4 Works

WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0)

Stefan Lange, Christoph Menz, Stephanie Gleixner, Marco Cucchi, Graham P. Weedon, Alessandro Amici, Nicolas Bellouin, Hannes Müller Schmied, Hans Hersbach, Carlo Buontempo & Chiara Cagnazzo
The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b).
Version 2.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2019. Data sources of W5E5 are version 2.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al.,...

Pollution control can help mitigate future climate change impacts on European grayling in the UK

J. Vanessa Huml, W. Edwin Harris, Martin I. Taylor, Robin Sen, Christel Prudhomme & Jonathan S. Ellis
Aim We compare the performance of habitat suitability models using climate data only or climate data together with water chemistry, land cover and predation pressure data to model the distribution of European grayling (Thymallus thymallus). From these models, we (1) investigate the relationship between habitat suitability and genetic diversity; (2) project the distribution of grayling under future climate change and (3) model the effects of habitat mitigation on future distributions. Location United Kingdom Methods Maxent...

Historic reconstructions of daily river flow for 303 UK catchments (1891-2015)

K.A. Smith, M. Tanguy, J. Hannaford & C. Prudhomme
This dataset is model output from the GR4J lumped catchment hydrology model. It provides 500 model realisations of daily river flow, in cubic metres per second (cumecs, m3/s), for 303 UK catchments for the period between 1891-2015. The modelled catchments are part of the National River Flow Archive (NRFA) (https://nrfa.ceh.ac.uk/) and provide good spatial coverage across the UK. These flow reconstructions were produced as part of the Research Councils UK (RCUK) funded Historic Droughts and...

Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'

Tom R. Andersson & J. Scott Hosking
This dataset encompasses data produced in the study 'Seasonal Arctic sea ice forecasting with probabilistic deep learning', published in Nature Communications. The study introduces a new Arctic sea ice forecasting AI system, IceNet, which predicts monthly-averaged sea ice probability (SIP; probability of sea ice concentration > 15%) up to 6 months ahead at 25 km resolution. The study demonstrated IceNet's superior seasonal forecasting skill over a state-of-the-art physics-based sea ice forecasting system, ECMWF SEAS5, and...

Registration Year

  • 2021
  • 2020
  • 2018

Resource Types

  • Dataset


  • European Centre for Medium-Range Weather Forecasts
  • National Oceanography Centre
  • Plymouth University
  • University of Washington
  • Institute of Atmospheric Sciences and Climate
  • Centre for Ecology & Hydrology
  • UK Centre for Ecology & Hydrology
  • University of Glasgow
  • Harper Adams University
  • University of Cambridge