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The configuration files provided here produced the CROCO model output analysed in "The land-sea breeze influences the oceanography of the southern Benguela upwelling system at multiple time-scales" by Fearon et. al (2023) submitted to Frontiers in Marine Science, section Physical Oceanography. The provided files include the adopted compile options, code used to generate the model forcing files (grids, boundary conditions and surface forcing), and model runtime settings.

{"references": ["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 Python reference implementation of the CF data model.

There are several research infrastructures or other data services running in Europe that cover a multitude of marine-related sciences, providing specific datasets coming from observations collected with different methods. These infrastructures constitute a diverse world, each looking at a piece of the big picture, sometimes hindering collaboration and data sharing. Blue-Cloud aims to overcome fragmentation and build a bridge between thematic science clusters - such as marine, climate, food and agriculture sciences - and EOSC, creating a data federation and providing a common access to a so-called thematic EOSC for marine data. By connecting leading marine data management infrastructures with horizontal e-infrastructures, the project aims to maximise the exploitation of data resources available from different sources. The Blue-Cloud framework consists of two major technical components: (1) a Blue-Cloud Data Discovery and Access service, already presented in a previous EOSC in practice story, to serve federated discovery and access to blue data infrastructures, and (2) a Blue-Cloud Virtual Research Environment (VRE) to provide computing platforms and analytical services facilitating the collaboration between researchers, which is detailed hereafter. The Blue-Cloud VRE is powered by the D4Science Infrastructure. [M. Assante et al. (2019) Enacting open science by D4Science. Future Gener. Comput. Syst. 101: 555-563 10.1016/j.future.2019.05.063 ] The full list of EOSC in practice stories is available here

Argo is a real-time global ocean in situ observing system. It provides thousands of highly accurate ocean measurements every day. The Argo dataset has now accumulated more than 2.3 million vertical ocean profiles and accessing it for scientific analysis remains a challenge.The Argo expert community, focused on delivering a curated dataset of the best scientific quality possible, has never provided its user base with a Python software package to easily access and manipulate Argo measurements: the argopy software aims to fill this gap. The argopy software can be used to easily fetch and manipulate measurements from Argo floats. It is dedicated to scientists without knowledge of the Argo data management system but is also designed to accommodate expert requirements.

{"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.

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.

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

This bundled publication contains data sets on 1) Polyp size and sex: biometric data of polyps from Dendrophyllia ramea, as well as the sex of each analysed polyp; 2) Oocyte per mesentery: number of oocytes per analysed mesentery; 3) Oocytes per polyp: these data have been used to calculate fecundity; 4) Oocyte developmental stages: oocyte sizes and developmental stage are included; 5. Spermatic cysts: spermatic cysts sizes and developmental stage are included. A total of three polyps per each coral colony have been analysed by histological methods and a total of three coral colonies have been analysed for each sampling month: February 2018, May 2017, July 2018 and October 2017. Samples have been collected in the protected area of Punta La Mona (Granada, Alborán Sea, western Mediterranean) between 30 and 37 meters depth by scuba diving.

Three high resolution multicore records have been collected at three sites in the western Mediterranean with a MC400-Multicorer system during the MedSeA cruise (Mediterranean Sea Acidification in a changing climate) on 2 May to 2 June 2013 onboard the R/V Angeles Álvarino. Core MedSeA-S3-c1 was retrieved in the Alboran basin (Lat. 36.0746° N, Long. 04.11040° W) at a water depth of 1137 m, with a core length of 33 cm. Core MedSeA-S23-c1 was recovered at a water depth of 1156 m in the Balearic basin offshore Barcelona (Lat. 41.1121° N, Long. 2.38200° E) with a core length of 43 cm. MedSeA-S7-c2 was collected at the Strait of Sicily (Lat. 37.7080° N, Long. 12.40553° E) at a water depth of 263 m, with a core length of 46.5 cm. All three cores have been analyzed for changes in size normalized weight (SNW) and stable carbon isotopes (δ13C), measured in planktic foraminiferal clacite shells of the two species Globigerina bulloides and Globigerinoides elongatus. Boron (δ11B) isotopes have been measured in tests of Globigerinoides elongatus at the Alboran site, and in Globigerinoides ruber albus at the Strait of Sicily. Complementary data for the Strait of Sicily record has been obtained, including a 210Pb based age depth model, sea surface temperatures (SST), alkenone concentrations and planktic foraminiferal assemblage changes. The Strait of Sicily record (MedSeA-S7-c2) covers around the last 200 a, describing environmental changes throughout the Industrial Era (IE) at high temporal resolution. The Alboran (MedSeA-S3-c1) and Balearic Sea (MedSeA-S23-c1) records spanning the last about 1 ka at lower temporal resolution, displaying oceanographic changes throughout the transition from the pre-industrial era to present, as discussed in (Pallacks et al., 2021; doi:10.1016/j.gloplacha.2021.103549). Data has been collected to investigate the response of marine calcifiers to the combined effects of climate change stressors on decadal to centennial timescales, caused by anthropogenic CO2 emissions.

Results from six firn cores obtained during a 426 km long northern Greenland traverse in 2015 between the NEEM and the EGRIP deep drilling stations situated on the Western and Eastern side of the Greenland ice sheet, respectively. The cores (9 to 14 m long) are analysed for chemical impurities by means of Continuous Flow Analysis (CFA); Insoluble dust, ammonium, calcium, acid, conductivity and peroxide. The data was dated by means of annual layer counting of mainly peroxide supplemented by calcium seasonal cycles and spans 18 to 53 years (±3 yrs) depending on local snow accumulation that decreases from west to east. Insoluble dust, ammonium, and calcium concentrations in the 6 firn cores overlap, and also the seasonal cycles are similar in timing and magnitude across sites, while peroxide (H2O2) and conductivity both have spatial variations. H2O2 is driven by the accumulation pattern and conductivity is likely influenced by sea salt. Data is published as part of Kjær et al. 2022, Climate of the past, https://doi.org/10.5194/cp-2021-99