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Salinity from Space Unlocks Satellite-Based Assessment of Ocean Acidification
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† Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, U.K.
‡ University of Exeter, Penryn Campus, Cornwall TR10 9FE, U.K.
§ Institut Francais Recherche Pour ĹExploitation de la Mer, Pointe du Diable, 29280 Plouzané France
∥ Telespazio-Vega U.K. for European Space Agency (ESA), ESTEC, Noordwijk, The Netherlands
⊥ Ocean Processes Analysis Laboratory, University of New Hampshire, Durham, New Hampshire 3824, United States
# Norwegian Institute for Water Research, Thormøhlensgate 53 D, N-5006 Bergen, Norway
∇ Department of Biological Sciences, Indian Institute of Science Education and Research-Kolkata, Mohanpur 741 246, West Bengal India
*E-mail: [email protected]
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2015, 49, 4, 1987–1994
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https://doi.org/10.1021/es504849s
Published January 8, 2015

Copyright © 2015 American Chemical Society. This publication is available under these Terms of Use.

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Abstract

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Approximately a quarter of the carbon dioxide (CO2) that we emit into the atmosphere is absorbed by the ocean. This oceanic uptake of CO2 leads to a change in marine carbonate chemistry resulting in a decrease of seawater pH and carbonate ion concentration, a process commonly called “Ocean Acidification”. Salinity data are key for assessing the marine carbonate system, and new space-based salinity measurements will enable the development of novel space-based ocean acidification assessment. Recent studies have highlighted the need to develop new in situ technology for monitoring ocean acidification, but the potential capabilities of space-based measurements remain largely untapped. Routine measurements from space can provide quasi-synoptic, reproducible data for investigating processes on global scales; they may also be the most efficient way to monitor the ocean surface. As the carbon cycle is dominantly controlled by the balance between the biological and solubility carbon pumps, innovative methods to exploit existing satellite sea surface temperature and ocean color, and new satellite sea surface salinity measurements, are needed and will enable frequent assessment of ocean acidification parameters over large spatial scales.

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Approximately a quarter of the carbon dioxide (CO2) that we emit into the atmosphere is absorbed by the ocean. This oceanic uptake of CO2 leads to a change in marine carbonate chemistry resulting in a decrease of seawater pH and carbonate ion concentration, a process commonly called “Ocean Acidification”. Salinity data are key for assessing the marine carbonate system, and new space-based salinity measurements will enable the development of novel space-based ocean acidification assessment. Recent studies have highlighted the need to develop new in situ technology for monitoring ocean acidification, but the potential capabilities of space-based measurements remain largely untapped. Routine measurements from space can provide quasi-synoptic, reproducible data for investigating processes on global scales; they may also be the most efficient way to monitor the ocean surface. As the carbon cycle is dominantly controlled by the balance between the biological and solubility carbon pumps, innovative methods to exploit existing satellite sea surface temperature and ocean color, and new satellite sea surface salinity measurements, are needed and will enable frequent assessment of ocean acidification parameters over large spatial scales.

1 Introduction

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Each year global emissions of carbon dioxide (CO2) into our atmosphere continue to rise. These increasing atmospheric concentrations cause a net influx of CO2 into the oceans. Of the roughly 36 billion metric tons of CO2 that is emitted into our atmosphere each year, approximately a quarter transfers into the oceans. (1) This CO2 addition has caused a shift in the seawater–carbonate system, termed ocean acidification (OA), resulting in a 26% increase in acidity and a 16% decrease in carbonate ion concentration since the industrial revolution. (2) Recently there has been recognition that this acidification is not occurring uniformly across the global oceans, with some regions acidifying faster than others. (3, 4) However, the overall cause of OA remains consistent: the addition of CO2 into the oceans, and as such, it remains a global issue. Continual emissions of CO2 into the atmosphere over the next century will decrease average surface ocean pH to levels which will be deleterious to many marine ecosystems and the services they provide. (5)
While the seawater–carbonate system is relatively complex, two parameters have been suggested as pertinent to the monitoring and assessment of OA through time and space. These are pH (the measure of acidity) and calcium carbonate (CaCO3) mineral saturation state, with aragonite generally considered to be an important CaCO3 mineral to be monitored because of its relevance to marine organisms (e.g., corals) and its relative solubility. Thermodynamically, CaCO3 is stable when the saturation state (an index of the concentrations of calcium and carbonate ions) is greater than one and becomes unstable when seawater becomes undersaturated with these ions (saturation <1). While there is significant variability between types of organism, there is ample experimental evidence that many calcifying organisms are sensitive to OA, (6) and that thresholds exist below which some organisms become stressed and their well-being and existence becomes threatened. (7) Increasingly evidence suggests that the physiology and behavior of calcifying and noncalcifying organisms can be impacted by increasing OA, (8) with cascading effects on the food chain and protein supply for humans, (3) and alterations to the functioning of ecosystems and feedbacks to our climate. (9)
In 2012 the Global Ocean Acidification Observing Network (GOA-ON, www.goa-on.org) was formed in an attempt to bring together expertise, data sets and resources to improve OA monitoring. At present, OA monitoring efforts are dominated by in situ observations from moorings, ships and associated platforms. While key to any monitoring campaign, in situ data tend to be spatially sparse, especially in inhospitable regions, and so on their own are unlikely to provide a comprehensive, robust and cost-effective solution to global OA monitoring. The need to monitor and study large areas of the Earth has driven the development of satellite-based sensors.
Increasingly, as in situ data accumulate, attempts are being made to use in situ hydrographic data (10-13) and/or remotely sensed data (14, 15) to provide proxies and indicators for the condition of the carbonate system, enabling data gaps to be filled in both space and time. The increased availability of in situ data creates a substantial data set to develop and test the capabilities of satellite-derived products, and we suggest that the recent availability of satellite-based salinity measurements provides new key insights for studying and assessing OA from space.

2 The Complexities of the Carbonate System

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The oceanic carbonate system can be understood and probed through four key parameters: total alkalinity (TA), dissolved inorganic carbon (DIC), pH, and fugacity of CO2 (fCO2). The latter may be replaced with the related partial pressure of CO2, pCO2, from which fCO2 can be calculated, and the two are often used interchangeably. In principle, knowledge of any two of these four is sufficient to solve the carbonate system equations. However, overdetermination, the process of measuring at least three parameters, is advantageous.
The relationships between the different carbonate system parameters are fundamentally driven by thermodynamics, hence influenced by temperature and pressure, and knowing these is fundamental for calculating the carbonate system as a whole. (16) Water temperature is the major controller of the solubility of CO2, (17) so seasonal changes in sea temperature can, depending on the region, be significant for driving changes in fCO2 (and consequently DIC and pH). Salinity affects the coefficients of the carbonate system equations. Hence to solve the equations, it is necessary to estimate temperature, salinity and pressure along with carbonate parameters.
The ratio between ions (the constituents of salinity) will tend to remain constant anywhere in the global oceans, resulting in a strong relationship between TA and salinity. (18) Unfortunately, a universal relationship between TA and salinity does not apply in certain regions, for instance in areas influenced by freshwater outflows from rivers, (7) or areas where calcification and/or CaCO3 dissolution occurs, such as where calcifying plankton are prevalent. (19) In these regions, it is therefore critical to gain additional local knowledge. For example, different rivers will have different ionic concentrations (and therefore different TA concentrations) depending on the surrounding geology and hydrology.
For DIC, fCO2 (or pCO2), and pH, the other important process is biological activity. (19) Removal or addition of CO2 by plankton photosynthesis or respiration can be a significant component of the seasonal signal. (20) Biological activity, in turn, is driven by factors such as nutrient dynamics and light conditions, which again are regionally specific. Measurements of chlorophyll (a proxy for biomass) and/or oxygen concentration can be useful for interpreting the biological component of the carbon signal.
The combination of these processes means that it is extremely challenging to produce a global relationship between any component of the carbonate system and its drivers. To enable us to understand these dynamics, extrapolation from collected data points to the global ocean is needed, and along with model predictions, empirical relationships and data sets are important and need to be studied and developed. OA needs to be assessed using these relationships on a global scale, but regional complexities, particularly where riverine and coastal processes dominate, (21, 22) cause significant challenges for global empirical relationships.

3 Current in Situ Approaches and Challenges

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Laboratory measurements are the gold standard for assessing the carbonate system in seawater, with accuracy far in excess of that achievable from satellites. (23-25) However, research vessel time is expensive and limited in coverage, so autonomous in situ instruments are also deployed, for example, on buoys, with less accuracy. (26) A notable example is the Argo network of over 3000 drifters, which measure temperature and salinity throughout the deep global ocean. Interpolation of Argo data is much less challenging than for most in situ measurements. Argo is the closest in situ data have come to the global, synoptic measurements possible with satellites, but shallow or enclosed seas are not represented (there are as yet no Argo instruments in the open Arctic Ocean). Table 1 lists more examples. Of the four key parameters, only fCO2 (or pCO2) and pH are routinely monitored in situ. As yet there are limited capabilities to measure DIC and TA autonomously, hence these parameters must be measured either in a ship-based laboratory or on land.
Table 1. In Situ Datasets and Programs than Can Be Used for the Development and Validation of OA Remote Sensing Algorithms
data set name and referencetemporal periodgeographic locationvariablesno. of data points
SOCAT v2.0 (27)1968–2011global*fCO2, SSS, SST6 000 000+
LDEO v2012 (28)1980-presentglobal*pCO2, SSS, SST6 000 000+
GLODAP (29)1970–2000globalTA, DIC, SSS, SST, Nitrate10 000+
CARINA AMS v1.2 (30)1980–2006ArcticTA, DIC, SSS, SST1500+
CARINA ATL v1.0 (31)Atlantic
CARINA SO v1.1 (32)Southern Ocean
AMT (33)1995-presentAtlanticpCO2W, SSS, SST, Chl, pH1000+
NIVA Ferrybox (34)2008-presentArcticpCO2W, TA, DIC, SSS, SST1000+
OWS Mike (35)1948–2009ArcticTA, DIC, SSS, SST, Chl1000+
RAMA Moored buoy array (36)2007-presentBay of BengalSSS, SST1000+
ARGO buoys (37)2003-presentglobalSSS, SST1 000 000+
OOI (38)2014 onwardglobal (six sites)pCO2, SSS, SST, nitratenew program
SOCCOM (39)2014 onwardSouthern OceanSSS, SST, pH, nitratenew program

4 Potential of Space Based Observations

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4.1 Advantages and Disadvantages

While it has proven difficult to use remote sensing to directly monitor and detect changes in seawater pH and their impact on marine organisms, (22) satellites can measure sea surface temperature and salinity (SST and SSS) and surface chlorophyll-a, from which carbonate system parameters can be estimated using empirical relationships derived from in situ data. Although surface measurements may not be representative of important biological processes, for example, fish or shellfish, observations at the surface are particularly important for OA because the change in carbonate chemistry due to atmospheric CO2 occurs in the surface first. Thus, satellites have great potential as a tool for assessing changes in carbonate chemistry.
SST has been measured from space with infrared radiometry since the 1960s, but the data are only globally of sufficient quality for climate studies since 1991. (40) Satellite measurements of chlorophyll-a in the visible are more recent, starting in 1986 and delivering high quality global data since 1997. (41) Both measurements are made globally at high spatial and temporal resolution, but with data gaps due to effects such as cloud, which can greatly affect data availability in cloudy regions. SST is measured in the top few microns, and chlorophyll-a is generally measured to depths around 1–100 m, depending on water clarity. Data quality can be affected by many issues, for example, adjacent land or ice may affect both SST and chlorophyll-a retrievals, and suspended sediment may affect chlorophyll-a retrievals.
Only since 2009 has a satellite-based capability for measuring SSS existed. Increasing salinity decreases the emissivity of seawater and so changes the microwave radiation emitted at the water surface. ESA Soil Moisture and Ocean Salinity (SMOS) and NASA-CONAE Aquarius (launched in 2009 and 2011 respectively, both currently in operation), are L-band microwave sensors designed to detect variations in microwave radiation and thus estimate ocean salinity in the top centimeter. The instruments are novel and the measurement is very challenging, and research is ongoing to improve data quality. (42) The instruments can measure every few days at a spatial resolution of 35–100 km, but single measurements are very noisy, so the instantaneous swath data are generally spatially and temporally averaged over 10 days or a month, with an intended accuracy around 0.1–0.2 g/kg for monthly 200 km data. A particular issue close to urban areas is radio frequency interference from illegal broadcasts, which are gradually being eliminated but still result in large data gaps, particularly for SMOS. The signal can be affected by nearby land or sea ice, and the sensitivity to SSS decreases for cold water, by about 50% from 20 to 0 °C. (43)
With these challenges, a central question is whether satellite SSS can bring new complementary information to in situ SSS measurements such as Argo for assessing OA. Direct comparisons (44, 45) indicate differences of 0.15–0.5 g/kg in a 1° × 1° region over 10–30 days. The two are difficult to compare directly, however, as Argo measures 5 m or more from the surface, so some differences are expected even in the absence of errors, especially where the water column is stratified. A better strategy might be to compare their effectiveness in estimating OA. How the uncertainties propagate through the carbonate system calculations is the subject of ongoing research.
Despite biases and uncertainties, satellite measurements of SSS in the top centimeter contain geophysical information not detected by Argo. (46, 47) In addition, Argo coverage can be much poorer than satellite SSS in several regions such as the major western boundary or equatorial currents and across strong oceanic fronts. The use of interpolated Argo products presents an additional source of uncertainty due to the interpolation scheme. (48) Satellite SSS can also resolve mesoscale spatial structures not resolved by Argo measurements, (49) and unlike Argo, satellites provide a synoptic “snapshot” of a region at a given time.
Regular mapping of the SSS field with unprecedented temporal and spatial resolution at global scale is now possible from satellites. The impact of using satellite SSS for carbonate system algorithms can now be tested, where previously there was a reliance on climatology, in situ or model data. For example, this provides the means to study the impact that freshwater influences (sea ice melt, riverine inputs and rain) can have on the marine carbonate system. The use of satellite SSS data will also allow evaluation of the impact on the carbonate system of the inter- and intra-annual variations in SSS.
Recent advances in radar altimetry (e.g., Cryosat-2 and Sentinel 1 satellites and sensors) are already enabling significant improvements in satellite sea-ice thickness measurements. (50) Thin sea ice thickness can now also be determined from SMOS, complementing altimeter estimates mostly valid for thick sea ice. (51) Sea ice thickness is important for OA research as it indicates whether ice is seasonal or multiyear, supporting the interpretation of carbonate parameters. Altimetry is also used to measure wind speeds and increases the coverage of scatterometer estimates in polar regions. It provides higher-resolution (along track) estimates of surface wind stress, which can potentially be used to indicate regions of upwelling. Wind-driven upwelling causes dense cooler water (with higher concentrations of CO2 and thus more acidic) to be drawn up from depth to the ocean surface. This upwelling can have significant impacts on local OA and ecosystems, (4, 52) especially at eastern oceanic boundaries. (53, 54)
It is important to emphasize that the use of Earth observation data to derive carbonate parameters should not be seen as a replacement for in situ measurement campaigns, especially due to the current reliance on empirical and regional algorithms. Earth observation algorithms need calibration and validation with in situ data such as those taken by GOA-ON, and if the carbonate system response changes over time, empirical and regional algorithms tuned to previous conditions may become less reliable.

4.2 Algorithms for Estimating Carbonate Parameters

The four key OA parameters (pCO2, DIC, TA, pH) are largely driven by temperature, salinity and biological activity, allowing empirical relationships to be developed using in situ measurements of OA parameters. Table 2 shows a range of published algorithms based on such relationships, while Figure 1 shows their geographical coverage. Both illustrate that most of the literature has focused on the northern basins of the Pacific and Atlantic and the Arctic, especially the Barents Sea, with all other regions only attracting algorithms for a single parameter or none at all.. (55)
Table 2. Example Regional Algorithms for Each Carbonate Parameter Illustrating the Variable Dependencies. Chl is Chlorophyll-a and MLD is Mixed Layer Depth
parameterdependenciesregion and references
pCO2SSTglobal, (56) Barents Sea (57)
SST, SSSBarents Sea, (58) Caribbean (14)
SST, ChlNorth Pacific (59)
SSS, ChlNorth Sea (60)
SST, SSS, ChlNorth Pacific (61)
SST, Chl, MLDBarents Sea (62)
 
TASSSBarents Sea (57)
SST, SSSglobal, (18, 63) Arctic (15)
SSS, nitrateGlobal (55)
 
DICSST, SSSEquatorial pacific (64)
SST, SSS, ChlArctic (15)
 
pHSST, ChlNorth Pacific (10)

Figure 1

Figure 1. Number of key carbonate parameters (fCO2 or pCO2, TA, DIC, pH) for which regional algorithms exist in the literature that can be implemented using just satellite Earth observation data. Regions are indicative of open ocean areas, as implementation of algorithms in coastal areas may be problematic.

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NOAA’s experimental Ocean Acidification Product Suite (OAPS) is a regional example of using empirical algorithms with a combination of climatological SSS and satellite SST to provide synoptic estimates of sea surface carbonate chemistry in the Greater Caribbean Region. (14)pCO2 and TA were derived from climatological SSS and satellite SST, then used to calculate monthly estimates of the remaining carbonate parameters, including aragonite saturation state and carbonate ion concentration. In general the derived data were in good agreement with in situ measured data (e.g., mean derived TA = 2375 ± 36 μmol kg–1 compared to a mean ship-measured TA = 2366 ± 77 μmol kg–1). OAPS works well in areas where chlorophyll-a is low, however in regions of high chlorophyll-a, where net productivity is likely to perturb the carbonate system, and in areas where there are river inputs, the approach tends to underestimate aragonite saturation state, for example. (21)
A quite different approach is the assimilation of satellite data into ocean circulation models. (65) The model output carbonate parameters can then be used directly. This allows satellite-observed effects to be extended below the water surface, albeit with the uncertainties inherent in model data. Here we seek to assess the direct use of satellite data through empirical algorithms to improve OA estimates.

4.3 Regions of Interest for Earth Observation

Arctic Seas

It is increasingly recognized that the Polar Oceans (Arctic and Antarctic) are particularly sensitive to OA. (66) Lower alkalinity (and thus buffer capacity), enhanced warming, reduced sea-ice cover resulting in changes in the freshwater budget, (67) and nutrient limitation make it more vulnerable to future OA. (68) Retreating ice also provides increased open water for air-sea gas exchange and primary production. (69)
The remote nature of the Arctic Ocean provides difficulties for collecting in situ data sets, with limited ship, autonomous vehicle and buoy access, and in situ data collection during winter months is often impossible. Therefore, the use of remote sensing techniques is very attractive, if sufficient in situ data can be found to calibrate satellite algorithms, and if the challenges of Arctic remote sensing can be overcome. These waters are very challenging regions for satellite remote sensing. For instance, low water temperatures reduce the sensitivity range of SSS sensors, (43) and sea ice can complicate retrievals of SSS and chlorophyll-a. (70, 71) Improvement in the accuracy of high latitude satellite SSS is expected soon by combining observations from SMOS, Aquarius and the upcoming SMAP sensor, all polar-orbiting L-band radiometers.

The Bay of Bengal

This region is clearly a focus of current OA research with unique characteristics due to the large freshwater influence. The flow of fresh water from the Ganges Delta into Bay of Bengal (42 000 m3/sec) represents the second greatest discharge source in the world. Additionally, rainfall along with freshwater inputs exceeds evaporation, resulting in net water gain annually in the Bay of Bengal. Collectively these provide an annual positive water balance that reduces surface salinity by 3–7 g/kg compared to the adjacent Arabian Sea, (72, 73) resulting in distinctly different biogeochemical regimes. (74) Biogeochemically, the Indian Ocean is one of the least studied and most poorly understood ocean basins in the world. (74) This is particularly true for the Bay of Bengal where a relatively small number of hydrographic sections and underway surface observations have been undertaken, despite the notable influence of freshwater on particle dynamics, air-sea carbon flux and surface carbonate chemistry. (75-79) North of 15° S, TA increases relative to salinity, (80) indicating the presence of an important land source that can broadly affect acidification dynamics.
To date there is little work on acidification dynamics and air sea exchange of CO2 in the Bay of Bengal. (81-83) In 2013, the Bay of Bengal Ocean Acidification (BOBOA) Mooring was deployed for the first time in Bay of Bengal (15°N, 90°E) by PMEL (NOAA) and the Bay of Bengal Large Marine Ecosystem Program (BOBLME). Data from the buoy will improve our understanding of biogeochemical variations in the open ocean environment of the Bay of Bengal.
It is an open question whether SSS can be used to estimate TA in the Bay of Bengal. An important step toward answering this question would be to investigate the spatial variability of the TA to salinity relationship in the region. Use of satellite SSS in the region is also challenged by heavy radio frequency interference.

The Greater Caribbean and the Amazon plume

The reefs in the Greater Caribbean Region are economically important to the US and Caribbean nations with an estimated annual net value of US$3.1–4.6 billion in 2000. (84) At least two-thirds of these reefs are threatened from human impacts including OA. The skeleton of a coral is made of aragonite and the growth of their skeletons is reduced by OA, (6) and numerous studies have shown a net decline in coral calcification (growth) rates in accordance with declining CaCO3 saturation state. (85) The waters of the Greater Caribbean region are predominantly oligotrophic and similar to the subtropical gyre from which it receives most of its water. (14) While the often shallow water environments of coral reefs and the plethora of small islands can make it challenging for Earth observation instruments to collect reliable data, the oligotrophic nature and the similarities in water type across the whole region make it ideal for the development of novel products. This region therefore provides an ideal case study to develop and evaluate algorithms representative of a shallow, oligotrophic environment.
The Amazon plume, south of the Greater Caribbean, is the largest freshwater discharge source in the world (209 000 m3/sec). It can cause SSS decreases of several units many hundreds of kilometers from land, and has an area that seasonally can reach 106 km2. These characteristics make it an ideal case study for testing and evaluating remote sensing algorithms, particularly to study the space-time resolution trade-offs using SSS sensors.

5 Future Opportunities and Focus

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The Copernicus program is a European flagship initiative, worth more than €7 billion, which aims to provide an operational satellite monitoring capability and related services for the environment and security. (86) The launch of the Sentinel-1A satellite in 2014 signaled its start. Of the five Sentinel satellite types, Sentinels 2 and 3 are most appropriate for assessment of the marine carbonate system. (87-89) These satellites will provide chlorophyll-a and SST with unprecedented spatial and temporal coverage. The development of higher spatial resolution geostationary sensors that continually monitor chlorophyll-a and SST over the same area of the Earth also holds much potential for the future of OA assessment and research. (90) These satellites and sensors are able to provide 10 or more observations per day, allowing the study of the effect of tidal and diurnal cycles on OA. The societal importance of measuring and observing the global carbon cycle was further highlighted with the launch of the NASA Orbiting Carbon Observatory (OCO-2) in 2014. This satellite and its sensors are designed to observe atmospheric CO2 concentrations, but its potential for marine carbon cycle and OA is likely to be a focus of future research.
SMOS and Aquarius have recently passed their nominal lifetimes, with SMOS now extended until 2017. Based on the lifetimes of previous satellite Earth observation sensors, they may well operate until the early 2020s. NASA’s SMAP satellite, to be launched in January 2015, should provide short-term continuity. The development of the technology and the clear importance of monitoring ocean salinity are likely to support the development of future satellite sensors. Also, historical time series data from alternative microwave sensors hold the potential for a 10+ year time series of satellite based SSS observations, (91) and this sort of measurement record is likely to extend into the future as it forms the basis of a global SSS monitoring effort.
In summary, satellite products developed up to now in the OA context have been regional, empirical or derived with a limited variety of satellite data sets, rendering an effort to systematically exploit remote sensing assets (capitalizing on the recent advent of satellite salinity measurements) absolutely timely. To-date there is only regional application of satellite SST to address the issue of assessing OA, (62) along with two nonpeer-reviewed attempts to calculate carbonate system products using satellite SSS data. (92, 93) Supported by good in situ measurement campaigns, especially in places with currently poor in situ coverage such as the Arctic, satellite measurements are likely to become a key element in understanding and assessing OA.

Author Information

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  • Corresponding Author
    • Peter E. Land - Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, U.K.;  Email: [email protected]
  • Authors
    • Jamie D. Shutler - University of Exeter, Penryn Campus, Cornwall TR10 9FE, U.K.
    • Helen S. Findlay - Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, U.K.
    • Fanny Girard-Ardhuin - Institut Francais Recherche Pour ĹExploitation de la Mer, Pointe du Diable, 29280 Plouzané France
    • Roberto Sabia - Telespazio-Vega U.K. for European Space Agency (ESA), ESTEC, Noordwijk, The Netherlands
    • Nicolas Reul - Institut Francais Recherche Pour ĹExploitation de la Mer, Pointe du Diable, 29280 Plouzané France
    • Jean-Francois Piolle - Institut Francais Recherche Pour ĹExploitation de la Mer, Pointe du Diable, 29280 Plouzané France
    • Bertrand Chapron - Institut Francais Recherche Pour ĹExploitation de la Mer, Pointe du Diable, 29280 Plouzané France
    • Yves Quilfen - Institut Francais Recherche Pour ĹExploitation de la Mer, Pointe du Diable, 29280 Plouzané France
    • Joseph Salisbury - Ocean Processes Analysis Laboratory, University of New Hampshire, Durham, New Hampshire 3824, United States
    • Douglas Vandemark - Ocean Processes Analysis Laboratory, University of New Hampshire, Durham, New Hampshire 3824, United States
    • Richard Bellerby - Norwegian Institute for Water Research, Thormøhlensgate 53 D, N-5006 Bergen, Norway
    • Punyasloke Bhadury - Department of Biological Sciences, Indian Institute of Science Education and Research-Kolkata, Mohanpur 741 246, West Bengal India
  • Author Contributions

    The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

  • Funding

    This work was funded by the European Space Agency Support to Science Element Pathfinders Ocean Acidification project (contract No. 4000110778/14/I-BG).

  • Notes
    The authors declare no competing financial interest.

Biography

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Peter Land is a remote sensing scientist at Plymouth Marine Laboratory (PML), specializing in atmosphere-ocean gas exchange and carbonate chemistry. Jamie Shutler is an oceanographer and former European Space Agency (ESA) fellow specializing in atmosphere-ocean gas exchange at the University of Exeter. Helen Findlay is an oceanographer at PML specializing in ocean acidification and carbonate chemistry. Fanny Girard-Ardhuin is a remote sensing scientist specializing in sea ice at l’Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer). Nicolas Reul is a remote sensing scientist at Ifremer and member of the SMOS scientific team. Jean-Francois Piolle is a computer scientist at Ifremer. Bertrand Chapron leads remote sensing research at Ifremer. Yves Quilfen is an altimetry remote sensing scientist at Ifremer. Joseph Salisbury and Douglas Vandemark are oceanographers at the University of New Hampshire focusing on biogeochemistry and ecology in coastal areas. Richard Bellerby is a chemical oceanographer at the Norwegian Institute for Water Research, a member of the GOA-ON executive committee, and leader of the AMAP and SCAR ocean acidification working groups. Punyasloke Bhadury is a coastal ecologist at the Indian Institute of Science Education and Research-Kolkata. Roberto Sabia is a specialist in remote sensing of salinity working for ESA.

Acknowledgment

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This work was enabled by European Space Agency (ESA) Support to Science Element (STSE) Pathfinders Ocean Acidification project (contract No. 4000110778/14/I-BG). The authors gratefully acknowledge the assistance of Diego Fernandez (STSE programme manager).

References

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    Ridgwell, A.; Schmidt, D. N.; Turley, C.; Brownlee, C.; Maldonado, M. T.; Tortell, P.; Young, J. R. From laboratory manipulations to Earth system models: Scaling calcification impacts of ocean acidification Biogeosciences 2009, 6 (11) 2611– 2623
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    From laboratory manipulations to Earth system models: scaling calcification impacts of ocean acidification
    Ridgwell, A.; Schmidt, D. N.; Turley, C.; Brownlee, C.; Maldonado, M. T.; Tortell, P.; Young, J. R.
    Biogeosciences (2009), 6 (11), 2611-2623CODEN: BIOGGR; ISSN:1726-4170. (Copernicus Publications)
    A review. The obsd. variation in the calcification responses of coccolithophores to changes in carbonate chem. paints a highly incoherent picture, particularly for the most commonly cultured "species", Emiliania huxleyi. The disparity between magnitude and potentially even sign of the calcification change under simulated end-of-century ocean surface chem. changes (higher pCO2, lower pH and carbonate satn.), raises challenges to quantifying future carbon cycle impacts and feedbacks because it introduces significant uncertainty in parameterizations used for global models. Here we compile the results of coccolithophore carbonate chem. manipulation expts. and review how ocean carbon cycle models have attempted to bridge the gap from expts. to global impacts. Although we can rule out methodol. differences in how carbonate chem. is altered as introducing an exptl. bias, the absence of a consistent calcification response implies that model parameterizations based on small and differing subsets of exptl. observations will lead to varying ests. for the global carbon cycle impacts of ocean acidification. We highlight two pertinent observations that might help: (1) the degree of coccolith calcification varies substantially, both between species and within species across different genotypes, and (2) the calcification response across mesocosm and shipboard incubations has so-far been found to be relatively consistent. By analogy to descriptions of plankton growth rate vs. temp., such as the "Eppley curve", which seek to encapsulate the net community response via progressive assemblage change rather than the response of any single species, we posit that progressive future ocean acidification may drive a transition in dominance from more to less heavily calcified coccolithophores. Assemblage shift may be more important to integrated community calcification response than species-specific response, highlighting the importance of whole community manipulation expts. to models in the absence of a complete physiol. understanding of the underlying calcification process. However, on a century time-scale, regardless of the parameterization adopted, the atm. pCO2 impact of ocean acidification is minor compared to other global carbon cycle feedbacks.
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    Reconstruction of pH in the surface seawater over the North Pacific Basin for all seasons using temperature and chlorophyll-a
    Nakano, Yoshiyuki; Watanabe, Yutaka W.
    Journal of Oceanography (2005), 61 (4), 673-680CODEN: JOOCE7; ISSN:0916-8370. (Terra Scientific Publishing Co.)
    A simple function of pH which relates to sea surface temp. (SST, K) and chlorophyll-a (Chl, μg/L) using measured surface seawater pH, SST, and Chl datasets over the North Pacific was detd.: pH (total H scale at 25°) = 0.01325 SST - 0.0253 Chl + 4.150 (R2 = 0.95, p <0.0001, n = 483). Moreover, evaluating the seasonal variation of pH based on this algorithm, measured pH was compared with predicted pH at observational time series sites in sub-polar and sub-tropical regions. Av. ΔpH (measured - predicted; n = 52) was 0.006 ± 0.022 pH. Thus, the combination of SST and Chl permits a detn. of the spatiotemporal distribution of pH over the North Pacific. Using climatol. SST and Chl datasets with this pH algorithm, the seasonal distribution of pH at 25° (pH(25)) and pH in-situ temp. (pH(T)) over North Pacific surface seawater was detd.
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    Juranek, L. W.; Feely, R. A.; Peterson, W. T.; Alin, S. R.; Hales, B.; Lee, K.; Sabine, C. L.; Peterson, J. A novel method for determination of aragonite saturation state on the continental shelf of central Oregon using multi-parameter relationships with hydrographic data Geophys. Res. Lett. 2009, 36 (24) L24601
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    Midorikawa, T.; Inoue, H. Y.; Ishii, M.; Sasano, D.; Kosugi, N.; Hashida, G.; Nakaoka, S.-i.; Suzuki, T. Decreasing pH trend estimated from 35-year time series of carbonate parameters in the Pacific sector of the Southern Ocean in summer Deep Sea Res., Part I 2012, 61, 131– 139
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    Decreasing pH trend estimated from 35-year time series of carbonate parameters in the Pacific sector of the Southern Ocean in summer
    Midorikawa, Takashi; Inoue, Hisayuki Y.; Ishii, Masao; Sasano, Daisuke; Kosugi, Naohiro; Hashida, Gen; Nakaoka, Shin-ichiro; Suzuki, Toru
    Deep Sea Research, Part I: Oceanographic Research Papers (2012), 61 (), 131-139CODEN: DRORE7; ISSN:0967-0637. (Elsevier Ltd.)
    The Southern Ocean is an important region for investigation because it has a major effect on global air-to-sea CO2 fluxes and because of the ocean acidification resulting from the uptake of anthropogenic carbon, leading to serious consequences for marine ecosystems in the near future. We estd. long-term trends of ocean acidification in surface waters of the Pacific sector of the Southern Ocean, based on the summer observational records of oceanic CO2 partial pressure and related surface properties during 1969-2003. The computed pH time series exhibited substantial decreasing trends in the extensive region from the subtropical to polar zones. The mean rates of pH decrease over the 35-yr period were 0.0011 to 0.0013 yr-1 in the zones north of the Polar Front and were larger in the polar zone (0.0020 yr-1). The contribution of trends in sea surface temp. to the trends of pH decrease was small in all zones. The high rate of pH decrease in the polar zone was attributed to the supply of dissolved inorg. carbon from lower layers, enhanced by intensified wind stress and superimposed onto the accumulation of anthropogenic CO2. A preliminary evaluation of thermodn. changes in the upper carbonate system, using observational results, projected that the polar zone south of the Polar Front would be undersatd. with respect to aragonite in summer after 80 years.
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    Carbon dioxide in water and sea water. Solubility of a nonideal gas
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    New measurements of the soly. of CO2 in H2O and sea water confirm the accuracy of the measurements of Murray and Riley (1971), as opposed to those of Li and Tsui (1971). Corrections for non-ideal behavior in the gas phase and for dissocn. in distilled H2O are required to calc. soly. coeffs. from these sets of data. Equations for the solubilities of real gases are presented and discussed. Soly. coeffs. for CO2 in H2O and sea water are calcd. for the data of Murray and Riley, and are fitted to equations in temp. and salinity of the form used previously to fit the solubilities of other gases.
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    Lee, K.; Tong, L. T.; Millero, F. J.; Sabine, C. L.; Dickson, A. G.; Goyet, C.; Park, G. H.; Wanninkhof, R.; Feely, R. A.; Key, R. M., Global relationships of total alkalinity with salinity and temperature in surface waters of the world’s oceans. Geophys. Res. Lett. 2006, 33, (19).
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    Carbon dioxide and metabolism in marine environments
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    The marine CO2 [124-38-9] system is discussed in relation to photosynthesis, respiration, calcification, and soln. within a closed body of water. Photosynthesis and calcification both lower the CO2 content of the water, while respiration and CaCO3 [471-34-1] soln. raise it. Only the pptn. or soln. of CaCO3 significantly alters the total alkalinity of the water, so alkalinity changes are used to calc. the effects of CaCO3 on total CO2 changes. Changes in CO2 not attributable to CaCO3 reactions represent photosynthesis or respiration and can be related to org. C production or consumption.
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    Measuring Ocean Acidification: New Technology for a New Era of Ocean Chemistry
    Byrne, Robert H.
    Environmental Science & Technology (2014), 48 (10), 5352-5360CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Human addns. of carbon dioxide to the atm. are creating a cascade of chem. consequences that will eventually extend to the bottom of all the world's oceans. Among the best-documented seawater effects are a worldwide increase in open-ocean acidity and large-scale declines in calcium carbonate satn. states. The susceptibility of some young, fast-growing calcareous organisms to adverse impacts highlights the potential for biol. and economic consequences. Many important aspects of seawater CO2 chem. can be only indirectly obsd. at present, and important but difficult-to-observe changes can include shifts in the speciation and possibly bioavailability of some life-essential elements. Innovation and invention are urgently needed to develop the in situ instrumentation required to document this era of rapid ocean evolution.
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    Testing the Honeywell Durafet for seawater pH applications
    Martz, Todd R.; Connery, James G.; Johnson, Kenneth S.
    Limnology and Oceanography: Methods (2010), 8 (May), 172-184CODEN: LOMIBY; ISSN:1541-5856. (American Society of Limnology and Oceanography)
    We report on the first seawater tests at 1 atm of the Honeywell Durafet pH sensor, a com. available ion sensitive field effect transistor (ISFET). Performance of this sensor was evaluated in a no. of different situations including a temp.-controlled calibration vessel, the MBARI test tank, shipboard underway mapping, and a surface mooring. Many of these tests included a secondary ref. electrode in addn. to the internal ref. supplied with the stock Durafet sensor. We present a theor. overview of sensor response using both types of ref. electrode. The Durafet sensor operates with a short term precision of ± 0.0005 pH over periods of several hours and exhibits stability of better than 0.005 pH over periods of weeks to months. Our tests indicate that the Durafet pH sensor operates at a level of performance satisfactory for many types of biogeochem. studies at low pressure.
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    Skjelvan, I.; Falck, E.; Rey, F.; Kringstad, S. B.
    Biogeosciences (2008), 5 (2), 549-560CODEN: BIOGGR; ISSN:1726-4170. (Copernicus Publications)
    Dissolved inorg. carbon (CT) has been collected at Ocean Weather Station M (OWSM) in the Norwegian Sea since 2001. Seasonal variations in CT are confined to the upper 50 m, where the biol. is active, and below this layer no clear seasonal signal is seen. From winter to summer the surface CT concn. typical drop from 2140 to about 2040 μmol kg-1, while a deep water CT concn. of about 2163 μmol kg-1 is measured throughout the year. Observations show an annual increase in salinity normalized carbon concn. (nCT) of 1.3 ± 0.7 μmol kg-1 yr-1 in the surface layer, which is equiv. to a pCO2 increase of 2.6 ± 1.2 μatm yr-1, i.e. larger than the atm. increase in this area (2.1 ± 0.2 μatm yr-1). Observations also show an annual increase in the deep water nCT of 0.57 ± 0.24 μmol kg-1 yr-1, of which about 15% is due to inflow of old Arctic water with larger amts. of remineralized matter. The remaining part has an anthropogenic origin and sources for this might be Greenland Sea surface water, Iceland Sea surface water, and/or recirculated Atlantic Water. By using an extended multi linear regression method (eMLR) it is verified that anthropogenic carbon has entered the whole water column at OWSM.
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    Font, Jordi; Camps, Adriano; Borges, Andres; Martin-Neira, Manuel; Boutin, Jacqueline; Reul, Nicolas; Kerr, Yann H.; Hahne, Achim; Mecklenburg, Susanne
    Proceedings of the IEEE (2010), 98 (5), 649-661CODEN: IEEPAD; ISSN:0018-9219. (Institute of Electrical and Electronics Engineers)
    Soil Moisture and Ocean Salinity, European Space Agency, is the first satellite mission addressing the challenge of measuring sea surface salinity from space. It uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) that generates brightness temp. images, from which both geophys. variables are computed. The retrieval of salinity requires very demanding performances of the instrument in terms of calibration and stability. This paper highlights the importance of ocean salinity for the Earth's water cycle and climate: provides a detailed description of the MIRAS instrument, its principles of operation, calibration, and image-reconstruction techniques; and presents the algorithmic approach implemented for the retrieval of salinity from MIRAS observations, as well as the expected accuracy of the obtained results.
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Cite this: Environ. Sci. Technol. 2015, 49, 4, 1987–1994
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  • Abstract

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    Figure 1

    Figure 1. Number of key carbonate parameters (fCO2 or pCO2, TA, DIC, pH) for which regional algorithms exist in the literature that can be implemented using just satellite Earth observation data. Regions are indicative of open ocean areas, as implementation of algorithms in coastal areas may be problematic.

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  • References

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      Gledhill, D. K.; Wanninkhof, R.; Eakin, C. M., Observing ocean acidification from space. Oceanography 2009, 22.
      There is no corresponding record for this reference.
    22. 22
      Sun, Q.; Tang, D.; Wang, S. Remote-sensing observations relevant to ocean acidification Int. J. Rem. Sensing 2012, 33 (23) 7542– 7558
      There is no corresponding record for this reference.
    23. 23
      Dickson, A. G., The carbon dioxide system in seawater: Equilibrium chemistry and measurements. In Guide to Best Practices for Ocean Acidification Research and Data Reporting, Riebesell, U.; Fabry, C. J.; Hansson, L.; Gattuso, J.-P., Eds.; European Commission: Brussels, 2011; pp 17– 40.
      There is no corresponding record for this reference.
    24. 24
      Dickson, A. G.; Sabine, C. L.; Christian, J. R.Guide to Best Practices for Ocean CO2 Measurements, PICES Special Publication 3, 2007
      There is no corresponding record for this reference.
    25. 25
      Byrne, R. H. Measuring Ocean Acidification: New Technology for a New Era of Ocean Chemistry Environ. Sci. Technol. 2014, 48 (10) 5352– 5360
      25
      Measuring Ocean Acidification: New Technology for a New Era of Ocean Chemistry
      Byrne, Robert H.
      Environmental Science & Technology (2014), 48 (10), 5352-5360CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Human addns. of carbon dioxide to the atm. are creating a cascade of chem. consequences that will eventually extend to the bottom of all the world's oceans. Among the best-documented seawater effects are a worldwide increase in open-ocean acidity and large-scale declines in calcium carbonate satn. states. The susceptibility of some young, fast-growing calcareous organisms to adverse impacts highlights the potential for biol. and economic consequences. Many important aspects of seawater CO2 chem. can be only indirectly obsd. at present, and important but difficult-to-observe changes can include shifts in the speciation and possibly bioavailability of some life-essential elements. Innovation and invention are urgently needed to develop the in situ instrumentation required to document this era of rapid ocean evolution.
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    26. 26
      Martz, T. R.; Connery, J. G.; Johnson, K. S. Testing the Honeywell Durafet® for seawater pH applications Limnol. Oceanogr. Methods 2010, 8, 172– 184
      26
      Testing the Honeywell Durafet for seawater pH applications
      Martz, Todd R.; Connery, James G.; Johnson, Kenneth S.
      Limnology and Oceanography: Methods (2010), 8 (May), 172-184CODEN: LOMIBY; ISSN:1541-5856. (American Society of Limnology and Oceanography)
      We report on the first seawater tests at 1 atm of the Honeywell Durafet pH sensor, a com. available ion sensitive field effect transistor (ISFET). Performance of this sensor was evaluated in a no. of different situations including a temp.-controlled calibration vessel, the MBARI test tank, shipboard underway mapping, and a surface mooring. Many of these tests included a secondary ref. electrode in addn. to the internal ref. supplied with the stock Durafet sensor. We present a theor. overview of sensor response using both types of ref. electrode. The Durafet sensor operates with a short term precision of ± 0.0005 pH over periods of several hours and exhibits stability of better than 0.005 pH over periods of weeks to months. Our tests indicate that the Durafet pH sensor operates at a level of performance satisfactory for many types of biogeochem. studies at low pressure.
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    27. 27
      Bakker, D. C. E.; Hankin, S.; Olsen, A.; Pfeil, B.; Smith, K.; Alin, S. R.; Cosca, C.; Hales, B.; Harasawa, S.; Kozyr, A. An update to the surface ocean CO2 Atlas (SOCAT version 2) Earth Syst. Sci. Data 2014,  DOI: 10.5194/essd-6-69-2014
      There is no corresponding record for this reference.
    28. 28
      Takahashi, T.; Sutherland, S. C.; Kozyr, A. Global Ocean Surface Water Partial Pressure of CO2 Database: Measurements Performed During 1957–2012 (Version 2012), ORNL/CDIAC-160, NDP-088(V2012); Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy: Oak Ridge, TN, 2013.
      There is no corresponding record for this reference.
    29. 29
      Key, R. M.; Kozyr, A.; Sabine, C. L.; Lee, K.; Wanninkhof, R.; Bullister, J. L.; Feely, R. A.; Millero, F. J.; Mordy, C.; Peng, T. H., A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP). Global Biogeochem. Cycles 2004, 18, (4).
      There is no corresponding record for this reference.
    30. 30
      CARINA group. Carbon in the Arctic Mediterranean Seas Region—The CARINA Project: Results and Data, Version 1.2; Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy: Oak Ridge, TN, 2009.
      There is no corresponding record for this reference.
    31. 31
      CARINA group. Carbon in the Atlantic Ocean Region—The CARINA Project: Results and Data, Version 1.0.; Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy: Oak Ridge, TN, 2009.
      There is no corresponding record for this reference.
    32. 32
      CARINA group. Carbon in the Southern Ocean Region—The CARINA Project: Results and Data, Version 1.1.; Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy: Oak Ridge, TN, 2010.
      There is no corresponding record for this reference.
    33. 33
      Robinson, C.; Holligan, P.; Jickells, T.; Lavender, S. The Atlantic Meridional Transect Programme (1995–2012) Deep Sea Res., Part II 2009, 56 (15) 895– 898
      There is no corresponding record for this reference.
    34. 34
      Yakushev, E. V.; Sørensen, K. On seasonal changes of the carbonate system in the Barents Sea: Observations and modeling Mar. Biol. Res. 2013, 9 (9) 822– 830
      There is no corresponding record for this reference.
    35. 35
      Skjelvan, I.; Falck, E.; Rey, F.; Kringstad, S. B. Inorganic carbon time series at Ocean Weather Station M in the Norwegian Sea Biogeosciences 2008, 5, 549– 560
      35
      Inorganic carbon time series at Ocean Weather Station M in the Norwegian Sea
      Skjelvan, I.; Falck, E.; Rey, F.; Kringstad, S. B.
      Biogeosciences (2008), 5 (2), 549-560CODEN: BIOGGR; ISSN:1726-4170. (Copernicus Publications)
      Dissolved inorg. carbon (CT) has been collected at Ocean Weather Station M (OWSM) in the Norwegian Sea since 2001. Seasonal variations in CT are confined to the upper 50 m, where the biol. is active, and below this layer no clear seasonal signal is seen. From winter to summer the surface CT concn. typical drop from 2140 to about 2040 μmol kg-1, while a deep water CT concn. of about 2163 μmol kg-1 is measured throughout the year. Observations show an annual increase in salinity normalized carbon concn. (nCT) of 1.3 ± 0.7 μmol kg-1 yr-1 in the surface layer, which is equiv. to a pCO2 increase of 2.6 ± 1.2 μatm yr-1, i.e. larger than the atm. increase in this area (2.1 ± 0.2 μatm yr-1). Observations also show an annual increase in the deep water nCT of 0.57 ± 0.24 μmol kg-1 yr-1, of which about 15% is due to inflow of old Arctic water with larger amts. of remineralized matter. The remaining part has an anthropogenic origin and sources for this might be Greenland Sea surface water, Iceland Sea surface water, and/or recirculated Atlantic Water. By using an extended multi linear regression method (eMLR) it is verified that anthropogenic carbon has entered the whole water column at OWSM.
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    36. 36
      McPhaden, M. J.; Meyers, G.; Ando, K.; Masumoto, Y.; Murty, V. S. N.; Ravichandran, M.; Syamsudin, F.; Vialard, J.; Yu, L.; Yu, W. RAMA: The Research Moored Array for African–asian–australian Monsoon Analysis and Prediction, 2009
      There is no corresponding record for this reference.
    37. 37
      ARGO Argo - part of the integrated global observationstrategy. http://www.argo.ucsd.edu (14/12/ 2014) ,.
      There is no corresponding record for this reference.
    38. 38
      OOI Ocean Observatories Initiative. http://oceanobservatories.org (14/12/ 2014) ,.
      There is no corresponding record for this reference.
    39. 39
      SOCCOM, Southern Ocean Carbon and Climate Observations and Modeling. http://soccom.princeton.edu (accessed December 14, 2014).
      There is no corresponding record for this reference.
    40. 40
      Merchant, C. J.; Embury, O.; Rayner, N. A.; Berry, D. I.; Corlett, G. K.; Lean, K.; Veal, K. L.; Kent, E. C.; Llewellyn-Jones, D. T.; Remedios, J. J., A 20 year independent record of sea surface temperature for climate from Along–Track Scanning Radiometers. J. Geophys. Res.: Oceans 2012, 117, (C12).
      There is no corresponding record for this reference.
    41. 41
      McClain, C. R.; Feldman, G. C.; Hooker, S. B. An overview of the SeaWiFS project and strategies for producing a climate research quality global ocean bio-optical time series Deep Sea Res., Part II 2004, 51 (1) 5– 42
      There is no corresponding record for this reference.
    42. 42
      Font, J.; Boutin, J.; Reul, N.; Spurgeon, P.; Ballabrera-Poy, J.; Chuprin, A.; Gabarró, C.; Gourrion, J.; Guimbard, S.; Hénocq, C. SMOS first data analysis for sea surface salinity determination Int. J. Rem. Sens. 2013, 34 (9–10) 3654– 3670
      There is no corresponding record for this reference.
    43. 43
      Font, J.; Camps, A.; Borges, A.; Martín-Neira, M.; Boutin, J.; Reul, N.; Kerr, Y. H.; Hahne, A.; Mecklenburg, S. SMOS: The challenging sea surface salinity measurement from space Proc. IEEE 2010, 98 (5) 649– 665
      43
      SMOS: the challenging sea surface salinity measurement from space
      Font, Jordi; Camps, Adriano; Borges, Andres; Martin-Neira, Manuel; Boutin, Jacqueline; Reul, Nicolas; Kerr, Yann H.; Hahne, Achim; Mecklenburg, Susanne
      Proceedings of the IEEE (2010), 98 (5), 649-661CODEN: IEEPAD; ISSN:0018-9219. (Institute of Electrical and Electronics Engineers)
      Soil Moisture and Ocean Salinity, European Space Agency, is the first satellite mission addressing the challenge of measuring sea surface salinity from space. It uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) that generates brightness temp. images, from which both geophys. variables are computed. The retrieval of salinity requires very demanding performances of the instrument in terms of calibration and stability. This paper highlights the importance of ocean salinity for the Earth's water cycle and climate: provides a detailed description of the MIRAS instrument, its principles of operation, calibration, and image-reconstruction techniques; and presents the algorithmic approach implemented for the retrieval of salinity from MIRAS observations, as well as the expected accuracy of the obtained results.
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    44. 44
      Boutin, J.; Martin, N.; Reverdin, G.; Morisset, S.; Yin, X.; Centurioni, L.; Reul, N. Sea surface salinity under rain cells: SMOS satellite and in situ drifters observations J. Geophys. Res.: Oceans 2014, 119 (8) 5533– 5545
      There is no corresponding record for this reference.
    45. 45
      Reul, N.; Chapron, B.; Lee, T.; Donlon, C.; Boutin, J.; Alory, G. Sea surface salinity structure of the meandering Gulf Stream revealed by SMOS sensor Geophys. Res. Lett. 2014, 41 (9) 3141– 3148
      There is no corresponding record for this reference.
    46. 46
      Boutin, J.; Martin, N.; Reverdin, G.; Yin, X.; Gaillard, F. Sea surface freshening inferred from SMOS and ARGO salinity: Impact of rain Ocean Sci. 2013, 9, 183– 192
      There is no corresponding record for this reference.
    47. 47
      Sabia, R.; Klockmann, M. Fernández-Prieto, D.; Donlon, C., A first estimation of SMOS-based ocean surface T-S diagrams J. Geophys. Res.: Oceans 2014, 119 (10) 7357– 7371
      There is no corresponding record for this reference.
    48. 48
      Hosoda, S.; Ohira, T.; Nakamura, T. A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations JAMSTEC Rep. Res. Dev 2008, 8, 47– 59
      There is no corresponding record for this reference.
    49. 49
      Reul, N.; Fournier, S.; Boutin, J.; Hernandez, O.; Maes, C.; Chapron, B.; Alory, G.; Quilfen, Y.; Tenerelli, J.; Morisset, S. Sea surface salinity observations from space with the SMOS satellite: A new means to monitor the marine branch of the water cycle Surv. Geophysics 2014, 35 (3) 681– 722
      There is no corresponding record for this reference.
    50. 50
      Laxon, S. W.; Giles, K. A.; Ridout, A. L.; Wingham, D. J.; Willatt, R.; Cullen, R.; Kwok, R.; Schweiger, A.; Zhang, J.; Haas, C. CryoSat-2 estimates of Arctic sea ice thickness and volume Geophys. Res. Lett. 2013, 40 (4) 732– 737
      There is no corresponding record for this reference.
    51. 51
      Kaleschke, L.; Tian-Kunze, X.; Maaß, N.; Mäkynen, M.; Drusch, M., Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period. Geophys. Res. Lett. 2012, 39, (5).
      There is no corresponding record for this reference.
    52. 52
      Mathis, J. T.; Pickart, R. S.; Byrne, R. H.; McNeil, C. L.; Moore, G. W. K.; Juranek, L. W.; Liu, X.; Ma, J.; Easley, R. A.; Elliot, M. M., Storm-induced upwelling of high pCO2 waters onto the continental shelf of the western Arctic Ocean and implications for carbonate mineral saturation states. Geophys. Res. Lett. 2012, 39, (7).
      There is no corresponding record for this reference.
    53. 53
      Mahadevan, A.; Tagliabue, A.; Bopp, L.; Lenton, A.; Memery, L.; Lévy, M. Impact of episodic vertical fluxes on sea surface pCO2 Philos. Trans. R. Soc., A 2011, 369 (1943) 2009– 2025
      53
      Impact of episodic vertical fluxes on sea surface pCO2
      Mahadevan, A.; Tagliabue, A.; Bopp, L.; Lenton, A.; Memery, L.; Levy, M.
      Philosophical Transactions of the Royal Society, A: Mathematical, Physical & Engineering Sciences (2011), 369 (1943), 2009-2025CODEN: PTRMAD; ISSN:1364-503X. (Royal Society)
      Episodic events like hurricanes, storms and frontal- and eddy-driven upwelling can alter the partial pressure of CO2 (pCO2) at the sea surface by entraining subsurface waters into the surface mixed layer (ML) of the ocean. Since pCO2 is a function of total dissolved inorg. carbon (DIC), temp. (T), salinity and alky., it responds to the combined impacts of phys., chem. and biol. changes. Here, we present an anal. framework for assessing the relative magnitude and sign in the short-term perturbation of surface pCO2 arising from vertical mixing events. Using global, monthly, climatol. datasets, we assess the individual, as well as integrated, contribution of various properties to surface PCO2 in response to episodic mixing. The response depends on the relative vertical gradients of properties beneath the ML. Many areas of the ocean exhibit very little sensitivity to mixing owing to the compensatory effects of DIC and T on PCO2, whereas others, such as the eastern upwelling margins, have the potential to generate large pos./neg. anomalies in surface pCO2. The response varies seasonally and spatially and becomes more intense in subtropical and subpolar regions during summer. Regions showing a greater PCO2 response to vertical mixing are likely to exhibit higher spatial variability in surface pCO2 on time scales of days.
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    54. 54
      Mahadevan, A. Ocean science: Eddy effects on biogeochemistry Nature 2014, 506, 168– 169
      There is no corresponding record for this reference.
    55. 55
      Takahashi, T.; Sutherland, S. Climatological Mean Distribution of pH and Carbonate Ion Concentration in Global Ocean Surface Waters in the Unified pH Scale and Mean Rate of Their Changes in Selected Areas, OCE 10-38891; National Science Foundation: Washington, D. C., USA,, 2013.
      There is no corresponding record for this reference.
    56. 56
      Goddijn-Murphy, L. M.; Woolf, D. K.; Land, P. E.; Shutler, J. D.; Donlon, C. Deriving a sea surface climatology of CO2 fugacity in support of air-sea gas flux studies Ocean Sci. Discuss. 2014, 11, 1895– 1948
      There is no corresponding record for this reference.
    57. 57
      Årthun, M.; Bellerby, R. G. J.; Omar, A. M.; Schrum, C. Spatiotemporal variability of air–sea CO < sub> 2</sub> fluxes in the Barents Sea, as determined from empirical relationships and modeled hydrography J. Mar. Syst. 2012, 98, 40– 50
      There is no corresponding record for this reference.
    58. 58
      Friedrich, T.; Oschlies, A., Basin-scale pCO2 maps estimated from ARGO float data: A model study. J. Geophys. Res.: Oceans 2009, 114, (C10).
      There is no corresponding record for this reference.
    59. 59
      Ono, T.; Saino, T.; Kurita, N.; Sasaki, K. Basin-scale extrapolation of shipboard pCO2 data by using satellite SST and Chla Int. J. Rem. Sens. 2004, 25 (19) 3803– 3815
      There is no corresponding record for this reference.
    60. 60
      Borges, A. V.; Ruddick, K.; Lacroix, G.; Nechad, B.; Asteroca, R.; Rousseau, V.; Harlay, J., Estimating pCO2 from remote sensing in the Belgian coastal zone. ESA Spec. Publ. 2010, 686.
      There is no corresponding record for this reference.
    61. 61
      Sarma, V. V. S. S.; Saino, T.; Sasaoka, K.; Nojiri, Y.; Ono, T.; Ishii, M.; Inoue, H. Y.; Matsumoto, K., Basin-scale pCO2 distribution using satellite sea surface temperature, Chl a, and climatological salinity in the North Pacific in spring and summer. Global Biogeochem. Cycles 2006, 20, (3).
      There is no corresponding record for this reference.
    62. 62
      Lauvset, S. K.; Chierici, M.; Counillon, F.; Omar, A.; Nondal, G.; Johannessen, T.; Olsen, A. Annual and seasonal fCO2 and air–sea CO2 fluxes in the Barents Sea J. Mar. Syst. 2013,  DOI: 10.1016/j.jmarsys.2012.12.011
      There is no corresponding record for this reference.
    63. 63
      Millero, F. J.; Lee, K.; Roche, M. Distribution of alkalinity in the surface waters of the major oceans Mar. Chem. 1998, 60 (1) 111– 130
      There is no corresponding record for this reference.
    64. 64
      Loukos, H.; Vivier, F.; Murphy, P. P.; Harrison, D. E.; Le Quéré, C. Interannual variability of equatorial Pacific CO2 fluxes estimated from temperature and salinity data Geophys. Res. Lett. 2000, 27 (12) 1735– 1738
      64
      Interannual variability of equatorial Pacific CO2 fluxes estimated from temperature and salinity data
      Loukos, H.; Vivier, F.; Murphy, P. P.; Harrison, D. E.; Le Quere, C.
      Geophysical Research Letters (2000), 27 (12), 1735-1738CODEN: GPRLAJ; ISSN:0094-8276. (American Geophysical Union)
      Based on atm. data and models, the tropical CO2 source anomaly reaches up to 2 GtC yr-1, but the resp. contributions of the terrestrial biosphere and the oceans to this flux are difficult to quantify. Here we present a new method for estg. CO2 fluxes from oceanic observations based on the surprisingly good predictive skill of temp. and salinity for surface dissolved inorg. carbon. Using historical temp. and salinity data, we reconstruct the basin scale CO2 flux to the atm. in the equatorial Pacific from 1982 to 1993. We find that interannual anomalies do not exceed 0.4 ±0.2 GtC yr-1 which suggests that up to 80% of the tropical CO2 source anomaly is due to the terrestrial biosphere.
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    65. 65
      Anderson, D.; Sheinbaum, J.; Haines, K. Data assimilation in ocean models Rep. Prog. Phys. 1996, 59 (10) 1209
      There is no corresponding record for this reference.
    66. 66
      Steinacher, M.; Joos, F.; Frölicher, T. L.; Plattner, G. K.; Doney, S. C. Imminent ocean acidification in the Arctic projected with the NCAR global coupled carbon cycle-climate model Biogeosciences 2009, 6 (4) 515– 533
      66
      Imminent ocean acidification in the Arctic projected with the NCAR global coupled carbon cycle-climate model
      Steinacher, M.; Joos, F.; Frolicher, T. L.; Plattner, G.-K.; Doney, S. C.
      Biogeosciences (2009), 6 (4), 515-533CODEN: BIOGGR; ISSN:1726-4170. (Copernicus Publications)
      Ocean acidification from the uptake of anthropogenic carbon is simulated for the industrial period and IPCC SRES emission scenarios A2 and B1 with a global coupled carbon cycle-climate model. Earlier studies identified seawater satn. state with respect to aragonite, a mineral phase of calcium carbonate, as a key variable governing impacts on corals and other shell-forming organisms. Globally in the A2 scenario, water satd. by more than 300%, considered suitable for coral growth, vanishes by 2070 AD (CO2≈630 ppm), and the ocean vol. fraction occupied by satd. water decreases from 42% to 25% over this century. The largest simulated pH changes worldwide occur in Arctic surface waters, where hydrogen ion concn. increases by up to 185% (ΔpH=-0.45). Projected climate change amplifies the decrease in Arctic surface mean satn. and pH by more than 20%, mainly due to freshening and increased carbon uptake in response to sea ice retreat. Modeled satn. compares well with observation-based ests. along an Arctic transect and simulated changes have been cor. for remaining model-data differences in this region. Aragonite undersatn. in Arctic surface waters is projected to occur locally within a decade and to become more widespread as atm. CO2 continues to grow. The results imply that surface waters in the Arctic Ocean will become corrosive to aragonite, with potentially large implications for the marine ecosystem, if anthropogenic carbon emissions are not reduced and atm. CO2 not kept below 450 ppm.
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    67. 67
      Peterson, B. J.; Holmes, R. M.; McClelland, J. W.; Vörösmarty, C. J.; Lammers, R. B.; Shiklomanov, A. I.; Shiklomanov, I. A.; Rahmstorf, S. Increasing river discharge to the Arctic Ocean Science 2002, 298 (5601) 2171– 2173
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      Increasing River Discharge to the Arctic Ocean
      Peterson, Bruce J.; Holmes, Robert M.; McClelland, James W.; Voeroesmarty, Charles J.; Lammers, Richard B.; Shiklomanov, Alexander I.; Shiklomanov, Igor A.; Rahmstorf, Stefan
      Science (Washington, DC, United States) (2002), 298 (5601), 2171-2173CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Synthesis of river-monitoring data reveals that the av. annual discharge of fresh water from the six largest Eurasian rivers to the Arctic Ocean increased by 7% from 1936 to 1999. The av. annual rate of increase was 2.0 ± 0.7 cubic kilometers per yr. Consequently, av. annual discharge from the six rivers is now about 128 cubic kilometers per yr greater than it was when routine measurements of discharge began. Discharge was correlated with changes in both the North Atlantic Oscillation and global mean surface air temp. The obsd. large-scale change in freshwater flux has potentially important implications for ocean circulation and climate.
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    68. 68
      Shadwick, E. H.; Trull, T. W.; Thomas, H.; Gibson, J. A. E., Vulnerability of polar oceans to anthropogenic acidification: Comparison of arctic and antarctic seasonal cycles. Sci. Rep. 2013, 3.
      There is no corresponding record for this reference.
    69. 69
      McGuire, A. D.; Anderson, L. G.; Christensen, T. R.; Dallimore, S.; Guo, L.; Hayes, D. J.; Heimann, M.; Lorenson, T. D.; Macdonald, R. W.; Roulet, N. Sensitivity of the carbon cycle in the Arctic to climate change Ecol. Monogr. 2009, 79 (4) 523– 555
      There is no corresponding record for this reference.
    70. 70
      Zine, S.; Boutin, J.; Font, J.; Reul, N.; Waldteufel, P.; Gabarró, C.; Tenerelli, J.; Petitcolin, F.; Vergely, J. L.; Talone, M. Overview of the SMOS sea surface salinity prototype processor IEEE Trans. Geosci. Rem. Sens. 2008, 46 (3) 621– 645
      There is no corresponding record for this reference.
    71. 71
      Bélanger, S.; Ehn, J. K.; Babin, M. Impact of sea ice on the retrieval of water-leaving reflectance, chlorophyll a concentration and inherent optical properties from satellite ocean color data Rem. Sens. Environ. 2007, 111 (1) 51– 68
      There is no corresponding record for this reference.
    72. 72
      Varkey, M. J.; Murty, V. S. N.; Suryanarayana, A. Physical oceanography of the Bay of Bengal and Andaman Sea Oceanogr. Mar. Biol.: Annu. Rev. 1996, 34, 1– 70p
      There is no corresponding record for this reference.
    73. 73
      Vinayachandran, P. N.; Murty, V. S. N.; Ramesh Babu, V. Observations of barrier layer formation in the Bay of Bengal during summer monsoon J. Geophys. Res.: Oceans 2002, 107 (C12) SRF-19
      There is no corresponding record for this reference.
    74. 74
      International CLIVAR Project Office Understanding The Role Of The Indian Ocean In The Climate System—Implementation Plan For Sustained Observations; International CLIVAR Project Office: 2006.
      There is no corresponding record for this reference.
    75. 75
      Sarma, V. V. S. S.; Krishna, M. S.; Rao, V. D.; Viswanadham, R.; Kumar, N. A.; Kumari, T. R.; Gawade, L.; Ghatkar, S.; Tari, A. Sources and sinks of CO2 in the west coast of Bay of Bengal Tellus B 2012, 64, 10961
      75
      Sources and sinks of CO2 in the west coast of Bay of Bengal
      Sarma, V. V. S. S.; Krishna, M. S.; Rao, V. D.; Viswanadham, R.; Kumar, N. A.; Kumari, T. R.; Gawade, L.; Ghatkar, S.; Tari, A.
      Tellus, Series B: Chemical and Physical Meteorology (2012), 64 (), 10961CODEN: TSBMD7; ISSN:1600-0889. (Co-Action Publishing)
      Observations at high spatial resoln. (100 × 50 km2) in the western continental shelf of Bay of Bengal during southwest monsoon, when peak discharge occurs into the Bay through major rivers of the Indian subcontinent, revealed that freshwater discharge exerts dominant control on the inorg. carbon components in surface waters. Lower than present atm. pCO2 levels were found in the northwestern (NW) than southwestern (SW) coastal Bay of Bengal. The pCO2 levels in the peninsular rivers were an order of magnitude higher (5000-17000 μatm) than that of atm. levels and glacial river Ganges (∼500 μatm). The discharge from the peninsular rivers has a stronger influence in the SW region, whereas the Ganges river discharge has a stronger influences in the NW region. Source or sink of CO2 in the shelf region depends on the discharged river characteristics and the East India Coastal Current that distributes discharged water along the coast. Although during northeast monsoon, the situation is briefly reversed and the region acts as a sink and on annual scale, the western Bay of Bengal acts as a source for atm. CO2 than hitherto hypothesized.
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    76. 76
      Madhupratap, M.; Gauns, M.; Ramaiah, N.; Prasanna Kumar, S.; Muraleedharan, P. M.; De Sousa, S. N.; Sardessai, S.; Muraleedharan, U. Biogeochemistry of the Bay of Bengal: Physical, chemical and primary productivity characteristics of the central and western Bay of Bengal during summer monsoon 2001 Deep Sea Res., Part II 2003, 50 (5) 881– 896
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      Biogeochemistry of the Bay of Bengal: physical, chemical and primary productivity characteristics of the central and western Bay of Bengal during summer monsoon 2001
      Madhupratap, M.; Gauns, Mangesh; Ramaiah, N.; Prasanna Kumar, S.; Muraleedharan, P. M.; de Sousa, S. N.; Sardessai, S.; Muraleedharan, Usha
      Deep-Sea Research, Part II: Topical Studies in Oceanography (2003), 50 (5), 881-896CODEN: DSROEK; ISSN:0967-0645. (Elsevier Science Ltd.)
      Reliable data on biol. characteristics from the Bay of Bengal are elusive. Results of simultaneously measured physics, chem., and biol. during the summer monsoon, 2001, from open-ocean and coastal areas of the region are reported. This period was characterized by cold-core eddies and thermocline oscillations; however, these were capped by a prevalent low-salinity upper regime which prevented surfacing of nutrients. River plume effects were evident from low salinity values obsd. in the surface layer of the upper bay, but this did not provide significant amts. of nutrients. Chlorophyll a concns. (10-20 mg/m2) and primary productivity values (40-502 mg C/m2-day) were low and not up to Arabian Sea values for the same season. Diatoms dominated the phytoplankton community and contained more genera than the Arabian Sea. Large colonies of the tunicate, Pyrosoma, which occurred at the surface and mid-depths, could have consumed a portion of the phytoplankton population. These results, although limited, have implications on the biogeochem. of the region.
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    77. 77
      Ittekkot, V.; Nair, R. R.; Honjo, S.; Ramaswamy, V.; Bartsch, M.; Manganini, S.; Desai, B. N. Enhanced particle fluxes in Bay of Bengal induced by injection of fresh water Nature 1991, 351 (6325) 385– 387
      There is no corresponding record for this reference.
    78. 78
      Ramaswamy, V.; Nair, R. R. Fluxes of material in the Arabian Sea and Bay of Bengal—Sediment trap studies Proc. - Indian Acad. Sci., Earth Planet. Sci. 1994, 103 (2) 189– 210
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      Fluxes of material in the Arabian Sea and Bay of Bengal - Sediment trap studies
      Ramaswamy, V.; Nair, R R.
      Proceedings - Indian Academy of Sciences, Earth and Planetary Sciences (1994), 103 (2), 189-210CODEN: PIESDS; ISSN:0253-4126.
      Particle flux data collected during May 1986 to Nov. 1991 and Nov. 1987 to Nov. 1992 in the Arabian Sea and Bay of Bengal resp, are presented . Particle fluxes were high during both the SW and NE monsoons in the Arabian Sea as well as in the Bay of Bengal. The mechanisms of particle prodn. and transport, however, differ in both the regions. In the Arabian Sea, av. annual fluxes are over 50/ gm2-year in the western Arabian Sea and less than 27 gm-2y-1 in the central part. Biogenic matter is dominant at sites located near upwelling centers, and is less degraded during peak flux periods. High particle fluxes in the offshore areas of the Arabian Sea are caused by injection of nutrients into the euphotic zone due to wind-induced mixed layer deepening. In the Bay of Bengal, av. annual fluxes are highest in the central Bay of Bengal (>50 gm-2y-1) and are least in the southern part of the Bay (37 gm-2y-1). Particle flux patterns coincide with freshwater discharge patterns of the Ganges-Brahmaputra river system. Opal/carbonate and org. carbon/carbonate carbon ratios increase during the SW monsoon due to variations in salinity and productivity patterns in the surface waters as a result of increased freshwater and nutrient input from rivers. Comparison of 5 yr data show that fluxes of biogenic and lithogenic particulate matter are higher in the Bay of Bengal even though the Arabian Sea is considered to be more productive. Our results indicate that in the northern Indian Ocean interannual variability in org. carbon flux is directly related to the strength and intensity of the SW monsoon while its transfer from the upper layers to the deep sea is partly controlled by input of lithogenic matter from adjacent continents.
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    79. 79
      Gomes, H. R.; Goes, J. I.; Saino, T. Influence of physical processes and freshwater discharge on the seasonality of phytoplankton regime in the Bay of Bengal Continental Shelf Research 2000, 20 (3) 313– 330
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      Sabine, C. L.; Key, R. M.; Feely, R. A.; Greeley, D. Inorganic carbon in the Indian Ocean: Distribution and dissolution processes Global Biogeochem. Cycles 2002, 16 (4) 1067
      There is no corresponding record for this reference.
    81. 81
      Biswas, H.; Mukhopadhyay, S. K.; De, T. K.; Sen, S.; Jana, T. K. Biogenic controls on the air-water carbon dioxide exchange in the Sundarban mangrove environment, northeast coast of Bay of Bengal, India Limnolo. Oceanogr. 2004, 49 (1) 95– 101
      There is no corresponding record for this reference.
    82. 82
      PrasannaKumar, S.; Sardessai, S.; Ramaiah, N.; Bhosle, N. B.; Ramaswamy, V.; Ramesh, R.; Sharada, M. K.; Sarin, M. M.; Sarupria, J. S.; Muraleedharan, U. Bay of Bengal Process Studies Final Report; NIO: Goa, India, 2006; p 141.
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    83. 83
      Akhand, A.; Chanda, A.; Dutta, S.; Manna, S.; Hazra, S.; Mitra, D.; Rao, K. H.; Dadhwal, V. K. Characterizing air–sea CO2 exchange dynamics during winter in the coastal water off the Hugli-Matla estuarine system in the northern Bay of Bengal, India J. Oceanogr. 2013, 69 (6) 687– 697
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      Characterizing air-sea CO2 exchange dynamics during winter in the coastal water off the Hugli-Matla estuarine system in the northern Bay of Bengal, India
      Akhand, Anirban; Chanda, Abhra; Dutta, Sachinandan; Manna, Sudip; Hazra, Sugata; Mitra, Debasis; Rao, K. H.; Dadhwal, V. K.
      Journal of Oceanography (2013), 69 (6), 687-697CODEN: JOOCE7; ISSN:0916-8370. (Springer)
      The distribution of the fugacity of CO2 (fCO2) and air-sea CO2 exchange were comprehensively investigated in the outer estuary to offshore shallow water region (lying adjacent to the Sundarban mangrove forest) covering an area of ∼2,000 km2 in the northern Bay of Bengal during the winter. A total of ten sampling surveys were conducted between 1 Dec., 2011 and 21 Feb., 2012. Physico-chem. variables like sea surface temp. (SST), salinity, pH, total alky. (TAlk), dissolved inorg. carbon (DIC) and in vivo chlorophyll-a along with atm. variables were measured in order to study their role in controlling the CO2 flux. Surface water fCO2 ranged between 111 and 459 μatm which correlated significantly with the SST (r = 0.71, p < 0.001, n = 62). Neither DIC nor TAlk showed any linear relationship with varying salinity in the estuarine mixing zone, demonstrating the significant presence of non-carbonate alky. An overall net biol. control on the surface fCO2 distribution was established during the study, although no significant correlation was found between chlorophyll-a and fCO2 (water). The shallow water region studied was mostly under-satd. with CO2 and acted as a sink for atm. CO2. The difference between surface water and atm. fCO2 (ΔfCO2) ranged from -274 to 69 μatm, with an av. seaward flux of -10.5 ± 12.6 μmol m-2 h-1. The ΔfCO2 and hence the air-sea CO2 exchange was primarily regulated by the variation in sea surface fCO2, since atm. fCO2 varied over a comparatively narrow range of 361.23-399.05 μatm.
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    84. 84
      Burke, L. M.; Maidens, J. Reefs at Risk in the Caribbean; World Resources Institute: Washington, DC, 2004.
      There is no corresponding record for this reference.
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      Langdon, C.; Atkinson, M. J., Effect of elevated pCO2 on photosynthesis and calcification of corals and interactions with seasonal change in temperature/irradiance and nutrient enrichment. J. Geophys. Res.: Oceans 2005, 110, (C9).
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      Aschbacher, J.; Milagro-Pérez, M. P. The European Earth monitoring (GMES) programme: Status and perspectives Rem. Sens. Environ. 2012, 120, 3– 8
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      Berger, M.; Moreno, J.; Johannessen, J. A.; Levelt, P. F.; Hanssen, R. F. ESA’s sentinel missions in support of Earth system science Rem. Sens. Environ. 2012, 120, 84– 90
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      Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services Rem. Sens. Environ. 2012, 120, 25– 36
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    89. 89
      Donlon, C.; Berruti, B.; Buongiorno, A.; Ferreira, M. H.; Féménias, P.; Frerick, J.; Goryl, P.; Klein, U.; Laur, H.; Mavrocordatos, C. The global monitoring for environment and security (GMES) sentinel-3 mission Rem. Sens. Environ. 2012, 120, 37– 57
      There is no corresponding record for this reference.
    90. 90
      IOCCG. http://www.ioccg.org/sensors/GOCI.html (accessed August 27, 2014).
      There is no corresponding record for this reference.
    91. 91
      Reul, N.; Saux-Picart, S.; Chapron, B.; Vandemark, D.; Tournadre, J.; Salisbury, J., Demonstration of ocean surface salinity microwave measurements from space using AMSR-E data over the Amazon plume. Geophys. Res. Lett. 2009, 36, (13).
      There is no corresponding record for this reference.
    92. 92
      Sabia, R.; Fernández-Prieto, D.; Donlon, C.; Shutler, J.; Reul, N. In A Preliminary Attempt to Estimate Surface Ocean pH from Satellite Observations; IMBER Open Science Conference: Bergen, Norway, 2014.
      There is no corresponding record for this reference.
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      Willey, D. A.; Fine, R. A.; Millero, F. J. Global surface alkalinity from Aquarius satellite. In Ocean Sciences Meeting, Honolulu, HI, 2014.
      There is no corresponding record for this reference.