3.1. Analysis of Weather Characteristics During the Flood Season according to the Occurrence of El Niño and La Niña
To identify the effects of ocean and atmospheric fluctuations on the summer temperature and precipitation in Korea considering the El Niño and La Niña phenomena, the data were classified into El Niño, neutral, and La Niña. The characteristics of the temperature and precipitation during each period were compared and analysed. The results were obtained by comparing the distribution of the monthly mean temperature and precipitation data for each state during the flood period from June 1993 to September 2016.
From the distribution of the monthly mean temperature in the form of a box plot based on the classification of the periods (Fig. 5), the monthly mean temperature during the El Niño period was 19–28.1°C and the mean value was lower with a wider range of distribution compared with the neutral and La Niña periods. The deviation range of the quartile values for each classification was less than 1°C and there were no significant characteristics for all studied periods (Table 4).
From the distribution of the monthly precipitation in a box plot for each classification (Fig. 6), the largest mean and range were noted in the La Niña period, followed by the neutral and El Niño periods. During the El Niño period, the mean monthly precipitation was 181.7 mm with a median of 145.7 mm. Meanwhile, the mean was 263.7 mm with a median of 216.7 mm during the La Niña period, indicating a wider range of distribution. In the case of the neutral period, the mean was 232.1 mm and the median was 209.7 mm, demonstrating values between those of the El Niño and La Niña periods. For the distribution of the monthly precipitation (Table 5), there were differences for each classified period, suggesting that the climate factors of the El Niño and La Niña periods directly or indirectly influenced the monthly precipitation during the flood season.
One-way analysis of variance (ANOVA) was performed to quantitatively analyse the statistical significance in the difference in the distribution of each classification. This analysis method is used to compare the variation between and within three or more groups to determine the significance of the difference between them. The results of the analysis based on the division of the monthly precipitation and mean temperature as a function of the classification of the period are listed in Tables 6 and 7. The F-ratio is the ratio of the mean-of-squares between and within the groups. If the P-value is within 5% of the significance level, the difference between the groups was considered significant. For the monthly mean temperature, the P-value was 0.25, indicating that the difference between the groups was not significant. In contrast, the P-value for the monthly precipitation was 0.02, suggesting a statistically significant difference. Therefore, the data characteristics for the period of each status should be reflected to predict the monthly precipitation.
Precipitation during the flood season is known to be affected by the El Niño and La Niña phenomena. Every summer, the warm and humid North Pacific anticyclone from the southwest of the Korean peninsula rises to the north, and the cold and humid Okhotsk sea anticyclone descends from the northeast and forms a seasonal rain front. If El Niño occurs during this period, the trade winds, which blow from east to west and cause the spread of hot water from the Western to the Eastern Pacific, are weakened. Correspondingly, the North Pacific anticyclone, which develops in the east of Japan, fails to receive sufficient water vapor, thereby considerably inhibiting its development. As the force of the North Pacific anticyclone is weakened, the seasonal rain front cannot be formed or cannot move to the north, thereby affecting the southern region only. Therefore, the occurrence of El Niño during the rainy season results in a “dry rainy season” with considerably lower rainfall. Accordingly, the precipitation decreases compared to the same period in a normal year and the number of typhoons also decreases. This further results in a decrease in the precipitation in the second rainy season during the late summer. In contrast, the opposite occurs due to the La Niña phenomenon. Particularly, when La Niña occurs in the summer, the number of typhoons in the Korean Peninsula increases and the precipitation during the second rainy season tends to increase.
This identifies the impacts of El Niño and La Niña on precipitation during the flood season in Korea. Based on this, the impacts on temperature were considered insignificant. Therefore, developing a model to predict monthly precipitation in the summer considering the classification of the period (El Niño/La Niña/neutral) would increase the predictive power.
3.2. Seasonal Climate Prediction based on Lagged Teleconnection
a. General Seasonal Prediction based on Teleconnection
To identify the climate factors that will be used as independent variables for the prediction model of temperatures and precipitation in the target basin, the correlation between the meteorological data, and global-scale ocean and atmospheric climate data were analysed. The monthly mean temperature and observation data collected from the six weather stations were used as the representative values. The global-scale SST anomaly and GPH data from the same period were also used. The correlation coefficient was calculated for each grid in the GCM considering the lag time (1–6 months).
A linear regression model was constructed to predict temperature and precipitation using the SST anomaly and GPH data with the highest correlation calculated for each lag time. The time series data of the grid with the highest correlation coefficient (for a lag time greater than the lead time) was used as the independent variable of the prediction model. For example, to achieve accurate predictions one month ahead, all teleconnection climate factors selected with lag times of 1–6 months can be used as independent variables. However, for predictions two months ahead, the teleconnection climate factors with a lag time of one month were excluded. Table 8 lists the equations of the prediction model for temperature and precipitation based on teleconnection as a function of lag time. In the model, the SST anomaly and GPH were expressed as SST and GPH, respectively, and the numbers in the parentheses indicate the lag time.
The results obtained by the prediction model for the monthly mean temperature are shown in Fig. 7 and Table 9. As shown in the graphs, the prediction values were slightly higher than the observation results obtained in September 2014 for lead times in the range of 1–4 months and temperature was generally underestimated from July to August 2013. Nonetheless, the trend of the observation matched well with the general prediction for all lead times.
The results obtained by the prediction model for the monthly precipitation are shown in Fig. 8 and Table 10. As shown in the graphs, the trends of the observation data and predicted were similar with slight overestimations. Specifically, for the results from the flood season of 2015, the prediction was considerably greater than the actual precipitation, which has a low value.
The prediction model was evaluated using the correlation coefficient between the predicted and observed monthly mean temperatures and precipitation for each lead time. In Table 11, the correlation coefficient was 0.6 or higher for the monthly mean temperature for all lead times. With the exception of the predicted results obtained for a lead time of six months, outstanding results with a correlation coefficient of 0.7 were obtained. For the predicted monthly precipitation, the overall correlation coefficient was less than 0.3 regardless of the lead time. Therefore, the prediction model based on lagged teleconnection was applicable to the monthly mean temperature but not to the monthly precipitation.
b. Development and Application of the Prediction Model Considering the Occurrence of El Niño and La Niña Phenomena
In the previous section, the prediction obtained by the general prediction model for monthly precipitation did not simulate the observation well for the flood season of 2015 when El Niño occurred. Therefore, in this study, a model for predicting the monthly mean temperature and precipitation was presented considering the effects of El Niño and La Niña.
To consider the effects of the El Niño and La Niña phenomena, the training period from June 1993 to September 2012 was divided into three stages: El Niño, La Niña, and neutral. Table 12 shows the training period classified as either El Niño, La Niña, and neutral. The observed temperature and precipitation data, and SST anomaly and GPH data were established according to the classification of the status. A regression model was constructed based on the analysis of the teleconnection as a function of the lag time. After constructing three prediction models for each status, the monthly mean temperature and precipitation were predicted using their corresponding model during the verification period of June 2013 to September 2016. Table 13 shows the classification of the verification period.
The prediction model methods for the monthly mean temperature and precipitation based on the effects of El Niño and La Niña were referred to as the “modified method” to distinguish it from the general method. The results of the prediction model for the monthly mean temperature using the modified method are shown in Fig. 9 and Table 14. According to the results, the prediction reflected the tendency of the observation data except for the prediction with a lead time of 6 months. In addition, the temperature predictions in June 2015 were overestimated for all lead times. The results of the prediction model for the monthly precipitation using the modified method are shown in Fig. 10 and Table 15. In the resulting graphs, the precipitation was overestimated but the predicted precipitation was reduced in 2015 as the drought during this period was considered.
The modified prediction model was evaluated using the correlation coefficient between the predicted and observed monthly mean temperatures and precipitation for each lead time. As shown in Table 16, the predicted monthly mean temperature demonstrated a correlation coefficient ≤ 0.6 for all lead times and the predicted monthly precipitation demonstrated a correlation coefficient in the range of 0.43–0.65 depending on the lead time.
c. Comparison of the Results of the Seasonal Prediction Models
In this section, a comparative analysis of the predicted results obtained using the modified method and general method was performed, and the applicability of the modified method was evaluated. The correlation coefficients of the predicted results obtained using the general method and modified methods with the observed results were obtained as a function of the lead time. The NRMSE and MAPE values are listed in Tables 17 and 18. In comparison of the results obtained using the two methods, lower NRMSE and MAPE values denote better predictive power.
Comparing Figs. 7 and 9, the general method accurately predicted the temperature, which corresponds with the observed values, while the modified method overestimated the temperature in some periods. Comparing the evaluation indicators in Table 7, the predicted results obtained by applying the general model were more accurate than the modified method. For the monthly precipitation, the general method overestimated the values during 2015, which was likely caused by the effects of El Niño on precipitation. As a result of the application of the modified method, which considered the effects of El Niño, the prediction errors of the 2015 results were reduced. The results in Table 18 suggest that the modified method achieved outstanding results, except for the NRMSE results with lead times of 5 and 6 months. However, the NRMSE results obtained by the general method and modified method were relatively similar. Therefore, it is more advantageous to apply the general model to predict the average monthly temperature data and the modified model to predict the monthly precipitation data.