The Asia Pacific Economic Cooperation (APEC) Climate Center (APCC) inhouse model (Seamless Coupled Prediction System: SCoPS) has been newly developed for operational seasonal forecasting. SCoPS has generated ensemble retrospective forecasts for the period 1982-2013 and real-time forecasts for the period 2014-current.In this study, the seasonal prediction skill of the SCoPS hindcast ensemble was validated compared to those of the previous operation model (APEC Climate Center Community Climate System Model version 3: APCC CCSM3). This study validated the spatial and temporal prediction skills of hindcast climatology, large-scale features, and the seasonal climate variability from both systems. A special focus was the fidelity of the systems to reproduce and forecast phenomena that are closely related to the East Asian monsoon system. Overall, both CCSM3 and SCoPS exhibit realistic representations of the basic climate, although systematic biases are found for surface temperature and precipitation. The averaged temporal anomaly correlation coefficient for sea surface temperature, 2-m temperature, and precipitation from SCoPS is higher than those from CCSM3. Notably, SCoPS well captures the northward migrated rainband related to the East Asian summer monsoon. The SCoPS simulation also shows useful skill in predicting the wintertime Arctic Oscillation. Consequently, SCoPS is more skillful than CCSM3 in predicting seasonal climate variability, including the ENSO and the Arctic Oscillation. Further, it is clear that the seasonal climate forecast with SCoPS will be useful for simulating the East Asian monsoon system.

Surface ozone (O3) is a harmful pollutant and effective strategies must be developed for its reduction. In this study, the impact of meteorological factors on the annual O3 variability for South Korea were analyzed. In addition, the regional differences of meteorological factors in six air quality regions in South Korea (Seoul Metropolitan Area, SMA; Central region, CN; Honam, HN; Yeongnam, YN; Gangwon, GW; Jeju, JJ) were compared. The analysis of ground observation data from 2001 to 2017 revealed that the long-term variability of O3 concentration in South Korea continuously increased since 2001, and the upward trend in 2010 to 2017 (Period 2, PRD2) was 29.8% higher than that in 2001 to 2009 (Period 1, PRD1). This was because the meteorological conditions during PRD2 became relatively favorable for high O3 concentrations compared to conditions during PRD1. In particular, the increase in the solar radiation (SR) and maximum temperature (TMAX) and the decrease in the precipitation (PRCP) and wind speed (WS) of South Korea in PRD2 were identified as the main causes for the rise in O3 concentrations. When meteorological factors and O3 variability were compared among the six air quality regions in South Korea during PRD1 and PRD2, significant differences were observed. This indicated that different meteorological changes occurred in South Korea after 2010 due to the different topographical characteristics of each region; thus, O3 variability also changed differently in each region. Interestingly, for the regions with almost similar meteorological changes after 2010, the O3 concentration changed differently depending on the difference in the distribution of emissions. These results indicate that the O3–meteorology relationship shows spatiotemporal differences depending on the topographical and emission distribution characteristics of each area and implies that it is necessary to fully consider such differences for efficient O3 reduction.

As the climate changes, increasing variations in environmental factors directly influence crop cultivation at different magnitudes over a broad range of local communities worldwide. As a result, there is an urgent need to develop local impact assessments and adaptation strategies for use at local, rather than national or global, levels. In this study, we predicted the future frost damage of kiwifruit in the Jeonnam province, Korea, as a case study for the local impact assessment of climate change. This study included a series of models that integrated both the biological responses of plants and the physical influences of climatic factors. First, potential changes in the suitable area for kiwifruit cultivation under a changing climate were simulated using downscaled high resolution (1 km) climate data. Through the development of a frost‐forecasting model and linking it to a kiwifruit phenology model, we also assessed the interaction of plant and climatic factors. Because of the warming climate, the last frost date in spring occurred 13.7 days earlier in average under climate change. Nevertheless, the potential risk of spring frost damage of kiwifruit continued to exist at a similar magnitude in the future. Additional study at the county level indicated that the date of bud burst is advancing even faster than the last frost date (approximately 1 day per every decade), resulting in the increasing risk of spring frost damage for kiwifruit through 2100. In this study, the local impacts of climate change on kiwifruit frost damage were assessed using the integrated modelling approach. As such, local policy makers and stakeholders will be able to prepare more realistic adaptation strategies to cope with upcoming threats in a changing climate.

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In this study, the characteristics of systematic errors in subseasonal prediction for East Asia are investigated using an ensemble hindcast (1991–2010) produced by the Global Seasonal Forecasting System version 5 (GloSea5). GloSea5 is a global prediction system for the subseasonal-to-seasonal time scale, based on a fully coupled atmosphere, land, ocean, and sea ice model. To examine the fidelity of the system with respect to reproducing and forecasting phenomena, this study assesses the systematic biases in the global prediction model focusing on the prediction skill for the East Asian winter monsoon (EAWM), which is a major driver of weather and climate variability in East Asia. To investigate the error characteristics of GloSea5, the hindcast period is analyzed by dividing it into two periods: 1991–2000 and 2001–2010. The main results show that the prediction skill for the EAWM with a lead time of 3 weeks is significantly decreased in the 2000s compared to the 1990s. To investigate the reason for the reduced EAWM prediction performance in the 2000s, the characteristics of the teleconnections relating to the polar and equatorial regions are examined. It is found that the simulated excessive weakening of the East Asian jet relating to the tropics and a failure in representing the Siberian high pressure relating to the Arctic are mainly responsible for the decreased EAWM prediction skill.