Below is a list of DPhil (PhD) project areas within Climate Physics. If you are interested in any of the research areas listed, please contact the relevant supervisor directly. They will be happy to discuss potential project opportunities and answer any questions you may have.
Please note that some projects may be filled as applications are reviewed. We therefore particularly encourage candidates who are considering applying after the January deadline to contact prospective supervisors in advance to discuss available project options.
- Atmospheric composition, observations and modelling: Paul Palmer
- Cryosphere and polar processes: Andrew Wells
- Earth and planetary climate dynamics: Raymond Pierrehumbert
- Geophysical fluid dynamics: Andrew Wells, David Marshall, Raymond Pierrehumbert, Tim Woollings
- Machine learning for weather and climate science: Hannah Christensen, Milan Klöwer, Paul Palmer, Philip Stier
- Natural and anthropogenic drivers of climate change: Anu Dudhia, Don Grainger, Paul Palmer, Philip Stier, Raymond Pierrehumbert
- Ocean physics: Andrew Wells, David Marshall
- Physics of clouds and aerosols and their effects on climate: Don Grainger, Philip Stier
- Predictability of weather and climate: Antje Weisheimer, Hannah Christensen, Milan Klöwer, Myles Allen, Tim Woollings
- Remote sensing and earth observations: Anu Dudhia, Don Grainger, Neil Bowles, Paul Palmer, Philip Stier
- Response to natural and anthropogenic drivers of climate change: Antje Weisheimer, Myles Allen
- Tropical climate variability: Antje Weisheimer, Hannah Christensen
- Variability of atmospheres and oceans: Antje Weisheimer, David Marshall, Hannah Christensen, Myles Allen, Tim Woollings
- Weather and climate modelling: Hannah Christensen, Milan Klöwer
Project examples
The following projects are listed to give candidates with Overseas fee status an indication of the research opportunities available. Candidates with Home fee status are encouraged to apply through ILESLA or the CDT in Intelligent Earth.
The following projects are available for the 2027/28 academic year. Some project descriptions are currently being updated; this will be clearly indicated in the relevant descriptions.
- Observing the origins of tropical cyclones (currently being edited) -
Supervisors: Anu Dudhia and Tony McNally
Tropical cyclones are potentially devastating weather systems for which accurate early warning is critical. However, their origins and behaviour often depend on rather small scale atmospheric structures which are not well-resolved in global numerical weather prediction models. The aim of this project is to explore if key features signalling the genesis, or sudden intensification, of tropical cyclones can be observed in infrared spectra from a 12km footprint measured by the IASI instrument on the MetOp satellites. Such features include warm Sea Surface Temperatures (SSTs), high levels of mid-tropospheric humidity (fuel) and cloud top temperatures. The work would combine the IASI expertise in AOPP with the forecast modelling and data assimilation at ECMWF.
The conclusions from this project will be relevant for improving the use of data from current satellite instruments in operational tropical cyclone prediction. In addition this study will help pave the way for the successful exploitation of future satellite instruments. The next generation geostationary Meteosat sounders (2023 onwards) will observe the genesis of Atlantic tropical cyclones with high temporal resolution (up to every 15 minutes) and IASI-NG instruments (2021 onwards) will provide enhanced information about the vertical structure of these storms.
Relevant skills: computing, atmospheric radiative transfer and thermodynamics.
Full details can be found here.
- Satellite observations of air quality (currently being edited) -
Supervisors: Anu Dudhia and Brian Kerridge (Remote Sensing Group, RAL Space)
Ammonia (NH3) and carbon monoxide (CO) are important atmospheric pollutants, contributing to the production of ozone and particulates, which are important to air quality and also to climate change. Ammonia is generated from agricultural processes and carbon monoxide from fossil fuel combustion, but both are also products of biomass burning.
Concentrations of these (and many other molecules) can be retrieved from their infrared signatures in the spectra taken by the current generation of polar-orbiting interferometers, such as the IASI instrument on the MetOp satellites.
Both RAL (CO) and Oxford (NH3) have developed retrieval algorithms for IASI and the first part of the project will be to compare these results with other sources, eg other IASI retrievals, other satellite instruments, models, and surface measurements.
The second part of the project involves the adaptation of these algorithms for two new satellite instruments:
IASI-NG, the next generation IASI instruments, the first of which will be launched in 2021 and should provide data in the time-frame of the DPhil. This will have improved spectral resolution, allowing more species to be retrieved and with greater accuracy.
MTG-S, the third generation of the Meteosat geostationary satellite which will carry an interferometer viewing the entire earth's disk every hour. The first of these is due for launch in 2023. Unlike the IASI instruments which are on polar-orbiters and hence view each location only every 12 hours, the MTG-S will allow the full diurnal cycle to be observed.
Relevant skills: ability to write computer code in Fortran, C, IDL or Python, basic knowledge of radiative transfer and inverse methods.
Full details can be found here.
- Retrieval of aerosol from single-view imagery (currently being edited) -
Supervisors: Don Grainger
Atmospheric aerosols and cloud influence the climate by scattering and absorbing light. The most recent IPCC report labelled these interactions the most uncertain aspect of our understanding of the Earth's radiation budget. An accurate and long-term record of aerosol properties is essential to understanding the mechanics of our atmosphere, monitoring the effects of wildfires, and adapting to the growing problem of air pollution. As part of a European Space Agency project, researchers at Oxford helped produce a 30+ year record of cloud properties from satellite observations. A successful DPhil candidate will join that team and devise a means to extend our aerosol record to cover the same period. The method could be adapted to the next generation of geostationary imagery, providing near-real-time observations of aerosol and cloud of unrivalled detail and duration.
Aim: this project aims to produce an accurate retrieval of aerosol properties from single-view satellite imagery. The Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm is open-source implementation of optimal estimation retrieval developed by EODG in conjunction with scientists at the Rutherford Appleton Laboratory. The code is used to infer aerosol (and cloud) properties from radiometer imagers, such as MODIS, the Sea and Land Surface Temperature Radiometer (SLSTR) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Currently, ORAC requires two views of the atmosphere to produce an aerosol retrieval. This limits us to observations since 1995 despite acceptable satellite records beginning in 1978. This also excludes observations from the SEVIRI geostationary imagers, which provide planetary-scale imagery every 15-minutes. Aerosol retrievals at such resolution are necessary to evaluate some of the most uncertain processes.
This project will determine the best means to produce aerosol retrievals from single-view imagery. This is expected to involve constraining the surface reflectance, through some combination of theoretical modelling and independent observations. Various single-view aerosol retrievals exist, such as NASA's Deep Blue, that are expected to provide a starting point.
Depending on the student's interest, there is scope to manage and operate remote sensing instruments in Oxford to generate data that can be compared to the new retrievals for validation. Products can also be intercompared to trace gas and volcanic products generated within the group.
Skills that would be helpful: computing, statistics, atmospheric radiative transfer, remote sensing (especially of the surface).
Full details can be found here.
- Satellite remote sensing of volcanic plumes (currently being edited) -
Supervisors: Don Grainger, Isabelle Taylor and Tamsin Mather (Earth Sciences)
Plumes of ash and gas are one of the far-reaching hazards associated with volcanic eruptions. Monitoring them helps to mitigate their effects, understand the impacts they have on the environment and climate, and interpret volcanic activity. Satellite remote sensing offers a cost-effective solution to some of the limitations of ground-based monitoring and can be used to study the propagation of these plumes as they move across the globe and interact with different weather systems. The IASI instrument is an infrared hyperspectral sensor on-board three of EUMETSAT’s meteorological satellites, with each obtaining global coverage twice a day. IASI can be used to obtain the mass and height of both ash and SO2 and previous studies have demonstrated its capability for studying the plumes from large explosive eruptions and also continuous degassing.
Aim: to explore some of the ways in which the IASI instruments (and future generations of this instrument) can be used to study volcanic plumes of ash and gas. This project will build on the previous research in Oxford using IASI to study volcanic plumes. The focus of the PhD can be tailored to the student’s strengths and interests. A student from a physics or more technical background may wish to work on the further development of retrievals for IASI or other hyperspectral satellites; while a student with an Earth Sciences or Geography background may wish to explore how this data may be used to understand volcanic activity. Students may be interested to explore the impacts of these plumes on atmospheric dynamics. Opportunities may arise during the PhD to study recent eruptions.
Skills that would be helpful: programming (IDL, python), experience with remote sensing, experienced with multi-disciplinary work.
- Using trace gas measurements to infer aerosol type (currently being edited) -
Supervisors: Don Grainger and Anu Dudhia
The effect of atmospheric aerosols is the most poorly quantified element of the Earth's radiative budget (see Figure 1). Aerosols act directly by reflecting or absorbing solar radiation, or indirectly by altering cloud properties. This project focuses on understanding the direct effect of aerosols. Ground and aircraft based instruments can be used to measure aerosol properties locally whereas satellite measurements provide a global perspective. Progress has been made in using satellite instruments to quantify aerosol optical depth and particle size (using, for example, NASA's Moderate Resolution Imaging Spectrometer, MODIS, or ESA's Advanced Along Track Scanning Radiometer, ATSR) but much uncertainty remains in the type of aerosol being observed. Understanding the type of aerosol is critical as measurements from a thin layer of highly reflective aerosol can be indistinguishable from a thicker layer of strongly absorbing aerosol. Without knowledge of aerosol type it is not possible to tell if solar radiation has been reflected harmlessly back to space or absorbed into the atmosphere, further heating the Earth-atmosphere system.
The aim of this project is to quantify aerosol absorption (as opposed to scattering) over an annual cycle, globally. The Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm is open source software developed by EODG in conjunction with scientists at the Rutherford Appleton Laboratory. The code is used to infer aerosol (and cloud) properties from radiometer imagers such as MODIS, the Sea and Land Surface Temperature Radiometer (SLSTR) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Currently ORAC uses a combination of spectral signature augmented by geographic location to select a most probable aerosol type. This is not always successful. In this project additional measurements of aerosol-forming gases will be used to construct a prior probability of aerosol type. This will be used within ORAC to create a best guess of aerosol type.
The Infrared Atmospheric Sounding Interferometer (IASI) is a nadir-viewing Fourier transform spectrometer carried on the MetOp series of satellites (October 2006 - ~2030). Work within EODG has shown IASI can provide information on gases that form aerosol (e.g. SO2 or NH3) or gases associated with industrial pollution or biomass burning (e.g. CO or HCN). The goal of this project is to use the IASI gas measurements to improve ORAC's prediction of aerosol type. For example an enhanced value in the linear flag of SO2 will be used to increase the likelihood of the ORAC algorithm selecting sulphate aerosol. Much of the work will involve optimising the choice of aerosol given the values of the gas measurements. Once the method is finalised the algorithm will be applied to at least a year of data and the results analysed to give a measure of absorbing versus non-absorbing aerosol. If time permits further investigations could include fire emission indices, aerosol-formation mass budgets or aerosol composition climatologies.
Skills that would be helpful: computing, atmospheric radiative transfer, remote sensing, chemistry.
Full details can be found here.
- Differentiable atmospheric modelling: Learning from data between Earth’s and exoplanetary climates -
Supervisors: Milan Klöwer and Thaddeus Komacek
Atmospheric general circulation models are the backbone of climate models, being used to understand and predict climate change on Earth. Founded in physical laws, general circulation models can be generalised to exoplanetary atmospheres. While many atmospheric processes on Earth are well understood and accurately simulated as evident through the success of weather forecasting, some processes such as cloud formation and precipitation are less certain. Societally-relevant surface climate and extreme events like heat waves, however, strongly depend on those. At the same time, observations can constrain such uncertainties, improving climate predictions on Earth. On exoplanets this will reveal insights about the parameter regimes in which planetary habitability is possible. How to make atmospheric models automatically learn from observational data?
This project will build on top of SpeedyWeather, an atmospheric general circulation model written in the Julia programming language. SpeedyWeather is easy to use and extend, inspired by modern software engineering, covering functionality from data visualisation to high-performance computing. The candidate will continue to develop its differentiability through the automatic differentiation framework Enzyme. Differentiating through SpeedyWeather, we can optimise the unknown or less certain parameters determining the planetary surface climate and resulting habitability in the same way as neural networks are trained towards observations. And we can add neural networks to SpeedyWeather forming a hybrid physics and data-driven model.
For exoplanets, observations of gas giants down to hot terrestrial planets are currently used to constrain their atmospheric composition and thermal structure. Critically, upcoming space missions such as the Large Interferometer for Exoplanets and Habitable Worlds Observatory will have the ability to measure the thermal emission and reflected light of Earth-like exoplanets, enabling a direct test of our Earth-based understanding of planetary climate. This SpeedyWeather/Enzyme framework will enable novel rapid inverse characterization of exoplanetary atmospheres with a 3D model that can be applied to exoplanet observations.
Part 2 of this project will scale up the same method to the Earth’s atmosphere, increasing data and complexity. For earth, the current surface climate is observable, but a model may still mispredict frequencies and intensities of extreme events like heat waves under global warming. But with automatic differentiation we can train the model to correct for the missing physics of heat waves towards a more reliable generalisation into future climates, assessing the Earth’s habitability under global warming.
This PhD project will bridge several fields: The applicant is expected to have a strong background on the spectrum between physics, mathematics, and computer science, with enthusiasm for computer science and machine learning. Prior experience with Julia is not required, but experience with another programming language like Python, Matlab, C(++), or Fortran is preferred.
- Projects on Atmospheric Composition -
Supervisors: Paul Palmer (joining the University of Oxford in 2027)
Atmospheric composition plays a central role in the Earth system through its influence on climate, air quality, and ecosystem health. Greenhouse gases such as methane and carbon dioxide drive climate change, while pollutants including ozone and particulate matter affect human health and agriculture. Despite major advances in observations and modelling, substantial uncertainties remain in emissions, atmospheric processes, and their response to natural and anthropogenic change.
Aims: Projects in this subject area will be developed jointly with the student and may focus on greenhouse gas emissions and sinks, air quality, atmospheric chemistry, satellite observations, inverse modelling, data assimilation, machine learning, or climate interactions.
Methods to be used: Depending on the specific project and student preferences, these questions can be tackled using atmospheric chemistry and transport models, inverse modelling and data assimilation systems, satellite and in situ observations, statistical methods, machine learning techniques, and high-performance computing, or any combination thereof.
Skills required: A solid quantitative background in Physics, Mathematics, Data Science, or a related discipline. Experience in programming (Python, FORTRAN), numerical modelling, data analysis, or machine learning would be advantageous but is not essential.
- How do large-scale climate anomalies influence methane emissions from tropical African wetlands? -
Supervisors: Paul Palmer (joining the University of Oxford in 2027)
Atmospheric methane is the second most important anthropogenic greenhouse gas and is increasing at rates that remain difficult to explain. Tropical wetlands are thought to play a major role in driving this variability, with emissions strongly influenced by rainfall, temperature, and hydrology. Large-scale climate anomalies, including El Niño events, provide a natural experiment to investigate how changes in climate affect wetland methane emissions. However, estimates of methane emissions from tropical Africa remain highly uncertain due to sparse observations and limited understanding of underlying processes.
Aims: This project will examine how climate variability influences methane emissions from tropical African wetlands using satellite observations, atmospheric inverse modelling, land-surface modelling, and machine learning techniques. An emphasis will be placed on understanding the influence of ENSO-driven changes in rainfall and hydrology, and on improving the representation of wetland methane emissions in Earth system models.
Methods to be used: Depending on the specific direction of the project and student preferences, the research will combine satellite observations (e.g. GOSAT, TROPOMI, CO2M), land-surface and atmospheric transport models (e.g. JULES and GEOS-Chem), inverse modelling, data assimilation, and machine learning approaches. The project will benefit from links to the NERC-funded CurFEW programme, which is developing new observational and process-level understanding of tropical wetland methane emissions.
Skills required: A solid quantitative background in Physics, Mathematics, Data Science, or a related discipline. Experience in programming (Python, FORTRAN), numerical modelling, data analysis, or machine learning would be advantageous but is not essential.
Further reading:
https://doi.org/10.5194/acp-18-18149-2018 McNorton et al. (2018) https://doi.org/10.5194/acp-19-14721-2019 Lunt et al. (2019) https://doi.org/10.1038/s41467-021-27786-4 Helfter et al. (2022) https://www.nature.com/articles/s41467-022-28989-z Feng et al. (2022) https://doi.org/10.5194/acp-23-4863-2023 Feng et al. (2023) https://www.nature.com/articles/s43017-023-00397-x Palmer et al. (2023) https://doi.org/10.5194/essd-17-1873-2025 Saunois et al. (2025)
- Revealing carbon cycle dynamics and their response to climate using satellite observations and AI -
Supervisors: Paul Palmer (joining the University of Oxford in 2027)
Atmospheric carbon dioxide and methane are increasing at unprecedented rates, reflecting changes in the global carbon cycle that remain only partly understood. Variability in terrestrial carbon uptake and release is thought to play a major role, particularly in tropical and high-latitude ecosystems, but the mechanisms driving these changes remain uncertain. Recent advances in satellite observations and AI provide an opportunity to identify the climatic drivers of ecosystem behaviour and improve understanding of how the carbon cycle responds to environmental change.
Aims: This project will investigate how climate variability influences terrestrial carbon cycling using satellite observations and emerging AI approaches. Emphasis will be placed on understanding ecosystem responses to large-scale climate anomalies, such as El Niño events, and on developing interpretable relationships between climate, ecosystem function, and atmospheric carbon dioxide.
Methods to be used: Depending on the specific direction of the project and student interests, the research will combine satellite observations of atmospheric composition, solar-induced fluorescence, vegetation dynamics, phenology, hydrology, and fire with machine-learning techniques including physics-informed neural networks, neural operators, and equation discovery. The resulting process relationships and predictive models will be evaluated using ecological measurements and compared with state-of-the-art land-surface and Earth system models.
Skills required: A solid quantitative background in Physics, Mathematics, Data Science, or a related discipline. Experience in programming (Python, FORTRAN), numerical modelling, data analysis, or machine learning would be advantageous but is not essential.
- Clouds in a Changing Climate: Earth Observations, AI and Global Kilometre-Scale Modelling -
Supervisor: Philip Stier
Clouds play a fundamental role in the climate system by regulating the Earth’s energy balance and hydrological cycle, but remain a major source of uncertainty in climate predictions. In particular, we need to understand how clouds respond to greenhouse-gas-induced warming through cloud feedbacks and to changing air pollution through aerosol-cloud interactions. New Earth observations, global kilometre-scale climate models and rapid advances in artificial intelligence now provide exciting opportunities to study clouds and their evolution in unprecedented detail.
Potential DPhil projects include:
1. Geospatial foundation model for clouds
Develop an observational AI foundation model that learns relationships between environmental conditions, clouds, aerosols and radiation from multi-modal Earth observations. The project could explore generative models for 2D/3D cloud reconstruction and evolution, physically structured representations of cloud regimes, or inference of poorly observed cloud and aerosol properties, with particular emphasis on physical interpretability and generalisation to changing climate conditions.
2. Calibration of global kilometre-scale models and aerosol-convection interactions
Develop innovative approaches to calibrate global kilometre-scale climate models using observations of cloud structure, lifecycle and radiative effects, combining perturbed-parameter ensembles with statistical and machine-learning emulators. Calibrated models will then be used to investigate aerosol effects on convection, precipitation and convective anvils using the ICON-HAM-lite modelling framework.
3. Lagrangian analysis of aerosol effects on convection using cloud tracking
Use the open-source tobac cloud-tracking framework developed in our group to follow individual convective systems through their lifecycle and determine how aerosol perturbations affect their development, precipitation, anvils and radiative effects. The project could combine satellite observations with regional and global kilometre-scale ICON-HAM-lite simulations.
4. AI nowcasting of extreme precipitation and haboob dust storms
Develop AI approaches for short-term prediction of deep convection, extreme precipitation and associated haboob dust storms, particularly in regions without extensive weather-radar networks. Building on our ConvCast concept, the project will combine sequences of geostationary satellite observations with atmospheric-state information to generate probabilistic nowcasts, with opportunities to explore prediction from convective precursors, uncertainty quantification, transfer to data-sparse regions and high-resolution simulations of storm evolution.
Aims: The precise scientific questions will be developed jointly with the student within one of these themes, spanning atmospheric physics, climate modelling, Earth observation and artificial intelligence.
Methods to be used: Depending on the project and student interests, methods may include global and regional high-resolution atmospheric modelling, satellite remote sensing, aircraft and ground-based observations, cloud tracking, statistical model calibration, computer vision, and modern machine-learning and generative-AI techniques.
Skills required: A strong quantitative background in Physics, Atmospheric Physics/Meteorology, Mathematics, Computer Science, Data Science or a related discipline. Prior atmospheric-science experience is advantageous but not essential for candidates with strong quantitative or computational skills. Programming experience is desirable.
- Climate variability and predictability during the 20th Century -
Supervisor: Antje Weisheimer
This project will look into the variability of the coupled atmosphere-ocean-sea-ice climate system during the 20th Century which was characterised by complex variations linked to natural variability (internal and external) as well as anthropogenic forcings. A key aim of the project will be to link the multi-decadal climate variability as seen in observational datasets with the fluctuations that state-of-the-art climate forecast models used in operational seasonal forecasts exhibit.
Forecasts of seasonal climate anomalies using physically based global circulation models are routinely made at operational meteorological centres around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years that are used to estimate skill and to calibrate the forecasts. You will be working with a new hindcast data set called Coupled Seasonal Forecasts of the 20th Century (CSF-20C) to improve our understanding of the physical mechanisms in the atmosphere, ocean and sea-ice responsible for the low-frequency modulations of forecast skill that have been found in these hindcasts.
Further reading: Weisheimer et al., 2020 and references therein
The project requires strong analytical skills in Physics, Mathematics, Meteorology, Environmental sciences or related disciplines and a high level of curiosity to explore the marvels of nature. In return, it offers a wide range of opportunities and applications in the broad field of weather and climate predictions.