Climatological mean salinity for the global ocean from in situ profile data _NCProperties=version=2,netcdf=4.9.2,hdf5=1.14.3 cdm_data_type=Grid comment=Global Climatology as part of the World Ocean Atlas Project.Modified at NOAA/AOML to keep only the upper 500m s_an values contributor_name=Ocean Climate Laboratory contributor_role=Calculation of climatologies Conventions=CF-1.6, ACDD-1.3, COARDS Easternmost_Easting=179.875 geospatial_lat_max=89.875 geospatial_lat_min=-89.875 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=179.875 geospatial_lon_min=-179.875 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east id=woa23_decav_s12_04.nc infoUrl=https://www.nodc.noaa.gov/OC5/woa18/ institution=NCEI keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=Unidata Dataset Discovery v1.0 metadata_link=https://www.ncei.noaa.gov/products/world-ocean-atlas naming_authority=gov.noaa.ncei ncei_template_version=NCEI_NetCDF_Grid_Template_v1.0 Northernmost_Northing=89.875 processing_level=processed project=World Ocean Atlas references=Reagan, J.R., D. Seidov, Z. Wang, D. Dukhovskoy, T.P. Boyer, R.A. Locarnini, O.K. Baranova, A.V. Mishonov, H.E. Garcia, C. Bouchard, S.L. Cross, and C.R. Paver (2023). World Ocean Atlas 2023, Volume 2: Salinity. A. Mishonov Technical Editor, NOAA Atlas NESDIS 90, https://doi.org/10.25923/70qt-9574. sourceUrl=(local files) Southernmost_Northing=-89.875 standard_name_vocabulary=CF-1.6 time_coverage_duration=P68Y time_coverage_end=1988-12-16T00:00:00Z time_coverage_resolution=P01M time_coverage_start=1988-01-16T00:00:00Z Westernmost_Easting=-179.875

The climatology provides temperature and salinity data at 1X1 degree intervals for all the earth's oceans, down to a depth of 5500m, at incremental depths identical to those provided in the National Oceanographic Data Center's (NODC) World Ocean Atlas (WOA). This global climatology is the combination of NODC's 1998 world climatology (WOA), the EWG Arctic Ocean Atlas (AOA), and select Canadian data provided by the Bedford Institute of Oceanography (BIO). While the NODC data includes the Arctic ocean, the AOA data provides a better description of this region. Neither of these data fields provides a good representation of the Canadian Archipelago region and nearby bays in the winter months. The data from the Bedford Institute of Oceanography allowed us to bridge this data hole. These three data sets were merged using an optimal interpolation routine such that our PHC retains the high quality world description provided by the WOA while improving the Arctic with the AOA fields and Canadian data.

In summary, PHC = WOA (Levitus) '98 everywhere except in our arctic domain, where we have blended in the AOA field (from EWG), and the BIO data to produce a more realistic arctic region.

These Version 3.0 data were received from the Polar Science Center, Applied Physics Lab., University of Washington, during April 2005.

Data for Figure 3.23 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.23 shows multi-model-mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 multi-model mean and the climatology from the World Ocean Atlas 2018.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.

List of data provided

This dataset contains:

• Global bias of modelled annual-mean sea surface temperature (°C) of CMIP6 for the period 1995–2014 to WOA 2018
• Global bias of modelled annual-mean sea surface salinity (PSS-76) of CMIP6 for the period 1995–2014 to WOA 2018

CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. WOA 2018 is the World Ocean Atlas 2018.

Data provided in relation to figure

• panel_a/fig_3_23_a.nc (sst, CMIP6 Sea Surface Temperature Bias)
• panel_b/fig_3_23_b.nc (sss, CMIP6 Sea Surface Salinity Bias)

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

the dataset made available here is the monthly climatology of an estimate of the ocean surface mixed layer depth (mld) over the global ocean, at 1 degree by 1 degree spatial resolution. this climatology is based on about 7.3 million casts/profiles of temperature and salinity measurements made at sea between january 1970 and december 2021. those profiles data come from the argo program and from the ncei-noaa world ocean database (wod, boyer et al. 2018), including e.g. data from marine mammals or ice-tethered profilers for the high latitudes.here, the mld is computed on each individual cast/profile using the threshold criterion method. the depth of the mixed layer is defined as the shallowest depth where the surface potential density of the profile is superior to a reference value (usually taken close to the surface) added with the chosen threshold. in this work, we take a fixed threshold value for the density of 0.03 kg/m3, and a surface reference depth at 5 m. this surface mixed layer is by definition homogeneous in density (up to 0.03 kg/m3 variations) and can also be called an isopycnal layer. the latter value of 5 m for the reference depth differs from the rather usual 10 m value used in de boyer montégut et al., 2004 (see dataset at https://www.seanoe.org/data/00806/91774/). it is important to note that this shallower value has been chosen for a specific study dedicated to the arctic ocean (allende et al., submitted to geoscientific model development), where both the presence of ice and a very weak diurnal cycle may lead to mld shallower than 10 m and possibly lasting for several days or more (which should be rare outside polar latitudes). differences with the mld climatology using a 10 m reference depth, are located mostly in the polar high latitudes (poleward of 60 degree) and somehow in the north atlantic in winter. they are rather weak in other places of the global ocean (about less than 10m, mld with ref depth at 5 m being shallower).this product is rather intended for studies in the polar latitudes (e.g. arctic ocean with the presence of sea ice), and/or for validation of mld fields in such areas from ocean models like ocean general circulation models (e.g. see allende et al. submitted to geoscientific model development). in any case, we recall that it is important to estimate, as much as possible, the mld from the model with the same method as in the chosen observed climatology i.e. here with the same criterion values (0.03 kg/m3 threshold, and 5 m reference depth).this dataset (netcdf file) and its more detailed product documentation (pdf file) are produced at lops laboratory (associated research unit with following french research institute and university : ifremer, university of brest, iuem, cnrs, and ird). those files are available by clicking the download buttons hereunder on this page. some links to other related climatologies of ocean surface variables (e.g. mld with other criteria, barrier layer thickness...) can be found at the lops-ifremer mld webpages : https://cerweb.ifremer.fr/mld, or similarly at : https://www.umr-lops.fr/en/data/mld.

This repository contains all publicly available data required in the Indian Ocean modeling using the MASNUM ocean circulation model, as well as the simulated temperature, diffusive term and current results. Gebco_08: the topography based on the global General Bathymetric Chart of the Oceans 2008 (GEBCO_08) data. Levitus: the annually mean Levitus data are interpolated as the initial temperature and salinity. NCEP_NCAR: The surface forcing including the momentum, heat and wind stress fluxes are calculated from the monthly mean surface fields of the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set from 1948 to 2021. OSCAR: the multi-year (1993-2021) monthly mean Ocean Surface Current Analyses Real-time (OSCAR) data are regarded as a reference in the comparison of the simulated current. results: the simulated temperature, diffusive term and current results using the MASNUM ocean model. result_glob: the global simulated results that can provide the data for the open boundary conditions in the Indian Ocean modeling. WOA13: The simulated temperature in the last 1 year are compared with the monthly World Ocean Atlas 2013 (WOA13) climatologic data which is regarded as a reference.

This dataset contains recalibrations of previously deployed Argo oxygen floats using a procedure described in Drucker, R. and S. C. Riser (2016), In situ phase-domain calibration of oxygen optodes on profiling floats, Methods in Oceanography, 17, 206-318, https://doi.org/10.1016/j.mio.2016.09.007.

Comparison of profiles from oxygen Optodes deployed on profiling floats with ship-based bottle casts taken at the time of deployment shows typical low biases of approximately 0 to â 40 μmol kgâ 1.

in situ calibrations were performed on 147 Optodes floats deployed on UW floats between 2004 and 2015 using only World Ocean Database (WOD) references. Median differences to World Ocean Atlas (WOA) 2009 climatology were reduced from â ¼6% to â ¼1%. Deployment casts were used to estimate error for eight Argo floats deployed in the Indian and Pacific Oceans; the aggregate error was reduced from 8% to 0.3%.

"The COriolis Ocean Dataset for Reanalysis (hereafter ""CORA"") product is a global dataset of in situ temperature and salinity measurements. The CORA observations comes from many different sources collected by Coriolis data centre in collaboration with the In Situ Thematic Centre of the Copernicus Marine Service (CMEMS INSTAC). The observation integrated in the CORA product have been acquired both by autonomous platforms (Argo profilers, fixed moorings , gliders , drifters, sea mammals) , research or opportunity vessels (CTDs, XBTs, ferrybox). From the near real time CMEMS In Situ Thematic Centre product validated on a daily and weekly basis for forecasting purposes, a scientifically validated product is created. It s a ""reference product"" updated on a yearly basis since 2007. This product has been controlled using an objective analysis (statistical tests) method and a visual quality control (QC). This QC procedure has been developed with the main objective to improve the quality of the dataset to the level required by the climate application and the physical ocean re-analysis activities. It provides T and S weekly gridded fields and individual profiles both on their original level with QC flags and interpolated level. The measured parameters, depending on the data source, are : temperature, salinity. The reference level of measurements is immersion (in meters) or pressure (in decibars). CORA contains historical profiles extracted from the EN.4 global T&S dataset, World Ocean Atlas, SeaDataNet, ICES and other data aggregators." analysis_name=OA_CORA5.2_ cdm_data_type=Grid citation=Szekely et al. 2020, doi: 10.17882/46219 comment=V8.0 reference climatology and analysis parameters Conventions=CF-1.4 conventions=CF-1.6, COARDS, ACDD-1.3 creation_date=20240925T091022L data_doi=10.17882/46219 data_manager=Tanguy Szekely data_update=2024-02 Easternmost_Easting=179.5 geospatial_lat_max=89.89626 geospatial_lat_min=-77.01048 geospatial_lat_units=degrees_north geospatial_lon_max=179.5 geospatial_lon_min=-180.0 geospatial_lon_resolution=0.5 geospatial_lon_units=degrees_east history=Wed Oct 2 16:06:46 2024: ncks -d depth,0,151 OA_CORA5.2_20230615_fld_PSAL.nc -o OA_CORA5.2_20230615_fld_PSAL.nc 20240925T091022L : Creation infoUrl=https://www.seanoe.org/data/00351/46219/ institution=Copernicus Marine Service (CMEMS) institution_country=FR institution_edmo_code=5938 keywords_vocabulary=GCMD Science Keywords naming_authority=OCEAN:ICE NCO=netCDF Operators version 4.9.0 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=89.89626 owner_name=OceanScope product_DOI=https://doi.org/10.17882/46219 project_name=CMEMS Ins-TAC references=https://www.seanoe.org/data/00351/46219/ software_version=POSTOA_main - 7.0 source=Reanalysis sourceUrl=(local files) Southernmost_Northing=-77.01048 standard_name_vocabulary=CF Standard Name Table v85 time_coverage_end=2023-06-15T00:00:00Z time_coverage_start=1960-01-01T00:00:00Z variables=Time, Depth, Latitude, Longitude, Sea Temperature, Temperature Error, Error on temperature (% variance), Practical Salinity, Practical Salinity Error, Error on Salinity (% variance) Westernmost_Easting=-180.0

The ocean absorbs one quarter of the global CO2 emissions from human activity. The community-led Surface Ocean CO2 Atlas (www.socat.info) is key for the quantification of ocean CO2 uptake and its variation, now and in the future. SOCAT version 2025 has quality-controlled in situ surface ocean fCO2 (fugacity of CO2) measurements on ships, moorings, sailing yachts, autonomous and drifting surface platforms for the global ocean and coastal seas from 1957 to 2024. The main SOCAT synthesis and gridded products contain fCO2 values with an estimated accuracy of better than 5 μatm. Sensor fCO2 data with an estimated accuracy of better than 10 μatm are separately available. During secondary quality control, marine scientists assign a flag to each data set, as well as WOCE flags of 2 (good), 3 (questionable) or 4 (bad) to individual fCO2 values. Data sets are assigned flags of A and B for an estimated accuracy of better than 2 μatm, flag of C (and D) for an accuracy of better than 5 μatm and a flag of E for an accuracy of better than 10 μatm. Bakker et al. (2016) describe the quality control criteria used from SOCAT version 3 onward. SOCAT quality control cookbooks provide quality control updates (www.socat.info), with (Gkritzalis et al., 2024) used for version 2025. Quality control comments for individual data sets can be accessed via the SOCAT Data Set Viewer (www.socat.info). All data sets, where data quality has been deemed acceptable, have been made public. The main SOCAT synthesis files and the gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Access to data sets with an estimated accuracy of better than 10 µatm (flag of E) and fCO2 values with flags of 3 and 4 is via additional data products and the Data Set Viewer (Table 8 in Bakker et al., 2016). SOCAT publishes a global gridded product with a 1° longitude by 1° latitude resolution without gap filling. A second product with a higher resolution of 0.25° longitude by 0.25° latitude is available for the coastal seas. The gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Gridded products are available monthly, per year and per decade. Two powerful, interactive, online viewers, the Data Set Viewer and the Gridded Data Viewer (www.socat.info), enable investigation of the SOCAT synthesis and gridded data products. SOCAT data products can be downloaded. Matlab code is available for reading these files. Ocean Data View also provides access to the SOCAT data products (www.socat.info). SOCAT data products are discoverable, accessible and citable. The SOCAT Data Use Statement (www.socat.info) asks users to generously acknowledge the contribution of SOCAT scientists by invitation to co-authorship, especially for data providers in regional studies, and/or reference to relevant scientific articles. It also asks users to cite the relevant SOCAT data set, the relevant methods paper(s), and to use acknowledgement text (https://socat.info/index.php/citing-socat/). The SOCAT website (www.socat.info) provides a single access point for online viewers, downloadable data sets, the Data Use Statement, a list of contributors and an overview of scientific publications on SOCAT and using SOCAT. Automation of data upload and initial data checks have allowed annual releases of SOCAT from version 4 onwards. Automation of metadata upload is ongoing. SOCAT is used for quantification of ocean CO2 uptake and ocean acidification and for evaluation of earth system models and sensor data. SOCAT products inform on ocean CO2 uptake in the annual Global Carbon Budget since 2013. SOCAT is a key element of the World Meteorological Organization’s (WMO) Global Greenhouse Gas Watch (G3W) program and is a key resource for Copernicus’ evaluations. The annual SOCAT releases by the SOCAT scientific community contribute to United Nations (UN) Sustainable Development Goal (SDG) 13 and SDG 14 (Life Below Water), and to the UN Decade of Ocean Science for Sustainable Development. However, since 2022 SOCAT critically relies on support provided by the Pacific Marine Environmental Laboratory of the National Oceanic and Atmospheric Administration in the US. This has been sufficient to keep the basic operation running, however this limited support has resulted in SOCAT data architecture not being updated, leading to a system with limited resilience that is highly vulnerable to external factors. Hundreds of peer-reviewed scientific publications and high-impact reports cite SOCAT. The SOCAT community-led synthesis product is a key step in the value chain based on in situ surface ocean carbon measurements, which provides policy makers with critical information on ocean CO2 uptake for policy makers. The need for accurate knowledge of global ocean CO2 uptake and its (future) variation makes sustained funding of in si…

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.The Ocean Base map currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas.The World Ocean Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the World Ocean Base with the Reference service drawn on top. Article in the Fall 2011 ArcUser about this basemap: "A Foundation for Ocean GIS".The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, and Esri for topographic content. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details on the users who contributed bathymetric data for this map via the Community Maps Program, view the list of Contributors for the Ocean Basemap. The basemap was designed and developed by Esri. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO's website: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/.The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system.Tip: Here are some famous oceanic locations as they appear this map. Each URL launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

Based on the quantitative study of diatoms and radiolarians, summer sea-surface temperature (SSST) and sea ice distribution were estimated from 122 sediment core localities in the Atlantic, Indian and Pacific sectors of the Southern Ocean to reconstruct the last glacial environment at the EPILOG (19.5-16.0 ka or 23 000-19 000 cal yr. B.P.) time-slice. The statistical methods applied include the Imbrie and Kipp Method, the Modern Analog Technique and the General Additive Model. Summer SSTs reveal greater surface-water cooling than reconstructed by CLIMAP (Geol. Soc. Am. Map Chart. Ser. MC-36 (1981) 1), reaching a maximum (4-5 °C) in the present Subantarctic Zone of the Atlantic and Indian sector. The reconstruction of maximum winter sea ice (WSI) extent is in accordance with CLIMAP, showing an expansion of the WSI field by around 100% compared to the present. Although only limited information is available, the data clearly show that CLIMAP strongly overestimated the glacial summer sea ice extent. As a result of the northward expansion of Antarctic cold waters by 5-10° in latitude and a relatively small displacement of the Subtropical Front, thermal gradients were steepened during the last glacial in the northern zone of the Southern Ocean. Such reconstruction may, however, be inapposite for the Pacific sector. The few data available indicate reduced cooling in the southern Pacific and give suggestion for a non-uniform cooling of the glacial Southern Ocean.

This repository contains annual 3D sea water age since surface contact files from a subset of simulations with GFDL's ESM4.1 climate model (Dunne et al., 2020) that were contributed to the 6th Coupled Model Intercomparison Project (CMIP6).

The files have been regridded from MOM6 native grid to standard World Ocean Atlas 1x1 horizontal grid. They have been remapped from the lagrangian vertical coordinate used by GFDL's MOM6 ocean model (Adcroft et al., 2019) to standard World Ocean Atlas depths.

Files have undergone QA/QC. All data files are NetCDF.

Enquiries should be directed to Jasmin.John (Jasmin.John@noaa.gov) or John Dunne (John.Dunne@noaa.gov)

Anyone using these data should cite Dunne et al. (2020) and Adcroft et al. (2019). See references below.

Other variables associated with these simulations, along with assigned DOI's, are available through the ESGF CMIP6 portal:

https://esgf-node.llnl.gov/projects/cmip6/

_

The organization of this repository is as follows:

Experiments:

Future scenario

NOAA-GFDL/CMIP6/ScenarioMIP/GFDL-ESM4/ESM4_ssp585_D1

For each experiment, 3D annual sea water age data can be found in the sub-directory:

/ocean_annual_z_1x1deg

_

NOTES:

Provenance:

The data provided here do not have metadata that can be used for provenance and traceability back to NOAA/GFDL. The data DOI assigned should be used when sharing and citing these data.

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Silica is most common on the ocean floor beneath cold surface waters as it dissolves quickly in warm water. Silica is formed from the remains of organisms with silica based skeletons like diatoms or radiolarians. When large plankton blooms with a sudden die-off occur massive amounts of diatoms that associate die and contribute to the siliceous material on the seafloor. Silicate directly influences marine primary productivity. Phenomenon Mapped: Seafloor silicateUnits: Micromoles per literCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Unsigned integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Garcia HE, Locarnini RA, Boyer TP, Antonov JI (2006) World Ocean Atlas 2005, Volume 4: Nutrients (phosphate, nitrate, silicate). In: Levitus S, editor. 396 p. NOAA Atlas NESDIS 64, U.S. Government Printing Office, Washington DC.Publication Date: 2006ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Cartographic Renderer - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

North Sea - DIVAnd 4D 6-year seasonal analysis of Water body dissolved oxygen concentration 1980/2020 v2021. Moving 6-year analysis and visualization of Water body dissolved oxygen concentration winter (dec-feb) in the North Sea. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.2. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths. abstract=Moving 6-year analysis and visualization of Water body dissolved oxygen concentration in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.9. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths. acknowledgement=EMODNET-chemistry acknowledgment=Aggregated data products are generated by EMODnet Chemistry under the support of DG MARE Call for Tenders EASME/EMFF/2020/3.1.11/Lot 5/SI2.846161) area_keywords=North Sea area_keywords_urn=SDN:C19::1_2 Author_e_mail=Martin Mørk Larsen mml@bios.au.dk, Jonas K. Rømer jkr@bios.au.dk bathymetry_source=GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9) cdm_data_type=Grid citation=Usage is subject to mandatory citation: "This resource was generated in the framework of EMODnet Chemistry, under the support of DG MARE Call for Tender EASME/EMFF/2020/3.1.11/European Marine Observation and Data Network (EMODnet) - Lot 5 - Chemistry" comment=Every year of the time dimension corresponds to a 6-year centred average for the seasons: winter (Dec-Feb), spring (Mar-May), summer (Jun-Aug), autumn (Sep-Nov). Horizontal resolution 0.1 degree by 0.1 degree. Conventions=CF-1.6, COARDS, ACDD-1.3 data_access=http://ec.oceanbrowser.net/data/emodnet-domains date=2023-05-12T15:29:22 DIVA_references=Barth, A., Beckers, J.-M., Troupin, C., Alvera-Azcarate, A., and Vandenbulcke, L. (2014): divand-1.0: n-dimensional variational data analysis for ocean observations, Geosci. Model Dev., 7, 225-241, doi: 10.5194/gmd-7-225-2014 documentation=https://doi.org/doi_of_doc doi=https://doi.org/10.13120/c0155a06-6798-4c77-aab6-64165df57d88 Easternmost_Easting=25.0 file_name=./NorthSea/output/Water_body_dissolved_oxygen_concentration_NorthSea.4Danl.nc geospatial_lat_max=63.0 geospatial_lat_min=47.0 geospatial_lat_resolution=0.1 geospatial_lat_units=degrees_north geospatial_lon_max=25.0 geospatial_lon_min=-20.0 geospatial_lon_resolution=0.1 geospatial_lon_units=degrees_east history=Fri Sep 1 10:25:44 2023: ncks -O Water_body_dissolved_oxygen_concentration.4Danl.nc Water_body_dissolved_oxygen_concentration.4Danl.nc id=DatasetScan/emodnet-domains/By_sea_regions/North_Sea/Water_body_dissolved_oxygen_concentration.4Danl.nc infoUrl=https://doi.org/10.6092/A8CFB472-10DB-4225-9737-5A60DA9AF523 institution=Aarhus University, Department of Bioscience, Marine Ecology Roskilde institution_urn=SDN:EDMO::729 keywords_vocabulary=GCMD Science Keywords NCO=netCDF Operators version 4.9.9 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=63.0 parameter_keyword=Water body dissolved oxygen concentration parameter_keyword_urn=SDN:P35::EPC00002 product_code=AU-ECOS-North-SEA-DIN-2.0.ANA product_id=c0155a06-6798-4c77-aab6-64165df57d88 project=EMODNET Chemistry phase 5 search_keywords=Dissolved oxygen parameters in the water column search_keywords_urn=SDN:P02::DOXY source=observational data from SeaDataNet and World Ocean Atlas sourceUrl=http://opendap.oceanbrowser.net/thredds/dodsC/data/emodnet-domains/By_sea_regions/North_Sea/Water_body_dissolved_oxygen_concentration.4Danl.nc Southernmost_Northing=47.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2018-10-16T00:00:00Z time_coverage_start=1982-01-16T00:00:00Z WEB_visualisation=http://ec.oceanbrowser.net/emodnet/ Westernmost_Easting=-20.0

this product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: surface ocean carbon atlas socatv2023 and global ocean data analysis project glodapv2.2023.the socatv2023-obs dataset contains >25 million observations of fugacity of co2 of the surface global ocean from 1957 to early 2023. the quality control procedures are described in bakker et al. (2016). these observations form the basis of the gridded products included in socatv2023-gridded: monthly, yearly and decadal averages of fco2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean.glodapv2.2023-obs contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and ph from 1972 to 2021. these data were subjected to an extensive quality control and bias correction described in olsen et al. (2020). glodapv2-gridded contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and ph over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of glodap, glodapv2.socat and glodap are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see references below) in order to support future sustainability of the data products.

Swath sonar bathymetry data used for that dataset was recorded during RV METEOR cruise M79/2 using Kongsberg EM 120 multibeam echosounder. The cruise took place between 26.08.2009 and 21.09.2009 in the northeastern Atlantic. The main objectives of the cruise were the analysis of the structure, dynamic and natural hazard potential of the Atlantic section of the European-African plate boundary in the area of the eastern Azores (São Miguel region) and Gloria Fault. The measurements included marine geophysical experiments like refraction and reflection seismics, potential field recordings (gravity & magnetics), parametric sediment subbottom profiling and multibeam bathymetry [DOI:10.2312/cr_m79_2]. CI Citation: Paul Wintersteller (seafloor-imaging@marum.de) as responsible party for bathymetry raw data ingest and approval. Description of the data source: During the M79/2 cruise, the hull-mounted KONGSBERG EM120 multibeam ecosounder (MBES) was utilized to perform bathymetric mapping in middle to deep water depths. Two linear transducer arrays in a Mills Cross configuration transmit acoustic signals of a nominal sonar frequency of 12 kHz. With 191 beams, the emission cone has a dimension of max. 140° across track and 1° along track. With a reception obtained from 288 beams, the actual beam footprint is 1° by 2°. Depending on the roughness of the seafloor, the swath width on a flat bottom is generally maximum six times the water depth. For further information on the system, consult https://www.km.kongsberg.com/. Responsible person during this cruise / PI: Luis Batista. Description of data processing: Postprocessing and products were conducted by the Seafloor-Imaging & Mapping group of MARUM/FB5, responsible person Paul Wintersteller (seafloor-imaging@marum.de). The open source software MB-System (Caress, D. W., and D. N. Chayes, MB-System: Mapping the Seafloor, https://www.mbari.org/products/research-software/mb-system, 2017) was utilized for this purpose. . SVPs taken during this cruise were not sufficient enough to correct the recorded bathymetric data. Therefor sound velocity profiles were modelled using reference profiles from the world ocean atlas (S. Levitus, 1982), extracted and calculated through the MB-System program mblevitus by utilizing the DelGrosso equation. The surface sound speed has then been adapted according to the recordings during this cruise while there were no further corrections for roll, pitch and heave applied during postprocessing. A tide correction was applied, based on the Oregon State University (OSU) tidal prediction software (OTPS) that is retrievable through MB-System. CTD measurements during the cruise were sufficient to represent the changes in the sound velocity throughout the study area. Using Mbeditviz, artefacts were cleaned manually. NetCDF (GMT) grids of the edited data as well as statistics were created with mbgrid. The published bathymetric EM120 grid of the cruise M79/2 has a resolution of 35 m. No total propagated uncertainty (TPU) has been calculated to gather vertical or horizontal accuracy. A higher resolution is, at least partly, achievable. The grid extended with _num represents a raster dataset with the statistical number of beams/depths taken into account to create the depth of the cell. The extended _sd -grid contains the standard deviation for each cell. The DTMs projections are given in Geographic coordinate system Lat/Lon; Geodetic Datum: WGS84. All grids produced are retrievable through the PANGAEA database (www.pangaea.de). Chief Scientist: Christian Hübscher christian.huebscher@uni-hamburg.de CR: https://www.tib.eu/de/suchen/id/awi%3Adoi~10.2312%252Fcr_m79_2/ CSR: https://epic.awi.de/id/eprint/37063/31/m79-expeditionsheft.pdf

Data for Figure Atlas.28 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure Atlas.28 shows changes in annual mean surface air temperature, precipitation and sea level rise relative to the 1995–2014 baseline for the reference regions in the Small Islands region for different lines of evidence (CMIP5, CORDEX and CMIP6).

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citations: For the report component from which the figure originates: Gutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021

Iturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760

Figure subpanels

The figure has twelve panels, with data provided for all panels in the master GitHub repository linked in the documentation.

List of data provided

This dataset contains global monthly precipitation and near surface temperature aggregated by reference region for model output datasets: - CMIP5, CMIP6 (1850-2100) - CORDEX (1970-2100) These are presented separately for land, sea, and land-sea gridboxes (a single run per model). Regional averages are weighted by the cosine of latitude in all cases. An observation-based product (1979-2016) is also provided in the same format for reference: W5E5 (Lange, 2019). Sea level rise data from the CMIP6 ensemble is also used for the right-hand plot of each panel.

Data provided in relation to figure

All datasets of monthly precipitation and near surface temperature aggregated by region for CMIP5, CMIP6 and CORDEX models are provided in the labelled directories and regions in the Small Islands are used for the production of this figure.

Sea level projections data can be found here: https://www.wdc-climate.de/ui/entry?acronym=IPCC-DDC_AR6_Sup_SLPr

CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. SSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100. SSP2-4.5 is based on SSP2 with medium challenges to climate change mitigation and adaptation and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100. SSP5-8.5 is based on SSP5 where climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100. RCP2.6 is the Representative Concentration Pathway for 2.6 Wm-2 global warming by 2100. RCP4.5 is the Representative Concentration Pathway for 4.5 Wm-2 global warming by 2100. RCP8.5 is the Representative Concentration Pathway for 8.5 Wm-2 global warming by 2100. GWL stands for global warming levels. JJA and DJF stand for June, July, August and December, January, February respectively.

Notes on reproducing the figure from the provided data

Data and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. To reproduce each panel in this figure, use the 'regional-scatter-plots_R.ipynb' notebook. Information on reproducibility can be found in the 'reproducibility/projections' folder of the Atlas GitHub repository.

The notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Inte... For full abstract see: https://catalogue.ceda.ac.uk/uuid/89e1b69ad74146cfa8b0a941108811c2.

'''Short description:'''

Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2024 and GLobal Ocean Data Analysis Project GLODAPv2.2023. The SOCATv2024-OBS dataset contains >38 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2024. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2024-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.

'''DOI (product) :'''
https://doi.org/10.17882/99089

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu

'''Short description:''' Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2021 and GLobal Ocean Data Analysis Project GLODAPv2.2021. The SOCATv2021-OBS dataset contains >25 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2021. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2020-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2021-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2019. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.

'''DOI (product) :'''
https://doi.org/10.48670/moi-00035

North Sea - DIVAnd 4D 6-year seasonal analysis of Water body silicate 1980/2021 v2023. Moving 6-year analysis and visualization of Water body silicate in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.9. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths. abstract=Moving 6-year analysis and visualization of Water body silicate in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.9. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths. acknowledgement=EMODNET-chemistry area_keywords=North Sea area_keywords_urn=SDN:C19::1_2 Author_e_mail=Martin Moerk Larsen mml@ecos.au.dk, Jonas K. Roemer jkr@ecos.au.dk bathymetry_source=GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9) cdm_data_type=Grid citation=Usage is subject to mandatory citation: "This resource was generated in the framework of EMODnet Chemistry, under the support of DG MARE Call for Tender EASME/EMFF/2020/3.1.11/European Marine Observation and Data Network (EMODnet) - Lot 5 - Chemistry" comment=Every year of the time dimension corresponds to a 6-year centred average for the seasons: winter (Dec-Feb), spring (Mar-May), summer (Jun-Aug), autumn (Sep-Nov). Horizontal resolution 0.1 degree by 0.1 degree. Conventions=CF-1.8, COARDS, ACDD-1.3 data_access=http://ec.oceanbrowser.net/data/emodnet-domains date=2023-03-31T06:59:31 DIVA_references=Barth, A., Beckers, J.-M., Troupin, C., Alvera-Azcarate, A., and Vandenbulcke, L. (2014): divand-1.0: n-dimensional variational data analysis for ocean observations, Geosci. Model Dev., 7, 225-241, doi: 10.5194/gmd-7-225-2014 documentation=https://doi.org/doi_of_doc doi=https://doi.org/10.13120/97f6c2a8-fb4a-4870-9f69-04bc374b3257 Easternmost_Easting=25.0 file_name=./NorthSea/output/Water_body_silicate_NorthSea.4Danl.nc geospatial_lat_max=63.0 geospatial_lat_min=47.0 geospatial_lat_resolution=0.1 geospatial_lat_units=degrees_north geospatial_lon_max=25.0 geospatial_lon_min=-20.0 geospatial_lon_resolution=0.1 geospatial_lon_units=degrees_east history=Fri Sep 1 10:27:18 2023: ncks -O Water_body_silicate.4Danl.nc Water_body_silicate.4Danl.nc id=DatasetScan/emodnet-projects/v2023/By_sea_regions/North_Sea/Water_body_silicate.4Danl.nc infoUrl=https://doi.org/doi_of_doc institution=Aarhus University, Department of Bioscience, Marine Ecology Roskilde institution_urn=SDN:EDMO::729 keywords_vocabulary=GCMD Science Keywords NCO=netCDF Operators version 4.9.9 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=63.0 parameter_keyword=Water body silicate parameter_keyword_urn=SDN:P35::EPC00008 product_code=AU-ECOS-North-SEA-DIN-2.0.ANA product_id=97f6c2a8-fb4a-4870-9f69-04bc374b3257 project=EMODNET Chemistry phase 5 search_keywords=Silicate concentration parameters in the water column search_keywords_urn=SDN:P02::SLCA source=observational data from SeaDataNet and World Ocean Atlas sourceUrl=http://opendap.oceanbrowser.net/thredds/dodsC/data/emodnet-projects/v2023/By_sea_regions/North_Sea/Water_body_silicate.4Danl.nc Southernmost_Northing=47.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_end=2018-10-16T00:00:00Z time_coverage_start=1982-01-16T00:00:00Z WEB_visualisation=http://ec.oceanbrowser.net/emodnet/ Westernmost_Easting=-20.0