About the Project
Climatologies such as the World Ocean Atlas are calculated by averaging, on a month-by-month basis, many years of observational climate data to produce a representation of the mean state across the globe, and the seasonal variability. Such climatologies are widely used in many branches of climate science, and are often treated as if they represent “the truth”. For example, climate models are frequently verified by comparison with climatologies. But how accurate are these climatologies? A recent study found that the sea surface temperature in two different recent climatologies differed by as much as 2°C in some areas - yet sea surface temperature is relatively easy to observe compared with other important climate variables such as the fluxes of heat and moisture between the ocean and atmosphere.
You will begin by analysing two or more recent climatologies to explore which variables differ in a significant way, in which areas of the globe, and at what times of year. From this, you will create masks to cover areas of high uncertainty, and will explore the difference this makes in model evaluations. You will assess whether the differences between climatologies are largely due to data scarcity, quality control, or interpolation methods, informing future observational campaigns and/or the methods used to create climatologies.
This project will provide you with thorough training in climate science and data processing, analysis and visualisation. We anticipate that you will participate in an ocean research cruise to gain observational expertise. There will also be opportunities for you to attend summer schools and to present your research at conferences and workshops.
You will have a physical science degree or similar (e.g. oceanography, meteorology, environmental sciences, natural sciences, physics, mathematics) with good numerical skills. Experience with a computer programming language (e.g. Matlab, Python) will be an advantage.
i) Wang, Y., Heywood, K. J., Stevens, D. P., and Damerell, G. M.: Seasonal extrema of sea surface temperature in CMIP6 models, Ocean Sci. Discuss. [preprint], https://doi.org/10.5194/os-2021-102, in review, 2021.
ii) Shahzadi, K., Pinardi, N., Barth, A., Troupin, C., Lyubartsev, V., Simoncelli, S. (2021): A New Global Ocean Climatology, Front. Environ. Sci. 9, 711363, https://doi.org/10.3389/fenvs.2021.711363
iii) Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M. (2013). Evaluation of Climate Models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, pages 741-866.
iv) Kent, E.C., Fangohr, S. and Berry, D.I. (2013), A comparative assessment of monthly mean wind speed products over the global ocean. Int. J. Climatol, 33: 2520-2541. https://doi.org/10.1002/joc.3606