the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jun 2026
Base-state dependence of parameter sensitivity for the Twomey effect in a perturbed parameter ensemble
Abstract. A major source of uncertainty in the effective radiative forcing due to aerosol-cloud interactions (ERFaci) arises from parameters that represent unresolved sub-grid-scale processes in global climate models. Constraining parametric uncertainty requires identifying parameters driving the largest spread in ERFaci. Perturbed parameter ensembles (PPE) address this by systematically varying parameters related to aerosol emission, cloud microphysics, and associated small-scale processes. We perform a global sensitivity analysis of the three components of ERFaci from liquid clouds, the Twomey effect, liquid water path (LWP), and cloud fraction (CF) adjustments, in a PPE with and without constraining to the model's present-day base state. Under uniform prior sampling, autoconversion parameters unrelated to aerosol activation counterintuitively dominate the Twomey effect. Only after constraining the base state are aerosol emission and activation parameters revealed as controlling parameters for the Twomey effect. The apparent control of autoconversion parameters over the Twomey effect arises because these parameters control the cloud base state, which in turn affects the strength of the Twomey effect. The dominant parameters for LWP and CF adjustments remain related to autoconversion before and after the constraint. Constraining to the base state is therefore essential to inferring the correct parameters and processes controlling the model’s climate responses.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-2811', Léa Prévost, 10 Jul 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2811/egusphere-2026-2811-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2026-2811-RC1 -
RC2: 'Comment on egusphere-2026-2811', Anonymous Referee #2, 20 Sep 2026
Review Comments on “Base-State Dependence of Parameter Sensitivity for the Twomey Effect in a Perturbed Parameter Ensemble”
The authors perform a global sensitivity analysis for three components of ERFaci: the Twomey effect, LWP adjustment, and cloud-fraction adjustment. The uniformly sampled CAM6 PPE is constrained using the present-day model base state defined by the global-mean LWP, ice water path (IWP), TOA net shortwave flux, and liquid cloud fraction from the CAM6 ensemble member with default parameter values. For the prior PPE, the autoconversion-related parameter appears to dominate the sensitivity of the Twomey effect. After constraining the ensemble toward the CAM6 base state, the relative importance of several other parameters increases. The regional analysis in Figure 2 further shows that parameter importance is spatially dependent and is not controlled solely by the autoconversion process. This work appears to provide a computationally inexpensive way of identifying alternative parameter combinations that can reproduce a given model base state (CAM6 default). The authors then examine which parameters are important for achieving consistency with this base state and it demonstrates that the inferred parameter sensitivities can change substantially after excluding ensemble members that are inconsistent with the selected model base state. I think this is a meaningful and useful approach, although the rationale for using the model base state, rather than observations, as the constraint should be clarified.
My main concern is the choice of the CAM6 default model state as the constraint. The default CAM6 configuration has already undergone model development and tuning, including tuning aimed at obtaining a realistic global-mean TOA energy balance. Therefore, the selected base state is not completely independent of prior observational evaluation and model tuning. Could the authors explain more clearly why the CAM6 base state is used to constrain the PPE rather than applying observational constraints directly?
It would also be useful to identify which of the four base-state variables contributes most strongly to the change in parameter sensitivities. In particular, which constraint causes aerosol-emission- and aerosol-activation-related parameters to become more important after conditioning the PPE? Such an analysis would help clarify the physical reason why the dominant sensitivities differ between the prior ensemble and the base-state-constrained ensemble.
Additional comments:
Line 38: state explicitly what the base state is? Model’ s base state?
Line 47: Please also provide the approximate grid resolution in kilometers (km) in addition to the 1° resolution.
Line 58: Bellouin, Nicolas, et al. "Bounding global aerosol radiative forcing of climate change." Reviews of Geophysics 58.1 (2020): e2019RG000660.
Line 58-59: Precipitation suppression is one pathway by which smaller cloud droplets, through reduced collision–coalescence efficiency and slower gravitational settling, can increase cloud LWP and lifetime, leading to a more negative ERFaci (cooling).
Line 80: shortwave
Line 98: “Other parameters that exhibit influence on the Twomey effect are related to the warm rain process (micro_mg_autcon_nd_exp, micro_mg_accre_enhan_fact) …” It may be helpful to describe these parameters more specifically. For example: “Other parameters that influence the Twomey effect are associated with warm-rain microphysics, including the CDNC exponent in the autoconversion parameterization (micro_mg_autocon_nd_exp) and the accretion enhancement factor (micro_mg_accre_enhan_fact).”
Also, in this paragraph, if the Twomey effect is sensitive to aerosol emissions and activation, why was CDNC not included as a constraint on the model’s baseline state? If CDNC were added as an additional constraint on the prior ensemble, would the inferred parameter importance change?
Line 127: Could the authors briefly explain the physical reason why microp_aero_npccn_scale shows the dominant sensitivity in this region?
219: LWP has already been defined earlier in the manuscript. Please check the entire manuscript and use the abbreviation consistently throughout.
230: define CRK.
It is unclear why the model base state, rather than observations, is used to constrain the prior ensemble. The authors should also clarify how “unrealistic” simulations are defined in this context. Are simulations considered unrealistic simply because their global-mean present-day states differ from the base model, even if they may still be consistent with observations?
Line 234: How were the prior samples that matched the global-mean model base state identified? Were samples required to reproduce exactly the same global-mean values, or was a tolerance range applied? What fraction of the prior samples was retained after applying this constraint?
Discussion:
I see that the authors also apply observational constraints to the PPE in the Supplementary Material. It would be interesting to compare the sensitivity indices among the prior ensemble, the observation-constrained posterior, and the posterior constrained by the model base state. Such a comparison could help clarify how the choice of constraint affects the inferred parameter importance.
Although the CAM6 base-state TOA radiation is not an observational constraint itself, the reference model has already undergone tuning to achieve a realistic TOA energy balance. It would therefore be useful to discuss how constraining the PPE toward the model base state differs from directly constraining it with observations.
Citation: https://doi.org/10.5194/egusphere-2026-2811-RC2
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