This dataset containing traits of marine fish is based on fish taxa observed during international scientific bottom-trawl surveys regularly conducted in the Northeast Atlantic, Northwest Atlantic and the Northeast Pacific. These scientific surveys target primarily demersal (bottom-dwelling) fish species, but pelagic species are also regularly recorded. The overarching aim of this dataset was to collect information on ecological traits for as many fish taxa as possible and to find area-specific trait values to account for intraspecific variation in traits, especially for widely distributed species. We collected traits for species, genera and families. The majority of trait values were sourced from FishBase (Froese and Pauly, 2019), and have been supplemented with values from the primary literature.

Assessments run at AFWG provide the scientific basis for the management of cod, haddock, saithe, redfish, Greenland halibut and capelin in subareas 1 and 2. Taking the catch values provided by the Norwegian fisheries ministry for Norwegian catches1 and raising the total landed value to the total catches gives an approximate nominal first-hand landed value for the combined AFWG stocks of ca. 20 billion NOK or ca. 2 billion EUR (2018 estimates).

The purpose of this table is to present the best available abundance estimates for cetacean species in areas of relevance to the work of NAMMCO. It is intended to be used as a starting point for researchers, and the original sources are provided if additional information is required. The Scientific Committee of NAMMCO maintains a Working Group on Abundance Estimates, composed of invited experts in the field as well as some Committee members. This Working Group meets periodically to review new abundance estimates from recent surveys or, in some cases, re-analyses of older data. The reports of the Working Group are brought to the Scientific Committee at their annual meetings, and used to formulate advice on stock status, allowable removals or other matters. In most cases, the Scientific Committee will formally endorse estimates approved by the Working Group, and if so, this is indicated on the Table. Some estimates have been endorsed by the Scientific Committee of the International Whaling Commission (IWC).

Excel file containing CPR data from Standard Areas B4,C3,C4,D3,D4,D5,E4,F4 for the plankton Calanus finmarchicus and helgolandicus, total traverse (small) copepods, total large copepods, Phytoplankton Colour Index and Cnidaria (presence denoted by a 1, absence by a zero). All taxa are from 1980, except Cnidaria which are from 2011. Dataset is in the format of sample level data, with each row being a discrete sample, with a sample being 3m3 filtered seawater, and 10nm of tow. For each row, a sample has the following information, starting at column a: Standard area of sample, sample id, latitude (decimal degrees) of sample mid point, longitude (decimal degrees) of sample midpoint, sample midpoint date and local time, year of sample, month of sample, then plankton abundance values (or PCI index or cnidaria presence/absence). All taxa have been looked for during the period this dataset spans, so zero values represent true absence.

M2TMNXAER (or tavgM_2d_aer_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated aerosol diagnostics, such as column mass density of aerosol components (black carbon, dust, sea salt, sulfate, and organic carbon), surface mass concentration of aerosol components, and total extinction (and scattering ) aerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10 may be derived with the formula described in the FAQs under the Documentation tab of this page. The collection also includes variance of certain parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month.

The Commission for the Conservation Southern Bluefin Tuna collects a variety of data types from its Members and Cooperating Non-Members, including total catch, catch and effort data, and catch at size data. Catch, size and trade information is also collected through the Commission's Catch Documentation Scheme, Japanese import statistics, and other monitoring programs. Annual catches provided on this page are reported on a calendar year basis. CCSBT Members use quota years (not calendar years) for managing catching limits, but quota years differ between Members, so calendar years are used to provide catches on a common timescale. Relevant subsets and summaries of these data are provided below. All figures are subject to change as improved data or estimates become available. In particular, reviews of SBT data in 2006 indicated that southern bluefin tuna catches may have been substantially under-reported over the previous 10-20 years and the data presented here do not include estimates for this unreported catch. Also, data for the last reported year of catch (2020) are preliminary and are subject to revision. Any latitudes and longitudes presented in these summaries represent the north western corner of the relevant grid, which is a 5*5 grid unless otherwise specified. Other information on Members and Cooperating Non-Members fishing activities appears in the reports of the Extended Scientific Committee, Compliance Committee and Extended Commission.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has not been the case and when this does occur users will be notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1979 to present".

All statistics of UNCTAD are harmonized and integrated into UNCTADstat- free to use dissemination platform. It gives access to basic and derived indicators built upon common rules, harmonized environment and clear methodology supported by powerful data browsing system. The statistical series are regularly updated and classified into easy-to-navigate themes. UNCTADstat offers ready-to-use analytical groupings, with a unique coverage for countries and products and a particular focus on developing and transition economies. This approach ensures data consistency across multiple data series, and enables users to harness its full potential by mixing and matching data from various domains. The navigation browser allows table or graphic presentations, easy selection and reorganization of data, personalized functionalities and several straightforward extraction options.

The Atlantic Multi-decadal Oscillation (AMO) has been identified as a coherent mode of natural variability occurring in the North Atlantic Ocean with an estimated period of 60-80 years. It is based upon the average anomalies of sea surface temperatures (SST) in the North Atlantic basin, typically over 0-80N. To remove the climate change signal from the AMO index, users typically detrend the SST data at each gridpoint or detrend the spatially averaged time series. Trenberth and Shea (2006) recommend that the detrending be done by subtracting the global-mean SST anomaly time series from the spatially averaged time series. See the Expert Guidance by Dr. Kevin Trenberth for the rationale for the global-mean detrending approach. The Expert Guidance by Dr. Rong Zhang discusses the impacts and mechanisms of the AMO.

This is the FAO Fishery and Aquaculture Reference Data repository: Codes and reference data for fishing gear, species, currencies, commodities, countries and others.