Postdoctoral Researcher in Climate Modeling and Uncertainty Quantification

Position Summary

The International Water Research Institute (IWRI) of the College of Agriculture and Environmental Sciences (CAES) at UM6P invites applications for a postdoctoral researcher in climate modeling and uncertainty quantification, focusing on extreme precipitation in data-scarce regions, with Morocco as a primary case study.

Extreme precipitation events are inherently rare and poorly observed, particularly in regions with sparse and heterogeneous monitoring networks. Climate models therefore play a central role in understanding and projecting such extremes, yet they introduce multiple sources of uncertainty that propagate across scales. This project aims to systematically quantify and interpret these uncertainties across the hierarchy of climate models, from global to convection-permitting scales.

Scientific context

Climate projections rely on a hierarchy of models with increasing spatial resolution, each introducing distinct sources of uncertainty that affect the simulation of extreme precipitation. Global Climate Models (GCMs) provide physically consistent representations of large-scale atmospheric circulation and climate variability, but their coarse resolution limits their ability to resolve key processes such as orographic forcing and mesoscale convection. Regional Climate Models (RCMs) dynamically downscale GCM outputs to finer resolutions, improving the representation of topography and land–atmosphere interactions, yet they remain strongly dependent on boundary conditions and continue to rely on parameterized convection, which is a major source of error in extreme precipitation. Convection-permitting models (CPMs), operating at kilometer-scale resolution, explicitly resolve deep convection and have demonstrated improved skill in simulating short-duration and high-intensity precipitation events; however, their high computational cost limits ensemble sizes, and their uncertainty structure remains insufficiently explored, particularly due to sensitivities to boundary forcing and physical parameterizations. Understanding how uncertainty propagates across the GCM–RCM–CPM modeling chain is therefore a central challenge, especially in regions such as Morocco, where complex topography (Atlas Mountains, Rif Plateau), diverse climatic influences, and limited observational data combine to produce highly variable and poorly constrained extreme precipitation patterns.

Research Scope

The postdoctoral researcher will investigate uncertainty propagation across multi-scale climate modeling frameworks by combining multi-model datasets (e.g., CMIP6, MIT Regional Climate Model simulations, and convection-permitting simulations) with dynamical and statistical downscaling approaches, extreme value theory, and spatial statistics. The work will focus on decomposing uncertainty into its key components while assessing the added value of high-resolution convection-permitting models for extreme precipitation. Particular attention will be given to observational uncertainty in sparse data environments through the integration of station, satellite, and reanalysis datasets. The project will also develop and apply advanced uncertainty quantification methods with the objective of providing robust guidelines for climate modeling of extremes in data-scarce and topographically complex regions.

Candidate Profile

• PhD in climate science, atmospheric physics, applied mathematics, or related field
• Expertise in climate modeling and/or uncertainty quantification
• Strong programming skills (Python, MATLAB, Fortran, or similar)
• Experience with climate datasets and/or extreme event analysis is desirable
• Proven ability to handle HPC environements is an added asset