Abrupt changes in the climate system could have potentially devastating socio-economic impacts. Simulating them in future climate scenarios is therefore of great importance. Future climate change scenarios are often based on the average or mean of climate model projections where abrupt changes and tipping points can appear in one or a few realizations of a subset of the models (Drijfhout et al. 2015). Additionally, climate models are often tuned to simulate a stable version of the present-day climate and remain untested in abrupt climate shift scenarios (Valdes 2011).
We propose to extend the findings of Drijfhout et al. (2015) with the newest generation of climate model simulations, generating a catalog of abrupt changes, along with analysis of the relevant feedbacks that may lead to abrupt change. For instance, changes in ocean heat and freshwater transports have a large impact on the Atlantic Meridional Overturning Circulation (AMOC), which is the main mode of ocean circulation in the Atlantic and impacts European climate. The primary focus will be on AMOC tipping points, with an additional focus on other tipping points that can be chosen from the catalogue of abrupt changes (i.e. abrupt sea ice loss, vegetation changes, etc.) Through this project the candidate will improve our understanding of the factors that can limit the ability of climate models to simulate abrupt climate shifts.
In Drijfhout et al. (2015) search, selection and classification criteria were developed allowing identification of abrupt climate changes in model simulations. In a first phase, the student will apply available scripts to the newest set of climate model projections (CMIP6). Preliminary feedback analyses for different types of tipping elements will be sketched out following Drijfhout et al. (2015).
In a second phase, the student will focus on the Atlantic meridional overturning circulation (AMOC). They will expand on the methodology of Mecking et al. (2017), where the influence of salinity bias on the freshwater transports in the Atlantic was evaluated. These biases in salinity impact the freshwater transports, that define the stability characteristics of the AMOC. AMOC estimates from the RAPID and OSNAP arrays, and temperature and salinity from gridded observational datasets will be used to evaluate model biases in the climate states which may impact stability.
In a third phase, we will encourage the student to pick a tipping element of their own choice, perform a relevant feedback analysis, and subsequently investigate how the biased climate states simulated in the coupled climate models affect these feedbacks, amplifying or hampering nonlinear growth and abrupt climate change.
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 National Oceanography Centre (NOC). Specific training will include:
- Learning to use a range of tools from simple numerical and mathematical models to advanced climate models.
- Receive training in the use of coding languages such as Python, MATLAB, Ferret and Fortran.
The student will join the multi-disciplinary modelling team at NOC, and receive training in analysis of large model/observational datasets. They will acquire a solid background in ocean circulation and climate change theories. They will join an active team of postdoctoral researchers and postgraduate students focusing on the role of the ocean in climate. They will be encouraged to present results at national and international conferences and have the potential to join a research expedition if interested.
Drijfhout et al. 2015: Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proceedings of the National Academy of Sciences of the United States of America, 112, E5777-E5786, doi:10.1073/pnas.1511451112.
Valdes 2011:. Built for stability. Nature Geoscience, 4(7), p.414.
Mecking et al. 2017: The effect of model bias on Atlantic freshwater transport and implications for AMOC bi-stability. Tellus A: Dynamic Meteorology and Oceanography, 69(1), p.1299910.