| 09 Sep 2026
The moving target of simulating ENSO: A timeline of Community Earth System Model version 3 (CESM3) Development
Abstract. The process of developing Earth System Models (ESMs) varies across modeling centers globally, but the overarching goal is largely the same – to improve the representation of physically-based processes such that biases in the mean climate state and its variability are minimized. Developers face a number of scientific and technical hurdles to ensure the best use of limited computational resources, storage space, and time. These challenges are particularly pronounced for modes of climate variability that are characterized by high internal variability, like the El Niño Southern Oscillation (ENSO). In this study, we leverage the development of the Community Earth System Model version 3 (CESM3) as a case study to illustrate some of these difficulties as well as to highlight new model developments and their impacts on ENSO.
ENSO is a complicated indicator of model performance in that it can be characterized by a wide number of metrics to assess how well (or poorly) it is simulated. This is at odds with the model development process as a whole, which requires that a manageably small number of metrics be selected for model analysis as teams conduct hundreds of simulations to arrive at a single "best" model configuration. Selecting too many metrics runs the risk of dramatically slowing progress in developing a coupled ESM. As a result, we discuss a minimal set of ENSO metrics here that we consider indicative of overall model performance. We find that biases in the spatial extent of ENSO events persisted throughout the development cycle, with sea surface temperature anomalies (SSTa) that extend too far into the West Pacific for all simulations. Other metrics are more sensitive to model changes, including ENSO amplitude and duration. Such metrics are, however, prone to significant internal variability. This is confirmed by temporally sub-sampling long pre-industrial control simulations of previous model versions (CESM1 and CESM2), which also adds critical context to the evaluation of CESM3; any new model version should ideally not be markedly worse than past iterations. Ultimately, CESM3 produces a reasonable ENSO in comparison to previous model versions and relative to observations, but it remains difficult to attribute changes in its representation to individual model changes due to the significant internal variability.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.
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Review of 'The moving target of simulating ENSO: A timeline of COmmunity Earth System MOdel version 3 (CESM3) development' by Fowler et al.
The manuscript examines the challenges of simulating ENSO during the development cycle of CESM3. The authors track ENSO performance across a range of developmental model configurations rather than relying solely on controlled sensitivity experiments. One important finding is that the high internal variability of ENSO requires relatively long simulations—often exceeding 100 years—to robustly assess model performance, which is consistent with the general understanding of ENSO variability.
I appreciate the authors’ considerable effort and exceptional transparency in documenting ENSO behavior throughout the model-development process. The framework and methodologies presented here could also be useful to other modeling centers. Overall, I am generally supportive of publication, and I hope the authors can consider addressing the following comments, which I believe could further strengthen the manuscript.
1) Broader Applicability
While the manuscript transparently documents the inherently iterative and sometimes messy reality of model development, I feel that it currently provides relatively limited actionable guidance for other modeling centers. Contrasting the “simulations of opportunity” approach with more systematic atmospheric-parameter optimization methods, such as those described by Yu et al. (A systematic atmospheric parameter optimization method to improve ENSO simulation in the ICON Earth system model), could help elevate the manuscript from a documentation of the CESM3 development history to a more broadly applicable methodological guide. I fully recognize that this may not be straightforward, since model development typically involves optimizing multiple metrics simultaneously and balancing realistic representations of individual physical processes against overall model performance. Nevertheless, along these lines, I encourage the authors to explore the underlying model physics in greater depth, such as the discussion of the treatmen of gustness and its impacts on ENSO in this paper.
2) Link to AMIP Simulations
ENSO is a highly air-sea coupled mode and is strongly impacted by both intrinsic atmospheric and oceanic model physics and the coupled mean climate state. For model development, it is common to build a reasonable atmospheric and oceanic models before coupling them. I think incorporating AMIP simulations would significantly strengthen the analysis by using prescribed SST forcing. It may help distinguish whether the identified ENSO-related biases originate primarily from intrinsic atmospheric model physics or emerge through interactions with the coupled mean state.
3) Other factors/metrics critical for ENSO simulations:
The analysis currently places substantial emphasis on atmospheric diagnostics. A more comprehensive evaluation of the oceanic and thermodynamic contributions would help complete the physical picture. In particular, I encourage the authors to consider expanding the analysis to include something like: thermocline depth/tilt, mean upwelling, thermodynamic damping. These factors all influence ENSO evolution.
The manuscript identifies a weakened Bjerknes feedback, particularly through a muted wind-stress response to SST anomalies. I think this is, at least partially, related to the model’s mean state: CESM3 exhibits a mean cold SST bias, which may make the threshold for deep convection more difficult to reach. Consequently, even for comparable SST anomalies, the associated convective response—and therefore the subsequent wind-stress response—could be systematically weaker. Given the mean SST bias, this may not be quite surprising. Again, if the AMIP model does show the underestimated wind stress response, it could be a big concern and should be related to convection parameterization and PBL processes.