TITLE:

GLOBAL MONITORING OF PARTICULATE ORGANIC CARBON USING SATELLITE OCEAN COLOR OBSERVATIONS

DATE:

Friday, January 18th, 2008

TIME:

3:30 PM

LOCATION:

GMCS 214

SPEAKER:

Malgorzata Stramska, Modeling Ecosystems Dynamics Group, CHORS and CSRC, San Diego State University

ABSTRACT:

The development of bio-optical algorithms linking measurable optical properties to Chla has been the focus of numerous studies over the last three decades. These algorithms and the successful operation of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) on OrbView-2 satellite since 1997, allowed for the fact that the multi-year global time series data on ocean surface chlorophyll a concentrations (Chl) with excellent temporal coverage have become available. Although such satellite-based Chl estimates are subject to some uncertainty (nominal accuracy goal of 30%), they have become an invaluable research tool for studying phytoplankton biomass of ocean waters. However, for studies of ocean biogeochemistry and its potential role in climate it is carbon, not chlorophyll, which is of most direct interest. The particulate organic carbon (POC) pool in the surface ocean, which includes autotrophic and heterotrophic organisms and biogenic detrital particles, represents one of carbon reservoirs of substantial interest. Sinking of POC from surface waters is part of the biological pump that provides a mechanism for storing carbon in the deep ocean and a long-term sink for atmospheric CO2. Satellite capabilities to monitor changes in particulate carbon pools can effectively aid in studies related to global carbon cycling and the role of the biological pump in the ocean. This is why we have proposed to develop ocean color POC algorithms. In this presentation I will show a summary of our recent work where we have examined several approaches for estimating the surface concentration of particulate organic carbon, POC, from optical measurements of remote-sensing reflectance, Rrs(l). The best error statistics were found for power function fits to the data of POC vs. Rrs(443)/Rrs(555) and POC vs. Rrs(490)/Rrs(555). For the data set collected by us, which includes over 50 data pairs, these relationships are characterized by the mean normalized bias of about 2% and the normalized root mean square error of about 20%. We recommended that these algorithms be implemented for routine NASA processing of ocean color satellite data to produce global maps of surface ocean POC with the status of an evaluation data product for continued work on algorithm development and refinements. In this presentation I will also discuss our future plans to use satellite derived POC maps to study oceanic POC budgets and fluxes.

HOST:

Jose Castillo

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