2020
DOI: 10.1029/2019jc015790
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Effects on Ocean Biology Induced by El Niño‐Accompanied Positive Freshwater Flux Anomalies in the Tropical Pacific

Abstract: The El Niño and Southern Oscillation (ENSO) can induce large perturbations in freshwater flux (FWF, defined as precipitation minus evaporation) and ocean ecosystem in the tropical Pacific. However, how El Niño-induced FWF can affect the tropical Pacific ecosystem (e.g., chlorophyll) is still unknown. Here, a series of ocean-only experiments are performed using a coupled ocean-physical biogeochemistry model forced by prescribed climatological wind stress. Interannual FWF anomalies observed during the 1997-1998 … Show more

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Cited by 6 publications
(2 citation statements)
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References 73 publications
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“…These unanswered sources in TCV may constitute a quarter of midlatitude variability and nearly less than half of the tropical variabilities (Figure S7 in Supporting Information ). Potential sources of explanation go far beyond the simple physical climate metrics discussed in the present work and include previously identified mechanisms such as: internal dynamics of biogeochemical and ecological cycling on zooplankton grazing pressures on phytoplankton (Gorgues et al., 2010; Tian et al., 2021); anomalous phytoplankton blooms driven by volcanic ash increases in iron supply (Achterberg et al., 2013; Hamme et al., 2010); high contribution of the Amazon and Orinoco river plumes into the tropical Atlantic (Grodsky et al., 2008); widespread phytoplankton blooms triggered by Australian wild fires in the Southern Pacific (Tang et al., 2021); diversity and asymmetry of ENSO (Lee et al., 2014; Park et al., 2014); ENSO‐driven freshwater flux (Tian et al., 2020); nonlinearity of horizontal nitrate advection (Ham et al., 2021); nonlinear statistical approach (Martinez et al., 2020); and tropical instability wave driven nutrient supply (Evans et al., 2009) in the tropical Pacific phytoplankton variation.…”
Section: Discussionmentioning
confidence: 99%
“…These unanswered sources in TCV may constitute a quarter of midlatitude variability and nearly less than half of the tropical variabilities (Figure S7 in Supporting Information ). Potential sources of explanation go far beyond the simple physical climate metrics discussed in the present work and include previously identified mechanisms such as: internal dynamics of biogeochemical and ecological cycling on zooplankton grazing pressures on phytoplankton (Gorgues et al., 2010; Tian et al., 2021); anomalous phytoplankton blooms driven by volcanic ash increases in iron supply (Achterberg et al., 2013; Hamme et al., 2010); high contribution of the Amazon and Orinoco river plumes into the tropical Atlantic (Grodsky et al., 2008); widespread phytoplankton blooms triggered by Australian wild fires in the Southern Pacific (Tang et al., 2021); diversity and asymmetry of ENSO (Lee et al., 2014; Park et al., 2014); ENSO‐driven freshwater flux (Tian et al., 2020); nonlinearity of horizontal nitrate advection (Ham et al., 2021); nonlinear statistical approach (Martinez et al., 2020); and tropical instability wave driven nutrient supply (Evans et al., 2009) in the tropical Pacific phytoplankton variation.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, it is invaluable to fully understand ENSO‐related mechanisms that control phytoplankton biomass and community composition under various climate scenarios. Meanwhile, ENSO can also be modulated by changes in marine ecosystems in the tropical Pacific through a direct warming effect and an indirect cooling effect, since chlorophyll in phytoplankton affects the absorption of shortwave radiation and subsequently the vertical and meridional heat gradient (Heinemann et al., 2011; Lengaigne et al., 2007; Nakamoto et al., 2001; Park et al., 2014; Shi et al., 2023; Tian et al., 2020; Zhang et al., 2009, 2018). Though the overall effect of phytoplankton on ENSO remains uncertain, the facts of biologically induced changes in the physical environment are undeniable and have been proven to be a crucial factor in improving the accuracy of numerical models (Gnanadesikan et al., 2004; Manizza et al., 2005; Murtugudde et al., 2002).…”
Section: Introductionmentioning
confidence: 99%