Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends

A 16-year (1998-2013) analysis of trends and seasonal patterns was conducted for the five subtropical ocean gyres using satellite data: chlorophyll-a (Chl-a) retrievals from ocean color, sea surface temperature (SST), and sea-level anomaly (SLA). Trend analysis was also performed on mixed-layer data derived from ocean model gridded temperature and salinity profiles (1998-2010). The Chl-a monthly composites were constructed from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate-resolution Imaging Spectroradiometer (MODIS) on Aqua using two different algorithms: the standard algorithm (STD) that has been in use since the start of the SeaWiFS mission in 1997, and a more recently developed Ocean Color Index (OCI) algorithm with improved accuracy in low Chl-a waters. Trends were obtained for all gyres using both STD and OCI algorithms, which demonstrated generally consistent results. The North Pacific, Indian Ocean, North Atlantic and South Atlantic gyres showed significant downward trends in Chl-a, while the South Pacific gyre has a much weaker upward trend with no statistical significance. Time series of satellite-derived net primary production (NPP) showed downward trends for all the gyres, while all five gyres exhibited positive trends in SST and SLA. The seasonal variability of Chl-a in each gyre is tightly coupled to the variability in mixed layer depth (MLD) with peak values in winter in both hemispheres when vertical mixing is more vigorous, reaching depths approaching the nutricline. On a seasonal basis, Chl-a concentrations increase when the MLD approaches or is deeper than the nutricline depth, in agreement with the concept that vertical mixing is the major driving mechanism for phytoplankton photosynthesis in the interior of the gyres. The combination of surface warming trends and biomass reduction over the 16-year period has the potential to reduce atmospheric CO2 uptake by the gyres and therefore influence the global carbon cycle.

[1]  W. Gregg,et al.  Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models , 2014, Journal of geophysical research. Oceans.

[2]  Fabrizio D'Ortenzio,et al.  Understanding the seasonal dynamics of phytoplankton biomass and the deep chlorophyll maximum in oligotrophic environments: A Bio‐Argo float investigation , 2014 .

[3]  Bryan A. Franz,et al.  Corrections to the MODIS Aqua Calibration Derived From MODIS Aqua Ocean Color Products , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  C. McClain,et al.  Subtropical gyre variability as seen from satellites , 2012 .

[5]  Bryan A. Franz,et al.  Quality and Consistency of the NASA Ocean Color Data Record , 2012 .

[6]  Bryan A. Franz,et al.  Chlorophyll aalgorithms for oligotrophic oceans: A novel approach based on three‐band reflectance difference , 2012 .

[7]  Bryan A. Franz,et al.  Special Supplement to the Bulletin of the American Meteorological Society , 2012 .

[8]  André Morel,et al.  The most oligotrophic subtropical zones of the global ocean: similarities and differences in terms of chlorophyll and yellow substance , 2010 .

[9]  C. McClain,et al.  Effect of uncertainties in climatologic wind, ocean pCO2, and gas transfer algorithms on the estimate of global sea‐air CO2 flux , 2009 .

[10]  David A. Siegel,et al.  Carbon‐based primary productivity modeling with vertically resolved photoacclimation , 2008 .

[11]  Melanie Abecassis,et al.  Ocean's least productive waters are expanding , 2008 .

[12]  Thomas M. Smith,et al.  Daily High-Resolution-Blended Analyses for Sea Surface Temperature , 2007 .

[13]  D. Karl,et al.  On the relationships between primary, net community, and export production in subtropical gyres , 2006 .

[14]  P. J. Werdell,et al.  An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation , 2005 .

[15]  Charles R. McClain,et al.  Subtropical Gyre Variability Observed by Ocean Color Satellites , 2004 .

[16]  M. Behrenfeld,et al.  High variability of primary production in oligotrophic waters of the Atlantic Ocean : uncoupling from phytoplankton biomass and size structure , 2003 .

[17]  M. Mcphaden,et al.  Remotely Sensed Biological Production in the Equatorial Pacific , 2001, Science.

[18]  P. Holligan,et al.  Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments , 2001 .

[19]  Patrick M. Holligan,et al.  Basin-scale variability of phytoplankton biomass, production and growth in the Atlantic Ocean , 2000 .

[20]  James A. Carton,et al.  A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950–95. Part I: Methodology , 2000 .

[21]  M. Kahru,et al.  Ocean Color Chlorophyll Algorithms for SEAWIFS , 1998 .

[22]  Ricardo M Letelier,et al.  Seasonal variability in the phytoplankton community of the North Pacific Subtropical Gyre , 1995 .

[23]  R. Huang,et al.  Ventilation of the subtropical North Pacific , 1994 .

[24]  B. Qiu,et al.  Three-Dimensional Structure of the Wind-Driven Circulation in the Subtropical North Pacific , 1994 .

[25]  R. Bidigare,et al.  Temporal variability of phytoplankton community structure based on pigment analysis , 1993 .

[26]  C. McClain,et al.  An investigation of Ekman upwelling in the North Atlantic , 1993 .

[27]  J. Pedlosky The Dynamics of the Oceanic Subtropical Gyres , 1990, Science.

[28]  J. Marra,et al.  Primary production in the North Pacific gyre: a comparison of rates determined by the 14C, O2 concentration and 18O methods , 1989 .

[29]  J. Marra,et al.  Primary production in the North Pacific Central Gyre: some new measurements based on 14C , 1987 .

[30]  E. Laws,et al.  High phytoplankton growth and production rates in the North Pacific subtropical gyre1,2 , 1987 .