Details of Award
NERC Reference : NE/R009686/1
MOCCHA Analysis of Dynamic, Cloud, and Aerosol Processes
Grant Award
- Principal Investigator:
- Professor IM Brooks, University of Leeds, School of Earth and Environment
- Co-Investigator:
- Professor P Field, University of Leeds, School of Earth and Environment
- Co-Investigator:
- Professor BJ Murray, University of Leeds, School of Earth and Environment
- Co-Investigator:
- Dr RR Neely, University of Leeds, National Centre for Atmospheric Science
- Grant held at:
- University of Leeds, School of Earth and Environment
- Science Area:
- Atmospheric
- Overall Classification:
- Panel B
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Science Topics:
- Climate modelling
- Regional climate
- Remote sensing
- Water vapour
- Climate & Climate Change
- Aerosols
- Boundary layer models
- Cloud droplets
- Cloud dynamics
- Heat fluxes
- Marine boundary layer
- Moisture fluxes
- Radiation balance
- Surface temperature
- Turbulence
- Weather prediction
- Boundary Layer Meteorology
- Aerosols
- Cloud distribution
- Cloud formation
- Cloud physics
- Heat fluxes
- Ice nucleation
- Lidar sensing
- Radiation budget
- Radiation modelling
- Remote sensing
- Solar radiation
- Weather forecasting
- Radiative Processes & Effects
- Aerosols and particles
- Atmospheric humidity
- Atmospheric ice
- Atmospheric modelling
- Atmospheric profiling
- Boundary layer
- Cloud droplets
- Cloud dynamics
- Cloud physics
- Infrared radiation
- Lidar sensing
- Mixed phase cloud
- Radiation balance
- Radiative forcing
- Rain formation
- Solar radiation
- Uncertainty estimation
- Weather forecasting
- Water In The Atmosphere
- Abstract:
- The climate of the Arctic is changing rapidly, warming at least twice as fast as the global average. While climate models are generally consistent with this amplified warming, they show much greater variability between model in the Arctic than elsewhere, and have conspicuously failed to reproduce some key features of observed climate change - notably the rapid reduction in summer sea ice extent. The greatest source of uncertainty in models of Arctic climate, indeed for climate in general, is clouds. Clouds are the dominant control of the surface energy budget through their impact on both solar and infra red radiation. The effect of clouds on the surface radiation budget is commonly expressed as the 'cloud radiative effect' (CRE): the difference between the radiation under clear skies and with clouds present. Results from the latest Climate Model Intercomparison Project (CMIP5) show that the annual mean CRE of all the models is biased by -10 W m-2 with a standard deviation across the models of similar magnitude. The bias for the summer months only is +20 W m-2. A bias of just 1 W m-2 over a year is sufficient to melt/freeze a 10 cm thick layer of sea ice; these model biases are thus highly significant. We aim to reduce the bias and uncertainty resulting from model summertime clouds in the Arctic, by improving understanding and parameterization of key processes affecting their formation, development, and dissipation. Arctic summer boundary-layer clouds are mixed-phase - containing both liquid water and ice crystals. This is a thermodynamically unstable non-equilibrium state because saturation water vapour pressure over ice is lower than that over water, thus ice crystals should grow at the expense of water droplets. The clouds are maintained in a quasi-equilbrium through a complex set of interactions between aerosol (on which droplets and ice crystals form), thermodynamic, microphysical, radiative, and turbulent mixing processes. All of these individual processes have significant uncertainties associate with them, and current model parameterizations have been developed almost entirely based on measurements in mid-latitude clouds where conditions are very different. We will use a combination of state-of-the-art measurements of cloud and boundary layer properties and processes, with detailed modelling studies using the Met Office Unified Model. The measurements utilise high resolution active remote sensing systems such as cloud radar and lidar, processed through the CLOUDNET algorithm to generate detail cloud property data. These are supplemented with in situ measurements of boundary layer structure and aerosol properties. Analysis of combined model and observational data will be used to identify key failings in model parameterizations, and new understanding of the physics that must be represented in the models. An iterative process of parameterization updates and testing against observations will be used to improve the model physics. The updates will then be tested in 20-year climate model runs using cases from the Atmospheric Model Intercomparison Project (AMIP) to evaluate their impact on climate projections. The Unified Model is chosen as an ideal tool because of its seamless philosophy, using the same model physics in both operational forecast and climate models. This allows direct testing against observational cases, and easy transfer of new model updates to the climate version. The final model updates will then be put forward to the Met Office for integration into the official model releases.
- NERC Reference:
- NE/R009686/1
- Grant Stage:
- Completed
- Scheme:
- Standard Grant FEC
- Grant Status:
- Closed
- Programme:
- Standard Grant
This grant award has a total value of £649,234
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DI - Staff | DA - Estate Costs | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £28,843 | £236,981 | £68,661 | £193,789 | £81,026 | £21,993 | £17,942 |
If you need further help, please read the user guide.