Bio-Optical Models and the Problems of Scaling

Bidigare, Robert R.; Prézelin, Barbara B.; Smith, Raymond C.

doi:10.1007/978-1-4899-0762-2_11

Cited by 72 publications

(45 citation statements)

References 132 publications

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“…Quantum yield is parameterized from a maximum value and a light dependent term (Waters et al, 1994;Bidigare et al, 1992). The chlorophyll profile is assumed vertically uniform.…”

Section: Strategy For Model Improvementmentioning

confidence: 99%

A comparison of global estimates of marine primary production from ocean color

Carr

Friedrichs

Schmeltz

et al. 2006

Deep Sea Research Part II: Topical Studies in Oceanography

636

477

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The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depthintegrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 C, and chlorophyll concentration exceeding 1 mg Chl m À3 . Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate. r

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“…Quantum yield is parameterized from a maximum value and a light dependent term (Waters et al, 1994;Bidigare et al, 1992). The chlorophyll profile is assumed vertically uniform.…”

Section: Strategy For Model Improvementmentioning

confidence: 99%

A comparison of global estimates of marine primary production from ocean color

Carr

Friedrichs

Schmeltz

et al. 2006

Deep Sea Research Part II: Topical Studies in Oceanography

636

477

View full text Add to dashboard Cite

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“…Bidigare et al, 1992], which has traditionally been measured using discrete water samples or estimated empirically [Bricaud et al, 1995;Cleveland, 1995]. Often is derived from the product of biomass and biomass-normalized phytoplankton absorption [Sakshaug et al, 1997].…”

Section: Biooptical Modeling Of Photosynthesis In Coastal Watersmentioning

confidence: 99%

“…Resolving the impact of primary production on any oceanic system is ultimately a question of scale [Bidigare et al, 1992], which has been recently addressed with comparisons of local, regional, and global productivity models in ocean observatories. Comparisons of modeled and measured primary production in these observatories showed mixed results.…”

Section: Introductionmentioning

confidence: 99%

“…This model was dependent on chlorophyll specific mean spectrally weighted absorption of phytoplankton (ā*ph), which explained 82% of the variance and was able to resolve small-timescale phytoplankton blooms. Productivity models that incorporate ā*ph performed well in many different waters ā*ph [Smith et al, 1989;Bidigare et al, 1992;Waters et al, 1994;Morel et al, 1996] because they describe the fraction of photosynthetically available radiation (PAR) that is absorbed, which is a function of phytoplankton abundance, distribution, community structure, and physiology.…”

Section: Introductionmentioning

confidence: 99%

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Deriving in situ phytoplankton absorption for bio‐optical productivity models in turbid waters

et al. 2004

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As part of Hyperspectral Coupled Ocean Dynamics Experiment, a high-resolution hydrographic and bio-optical data set was collected from two cabled profilers at the Long-Term Ecosystem Observatory (LEO). Upwelling-and downwelling-favorable winds and a buoyant plume from the Hudson River induced large changes in hydrographic and optical structure of the water column. An absorption inversion model estimated the relative abundance of phytoplankton, colored dissolved organic matter (CDOM) and detritus, as well as the spectral exponential slopes of CDOM and detritus from in situ WET Labs nine-wavelength absorption/attenuation meter (ac-9) absorption data. Derived optical weights were proportional to the parameter concentrations and allowed for their absorptions to be calculated. Spectrally weighted phytoplankton absorption was estimated using modeled spectral irradiances and the phytoplankton absorption spectra inverted from an ac-9. Derived mean spectral absorption of phytoplankton was used in a bio-optical model estimating photosynthetic rates. Measured radiocarbon uptake productivity rates extrapolated with water mass analysis and the bio-optical modeled results agreed within 20%. This approach is impacted by variability in the maximum quantum yield (Ф) and the irradiance light-saturation parameter (Ek (PAR)). An analysis of available data shows that Фmax variability is relatively constrained in temperate waters. The variability of Ek(PAR) is greater in temperate waters, but based on a sensitivity analysis, has an overall smaller impact on water-column-integrated productivity rates because of the exponential decay of light. This inversion approach illustrates the utility of bio-optical models in turbid coastal waters given the measurements of the bulk inherent optical properties.

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“…Several other reports and papers have reviewed specific ocean technologies and sampling issues (e.g. Bidigare et al 1992;Dickey, 2001Dickey, , 2002Dickey, , 2003Dickey, , 2004Glenn and Dickey, 2003;Bishop et al, 2001Bishop et al, , 2002Davis et al, 2001;Dickey and Chang, 2001;Eriksen et al, 2001;Jaffe et al, 2001;Chang et al, 2004), therefore emphasis here is placed more upon our personal views of some of the remaining oceanographic problems and the types of technologies that may be used for advanced ocean sampling technologies.…”

Section: Introductionmentioning

confidence: 99%

Interdisciplinary oceanographic observations: the wave of the future

Dickey¹,

Bidigare²

2005

Sci. Mar.

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SUMMARY: Oceanographic measurements, though difficult and expensive, are essential for effective study, stewardship, preservation, and management of our oceanic and atmospheric systems. Ocean sciences have been driven by technologies enabling new observations, discoveries, and modelling of diverse interdisciplinary phenomena. Despite rapid advances in ocean sampling capabilities, the numbers of disciplinary variables that are necessary to solve oceanographic problems are large and increasing. In addition, the time and space scales of key processes span over ten orders of magnitude; presently, there remain major spectral gaps in our sampling. Thus, undersampling presents the main limitation to our understanding of global climate change; variability in fish biomass and regime shifts; and episodic and extreme events. Fortunately, recent advances in ocean platforms and in situ autonomous sampling systems and satellite sensors are enabling unprecedented rates of data acquisition as well as the expansion of temporal and spatial coverage. Consequently, improved sampling strategies will lead to a reduction in ocean forecasting error for predictions of a multitude of atmospheric and oceanic processes. Nonetheless, major challenges remain to massively increase the variety and quantity of ocean measurements and to effectively coordinate, synthesize, and distribute oceanographic data sets. In particular, numbers of measurements are limited by the costs of instruments and their deployment as well as data processing and production of useful data products and visualizations. Looking forward, many novel and innovative technologies involving computing, nanotechnology, robotics, information and telemetry technologies, space sciences, and molecular biology are being developed at a fast pace for numerous applications (Kaku, 1997;Kurzweil, 1999). It is anticipated that several of these can and will be transitioned to the ocean sciences and will prove to be extremely beneficial for oceanographers in the next few decades. Already, autonomous, 'robotic' in situ sampling, high spectral resolution optical and chemical instrumentation, multi-frequency acoustics, and biomolecular techniques are being utilized by a limited number of oceanographers. Also, increased temporal and spatial sampling capabilities for expanding numbers of interdisciplinary variables are being accelerated thanks to both new technologies and utilization of data assimilation models coupled with autonomous sampling platforms. Data networks coupled with internet connectivity are rapidly increasing access to and utilization of data sets. In this essay, we review recent technological progress for solving some key oceanographic problems and highlight some of the foreseeable challenges and opportunities of ocean science technologies and their applications.Keywords: technology, instrumentation, platforms, modeling, interdisciplinary, observatories, observational systems. RESUMEN: OBSERVACIONES OCEANOGRÁFICAS INTERDISCIPLINARES: NUEVAS TENDENCIAS. -Las medidas de parámetros o...

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Bio-Optical Models and the Problems of Scaling

Cited by 72 publications

References 132 publications

A comparison of global estimates of marine primary production from ocean color

A comparison of global estimates of marine primary production from ocean color

Deriving in situ phytoplankton absorption for bio‐optical productivity models in turbid waters

Interdisciplinary oceanographic observations: the wave of the future

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