Monthly Arctic sea ice lead fraction in 0.5° x 0.5° resolution from SARAL/Altika altimeter, link to datasets in NetCDF 4 format

Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geochemistry, and in ice dynamics. Their precise detection is crucial for altimetric estimations of sea ice thickness and volume. This study evaluates the performance of the SARAL/AltiKa (Satellite with ARgos and ALtiKa) altimeter to detect leads and to monitor their spatio-temporal dynamics. We show that a pulse peakiness parameter (PP) used to detect leads by Envisat RA-2 and ERS-1,-2 altimeters is not suitable because of saturation of AltiKa return echoes over the leads. The signal saturation results in loss of 6-10% of PP data over sea ice. We propose a different parameter-maximal power of waveform-and define the threshold to discriminate the leads. Our algorithm can be applied from December until May. It detects well the leads of small and medium size from 200 m to 3-4 km. So the combination of the high-resolution altimetric estimates with low-resolution thermal infra-red or radiometric lead fraction products could enhance the capability of remote sensing to monitor sea ice fracturing.

222Radon flux map for Europe in netCDF format

A high-resolution 222Radon (222Rn) flux map for Europe was developed, based on a parameterization of 222Rn production and transport in the soil. The 222Rn exhalation rate is parameterized based on soil properties, uranium content, and modelled soil moisture from two different land-surface reanalysis data sets. Spatial variations in exhalation rates are primarily determined by the uranium content of the soil, but also influenced by soil texture and local water table depth. Temporal variations are related to soil moisture variations as the molecular diffusion in the unsaturated soil zone depends on available air-filled pore space. Monthly 222Rn exhalation rates from European soils were calculated with a nominal spatial resolution of 0.083° x 0.083°. The two realizations of the 222Rn flux map, based on the different soil moisture data sets, both realistically reproduce the observed seasonality in the fluxes but yield considerable differences for absolute flux values. The mean 222Rn flux from soils in Europe is estimated to be 10 mBq/m**2/s (ERA-Interim/Land soil moisture) or 15 mBq/m**2/s (GLDAS-Noah soil moisture) for the period 2006-2010. The 222Rn flux maps for Europe are available for the application in atmospheric transport studies, e.g to evaluate the performance of atmospheric transport models.

E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first assemble an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 12) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950 - December 2015) on a 0.5° x 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

Results of the coupled atmosphere-land surface model ECHAM5-JSBACH in NetCDF format

In this study we present first results of a new model development, ECHAM5-JSBACH-wiso, where we have incorporated the stable water isotopes H218O and HDO as tracers in the hydrological cycle of the coupled atmosphere-land surface model ECHAM5-JSBACH. The ECHAM5-JSBACH-wiso model was run under present-day climate conditions at two different resolutions (T31L19, T63L31). A comparison between ECHAM5-JSBACH-wiso and ECHAM5-wiso shows that the coupling has a strong impact on the simulated temperature and soil wetness. Caused by these changes of temperature and the hydrological cycle, the d18O in precipitation also shows variations from -4 permil up to 4 permil. One of the strongest anomalies is shown over northeast Asia where, due to an increase of temperature, the d18O in precipitation increases as well. In order to analyze the sensitivity of the fractionation processes over land, we compare a set of simulations with various implementations of these processes over the land surface. The simulations allow us to distinguish between no fractionation, fractionation included in the evaporation flux (from bare soil) and also fractionation included in both evaporation and transpiration (from water transport through plants) fluxes. While the isotopic composition of the soil water may change for d18O by up to +8 permil:, the simulated d18O in precipitation shows only slight differences on the order of ±1 permil. The simulated isotopic composition of precipitation fits well with the available observations from the GNIP (Global Network of Isotopes in Precipitation) database.

E-RUN version 1.0: Observational gridded runoff estimates for Europe, link to data in NetCDF format (68 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first collect an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 11) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950-December 2014) on a 0.5° × 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

Potential temperature in Espartel Sill (Strait of Gibraltar), from 2004-09-30 to 2016-03-02 at 344 meters depth, link to raw data in MATLAB format

Potential temperature measured with a SBE37 at 35.862ºN, 5.97ºW at 344 meters Depth. Data expand from September the 30th, 2004 to March the 2nd, 2016. Original measurement frequency was 30 minutes, the data presented here is a subsampling that extract the coldest temperature found each 12 hours. The time vector corresponds with the moment in which this minimun temperature is observed.

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format

Topographic variation, the spatial variation in elevation and terrain features, underpins a myriad of patterns and processes in geography and ecology and is key to understanding the variation of life on the planet. The characterization of this variation is scale-dependent, i.e. it varies with the distance over which features are assessed and with the spatial grain (grid cell resolution) of analysis. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale basic research and analytical applications, however to date, such technique is unavailable. Here we used the digital elevation model products of global 250 m GMTED and near-global 90 m SRTM to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile and tangential curvature, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches (median, average, minimum, maximum, standard deviation, percent cover, count, majority, Shannon Index, entropy, uniformity). While a global cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at http://www.earthenv.org and can serve as a basis for standardized hydrological, environmental and biodiversity modeling at a global extent.