the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jul 2026
A pathway-based evaluation of MJO simulation in the Community Atmosphere Model version 6 (CAM6): linking parameter sensitivities to moisture and cloud–radiation processes
Abstract. A pathway-based diagnostic framework is proposed to bridge perturbed parameter ensemble (PPE) sensitivity analysis and process-oriented moist static energy (MSE) budget diagnostics for evaluating Madden–Julian Oscillation (MJO) simulation in general circulation models. The framework connects statistical parameter sensitivities to the physical processes that shape MJO behavior and provides a mapping between influential parameter perturbations and process-level pathways. Applied to the Community Atmosphere Model version 6 (CAM6) using a 128-member PPE, the framework identifies three dominant parameters: the fractional entrainment rate (dmpdz) and the convective adjustment timescale (tau) in the Zhang-McFarlane deep convection scheme, and the stratiform ice fall speed (ai) in the Morrison-Gettelman version 2 microphysics scheme. Targeted experiments show that improved MJO skill is associated with two well-supported pathways. The first pathway involves an increase in dmpdz, which suppresses premature deep convection and restores the equatorial low-level moisture distribution, enabling realistic meridional MSE advection that drives MJO eastward propagation. The second pathway involves a reduction in ai, which prolongs the residence time of upper-tropospheric ice and enhances high cloud cover along the MJO propagation pathway, strengthening the developmental-phase longwave cloud–radiation feedback that supports MJO maintenance. The role of tau is also important, but its specific pathway cannot be fully isolated within the present experimental design. The improved MJO simulation does not degrade the mean precipitation metrics examined here, indicating that the targeted parameter changes do not come at the expense of the simulated mean rainfall pattern in CAM6. This framework provides a process-based strategy for model evaluation and parameter calibration in general circulation models with comparable convection and cloud microphysics schemes.
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Status: open (until 11 Oct 2026)
- RC1: 'Comment on egusphere-2026-3746', Anonymous Referee #1, 17 Aug 2026 reply
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RC2: 'Comment on egusphere-2026-3746', Anonymous Referee #2, 23 Aug 2026
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The manuscript titled ”A pathway-based evaluation of MJO simulation in the Community Atmosphere Model version 6 (CAM6): linking parameter sensitivities to moisture and cloud–radiation processes” by Zhou et al presents a diagnostic framework to evaluate the performance of Madden-Julian Oscillation (MJO) in climate models. I find this study to be novel and extremely useful in several ways. While various process diagnostic metrics exist for evaluating the Madden-Julian Oscillation (MJO), most are not specifically designed to target individual models or to clarify the interactions among the physical processes that influence MJO propagation and maintenance. The authors have identified three key parameters: the fractional entrainment rate, convective adjustment timescale, and stratiform ice fall speed. They explored the role of these parameters in understanding the process-level pathways of MJO simulation within the Community Atmosphere Model (CAM) by using perturbed parameter ensemble (PPE) sensitivity analysis and process-oriented moist static energy (MSE) budget diagnostics. The manuscript is well-written, and the figures along with their captions are clear and effectively communicate the intended message. I recommend accepting the manuscript for publication.
Suggestion.
I have one suggestion. It is well known that the MSE budget cannot often be closed for various reasons. Additionally, understanding the propagation and maintenance of the MJO relies on the balance among the major components of the MSE equation. Therefore, it would be ideal to include a cautionary statement about the limitations of using reanalysis and model simulation data for the MSE budget. For more details, please refer to the provided reference.
Inoue, K et al (2026): Accurate Column Moist Static Energy Budget in Climate Models. Part 1: Conservation Equation Formulation, Methodology, and Primary Results Demonstrated Using GISS ModelE3, Journal of Advances in Modeling Earth Systems, 18(3), e2025MS005564, https://doi.org/10.1029/2025MS005564.
Citation: https://doi.org/10.5194/egusphere-2026-3746-RC2 -
RC3: 'Comment on egusphere-2026-3746', Anonymous Referee #3, 01 Oct 2026
reply
This manuscript addresses a relevant question for Geoscientific Model Development: how parameter sensitivities can inform the evaluation and improvement of MJO simulation in CAM6. Its contribution is the integration of a 128-member perturbed parameter ensemble with process-oriented moist static energy (MSE) diagnostics to connect parameter changes with moisture and cloud–radiation pathways. The targeted experiment shows improved MJO simulation in enhanced eastward propagation and a higher eastward-to-westward spectral power ratio. The approach is suitable for the modelling question, and the comparison with observations and CTL provides a useful basis for interpreting the combined parameter changes.
The manuscript is generally well structured. The code and data availability statement describes an archive of parameter settings, run configurations, diagnostic scripts, figure scripts, and simulation data, providing a useful basis for reproducibility. The methodological clarifications below would further improve the traceability of the reported diagnostics.
Therefore, I recommend acceptance after minor revision, with the following comments aimed at refining the interpretation, clarifying the methods, and improving the figures and wording.
Minor comments
1. Lines 253–259 and 321–324: Please describe the E/W ratio as a measure of the relative dominance of eastward over westward spectral power. Since the ratio can increase through changes in either component, it would be helpful to clarify that. If available, the eastward spectral power could provide complementary context for the MJO amplitude discussion.
2. Lines 260–266 and Figure 2d-f: It’s difficult to understand why regressing OLR on its domain-averaged time series would lead to negative values around day 0 over the same domain. I’m suspecting that the authors flipped the sign of the domain-averaged OLR time series. If that is the case, please specify it. Also, it would be helpful to know if the OLR time series is standardized before regression, as the regressed OLR values seem high. A brief statement of these conventions for observations and simulations would help readers interpret the regression patterns.
3. Table.1: are the values used in CTL simulation the same as CAM6 default ones? If yes, it’s surprising to see the much biased MJO propagation in CTL simulation, e.g., Fig.2e. If not, please specify that.
4. Lines 303–310: It would be helpful to indicate whether the GLM coefficients are statistically significant and, where available, report their uncertainty or significance levels.
5. Figure 4 and lines 248–251, 354–355, and 366–371: Please add a brief explanation for choosing 130°E as the eastern boundary of the MSE projection domain, since the observed eastward tendency maximum extends beyond this boundary. A short discussion of how the domain choice may affect the projections would be useful.
6. Lines 361–365 and Figures 2 and 4: The improvements in DTA are encouraging. Please also briefly discuss the remaining differences from observations in the strength and spatial structure of the MSE anomaly and tendency patterns.
7. Figure 9: Please specify the precipitation bin width or increment, whether the bins are linear or logarithmic.
8. Figure 11 and lines 520–523: This looks like a new way to test uncertainty. It would be helpful to know how many sensitivity tests were done to generate the error bars. Also, any justifications for this?
9. Lines 526–537: Please refine the explanation of the vertical structure by distinguishing lower-tropospheric moisture convergence leading MJO convection from upper-tropospheric processes supporting stratiform anvils. Since the pressure-velocity diagnostic is averaged over 1000–200 hPa, a brief qualification of the inferred vertical structure and timing would help align the discussion with the diagnostics shown.
10. Lines 59–62: “convection schemes” -> convective parameterization schemes.
11. Lines 98–101: Please clarify what is meant by “did not conduct targeted experiments to verify their effects”. As far as I know, Huang et al., (2019) did examine how the changed parameter values may impact MJO simulation from process-oriented diagnostics.
13. Lines 166–167: Please specify which earlier PPE studies you are referring to by adding the relevant citations.
14. Lines 178–179: This is not accurate. Several CTL parameter values are offset from the midpoint, including the default downdraft fraction of 0.1 within 0.01–0.99 and convective timescale of 3600 s within 1800–7200 s. Please also adjust the related wording at lines 325–326.
15. Line 228: “obtained via a 20–90 day band-pass filter” -> obtained via 20–90-day bandpass filtering.
16. Figure 2 appears twice on consecutive pages. Please remove the duplicate.
17. Figure 4 caption, line 352 “obtained by regression total fields” -> obtained by regressing total fields.
18. Lines 378: Please clarify what “though still about half of the observed value of unity” means.
19. Figure 12 and lines 606–614: Please provide a separate diverging color scale with negative and positive values for the DTA minus CTL difference in panel (d). The current color bar contains only nonnegative values, making the negative differences discussed in the text difficult to interpret.
Citation: https://doi.org/10.5194/egusphere-2026-3746-RC3
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