Identification and classification of vertical chlorophyll patterns in the Benguela upwelling system and Angola-Benguela front using an artificial neural network

Information on the vertical chlorophyll structure in the ocean is important for estimating integrated chlorophyll a and primary production from satellite. For this study, vertical chlorophyll profiles from the Benguela upwelling system and the Angola-Benguela front were collected in winter to identify characteristic profiles. A shifted Gaussian model was fitted to each profile to estimate four parameters that defined the shape of the curve: the background chlorophyll concentration (B 0), the height parameter of the peak (h), the width of the peak (σ) and the depth of the chlorophyll peak (zm ). A type of artificial neural network called a self-organizing map (SOM) was then used on these four parameters to identify characteristic profiles. The analysis identified a continuum of chlorophyll patterns, from those with large surface peaks (>10 mg m−3) to those with smaller near-surface peaks (<2 mg m−3). The frequency of occurrence of each chlorophyll pattern identified by the SOM showed that the most frequent pattern (∼12%) had a near-surface peak and the least frequent pattern (∼2%) had a large surface peak. These characteristic profile shapes were then related to pertinent environmental variables such as sea surface temperature, surface chlorophyll, mixed layer depth and euphotic depth. Partitioning the SOM output map into environmental categories showed large peaks of surface chlorophyll dominating in water with cool temperature, high surface chlorophyll concentration and shallow mixed layer and euphotic depth. By contrast, smaller peaks of subsurface chlorophyll were in water with warmer temperature, lower surface chlorophyll concentration, intermediate mixed layer and deep euphotic depth. These relationships can be used semi-quantitatively to predict profile shape under different environmental conditions. The SOM analysis highlighted the large variability in shape of vertical chlorophyll profiles in the Benguela. This suggests that an ideal typical chlorophyll profile, as used in the framework of biogeochemical provinces, may not be applicable to this dynamic upwelling system.

[1]  Ding-Geng Chen,et al.  A neural network model for forecasting fish stock recruitment , 1999 .

[2]  Satsuki Matsumura,et al.  Chlorophyll biomass off Sanriku, northwestern Pacific, estimated by Ocean Color and Temperature Scanner (OCTS) and a vertical distribution model , 1998 .

[3]  I. Aoki,et al.  Analysis and prediction of the fluctuation of sardine abundance using a neural network , 1997 .

[4]  Jorma Laaksonen,et al.  SOM_PAK: The Self-Organizing Map Program Package , 1996 .

[5]  Trevor Platt,et al.  Regionally and seasonally differentiated primary production in the North Atlantic , 1995 .

[6]  T. Platt,et al.  An estimate of global primary production in the ocean from satellite radiometer data , 1995 .

[7]  Thomas Brey,et al.  Exploring the use of neural networks for biomass forecasts in the Peruvian upwelling ecosystem , 1995 .

[8]  T. Platt,et al.  Basin-scale estimates of oceanic primary production by remote sensing - The North Atlantic , 1991 .

[9]  G. Pitcher Phytoplankton seed populations of the cape peninsula upwelling plume, with particular reference to resting spores of Chaetoceros (bacillariophyceae) and their role in seeding upwelling waters , 1990 .

[10]  T. Platt,et al.  Ocean primary production and available light: further algorithms for remote sensing , 1988 .

[11]  L. Hutchings,et al.  The development and decline of phytoplankton blooms in the southern Benguela upwelling system. 1. Drogue movements, hydrography and bloom development , 1987 .

[12]  Trevor Platt,et al.  Phytoplankton and thermal structure in the upper ocean: Consequences of nonuniformity in chlorophyll profile , 1983 .

[13]  John J. Cullen,et al.  The deep chlorophyll maximum comparing vertical profiles of chlorophyll a , 1982 .