Earth’s changing climate is a defining scientific and societal issue of the 21st century. However, there is still considerable uncertainty in how climate will respond to anthropogenic greenhouse gas emissions. Complex Intergovernmental Panel on Climate Change-type climate models contain thousands of input parameters, but are unable to quantify how uncertainty in these parameters leads to uncertainty in future climate (‘parametric uncertainty’). In contrast, simplified faster climate models can fully sample parametric uncertainty, provided that uncertainty in how they represent key processes is adequately treated (‘structural uncertainty’). The dominant source of structural uncertainty arises in how models treat the ocean circulation, because there are multiple theories for how the large-scale ocean circulation operates and thus how the deep ocean will exchange heat and carbon with the atmosphere. By incorporating different simple models of the ocean circulation into a pre-existing simple climate model framework well-suited to quantifying uncertainty in climate projections, the student will be able to assess how different circulation models affect climate projections and sensitivity, and whether some match observations better than others. The student will then be able to address the question: How does incorporating our uncertainty in the ocean circulation affect overall uncertainty in climate projections?
The student will primarily utilize an efficient climate modeling framework (developed by Goodwin, ref. 1) to explore impact of different conceptual representations of the ocean circulation (e.g. refs. 2, 3) on climate projections. This framework uses a simple model representation of the Earth system and a large ensemble of simulations to quantify climate projection uncertainties as is not possible with IPCC-type models. The student will identify a suite of different conceptual ocean circulation models from the literature which represent different conceptualizations of the fundamental controls on the exchanges between the surface and deep ocean (e.g. whether interior mixing produces welling or downwelling, whether density contrasts and overturning are positively or negatively related), potentially also developing their own theory/ies. The student will then substitute these different idealized ocean circulation models into the existing model (which currently ignores changes in ocean circulation) and will generate large observationally-consistent ‘ensembles’ of model runs that can be used to evaluate climate projections and sensitivity for each ocean representation, to quantitatively compare the climate implications of different theories. These will then be compared in terms of their projections and fits to observations to answer the above questions and to generate the first climate projection that addresses both uncertainty types (structural and parametric) in the ocean circulation.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi- disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the National Oceanography Centre, Southampton.
Numerical modelling and statistical methods are among the most in-demand skills within international environmental research; this PhD will enable the student to develop those techniques, with the support of a team of supervisors with extensive expertise in ocean and climate modelling and statistics. Specific training will include:
– Implementation of Monte Carlo algorithms to Bayesian statistical modeling
– Development of theoretical models of the ocean and Earth system
– Presentation of results at national and international conferences
– Participation in a research cruise
– Contribution to relevant workshops to the research topic
– Participating in ocean physics and climate dynamics content at the University of Southampton, where relevant
Training may be further facilitated through research visits to the University of Chicago and/or to the Massachusetts Institute of Technology to work with external collaborators who are experts in conceptual ocean circulation modelling
1. Goodwin, Philip. "How historic simulation–observation discrepancy affects future warming projections in a very large model ensemble." Climate Dynamics 47.7-8 (2016): 2219-2233.
2. Gnanadesikan, Anand. "A simple predictive model for the structure of the oceanic pycnocline." Science 283.5410 (1999): 2077-2079.
3. Jansen, Malte F., and Louis-Philippe Nadeau. "A toy model for the response of the residual overturning circulation to surface warming." Journal of Physical Oceanography 49.5 (2019): 1249-1268.