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.

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This study presents the ability of seasonal forecast models to represent the observed mid-latitude teleconnection associated with El Niño-Southern Oscillation (ENSO) events over the North American region for the winter months of December, January, and February. Further, the impacts of the associated errors on regional forecast performance for winter temperatures are evaluated, with a focus on one-month lead time forecasts. In most models, there exists a strong linear relationship of temperature anomalies with ENSO and, thus, a clear anomaly sign separation between both ENSO phases persists throughout the winter, whereas linear relationships are weak in observations. This leads to a difference in the temperature forecast performance between the two ENSO phases. Forecast verification scores show that the winter season warming (cooling) events during El Niño in northern (southern) North America are more correctly forecasted in the models than the cooling (warming) events during La Niña. One possible reason for this result is that the remote atmospheric teleconnection pattern in the models is almost linear or symmetric between the El Niño and La Niña phases. The strong linear atmospheric teleconnection appears to be associated with the models’ failure in simulating the westward shift of the tropical Pacific rainfall response for the La Niña phase compared to that for the El Niño phase, which is attributed to the warmer central tropical Pacific in the models. This study highlights that understanding how the predictive performance of climate models varies according to El Niño or La Niña phases is very important when utilizing predictive information from seasonal forecast models.

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In this study, a series of analyses were performed to verify the performance of the current Asia‐Pacific Economic Cooperation Climate Center (APCC) in‐house model, the Seamless Coupled Prediction System (SCoPS), for predicting major climate variabilities and the related dynamical processes. The prediction skills for forecasting the major atmospheric and oceanic climate modes and the related circulation patterns in the tropical Pacific, Indian, and Atlantic Oceans were examined. The interbasin relationships between the major modes were also analyzed. Despite slight spatial and temporal shifts, SCoPS simulates the atmospheric responses to the El Niño–Southern Oscillation (ENSO) well over both the tropical and the northwestern Pacific. The major modes over the Indian Ocean are also reasonably well predicted. The dynamical interaction between the Indian and Pacific Oceans, which is involved in the development of the tropical Pacific modes, is also represented with good accuracy. In case of the Atlantic Ocean, the northern tropical sea surface temperature (SST) variability is better predicted than the southern tropical SST variability. Through examining the characteristics of El Niño‐La Niña asymmetries in forcing the tropical Atlantic variability SST modes, we found that this skill difference could be partly because of too strong linear response of the SST variability in the southern region to ENSO. The North Atlantic Oscillation, one of the major atmospheric modes during boreal winter, was also investigated. It was found that SCoPS reasonably replicates the observed loading pattern and temporal variation but tends to overestimate the relationship between North Atlantic Oscillation and ENSO.