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Abstract Physics‐based atmosphere‐land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared to observations. Recently, AI emulators and hybrid models have emerged with the potential to overcome these biases, but still require systematic evaluation against metrics grounded in fundamental atmospheric dynamics. We evaluate the representation of four atmospheric variability benchmarking metrics in a fully data‐driven AI emulator (ACE2‐ERA5) and hybrid model (NeuralGCM). The hybrid model and emulator can capture the spectra of large‐scale tropical waves and extratropical eddy‐mean flow interactions, including critical levels. However, both struggle to capture the timescales associated with quasi‐biennial oscillation (QBO, ∼28 months) and Southern annular mode propagation (∼150 days). These dynamical metrics serve as an initial benchmarking tool to inform AI model development and understand their limitations, which may be essential for out‐of‐distribution applications (e.g., extrapolating to unseen climates). 

The circulation response to climate change shapes regional climate and extremes. Over the last decade an increasing number of atmospheric circulation signals have been documented, with some attributed to human activities. The circulation signals represent an exciting opportunity for improving our understanding of dynamical mechanisms, testing our theories and reducing uncertainties. The signals have also presented puzzles that represent an opportunity for better understanding the circulation response to climate change, its contribution to climate extremes, interactions with moisture, and connection to thermodynamic discrepancies. The next decade is likely to be a golden age for dynamics with many advances possible. 

Abstract The modern Arctic climate during wintertime is characterized by sea-ice cover, a strong surface temperature inversion, and the absence of convection. Correspondingly, the energy balance in the Arctic atmosphere today is dominated by atmospheric radiative cooling and advective heating, so-called radiative advective equilibrium. Climate change in the Arctic involves sea-ice melt, vanishing of the surface inversion, and emergence of convective precipitation. Here we show climate change in the Arctic involves the emergence of a new energy balance regime characterized by radiative cooling, convective heating, and advective heating, so-called radiative convective advective equilibrium. A time-dependent decomposition of the atmospheric energy balance shows the regime transition is associated with enhanced radiative cooling followed by decreased advective heating. The radiative cooling response consists of a robust clear-sky greenhouse effect and a transient cloud contribution that varies across models. Mechanism-denial experiments in an aquaplanet with and without interactive sea ice highlight the important role of sea-ice melt in both the radiative cooling and advective heating responses. The results show that climate change in the Arctic involves temporally evolving mechanisms, suggesting that an emergent constraint based on historical data or trends may not constrain the long-term response.