The Indian summer monsoon provides rainfall to over a billion people and has a profound effect on many sectors of industry, especially agriculture. Monsoon depressions, which are one of the major sources of rainfall within the summer monsoon, remain relatively poorly understood with research disagreeing on where they come from, how they intensify, and how they will respond to future climate change. In this project, you will have the opportunity to tackle some of these questions by exploring the relationship between sea-surface temperature (SST) and depressions. Although we know SSTs are extremely important in predicting tropical cyclone genesis and intensity, little work has been done to extend that theory to monsoon depressions. Understanding this relationship is crucial in the context of climate change, where small changes in SST can lead to large changes in near-surface humidity, one of the important ingredients for depression growth.
A suggested outline of this project is:
- to use modern data to test historical theories. Is the threshold SST of 29°C for depressions to form, stated nearly fifty years ago, accurate? Are there exceptions?
- to explore the role of SSTs in depression genesis and intensification. This could involve using a variety of data (e.g., buoys, reanalysis) to explore whether parallel theories for tropical cyclones (e.g. wind-induced surface heat exchange) are applicable to depressions, or whether other concepts, such as SST gradients, are important.
- to find whether these results are correctly simulated in weather and climate models.
- to develop your own theory on the relationship between SSTs and depressions and use these to make projections about how depressions might respond to climate change, and then to test these theories using state-of-the-art climate model output.
You will have the opportunity to collaborate with our tropical meteorology research group at Reading, as well as our colleagues at the UK Met Office, giving you access to state-of-the-art models and computing facilities and a wealth of expertise.
Eligibility requirements: Applicants should hold, or be predicted, a strong Bachelor's (first or upper second class or equivalent), or Master's (merit or distinction level) degree, in a physical, mathematical, or computational science.