A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi: 10.1594/PANGAEA.854832 (Valente et al., 2015).

[This corrects the article DOI: 10.1371/journal.pone.0199287.].

The calving of A-68, the 5,800 km2 iceberg that separated from the Larsen C Ice Shelf in July 2017, presents a unique and time-sensitive research opportunity. This event and other ice-shelf losses (e.g., from Larsen A and B, Wilkins, Wordie) are harbingers of warming effects along the Antarctic Peninsula in particular, and ultimately around all of Antarctica. The scientific momentum and public interest created by this most recent event led to an NSF funded workshop in November 2017. A consortium of US and international researchers with a diversity of expertise in Antarctic glaciological, geological, biological, and ecological sciences shared and reviewed the latest research, identified important research priorities and knowlegde gaps, and outlined strategic plans for the research community to advance understanding of the continent-wide changes that Antarctic ice shelves and surrounding ecosystems will experience in response to warming. The workshop has leveraged an opportunity to synergize efforts in investigating Antarctic ecosystems under the direct and indirect effects of ice-shelf collapse, and climatic warming in general. Here we present a review on the known and unknown ecosystem processes that will occur in the wake of massive, abrupt ice-shelf break-off and how they will affect ice-associated, pelagic, hard-bottom and soft-sediment substrates. We also present a view on future research approaches to address gaps in our knowledge and advance our understanding of the widespread effects of ice-shelf break-off and collapse.

The Arctic is warming at approximately twice the global rate in response to anthropogenic climate change, resulting in disappearing sea ice, increased open water area, and a longer growing season (IPCC, 2013). This loss of sea ice has resulted in a 30% increase in annual net primary production (NPP) by Arctic Ocean phytoplankton between 1998 and 2012 (Arrigo and van Dijken, 2015). To quantify NPP, many algorithms require input of chlorophyll a (Chl a) concentration, which serves as a biomass proxy for phytoplankton. While satellites provide temporally and spatially extensive data, including Chl a, the standard global ocean color algorithms are prone to errors in Arctic Ocean waters due to higher than average phytoplankton pigment-packaging and chromophoric dissolved organic matter (CDOM) concentrations. Here, we evaluate retrievals of Chl a using existing ocean color algorithms, test and develop a new empirical ocean color algorithm for use in the Chukchi Sea, and evaluate the effect of using different satellite Chl a products as input to an NPP algorithm. Our results show that in the Chukchi Sea, Chl a was overestimated by the global algorithm (MODIS OC3Mv6) at concentrations lower than 0.9mgm−3 because of contamination by CDOM absorption, but underestimated at higher concentrations because of pigment packaging. Only within the in situ Chl a range of 0.6–2mgam−3 was the satellite retrieval error by the OC3Mv6 algorithm below the ocean color community goal of <35%. Using coincident in situ Chl a concentrations and optical data, a new linear empirical algorithm is developed (OC3L) that yields the lowest statistical error when estimating Chl a in the Chukchi Sea, compared to existing ocean color algorithms (OC3Mv6, OC4L, OC4P). When we estimated regional NPP using different Chl a satellite products as input, three distinct bio-optical provinces within the Arctic Ocean emerged. These provinces correspond to the inflow shelves, interior shelves, and outflow shelves+deep basin as defined by Carmack et al. (2006). Eleven sub-regions within the Arctic Ocean were grouped into each of these three provinces based on their mean value for R, the ratio of blue to green remote sensing reflectance (RRS). Our results suggest that three algorithms tuned to each of the three bio-optical provinces may be sufficient to capture the bio-optical heterogeneity within the Arctic Ocean. Currently, only within the inflow shelf province do we feel confident that Chl a and NPP can be accurately estimated by satellite using the OC3L algorithm. The interior and outflow shelf+basin provinces require development of ocean color algorithms specific to their respective bio-optical conditions.

The observed seasonal cycles in atmospheric potential oxygen (APO) at a range of mid- to high-latitude surface monitoring sites are compared to those inferred from the output of six Earth system models (ESMs) participating in the fifth phase of the Coupled Model Intercomparison Project phase 5 (CMIP5). The simulated air–sea O2 fluxes are translated into APO seasonal cycles using a matrix method that takes into account atmospheric transport model (ATM) uncertainty among 13 different ATMs. Three of the ocean biogeochemistry models tested are able to reproduce the observed APO cycles at most sites, to within the large TransCom3-era ATM uncertainty used here, while the other three generally are not. Net primary production (NPP) and net community production (NCP), as estimated from satellite ocean color data, provide additional constraints, albeit more with respect to the seasonal phasing of ocean model productivity than overall magnitude. The present analysis suggests that, of the tested ocean biogeochemistry models, the community ecosystem model (CESM) and the Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M are best able to capture the observed APO seasonal cycle at both northern and southern hemispheric sites. In most models, discrepancies with observed APO can be attributed to the underestimation of NPP, deep ventilation or both in the northern oceans.

Retrievals of ocean surface chlorophyll-a concentration (Chla) by multiple ocean color satellite sensors (SeaWiFS, MODIS-Terra, MODIS-Aqua, MERIS, VIIRS) using standard algorithms were evaluated in the California Current using a large archive of in situ measurements. Over the full range of in situ Chla, all sensors produced a coefficient of determination (R2) between 0.79 and 0.88 and a median absolute percent error (MdAPE) between 21% and 27%. However, at in situ Chla > 1 mg m−3, only products from MERIS (both the ESA produced algal_1 and NASA produced chlor_a) maintained reasonable accuracy (R2 from 0.74 to 0.52 and MdAPE from 23% to 31%, respectively), while the other sensors had R2 below 0.5 and MdAPE higher than 36%. We show that the low accuracy at medium and high Chla is caused by the poor retrieval of remote sensing reflectance.

Coral reefs are increasingly affected by high-temperature stress events and associated bleaching. Monitoring and predicting these events have largely utilized sea surface temperature data, due to the convenience of using large-scale remotely sensed satellite measurements. However, coral bleaching has been observed to vary in severity throughout the water column, and variations in coral thermal stress across depths have not yet been well investigated. In this study, in situ water temperature data from 1999 to 2011 from three depths were used to calculate thermal stress on a coral reef in Bahia Almirante, Bocas del Toro, Panama, which was compared to satellite surface temperature data and thermal stress calculations for the same area and time period from the National Oceanic and Atmospheric Administration Coral Reef Watch Satellite Bleaching Alert system. The results show similar total cumulative annual thermal stress for both the surface and depth-stratified data, but with a striking difference in the distribution of that stress among the depth strata during different high-temperature events, with the greatest thermal stress unusually recorded at the deepest measured depth during the most severe bleaching event in 2005. Temperature records indicate that a strong density-driven temperature inversion may have formed in this location in that year, contributing to the persistence and intensity of bleaching disturbance at depth. These results indicate that depth may not provide a stress refuge from high water temperature events in some situations, and in this case, the water properties at depth appear to have contributed to greater coral bleaching at depth compared to near-surface locations. This case study demonstrates the importance of incorporating depth-stratified temperature monitoring and small-scale oceanographic and hydrologic data for understanding and predicting local reef responses to elevated water temperature events.

Introduction Conclusions References

In addition to scattering coefficients, the light absorption coefficients of particulate and dissolved materials are the main factors determining the light propagation of the visible part of the spectrum and are, thus, important for developing ocean color algorithms. While these absorption properties have recently been documented by a few studies for the Arctic Ocean (e.g., Matsuoka et al., 2007, 2011; Ben Mustapha et al., 2012), the data sets used in the literature were sparse and individually insufficient to draw a general view of the basin-wide spatial and temporal variations in absorption. To achieve such a task, we built a large absorption database of the Arctic Ocean by pooling the majority of published data sets and merging new data sets. Our results show that the total nonwater absorption coefficients measured in the eastern Arctic Ocean (EAO; Siberian side) are significantly higher than in the western Arctic Ocean (WAO; North American side). This higher absorption is explained by higher concentration of colored dissolved organic matter (CDOM) in watersheds on the Siberian side, which contains a large amount of dissolved organic carbon (DOC) compared to waters off North America. In contrast, the relationship between the phytoplankton absorption (a&varphi;(λ)) and chlorophyll a (chl a) concentration in the EAO was not significantly different from that in the WAO. Because our semianalytical CDOM absorption algorithm is based on chl a-specific a&varphi;(λ) values (Matsuoka et al., 2013), this result indirectly suggests that CDOM absorption can be appropriately derived not only for the WAO but also for the EAO using ocean color data. Based on statistics, derived CDOM absorption values were reasonable compared to in situ measurements. By combining this algorithm with empirical DOC versus CDOM relationships, a semianalytical algorithm for estimating DOC concentrations for river-influenced coastal waters of the Arctic Ocean is presented and applied to satellite ocean color data.

1 Evaluating the ocean biogeochemical components of earth system models 1 using atmospheric potential oxygen (APO) and ocean color data 2 3 C.D. Nevison, M. Manizza, R.F. Keeling, M. Kahru, L. Bopp, J. Dunne, J. Tiputra, T. 4 Ilyina, B.G. Mitchell 5 1 University of Colorado, Boulder, Institute for Arctic and Alpine Research, Boulder, Colorado 6 2 Scripps Institution of Oceanography, La Jolla, California. 7 3 IPSL/LSCE, UMR8212, CNRS-CEA-UVSQ, Gif sur Yvette, France 8 4 National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, 9 Princeton, New Jersey 10 5 Uni Climate, Uni Research and Bjerknes Centre for Climate Research, Bergen, Norway. 11 6 Max Planck Institute for Meteorology, Hamburg, Germany. 12

A global pigment database consisting of 35 634 pigment suites measured by high performance liquid chromatography was assembled in support of the MARine Ecosytem DATa (MAREDAT) initiative. These data originate from 136 field surveys within the global ocean, were solicited from investigators and databases, compiled, and then quality controlled. Nearly one quarter of the data originates from the Laboratoire d'Océanographie de Villefranche (LOV), with an additional 17% and 19% stemming from the US JGOFS and LTER programs, respectively. The MAREDAT pigment database provides high quality measurements of the major taxonomic pigments including chlorophylls a and b, 19'-butanoyloxyfucoxanthin, 19'-hexanoyloxyfucoxanthin, alloxanthin, divinyl chlorophyll a, fucoxanthin, lutein, peridinin, prasinoxanthin, violaxanthin and zeaxanthin, which may be used in varying combinations to estimate phytoplankton community composition. Quality control measures consisted of flagging samples that had a total chlorophyll a concentration of zero, had fewer than four reported accessory pigments, or exceeded two standard deviations of the log-linear regression of total chlorophyll a with total accessory pigment concentrations. We anticipate the MAREDAT pigment database to be of use in the marine ecology, remote sensing and ecological modeling communities, where it will support model validation and advance our global perspective on marine biodiversity. The original dataset together with quality control flags as well as the gridded MAREDAT pigment data may be downloaded from PANGAEA: http://doi.pangaea.de/10.1594/PANGAEA.793246.

The seasonal cycle of atmospheric potential oxygen (APO ∼ O2 + 1.1 CO2) reflects three seasonally varying ocean processes: 1) thermal in‐ and outgassing, 2) mixed layer net community production (NCP) and 3) deep water ventilation. Previous studies have isolated the net biological seasonal signal (i.e., the sum of NCP and ventilation), after using air‐sea heat flux data to estimate the thermal signal. In this study, we resolve all three components of the APO seasonal cycle using a methodology in which the ventilation signal is estimated based on atmospheric N2O data, the thermal signal is estimated based on heat flux or atmospheric Ar/N2 data, and the production signal is inferred as a residual. The isolation of the NCP signal in APO allows for direct comparison to estimates of NCP based on satellite ocean color data, after translating the latter into an atmospheric signal using an atmospheric transport model. When applied to ocean color data using algorithms specially adapted to the Southern Ocean and APO data at three southern monitoring sites, these two independent methods converge on a similar phase and amplitude of the seasonal NCP signal in APO and yield an estimate of annual mean NCP south of 50°S of 0.8–1.2 Pg C/yr, with corresponding annual mean NPP of ∼3 Pg C/yr and a mean growing season fratio of ∼0.33. These results are supported by ocean biogeochemistry model simulations, in which air‐sea O2 and N2O fluxes are resolved into component thermal, ventilation and (for O2) NCP contributions.

Time series of satellite‐derived surface chlorophyll‐a concentration (Chl) in 1997–2009 were used to examine for trends in the timing of the annual phytoplankton bloom maximum. Significant trends towards earlier phytoplankton blooms were detected in about 11% of the area of the Arctic Ocean with valid Chl data, e.g. in the Hudson Bay, Foxe Basin, Baffin Sea, off the coasts of Greenland, in the Kara Sea and around Novaya Zemlya. These areas roughly coincide with areas where ice concentration has decreased in early summer (June), thus making the earlier blooms possible. In the selected areas, the annual phytoplankton bloom maximum has advanced by up to 50 days which may have consequences for the Arctic food chain and carbon cycling. Outside the Arctic, the annual Chl maximum has become earlier in boreal North Pacific but later in the North Atlantic.

Oceans represent a significant natural source of gases and particles to the atmosphere. Relative to gas phase compounds, less is known regarding the influence of changes in biological activity in the ocean on the chemistry of sea spray aerosols produced in marine environments. To gain insight into the influence of ocean biology and chemistry on atmospheric aerosol chemistry, simultaneous real-time measurements were made of atmospheric aerosol size and chemical mixing-state, gas phase dimethyl sulfide (DMS), as well as seawater DMS and chlorophyll a. In three different marine environments with elevated chlorophyll a and DMS, unique Mg particles were detected containing Mg2+, Ca2+, K+, and organic carbon. These particles were segregated from sea salt particles highlighting that two subpopulations within the sea spray were being ejected from the ocean. Strong temporal correlations were observed between these unique ocean-derived particles and freshly emitted sea salt particles (R-2 = 0.86), particularly as wind speed increased to at least 10 m/s, and atmospheric DMS (R-2 = 0.76). Time series correlations between ocean measurements and atmospheric aerosol chemistry suggest that chlorophyll a and DMS serve as indicators of changes in the chemistry of the ocean, most likely an increase in organic material, which is directly reflected in the single particle mixing-state. This is the first time such real-time correlations are shown between ocean chemistry and atmospheric aerosol mixing-state. The reasons behind these observed changes in aerosol chemistry are critical for understanding the heterogeneous reactivity, water uptake, and cloud forming potential of sea spray aerosols.