This manuscript discusses an important issue of international interest. However, it is premature because of major flaws.

The major flaw is the erroneous assumption that riverine input is the primary source of nutrients(line 27). Much of the Introduction and Methods concerns river input. However, the Kuroshio Intermediate Water is the primary source of nutrients for the East China Sea shelf. Many papers substantiated this notion. The authors used the results presented in Figs. 2 and 3 to prove the importance of riverine inputs of nutrients. Yet, Figs. 2 and 3 only provided a good correlation between chlorophyll and nutrients that mostly did not come from rivers. Instead, the upwelling of nutrient-rich Kuroshio subsurface waters provided most of the nutrients. Of course, upwelling could be induced by the buoyance effect caused by the river water(e.g., Chen 2008, Acta Oceanologica Sinica, 27,133, 2008). To summarize, the so-called runoff-driven blooming mechanism is not caused by riverine nutrients. Instead, it is caused by buoyancy-driven upwelling and vertical mixing. BTW, validation is needed to substantiate the modeled results presented in these two figures.

Another factor worth mentioning is that the outflow of the South China Sea is the primary source of Kuroshio waters entering the East China Sea. During El Nino years, there is a more substantial outflow of the more nutrient-rich SCS water(e.g., Chen et al., Deep-Sea Res. I, 103,13, 2015), which may enhance productivity on the ECS shelf.

As a final note, many of the figures are not labeled correctly. Figures 1, 2, 3, 4, 5, 6, 9, and 10 are all related to anomalies, not the actual values. The "p" values should also be provided.

Based on limited observations, previous studies suggested that in post-El Nino summers (JJA), increased runoff from the Yangtze River reduces salinity and increases Ch-a concentrations in the surface layer of the East China Sea (ECS). The present study extends the previous studies by using longer observations, suggesting that the western North Pacific anticyclone (WNPAC) in the lower troposphere during post-El Nino summers contributes to positive Ch-a anomalies in the ECS also through positive wind stress curl anomalies, which enhance upwelling of high nutrients from below the seasonal thermocline. The paper is generally well written and should be eventually published after addressing the following concerns.

Major comments

1. The new mechanism of wind curl-induced upwelling is mostly based on a coarse-resolution ESM but the observational support is marginal. While observed Ch-a increase during post-El Nino summers is east of the Yangtze River estuary (Fig. 1a), positive wind curl anomalies are further to the south (Fig. 9b), almost entirely outside the ECS. The geographical discrepancy between Ch-a and wind curl anomalies needs to be reconciled. Just a thought: rainfall over eastern China takes a while to reach the coast and affects ECS Ch-a. Could this delay be important? Also there are many big reservoirs along the Yangtze River and water is being diverted from the river. Are these human controls important for discharges at the estuary?
2. ENSO is but one driver, but the WNPAC is an intrinsic mode of Asian summer monsoon variability and could be active in non-ENSO summers (P. Zhang et al. 2024, JC). The super-active Meiyu season in 2020 is a recent example (Z.Q. Zhou et al. 2021, PNAS). Could this explain the discrepancy between Figs. 1a and 9b?

Minor comments

1. The discussion of WNPAC dynamics is quite dated (L235-240, L386-394). See Xie et al. (2016, Adv Atmos Sci) and Chowdary et al. (2019, Current Clim Change Reps) for recent reviews.
2. Regarding the global warming effect on WNPAC (L383-386), a CMIP6 analysis suggests that ENSO-induced variability does not change much but ENSO-unrelated variability intensifies with warming (C.Y. Wang et al. 2023, JC). This would imply a weakened ENSO effect on ECS Ch-a.
3. The writing is overall good but please check English grammar. L21: add “the” in front of “East China Sea”. L35: add “The” in front of “Western North.”  

General comments: 

• The study uses satellite remote sensing, reanalysis data, and model simulation to investigate two mechanisms (river discharge and upwelling) behind how ENSO cycle influences phytoplankton biomass in East China Sea, an area of fisheries and biogeochemical importances, and is subjected to anthropogenic perturbations.

• The authors conclude that both mechanisms are relevant to the positive surface Chl a anomalies during ENSO phase based mainly on correlation analysis. However, correlation does not mean causality. If you rely on correlation analysis, the correlation between nutrient fluxes (horizontal transport or vertical transport) and Chl a under variable phases of ENSO cycle may be more robust.

• The main weakness of this study is the coarse resolution of the model used. With 1° by 1° resolution, mesoscale processes related to river  discharge may not be resolved. In fact, the model failed to reproduce the distribution of Chl anomalies of ECS as observed in remote sensing 

(compare Figure 1a and Figure 3a). The modeled Chl distribution pattern is more related to Taiwan Strait Current from the south than due to river plume. Does the model sufficiently simulate the observed nutrient distributions?

•The authors stated that phosphate input from river was not considered in the model, however within the Yangtze river estuary, there is a positive correlation between P and Chl (Figure 2c), that is, the Chl is stimulated by other water masses rich in phosphate. This can’t be explained by increasing river water discharge, as Yangtze river water is high in N/P ratio in reality, and not included in the model. There are reports showing phosphate rich water with low N/P ratio is originated from Taiwan Strait Current (Huang et al., 2019). Therefore, the model might be simulating the straightening Taiwan Strait Current during ENSO phase, assuming the model represents the N/P correctly.

• Another caveat of simulating the Chl using plankton ecosystem model coupled with ocean GCM is that top-down control is not considered in majority of the models. Under same nutrient supply, Chl a may increase if grazers are suppressed by grazing of upper trophic levels.

• The descriptions in the Data and Methods are in sufficient. It is not quite clear to me what the exact time frame of the analysis in this study. Line 65-66 stated 25 years of remote sensing data were used, does that mean the same time period is covered in model and re-analysis data? But in Figure 3, the figure legend mentioned 176 years of El Nino cases in ESM were analyzed. Some statistical analysis such as “joint composition analysis” may not be familiar to readers, me included, a reference should be very helpful.

• In describing the TOPAZ model, the authors stated Dunne et al. (2010, 2013) TOPAZ models is implemented in this study. As TOPAZ is developed in completely different context. I am not sure if the authors have done anything to tune the model parameters for ECS regions? If so, the model parameters should be listed in a table, if not in the main texts, should be in supplementary materials.

• English is mostly well written, but there are places where comprehension is compromised due to incorrect English.

Detailed comments:

• Line 39: citation of Racault et al. (2017) is not appropriate, as that paper does not address ECS specifically.

• Line 42: What does YECS stand for?

• Line 89: Fe is a ”micronutrients”, but others are macronutrients.

• Figure 1: How were the anomalies calculated? Are they against annual mean? or a climatology of certain period? Need to be precisely stated.

• Line 102: Yamaguchi et al. (Year?)

• Line 113-114: “This anomalous phytoplankton bloom during the boreal summer season of the following El Niño events has been explained by enhanced precipitation.” This sentence is hard to read. Are you trying to say the anomalous phytoplankton bloom following the El Nino events has been explained by enhanced precipitation?

• Figure 2(e, and f): What do red and red squares stand for? Describe it in the legend.

• Figure 4a: What are the gray dots for?

• Line 173-174: Where is the observed patterns of NO3 and PO4?

• Fig. 5: How did you simulate runoff? It appears there is no grid close to the coast due to coarse resolution.

• Line 260: “ within the red box in Fig. 7g”. The red box is in Fig. 7c.

• Line 261-263: How deep could be the upwelling, and what is the nutrient concentration of the upwelling horizon?

• Line 274: “Joint composition analysis” may not be familiar to large number of readers, including myself. A reference should be provided.

• Fig 9(a) and Fig 7(a-c): The position of modelled and observed position of GPH anomalies are quite different in latitude. Need some

explanations.

• Line 368: What do you mean by “two seasons”?

• Lines 370-371: Not clear about the correlation between what? Do you mean the correlation between observation and ESM results?

Reference:

Huang, T. H., Chen, C. T. A., Lee, J., Wu, C. R., Wang, Y. L., Bai, Y., He, X., Wang, S. L., Kandasamy, S., Lou, J. Y., Tsuang, B. J., Chen, H. W., Tseng, R. S., & Yang, Y. J. (2019). East China Sea increasingly gains limiting nutrient P from South China Sea. Scientific Reports, 9(1), 5648. https://doi.org/10.1038/s41598-019-42020-4.

Publisher’s note: the content of this comment was removed on 2 January 2025 since the comment was posted by mistake.