Postdoctoral Research Assistant on climate predictions and projections

Applications are invited for a Postdoctoral Research Assistant in the Predictability of Weather and Climate group at AOPP. The post is available immediately for a fixed-term duration until May 2022.

This project will develop an innovative ensemble climate prediction system based on high-resolution climate models for Europe for the near-term (~1-40 years) including improved methods to characterise uncertainty in climate prediction, regional downscaling, and evaluation against observations. The climate prediction system will be used to produce consistent, authoritative and actionable climate information which will form the basis for Europe-wide climate service activities to support climate-related risk assessments and climate change adaptation programmes. The post offers flexibility for novel ideas.

The successful candidate will be responsible for the following tasks:

  • Develop novel scientific methodologies to explore how to merge, or homogenise initialised predictions with non-initialised scenario projections on time scales of 1-40 years. This will include developing a better understanding of the skill in initialised and non-initialised global projections for overlapping prediction time scales including estimation of the prediction time until which the initialised predictions add skill compared to the non-initialised predictions, for different variables, seasons and the European focus region.
  • Develop storylines and cases of plausible future weather consistent with the result of the combined forecasts across the full range of 1 to 40+ years by drawing on the initialised and non-initialised ensemble members. Derive new methods to construct small ensembles of realisations from the initialised simulations until the merge point in time. The approaches will include matching indices of the major modes of variability, especially those most relevant to the European region.
  • Develop your own and contribute to joint new research ideas

The successful applicant is expected to work closely with other project partners and will take responsibility for the relevant research in Oxford. This post not only is an exciting opportunity to carry out original research in a highly society- relevant area of climate science in close collaboration with the leading European climate prediction and projection groups but also offers the prospects to develop and follow independent scientific ideas.

Applicants should possess, or be very close to obtaining a doctorate degree in climate science, mathematics, physics, statistical science or a related field.

The candidate we are seeking should have a sound knowledge of climate science, climate modelling and climate prediction, or some experience with statistical verification of ensemble predictions and/or big data analysis from other areas of science. Candidates are expected to demonstrate excellent communication skills.

Please direct enquiries about the role to Dr Antje Weisheimer.

Only applications received before midday 23 April 2021 can be considered. You will be required to upload a brief statement of research interests, CV and details of two referees as part of your online application.

Please apply for this job via

Projects for Students

We welcome expressions of interest from potential DPhil (=PhD) candidates who would like to join our group. UK students can be funded through the University of Oxford's Doctoral Training Partnership in Environmental Research. There may be alternative funding opportunities for non-UK candidates.

We also welcome expressions of interest from undergraduate students who are interested in working in our group for their MPhys project or during the summer vacation.

MPhys project "Retrospective forecasts of winter and summer large-scale circulation changes during the 20th Century"

Forecasts of seasonal-mean anomalies of the climate using dynamical atmosphere-ocean circulation models based on the laws of physics are now routinely made at many operational meteorological forecast centres around the world. Such seasonal predictions provide estimates of seasonal-mean statistics of weather, typically up to four months ahead. In order to estimate how skilful seasonal forecasts are, the models are run in so-called retrospective forecast mode. This means that a period in the past, that can be verified with observations, is predicted using only information that would have been available at the time of the start of the forecasts. This project works with a long seasonal retrospective forecast data set that covers the entire 20th Century. The unusually long model forecast record allows the analysis of dominant modes of large-scale atmospheric variability, predictability and their changes on multi-decadal time scales. The student is going to analyse the existing ensemble forecast data set and compare it with a proxy data set of global observations.
Supervisors: Dr. Antje Weisheimer, Prof. Tim Palmer
Contact: Dr. Antje Weisheimer

Summer vacation projects for undergraduates

These projects run for typically 8 weeks, nominally from late June to August, though the duration may be shorter to accommodate summer travel. Students are paid a living wage for their time, around £350 per week (subject to tax and National Insurance deductions). 75% of the salary due for the entire project will be advanced during the first week, and the rest will be paid after completion of the project. The project is full-time but hours can be discussed with your supervisor. Please note that projects are not available to applicants that require a work permit.

Climate trends and seasonal forecasts

Summer 2021

In this project we look at climate trends at regional scales and their role in global seasonal forecasts. Non-stationarities in the climate system such as trends can be problematic for the analysis and interpretation of climate forecasts because these are often based on assumptions of stationary behaviour. Probabilistic predictions of the global coupled climate state over the coming season are routinely issued by many meteorological forecasting centres around the world. Data from retrospective forecasts of several European prediction models from the Copernicus Climate Change Service (C3S), including the UK Met Office’s GloSea system, will be used in this project to analyse long-term trends and contrast them to observed climate trends with the wider aim to evaluate the role of trends for forecast signals. The study involves to a large extent the numerical and statistical analysis of big data sets based on climate forecast models and observations. Knowledge of a modern programming and analysis language (e.g., python) will be of help. Curiosity to learn about the behaviour of the climate system and its statistical properties are ideally the primary motivation for your interest in this project.

Contact: Dr Antje Weisheimer

Please check at for details and how to apply.