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.

This study was conducted aiming to figure out two things. First, knowledge of the change in spatial distribution of Tetranychus urticae depending on how to control it (using pesticide or natural enemy). Second, spatial association of T. urticae and Phytoseiulus persimilis in biocontrol plot (B.P). The data was analyzed by spatial analysis by distance indices (SADIE) using global aggregation index, Ia . Ia values were 0.77-1.37 in conventional plot (C.P) and 0.88-1.68 in B.P, respectively. However, the fluctuation level of Ia in B.P was higher than C.P. Therefore, the results indicated that there was a clear spatial pattern change in B.P, i.e. prey’s spatial distribution is affected by natural enemy. And spatial association analysis showed that T. urticae and P. persimilis have positively associated. It means that T. urticae is relatively low mobile prey, and P. persimilis is relatively high mobile predator.

Map-based cloning is a basic method for identifying the mutated gene in plants. We selected the gametophytic mutant, named as AP-26-09, in activation-tagging pool. Mutant plant showed various kinds of pollen phenotype, such as the different number of nucleus or abnormal shapes. For the map-based gene cloning, we conducted phenotypic analysis of F2 mapping population through the screening of DAPI-stained pollen using fluorescence microscopy. Genomic DNA of F2 plants is prepared from leaves of approximately 1000 plants. In order to define chromosomal region where mutation is located, we designed SSLP markers and performed PCR amplification. In this study, we characterized gametophytic mutant and determined the chromosomal location using map-based approach.