Photosynthetic rates derived from satellite-based chlorophyll concentration
نویسندگان
چکیده
We assembled a dataset of 14C-based productivity measurements to understand the critical variables required for accurate assessment of daily depth-integrated phytoplankton carbon fixation (PP,,) from measurements of sea surface pigment concentrations (C,,,). From this dataset, we developed a light-dependent, depth-resolved model for carbon fixation (VGPM) that partitions environmental factors affecting primary production into those that influence the relative vertical distribution of primary production (P,) and those that control the optimal assimilation efficiency of the productivity profile (P”,,,). The VGPM accounted for 79% of the observed variability in P, and 86% of the variability in PP,, by using measured values of PBop,. Our results indicate that the accuracy of productivity algorithms in estimating PP,, is dependent primarily upon the ability to accurately represent variability in PBopt. We developed a temperature-dependent PBopt model that was used in conjunction with monthly climatological images of C,,,, sea surface temperature, and cloud-corrected estimates of surface irradiance to calculate a global annual phytoplankton carbon fixation (PP,,,,) rate of 43.5 Pg C yr-‘. The geographical distribution of PP,,,, was distinctly different than results from previous models. Our results illustrate the importance of focusing Psopt model development on temporal and spatial, rather than the vertical, variability. Thousands of measurements of marine phytoplankton productivity have been made at discrete locations throughout the world’s oceans since the introduction of the radiolabelled carbon uptake method (i.e. 14C method) in 1952 (Steemann Nielsen 1952). Although numerous, these discrete primary productivity measurements only provide information for infinitesimally small points over the oceans’ surfaces. Scaling these discrete measurements to global projections by means of satellite-based estimates of chlorophyll concentration (C,,,) requires mathematical models that quantitatively relate primary productivity to chlorophyll (Bidigare et al. 1992). Acknowledgments We thank Monica Chen for global primary production calculations and images of productivity distributions, David Siegel for assistance with the BATS productivity data, Richard Barber for generously contributing the EQPAC data, William Behrens for assistance with the BNL database, and Jay O’Reilly for the MARMAP contribution to the dataset. We especially thank Andre Morel for the LPCM data, the source code for his bio-optical model, and numerous helpful discussions. We also thank Peter Minnett and Kevin Tut-pie for assistance with data retrieval and analysis and Creighton Wirick, Zbigniew Kolber, and two anonymous reviewers for helpful recommendations and discussions. This research was supported by the U.S. National Aeronautics and Space Administration grant UPN161-35-05-08 and by the U.S. Department of Energy contract DE-AC02-76CH00016. This manuscript is dedicated to William Winner for his inspiration and friendship. The simplest productivity models estimate timeand depth-integrated primary production as a function of sea surface chlorophyll (e.g. Smith et al. 1982; Eppley et al. 1985). The next step in algorithm complexity introduces surface irradiance as a second factor controlling productivity, where depth-integrated production is the product of depth-integrated chlorophyll, daily surface irradiance, and a constant, water-column averaged quantum yield (?@ for photosynthesis (Morel 1978; Falkowski 1981; Platt 1986; Morel 1991). Diverse empirical relationships also exist that relate depth-integrated production to Csat, euphotic depth, and a photoadaptive parameter (see Balch et al. 1992). A more mechanistic approach to productivity modeling has been attempted by use of complex bio-optical models (Platt and Sathyendranath 1988; Morel and Berthon 1989; Morel 1991; Platt et al. 199 1). Bio-optical models attempt to improve productivity estimates over the depth-integrated empirical relationships by including model variables that account for the spectrally dependent attenuation of photosynthetically active radiation (PAR, 400-700 nm) through the water column, as well as vertical and spatial variability in phytoplankton optical absorption cross sections (a*) and photosynthesis vs. irradiance (P vs. E) parameters (CU, PB,,,, p). An advantage of bio-optical models is that they account for the variable fraction of the euphotic zone that is lightsaturated. However, it is not clear how much of the added complexity in bio-optical models reflects the level of under2 Behren.eld and Falkowski standing about a particular variable rather than the importance of the exact representation of the variable on the predictive capacity of the model. At all levels of complexity, productivity models often perform well in predicting integrated productivity when comparisons are limited to the datasets from which they were derived. However, when these models are used to predict column productivity from different datasets, model performance is often dramatically reduced (Campbell and O’Reilly 1988; Balch et al. 1992). Clearly, some of the disagreement between modeled and measured production is due to methodological differences in 14C measurements and errors in the 14C data, but much of the discrepancy must also result from limitations of the models. To better understand the level of complexity and the critical factors required for making reliable estimates of daily integrated phytoplankton production based on C,,,, we investigated the variability observed in phytoplankton primary production by assembling a dataset of 11,283 14C-based measurements of daily carbon fixation collected at 1,698 oceanographic stations in both open ocean and coastal waters. The dataset includes measurements that have previously been used for testing productivity models, as well as previously unutilized data. By removing the influences of euphotic depth, photoperiod, and chlorophyll concentration, we discovered a consistent trend in the vertical distribution of primary production. We therefore developed an irradiance-dependent, depth-resolved productivity model that accounted for the observed vertical trends in normalized productivity. Parameters of the model were established with data from a single research program and model performance was tested with the full 14C dataset. We then simplified the model by removing the vertical resolution, thereby allowing rapid assessment of depthintegrated estimates of euphotic zone productivity. We identified a key parameter, Ptlopt, required for modeling phytoplankton primary production and developed a preliminary model for estimating PBopt. By knowing PBopt alone, our vertically generalized production model (VGPM) accounts for 86% of the observed variability in measured values of daily integral production. When Pnopl is estimated by means of the relationship developed from the dataset, the VGPM accounts for 58% of the observed variability. We applied the VGPM to monthly coastal zone color scanner C,,, images to estimate annual global primary production and to compare results using our PBopt model, in terms of the distribution and total primary production, to similar calculations from other primary production models. These results have implications for future development of satellite-based models of global oceanic primary production.
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