Remotely Sensed Biological Production in the Equatorial Pacific

Turk, Daniela; McPhaden, Michael J.; Busalacchi, Antonio J.; Lewis, Marlon R.

doi:10.1126/science.1056449

Cited by 69 publications

(55 citation statements)

References 27 publications

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“…In the tropics and subtropics, zonal wind stress patterns cause convergence zones and increase sea level (Palanisamy et al 2015). Such zones are also characterized by deep thermoclines and low nutrient availability, which are unfavourable for phytoplankton production (Kahru et al 2010); a negative correlation between SSH and chlorophyll concentration can be observed (Turk et al 2001). In high latitudes, oceanic convergence zones also show an increase in SSH, but the conditions may still favour phytoplankton production due to enhanced upper ocean light availability and supply of nutrients from winter mixing.…”

Section: General Principlesmentioning

confidence: 99%

Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

et al. 2016

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We analyse the regional variability in observed sea surface height (SSH), sea surface temperature (SST) and ocean colour (OC) from the ESA Climate Change Initiative datasets over the period [1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011]. The analysis focuses on the signature of the ocean large-scale climate fluctuations driven by the atmospheric forcing and do not address the mesoscale variability. We use the ECCO version 4 ocean reanalysis to unravel the role of ocean transport and surface buoyancy fluxes in the observed SSH, SST and OC variability. We show that the SSH regional variability is dominated by the steric effect (except at high latitude) and is mainly shaped by ocean heat transport divergences with some contributions from the surface heat fluxes forcing that can be significant regionally (confirming earlier results). This is in contrast with the SST regional variability, which is the result of the compensation of surface heat fluxes by ocean heat transport in the mixed layer and arises from small departures around this background balance. Bringing together the results of SSH and SST analyses, we show that SSH and SST bear some common variability. This is because both SSH and SST variability show significant contributions from the surface heat fluxes forcing. It is evidenced by the high correlation between SST and buoyancy-forced SSH almost everywhere in the ocean except at high latitude. OC, which is determined by phytoplankton biomass, is governed by the availability of light and nutrients that essentially depend on climate fluctuations. For this reason, OC shows significant correlation with SST and SSH. We show that the correlation with SST displays the same pattern as the correlation with SSH with a negative correlation in the tropics and subtropics and a positive correlation at high latitude. We discuss the reasons for this pattern.

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Section: General Principlesmentioning

confidence: 99%

Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

et al. 2016

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“…During the 1997-1999 El Niño/La Niña transition period, phytoplankton biomass increased by 10 % globally (Behrenfeld et al 2001), and new production (dependent on new nitrogen) varied by more than a factor of two in the Equatorial Pacific (Turk et al 2001). Short-term variability (less than one decade) in chlorophyll concentration, primary production and phenology of phytoplankton populations have been shown to correlate with ENSO variability in the Equatorial regions and in the global oceans albeit with marked regional differences (Yoder and Kennelly 2003;Behrenfeld et al 2006;Vantrepotte and Mélin 2011;Chavez et al 2011;Messié and Chavez 2012;Racault et al 2012;Raitsos et al 2015).…”

Section: Enso Impact On Chlorophyll Primary Production and Phenologymentioning

confidence: 99%

Phenological Responses to ENSO in the Global Oceans

et al. 2016

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Phenology relates to the study of timing of periodic events in the life cycle of plants or animals as influenced by environmental conditions and climatic forcing. Phenological metrics provide information essential to quantify variations in the life cycle of these organisms. The metrics also allow us to estimate the speed at which living organisms respond to environmental changes. At the surface of the oceans, microscopic plant cells, so-called phytoplankton, grow and sometimes form blooms, with concentrations reaching up to 100 million cells per litre and extending over many square kilometres. These blooms can have a huge collective impact on ocean colour, because they contain chlorophyll and other auxiliary pigments, making them visible from space. Phytoplankton populations have a high turnover rate and can respond within hours to days to environmental perturbations. This makes them ideal indicators to study the first-level biological response to environmental changes. In the Earth's climate system, the El Niño-Southern Oscillation (ENSO) dominates large-scale inter-annual variations in environmental conditions. It serves as a natural experiment to study and understand how phytoplankton in the ocean (and hence the organisms at higher trophic levels) respond to climate variability. Here, the ENSO influence on phytoplankton is estimated through variations in chlorophyll concentration, primary production and timings of initiation, peak, termination and duration of the growing period. The phenological variabilities are used to characterise phytoplankton responses to changes in some physical variables: sea surface temperature, sea surface height and wind. It is reported that in oceanic regions experiencing high annual Surv Geophys (2017) 38:277-293 DOI 10.1007 variations in the solar cycle, such as in high latitudes, the influence of ENSO may be readily measured using annual mean anomalies of physical variables. In contrast, in oceanic regions where ENSO modulates a climate system characterised by a seasonal reversal of the wind forcing, such as the monsoon system in the Indian Ocean, phenologybased mean anomalies of physical variables help refine evaluation of the mechanisms driving the biological responses and provide a more comprehensive understanding of the integrated processes.

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“…This correlation has been used by Turk et al (2001), for instance, to infer new production in the equatorial Pacific. However, the dynamic center of the gyres does not coincide with the "biological" center of the gyres, i.e., the region of minimum surface chlorophyll-a (chl-a) concentration as derived from the SeaWiFS data set.…”

Section: Offset Between the Dynamic And Biological Gyre Centersmentioning

confidence: 99%