Understanding the responses of air‐sea carbon dioxide (CO₂) flux to rapid environmental changes in the highly productive Chukchi Sea is of great significance for the climate prediction in the Arctic Ocean. Through analyzing the 1998–2015 hindcast simulation of a coupled ocean–sea ice–biogeochemical model, this study identifies that the key factors influencing oceanic carbon uptake in the Chukchi Sea vary seasonally. The effects of these seasonal carbon uptakes on the total annual carbon sink vary at different timescales. The model solution suggests that the Chukchi Sea acts as a perennial carbon sink of atmospheric CO₂ with the annual mean influx of −3.73 ± 3.90 mmol m⁻² d⁻¹. Quantitative analyses reveal that the sea ice concentration predominates the annual cycle of air‐sea CO₂ flux, while primary production and wind speed strengthen the capacity for carbon uptake during June–July (melt season) and October–December (freeze season), respectively. The total annual carbon sink shows an increasing trend during 1998–2015 with a multi‐year average of 9.22 ± 1.93 Tg C y⁻¹. The carbon uptakes during June–July and August–September (open water season), which are respectively regulated by primary production and open water area at interannual scale, present significant long‐term increases and jointly determine the secular increase trend of the total annual carbon sink. Interannual CO₂ absorption during the freeze season is sensitive to the sea ice concentration and wind conditions, presenting the greatest year‐to‐year variation but insignificant long‐term trend, and modulates the interannual amplitude of the total annual carbon sink.

High-frequency measurements of tides, waves, and turbulence were made using the bottom-mounted tripod equipped with the Nortek 6-MHz acoustic Doppler velocimetry during 20–23 February 2016 (winter) and 12–26 June 2017 (summer) in Heini Bay, Yellow Sea. The synchro-squeezed wavelet transform was applied for wave-turbulence decomposition, and an iterative procedure was developed to identify the turbulence inertial subrange in the bottom boundary layer. The analysis results reveal the dependency of the inertial subrange on the tidal current and turbulence intensities. The flood-ebb tidal flows are different between the summer and winter seasons, without and with the presence of dense raft aquaculture for kelp, respectively. In summer, the turbulent kinetic energy (TKE), turbulent Reynolds stress (TRS), and dissipation rate ($\epsilon$) of TKE increase smoothly with the increasing tidal flow magnitude, and $\epsilon$ is approximately in balance with TKE production related to the vertical shear. The presence of heavy kelp aquaculture in winter causes the reduction in flow speeds and TRS, while keeping TKE and $\epsilon$ at high levels.

The Beaufort Sea has experienced significant sea ice retreat in recent decades, driven by both thermodynamic and dynamic processes. This study investigates the drivers and predictability of summer sea ice retreat in the Beaufort Sea by integrating an ocean–sea ice model with satellite-derived sea ice concentration data and atmospheric reanalysis products. Model diagnostics from 1994 to 2019 reveal that thermodynamic processes dominate annual sea ice loss (approximately 90%), with vertical heat flux accounting for roughly 85% of total oceanic heat input. The summer sea ice minimum area and the day of opening, derived from either model results and satellite observations, have a strong correlation with R² = 0.60 and R² = 0.77, respectively, enabling regression equations based solely on remote sensing data. Further multiple linear regression incorporating preceding winter (January to April) accumulated temperature and easterly wind yields moderately robust forecasts of minimum sea ice area (R² = 0.49) during 1998–2020. Additionally, analysis of reanalysis wind data shows that the timing of minimum sea ice area is significantly influenced by the frequency and intensity of sub-seasonal easterly wind events during melt season. These results demonstrate the critical importance of remote sensing in monitoring Arctic sea ice variability and enhancing seasonal prediction capability under a rapidly changing climate.