Spatial scales in CZCS‐chlorophyll imagery of the southeastern U.S. continental shelf1

Abstract

A 1-yr time series of Coastal Zone Color Scanner (CZCS) imagery of continental shelf waters off the southeastern U.S. was statistically analyzed to determine dominant scale lengths in the range from 2 to 100 km for near-surface chlorophyll (Chl) variability. For each image, we extracted two-dimensional matrices of CZCS-Chl concentrations from each of 10 study areas. Variograms (structure functions) were calculated for both the along-and across-shelf dimension of each data matrix, although we designed the study areas to focus on along-shelf variability.The variograms were used to determine whether the images contained nonrandom structure and, if so, to estimate its scale lengths. Outer shelf CZCS-Chl distributions lacked apparent spatial structure in about 50% of the data matrices vs. < 10% for the inner shelf matrices. In general, scale length increased with distance offshore and did not change with season. For example, the mean scale length (n = 30 images) for along-shelf CZCS-Chl variability on the inner shelf off Georgia was 32 km vs. 84 km (n = 18 images) for the outer shelf. WC propose that Gulf Stream frontal eddies are the principal determinants of outer shelf scale length and that topographic effects on the position of a coastal salinity front determine scale length on the inner shelf off Georgia and South Carolina.

Physical and biogeochemical spatial scales of variability in the East Australian Current separation from shelf glider measurements

“…At the interface between oceanic and coastal processes, continental shelf regions are characterized by complex dynamics resulting from the interaction between different water masses at smaller spatial scales than the open ocean (Yoder et al, 1987). While wind, topography, or density-driven processes mostly influence the mixing and advection of the physical characteristics (temperature and salinity) of the shelf water masses, locally acting ecological processes are also determinant for biogeochemistry (Ballantyne et al, 2011).…”

A simple optical index shows spatial and temporal heterogeneity in phytoplankton community composition during the 2008 North Atlantic Bloom Experiment

“…Phytoplankton biomass is assessed through several different optical proxies, including in situ chlorophyll a fluorescence (Chl F ; Lorenzen, 1966), the phytoplankton absorption coefficient (a phy (λ)) or particulate absorption coefficient in waters dominated by phytoplankton (Bricaud et al, 1995;Roesler and Barnard, 2014), and chlorophyll derived from in situ or remotely sensed ocean reflectance at visible wavelengths (O'Reilly et al, 1998). High-frequency optical measurements are ideal for detecting temporal change and spatial patchiness and for improving our understanding of the role of meso-and submesoscale physics in the distribution of phytoplankton in the ocean (Denman and Platt, 1976;Yoder et al,…”

Sub-kilometer length scales in coastal waters

“…Previous work has shown that physical and biological spatial and temporal variabilities increase with increasing proximity to shore Chang et al, 2002;Lovejoy et al, 2001;Yoder et al, 1987). This phenomenon has been referred to often in Stommel diagrams (Stommel, 1963;Lovejoy et al, 2001;Dickey, 1991) and on broad scales, has been accepted as common knowledge.…”

“…Considerable attention has been given to determine scales of biological and physical oceanographic processes and to identify the drivers of plankton patchiness. These questions have been addressed using a variety of different methods, including spectral analysis (Denman and Platt, 1976;Losee et al, 1989;Lovejoy et al, 2001;Washburn et al, 1998;Wiebe et al, 1996), autocorrelation or autocovariance functions (Chang et al, 2002;Mackas, 1984;Yu et al, 2002) correlograms or variograms (Dustan and Pinckney, 1989;Mackas, 1984;Yoder et al, 1987), wavelet analysis (Deutschman et al, 1993;Machu et al, 1999;Charria et al, 2003), and multifractal analysis (Seuront et al, 1996(Seuront et al, , 1999. Better understanding of the scales of variability can be used to help identify the physical and biological processes structuring biomass distribution and community structure, and help distinguish which processes are responsible at different scales (Chang et al, 2002;Cunningham et al, 2003;Dustan and Pinckney, 1989;Seliger et al, 1981).…”