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Collection of figures to explore intra-regime weather variability of North Atlantic-European year-round weather regimes as Supplementary Dataset for Gerighausen et al. (2024)
<p>This is a supplementary dataset accompanying the publication <strong>Gerighausen et al. (2024) </strong>submitted to Meteorological Applications. It contains a collection of browsable figures, complementing selected regimes, seasons, and countries in the paper. The figures are provided as a zipped archive. The ZIP-File (1.2 GB) contains 4 subfolders and 4 auxiliary files as described in <strong>readme.md </strong>in the main folder. Once downloaded and unpacked, the .html navigation panels can be used in any browser to navigate through the plots. </p> <p>Data and methods used to generate the figures are explained in Gerighausen et al. (2024). In brief the analysis is based on ERA5 reanalysis 1979-2021 at 1° grid spacing and 6h temporal resolution aggregated to daily data. Anomalies are computed with respect to a 31-day running mean climatology. The figures are explained in the table below and in the navigation panel.</p> <p><strong>Gerighausen</strong>, J., J. Dorrington, M. Osman, and C. M. Grams, <strong>2024</strong>: Quantifying intra-regime weather variability for energy applications, <em>submitted to Meteorological Applications.</em> <a href="https://doi.org/10.48550/arXiv.2408.04302">doi:10.48550/arXiv.2408.04302</a></p>
Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
<p>This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023. </p> <p>The wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution. The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL. </p> <p>Like the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. </p> <p>The above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data. </p> <p><strong>Note that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields.</strong></p> <p>Since the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field. The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale. </p> <p>Furthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D’Errico, 2023). </p> <p>Each monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution. </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>
Lagrangian overturning in the eastern subpolar North Atlantic Ocean - ORCA025-GJM189 Particle Trajectory Dataset
<p>This dataset contains the output of Lagrangian particle tracking experiments using 5-day mean velocity and hydrographic fields from the ORCA025-GJM189 ocean sea-ice model hindcast configured during the Drakkar project in which numerical particles are initialised along the northward inflows across the Overturning in the Subpolar North Atlantic Program (OSNAP) East section. Particles are advected using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool using the regular step-wise stationary advection scheme and an adapted implementation of the vertical turbulent mixing parameterisation created by Paris et al. (2013) for the Connectivity Modelling System Lagrangian particle tracking tool. This vertical turbulent mixing scheme only acts on particles found within the surface mixed layer (as evaluated along particle trajectories) and randomly reshuffles them according to a maximum vertical velocity of 10 cm/s - characteristic of vertical convective plumes. Note, particles cannot be artificially subducted across the base of the mixed layer into the ocean interior using this scheme.</p> <p>Particles are initialised on the first-available day of each month (based on the centre of the model fields 5-day mean windows) between 1976 and 2008 (inclusive) before being advected within the Iceland and Irminger Basins until any one of three termination conditions are met: 1) particles return southward across OSNAP East, 2) particles flow northward across the Greenland-Scotland Ridge, or 3) particles reach the maximum advection time of 7-years. The 7-year maximum advection time ensures >99.1% of all initialised particles meet one of conditions 1) or 2), hence only 0.9% of all particles are terminated between OSNAP East and the Greenland-Scotland Ridge.</p> <p>The number of particles initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 2.5 mSv (mSv == 1E-3 Sv), enabling the calculation of robust Lagrangian statistics.</p> <p>Particle locations (referenced to the original ORCA025 model grid) and properties (potential temperature, salinity, potential density and local mixed layer depth) are stored in the output files on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points.</p> <p>In total, four Lagrangian experiments were conducted at the Department of Earth Sciences, University of Oxford. Please see README.md for a full description of all Lagrangian experiments and the accompanying output files.</p> <p><strong>For a complete description of the ORCA025-GJM189 hindcast configuration see:</strong> https://github.com/meom-configurations/ORCA025.L75-GJM189.</p> <p><strong>For a complete description of TRACMASS v7.1 see</strong>: https://github.com/TRACMASS/tracmass</p>
Modal shift in North Atlantic seasonality during the last deglaciation
<p>Raw data for Brummer et al. (2020) published in Climate of the Past (https://doi.org/10.5194/cp-16-1-2020)</p>
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
North Atlantic jet stream clusters: daily and seasonal occurence
<p>This dataset contains the time series used in Madonna et al 2020 (Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns, DOI: 10.5194/wcd-2021-6)</p> <p><br> Filenames:</p> <p>1) seasonal_timeseries.txt</p> <p>Time series of the occurrence (in % = days/season*100) of time during winter of each jet cluster, blocking and the NAO.<br> Winters are defined as December, January and February (DJF). The season name is given by the last month (i.e. 1980 is December 1979, January 1980 and February 1980). 29 February is removed from the data so that each winter season has 90 days.</p> <p>Jet clusters are calculated following Madonna et al 2017. The five clusters are named as in Madonna et al 2017: Northern (N), Central (C), Mixed (M), Southern (S) and Tilted (T).<br> Blocking are calculated following Scherrer et al. 2006 and averaged over Greenland (GB), offshore of the Iberian Peninsula also called Iberian wave breaking (IWB) and over Scandinavia (SBL). The exact definition of the regions can be found in Madonna et al 2020.</p> <p>The NAO index was downloaded from ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii. Positive (NAO+) and negative (NAO-) days are defined as those that exceed 0.5 DJF standard deviation, corresponding to values greater than 0.613 and lower than -0.177, respectively.</p> <p>Example: during winter 1980, 7.78% of the days were in the North jet cluster. This is equivalent to 7 days -> 7.78 * 90 (days per season) /100</p> <p><br> 2) daily_inverse_distance_from_centroid.txt contains information about the similarity of the 2D zonal wind field to the cluster centroids which is used to determine the jet state.</p> <p>The file has 12 columns, labelled as follow:<br> date, lat, speed, N4, C4, M4, S4, N5, C5, M5, S5, T5</p> <p>The first column (date) shows the day in YYYYMMDD format, the second (lat) is the latitude (in °N) of the maximum zonal wind in the 60°W-0°W sector (i.e. the jet latitude index, see Woollings et al. 2010 or Madonna et al. 2017 for more details), and the third (speed) is the zonal averaged (60°W-0°) zonal wind speed (in m/s) at the latitude given by column 2.</p> <p>Columns 4-7 give the inverse distance from each cluster centroids using four (4) clusters: Northern (N4), Central (C4), Mixed (M4), Southern (S4) and is normalized from 0 to 1. Values close to 1 means that the clusters are similar to its centroid. The distances sum up to 1.</p> <p>Columns 8-12 show similar to 4-7 the inverse distance from the centroids using five (5) clusters: Northern (N5), Central (C5), Mixed (M5), Southern (S5) and Tilted (T5). Distances are also normalized and sum up to 1.</p> <p>In the study of Madonna et al 2020, a day has a defined cluster X (X=N, C, M, S, T), if the inverse distance from the cluster centroid X exceeds 0.5 and it clearly dominates over the other clusters.</p> <p><br> Example: 1 January 1979, the zonal mean zonal wind is maximum at 47°N and has a value of 15.61 m/s.<br> Considering 4 clusters, the jet resembles most the Mixed cluster (M4=0.36), followed by the Southern (S4=0.23), Northern (N4=0.22) and Central (C4=0.18). The sum of the distances (0.36 + 0.23 + 0.22 + 0.18 = 0.99 due to decimal approximation) is equal to 1. Using 4 clusters, this day would be assigned to cluster M4. The day is, however, not clearly identified as a Mixed jet, as the inverse distance (M4=0.36) is smaller than 0.5. The threshold of 0.5 is set to identify days where a centroid clearly leads over the others.<br> If we consider 5 clusters, the jet on 1 Jan 1979 resembles the tilted jet (T5 = 0.73) and has very little in common with the other centroids (values of 0.05-0.08). Thus, considering 5 clusters, this day is classified as a tilted jet. It is also clearly defined, as 0.73 > 0.5.</p> <p><br> References:</p> <p>Madonna, E., Li, C., Grams, C.M. and Woollings, T. (2017), The link between eddy‐driven jet variability and weather regimes in the North Atlantic‐European sector. Q.J.R. Meteorol. Soc, 143: 2960-2972. https://doi.org/10.1002/qj.3155</p> <p>Scherrer, S. C., Croci‐Maspoli, M., Schwierz, C., and Appenzeller, C. (2006). Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region. International Journal of Climatology, 26(2), 233-249</p> <p>Woollings T, Hannachi A and Hoskins B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Q. J. R. Meteorol. Soc. 136: 856– 868.</p> <p> </p> <p> </p> <p> </p>
Stable isotope data of a community of mid‑trophic level epipelagic species in the North Atlantic
<p>A total of 495 individuals from a guild of 14 species (fishes, squids and crustaceans) were sampled along the west and south Iberia (Portuguese coast) aboard a research vessel during 2014, 2016 and 2017. Two seasonal oceanographic surveys were conducted each year to sample pelagic mid-trophic level species during spring and autumn. Specimens were captured with trawl hauls, and information on date, time, position of capture (latitude and longitude) and depth (m) were recorded. The body mass (g) and total length (in fishes; mm), mantle length (in squids; mm) or carapace width (in crustaceans; mm) were recorded for each individual. Stable isotopes of carbon (δ13C) and nitrogen (δ15N) were analysed in the muscle of each individual. Lipids were removed from samples by successive rinses in a 2:1 chloroform–methanol solution. More details of the methodology can be found in:</p> <p>Ceia FR, Cherel Y, Silva AV, Garrido S, Angélico MM, da Silva JM, Laranjeiro MI, Ramos JA (2023) Drivers of niche partitioning in a community of mid-trophic level epipelagic species in the North Atlantic. Hydrobiologia, 850: 1583–1599. (DOI:10.1007/s10750-023-05160-3).</p>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Water Body Checklists 2019: North Atlantic Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the North Atlantic Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists: North Atlantic Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the North Atlantic Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Carbon-specific remineralization rates of small and large organic particles in the North Atlantic
<p>This repository provides the carbon-specific remineralization rates of small and large organic particles in the North Atlantic which were calculated by using BGC-Argo observations of backscatter (a proxy of particulate organic carbon, POC) and dissolved oxygen. Details are given in the following article:</p> <p>Wang, B. and Fennel, K. (2022), Biogeochemical-Argo data suggest significant contributions of small particles to the vertical carbon flux in the subpolar North Atlantic. Limnol Oceanogr, 67: 2405-2417. <a href="https://doi.org/10.1002/lno.12209">https://doi.org/10.1002/lno.12209</a></p>
Seismicity datasets of the 2022 seismic unrest at the North Mid-Atlantic Ridge
<p>These datasets contain information on the seismicity during the 2022 seismic unrest at the North Mid Atlantic Ridge. The seismic catalogs have been produced using seismological techniques dedicated to earthquake location and moment tensor inversion.</p> <p>Data Set 1 is the catalog of relocated earthquake, containing 61 events.<br> Note, that event nar_2022_09_26_14_49_21 is not been associated with one cluster and is therefore not shown in the final plot of relocated seismicity.</p> <p>Data Set 2 is a full moment tensor catalog, containing 77 events, with absolute centroid locations (i.e. out of the moment tensor inversion and not relocated).</p> <p>Data Set 3 is a deviatoric moment tensor catalog, containing 77 events, with absolute centroid locations (i.e. out of the moment tensor inversion and not relocated).</p>
Bischof et al. 2023 - The role of the North Atlantic for heat wave characteristics in Europe, an ECHAM6 study
<p><strong>Data to reproduce the figures in Bischof et al. 2023: "The role of the North Atlantic for heat wave characteristics in Europe, an ECHAM6 study" submitted to GRL in July 2023. </strong></p> <p>Modelling experiments are based on the FOCI model using ECHAM6 in an AMIP-like setup to carry out time slice experiments using 2018 background conditions in the atmosphere and on land (both following the SSP5-8.5 scenario) as well as in the ocean (daily forcing data based on ERA5). A sensitivity experiment without the cold SST anomaly in the subpolar North Atlantic that was observed in 2018 is also carried out.</p> <p><strong>COLD-SH009</strong> refers to the 2018 experiment using 2018 SSTs as observed, <strong>NEUTRAL-RP006</strong> refers to the experiment with altered SSTs in the North Atlantic region only. Details on the method and model setup can be found in the associated publication. </p>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
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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)
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DANDI Archive for NWB datasets
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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.