MPE CDT Student Cohort 2018

Lily Greig

Based at: University of Reading
Leads are fractures in sea ice. They provide a significant contribution to the polar heat balance despite making up only 5-10% of the sea ice cover, as gradients of sea ice concentration can result in lateral gradients in surface forcing and density gradients in the mixed layer. Through baroclinic instability, these fronts can energise submesoscale eddies. Submesoscale eddies have relatively fast time scales (hours to days), living in a parameter regime with finite Rossby and Richardson numbers. If energised they drive large horizontal exchange between ice-free and ice-covered ocean, and previous work showed that such dynamics could have an order 1 impact on the sea ice melt. Grid scales in the current generation of climate models are greater than the scale of submesoscale eddies and sea ice leads and ignore the effects of the sub-grid scale processes on the net polar heat balance. This project aims to explore these effects. It will start by building a mathematical model to develop understanding of the time and space scales of the density fronts formed under leads. Next it will explore under which conditions the density fronts may become unstable and spawn a submesoscale eddy field. Finally, this project will assess how subsmesoscale dynamics modulate air sea exchanges and if these processes should be included in climate models.

Calvin Nesbitt

Based at: University of Reading

Chiara Cecilia Maiocchi

Based at: University of Reading

Niccolò Zagli

Based at: Imperial College London

Ollie Street

Based at: Imperial College London
Research project: SPDEs in fluid dynamics and their application to ocean debris
The issue of ocean plastics has recently been much discussed by academics, policy makers, and environmental campaigners. The mathematical models which are used to describe the advection of plastics have largely ignored key factors such as sub-grid-scale dynamics and the mass of the debris. This raises the interesting question of how inertial particles move in a fluid governed by a SPDE. Using recent developments in stochastic fluid equations [Holm 2015] as a springboard, we will explore how the introduction of transport noise affects the properties (such as well posedness and smoothness) of a fluid model. In particular, can this type of noise restore uniqueness to a model? Furthermore, we will input the velocity field of the fluid into an equation which will return the velocity of the debris [Maxey & Riley, 1983], exploring the validity of doing this and whether this accurately models reality. Such a model would have applications in predicting the motion of ocean debris (such as icebergs, plastics, or aircraft wreckage) and, considering the model as an inverse problem, calibrating ocean models from drifter buoy data by understanding how the movement of the buoys differs from that of the fluid.

Swinda Falkena

Based at: University of Reading
Research project: Storyline descriptions of climate variability and change
Predictions of future climate change are usually represented as best estimates with an uncertainty range. However, at the regional scale, changes in atmospheric circulation play a large role and several outcomes may be possible. Under such conditions, an aggregate approach does not provide informative statements about risk. Storylines provide a way to represent the uncertainty in climate change itself, but need to be embedded within a probabilistic framework to account for the uncertainty in the particular realization of climate that will occur.

In this PhD project we use Bayesian causal networks to combine the storyline approach with probability. We focus on atmospheric circulation regimes in the Euro-Atlantic sector, since these have a large influence on the weather over Europe, and study their link with regional changes in extreme events. To inform the derivation of the causal network, expert knowledge will be used, which can be (partially) based on dynamical relationships derived from complex simulation models. The network will incorporate memory effects present in these dynamical relationships, which can give rise to persistent circulation anomalies. This will lead to a stronger physical foundation of the derived causal networks.

Ryo Kurashina

Based at: Imperial College London

Oliver Phillips

Based at: University of Reading

James Woodfield

Based at: University of Reading
Research project: Advection and Convection for Weather and Climate Models
Supervisors:
Hilary Weller (Reading Meteorology)
Colin Cotter (Mathematics, Imperial)
Christian Kühnlein (ECMWF)

Transport, or advection, is arguably the most important part of an atmospheric prediction model. Everything in the atmosphere is transported by the wind - temperature, pollutants, moisture, clouds and even the wind itself (non-linear advection). Operational weather and climate centres, such as the Met Office and ECMWF, are developing new atmospheric dynamical cores to run on modern computer architectures and they need accurate, efficient and conservative advection schemes that are stable for long time steps suitable for their new models. Their current transport schemes are accurate and stable for long time steps but do not conservative. This project will develop implicit methods to achieve long stable time steps on arbitrary grids of the sphere for linear and non-linear problems. We will start by creating a model for Rayleigh-Benard convection and we will develop a Newton solver to achieve long, stable time steps.

Sam Harrison

Based at: University of Reading

Tom Gregory

Based at: Imperial College London

Manu Sidhu

Based at: University of Reading

Robin Evers

Based at: Imperial College London

Philipp Breul

Based at: Imperial College London

Lois Baker

Based at: Imperial College London
Research project: Transition to turbulence in topographically induced internal wave breaking
It is an emerging picture that deep ocean turbulence exerts a control over the climate system through regulating the oceanic uptake and redistribution of heat, carbon, nutrients and other tracers. Observations of such turbulence, and our ability to model it numerically, however, have been limited if non-existent until very recently. In recent years, a few major international field programs have shed light on deep ocean turbulence by state-of-art observations of turbulence generated by deep ocean waves that can be as small as few meters tall or as tall as a few skyscrapers.

Our ability to mimic such turbulence in numerical models of high resolution is recent and helps out putting the isolated, yet expensive, observational data in the context of the large scale climate dynamics. The challenge ahead is to understand physics of such turbulence to help represent them properly in climate models that are coarse resolution, hence incapable of resolving such waves. This project aims at significantly enhancing our understanding of deep ocean turbulence and its representation in climate models. It will build on theoretical study of turbulence transition through stability analysis, numerical verification, and comparison with recent observational data.

Supervisors:

Lead Supervisor: Dr Ali Mashayek (Imperial College London)
Co-supervisors: Dr John Taylor (University of Cambridge), Professor Martin Siegert (Imperial College London)

Edward Calver

Based at: University of Reading

Cathie Wells

Based at: University of Reading
Research project: Reformulating aircraft routing algorithms to improve flight routes
Air travel is the subject of much current controversy. Statistics for fuel use and CO2 emissions make uncomfortable reading for both airlines and environmental groups. Today’s flight routes avoid areas of strong headwinds and make use of available tailwinds, for a set optimal low fuel burn air speed. During the MRes phase of the project, however, it was shown that these trajectories do not always minimise fuel burn.
Airlines are keen to be able to provide a timetable that is unaffected by a particularly strong wind field. Delays are costly and early arrival can often result in extra fuel burn due to holding patterns. This PhD project will find optimal routes to minimise fuel burn for set departure and arrival times. Varying both airspeed and altitude, whilst considering the expected background wind field and the change in aircraft mass due to fuel burn, will provide a realistic model for the cruise phase of transatlantic flights.
Using Optimal Control theory, the dynamical system of routing equations derived in each situation can be solved numerically. The fuel burn statistics from the model can then be compared with recent actual flight data and recommendations made to the airline industry.

Lead supervisor: Paul Williams (Reading)
Co-supervisors: Dante Kalise (Imperial) and Nancy Nichols (Reading) Industrial co-supervisor: Ian Poll (Poll AeroSciences Ltd)