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  • This dataset collection holds high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. These were produced by the Department of Meteorology at the University of Reading. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). The data was recorded over a 5 year period from 2005-2010 on a global scale. It is expected that new and updated datasets will be added in the future.

  • These data are high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. This includes: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates on a high-resolution (1/360x1/360 degree) grid, produced by the Department of Meteorology at the University of Reading. Data was derived using the ESA CCI Land Cover Map (see linked documentation). Datasets containing information to locate and identify water bodies have been generated from high-resolution (1/360x1/360 degree, about 300mx300m) data locating static-water-bodies recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The new datasets provide: distance to land, distance to water, water body identifiers and lake centre locations. The lake identifiers (IDs) are from the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. The LC CCI water bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR (Advanced Synthetic Aperture Radar) on Envisat between 2005 and 2010. Temporal change in water body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated. The paper associated with this dataset is: L.Carrea O. Embury C.J. Merchant "High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates" Geoscience Data Journal, vol. 2 issue 2, pp. 83-97, November 2015. DOI: 10.1002/gdj3.32

  • This data set represents the model results plotted in the figures in Bett et al. (2020). Data portrays Amundsen Sea freshwater fluxes and freshwater passive tracer results, along with the results on the effect of grounded icebergs and iceberg melt on sea ice and oceanic heat content. These results are derived from Amundsen Sea regional model simulations over the period 1979-2018, with the first 10 years regarded as model spin up. For full descriptions of the results plotted in each figure see Bett et al. (2020).

  • The National Oceanography Centre Southampton (NOCS) Version 2.0 Surface Flux Dataset is a monthly mean gridded dataset of marine surface measurements and derived fluxes constructed using optimal interpolation. Input for the period 1973 to 2006 are ICOADS Release 2.4 ship data, the update from 2007 to 2014 uses ICOADS Release 2.5 and data after 2007 are preliminary. The dataset is presented as a time series of monthly mean values on a 1 degree area grid. The quality of the gridded data is quantified by estimates of random, bias and total uncertainty. The monthly means were derived from daily estimates of each variable and the standard deviation of these daily values is also available. Please see nocs2_variable_defs document for detailed variable information. Users are advised to take account of the uncertainty estimates provided, and to note that in very poorly sampled regions, such as the Southern Ocean, the uncertainty estimates themselves may be unreliable. Surface meteorological fields have been adjusted to account for varying measurement heights and for known biases (Berry and Kent 2009, Berry and Kent 2011). Surface fluxes have been calculated from daily fields of the surface meteorological parameters using bulk parameterisations (Reed 1977; Clark et al. 1974; Smith 1980; 1988). The NOCS v2.0 flux dataset was funded by the Oceans2025 project.

  • This dataset contains climatological monthly mean files of air-sea fluxes on a global grid in netCDF format produced at the National Oceanography Centre (NOC). It includes freshwater flux, heat flux and windstress and selected meteorological variables. Each data file contains 12 climatological monthly means on a global 1 x 1 grid for a particular flux field: Heat flux and windstress: latent heat flux (hfls), net heat flux (hfns), sensible heat flux (hfss), precipitation (pr), net longwave flux (rls), net shortwave flux (rss), wind stress (eastward) (tauu), wind stress (northward) (tauv). Units are W/m2 for the heat flux and N/m2 for the stress. Also available are freshwater fields: evaporation (emy), precipitation (pmy), net evaporation (epmy) Units are m/yr in each case (divide by 12 to get m/month). Meteorology fields are: u10 - 10m wind speed, units m/s t10 - 10m air temperature, units deg C q10 - 10m specific humidity, units g/kg sst - sea surface temperature, units deg C ana - total cloud amount, units octas slp - sea level pressure, units mb The flux fields have been derived from the COADS1a (1980-93) dataset enhanced with additional metadata from the WMO47 list of ships. A full description of the fields is given in The Southampton Oceanography Centre (SOC) Ocean - Atmosphere, Heat, Momentum and Freshwater Flux Atlas (see link under Docs) and a parallel journal paper (Josey et al, 1999) describes the results of various evaluation studies (see links under Docs). It is important to note that the quality of the fields has a strong spatial dependence which reflects the global distribution of ship observations. Quality is likely to be high in the well sampled North Atlantic & North Pacific but to decrease in the Southern Hemisphere. In particular, south of 40 S the errors in the fields are likely to be large and we recognise the existence of spurious features which have been generated during the objective analysis of the original raw fields. NOC stress that caution must be taken when interpreting the fields in this region. In addition, note that the current version of the fields does not give closure of the global heat budget, the imbalance being a global mean net heat gain by the ocean of 29 W/m2. Work was carried out to identify regions in which NOC scientists believe the net heat gain has been overestimated. Results from several regional comparisons against high quality meteorological buoy data indicate that in those regions for which comparisons have been possible the NOC net heat flux estimates agree well with independent buoy measurements. Hence, NOC have not applied global adjustments to the heat flux components in order to balance the heat budget at this stage of their analysis. See NOC1.1a for adjusted heat fluxes. Funding has been received from the Hadley Centre, UK Meteorological Office for the production and analysis of this dataset. Please note that NOC1.1 - Previously the 'Original' SOC climatology (climatological and individual monthly fields)