- This project will develop a state-of -the-art global energy balance climate model with an improved and enhanced representation of the water cycle. Particular focus will be on changes to global precipitation patterns and polar regions for past, present and future warming scenarios.
- You will combine current energy balance modelling approaches, earth observation datasets, and apply statistical and machine learning techniques to advance the current state of hydrological representation within the new model. The resulting changes to the model will be assessed through a number of experiments based on either current climate conditions and future warming scenarios.
- Provides the opportunity to join the international activities within the Global Energy and Water Exchanges (GEWEX) community and the European Space Agency Water Vapour Climate Change Initiative. Furthermore, you will collaborate with scientists from the UK Met Office and National Centre for Earth Observation with the opportunity to work on site through placements.
Energy balance climate models are a simplified representation of the Earth’s climate system and are less detailed and comprehensive than complex general circulation models (GCMs). Rather than try to attempt to resolve the dynamics of the climate system (e.g. large-scale wind and atmospheric circulation systems, ocean currents, atmosphere and ocean convective motion), they instead focus on the thermodynamics and energetics of the climate system. A key advantage of this simplicity is that their underlying assumptions and equations have greater transparency, making it easier for researchers to trace and understand the factors influencing climate change. Energy balance models can run quickly, allowing for rapid experimentation and analysis. This speed makes them helpful in exploring a wide range of climate-related questions without the computational burden of more complex models.
Energy balance models are often used to estimate the Earth’s climate sensitivity, a crucial parameter in understanding how the climate system responds to changes in greenhouse gas concentrations. They provide a simple framework for studying the relationship between radiative forcing and temperature change. For example, they can assess the temperature increase associated with different levels of CO2 emissions or perform sensitivity analyses, helping to identify the most influential factors affecting climate outcomes and guiding further research.
With globally resolved energy balance models, sensitivity to changes in the hydrological cycle significantly impacts results as the model is now sensitive to the spatial distribution of water in all its phases. For instance, sea ice extent or changes to regional rainfall will affect the model response to rising atmospheric CO2 levels. Figure 1 presents an example of the model run for 1850-2100 using a variety of possible atmospheric CO2 levels. The top row of plots shows that while the model agrees well with current observations at the global scale, this breaks down when we focus on the polar regions, reducing confidence in projections beyond 2030. However, the results are spatially resolved to identify areas where biases may occur. This PhD seeks to improve and enhance the representation of the water cycle in the model, making it a valuable tool for climate studies.
Figure 1: (a) Model results of global and polar mean changes in surface temperature relative to 1961-1990 average for historical, and future atmospheric carbon dioxide (CO2) levels. Observed temperate changes (ERA5) between 1940-2022 are also shown. (b) Annual concentration levels of atmospheric CO2 used to drive the globally resolved energy balance climate model. (c) Spatial pattern of average surface temperature differences for 2080-2099 relative to the reference period (1961-1990) for the lowest and highest CO2 scenario (RCP26 and RCP85, respectively).
This is a CENTA Flagship Project
HostNational Centre for Earth Observation
- Climate and Environmental Sustainability
Initially, the student will take advantage of freely available globally resolved energy balance climate models to investigate similarities and differences in the inclusion and parameterisation of the water cycle and the resulting model sensitivities/biases. The project’s next phase will see the student working with state-of-the-art climate quality observations from satellites and ground-based measurements to develop data-driven methods to enhance and improve the water cycle representation in the Globally Resolved Energy Balance (GREB) model. These include data assimilation/Bayesian and machine learning approaches, with additional scope to bring in existing emulators for water reservoirs yet to be included in the model. Finally, the student will run multiple experiments within the current era to assess the model’s improvements and further experiments that will mirror existing or upcoming model intercomparison efforts within the Coupled Model Intercomparison Project (CMIP).
Training and skills
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
NCEO will provide access to its Researcher Forum, staff conferences/workshops and national-level training. This includes machine learning and data assimilation courses.
University of Leicester (UoL) will provide training on using the energy balance model and data processing on the ALICE (Leicester) and JASMIN (NERC) HPC facilities.
There will be the opportunity to receive training at the UK Met Office and University of Reading in working with state-of-the-art observational and climate model data sets.
The student will have the opportunity to take the UG Climate Physics and MSc Satellite Data Analysis in Python courses at UoL and other modules deemed suitable.
Partners and collaboration
This project has been developed in collaboration with the UK Met Office and University of Reading, who will co-supervise the project. In addition, a variety of other collaborations include:
- European Space Agency – Climate Change Initiative (via Dr Trent, Dr Povey ESA Water Vapour and Cloud projects)
- NCEO collaborators working on machine-learning emulation and data assimilation projects at University of Leicester and University of Reading.
- Global Energy and Water Exchanges (GEWEX) – (via Dr Trent, initially through the GEWEX Water Vapor Assessment)
- The broader Met Office Academic Partnership community – (via Prof Allen & Dr Willet)
Further details on how to contact the supervisor for this project and how to apply for this project can be found here:
We strongly encourage anyone considering an application to contact us in advance for an informal chat about the project at an early stage of any application.
Please get in touch with Dr Tim Trent (University of Leicester) at [email protected] with any questions regarding this project.
The successful applicant would be registered at the University of Leicester.
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2024.
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://le.ac.uk/study/research-degrees/funded-opportunities/centa-phd-studentships. Please scroll to the bottom of the page and click on the “Apply for NERC CENTA Studentship” button. Your CV can uploaded to the Experience section of the online form, the CENTA application form 2024 can be uploaded to the Personal Statement section of the online form. Please quote CENTA 2024-L21-CENTA2-PHYS5-TREN when completing the application form.
Applications must be submitted by 23:59 GMT on Wednesday 10th January 2024.
Review of existing literature and open access models, refinement of research plan & objectives, and training. Initial training will focus on computing skills needed for the project, including (but not restricted to) coding, HPC usage, running of the GREB model, and attendance at 1-2 suitable meetings/workshops. Analysis of model output from existing default setup with climate quality in situ and satellite products will allow the student to refine the research plan and objectives. The final objective for the year would be for the student to present their work as a poster at an appropriate national/international conference.
Implementing updates to the model based on refined research objectives, with the objective to publish the results in a peer-reviewed journal. The student would also look to present at one international meeting/conference and one national conference during this year. Placements at the UK Met Office and the University of Reading will also occur at this stage to provide support and additional training.
Analysis of new global energy balance climate model driven with IPCC future climate scenarios. Attendance to 2 national/international conferences/workshops to present work with publication of results of key science questions addressed by experiments defined in years 1+2. Potential for further placements at UK Met Office/University of Reading as required.
- Dommenget, D. and Flöter, J., 2011. Conceptual understanding of climate change with a globally resolved energy balance model. Climate dynamics, 37, pp.2143-2165. https://doi.org/10.1007/s00382-011-1026-0
- Stassen, C., Dommenget, D. and Loveday, N., 2019. A hydrological cycle model for the Globally Resolved Energy Balance (GREB) model v1. 0. Geoscientific Model Development, 12(1), pp.425-440. https://doi.org/10.5194/gmd-12-425-2019
- Watters, D., Battaglia, A. and Allan, R.P., 2021. The diurnal cycle of precipitation according to multiple decades of global satellite observations, three CMIP6 models, and the ECMWF reanalysis. Journal of Climate, 34(12), pp.5063-5080. https://doi.org/10.1175/JCLI-D-20-0966.1
- Xie, Z., Dommenget, D., McCormack, F.S. and Mackintosh, A.N., 2021. GREB-ISM v0. 3: A coupled ice sheet model for the Global Resolved Energy Balance model for global simulations on time-scales of 100 kyr. Geoscientific Model Development Discussions, 2021, pp.1-46. https://doi.org/10.5194/gmd-15-3691-2022