Validation of SeaWiFS chlorophyll a concentrations in the Southern Ocean: A revisit

Marrari, Marina; Hu, Chuanmin; Daly, Kendra L.

doi:10.1016/j.rse.2006.07.008

Cited by 108 publications

(63 citation statements)

References 40 publications

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“…Taking into consideration the lognormal distribution of chlorophyll data (Campbell, 1995), all error estimates and corrections were made to the log-transformed (base 10) data. The root mean square error (RMS) and bias were calculated at each pixel according to Gregg and Casey (2004) and Marrari et al (2006):…”

Section: Methodsmentioning

confidence: 99%

Variability and 20-Year Trends in Satellite-Derived Surface Chlorophyll Concentrations in Large Marine Ecosystems around South and Western Central America

2017

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Section: Methodsmentioning

confidence: 99%

Variability and 20-Year Trends in Satellite-Derived Surface Chlorophyll Concentrations in Large Marine Ecosystems around South and Western Central America

2017

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“…The global improvement in model-observation bias produced by application of a single scaling factor of ∼6, would translate to a significant global-scale bias in satellite chlorophyll-a concentrations or MLD climatology values. Based on past evaluations of these products (de Boyer et al, 2004;Marrari et al, 2006), a systematic bias of such magnitude seems unlikely. The assumption that isoprene is well-mixed through the MLD is an approximation.…”

Section: Evaluation Of Marine Isoprene Emissionsmentioning

confidence: 99%

Evaluation of the global oceanic isoprene source and its impacts on marine organic carbon aerosol

et al. 2009

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Abstract. We have combined the first satellite maps of the global distribution of phytoplankton functional type and new measurements of phytoplankton-specific isoprene productivities, with available remote marine isoprene observations and a global model, to evaluate our understanding of the marine isoprene source and its impacts on organic aerosol abundances. Using satellite products to scale up data on phytoplankton-specific isoprene productivity to the global oceans, we infer a mean "bottom-up" oceanic isoprene emission of 0.31±0.08 (1σ )Tg/yr. By minimising the mean bias between the model and isoprene observations in the marine atmosphere remote from the continents, we produce a "topdown" oceanic isoprene source estimate of 1.9 Tg/yr. We suggest our reliance on limited atmospheric isoprene data, difficulties in simulating in-situ isoprene production rates in laboratory phytoplankton cultures, and limited knowledge of isoprene production mechanisms across the broad range of phytoplankton communities in the oceans under different environmental conditions as contributors to this difference between the two estimates. Inclusion of secondary organic aerosol (SOA) production from oceanic isoprene in the model with a 2% yield produces small contributions (0.01-1.4%) to observed organic carbon (OC) aerosol mass at three remote marine sites in the Northern and Southern Hemispheres. Based on these findings we suggest an insignificant role for isoprene in modulating remote marine aerosol abundances, giving further support to a recently postulated primary OC source in the remote marine atmosphere.

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“…The straight lines represent several different bio-optical models (4,37,38). methods, however, will yield different results depending on the composition of pigments within the phytoplankton, and errors can be on the order of 50% (24)(25)(26). The presence of significant amounts of chlorophyll b, characteristic of chlorophytes, prochlorophytes, and cryptophytes, causes fluorometric techniques to underestimate Chl.…”

Section: Data Uncertaintiesmentioning

confidence: 99%

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

Dierssen

2010

Proc. Natl. Acad. Sci. U.S.A.

168

107

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Phytoplankton biomass and productivity have been continuously monitored from ocean color satellites for over a decade. Yet, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more. Such variability is due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals. The empirical algorithms have built-in assumptions that follow the basic precept of biological oceanography -namely, oligotrophic regions with low phytoplankton biomass are populated with small phytoplankton, whereas more productive regions contain larger bloom-forming phytoplankton. With a changing world ocean, phytoplankton composition may shift in response to altered environmental forcing, and CDOM and mineral concentrations may become uncoupled from phytoplankton stocks, creating further uncertainty and error in the empirical approaches. Hence, caution is warranted when using empirically derived Chl to infer climaterelated changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellitederived Chl for different phytoplankton assemblages. Accurate global assessments of phytoplankton will require improved technology and modeling, enhanced field observations, and ongoing validation of our "eyes in space." biological oceanography | phytoplankton | primary productivity | satellite oceanography | ocean optics

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Validation of SeaWiFS chlorophyll a concentrations in the Southern Ocean: A revisit

Cited by 108 publications

References 40 publications

Variability and 20-Year Trends in Satellite-Derived Surface Chlorophyll Concentrations in Large Marine Ecosystems around South and Western Central America

Variability and 20-Year Trends in Satellite-Derived Surface Chlorophyll Concentrations in Large Marine Ecosystems around South and Western Central America

Evaluation of the global oceanic isoprene source and its impacts on marine organic carbon aerosol

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

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