Wetlands in central Siberia from ASAR WS 2003, link to shapefiles

An area in central Siberia (partial coverage of Turukhansky und Yeniseysky districts) was investigated using satellite data. It covers freshwater ecosystems of non-forested peatlands in boreal forests. The satellite data represent the growing seasons of 2003/2004. Microwave data were acquired by the Advanced Synthetic Aperture Radar (ASAR) instrument onboard ENVISAT. The multi-temporal capabilities and resolution (150mx150m in WS mode) of the ASAR wide swath mode enabled the detection of dynamic features >2ha over this vast area. Scatterometer (QuikScat) data could be employed to distinguish hydro-periods. Wetland types have been identified on the basis of seasonal changes in backscatter. Results for peatlands have been compared with Russian forest inventory data which contain information on wetland distribution.

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.

Permafrost cores and active layer pits on Herschel Island: link to shape files

Twelve permafrost cores and active layer pits were drilled/dug on Herschel Island in order to estimate the soil organic carbon and total nitrogen contents in the first 30, 100 and 200 cm of ground. The data are shapefile points with attribute table, which contains different core information.

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.

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.