Surface Ocean CO2 Atlas (SOCAT) gridded data products

As a response to public demand for a well-documented, quality controlled, publically available, global surface ocean carbon dioxide (CO2) data set, the international marine carbon science community developed the Surface Ocean CO2 Atlas (SOCAT). The first SOCAT product is a collection of 6.3 million quality controlled surface CO2 data from the global oceans and coastal seas, spanning four decades (1968–2007). The SOCAT gridded data presented here is the second data product to come from the SOCAT project. Recognizing that some groups may have trouble working with millions of measurements, the SOCAT gridded product was generated to provide a robust, regularly spaced CO2 fugacity (fCO2) product with minimal spatial and temporal interpolation, which should be easier to work with for many applications. Gridded SOCAT is rich with information that has not been fully explored yet (e.g., regional differences in the seasonal cycles), but also contains biases and limitations that the user needs to recognize and address (e.g., local influences on values in some coastal regions).

Coccolithophore surface distributions in the North Atlantic and their modulation of the air-sea flux of CO2 from 10 years of satellite Earth observation data

Coccolithophores are the primary oceanic phytoplankton responsible for the production of calcium carbonate (CaCO3). These climatically important plankton play a key role in the oceanic carbon cycle as a major contributor of carbon to the open ocean carbonate pump (similar to 50 %) and their calcification can affect the atmosphere-to-ocean (air-sea) uptake of carbon dioxide (CO2) through increasing the seawater partial pressure of CO2 (pCO(2)). Here we document variations in the areal extent of surface blooms of the globally important coccolithophore, Emiliania huxleyi, in the North Atlantic over a 10-year period (1998-2007), using Earth observation data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). We calculate the annual mean sea surface areal coverage of E. huxleyi in the North Atlantic to be 474 000 +/- 104 000 km(2), which results in a net CaCO3 carbon (CaCO3-C) production of 0.14-1.71 Tg CaCO3-C per year. However, this surface coverage (and, thus, net production) can fluctuate inter-annually by -54/+81% about the mean value and is strongly correlated with the El Nino/Southern Oscillation (ENSO) climate oscillation index (r = 0.75, p < 0.02). Our analysis evaluates the spatial extent over which the E. huxleyi blooms in the North Atlantic can increase the pCO(2) and, thus, decrease the localised air-sea flux of atmospheric CO2. In regions where the blooms are prevalent, the average reduction in the monthly air-sea CO2 flux can reach 55%. The maximum reduction of the monthly air-sea CO2 flux in the time series is 155 %. This work suggests that the high variability, frequency and distribution of these calcifying plankton and their impact on pCO(2) should be considered if we are to fully understand the variability of the North Atlantic air-to-sea flux of CO2. We estimate that these blooms can reduce the annual N. Atlantic net sink atmospheric CO2 by between 3-28 %.

Variation in ocean colour may help predict cod and haddock recruitment

Characteristics of the spring and fall phytoplankton blooms in spawning areas on the Scotian Shelf, Canada, were estimated from remote sensing data. These blooms, along with anomalies in the North Atlantic Oscillation, were used to explain variation in the recruitment of 4 populations of cod and haddock. We tested the effects of the timing of the bloom using the chlorophyll a (chl a) signal, the maximum amount of chl a, the timing of the diatom bloom, and the maximum relative dominance of diatoms on the recruitment (to Age 1) of cod and haddock on the Scotian Shelf. Models were run separately for the effects of the spring and fall blooms. Only 3 of 10 models tested (0-lag) explained significant (80 to 92%) variation in recruitment. However, the performance of these models was not consistent across populations or species, suggesting that generalities about how spring and fall phytoplankton blooms affect recruitment cannot yet be made. The differences among models suggest that fish larvae are probably adapted locally to food production and thus indirectly to the characteristics of the phytoplankton bloom, which in turn are influenced by regional (meso-scale) oceanographic conditions.

Generalized ocean color inversion model for retrieving marine inherent optical properties

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

Real-time sea surface air temperature retrieval over Southern Ocean using AMSR-E data

The AMSR-E satellite data and in-situ data were applied to retrieve sea surface air temperature (Ta) over the Southern Ocean. The in-situ data were obtained from the 24~(th) -26~(th) Chinese Antarctic Expeditions during 2008-2010. First, Ta was used to analyze the relativity with the bright temperature (Tb) from the twelve channels of AMSR-E, and no high relativity was found between Ta and Tb from any of the channels. The highest relativity was 0.38 (with 23.8 GHz). The dataset for the modeling was obtained by using in-situ data to match up with Tb, and two methods were applied to build the retrieval model. In multi-parameters regression method, the Tbs from 12 channels were used to the model and the region was divided into two parts according to the latitude of 50°S. The retrieval results were compared with the in-situ data. The Root Mean Square Error (RMS) and relativity of high latitude zone were 0.96℃and 0.93, respectively. And those of low latitude zone were 1.29 ℃ and 0.96, respectively. Artificial neural network (ANN) method was applied to retrieve Ta.The RMS and relativity were 1.26 ℃ and 0.98, respectively.

The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situRrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.

Coccolithophore surface distributions in the North Atlantic and their modulation of the air-sea flux of CO2 from 10 years of satellite Earth observation data

Coccolithophores are the primary oceanic phytoplankton responsible for the production of calcium carbonate (CaCO3). These climatically important plankton play a key role in the oceanic carbon cycle as a major contributor of carbon to the open ocean 5 carbonate pump (�50%) and their formation can affect the atmosphere-to-ocean (airsea) uptake of carbon dioxide (CO2) through increasing the seawater partial pressure of CO2 (pCO2). Here we document variations in the areal extent of surface blooms of the globally important coccolithophore, Emiliania huxleyi, in the North Atlantic over a 10-year period (1998–2007), using Earth observation data from the Sea-viewing Wide 10 Field of view Sensor (SeaWiFS).We calculate the annual mean surface areal coverage of E. huxleyi in the North Atlantic to be 474 000±119 000km2 yr−1, which results in a net CaCO3 production of 0.62±0.15 Tg CaCO3 carbon per year. However, this surface coverage and net production can fluctuate by −54/+81% about these mean values and are strongly correlated with the El Ni˜no/Southern Oscillation (ENSO) climate os15 cillation index (r =0.75, p<0.02). Our analysis evaluates the spatial extent over which the E. huxleyi blooms in the North Atlantic can increase the pCO2 and thus decrease the localised sink of atmospheric CO2. In regions where the blooms are prevalent, the average reduction in the monthly CO2 sink can reach 12 %. The maximum reduction of the monthly CO2 sink in the time series is 32 %. This work suggests that the high 20 variability, frequency and distribution of these calcifying plankton and their impact on pCO2 should be considered within modelling studies of the North Atlantic if we are to fully understand the variability of its air-to-sea CO2 flux.

ALES: a multi-mission adaptive sub-waveform retracker for coastal and open ocean altimetry

Satellite altimetry has revolutionized our understanding of ocean dynamics thanks to frequent sampling and global coverage. Nevertheless, coastal data have been flagged as unreliable due to land and calm water interference in the altimeter and radiometer footprint and uncertainty in the modelling of high-frequency tidal and atmospheric forcing. Our study addresses the first issue, i.e. altimeter footprint contamination, via retracking, presenting ALES, the Adaptive Leading Edge Subwaveform retracker. ALES is potentially applicable to all the pulse-limited altimetry missions and its aim is to retrack both open ocean and coastal data with the same accuracy using just one algorithm. ALES selects part of each returned echo and models it with a classic ”open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder-Mead nonlinear optimization technique. By avoiding echoes from bright targets along the trailing edge, it is capable of retrieving more coastal waveforms than the standard processing. By adapting the width of the estimation window according to the significant wave height, it aims at maintaining the accuracy of the standard processing in both the open ocean and the coastal strip. This innovative retracker is validated against tide gauges in the Adriatic Sea and in the Greater Agulhas System for three different missions: Envisat, Jason-1 and Jason-2. Considerations of noise and biases provide a further verification of the strategy. The results show that ALES is able to provide more reliable 20-Hz data for all three missions in areas where even 1-Hz averages are flagged as unreliable in standard products. Application of the ALES retracker led to roughly a half of the analysed tracks showing a marked improvement in correlation with the tide gauge records, with the rms difference being reduced by a factor of 1.5 for Jason-1 and Jason-2 and over 4 for Envisat in the Adriatic Sea (at the closest point to the tide gauge).

Plankton indicators and ocean observing systems: support to the marine ecosystem state assessment

Ecological indicators are used extensively as tools to manage environmental resources. In the oceans, indicators of plankton can be measured using a variety of observing systems including: mooring stations, ships, autonomous floats and ocean colour remote sensing. Given the broad range of temporal and spatial sampling resolutions of these different observing systems, as well as discrepancies in measurements obtained from different sensors, the estimation and interpretation of plankton indicators can present significant challenges. To provide support to the assessment of the state of the marine ecosystem, we propose a suite of plankton indicators and subsequently classify them in an ecological framework that characterizes key attributes of the ecosystem. We present two case studies dealing with plankton indicators of biomass, size structure and phenology, estimated using the most spatially extensive and longest in situ and remote-sensing observations. Discussion of these studies illustrates how some of the challenges in estimating and interpreting plankton indicators may be addressed by using for example relative measurement thresholds, interpolation procedures and delineation of biogeochemical provinces. We demonstrate that one of the benefits attained, when analyzing a suite of plankton indicators classified in an ecological framework, is the elucidation of non-trivial changes in composition, structure and functioning of the marine ecosystem.

Global assessment of ocean carbon export by combining satellite observations and food-web models

The export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food-web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional-scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional-scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of ~6 Pg C yr−1. Global export estimates show small variation (typically < 10%) to factor of 2 changes in model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump.

Enhancement of primary production in the North Atlantic outside of the spring bloom, identified by remote sensing of ocean colour and temperature

The heterogeneity in phytoplankton production in the North Atlantic after the spring bloom is poorly understood. We analysed merged microwave and infrared satellite sea surface temperature (SST) data and ocean colour phytoplankton size class biomass, primary production (PP) and new production (ExP) derived from SeaWiFS data, to assess the spatial and temporal frequency of surface thermal fronts and areas of enhanced PP and ExP. Strong and persistent surface thermal fronts occurred at the Reykjanes Ridge (RR) and sub-polar front (SPF), which sustain high PP and ExP and, outside of the spring bloom, account for 9% and 15% of the total production in the North Atlantic. When normalised by area, PP at the SPF is four times higher than the RR. Analysis of 13 years of satellite ocean colour data from SeaWiFS, and compared with MODIS-Aqua and MERIS, showed that there was no increase in Chla from 1998 to 2002, which then decreased in all areas from 2002 to 2007 and was most pronounced in the RR. These time series also illustrated that the SPF exhibited the highest PP and the lowest variation in Chla over the ocean colour record. This implies that the SPF provides a high and consistent supply of carbon to the benthos irrespective of fluctuations in the North Atlantic Oscillation.