Colm-cille P Caulfield

Applied mathematician with interests in modelling fluid flows in the ocean and the atmosphere, particularly where turbulence and buoyancy forces interact, using modern mathematical, computational and experimental methods. 

 

Research Area

My research interests include instability, turbulence transition and turbulent mixing processes in stratified flows. Stratified flows (i.e. flows where the fluid density is not constant) are ubiquitous in geophysical and environmental flows. For example, the ocean and atmosphere are (on average) statically stable, with the density decreasing upwards.  Due to the buoyancy force,  such statically stable density distributions typically suppress vertical motions. Such inevitable anisotropy complicates even further developing an understanding of stratified turbulence, as if the “turbulence problem” wasn’t hard enough. Indeed, stratified turbulence  is not “just” an interesting research challenge in classical physics, since stratified turbulence actually plays a leading order role in the transport of heat and other scalars (such as carbon dioxide, pollutants etc) in the world’s oceans and atmosphere. Indeed, stratified turbulent mixing is a central (and still highly controversial) component of the (rapidly changing) global climate system.  This is a particularly exciting (and important) time to be studying the problem of stratified turbulence and mixing, not least because the data-driven revolution is adding several innovative potential approaches, just as computational, observational and experimental developments are generating vast quantities of data to analyse and (hopefully) understand. 

 

Project Interests

I would be very happy to develop projects in any area of environmental/geophysical fluid dynamics where flow inertia and buoyancy effects play central roles. I am interested in the whole spectrum of understanding our global climate system, from “fundamental” fluid dynamics problems in idealized domains to the development and implementation of improved “parameterizations” in large scale models. I am particularly interested in exploring ways that modern data-driven methods can be combined with more classical physics-based models to improve the usefulness, robustness and applicability of academic research to understanding (and hence hopefully addressing) the global climate challenge.