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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.

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.

The positive effect of fully protected Marine Protected Areas (MPAs) on marine biodiversity, specifically on fishes, has been widely documented. In contrast, the potential of MPAs to mitigate the impact of adverse climatic conditions has seldom been investigated. Here, we assessed the effectiveness of MPAs, quantified as increasing fish biomass, across wide geographic and environmental gradients across the Mediterranean Sea. We performed underwater visual surveys within and outside MPAs to characterize fish assemblages in 52 rocky reef sites across an extent of over 3,300 km. We used the steep spatial temperature gradient across the Mediterranean as a 'space-for-time' substitution to infer climate-driven temporal changes. We found that, as expected, Mediterranean MPAs increased fish biomass. At the same time, higher seawater temperatures are associated with decreased fish biomass, changes in species composition, and shifts towards more thermophilic species. Importantly, we found that the rate of decrease in fish biomass with temperature was similar between protected and fished sites. Taken together, these results suggest that the capacity of MPAs to harbor higher fish biomass, compared to surrounding areas, is maintained across a broad temperature range. At the same time, MPAs will not be able to offset larger-scale biotic alterations associated with climate change. Policy implications: Our results suggest that sustained warming will likely reduce fish biomass in the Mediterranean Sea and shift community structure, requiring more conservative targets for fishery regulations. At the same time, protection from fishing will remain an important management tool even with future high-water temperatures, and MPAs are expected to continue to provide local-scale benefits to conservation and fisheries.

Triple threat processes and/or other forcings can lead to changes in the ocean happening fast and abruptly. These changes, referred to as “tipping points”, are critical thresholds in a marine system that, when exceeded, can lead to a significant change in the state of the system, which often can be irreversible. This product has been prepared with the financial support of Norges forskningsråd (Research Council of Norway) (309382) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points). The work reflects only the author’s/authors’ view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains.

Triple threat processes and/or other forcings can lead to changes in the ocean happening fast and abruptly. These changes, referred to as “tipping points”, are critical thresholds in a marine system that, when exceeded, can lead to a significant change in the state of the system, which often can be irreversible. This leaflet has been prepared mainly (but not only) for high school pupils with the financial support of Norges forskningsråd (Research Council of Norway) (309382).

In work package 2 of FLOATECH a detailed validation and verification of the capabilities of QBlade-Ocean is ongoing. Thereby, three wind turbine models mounted on floating substructures with differing characteristics serve as the means for the validation.This dataset contains Floating Offshore Wind Turbine (FOWT) calculations in various design situations, computed with three different codes. In more detail, three floating platform archetypes are used in the code-to-code comparison ongoing in work package 2: a semi-submersible-type floater and a spar-type floater as well as the Hexafloat® concept recently proposed by Saipem®. The three test-cases are the NREL 5MW RWT mounted on the DeepCwind semi-submersible platform, the DTU 10MW RWT mounted on the SOFTWIND spar-type platform and the DTU 10MW RWT mounted on the Hexafloat® platform. More details regarding the dataset structure and the testcases can be found in the accompanying document.

This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. 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 submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea).

Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century. During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values. Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005). Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": Column 1: Scenario: RCP scenario used Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka Column 3: Year: Year in CE of the simulation in 10 year steps Column 4: Forest line in m Column 5: Treeline in m Column 6: Single-tree line in m Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": Column 1: Scenario: RCP scenario used Column 2: Year: Year in CE of the simulation in 10 year steps Column 3: Tundra area at region Taimyr Peninsula in km² Column 4: Tundra area at region Buor Khaya Peninsula in km² Column 5: Tundra area at region Kolyma River Basin in km² Column 6: Tundra area at region Chukotka in km² The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") This study was supported 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": ["Kruse, S., Gerdes, A., Kath, N. J., Epp, L. S., Stoof-Leichsenring, K. R., Pestryakova, L. A., & Herzschuh, U. (2019). Dispersal distances and migration rates at the arctic treeline in Siberia \u2013 a genetic and simulation-based study. Biogeosciences, 16(6), 1211\u20131224. doi:10.5194/bg-16-1211-2019", "Kruse, S., Gerdes, A., Kath, N. J., & Herzschuh, U. (2018). Implementing spatially explicit wind-driven seed and pollen dispersal in the individual-based larch simulation model: LAVESI-WIND 1.0. Geoscientific Model Development, 11(11), 4451\u20134467. doi:10.5194/gmd-11-4451-2018", "Kruse, S., Wieczorek, M., Jeltsch, F., & Herzschuh, U. (2016). Treeline dynamics in Siberia under changing climates as inferred from an individual-based model for Larix. Ecological Modelling, 338, 101\u2013121. doi:10.1016/j.ecolmodel.2016.08.003", "Walker, D. A., Raynolds, M. K., Dani\u00ebls, F. J. A. A., Einarsson, E., Elvebakk, A., Gould, W. A., \u2026 Team, the other members of the C. (2005). The circumpolar Arctic vegetation map. Journal of Vegetation Science, 16(3), 267\u2013282. doi:10.1658/1100-9233(2005)016[0267:TCAVM]2.0.CO;2"]}

L'inventaire officiel national de gaz à effet de serre du Canada est préparé et présenté à la Convention cadre des Nations Unies sur les changements climatiques (CCNUCC) au plus tard le 15 avril de chaque année, conformément aux Directives pour l'établissement des communications nationales des Parties visées à l'annexe 1 de la Convention, première partie : directives FCCC pour la notification des inventaires annuels (directives de la CCNUCC pour la notification des inventaires) adoptées par la décision 24/CP.19 en 2013. Le rapport annuel d'inventaire se compose du Rapport d'inventaire national (RIN) et des tableaux du Cadre uniformisé de présentation de rapports (CUPR).Les gaz pour lesquels les émissions sont estimées comprennent le dioxyde de carbone (CO2), le méthane (CH4), l'oxyde de diazote (N2O), les hydrofluorocarbones (HFC), les perfluorocarbones (PFC), l'hexaflorure de soufre (SF6) et la triflorure d'azote (NF3). Notez que pour tenir compte de la création du Nunavut en 1999, les données pour les années précédant 1999 montrent le Nunavut et les Territoires du Nord-Ouest comme une seule région. En apprendre plus sur l'inventaire canadien des gaz à effet de serre : https://www.canada.ca/inventaire-ges Nous contacter : https://www.canada.ca/fr/environnement-changement-climatique/services/changements-climatiques/emissions-gaz-effet-serre/coordonnees-equipe.html Renseignements supplémentaires Le sommaire du RIN est disponible sur le site web d'Environnement Canada : https://www.canada.ca/fr/environnement-changement-climatique/services/changements-climatiques/emissions-gaz-effet-serre.html Le RIN complète est disponible sur le site web de la Convention-cadre des Nations Unies sur les changements climatiques : https://unfccc.int/ghg-inventories-annex-i-parties/2022 Soutien aux projets : Ces données sont une partie des travaux publiés par le Canada dans la présentation du Rapport d'inventatire national (RIN) en 2022. Canada's official national greenhouse gas inventory is prepared and submitted annually to the United Nations Framework Convention on Climate Change (UNFCCC) by April 15 of each year, in accordance with the revised Guidelines for the preparation of national communications by Parties included in Annex I to the Convention, Part I: UNFCCC reporting guidelines on annual inventories (UNFCCC Reporting Guidelines), adopted through Decision 24/CP.19 in 2013. The annual inventory submission consists of the National Inventory Report (NIR) and the Common Reporting Format (CRF) tables. Gases for which emissions are estimated include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFC), perfluorocarbons (PFC), sulfur hexafluoride (SF6) and nitrogen trifluoride (NF3). Note that to account for the creation of Nunavut in 1999, data for years preceding 1999 show Nunavut and Northwest Territories as a combined region. Learn more about Canada's greenhouse gas inventory: https://www.canada.ca/ghg-inventory Contact us: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions/contact-team.html Supplemental Information The Executive Summary of Canada's NIR is available on Environment Canada's website: https://www.canada.ca/en/environment-climate-change/services/climate-change/greenhouse-gas-emissions.html The complete NIR is available on the United Nations Framework Convention on Climate Change's website: https://unfccc.int/ghg-inventories-annex-i-parties/2022 Supporting Projects: This data is a portion of the work published by Canada in the 2022 submission of the National Inventory Report (NIR).

These datasets include measurements of particulate organic and total carbon of: - Bottom-ice algal communities and Melosira arctica - Phytoplankton and melt pond communities. Data were collected in Hudson Bay and rivers exiting into the Bay during the BaySys cruise in June/ July 2018.