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, from reduced-complexity models to machine learning 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, see also Balaji et al. 2022) challenges our ability to transfer insight from conceptual models to reality, and distill the physical mechanisms underlying the behavior of state-of-the-art ESMs. This calls for a concerted effort to learn from the entire model hierarchy — or rather, model spectrum — to understand the differences and similarities across its various levels of complexity for increased confidence in climate predictions.

A diverse and well-integrated model ecosystem is also an indispensable prerequisite for the timely assessment of climate risks and effective decision-making. This places renewed emphasis on the concept of fit-for-purpose modeling, which is intrinsically linked to the climate model spectrum through the need to understand what levels of complexity are required and sufficient for a given scientific question or application. The climate community is increasingly interested in making models useful also beyond the academic domain (Mansfield et al., 2023).

In this session, we bring together contributions from all subfields of climate science that showcase how different modeling approaches advance our understanding of the Earth system, highlight inconsistencies in the model spectrum, and/or enable applications in climate impact projections. The key goal is to foster exchange between researchers working on different rungs of the model complexity ladder, focusing on process understanding. Contributions may employ dynamical systems models, physics-based low-order models, explainable machine learning, fast climate models and Earth System Models of Intermediate Complexity (EMICs), simplified or idealized setups of ESMs, CMIP models, and km-scale models. Processes and phenomena of interest include the Earth system response to transient forcing, tipping behavior, climate variability and extremes, and predictability.