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138 results for βUpwellingβ
Data from: Satellite-based Lagrangian model reveals how upwelling and oceanic circulation shape krill hotspots in the California Current System [updated]
<p><strong>Abstract</strong></p> <p>In the California Current System, wind-driven nutrient supply and primary production, computed from satellite data, provide a synoptic view of how phytoplankton production is coupled to upwelling. In contrast, linking upwelling to zooplankton populations is difficult due to relatively scarce observations and the inherent patchiness of zooplankton. While phytoplankton respond quickly to environmental forcing, zooplankton grow slower and tend to aggregate into mesoscale “hotspot” regions spatially decoupled from upwelling centers. To better understand mechanisms controlling the formation of zooplankton hotspots, we use a satellite-based Lagrangian method where variables from a plankton model, forced by wind-driven nutrient supply, are advected by near-surface currents following upwelling events. Modeled zooplankton distribution reproduces published accounts of euphausiid (krill) hotspots, including the location of major hotspots and their interannual variability. This satellite-based modeling tool is used to analyze the variability and drivers of krill hotspots in the California Current System, and to investigate how water masses of different origin and history converge to form predictable biological hotspots. The Lagrangian framework suggests that two conditions are necessary for a hotspot to form: a convergence of coastal water masses, and above average nutrient supply where these water masses originated from. The results highlight the role of upwelling, oceanic circulation, and plankton temporal dynamics in shaping krill mesoscale distribution, seasonal northward propagation, and interannual variability.</p> <p><strong>Data set description</strong></p> <p>This data set includes 2 files:</p> <ul> <li>a satellite-based 1993-2023 monthly retrospective of krill concentrations (Zbig) modeled using the growth-advection method in the California Current upwelling system. Inputs include the nitrate supply product described below and GlobCurrent 15 m oceanic currents. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/krill-hotspots-in-the-california-current/.</li> <li>a satellite-based 1993-2023 monthly retrospective of wind-driven nitrate supply estimated in a 150 km coastal band at 0.125° latitudinal resolution. Nitrate supply was calculated based primarily on CCMP v3.1 winds, AVISO geostrophic currents, and a climatology of in situ nitrate at 60m. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/nitrate-supply-estimates-in-upwelling-systems/.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://doi.org/10.3389/fmars.2022.835813">Messié et al. (2022)</a>.</p> <p>[IMPORTANT NOTE:] There is an error in the Ekman pumping fields (trans_pump, Nsupply_pump, Nsupply_total) that will be corrected soon (those fields are not used in publications where only coastal transport was considered). Please contact me if you need Ekman pumping fields before this is fixed.</p>
Data from: Coastal upwelling drives ecosystem temporal variability from the surface to the abyssal seafloor.
<p><strong>Abstract</strong></p> <p>Long-term biological time series that monitor ecosystems across the ocean’s full water column are extremely rare. As a result, classic paradigms have yet to be tested. One such paradigm is that variations in coastal upwelling drive changes in marine ecosystems throughout the water column. We examine this hypothesis by using data from three multi-decadal time series spanning surface (0 m), midwater (200-1000 m), and benthic (~ 4000 m) habitats in the central California Current Upwelling System. Data include microscopic counts of surface plankton, video quantification of midwater animals, and imaging of benthic seafloor invertebrates. Taxon-specific plankton biomass and midwater and benthic animal densities were separately analyzed with principal component analysis. Within each community, the first mode of variability corresponds to most taxa increasing and decreasing over time, capturing seasonal surface blooms and lower-frequency midwater and benthic variability. When compared to local wind-driven upwelling variability, each community correlates to changes in upwelling damped over distinct timescales. This suggests that periods of high upwelling favor increases in organism biomass or density from the surface ocean through the midwater down to the abyssal seafloor. These connections most likely occur directly via changes in primary production and vertical carbon flux, and to a lesser extent indirectly via other oceanic changes. The timescales over which species respond to upwelling are taxon-specific and are likely linked to the longevity of phytoplankton blooms (surface) and of animal life (midwater and benthos), that dictate how long upwelling-driven changes persist within each community.</p> <p> </p> <p><strong>Data set description</strong></p> <p>This data set includes 3 files, one for each community. The files contain plankton biomass (for the surface community) or animal density (for midwater and benthos communities) as a function of sampling time and taxonomic group. </p> <ul> <li>surface.csv: autotrophic and heterotrophic surface plankton sampled in Monterey Bay by CTD-rosette and analyzed by epifluorescence microscopy and flow cytometry</li> <li>midwater.csv: midwater animals observed by ROV in the Monterey Bay mesopelagic zone from 200-1000m</li> <li>benthos.csv: benthic animals observed by ROV in a ~ 4000 m abyssal seafloor habitat at the base of the Monterey deep-sea fan</li> </ul> <p><strong>Detailed description </strong>(see additional details and references in <a href="https://www.pnas.org/doi/10.1073/pnas.2214567120">Messié et al., 2023</a>):</p> <p><strong>Surface time series:</strong> Plankton biomass was estimated from surface plankton counts collected using ship-based CTD-rosette at station M1 in Monterey Bay (122.022°W, 36.747°N). This station is part of a 3-station time series program operating in Monterey Bay since 1989 at 3-4 week intervals. Epifluorescence microscopy was used to enumerate and size auto- and heterotrophic plankton. Starting in 1998, flow cytometry samples provided more precise numbers for <em>Synechococcus</em> and eukaryotic picoplankton (<em>Prochlorococcus</em> was not included as no information is available prior to 1998). Standard geometric equations (e.g., ellipsoid, sphere, cylinder, pennate diatom shape) were used to calculate biovolumes of individual cells, and biomass of each plankton group was assessed using biovolume-based carbon conversions. For picoplankton an average value per cell was used: 82 fgC cell<sup>-1</sup> for <em>Synechococcus</em> and 530 fgC cell<sup>-1</sup> for eukaryotic picophytoplankton (red fluorescing picoplankton). Diatom biovolumes were converted to biomass using log<sub>10</sub>(Biomass) = 0.76 log<sub>10</sub>(Volume) - 0.29 where Biomass is in gC and Volume is in πm<sup>3</sup>. The ciliate conversion was Biomass = 0.08 * Volume. For all other plankton we used log<sub>10</sub>(Biomass) = 0.94 log<sub>10</sub>(Volume) - 0.6.</p> <p><strong>Midwater time series: </strong>Quantitative mesopelagic video transects were conducted at a single station in Monterey Bay (Midwater 1, 36°42′N, 122°02′W). The station is located over the axis of the Monterey Submarine Canyon, where the water column is approximately 1600 m deep. Data were collected using remotely operated vehicles (ROVs). Estimates of animal densities using ROV imaging underestimate some groups (notably fishes), but provide a more complete view of life in the ocean than traditional methods such as nets and acoustics, particularly for gelatinous animals. The ROVs conducted horizontal video transects while moving at about 0.5 m s<sup>-1</sup> for 10 min. Data for this paper come from approximately monthly transects made at 100 m intervals between 200 - 1000 m from 1997-2017. These years were chosen because the entire mesopelagic water column was more evenly surveyed than in the years prior. In each transect, the community of animals was annotated by professional annotators using the open-source Video Annotation and Referencing System (VARS) software. Annotators identified organisms in transect video to the lowest taxon possible; in many cases to species. We selected 63 taxonomic groups defined at the highest possible taxonomic resolution; annotations not included represent 31% of the total (84% of which are euphausiids, chaetognaths, and unidentified appendicularians). Calibrated cameras on MBARI ROVs and accurate measurement of ROV speed through water, allow for the calculation of volume for each transect. Animal density was calculated for each taxonomic group and each depth-specific transect as the number of individuals divided by the corresponding transect volume, further averaged over the water column from 200 - 1000 m. Midwater transecting methods and their efficacy are well-documented.</p> <p><strong>Benthic time series: </strong>Two comparable methods were used to assess benthic communities at Station M (34°50′N, 123°00′W). From 1989-2005, the identification to the lowest possible taxon, and quantity of benthic animals were recorded from images taken by a camera-sled towed along a horizontal transect above the sea floor at a speed of approximately 1 m s<sup>-1</sup>, taking a film image every 4-5 seconds (water depth ~ 4,100 m). The developed film was projected by a Beseler model 23C-II enlarger for annotation of identifiable animals in images. From 2006-2018, benthic communities were assessed using ROV video transects recorded from approximately 1.3 m above the sea floor, with a view of approximately 1 m wide, and length typically approximately 1 km. Water depth for these transects was approximately 4,000 m, the lower depth limit of the ROV. Animals visible in the video were identified and annotated using VARS. The 2006 change in sampling method and in time series location and depth was found to have little impact on the megafauna time series. </p>
Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019
<p>This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details. </p> <p>After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. </p> <p>The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). </p> <p>Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details. </p> <p>The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution. </p> <p>Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. </p> <p>Filenames: </p> <p><strong>monthly_windstress_wsc_upwelling.nc</strong>: 480 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>seasonal_windstress_wsc_upwelling.nc</strong>: 160 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>annual_windstress_wsc_upwelling.nc</strong>: 40 time steps from 80W to 45W and 30N to 45N.</p>
A Lagrangian study of the contribution of the Canary coastal upwelling to the nitrogen budget of the open North Atlantic
<p>The attached datasets constitute the particle trajectory data produced in the experiment for Hailegeorgis et al..</p> <p>The "traj_upwell_1d_70m_1d-variables.nc" contains variables that describe different aspects of each upwelled particle (mostly regarding a particle's release or its initial or final conditions).</p> <p>The rest of the files with the format "traj_upwell_1d_70m_XXX-traj.nc" describe an attribute XXX (location or nutrient concentration) along the trajectory of upwelled particles tracked as part of the experiment.</p> <p>With ARIANE, particles are released and tracked in a ROMS simulation of the Canary coastal upwelling region. Out of the ~10M particles, the trajectories of the ~353K (~3.6%) that upwell are included. The variable "index_in_full_exp" in file "traj_upwell_1d_70m_1d-variables.nc" shows the index of each of these upwelling particles in the larger pool of released particles. For each upwelled particle, out of the 720-day trajectories starting from its release into the coast, the values from its upwelling step to its exit from the experiment are included, with the values outside this range being filled with a generic value (1.e20). An upwelled particle exits the experiment when it leaves the regional ROMS simulation altogether or when it leaves the coast and returns to the coast to re-upwell (more details in the paper).</p> <p>Be mindful of the different values of time. In "traj_upwell_1d_70m_1d-variables.nc", the variable "release_time" tells each particle's release time, in days since onset of the ROMS simulation, while variable "coast_exit_time" tells each particle's day of exiting coast, in days since its release. In each particle's trajectory (in traj_lon, traj_lat, etc), the first and last steps with valid values are the same as the days of its upwelling and its exit, respectively, since its release.</p> <p>The files contain the name and description of each variable. Along with the details in the publication, the descriptions here should be enough to fully interpret the information and replicate our analysis.</p>
BIOLOGICAL DATA in CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system
<p>BIOLOGICAL DATA to estimate the carbon dioxide budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system.</p> <p>Álvarez-Salgado et al. (2022) estimate the carbon dioxide and total alkalinity budgets due to the Mediterranean mussels (Mytilus galloprovicialis) growing in suspended culture in a low seston environment such as the Galician Rías (NW Spain). This database contains the biological data needed to estimate the carbon dioxide fluxes and changes in total alkalinity induced by the different biological processes involved in mussel growth. </p> <p>Manuscript available at: <a href="https://doi.org/10.1016/j.scitotenv.2022.157867">https://doi.org/10.1016/j.scitotenv.2022.157867</a></p> <p>Álvarez-Salgado, X.A., Fernández-Reiriz, M.J., Fuentes-Santos, I., Antelo, L.T., Alonso, A.A., Labarta, U., 2022. CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system. Sci. Total Environ. 849, 157867.</p>
Indicative distribution map for Ecosystem Functional Group M1.9 Upwelling zones
<p>This archive contains indicative distribution maps and profiles for <strong>M1.9 Upwelling zones</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Processing of MODIS-Aqua data with Self-Organizing Maps NeuroVaria method for the southern canary upwelling system
<p>Abstract</p> <p>This ocean color dataset is derived from MODIS_Aqua sensor measurements covering the Southern Canary upwelling system. The raw L1A measurements were downloaded from NASA's Ocean Color web site and then processed using the Ocean Biology Processing Group's (OBPG) Multi-Sensor Level-1 to Level-2 (MSL12) code. The l2gen program, based on its standard process, generates Level-2 parameters consisting of the top of atmosphere radiance, the radiance of each ocean and atmosphere component, the measurement angles, Level-2 flags, ... The top of atmosphere radiance is pre-corrected to keep only a dependence on the diffuse transmittance, the aerosol contribution and the water leaving radiance.</p> <p><br> The pre-corrected product and measurement angles are assimilated using the Self-Organizing Map<br> NeuroVaria (SOM-NV) code (Diouf et al., 2013). SOM-NV is an algorithm based on two statistical models<br> that classify a dataset into a map, and then use the information from that map to deliver atmospheric and oceanic parameters from the satellite observation.</p> <p>The parameters of interest are the remote sensing reflectance spectra (Rrs(λ)) and the aerosol optical thickness (AOT) at 869 nm (aot_869). The Rrs at blue (443 and 488 nm) and green (547 nm) are used to calculate chlorophyll-a concentration from the OBPG OCx algorithm (chl_ocx, O'Reilly et al., 1998; Mobley et al., 2016).</p> <p>These geophysical parameters are projected onto a fixed grid at 1/96° resolution and archived in a daily netcdf format files. Each file contains five visible reflectances Rrs(λ) (with λ = 412, 443, 488, 531, and 547 nm), chl_ocx, aot_869, and latitude and longitude coordinates. These parameters are described in the files, along with the global attributes.</p> <p><br> The netcdf files are formatted as follows: SOM-NV-Ayyyydddhhmmss.nc; where yyyy = year; ddd = Julian<br> day; hh = hour; mm = minute; ss = second. The extension "Ayyyydddhhmmss.nc", corresponds to the name<br> of the MODIS_aqua file of the day. When two input files exist for the same day, within 5 minutes, the two<br> scans are concatenated and the orbit keeps the name of the second file.<br> All files are compressed internally to a size of 4, to facilitate transfers.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Résumé</p> <p>Ce jeu de données de couleur de l’eau est issu des mesures du capteur MODIS_Aqua sur la partie sud du système d’upwelling des Canaries. Les mesures brutes L1A ont été téléchargées du site Ocean Color de la NASA, puis traitées à l’aide du code de traitement « Multi-Sensor Level-1 to Level-2 (MSL12) » du groupe Ocean Biology Processing Group (OBPG). La version standard du programme l2gen génère les paramètres de niveau 2 constitués de la luminance totale mesurée, de la luminance de chaque composante du système océan-atmosphère, des angles de mesures, des masques de niveau 2, …. La luminance totale est pré-corrigée pour ne garder qu’une dépendance à la transmittance diffuse, à la contribution des aérosols et à la luminance marine.<br> <br> Le produit pré-corrigé et les angles de mesure sont assimilés à l’aide du code Self-Organizing Map NeuroVaria (SOM-NV) de Diouf et al. (2013). SOM-NV est un algorithme basé sur deux modèles statistiques qui permettent de classer un ensemble de données sur une carte, puis d’utiliser les informations de cette carte pour restituer les paramètres atmosphériques et océaniques de l’observation satellite.<br> <br> Les paramètres restitués sont les spectres de réflectance marine (Rrs(λ)) et l’épaisseur optique des aérosols (AOT) à 869 nm (aot_869). Les Rrs au bleu (443 et 488 nm) et au vert (547 nm) servent à calculer la concentration en chlorophylle-a à partir de l’algorithme OCx de OBPG (chl_ocx).<br> <br> Ces paramètres géophysiques sont projetés sur une grille fixe à 1/96° de résolution et archivés au format de fichiers netcdf journaliers. Chaque fichier netcdf contient cinq réflectances du visible Rrs(λ) (avec λ = 412, 443, 488, 531 et 547 nm), la chl_ocx, l’aot_869, et les coordonnées latitude et longitude. Ces paramètres sont décrits dans les fichiers, ainsi que les attributs globaux.</p> <p><br> Les fichiers netcdf sont formatés comme suite : SOM-NV-Ayyyydddhhmmss.nc ; avec yyyy = année ; ddd =<br> jour julien ; hh = heure ; mm = minute ; ss = seconde. L'extension "Ayyyydddhhmmss.nc", correspond au<br> nom du fichier MODIS_aqua du jour. Dans le cas où deux fichiers existent pour un même jour, à 5 minutes<br> près, les deux scans sont concaténés et l'orbite garde le nom du deuxième fichier.<br> Tous les fichiers sont compressés en interne à un niveau 4, pour faciliter le transfert.</p>
CCE LTER P1908 Upwelling Filament Ocean Acidification Phytoplankton Iron Incubation Experiments
<p>Metatranscriptome assembly, read counts, and annotations from four sets of trace metal clean ocean acidification experimeints in the California Current Ecosystem. Experiments were conducted during August 2019 as part of the CCE LTER program. Samples were collected at the initial time point (T0), and three pCO<sub>2</sub> treatments (400, 800, and 1200 ppm) with two time points for each experiment. </p> <p>Poly-A selected mRNA was sequenced on an Illumina NovaSeq 6000 and then assembled with Trinity for each separate experiment. Proteins from the assembly were then predicted with Genemark S-T. Taxonomic annotation was performed with DIAMOND BLASTP searches against PhyloDB v1.076 and based on the Lineage Proability Index from the top hits. Functional annotation was similarly performed using KEGG with KEGG Orthology annotation based on KofamKOALA results. Read quantification was conducted with Bowtie2. </p> <p>Predicted proteins from each assembly is provided in fasta format. The read counts and annotations for each experiment are in tab-delimited files. </p>
Dataset associated with paper "Topographic hotspots of Southern Ocean eddy upwelling"
<p><strong>Data repository for Yung, Morrison and Hogg (2022) <em>Topographic hotspots of Southern Ocean eddy upwelling</em>, submitted to Frontiers in Marine Science</strong></p> <p> </p> <p>This repository contains processed data, created using scripts available in the github repository <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code</a>.</p> <p> </p> <p>The data comes from the repeat year atmospheric forcing version of the ACCESS-OM2 modelling suite 0.1 degree model (see Kiss et al. (2020), http://www.cosima.org.au, model available at <a href="https://github.com/COSIMA/access-om2">https://github.com/COSIMA/access-om2</a>). The model was spun up for 270 years, and the next 10 years of output were used.</p> <p> </p> <p>Daily resolution data was used in the calculation of quantities, which results in a large amount of raw data (~4TB for Southern Ocean latitudes (35-70S), 10 years). Therefore, we only provide relevant processed data. Details of these calculations are available in the above github repository.</p> <p> </p> <p>Any quantities calculated along sea surface height contours are labelled with a letter. These are referred to in the following table.</p> <p> </p> <p>| Letter | SSH |</p> <p>| :---: | :---: |</p> <p>| A | -0.1m |</p> <p>| B | -0.2m |</p> <p>| C | -0.3m |</p> <p>| D | -0.4m |</p> <p>| E | -0.5m |</p> <p>| F | -0.6m |</p> <p>| G | -0.7m |</p> <p>| H | -0.8m |</p> <p>| I | -0.9m |</p> <p>| J | -1.0m |</p> <p>| K | -1.1m |</p> <p>| L | -1.2m |</p> <p>| M | -1.3m |</p> <p>| N | -1.4m |</p> <p>| O | -1.5m |</p> <p>| P | -0.15m|</p> <p>| Q | -0.25m|</p> <p>| R | -0.35m|</p> <p>| S | -0.45m|</p> <p>| T | -0.55m|</p> <p>| U | -0.65m|</p> <p>| V | -0.75m|</p> <p>| W | -0.85m|</p> <p>| X | -0.95m|</p> <p>| Y | -1.05m|</p> <p>| Z | -1.15m|</p> <p>| Z1 | -1.25m|</p> <p>| Z2 | -1.35m|</p> <p>| Z3 | -1.45m|</p> <p> </p> <p>There are two versions of the along-contour coordinates for each contour. The latlon named files are more useful for analysis, the other is used while computing transport across contours. These are made using <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb</a>.</p> <p> </p> <p>Distance along contour files contain the cumulative distance along the contour in 10^3 km from 80E (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb</a>). Dimensions are contour index, counting from 80E. There are also segment length files of each part of the contour.</p> <p> </p> <p>vh_eddy files contain the eddy transport across the contours, averaged over 10 years. These are calculated by taking the time mean of the residual transport (<span class="math-tex">\(\overline{vh}\)</span>), e.g. SO_L_vol_trans_across_contour_binned.nc, and subtracting the mean transport <span class="math-tex">\(\overline{v}\overline{h}\)</span>, calculated from the time mean isopycnal thicknesses along contours (e.g. SO_L_dzu_across_contour_binned) and the time mean velocity, <span class="math-tex">\(v = vh/h\)</span> (calculated in <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb</a>). The full files are provided for the contour L (SSH=-1.2 m) as is provided in the paper manuscript Fig. 6. These eddy transports have dimensions of sigma1 and contour index (the two extra SO_L files have time too).</p> <p> </p> <p>vh_eddy_interp files contain the interpolated eddy transports at hotspots, for the density range 1032.2kg/m^3 <= sigma_1 <= 1032.5kg/m^3. Calculation method provided at <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb</a>.</p> <p> </p> <p>We also provide files that summarise the transport in density and SSH space for hotspots and the circumpolar total. See <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb</a> for calculation details. The exact names and specifications are provided in the README.</p> <p> </p> <p>We provide 10 year mean files of the energy conversion and energy terms over the Southern Ocean latitude range. These are made by binning daily transports and layer thicknesses into sigma 1 bins over the Southern Ocean (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb</a>) and then calculating energy terms (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb</a>, <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb</a>)</p> <p> </p> <p> </p> <p>The files named with contour_energies contain the EKE, Form stress and Reynolds stress averaged over 10 years but extracted along the same contours as eddy transport. <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb</a></p> <p> </p> <p>We also provide 10 year averaged density binned transport, layer thickness and densities.</p> <p> </p> <p>Please refer to the README file for additional details.</p>
Data for: Wind-induced hypolimnetic upwelling between the multi-depth basins of Lake Geneva during winter: An overlooked deepwater renewal mechanism?
<p>Combining field observations, 3D hydrodynamic modeling and particle tracking, we investigated wind-driven interbasin exchange, and in particular hypolimnetic upwelling, between the deep <em>Grand Lac</em> (max. depth 309 m) and shallow <em>Petit Lac</em> (max. depth 75 m) basins of Lake Geneva (Switzerland/France) during the weakly stratified fall/winter period 2018-2019.</p> <p><br> The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs) and vertical thermistor lines along with the corresponding 3D modeling and particle tracking results.</p> <p><br> The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).</p> <p><br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118)</p>
Data to reproduce figures in "Tropical thermocline helps power Pacific equatorial upwelling"
<p>A set of netcdf include results of the energetics in the Pacific STC region. </p> <p>A jupyter notebook uses all those dataset to reproduce the main plots in the paper. Code used to compute the energetics can be found within the 'Tailleux' class inside this module: https://github.com/inciente/EastPac/blob/main/KE_tools.py</p> <p>Please feel free to reach out if you're trying to use the data, or apply the energetics framework to your own simulations.</p>
Salinity is diagnostic of maximum potential chlorophyll and phytoplankton community structure in an Eastern Boundary Upwelling System
Coastal upwelling ecosystems associated with strong physical stirring exhibit fine-scale hydrographic and biological patchiness. Though many studies have found broad correlations between hydrographic properties (e.g., temperature and salinity) and phytoplankton biomass, we lack a detailed understanding of the underlying mechanisms and how to diagnose patchy distributions. Here, using observational data from coastal waters in the California Current System, we demonstrate that the maximum observed chlorophyll in a water parcel increases with salinityβa conservative water-mass tracer. This relationship arises from sub-euphotic zone nitrate concentrations, which also increase with salinity. Therefore, we can define maximum potential chlorophyll as a function of salinity and nitrate. We show that variations in salinity explain patterns in phytoplankton community structure and discuss how growth, grazing, and light and micronutrient limitation can generate chlorophyll values below the maximum potential. Our mechanistic explanation provides a novel framework for diagnosing biological patchiness using salinity observations.
A 3-km model configuration of the southern Benguela Current upwelling system: ROMS model data and Pyticles Lagrangian data
<p>This dataset contains model output data from the Regional Ocean Modelling System (ROMS) configuration of the southern Benguela upwelling system (SBUS) to study the interannual variability of Lagrangian transport in the SBUS. This is a 3-km model resolution that ran for 22 years from 1989-2011 period with the first 3 years considered as spin-up. The model outputs were archived at a daily frequency. The 3-km model was nested in a 7.5 km model resolution described by Ragoasha et.al., 2019.</p> <p>The model output data provided here is a monthly climatology (1995-2011) NetCDF file of the surface temperature, salinity, the velocity fields (<em>u,v & w</em>), and sea surface height (SSH). The file that contains the model grid is also provided.</p> <p>An eddy detection and tracking algorithm were also performed on the daily 3-km SSH model outputs to study mean eddy characteristics of the region for the 1992-2011 period. The file contains identifications of the Eddies detected and tracked in out model domain, their position (longitude and latitude), vorticity, amplitude, propagation and rotational speed.</p> <p> </p> <p>An example of a Pyticles (Gula et al., 2014; Ragoasha et.al., 2019) Lagrangian output subset for 3000 Lagrangian drifters tracked for 60 days. The drifters were released in the upper 100 m depth at an across-shore transect off Cape Point (34<sup>o</sup>S). A Matlab file is also provided for monthly (1992-2011) percentage of drifters that reach St Helena Bay (32<sup>o</sup>S) from Cape Point. </p> <p> </p> <p> </p> <p><strong>Dataset provided:</strong></p> <p>Monthly climatology file: “<em>roms_avg_Y1995M1-Y2011M12.nc”</em></p> <p>Model grid file: “<em>grid_roms_avg_r3km.nc”</em></p> <p>Eddy tracking file: “<em>TRA02_SEL01_DET02_eddies_r3km_1992M1_2011M12.nc”</em></p> <p>Pyticles Lagrangian experiment output example file: “<em>Pyticles_Y2010M10.nc”</em></p> <p>Monthly transport success Matlab file: <em>"R3km_monthly_transport_1992_2011.mat"</em></p> <p> </p> <p> </p> <p><strong>Citations:</strong></p> <p> </p> <p><strong>Ragoasha, N</strong>., Herbette, S., Cambon, G., Reason, C., Roy, C., 2019. Lagrangian pathways in the southern Benguela upwelling system. <em>Journal of Marine Systems</em>, 195: 50-66.</p> <p> </p> <p>Gula, J., Molemaker, M. J., & McWilliams, J. C., 2014. Submesoscale Cold Filaments in the Gulf Stream. <em>Journal of Physical Oceanography.,</em> 44 (10), 2617–2643. DOI: 10.1175/JPO-D-14-0029.1</p> <p> </p> <p><strong>Corresponding author:</strong></p> <p>M.N. Ragoasha, ORCID identifier: 0000-0002-1500-6259. Email: moagaboragoasha@gmail.com</p> <p> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors acknowledge the funding of N. Ragoasha’s PhD by the South-Africa’s National Research Foundation (NRF, South Africa) and the French Institute for Research and Sustainable Development (IRD, France). This work was also supported by the French National Program LEFE/INSU under the project’s name Benguela Upwelling Innershelf</p> <p>647 Circulation (BUIC). This work was granted access to the HPC resources of [TGCC/CINES/IDRIS] under the allocation 2017- [DARI n<sup>β¦</sup>A0020107443] attributed by GENCI (Grand Equipement National de Calcul Intensif).</p>
The Disproportionate Role of Ocean Topography on the Upwelling of Carbon in the Southern Ocean
<p>This repository contains the filtered Lagrangian particle trajectories associated with the pre-print submitted to Geophysical Research Letters titled "The Disproportionate Role of Ocean Topography on the Upwelling of Carbon in the Southern Ocean." These trajectories follow the 19,002 particles that last upwell across 1000 m in the Antarctic Circumpolar Current in the Southern Ocean. Included are their x, y, z positions and a variety of tracers recorded along their transit in 2-day intervals, as well as a few Eulerian mesh files that are pertinent to the study. The output is the result of a global ocean biogeochemical simulation with the Model for Prediction Across Scales with online Lagrangian particle tracking. Also included are python notebooks which showcase the analysis and visualization used to create the main figures and tables in the text of the manuscript.</p>
Dataset from "Quantifying air-sea gas exchange using noble gases in a coastal upwelling zone"
<p>Dataset of dissolved noble gas (He, Ne, Ar, Kr, and Xe) measurements in Monterey Bay, CA. Published as a supplement to: Manning, C.C., R.H.R. Stanley, D.P. Nicholson, and M.E. Squibb (2016). Quantifying air-sea gas exchange using noble gases in a coastal upwelling zone. <em>IOP Conference Series: Earth and Environmental Science</em>, 35, 012017 (13 pp). doi: 10.1088/1755-1315/35/1/012017</p>
Decoupling silicon metabolism from carbon and nitrogen assimilation poises diatoms to exploit episodic nutrient pulses in a coastal upwelling system
<p>Diatoms serve as the major link between the marine carbon (C) and silicon (Si) biogeochemical cycles through their contributions to primary productivity and requirement for Si during cell wall formation. Although several culture-based studies have investigated the molecular response of diatoms to Si and nitrogen (N) starvation and replenishment, diatom silicon metabolism has been understudied in natural populations. A series of deckboard Si-amendment incubations were conducted using surface water collected in the California Upwelling Zone near Monterey Bay. Steep concentration gradients in macronutrients in the surface ocean coupled with substantial N and Si utilization led to communities with distinctly different macronutrient states: replete ('healthy'), low N ('N-stressed'), and low N and Si ('N- and Si-stressed'). Biogeochemical measurements of Si uptake combined with metatranscriptomic analysis of communities incubated with and without added Si were used to explore the underlying molecular response of diatom communities to different macronutrient availability. Metatranscriptomic analysis revealed that N-stressed communities exhibited dynamic shifts in N and C transcriptional patterns suggestive of compromised metabolism. Expression patterns in communities experiencing both N and Si stress imply that the presence of Si stress may partially ameliorate N stress and dampen the impact on organic matter metabolism. This response builds upon previous observations that the regulation of C and N metabolism is decoupled from Si limitation status, where Si stress allows the cell to optimize the metabolic machinery necessary to respond to episodic pulses of nutrients. Several well-characterized Si-metabolism associated genes were found to be poor molecular markers of Si physiological status; however, several uncharacterized Si-responsive genes were revealed to be potential indicators of Si stress or silica production.</p>
The impact of Indonesian Throughflow constrictions on eastern Pacific upwelling and water-mass transformation
<p>Netcdf data and Matlab processing scripts for the article: </p> <p>Eabry, Holmes and Sen Gupta (2022): The impact of Indonesian Throughflow constrictions on eastern Pacific upwelling and water-mass transformation. Journal of Geophysical Research: Oceans. <a href="https://doi.org/10.1029/2022JC018509">https://doi.org/10.1029/2022JC018509</a></p> <p>Included are netcdf files with output from the ACCESS-OM2 1-degree ocean model averaged over years 500-600 of the spin-up simulation. CONTROL indicates the control simulation (realistic ITF topography), OPENITF indicates the Open ITF experiment and DIFF indicates difference files between the two. Please refer to the meta-data within the netcdf files for more information. Scripts to help with plotting standard variables are part of the COSIMA cookbook repository at <a href="https://github.com/COSIMA/cosima-recipes">https://github.com/COSIMA/cosima-recipes</a>.</p> <p>An example script Control_WMT_budget.m is provided to plot the control WMT budget and can be easily modified to plot the Open ITF or anomalous WMT budget. This script uses the Pacific masks found in mask.mat. The small tendency term is provided separately as dV_dt_nrho.mat.</p>
Simulated tropical stratospheric upwelling and O$_{3}$ interactions with the 1D RCE model konrad
<p>Model output of the control and CO$_{2}$-doubling experiments with the 1D radiative-convective equilibrium (RCE) model `konrad`. The model output consists of temperature, atmospheric composition, radiative flux, heating rate profiles, time series of the convective top, surface temperature, and the net radiative flux at the top of the atmosphere. It also includes metadata such as the parameters used for each run.</p> <p>The experiments varied the relative humidity profile, the representation of O$_{3}$ chemistry, and, more importantly, a parameterization of the cooling due to the tropical stratospheric upwelling caused by the Brewer-Dobson Circulation. With the dataset, one can study the effects the tropical stratospheric upwelling on the tropical equilibrium climate sensitivity.</p>
manuscript (atmosphere-3145955) titled: The Black Sea Upwelling System: Analysis on the Western Shallow Waters Authored by: Maria Emanuela Mihailov was accepted in Atmosphere (ISSN 2073-4433) on 15 August 2024
<p>Datasets represents the modelling results for:</p> <p>- Coastal Upwelling Transport Index (CUTI) of the National Oceanic and Administrative Administration’s Environmental Research Division (NOAA-ERD) was used to derive the time series of the coastal upwelling index in four locations on the north-western Black Sea coast. The coastal upwelling index time series was calculated using monthly average wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis and MATLAB software to compute the CUTI </p> <p>- The upwelling index (UI) is computed using the CUTI Formula (<span>Bakun Index </span>), defined as CUTI (m3·s−1·100 m−1), representing the volume transport per distance unit of an alongshore section. The sign of Ekman transport is changed to define positive or negative values of UI as a response to upwelling or downwelling favourable winds.<br>To compute the BEUTI, Copernicus Marine Service [1] data are used for dedicated locations.</p> <p>[1] <span>Grégoire,<em> </em>M.;<em> </em>Vandenbulcke,<em> </em>L.;<em> </em>Capet,<em> </em>A.<em> </em>Black<em> </em>Sea<em> </em>Biogeochemical<em> </em>Reanalysis<em> </em>(CMEMS<em> </em>BS-Biogeochemistry)<em> </em>(Version<em> </em>1)<em> </em>set.<em> </em>Copernicus<em> </em>Monitoring<em> </em>Environment<em> </em>Marine<em> </em>Service<em> </em>(CMEMS).<em> </em>2020.<em> </em>Available<em> </em>online:<em> </em>https://marine.copernicus.eu/<em> </em>(accessed<em> </em>on<em> </em>10<em> </em>November<em> </em>2023).</span></p>
Numerical study on advective fog formation and its characteristic associated with cold water upwelling
<p>Recent rapid industrial development in the Korean Peninsula has increased the impacts of meteorological disasters on marine and coastal environments. In particular, marine fog driven by summer cold water masses can inhibit transport and aviation; yet a lack of observational data hinders our understanding. The present study aimed to analyze the differences in cold water mass formation according to sea surface temperature (SST) resolution and its effects on the occurrence and distribution of sea fog over the Korean Peninsula from June 23–July 1, 2016, according to the Weather Research and Forecasting model. Data from the Final Operational Model Global Tropospheric Analyses were provided at 1° and 0.25° resolutions and NOAA real-time global SST (RTG-SST) data were provided at 0.083°. While conventional analyses have used initial SST distributions throughout the entire simulation period, small-scale, rapidly developing oceanic phenomena (e.g., cold water masses) lasting for several days act as an important mediating factor between the lower atmosphere and sea. RTG-SST was successful at identifying fog presence and maintained the most extensive horizontal distribution of cold water masses. In addition, it was confirmed that the difference in SST resolution led to varying sizes and strengths of the warm pools that provided water vapor from the open sea area to the atmosphere. On examining the horizontal water vapor transport and the vertical structure of the generated sea fog using the RTG-SST, water vapors were found to be continuously introduced by the southwesterly winds from June 29 to 30, creating a fog event throughout June 30. Accordingly, high-resolution SST data must be input into numerical models whenever possible. It is expected that the findings of this study can contribute to the reduction of ship accidents via the accurate simulation of sea fog.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.