The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

In the summer of 2018, the Korea-Japan (KJ) region experienced an extremely severe and prolonged heatwave. This study examines the GloSea6 model's prediction performance for the 2018 KJ heatwave event and investigates how its prediction skill is related to large-scale circulation patterns identified by the k-means clustering method. Cluster 1 pattern is characterized by a KJ high-pressure anomaly, Cluster 2 pattern is distinguished by an Eastern European highpressure anomaly, and Cluster 3 pattern is associated with a Pacific-Japan pattern-like anomaly. By analyzing the spatial correlation coefficients between these three identified circulation patterns and GloSea6 predictions, we assessed the contribution of each circulation pattern to the heatwave lifecycle. Our results show that the Eastern European highpressure pattern, in particular, plays a significant role in predicting the evolution of the development and peak phases of the 2018 KJ heatwave approximately two weeks in advance. Furthermore, this study suggests that an accurate representation of large-scale atmospheric circulations in upstream regions is a key factor in seasonal forecast models for improving the predictability of extreme weather events, such as the 2018 KJ heatwave.

The prolonged and heavy East Asian summer precipitation in 2020 may have been caused by an enhanced Madden-Julian Oscillation (MJO), which requires evaluation using forecast models. We examined the performance of GloSea6, an operational forecast model, in predicting the East Asian summer precipitation during July 2020, and investigated the role of MJO in the extreme rainfall event. Two experiments, CON and EXP, were conducted using different convection schemes, 6A and 5A, respectively to simulate various aspects of MJO. The EXP runs yielded stronger forecasts of East Asian precipitation for July 2020 than the CON runs, probably due to the prominent MJO realization in the former experiment. The stronger MJO created stronger moist southerly winds associated with the western North Pacific subtropical high, which led to increased precipitation. The strengthening of the MJO was found to improve the prediction accuracy of East Asian summer precipitation. However, it is important to note that this study does not discuss the impact of changes in the convection scheme on the modulation of MJO. Further research is needed to understand other factors that could strengthen the MJO and improve the forecast.

Given the significant social and economic impact caused by heat waves, there is a pressing need to predict them with high accuracy and reliability. In this study, we analyzed the real-time forecast data from six models constituting the Subseasonal-to-Seasonal (S2S) prediction project, to elucidate the key mechanisms contributing to the prediction of the recent record-breaking Korean heat wave event in 2018. Weekly anomalies were first obtained by subtracting the 2017- 2020 mean values for both S2S model simulations and observations. By comparing four Korean heat-wave-related indices from S2S models to the observed data, we aimed to identify key climate processes affecting prediction accuracy. The results showed that superior performance at predicting the 2018 Korean heat wave was achieved when the model showed better prediction performance for the anomalous anticyclonic activity in the upper troposphere of Eastern Europe and the cyclonic circulation over the Western North Pacific (WNP) region compared to the observed data. Furthermore, the development of upper-tropospheric anticyclones in Eastern Europe was closely related to global warming and the occurrence of La Niña events. The anomalous cyclonic flow in the WNP region coincided with enhancements in Madden- Julian oscillation phases 4-6. Our results indicate that, for the accurate prediction of heat waves, such as the 2018 Korean heat wave, it is imperative for the S2S models to realistically reproduce the variabilities over the Eastern Europe and WNP regions.

Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.

Ocean biogeochemistry plays a crucial role in sustaining the marine ecosystem and global carbon cycle. To investigate the oceanic biogeochemical responses to iron parameters in the tropical Pacific, we conducted sensitivity experiments using the Nucleus for European Modelling of the Ocean–Tracers of Ocean Phytoplankton with Allometric Zooplankton (NEMO-TOPAZ) model. Compared to observations, the NEMO-TOPAZ model overestimated the concentrations of chlorophyll and dissolved iron (DFe). The sensitivity tests showed that with increasing (+50%) iron scavenging rates, chlorophyll concentrations in the tropical Pacific were reduced by approximately 16%. The bias in DFe also decreased by approximately 7%; however, the sea surface temperature was not affected. As such, these results can facilitate the development of the model tuning strategy to improve ocean biogeochemical performance using the NEMOTOPAZ model.

Although recent reports suggest that the negative correlation between the Arctic Oscillation (AO) and the East Asian winter monsoon (EAWM) has been strengthened, it is not clear whether this intermittent relationship is an intrinsic oscillation in the climate system. We investigate the oscillating behavior of the AO-EAWM relationship at decadal time scales using the long-term (500-yr) climate model simulation. The results show that ice cover over the East Siberian Seas is responsible for the change in the coupling strength between AO and EAWM. We found that increased ice cover over these seas strengthens the AO-EAWM linkage, subsequently enhancing cold advection over the East Asia due to anomalous northerly flow via a weakened jet stream. Thus, this strengthened relationship favors more frequent occurrences of cold surges in the EAWM region. Results also indicate that the oscillating relationship between AO and EAWM is a natural variability without anthropogenic drivers, which may help us understand the AO-EAWM linkage under climate change.

Extreme temperatures and precipitations are expected to be more frequently occurring due to the ongoing global warming over the Korean Peninsula. However, few studies have analyzed the synoptic weather patterns associated with extreme events in a warming world. Here, the atmospheric patterns related to future extreme events are first analyzed using the HadGEM3-RA regional climate model. Simulations showed that the variability of temperature and precipitation will increase in the future (2051-2100) compared to the present (1981-2005), accompanying the more frequent occurrence of extreme events. Warm advection from East China and lower latitudes, a stagnant anticyclone, and local foehn wind are responsible for the extreme temperature (daily T>38 o C) episodes in Korea. The extreme precipitation cases (>500 mm day−1 ) were mainly caused by mid-latitude cyclones approaching the Korean Peninsula, along with the enhanced Changma front by supplying water vapor into the East China Sea. These future synoptic-scale features are similar to those of present extreme events. Therefore, our results suggest that, in order to accurately understand future extreme events, we should consider not only the effects of anthropogenic greenhouse gases or aerosol increases, but also small-scale topographic conditions and the internal variations of climate systems.

Controversy has surrounded the potential impacts of phytoplankton on the tropical climate, since climate models produce diverse behaviors in terms of the equatorial mean state and El Niño-Southern Oscillation (ENSO) amplitude. We explored biophysical impacts on the tropical ocean temperature using an ocean general circulation model coupled to a biogeochemistry model in which chlorophyll can modify solar attenuation and in turn feed back to ocean physics. Compared with a control model run excluding biophysical processes, our model with biogeochemistry showed that subsurface chlorophyll concentrations led to an increase in sea surface temperature (particularly in the western Pacific) via horizontal accumulation of heat contents. In the central Pacific, however, a mild cold anomaly appeared, accompanying the strengthened westward currents. The magnitude and skewness of ENSO were also modulated by biophysical feedbacks resulting from the chlorophyll affecting El Niño and La Niña in an asymmetric way. That is, El Niño conditions were intensified by the higher contribution of the second baroclinic mode to sea surface temperature anomalies, whereas La Niña conditions were slightly weakened by the absorption of shortwave radiation by phytoplankton. In our model experiments, the intensification of El Niño was more dominant than the dampening of La Niña, resulting in the amplification of ENSO and higher skewness.

It is important to understand the variability of tropospheric ozone since it is both a major pollutant affecting human health and a greenhouse gas influencing global climate. We analyze the characteristics of East Asia tropospheric ozone simulated in a chemistry-climate model. We use a global chemical transport model, driven by the prescribed meteorological fields from an air-sea coupled climate model simulation. Compared with observed data, the ozone simulation shows differences in distribution and concentration levels; in the vicinity of the Korean Peninsula, a large error occurred in summer. Our analysis reveals that this bias is mainly due to the difference in atmospheric circulation, as the anomalous southerly winds lead to the decrease in tropospheric ozone in this region. In addition, observational data have shown that the western North Pacific subtropical high (WNPSH) reduces tropospheric ozone across the southern China/ Korean Peninsula/Japan region. In the model, the ozone changes associated with WNPSH are shifted westward relative to the observations. Our findings suggest that the variations in WNPSH should be considered in predicting tropospheric ozone concentrations.

엘니뇨와 남방진동(엔소)은 변동 주기가 2-8년으로 넓게 걸쳐있으며 그 진폭과 주기 또한 천천히 변하는데 이런 특징을 각각 엔소 불규칙성과 엔소 변조라 한다. 이 연구는 비선형 대기 변동성을 나타나는 Lorenz-63 모형과 간단한 충전 진동자 모형을 결합함으로써 비선형 저차 기후모델을 개발하였다. 이 모델은 동태평양의 해수면 온도 변동의 중심 주기, 넓은 주기성, 강도의 수십 년 변동 등과 같은 관측에서 보이는 엔소 특징을 잘 재현하였다. 이것은 대기 카오스 강제력이 엔소의 불규칙성과 변조를 이끌 수 있음을 보여준다. 덧붙여 모델은 서태평양 온난역의 대류활동이 강해지면 라니냐 발생 가능성이 높아지는 것을 제시하였다. 이 모델은 간단하면서도 적도 태평양의 대기-해양 비선형 상호작용을 잘 모사하고 있기에 향후 장기 기후변화 연구에 활동될 것으로 기대된다.

북대서양 자오면 순환(AMOC)은 그린란드 부근에서 고밀도 해수의 침강으로 유도되는데, 이것은 열과 물질을 수송시키기 때문에 기후 시스템의 중요한 요소이다. 이 연구는 전 지구 기후모델 중 하나인 HadGEM2-AO 모델에서 모의된 AMOC의 특징과 장기변동 메커니즘을 분석하였다. AMOC 지수를 이용한 지연 상관 분석을 통해 AMOC의 수십 년 변화는 해양 자체유지 변동으로 간주할 수 있었다. 즉 AMOC의 장기 변화는 남북 수온 경도와 해양 순환의 위상차로 인해 발생하는 불안정성에 의한 것으로 분석되었다. AMOC가 강해지면서 열의 북향 수송에 의해 남북 수온 경도가 작아지고, 따라서 해수의 순환과 열 수송이 줄어드는데, 이와 함께 고위도에서는 냉각이 유도되어 결과적으로 다시 AMOC가 강해지게 된다. 이 메커니즘은 저위도로부터 이류되는 열의 양에 따라 고위도 지역의 밀도 변화가 결정되기 때문에 AMOC의 변동을 염분 유도가 아닌 열적 유도 과정으로 이해할 수 있다.