Challenges in global climate models to represent cloud response to aerosols: insights from volcanic eruptions

Domain: earth_science
Published: 2025-12-18
Editorial outcome: major_revision

Abstract
Aerosol-cloud interactions (ACI) remain a major source of climate uncertainty due to missing large-scale observational constraints. Such a constraint, with global cloud representativeness, has recently been developed based on the Holuhraun-2014 volcanic eruption from machine learning with satellite observations. Here, we confront this large-scale observational constraint against six diverse global climate models to advance our understanding of ACI simulation uncertainty. We show that marine liquid cloud optical depth responses to aerosols are reasonably well simulated, although through compensating errors. However, all models largely underestimate cloud cover responses to aerosols, with five of them outside the 90% confidence level. This persistent bias remains despite tuning five distinct cloud schemes and testing various key cloud processes. Such bias in cloud cover response is a major driver of simulation uncertainty in ACI cooling and need to be addressed urgently to improve climate projections and estimations of climate sensitivity.

Introduction
The global mean near-surface temperature in 2024 was a record breaking +1.55°C above pre-industrial levels, according to WMO, temporarily overshooting the +1.5°C threshold. Under current emission trajectories, a warming well above +2°C is expected by 2100, driving devastating hazards1,2. Accurate climate projections are needed to guide policymaking for decarbonisation strategies and climate interventions to minimise losses from subsequent climate and weather extremes1,2.

A series of Intergovernmental Panel on Climate Change (IPCC) reports has identified large uncertainties in climate assessments1,2. One major source of uncertainty is aerosols, tiny particles suspended in the atmosphere, and their impacts on clouds1,2. Modern-day global observations (e.g., satellite, aircraft, in-situ) have improved constraining simulations of aerosol and cloud in terms of the present-day climate (e.g., refs. 3,4). However, how clouds respond to aerosol perturbations (usually quantified as the susceptibility of cloud properties to changes in aerosol, “dlnCloud/dlnAerosol”) is still poorly constrained5–7. Cloud susceptibility is critical for climate projections, since “Future clouds=Current cloud state+Exp(dlnCloud/dlnAerosol×ΔlnAerosol)+Feedback of clouds to climate change”. The long-standing challenge for constraining cloud susceptibility is the lack of large-scale observational constraints to calibrate global climate models (GCMs), leaving discrepancies of more than a factor of 10 that have existed for decades amongst GCMs8. These large discrepancies in cloud susceptibility can propagate significant uncertainties in climate projections across future scenarios, even with similar current states of clouds and aerosols. Therefore, constraining cloud processes and susceptibility is critical for reducing climate uncertainty5,8,9.

Aerosols can influence cloud formation by serving as cloud condensation nuclei (CCN) and ice-nucleating particles1. In this study, we focus on marine warm clouds given that their net effect on radiative forcing is the largest and their likeliness to be perturbed by human emissions due to their low altitude10. Increased aerosol concentration results in more CCN and more, yet smaller, cloud droplets (effective droplet radius (Re) decrease), making clouds brighter (Twomey effect)11. In response, clouds tend to adjust their liquid water path (LWP) and liquid cloud cover (LCC) through complex cloud microphysical processes. Two competing effects determine the changes of LWP and LCC to aerosol perturbations. On the one hand, numerous smaller cloud droplets could suppress precipitation, leading to an increase in LWP and LCC, and prolonging cloud lifetime (Albrecht effect)12,13. On the other hand, numerous smaller cloud droplets could increase cloud-top entrainment of dry air, leading to droplet evaporation and hence decreasing LWP and LCC (entrainment effect)14. Both the Twomey effect and the larger LWP result in a negative forcing via optically thicker and brighter clouds, resulting in more reflection of sunlight and cooling. In addition, a larger LCC has been found to contribute to a large negative forcing6,7.

To date, the Twomey effect11 has been well documented in multiple lines of evidence, including modelling (e.g., refs. 15,16) and observations (e.g., refs.6,7,17–21). However, evidence for the Albrecht effect, including LWP and LCC adjustments, is much less clear, with signals in the range from positive to negative reported17,21–29. A major challenge is disentangling aerosol impacts on clouds from confounding meteorological co-variability, especially on a climate-relevant scale of thousands of km where meteorological conditions can differ spatially and temporally21. In 2014, the Icelandic effusive eruption known as Holuhraun resulted in an aerosol plume over the North Atlantic for months, affecting clouds with a regime spectrum analogous to global cloud regimes21,30. This opportunistic experiment has been proposed to serve as a benchmark case for studying ACI at a climate-relevant scale21,30. The challenge of confounding meteorology has been addressed in a recent work6, using machine-learning-based counterfactual clouds without volcanic influence to contrast real-world observations (i.e. clouds with volcanic influence). The detected ACI signals effectively minimise the noise from satellite observational uncertainty, by aggregating over an extensive region spanning millions of square kilometres6. By applying this machine-learning approach in Hawaii trade wind region, large ACI signals were detected in the downstream of volcanic aerosol plume while negligible signals in the upstream7; suggesting this methodology is able to estimate cloud susceptibility at large scale from satellite observations, making it possible to calibrate cloud susceptibility in GCMs.

Now, this study leverages the observational constraint of cloud susceptibility at a climate-relevant scale to challenge a group of six diverse GCMs with distinct cloud schemes, the group chosen as they well represent the uncertainty range of current state-of-the-art GCMs8. We found that all models significantly underestimate cloud cover response to the aerosol perturbation, although they simulate cloud optical depth response reasonably well, but with compensating errors. This multi-model intercomparison pinpointed key ACI uncertainties. Here, we echo Malavelle et al.21 to call for the wider climate community takes the Holuhraun-2014 eruption as an anchor case, and stress validation of cloud susceptibilities in addition to cloud properties to decompose and unravel the largest uncertainty in climate radiative forcing, i.e. ACI simulation, therefore pointing towards a vital direction to improve future climate simulations.

Results
Multi-model ACI structural uncertainty
The structural uncertainty in ACI simulations refers to the uncertainty arising from limitations in the representations of physical processes in models8. Here we use the large-scale observational constraint of cloud susceptibility from Holuhraun-20146, to challenge six GCMs and uncover their structural uncertainty of ACI. These GCMs include ECHAM6.3-HAM2.331, CESM2.1.032, UKESM133, CNRM-ESM2-134, ECHAM6.3-SALSA2.035, CAM5.3-Oslo36. The discussion and evaluation of cloud state simulations are consistent with a previous study by Malavelle et al.21, see details in the Supplementary Discussion Section S1. In contrast to the widely used method of evaluating cloud and aerosol states, here we re-evaluate the performance of GCMs using cloud susceptibilities as a proxy for ACI. Cloud susceptibilities are defined as changes in cloud properties (i.e., Re, LWP) and LCC in response to volcanic aerosol-induced changes in cloud droplet number concentration, Nd (i.e., -dlnRe/dlnNd, dlnLWP/dlnNd, and dlnLCC/dlnNd), using paired GCM simulations with and without Holuhraun-2014 volcanic emissions (see Methods). Together, these cloud susceptibilities determine the change in cloud radiative effect.

Figure 1a displays the results of the evaluation of GCMs overlaid on violin plots illustrating the observational ACI constraints6. These violin plots represent the probability distributions of each cloud susceptibility with a 90% confidence interval, i.e., “very likely” as defined in the IPCC uncertainty guideline37. The best estimates (median values [90% confidence interval]) are: -dlnRe/dlnNd = 0.37 [0.16–0.92], dlnLWP/dlnNd = 0.02 [−0.18–0.21], dlnCOD/dlnNd = 0.39 [0.11–0.96], and dlnLCC/dlnNd = 0.42 [0.09–1.06], where COD stands for cloud optical depth. The Twomey effect, indicated by -dlnRe/dlnNd, is relatively well simulated in six GCMs, falling within the 90% likelihood of the observational constraint, although they are slightly underestimated (below the 50th percentile), ranging from 0.15 in ECHAM6.3-HAM2.3/UKESM1 to 0.32 in CNRM-ESM2-1.

Regarding the liquid water path adjustments (dlnLWP/dlnNd), all models except for CNRM-ESM2-1 overestimate the LWP response to changes in Nd compared to the observational constraint. Four GCMs fall within the observational uncertainty range, from the 50th percentile for CNRM-ESM2-1 to the 90th percentile for CAM5.3-Oslo. Two GCMs, ECHAM6.3-HAM2.3 and ECHAM6.3-SALSA2.0, show excessive LWP adjustments, far exceeding the observational constraints. These overly strong LWP adjustments in some of the GCMs compared to machine-learning-based observational constraints6 are in line with Malavelle et al.21, who compared four GCMs with climatological anomalies from satellite observations21. However, we further show that this overestimation of LWP adjustment in GCMs is balanced by the underestimation of the Twomey effect in all six GCMs to give a reasonable value of cloud optical depth response to ACI.

The COD (an indicator of albedo) response can be obtained by combining the Twomey effect and LWP adjustment (i.e., dlnCOD/dlnNd = -dlnRe/dlnNd + dlnLWP/dlnNd)8. All models simulate the COD response well within 25–75th percentiles of the observational constraint. Although, except for CNRM-ESM2-1, five GCMs do so by compensating between low biases in the Twomey effect and high biases in LWP adjustment. An exception is CNRM-ESM2-1, which excels in simulating both the Twomey effect and the LWP adjustments in best agreement with observations. The dlnCOD/dlnNd in ECHAM6.3-HAM2.3 and ECHAM6.3-SALSA2.0 is higher than the best estimate, primarily driven by the excessively strong LWP adjustment. Meanwhile, the low dlnCOD/dlnNd in UKESM1 is driven by an overly weak Twomey effect.

We find that the susceptibility of cloud cover is largely underestimated in all six GCMs. Almost all GCMs fall out of the 90% confidence interval of observational constraints, with only ECHAM6.3-SALSA2.0 touching the lower bound, indicating “very unlikely” that current state-of-the-art climate models are able to capture the aerosol-induced cloud cover increase. Liquid cloud cover is usually parameterised in terms of relative humidity (RH) and liquid water content in CESM2.1.038,39 or only a function of RH in the other five GCMs40–43 (Table 1 in Methods). This systematic bias indicates a lack of connection between aerosols and cloud cover parameterisations in GCMs. Further improvements in representations in entrainment or rain formation could improve cloud lifetime44,45 and thus cloud cover responses to aerosol changes in GCMs.

Multi-scheme ACI structural uncertainty
Although the cloud cover in models is diagnosed by grid-mean RH, it remains closely linked to cloud microphysical processes through the partitioning of water into the vapour, liquid, and ice phases. Thus, the chosen cloud schemes in the model are expected to influence cloud susceptibilities, causing structural uncertainty of ACI. To further understand this issue, we select ECHAM6.3-HAM2.3 as a showcase model to explore whether ACI uncertainty, especially for cloud cover, can be reduced by implementing different cloud schemes. ECHAM6.3-HAM2.3 is chosen because its LCC adjustment is about in the middle of six GCMs, indicating a good representative (in line with Ghan et al.8) and because multiple cloud schemes (both diagnostic and prognostic ones) have been embedded in the model.

In total, five different cloud schemes have been implemented in the ECHAM6.3-HAM2.3 model to test various cloud microphysics and cover parameterisations. The default cloud schemes include a two-moment cloud microphysics scheme46 and an RH-based cloud cover scheme40, referred to as REF. One additional cloud microphysics scheme is implemented: Predicted Particle Properties scheme (P3)47. Next to the default RH-based cloud cover scheme, we implement the Xu and Randall cloud cover scheme (XR)48 with REF and P3 microphysics schemes. In the XR scheme, the cloud cover is determined by both liquid water and RH48. In addition, we used the Prognostic cloud cover scheme (PROG_CC)49, a Tiedtke-like scheme50 similar to that in GFDL’s CM4.0 Climate Model51,52, which simulates cloud fraction prognostically based on in-cloud water vapour and convective activity and uses an updated cloud microphysics scheme. In total, we have five different cloud scheme setups in ECHAM6.3-HAM2.3, including REF, REF-XR, P3, P3-XR, and PROG_CC.

As shown in Fig. 1b, all five cloud schemes in ECHAM6.3-HAM2.3 demonstrate a reasonable simulation of the COD response. The reasonable COD response is again due to the compensation of biases in the Twomey effect and the LWP adjustment. The Twomey effect (-dlnRe/dlnNd) is underestimated, falling below the 25th percentile of the observational constraint, although the simulations with the P3 scheme perform slightly better than with the REF and PROG_CC schemes. The LWP adjustment (dlnLWP/dlnNd) in all schemes exceeds the observational constraint, showing excessively strong signals, even though PROG_CC is much improved (close to the 90th percentile) compared to others schemes. As a result, the ensemble spans the range of the observed COD response (dlnCOD/dlnNd) within the 45–85th percentiles. Nevertheless, none of the cloud schemes reproduce the observed increase in cloud cover (dlnLCC/dlnNd), even the cloud cover schemes that depend on liquid water (XR) or prognostic scheme (PROG_CC) still underestimate dlnLCC/dlnNd, suggesting a major structural uncertainty in current cloud cover schemes. Further investigations of the cloud cover schemes and relevant cloud processes are imperative to clarify cloud cover response to aerosol changes and to inform model improvements.

ACI parametric uncertainty
Similar to the cloud schemes, tuned cloud microphysical processes impact the partitioning of water into the vapour, liquid, and ice phases, therefore impacting cloud cover and causing parametric uncertainty of ACI. To further explore the influence of individual cloud microphysical processes on cloud susceptibilities, we selected two distinct cloud schemes, REF (diagnostic cloud cover scheme) and PROG_CC (prognostic cloud cover scheme). Detailed parameter settings of the sensitivity studies are listed in Table 2 (Methods). Briefly, based on a previous ECHAM model tuning study53, we tuned the parameters of key microphysical processes that influence cloud cover towards maximising cloud cover response to changes in aerosols53. For the default scheme (REF), the perturbed processes include autoconversion rates for stratiform and convective clouds, the RH threshold for cloud formation, entrainment rate, and the fraction of shallow convection into the free troposphere (details in Table 2). Similarly, we perturbed processes for the prognostic cloud cover schemes (PROG_CC), including the detrainment rate, entrainment rate, turbulence, and the RH threshold for large-scale cloud formation.

As shown in Fig. 1c, for the REF scheme, despite the perturbed simulations slightly increasing the cloud cover susceptibility compared to the default setup, as expected, the dlnLCC/dlnNd fell outside the range of the observational constraint. As a side effect, the perturbed runs simulated an even higher bias in LWP adjustment (dlnLWP/dlnNd) than the default setting (except for REF-S2), whilst the Twomey effect (-dlnRe/dlnNd) is relatively insensitive, leading to a greater overestimation of the COD response (green hue lines). For the PROG_CC scheme (blue hue lines), perturbed runs only changed cloud cover responses and COD responses slightly. The reasons for this are unclear, but the prognostic cloud cover scheme49 has more degrees of freedom to respond to cloud processes compared to the default diagnostic scheme REF46. These dichotomous results further highlight the complexity of influencing the cloud cover response through cloud microphysical processes. Since different cloud schemes and models may provide various responses, further investigations with a more detailed evaluation of the results with ECHAM6.3-HAM2.3 and more models are required to address this issue.

Global representativeness and robustness of the Holuhraun eruption
To evaluate the global representativeness of the Holuhraun-2014 eruption case, we compared its cloud regime distribution and cloud susceptibilities to global ones. Malavelle et al.21 and Chen et al.6 have shown that the Holuhraun-2014 eruption occurred in a region encompassing the full range of cloud regimes, whose distribution is closely matched with the global distribution observed by satellite from 2002 to 2014 (see Extended Data Fig. 1 in Chen et al.6). In addition, we assessed cloud susceptibilities at a global scale resulting from anthropogenic aerosol emissions throughout 2000–2014 in CMIP6 simulations (MPI-ESM-1-2-HAM and UKESM1-0-LL, equivalent to ECHAM2.3-HAM2.3 and UKESM1 in this study, respectively). We compare CMIP6 simulations of “historical” and “hist-piAer” experiments, representing with and without anthropogenic aerosol influence, respectively; details are given in the Methods. The susceptibilities of clouds to anthropogenic aerosol emissions at the global scale are consistent with the Holuhraun-2014 eruption in our GCMs simulations (Fig. 1a). The consistency in both cloud regimes distribution and cloud susceptibilities suggests that the Holuhraun-2014 eruption provides a valid and representative analogue for understanding global cloud responses to aerosol perturbations due to human emissions in recent years.

To strengthen robustness of our results, we quantified model uncertainty caused by the inherently chaotic nature of cloud dynamics and radiative effects through ensemble simulations. We used ECHAM6.3-HAM2.3 as a showcase model and explored uncertainty across five representative experiments, each reflecting distinct cloud schemes and process sensitivities, with ten ensemble simulations per experiment. We find only a small degree of uncertainty is introduced by model internal variability. For cloud cover, the ensemble uncertainty is a magnitude smaller than the gap between GCMs and the observation (Fig. 2). Furthermore, we demonstrate the robustness in global representativeness using ensembles in CMIP6 simulations (three ensembles for each). Again, a negligible difference is found between CMIP6 ensembles (Fig. 2). These well demonstrate the robustness of the key conclusions and its global representativeness in this study.

Methods
Model setup
The effusive volcanic eruptions at Holuhraun (Iceland, 2014) emitted tens to hundreds of kilotons of SO2, creating a sulfate aerosol plume in the marine boundary layer which spread over the whole North Atlantic21, providing an ideal natural experiment to investigate aerosol-cloud interactions. Malavelle and Yuan proposed an AeroCom multi-model intercomparison project (VolcACI)62–64 to use this Holuhraun eruption as a benchmark case to study uncertainty in aerosol-cloud interaction simulations. In this study, we used five GCMs from the VolcACI project, based on available data for analysis, including UKESM133, CNRM-ESM2-134, ECHAM6.3-SALSA2.035, CAM5.3-Oslo36,45, ECHAM6.3-HAM2.331. In addition, CESM2.1.032,65 was added for this experiment following the VolcACI protocol.

The model setups and the simulations followed the VolcACI protocols62. Model treatments of aerosols and cloud schemes are summarised in Table 1. A brief description is provided below. Emissions from the Holuhraun eruption were specified in the VolcACI protocol62, while other anthropogenic and natural emissions follow the AeroCom Phase III guidelines. Sea surface temperature and sea ice extent were prescribed using time-varying monthly mean values of the year 2014, as given in the AMIP data66. Model winds and surface pressure are nudged towards time-varying reanalysis data (e.g., ERA-Interim or MERRA2). The spin-up period was set to one year to allow the models to stabilise prior to the volcano simulations. Significant ACI signals and increase of cloud fraction have been detected using MODIS observations during September-October 20146. Here, we focus on the month of October 2014, because the volcanic plume has been well dispersed over the whole North Atlantic by then, influencing a cloud regime spectrum that closely represents the global clouds6,21. Control and volcano scenarios were run with and without Holuhraun emissions for October 2014, respectively. The difference between the two scenarios dictates the clouds’ response to volcanic aerosol perturbations.

Cloud scheme and process sensitivities
ECHAM6.3-HAM2.3 has 47 vertical levels from the surface to the top level at 0.01 hPa and runs at T63 spectral horizontal resolution (1.875°×1.875°). The two-moment cloud microphysics scheme (REF) is used as the default scheme for stratiform clouds in ECHAM-HAM46. This scheme simulates number concentrations and mass mixing ratios of cloud droplets and ice crystals prognostically. These hydrometeors interact with rain drops, snow, and water vapour, through various microphysical processes. A schematic figure showing the interconnections of microphysical processes in REF scheme can be found in ref.67. Liquid cloud cover is diagnosed based on grid-mean RH and a height-dependent threshold RH68. To test different cloud microphysics and cloud cover schemes, we employed five different cloud scheme setups in ECHAM6.3-HAM2.3, including REF, REF-XR, P3, P3-XR, and PROG_CC. To test the sensitivity of individual processes, we perturbed specific microphysical processes in the REF and PROG_CC schemes to assess the sensitivity of liquid water path and cloud cover to these processes. Detailed parameter settings for the sensitivity studies are shown in Table 2.

CMIP6 data for global cloud susceptibility
Global cloud property changes (i.e., cloud susceptibility) in response to present-day anthropogenic aerosol emissions are calculated through CMIP6 simulations and compared with Holuhraun-2014 eruption in this study to validate its global representativeness. Among the six GCMs used in our study, only two models from CMIP6 provide complete outputs for both cloud microphysical and macro-physical properties: MPI-ESM-1-2-HAM and UKESM1-0-LL (equivalent to ECHAM6.3-HAM2.3 and UKESM1 in this study). We used paired “historical” and “hist-piAer” simulations (2000–2014) for MPI-ESM-1-2-HAM69,70 and UKESM1-0-LL71,72 from CMIP6. For each experiment, three ensemble runs (r1, r2, r3) are available and used to assess the robustness of the results. The “historical” experiments simulate Earth’s climate from 1850 to 2014, including both natural and anthropogenic forcings. In contrast, the “hist-piAer” experiments follow the same setup but exclude anthropogenic aerosols. Comparing these two experiments over the present-day period (2000–2014) allows us to isolate the influence of anthropogenic aerosols on global cloud properties. Consistent with Holuhraun-2014 simulations, monthly cloud variables (i.e. Nd, Re, LWP, CC) from “hist-piAer” and “historical” experiments are used to calculate cloud susceptibilities (i.e., -dlnRe/dlnNd, dlnLWP/dlnNd, dlnCOD/dlnNd, and dlnLCC/dlnNd). Note that liquid cloud cover is not available from the CMIP6 simulations, so total cloud cover is used to calculate cloud cover response. Detailed information of CMIP6 simulations is given in Table 3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper investigates the ability of a selection of climate models to simulate the liquid cloud response to the massive injection of sulfur into the lower atmosphere associated with an effusive eruption in Iceland in 2014. The paper follows a similar methodology to reference 22, Malavelle et al. 2017. It is well organized and relatively concise in showing that climate models are fairly consistent in their errors in simulating this response. The models do relatively well on cloud optical depth responses, but underestimate the Twomey response and have almost no response in cloud fraction, compared to the satellite observations against which the models are tested. Trying many different parameterization schemes within one of the climate models, produces no significant change in these basic deficiencies.\nPerhaps a little more detail on the ability of the satellite measurements to distinguish Re, LWP and LCC changes would be helpful. I imagine compensating errors in the measurements between Re, LWP and LCC, too.\nThe paper calls for a continued effort to understand these discrepancies and improve their simulation compared the observations, as this eruption and the associated data comprise a unique natural experiment. The paper does not comment on the special character of the observed situation relative to the global cloud problem. It occurs in high latitudes over the ocean in the summer season in the vicinity of Iceland. The sulfate injection is huge. It might be that fine tuning simulations to this particular situation, may not serve particularly well for the global problem. Also, I believe the cloud fractions in this location and season are very high, so that the logarithmic error seen here is not that significant in a real sense , and maybe not that robustly measured. That said, however, it well might be that the inability to simulate these data constitutes a fundamental inability of climate model parameterizations to simulate cloud fraction responses to condensation nuclei changes. I am not sure which it is.\nComments on text:\nLine 111: ‘theirs’ does not make grammatical sense here.\n137: Do you really want to say ‘our previous study’ here in relation to reference 22? I don’t think the author list is identical. It also raises the question of what is really new here also.\n192: When you say cloud albedo effect, to which of the feedbacks in Figure 1 are you referring. Cloud albedo is not shown there.", "reviewer_2": "The author provides an evaluation of aerosol-cloud interactions in six atmospheric models during the October 2014 Holuhraun volcanic event. The results are intriguing, and the manuscript is concise. However, I believe significant improvements in methodology and analysis are needed before publication. My detailed concerns are outlined below.\nMajor:\nThe analysis relies on a single month of model simulations, which may introduce significant noise due to the inherently chaotic nature of cloud dynamics and radiative effects, especially over short timescales. To strengthen robustness, I recommend quantifying uncertainty through ensemble simulations. If unavailable, multi-year control experiments could serve as a proxy to estimate noise magnitude.\nThe study’s title and conclusions suggest broader relevance, but the connection between the Holuhraun volcanic event (a localized, sulfate-dominated aerosol source) and global aerosol-cloud interactions (ACI) remains unclear. If the models’ responses are context-specific, consider revising the title to reflect this limitation (e.g., “Global climate models fail to reproduce cloud cover response to volcanic aerosol”). Clarifying the generalizability to other aerosol types or regions would strengthen the analysis. For example, a good start point is to show the vertical profile of the cloud change in these experiment (or a simple high and low cloud decomposition) and compare it with the historical aerosol only simulations.\nFigure 1a shows CESM2 and CNRM-ESM with similar ACI values (if I understand correctly), yet Smith et al (2020) (https://acp.copernicus.org/articles/20/9591/2020/acp-20-9591-2020.html#&gid=1&pid=1) reports stronger ACI for CESM2 in CMIP6 (Table 6). This could due to many reasons, include the two major concerns that I raise above. So, it is good to understand the reason as a sanity check for you results overall.", "reviewer_3": "See uploaded review."}, "author_response": "1 Response to Reviewer #1 (manuscript NCOMMS-25-29057):\n2\n5 response to the massive injection of sulfur into the lower atmosphere associated with an\n6 effusive eruption in Iceland in 2014. The paper follows a similar methodology to reference 22,\n8 are fairly consistent in their errors in simulating this response. The models do relatively well\n9 on cloud optical depth responses, but underestimate the Twomey response and have almost no\n10 response in cloud fraction, compared to the satellite observations against which the models are\n11 tested. Trying many different parameterization schemes within one of the climate models,\n12 produces no significant change in these basic deficiencies.\n13\n16 have been carefully considered, and the manuscript has been revised accordingly. In particular,\n17 we have addressed the global representativeness of the Holuhraun-2014 eruption by comparing\n18 it with global cloud responses to anthropogenic aerosol emissions in the present-day period\n19 using CMIP6 outcomes. A new section in the Results has been added to discuss the global\n20 representativeness. Please find our detailed point-by-point responses below. A change-tracked\n21 revision of the manuscript has also been uploaded.\n22\n24 1) Perhaps a little more detail on the ability of the satellite measurements to distinguish Re,\n26 between Re, LWP and LCC, too.\n27\n30 cover (LCC) from the MODIS Terra & Aqua satellites provide a unique dataset of global and\n31 long-term consistent observations of aerosols and clouds1, representing one of the most widely\n32 used datasets for aerosol-cloud interaction studies (e.g., refs2, 3, 4, 5, 6, 7 and references therein).\n34 propagate to the analyses of aerosol-cloud interactions (ACI).\n35\n37 uncertainty. Because, Chen et al. (2022)7 demonstrated that uncertainties in MODIS retrievals\n38 can be effectively minimised in the derived ACI signals used ML-surrogate of MODIS:\n39 “Uncertainty in the MODIS retrievals can be decomposed into systematic errors and random\n1\n40 errors. Random errors are greatly suppressed by averaging over a geographical region of\n41 thousands of kilometres8, while systematic errors are largely cancelled when taking differences\n42 between MODIS and ML-MODIS2.”\n43\n45 is used in this study, is indeed due to aerosol perturbation rather than bias from MODIS errors.\n47 regions of Hawaii-volcanoes; they find no ACI signal in the upwind clean region but a strong\n49 signal was due to satellite error, we should expect similar signals for the downstream and\n50 upstream regions.\n51\n52\n54 upwind clean region. Source: figure is adapted from Figs. 2 and S2 in Chen et al. (2024)6\n55\n56\n58 satellite-derived observational constraints7.\n59\n“The challenge of confounding meteorology has been addressed in a recent work7,\nusing machine-learning-based counterfactual clouds without volcanic influence to contrast\nreal-world observations (i.e. clouds with volcanic influence). The detected ACI signals\neffectively minimise the noise from satellite observational uncertainty, because random\n2\nerrors in observations are mostly cancelled out by averaging across an extensive region\nspanning millions of square kilometres, and systematic errors are largely suppressed when\ncomparing differences between satellite and a machine-learned surrogate satellite7.This\nmethodology has enabled robust estimation of cloud susceptibility at a large scale from\nsatellite observations, making it possible to calibrate cloud susceptibility in GCMs.”\n60\n61\n62\n63 2) The paper calls for a continued effort to understand these discrepancies and improve their\n64 simulation compared the observations, as this eruption and the associated data comprise a\n65 unique natural experiment. The paper does not comment on the special character of the\n66 observed situation relative to the global cloud problem. It occurs in high latitudes over the\n67 ocean in the summer season in the vicinity of Iceland. The sulfate injection is huge. It might be\n68 that fine tuning simulations to this particular situation, may not serve particularly well for the\n69 global problem.\n70\n73 simulations to demonstrate the global representativeness of ACI in Holuhraun-2014 eruption.\n75 demonstrate the robustness of our analysis. Detailed discussion has been added in a new section\n76 in the Results part of the main text, see below:\n“Global representativeness and robustness of the Holuhraun eruption\nTo evaluate the global representativeness of the Holuhraun-2014 eruption case, we\ncompared its cloud regime distribution and cloud susceptibilities to global ones. Malavelle\net al.3 and Chen et al.7 have shown that the Holuhraun-2014 eruption occurred in a region\nencompassing the full range of cloud regimes, whose distribution is closely matched with the\nglobal distribution observed by satellites from 2002 to 2014 (see Extended Data Fig.1 in\nChen et al.7, copied to below). In addition, we assessed cloud susceptibilities at a global\nscale resulting from anthropogenic aerosol emissions throughout 2000 to 2014 in CMIP6\nsimulations (MPI-ESM-1-2-HAM and UKESM1-0-LL, equivalent to ECHAM6.3-HAM2.3\nand UKESM1 in this study, respectively). We compare CMIP6 simulations of “historical”\nand “hist-piAer” experiments, representing with and without anthropogenetic aerosol\ninfluence respectively; details are given in the Methods. The susceptibilities of clouds to\nanthropogenic aerosol emissions at the global scale are consistent with the Holuhraun-2014\neruption in our GCMs simulations (Fig. 1a). The consistency in both cloud regimes\ndistribution and cloud susceptibilities suggests that the Holuhraun-2014 eruption provides\na valid and representative analogue for understanding global cloud responses to aerosol\nperturbations due to human emissions in recent years.\nTo strengthen robustness of our results, we quantified model uncertainty caused by the\ninherently chaotic nature of cloud dynamics and radiative effects through ensemble\nsimulations. We used ECHAM6.3-HAM2.3 as a showcase model and explored uncertainty\nacross five representative experiments, each reflecting distinct cloud schemes and process\nsensitivities, with ten ensemble simulations per experiment. We find only a small degree of\nuncertainty is introduced by model internal variability. For cloud cover the ensemble\nuncertainty is a magnitude smaller than the gap between GCMs and the observation (Fig.2).\nFurthermore, we demonstrate the robustness in global representativeness using ensembles\nin CMIP6 simulations (three ensembles for each). Again, a negligible difference is found\nbetween CMIP6 ensembles (Fig.2). These well demonstrate the robustness of the key\nconclusions and its global representativeness in this study.\naerosol perturbations (i.e., cloud susceptibilities) from volcanic degassing at Holuhraun,\nIceland (October 2014), based on multiple model simulations with varying cloud schemes\nand process sensitivities, alongside observational constraints7. a Six GCMs from this study,\nrepresentative of the Holuhraun region behaviour, and two GCMs from the Coupled Model\nIntercomparison Project Phase 6 (CMIP6), representative of the global behaviour.”\n77\n78\n80\n81\n82\n83\n84\n85\n87 experiments that were used in this study, as shown below:\n“CMIP6 data for global cloud susceptibility\nGlobal cloud property changes (i.e., cloud susceptibility) in response to present-day\nanthropogenic aerosol emissions are calculated through CMIP6 simulations and compared\nwith Holuhraun-2014 eruption in this study to validate its global representativeness.\nAmong the six GCMs used in our study, only two models from CMIP6 provide complete\noutputs for both cloud microphysical and macro-physical properties: MPI-ESM-1-2-HAM\nand UKESM1-0-LL (equivalent to ECHAM6.3-HAM2.3 and UKESM1 in this study). We\nused paired “historical” and “hist-piAer” simulations (2000-2014) for MPI-ESM-1-2-\nHAM10, 11 and UKESM1-0-LL12, 13 from CMIP6. For each experiment, three ensemble runs\n(r1, r2, r3) are available and used to assess the robustness of the results. The “historical”\nexperiment simulates Earth’s climate from 1850 to 2014, including both natural and\nanthropogenic forcings. In contrast, the “hist-piAer” experiment follows the same setup but\nexcludes anthropogenic aerosols. Comparing these two experiments over the present-day\nperiod (2000-2014) allows us to isolate the influence of anthropogenic aerosols on global\ncloud properties. Consistent with Holuhraun-2014 simulations, monthly cloud variables\n(i.e. Nd, Re, LWP, CC) from “hist-piAer” and “historical” experiments are used to calculate\ncloud susceptibilities (i.e., -dlnR /dlnN , dlnLWP/dlnN , dlnCOD/dlnN , and\ne d d d\ndlnLCC/dlnN ). Note that liquid cloud cover is not available from the CMIP6 simulations,\nd\nso total cloud cover is used to calculate cloud cover response. Detailed information of\nCMIP6 simulations is given in Table 3.”\nTable 3 (newly added). Experiment information for selected CMIP6 models: MPI-ESM-1-2-HAM\nand UKESM1-0-LL.\nModel MPI-ESM-1-2-HAM UKESM1-0-LL\nExperiment ID “hist-piAer” “historical” “hist-piAer” “historical”\nActivity ID AerChemMIP CMIP AerChemMIP CMIP\nInstitution ID HAMMOZ- HAMMOZ- MOHC MOHC\nConsortium Consortium\nNominal 250 km 250 km 250 km 250 km\nResolution\nFrequency Monthly Monthly Monthly Monthly\nVariant label r1i1p1f1 r1i1p1f1 r1i1p1f2 r1i1p1f2\n[data version] [20190627], [20190627], [20190813], [20190406],\n6\nr2i1p1f1 r2i1p1f1 r2i1p1f2 r2i1p1f2\n[20190627], [20190627], [20191104], [20190502],\nr2i1p1f1 r3i1p1f1 r3i1p1f2 r3i1p1f2\n[20190627 [20190627 [20200128] [20190502]\n/20190628]#, /20191218]*\nr3i1p1f1\n[20190627\n/20191218]*\n#Note: data versions are 20190627 for Nd, Re, LWP and 20190628 for CC.\n*Note: data versions are 20190627 for Nd, Re, LWP and 20191218 for CC.\n88\n89\n90 3) Also, I believe the cloud fractions in this location and season are very high, so that the\n91 logarithmic error seen here is not that significant in a real sense, and maybe not that robustly\n92 measured. That said, however, it well might be that the inability to simulate these data\n93 constitutes a fundamental inability of climate model parameterizations to simulate cloud\n94 fraction responses to condensation nuclei changes. I am not sure which it is.\n95\n98 are higher than the multi-year global annual averages (30% over ocean) from 2000 to 2020\n99 (excluding 2014). We chose and still prefer to use the logarithmic form in our analysis because\n100 it is an appropriate and better way to evaluate cloud susceptibility to aerosol perturbation, for\n101 the following reasons:\n102\n103 1) The logarithmic form implies the relative change, for example:\nN\n104 dlnN =-ln=N lnN ln( d1)\nd d1 d0 N\nd0\n106\n109 (preindustrial) N , can vary widely (a 33-63 #/cm3 range across nine global climate\nd\n110 models averaged over the global ocean areas14). However, if all models were to follow\n111 a similar aerosol-cloud interaction principle (or similar cloud susceptibility, e.g.:\n112 dlnLCC/dlnNd), then the relative changes of cloud optical depth and cloud cover (and\n113 hence the reflected sunlight) will be proportional to the relative changes of N , but\nd\n114 relatively insensitive to the very uncertain baseline.\n115\n7\n116 2) Given the aforementioned importance of relative change, the logarithmic form (hence\n117 relative changes) is commonly used in many previous modelling and observational\n118 aerosol-cloud-climate studies. For example, Ghan et al. (2016)14 compared the\n119 performance of nine global climate models using the logarithmic form; Rosenfeld et al.\n120 (2019)15 and Toll et al. (2019)2 used observationally-derived logarithmic terms to\n121 discuss aerosol-cloud interactions.\n122\n124 more readily for comparison with other studies, and easier to be directly used/compared by the\n125 community in future studies.\n126\n129 analysis points towards a fundamental inability of climate model parameterisations to simulate\n130 cloud cover responses to aerosol changes, and this is a critical common gap with global\n131 representativeness rather than a context-specified issue.\n132\n133\n134\n136\n138\n141\n142\n143 137: Do you really want to say ‘our previous study’ here in relation to reference 22? I don’t\n144 think the author list is identical. It also raises the question of what is really new here also.\n145\n148 involved in this study, we agree that the author list is not identical between Malavelle et al.\n149 (2017)3 and this study.\n150\n151\n152 192: When you say cloud albedo effect, to which of the feedbacks in Figure 1 are you referring.\n8\n156 as an indicator of cloud albedo; however, we agree with the reviewer that they are not the same\n157 and albedo is not directly shown in Figure 1. We therefore have corrected it to avoid the\n158 confusion.\n159\n160\n161\n162\n163\n9\n165 1. Platnick S, et al. The MODIS Cloud Optical and Microphysical Products: Collection 6\n168\n169 2. Toll V, Christensen M, Quaas J, Bellouin N. Weak average liquid-cloud-water response\n170 to anthropogenic aerosols. Nature 572, 51-55 (2019).\n171\n172 3. Malavelle FF, et al. Strong constraints on aerosol–cloud interactions from volcanic\n173 eruptions. Nature 546, 485-491 (2017).\n174\n175 4. Quaas J, et al. Robust evidence for reversal of the trend in aerosol effective climate\n176 forcing. Atmos Chem Phys 22, 12221-12239 (2022).\n177\n178 5. Yuan T, et al. Observational evidence of strong forcing from aerosol effect on low cloud\n179 coverage. Science Advances 9, eadh7716 (2023).\n180\n181 6. Chen Y, et al. Substantial cooling effect from aerosol-induced increase in tropical\n182 marine cloud cover. Nature Geoscience 17, 404-410 (2024).\n183\n184 7. Chen Y, et al. Machine learning reveals climate forcing from aerosols is dominated by\n185 increased cloud cover. Nature Geoscience 15, 609-614 (2022).\n186\n187 8. Grosvenor DP, et al. Remote Sensing of Droplet Number Concentration in Warm\n190\n191 9. Oreopoulos L, Cho N, Lee D, Kato S. Radiative effects of global MODIS cloud regimes.\n193\n194 10. Neubauer D, et al. HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared\n195 for CMIP6 CMIP historical. Earth System Grid Federation.\n196 https://doi.org/10.22033/ESGF/CMIP6.5016. (2019).\n197\n198 11. Neubauer D, et al. HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared\n199 for CMIP6 AerChemMIP hist-piAer. Earth System Grid Federation.\n200 https://doi.org/10.22033/ESGF/CMIP6.5007. (2019).\n201\n202 12. Tang Y, et al. MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP\n203 historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6113.\n204 (2019).\n205\n206 13. O'Connor F. MOHC UKESM1.0-LL model output prepared for CMIP6 AerChemMIP\n207 hist-piAer. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6062.\n208 (2019).\n209\n210 14. Ghan S, et al. Challenges in constraining anthropogenic aerosol effects on cloud\n211 radiative forcing using present-day spatiotemporal variability. Proceedings of the\n213\n10\n214 15. Rosenfeld D, Zhu Y, Wang M, Zheng Y, Goren T, Yu S. Aerosol-driven droplet\n215 concentrations dominate coverage and water of oceanic low-level clouds. Science 363,\n216 eaav0566 (2019).\n217\n218\n11\n2\n5 during the October 2014 Holuhraun volcanic event. The results are intriguing, and the\n6 manuscript is concise. However, I believe significant improvements in methodology and\n7 analysis are needed before publication. My detailed concerns are outlined below.\n8\n11 have been carefully considered, and the manuscript has been revised accordingly. In particular,\n12 we have addressed the global representativeness of the Holuhraun-2014 eruption by comparing\n13 it with global cloud responses to anthropogenic aerosol emissions in the present-day period\n14 using CMIP6 data. Model uncertainty has been evaluated through ensemble runs in a showcase\n15 model, ECHAM6.3-HAM2.3 and CMIP6. A new section discussing this comparison has been\n16 added to the Results. Please find our detailed point-by-point responses below. A change-tracked\n17 revision of the manuscript has also been uploaded.\n18\n19\n21 1) The analysis relies on a single month of model simulations, which may introduce significant\n22 noise due to the inherently chaotic nature of cloud dynamics and radiative effects, especially\n23 over short timescales. To strengthen robustness, I recommend quantifying uncertainty through\n24 ensemble simulations. If unavailable, multi-year control experiments could serve as a proxy to\n25 estimate noise magnitude.\n26\n29 to quantify uncertainty of model internal variability and thus strengthen robustness of results.\n32 better represent the uncertainty in diverse modelling configurations, we performed\n34 processes (each with 10 ensembles). We found a minor uncertainty in all ECHAM6.3-HAM2.3\n35 and CMIP6 ensembles. Detailed discussion has been added in a new section (“Global\n36 representativeness and robustness of the Holuhraun eruption”) in the Results of main text, as\n37 shown following point-2.\n38\n39\n1\n40 2) The study’s title and conclusions suggest broader relevance, but the connection between the\n42 cloud interactions (ACI) remains unclear. If the models’ responses are context-specific,\n43 consider revising the title to reflect this limitation (e.g., “Global climate models fail to\n44 reproduce cloud cover response to volcanic aerosol”). Clarifying the generalizability to other\n45 aerosol types or regions would strengthen the analysis. For example, a good start point is to\n46 show the vertical profile of the cloud change in these experiment (or a simple high and low\n47 cloud decomposition) and compare it with the historical aerosol only simulations.\n48\n51 in the new section: “Global representativeness and robustness of the Holuhraun eruption”, the\n54 “Challenges in global climate models to represent cloud response to aerosol: insights from\n55 volcanic eruptions”.\n56\n58 cloud properties (Re, LWP, LCC) changes as a response to an increase in Nd. These cloud\n59 adjustments happened after activation of aerosol particles to serve as cloud condensation nuclei\n60 (CCN). The CCN can come from any aerosol type, and is not specific to volcanic aerosol.\n62 increased Nd.\n63\n“Global representativeness and robustness of the Holuhraun eruption\nTo evaluate the global representativeness of the Holuhraun-2014 eruption case, we\ncompared its cloud regime distribution and cloud susceptibilities to global ones. Malavelle\net al.1 and Chen et al.2 have shown that the Holuhraun-2014 eruption occurred in a region\nencompassing the full range of cloud regimes, whose distribution is closely matched with the\nglobal distribution observed by satellites from 2002 to 2014 (see Extended Data Fig.1 in\nChen et al.2, copied to below). In addition, we assessed cloud susceptibilities at a global\nscale resulting from anthropogenic aerosol emissions throughout 2000 to 2014 in CMIP6\nsimulations (MPI-ESM-1-2-HAM and UKESM1-0-LL, equivalent to ECHAM6.3-HAM2.3\nand UKESM1 in this study, respectively). We compare CMIP6 simulations of “historical”\nand “hist-piAer” experiments, representing with and without anthropogenetic aerosol\ninfluence respectively; details are given in the Methods. The susceptibilities of clouds to\nanthropogenic aerosol emissions at the global scale are consistent with the Holuhraun-2014\neruption in our GCMs simulations (Fig. 1a). The consistency in both cloud regimes\ndistribution and cloud susceptibilities suggests that the Holuhraun-2014 eruption provides\na valid and representative analogue for understanding global cloud responses to aerosol\nperturbations due to human emissions in recent years.\nTo strengthen robustness of our results, we quantified model uncertainty caused by the\ninherently chaotic nature of cloud dynamics and radiative effects through ensemble\nsimulations. We used ECHAM6.3-HAM2.3 as a showcase model and explored uncertainty\nacross five representative experiments, each reflecting distinct cloud schemes and process\nsensitivities, with ten ensemble simulations per experiment. We find only a small degree of\nuncertainty is introduced by model internal variability. For cloud cover the ensemble\nuncertainty is a magnitude smaller than the gap between GCMs and the observation (Fig.2).\nFurthermore, we demonstrate the robustness in global representativeness using ensembles\nin CMIP6 simulations (three ensembles for each). Again, a negligible difference is found\nbetween CMIP6 ensembles (Fig.2). These well demonstrate the robustness of the key\nconclusions and its global representativeness in this study.”\naerosol perturbations (i.e., cloud susceptibilities) from volcanic degassing at Holuhraun,\nIceland (October 2014), based on multiple model simulations with varying cloud schemes\nand process sensitivities, alongside observational constraints2. a Six GCMs from this study,\nrepresentative of the Holuhraun region behaviour, and two GCMs from the Coupled Model\nIntercomparison Project Phase 6 (CMIP6), representative of the global behaviour.\nFigure 2 (newly added). Intercomparison of liquid cloud properties response to aerosol\nperturbations (i.e., cloud susceptibilities) from Holuhraun-2014 eruption in ECHAM6.3-\nHAM2.3 with different cloud schemes and sensitivities (each experiment with ten ensemble\nruns, ensemble mean values and one standard deviation are shown) and CMIP6 data (MPI-\nESM-1-2-HAM and UKESM1-0-LL, each with three ensemble runs). The violin plots\nrepresent 90% confidence intervals of machine-learning-based observational constraints2,\nwith the inner box showing the 25th, 50th, and 75th percentiles. Note: the three ensemble\nruns for each CMIP6 model are all plotted, but they overlap with each other due to negligible\ndifferences.\n64\n65\n4\n66\n68\n73\n75 experiments that were used in this study, as shown below:\n“CMIP6 data for global cloud susceptibility\nGlobal cloud property changes (i.e., cloud susceptibility) in response to present-day\nanthropogenic aerosol emissions are calculated through CMIP6 simulations and compared\nwith Holuhraun-2014 eruption in this study to validate its global representativeness.\nAmong the six GCMs used in our study, only two models from CMIP6 provide complete\noutputs for both cloud microphysical and macro-physical properties: MPI-ESM-1-2-HAM\nand UKESM1-0-LL (equivalent to ECHAM6.3-HAM2.3 and UKESM1 in this study). We\nused paired “historical” and “hist-piAer” simulations (2000-2014) for MPI-ESM-1-2-\nHAM4, 5 and UKESM1-0-LL6, 7 from CMIP6. For each experiment, three ensemble runs (r1,\nr2, r3) are available and used to assess the robustness of the results. The “historical”\nexperiment simulates Earth’s climate from 1850 to 2014, including both natural and\nanthropogenic forcings. In contrast, the “hist-piAer” experiment follows the same setup but\nexcludes anthropogenic aerosols. Comparing these two experiments over the present-day\nperiod (2000-2014) allows us to isolate the influence of anthropogenic aerosols on global\ncloud properties. Consistent with Holuhraun-2014 simulations, monthly cloud variables\n(i.e. Nd, Re, LWP, CC) from “hist-piAer” and “historical” experiments are used to calculate\ncloud susceptibilities (i.e., -dlnR /dlnN , dlnLWP/dlnN , dlnCOD/dlnN , and\ne d d d\ndlnLCC/dlnN ). Note that liquid cloud cover is not available from the CMIP6 simulations,\nd\nso total cloud cover is used to calculate cloud cover response. Detailed information of\nCMIP6 simulations is given in Table 3.”\nTable 3 (newly added). Experiment information for selected CMIP6 models: MPI-ESM-1-2-HAM\nand UKESM1-0-LL.\nModel MPI-ESM-1-2-HAM UKESM1-0-LL\nExperiment ID “hist-piAer” “historical” “hist-piAer” “historical”\nActivity ID AerChemMIP CMIP AerChemMIP CMIP\nInstitution ID HAMMOZ- HAMMOZ- MOHC MOHC\nConsortium Consortium\nNominal 250 km 250 km 250 km 250 km\nResolution\nFrequency Monthly Monthly Monthly Monthly\nVariant label r1i1p1f1 r1i1p1f1 r1i1p1f2 r1i1p1f2\n[data version] [20190627], [20190627], [20190813], [20190406],\n6\nr2i1p1f1 r2i1p1f1 r2i1p1f2 r2i1p1f2\n[20190627 [20190627], [20191104], [20190502],\nr3i1p1f1 r3i1p1f2 r3i1p1f2\n[20190627 [20200128] [20190502]\n/20191218]*\n#Note: data versions are 20190627 for Nd, Re, LWP and 20190628 for CC.\n*Note: data versions are 20190627 for Nd, Re, LWP and 20191218 for CC.\n76\n77 3) Figure 1a shows CESM2 and CNRM-ESM with similar ACI values (if I understand\n78 correctly), yet Smith et al (2020) (https://acp.copernicus.org/articles/20/9591/2020/acp-20-\n79 9591-2020.html#&gid=1&pid=1) reports stronger ACI for CESM2 in CMIP6 (Table 6). This\n80 could due to many reasons, include the two major concerns that I raise above. So, it is good to\n81 understand the reason as a sanity check for you results overall.\n82\n86 the reviewer that CEMS2’s ACI-induced shortwave radiative forcing is about three times larger\n87 than the one from CNRM-ESM. While ACI in our study refers to the interactions themselves\n88 between aerosol and cloud, i.e. changes in clouds due to aerosol.\n89\n91 (2020) and our study may point towards an important ACI uncertainty pathway in the GCMs.\n93 below:\n“Due to the complexity of clouds and climate systems, even a perfect simulation of cloud\nsusceptibilities still cannot guarantee an unbiased estimate of ACI radiative forcing, this is\nbecause the efficiency in radiative effect of clouds varies significantly between GCMs; for\nexample, CESM2 shows similar cloud susceptibilities to CNRM-ESM2-1 (Fig. 1a), but\nshows some of three times larger ACI radiative forcing8. A recent study highlighted that\nhigh equilibrium climate sensitivity (³ 2.93 K) is needed to reproduce the trend in the\nobserved Earth energy imbalance, where changes of clouds are critical for climate\nassessments but highly uncertain9. Therefore, radiative transfer processes of clouds and\ncloud feedback following ACI radiative forcing are also key to improving climate\nprojections.”\n94\n7\n96 1. Malavelle FF, et al. Strong constraints on aerosol–cloud interactions from volcanic\n97 eruptions. Nature 546, 485-491 (2017).\n98\n99 2. Chen Y, et al. Machine learning reveals climate forcing from aerosols is dominated by\n100 increased cloud cover. Nature Geoscience 15, 609-614 (2022).\n101\n102 3. Oreopoulos L, Cho N, Lee D, Kato S. Radiative effects of global MODIS cloud regimes.\n104\n105 4. Neubauer D, et al. HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared\n106 for CMIP6 CMIP historical. Earth System Grid Federation.\n107 https://doi.org/10.22033/ESGF/CMIP6.5016. (2019).\n108\n109 5. Neubauer D, et al. HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared\n110 for CMIP6 AerChemMIP hist-piAer. Earth System Grid Federation.\n111 https://doi.org/10.22033/ESGF/CMIP6.5007. (2019).\n112\n113 6. Tang Y, et al. MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP\n114 historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6113.\n115 (2019).\n116\n117 7. O'Connor F. MOHC UKESM1.0-LL model output prepared for CMIP6 AerChemMIP\n118 hist-piAer. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6062.\n119 (2019).\n120\n121 8. Smith CJ, et al. Effective radiative forcing and adjustments in CMIP6 models. Atmos\n123\n124 9. Myhre G, Hodnebrog Ø, Loeb N, Forster PM. Observed trend in Earth energy\n125 imbalance may provide a constraint for low climate sensitivity models. Science 388,\n126 1210-1213 (2025).\n127\n128\n8\n2\n6\n8\n10 structural and parameter variations for one of them) with measures of these interactions inferred\n11 from observed cloud responses to an effusive volcano eruption, which generated large sulfur\n12 dioxide emissions. The resulting sulfate plume spread broadly over the North Atlantic, so\n13 analyzing that area over a month covers a range of synoptic regimes to bolster the significance\n14 of the results. The results provide an important evaluation of the ability of climate models to\n15 simulate ACI. Publication is recommended. Suggested revisions follow.\n16\n19 have been carefully considered, and the manuscript has been revised accordingly. In particular,\n20 we have added discussions on the prognostic cloud scheme in the results part and updated “data\n21 and code availability”. Please find our detailed point-by-point responses below. A change-\n22 tracked revision of the manuscript has also been uploaded.\n23\n24\n26 1) Four measures of ACI are presented. Except for the cloud cover susceptibility and the liquid\n27 water path susceptibility for one of the models, all lie in or very close to the observed estimates.\n29 be retitled to capture better the balance of results.\n32 have changed to a new title: “Challenges in global climate models to represent cloud response\n33 to aerosol: insights from volcanic eruptions”.\n34\n35 2) l. 117: LCC is presumably a fraction, so, unlike re and LWP which have distinct in-cloud\n36 values, it is a grid property and should not be included with the others listed as in-cloud.\n39\n1\n40 3) l. 138: The meaning of the phrase “comparing…observations” is not clear.\n43 “These overly strong LWP adjustments in GCMs compared to machine-learning-based\n44 observational constraints1 are in line with Malavelle et al.2, who compared four GCMs with\n45 climatological anomalies from satellite observations2.”\n46\n47\n48 4) l. 173: Do any of the models included in this study use a process-based, prognostic cloud\n49 cover parameterization along the lines of Tiedkte (1989, Mon. Wea. Rev.), which is used, for\n50 example, in GFDL CM4 (Zhao et al., 2018, 10.1002/2017MS001209; Held et al., 2019,\n51 10.1029/2019MS001829}? It is possible that the prognostic scheme in ECHAM6.3-HAM2.3\n52 is of this nature. In revision, discuss this among the structural aspects of the models.\n55 sub-saturation and supersaturation with respect to ice separately in the cloud-free and cloudy\n56 air. So, it is along the lines of the scheme used in GFDL CM4 as you correctly mentioned. We\n57 have added words to discuss the prognostic cloud schemes as suggested, see below.\n58\n59 “Multi-scheme ACI structural uncertainty\n60 …\n63 addition, we used the Prognostic cloud cover scheme (PROG_CC)3, a Tiedtke-like scheme4\n64 similar to that in GFDL’s CM4.0 Climate Model5, 6, which simulates cloud fraction\n65 prognostically based on in-cloud water vapour and convective activity and uses an updated\n66 cloud microphysics scheme. …\n67\n69 reasonable simulation of the COD response. The reasonable COD response is again due to the\n70 compensation of biases in the Twomey effect and the LWP adjustment. … Nevertheless, none\n71 of the cloud schemes reproduce the observed increase in cloud cover (dlnLCC/dlnN ), even the\nd\n72 cloud cover schemes that depend on liquid water (XR) or are prognostic (PROG_CC) still\n73 underestimate dlnLCC/dlnN , suggesting a major structural uncertainty in current cloud cover\nd\n74 schemes. Further investigations of the cloud cover schemes and relevant cloud processes are\n75 imperative to clarify cloud cover response to aerosol changes and to inform model\n76 improvements.”\n77\n78\n2\n79 5) ll. 238-240: Nordling et al. (2024, 10.5194/acp-24-869-2024) is a parameterization of the\n80 type described here and should be referenced.\n83\n85 “A combination of machine learning and the results of high-resolution cloud-resolving models\n86 would be a plausible way forward for improving cloud parameterisations in global models to\n87 better represent the cloud cover’s responses to aerosol changes7. It is important to note that\n88 clouds respond differently to aerosol changes under various meteorological conditions8, 9 (e.g.,\n89 precipitation-dominant vs entrainment-dominant stratocumulus regimes) and background\n90 aerosol levels10, so more observational constraints in diverse conditions are critical to validate\n91 and calibrate cloud sensitivities in future.”\n92\n93\n94 6) The data availability section includes ECHAM-HAMOZ but not the other models.\n97 which will be published with the manuscript. We have updated “Data Availability” and “Code\n99\n100 “Data Availability\n102 (https://doi.org/10.5281/zenodo.16926288). The MODIS cloud products from Aqua\n103 (MYD08_L3) and Terra (MOD08_L3) are openly available from the Atmosphere Archive and\n106 data for MPI-ESM-1-2-HAM and UKESM1-0-LL models are openly available through the\n108 ceda/).”\n109\n110 “Code Availability\n112 to the scientific community under the HAMMOZ Software License Agreement at\n113 https://redmine.hammoz.ethz.ch/projects/hammoz/wiki/2_How_to_get_the_sources. The\n116 and code of each module of CNRM-ESM2-1 are documented on the CNRM website\n3\n117 (https://www.umr-cnrm.fr/cmip6/). CAM5.3-Oslo (the atmospheric and aerosol module of\n4\n120 1. Chen Y, et al. Machine learning reveals climate forcing from aerosols is dominated by\n121 increased cloud cover. Nature Geoscience 15, 609-614 (2022).\n122\n123 2. Malavelle FF, et al. Strong constraints on aerosol–cloud interactions from volcanic\n124 eruptions. Nature 546, 485-491 (2017).\n125\n126 3. Muench S, Lohmann U. Developing a Cloud Scheme With Prognostic Cloud Fraction\n127 and Two Moment Microphysics for ECHAM-HAM. Journal of Advances in Modeling\n129\n130 4. Tiedtke M. Representation of Clouds in Large-Scale Models. Monthly Weather\n132\n133 5. Zhao M, et al. The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2.\n136\n137 6. Held IM, et al. Structure and Performance of GFDL's CM4.0 Climate Model. Journal\n138 of Advances in Modeling Earth Systems 11, 3691-3727 (2019).\n139\n140 7. Nordling K, et al. Technical note: Emulation of a large-eddy simulator for\n141 stratocumulus clouds in a general circulation model. Atmos Chem Phys 24, 869-890\n142 (2024).\n143\n144 8. Prabhakaran P, Hoffmann F, Feingold G. Evaluation of Pulse Aerosol Forcing on\n146 the Atmospheric Sciences 80, 1585-1604 (2023).\n147\n148 9. Chen Y, et al. Substantial cooling effect from aerosol-induced increase in tropical\n149 marine cloud cover. Nature Geoscience 17, 404-410 (2024).\n150\n151 10. Carslaw KS, et al. Large contribution of natural aerosols to uncertainty in indirect\n152 forcing. Nature 503, 67-71 (2013).\n153\n154\n5\n2\n5 from volcanic eruptions. By Wang et al.\n6\n8 through the revised paper. The main point of the paper is that models simulate the AOD change\n9 associated with the 2014 effusive eruption well, but do not get the apportionment between Re,\n11 aerosols. The latter point is based on an acceptance of the Chen et al. (2022) paper, in which\n12 machine learning is used to predict these quantities as measured by MODIS, using\n13 meteorological data from ER5. I am not sure this result has been independently verified.\n15 although I assume many 1-degree grid boxes. I am left feeling a bit skeptical about whether the\n17 to. I think this dependence should be a bit more emphasized in the paper. The paper makes it\n18 seem like the satellite data are rock solid, but there are major uncertainties in both the\n19 observational apportionment and its fitting with the ML method and these things are critical to\n20 the conclusions. It seems a bit worrisome that straightforward comparison of climatologies\n21 reveals no change in CF in 2014, but using the ML residual method introduces one.\n22\n24 changes compared to MODIS, but previous work with MODIS data by McCoy and\n26 or fraction. Malavelle et al(2017) use MODIS data and state that “cloud amount changes are\n27 undetectable” What changed in the interim to make cloud amount changes a significant effect?\n29 monthly mean data to estimate the meteorological effects on cloud properties. Although\n30 straightforward average comparisons suggest no change in LWP or CF between September or\n32 increase in cloud fraction in October 2014 in response to aerosols. This seems to suggest that\n33 the ML model predicted a decrease in CF in October 2014 that was not observed and then\n34 attributed the difference to the aerosols. I have not studied the methods thoroughly, but I am\n35 somewhat unconvinced, particularly since the September data were not used, which would\n36 have given an independent data point. I find the simple result that no significant difference in\n37 cloud fraction was observed in 2014 more compelling than the residual of a ML computation\n38 based on monthly data (20 months total) and the residual derived therefrom. What am I missing?\n39\n1\n42 about the increase of cloud fraction (CF) in Chen et al. (2022) using machine-learning (ML),\n43 while Malavelle et al (2017) found “cloud amount changes are undetectable” using\n44 climatological anomaly. We will clearly explain this with multiple lines of evidence, shown as\n45 below.\n46\n47 i) Discrepancy between Malavelle et al. (2017) and Chen et al. (2022)\n50 not been used for cloud fraction analysis.\nCited from Page-98 in the MODIS User Guide (Platnick et al., 2018):\n“In C5, those pixels restored to clear by the Clear Sky Restoral algorithm\n(i.e., those with flag CSR=2) were inadvertently excluded from the\ndenominator in the calculation of fraction. In C6, the retrieval fraction is\nnow correctly defined …”\n51\n53 fraction (same approach as used in (Malavelle et al., 2017)), a strong increase of CF can also\n54 be clearly seen across the whole north Atlantic (see Figure below).\nExtended Data Fig. 3c from Chen et al. (2022) | Climatological anomaly of CF\nobserved by MODIS-Aqua in October 2014.\n55\n57 ease the concern of reviewer. In Chen et al. (2022), they have explicitly noted the caveat in CF\n58 in C5 and the correction of the latest C6 version (text provided below).\nCited from Method section in Chen et al. (2022):\n“An inadvertent artifact in the calculations of cloud fraction (derived from\nCloud Optical Property) in Collection 5.1 has also been removed in\n2\n61 2019).”\n62\n64 been discussed in detail in Chen et al. (2022) Supplementary Information Section-S2. We do\n65 not include this discussion or clarification in our manuscript, because our study is a modelling\n66 study rather than satellite observation analysis, and because this information has already been\n67 provided in Chen et al. (2022) and we feel it is not necessary to duplicate in this manuscript.\n69 observations have. Following reviewer suggestion, we have acknowledged the uncertainty and\n70 limitation of this study in the manuscript, revised text provided in the Point-3 response.\n71\n73 liquid water path (LWP) retrieval in C5 is still reliable, Chen et al. (2022) find consistent nearly\n74 zero response in LWP as in Malavelle et al. (2017); therefore, the main conclusion of Malavelle\n75 et al. (2017) of weak LWP response is still validated.\n76\n77 ii) Independent data points as evidence: September 2014 and Hawaii volcanoes.\n79 data point to further validate the increase of CF from ML. This analysis has been performed in\n81 climatological anomaly approaches (see figure below), which is in line with Oct. 2014 case;\n82 see detailed discussion in Chen et al. (2022) -- Supplementary Information Section-S1. Here,\n83 we would like to focus on Oct. 2014, this is in line with Malavelle et al. (2017) who also focus\n84 on Oct. 2014 and only some Sep. 2014 discussion in their supplement; this is because October\n85 is more representative of the global distribution of cloud regimes due to more limited areal\n86 extent of the plume in September, and because noise from continental pollution in lower part\n87 of the domain in September (Malavelle et al., 2017).\nExtended Data Fig. 9 in Chen et al. (2022) | Cloud fraction (CF) responses to Holuhraun\nvolcanic aerosol in September 2014.\nHawaii, also provide independent data point to prove the fidelity of ML approach and\nincrease of CF. As we provided in the first-round review, Chen et al. (2024) applied the same\nmachine-learning approach to the trade wind regions of Hawaii-volcanoes; they find no ACI\nsignal in the upwind clean region but a strong ACI signal only in the downwind region\npolluted by volcanic aerosols (see Fig. R1). This strongly validates the fidelity of ML\napproach for detecting CF increase.\nFigure R1. Cloud susceptibility to volcanic aerosol: (a) downstream polluted region; (b)\nupstream clean region. Ratio = MODIS / ML-MODIS. Black boxplots are the validation\nagainst non-eruption years; red boxplots are the detected ACI signals. Monto-Carlo 648\nensembles are analysed. Source: figure is adapted from Figs. 2 and S2 in Chen et al. (2024).\nIn summary, multiple lines of evidence all suggest that ML detected increase of CF is robust,\nincluding i) the consistent increase of CF using both ML and climatological approach (same\napproach as in Malavelle et al. (2017)), ii) strong increase of CF in September 2014 from\nHoluhraun volcanic aerosol, iii) strong increase of CF in downstream regions of Kilauea\naerosol plume but insignificant change of CF in upstream clean region.\n88\n89\n90\n4\n92 1) Abstract concludes with “, and support Net-Zero Policymaking” makes it seems like the\n93 purpose of scientific research is to support a particular policy option. I strongly recommend\n94 against using this phrasing.\n97\n98 2) 45-46 “none of state-of-the-art climate models are able to fully account for this recent\n99 warming”. This statement is hyperbolic and makes it seem like we don’t know what we are\n100 doing, and it also is unbalanced in a similar way to people who said warming had stopped after\n101 1998. There was a big jump in 2023 from 2022, but equally remarkable is the small amount of\n102 warming from 1998 to 2014. Smoothing over these anomalies, the overall trend has continued\n103 apace at about 0.2K/decade, which is what the models are predicting (if this trend is extraplated\n104 from 2024 to 2100 it reaches about 3K anomaly in 2100, in good agreement with central\n105 estimates for a scenario that does not rapidly reduce CO2 emissions.). One should not expect\n106 models to exactly explain one year’s anomaly because of the major influences of natural\n107 variability and the uneven, sometimes stepwise response to human influences. The statement\n108 that models cannot “fully account” for the anomaly in 2023 is probably strictly true for every\n109 year in the record. It is not helpful to make things seem more uncertain than they probably are.\n112\n113\n114 3) 82 “both positive and negative results reported” Also a lot of zero effect on LWP and CF.\n116 background. Evidence that background levels decrease the effect of emissions of sulfate and\n117 the effect is also not linear, diminishing for very large sulfate loading. Therefore one case like\n121 positive to negative reported”. We have also added the discussion of dependence on the\n122 meteorology and aerosol source strength compared to background, as well as acknowledge that\n123 one case like Iceland volcano cannot solve all problem and studies of more opportunistic\n124 experiments are needed, such as IMO shipping emission reduction since 2020 (Gettelman et\n125 al., 2024). The revised text read as below:\n“Our study is not without limitation. The observational constraints may suffer\nfrom uncertainty in satellite observations, despite that satellites are currently the\nonly measure to provide continuous long-term cloud cover observations over a\n5\nlarge scale. In future studies, using multiple satellites along with machine-\nlearning development could further reduce uncertainty in the observations of\ncloud cover and cloud susceptibility to better constrain global climate models.\nThe nonlinearity of cloud response to aerosol means that cloud susceptibilities\ncan also depend on meteorology (Gettelman et al., 2015; Chen et al., 2024) and\nthe strength of aerosol source compared to background (Gettelman et al., 2015;\nCarslaw et al., 2013). For example, Gettelman et al. (2015) performed sensitivity\nsimulations of Holuhraun-2014 using a GCM and demonstrated that\nmeteorological diversity could introduce up to 30% difference in ACI radiative\nforcing, and that the forcing is not proportional to the change in aerosol\nemissions. This suggests the need of more opportunistic experiment studies, such\nas IMO shipping emission reduction since 2020 (Gettelman et al., 2024),\ntogether with the Holuhraun-2014 to have a holistic investigation of ACI in\ndiverse global conditions.”\n126\n127\n128 4) 95-96 Are there references for this statement that the observational uncertainty has been\n129 removed? Also, explain why the observational response of CF is now large, compared to earlier\n130 observational work that suggested it is small. I would be skeptical if it is based on AI\n131 methodologies that do not have a strong physical basis and explanation. The LWC and CF\n132 changes do now show up in simple averages, only the Twomey effect.\n135 well accepted and practised in many disciplines, and this is why large sample size is always\n136 recommended for robust studies in almost all disciplines although need to compromise with\n137 the required resources (Schillaci and Schillaci, 2022). Specific to MODIS, for example, (Wei\n138 et al., 2020) demonstrate that by aggregating relatively larger sample size in 10 km resolution\n139 shows higher accuracy than 3 km resolution data. In Chen et al. (2022), they looked into and\n140 aggregate CF over a region of about 3000 km × 6000 km (over 1000 samples), suggesting a\n141 large reduce of random uncertainty. Again, if the detected CF signal was dominated by the\n142 satellite uncertainty, can we expect signals as shown in Fig. R1? Absolutely, not. Because we\n143 would expect similar signals in both downwind and upwind region. However, what we see is:\n144 very large ACI signals only in the volcanic aerosol plume downwind region (Fig. R1a)\n145 contrasting with near zero signal in the upwind clean region (Fig. R1b).\n147 they claimed physical basis, and this is exactly why we need to use and improve physical-based\n148 models (such as this study) to understand the phenomenon observed in Chen et al. (2022). No\n149 strong physical basis does not hamper ACI signal detection in Chen et al. (2022). The reviewer\n150 state that (s)he did not study Chen et al. (2022) method thoroughly, below we try to explain\n6\n151 their method in plain language as best as we can. In Chen et al. (2022), they use ML with\n152 extensive meteorological parameters as inputs to build the counterfactual clouds for given\n153 meteorology, i.e. ML-MODIS. Then, ML-MODIS for 2014 could be understood as the\n154 counterfactual “experimental” case, and ML-MODIS for other years without volcano as the\n155 counterfactual “control” cases. The “control” cases are well validated against MODIS\n156 observations (e.g., black boxplots in Fig. R1a -- lying on 1.0 line means well validation) with\n157 hundreds Monte-Carlo ensembles; however, the “experimental” case show a large “gap”\n158 between ML-MODIS and MODIS (e.g. red boxplots in Fig. R1a). This means the observed\n159 “gap” in the “experimental” case cannot be explained by the meteorology, therefore rejecting\n160 the null hypothesis: this “gap” is due to meteorology. The major difference between “control”\n161 and “experimental” is the volcanic aerosol, therefore, the “gap” needs to be explained by\n162 volcanic ACI. This ML approach overcomes the challenge in climatological anomaly approach\n163 -- we do not know if the anomaly is due to aerosol or meteorology (since meteorology in 2014\n164 is not identical to the climatological average). Hope this is helpful for understanding the logic\n165 of ACI signal detection in Chen et al. (2022), where physical basis is not the key.\n166\n168 the calculations of cloud fraction in Collection 5.1 (Platnick et al., 2018), which in retrospect\n169 should have not been used for cloud fraction analysis.\n170\n171 5) 101 – “we suggest that the wider climate community takes the Holuhraun-2014 eruption as\n172 an anchor case” Several papers have already investigated the eruptions effect on clouds and\n173 compared with models, so what is being suggested is not new. Malavelle et al. (2017)\n176 al.(2025a), Zoega et al.(2025b), Marelle et al. (2025).\n179 as how much cloud fraction) but also cloud susceptibilities (especially for cloud fraction), as\n180 we highlighted in the beginning of the manuscript: “Cloud susceptibility is critical for climate\n181 projections, …. large discrepancies in cloud susceptibility can propagate significant\n182 uncertainties into climate projections, even with similar current states of clouds and\n183 aerosols …”.\n185 method of evaluating cloud and aerosol states, here we re-evaluate the performance of GCMs\n186 using cloud susceptibilities …”, we agree that this new point is not emphasized in this sentence.\n188 case, and emphasized again this new point in cloud susceptibility, read as:\n7\n“We echo Malavelle et al. (2017) to call for the wider climate community\ntakes the Holuhraun-2014 eruption as an anchor case, and stress\nvalidation of cloud susceptibilities in addition to cloud properties to\ndecompose and unravel the largest uncertainty in climate radiative\nforcing, i.e. ACI simulation …”\n189\n190\n191 6) 156-157: Getting the albedo change right is central. It is also much more accurately measured\n192 that apportionment of albedo change to Re, LWP and CF. I wonder if it can be shown that the\n193 observational apportionment between Re, LWP and CF is less uncertain than that in the models.\n195 not sure, Is the ML method used here good enough to use to change models?\n199 have explained the discrepancy between Chen et al. (2022) and previous study and provided\n200 independent data points following reviewer’s suggestion.\n201\n202\n203\n207 contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67,\n208 10.1038/nature12674, 2013.\n211 cooling effect from aerosol-induced increase in tropical marine cloud cover, Nature\n215 and Lohmann, U.: Machine learning reveals climate forcing from aerosols is dominated\n216 by increased cloud cover, Nature Geoscience, https://doi.org/10.1038/s41561-022-\n217 00991-6, 2022.\n223 e2024GL109077, https://doi.org/10.1029/2024GL109077, 2024.\n226 https://icdc.cen.uni-\n8\n227 hamburg.de/fileadmin/user_upload/icdc_Dokumente/MODIS/MODIS_Collection6_A\n228 tmosphereL3_GriddedProduct_ATBDandUsersGuide_v4.1_Sep22_2015.pdf, 2019.\n235 from volcanic eruptions, Nature, 546, 485-491, 10.1038/nature22974, 2017.\n240 imager.gsfc.nasa.gov/sites/default/files/ModAtmo/MODISCloudOpticalPropertyUser\n248 and coefficient of variation: Sample size and accuracy, Journal of Human Evolution,\n249 171, 103230, https://doi.org/10.1016/j.jhevol.2022.103230, 2022.\n251 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty\n252 analysis, Atmospheric Environment, 240, 117768,\n253 https://doi.org/10.1016/j.atmosenv.2020.117768, 2020.\n254\n9\n2\n4 manuscript.\n7 improved this study greatly.\n8\n9\n1"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the energy with which the authors have addressed my comments on the previous version of this paper. I think my concerns have been adequately addressed and I now recommend publication.", "reviewer_2": "My comments have been addressed appropriately, and I recommend acceptance of the manuscript."}, "author_response": ""}]

Decision letter