Dr Helen He (UEA)
Prof Manoj Joshi (UEA)
Predicting how climate change will alter precipitation patterns is crucial for society yet very uncertain (Park et al. 2018; Cloke et al. 2013). Recent work (Polade et al. 2014) suggests that we can better understand the predicted changes if we break them down into how often it rains (frequency) and how much it rains (intensity). By linking these to the distinct processes that cause precipitation to change (changes in atmospheric circulation and changes in humidity) we can test this understanding, assess which projections are most reliable and identify where the uncertainties are largest (e.g. Pfahl et al. 2017; Maraun et al. 2012).
You will utilize new compilations of global sub-daily precipitation observations and high resolution climate model data to apply this partitioning into frequency-intensity and circulation-humidity components. This will enable you to:
- Identify consistent changes in precipitation frequency on timescales from hours to days in climate model simulations.
- Test whether these predicted changes can be detected in the observations.
- Calculate how the changing frequency and intensity of precipitation rates combine together to generate changes in different precipitation characteristics that are important for society: mean precipitation, dry spells and extreme precipitation events.
- Explore how a combination of less frequent precipitation occurrence together with more intense precipitation rates will change the likelihood of extreme precipitation and how this depends on timescale (from hours to days), spatial scale (from rain gauges to catchment averages) and on the return period of the extreme precipitation.
- Use hydrological models spanning a range of different catchment types to translate these changes into discharge estimates and identify improved performance for simulating peak discharges.
You will gain transferable skills necessary to pursue a range of academic and non-academic careers: scientific computing for dealing with “big data”, programming for data science (e.g. R or Python), the ability to use and interpret computer model outputs, and communication at technical and scientific levels.
A Bachelor or Masters degree in a relevant subject area (Environmental Sciences, Physics, Maths, Statistics, Geography or a related discipline), an aptitude for research, numerate and a clear communicator.