Research Fellow in Climate Intervention

Salary: Full time starting salary is normally in the range £36,636 to £46,049 with potential progression once in post to £48,822

Contract Type: Fixed Term contract up to January 2029

Background

World Meteorological Organization (WMO) has confirmed that 2024 was the first year that global temperatures reached +1.5°C higher than pre-industrial levels. While a temperature rise of 1.5°C for a single year doesn’t mean that we have breached our commitments under the Paris Agreement (as global warming is an average over decades), it is a strong warning signal that we are getting closer. To address this, some countries are considering Solar Radiation Management (SRM) methods to artificially cool the Earth by reflecting more sunlight back into space, buying time for global decarbonisation.

The main SRM methods are: 1) Stratosphere Aerosol Injection (SAI), which creates a protective aerosol layer; 2) Marine Cloud Brightening (MCB), which increases cloud reflectivity over oceans.

While these methods could slowdown warming, their effectiveness and side effects, such as ozone depletion and altered weather patterns, are uncertain.

‘Quantifying efficacy and risks of solar radiation management approaches using natural analogues’ (QUESTION) aims to study SRM using natural analogues, such as wildfires and volcanic eruptions. These analogues have happened in the recent Earth history and therefore no worrying of side-effects from any new field experiments, and will give us clues what might happen should we attempt to do solar radiation management.

In QUESTION, the University of Birmingham and University of Edinburgh, and the Center for International Climate Research (CICERO, Norway) will work together to will address challenges in separating SRM signals from other factors, improving climate models, and attributing climate responses of SRM.

This research will help assess SRM’s risks and benefits, supporting informed climate action.

This position will use further develop the novel AI/machine-learning (ML) approach in Chen et al. (2022 & 2024, Nature Geoscience) and apply to stratosphere, in order to investigate SAI’s impacts, using natural analogues. You will quantify the influence of SAI on stratospheric chemistry, such as aerosol, ozone, chloride, water vapour, etc.; as well as to understand SAI’s highly influential impacts, such as ozone layer depletion, tropics drying, ITCZ shifting, extreme precipitation, etc. Satellite remote sensing will be used in conjunction with meteorological reanalysis, AI and global climate models to understand the efficacy of SAI and its unintended consequence to underpin risk-risk assessment for policymaking. 

Person Specification

• PhD (or close to completion) or equivalent experience, in a relevant, quantitative field, e.g. atmospheric or climate science, meteorology, computer science, data-science, physics, mathematics, statistics, environmental science or related fields
• Evidence of a good understanding (or capacity to develop understanding) in relevant field
• Good programming skills in a data processing and visualization language such as Python, MATLAB, R or NCL
• Experience with UNIX/LINUX and High Performance Computer

Informal enquiries can be made to Dr Ying Chen, email: y.chen.21@bham.ac.uk

To download the full job description and details of this position and submit an electronic application online please click on the 'Apply' button above.

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