Executive Summary

Due to climate change caused by global warming, extreme climate events are occurring more frequently and more intensely around the world. For coping with the climate change, the importance of climate prediction information is increasing. Therefore, the APEC Climate Center (APCC) has been working on improving long-term climate forecasts that the public can feel directly, with a focus on monitoring, analyzing, and predicting extreme climate events, as part of a six-phase (2022-2024) APCC climate information service and research development project that has been performed over a three-year period and aimed to contribute to enhancing the reliability of observation-based climate predictions. In the first year (2022) of the six-phase project, we analyzed climate factors that have recently gained increased importance, such as the South Asian High system during the summer and the North Atlantic Oscillation (NAO) in the spring. It was found that when the NAO is in a positive phase, South Korea experiences higher temperatures in connection with the sea surface temperature anomalies in the central Pacific. Conversely, in the negative phase, the Barents Sea ice conditions are linked to colder temperatures. Furthermore, the expansion mode of the South Asian monsoon system in the summer corresponds with low-pressure systems over the Korean Peninsula, which is associated with lower temperatures and positive precipitation anomalies. The north-south mode of the South Asian High system is linked to higher temperatures in South Korea. Additionally, we analyzed the possible change in flood risks and typhoon for the Korean Peninsula due to future climate change, concluding that extreme rainfall intensity will increase, and stronger typhoons impacting the Korean Peninsula will become more frequent due to global warming. Moreover, improvements were made to the Climate Analysis System (CAS), including the development of an automatic monitoring and forecasting system for prediction discussions, as well as the display of climate predictor and composite fields for climate analysis.

In the second year (2023) of the 6-phase project, the goal was to develop techniques for optimally utilizing various monitoring and analysis information to support seasonal forecasts. To achieve this, we first proposed ways to improve the usefulness and predictive skills of key predictors during the summer. Specifically, it was found that by monitoring the South Asian Monsoon Index in June when the European Z500 index in March is positive, and monitoring snow conditions in Central Asia in April and sea surface temperature (SST) anomalies in the Gulf of Mexico in June when the index shows negative values, can help improve the accuracy of temperature forecasts for the Korean Peninsula in July. Similarly, by monitoring the South Asian Monsoon Index in June when the positive phase of the tropical SST tri-pole index in April is observed, and tracking the negative tropical SST tri-pole conditions in Maywhen in the negative phase, it was shown that we can improve the reliability of July temperature forecasts. We also analyzed to improve the utilization of the North Pacific Oscillation (NPO) atmospheric variability mode, which affects winter temperatures in South Korea. It was found that the high-pressure anomalies over the Korean Peninsula related to the NPO in December continue through January, influencing the January temperatures. Notably, the skills of temperature forecasts increased when the NPO was combined with ENSO (El Niño-Southern Oscillation) predictor. Additionally, as in the first year, the project carried out scientific analyses of climatic issues under future climate change, such as renewable energy generation potential and extreme droughts. Under high-carbon scenarios, the solar energy generation potential showed significant decreases during spring and winter and also wind energy potential decreased significantly during spring and autumn. The future changes in drought indices for South Korea indicated that drought conditions would worsen in the spring and autumn, especially in the autumn and under high-carbon scenarios, compared to the spring and low-carbon scenarios. Finally, improvements to the CAS were made, enhancing user convenience, as well as expanding composite analysis. These enhancements increased the speed and efficiency of monitoring and analysis tasks.

In the third year (2024) of the project, it is aimed to further advance the optimal utilization techniques for monitoring and analysis information to better cope with extreme climate events. In the study on developing new predictors for Atlantic and mid-latitude wave propagation, predictors for January temperatures in Korea were identified and their dynamic processes analyzed. All potential predictors were closely linked to NAO-like patterns in December, with the most reliable being the average geopotential height anomalies at 500 hPa over Europe and the Baikal region. When both anomalies were below average, a negative NAO pattern developed over the North Atlantic, inducing upper-level negative anomalies over Europe and the Baikal region through wave propagation. Simultaneously, Siberia experienced negative MSLP anomalies, lower surface temperatures, and increased snowfall. These conditions led to the development of the Siberian high, causing below-normal temperatures from the Baikal region to Korea in early January. An analysis of early and late January temperatures revealed that these periods are influenced by distinct climate factors. The predictors related to mid-latitude wave propagation identified in this study primarily affect early January temperatures. Considering the correlation between early January temperatures and the overall January average temperatures, these predictors are particularly effective when forecasting below-normal January conditions. To apply these 1-month lead predictors in operational forecasting, a method combining reanalysis data with sub-seasonal prediction model outputs is proposed. This approach is expected to enhance the practical applicability of the identified predictors in forecasting operations.

A significant lead-lag relationship is found between the western Indian Ocean (WIO) SST anomalies in December and precipitation anomalies over South Korea in January. The December WIO SST anomalies are responsible for positive precipitation anomalies over South Korea that peak in January, exhibiting a 1-month leading role. The December WIO SST anomalies effectively drive the precipitation anomalies in the tropical eastern Indian Ocean, which do persist and strengthen into January. Then, the upper-level anticyclonic anomalies located in the Arabian Sea as a result of Gill-type response favor the enhancement of the Rossby wave train that propagate poleward from this region into East Asia. The resultant anticyclonic anomalies in East Asia tend to modulate the precipitation anomalies over South Korea in January. Therefore, the December WIO SST index as a precursor may help us to better understand and predict the precipitation variability over South Korea in January with 1-month lag.

In a study that suggested how tropical Pacific convection activities in autumn can be used to predict the south Korean temperatures in winter, new predictors were discovered to predict temperatures in the middle and late of winter when predictors are insufficient, and furthermore, a method was proposed to increase the predictive skill and utilization of the discovered predictors. First, from the SVD analysis that was performed on the precipitation anomalies in the tropical Pacific Ocean in autumn and the winter temperature anomalies in Korea, a TSPM (Tropical South Pacific Mode) in autumn were defined as a predictor for the prediction of temperature in winter. A dynamical process and schematic diagram of the effect of TSPM in autumn on temperature in mid to late winter over South Korea were presented through performing a series of composite analysis. In addition, the analysis of the relationship with the October MJO showed that the utilization of TSPM predictors could be increased, and the anomalous sea surface temperatures in the central Pacific Ocean in October could be used to increase predictive skills in forecasting warmer-than-normal temperatures in winter. Moreover, in order to analyze the impact of convective activity in the tropical Pacific Ocean on winter precipitation in South Korea, the K-means cluster analysis was performed and atmospheric circulation characteristics were analyzed based on the events included in each cluster. In other words, one of the two clusters of positive and negative precipitation events is characterized by strong convective activity in the tropical northwest Pacific Ocean, which affects the lower-level atmospheric circulation pattern around the Korean Peninsula, influencing south Korean precipitation changes, and the other is characterized by the weakening of the intensity of the tropical Pacific convective activity in autumn as winter approaches, and the influence of mid-latitude wave propagation is more predominant.

In the study of the projection of the extreme climate events under future global warming, we analyzes the future changes in winter temperature drops on the Korean Peninsula using high-resolution climate change scenario data for East Asia. Sudden Daily Temperature Drop (SDTD) events are a major extreme weather phenomenon in Korea's winter climate, reflecting the sensitivity to climate change and providing important information for understanding climate variability. Using reanalysis data, SDTD event days were selected, and the frequency of occurrences and composite patterns of climate variables for each winter month over the historical period were compared. Based on the observational period analysis, a frequency analysis of SDTD events for the same period was conducted using 3models in CORDEX-EA historical experiment. The results showed that SDTD events were most frequent in December and least frequent in February. In future climate projections under both SSP126 and SSP585 scenarios, the frequency of these events decreases in December and January, while the frequency in February increases. Through the analysis of the composite patterns of climate variables, changes in the East Asian winter monsoon index and Siberian high-pressure index, and correlations with winter temperatures in Korea, this study deepens the understanding of climate change and provides crucial baseline data for predicting future changes in extreme weather events such as SDTD events.

Finally, due to the increasing frequency of extreme weather events worldwide, the need for rapid climate monitoring systems has grown significantly. Continuous monitoring of intensifying extreme weather phenomena across the globe has become essential to minimize property damage and loss of life. This has underscored the importance of collecting up-to-date observational data and establishing a systematic climate monitoring framework. To ensure stable operation and improvement of these systems, observational data provided by NCEP and the Meteorological Administration have been validated, and previously inconvenient climate monitoring and analysis services have been enhanced. The climate monitoring service now offers the latest information on various climate variables, while the climate analysis service synthesizes data, provides time-series analysis results, and standardizes data collection systems for rapid response to abnormal weather events. To facilitate the swift delivery of monitoring results, features such as automated generation of predictive indicators, information provision systems, and time-series-based synthetic information tools have been developed. For monthly forecasting discussions, the system automatically calculates relevant climate factors and provides guidance on their utilization, significantly improving operational efficiency.