Global seasonal climatologies of ocean chlorophyll: Blending in situ and satellite data for the Coastal Zone Color Scanner era

Abstract

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...

Integrating global chlorophyll data from 1890 to 2010

“…6); these include elevated chlorophyll in coastal, high-latitude, and upwelling regions, as well as low chlorophyll in the oligotrophic gyres. Although there are potential issues regarding the intercalibration of Chl CZCS (Antoine et al 2005;Gregg and Conkright 2001), the Chl C fields were positively correlated with concurrent (1° ¥ 1° ¥ 1 month bins) Chl CZCS (r = 0.76, P < 0.0001), and exhibited approximate linear scaling on a log-scale (intercept = -0.01, slope = 0.84, r 2 = 0.58; Fig. 6C; Table 2).…”

Sampling biases in MODIS and SeaWiFS ocean chlorophyll data

“…The method is used for bias correction in Optimal Interpolation Sea Surface Temperature (OISST) data (Reynolds, 1988;Reynolds and Smith, 1994), and has been used successfully for ocean color applications (Gregg and Conkright, 2001;2002;Conkright and Gregg, 2003). Assimilation is performed daily, using log-transformed remote sensing and model chlorophyll.…”

Towards a merged satellite and in situ fluorescence ocean chlorophyll product

“…Among the three main approaches that exist for measuring Chl a C (i.e. water sampling, ocean colour and induced fluorescence; see later), fluorescence is the only one that has not been included in global re-analysis, as, for example, open ocean climatologies of Chl a C (Gregg and Conkright, 2001). However, it represents the most important source of in situ data in terms of numbers of observations (i.e.…”

“…They have been limited to the merging of ocean colour satellite observations with water sample-derived estimations. A pure blending method (Gregg and Conkright, 2001) was developed to directly merge satellite and in situ data. A more indirect approach used satellite and in situ data to establish empirical relationships between the surface Chl a C and its vertical signature (Morel and Berthon, 1989;Uitz et al, 2006), in order to reconstruct a vertical profile for each available satellite pixel.…”