostly skillful predictions of the positive WP phase occur in the years of “super” El Niño (SEN) episodes (the DJF Niño3.4 index exceeds 2.0; the years of 1982, 1997, and 2015). When three SEN years were removed, the TCC dropped down to 0.54 and the sign consistency became 64.8%, which exceeds that of random guessing only at the 90% confidence level based on the binomial probability distribution. The predictability of the WP phase for these winters became ambivalent. Particularly, for all 10 ordinary El Niño (not SEN) winters (DJF Niño3.4 index within 0.5–2.0) predicted, there was the positive WP index polarity; meanwhile, during four of these ten winters observed, there was the negative one. For three strong La Niña winters (DJF Niño3.4 index below −1.5) predicted, there was the negative WP index, but the negative one occurred only once. Surprisingly, the highest predictability was for the ordinary La Niña winters (DJF Niño3.4 index within −1.5–−0.5). They occurred 11 times, with the WP index being predicted correctly for 10 of them (eight negative and two positive). In general, the MME tended to predict the positive phase of the WP pattern for the El Niño winters and the negative phase for the La Niña winters. However, in observations, the probabilities of occurrence of the positive or negative phases of the WP pattern did not differ from each other in terms of the 95% confidence intervals of the binomial probability distribution for both the El Niño and La Niña winters.
The TCC for individual model predictions ranged within 0.37 and 0.54, being significant at the 99% confidence level with the exception of KMA (Figure 3b–f), although expectably lower than for MME [43]. All the models succeeded in predicting the positive WP phase for the SEN winters. However, when the SEN years were removed, the individual models’ TCC range lowered down to 0.27–0.42 and became insignificant at the 95% confidence level for KMA and NCEP models.
We also calculated correlation coefficients between the observed and predicted WP indices for the common set of years used for all the models (1991–2010, 2015, 2016, and 2021; 23 years in total) (Table S1). The 23-year series correlations were slightly less than those for the 40-year series for the CWB, NASA, and NCEP models, and they were noticeably increased for the KMA and PNU models (up to 0.48 and 0.61, correspondingly). However, for the series without two SEN years (1997 and 2015), correlations increased only for the KMA and PNU models, while they noticeably decreased for other models.
The correlation pattern between Z500 from the observations and the WP_MME index (Figure 4a) essentially differed from the observed one shown in Figure 2. The two negative signals covered most of the North Pacific region centered in the Kamchatka Peninsula and eastern north Pacific and the positive signals located to the southwest and northeast of it. That is, the WP_MME pattern on Z500 resembles the PNA, which expanded more westward than WP. Similar correlation patterns appeared in most of the models (Figure 4b–f). When three SEN years (out of total 40 years) were excluded (Figure 5a), the negative signal in the eastern Pacific almost disappeared, and the WP_MME pattern became similar to the WP one rather than the PNA. However, in two models (CWB, NCEP) the negative signal of the Kamchatka Peninsula became insignificant (Figure 5b–f); the KMA pattern remained, but all signals were weakened. NASA’s pattern was most similar to that of MME, and PNU’s pattern was most similar to the WP definition (Figure 2).
The difference in the WP spatial patterns shown in Figure 4 and Figure 5 indicates that, during the SEN years, the SEN excited a strong positive PNA pattern, which expands westward exciting or maintaining the positive WP pattern that corresponds to the results of Dai and Tan [44]. When the SEN years were excluded, this mechanism weakened (the TCC between the WP and Niño3.4 indices decreased down to 0.19 from the 0.36 obtained from the entire series), and some other mechanisms played the main role in the exciting of the WP pattern. These mechanisms are discussed in the next section.
Thus, the skill of the WP index prediction assessed on the entire hindcast series looks quite high (TCC = 0.61, sign consistency is significant at the 95% confidence level); however, it appears somewhat confusing because the skill was mainly contributed by the SEN years. Meanwhile, the exclusion of just three SEN years out of the total 40 led to a TCC decrease down to 0.54, with the sign consistency exceeding that of random guessing only at the 90% confidence level. Since the SEN winters are rather seldom episodes, we performed research for possible sources of the WP predictability, with special attention paid to the non-SEN winters.

3.2. Sources of Predictability in Observations and MME

3.2.1. Observations

We performed a study on the relationships between the WP and SST in observations and CGCM simulations. We constructed correlation maps between observed SST and the WP index (Figure 6). On the correlation map for the full series (Figure 6a), there are prominent signals in the western North Pacific, in the eastern equatorial Pacific, resembling the El Niño pattern, and in the Indian ocean, resembling the Indian Ocean Dipole mode (IOD, [45]). For the series without SEN years (Figure 6b), only the pattern of the seesaw between the latitudinal belts of the positive correlations (~20–35 °N) and the negative correlations (~45–55 °N) in the western North Pacific remained unchanged in both location and amplitude. Meanwhile, the correlation patterns in the eastern equatorial Pacific and in the Indian ocean became much weaker.
For the numerical representation of this pattern, we suggest a Northwestern Pacific (NWP) SST index defined as the difference between the SST anomalies averaged over the areas within the seesaw pattern (blue boxes in Figure 6):
NWP = [SST (20.0–32.5 °N, 125.0–160.0 °E) − SST (42.5–50.0 °N, 165.0–190 °E)]
Noteworthy, the northern domain of the index is embraced by the North Pacific SST domain of Hurwitz et al. [46] that strongly relates to the WP northern node. The NWP index correlated with the WP index, with TCC being about 0.8 for both the entire series and those without the SEN years (Figure 6c). Correlations of the WP index with the Niño3.4 and IOD indices were much weaker, 0.36 (0.19 without SEN years) and 0.34 (0.46), correspondingly., with the difference being significant at a 95% confidence level compared to the NWP. The relationships between the NWP index and the ENSO and IOD indices were not significant at the 95% confidence level, with TCCs of 0.23 (0.13 without the SEN years) and 0.22 (0.30), correspondingly.
The NWP pattern persisted from November (Figure S1), regardless of SEN, with the correlation between DJF and November NWP indices being 0.67 without the SEN years and 0.62 for entire series (Figure S1c). The persistence from October was weaker; the TCC between the DJF NWP index and the October NWP index was 0.30 for the series without SEN years and dropped down to 0.15 with the SEN years added. The significant persistence indicates that the NWP pattern precursors the tropospheric WP pattern by one month at least; however, this relationship is not strong enough to consider the November NWP as a possible predictor of the DJF WP, with the TCC between them being 0.27. Furthermore, the high concurrent correlation between the DJF WP and NWP (0.79) suggests that there could be positive feedback between them. The NWP impacts the WP by forcing a negative (positive) low troposphere temperature anomaly over the northern (southern) domain, with the corresponding negative (positive) geopotential height anomalies in the overlying middle and upper troposphere, which is supported by Hurwitz et al. [46], who showed in a model study with prescribed SST that the negative (positive) SST anomaly in the northern North Pacific excites the positive (negative) WP phase. In contrast, the WP could force the NWP via associated surface wind anomalies (Figure S4), which cause the enhanced (weakened) surface cooling in the northern (southern) domain of the NWP due to enhanced (weakened) sensible and latent heat fluxes during the positive phase of the WP and NWP and vice versa during the negative phase. Meanwhile, it is noteworthy that Nigam [47] showed that the negative SST anomaly in the northern domain of the NWP pattern may also be excited by the PNA impact. Particularly, the positive PNA phase is associated with enhanced winds over the northern domain, leading to sea surface cooling caused by enhanced sensible and latent heat fluxes.
Thus, in the observations, the main source of predictability of the wintertime WP pattern was the NWP, that is, the SST seesaw pattern in the north-western North Pacific, which persists through winter from October-November. When the NWP pattern is obtained through the correlation between Z500 and NWP, a negative signal appears over the Kamchatka Peninsula, and a positive signal appears near 25 °N, which is quite similar compared to the WP pattern (Figure S2a). Strong relationships (TCC = 0.8) between the WP and NWP remain unchanged regardless of the ENSO status (Figure 6c). Therefore, the skillful prediction of the WP pattern requires the reproducibility of the WP-NWP relationships by the MME.
We performed the PCA of the western North Pacific (0–70 °N, 100–200 °E) DJF SST anomalies (Figure S3) to check whether the NWP is a pattern of natural variability or an artifact from construction of the correlation map shown in Figure 6. The NWP pattern closely resembles the third EOF, accounting for 12.97% of the total variability, and is well separated from other EOFs [48]. The TCC between the NWP index and the third principal component (PC3) is 0.83 (Figure 7). Therefore, the NWP SST seesaw pattern reflects the natural variability of the SST anomalies in the north-western North Pacific that is able to excite the WP pattern of variability in the middle troposphere.

3.2.2. MME

The pattern of significant (95% confidence level) correlations between the WP index and SST in MME (WP_MME and SST_MME) and the WP index and SST in individual models closely resembles that of the El Niño and IOD (Figure 8). Meanwhile, the pattern of the NWP is also apparent; however, correlations are less than those in the equatorial Pacific and Indian Ocean. When the SEN years were excluded (Figure 9), the correlations essentially weakened in the El Niño and IOD patterns. Weakening was hardly noticeable in the NWP pattern. Particularly, for the PNU model, correlations became insignificant along the equator and slightly increased in the NWP pattern. Thus, similar to the observations, the predictability of the WP in the models was provided by all three patterns, the NWP, ENSO, and IOD. However, their roles changed. While, in the observation, the leading role is played by the NWP with the correlation 0.81 (0.79 without SEN years) followed by the IOD (0.34 (0.46)) and the ENSO (0.36 (0.19)), in the MME, the main role is played by the ENSO with the correlation 0.74 (0.60) and IOD (0.61 (0.51)) followed by the NWP with the correlation 0.50 (0.48).
The WP-ENSO and WP-IOD relationships are rather weak in observation, whereas they are quite strong in model simulations. Overestimation of the ENSO and IOD impacts on the WP is one of the main shortcomings in the seasonal prediction of the WP by the MME. Overestimation of the ENSO and IOD impacts in the seasonal CGSM simulations was documented for the prediction of the East Asia summer monsoon indices [49,50]. Furthermore, it is interesting to note that the closest to the WP definition pattern, although much weaker, was predicted by the PNU model (Figure 5f), for which the main source of predictability, particularly in the non-SEN years, is the NWP pattern (Figure 9f). It corresponds to the conclusion that large ensemble models are able to predict regional modes of variability (the NAO in their case), although with essentially reduced amplitude, with sources of predictability not confined to the tropical ocean [29,51].

3.3. The Role of the NWP

Relationships between the NWP and WP pattern are significant regardless of the ENSO status in both the observations and MME. We tested whether the NWP_MME could be used for the WP prediction. Correlation patterns between SST and NWP_MME (Figure 10a) closely resemble those obtained for SST and the WP (Figure 6), i.e., the strong NWP pattern and weaker signals in the equatorial eastern Pacific and the Indian Ocean. The NWP pattern is also significant in the correlation maps for individual models (Figure 10b–f). The NWP pattern is well predictable, with the TCC between the NWP and NWP_MME being 0.75 and varying between 0.60 and 0.68 for individual models (Figure 11). The TCC between the WP and NWP_MME is 0.49 (0.45) and varies between 0.31 and 0.47 for individual models (Figure 12). Interestingly, the correlation patterns between Z500 and the model-predicted NWP indices (Figure 13) are closer to the WP definition (Figure 2) than those predicted by the models directly (Figure 4 and Figure 5). Particularly, the PNU model’s pattern (Figure 13f) exactly reproduced the WP definition pattern (Figure 2), with the negative anomalies over the Kamchatka Peninsula and the positive anomalies spanning from the Eastern China Sea to the Central Pacific at latitude 25 °N. However, even for the series with SEN years excluded, the TCC between the WP and NWP_MME (0.45) was lower than that for the directly model-predicted WP-MME (0.54), even though the TCCs between the WP index and individual model predictions of the NWP index are mostly higher than the TCCs between the WP index and individual model predictions of the WP index (Table 4). Discussion on the rationality of MME and conditions of its efficiency is beyond the scope of this paper; we should note that the errors of the model predictions of WP are more independent between the models than the errors of the NWP model predictions.
Nonetheless, the predicted NWP is useful for the improvement of the reliability of the predictions of the WP. Figure 14 shows composites of SST for the years in which the WP predictions were successful and the years in which the WP predictions were not successful. The average of the corresponding years observed and predicted indices are shown under the corresponding panels. Predictions of the positive (negative) phase of the WP were correct on the background of the warn (cold) ENSO episodes and the positive (negative) phase of the NWP, with the predicted positive (negative) anomalies of the WP and NWP indices being significant. Predictions of the positive (negative) phase of the WP were incorrect on the background of the warm (cold) ENSO episodes and negative (positive) or uncertain phase of the NWP, with the predicted NWP index anomaly being insignificant. Based on only predicted WP and NWP indices, the recommendation is as follows: the prediction of the WP phase is reliable when both predicted WP and NWP anomalies have the same sign and are significant at the 95% confidence level. Otherwise, the prediction is uncertain. Using this rule, the reliability of the performed real-time prediction of the WP index, at least its polarity, could be assessed in advance, when the prediction is issued.

4. Conclusions

The performed study revealed that the overall predictability of the WP pattern strongly depends upon the state of the background climate status. The overall assessment of the skill of the WP predictions is high, with the TCC between observed and predicted WP indices being 0.61. However, when super El Niño years were excluded, the correlation decreased down to 0.54, which reflects a decrease in the reliability of the WP predictions.
The positive polarity of the WP index was predicted correctly for the SEN years when the SEN excited the strong positive phase of the PNA. The WP positive phase in the SEN years resulted from the expansion of the PNA-associated anomalies westward on the background of the positive NWP phase. When the SEN episode was expected, the prediction of a positive phase of the WP pattern was reliable.
In the non-SEN years, the predictability of the WP index was ambiguous. Successful and unsuccessful predictions of the WP phase may appear regardless of the ENSO status. The WP-ENSO relationships were weak (TCC = 0.19), and the WP was mainly governed by the NWP (TCC between the WP and NWP indices is 0.79). However, in the model simulations, the WP_MME was mainly governed by the ENSO (TCC with Niño3.4_MME index is 0.60), whereas its relationships with the NWP_MME were much weaker (TCC = 0.48). Meanwhile, the NWP pattern in SST was well predictable, with the TCC between the NWP_MME and NWP being 0.73.
We suggest a rule for forecasters, helping them to assess the reliability of the issued real-time prediction of the WP phase in advance of the forecast period. The prediction of the WP phase is reliable when both the predicted WP and NWP index anomalies are significant and indicate the same WP sign; otherwise, the prediction is unreliable. It is proposed to use the sign and statistical significance of the predicted NWP as a factor to determine the reliability of the WP prediction in APCC MME.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/atmos13111772/s1, Table S1: Correlations between the WP indices observed and predicted for common years, Figure S1: Correlation patterns between November SST and the DJF NWP index in observations, Figure S2: Correlation patterns between Z500 and the NWP index in observations, Figure S3: Four leading EOFs of the north-western Pacific DJF SST anomalies, Figure S4: Correlation patterns between surface wind velocity, u-, v-components and the WP index.

Author Contributions

Conceptualization, J.-B.A.; methodology, J.-B.A. and V.N.K.; software, E.-S.K.; validation, J.-B.A. and V.N.K.; formal analysis, E.-S.K.; writing—original draft preparation, E.-S.K.; writing—review and editing, J.-B.A. and V.N.K.; visualization, E.-S.K.; supervision, J.-B.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01475503)" Rural Development Administration, Republic of Korea.

Data Availability Statement

Data (seasonal model forecasts) used in this study are available upon reasonable request from the APEC Climate Center and are publicly available at https://cliks.apcc21.org/ (accessed on 31 May 2022) in the future. ERA5 dataset is publicly available: https://cds.climate.copernicus.eu/ (accessed on 31 May 2022). OISSTv2 dataset is publicly available: https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html (accessed on 31 May 2022).

Acknowledgments

The authors appreciate the institutes participating in the APEC Climate Center seasonal forecast system for providing the hindcast and forecast experimental data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Barnston, A.G.; Livezey, R.E. Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Weather Rev. 1987, 115, 1083–1126. [Google Scholar] [CrossRef]
  2. Mo, K.C.; Livezey, R.E. Tropical-extratropical geopotential height teleconnections during the Northern Hemisphere winter. Mon. Weather Rev. 1986, 114, 2488–2515. [Google Scholar] [CrossRef]
  3. Wallace, J.M.; Gutzler, D.S. Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Weather Rev. 1981, 109, 784–812. [Google Scholar] [CrossRef]
  4. Ma, X.; Zhang, Y. Interannual variability of the North Pacific winter storm track and its relationship with extratropical atmospheric circulation. Clim. Dyn. 2018, 51, 3685–3698. [Google Scholar] [CrossRef]
  5. Rivière, G. Role of Rossby wave breaking in the west Pacific teleconnection. Geophys. Res. Lett. 2010, 37, L11802. [Google Scholar] [CrossRef] [Green Version]
  6. Tanaka, S.; Nishii, K.; Nakamura, H. Vertical structure and energetics of the western Pacific teleconnection pattern. J. Clim. 2016, 29, 6597–6616. [Google Scholar] [CrossRef]
  7. Wettstein, J.J.; Wallace, J.M. Observed patterns of month-to-month storm-track variability and their relationship to the background flow. J. Atmos. Sci. 2010, 67, 1420–1437. [Google Scholar] [CrossRef]
  8. Yuan, J.; Tan, B.; Feldstein, S.B.; Lee, S. Wintertime North Pacific teleconnection patterns: Seasonal and interannual variability. J. Clim. 2015, 28, 8247–8263. [Google Scholar] [CrossRef] [Green Version]
  9. Dai, Y.; Tan, B. The western Pacific pattern precursor of major stratospheric sudden warmings and the ENSO modulation. Environ. Res. Lett. 2016, 11, 124032. [Google Scholar] [CrossRef]
  10. Kodera, K. Consideration of the origin of the different midlatitude atmospheric responses among El Niño events. J. Meteorol. Soc. Jpn. 1998, 76, 347–361. [Google Scholar] [CrossRef]
  11. Garfinkel, C.I.; Butler, A.H.; Waugh, D.W.; Hurwitz, M.M.; Polvani, L.M. Why might stratospheric sudden warmings occur with similar frequency in El Niño and La Niña winters? J. Geophys. Res. Atmos. 2012, 117, D19106. [Google Scholar] [CrossRef] [Green Version]
  12. Garfinkel, C.I.; Hartmann, D.L.; Sassi, F. Tropospheric precursors of anomalous Northern Hemisphere stratospheric polar vortices. J. Clim. 2010, 23, 3282–3299. [Google Scholar] [CrossRef]
  13. Lim, Y.K.; Kim, H.D. Impact of the dominant large-scale teleconnections on winter temperature variability over East Asia. J. Geophys. Res. Atmos. 2013, 118, 7835–7848. [Google Scholar] [CrossRef] [Green Version]
  14. Baxter, S.; Nigam, S. Key role of the North Pacific Oscillation–west Pacific pattern in generating the extreme 2013/14 North American winter. J. Clim. 2015, 28, 8109–8117. [Google Scholar] [CrossRef] [Green Version]
  15. Cheung, H.H.; Zhou, W.; Lee, S.M.; Tong, H.W. Interannual and interdecadal variability of the number of cold days in Hong Kong and their relationship with large-scale circulation. Mon. Weather Rev. 2015, 143, 1438–1454. [Google Scholar] [CrossRef]
  16. Kim, G.; Ahn, J.B.; Kryjov, V.N.; Lee, W.S.; Kim, D.J.; Kumar, A. Assessment of MME methods for seasonal prediction using WMO LC-LRFMME hindcast dataset. Int. J. Climatol. 2021, 41, E2462–E2481. [Google Scholar] [CrossRef]
  17. Lee, J.; Lee, M.I.; Ahn, J.B. Importance of ocean initial conditions of late autumn on winter seasonal prediction skill in atmosphere–land–ocean–sea ice coupled forecast system. Clim. Dyn. 2022, 58, 3427–3440. [Google Scholar] [CrossRef]
  18. Buontempo, C.; Burgess, S.N.; Dee, D.; Pinty, B.; Thépaut, J.N.; Rixen, M.; Almond, S.; Armstrong, D.; Brookshaw, A.; Alos, A.L.; et al. The Copernicus Climate Change Service: Climate Science in Action. Bull. Amer. Meteor. 2022. [Google Scholar] [CrossRef]
  19. Graham, R.J.; Yun, W.T.; Kim, J.; Kumar, A.; Jones, D.; Bettio, L.; Gagnon, N.; Kolli, R.K.; Smith, D. Long-range forecasting and the Global Framework for Climate Services. Clim. Res. 2011, 47, 47–55. [Google Scholar] [CrossRef]
  20. Kirtman, B.P.; Min, D.; Infanti, J.M.; Kinter, J.L.; Paolino, D.A.; Zhang, Q.; Dool, H.; Saha, S.; Mendez, M.P.; Becker, E.; et al. The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bull. Am. Meteorol. Soc. 2014, 95, 585–601. [Google Scholar] [CrossRef] [Green Version]
  21. Min, Y.M.; Kryjov, V.N.; Oh, S.M.; Lee, H.J. Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008–2015. Clim. Dyn. 2017, 49, 4141–4156. [Google Scholar] [CrossRef]
  22. Jeong, H.I.; Ahn, J.B.; Lee, J.Y.; Alessandri, A.; Hendon, H.H. Interdecadal change of interannual variability and predictability of two types of ENSO. Clim. Dyn. 2015, 44, 1073–1091. [Google Scholar] [CrossRef]
  23. Jeong, H.I.; Lee, D.Y.; Ashok, K.; Ahn, J.B.; Lee, J.Y.; Luo, J.J.; Schemm, J.-K.E.; Hendon, H.H.; Braganza, K.; Ham, Y.G. Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter. Clim. Dyn. 2012, 39, 475–493. [Google Scholar] [CrossRef] [Green Version]
  24. Sohn, S.J.; Tam, C.Y.; Ashok, K.; Ahn, J.B. Quantifying the reliability of precipitation datasets for monitoring large-scale East Asian precipitation variations. Int. J. Climatol. 2012, 32, 1520–1526. [Google Scholar] [CrossRef]
  25. Domeisen, D.I.; Badin, G.; Koszalka, I.M. How predictable are the Arctic and North Atlantic Oscillations? Exploring the variability and predictability of the Northern Hemisphere. J. Clim. 2018, 31, 997–1014. [Google Scholar] [CrossRef]
  26. Kang, D.; Lee, M.I.; Im, J.; Kim, D.; Kim, H.M.; Kang, H.S.; Schubert, S.D.; Arribas, A.; MacLachlan, C. Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systems. Geophys. Res. Lett. 2014, 41, 3577–3585. [Google Scholar] [CrossRef] [Green Version]
  27. Kim, H.J.; Ahn, J.B. Improvement in prediction of the Arctic Oscillation with a realistic ocean initial condition in a CGCM. J. Clim. 2015, 28, 8951–8967. [Google Scholar] [CrossRef]
  28. Sun, J.; Ahn, J.B. Dynamical seasonal predictability of the Arctic Oscillation using a CGCM. Int. J. Climatol. 2015, 35, 1342–1353. [Google Scholar] [CrossRef]
  29. Scaife, A.A.; Arribas, A.; Blockley, E.; Brookshaw, A.; Clark, R.T.; Dunstone, N.; Eade, R.; Fereday, D.; Folland, C.K.; Gordon, M.; et al. Skillful long-range prediction of European and North American winters. Geophys. Res. Lett. 2014, 41, 2514–2519. [Google Scholar] [CrossRef] [Green Version]
  30. Lee, Y.Y.; Oh, J.H. West Pacific teleconnection pattern in dynamical seasonal predictions: How is it connected to the Atlantic atmospheric mean bias? Clim. Dyn. 2020, 54, 3671–3683. [Google Scholar] [CrossRef]
  31. Paek, H.; Yu, J.Y.; Hwu, J.W.; Lu, M.M.; Gao, T. A source of AGCM bias in simulating the western Pacific subtropical high: Different sensitivities to the two types of ENSO. Mon. Weather Rev. 2015, 143, 2348–2362. [Google Scholar] [CrossRef] [Green Version]
  32. MacLachlan, C.; Arribas, A.; Peterson, K.A.; Maidens, A.; Fereday, D.; Scaife, A.A.; Gordon, M.; Vellinga, M.; Williams, A.; Comer, R.E.; et al. Global Seasonal forecast system version 5 (GloSea5): A high-resolution seasonal forecast system. Q. J. R. Meteorol. Soc. 2015, 141, 1072–1084. [Google Scholar] [CrossRef]
  33. Borovikov, A.; Cullather, R.; Kovach, R.; Marshak, J.; Vernieres, G.; Vikhliaev, Y.; Zhao, B.; Li, Z. GEOS-5 seasonal forecast system. Clim. Dyn. 2019, 53, 7335–7361. [Google Scholar] [CrossRef]
  34. Saha, S.; Moorthi, S.; Pan, H.L.; Wu, X.; Wang, J.; Nadiga, S.; Tripp, P.; Kistler, R.; Woollen, J.; Behringer, D.; et al. The NCEP climate forecast system reanalysis. Bull. Am. Meteorol. Soc. 2010, 91, 1015–1058. [Google Scholar] [CrossRef] [Green Version]
  35. Ahn, J.B.; Kim, H.J. Improvement of 1-month lead predictability of the wintertime AO using a realistically varying solar constant for a CGCM. Meteorol. Appl. 2014, 21, 415–418. [Google Scholar] [CrossRef]
  36. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  37. Reynolds, R.W.; Rayner, N.A.; Smith, T.M.; Stokes, D.C.; Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 2002, 15, 1609–1625. [Google Scholar] [CrossRef]
  38. Wilks, D.S. Statistical Methods in the Atmospheric Sciences, 2nd ed.; Elsevier Academic Press: San Diego, CA, USA, 1995; p. 467. [Google Scholar]
  39. Leung, M.Y.T.; Zhou, W. Variation of circulation and East Asian climate associated with anomalous strength and displacement of the East Asian trough. Clim Dyn. 2015, 45, 2713–2732. [Google Scholar] [CrossRef]
  40. Nishii, K.; Nakamura, H.; Orsolini, Y.J. Cooling of the wintertime Arctic stratosphere induced by the western Pacific teleconnection pattern. Geophys. Res. Lett. 2010, 37, L13805. [Google Scholar] [CrossRef]
  41. Feddersen, H.; Navarra, A.; Ward, M.N. Reduction of model systematic error by statistical correction for dynamical seasonal predictions. J. Clim. 1999, 12, 1974–1989. [Google Scholar] [CrossRef]
  42. Kang, I.S.; Shukla, J. Dynamic seasonal prediction and predictability of the monsoon. In The Asian Monsoon; Wang, B., Ed.; Springer: Berlin/Heidelberg, Germany, 2006; pp. 585–612. [Google Scholar]
  43. Hagedorn, R.; Doblas-Reyes, F.J.; Palmer, T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus A Dyn. Meteorol. Oceanogr. 2005, 57, 219–233. [Google Scholar] [CrossRef]
  44. Dai, Y.; Tan, B. Two types of the western Pacific pattern, their climate impacts, and the ENSO modulations. J. Clim. 2019, 32, 823–841. [Google Scholar] [CrossRef]
  45. Saji, N.H.; Goswami, B.N.; Vinayachandran, P.N.; Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 1999, 401, 360–363. [Google Scholar] [CrossRef] [PubMed]
  46. Hurwitz, M.M.; Newman, P.A.; Garfinkel, C.I. On the influence of North Pacific sea surface temperature on the Arctic winter climate. J. Geophys. Res. Atmos. 2012, 117, D19110. [Google Scholar] [CrossRef]
  47. Nigam, S. Teleconnections. In Encyclopedia of Atmospheric Sciences; Holton, J.R., Pyle, J.A., Curry, J.A., Eds.; Academic Press: London, UK, 2003; pp. 2243–2269. [Google Scholar]
  48. North, G.R.; Bell, T.L.; Cahalan, R.F.; Moeng, F.J. Sampling errors in the estimation of empirical orthogonal functions. Mon. Weather Rev. 1982, 110, 699–706. [Google Scholar] [CrossRef]
  49. Park, H.J.; Kryjov, V.N.; Ahn, J.B. One-month-lead predictability of Asian summer monsoon indices based on the zonal winds by the APCC multimodel ensemble. J. Clim. 2018, 31, 8945–8960. [Google Scholar] [CrossRef]
  50. Lu, B.; Ren, H.L.; Scaife, A.A.; Wu, J.; Dunstone, N.; Smith, D.; Wan, J.; Eade, R.; MacLachlan, C.; Gordon, M. An extreme negative Indian Ocean Dipole event in 2016: Dynamics and predictability. Clim. Dyn. 2018, 51, 89–100. [Google Scholar] [CrossRef]
  51. Scaife, A.A.; Smith, D. A signal-to-noise paradox in climate science. Npj Clim. Atmos. Sci. 2018, 1, 1–8. [Google Scholar] [CrossRef]
Atmosphere 13 01772 g001 550
Figure 1. CGCM data used in this study: coverage with individual model historical and real-time forecasts. The cyan, gray, and white box indicated forecast, hindcast, and no data, respectively.
Figure 1. CGCM data used in this study: coverage with individual model historical and real-time forecasts. The cyan, gray, and white box indicated forecast, hindcast, and no data, respectively.
Atmosphere 13 01772 g001
Atmosphere 13 01772 g002 550
Figure 2. Correlation patterns between Z500 and the WP indices (a) WG81, (b) CPC, and (c) that were used in this study. Green boxes enclose the areas of averaging. The ACC between the WP index used in the study and the WG81 WP and CPC WP patterns are shown in the top-right corner of the panels. Significant at the 99% (95%) confidence level values are shown by hatch pattern (shading).
Figure 2. Correlation patterns between Z500 and the WP indices (a) WG81, (b) CPC, and (c) that were used in this study. Green boxes enclose the areas of averaging. The ACC between the WP index used in the study and the WG81 WP and CPC WP patterns are shown in the top-right corner of the panels. Significant at the 99% (95%) confidence level values are shown by hatch pattern (shading).
Atmosphere 13 01772 g002
Atmosphere 13 01772 g003 550
Figure 3. Time series of the WP index observed (solid line) and predicted (dashed line) by the (a) MME and (b–f) individual models. The status of the ENSO is shown with the circles on the time-axis. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Significant at the 99% (95%) level is denoted with a double (single) asterisk.
Figure 3. Time series of the WP index observed (solid line) and predicted (dashed line) by the (a) MME and (b–f) individual models. The status of the ENSO is shown with the circles on the time-axis. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Significant at the 99% (95%) level is denoted with a double (single) asterisk.
Atmosphere 13 01772 g003
Atmosphere 13 01772 g004 550
Figure 4. Correlation patterns between Z500 and the WP index predicted by the (a) MME and (b–f) individual models for the entire series. Green boxes enclose the areas of Z500 averaging used for the estimation of the WP index. Significant at the 99% (95%) confidence level values are shown by hatch pattern (shading).
Figure 4. Correlation patterns between Z500 and the WP index predicted by the (a) MME and (b–f) individual models for the entire series. Green boxes enclose the areas of Z500 averaging used for the estimation of the WP index. Significant at the 99% (95%) confidence level values are shown by hatch pattern (shading).
Atmosphere 13 01772 g004
Atmosphere 13 01772 g005 550
Figure 5. Correlation patterns between Z500 and the WP index predicted by the (a) MME and (b–f) individual models for the entire series without the SEN years. Green boxes enclose the areas of Z500 averaging used for the estimation of the WP index. Significant at the 99% (95%) confidence level values are shown by hatch pattern (shading).
Figure 5. Correlation patterns between Z500 and the WP index predicted by the (a) MME and (b–f) individual models for the entire series without the SEN years. Green boxes enclose the areas of Z500 averaging used for the estimation of the WP index. Significant at the 99% (95%) confidence level values are shown by hatch pattern (shading).
Atmosphere 13 01772 g005
Atmosphere 13 01772 g006 550
Figure 6. Correlation patterns between SST and the WP index in observations (a) for the entire series, (b) for the series without the SEN years, and (c) for the time series of the WP (solid line) and NWP (dashed line) indices. Significant at the 95% confidence level values are enclosed by a thick line. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 99% (95%) level is denoted by a double (single) asterisk.
Figure 6. Correlation patterns between SST and the WP index in observations (a) for the entire series, (b) for the series without the SEN years, and (c) for the time series of the WP (solid line) and NWP (dashed line) indices. Significant at the 95% confidence level values are enclosed by a thick line. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 99% (95%) level is denoted by a double (single) asterisk.
Atmosphere 13 01772 g006
Atmosphere 13 01772 g007 550
Figure 7. (a) The correlation pattern between SST and the WP index. Significant at the 95% confidence level values are enclosed by a thick line, and blue boxes enclose the areas of SST averaging used for the estimation of the NWP index; (b) the third EOF of the north-western Pacific SST anomalies. (c) The time series of the corresponding NWP index (solid line) and the PC3 (dashed line). Significance at the 99% (95%) level is denoted by a double (single) asterisk.
Figure 7. (a) The correlation pattern between SST and the WP index. Significant at the 95% confidence level values are enclosed by a thick line, and blue boxes enclose the areas of SST averaging used for the estimation of the NWP index; (b) the third EOF of the north-western Pacific SST anomalies. (c) The time series of the corresponding NWP index (solid line) and the PC3 (dashed line). Significance at the 99% (95%) level is denoted by a double (single) asterisk.
Atmosphere 13 01772 g007
Atmosphere 13 01772 g008 550
Figure 8. Correlation patterns between Z500 and the WP index predicted by the (a) MME (b–f) and individual models for the entire series. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 95% confidence level values are enclosed by a thick line.
Figure 8. Correlation patterns between Z500 and the WP index predicted by the (a) MME (b–f) and individual models for the entire series. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 95% confidence level values are enclosed by a thick line.
Atmosphere 13 01772 g008
Atmosphere 13 01772 g009 550
Figure 9. Correlation patterns between Z500 and the WP index predicted by the (a) MME (b–f) and individual models for the entire series without the SEN years. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 95% confidence level values are enclosed by a thick line.
Figure 9. Correlation patterns between Z500 and the WP index predicted by the (a) MME (b–f) and individual models for the entire series without the SEN years. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 95% confidence level values are enclosed by a thick line.
Atmosphere 13 01772 g009
Atmosphere 13 01772 g010 550
Figure 10. The correlation pattern between SST and the NWP index predicted by (a) MME and (b–f) individual model NWP index. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 95% confidence level values are enclosed by a thick line.
Figure 10. The correlation pattern between SST and the NWP index predicted by (a) MME and (b–f) individual model NWP index. Blue boxes enclose the areas of SST averaging used for the estimation of the NWP index. Significant at the 95% confidence level values are enclosed by a thick line.
Atmosphere 13 01772 g010
Atmosphere 13 01772 g011 550
Figure 11. Time series of the NWP index observed (solid line) and predicted (dashed line) by the (a) MME and (b–f) individual models. The status of the ENSO is shown with the circles on the time-axis. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Significant at the 99% (95%) level is denoted with a double (single) asterisk.
Figure 11. Time series of the NWP index observed (solid line) and predicted (dashed line) by the (a) MME and (b–f) individual models. The status of the ENSO is shown with the circles on the time-axis. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Significant at the 99% (95%) level is denoted with a double (single) asterisk.
Atmosphere 13 01772 g011
Atmosphere 13 01772 g012 550
Figure 12. Time series of the WP index (solid line) and the NWP index (dashed line) predicted by (a) MME and (b–f) individual models. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Significant at the 99% (95%) level is denoted with a double (single) asterisk.
Figure 12. Time series of the WP index (solid line) and the NWP index (dashed line) predicted by (a) MME and (b–f) individual models. The TCC for the entire series (series without SEN years) is shown in the top (bottom) right corner of the panels. Significant at the 99% (95%) level is denoted with a double (single) asterisk.
Atmosphere 13 01772 g012
Atmosphere 13 01772 g013 550
Figure 13. Correlation patterns between Z500 and the NWP index predicted by (a) MME and (b–f) individual models. Green boxes enclose the areas of Z500 averaging used for the estimation of the WP index. Significant at the 95% confidence level values are enclosed by a thick line.
Figure 13. Correlation patterns between Z500 and the NWP index predicted by (a) MME and (b–f) individual models. Green boxes enclose the areas of Z500 averaging used for the estimation of the WP index. Significant at the 95% confidence level values are enclosed by a thick line.
Atmosphere 13 01772 g013
Atmosphere 13 01772 g014 550
Figure 14. Composites of SST for the years of correct predictions of (a) positive WP, (b) negative WP, and incorrect predictions of (c) positive WP and (d) negative WP. The averages of the corresponding years observed and predicted index anomalies are shown under the panels. Significant at the 95% confidence level index anomalies are typed in bold.
Figure 14. Composites of SST for the years of correct predictions of (a) positive WP, (b) negative WP, and incorrect predictions of (c) positive WP and (d) negative WP. The averages of the corresponding years observed and predicted index anomalies are shown under the panels. Significant at the 95% confidence level index anomalies are typed in bold.
Atmosphere 13 01772 g014
Table
Table 1. Configurations of the models.
Table 1. Configurations of the models.
System NameOrganization/
Country		AGCM/
Resolution		OGCM/
Resolution		Arrangement of Members
(Hindcast/Forecast)		No. ofReference
Taiwan Central Weather Bureau 1 Tier model version 1.1
(TCWB1Tv1.1)		Central Weather Bureau
(CWB)/
Chinese Taipei		GFS/
T119L40		MOM3/
1° × 1°		30 days before the 15th of each month30Paek et al. [31]
Global Seasonal Forecasting System version 5 Global Coupled configuration 2
(GloSea5GC2)		Korea Meteorological Administration (KMA)/
South Korea		UM8.6/
N216L85		NEMO3.4/
N216L85		Every day/1st, 9th, 17th, 25th of the month42/12MacLachlan et al. [32]
Goddard Earth Observing System Atmosphere-Ocean General Circulation Model and Data Assimilation System Version S2S-2_1
(GOES-S2S-2.1)		National Aeronautics and Space Administration (NASA)/
United States of America		MERRA-2/
0.5° × 0.5°		MOM5/
0.5° × 0.5°		Every 5 days of the month10/4Borovikov et al. [33]
Climate Forecast System Version 2
(CFSv2)		National Centers for Environmental Prediction (NCEP), National Weather Service (NWS), and National Oceanic and Atmospheric Administration (NOAA)/
United States of America		GFS/
T126L64		MOM4/
0.25° − 0.5° × 0.5°, L40		Latest 5 days in previous month/Every 5 days of the month20Saha et al. [34]
Pusan National University Coupled General Circulation Model Version 2.0
(PNU CGCM v2.0)		Pusan National University
(PNU)/South Korea		CCM3/
T42L18		MOM3/
2.8125°, L40		Different 5 days of the month35Ahn and Kim [35]
Table
Table 2. Definitions of the WP indices.
Table 2. Definitions of the WP indices.
ReferenceDefinition
Wallace and Gutzler
([3], WG81)		–(0.5 × [(Z*(60 °N, 155 °E)–Z*(30 °N, 155 °E)])
Z*: Normalized Z500
WG81 in this study has an opposite sign with the original WP index defined by WG81.
NOAA/CPC
([1], CPC)		PC Time series of RPCA of standardized Z500 anomalies over the extratropical Northern Hemisphere (20 °N–).
Table
Table 3. TCC between the WP indices.
Table 3. TCC between the WP indices.
WPWG81CPC
WP1.000.970.89
WG81 1.000.87
CPC 1.00
Table
Table 4. Correlation coefficients between observed WP (NWP, WP) and predicted by MME and individual models WP (NWP, NWP). Significance at the 99% (95%) level is denoted with a double (single) asterisk.
Table 4. Correlation coefficients between observed WP (NWP, WP) and predicted by MME and individual models WP (NWP, NWP). Significance at the 99% (95%) level is denoted with a double (single) asterisk.
WP & WP_MMEMMECWBKMANASANCEPPNU
Correl.
(w/o SEN)		0.61 **0.50 **0.37 *0.54 **0.43 **0.50 **
0.54 **0.38 *0.270.42 **0.290.41 **
NWP and NWP_MMEMMECWBKMANASANCEPPNU
Correl.0.75 **0.60 **0.65 **0.64 **0.68 **0.68 **
(w/o SEN)0.73 **0.56 **0.64 **0.62 **0.68 **0.71 **
WP and NWP_MMEMMECWBKMANASANCEPPNU
Correl.0.49 **0.46 **0.31 *0.47 **0.47 **0.42 **
(w/o SEN)0.45 **0.37 *0.230.42 **0.46 **0.46 **
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kim, E.-S.; Kryjov, V.N.; Ahn, J.-B. Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble. Atmosphere 2022, 13, 1772. https://doi.org/10.3390/atmos13111772

AMA Style

Kim E-S, Kryjov VN, Ahn J-B. Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble. Atmosphere. 2022; 13(11):1772. https://doi.org/10.3390/atmos13111772

Chicago/Turabian Style

Kim, Eung-Sup, Vladimir N. Kryjov, and Joong-Bae Ahn. 2022. "Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble" Atmosphere 13, no. 11: 1772. https://doi.org/10.3390/atmos13111772

APA Style

Kim, E.-S., Kryjov, V. N., & Ahn, J.-B. (2022). Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble. Atmosphere, 13(11), 1772. https://doi.org/10.3390/atmos13111772

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop