The Arabian Sea has undergone significant warming since the mid-20th century, highlighting the importance of assessing how decadal climate patterns influence chlorophyll-a (Chl-a) and broader marine ecosystem dynamics. This study investigates the variability of Chl-a, sea surface temperature (SST), and sea level anomaly (SLA) over the past three decades, and their relationships with the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The mean Chl-a concentration was 1.10 mg/m³, with peak levels exceeding 2 mg/m³ between 2009 and 2013, and the lowest value (0.6 mg/m³) was recorded in 2014. Elevated Chl-a levels were consistently observed in February and March across both coastal and offshore regions. Empirical orthogonal function (EOF) analysis revealed distinct spatial patterns in Chl-a and SST, indicating dynamic regional variability. The SST increased by 0.709 °C over the past four decades, accompanied by a steady rise in the SLA of approximately 1 cm. The monthly mean Chl-a exhibited a strong inverse relationship with both the SST and SLA and a positive correlation with SST gradients (R² > 0.5). A positive correlation (R² > 0.5) was found between the PDO and Chl-a, whereas the PDO was negatively correlated with the SST and SLA. In contrast, the AMO was negatively correlated with Chl-a but positively associated with warming and SLA rise. These findings underline the contrasting roles of the PDO and AMO in modulating productivity and ocean dynamics in the Arabian Sea. This study emphasizes the need for continued monitoring to improve predictions of ecosystem responses under future climate change scenarios.

The sustainable exploitation of fishery resources in Pakistan was assessed using the catch-based Monte Carlo method (CMSY) and the length-based Bayesian biomass (LBB) method to evaluate the data-limited fishery of the Spangled Emperor, Lethrinus nebulosus. CMSY relies on catch data, resilience parameters, and quantitative stock status metrics, while LBB exclusively uses length–frequency (LF) data for stock assessments. This study utilized twenty-two years of catch–effort and LF data from 7230 fish along the Balochistan coastline in Pakistan. The study revealed that the relative biomass of the exploited stock, with a B/B_MSY ratio of 0.557, indicates significant depletion. The relative exploitation rate (F/F_MSY = 2.47) confirms that the stock is being severely overfished. The discrepancy between the optimal length at first capture (L_{c_opt} = 43.1 cm) and the length at first capture (L_c = 38.8 cm) further proves the overexploitation of L. nebulosus. The convergence of findings from both methodologies strengthens the reliability of stock status estimates. By integrating diverse data types and analytical frameworks, this study provides valuable insights into the sustainability of L. nebulosus populations. This dual approach not only underscores the importance of varied data sources but also informs management strategies for effective fisheries conservation, contributing to a deeper understanding of resource dynamics along the Balochistan coast of Pakistan.

Landfalling typhoons frequently cause severe flooding and socioeconomic losses along the eastern coast of China, yet the relative influence of oceanic and storm-related factors on precipitation variability remains insufficiently understood. This study investigates the relationships among sea surface temperature (SST), translation speed, typhoon intensity, and precipitation characteristics for 23 typhoons that made landfall in Zhejiang Province during 2000–2025. Typhoon track, intensity, precipitation, and SST data were analyzed to examine pre-landfall evolution, precipitation variability across intensity categories, and the environmental factors associated with typhoon-induced precipitation. Results showed that SST generally decreased as typhoon approached the coast, with mean values declining from 27.9 °C offshore to 24.5 °C at landfall. Translation speed exhibited a non-linear evolution during the 120 h preceding landfall, with an average speed of 9.8 km h⁻¹ and a weak positive tendency toward the coast. Peak 24 h precipitation varied considerably among intensity categories. Severe typhoons (STY) produced the highest mean precipitation (120.9 ± 92.3 mm), whereas super typhoons (SuperTY) exhibited lower but more consistent rainfall totals. Correlation analysis revealed a weak relationship between translation speed and rainfall (r = −0.18), indicating that storm motion alone was insufficient to explain rainfall variability. SST showed a moderate negative correlation with rainfall (r = −0.53), highlighting the complex relationship between ocean thermal conditions and precipitation variability. These findings show that typhoon precipitation over Zhejiang is associated with the combined variability of storm intensity, ocean thermal conditions, atmospheric moisture availability, and coastal environmental conditions, providing scientific insights for understanding typhoon-related hazards under climate change.