Details of Award
NERC Reference : NE/K006932/1
Improved predictions of the Indian Monsoon
Training Grant Award
- Lead Supervisor:
- Professor J Marsham, University of Leeds, School of Earth and Environment
- Grant held at:
- University of Leeds, School of Earth and Environment
- Science Area:
- Atmospheric
- Overall Classification:
- Atmospheric
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Science Topics:
- None
- Abstract:
- This project will provide science necessary to improve predictions of the Indian monsoon on both weather and climate timescales. Convective storms generate the monsoon rains, but are unresolved in operational models. We will use new simulations run at the Met Office to understand how these storms couple with the monsoon and how the known model biases in convective storms contribute to the large model biases in the Indian monsoon and its rainfall. We will evaluate how treatments of convection can be improved to improve predictions of the monsoon and the monsoon rains. MOTIVATION Monsoons are the dominant annual mode of variation in the energy budget and water cycle of our global circulation. Over 60% of the world's population rely on monsoons, with over one billion people relying on the Indian monsoon. Forecast and climate models currently exhibit large biases in monsoon regions. The current prediction of the Indian Monsoon precipitation is considered by the Met Office to be one of the most significant biases in their global climate model and a very high priority area for model improvement. Global models also exhibit major biases in the systems of deep convective clouds that generate the monsoon rains. The model errors in convection and monsoon are connected, with biases in convection leading to continental-scale biases in monsoons. Convective storms are sub-grid in operational global models and are therefore parameterised. Although some global weather models may start to crudely resolve the largest convective systems in the coming decades, convection will be subgrid in climate models for the foreseeable future. In the EMBRACE project, the Met Office has recently run simulations spanning 4500 by 4500 km over India at a grid-spacing of 1.5km. For the first time, this allows us to study the convectivescale and the monsoon-scale in the same model. AIMS Using EMBRACE simulations and observations, we will understand and quantify: (1) the upscale impact of deep moist convection and rainfall on the Indian monsoon. (2) how the representation of moist convection and rainfall in models affects predictions of the monsoon compared with observations. (3) which aspects of the model treatment of moist convection are critical for improved predictions and so how models can be improved WORKPLAN Using existing simulations and observations provides a low-risk basis for the PhD. The student will write a literature review on the monsoon and moist convection in monsoon systems. Their research will then have four main components: (1) Evaluation of simulations with observations, (2) Upscale impacts of convection, (3) Biases in operational predictions, (4)Improving models. The first one-week visit to the Met Office will take place in Oct 2013 to obtain the EMBRACE data, discuss analysis and operational prediction biases. The student will then make two two-week visits/year to the Met Office, or more as required. IMPACT Improving predictions of the Indian monsoon on any time-scale will provide enormous socio-economic benefits: on the shortest time scales monsoon rains and associated floods are a hazard, but support hydro-electricity production. Improved seasonal predictions would benefit agriculture - a key employer. On a climate time-scale, an increase in precipitation is projected, but major uncertainties remain, with models disagreeing whether the Indian monsoon region will get wetter or drier. It is known that moist convection is a major source of model error in all models. Furthermore, improved monsoon predictions are key for predictions of many aspects of the Earth system: the biosphere, aerosols, air-pollution, agriculture etc. The shared supervision with the Met office will allow direct exchange with scientists working on modelling and predicting the monsoon on all time-scales, and direct communication with policy-makers. Improvements to Met Office predictions will directly feed through to IPCC assessments.
- NERC Reference:
- NE/K006932/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Open CASE
This training grant award has a total value of £68,671
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - Student Stipend | Total - RTSG |
|---|---|---|
| £13,978 | £49,193 | £5,499 |
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