In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak (Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha-1, with a range of 3.30-8.19 Mg C ha-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through processbased models.

Biogeochemical processes play an important role in ocean environments and can affect the entire Earth’s climate system. Using an ocean-biogeochemistry model (NEMO-TOPAZ), we investigated the effects of changes in albedo and wind stress caused by phytoplankton in the equatorial Pacific. The simulated ocean temperature showed a slight decrease when the solar reflectance of the regions where phytoplankton were present increased. Phytoplankton also decreased the El Niño-Southern Oscillation (ENSO) amplitude by decreasing the influence of trade winds due to their biological enhancement of upper-ocean turbulent viscosity. Consequently, the cold sea surface temperature bias in the equatorial Pacific and overestimation of the ENSO amplitude were slightly reduced in our model simulations. Further sensitivity tests suggested the necessity of improving the phytoplankton-related equation and optimal coefficients. Our results highlight the effects of altered albedo and wind stress due to phytoplankton on the climate system.