Details of Award
NERC Reference : NE/T006412/1
CIRCULATES - Circulation, Clouds and Climate Sensitivity
Grant Award
- Principal Investigator:
- Professor C Huntingford, UK Centre for Ecology & Hydrology, Hydro-climate Risks
- Grant held at:
- UK Centre for Ecology & Hydrology, Hydro-climate Risks
- Science Area:
- Atmospheric
- Marine
- Overall Classification:
- Unknown
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Science Topics:
- Boundary Layer Meteorology
- Cloud dynamics
- Climate & Climate Change
- Climate modelling
- Large scale atmos modelling
- Ocean atmosphere interaction
- Regional & Extreme Weather
- Convective cloud & precip
- Abstract:
- Climate models are numerical models used to make projections of future climate change. Because of limitations in computing power, approximations to some parts of the model are required, particularly on small scales where important processes occur that are smaller than the model grid on which calculations are carried out. It is not clear how best to approximate small-scale processes and as a result, different GCMs use different approximations and produce different predictions of future climate change. One of the most important of these uncertainties is how low clouds are represented, and that is the focus of the CIRCULATES proposal. We now have access to new, high resolution satellite observations that we can use to build datasets that give us a much better idea of how clouds form and disperse, and how they interact with the environment in which they find themselves. We also have high resolution modelling tools that are able to represent the physical processes necessary to simulate clouds with much higher accuracy. High resolution models are far too computationally expensive to run for many model years over the whole globe in a way that could be used to project changes in climate directly. However, in conjunction with the satellite data, they can be used to determine the best way to represent the effects of clouds on the GCM model grid. This information can be transferred to the climate model, which can then be run to discover the impact of our findings on global climate change. In CIRCULATES, we propose to develop both new satellite data and high resolution simulations that are specifically designed to assist with improving and understanding the response of climate models with a focus on tropical and sub-tropical clouds. The project aims to assist the climate science and policy communities in two ways. First, the discoveries that we make will be used to assess the simulations made by climate models run by modelling centres around the world for the Intergovernmental Panel on Climate Change (IPCC) reports. How well are IPCC GCMs representing cloud processes in the present day? How does their representation change for simulations of the future and is this appropriate? By determining the fidelity of simulation in comparison with high resolution satellite and model data, we will determine the extent to which model simulations can be trusted, with the aim of constraining the likely range of future climate change. Second, we will develop metrics that are useful not only for constraining projections but also for model developers who are building the next generation of models. Our project has strong collaboration with the Met Office, who, together with the academic community, are the primary developers of models used for understanding climate change in the UK. We will engage with key Met Office and UKESM staff on a regular basis in order to determine how our results may be made most useful to model development.
- Period of Award:
- 1 Feb 2020 - 31 Jan 2024
- Value:
- £50,930 Split Award
Authorised funds only
- NERC Reference:
- NE/T006412/1
- Grant Stage:
- Completed
- Scheme:
- Directed (Research Programmes)
- Grant Status:
- Closed
- Programme:
- Clouds
This grant award has a total value of £50,930
FDAB - Financial Details (Award breakdown by headings)
| Indirect - Indirect Costs | DA - Estate Costs | DI - Staff | DI - T&S |
|---|---|---|---|
| £14,229 | £6,521 | £22,918 | £7,266 |
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