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Understanding historical climate change using multiple data sources


Department of Chemistry

About the Project

Background

Understanding how temperatures have changed since the start of the industrial revolution is key to understanding how human activity is likely to shape future climate, as well as for the evaluation of climate model simulations. Numerous sources of historical temperature data are available, including data from weather stations, ship deck observations, sea temperatures measurements, natural temperature proxies and weather model simulations using air pressure and other data. However all of these have limitations in spatial coverage, precision and observational biases. Our previous work has shown that careful comparison of these different datasets can help use to understand both errors and uncertainties in the historical data, and in some cases to produce more accurate estimates of historical temperature change. The purpose of this project is to expand this kind of analysis to investigate regional biases across a wider range of record types, and in doing so to examine periods which remain problematic, for example sea surface temperature change in the early 20th century.

Objectives

The objectives of the project are as follows:
1. Understand when and where existing historical temperature datasets differ.
2. Develop models to explain those differences in terms of observation types and physical factors.
3. Produce reconciled temperature datasets which allow a better understanding of historical temperature change.
4. Provide a better description of remaining sources of uncertainty.

Experimental approach

The work of the project will be entirely computational, and will involve the following steps:
1. Perform a detailed analysis of spatiotemporal differences between historical temperature datasets, spanning multiple domains of historical observations, natural proxies and weather model reanalyses.
2. Identify interesting divergences between the datasets and investigate their causes, initially looking for outlier data sources and comparisons across measurement domains, followed by later inclusion of metadata information to isolate and correct for biases.
3. Obtain better estimates of the size and nature of errors in different observational datasets.

Statistical and data analysis methods, including machine learning, will be used to model differences between datasets.

Novelty

Most work so far has focussed on improving models under the assumption that the observations are right. Our work with the observations so far has revealed biases in both the observations themselves, and in how they are used, which has proven to be very productive. The extension of this work to regional rather than global analysis, and to a broader range of observations is expected to produce significant further insights.

Training

The student will work as part of a new research group with Professor Cowtan and one other researcher, but will also benefit from contacts in the environment department and the Wolfson Atmospheric Chemistry laboratory, and from contacts with the UK Met Office and other UK institutions involved in the GloSAT project. They will develop their computational science and data analysis skills and become familiar with climate data sources and formats. They will benefit from postgraduate training courses both in Chemistry and other departments at York.
All Chemistry research students have access to our innovative Doctoral Training in Chemistry (iDTC): cohort-based training to support the development of scientific, transferable and employability skills: https://www.york.ac.uk/chemistry/postgraduate/idtc/

Equality, Diversity and Inclusion

The Department of Chemistry holds an Athena SWAN Gold Award and is committed to supporting equality and diversity for all staff and students. The Department strives to provide a working environment which allows all staff and students to contribute fully, to flourish, and to excel: https://www.york.ac.uk/chemistry/ed/.

Your background

You should expect hold or expect to achieve the equivalent of at least a UK upper second class degree in Chemistry or a related subject. Please check the entry requirements for your country: https://www.york.ac.uk/study/international/your-country/

Funding Notes

This project is available to students from any country who can fund their own studies. The Department of Chemistry at the University of York is pleased to offer Wild Fund Scholarships. Applications are welcomed from those who meet the PhD entry criteria from any country outside the UK. Scholarships will be awarded on supervisor support, academic merit, country of origin, expressed financial need and departmental strategy. For further details and deadlines, please see our website: View Website

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