Use and understanding of EuroCORDEX data over the UK

This project intends to enable the use of, and produce improved understanding of EuroCORDEX data over the UK.

Background

The 2018 UK Climate Projections (UKCP) provide information about the future climate of the UK at coarse (60km), medium (12km) and local (2.2km) spatial resolutions. The medium-resolution projections are proving beneficial in understanding future climate risks to the UK. However, they were driven by a relatively small subset of global climate models and so may not sample the full range of uncertainty that is consistent with current scientific knowledge.

The project will extend the current suite of UKCP climate projections by incorporating information from a broader range of high-resolution climate simulations. These will be predominantly obtained from the EuroCORDEX downscaling experiment, but also by incorporating other currently available high-resolution information.

This will support a more comprehensive sampling of uncertainty in high-resolution UK climate projections than has previously been possible. This, in turn, will offer the potential to develop better informed strategies for adapting to, and mitigating the effects of, future weather and climate.

Aims

The overall aims of the project are: 1) to deliver an improved understanding of future climate scenarios for the UK by examining additional high‐resolution simulations alongside the existing UKCP; and 2) to incorporate these additional simulations into a publicly accessible extension to the existing UKCP dataset.

Individual work packages are designed to:

  • provide insights into the physical plausibility of the various simulations
  • assess the value to be gained from the use of high-resolution information
  • identify the dominant sources of uncertainty in future projections of a variety of weather indices
  • attempt to assess the extent to which the existing UKCP ensemble provides a decision-relevant characterisation of this uncertainty.

To achieve these challenging objectives, the project takes a multidisciplinary approach combining expertise in climate modelling, modern statistics and uncertainty quantification, and software engineering.

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