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  • purpose: The map shows the median grain size (or d50) of surface sediments in the North Sea predicted by interpolation of legacy grain size distribution data. It has been produced to aid in describing physical habitat characteristics and to supply consistent baseline data and boundary conditions for ecological and biophysical modelling. abstract: In grain size analysis, the median is the midpoint of the cumulative particles size distribution curve of a sediment sample. The median grain size is an important biophysical variable that relates to sediment stability and often can be mapped with a quantifiable correspondence to the occurrence of benthic species and assemblages. This map conveys information on the median grain size of seabed sediments in the North Sea. It has been produced with multivariate geostatistics (external drift kriging) using the percentage mud content as a trend variable. The underlying data set is a compilation of over 30,000 sediment samples from many national and European surveys conducted over a period of more than 50 years. Due to the vintage of some samples in the database, users are advised to consider the dynamic nature of the seafloor when using the data and when creating derived surrogate based habitat maps. Also, due to the diversity of sources for the pointdata, users should be aware of the differing methods by which the grain size analyses were conducted. As a consequence, map confidence is not necessarily uniform and thus areas not always comparable, even though the interpolation surface my look continuous.

  • CHELSA_v1.1 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns as well as derived bioclimatic and interannual parameters for the time period 1979-2013. CHELSA_v1.1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction.

  • abstract: In grain size analysis, the proportion of particles with a diameter of less than 63 µm is commonly referred to as the mud content of a sediment sample. The mud content is an important biophysical variable that often can be mapped with a quantifiable correspondence to organic matter, contaminants and the occurrence of benthic species and assemblages. Thismap conveys information on the percentage mud content of seabedsediments in the North Sea. It has been produced with multivariate geostatistics (external drift kriging) using water depth as a trend variable. The underlying data set is a compilation of over 30,000 sediment samples from many national and Europaen surveys conducted over a period of more than 50 years. Due to the vintage of some samples in the database, users are advised to consider the dynamic nature of the seafloor when using the data and when creating derived surrogate based habitatmaps. Also, due to the diversity of sources for the pointdata, users should be aware of the differing methods by which the grain size analyses were conducted. As a consequence, map confidence is not necessarily uniform and thus areas not always comparable, even though the interpolation surface may look continuous. purpose: The map shows the percentage mud content (silt + clay) of surface sediments in the North Sea predicted by interpolation of legacy grain size distribution data. It has been produced to aid in describing physical habitat characteristics and to supply consistent baseline data and boundary conditions for ecological and biophysical modelling.

  • purpose: This map shows the total organic carbon content (TOC) of surface sediments in the North Sea. It was produced by interpolation of legacy data from more than 3000 samples collected between 1960 and 2014. The distribution of this map allows the user to visualize an important marine habitat characteristic and to exploit the dataset for ecological and biogeochemical modelling. abstract: Weight percent total organic carbon (TOC) is one of the most commonly used descriptors for marine sediments. It is used to judge primary productivity of the overlying water column and refers to the amount of organic matter preserved within sediment. TOC has a major influence on biogeochemical processes occurring in sediments, including the regulation of the behavior of the other chemical species such as metals and organic pollutants. Therefore, determination of TOC is an essential component of environmental characterization analysis.This map conveys information on the weight percent TOC of seabed sediments in the North Sea. It has been produced with multivariate geostatistics (external drift kriging) using the percentage mud content as a trend variable. The underlying data set is a compilation of over 3,000 sediment samples from many national and European surveys conducted between 1960 and 2014. Due to the vintage of some samples in the database, users are advised to consider the dynamic nature of the seafloor when using the data and when creating derived surrogate based habitat maps. Also, due to the diversity of sources for the point data, users should be aware of the differing methods by which the TOC analyses were conducted. As a consequence, map confidence is not necessarily uniform and thus areas not always comparable, even though the interpolation surface may look continuous.

  • Note: please use https://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=EKF400_v1.1 data instead of EKF400_v1 (details see Quality information EKF400_v1.1)! EKF400 is a monthly resolved paleo-reanalysis covering the period 1603 to 2003. Early instrumental temperature and surface pressure observations, temperature indices derived from historical documents and temperature and moisture sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time.

  • Simulated 2D residual velocity fields in the inner German Bight were subjected to Principal Component Analysis (PCA). Residual currents were obtained from coastDat2 barotropic 2D simulations with the hydrodynamic model TRIM-NP V2.1.22 in barotropic 2D mode on a Cartesian grid (1.6km spatial resolution) stored on an hourly basis for the years 1948 - 2012 (doi:10.1594/WDCC/coastDat-2_TRIM-NP-2d) and later extended until August 2015. The present analysis refers to the period Jan 1958 - Aug 2015. The spatial domain considered is the region to the east of 6 degrees east and to the south of 55.6 degrees north. All grid nodes with a bathymetry of less than 10m were excluded. Residual velocities were calculated in two different ways: 1.) as 25h means, 2.) as monthly means. Both types of residual current data are available from * RESIDUAL_CURRENTS_195801_201508 The directory contains sub-directories for years and months. Daily residual currents for the 13th of September 1974, for instance, are stored in * RESIDUAL_CURRENTS_195801_201508/YEAR_1974/MONTH_09/TRIM2D_1974_09_13_means.nc while monthly mean residual currents for September 1974 are stored in: * RESIDUAL_CURRENTS_195801_201508/YEAR_1974/TRIM2D_1974_09_means.nc All current fields provided were interpolated from the original Cartesian model grid to a more convenient regular geographical grid (116x76 nodes). Mean residual currents are stored in: * mean_residual_currents.nc This data set contains residual velocities both on original Cartesian grid nodes and interpolated to the geographical grid. An example plot is provided: * mean_residual_currents.png For PCA, two residual velocity components from each of 12133 Cartesian grid nodes were combined into one data vector (length 2x12133), referring to 21061 daily or 692 monthly time levels. Results of two independent PCAs for either daily or monthly mean fields are stored in: * PCA_daily_residual_currents.nc * PCA_monthly_residual_currents.nc Files contain three leading Principal Components (PCs) and corresponding Emipirical Orthogonal Functions (EOFs). Again EOFs were also interpolated to a regular geographical grid. PC time series are also stored in plain ASCII format: * PCs_daily.txt * PCs_monthly.txt For monthly fields the number N of variables (N=2x12133) is much larger than the number T of time levels (T=692). Therefore, to reduce computational demands, the roles of time and space were formally interchanged. Having conducted the PCA the EOFs were then transformed back to the original spatial coordinates (cf. Section 12.2.6 in von Storch and Zwiers (1999), Statistical Analysis in Climate Research, Cambridge University Press). A much larger number of time levels made even this approach prohibitive for the full set of daily data. Therefore, PCAs were performed for six sub-periods (1958-1965, 1966-1975, 1976-1985, 1986-1995, 1996-2005, 2006-2015(Aug)) independently. EOFs obtained from these six sub-periods were then averaged to obtain EOFs representative for the whole period. Corresponding PCs were calculated by projecting daily fields onto these average EOFs. IMPORTANT: In contrast with PCA of monthly data, the PCA of daily data INVOLVES SOME APPROXIMATIONS! EOFs on the original nodes were normalized to have unit lengths. The following figures, * daily_EOF1.png * daily_EOF2.png * daily_EOF3.png show the first three EOFs obtained from daily data, assuming that corresponding PCs have the value of one standard deviation. The following two plots, * monthly_EOF1.png * monthly_EOF2.png show the leading EOFs for monthly mean data. EOF3 is omitted as it represents just a very small percentage of overall variance (1.7%).

  • Hydrological forecasts with hydrological model LARSIM (LARSIM=LArge Area Runoof Simulation Model, BY=Bavaria, Conceptual RR-model. Forecast depth: 72 hours.) for rivers Iller and Lech (DE) driven by numerical weather prediction models LME, GME, GFS. The runs were performed by "Wasserwirtschaftsamt Kempten" (WWA-KE).

  • LARSIM (LARSIM=LArge Area Runoff Simulation Model BW= Baden-Wuerttemberg) is described in "Freiburger Schriften zur Hydrologie", Band 22. 2006 (Ludwig, K.; Bremicker, M.: The water Balance Model LARSIM) The calculated results from LARSIM for the gauges Murg at Rotenfels and Kinzig at Schwaibach were handed over. The results are calcultaed in operational mode of the flood forecasting centre Karlsruhe (HVZ). The forecasts were corrected with ARIMA (0,1,0), i.e. the forecasted discharges were shifted with a constant amount, so, that the first forecast value attaches directly to the last measured value. During low water periods, the forecast is adapted to the average value of the last 24 h of the measured values. The forecasts were calculated for 72 hours. The runs driven by the DWD forecast LMK takes the LMK (new name: COSMO-DE) for the first 21 hours and then the LME-forecast. The runs called LME take only the LME (new name: COSMO-EU) forecast into accuont. For the period up to the forecast time measured values were used. The model uses precipitation, temperature, wind velocity, dew point or rel. humidity and the solar radiation. The measurement network uses the stations of the German Weatherservice DWD, the stations of the federal state Baden-Wuerttemberg (called "LUBW Luft" and "LUBW Ombro") and stations of third parties. The measurement network is very dense, but the equipement of the different stations may be dissimilar. You can see the network of the precipitation stations at http://www.hvz.baden-wuerttemberg.de/ -> Niederschlag -> Stationskarte. The forecasts were performed by the Flood Forecasting Centre Karlsruhe (HVZ) with its operational model "Oberrheinzf" (for Oberrheinzufluesse = tributaries of the river Rhine). The HVZ is part of the "Landesanstalt fuer Umwelt, Messungen und Naturschutz Baden-Wuerttemberg" (LUBW)". The model covers the region: 7°42' / 48°04' und 8°33' / 49°02'