Monitoring marine primary productivity (PP) is crucial for understanding changes in the marine ecosystem. Based on satellite data and the vertically generalized production model (VGPM), this study investigated the spatiotemporal distribution and long-term trend of PP in the Yellow and Bohai Seas (YBSs) from 2003 to 2020. By using the calibrated satellite data and optimized parameterization scheme, the accuracy of the PP results in the YBSs was significantly improved compared to online PP products. The annual mean PP in the YBSs from 2003 to 2020 was 523.8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, with significant seasonal and interannual differences. Seasonally, PP in the Yellow Sea and the Bohai Sea exhibited bimodal (two peaks in May and October) and unimodal (one peak in June) variation, respectively. The magnitude of mean PP in the YBSs was ranked as spring > summer > autumn > winter, with spring PP (~1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) contributing more than 40% of the annual PP. The annual mean PP in the YBSs showed an overall decrease from 2003 to 2020, with a decrease rate of 5–6 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})/\mathrm{y}$. The interannual variation of the PP was mainly related to the variability of the chlorophyll-a concentration and was essentially inverse to the phases of the Pacific Decadal Oscillation and the El Niño-Southern Oscillation.

Sufficient and accurate air pollutant data are essential to analyze and control air contamination problems. An orthogonal polynomial fitting (OPF) method using Chebyshev basis functions is introduced to produce spatial distributions of fine particle (PM_2.5) concentrations in central and southern regions of China. Idealized twin experiments (IE1 and IE2) are designed to validate the feasibility of the OPF method. IE1 is designed in accordance with the most common distribution of PM_2.5 concentrations in China, whereas IE2 represents a common distribution in spring and autumn. In both idealized experiments, prescribed distributions are successfully estimated by the OPF method with smaller errors than kriging or Cressman interpolations. In practical experiments, cross-validation is employed to assess the interpolation results. Distributions of PM_2.5 concentrations are well improved when OPF is applied. This suggests that errors decrease when the fitting order increases and arrives at the minimum when both orders reach 6. Results calculated by the OPF method are more accurate than kriging and Cressman interpolations if appropriate fitting orders are selected in practical experiments.

This study focuses on different evolutions of the low-level atmospheric circulations between eastern Pacific (EP) El Niño and central Pacific-II (CP-II) El Niño. The western North Pacific anomalous anticyclone (WNPAC) originates from the northern South China Sea for EP El Niño, and moves to the western North Pacific (WNP) afterward. Compared with EP El Niño, the origin of the WNPAC is farther west during CP-II El Niño, with the center over the Indochina Peninsula. Moreover, the WNPAC shows a weaker eastward shift. Such discrepancies are attributed to different evolutions of the cyclonic response over the WNP, which can suppress the convection in the western flank of the anomalous cyclone. The eastward retreat of the anomalous cyclone is significant for EP El Niño, but less evident for CP-II El Niño. These discrepancies are related to zonal evolutions of the increased precipitation over the equatorial Pacific. Following the southward migration of the intertropical convergence zone (ITCZ), the deep-convection region extends eastward along the equator, reinforcing the atmospheric response to the eastern Pacific warming in EP El Niño. For CP-II El Niño, the atmospheric response is insignificant over the eastern Pacific without warming. Moreover, the meridional migration of the ITCZ can modulate zonal variations of the easterly trade wind and specific humidity as well. Due to the combined effects of the climatological background and atmospheric anomalies, the specific humidity–induced and wind-induced moist enthalpy advection contribute to different shifts of the precipitation center.

Water residence time (WRT) is a crucial parameter for evaluating the rate of water exchange and it serves as a timescale for elucidating hydrodynamic processes, pollutant dispersion, and biogeochemical cycling in coastal waters. This study investigates the tidal-driven WRT patterns in the Bohai and Yellow Seas (collectively known as BYS) by employing a tidal model in conjunction with an adjoint WRT diagnostic model and explores the influence of tidal constituents on WRT. The findings indicate that the tidal-driven WRT in the BYS is approximately 2.11 years, exhibiting a significant spatially heterogeneous distribution. The WRT pattern shows a strong correlation with the pattern of tidal-driven Lagrangian residual currents (LRCs). Semidiurnal tides have a more pronounced effect on WRT than diurnal tides. Semidiurnal tides significantly reduce WRT across the entire BYS, while diurnal tides predominantly influence WRT in the Bohai Sea (BS). The M₂ tidal constituent is the most influential in decreasing WRT and enhancing water exchange, owing to its dominant energy contribution within the tidal system. In contrast, the S₂ tidal constituent has a minimal effect; however, its interaction with the M₂ tidal constituent plays a significant role in reducing the WRT. The K₁ and O₁ constituents exert more localized effects on WRT, particularly in the central BS, where their energy ratios relative to M₂ are relatively high. Although the amplitude of the S₂ constituent exceeds that of K₁ and O₁, its contribution to LRC—and consequently to WRT—is limited due to the overlapping tidal wave with M₂. This research contributes to a deeper understanding of the influence of tidal dynamics on long-term water transport and associated timescales, which are vital for enhancing predictions of material transport and ecosystem dynamics in tidal-dominated environments.

The vertically generalized production model (VGPM) is one of the most important methods for estimating marine net primary productivity (PP) using remote sensing. However, different data sources and parameterization schemes of the input variables for the VGPM can introduce uncertainties to the model results. This study compared the PP results from different data sources and parameterization schemes of three major input variables (i.e., chlorophyll-a concentration ($C_{opt}$), euphotic depth ($Z_{eu}$), and maximum photosynthetic rate ($P_{opt}^B$)) and evaluated the sensitivity of VGPM in the Yellow and Bohai Seas on the inputs. The results showed that the sensitivity in the annual mean PP was approximately 40%. Seasonally, the sensitivity was lowest in the spring (35%), highest in the winter (70%), and approximately 60% in the summer and autumn. Spatially, the sensitivity in nearshore water (water depth < 40 m) was more than 60% and around two times higher than that in deep water areas. Nevertheless, all VGPM results showed a decline trend in the PP from 2003 to 2020 in the Yellow and Bohai Seas. The influence of $P_{opt}^B$ and $C_{opt}$ was important for the magnitude of annual mean PP. The PP seasonal variation pattern was highly related to the parameterization scheme of $P_{opt}^B$, whereas the spatial distribution was mostly sensitive to the data sources of $C_{opt}$.