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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.

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

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.

This REcoM code version was used in Seifert et al. 2022 (doi:10.1525/elementa.2021.00104). More information in readme.txt. Full Changelog: https://github.com/mseifert93/Seifert_et_al_2022_REcoM_code/commits/v1.0.0

DIVAnd v2.7.9 Diff since v2.7.8 Closed issues: Bulls eye in region without data (#90) How to leverage other data while gridding? (Enhancement) (#96) Failed test (#104) Merged pull requests: CompatHelper: bump compat for Interpolations to 0.14, (keep existing compat) (#103) (@github-actions[bot]) {"references": ["Barth. A. et al. (2014) divand-1.0: n-dimensional variational data analysis for ocean observations"]}

The Last Interglacial (LIG; ~129,000 to 116,000 years ago) is the most recent geologic period with a warmer-than-present climate. Consequently, climate reconstructions from this period provide an opportunity to relate current and projected climatic trends to natural climate variability from a warmer world. We established a ~4.800 year-long record of sea surface temperature (SST) variability from the Eastern Mediterranean Sea at 1-to-4-year resolution by applying mass spectrometry imaging of long-chain alkenones on the finely laminated sapropel deposited during the LIG. Reconstructed SSTs rapidly increased during the early part of sapropel deposition and subsequently remained at a level similar or above modern SSTs. In the early stage of sapropel deposition, the maximal amplitude of decadal variability reached higher values than recorded in the same region during the instrumental era, plausibly due to a highly stratified water column that led to reduced vertical mixing and thus different forcing of SST than today. The later stage of sapropel deposition is characterised by varve deposition, suggesting weakened stratification and its breakdown in the colder months; this implies the establishment of oceanographic conditions with SST forcing similar to modern. A comparison of the decadal SST variabilities from this varved part and the instrumental era reveals that the maximal amplitude of reconstructed decadal SST variability did not exceed the range of the recent period of warming climate. However, examining the more gradual SST trend reveals that the maximal centennial-scale SST increase from the varved sapropel section is below projected centennial-scale SST warming in the 21st century under medium-to-low-emissions scenarios, while the high-emissions scenarios suggest that future SST rise could even outpace the warming trends observed during early sapropel deposition underneath a highly stratified water column.

The Cage Aquaculture Particulate Output and Transport (CAPOT) model is an easy to use and flexible farm-scale model that can be used to rapidly estimate particulate waste deposition from cage production. CAPOT is a suitable initial, rapid assessment model to give an overview of potential impact of particulate waste from new or expanded fish cage farms, with little operator expertise by a wide range of stakeholders. The model is wholly encapsulated within standard spreadsheet format (.xlsm). Visualisation of the output is achieved using proprietary contour plotting software, such as SurferTM (Golden Software. CA, USA) and open-source Geographic Information System (GIS) software, such as QGIS, but can be achieved in a more limited form within the spreadsheet itself. Visual Basic for Applications (VBA) macro scripts within the spreadsheets perform calculations and data formatting to produce outputs in Surfer and ESRI (GIS) grid formats. The model should not be used for environment regulation/licensing purposes without checking with the relevant regulatory authority. Disclaimer: This model is distributed in good faith as a work in progress and potentially subject to error. As such, the authors and the University of Stirling do not accept any liability for inaccurate predictions or their consequences. This model is employed at the users own risk.

Programs used to detect Island Mass Effects (IMEs) in the tropical Pacific, accompanying the paper "Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean" (Messié et al., 2022). This code runs the IME detection method on tropical Pacific islands on a climatological basis, and reproduces results presented in Fig. 1, Extended Fig. 1, and Table 1 of the paper. The method was developed within the SAPPHIRE project funded by Horizon 2020 (Marie Skłodowska-Curie grant agreement No. 746530). The programs are written for Matlab and require the Image Processing toolbox. The main .zip file (messiem/toolbox_IME_detection-v1.0.0.zip) contains the toolbox architecture and code, as well as output figures. Files with .mat extensions are not correctly represented in the main .zip file because of using Git LFS on GitHub. They are provided within the additional zip file toolbox_IME_detection_mat_files.zip. Tu use the toolbox, replace the .mat files from the main zip by the corresponding files within toolbox_IME_detection_mat_files.zip.