We use the ICOsahedral Non-hydrostatic modelling framework of the Max Planck Institute for Meteorology (ICON-MPIM), which simulates all Earth system components on an icosahedral-triangular C grid . ICON comprises three main components: ocean, land, and atmosphere. For the land surface, it employs the Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg (JSBACH) version 4, providing boundary conditions such as albedo, roughness length, and flux parameters, while solving the surface energy balance coupled with atmospheric diffusion equations . It features a multi-layer soil hydrology scheme for water storage and a hydrological discharge model for routing runoff into the oceans . In the atmospheric component, ICON-MPIM utilises a hybrid sigma-z vertical coordinate system (the SLEVE scheme), incorporating a Rayleigh damping layer in the upper atmosphere . Within its regional configuration, a sponge layer is implemented along the lateral boundaries to prevent outward-propagating waves from reflecting, with the interior flow relaxed towards externally specified boundary data. In supersaturated regions, positive water vapour increments are cut to zero in the nudging zone to avoid an artificial increase in cloud water . ICON-MPIM employs radiation, cloud microphysics, and turbulence schemes, while avoiding parameterisation for shallow convection or subgrid-scale clouds to better resolve km-scale dynamics . Its radiation scheme employs the Rapid Radiative Transfer Model for General circulation model applications-Parallel (RRTMGP) , whereas turbulence is parameterised using a modified Smagorinsky scheme suited for cloud-resolving simulations . This configuration prioritises computational efficiency to explore Earth system dynamics and sensitivities to unresolved processes . Cloud microphysical processes are represented using the single- and double-moment schemes of and , respectively.

The single-moment scheme predicts mass mixing ratios of cloud water, rain, cloud ice, snow, and graupel. Warm-phase processes are represented by the parameterisation of , reduced to one-moment form by assuming a fixed cloud droplet number concentration of Nc=200 cm-3, consistent with the ICON one-moment microphysics default for continental conditions, and within the range reported for moderately polluted conditions in the Amazon during the wet season see, e.g.,. Raindrop growth and sedimentation are modelled using exponential size distributions with empirically derived terminal fall speeds. Cold-phase processes include graupel formation via raindrop freezing, cloud ice riming, and snow-to-graupel conversion when cloud water exceeds 0.2 gkg-1. Snow microphysics utilise temperature-dependent intercept parameters in exponential size distributions, enhancing the treatment of slower-falling aggregates at higher altitudes . Graupel particles are assumed to have low density and moderate fall speeds based on the relationships of .

The double-moment scheme predicts both mass and number concentrations of the same hydrometeors, including hail. Raindrop formation employs a stochastic bulk parameterisation, with autoconversion and accretion rates depending on both mass and number concentrations, making them sensitive to droplet size and concentration. Hydrometeor size spectra are represented with a modified gamma distribution, whose parameters are diagnosed from the prognosed mass and number concentrations. Cold-phase processes include size-dependent collection and freezing efficiencies. Graupel forms through riming of snow and cloud ice, as well as melting and refreezing. Ice-phase interactions use collision efficiencies derived from , which account for the dependence of collection probability on particle size and relative fall speeds, providing a more realistic representation than the constant efficiencies assumed in simpler schemes. By prognosing number concentrations, the scheme explicitly represents processes such as ice multiplication, sedimentation velocities, and phase transitions under varying humidity and temperature conditions.

To explore the sensitivity of cloud microphysics schemes, we compare two regional simulations using different microphysics schemes. To examine how these sensitivities interact with domain configuration, we also analyse a global simulation. All simulations are conducted using a horizontal grid spacing of approximately 5 km. The atmosphere and land components use time steps of 10 and 40 s in the regional and global setups, respectively, with radiation calculated every 3 and 12 min, consistent with stable configurations commonly used within the respective ICON configurations and compatible with available computational resources. Additional sensitivity tests using a regional configuration with the same atmospheric time step as the global simulation showed negligible differences in the analysed fields, indicating that the selected time steps do not materially affect the results presented here.

The regional simulations use single- and double-moment microphysics schemes and are centred over the Amazon basin, spanning approximately 26° longitudinally (68–42°W) and 18° latitudinally (15°S–3°N). The global simulation employs the single-moment scheme and uses identical physical and numerical settings. From this simulation, the same Amazon region is selected (see Fig. 1) to enable direct comparison with the regional runs and to isolate the effects of domain configuration from those of microphysics representation. A 12 d spin-up (20–31 January 2020) is included to ensure the global simulation reaches a statistically stable state , with data collected and analysed across all simulations from 1 to 7 February 2020.

ERA5 reanalysis data from the Copernicus Climate Change Service is used to initialise all three simulations and provide lateral boundary conditions for the regional runs. Although all simulations undergo a spin-up period, the global configuration requires a substantially longer spin-up than the regional runs. Consequently, the atmospheric states of the simulations can diverge at the start of the analysis period, as shown in Fig. 2. The two regional simulations (Fig. 2b, c) remain nearly identical and closely resemble the ERA5 initial state (Fig. 2a). In contrast, the global simulation (Fig. 2d), despite reproducing the main large-scale features of ERA5, exhibits noticeable deviations characterised by cooler surface temperatures and a less organised near-surface wind pattern.

Boundary conditions are updated every 6 h and linearly interpolated between time intervals. As with the initial conditions, the atmospheric state within the boundary zone remains closely constrained to ERA5 in both regional simulations, while the freely evolving global simulation can diverge substantially over time. To quantify these differences, Fig. 3 compares ERA5 boundary conditions with the corresponding boundary-region fields from the global simulation, sampled at the same frequency as ERA5 input. The comparison includes mean vertical profiles of specific humidity (Fig. 3a) and cloud water and ice (Fig. 3b), where solid lines indicate cloud water and dashed lines indicate cloud ice, as well as time series of surface pressure (Fig. 3c) and surface temperature (Fig. 3d). Figure 3 shows notable differences between ERA5 and the global single-moment simulation, including a near-surface specific humidity offset of about 2gkg-1, divergence in vertical cloud profiles, and differences in the temporal evolution of surface pressure. Additional filtering tests (not shown) indicate that these differences persist beyond the dominant diurnal variability, suggesting that the global simulation evolves toward a distinct large-scale state during the analysis period. This behaviour is absent in the regional simulations, which remain continuously relaxed toward ERA5 at their lateral boundaries. In addition, the global simulation produces substantially more low-level cloud, whereas ERA5 shows greater amounts of mid-level and anvil cloud.

To assess simulated surface precipitation rates and distributions, we use the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Mission (IMERG; ). IMERG estimates are derived from a combination of satellite observations and ground-based gauge measurements. Its high temporal (30min) and spatial (0.1°) resolutions make it ideal for verification and comparison with CRM simulations. However, its evaluation for regional analysis is uncertain due to the lack of consistent ground-truth data .

Despite this, IMERG effectively captures spatial precipitation patterns in regions with sparse rain gauges and complex topography , although its performance varies seasonally and geographically. Biases have been reported in the accuracy of winter precipitation over South America , including misrepresentation of heavy rainfall and overestimation of moderate events , and underdetection of low-intensity precipitation due to limitations of passive microwave retrieval . These issues are especially relevant in regions with strong seasonal variability, where the reliability of IMERG diminishes . However, IMERG remains valuable for evaluating simulated precipitation due to its high spatial and temporal resolution.

For deep-convection spatial distribution and occurrence, we use brightness temperature (BT) observations from the Advanced Baseline Imager (ABI) of the first satellite of the Geostationary Operational Environmental Satellites (GOES-16) series as a proxy for outgoing longwave radiation (OLR). We estimate OLR from ABI level 2 in the cloud and moisture imagery product (MCMIP) channel 11.2µm using the relationship from between the observed longwave window BT and the flux equivalent BT. This provides an OLR estimate that is most representative of high cloud tops, although it does not capture the full longwave spectrum.

To evaluate the simulations against in-situ observations, we use measurements from the Amazon Tall Tower Observatory (ATTO; ), located in the central Amazon rainforest of Brazil. To constrain near-surface fog occurrence, we use ATTO's CHM15k ceilometer (Lufft, Fellbach, Germany), a vertically pointing backscatter lidar that provides continuous attenuated backscatter profiles and cloud base height retrievals, from which fog occurrence is inferred when the reported cloud base height falls at or near the surface (below 200 m). In addition, station visibility reports from the global Surface Synoptic Observations (SYNOP) network (; https://library.wmo.int/idurl/4/35713, last access: 24 March 2026) are used to evaluate near-surface fog occurrence across the wider region. SYNOP (code form FM-12) is the World Meteorological Organization standard alphanumeric format for reporting surface meteorological observations from fixed land stations, both staffed and automatic, at standard synoptic times (00:00, 06:00, 12:00, and 18:00 UTC).

Fog formation is fundamentally governed by thermodynamic and dynamic conditions, including surface temperature, near-surface humidity, and boundary-layer structure. In this study, we do not aim to evaluate the absolute realism of simulated fog, but rather to characterise how fog responds to the two controlled differences between runs: the choice of microphysics scheme and the domain configuration. Although the regional simulations evolve freely within the domain interior, their large-scale thermodynamic evolution remains similar throughout the analysis period (not shown). Differences in fog characteristics between the regional runs can therefore be largely interpreted in terms of the differing microphysical representation, while differences between the regional and global runs additionally reflect the influence of large-scale dynamics. Figure 6 compares the spatial distribution of simulated fog occurrence frequency with SYNOP surface observations. Observed fog events are defined by horizontal visibility below 1.5 km, while model fog occurrence is defined as the normalised frequency of near-surface cloud liquid water mixing ratio exceeding 0.01 gkg−1. To ensure consistency, the model evaluation is restricted to the observation times. The two regional simulations exhibit broadly similar spatial fog distributions and occurrence frequencies, with enhanced fog occurrence near the northeastern coastal region and over parts of the southwestern domain. In contrast, the global single-moment simulation produces substantially broader and more persistent fog occurrence, particularly across the western half of the domain, with fog extending farther northward than in the regional configurations. The SYNOP observations indicate the highest fog occurrence frequencies at several stations within the western part of the domain. This behaviour is more consistent with the broader fog distribution produced by the global simulation, whereas the regional simulations remain comparatively fog-free in parts of this region. However, the global configuration also produces extensive fog occurrence in areas where the observational frequency remains relatively low, suggesting a positive bias in fog spatial coverage. Overall, the regional simulations produce more spatially confined fog occurrence patterns, while the global configuration favours more widespread and persistent fog formation.

As shown in Fig. 6, the domain-mean fog time series in Fig. 7a highlights significant differences in the fog diurnal cycle among the simulations. All three simulations exhibit similar diurnal cycles, starting around 20:00 LT and lasting approximately 12 h. As shown in Fig. 7c, this timing aligns well with the ATTO ceilometer observations, suggesting that ICON predicts the onset and peak of fog occurrence with relatively high reliability. However, amplitude differences are notable, with the double-moment simulation peaking at 0.025 gkg-1, the regional single-moment at 0.130 gkg-1, and the global single-moment at 0.295 gkg-1. Despite these large differences in magnitude, the consistent diurnal phase across all runs suggests that the underlying dynamical and thermodynamic triggers are similarly represented across configurations. Furthermore, while the onset is well-captured, all simulations exhibit a clear bias during the dissipation phase compared to observations, with fog persisting longer in the models than indicated by ATTO reports. Interestingly, the global simulation shows a decrease in fog on day five, the only instance where its mean value falls below that of the regional simulation. The subsequent recovery by day seven coincides with changes in circulation patterns. Because the regional simulations are constrained by reanalysis data at their lateral boundaries, whereas the global run evolves freely, external influences outside the Amazon basin modify local conditions through internally generated dynamics. These changes enhance vertical mixing and horizontal transport, which remove moisture from the boundary layer and reduce fog formation. While the time series in Fig. 7a shows some similarities, the probability density function (PDF) in Fig. 7b (shown on log-log axes) highlights clearer differences. The single-moment simulations display comparable distributions, with more frequent fog occurrences across most of the range. However, the distribution predicted by the double-moment simulation is shifted to lower values compared to the regional simulations, with a maximum value reaching approximately 1gkg-1. This highlights that, under near-identical thermodynamic boundary forcing, the microphysics scheme is the primary source of inter-simulation variability in fog intensity, consistent with , who identify droplet size distribution tuning as a key driver for fog variability.

Analysis of the warm-phase processes reveals time series differences in water vapour path, LWP, and rain water path across all three simulations (Fig. 8). While all simulations agree on the general cycle, differences in amplitude are evident. The regional simulations show nearly identical water vapour distributions (Fig. 8a), with differences less than 0.5 %, whereas the global simulation exhibits levels up to 5 % higher. For LWP and rain (Fig. 8b and c), the single-moment simulations remain closely aligned throughout most of the experiment, while the double-moment simulation shows significantly lower LWP (up to half as much) and higher rain values (up to twice more). The global simulation also demonstrates greater diurnal variability in LWP, with a larger peak-to-peak range compared to the regional runs. Maximum LWP values differ notably; the average difference is 102.7gm-2 between the regional simulations and 16.6gm-2 between the single-moment simulations.

To complement warm-phase processes, we examine surface precipitation. Unlike rain water path, which represents the vertically integrated rainwater content, precipitation refers to the flux of falling hydrometeors reaching the surface. The time-averaged surface precipitation rate maps for the three simulations are presented alongside IMERG estimates (Fig. 9) to directly compare spatial distribution. The simulations closely match IMERG in spatial distribution, with the northern region remaining relatively dry. Both simulations accurately predict heavier coastal precipitation in the northeast and several organised convection centres, mainly in the south. However, both the single- and double-moment simulations overestimate precipitation intensity compared to IMERG. To enable consistent spatial comparison, the simulated data is regridded to the IMERG grid (approx. 10 km). However, it is important to interpret these discrepancies with caution. Although IMERG provides precipitation data on a fine spatial grid, its effective resolution, i.e., the smallest reliably resolved feature, is coarser than the nominal 0.1° grid due to limitations in satellite retrieval capabilities . Its accuracy varies by geographic setting and precipitation type; it tends to underestimate heavy rainfall and overestimate moderate events, especially in tropical and mountainous regions, and performs less reliably during winter or in complex terrain . These biases may partly explain the differences in precipitation intensity.

To further examine these differences, Fig. 10 presents the time series of domain-mean surface precipitation rate (Fig. 10a) alongside its logarithmic scale PDF (Fig. 10b). While the simulated results broadly capture the diurnal cycle observed by IMERG, they tend to overestimate precipitation, often by nearly a factor of two, as on day four. Although precipitation values are generally similar across simulations, subtle differences arise; the global simulation consistently diverges from regional counterparts, particularly at minima and increasingly at maxima from day five onward. Notably, the relative ordering of simulations differs between rain water path (Fig. 8c) and surface precipitation rate (Fig. 10a). This discrepancy is particularly pronounced in the double-moment scheme, which sustains the highest rain water path despite similar domain-averaged precipitation rates. This apparent decoupling indicates that a larger in-column rainwater burden does not translate directly into enhanced surface precipitation, which likely reflects differences in the partitioning of rainwater within the column and in processes affecting its conversion to surface precipitation, such as evaporation or recycling into mixed-phase cloud processes.

Figure 10b reveals that all simulations overpredict the frequency of precipitation across most intensities compared to IMERG, especially at higher precipitation rates. The regional simulations exhibit similar distributions, with the double-moment predicting marginally lower probabilities for rates exceeding 150mmh-1. The global simulation diverges from the regional simulations, particularly at the tail of the distribution, underestimating the probabilities of extreme precipitation. At low rates, IMERG data decrease more gradually than the simulations, which could reflect genuine model overestimation of light rainfall, but may equally arise from IMERG's known underdetection of weak precipitation due to limitations of passive microwave retrieval e.g.,.

Differences in the ice-phase hydrometeors are illustrated by the time series of domain-mean IWP, and graupel and snow water paths from the three simulations (Fig. 11). For IWP (Fig. 11a), the single-moment runs remain closely aligned during the first half of the experiment (days one through four), whereas the double-moment simulation produces nearly twice the peak values. In the second half of the experiment (days four through seven), IWP in the global single-moment run increases sharply, by about 10gm-2, and exceeds that of the double-moment run, which decreases and approaches the regional single-moment values. The regional single-moment simulation shows a slight decline in IWP after day four, but the change is less pronounced. Graupel behaviour (Fig. 11b) differs substantially across schemes. The single-moment simulations exhibit similar diurnal cycles, although the regional run maintains elevated graupel concentrations slightly later into the night. The double-moment simulation produces markedly larger graupel amounts, reaching peaks over 400gm-2, about five times higher than the single-moment runs, and maintains high values with little nocturnal relaxation. Snow evolution (Fig. 11c) shows the opposite tendency. The double-moment simulation maintains snow levels nearly constant at low values, around 10 gm-2, whereas both single-moment runs produce considerably higher and more variable amounts. During the first half of the experiment, snow in the single-moment simulations ranges between 50 and 100 gm-2; after day four, peak values increase to about 120 gm-2 in the regional run and up to 200 gm-2 in the global run. The contrasting behaviour of graupel and snow across schemes highlights the differing representations of mixed-phase processes and tuning.

The time-averaged OLR maps from the three simulations are compared with GOES-16 observations (Fig. 12) to assess the simulated cloud coverage. As noted in Sect. 2.3, the window-channel OLR proxy should be interpreted with caution for low- and mid-level clouds. GOES-16 shows suppressed OLR in the southern part of the domain (200–220 Wm-2), with a compact low-OLR core extending southeastward and a smooth gradient toward higher OLR over the north. Both regional simulations reproduce the north–south gradient, though with sharper transitions than observed. The double-moment run (Fig. 12b) captures the location and magnitude of the low OLR core more closely, while the global run (Fig. 12c) extends the suppressed region too broadly. In contrast, the regional single-moment simulation (Fig. 12a) maintains generally higher OLR values and weaker suppression, underestimating deep convection compared to GOES-16. While neither simulation fully reproduces the observations, the double-moment run exhibits better agreement in matching the structure of the suppressed region. In contrast, the global simulation effectively captures the magnitude of this feature, albeit with an exaggerated spatial extent.

The time series of domain-mean OLR in Fig. 13a shows that all simulations capture the diurnal cycle, with peaks and troughs generally aligning with GOES-16. In the first half of the experiment (days one through four), the global simulation maintains higher OLR daily mean values than both regional simulations and GOES-16, then drops from day five onwards. This difference, as expected from the free-running global setup, aligns with the transition seen in IWP (Fig. 11a), suggesting evolving cloud characteristics. Taken together, the decrease in fog, increase in IWP, and drop in OLR in the global simulation during the second half of the experiment suggest a coherent shift in cloud regime, discussed further in Sect. 4. Among the simulations, the double-moment model most closely follows GOES-16 retrievals at minima, typically associated with deep convection. All simulations, however, show higher daytime OLR maxima than GOES-16 (except during days five to seven in the global simulation), indicating shorter-lived or less extensive upper-level clouds, including cirrus and anvil clouds, which can maintain low OLR after convection collapses. From the PDFs shown in Fig. 13b, all simulations predict enhanced probabilities at the high-OLR tail, possibly indicating warm surface biases or reduced high cloud cover. They also show a broader peak around 275Wm-2, compared to the narrower GOES-16 peak. A secondary peak near 220Wm-2, potentially representing mid-level convective cloud tops, is captured in both regional simulations, although its width is underestimated relative to GOES-16. As mentioned above, these regions of the distribution should therefore be interpreted with caution when compared to GOES-16, particularly where the window-channel proxy is less reliable. At lower OLR values (below 150Wm-2), indicative of deep convection cloud tops, the single-moment regional simulation underpredicts occurrence; the global simulation aligns closer with the double-moment model, implying similar anvil coverage, consistent with Figs. 4, 11a and 13a.

The choice of microphysics scheme influences the simulated cloud processes and hydrometeor distributions. The single-moment scheme consistently produces higher LWP, fog, and snow values compared to the double-moment scheme, with differences reaching up to 100 %, 400 %, and 1400 %, respectively (Figs. 5, 7, 8, 11). These differences arise from the way hydrometeor evolution responds to the underlying microphysical assumptions within each scheme. Overall, these results are consistent with previous studies, showing that single-moment schemes tend to overestimate low-level cloud condensate and precipitation rates, particularly in convective environments e.g.,.

On average, the single-moment simulations produce more warm-phased clouds and thinner, less vertically extended anvil clouds compared to the double-moment scheme (Fig. 5a and b). The reduced anvil cloud cover in the single-moment scheme may be due to its reliance on fixed-size distributions and riming thresholds, which can suppress effective sedimentation or ice mass flux aloft. In contrast, the increased flexibility of the double-moment scheme in representing evolving size spectra can support sustained anvil development. This aligns with previous studies suggesting that changes in size distribution parameters and number concentrations can influence both precipitation efficiency and the vertical extent of ice clouds .

On the other hand, the single-moment scheme consistently predicts less rain, graupel, and IWP compared to the double-moment scheme, with observed differences reaching up to 100 %, 400 %, and 100 %, respectively (Figs. 8, 11). The formation of graupel, a key contributor to precipitation efficiency, relies on the accretion of cloud water, which is treated with greater process-level flexibility in the double-moment scheme. Additionally, differences in warm-rain processes, such as autoconversion and collection efficiencies, likely contribute to the observed discrepancies in precipitation (Fig. 9), with studies showing that double-moment schemes more accurately represent the sensitivity of rain formation to variations in droplet size and concentration, which can modulate rainfall efficiency across different convective regimes . Despite this, the two schemes yield similar levels of domain-averaged precipitation, OLR, and water vapour (Figs. 10, 13, and 8). This behaviour is consistent with the differing evolution of rain water path and surface precipitation (Sect. 3.2), highlighting that microphysical changes can redistribute rainwater within the column without proportionally altering surface fluxes. This suggests that while microphysical processes strongly influence the distribution and characteristics of hydrometeors, the domain-integrated outputs appear less sensitive to the specific microphysical parameterisation over these scales. This finding aligns with the conceptual framework of , which posits that at large spatial scales, atmospheric moisture and energy constraints can limit the impact of microphysical perturbations. While our simulation period is too short to assume a balance between precipitation and evaporation, the results support the idea that at broader scales, the divergence of water vapour (i.e., atmospheric dynamics) acts as a primary driver of precipitation variability, potentially dampening the signature of individual microphysical pathways on larger scales.

Interestingly, there are cases where the single-moment scheme simulation has demonstrated closer approximations to observed storm characteristics, such as in simulations of Super Typhoon Sarika (2016), where single-moment schemes produced a stronger storm than double-moment schemes . These findings highlight that the relative performance of microphysics schemes may depend on the specific meteorological context and the processes dominating the cloud system under consideration.

However, it is important to address additional limitations associated with the choice of microphysics schemes. Double-moment schemes require greater computational load due to the larger set of prognostic variables they introduce (typically twice as many). Although this enables a more detailed microphysical representation, it does not necessarily improve the accuracy of predicted integrated quantities, such as precipitation or radiative fluxes . Beyond computational cost, both schemes rely on tuning parameters that influence their simulated behaviour and may contribute to the observed divergence. Although tuning can improve agreement with observations, it reduces generalisability across regimes and complicates direct comparisons between schemes. While these aspects are beyond the scope of this study, they should be considered when interpreting the results.

While microphysics schemes primarily drive differences in cloud structure and hydrometeor distributions, the domain configuration modulates these sensitivity manifestations. The global single-moment run produces higher values of cloud ice, fog, precipitation, and water vapour than the regional single-moment run, with differences reaching up to 20 %, 150 %, 30 %, and 10 % respectively (Figs. 5, 7, 8, 10). These contrasts reflect the influence of large-scale circulation and energy transport, which are shaped by the domain size and the treatment of boundary conditions . Whereas the regional setup constrains circulation and thermodynamic profiles through sponge layers and prescribed boundary conditions, the global configuration allows the development of internally consistent synoptic-scale convection, enhancing moisture transport and convective organisation.

Temporal evolution highlights how the differences accumulate over time, with similar initial IWP values between the single-moment simulations and nearly doubled peak values in the global run during the second half of the experiment (Fig. 11a). This deviation, independent of the initial state (Fig. 2), points to the growing impact of boundary effects on cloud properties and moisture structure as simulations progress, consistent with studies showing that convective triggering and intensity, boundary-zone divergence and large-scale advection are sensitive to boundary conditions . This behaviour is consistent with the large-scale boundary discrepancies identified in Fig. 3, which indicate that the global simulation progressively evolves toward a distinct large-scale state relative to the ERA5-constrained regional simulations. Such divergence likely contributes to the increasing differences in cloud structure between the global and regional configurations over time. A targeted way to disentangle these effects would be to repeat the global simulations with a substantially shorter spin-up, so that the configurations are compared under nearly identical synoptic conditions, thereby separating differences in model physics from those caused by divergence in the evolving large-scale state.

Fog formation provides a clear example of this interaction between microphysics and domain-scale dynamics. Although fog is mainly influenced by the microphysics scheme, the higher near-surface water vapour in the global simulation (Fig. 5f) promotes a more persistent fog (Fig. 7), likely due to weaker boundary-layer ventilation and reduced horizontal advection. This persistence is reinforced by a feedback mechanism in which fog reduces surface heating, slows vapour removal, and sustains itself under inversion capping until dissipated by solar forcing. Such processes illustrate how domain-scale dynamics can amplify or prolong microphysics-induced variability . The simultaneous increase in IWP and decrease in OLR during the latter part of the global simulation is consistent with a transition toward deeper, more organised convection, which may contribute to enhanced vapour removal and the subsequent reduction in fog.

To assess whether thermodynamic biases underpin the fog differences, we compared domain-mean diurnal cycles of surface temperature, pressure, relative humidity, and wind between the runs (not shown). The two regional simulations exhibit nearly identical thermodynamic profiles, consistent with their shared lateral boundary conditions, whereas the global run diverges. Crucially, fog characteristics differ strongly between the two regional runs despite their thermodynamic similarity, which supports the interpretation that the microphysics scheme, rather than thermodynamic state, is the primary driver of inter-simulation fog variability. The global run enhanced fog is therefore influenced by both its distinct thermodynamic environment and unconstrained dynamics, acting on top of the microphysical differences. Although microphysical and thermodynamic processes are inherently coupled, the similarity of thermodynamic conditions between the regional runs provides a useful natural control that helps isolate the microphysical contribution in this case.

The longer time step used in the global simulation (40s vs. 10s regionally) may also influence process rates and biases such as fog and warm-cloud formation . However, sensitivity tests (not shown) with the global time step applied within the regional configuration show only modest quantitative reductions in domain-mean fog, without altering the qualitative differences between the configurations. This suggests that the time-step choice contributes to the magnitude of the divergence, and does not affect the primary sensitivities to domain configuration and microphysics identified here.

Taken together, these results demonstrate that, while the microphysics primarily controls cloud structure and hydrometeor characteristics, domain size shapes the dynamical environment in which these sensitivities are expressed. Global simulations enable the growth of large-scale circulations and moisture transport, which can amplify or reshape microphysics-driven differences, thus reinforcing the broader conclusion that integrated outputs, such as precipitation and OLR, are ultimately governed by this large-scale dynamical framework.