the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 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.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-6017', Anonymous Referee #1, 15 Feb 2026
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RC2: 'Comment on egusphere-2025-6017', Anonymous Referee #2, 21 Feb 2026
This manuscript examines the impact of horizontal resolution on ENSO amplitude in the FGOALS-f3 model. The authors show that differences in the meridional structure of zonal wind stress anomalies lead to changes in thermocline and zonal advective feedbacks within the BJ framework, thereby modulating ENSO amplitude and regularity.
Overall, the manuscript is well-organized and mostly clear. The conclusions drawn from the analysis are generally sound and suggest important implications. However, further refinements in the presentation and result interpretation would significantly enhance its overall impact and persuasiveness. Please see the following comments:
- The manuscript contains numerous equations and symbolic representations. It would improve clarity if the notation were made more consistent throughout the paper. For example, overbars and primes are both used to denote anomalies in different places, and in some places, primes seem to indicate filtered anomalies. A clearer and more unified notation system would help readers follow the derivations more easily.
- The BJ index calculation assumes a fixed mixed layer depth of 65 m. Since model resolution may affect vertical stratification and mixed layer structure, it would be helpful if the authors could discuss the potential sensitivity of their results to this assumption. For example, would the use of model-specific mixed layer depths change the quantitative estimates of the feedback components?
- The comparison between the CMIP experiments and the OMIP experiments is interesting. However, it seems that the response of thermocline depth anomalies to zonal wind stress differs between OMIP and CMIP in a resolution-dependent manner (i.e., OMIP shows a stronger response than CMIP at high resolution, but a weaker response at low resolution). What are the physical reasons for this contrasting behavior?
- The BJ index provides a useful linear stability framework for interpreting ENSO amplitude changes. However, given the relatively limited simulation length (~65 years) and the analysis being based on a single model family, it would be helpful for the authors to briefly acknowledge the potential limitations of applying a linear BJ framework to interpret resolution-dependent changes in ENSO dynamics in the discussion, particularly considering the role of nonlinear and stochastic processes.
- In Figure 7a, there seems to be a horizontal black line around 0.2, but it is not described in the caption. Please clarify what this line represents.
- Lines 29-30. The sentence “The low-resolution severely overestimates ENSO amplitude” lacks a noun after “low-resolution.” Please revise (e.g., “low-resolution version”).
- Line 38: The word “feeble” sounds somewhat informal in this context. Please consider replacing it with “weak”.
- Line 422: “may be not the primary driver” contains a word order issue. Please revise to “may not be the primary driver.”
- Line 500: “yielding a more realistic characteristics of TC activity” contains a number agreement issue. Please revise to either “yielding more realistic characteristics of TC activity” or “yielding a more realistic representation of TC activity.”
Citation: https://doi.org/10.5194/egusphere-2025-6017-RC2 -
EC1: 'Comment on egusphere-2025-6017', Xianan Jiang, 20 Mar 2026
In addition to the reviewers' comments, I have several additional suggestions for further improving this manuscript:
I strongly suggest performing a thorough proofreading of the manuscript, as there are numerous grammatical errors throughout. Examples include: (L45) one of the most prominent "modes" of interannual variability, (L54) "overly regular ENSO oscillation"?, (L134) "uses", (L211) the first term "are", among many others not listed here.
For Figure 4, I suggest also including the observed counterparts to allow for a direct validation of the model results.
While the source code related to the diagnostics is provided in the data archive (https://zenodo.org/records/17778266), it needs to be well-documented with README files. Specifically, explicit step-by-step instructions for the calculations and figure plotting, along with the sample data, must be provided for each figure shown in the manuscript. This will ensure that readers can reproduce the results of this study and easily apply the approach to similar analyses. Furthermore, the current organization of the data structure (e.g., "BJ index", "TC Detection") could be improved, for instance, by sorting the files/folders according to the figure numbers in the paper.
Citation: https://doi.org/10.5194/egusphere-2025-6017-EC1
Data sets
The output of FGOALS-f3 models Q. Bao and B. He http://doi.org/10.22033/ESGF/CMIP6.3312
The data for ORAS5 and ERA5 dataset H. Zuo and H. Hersbach https://cds.climate.copernicus.eu/datasets
The data for GPCP dataset R. F. Adler https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access
The data for SODA dataset J. A. Carton http://apdrc.soest.hawaii.edu/datadoc/soda_2.2.4.php
The data for the TC best track in observation M. Ying https://tcdata.typhoon.org.cn/en/zjljsjj.html
The data for HadISST dataset N. A. Rayner https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html
Model code and software
The code for FGOALS-f3 model M. E. Song https://doi.org/https://doi.org/10.5281/zenodo.17778266
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- 1
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.