Publication

The ocean mixed layer model (OMLM) is improved using the large eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green’s function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLM grid resolution on the optimal empirical constants is also investigated.

Marine extreme temperature events (METs), including marine heatwaves (MHWs) and cold spells, have recently gained much attention owing to their vital influence on the marine ecosystem and social economy. Since METs can alter the upper ocean stratification and wintertime convective mixing in the northwestern North Pacific subtropical gyre (NPSG), their activities may modulate phytoplankton blooms by regulating entrainment of the subtropical mode water (STMW) with high NO. Furthermore, because STMW formed in the previous winter reemerges east of its formation site in the following winter, the METs activities imprinted in STMW affect phytoplankton blooms remote from its formation site. Here, we examined the relationship between the MET activities, STMW volume, and phytoplankton blooms using satellite observations and a data-assimilative coupled physical-biogeochemical model dataset. MET activities appearing in the STMW formation region during winter regulate the formation of STMW and the supply of NO from the subsurface, with the latter controlling the spring/autumn blooms in that region under NO-limited conditions. Subsequently, this water mass is transported eastward in the subsurface within the northern flank of the NPSG before reemerging east of the STMW formation site the following spring. This process results in a negative lag-correlation between MET activities and surface chlorophyll in the reemergence region; for example, MHWs in winter at the STMW formation site tend to lower the surface chlorophyll concentration one year later in the reemergence region. Our study suggests that the oceanic processes allow one year of predictability of the marine ecosystems by monitoring METs in the STMW formation site.

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluated the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast, particularly focusing on the western North Pacific (WNP), where the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface net heat flux. At the beginning of model integration, the ocean receives excessive heat from the atmosphere because of the predominant positive bias in the downward shortwave radiation (SW), which rapidly decreased within a few days as cloud cover builds. Then, the negative bias in the latent heat (LH) flux increases over time and induces a negative bias in the surface net heat flux. Although the magnitude of the negative bias in LH flux gradually decreases, it remains the most significant contributor to the negative bias in the net heat flux bias for more than 250 days. Uncoupled ocean model experiments showed that the ocean model is unlikely to be the primary source of the SST bias.

The Southern Ocean, an important region for the uptake of anthropogenic carbon dioxide (CO2), features strong surface currents due to substantial mesoscale meanders and eddies. These features interact with the wind and modify the momentum transfer from the atmosphere to the ocean. Although such interactions are known to reduce momentum transfer, their impact on air-sea carbon exchange remains unclear. Using a 1/20° physical-biogeochemical coupled ocean model, we examined the impact of the current-wind interaction on the surface carbon concentration and the air-sea carbon exchange in the Southern Ocean. The current-wind interaction decreased winter partial pressure of CO2 (pCO2) at the ocean surface mainly south of the northern subantarctic front. It also reduced pCO2 in summer, indicating enhanced uptake, but not to the same extent as the winter loss. Consequently, the net outgassing of CO2 was found to be reduced by approximately 17% when including current-wind interaction. These changes stem from the combined effect of vertical mixing and Ekman divergence. A budget analysis of dissolved inorganic carbon (DIC) revealed that a weakening of vertical mixing by current-wind interaction reduces the carbon supply from below, and particularly so in winter. The weaker wind stress additionally lowers the subsurface DIC concentration in summer, which can affect the vertical diffusive flux of carbon in winter. Our study suggests that ignoring current-wind interactions in the Southern Ocean can overestimate winter CO2 outgassing.