Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection
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The prediction skills of climate model simulations in the western tropical Pacific (WTP) and East Asian region are assessed using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers (June–August) during the period 1983–2005, along with corresponding observed and reanalyzed data. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP Indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index or each MPI. Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by hybrid dynamical–statistical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using a hybrid dynamical–statistical approach compared to the dynamical forecast alone.
KeywordsWestern tropical Pacific convection East Asian summer rainfall Maritime continent-western tropical Pacific Index East Asia Rainfall Index Reconstructed rainfall
As one of the major monsoon climate regions in the world, East Asia suffers frequently from severe floods and/or droughts in summer. Many researchers have studied the characteristics of the East Asian summer monsoon (EASM), including the large-scale circulation related to the rainband (Huang and Sun 1992; Huang 2004; Gong et al. 2011; Lee et al. 2013a). The atmospheric circulation of EASM is characterized by a low-level convergence zone between southerlies from the tropics and weak northerlies from mid-latitudes, and upper-tropospheric westerlies in the north of the subtropical ridge (Wang and Xu 1997).
The seasonal climate prediction of East Asian summer rainfall remains a challenging issue with various limitations, in spite of the facts that refined statistical methods have reasonably improved the predictability of the simulated outcomes (Zhu et al. 2008; Wang and Fan 2009; Lang and Wang 2010; Ke et al. 2011) and state-of-the art climate prediction models have been improved significantly (Wang et al. 2007, 2008a, 2009). Several studies have also revealed that the models have not only difficulties in simulating the mean climate state over the EASM region, but also deficiencies in predicting the variability of mean summer rainfall anomalies (Kang et al. 2002; Wang et al. 2004, 2008a, 2009; Yang et al. 2008; Liang et al. 2009; Lee et al. 2010).
The present study investigates the limitations and possibilities for seasonal climate prediction, with a particular focus on summer rainfall anomalies in the WTP and East Asian region, using available hindcast datasets for the 23-year period of 1983–2005 obtained from the operational climate forecast models in the Asia Pacific Economic Cooperation (APEC) Climate Center (APCC). We focus on developing a new approach to further improve the prediction skill of EASM rainfall using the characteristics of convective activity in the WTP affecting the East Asian monsoon rainfall. Further issues that are addressed in this study include: (1) an assessment of the predictive quality of the climate model simulations for the East Asian summer rainfall and WTP convection, (2) investigation of the possible methods to improve the predictability of the East Asian monsoon rainfall using predictable information and (3) an evaluation of the prediction skill for the reconstructed summer rainfall over the East Asian region by applying the developed approach method.
Section 2 presents a brief description of the observational data, coupled model data, and statistical methods used in this study. Section 3 describes the relationship between the WTP convective activity and East Asian summer rainfall, and compares the observed and simulated convective activity over the WTP. Section 4 describes the development of a hybrid dynamical–statistical method for improving the prediction skill of the East Asian summer rainfall and the assessment of the skills for the reconstructed rainfall in East Asia. The final section presents the summary and conclusions.
2 Data and statistical methods
Description of the coupled atmosphere–ocean general circulation models used
Institutes (model name)
POP 1.3 (gxlv3_L40)
Jeong et al. (2008)
BOM (POAMA v2.4)
BAM v3.0d (T47L17)
ACOM2 (0.5°–1.5°lat × 2°lon L25)
Lim et al. (2012)
OGCM4 (0.94°lat × 1.41°lon L40)
Kim et al. (2003)
OGCM4 (0.94°lat × 1.41°lon L40)
Simmons et al. (2004)
NASA GSFC (NASA)
GEOS-5 (288 × 181L72)
MOM4 (720 × 410 L40)
Rienecker et al. (2008)
NCEP (NCEP_CFS v2)
MOM4 (0.25° at the tropics, 0.5° northwards and southwards of 10°N and 10°S, L40)
Saha et al. (2014)
MOM3 (~0.7° (low lat), ~1.4° (mid lat), and ~2.8° (high lat) L29)
Ahn and Kim (2013)
2.2 Statistical methods
We adopt a simple composite MME method (Peng et al. 2002; Kang et al. 2009; Lee et al. 2008, 2009, 2011a, 2013a, b), which assigns equal weights to the ensemble mean predictions of individual models. The performance of this method is on par with the best available operational MME techniques (Lee et al. 2009). Henceforth, MME refers to this simple composite method, unless otherwise specified.
The MME results are generated by the application of a bias correction (Lee et al. 2009, 2011a) to model forecast anomalies, which are obtained from the standard ‘‘leave-one-out’’ cross-validation method (Michaelsen 1987; Jolliffe and Stephenson 2003; World Meteorological Organization 2006; Lee et al. 2013a, b). This cross-validation method essentially computes seasonal anomalies for each model parameter, from the corresponding yearly climatological means obtained by excluding information from the target year, as well as those of observations.
We use the Student’s two-tailed t test (Wilks 1995; Spiegel and Stephens 2008) to compute the statistical significance of temporal correlations. We compute the statistical significance using the simple number of degrees of freedom estimated as N-2, where N is 23, the number of summer seasons during the study period.
3 Relationship between the observed WTP convection and East Asian summer rainfall
3.1 Characteristics of convective activity over the WTP
Many studies (Nitta 1987; Huang and Sun 1992; Lau et al. 2000; Lu 2001; Sun et al. 2009) have addressed the effect of the low-level circulation in the tropical western Pacific on the variability of rainfall over the East Asian region. These findings clearly show that the convective activity induced by heating in the WTP is closely related to East Asian summer rainfall. We expect an investigation aimed at improving our understanding of the empirical relationship between the two regions to improve the prediction skills of the East Asian summer rainfall. First, the performance of the climate prediction models and their MME used in this study is assessed in terms of the simulated rainfall anomalies.
Figure 1 illustrates the spatial distribution of the prediction skills of all individual models and their MME for precipitation over the East Asian (90°E–160°E, 20°–60°N) region in terms of temporal correlation coefficient (TCC) at each grid point for the period of 1983–2005. The statistical significance of the TCCs is computed using Student’s two‐tailed t test. The region of significant correlation at the 90 % confidence level is outlined. In general, the predicted skills over the ocean areas are significantly superior to those over the land areas in most of the models. We also find that the MME prediction skills are considerably improved as compared to those of the individual models. However, certain limitations remain in improving the predicted rainfall in the mid-latitude region, particularly over East Asia. In contrast to these concerns, TCCs over the Maritime continent-WTP (MC-WTP: 100°E–180°E, 20S°–20°N) display considerably more significant values and higher correlations (Fig. 2). The prediction skills of most individual models except for MSC_CANCM3 and MSC_CANCM4 show high performance. Especially, the area average of the MME prediction skill reaches more than 0.4 for the period of 1983–2005, which is statistically significant at the 95 % confidence level based on Student’s two-tailed t test. Figure 3 shows the scatter diagram between the spatial pattern correlation coefficients (PCCs; Jolliffe and Stephenson 2003; World Meteorological Organization 2006) and normalized root mean square errors (NRMSEs; Jolliffe and Stephenson 2003; World Meteorological Organization 2006) with respect to the corresponding observed standard deviation for precipitation and temperature at 850 hPa from all seven models and MME predictions over the abovementioned two regions. We calculated the PCC and NRMSE between observed and predicted anomalies for each grid point over the regions in every year, and then time-averaged skill and error scores were computed for each model and MME. The prediction skills of MMEs as well as individual models for both variables differ markedly between the East Asia and MC-WTP regions, although the differences between the NRMSEs for MME predictions are relatively smaller than those of the PCCs. Furthermore, the PCC difference of the MME prediction between the two regions for precipitation is considerably higher than that for temperature.
MPI1 (MPI2) obtained from the dipole (tripole) pattern exhibits a nearly consistent variation with the PC1 (PC2) time series. The correlation coefficients between the MPIs and PCs reach 0.97 and 0.94, respectively. In addition, the correlation coefficient between the two MPIs is nearly similar to that between the two PC time series representing the orthogonal feature to each other.
3.2 Influences of convective activity over the WTP on the EASM region
(Upper triangle) Inter-spatial pattern correlations between the spatial pattern of the first EOF mode for the observed precipitation and regressed precipitation patterns on the observed various indices. (Lower triangle) Inter-temporal correlations between the PC time series of the first EOF mode for the observed precipitation and various indices
EOF 1st mode/PC1
Reg. prec. on/EASMI
Reg. prec. on/MPI1
Reg. prec. on/MPI2
Reg. prec. on/EARI
EOF 1st mode/PC1
Reg. prec. on/EASMI
Reg. prec. on/MPI1
Reg. prec. on/MPI2
Reg. prec. on/EARI
In Table 2, the precipitation patterns regressed on the MPIs are much more similar to the spatial pattern of the first EOF mode of the summer rainfall over East Asia than those on EASMI. The PCC between the spatial patterns associated with the EOF and EASMI is about 0.6, while the regressed patterns on the MPIs have much closer relationships (higher correlation of more than around 0.7) with the EOF1 spatial pattern. The regression pattern of the summer rainfall with respect to the EASMI shows a very similar structure (PCC is more than 0.9) compared to the pattern with respect to MPI2, unlike that to MPI1 which has a correlation close to zero. Figure 10 shows the interannual variability of each index. All indices except for EASMI have variation with a period of 4–5 years (figure not shown). Table 2 also illustrates that the first PC time series of the observed precipitation has a statistically significant and strong correlation with MPI2 (0.75) compared to the EASMI (0.54) and MPI1 (0.49). Furthermore, the relationship between the EASMI and MPI2 implies that the EASMI has a close relationship with the tripole structure, represented as the second EOF mode of the convective activity over the tropical western Pacific, related to the ENSO decaying.
The previous sections have used the observed MPIs produced by the major patterns of the rainfall representing convective activity over the WTP to investigate the relationship of the MPIs with the East Asian monsoon rainfall and the usefulness of the MPIs as the predictors in predicting monsoon rainfall over East Asia. In the next section, an intercomparison is conducted between the observed and predicted MPIs for the individual models and MME.
3.3 Comparison between the observed and simulated convective activity over the WTP
We analyze the interannual variability of the MPIs to determine whether the strong relationship between the observation and the models shown in Fig. 2 also presents in the comparisons between the observed and simulated MPIs indicating the significant feature of the convection over the WTP.
Temporal correlation between observed and simulated MPIs
4 Development of East Asian Rainfall Index (EARI) and skill assessment of the reconstructed rainfall
4.1 East Asian Rainfall Index (EARI)
The temporal relationship between the developed EARI and EOF PC1 for observed rainfall in East Asia has a considerably higher correlation (more than 0.85 in Table 2; Fig. 10), which is statistically significant at the 99 % confidence level based on Student’s two-tailed t test, and the East Asia summer rainfall regressed onto the EARI shows a significant relationship with the first EOF spatial mode of the observed rainfall (pattern correlation is more than 0.87 in Table 2; Fig. 9e). These results show that, compared to the EASMI and each MPI, the EARI is very closely related to the variation of the observed rainfall over East Asia, and particularly emphasize the necessity of using the two MPIs for the East Asia rainfall prediction. To examine the practicality of the prediction through the verification for the study period of 1983–2005, we use the leave-one-out cross-validation (Michaelsen 1987; Jolliffe and Stephenson 2003; World Meteorological Organization 2006; Lee et al. 2013a, b) for each target year in calculating the cross-validated EARI with normalized observed MPIs. Little difference is evident between the TCCs with the EOF PC1 of observed rainfall over East Asia on the cross-validated EARI (0.848) and EARI (0.855 in Table 2) in observation.
Temporal correlation between the PC time series of the first EOF mode for the observed precipitation and the normalized and cross-validated EARIs for individual models and their MME
4.2 Assessment of the skill of the reconstructed rainfall using EARI
5 Summary and conclusions
The impact of convective activity in the WTP on East Asian rainfall was investigated to develop a hybrid dynamical–statistical method using the inter-relationship between western Pacific convection and rainfall variance in East Asia. By applying the developed method to the individual models and MME, the summer rainfall over the East Asian region was predicted and the prediction skill was examined.
The model data used in this diagnostic study consisted of seasonal retrospective forecasts for boreal summer (JJA) over the 23-year period (1983–2005) from the seven APCC operational coupled forecast models. The NCEP-DOE reanalysis 2 (Kanamitsu et al. 2002) and CMAP precipitation (Xie and Arkin 1997) were used for the same period as the model data.
The EASM region experiences complex climate variation having wide spatial and temporal spectrum influenced by climate factors originating from both the extra-tropics and the tropics. In this study, we focused on the WTP affecting lower-level moisture transport into East Asia and thus strongly influencing rainfall in East Asia during the boreal summer. Using this relationship, we tried to overcome the limitations of dynamical model prediction for summer precipitation in East Asia.
We examined the characteristics of convection in the western Pacific affecting the East Asian monsoon rainfall and identified two types of distinct structure patterns in the WTP. In our results, the first and second patterns were closely related to ENSO developing and ENSO decaying modes, respectively. Using two distinct patterns, as the major influences on the East Asian summer rainfall, related to western Pacific convection, normalized MPIs were developed. We investigated the inter-relationship in terms of teleconnection patterns for the various variables to assess the possible use of the developed MPIs. Consequently, we found teleconnection patterns with a meridional tripole structure for each variable, which confirmed the suitability of the MPIs in representing the EASM variability. These patterns resembled the well-known PJ and EAP teleconnection patterns. Accordingly, the MPIs are useful predictors for precipitation prediction in the East Asia region. We also found that the regressed precipitation on MPIs and the interannual variability of MPIs had a statistically significant and strong correlation with the pattern and variability of observed precipitation over East Asia.
We selected the PC time series of the first EOF mode for precipitation over East Asia as a predict and two newly developed MPIs as predictors, and used a multiple linear regression method to develop the EARI, which indicates the interannual variability of the East Asian monsoon rainfall. The regressed rainfall pattern from EARI exhibited superior performance to that from the EASMI or each MPI. This result demonstrated the necessity of using these two new MPIs, which represent western Pacific convection, to improve the prediction skill of East Asian precipitation. For practical application to real-time forecasts, we used the leave-one-out cross-validation for each target year of the study period and obtained multiple linear regression coefficients for 23 years by calculating the cross-validated observed EARI. The normalized predicted MPIs obtained from the individual climate model and MME predictions were subjected to multiple regression analysis to produce the cross-validated and predicted EARI. The developed EARIs of the individual climate models and MME were applied statistically to the observed and regressed precipitation pattern to reconstruct the novel empirical predicted precipitation over the East Asia region. We evaluated the reconstructed East Asia summer precipitation of the individual models and MME, and found that the observed characteristics over East Asia are well captured. In general, the predictions of the individual models and MME using the hybrid dynamical–statistical method showed better performance than those of the dynamical models and their MME.
In the present study, the relatively small size of the available hindcast datasets posed a sampling limitation. In addition, we considered only climate factors from the WTP convection for the summer rainfall variability in East Asia, which is a complex area influenced by a variety of spatial and temporal climatic factors. As mentioned in many recent studies (Kwon et al. 2005; Ha et al. 2009; Choi et al. 2010), the trend of the change in rainfall variability over East Asia before and after the mid-1990s may be another potential consideration for seasonal prediction of East Asian monsoon rainfall. Nonetheless, the study methodology enhanced the prediction skill of the reconstructed summer rainfall, produced by the hybrid dynamical–statistical method using the predictors obtained from a statistically significant region. Consequently, the developed method can be applied to the operational seasonal forecast system for further improvement of forecast information. This research also emphasizes that the enhancement of forecasting capabilities of the individual models themselves should take precedence in attempts to improve seasonal prediction, not only in East Asia but worldwide.
The authors acknowledge the APCC MME Producing Centres for making their hindcast/forecast data available for analysis, the APEC Climate Center for collecting and archiving them and for organizing APCC MME prediction. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2014R1A6A3A01009455), and by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012-3100 and Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009353, Republic of Korea.
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