Satellite ocean-color observations of the tropical Pacific Ocean

McClain, Charles R.; Christian, James R.; Signorini, Sérgio R.; Lewis, Marlon R.; Asanuma, Ichio; Turk, Daniela; Dupouy, Cécile

doi:10.1016/s0967-0645(02)00047-4

Cited by 96 publications

(73 citation statements)

References 70 publications

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“…The temporal variations in the mixed layer DON and PON are smaller than most of the other biogeochemical parameters reported (varying by a factor of 3 to 10), including nutrients, chlorophyll, phytoplankton and zooplankton biomass, new production and export production (Chavez et al, 1999;McClain et al, 2002;Murtugudde et al, 1999;Wang et al, 2005aWang et al, , 2006a. (Fig.…”

Section: Interannual Variabilitymentioning

confidence: 82%

Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences

et al. 2008

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Abstract.To quote Libby and Wheeler (1997), "we have only a cursory knowledge of the distributions of dissolved and particulate organic nitrogen" in the equatorial Pacific. A decade later, we are still in need of spatial and temporal analyses of these organic nitrogen pools. To address this issue, we employ a basin scale physical-biogeochemical model to study the spatial and temporal variations of dissolved organic nitrogen (DON) and particulate organic nitrogen (PON). The model is able to reproduce many observed features of nitrate, ammonium, DON and PON in the central and eastern equatorial Pacific, including the asymmetries of nitrate and ammonium, and the meridional distributions of DON and PON. Modeled DON (5-8 mmol m −3 ) shows small zonal and meridional variations in the mixed layer whereas modeled PON (0.4-1.5 mmol m −3 ) shows considerable spatial variability. While there is a moderate seasonality in both DON and PON in the mixed layer, there is a much weaker interannual variability in DON than in PON. The interannual variability in PON is largely associated with the El Niño/Southern Oscillation (ENSO) phenomenon, showing high values during cold ENSO phase but low values during warm ENSO phase. Overall, DON and PON have significant positive correlations with phytoplankton and zooplankton in the mixed layer, indicting the biological regulation on distribution of organic nitrogen. However, the relationships with phytoplankton and zooplankton are much weaker for DON (r=0.18-0.71) than for PON (r=0.25-0.97). Such a difference is ascribed to a relatively larger degree of physical control (e.g., upwelling of low-organic-N deep waters into the surface) on DON than PON.

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Section: Interannual Variabilitymentioning

confidence: 82%

Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences

et al. 2008

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“…In 1992 and 1993, bio-optical sensors were added to the TAO mooring at 01, 1401W , and in 1996 bio-optical and chemical sensors were added to the moorings at 01, 1551W and 21S, 1701W (Chavez et al, 1999). Since the fall of 1996, the Ocean Color-Temperature Sensor (OCTS), the Polarization Detection Evironmental Radiometer (POLDER) and, the SeaViewing Wide Field-of-View Sensor (SeaWiFS) on earth orbiting satellites have provided a nearly continuous record of biological processes in surface waters in this region (McClain et al, 2002). These highly resolved data sets have allowed us to study the physical, chemical and biological processes over very large spatial scales.…”

Section: Equatorial Pacific Data Resourcesmentioning

confidence: 99%

“…The top-down control seems to be so strong that, to our knowledge, phytoplankton blooms have only been reported in the equatorial area on two occasions. The first occurred during the recovery of the equatorial upwelling after the strong 1997-1998 El Ni * no when the chlorophyll a at 01, 1401W increased by a factor of 3-4 in July-September 1998 McClain et al, 2002) and 10 at 21N, 1101W (Chavez et al, 1999). The rapid change from oligotrophic conditions to unusually high phytoplankton biomass were ascribed to the effects of macronutrient and iron inputs to the photic zone, associated with strong TIW activity (Chavez et al, 1999;Murtugudde et al, 1999).…”

Section: The Biological Pump In the Warm Pool And Hnlc Regionsmentioning

confidence: 99%

Carbon fluxes in the equatorial Pacific: a synthesis of the JGOFS programme

Borgne

Feely

Mackey

2002

Deep Sea Research Part II: Topical Studies in Oceanography

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“…Although there is considerable understanding of the photosynthetic process and knowledge of the ocean optics that determine ocean color signals, SatPPMs often have limited success reproducing the observed variability of PP data (Siegel et al, 2001;McClain et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…The size, temporal and spatial range, and consistent quality of this database help us meet the data requirements mentioned above for data-model intercomparisons. Although it is not possible to make conclusive statements concerning global model performance based on a single regional comparison, improving PP estimates in the tropical Pacific will increase the skill of global models because (1) this region represents a large fraction of the global ocean, and (2) this region represents one of the greatest current challenges to PP modelers (Campbell et al, 2002;McClain et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean

Friedrichs¹,

Carr²,

Barber³

et al. 2009

Journal of Marine Systems

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226

179

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Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color-based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BCGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of similar to 1000 C-14 measurements spanning more than a decade (1983-1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PR specifically yielding too few low PP (< 0.2 g Cm-2 d(-1)) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomassnormalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140 degrees W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison 6 years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill. (C) 2008 Elsevier BY. All rights reserved

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Satellite ocean-color observations of the tropical Pacific Ocean

Cited by 96 publications

References 70 publications

Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences

Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences

Carbon fluxes in the equatorial Pacific: a synthesis of the JGOFS programme

Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean

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