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The Arctic ice scape is composed by a mosaic of ridges, hummocks, melt ponds, leads, and snow. Under such heterogeneous surfaces, drifting phytoplankton communities are experiencing a wide range of irradiance conditions and intensities that cannot be sampled representatively using single-location measurements. Combining experimentally derived photosynthetic parameters with transmittance measurements acquired at spatial scales ranging from hundreds of meters (using a remotely operated vehicle, ROV) to thousands of meters (using a surface and underice trawl, SUIT), we assessed the sensitivity of water column primary production estimates to multiscale underice light measurements. Daily primary production calculated from transmittance from both the ROV and the SUIT ranged between 0.004 and 939 mgC.m(-2).day(-1). Upscaling these estimates at larger spatial scales using satellite-derived sea ice concentration reduced the variability by 22% (0.004-731 mgC.m(-2).day(-1)). The relative error in primary production estimates was two times lower when combining remote sensing and in situ data compared to ROV-based estimates alone. These results suggest that spatially extensive in situ measurements must be combined with large-footprint sea ice coverage sampling (e.g., remote sensing, aerial imagery) to accurately estimate primary production in ice-covered waters. Also, the results indicated a decreasing error of primary production estimates with increasing sample size and the spatial scale at which in situ measurements are performed. Conversely, existing estimates of spatially integrated phytoplankton primary production in ice-covered waters derived from single-location light measurements may be associated with large statistical errors. Considering these implications is important for modeling scenarios and interpretation of existing measurements in a changing Arctic ecosystem.

International audience; Comprehensively sampling the ocean in situ remains a challenge, even in the current era of rapid technological development. In less than a decade, the deployment of thousands of autonomous profiling floats increased the number of ocean temperature profiles by an order of magnitude compared to ship-based sampling in the past. But expendable floats cannot sample all the physical and biogeochemical regimes in the global ocean. A promising avenue that could guide in situ sampling is to partition oceans based on selected properties in order to identify "homogeneous" areas. This approach greatly reduces the number of measurements needed to represent the state of the ocean. However, homogeneous areas can be partitioned in many ways: depending on whether a single or several properties are considered; and on whether the definition of boundaries is left to expert knowledge or derived from objective analysis techniques. Here, we use a clustering method to map and partition many surface variables, and we further examine how this partitioning is affected by various ways of averaging or normalizing the input data. We performed this study using 15 different surface fields of physical and biological properties derived from satellite remote sensing observations and from global model outputs at a monthly resolution. The area of study is the Indian Ocean-one of the least-sampled oceans-which is the focus of a global research effort under the auspices of the second International Indian Ocean Expedition (IIOE-2). We show a strong effect of the average absolute values of the data, which can be removed to better examine the phenology of the properties. However, normalization is not mandatory; the technique selected should depend on the scientific questions at hand. Our clusters did generally did not match closely the regions identified by Longhurst in his seminal work on ocean provinces.

Hyperspectral (every 5 nm) absorption coefficients of ``pure'' seawater in the range of 350-550 nm are derived from remote sensing reflectance measured in oligotrophic oceans. The absorption spectrum is reduced by similar to 50-70% for the 350-400-nm range and similar to 5-10% for the 510-530-nm range compared with the commonly adopted standard for ocean color processing and shows different spectral curvatures. The application of this new spectrum resulted in better retrievals of the phytoplankton absorption coefficient in oligotrophic oceans and will provide better closure of remote sensing reflectance for the UV-visible domain. (C) 2015 Optical Society of America

Measured spectral absorption coefficients were inverted to infer phytoplankton concentration in three size classes (picoplankton, nanoplankton, and microplankton), chlorophyll concentration[Chl], and both magnitude and spectral shape of absorption by colored detrital matter (CDM). Our algorithm allowed us to solve for the nonlinear factor of CDM absorption slope separately from the other linear factors, thus fully utilizing the additive characteristic inherent in absorption coefficients. We validated the inversion with three datasets: two spatially distributed global datasets, the Laboratoire d'Oceanographie de Villefranche dataset and the NASA bio-Optical Marine Algorithm Dataset, and a time series coastal dataset, the Martha's Vineyard Coastal Observatory dataset. Comparison with high performance liquid chromatography analyses showed that the phytoplankton size classes can be retrieved with correlation coefficients (r)> 0.7, root mean square errors of 0.2, and median relative errors of 20% in oceanic waters and with similar performance in coastal waters. Much improved agreement was found for the entire phytoplankton population, with r > 0.90 for[Chl] and absorption coefficients (a(ph)) for all three datasets. The inferred a(CDM) (400) and CDM spectral slope agree within +/- 4% of measurements in both oceanic and coastal waters. The results indicate that the chlorophyll-a specific absorption spectra used as an inversion kernel represent well the global mean states for each of the three phytoplankton size classes. The method can be applied to either bulk or particulate absorption data and is spectrally flexible. (C) 2015 Optical Society of America

The changes in shape, amplitude, and timing in the diel variability of the particulate beam attenuation coefficient (c(p)) were investigated at 4 and 9 m during two seasonal cycles at an oceanic site in the northwestern Mediterranean Sea under contrasting physical and trophic situations. We observed a diel cycle in c(p) during the winter mixing of the water column, the development of the spring phytoplankton bloom, its collapse, and during the summer oligotrophy. The relative amplitude of the c(p) diel cycle was about 10-20% during winter mixing and summer oligotrophy and at least twice as large during the spring bloom. Diel c(p) minima generally occurred around sunrise and maxima a few hours before sunset. The specific particle rate of variation (r) was consistent over the diel cycle, with positive and negative values during daytime and nighttime, respectively. A striking feature of the r diel cycle was a morning maximum, i.e., before solar noon, which was successfully reproduced by a new model of particle assemblage growth rate based on three parameters: maximum growth rate, growth efficiency, and saturation irradiance. Each model parameter undergoes a diel cycle and shows a seasonal variation. A c(p)-based estimation of the particle net community production is computed from the measurements and model outputs. Results compare favorably with modeled primary production on the basis of continuous measurements of surface chlorophyll using fluorescence.

International audience; The light absorption properties of particulate and dissolved materials strongly influence the propagation of visible light in oceanic waters and therefore the accuracy of ocean color algorithms. While the general absorption properties of these materials have been reported for Arctic waters, their seasonal variability remains unknown. We investigated the light absorption coefficients of phytoplankton [a(phi)(lambda)], nonalgal particles [a(NAP)(lambda)], and colored dissolved organic matter [a(CDOM)(lambda)] in both coastal and oceanic waters of the western Arctic Ocean from spring to autumn. Values for the chlorophyll a-specific absorption coefficient of phytoplankton [a(phi)*(440)] declined significantly from the ice melt period in the early spring to the summer. Using high-performance liquid chromatography, we show that the decrease in a(phi)*(440) was due to a strong package effect that overwhelmed the influence of the pigment composition. A decrease in the a(NAP)(lambda) values from spring and summer to autumn likely originated from a decrease in the concentration of phytoplanktonic detritus. The a(CDOM)(lambda) near the surface decreased by 34% from spring to summer as a result of photobleaching by solar radiation. The colored dissolved organic matter (CDOM) absorption values then increased significantly during autumn, resulting from the cumulative injection of Alaskan Coastal Waters into the Arctic as well as CDOM generated in situ. Our results suggest that all of the absorption components are tightly linked to biogeochemical processes, and thus the seasonal variability in a 8 (lambda), a(NAP)(lambda), and a(CDOM)(lambda) should be taken into account in bio-optical models.

The very clear waters of the South Pacific Gyre likely constitute an end-member of oligotrophic conditions which remain essentially unknown with respect to its impact on carbon fixation and exportation. We describe a non-intrusive bio-optical method to quantify the various terms of a production budget (Gross community production, community losses, net community production) in this area. This method is based on the analysis of the diel cycle in Particulate Organic Carbon (POC), derived from high frequency measurements of the particle attenuation coefficient cp. We report very high integrated rates of Gross Community Production within the euphotic layer (average of 846±484 mg C m−2 d−1 for 17 stations) that are far above any rates determined using incubation techniques for such areas. Furthermore we show that the daily production of POC is essentially balanced by the losses so that the system cannot be considered as net heterotrophic. Our results thus agree well with geochemical methods, but not with incubation studies based on oxygen methods. We stress to the important role of deep layers, below the euphotic layer, in contributing to carbon fixation when incident irradiance at the ocean surface is high (absence of cloud coverage). These deep layers, not considered up to know, might fuel part of the heterotrophic processes in the upper layer, including through dissolved organic carbon. We further demonstrate that, in these extremely clear and stratified waters, integrated gross community production is proportional to the POC content and surface irradiance via an efficiency index ψ GCP*, the water column cross section for Gross Community Production. We finally discuss our results in the context of the role of oligotrophic gyre in the global carbon budget and of the possibility of using optical proxies from space for the development of growth community rather than primary production global models.

International audience; We analyzed a large dataset of simultaneous measurements of phytoplankton pigments, spectral specific absorption coefficient for phytoplankton [a*(lambda)], and photosynthesis versus irradiance (P versus E) curve parameters to examine the possible relationships between phytoplankton community structure and photophysiological properties at large spatial scales. Data were collected in various regions, mostly covering the trophic gradient encountered in the world's open ocean. The community composition is described in terms of biomass of three phytoplankton classes, determined using specific biomarker pigments. We present a general empirical model that describes the dependence of algal photophysiology on both the community composition and the relative irradiance within the water column (essentially reflecting photoacclimation). The application of the model to the in situ dataset enables the identification of vertical profiles of photophysiological properties for each phytoplankton class. The class-specific a*(lambda) obtained are consistent with results from the literature and with previous models developed for small and large cells, both in terms of the absolute values and the vertical patterns. Similarly, for the class-specific P versus E curve parameters, the magnitude and vertical distribution obtained with this method are coherent with previous observations. Large cells (mainly diatoms) may be more efficient in carbon storage than smaller cells, whereas their yield of light absorption is lower. We anticipate that such photophysiological parameterizations can improve primary production models by providing estimates of primary production that are specific to different phytoplankton classes on large scale.

International audience; Empirical algorithms based on first order relationships between ocean color and the chlorophyll concentration ([ChI]; mg m(-3)) are widely used, but cannot explain the statistical dispersion or ``anomalies'' around the mean trends. We use an empirical approach that removes the first order effects of [ChI] from satellite ocean color, thus allowing us to quantify the impact on the ocean color signal of optical anomalies that vary independently of the global mean trends with remotely sensed [ChI]. We then present statistical and modeling analyses to interpret the observed anomalies in terms of their optical sources (i.e. absorption and backscattering coefficients). We identify two main sources of second order variability for a given [ChI]: 1) the amount of non-algal absorption, especially due to colored dissolved organic matter; and 2) the amplitude of the backscattering coefficient of particles. The global distribution of the anomalies displays significant regional and seasonal trends, providing important information for characterizing the marine optical environment and for inferring biogeochemical influences. We subsequently use our empirically determined anomalies to estimate the backscattering coefficient of particles and the combined absorption coefficient for colored detrital and dissolved materials. This purely empirical approach provides an independent assessment of second order optical variability for comparison with existing methods that are generally based on semi-analytical models. (C) 2008 Elsevier Inc. All rights reserved.

Prochlorococcus, Synechococcus, picophytoeukaryotes and bacterioplankton abundances and contributions to the total particulate organic carbon concentration, derived from the total particle beam attenuation coefficient (cp), were determined across the eastern South Pacific between the Marquesas Islands and the coast of Chile. All flow cytometrically derived abundances decreased towards the hyper-oligotrophic centre of the gyre and were highest at the coast, except for Prochlorococcus, which was not detected under eutrophic conditions. Temperature and nutrient availability appeared important in modulating picophytoplankton abundance, according to the prevailing trophic conditions. Although the non-vegetal particles tended to dominate the cp signal everywhere along the transect (50 to 83%), this dominance seemed to weaken from oligo- to eutrophic conditions, the contributions by vegetal and non-vegetal particles being about equal under mature upwelling conditions. Spatial variability in the vegetal compartment was more important than the non-vegetal one in shaping the water column particle beam attenuation coefficient. Spatial variability in picophytoplankton biomass could be traced by changes in both total chlorophyll a (i.e. mono + divinyl chlorophyll a) concentration and cp. Finally, picophytoeukaryotes contributed ~38% on average to the total integrated phytoplankton carbon biomass or vegetal attenuation signal along the transect, as determined by size measurements (i.e. equivalent spherical diameter) on cells sorted by flow cytometry and optical theory. Although there are some uncertainties associated with these estimates, the new approach used in this work further supports the idea that picophytoeukaryotes play a dominant role in carbon cycling in the upper open ocean, even under hyper-oligotrophic conditions.