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.

곰보버섯의 자실체 색은 갈색 또는 회갈색이고 갓의 형태는 원뿔모양이며, 대의 색은 흰 크림색 또는 흰색이다. 자실체를 반으로 잘라보면 갓과 대는 속은 비어있다. 자실체의 현미경 관찰에서 자낭의 크기는 113.5~114.2×9.3~10.3㎛, 자낭포자의 크기는 17.6~20.9×9.4~10.7㎛, 측사는 40.0~45.2×5.8~6.4㎛, 대의 안쪽세포는 7.5~15.1㎛이었다. 곰보버섯 자실체 발생지 환경조건은 온도 13.8℃, 습도 75.8%, 광 538룩스이며, 토성은 sand 72.7%, silt 21.0%, clay 6.3%로 silt loamy 이며, 유기물 함량은 10.0%로 밭 토양보다 많고 인산함량은 낮았다. 균주별 균사배양 최적배지는 PDB 배지이며, 배양최적 온도는 25℃, pH는 5.0이었으며, 합성배지인 Czapek에 배지에 조정된 무기태 성분의 최적농도는 potassium phosphate 1%, sodium nitrate 2%, potassium chloride은 대조구에서 좋았다. 영양원 선발에서 최적 탄소원은 mannose이었으며, 최적농도는 5%이었다. 질소원은 대조구에 비하여 균사생육이 극히 저조하였다. 곰보버섯 균핵 형성 촉진배지 선발시험에서 균사생장과 균핵 형성이 좋은 배지는 폐면 이었으며, 톱밥에서는 참나무톱밥에서 균사생장이 빨랐다. 폐면과 참나무톱밥의 혼합비율은 참나무톱밥 20%+폐면 80% 혼합수준에서 균핵 형성이 좋았고 첨가제인 밀기울의 혼합비율은 15-20%, 황산칼슘은 2% 수준에서 균핵 형성이 좋았다. 토양종류별 균핵 형성은 피트모스 처리구에서 균사생장 및 균핵 형성이 가장 좋았으며, 피트모스의 혼합비율은 60-80%이나, 참나무를 혼합하면 50:50으로 혼합 처리한 구에 밀기울을 30% 첨가하면 균핵 형성이 가장 좋았다.