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This batch of script reproduces the results of "Watermass characteristics and circulation near 110 °E in the south-east Indian Ocean" research paper. This is written using MATLAB. I have also used M_Map(A mapping package for MATLAB) version 1.4m, GSW Oceanographic Toolbox and J.M.Lilly toolbox to save figures. For more information, tutorial and the details of the analysis please see our paper https://doi.org/10.1016/j.dsr2.2022.105149. If you find a bug or have a comment on the code, please create an issue on GitHub. Contact details: Please follow ORCID to get current email address For comments, questions, and if you find a bug please open an issue on the GitHub repository: https://github.com/rampatels/IIOE2-IntroToPO Disclaimer: the author provide this code as is, they do not take any responsibility.

This repository contains all material necessary to reproduce the figures and summary statistics presented in the publication Analysis of ecologically relevant sea ice and ocean variables for the Southern Ocean using a high-resolution model to inform ecosystem studies by Denisse Fierro-Arcos, Stuart Corney, Amelie Meyer, Hakase Hayashida, Andrew E. Kiss and Petra Heil. This manuscript has been submitted for publication to Progress in Oceanography and it is currently under review. In this study we examined the suitability of using outputs from the second run of ACCESS-OM2-01, a high-resolution coupled ocean-sea ice model, to answer questions about ecological impacts in the Southern Ocean. These notebooks can be used as a template for testing the suitability of model outputs for ecological applications, as well as quantitative estimates of changes in key environmental variables for the Southern Ocean over the past 50 years. The Marine Ecosystem Assessment for the Southern Ocean (MEASO) regions were used to evaluate and quantify the rate of change in the physical environment of the Southern Ocean. These regions were designed to establish a standard spatial scale for reporting and assessing environmental and ecosystem change in the SO, and to facilitate comparisons across studies and throughout time. The workflow presented in these notebooks can be adapted to evaluate different physical variables of ecological relevance and to outputs from different ocean models from an ecological perspective, as well as using different regional boundaries to examine change. If you use this software, please cite it as below.

{"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.

A Python reference implementation of the CF data model.

Code used in transport and flux analysis for Jones et al. 2023. "Observation-based estimates of volume, heat and freshwater exchanges between the subpolar North Atlantic interior, its boundary currents and the atmosphere"

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

Impact of Local Iron Enrichment on the Small Benthic Biota in the deep Arctic Ocean The study assesses the impact of local iron enrichment on the small benthic biota (bacteria, meiofauna) together with environmental parameters indicating the input of food at the deep seafloor. To evaluate the hypothesis that abundance, distribution, and diversity of the small benthic biota varies in relation to a local input of structural steel at the seabed, we analyzed sediment samples and the associated infauna along a short transect with increasing distance to an iron source, i.e., corroding steel weights of a free-falling observational platform (bottom-lander), lying on the seafloor for approximately seven years. Iron-enriched surface sediments in the vicinity of the bottom-weight left in summer 2008 after a short-term deployment of a bottom-lander in 2433 m water depth at the LTER (Long-Term Ecological Research) observation HAUSGARTEN in eastern parts of the Fram Strait were sampled on 28th July 2015 using push-corer (PC) handled by the Remotely Operated Vehicle (ROV) QUEST 4000 (MARUM Center for Marine Environmental Sciences, Germany) during Dive 369 from board RV Polarstern. The block-shaped steel bottom-weights (30 x 30 x 6 cm) were sitting about half of the height sunken into the seafloor and thus, almost not affecting near-bottom currents. During sampling in 2015, the plates were largely corroded. Surface sediments around the plates had an orange-red color with a gradient of decreasing color intensity with increasing distance from the source, i.e., the bottom weight. A total of eight push-corer samples (PC1-8) were taken at approx. regular distances (on average every 18 cm) along a short transect (about 1.5 m) crossing the iron gradient. Push-corers PC1-4 retrieved sediment from heavily impacted sediments, while samples taken from push-corers PC5-8 were visually indistinguishable from background sediments in the wider area. After recovery of the ROV, sediment cores (8 cm in diameter, and 20-25 cm in height) were sub-sampled using plastic syringes with cut-off anterior ends for meiofauna and nematode communities as well as for environmental parameters. The position specified in the data sets (longitude / latitude) refers to the position of the ROV.

The tropical Angolan upwelling system is a highly productive ecosystem with a distinct seasonal cycle in surface temperature and primary production. The lowest sea surface temperature, strongest cross-shore temperature gradient, and maximum productivity occur in austral winter when seasonally prevailing upwelling favorable winds are weakest. A multi cruise dataset of microstructure profiles collected between 2013 and 2022 in the tropical Angolan upwelling system was used to analyze the importance of mixing for cooling of the mixed layer. The data were collected during six cruises on board of the R/V Meteor. The results show that cooling due to turbulent heat fluxes at the base of the mixed layer is an important cooling term. This turbulent cooling, that is strongest in shallow shelf regions, is capable of explaining the observed negative cross-shore temperature gradient.

This document will form the basis for the EuropaBON virtual workshop on EBV workflows (22-24 February 2023) and for developing the co-design of the European Biodiversity Observation Network.