PROCEEDINGS ARTICLE | February 6, 1997
Proc. SPIE. 2963, Ocean Optics XIII
KEYWORDS: Carbon, Data modeling, Sensors, Satellites, Remote sensing, Quantum efficiency, Lamps, Optical testing, Photosynthesis, Absorption
The North Pacific Central Gyre is one of the largest homogenous bodies of water on Earth. Phytoplankton distributions appear to remain relatively constant for thousands of kilometers throughout the year. However, recent studies conducted at Station ALOHA as part of the Hawaii Ocean Time-series (HOT) program reveal significant seasonal and interannual variability in phytoplankton biomass and production rates. Despite the high resolution sampling performed at the HOT site, spatial and temporal variations in phytoplankton pigment biomass are difficult to resolve. This will require remote sensing platforms such as moorings and satellites. In situ measurements of the photo- physiological parameters necessary to bio-optically model primary production rates are an essential element for the interpretation of data that will result from the HOT and MOBY moorings and the SeaWiFS and OCTS satellite sensors. We participated in a transect cruise in the North Pacific Ocean from Station ALOHA to the CLIMAX site to document the spatial variability of photo-physiological parameters and to determine if the conditions at the HOT site are representative of the central gyre and, in particular, are comparable to the CLIMAX site. We measured the light limited rate of photosynthesis ((alpha) ), the irradiance at which photosynthesis becomes light saturated, the maximum rate of photosynthesis (Pmax), the phytoplankton spectral absorption coefficient, and the maximum quantum yield of photosynthesis ((Phi) max). The photosynthetical parameters were similar at the HOT and CLIMAX locations, however a diatom bloom at intermediate stations resulted in a doubling of Pmax, (alpha) , and (Phi) max. If these variations in photosynthetic parameter estimates are not accounted for when modeling production rates for the diatom- dominated stations, then carbon uptake estimates would be underestimated by 2-fold. This study demonstrates the need for temporally dynamic algorithms that account for variations in phytoplankton composition and physiology.
