Trends in vertical wind velocity variability reveal cloud microphysical feedback

Domain: earth_science
Published: 2025-12-22
Editorial outcome: major_revision

Abstract
By controlling supersaturation vertical air motion influences how aerosols activate into cloud droplets and ice crystals. This effect is difficult to represent accurately in atmospheric models as they cannot typically resolve the sub-kilometer scale component of wind motion, however it can be addressed by machine learning. Here we apply a generative technique combining storm-resolving simulations, observational and climate reanalysis data, to predict the spatial standard deviation in vertical wind velocity, σ W. This analysis reveals significant trends in σ W, reaching up to 1% yr−1 in low and mid-level oceanic regions, indicating enhanced atmospheric turbulence. We attribute these trends to global shifts in water vapor, temperature and convection, suggesting a feedback connection between enhanced warming and turbulence, which in turn has a microphysical effect through the activation of more cloud hydrometeors. Focusing on low level clouds this effect has had a radiative impact of about −0.10 ± 0.04 Wm−2 since the year 1900, slightly mitigating greenhouse warming.

Introduction
Vertical wind motion drives cloud formation by inducing water vapor supersaturation in air parcels, leading to the activation of aerosol particles into cloud droplets and ice crystals1. The characteristic scale of these processes ranges from meters to a few kilometers, whereas modern global atmospheric models, with typical spatial resolutions between 10km and 100km, cannot fully capture these small-scale phenomena. As a result, vertical transport and cloud formation processes are classified as subgrid-scale and are primarily represented through physical parameterizations in global circulation models (GCMs). A key component of these parameterizations is the distribution of vertical wind velocity (W) within each grid cell2. While only the mean W can be directly resolved, the moments of the subgrid distribution, particularly the standard deviation (σ W), must be estimated from the coarse atmospheric state. Since variability in W produces a spectrum of supersaturation, σ W plays a crucial role in determining grid-mean cloud particle formation rates1,2. Additionally, as it reflects the amplitude of vertical wind fluctuations, σ W serves as an indicator of turbulence3 and gravity wave activity4.

Vertical wind variability is difficult to parameterize accurately in global atmospheric models and is typically treated using either approximate solutions to the equations of motion at the subgrid scale5 or empirical parameterizations6–10 designed to represent specific processes such as turbulence, transport, and cloud microphysics. The diversity of these approaches introduces uncertainty in climate and weather predictions. Although observational data on σ W exists from field campaigns and ground-based measurements11–13, it is typically localized and episodic. Large Eddy Simulations have been used to develop parameterizations of σ W, mostly focusing on stratocumulus regimes14. These are however limited in domain and it is not clear to what extent their results are applicable across the wide diversity of meteorological environments present in the atmosphere. Currently, no global dataset exists to constrain σ W in atmospheric models. Since the representation of cloud formation depends strongly on σ W, this limitation may impact estimates of cloudiness and cloud feedback2,15.

In the past decade, the ability of atmospheric models to represent weather patterns has improved thanks to advances in computing power, with many achieving kilometer-scale resolution (i.e., with spatial grids between 3km and 7km)16, known as global storm resolving models (GSRMs). Compared to traditional GCMs these simulations can better capture convection, vertical transport and cloud microphysics17. However, even at such scale GSRMs still misrepresent high frequency gravity waves18 and turbulence14, for which even finer resolution may be required19–21. The GSRM simulations, and the extensive data they produce, however represent a wealth of information that can be used to understand and model σ W, particularly when combined with observational datasets. Artificial intelligence techniques and data-driven algorithms like deep learning are well-suited for this task. Unlike traditional numerical simulations or theories, these algorithms learn from data to make predictions.

Using deep learning to integrate model results with experimental measurements of σ W provides a way to correct for the physical approximations in simulated data. A recent study21 introduced a model of this kind, termed Wnet, designed to predict σ W using the coarse (spatial resolution Δx ~ 0.25°−1°) meteorological state resolved by typical climate models. Wnet was developed in two stages: first, it was trained on results from GSRM simulations (Δx ~ 6km), then refined using ground-based observations (Δx ~ 30−300m) from sites worldwide. To account for experimental uncertainty in the observations, the refinement process is based on generative adversarial networks22,23. This approach introduces an auxiliary network, called the discriminator, which learns to reject noise by identifying structured features in real data. The discriminator guides the training process by penalizing Wnet when it produces noisy output, forcing the model to distinguish meaningful patterns from unstructured noise. As a result, experimental error is gradually filtered out, aligning Wnet’s predictions with the observed distribution and converging to a clean approximation of the true data. This represents an advancement in the parameterization of σ W, as W retrievals are inherently challenging and often associated with substantial uncertainty11,12.

The global simulation data inform Wnet about the spatial distribution of σ W, enabling the neural network to extrapolate across different scenarios and cloud formation regimes. The refinement step corrects for unresolved variability in the simulations. This synergy between high-resolution simulations and observational data enables Wnet to predict σ W across a wider range of atmospheric conditions21. The Wnet model takes as input coarse-resolution meteorological and dynamical factors, including winds, temperature, water concentration and phase, and simplified turbulence metrics, such as the Richardson number and scalar diffusivity, to estimate unresolved variability in vertical motion, bridging large-scale and small-scale dynamics.

Wnet can be run online, applied to prior simulations, or used with weather forecast and reanalysis products. The latter is particularly beneficial, as climate reanalyses rely on traditional assimilation techniques to incorporate millions of observations, reconstructing the observed meteorological state24 and providing unique historical, global atmospheric data spanning multiple decades. Since Wnet was built to use variables that can be provided by reanalyses, it is possible to reconstruct σ W globally using their output. Analyzing long-term trends in such datasets help us to understand how changes in large-scale weather patterns affect small-scale dynamical processes, and how these in turn, feedback on climate. We will present an analysis showing significant trends in σ W emerging over recent decades, with implications not only for cloud formation but also climate change assessments, and weather and turbulence forecast.

Results
We used meteorological fields from two widely employed reanalyses, MERRA-225 and ERA526, as input to the Wnet model to create three-dimensional global climatologies of σ W, every 3h from 1980 to 2022. While both ERA5 and MERRA2 rely heavily on observed data, they differ in assimilation methods and in their underlying modeling approach, resulting in differences in the global distribution of clouds, water vapor, convection and winds27–29. Since MERRA-2 was used to provide the meteorological state during Wnet training (when refining against observations), using ERA5 allows us to assess how sensitive σ W is to the choice of input data.

Figure 1 displays the global mean spatial distribution of σ W, at a half-degree horizontal resolution as the average of the prediction from both reanalyses. There is a broad range of σ W values, peaking near mountain ranges and within the inter-tropical convergence zone (ITCZ). In the mid-latitudes, σ W tends to decrease with height as turbulence weakens and the effects of surface roughness and boundary layer mixing diminish30. In contrast, deep convection in the tropics generates high-frequency gravity waves through latent heat release in the middle and upper troposphere, which tend to enhance σ W 31. Additional factors such as boundary layer mixing and frontal systems contribute to variability32, producing regions of elevated σ W near the surface (see Fig. 1 at 900 hPa).

These features are also evident when using MERRA-2 (Fig. S4) and ERA5 (Fig. S6) separately as input to Wnet. Both reanalyses produce similar global distributions of σ W, with high values near orographic features and tropical convection. Some differences appear at 500 hPa, where ERA5 shows a slightly lower global average σ W compared to MERRA-2. This is likely due to more active convection in MERRA-2, which transports water vapor and detrains condensate at higher altitudes28. The Wnet model is sensitive to such differences, as the presence of clouds lead to latent heat release that perturbs the local environment and enhances turbulence, increasing σ W. Despite these variations, both datasets yield similar spatial patterns of σ W, providing confidence that the generative method used to train Wnet effectively filters out error producing an unbiased estimate of σ W.

Validation of the σ W datasets
In situ measurements and ground-based observations are used to validate the climatologies of σ W. Since σ W is generally not measured directly, it is derived from high-frequency (typically 1–10Hz) measurements of W[11,13, corresponding to spatial scales of roughly 30–300m, which encompass most vertical motions relevant for cloud formation. To calculate σ W, the high-resolution W fields are used to compute standard deviations over spatial windows corresponding to a characteristic scale of ~50–100km, representative of variability on scales comparable to a typical GCM grid cell. For aircraft measurements, σ W was calculated using a 430s averaging window, representing a horizontal scale of Δx ~ 100km13, covering both in-cloud and clear-sky regions. For ground-based retrievals, the characteristic time scale depends on the mean horizontal wind at each site and ranges from 15 to 30 min, corresponding to Δx ~ 50km21. Observations were collected from multiple locations worldwide, spanning a wide range of cloud regimes (cirrus, stratocumulus, altocumulus, convective) and employing various measurement techniques for W, providing independent validation of σ W.

We compare the estimates of σ W against in situ measurements from over 1000 h of research flight data, collected during 12 field campaigns across North and South America and the Tropical Pacific, covering much of the western hemisphere (Table S1)[cf. Table 113]. All field data were thoroughly quality-controlled to ensure reliability13,20. For each campaign, the σ W datasets were interpolated and compared to measured values along each flight track. Since these campaigns were designed to study high clouds, the data represent free-tropospheric conditions not included in the training set, providing a test of Wnet’s ability to generalize beyond its training data. Figure 2 shows that, for most campaigns, the agreement between predicted and measured σ W is within 0.1 ms−1, well inside the typical experimental uncertainty for vertical wind velocity measurements33. Wnet also reproduces much of the observed variability in σ W, though it is underestimated in two campaigns (ATTREX and POSIDON) where conditions were strongly influenced by convection. These environments were underrepresented in the training dataset21 and tend to have a larger experimental error (~0.3 ms−1)13. Using ERA5 tends to produce a slightly narrower range of σ W values compared to MERRA-2 and the observations, indicating that Wnet driven by ERA5 predicts less extreme values, also evident in Figs. S4 and S6. As ERA5 (Δx ~ 0.25°) has finer spatial resolution than MERRA-2 (Δx ~ 0.5°) it may result in a slightly larger fraction of resolved variability and a smaller subgrid-scale σ W.

We also compared the predicted σ W with remote sensing retrievals from 11 ground-based sites worldwide, as listed in Table S2, encompassing more than 100 years of high-frequency σ W data. As with the aircraft measurements, the predicted σ W generally falls within the typical experimental error, as shown in Fig. 2. Measurements taken within cirrus clouds using radar are restricted to cloudy regions and are about an order of magnitude lower than near the surface, within the planetary boundary layer (PBL). Wnet represents this wide variation in σ W performing well across diverse environments. The best agreement is obtained at the cirrus sites12 and in sites typical of low-level cloud decks like ENA34, NSA35 and ASI36. Larger discrepancies occur at Tropical sites dominated by convection like MAO37 and TWP38, likely due to the limited representation of convective regimes in the training dataset21. Wnet, driven by MERRA-2 meteorology, was partially trained using these data21 and as before represents a slightly wider variability in σ W than ERA5, although both still lie within the observed range and have means close to the observations.

Remote sensing retrievals mainly sampled cirrus clouds and the PBL, while aircraft measurements spanned a wider range of altitudes, primarily within stratocumulus and altocumulus clouds, and (c) ground-based remote sensing in the planetary boundary layer (PBL). For clarity the MAO data was scaled by a factor of 0.7. The location and time periods for each field campaign and ground site are summarized in Tables S1 and S2. environments. Some campaigns, such as ATTREX39, also captured high-altitude clouds with temperatures below 200K. Figure 2 shows that the derived climatologies reproduce the observed σ W well across these regimes. Neither the in situ aircraft observations nor the ERA5 data were used in developing Wnet, providing an independent validation. Wnet’s strong performance across diverse conditions and measurement techniques indicates that the training algorithm effectively reduced experimental errors and captured the physical relationships between the coarse atmospheric state and σ W.

Long-term trends in σ W
The long-term climatologies of σ W derived from reanalysis data offer a unique opportunity to examine its multi-decadal behavior in relation to changes in meteorological conditions. Wind variability is influenced by factors like convection and orography, which create gravity waves and turbulence32,40. Figure 1 shows the linear trend in the σ W anomaly for each grid cell calculated from monthly averages over the 1980−2022 period. On average, there is a statistically significant, positive trend in σ W on the order of 0.1% per year (equivalent to a global increase of 0.5−1 cm s−1 per decade). There is, however, substantial spatial variation, as positive trends are generally observed in the Tropics and midlatitudes, while negative trends are seen in the Arctic and Antarctic, and in some subtropical regions like the west coasts of South America and South Africa. These trends also vary with altitude, with the highest values reaching ~1% yr−1 in the middle troposphere (around 500 hPa) in the Tropics and the most negative in the Arctic (approximately −0.5% yr−1). Despite negative trends in some areas, the overall global average indicates an increase in σ W across most of the troposphere over the past four decades. The statistically significant trends highlight areas where atmospheric dynamics are evolving, possibly linked to changing climate patterns.

Significant anomalies in σ W are observed around 500 hPa, where some areas in the Tropics experienced up to a 30% increase between 1980 and 2020 (see Figs. S5 and S7). Conversely, high latitudes beyond 60S and 60N saw almost a 20% decrease over the same period. Near the surface (900 hpa) regions characterized by persistent stratocumulus, off the coasts of North and South America and east Africa, as well as the “roaring 40s” (40S-50S) also saw a significant increase in σ W between 1980 and 2020, although some regions near the ITCZ and west Africa showed negative trends. Similar patterns are observed at 250 hPa, although at this level, σ W generally increased in most oceanic regions. Anomalies tended to be higher over the ocean compared to land, except for South America at 900 hPa and Australia at 250 hPa. Positive anomalies also developed earlier at 250 hPa (starting after 1990) than at 900 hPa (starting after 2000), suggesting that different mechanisms may be responsible for the observed trends at different levels.

Similar patterns emerge for the ERA5 and MERRA-2 reanalyses, showing pronounced trends in the tropical Pacific (Figs. S4 and S6), particularly at 500 hPa and near the surface over the Indian Ocean and equatorial Pacific, the latter likely linked to boundary-layer turbulence. Negative trends in ERA5 are more pronounced in the Arctic at 500 hPa and in the western Pacific at 900 hPa, compared to MERRA-2. Consequently, the overall global trend in σ W tends to be weaker in ERA5 than in MERRA-2, although it remains statistically significant across most regions, with the highest values occurring in the tropical lower troposphere. These differences are expected, as the evolution of the meteorological state, particularly water vapor and cloudiness, differs significantly between the MERRA-2 and ERA5 reanalyses41, which influences how σ W evolves when Wnet is driven by each dataset.

Despite differences in the absolute magnitude of the trends derived from ERA5 and MERRA-2, both datasets consistently indicate a net global increase in σ W over recent decades. This strongly supports the hypothesis of a global intensification of subgrid-scale variability, with statistically significant changes concentrated in regions of active weather systems. These findings suggest that the evolution of large-scale climatic patterns influence subgrid scale variability, potentially impacting the long-term evolution of clouds.

Origin of the observed trends in σ w
We use Shapley Additive explanations, SHAP42, to investigate the contribution of each input variable to the trends depicted in Fig. 1. SHAP is an interpretable machine learning technique based on Shapley values, which indicate the average contribution of each input to the overall deviation in σ W from its average value. The Wnet model employs 14 scalar inputs, 10 at the grid level and 4 from the surface level at the same location. Combining surface variables with each grid-level input allows Wnet to account for the effects of orographic features and convection initiation on σ W, while remaining independent of vertical resolution. An alternative input set, which replaces scalar diffusivity and the Richardson number with PBL height (PBLH), was developed to accommodate a wider range of meteorological datasets. The core of Wnet remains unchanged, with only a transformation layer added to map the alternative inputs. Here, we focus on the original inputs, while Fig. S8 shows the Shapley values for the alternative set.

Figure 3 displays the Shapley values for each input of the Wnet model. While all input variables significantly influence σ W, water vapor mixing ratio (Q v), temperature (T), and density (both at the grid level and the surface) consistently show the highest Shapley values. These effects suggest the presence of active convection amplifying variability, as humidity at the surface and mid-levels strongly influence the formation and evolution of convective storms43. Increased humidity also causes fluctuations in the virtual potential temperature, which dictates the buoyancy of air parcels5. As satellite retrievals and reanalysis products indicate a positive trend in Q v near the surface over the last four decades41, the connection between water vapor and σ W explains some of the positive trends depicted in Fig. 1. These are likely related to a rise in sea surface temperature (SST) of about 0.1°C per decade in the Tropical regions of the low and middle troposphere, although they are also influenced by changes in atmospheric winds, the hydrological cycle, aerosols, and volcanic activity41,43.

The Shapley values for other input variables reflect the physical processes driving variability at different vertical levels. Near the surface (900 hPa), the presence of liquid water significantly impacts σ W, highlighting the strong connection between cloud formation, mixing, and turbulence44. Positive trends in σ W in persistent stratocumulus regions in the northern hemisphere may be linked to increased cloudiness from enhanced surface evaporation, although such is not the case for cloudy regions along the shores of South America and South Africa. At 500 hPa, vertical winds and the presence of ice condensate become increasingly important. These factors are associated with the presence of tropical middle and high clouds, which explain the trends depicted in Fig. 1, as reanalyses typically show positive trends in high cloud in this region28. This pattern is largely reproduced at 250 hPa, although some tropical regions exhibit negative trends, possibly due to unusually high cloud amounts during major El Niño events in 1982−1983 and 1997−199828.

Enhanced convection, however, cannot account for the negative trends in σ W observed in polar regions. Instead, the Shapley values found at 500 hPa in the Arctic suggest that horizontal winds may play a more significant role in variability than surface-initiated convection. It is likely that the negative trend in σ W in the polar regions is linked to the weakening of the polar jet stream, a phenomenon associated with Arctic amplification45. This connection between large-scale dynamics and microscale variability is known as chaotic advection, resulting from exponentially diverging paths of tracers within a grid cell43,46. Thus, the evolving meteorological patterns, like jet-activity, have systematically impacted the intensity and frequency of small-scale processes, such as turbulence and cloud formation.

Methods
Estimation of σ W
A schematic of the workflow used to produce the global datasets of σ W is shown in Fig. S2. The Wnet model was developed in two stages. First, the neural network, termed Wnet, was trained using W output from a global storm-resolving simulation (Δx ~ 6km), with σ W computed over areas corresponding to ~50km grid cells. This initial model serves as a feature extractor, identifying meaningful relative patterns in σ W. However, due to the relatively coarse resolution of the GSRM, it tends to underpredict the magnitude of σ W. To correct for this, a refinement step was introduced, where Wnet was further trained using long-term observations, which derive σ W from high-frequency radar and lidar retrievals of W, covering most atmospheric motion relevant to cloud formation. The W fields were processed using the average wind at each site to represent σ W over a ~50km grid cell, as detailed in ref. 21. Different sites were selected from around the world to sample a diversity of cloud and meteorological regimes as listed in Table S2 and depicted in Fig. S1.

The refinement against observations followed a generative approach, incorporating a second neural network, called the discriminator, which guides the training process21,22. During this stage, Wnet does not learn directly from the observational data but instead from the representation of σ W learned by the discriminator. This architecture allows the discriminator to filter out experimental error, exposing Wnet only to meaningful, structured relationships in the data. As a result, the model filters out noise and develops a robust parameterization of σ W.

Once trained, we carried out the inference step. Using MERRA-2 to inform the input to Wnet, we computed σ W using instantaneous values at 3-hourly intervals, on 72 model levels, then interpolated to 44 pressure levels. The input to the Wnet model (denoted as X in Fig. S2) consists of a 14-dimensional vector including the Richardson number (R i, dimensionless), total scalar diffusivity for momentum (K M, in m2s−1), the 3-dimensional wind velocity (U, V and W, in m s−1), the water vapor, liquid and ice mass mixing ratios (Q v, Q liq and Q ice in Kg Kg−1), air density (ρ a, in Kg m−3), and air temperature (T, in degrees K) at the resolution of the MERRA-2 reanalysis (0.5°×0.625°). Surface Q v, air density, K m and R i were concatenated to each level to account for the effect of orography and convection on upper tropospheric W variability.

Deseasonalized anomalies were calculated relative to the 1980−2022 long-term monthly climatology. Linear trends were obtained by performing regression on monthly means at each grid cell.

A second global dataset was generated for the period 1980–2022 using output from the ERA5 reanalysis26 to drive Wnet. The instantaneous input state was extracted at 3-hourly intervals with a nominal resolution of 0.25° and 37 pressure levels. Unlike MERRA-2, the ERA5 output does not include the parameters R i and K m. To address this, Wnet was adapted as a foundational model for subgrid-scale variability by introducing a transformation dense layer before Wnet to translate the input state, as shown in Fig. S3. This new layer consisted of 128 nodes, utilized LeakyReLU activation, and was trained on MERRA-2 data while keeping the original Wnet layers frozen. The input vector for ERA5 included 11 variables: PBL height (PBLH, in m), total integrated water vapor (TPW, in kg m−2), 2-m temperature (T2M, in K), 3-dimensional wind velocity components (U, V, W, in m s−1), water vapor, liquid, and ice mass mixing ratios (Q v, Q liq, Q ice in kg kg−1), air density (ρ a, in kg m−3), and air temperature (T, in K). Two-dimensional variables (PBLH, TPW, T2M) were broadcasted to three dimensions. After training, the ERA5 input was used to estimate σ W. A third, smaller σ W dataset was derived from the ERA-20C reanalysis55, spanning the years 1900–1905, computed using instantaneous values at 6-hourly intervals, at a resolution of 1° on 37 pressure levels.

Validation against observational data
Two sources of data were used to validate the σ W datasets, direct measurements taken during air flight campaigns and ground-based remote sensing retrievals at specific locations around the world. Table S1 describes the field campaign data. Measurements of W were used to estimate σ W by computing the standard deviation over a time window of about 430s; roughly the time it takes for the aircraft to cover an area equivalent to a standard GCM grid cell (i.e., 50−100km). W was measured at a frequency of 1Hz, corresponding to a spatial scale Δx ~ 300m, with an estimated error of about 0.1 to 0.3 ms−1. The measurement techniques and the quality screening of the data are detailed in ref. 13. The σ W datasets derived from MERRA-2 and ERA5 were interpolated to the spatial location and time of each campaign flight, generating one-dimensional curtains along the aircraft trajectories. Due to the discrepancy between the gridded data resolution and the frequency of measurements, it was challenging to pinpoint exactly the altitude of the aircraft at each time. Instead, σ W statistics were computed for each field campaign by extracting σ W for each curtain within two standard deviations of the measured mean temperature for each flight, and for pressure ranging between 50 and 600 hPa thus ensuring that only data for steady flight conditions were utilized for validation.

The remote sensing data used for validation is summarized in Table S2, with screening criteria detailed in ref. 21. The dataset includes lidar and radar retrievals of W with a frequency between 1 and 10Hz, and a corresponding spatial resolution Δx ~ 30−300m, covering periods between 6 months and 10 years, depending on the site. The mean horizontal wind at each site was used to estimate σ W, typically calculated over 5 min ≃ 20 min intervals. Radar retrievals focused on cloud-y cirrus layers, while lidar observations were limited to altitudes below 4 km. A single deep convective site (MAO) utilized scanning radar to profile W from the surface to 14 km. The estimated error in W is ~0.1 to 0.2 ms−111,12,37. The MERRA-2 and ERA5-derived σ W datasets were interpolated to match the location, time and altitude of each measurement and compared with observations. Overall, the observed σ W distributions were well represented in the datasets, with discrepancies within the experimental error. It is important to note that, unlike the aircraft data, the remote sensing retrievals were used in the development of Wnet, albeit through a different method.

Radiative forcing estimation
Since changes in σ W mainly impact N d , we adapted a methodology commonly used in the study aerosol-cloud interactions49,50 to calculate the radiative forcing associated with changes in σ W. This is given by:
ΔSW= SW down ×C f×A c×(1−A c)× 1/3 ∂/∂lnσ N d ×Δlnσ W (1)
where SW down is the downwelling shortwave flux at the top of the cloud layer (assumed here to be equal to SW at the surface49), C f is the liquid cloud fraction, and A c is the cloud albedo. Equation (1) assumes that the primary radiative impact of σ W arises from changes in cloud albedo, while adjustments in LWP and cloud fraction can be neglected. This assumption is justified because changes in N d induced by σ W are relatively small compared to other factors like aerosol emissions, and LWP is only weakly sensitive to N d 49. Climatological values averaged over 2015–2020 were used for C f, SW down, and A c. C f and SW down were obtained from the MCD06COSP_M3_MODIS58 and CERES EBAF-TOA59 datasets, respectively. MERRA-2 data were used to estimate A c, as it has been found to agree well with satellite observations60. Δlnσ W was estimated as the difference between the global mean σ W averaged over 2015–2020 from the combination of the MERRA-2 and ERA5 climatologies, and over 1900–1905 from the ERA-20C reanalysis55. Uncertainty in ΔSW was estimated by varying the vertical level for σ W and β (875–975hPa), present day (2015–2020) and beginning of the century (1900–1905) years, and the reanalysis dataset for a total of 378 combinations (see Table S3). This resulted in a calculated range of ΔSW=[−0.212,−0.043] Wm−2 with a standard deviation of 0.037 Wm−2 (Δσ W=[0.039,0.093] ms−1 with a standard deviation of 0.014 ms−1).

To calculate the susceptibility of N d to σ W, defined as β= ∂/∂lnσ N d, we followed the methodology detailed in ref. 50. This approach uses an auxiliary neural network, MAMnet61, to predict the aerosol size distribution (ASD) from MERRA-2 assimilated aerosol products. MAMnet emulates the Modal Aerosol Module [MAM762] and predicts number concentration and composition for seven internally mixed lognormal modes (accumulation, Aitken, coarse/fine dust, coarse/fine sea salt, and primary carbon matter). It was trained on 5 years of data from a simulation of the Goddard Earth Observing System (GEOS) model61 implementing MAM7 and validated against ground-based observations. To isolate the effect of σ W, a fixed, monthly mean climatological ASD was developed using the entire MERRA-2 dataset. This ASD was annually repeated, and, together with σ W from Wnet, used to predict N d for 1980–2022 following the approach detailed in ref. 63. The parameter β was then obtained as the slope of a linear regression of ln N d against ln σ W across the time dimension, on monthly means, at each grid cell. Since both σ W and β are three-dimensional fields, ΔSW is reported as the mean over the five lowest model levels (875–975hPa). All variables were regridded to 1° resolution to match the ERA-20C grid.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I have three primary criticisms of this article. After these are addressed, I think it will be suitable for publication. I have entered major revisions.\nComment 1:\nThere needs to be more information on how sigma_w actually influences a global model. Presumably this is via the\nparameterization of the turbulent flux terms? Is sigma_w diagnosed in the cumulus parameterization, and in you're sensitivity\nexperiments you are simply replacing the diagnosed value with your PI and PD values?\nComment 2:\nFig 2 and the associated discussion would benefit from a correlation analysis. Are the differences in sigma_w among the\nfield projects/locations statistically significantly correlated with the analogous differences computed from MERRA-2?\nComment 3:\nThe B24 model was derived using a 5 km grid spacing model, which under-resolve a lot of convective motions including\nthose in tropical deep convection, shallow convection, and smaller scale cloud features. What are the implications of this\nlimitation? At least some discussion of this issue is warranted.", "reviewer_2": "This study relates observed variability in vertical air motion (sigma_w_obs) to model-parameterized sub-grid-scale vertical\nair motion (sigma_w_model) using a deep learning technique. It then uses the global, multi-decade model (the MERRA-2\nreanalysis) to predict sigma_w globally and over multiple decades. A multi-decade (1980-2023) global trend in sigma_w is\ndescribed. This trend is attributed to changes in the climate system.\nThis trend is intriguing. A Shapley Additive explanations (SHAP) analysis shows that the trend is related to global warming\nand coincident increases in water vapor mixing ratio. Assuming that the described trend in sigma_w is accurate, one\nwonders whether this finding is noteworthy. Climate system feedbacks, in particular cloud feedbacks, do involve sigma_w,\ne.g. mid-tropospheric sigma_w is largely due to deep convection, so an independent measurement of a sigma_w trend\nwould be most noteworthy, as it would explain the observed global increase in precipitation rate (and the rate of hydrological\ncycling). But this paper does not present an independent observational finding. Rather, since sigma_w is parameterized in\nMERRA-2 , the documented trend simply is a dynamically consistent consequence of an increase in global precipitation rate\ncaptured by MERRA-2 and other reanalyses. So, I argue that the key finding of this paper lacks novelty. It merely confirms an\nalready established positive trend in convective precipitation.\nThis paper, and references cited, generally provide the details necessary for the method to be followed work and the results\nto be reproduced. However questions do remain, e.g. what assumptions are made to reduce Doppler hydrometeor vertical\nvelocity (from radar) to vertical air motion. The key reference [20], on which this study builds, does not detail these\nassumptions.\nWhile the paper is well written, the study in-depth, and the findings original, I believe there are flaws in the data analysis,\ninterpretation and conclusions. First, the training dataset of observations have unacknowledged uncertainties or limitations,\nadding noise to sigma_w_obs. Profiling Doppler Lidar (DL) observations are generally limited to the boundary layer (BL)\nwhere sigma_w is fundamentally different from that in the free troposphere, where the deep learning technique is mostly\napplied. Doppler radar (DR) data are used as well, but unknown variations in hydrometeor fallspeed contribute significantly\nto the observed variability, hence the DR variability is a poor proxy for sigma_w. Also, DR data are limited to precipitating\nregions only, while DL are limited to clear air, so their sigma_w values represent different atmospheric conditions.\nIndependent airborne estimates of sigma_w are used to validate the MERRA-2-based (predicted) sigma_w_model, but the\nairborne measurements are made elsewhere (above the boundary layer, outside precipitating areas, in clouds that neither\nDL nor DR can sample, and mostly in the upper troposphere, as indicated by the field campaigns listed in Table 1), and with\nan unacknowledged uncertainty (especially on the low-frequency side). sigma_w values in the upper troposphere typically\nare much lower than in the BL, so the validation does not capture the spectrum of variability.\nSecond, the frequency spectrum of data from ground-based profiling systems (DR and DL) and that from airborne gust\nprobes differ, and is different from models’ sub-grid-scale vertical velocity.\nThird, I question the robustness of the key finding, that in the last four decades, the global troposphere has experienced an\nincrease in sigma_w of about 1% per decade, reaching up to 10% per decade in some regions. This is according to the\nMERRA-2 dataset, subjected to a ML technique trained on data that have measurement uncertainty and lack\nrepresentativeness. The impact of measurements’ uncertainties and excessive clustering in the physical and the spectral\ndomains is not analyzed. Fourth, MERRA-2 is one reanalysis. The same technique was used on one other reanalysis,\nERA5, and the results are quite different (Fig. A3): the trend essentially vanishes above the boundary layer (500 and 250\nhPa) in ERA5, and the spatial patterns across the globe are different (Fig. 1 vs Fig. A3). The differences in values between\nERA5 (monthly-mean data) and MERRA-2 (6 hourly) makes sense: monthly-mean data do not capture the synoptic\nvariability in sigma_w. But that does not explain the differences in trend. Given this disagreement, I would recommend the\nuse of full-time-resolution ERA5 data, and other global or regional reanalyses, to build further evidence.\nSome of the terminology used in the paper is unfortunate and inconsistent with expected standards. For instance, vertical\n“wind” is used, where the term wind refers to horizontal air motion. I acknowledge that other papers have used the\nexpression “global cloud resolving models”, but of course, global models cannot truly resolve cloud processes: the latest\nDNS models, which capture cloud microphysical processes, have meter-scale volumes at most. I think they refer to global\nconvection-permitting models, which exist, e.g. the E3SM SCREAM. But then, MERRA-2 is not one of them. Maybe they\nsimply refer to numerical models in which cloud processes are parameterized. That has been done in climate models since\nthe beginning, going back to the early CMIPs. Statements like “cloud formation occurs at scales around 100 m” depart from\nthe established core knowledge, in this case understanding of the multiscale nature of clouds. There is no “Pacific Cold\nTongue at 250 hPa”. There are many other examples.\nIn summary, I believe that these weaknesses and flaws prohibit publication."}, "author_response": "Authors’ Response to Reviews of\nSignificant trend in vertical wind velocity variability over the\nlast 40 years\nDonifanBarahona,KatherineBreen,MinhguiDiao,DerekNgo,FlorVanessaMaciel,RyanPatnaude\nNatureCommunications,2025\nRC:Reviewers’Comment"}, {"round": 2, "reviewer_comments": {"reviewer_3": "Review of the manuscript „Trends in vertical wind velocity variability reveal cloud microphysical feedback“ by Barahona et\nal.\nIn the manuscript „Trends in vertical wind velocity variability reveal cloud microphysical feedback“ Barahona et al. apply\ntheir recently published generative AI model (Wnet) to predict the spatial variability (standard deviation) of vertical wind σW\nfrom large scale meteorological conditions from reanalysis (MERRA-2 and ERA-5). They find significant increases in σW\nthat are particularly strong over low (900hPa) and mid-level (500hPa) oceanic regions. This finding suggests increasing\nvariability in supersaturation and therefore changes in aerosol activation and cloud microphysics, and implies a (comparably\nsmall) negative feedback. This finding is important and justifies the publication in Nature Communications.\nMy main task as a reviewer was to evaluate the authors response to the criticism by reviewer 2 (R2), who, in her/his review\nwas very skeptical about the methodological approach, i.e. about the ability of the Wnet to accurately predict σW. After\nreading the manuscript, the reviews and the authors responses, I am convinced that the criticism of R2 is mainly founded on\nan incomplete understanding of the details of the generative AI method developed by the authors. In my opinion, the authors\nhave done substantial work to improve the communication and clarity of the manuscript in this regard.\nI do question the authors focus on one reanalysis to predict σW, though. In the manuscript, the results of σW data generated\nwith the large scale fields from MERRA-2 are highlighted, I guess because the trend signal is much more pronounced than\nin ERA-5. In my opinion this is not adequate and seems a bit like cherry picking of the results to me unless there is a\nphysical reason why we should trust MERRA-2 more than ERA5. If that is not the case I believe the two results should be\nequally weighted in the interpretation and in the headline statements. In this aspect, I agree with R2, comment 4, and think it\nis not appropriate to overrepresent the findings from the reanalysis that produces the stronger trend signal in σW. I\nacknowledge that ERA5 is now discussed more in the manuscript than initially, but still there seems to be a focus on the\naspects where the two data sets agree rather than clearly state the differences between them.", "reviewer_4": "Review of Barahona et al. - Trends in vertical wind velocity variability reveal cloud microphysical\nfeedback.\nI’m referring to the revised manuscript here.\nResponses to the comments of the other reviewers.\nReviewer#1\nThere was some confusion (comment#1) about what the ERF that was calculated represented, but I think that the main\nmanuscript has been changed to make it clear now that the ERF value is just due to the microphysical effect on droplet/ice\nactivation. However, this is still not the case in the abstract and I have made some suggestions for that below.\nI think that comment#2 has been addressed reasonably.\nComment#3 concerns the fact that the 5km resolution of the high resolution model won’t resolve a lot of the relevant motions\nand suggests more discussion. I agree with this point. Some discussion has been added, but I think that a little more\ndiscussion would be helpful. I made some similar comments below for Section 2., which are repeated here:-\n- Here it would be good to discuss that there is a large range of scales of updrafts/turbulence (from global circulations,\nthough to deep convection, shallow convection, stratocumulus, small-scale mixing and turbulence). And how sub-grid\nsigma_w will depend on the resolution being considered – i.e., what is the size of the “grid”. In this study you use a 5km\nmodel, but also include some smaller scales with the observations – what resolutions/scales do the observations used to\nbuild the model cover? And what is the resolution of the aircraft observations used to test the model? The results for the ERF\ncalculation rest on changes in stratocumulus clouds in the sub-tropics, off the west coasts of S America, N America and SW\nAfrica. However, there is little discussion about the spatial scales of vertical winds in these regimes and about how well your\nmodel captures them. One approach used in the past, which should be discussed in this paper, has been to use very high\nresolution (~35-150m) LES models to quantify sigma_w at stratocumulus-relevant scales\n(https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JD021218). Do the observations you used to build and test the\nmodel cover these smaller scales for stratocumulus clouds?\nReviewer#2\nSome of the comments may have come from unclear explanations in the introduction and methods sections and there has\nbeen some effort to improve that. However, this aspect could still be improved further and I’ve tried to make some\nsuggestions for that below. It is still not clearly stated whether the sub-grid parameterized sigma_w of the high resolution\nmodel is used along with the resolved updrafts (I don’t think it is, but I’m not sure that it’s stated clearly).\nThe comment “While the paper is well written, the study in-depth, and the findings original, I believe there are flaws in the\ndata analysis, interpretation and conclusions.”, also questions the types of regime sampled by the remote sensing used to\nbuild Wnet and the aircraft obs used to test it. I agree with this aspect – it’s not clear which types of cloud/meteorological\nregimes have been sampled by the aircraft observations from the tables and text provided. There is some useful information\nin Table S1 for the ground station data, but more information on the cloud types sampled would be useful – does “PBL”\ncover stratocumulus and trade cumulus, or just clear-air observations? Nor is it clear what the resolution of the observations\nwas. Some more discussion on this and the limitations is warranted (linking to the comments above about stratocumulus).\nI think the other comments have been addressed well.\nMy own comments\nOne of the key results (the “ERF” forcing due to sigma_w changes) rests on the difference in the sigma_w climatologies in\nthe PD run compared to that from the coupled PI run being representative and not just a random chance result. E.g., if\ndifferent 10 year samples were chosen or if longer samples were used, would the results be the same? I don't think that 10\nyears is long enough to capture the variability that is possible in a coupled simulation such as the PI simulation. In a coupled\nrun the SST patterns, sea-ice amount, etc. will vary a lot and 10 years won't capture this. Can you use the full 180 years?\nEven for the prescribed SST runs it is too short since there will be a lot of natural variability in the climate that you would\nneed to sample over. The exact 10-year period chosen may also influence the mean climatological winds, circulation, etc.\nand it's possible that different 10-year periods might give different results. I would expect a longer period to be used (around\n30 years). It would be good to compare results using 10, 20, 30 years to make sure they are robust. And similarly in the\ncoupled PI run, although you will probably have to extend to even longer periods for that.\nPossibly related to the comment above is the concern that the differences in sigma_w between the PD and PI runs don’t\nseem consistent with those between the +4K and PD runs (Fig. S7). There is also little resemblance to the long-term trends.\nThis is a concern as it suggests that the impact of warming and a changing climate is not being captured consistently with\nthe model experiments. There are some more detailed comments on this below, but the longer experiments above should\nhelp to address the issue. More discussion on these discrepancies in the results is needed.\nDetailed comments\nAbstract\n“This effect is however difficult to represent”\n- Better without the “however” here since it is also used later in the sentence.\n“however can be addressed by machine learning.” -> however, it can be addressed…\n“indicating enhanced turbulent atmosphere” -> indicated enhanced atmospheric turbulence?\n“suggesting a feedback connection between enhanced warming, turbulence and cloud microphysics, with an associated\nradiative forcing of about −0.1 +/- 0.21 W m−2, slightly mitigating greenhouse warming.”\n- You haven’t introduced the effect on microphysics here yet and it’s also not clear here that the “forcing” is solely due to the\nmicrophysical effect. I suggest something like this – “suggesting a feedback connection between enhanced warming and\nturbulence, which in turn has a microphysical effect through the activation of more cloud hydrometeors. The forcing\nassociated with the microphysical effect is estimated here to be about 0.1 +/- 0.21 W m−2, slightly mitigating\ngreenhouse warming.”\nIntroduction\n“modern atmospheric models”\n– You should specify the subset of models being talked about here rather than later in the paragraph - this only applies\ngenerally to global models. E.g., LES models have much higher resolution.\n“Nevertheless, the GSRM simulations and the extensive data they produce represent a wealth of information that can be\nused to understand and model σW. Artificial intelligence techniques and data-driven algorithms like deep learning, are well-\nsuited for this task. Unlike traditional numerical simulations or theories, these algorithms learn from data to make\npredictions.”\n– You haven’t introduced observational data yet – surely this is necessary to allow the AI approach to improve the\npredictions of the GSRM simulations?\n“it was trained on results from global storm-resolving model simulations,”\n- It would be helpful to quote the resolution of the model here to make it clear that it is built on high-resolution data. I don’t\nthink that it is mentioned in the whole paper. Also, to avoid confusion later, it would be good to clarify in this sentence that it\nis trained to predict sigma_w as output from the high-resolution model as a function of the low resolution meteorology (was\nthis from MERRA or from the coarsened fields of the high-res model?)\n“spatial distributino” typo.\n“By integrate physical relationships” -> By integrating…\n“Since the Wnet was built using data from reanalyses like MERRA-2,”\n- Was it built from MERRA2 or from coarsened fields from the high-res model? If the latter then it would perhaps be better to\nsay that the Wnet model uses input variables that can provided by MERRA2.\n“Our analysis revealed”\n- Better to say \"We will present an analysis that shows\" or similar since this sounds like you are referring to your past study.\nResults\n“We used the Wnet model to create three-dimensional global distributions of σW every 3 hours from 1980 to 2023.”\n- it would be good to say that you used the meteorological fields from the 1980-2023 MERRA2 reanalysis as inputs into\nWnet to create the climatology. Better here than in the next sentence.\nFig. 1 caption\n- “Stippling highlights statistical significance the 95% level” -> “…. at the 95% level.\nSection 2.1\n- Here it would be good to discuss that there is a large range of scales of updrafts/turbulence (from global circulations,\nthough to deep convection, shallow convection, stratocumulus, small-scale mixing and turbulence). And how sub-grid\nsigma_w will depend on the resolution being considered – i.e., what is the size of the “grid”. In this study you use a 5km\nmodel, but also include some smaller scales with the observations – what resolutions/scales do the observations used to\nbuild the model cover? And what is the resolution of the aircraft observations used to test the model? The results for the ERF\ncalculation rest on changes in stratocumulus clouds in the sub-tropics, off the west coasts of S America, N America and SW\nAfrica. However, there is little discussion about the spatial scales of vertical winds in these regimes and about how well your\nmodel captures them. One approach used in the past, which should be discussed in this paper, has been to use very high\nresolution (~35-150m) LES models to quantify sigma_w at stratocumulus-relevant scales\n(https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JD021218). Do the observations you used to build and test the\nmodel cover these smaller scales for stratocumulus clouds?\nSection 2.2\n“depicted with in Figure 1.” – typo.\n“frequency small scale processes” -typo\nSection 3\n“However, while turbulence indices are derived from the coarse meteorological state, σW indicates variability at the small\nscale,”\n- it’s not very clear what you mean here.\n“corresponds to a previously unexplored feedback mechanism mediated by the microphysical properties of clouds and the\nsmall scale dynamics.”\n- Although this will be inherently included in GCMs in some way since many predict TKE as a function of the grid properties,\nwhich then affects cloud droplet activation.\n“We employ global atmospheric modelling”\n- should say which model in the main text.\n“Near the surface, the difference between the PD and PI climatologies resembles the anomalies seen in Figure 1.”\n- I can't see any similarity between Fig. 1f and the lower-middle plot in Fig. S7 (900 hPa PD-PI) if that's what you're referring\nto. Nor for 500hPa. Plus, the PD-PI plots have the opposite sign to the SST+4K - PD plots. I would trust the latter a bit more\nsince both are driven by SSTs and don't rely on the 10-year coupled climatologies. The signals are certainly much stronger\nat higher altitudes.\n“Near the surface, the difference between the PD and PI climatologies resembles the anomalies seen in Figure 1.”\n- Why would a decrease in cloudiness lead to less convective transport of water. Where does the cloudiness decrease?\n“the radiative forcing obtained after 50 years of integration.”\n- so do you use the 10 year sigma_w climatology repeated 5 times? You should make it clearer what you did.\n“Enhanced σW in these regions leads”\n- Which regions? Low-cloud stratocumulus regions? Which ones in particular? I can't really see any enhanced sigma_w in\nthese regions in Fig. S7?\n“Figure S9). In constrast, extra-tropical regions in the Pacific experience the opposite effect. Drying at lower levels weakens\nσW as convective clouds deepen in the Tropics [37]. A sensitivity run using a σW climatology generated from a scenario\nwith present-day SSTs increased by 4 K [43] suggests that as drying intensifies, σW weakens even further,”\n- I can't see any evidence for this in Fig. S7? Where does sigma_w decrease in the extra-tropics in the PD experiment (vs\nPI). And the +4K experiments show the opposite response (vs PI) to the PD ones and stronger sigma_w in the extratropical\nPacific? How do you know that the convective clouds deepen? I think that it would be best just to focus on the stratocumulus\nregion effects rather than extratropics here? It's probably only the stratocumulus regions that are important here.\n“leading to decreased coverage of stratocumulus clouds and a positive net radiative forcing on climate (see Figure S10).”\n- The change in low cloud cover must come from the change in Nd since sigma_w only affects the microphysics. And so the\nreduction in cloud cover probably comes from the precipitation enhancement as a result of the reduced Nd? It would be good\nto mention this.\n“could profoundly impact climate predictions.”\n- This seems a bit of a stretch for a 0.2 W/m2 forcing with lots of uncertainty! The language needs modifying here.\nFigs. 4 and S10\n- It seems that the SWCE and LWCE changes should be negated to make them net incoming (instead of outgoing) TOA\nvalues for consistency with the ERF (since the value in Fig. 4 is negative giving an overall cooling)? And the definition of\nERF in the caption is wrong – it should be net incoming TOA radiative forcing?\nSection 5.1\n“trained using long-term retrievals of”\n- would be good to reiterate that these are observations.\nSection 5.2\n“ranging from 20 seconds to 5 minutes,”\n- Is it possible to give a horizontal resolution? E.g., for a 10 m/s horizontal wind speed this would be a resolution of 200-\n3000m.\nSection 5.3\n“the MERRA-2 reanalysis and the free-running simulation.”\n- Make it clearer what these two are - is the reanalysis the same as the \"long-term MERRA climatology run\" and is the \"free-\nrunning\" simulation the prescribed SST PD run?\n“, taken from each of the previously-developed σW climatologies.”\n- Make it clear what the 3 climatologies were. Presumably PI, PD and PD+4K?", "reviewer_author_response": "Authors’ Response to Reviews of\nTrends in vertical wind velocity variability reveal cloud micro-\nphysical feedback\nDonifanBarahona,KatherineBreen,MinhguiDiao,DerekNgo,FlorVanessaMaciel,RyanPatnaude\nNatureCommunications,2025\nRC:Reviewers’Comment", "reviewer_ar": "Authors’Response, □ManuscriptText\nWesincerelyappreciatethetimeandeffortinvestedinreviewingourwork. Below,weprovideadetailed\nresponsetoeachoftheconcernsraised.\n1. Reviewer#3\nRC: In the manuscript “Trends in vertical wind velocity variability reveal cloud microphysical feedback”\nBarahonaetal. applytheirrecentlypublishedgenerativeAImodel(Wnet)topredictthespatialvariability\n(standard deviation) of vertical wind W from large scale meteorological conditions from reanalysis\n(MERRA-2andERA-5).TheyfindsignificantincreasesinWthatareparticularlystrongoverlow(900hPa)\nandmid-level(500hPa)oceanicregions. Thisfindingsuggestsincreasingvariabilityinsupersaturation\nandthereforechangesinaerosolactivationandcloudmicrophysics,andimpliesa(comparablysmall)\nnegativefeedback. ThisfindingisimportantandjustifiesthepublicationinNatureCommunications. My\nmaintaskasareviewerwastoevaluatetheauthorsresponsetothecriticismbyreviewer2(R2),who,in\nher/hisreviewwasveryskepticalaboutthemethodologicalapproach,i.e. abouttheabilityoftheWnetto\naccuratelypredictW.Afterreadingthemanuscript,thereviewsandtheauthorsresponses,Iamconvinced\nthatthecriticismofR2ismainlyfoundedonanincompleteunderstandingofthedetailsofthegenerative\nAImethoddevelopedbytheauthors. Inmyopinion,theauthorshavedonesubstantialworktoimprovethe\ncommunicationandclarityofthemanuscriptinthisregard.\nAR: Wethankthereviewerforthepositiveassessmentonourwork.\nRC: IdoquestiontheauthorsfocusononereanalysistopredictW,though. Inthemanuscript,theresultsof\nWdatageneratedwiththelargescalefieldsfromMERRA-2arehighlighted,Iguessbecausethetrend\nsignal is much more pronounced than in ERA-5. In my opinion this is not adequate and seems a bit\nlikecherrypickingoftheresultstomeunlessthereisaphysicalreasonwhyweshouldtrustMERRA-2\nmore than ERA5. If that is not the case I believe the two results should be equally weighted in the\ninterpretationandintheheadlinestatements. Inthisaspect,IagreewithR2,comment4,andthinkitis\nnotappropriatetooverrepresentthefindingsfromthereanalysisthatproducesthestrongertrendsignalin\nσ . IacknowledgethatERA5isnowdiscussedmoreinthemanuscriptthaninitially,butstillthereseems\nW\ntobeafocusontheaspectswherethetwodatasetsagreeratherthanclearlystatethedifferencesbetween\nthem.\nAR: WeagreethatusingtworeanalysesinsteadofrelyingsolelyonMERRA-2makestheresultsmorerobust. Our\ninitialchoicewasnotaboutselectivelypresentingresults,butratherduetothepracticalityofperforminga\nlong-term,hightemporalresolutioncalculationofσ usingthelargeERA5datasetcomparedtoMERRA-2,\nW\nwhichisreadilystoredonNASAservers.\nW\ntemporalresolutionto3hoursandusingall37pressurelevelsoverthefullresearchperiod. Thisenableda\nclosercomparisonbetweenMERRA-2andERA5resultsandallowedustoevaluatebothdatasetsagainst\nobservations. We now report our main results as the average of both estimates. Sections 2 and 3 of the\nrevisedmanuscriptdiscussdifferencesbetweenthereanalyses,andtheindividualresultsarepresentedinthe\nsupplementaryfigures.\n2. Reviewer#4\n2.1. Responsestothecommentsoftheotherreviewers.\nReviewer1\nRC: Therewassomeconfusion(comment1)aboutwhattheERFthatwascalculatedrepresented,butIthink\nthatthemainmanuscripthasbeenchangedtomakeitclearnowthattheERFvalueisjustduetothe\nmicrophysicaleffectondroplet/iceactivation. However,thisisstillnotthecaseintheabstractandIhave\nmadesomesuggestionsforthatbelow.\nAR: Thanks. Weanswerthesebelow.\nRC: Ithinkthatcomment2hasbeenaddressedreasonably.\nAR: Thanksfortakingthetimetoverifyit.\nRC: Comment3concernsthefactthatthe5kmresolutionofthehighresolutionmodelwon’tresolvealotof\ntherelevantmotionsandsuggestsmorediscussion. Iagreewiththispoint. Somediscussionhasbeen\nadded,butIthinkthatalittlemorediscussionwouldbehelpful. Imadesomesimilarcommentsbelow\nforSection2.", "reviewer_whicharerepeatedhere": "Hereitwouldbegoodtodiscussthatthereisalargerangeof\nscalesofupdrafts/turbulence(fromglobalcirculations,thoughtodeepconvection,shallowconvection,\nstratocumulus,small-scalemixingandturbulence). Andhowsub-gridσ willdependontheresolution\nW\nbeingconsidered–i.e.,whatisthesizeofthe“grid”. Inthisstudyyouusea5kmmodel,butalsoinclude\nsomesmallerscaleswiththeobservations–whatresolutions/scalesdotheobservationsusedtobuildthe\nmodelcover? Andwhatistheresolutionoftheaircraftobservationsusedtotestthemodel?\nAR: All observations of W, ground and aircraft-based, are retrieved with a frequency between 1 and 10 Hz,\ncorrespondingtoaspatialscalebetweenabout30mand300m[KalesseandKollias,2013,Newsometal.,\n2019],coveringmostoftherangesofatmosphericmotion. Fromtheseσ isestimatedusingacharacteristic\nW\ntime of 7 minutes for aircraft measurements and 5-30 minutes for ground based so that σ represents\nW\nvariabilityovera0.5to1degreegridcell. ThereislittlevariabilityinW resolvedinthe25-50kmrange\n(andabove50km)sowestillapplyWnettotheERA5reanalysis,withthecaveatthatitmightleadtoslight\nunderestimation. WedonotrecommendWnetforfinergridswithoutproperscaling,althoughour7kmnature\nrunsshowsthatalotofvariabilityisstillunresolvedatthatscale(henceourusageofobservationsdirectly).\nWehaveexpandedthemethodssectionclarifyingthispoint.\nRC: TheresultsfortheERFcalculationrestonchangesinstratocumuluscloudsinthesub-tropics,offthe\nwestcoastsofSAmerica,NAmericaandSWAfrica. However,thereislittlediscussionaboutthespatial\nscalesofverticalwindsintheseregimesandabouthowwellyourmodelcapturesthem. Oneapproach\nusedinthepast,whichshouldbediscussedinthispaper,hasbeentouseveryhighresolution(35-150m)\nLESmodelstoquantifyσ atstratocumulus-relevantscales(https://agupubs.onlinelibrary.\nW\n2\nwiley.com/doi/full/10.1002/2013JD021218). Dotheobservationsyouusedtobuildandtest\nthemodelcoverthesesmallerscalesforstratocumulusclouds?\nAR: Yes,W isretrievedatspatialresolutions(assumingergodicity)oftheorderoftenthsofmeters.Thisisenough\ntoresolvemostatmosphericmotion. Toemphasizethispointwehaveaddedaseparatesectiononvalidation\nwhereweexplainourapproachmorethoroughly. WehavealsoaddedmoreinformationtoTablesS1andS2\n(oftherevisedwork)detailingtheenvironmentandcloudregimesassociatedwitheachobservationaldataset.\nThanksforpointingtheLESworktous. Wenowaddthisstatementtotheintroductionaswell: Similarly\nσ can be explicitly resolved using Large Eddy Simulations (LES) which have been used to develop\nW\nparameterizationsforclimatemodeling,mostlyfocusingonstratocumulusregimes[Malavelleetal.,2014].\nLESarehoweverlimitedindomainanditisnotcleartowhatextenttheseresultsareapplicableacrossthe\nwidediversityofmeteorologicalenvironmentspresentintheatmosphere.\nReviewer2\nRC: Someofthecommentsmayhavecomefromunclearexplanationsintheintroductionandmethodssections\nand there has been some effort to improve that. However, this aspect could still be improved further\nandI’vetriedtomakesomesuggestionsforthatbelow. Itisstillnotclearlystatedwhetherthesub-grid\nparameterizedσ ofthehighresolutionmodelisusedalongwiththeresolvedupdrafts(Idon’tthinkitis,\nw\nbutI’mnotsurethatit’sstatedclearly).\nAR: There is no parameterization of σ in the GEOS 5 km model, nor in MERRA-2; σ comes from the\nW w\ncoarsening of W from the high resolution model to half degree. For the initial training of Wnet, σ is\nw\ncalculatedusing64valuesofW pereachhalf-degreegridcell.Thisleadstoaσ toolowagainstobservations,\nw\nwhichwecorrectinthesecondstageoftraining. ThisstephowevertellsWnethowtoextrapolatebetween\ndifferentconditionsandlocations,asdetailedinourpreviouswork.\nThisisnowclearlystatedinthemethodssection.\nRC: Thecomment“Whilethepaperiswellwritten,thestudyin-depth,andthefindingsoriginal,Ibelievethere\nareflawsinthedataanalysis,interpretationandconclusions.”,alsoquestionsthetypesofregimesampled\nbytheremotesensingusedtobuildWnetandtheaircraftobsusedtotestit. Iagreewiththisaspect–\nit’snotclearwhichtypesofcloud/meteorologicalregimeshavebeensampledbytheaircraftobservations\nfromthetablesandtextprovided. ThereissomeusefulinformationinTableS1forthegroundstation\ndata,butmoreinformationonthecloudtypessampledwouldbeuseful–does“PBL”coverstratocumulus\nandtradecumulus,orjustclear-airobservations? Norisitclearwhattheresolutionoftheobservations\nwas. Somemorediscussiononthisandthelimitationsiswarranted(linkingtothecommentsaboveabout\nstratocumulus). Ithinktheothercommentshavebeenaddressedwell.\nAR: Thanksfortheassessment. Wecarryoutarigoroustestingoftheresults,coveringawiderangeofmete-\norological environments and cloud regimes. We however agree that it was nor properly conveyed in the\nmanuscript.\nToaddressthereviewersconcernwehaveexpandedtheexplanationoftherelevantscalesinSection2and3.\nInparticularwehaveaddedthefollowingparagraphtothenewSection2.1onvalidation:\n“Insitumeasurementsandground-basedobservationsareusedtovalidatetheclimatologiesofσ . Sinceσ\nW W\nisgenerallynotmeasureddirectly,itisderivedfromhigh-frequency(typically1–10Hz)measurementsof\nW [Macieletal.,2023,Newsometal.,2019],correspondingtospatialscalesofroughly30–300m,which\nencompassmostverticalmotionsrelevantforcloudformation. Tocalculateσ ,theW fieldsarecoarsened\nW\n3\nusingacharacteristictimescalechosensothatσ representsthestandarddeviationofW overaspatialscale\nW\nofapproximately50–100km,makingitcomparabletothatofaGCMgridcell. Foraircraftmeasurements,\nσ wascalculatedusinga430saveragingwindow,representingahorizontalscaleof∆x∼100km[Maciel\nW\netal.,2023],coveringbothin-cloudandclear-skyregions. Forground-basedretrievals,thecharacteristic\ntimescaledependsonthemeanhorizontalwindateachsiteandrangesfrom15to30minutes,corresponding\nto∆x ∼ 50km[Barahonaetal.,2024]. Observationswerecollectedfrommultiplelocationsworldwide,\nspanning a widerange of cloud regimes (cirrus, stratocumulus, altocumulus, convective)and employing\nvariousmeasurementtechniquesforW,providinganunbiasedandindependentvalidationofσ .”\nW\nWehavealsoexpandedthetablessummarizingthecampaignsandgroundbasedsites(nowtablesS1andS2),\ndetailingtheenvironmentandcloudregimes.\n2.2. Myowncomments\nMajorComments\nRC: One of the key results (the “ERF” forcing due to σ changes) rests on the difference in the σ cli-\nW W\nmatologiesinthePDruncomparedtothatfromthecoupledPIrunbeingrepresentativeandnotjust\narandomchanceresult. E.g.,ifdifferent10yearsampleswerechosenoriflongersampleswereused,\nwouldtheresultsbethesame? Idon’tthinkthat10yearsislongenoughtocapturethevariabilitythatis\npossibleinacoupledsimulationsuchasthePIsimulation. InacoupledruntheSSTpatterns,sea-ice\namount,etc. willvaryalotand10yearswon’tcapturethis. Canyouusethefull180years? Evenforthe\nprescribedSSTrunsitistooshortsincetherewillbealotofnaturalvariabilityintheclimatethatyou\nwouldneedtosampleover. Theexact10-yearperiodchosenmayalsoinfluencethemeanclimatological\nwinds,circulation,etc. andit’spossiblethatdifferent10-yearperiodsmightgivedifferentresults. Iwould\nexpectalongerperiodtobeused(around30years). Itwouldbegoodtocompareresultsusing10,20,30\nyearstomakesuretheyarerobust. AndsimilarlyinthecoupledPIrun,althoughyouwillprobablyhave\ntoextendtoevenlongerperiodsforthat. Possiblyrelatedtothecommentaboveistheconcernthatthe\ndifferencesinσ betweenthePDandPIrunsdon’tseemconsistentwiththosebetweenthe+4KandPD\nW\nruns(Fig. S7). Thereisalsolittleresemblancetothelong-termtrends. Thisisaconcernasitsuggests\nthat the impact of warming and a changing climate is not being captured consistently with the model\nexperiments. Therearesomemoredetailedcommentsonthisbelow,butthelongerexperimentsabove\nshouldhelptoaddresstheissue. Morediscussiononthesediscrepanciesintheresultsisneeded.\nAR: Weappreciatethesecomments,astheypromptedustoreconsiderourapproach. Inouroriginalcalculations,\nweattemptedtoisolatetheeffectofσ oncloudmicrophysicsusingpreviouslyrunsimulations. Whilethis\nW\napproachwasconsideredreasonableatthetime,weagreewiththereviewerthatitisproblematicforthe\nreasonsoutlined. AddressingtheseissuesthroughaGCM-basedapproachwouldrequiregreatlyexpanding\nthesimulations,whichwouldbechallengingandstillsubjecttouncertainties. Instead,wehaveoverhauled\nourradiativeforcingcalculation,adoptingmethodscommonlyusedinsatellite-basedestimationsthathave\nprovensuccessfulintheliterature. Insummary", "reviewer_wehave": "n• Calculatedpre-industrialσ usingtheERA-20Creanalysis,whichisconstrainedbyrecordsofSST,\nW\nwinds,andsurfacewatervapor. Weusedtheperiod1901–1905,theearliestavailableinthedataset.\n• Focusedsolelyontheeffectofσ onlow-levelclouds,whereitsinfluenceisexpectedtobedominant,\nW\nweextendedawell-knownrelationshipusedtostudymicrophysicaleffectsonshortwaveforcing[Yuan\netal.,2024]:\n1 ∂lnN\n∆SW =−SW C A (1−A ) d∆lnσ (1)\ndown f c c 3∂lnσ W\nW\n4\nwhereSW isthedownwellingshortwavefluxatthetopofthecloudlayer(assumedheretobeequalto\ndown\nSW atthesurface[Tolletal.,2019]),C istheliquidcloudfraction,andA isthecloudalbedo.\nf c\nAlthough less sophisticated than a full GCM simulation, we consider this approximation more intuitive,\nrobust,andconsistentwiththedata-driven,model-independentnatureofourwork.\nWealsoconductedadetailedsensitivityanalysisof∆SW withrespecttoeachfactorintheaboveexpression.\nWhile we acknowledge the limitations of this approach, such as neglecting liquid water path and cloud\nfractionadjustments,aswellastheeffectsofσ oniceclouds,weexpectthesetobeminorgiventhemodest\nW\ninfluenceofσ onN .\nW d\nSection3hasbeenfullyrevisedtodescribeourupdatedapproach.\nDetailedcomments\n*********Abstract\nRC:\n•“Thiseffectishoweverdifficulttorepresent”-Betterwithoutthe“however”heresinceitisalsoused\nlaterinthesentence.\n•“howevercanbeaddressedbymachinelearning.”->however,itcanbeaddressed...\n•“indicatingenhancedturbulentatmosphere”->indicatedenhancedatmosphericturbulence?\n•“suggestingafeedbackconnectionbetweenenhancedwarming,turbulenceandcloudmicrophysics,\nwith an associated radiative forcing of about 0.1±0.21 W m2, slightly mitigating greenhouse\nwarming.” - You haven’t introduced the effect on microphysics here yet and it’s also not clear\nhere that the “forcing” is solely due to the microphysical effect. I suggest something like this –\n“suggestingafeedbackconnectionbetweenenhancedwarmingandturbulence,whichinturnhas\namicrophysicaleffectthroughtheactivationofmorecloudhydrometeors. Theforcingassociated\nwith the microphysical effect is estimated here to be about 0.1±0.21 W m2, slightly mitigating\ngreenhousewarming.”\nAR: Thankyouforthesuggestions. Theyhavebeenincorporatedinthetext.\n*********Introduction\nRC: “modernatmosphericmodels”–Youshouldspecifythesubsetofmodelsbeingtalkedabouthererather\nthanlaterintheparagraph-thisonlyappliesgenerallytoglobalmodels. E.g.,LESmodelshavemuch\nhigherresolution.\nAR: Thestatementnowreads: “modernglobalatmosphericmodels”\nRC: “Nevertheless,theGSRMsimulationsandtheextensivedatatheyproducerepresentawealthofinformation\nthatcanbeusedtounderstandandmodelW.Artificialintelligencetechniquesanddata-drivenalgorithms\nlikedeeplearning,arewell-suitedforthistask. Unliketraditionalnumerical simulationsortheories,these\nalgorithmslearnfromdatatomakepredictions.”–Youhaven’tintroducedobservationaldatayet–surely\nthisisnecessarytoallowtheAIapproachtoimprovethepredictionsoftheGSRMsimulations?\nAR: Thestatementnowreads“TheGSRMsimulationsandtheextensivedatatheyproducerepresentawealthof\ninformationthatcanbeusedtounderstandandmodelσ ,particularlywhencombinedwithobservational\nW\n5\ndatasets.”\nRC: “itwastrainedonresultsfromglobalstorm-resolvingmodelsimulations,”-Itwouldbehelpfultoquote\ntheresolutionofthemodelheretomakeitclearthatitisbuiltonhigh-resolutiondata. Idon’tthinkthatit\nismentionedinthewholepaper. Also,toavoidconfusionlater,itwouldbegoodtoclarifyinthissentence\nthatitistrainedtopredictσ asoutputfromthehigh-resolutionmodelasafunctionofthelowresolution\nW\nmeteorology(wasthisfromMERRAorfromthecoarsenedfieldsofthehigh-resmodel?)\nRC: “spatialdistributino”typo. “Byintegratephysicalrelationships”->Byintegrating...\nAR: Corrected.\nRC: “SincetheWnetwasbuiltusingdatafromreanalyseslikeMERRA-2,”-WasitbuiltfromMERRA2or\nfromcoarsenedfieldsfromthehigh-resmodel? Ifthelatterthenitwouldperhapsbebettertosaythatthe\nWnetmodelusesinputvariablesthatcanprovidedbyMERRA2.\nAR: Thestatementhasbeenmodifiedto: “SincetheWnetwasbuilttousevariablesthatcanbeprovidedby\nreanalyses,itispossibletoreconstructσ globallyusingtheiroutput.”\nW\nRC: “Our analysis revealed” - Better to say \"We will present an analysis that shows\" or similar since this\nsoundslikeyouarereferringtoyourpaststudy.\nAR: Thestatementhasbeenmodifiedas: “Wewillpresentananalysisshowingsignificanttrendsinσ emerging\nW\noverrecentdecades,withimplicationsnotonlyforcloudformationbutalsoclimatechangeassessments,and\nweatherandturbulenceforecast.”\n**********Results\nRC: “WeusedtheWnetmodeltocreatethree-dimensionalglobaldistributionsofWevery3hoursfrom1980\nto2023.”-itwouldbegoodtosaythatyouusedthemeteorologicalfieldsfromthe1980-2023MERRA2\nreanalysisasinputsintoWnettocreatetheclimatology. Betterherethaninthenextsentence. Fig. 1\ncaption-“Stipplinghighlightsstatisticalsignificancethe95%level”->“.... atthe95%level.\nAR: Corrected.\n**********Section2.1\nRC: - Here it would be good to discuss that there is a large range of scales of updrafts/turbulence (from\nglobalcirculations,thoughtodeepconvection,shallowconvection,stratocumulus,small-scalemixingand\nturbulence). Andhowsub-gridσ willdependontheresolutionbeingconsidered–i.e.,whatisthesizeof\nW\nthe“grid”. Inthisstudyyouusea5kmmodel,butalsoincludesomesmallerscaleswiththeobservations–\nwhatresolutions/scalesdotheobservationsusedtobuildthemodelcover? Andwhatistheresolutionof\ntheaircraftobservationsusedtotestthemodel?\nAR: Thiscommentisaddressedintheresponsetothemajorcomments.\nRC: TheresultsfortheERFcalculationrestonchangesinstratocumuluscloudsinthesub-tropics,offthe\nwestcoastsofSAmerica,NAmericaandSWAfrica. However,thereislittlediscussionaboutthespatial\nscalesofverticalwindsintheseregimesandabouthowwellyourmodelcapturesthem. Oneapproach\nusedinthepast,whichshouldbediscussedinthispaper,hasbeentouseveryhighresolution(35-150m)\nLESmodelstoquantifyσ atstratocumulus-relevantscales(https://agupubs.onlinelibrary.\nW\nwiley.com/doi/full/10.1002/2013JD021218). Dotheobservationsyouusedtobuildandtest\nthemodelcoverthesesmallerscalesforstratocumulusclouds?\n2018]aretypicallycoveredwithlowlevelstratocumulus. Wnetperformsverywellatthoseconditions(see\nnewFigure2).\nWehaveexpandedtheSectiontoclarifyourapproachtothetestingandvalidation,detailingthescalesand\nregimesinvolved. WealsorefertotheLESapproachintheintroduction.\n**********Section2.2\nRC: “depictedwithinFigure1.”–typo. “frequencysmallscaleprocesses”-typo\nAR: Corrected.\n**********Section3\nRC: “However, while turbulence indices are derived from the coarse meteorological state, σ indicates\nW\nvariabilityatthesmallscale,”-it’snotveryclearwhatyoumeanhere.\nAR: Thestatementhasbeenremoved\nRC: “correspondstoapreviouslyunexploredfeedbackmechanismmediatedbythemicrophysicalpropertiesof\ncloudsandthesmallscaledynamics.”-AlthoughthiswillbeinherentlyincludedinGCMsinsomeway\nsincemanypredictTKEasafunctionofthegridproperties,whichthenaffectsclouddropletactivation.\nAR: Inprinciple,yes,butitisunclearhowaccuratelycurrentmodelsreproduceobservedσ oritsglobaltrends.\nW\nMoreover,theroleofσ asafeedbackmechanismlinkinglarge-scaledynamicsandcloudmicrophysicshas\nW\nnotbeenisolatednorthoroughlyexplored.\nWehaverewordedthestatementas:\n“Whileitiswellestablishedthatlarge-scalecloudpropertiessuchascloudfractionandliquidwaterpath\n(LWP)aresensitivetochangesinSSTandwatervaporshifts,theirimpactonmicrophysicalpropertieslike\nclouddropletnumberandsize,remainslargelyunexplored. Thisrepresentsapotentialfeedbackmechanism\nmediatedbythemicrophysicalpropertiesofcloudsandsmall-scaledynamics.”\nRC:\n•“Weemployglobalatmosphericmodelling”-shouldsaywhichmodelinthemaintext.\n•“Near the surface, the difference between the PD and PI climatologies resembles the anomalies\nseeninFigure1.”-Ican’tseeanysimilaritybetweenFig. 1fandthelower-middleplotinFig. S7\n(900hPaPD-PI)ifthat’swhatyou’rereferringto. Norfor500hPa. Plus,thePD-PIplotshavethe\noppositesigntotheSST+4K-PDplots. Iwouldtrustthelatterabitmoresincebotharedrivenby\nSSTsanddon’trelyonthe10-yearcoupledclimatologies. Thesignalsarecertainlymuchstronger\nathigheraltitudes.\n•“Nearthesurface,thedifferencebetweenthePDandPIclimatologiesresemblestheanomaliesseen\ninFigure1.”-Whywouldadecreaseincloudinessleadtolessconvectivetransportofwater. Where\ndoesthecloudinessdecrease?\n•“EnhancedWintheseregionsleads”-Whichregions? Low-cloudstratocumulusregions? Which\nonesinparticular? Ican’treallyseeanyenhancedσ intheseregionsinFig. S7? “FigureS9). In\nW\nconstrast,extra-tropicalregionsinthePacificexperiencetheoppositeeffect. Dryingatlowerlevels\n7\nweakensWasconvectivecloudsdeepenintheTropics[37]. AsensitivityrunusingaWclimatology\ngenerated from a scenario with present-day SSTs increased by 4 K [43] suggests that as drying\nintensifies,Wweakensevenfurther,”\n• Figs. 4andS10-ItseemsthattheSWCEandLWCEchangesshouldbenegatedtomakethemnet\nincoming(insteadofoutgoing)TOAvaluesforconsistencywiththeERF(sincethevalueinFig. 4\nisnegativegivinganoverallcooling)? AndthedefinitionofERFinthecaptioniswrong–itshould\nbenetincomingTOAradiativeforcing?\n•“the radiative forcing obtained after 50 years of integration.” - so do you use the 10 year σ\nW\nclimatologyrepeated5times? Youshouldmakeitclearerwhatyoudid.\n• -ThechangeinlowcloudcovermustcomefromthechangeinNdsinceσ onlyaffectsthemicro-\nW\nphysics. Andsothereductionincloudcoverprobablycomesfromtheprecipitationenhancementas\naresultofthereducedNd? Itwouldbegoodtomentionthis.\nAR: Wegreatlyappreciatethereviewer’sanalysis. Inouroriginalapproach,weused1-yearσ climatologies,\nW\nobtained by averaging the input state from a previous simulation over ten years and then running a new\nsimulationrepeatingtheclimatology50times. Whilethiswasconsideredsound,weacknowledgethatit\nreliedonasinglemodelandasinglesetofsimulations,makingitinherentlyuncertain.\nWehavesincerealizedthat,inattemptingtobeasrigorousaspossible,wemayhaveintroducedadditional\nuncertainty. TheindividualeffectsofmanyvariablesaredifficulttoisolateusingGCMsimulationsunlessthe\nmodelingframeworkisgreatlyexpanded,forexamplebyemployinglargeensemblesorlong-termcoupled\nruns.\nEven with such an expansion, extracting a clear signal would remain challenging. Therefore, we have\nreworkedthesectiontofocusexclusivelyontheeffectofσ onlow-levelclouds,asexplainedinthemajor\nW\ncommentssection. Wehavealsoincorporatedthereviewer’sfeedbackwhereapplicable.\nRC: “couldprofoundlyimpactclimatepredictions.”-Thisseemsabitofastretchfora0.2W/m2forcingwith\nlotsofuncertainty! Thelanguageneedsmodifyinghere.\nAR: Thestatementhasbeenremoved.\n************Section5.1\nRC: “trainedusinglong-termretrievalsof”-wouldbegoodtoreiteratethattheseareobservations.\nAR: Thestatementhasbeencorrected.\n***********Section5.2\nRC: “rangingfrom20secondsto5minutes,”-Isitpossibletogiveahorizontalresolution? E.g.,fora10m/s\nhorizontalwindspeedthiswouldbearesolutionof200-3000m.\nAR: Intheoriginaldescriptionpaper[Barahonaetal.,2024]thisreferstotheaveragingtimetoobtainσ . The\nW\nfrequencyofW retrievalsismuchhigher(1–10Hz),correspondingtoahorizontalresolutionofapproximately\n30–300m.\nWeacknowledgeitisamisleadingstatement. Ithasbeenmodifiedto:\n“To correct for this, a refinement step was introduced, where Wnet was further trained using long-term\nobservations,whichderiveσ fromhighfrequencyradarandlidarretrievalsofW (∼ 1−10Hzwitha\nW\n8\ncorrespondingspatialresolution∆x ∼ 30−300m),coveringmostatmosphericmotionrelevanttocloud\nformation.”\n********Section5.3\nRC:\n•“theMERRA-2reanalysisandthefree-runningsimulation.”-Makeitclearerwhatthesetwoare-\nisthereanalysisthesameasthe\"long-termMERRAclimatologyrun\"andisthe\"free-running\"\nsimulationtheprescribedSSTPDrun?\n•“,takenfromeachofthepreviously-developedWclimatologies.”-Makeitclearwhatthe3climatolo-\ngieswere. PresumablyPI,PDandPD+4K?\nAR: Thesestatementshavebeenremovedasthesectionhasbeenmodified.\nReferences\nHeikeKalesseandPavlosKollias. ClimatologyofhighclouddynamicsusingprofilingARMDopplerradar\nobservations. Journalofclimate,26(17):6340–6359,2013.\nRKNewsom,ChitraSivaraman,TRShippert,andLDRiihimaki. DopplerLidarverticalvelocitystatistics\nvalue-addedproduct. Technicalreport,DOEARMClimateResearchFacility,Washington,DC(United\nStates),2019.\nFlorentF.Malavelle,JimM.Haywood,PaulR.Field,AdrianA.Hill,StevenJ.Abel,AdrianP.Lock,BenJ.\nShipway,andKirstyMcBeath. Amethodtorepresentsubgrid-scaleupdraftvelocityinkilometer-scale\nmodels: Implicationforaerosolactivation. JournalofGeophysicalResearch: Atmospheres,119(7):4149–\n4173, 2014. . URL https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/\n2013JD021218.\nFlorVanessaMaciel,MinghuiDiao,andRyanPatnaude. Examinationofaerosolindirecteffectsduringcirrus\ncloudevolution. AtmosphericChemistryandPhysics,23(2):1103–1129,2023. .\nDonifan Barahona, Katherine H Breen, Heike Kalesse-Los, and Johannes Röttenbacher. Deep learning\nparameterization of vertical wind velocity variability via constrained adversarial training. Artificial\nIntelligencefortheEarthSystems,3(1):e230025,2024. .\nTianleYuan,HuaSong,LazarosOreopoulos,RobertWood,HuishengBian,KatherineBreen,MianChin,\nHongbinYu,DonifanBarahona,KerryMeyer,etal.Abruptreductioninshippingemissionasaninadvertent\ngeoengineering termination shock produces substantial radiative warming. Communications Earth &\nEnvironment,5(1):281,2024. .\nVelleToll,MatthewChristensen,JohannesQuaas,andNicolasBellouin. Weakaverageliquid-cloud-water\nresponsetoanthropogenicaerosols. Nature,572(7767):51–55,2019. .\nJasmineRémillard,PavlosKollias,EdwardLuke,andRobertWood. Marineboundarylayercloudobser-\nvationsintheazores. JournalofClimate, 25(21):7381–7398, 2012. . URLhttps://journals.\nametsoc.org/view/journals/clim/25/21/jcli-d-11-00610.1.xml.\nMeteorological Monographs"}, "author_response": ""}]

Decision letter