Contributing roughly half of the biosphere's net primary production (NPP), photosynthesis by oceanic phytoplankton is a vital link in the cycling of carbon between living and inorganic stocks. Each day, more than a hundred million tons of carbon in the form of CO2 are fixed into organic material by these ubiquitous, microscopic plants of the upper ocean, and each day a similar amount of organic carbon is transferred into marine ecosystems by sinking and grazing. The distribution of phytoplankton biomass and NPP is defined by the availability of light and nutrients (nitrogen, phosphate, iron). These growth-limiting factors are in turn regulated by physical processes of ocean circulation, mixed-layer dynamics, upwelling, atmospheric dust deposition, and the solar cycle. Satellite measurements of ocean colour provide a means of quantifying ocean productivity on a global scale and linking its variability to environmental factors. Here we describe global ocean NPP changes detected from space over the past decade. The period is dominated by an initial increase in NPP of 1,930 teragrams of carbon a year (Tg C yr(-1)), followed by a prolonged decrease averaging 190 Tg C yr(-1). These trends are driven by changes occurring in the expansive stratified low-latitude oceans and are tightly coupled to coincident climate variability. This link between the physical environment and ocean biology functions through changes in upper-ocean temperature and stratification, which influence the availability of nutrients for phytoplankton growth. The observed reductions in ocean productivity during the recent post-1999 warming period provide insight on how future climate change can alter marine food webs.

Abstract. On April 15 and 19, 1998, two intense dust storms were generated over the Gobi desert by springtime low-pressure systems descending from the northwest. The windblown dust was detected and its evolution followed by its yellow color on SeaWiFS satellite images, routine surface-based monitoring, and through serendipitous observations. The April 15 dust cloud was recirculating, and it was removed by a precipitating weather system over east Asia

During the 1997-98 El Nino, the equatorial Pacific Ocean retained 0. 7 x 10(15) grams of carbon that normally would have been lost to the atmosphere as carbon dioxide. The surface ocean became impoverished in plant nutrients, and chlorophyll concentrations were the lowest on record. A dramatic recovery occurred in mid-1998, the system became highly productive, analogous to coastal environments, and carbon dioxide flux out of the ocean was again high. The spatial extent of the phytoplankton bloom that followed recovery from El Nino was the largest ever observed for the equatorial Pacific. These chemical and ecological perturbations were linked to changes in the upwelling of nutrient-enriched waters. The description and explanation of these dynamic changes would not have been possible without an observing system that combines biological, chemical, and physical sensors on moorings with remote sensing of chlorophyll.

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Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Abstract. Phytoplankton photosynthesis links global ocean biology and climate-driven fluctuations in the physical environment. These interactions are largely expressed through changes in phytoplankton physiology, but physiological status has proven extremely challenging to characterize globally. Phytoplankton fluorescence does provide a rich source of physiological information long exploited in laboratory and field studies, and is now observed from space. Here we evaluate the physiological underpinnings of global variations in satellite-based phytoplankton chlorophyll fluorescence. The three dominant factors influencing fluorescence distributions are chlorophyll concentration, pigment packaging effects on light absorption, and light-dependent energy-quenching processes. After accounting for these three factors, resultant global distributions of quenching-corrected fluorescence quantum yields reveal a striking consistency with anticipated patterns of iron availability. High fluorescence quantum yields are typically found in low iron waters, while low quantum yields dominate regions where other environmental factors are most limiting to phytoplankton growth. Specific properties of photosynthetic membranes are discussed that provide a mechanistic view linking iron stress to satelliteCorrespondence to: M. Behrenfeld (mjb@science.oregonstate.edu) detected fluorescence. Our results present satellite-based fluorescence as a valuable tool for evaluating nutrient stress predictions in ocean ecosystem models and give the first synoptic observational evidence that iron plays an important role in seasonal phytoplankton dynamics of the Indian Ocean. Satellite fluorescence may also provide a path for monitoring climate-phytoplankton physiology interactions and improving descriptions of phytoplankton light use efficiencies in ocean productivity models.

In an Oceanography article published 13 years ago, three of us identified salinity measurement from satellites as the next ocean remote-sensing challenge. We argued that this represented the next "zeroth order" contribution to oceanography (Lagerloef et al., 1995) because salinity variations form part of the interaction between ocean circulation and the global water cycle, which in turn affects the ocean's capacity to store and transport heat and regulate Earth's climate. Now, we are pleased to report that a new satellite program scheduled for launch in the near future will provide data to reveal how the ocean responds to the combined effects of evaporation, precipitation, ice melt, and river runoff on seasonal and interannual time scales. These measurements can be used, for example, to close the marine hydrologic budget, constrain coupled climate models, monitor mode water formation, investigate the upper-ocean response to precipitation variability in the tropical convergence zones, and provide early detection of low-salinity intrusions in the subpolar Atlantic and Southern oceans. Sea-surface salinity (SSS) and sea-surface temperature (SST) determine sea-surface density, which controls the formation of water masses and regulates three-dimensional ocean circulation.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were u...