Researcher on Strategies for climate model calibration F/M

Climate models are essential tools for understanding the Earth’s climate system and anticipate its response to various external forcings, particularly those of anthropogenic origin. Their development is a long-term and collective endeavour, that brings together dozens of engineers and researchers over decades. This process encompasses a wide range of disciplines, from computational geophysical fluid dynamics to the representation of physical and biogeochemical processes, as well as model evaluation and validation to name only a few aspects of climate model development.

One of the most fundamental characteristics of the climate system is its continuum of spatial and temporal scales. Yet traditional numerical models used to solve the geophysical fluid dynamics equations must introduce a scale truncation: large scales are explicitly resolved using a discretized version of those equations, while smaller-scale processes are represented through physical parameterizations, which describe the statistical effects of smaller-scale processes on the resolved momentum, energy and water budgets. In addition, some processes – such as radiation or phase changes – are inherently outside the scope of fluid dynamics equation and must be parameterized regardless of model resolution.

Physical parameterizations are conceptual models that distil our current understanding of complex, multiscale processes in the climate system. These parameterizations are typically based on theoretical, empirical, or data-driven formulations, which include a number of parameters. Their values are often poorly constrained by observations. With climate models often involving several dozens of such parameters – and given the high computational cost of each model simulation – their calibration is a major bottleneck in climate model development. This calibration process directly impacts key emergent properties of climate models, including their climate sensitivity.

To address this challenge, the French climate modelling community has partnered with mathematicians to develop a novel, semi-automatic calibration framework (Couvreux et al., 2021). This approach integrates uncertainty quantification, to avoid overfitting, and machine learning, to reduce computational costs. It is based on the history matching technique (Williamson et al., 2013) which differs from traditional optimization methods by identifying the range of acceptable (“tuned”) model configurations rather than a single best-fit solution. This method supports the exploration of alternative plausible climates and enables the quantification of remaining uncertainties once performance targets are met within defined tolerances. Furthermore, the framework can accommodate diverse types of observational and theoretical constraints – from global metrics, to local measurements or diagnostics derived from large-eddy simulations or process models – applied across the entire hierarchy of climate model configurations.

As part of the long term DEPHY initiative, and in particular during the recent High-Tune project, the IPSL and CNRM teams have jointly developed ht-explo, a tool implementing the history matching framework for atmospheric model calibration, starting at the process level through comparisons between single-column model simulations and consistent large-eddy simulations. One of the general objective of the TRACCS-PC6-QUINTET project is to adapt/extend its use to the full coupled climate models.

Towards this broad objective, the position aims at:

• determining the link between atmospheric model standalone metrics and the coupled model behaviour, in particular the sea-ice state, ocean large-scale circulation and the ocean-atmosphere coupled modes of variability.

• understanding how key atmospheric metrics are impacted by the coupling

• analysing how atmospheric structural errors propagates on the ocean/sea-ice component in the coupled system

To this aim, we will develop and analyse a large ensemble of simulations obtained by parameter perturbation in atmospheric standalone mode and compare it to its counterpart in ocean-atmosphere coupled mode.

We are seeking a motivated and talented researcher to join the CNRM to investigate theses research questions and to leverage the calibration methods of the CNRM coupled climate model. In particular, the recruited researcher will contribute to the preparation of the new generation CNRM climate model that will participate in the upcoming seventh phase of the Climate Model Intercomparison Project (CMIP7), within the collaborative framework of the CNRM climate model developers. This includes identifying and documenting structural errors of the CNRM climate model.

The recruited researcher will publish results in peer-reviewed journals, present work at national and international conferences or workshops, especially those associated with the TRACCS programme and the QUINTET project. She/he will also have opportunities to supervise Master’s students.

The successful candidate will be recruited for a 3-year contract, renewable for 2 years (funding for 5 years in total already secured). Applicants are expected to provide a detailed CV and a cover letter.

Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, M.-P., Madeleine, J.-B., Rodier, Q., and Xu, W.: Process-Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement, J. Adv. Model. Earth Syst., 13, e2020MS002217, https://doi.org/10.1029/2020MS002217, 2021.

Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L., and Yamazaki, K.: History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble, Clim. Dyn., 41, 1703–1729, https://doi.org/10.1007/s00382-013-1896-4, 2013.

General scientific knowledge:

Applicants must hold a PhD thesis in climate science or a connected field, and have experience in climate modelling. Prior work on climate model calibration, evaluation, or uncertainty quantification will be appreciated.

Technical skills:

- Proficiency in Unix/Linux environments

- Solid experience with scientific programming and analysis tools such as Python or R

- Familiarity with NetCDF data handling tools, including NCO and CDO

- Basic knowledge of version control systems, particularly Git

Professional skills:

- Ability to work independently and manage time and tasks effectively

- Strong rigour in scientific development, testing, and analysis

- A demonstrated sense of initiative, motivation, and scientific curiosity

- Fluency in English (spoken and written) at a minimum B2 level

- French proficiency is not required, but any level will be a plus for day-to-day integration

Interpersonal skills:

- Strong interpersonal and communication skills

- Proven ability to work collaboratively: you will be part of the TRACCS community and contribute to its development and dynamism

- Responsiveness and availability

Additional information:

Application deadline: 30/10/2025

Duration of the contract: 3 years, renewable for 2 years (funding for 5 years in total already secured)

Teleworking friendly: Up to 2 days a week

Required level of education/ Diploma: PhD

Required level of experience: Beginner

Required level in french : Not required
Management : No

Email contacts for any further information : aurore.voldoire@meteo.fr and romain.roehrig@meteo.fr