Satellite‐in situ blended ocean chlorophyll records indicate that global ocean annual primary production has declined more than 6% since the early 1980's. Nearly 70% of the global decadal decline occurred in the high latitudes. In the northern high latitudes, these reductions in primary production corresponded with increases in sea surface temperature and decreases in atmospheric iron deposition to the oceans. In the Antarctic, the reductions were accompanied by increased wind stress. Three of four low latitude basins exhibited decadal increases in annual primary production. These results indicate that ocean photosynthetic uptake of carbon may be changing as a result of climatic changes and suggest major implications for the global carbon cycle.

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[1] The global ocean chlorophyll archive produced by the CZCS was revised using compatible algorithms with SeaWiFS. Both archives were then blended with in situ data to reduce residual errors. This methodology permitted a quantitative comparison of decadal changes in global ocean chlorophyll from the CZCS (1979CZCS ( -1986 and SeaWiFS (1997SeaWiFS ( -2000 records. Global spatial distributions and seasonal variability of ocean chlorophyll were similar, but global means decreased over the two observational segments. Major changes were observed regionally: chlorophyll concentrations decreased in the northern high latitudes while chlorophyll in the low latitudes increased. Mid-ocean gyres exhibited limited changes. The overall spatial and seasonal similarity of the two data records suggests that the changes are due to natural variability. These results provide evidence of how the Earth's climate may be changing and how ocean biota respond.

Abstract. The historical archives of in situ (National Oceanographic Data Center) and satellite (Coastal Zone Color Scanner (CZCS)) chlorophyll data were combined using the blended analysis method of Reynolds [1988] in an attempt to construct an improved climatological seasonal representation of global chlorophyll distributions. The results of the blended analysis differed dramatically from the CZCS representation: Global chlorophyll estimates increased 8-35% in the blended analysis depending upon season. Regional differences were even larger, up to 140% in the equatorial Indian Ocean in summer (during the southwest monsoon). Tropical Pacific chlorophyll values increased 25-41%. The results suggested that the CZCS generally underestimates chlorophyll. Regional and seasonal differences in the blended analysis were sufficiently large as to produce a different representation of global chlorophyll distributions than otherwise inferred from CZCS data alone. Analyses of primary production and biogeochemical cycles may be substantially impacted by these results. IntroductionSatellite observations of ocean color provide large-scale, repeat coverage sampling of global ocean chlorophyll that are necessary to help understand the role of phytoplankton on biogeochemical cycling, climate change, and fisheries. However, remotely sensed data are subject to several sources of error that affect their accuracy, for example, calibration, atmospheric correction algorithm errors, uncertainties in knowledge of the atmospheric optical state, and problems deriving chlorophyll from radiances. Conventional in situ methods (e.g., ships and buoys) typically provide high-quality, accurate data but can only produce extremely limited spatial observations because of the expense of sea operations and the large areal extent of the ocean. Thus in situ data provide high-quality chlorophyll information that satellites cannot, and satellites provide horizontal and temporal observations that in situ methods cannot. A blending of data sources can maximize the strengths of each data set and produce a high-quality, spatially large data set of ocean chlorophyll.In this paper we combine in situ chlorophyll data from the extensive archive maintained by the National Oceanic and Atmospheric Administration National Oceanographic Data Center (NODC) with remotely sensed data from the Coastal Zone Color Scanner (CZCS) in an attempt to provide an enhanced set of seasonal climatologies. We utilize the conditional relaxation analysis method [Oort, 1983] that has been successfully applied to sea surface temperature (SST) data [Reynolds, 1988] vastly larger number of observations by satellites from overwhelming the in situ data and, at the same time, takes advantage of the spatial variability observed from the satellite.We limit the analysis to the CZCS era (1978-1986) because of the availability of large amounts of in situ data (---70,000 surface observations or 54% of the total archive) and satellite data. Global primary production models [Iverson et al., 2000; Beh...

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