Researcher On Strategies for Climate Model Calibration F/m

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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 coupl