Atmospheric reanalyses combine historical observations with a consistent numerical weather prediction model and data assimilation system to provide spatially and temporally complete reconstructions of the atmosphere. They are indispensable tools for studying climate variability, atmospheric dynamics and high-impact weather. However, their utility in diagnosing the mechanisms behind extreme events can be limited by inherent structural uncertainties and data artefacts. While modern reanalyses generally agree on large-scale climatological means, discrepancies can emerge when evaluating transient sub-synoptic gradients, localised diabatic feedbacks, and circulation features during extreme events. These differences can be particularly pronounced in regions and periods with sparse observational coverage, where reanalyses are more weakly constrained by observations and therefore more susceptible to biases in the underlying model and data-assimilation system. Furthermore, changes in the assimilated observations can introduce non-climatic jumps, representation errors, and artificial trends.

This session provides a forum for diagnosing uncertainties, limitations and artefacts in global and regional reanalyses when studying tropospheric circulation and weather extremes. We invite contributions addressing:

1. Structural uncertainties from large-scale teleconnections to atmospheric blocking, jet stream waviness, Rossby wave breaking, and storm tracks across reanalysis products (e.g., ERA5, MERRA-2, JRA-3Q).
2. Artefacts, discontinuities, and spurious trends introduced by changes in the observing system over time, alongside robust variability and trends shared across reanalysis.
3. Representation errors in land-atmosphere and air-sea coupling, including how biases in sensible/latent heat fluxes, diabatic heating and water budgets alter synoptic- and mesoscale extremes (e.g., convective environments, atmospheric rivers, explosive cyclogenesis).
4. Physical consistency, uncertainties, and artefacts in emerging machine-learning-based or AI-assisted reanalyses, particularly during extreme weather.

We welcome both studies evaluating the suitability and robustness of reanalyses for diagnosing tropospheric circulation and weather extremes, and science-driven studies using multiple reanalyses to assess the robustness of specific atmospheric or climate questions.