| 21 Jan 2026
Process-based evaluation of ENSO simulation sensitivity to horizontal resolution in the Chinese Academy of Sciences FGOALS-f3 Climate System Model
Abstract. El Niño-Southern Oscillation (ENSO) is the most prominent interannual climate variability, hence its simulation performance represents a critical benchmark for evaluating the fidelity of coupled climate models. Increasing model resolution is an effective approach to improve the model simulation; however, the impact of refining horizontal resolution from the hundred-kilometer scale to the tens-of-kilometer scale on ENSO simulation, as well as the underlying mechanisms, remains unclear. This study provides a process-based evaluation of ENSO behaviour in two versions of the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Finite-Volume version 3 (FGOALS-f3) climate system model: a low-resolution configuration (~100 km; FGOALS-f3-L, hereafter f3-L) and a high-resolution configuration (~25 km; FGOALS-f3-H, hereafter f3-H). Using a reproducible diagnostic framework, we assess how horizontal resolution influences ENSO amplitude, oscillation characteristics, key air–sea coupling processes, and high-frequency (HF) atmospheric variability. The low-resolution severely overestimates ENSO amplitude, whereas f3-H produces amplitude closer to the observation. Process-based diagnostics show that this improvement arises from the more realistic representation of thermocline and zonal advection feedback processes in f3-H, which arises from the more realistic representation of the meridional structure of ENSO-related zonal wind stress anomalies over equatorial Pacific in f3-H and can be traced back to its improved horizontal resolution. The ENSO cycle in f3-L exhibits excessive regularity, featuring periodic warm-cold transitions; while f3-H reproduces an irregular oscillation resembling the observation. The excessive regularity in f3-L is attributed to its coarser resolution, which limits the simulation performance of tropical cyclones and consequently weakens high-frequency westerly wind activity over the tropical Pacific. The feeble stochastic forcing in f3-L is insufficient to disrupt its overly intense ENSO cycle, yielding an overly regular oscillation. By identifying the structural sources of ENSO biases across resolutions, this study provides a reproducible and model-agnostic framework for diagnosing resolution effects on ENSO performance in climate models and informs future development of FGOALS-f3 model family.
This manuscript presents a process-based evaluation of ENSO simulation sensitivity to horizontal resolution in the CAS FGOALS-f3 climate system model. By comparing low-resolution (~100 km) and high-resolution (~25 km) configurations, the authors diagnose differences in ENSO amplitude, oscillation regularity, and underlying air–sea feedback processes using a reproducible framework including BJ index decomposition and high-frequency wind diagnostics. The study is well structured, methodologically transparent, and aligns with the scope of Geoscientific Model Development, particularly under the “Model Evaluation Papers” category. The process-oriented approach and the explicit tracing of resolution-sensitive feedback pathways are meaningful for both model developers and modeler users. However, several issues still need clarification or strengthening before publication. Overall, I find the manuscript suitable for publication after minor revision. Below I outline specific comments and suggestions.
Main comments and suggestions.
1. One of the central arguments follows the logical chain “TC->HF westerlies->stochastic forcing-> ENSO irregularity”, which is physically plausible and well motivated. However, the manuscript does not quantify the relative magnitude of HF wind variance versus ENSO growth rate. It would be helpful to evaluate whether the stochastic forcing amplitude differs significantly relative to the linear growth rate (e.g., using a simple signal-to-noise ratio metric). Even a simple variance ratio metric or growth rate comparison would further strengthen this section.
2.The BJ framework in Section 2.3.2 should be presented more clearly to meet GMD’s reproducibility standards. Specifically, every symbol should be defined explicitly, units of each term should be provided, and the areas used for the eastern and western box regions in the BJ index calculation need to be specified.The full formulation can be provided either in the main text (with complete equations) or in an Appendix with a clean, self-contained mathematical definition.
3.For a GMD audience, it would be helpful to briefly discuss the computational cost increase from f3-L to f3-H and provide the implications for CMIP7 model development strategy. This would enhance model-development relevance of the manuscript.
4.Consider adding a short graphical summary (schematic) figure illustrating the two key pathways:
(1) “resolution->wind stress structure->feedback->amplitude”,
(2) “resolution->TC->HF noise-> irregularity”).
Such a conceptual figure would help readers quickly grasp the paper’s main messages.
5. Line 271-274: ENSO regularity is currently discussed mainly based on qualitative inspection of the Niño3.4 time series. It would be helpful to complement this with a simple quantitative metric of regularity (e.g., spectral peak sharpness/width, autocorrelation-based periodicity, coefficient of variation of event intervals, or an “irregularity index”). This would make the comparison more objective.
6.Several typos and grammatical refinements are still needed.
6.1 Line 184-185: Consider removing the full name after the abbreviation “HF” if it has already been defined earlier.
6.2 Replace “key influencing ENSO simulation” with “a key factor influencing ENSO simulation”.
6.3 Line 134: “use” should be “uses”.
6.4 Line 160: In Table 1, the land component abbreviation should be “CLM4.0”, not “CLIM4.0”.
6.5 Line 174: “are” should be “is”.
6.6 Data availability section contains a duplicated DOI string: https://doi.org/https://doi.org/... Please correct it.