Climate modeling is pushing the frontier towards increasingly complex, high-resolution earth system models (ESMs). At the same time, nonlinearities and emergent phenomena in the climate system are often studied by means of conceptual models, which offer qualitative understanding and permit theoretical approaches. Recent advancements in statistical and physical emulators – ranging from reduced-complexity climate models to machine learning-based techniques – are enabling rapid and computationally efficient assessments of climate trajectories, impacts, and risks.

Between these approaches, a persistent “gap between simulation and understanding” (Held 2005) challenges our ability to transfer insights from conceptual models to reality, and to distill the physical mechanisms underlying the behavior of complex (climate) models. This calls for a concerted effort to learn from the entire model hierarchy, understanding the differences and similarities across its various levels of complexity, to increase confidence in climate projections.

In this session, we invite contributions from all subfields of climate science that showcase how different modeling approaches advance our understanding of the Earth system and its components, and/or highlight inconsistencies in the model hierarchy. We also welcome studies exploring a single modeling approach, as we aim to encourage exchange between researchers working on different rungs of the model complexity ladder. Contributions may employ dynamical systems models, physics-based low-order models, explainable machine learning, Earth System Models of Intermediate Complexity (EMICs), simplified or idealized setups of ESMs (radiative-convective equilibrium, single-column models, aquaplanets, slab-ocean models, idealized geography, etc.), full ESMs or standalone models of components of the Earth system, and km-scale models.

Processes and phenomena of interest include, but are not limited to:
* Earth system response to climate forcing
* Tipping behavior and critical transitions
* (Coupled) modes of climate variability
* Extremes and predictability