Beschreibung: The potential impact of rising carbon dioxide (CO2) on carbon transfer from phytoplankton to bacteria was investigated during the 2005 PeECE III mesocosm study in Bergen, Norway. Sets of mesocosms, in which a phytoplankton bloom was induced by nutrient addition, were incubated under 1x (~350 µatm), 2x (~700 µatm), and 3x present day CO2 (~1050 µatm) initial seawater and sustained atmospheric CO2 levels for 3 weeks. 13C labelled bicarbonate was added to all mesocosms to follow the transfer of carbon from dissolved inorganic carbon (DIC) into phytoplankton and subsequently heterotrophic bacteria, and settling particles. Isotope ratios of polar-lipid-derived fatty acids (PLFA) were used to infer the biomass and production of phytoplankton and bacteria. Phytoplankton PLFA were enriched within one day after label addition, whilst it took another 3 days before bacteria showed substantial enrichment. Group-specific primary production measurements revealed that coccolithophores showed higher primary production than green algae and diatoms. Elevated CO2 had a significant positive effect on post-bloom biomass of green algae, diatoms, and bacteria. A simple model based on measured isotope ratios of phytoplankton and bacteria revealed that CO2 had no significant effect on the carbon transfer efficiency from phytoplankton to bacteria during the bloom. There was no indication of CO2 effects on enhanced settling based on isotope mixing models during the phytoplankton bloom, but this could not be determined in the post-bloom phase. Our results suggest that CO2effects are most pronounced in the post-bloom phase, under nutrient limitation.

Beschreibung: Ocean acidification and carbonation, driven by anthropogenic emissions of carbon dioxide (CO2), have been shown to affect a variety of marine organisms and are likely to change ecosystem functioning. High latitudes, especially the Arctic, will be the first to encounter profound changes in carbonate chemistry speciation at a large scale, namely the under-saturation of surface waters with respect to aragonite, a calcium carbonate polymorph produced by several organisms in this region. During a CO2 perturbation study in 2010, in the framework of the EU-funded project EPOCA, the temporal dynamics of a plankton bloom was followed in nine mesocosms, manipulated for CO2 levels ranging initially from about 185 to 1420 matm. Dissolved inorganic nutrients were added halfway through the experiment. Autotrophic biomass, as identified by chlorophyll a standing stocks (Chl a), peaked three times in all mesocosms. However, while absolute Chl a concentrations were similar in all mesocosms during the first phase of the experiment, higher autotrophic biomass was measured at high in comparison to low CO2 during the second phase, right after dissolved inorganic nutrient addition. This trend then reversed in the third phase. There were several statistically significant CO2 effects on a variety of parameters measured in certain phases, such as nutrient utilization, standing stocks of particulate organic matter, and phytoplankton species composition. Interestingly, CO2 effects developed slowly but steadily, becoming more and more statistically significant with time. The observed CO2 related shifts in nutrient flow into different phytoplankton groups (mainly diatoms, dinoflagellates, prasinophytes and haptophytes) could have consequences for future organic matter flow to higher trophic levels and export production, with consequences for ecosystem productivity and atmospheric CO2.

Beschreibung: Published empirical algorithms for oceanic total alkalinity (TA) and dissolved inorganic carbon (DIC) are used with monthly sea surface salinity (SSS) and temperature (SST) derived from satellite (SMOS, Aquarius, SST CCI) and interpolated in situ (CORA) measurements and climatological (WOA) ancillary data to produce monthly maps of TA and DIC at one degree spatial resolution. Earth system model TA and DIC (HADGEM2-ES) are also included. Results are compared with in situ (GLODAPv2) TA and DIC and results analysed in five regions (global, Greater Caribbean, Amazon plume, Amazon plume with in situ SSS 〈 35 and Bay of Bengal). Results are presented in three versions, denoted by 'X' in the lists below: using all available data (X = ''); excluding data with bathymetry 〈 500m (X = 'Depth500'); excluding data with both bathymetry 〈 500m and distance from nearest coast 〈 300 km (X = 'Depth500Dist300'). Datasets S1 to S5 are .csv lists of matchups in each region - date and location, in situ TA and DIC measurements and estimated uncertainties, all input datasets, estimates of TA and DIC from all outputs, and the best available output estimates of TA and DIC for each matchup. S1_GlobalAlgorithmMatchupsX.csv S2_GreaterCaribbeanAlgorithmMatchupsX.csv S3_AmazonPlumeAlgorithmMatchupsX.csv S4_AmazonPlumeLowSAlgorithmMatchupsX.csv S5_BayOfBengalAlgorithmMatchupsX.csv Datasets S6 to S10 are .csv statistical analyses of the performance of each combination of algorithm and input data - carbonate system variable, algorithm, input datasets used, (MAD, RMSD using all available data, output score, RMSD estimated from output score, output and in situ mean and standard deviation, correlation coefficient), all items in brackets presented both unweighted and weighted, number of matchups, number of potential matchups, matchup coverage, RMSD after subtraction of linear regression, percentage reduction in RMSD due to subtraction of linear regression and weighted score divided by number of matchups). S6_GlobalAlgorithmScoresX.csv S7_GreaterCaribbeanAlgorithmScoresX.csv S8_AmazonPlumeAlgorithmScoresX.csv S9_AmazonPlumeLowSAlgorithmScoresX.csv S10_BayOfBengalAlgorithmScoresX.csv Datasets S11 to S15 are zipped netCDF files containing error analyses of all outputs in each region, including the squared error of each output at each matchup, the weight of each squared error (1/squared uncertainty), weight * squared error, number of matchups available to each output, number of matchups available to each combination of two outputs, (score of each output in a given comparison of two outputs, overall output score and RMSD estimated from output score), all items in the last brackets presented both unweighted and weighted. S11_GlobalSquaredErrorsX.nc S12_GreaterCaribbeanSquaredErrorsX.nc S13_AmazonPlumeSquaredErrorsX.nc S14_AmazonPlumeLowSSquaredErrorsX.nc S15_BayOfBengalSquaredErrorsX.nc Datasets S16 to S20 are zipped netCDF files containing global maps of the mean and standard deviation of each of: in situ data; output data; output data - in situ data and number of matchups. Regional files show the same maps, but only including data within the region. S16_GlobalmapsX.nc S17_GreaterCaribbeanmapsX.nc S18_AmazonPlumemapsX.nc S19_AmazonPlumeLowSmapsX.nc S20_BayOfBengalmapsX.nc Datasets S21 and S22 are .csv files containing the effect on estimated RMSD of excluding various combinations of algorithms and/or inputs for TA and DIC in each region. For a given variable and region, the first line shows the algorithm, input data sources, estimated RMSD and bias of the output with lowest estimated RMSD. Subsequent lines show the effect of excluding combinations of algorithms and/or inputs, ordered first by the number of algorithms/inputs excluded (fewest first), then by effect on lowest estimated RMSD. So the first line(s) consist of the effects of excluding the best algorithm and each of the input sources to that algorithm, most important first. Each line consists of the item excluded, ratio of resulting estimated RMSD to original estimated RMSD, resulting bias and number of items excluded. Some exclusions are equivalent, for instance exclusion of WOA nitrate (the only nitrate source) is equivalent to excluding all algorithms using nitrate. Dataset S21 contains a comprehensive list of all possible exclusions, and so is rather hard to read and interpret. To mitigate this, Dataset S22 contains only those exclusion sets with effect greater than 1% and at least 0.1% greater than any subset of its exclusions. S21_importancesX.csv S22_importances2X.csv Dataset S23 is a .csv file containing like-for-like comparisons of RMSD between TA and DIC in each region. Bear in mind that the RMSD shown here is not the same as the estimated RMSD (RMSDe) shown elsewhere. S23_TA_DICcomparisonX.csv

Beschreibung: The track of the cruise, and the location of the different stations cover a large range of water masses, many of which take part in the exchange across the Greenland-Scotland Ridge, and of importance for the biogeochemical fluxes in the region. These water masses are of very different origins, which can be observed in the concentration of the different biogeochemical parameters. The concentrations are a result of the combination of the physical and biogeochemical environment in each formation region, and the processes acting on the water masses as they are transported away from the formation areas. The aim of the biogeochemistry measurements was to achieve a better understanding of the strength and variability of the biological carbon pump in the North Atlantic and Nordic Seas.

Beschreibung: The Deep Convection cruise repeatedly sampled two locations in the North Atlantic, sited in the Iceland and Norwegian Basins, onboard the RV Meteor (19 March - 2 May 2012). Samples were collected from multiple casts of a conductivity-temperature-depth (CTD) - Niskin rosette at each station. Water samples for primary production rates, community structure, chlorophyll a [Chl a], calcite [PIC], particulate organic carbon [POC] and biogenic silicic acid [BSi] were collected from predawn casts from six light depths (55%, 20%, 14%, 7%, 5% and 1% of incident PAR). Additional samples for community structure and ancillary parameters were collected from a second cast. Carbon fixation rates were determined using the 13C stable isotope method. Water samples for diatom and micro zooplankton counts, collected from the predawn casts, were preserved with acidic Lugol's solution (2% final solution) and counted using an inverted light microscope. Water samples for coccolithophore counts were collected onto cellulose nitrate filters and counted using polarising light microscopy. Water samples for Chl a analysis were filtered onto MF300 and polycarbonate filters and extracted in 90% acetone. PIC and BSi samples were filtered onto polycarbonate filters and analysed using an inductively coupled plasma emission optical spectrometer and a SEAL QuAAtro autoanalyser respectively.