Decadal modulation of El Nino Southern Oscillation and its global impacts

Image: (left) Strong El Nino event in the tropical Pacific in December 2015; (centre) phases of the Atlantic Multidecadal Variability over the past 140 years; (right) remote impacts of El Nino Southern Oscillation include extreme floods in many regions.

Project overview

There is a strong demand for seasonal-to-decadal climate predictions in the energy, agriculture and water sectors (Bruno-Soares et al., 2016). The skill and reliability of predictions is crucial for their usability. Long-range weather and climate predictions rely on slowly evolving predictable components. These are often fluctuations, or “modes of variability”, in the ocean that are predictable seasons to years ahead, and imprint signatures on weather and climate in remote regions through atmospheric teleconnections. While much research has addressed the effects of individual modes on climate prediction, an emerging research area is their mutual interactions and associated effects on predictability.

This exciting project will create new knowledge of the coupling between modes of climate variability and the associated effects on seasonal-to-decadal forecast skill. You will work at the intersection between atmospheric and oceanic sciences and develop excellent knowledge of climate processes, developing and running climate models, and advanced statistical techniques. You will work with the Met Office to identify potential areas for improvement of their operational seasonal forecasting system.

The research problem

Teleconnections describe interconnections between climate anomalies at distant locations across Earth. Teleconnections frequently arise through the response of atmospheric circulation to changing oceanic conditions. If these patterns can be accurately forecast, the signals provide a major source of predictive skill in seasonal-to-decadal forecasts (e.g., Scaife et al., 2016). Hence, there is a need to identify predictable climate signals and understand their effect on the risk of global climate hazards to improve seasonal-to-decadal prediction systems.

Most previous research has considered the teleconnections arising from modes of variability separately. However, there is growing evidence for coupling between modes of variability and their teleconnections on different timescales (Cai et al., 2019). Some recent studies have speculated Atlantic Multidecadal Variability – a fluctuating pattern in the North Atlantic ocean — augments the response of the winter North Atlantic Oscillation to El Nino Southern Oscillation, the dominant pattern of year-to-year variability in the tropics (e.g., Zhang et al., 2019; Ivasic et al., 2021). ENSO is a key driver of the NAO and this teleconnection is important for seasonal forecasting of European winter climate. However, questions remain about whether the AMV modulation of the ENSO-NAO teleconnection is robust and whether it can be distinguished from a local AMV impact on the NAO (Simpson et al., 2018). This PhD project will develop a new framework for assessing the AMV influence on the ENSO-NAO teleconnection using state-of-the-art large ensemble simulations from multiple climate models. This will provide the most comprehensive assessment to date of decadal modulation of ENSO-NAO coupling by the AMV and its mechanisms.

Another major topic of past research has been how ENSO affects climate in remote regions, including the North Atlantic (e.g. Trascasa-Castro et al., 2019). Few studies have considered teleconnections that may alter ENSO itself. Recent work performed at the University of Leeds (Trascasa-Castro et al., 2021) suggests the warm phase of AMV weakens ENSO on decadal timescales. This project will test this effect in a larger set of climate models and identify whether it is robustly simulated. New model simulations will also be performed to test hypotheses and isolate mechanisms. These experiments will exploit new ‘nudging’ capabilities within a climate model that enable specified regions to be constrained to observations to isolate remote impacts.

To close the loop on Pacific-Atlantic interactions, the third question addressed in the project will be: what are the impacts of the North Atlantic Oscillation on ENSO? The motivation comes from the observation that on seasonal timescales the NAO drives a tripolar pattern in North Atlantic sea surface temperatures through modified air-sea coupling (Deser et al., 2010). This raises the potential for longer-term multidecadal NAO trends to impact on climate outside the North Atlantic, but this is currently unexplored. This project will tackle this new question using a combination of statistical analysis of observations and a similar model experiment design where NAO anomalies are ‘nudged’ and the global response evolves.

The project would suit a candidate interested in large-scale climate processes, atmosphere-ocean coupling, atmospheric dynamics or climate prediction. There will be an opportunity to develop new modelling tools and to run the Met Office’s state-of-the-art climate model. The results will shine new light on the exciting and evolving field of long-range prediction.

Potential applicants should contact the lead supervisor ( before applying.


The specific objectives of the project will be adapted to fit the interests of the student and to pursue the most promising avenues of enquiry. Specific research objectives could include:

  • Quantify how Atlantic Multidecadal Variability affects the ENSO-NAO relationship including the associated mechanisms; test whether this is robust in climate models.
  • Explore the impact of the AMV on ENSO and its mechanisms via Atlantic-Pacific interactions.
  • Investigate the global impacts of interannual to decadal NAO variability, including potential impacts on ENSO.

The topics each offer the potential for innovative cutting-edge research and also freedom for the student to expand the research in the direction of their own interests.

Research tools

You will use a combination of observation datasets and modelling tools. The multi-model large ensemble archive comprises simulations from 8 state-of-the-art climate models that have performed hundreds of simulations of the historical period. These experiments provide comprehensive sampling of internal climate variability and the separation from anthropogenic forced changes. You will use a novel causal inference-based framework to test the influence of AMV on ENSO teleconnections (Kretschmer et al., 2021). Once you have identified potential teleconnection pathways in the large ensemble simulations, you will perform new targeted simulations to test hypotheses and isolate mechanisms. You will have access and training in the use of climate models and high performance computing systems, including the Leeds’ ARC4 HPC and Met Office supercomputer. Through expert training, you will develop excellent technical skills in computer programming, performing model simulations and data visualisation.

Scientific network

The project is co-supervised by Dr Jeff Knight at the Met Office Hadley Centre and Dr Yohan Ruprich-Robert at the Barcelona Supercomputing Center. The student will undertake visits to the Met Office to interact with the Monthly-to-Decadal prediction group, a world-leading team for long-range weather and climate prediction. You will also visit the Climate Prediction group at the Barcelona Supercomputing Center.

The supervision team are leading experts in their fields and will provide the student access to a large network of international scientists and activities. This includes projects within the World Climate Research Programme and the Coupled Model Intercomparison Projects. The student will benefit from being part of the EU CONSTRAIN project, a vibrant international research program involving 13 research institutes aimed at reducing uncertainty in climate projections, which is led by the University of Leeds.

Key outputs and potential for high impact

The project addresses a major and fundamental question in climate science that is closely aligned with high priority activities of strategic importance to the Met Office. There have been a series of recent high-profile papers published (e.g., Smith et al., 2020) highlighting problems with current seasonal-to-decadal predictions for the North Atlantic. This project will seek solutions to some of these problems by investigating the coupling between some of the major sources of predictability in current seasonal-to-decadal predictions (AMV, ENSO, NAO).

There is international recognition of the need for reliable seasonal climate predictions to provide information to a range of user groups and stakeholders. This project will address this need by advancing understanding of the role of decadal climate variability in seasonal prediction.

Training and research support

You will join a vibrant and dynamic group of academic staff, PhD and postdoctoral researchers within the Physical Climate Change group in the Institute for Climate and Atmospheric Science (ICAS) at the University of Leeds. We meet regularly providing a supportive forum to discuss latest research and ideas. More widely, ICAS provides a welcoming community to build a broad background in research topics across atmospheric and climate science including through seminars and lectures.

You will benefit from an excellent wider research environment that includes technical support through the Centre for Environmental Modelling and Computing, access to Met Office models and data through the Leeds-Met Office Academic Partnership, and the Priestley International Centre for Climate which promotes interdisciplinary climate research in Leeds. The Centre offers both a student society and the opportunity to apply to a PhD Scholars program. You will be part of the NERC Doctoral Training Partnership (DTP) PANORAMA cohort, which fosters a lively community amongst departments and provides many dedicated research, training and social opportunities.

You will have numerous opportunities to present your research at national and international conferences and meetings (e.g., EGU, AGU assemblies), as well as to attend summer schools and other training workshops.

Specific skills that will be developed during the project include:

  1. Techniques to handle large data sets produced by models.
  2. Application of statistical analysis methods to model simulations.
  3. Understanding of sources of predictability for long-range forecasts.
  4. Use of observational datasets to evaluate model simulations.
  5. Effective communication through presentations at conferences, informal talks at project meetings, and writing peer-reviewed journal articles.
  6. Working with people from a range of backgrounds.

Entry requirements
A good first degree (1 or high 2:1), Masters degree or equivalent in a physical or mathematical discipline such as Physics, Mathematics, Meteorology, Climate Science, Environmental/Geophysical Sciences, Chemistry, Physical Geography, Engineering or Computer Sciences. A desire to learn new programming and modelling techniques is essential.

Further reading (if you cannot access these please email

Cai et al., Pantropical Climate Interactions, Science, 2019, DOI: 10.1126/science.aav4236

Cai, W., Santoso, A., Collins, M. et al. Changing El Niño–Southern Oscillation in a warming climate. Nat Rev Earth Environ 2, 628–644 (2021). DOI: 10.1038/s43017-021-00199-z

Deser, C., M. A. AlexanderS.-P. XieA. S. Phillips, Sea Surface Temperature Variability: Patterns and Mechanisms, Annual Review of Marine Science 2010 2:1115-143

Ivasić, S., Herceg-Bulić, I. & King, M.P. Recent weakening in the winter ENSO teleconnection over the North Atlantic-European region. Clim Dyn 57, 1953–1972 (2021). DOI: 10.1007/s00382-021-05783-z

Kretschmer, M., Adams, S. V., Arribas, A., Prudden, R., Robinson, N., Saggioro, E., & Shepherd, T. G. (2021). Quantifying causal pathways of teleconnections, Bulletin of the American Meteorological Society (published online ahead of print 2021).

Scaife, A.A., Comer, R.E., Dunstone, N.J., Knight, J.R., Smith, D.M., MacLachlan, C., Martin, N., Peterson, K.A., Rowlands, D., Carroll, E.B., Belcher, S. and Slingo, J. (2017), Tropical rainfall, Rossby waves and regional winter climate predictions. Q.J.R. Meteorol. Soc., 143: 1-11.

Simpson, I. R., Deser, C., McKinnon, K. A., & Barnes, E. A. (2018). Modeled and Observed Multidecadal Variability in the North Atlantic Jet Stream and Its Connection to Sea Surface Temperatures, Journal of Climate, 31(20), 8313-8338.

Smith, D.M., Scaife, A.A., Eade, R. et al. North Atlantic climate far more predictable than models imply. Nature 583, 796–800 (2020). DOI: 10.1038/s41586-020-2525-0

Trascasa-Castro, P., Maycock, A. C., Scott Yiu, Y. Y., & Fletcher, J. K. (2019). On the Linearity of the Stratospheric and Euro-Atlantic Sector Response to ENSO, Journal of Climate, 32(19), 6607-6626.

Zhang, W., Mei, X., Geng, X., Turner, A. G., & Jin, F. (2019). A Nonstationary ENSO–NAO Relationship Due to AMO Modulation, Journal of Climate, 32(1), 33-43. DOI: 10.1175/JCLI-D-18-0365.1