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The effects of cloud aerosol interaction on earth albedo and radiation budget using Earth Observation Datasets and Numerical Weather Model Simulations. (SENSE CDT)

School of Geosciences

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Prof S Salter , Dr Alan Gadian No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

roject Aim: To combine earth observation datasets of albedo, cloud aerosol and cloud properties to examine changes in the earth’s radiation budget balance and analysis of numerical weather / climate model simulations for sensitivities in the cloud droplet characteristics; satisfying the research themes of atmospheric science and Earth Observation Datasets.

Background: The official policy of getting to zero greenhouse gas emissions at some date in the future will leave concentrations higher than at present by an amount depending on how long the reduction takes. This means that floods, droughts, storms, bushfires, ice melt, coral loss and sea level rise will all be worse, perhaps substantially worse, than at present when they seem quite bad enough. It may be necessary to do direct world cooling despite the enormous amount of energy involved. One possible method, due to John Latham, exploits the Twomey effect to increase marine cloud reflectivity by increasing the concentration of cloud condensation nuclei. This could be done by spraying submicron drops of filtered sea water into the turbulent marine boundary layer. Computer models show sufficiently large cooling and benign effects on precipitation.

Engineering design of hardware of spray vessels at Edinburgh University is well advanced but we need to understand the best way to deploy them. The strength of the Twomey effect depends on initial nuclei concentration levels, the depth of the turbulent marine boundary layer, the solar input, the vertical gradient of humidity, the cloud fraction, the time between rain showers and the direction and speed of the wind. These affect the extremes in, for example Sea Surface Temperatures (SSTs), which impacts on events such as the intensity of hurricanes developing between Africa and the Gulf of Mexico and SST dipoles in the Indian ocean are suggested to be connect to floods in Kenya and bush fires in Australia. Ocean cooling reverses sea level rise, half of which is caused by thermal expansion.

Remote sensing data from satellites provides useful data to examine the albedo of clouds and the droplet size. Latham 2012 ( shows in Figure 1 (from co-author Wood) how, in this case MODIS, can be used to determine the drop size distribution. Approximately 15 papers by Latham, Salter and Gadian, discuss the technology and the process of modelling. Wood and Rasch at PNNL / University of Washington, are developing high resolution modelling and experiments in parallel to this project. Using the HadGEM, UK Met Office model and Earth observation data sets funded by NERC and UK Space Agency will update this approach by Wood and enable both correlation with current model data and observations, and then interpret with model data sets where MCB has been applied.

Methodology: The project will use existing datasets to produce a seasonal merit-order of ocean regions analogous to the list used by electricity generation boards to bring on different generation plant. Information will be distilled and presented in a format that engineers can understand. The objective is to develop a facility to develop pseudo-real time analysis from past earth observation data sets. It will develop engineering useful data fields (Edinburgh) with derived meteorological and atmospheric cloud properties. Previous work (Parkes, PhD, University of Leeds), using state of the art climate and weather models detected teleconnections of cloud properties on temperature and precipitation fields. Sensitivity studies, increasing and decreasing cloud aerosol properties in different random sequences and correlating each sequence with model results will lead to estimations of regional global temperature and precipitation patterns. The computations will be carried out on the ARCHER-2, for which computer time will be required.

For more detail see :

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,409 for 2020/21), tax-free stipend at the UK research council rate (£15,285 for 2020/21), and a research training and support grant to support national and international conference travel.

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