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.