Find research datasets worth reusing

Figure 4 in Carbon-to-chlorophyll-a ratio in the phytoplankton of the Black Sea surface layer: variability and regulatory factors

Figure 4. Seasonal dynamic of monthly average values of C:Chl a ratio (mg С·mg Chl а-1) and their standard deviation (vertical lines) for all stations, located in the Black Sea coastal waters (during period 2000–2010).

Figure 3 in Carbon-to-chlorophyll-a ratio in the phytoplankton of the Black Sea surface layer: variability and regulatory factors

Figure 3. Seasonal variability of C:Chl a ratio (mg С·mg Chl а-1), phytoplankton biomass, water temperature, nitrate and ammonium concentrations in the surface water layer of Sevastopol Bay, as well as solar radiation intensity, which can reach the sea surface.

Figure 6 in Carbon-to-chlorophyll-a ratio in the phytoplankton of the Black Sea surface layer: variability and regulatory factors

Figure 6. Spatial distribution of chlorophyll a, phytoplankton biomass, relative share of diatoms in the total biomass of nano- and microphytoplankton and C:Chl a ratio in the surface water layer of the Black Sea in October 2010.

Figure 5 in Carbon-to-chlorophyll-a ratio in the phytoplankton of the Black Sea surface layer: variability and regulatory factors

Figure 5. Spatial distribution of chlorophyll a, phytoplankton biomass, relative share of diatoms in the total biomass of nano- and microphytoplankton and C:Chl a ratio in the surface water layer of the Black Sea in August 2011.

opencc-by-4.0Aug 2012View details →

Fig. 7. Phytoplankton from the Ivanye Zolote section. GIUS 4−3592. A in Palynology and microfacies of Lower Devonian mixed carbonate-siliciclastic deposits in Podolia, Ukraine

Fig. 7. Phytoplankton from the Ivanye Zolote section. GIUS 4−3592. A. Multiplicisphaeridium cf. M. raspa (Cramer) Wicander, 1986; IZ−4 sample. B. Multiplicisphaeridium cf. M. raspa (Cramer) Wicander, 1986; IZ−4 sample. C. Veryhachium trispinosum (Eisenack) Deunff, 1954; IZ−4 sample. D. Riculasphaera fissa Loeblich and Drugg, 1968; IZ−5 sample. E. Helosphaeridium cf. guttatum Playford, 1981; IZ−4 sample. F. Helosphaeridium microclavatum Playford, 1981; IZ−1 sample. G. Elektoriskos sp.; IZ−4 sample. H. Dictyotidium eurydictyotum Kirjanov, 1978; IZ−8 sample. I. Baltisphaeridium scabrosum Kirjanov, 1978; IZ−8 sample. J. Ammonidium cf. A. garrasinoi Ottone, 1996; IZ−1 sample. K. Tyligmasoma alargadum (Cramer) Ą

opencc-by-4.0Sep 2024View details →

Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"

<div>Brief Summary:</div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div>List of folders and subfolders and what they contain:</div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div>How to Replicate this Analysis:</div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div>   A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>. To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions. ocean_toolbox.py: import statsmodels.formula.api as smf import os import matplotlib.pyplot as plt import numpy as np from uncertainties import unumpy as unp from scipy import stats def file_grab(root,find,start): #grabs files by file extensions and location     filelst = []     for subdir, dirs, files in os.walk(root):         for file in files:             filepath = subdir + os.sep + file             if filepath.endswith(find):                 if filepath.startswith(start):                     filelst.append(filepath)     return filelst def sep_bbp(data, name_z, name_chla, name_bbp):          '''     data: Pandas Dataframe containing the profile data     name_z: name of the depth variable in data     name_chla: name of the chlorophyll-a variable in data     name_bbp: name of the particle backscattering variable in data              returns: the data variable with particle backscattering partitioned into      phytoplankton (bbpphy) and non-algal particle components (bbpnap).     '''     #name_chla = 'chla'     #name_z = 'depth'     #name_bbp = 'bbp470'     dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum     part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile              mod = smf.quantreg('bbp470 ~ ' + str(name_z),                         part_prof).fit(q=0.01) # Find model to 1 percentile     y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap          part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend     z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min()                                                   # Find depth where bbp NAP and bbp intersect     data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()     data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)               data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton     data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton     return data['bbpphy'], z_lim def bbp_to_cphy(bbp_data, sf):          '''     data: Pandas Dataframe containing the profile data     name_bbp: name of the particulate backscattering variable in data     name_bbp_err: name of particulate backscattering error variable in data          returns: the data variable with particle backscattering [/m] converted into     phytoplankton carbon [mg/m^3].     '''          cphy_data = bbp_data.mul(sf)         return cphy_data

opencc-by-4.0Dec 2020View details →

Differences in carbon acquisition could explain adaptive responses in a Baltic Sea pico-phytoplankton

The data uploaded are the basis of study on the carbon update in a Baltic Sea Pico-plankton species. Our study addresses a challenging question concerning the adaptive responses to warming and carbon uptake related strategies in a pico-phytoplankton species, which are, unlike bloom-forming, larger phytoplankton, contributing to the carbon cycle and the base of food-webs all year round. In addition, we address this question also in non-exponential growth conditions. This is important considering that most phytoplankton species are likely not at exponential phase all the time. We show that space for time substitution experiments provide a valid method for investigating questions about the adaptive potential in pico-phytoplankton that the measurements of potential growth on organic carbon sources can potentially highlight differences in carbon acquisition strategies.

dryad36/100

Phytoplanktonic polysaccharide-mediated sedimentation of particulate organic carbon triggers lagged methane emissions

Reservoirs act as carbon sinks when sedimentation of particulate organic carbon (POC) exceeds CO2 and CH4 emissions. Here, we study the poorly explored process where phytoplankton-derived acidic polysaccharides (APs) aggregate into particulate organic matter, promoting carbon export to sediments. This source of particulate organic carbon (POC) in sediments can mineralize to CO2 and CH4 over various timescales. Our research, centered on a Mediterranean reservoir, elucidates phenological trends of APs and POC sedimentation and identifies their predominant drivers. Our findings present synchronic sedimentation patterns of POC and APs but identify a two-week delay between POC sedimentation and CH4 emissions. Despite its eutrophic status, our data demonstrate that this reservoir acts as a carbon sink by sequestering 4.33 g C m-2 y-1, which accentuates the importance of temporal integration at multiple scales when analyzing carbon budget within reservoirs.

opencc-zeroDec 2023View details →

zenodo36/100

Size-Dependent Dynamics of the Internal Carbon Pool Drive Isotopic Vital Effects in Calcifying Phytoplankton

Isotopic offsets in biogenic calcite from equilibrium values can provide unique insights into the physiology and mechanisms of carbon regulation in calcifying phytoplankton. This study examines the impact of varying CO2 (controlled via pH) on five coccolithophore species chosen for varied cell sizes, physiology, and calcification. The study investigates isotopic offsets in coccolith calcite and organic matter, in relation to carbon demand and supply (µ/CO2). Species-specific CO2/pH optima for growth (µopt) were derived from variations in growth rates with varying CO2 concentrations. Growth rates for all species declined with rising CO2 (decreasing pH) due to H+-driven inhibition. C. braarudii and C. leptoporus exhibited high µopt values (suggesting high carbon-demand) and limited growth under low CO2 (high pH) suggesting carbon limitation. Under low CO2 supply, when growth rates were CO2-limited, both species exhibited coincident isotopic depletion in calcite and organic matter as a consequence of CO2 diffusion into the cell that experienced no equilibration as a result of a highly depleted internal carbon pool. In these two species, isotopic values in calcite  remained unaffected by growth rates and CO2 concentration (µ/CO2) when CO2 was sufficient for optimal growth. G. huxleyi and G. oceanica displayed optima for growth (µopt) at low CO2 concentrations and showed no growth limitation under low CO2 (indicating low carbon-demand). Both species experienced depleted (negative) vital effects caused by an excess of CO2 diffusion into the small internal carbon pool of the cell when diffusive carbon supply outpaced low demand (low µ/CO2). Enriched (positive) vital effects were observed under low carbon supply and high demand, likely due to diffusive loss of CO2 from the cell's intracellular carbon pool due to a lower intracellular pH. C. carterae exhibited an intermediate µopt and isotopically equilibrated intracellular carbon pool such that δ13C values in calcite and organic matter suggested a shared carbon pool. This study illustrates that pH-CO2 driven vital effects and fractionation into organic matter indicate the residence time for carbon in the intracellular carbon pool, and the size of this pool is proportional to cell size. C. leptoporus and C. carterae can elevate intracellular pH to minimise CO2 leakage, whereas vital effects in G. huxleyi and G. oceanica are caused by diffusive CO2 due to the small size of their internal carbon pool.

opencc-by-4.0Oct 2023View details →

zenodo36/100

Lipids of different phytoplankton groups differ in sensitivity to degradation: implications for carbon export

Data were collected on seven cruises on a monthly basis from February to August in 2010 in the northern Adriatic Sea (RV001 (N 45°04.8', E 013°36.6'), SJ107 (N 45°02.9', E 013°19.0'), SJ105 (N 45°02.0', E 0.13°09.3'), SJ103 (N 45°01.0', E 012°59.6'), SJ101 (N 44°59.9', E 012°50.3'), and SJ108 (N 44°48.7', E 012°45.0')).  A CTD probe (Seabird SBE25, Sea–Bird Electronics Inc., Bellevue, Washington, USA) was used to measure temperature and salinity. Total phosphorus, dissolved inorganic orthophosphates (PO43-), total inorganic nitrogen (TIN), including nitrates (NO3¯), nitrites (NO2¯), and ammonium (NH4+)), were determined by spectrophotometric methods (Parsons et al., 1984) on board and immediately after sampling using Shimadzu UV-Mini 1240 spectrophotometer with 10 cm quartz cuvettes. Organic phosphorus concentration was calculated as the difference between total and inorganic phosphorus concentrations. Subsamples for the determination of chlorophyll a (Chl a) were filtered on Whatman GF/C filters and stored frozen at -20°C until further processing. Chl a concentrations were determined following 3 h extraction in 90% acetone (in the dark, with grinding), on a Turner TD–700 fluorimeter (Parsons et al., 1984). We preserved 200 mL of seawater with 2% neutralized formaldehyde (final concentration) and performed nano- and microphytoplankton determination and enumeration within one month of sampling. The stored sample was homogenized by gentle shaking, and a subsample was added to the Utermöhl sedimentation chamber (volume: 50 mL; Hydro-Bios Apparatebau, Altenholz, Germany), where it settled for ~30 h. We performed the analysis on a Zeiss Axiovert 200 (Zeiss, Jena, Germany) following the inverted microscope method (Utermöhl, 1958, Hasle 1978). Total phytoplankton included all species counted in the microphytoplankton (20–200 µm) and nanophytoplankton (2-20 µm) groups (Sieburth et al., 1978). Taxa were identified to species level and subsequently grouped to diatoms, dinoflagellates, and nanophytoplankton coccolithophores and phytoflagellates (which included chlorophytes, chrysophytes, cryptophytes and prasinophytes) according to Tomas (1997). For POC determination, 1 L of seawater was filtered on board through 0.7 mm Whatman GF/F filters precombusted at 450 °C/5h. After filtration, the filters were rinsed with Milli-Q water to remove salts and stored in liquid nitrogen on board and at -80 oC in the laboratory until analysis. POC was analyzed using an SSM–5000A solid sample module connected to a Shimadzu TOC–VCPH carbon analyzer calibrated with glucose (Sugimura and Suzuki, 1988). POC concentrations were corrected based on filter blank measurements. The average filter blank value including the instrument blank value corresponded to 5 mg C L-1. The reproducibility obtained for the glucose standard was 3%. For particulate lipid analysis, we collected 3 L of seawater prefiltered through a 200 μm stainless steel screen to remove larger particles including microzooplankton. Lipids were collected on through precombusted (450 °C/5h) 47 mm GF/F filters and stored in liquid nitrogen until lipid extraction. It was performed using a modified one-phase solvent mixture of dichloromethane-methanol-water procedure (Bligh and Dyer, 1959; Vrana et al., 2023). In short, in order to assess recoveries in later stages of sample analysis we added 5 µg of standard methyl stearate to the sliced filters together with 10 mL of a one-phase solvent mixture (dichloromethane/methanol/deionized water (1:2:0.8 v/v/v)). This was then subjected to an ultrasonic treatment for three minutes and stored overnight in the refrigerator, afterwards we filtered the extracts through a sinter funnel into a separatory funnel, washed once with a the one-phase solvent mixture (10 mL), once with dichloromethane (5 mL) and 0.73% NaCl (5 mL) (1:1 v/v), and once with 10 mL dichloromethane. The dichloromethane extracts were concentrated by rotary evaporation under a nitrogen atmosphere and kept at -20 °C until measurements were made. To prepare the lipid extracts for analysis, the dichloromethane extracts were evaporated to dryness under nitrogen flow and then dissolved in 20 µL dichloromethane prior to analysis. Lipid classes were separated on Chromarods SIII and quantified with external calibration using a mixture of standard lipids by a thin-layer chromatograph-flame ionization detector (TLC-FID) Iatroscan Mark-VI (Iatron), using a hydrogen flow of 160 mL min-1 and an air flow of 2000 mL min-1. This method identify eighteen lipid classes: hydrocarbons (HC), steryl esters (SE), fatty acid methyl esters (ME), fatty ketone (KET), triacylglycerols (TG), free fatty acids (FFA), fatty alcohols (ALC), 1,3-diacylglycerols (1,3DG), sterols (ST), 1,2-diacylglycerols (1,2DG), pigments (PIG), monoacylglycerols (MG), three glycolipids (GL) including monogalactosyl-, digalactosyl-, and sulfoquinovosyl- diacylglycerol (MGDG, DGDG, and SQDG, respectively), and three phospholipids (PL) (phosphatidylglycerols (PG), phosphatidylethanolamines (PE), and phosphatidylcholines (PC)). Total lipid concentration is calculated by summing all detected classes. Full details can be found in Ga&scaron;parović et al. (2015; 2017). In this article we focused on lipid degradation indices trough the lipolysis index (Goutx et al., 2003), which characterize the degree of lipid degradation in seawater. Lipolysis index is calculated as the ratio of the sum of lipid degradation indices (ALC+FFA+MG+DG) to the sum of cell lipids TG, WE, and glyco- and phospho-lipids (Goutx et al., 2003).

opencc-by-4.0Dec 2022View details →

dryad36/100

Code and data for "Effects of phytoplankton species distribution on particulate organic carbon dynamics along a coastal gradient"

Open the record for dataset details and reuse information.

publicJul 2025View details →

opencc-zeroDec 2018View details →

Data from: Highest plasticity of carbon concentrating mechanisms in earliest evolved phytoplankton

Phytoplankton photosynthesis strongly relies on the operation of carbon‐concentrating mechanisms (CCMs) to accumulate CO2 around their carboxylating enzyme ribulose‐1,5‐bisphosphate carboxylase/oxygenase (RuBisCO). Earlier evolved phytoplankton groups were shown to exhibit higher CCM activities to compensate for their RuBisCO with low CO2 specificities. Here, we tested whether earlier evolved phytoplankton groups also exhibit a higher CCM plasticity. To this end, we collected data from literature and applied a Bayesian linear meta‐analytic model. Our results show that with elevated pCO2, photosynthetic CO2 affinities decreased strongest and most consistent for the earlier evolved groups, i.e., cyanobacteria and dinoflagellates, while CO2‐dependent changes in affinities for haptophytes and diatoms were smaller and less consistent. In addition, responses of maximum photosynthetic rates toward elevated pCO2 were generally small and inconsistent across species. Our results demonstrate that phytoplankton groups with an earlier origin possess a high CCM plasticity, whereas more recently evolved groups do not, which likely results from evolved differences in the CO2 specificity of RuBisCO.

zenodo32/100

DATASET: Isotope Ratios, Carbon and Nitrogen Concentrations, and Phytoplankton Composition Data from the ACE Expedition

This dataset represents the averaged observations derived from the Antarctic Circumnavigation Expedition (ACE - 2016/2017) and has been utilized for the analyses presented in the publication: "A circum-Antarctic plankton isoscape: Carbon export potential across the summertime Southern Ocean". It encompasses a comprehensive suite of biogeochemical measurements, specifically the isotopic ratios (δ13C, δ15N) and concentrations of carbon and nitrogen in Suspended Particulate Matter (SPM). Additionally, it includes data from High-Performance Liquid Chromatography (HPLC) analyses (i.e., Total Chl-a and fractions of pico-, nano-, and micro-phytoplankton).This dataset also contains the outputs of our calculations based on the two-endmember isotope mixing model (Fawcett et al., 2011), complemented by the Rayleigh model (Mariotti et al., 1981) to deduce the fraction of phytoplankton biomass originating from new nitrogen source (New Production).

opencc-by-4.0Oct 2023View details →

opencc-zeroJul 2022View details →

Physiological control on carbon isotope fractionation in marine phytoplankton

One of the great challenges in biogeochemical research over the past half a century has been to quantify and understand the mechanisms underlying stable carbon isotope fractionation (εp) in phytoplankton in response to changing CO2 concentrations. Partly, this interest is grounded in the use of fossil photosynthetic organism remains as a proxy for past atmospheric CO2 levels. Phytoplankton organic carbon is depleted in 13C compared to its source because of kinetic fractionation by the enzyme RubisCO during photosynthetic carbon fixation, as well as through physiological pathways upstream of RubisCO. Moreover, other factors such as nutrient limitation, variations in light regime as well as phytoplankton culturing systems and inorganic carbon manipulation approaches may confound the influence of aquatic CO2 concentration ([CO2]) on εp. Here, based on experimental data compiled from the literature, we assess which underlying physiological processes cause the observed differences in εp for various phytoplankton groups in response to C-demand/C-supply (i.e., POC production/[CO2]) and test potential confounding factors. Culturing approaches and methods of carbonate chemistry manipulation were found to best explain the differences in εp between studies, although daylength was an important predictor for εp in haptophytes. Extrapolating results from culturing experiments to natural environments and for proxy applications therefore requires caution, and it should be carefully considered whether culture methods and experimental conditions are representative of natural environments.

zenodo32/100

Code and model outputs for article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass"

Scripts from the scientific article "Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass" Submitted to Global Biogeochemical Cycles Camila Serra-Pompei, Anna Hickman, Gregory L. Britten, Stephanie Dutkiewicz This repository contains all the files needed to run the MITgcm Biogeochemical and Optical model (MITgcmBgc, a.k.a. The Darwin model) and generate the figures in the paper. Model outputs are also provided, so there is no need to run the entire model to generate the figures. The main folder "codes_phyto_bbp_paper.zip" contains all the files to process model output and reproduce the figures from the paper. The main files are:     - "Script_main.m" loads and processes model outputs and generates figures 2 to 9.     - "Script_new_optical_params" generates the optical parameters that are used as inputs in the MITgcmBgc model. It also generates figure 1.     - "Script_sensitivity_analysis" Performs offline sensitivity analysis and figure 10. The "mitgcm_darwin_3.zip" folder contain the code of the MITgcm with the Biogeochemical and Optical modules (Darwin package), with the configurations used in this study. Explanations of the model can be found in https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html  These folders also contain the BGC-Argo floats data and climatological bbp and Chl NASA MODIS-Aqua GIOP data used in this study. The BGC-Argo data used in this article (download date: 27/10/2021) is available in "Other_extra_files/Argo_matrix.mat". The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu,  https://www.ocean-ops.org, https://doi.org/10.17882/42182).  The Argo Program is part of the Global Ocean Observing System. BGC-Argo float data was extracted using the BGC-Argo-Mat Matlab toolbox (Frenzel et al. 2021). The MODIS-Aqua satellite remote sensing data used in this article (download date: 23/12/2022) is available here in the folder "nasa_data". Satellite remote sensing data was extracted from NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group; (2014): MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. http://dx.doi.org/10.5067/AQUA/MODIS_OC.2014.0

opencc-by-4.0Jan 2023View details →

zenodo32/100

Simulation data for "Impact of Dynamic Phytoplankton Stoichiometry on Global Scale Patterns of Nutrient Limitation, Nitrogen Fixation, and Carbon Export"

Simulation data for our submission to GBC. Some of the files are compressed in order to minimize the size of the archive.  Data files are stored as pickled python variables. The code for analysis of this data is in the github repository: https://github.com/georgehagstrom/BGCPaperFigureCode .

opencc-by-4.0Sep 2023View details →

publicJul 2022View details →

Physiological control on carbon isotope fractionation in marine phytoplankton

Open the record for dataset details and reuse information.

publicFeb 2019View details →

Data from: Highest plasticity of carbon concentrating mechanisms in earliest evolved phytoplankton

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zenodo28/100

Figure 1 in Carbon-to-chlorophyll-a ratio in the phytoplankton of the Black Sea surface layer: variability and regulatory factors

Figure 1. Location of sampling stations in the coastal waters of the Black Sea near Sevastopol and the southern coast of Crimea in 2000-2003 and 2006-2010: 1 - Sevastopol Bay, 2, 3 - Karantinnaya Bay, 4 - Kruglaya Bay, 5 - open coastal zone near Sevastopol, 6,7 - Golubaya Bay, 8, 9 - Laspi Bay.