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{"references": ["Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619\u20134646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.", "Hassell et al., (2020). cfdm: A Python reference implementation of the CF data model. Journal of Open Source Software, 5(54), 2717, https://doi.org/10.21105/joss.02717"]} A CF-compliant Earth Science data analysis library Version 3.14.0 is the first to use Dask.

This data set includes four files (CSV format) containing observational data from two oceanographic moorings, BB and FF, located in the Southern Adriatic Sea from the period between March 2012 and June 2020. The stand-alone moorings are equipped with a 300 kHz ADCP-RDI system, which measures currents along the last 100 meters of the water column and a CTD recorder equipped with SeaPoint turbidity meter sensor located approximatively 10 m above the bottom. The turbidity sensor measures in a range of 0-25 FTU. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic Sea submarine observatory system of the EMSO-ERIC European Consortium. The data were subjected to quality control (QC) and the coding numbers used, shown in a dedicated column, follow the SeaDataNet L20 measurement qualifiers flags. QC applied on echo data consists of detecting signal anomalies due to interactions with the seafloor and identifying if the signal falls below a minimum threshold for which the value is no longer considered reliable. For turbidity data, QC is addressed to the detections of possible spikes, anomalies, and sensor saturation in the recordings.

A Python reference implementation of the CF data model.

Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022. Three directories can be downloaded: DataOBS : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). FigPaper : Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). MY_SRC_PISCES_NEMO_3.6 : Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)

The multi-species ecosystem model SS-DBEM integrates a species based model (DBEM) with the spectrum approach (SS). This model includes a large number of mechanisms and ecological processes such as population growth, movement, and dispersal of adults and larvae, as well as the ecophysiological effects of temperature, oxygen, and pH on body size, growth, mortality, and reproduction. The SS-DBEM model provides spatially (at a 0.5x0.5º resolution) and temporally (yearly) resolved predictions of changes in species’ size, abundance and biomass with consideration of competition. The competition algorithm describes the resource allocation between different species co-occurring in a spatial unit (thereafter cell) by comparing the flux of energy (in biomass) that can be supported (estimated with the SS model) with the energy demanded by the species predicted to inhabit that cell (estimated with the DBEM model). In addition, the environmental conditions are considered in the mechanisms and since there are different environmental conditions that are provided by the biogeochemical models, species responses are also different spatially. See readme.txt for scientific publications developing and using the model.

Data from the paper "Late summer northwestward Amazon plume pathway under the action of the North Brazil Current rings" published in Journal of Geophysical Research: Oceans, Olivier et al., (2023). Thermosalinograph near surface temperature and salinity data from Mission Microbiomes AtlantECO legs 5,6 and 7, from Martinique (France) to Salvador da Bahia (Brazil) in August-September 2021 in a csv or txt format. CTD profiles from the stations effectuated during leg 5 (stations number 35 to 40), in a cnv format. The TSG file is composed of 5 columns: time (Matlab format), longitude (°), latitude (°), SST (°C), SSS (pss). Each CTD file contains one profile, and the corresponding metadata (station number, time, longitude, latitude, units).

This file contains two supplemental videos to chapter 4 of the PhD thesis of Evert de Froe which is registered as follows: ISBN/EAN: 978-90-6266-643-0 Title: Dinner's Served in the Deep Sea Subtitle: Environmental conditions, organic matter transport, and benthic fluxes at cold-water coral and sponge communities in the deep sea. Author: Froe, Evert de Uitgever: Universiteit Utrecht,Bibliotheek Geowetenschappen/TNO Bibliografische imprint: Universiteit Utrecht,Bibliotheek Geowetenschappen/TNO NUR-code: 930 NUR-omschrijving: Aardwetenschappen algemeen Reeks: Utrecht Studies in Earth Sciences Reeksnummer: 273 Druk: 1 Illustraties: Ja Aantal pagina's: 211 Taal: Engels Verschijningsvorm: Paperback / softback Verschijningsdatum: 13-03-2023

Momarsat 2022 cruise report: summary of dives and operations, and position of moorings and observation infrastructures and sampling locations

The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. This data set uses the data set of Kruse (2023) and applies a threshold of 0.68 km m-2 to differentiate forested areas according to the 2018 field inventories (Shevtsova et al., 2021). In this data set the total forest cover was summed up and the percent of total available areas is presented for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 1800, 1860, 1900, 1990, 2000 and in 5-year steps until 3000 CE and presents the mean over the three repeats of the sum of AGB of the whole study region: extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax). Format: csv, with headers 1-year, Year in CE, 2-average percent forests cover for the study region, 3-upper and 4-lower, is the minimum and maximum value of the three simulations, 5-RCP, is the RCP scenario, 6-Cooling, contains in case of the cooling scenario the string “Cooling”. {"references": ["Stefan Kruse, Mareike Wieczorek, Florian Jeltsch and Ulrike Herzschuh (2016) Treeline dynamics in Siberia under changing climates as inferred from an individual-based model for Larix. Ecological Modelling, 338, 101\u2013121. http://dx.doi.org/10.1016/j.ecolmodel.2016.08.003 Additional data and results are available at https://doi.pangaea.de/10.1594/PANGAEA.863584", "Stefan Kruse (2023). StefanKruse/LAVESI: LAVESI-WIND with landscape (v2.0). Zenodo. https://doi.org/10.5281/zenodo.7505539", "Shevtsova, Iuliia, Herzschuh, Ulrike, Heim, Birgit, & Kruse, Stefan. (2023). Simulated above ground biomass of forests (larch) aggregated over the vicinity of the Ilirney lake system region, Chukotka, Russia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7505616", "Shevtsova I, Herzschuh U, Heim B, Schulte L, St\u00fcnzi S, Pestryakova LA, Zakharov ES, Kruse S: Recent above-ground biomass changes in central Chukotka (Russian Far East) using field sampling and Landsat satellite data. Biogeosciences, 18, 3343\u20133366, https://doi.org/10.5194/bg-18-3343-2021, 2021."]} This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).

Forest density estimates visually determined by describing the amount of present trees satellite imagery from Esri basemap (Esri) at an area of ~30x30 m qualitatively for 6515 stratified sampled locations at an equal number of locations based on elevation, aspect and slope angle. The density was categorized ranging from 1: single trees to 4: dense tree stands present, and contains a 0: no trees present. Format: ESRI shapefile, points; projection UTM58N; extent: 642085.1, 654775.1, 7462263, 7492833 m (xmin, xmax, ymin, ymax) This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852). {"references": ["Esri: \"High-resolution satellite and aerial imagery, typically within 3-5 years\" [basemap]. Scale Not Given. \"World Imagery\". https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9, Accession: 15 December 2020."]}