Yuanli Zhu

and 10 more

Phytoplankton primary productivity (PP) varies significantly over environmental gradients, particularly in physically-dynamic systems such as estuaries and coastal seas. As the Changjiang River runoff peaks during summer time, large environmental gradients appear in both the Changjiang estuary and adjacent East China Sea (ECS), likely driving significant variability in PP. As satellite models of PP often underperform in coastal waters, we aimed to develop a novel approach for net PP estimation in such a dynamic environment. Parallel in situ measurements of Fast Repetition Rate (FRR) fluorometry and carbon (C) uptake rates were conducted for the first time in this region during two summer cruises in 2019 and 2021. A series of 13C-incubations (n=31) were performed, with measured PP ranging from ~6 - 1700 mgC m-3 d-1. Net PP values were significantly correlated with salinity (r = 0.45), phytoplankton chlorophyll a (Chl-a, r = 0.88), Photosystem II (PSII) functional absorption cross-section (, r = -0.76) and maximum PSII quantum yield (, r = 0.59). Stepwise regression analysis showed that Chl-a and were the strongest predictors of net PP. A generalized additive model (GAM) was also used to estimate net PP considering nonlinear effects of Chl-a and . We demonstrate that GAM outperforms linear modelling approaches in predicting net PP in this study, as evidenced by a lower root mean square error (~140 vs. 250 mgC m-3 d-1). Our novel approach provides a high resolution means to examine carbon cycling dynamics in this important region.

Yu Huan

and 5 more

The three-component model is often used to invert the phytoplankton size class (PSC) concentration globally, especially in open oceans. Limited by the three-component model’s assumption, new efforts were made to explore PSC in different water environments. Mass global cruise data sets were gathered and classified into coastal, mixed, and open ocean data sets depending on the variation in bathymetric depth. A new power three-component model was established for coastal water samples (<50 m), where the determination coefficient (R2) were 0.99, 0.51, and 0.38 for micro- (Micro), nano- (Nano), and picophytoplankton (Pico), respectively. We also updated the coefficients of the exponential three-component model in open ocean (>200 m) and found that the PSC verification results performed better in the north of −40°N oceans (R2: 0.83, 0.70, and 0.64, respectively). A smooth function for the samples in mixed ocean waters (50–200 m) was designed to obtain PSC by different weights between the power and exponential three-component models with relatively low accuracy (R2: 0.84, 0.37, and 0.14, respectively), indicative of the complex conditions in these regions. We assessed the published models’ performance in coastal and open ocean samples and found an apparent underestimation of the Nano and Pico chlorophyll concentrations when their concentrations were larger than 0.2 mg m-3. The PSC proportion distribution was consistent with existing knowledge. This study evaluated the preliminary consideration of the assumption of the exponential three-component model and found that it may fail in the South Ocean, based on the global open ocean data set.