Autoconversion Parameterization Research Articles

Improving Simulations of Warm Rain in a Bulk Microphysics Scheme

Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson–Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are 1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson–Eidhammer parameterization and 2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015/16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson–Eidhammer configuration. For the multimonth OLYMPEX period, we show that rain rates, rainwater mixing ratios, and raindrop number concentrations were increased relative to the Thompson–Eidhammer microphysical parameterization, while concurrently decreasing raindrop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest. Significance Statement Although the accurate simulation of warm rain is critical to forecasting the hydrology of coastal areas and windward slopes, many warm rain parameterizations underpredict precipitation in these locations. This study introduces and evaluates modifications to the Thompson–Eidhammer microphysics parameterization scheme that significantly improve the accuracy of rainfall prediction in those regions.

The Impact of Autoconversion Parameterizations of Cloud Droplet to Raindrop on Numerical Simulations of a Meiyu Front Heavy Rainfall Event

This study analyzes the different impacts of autoconversion of cloud droplets to raindrops (ACR) in a Meiyu front rainfall event by comparing two simulations using different parameterizations (KK00 and LD04) in the Weather Research and Forecasting (WRF) model. The Meiyu frontal clouds are further classified into stratiform and deep-convective cloud categories, and the precipitation and microphysical characteristics of the two simulations are compared with a budget analysis of raindrops. The simulated precipitation, radar composite reflectivity distribution, and rain rate evolution are overall consistent with observations while precipitation is overestimated, especially in the rainfall centers. The intensity and vertical structure of the ACR process between the two simulations are significantly different. The ACR rate in LD04 is larger than that in KK00 and there are two peak heights in LD04 but only one in KK00. Accretion of droplets by raindrops (CLcr), melting of ice-phase particles (ML), evaporation of raindrops (VDrv), and accretion of raindrops by ice-phase particles (CLri) are the dominant pathways to raindrop production. Limited distributional differences can be found in both the deep-convective and stratiform clouds between the two simulations during the growth stage of the Meiyu event. Stronger ACR in LD04 results in less cloud droplet content (Lc), more raindrop content (Lr), and larger raindrop number concentration (Nr) and the effect of ACR on Nr is greater than that on Lr. The ACR process also impacts other microphysical processes indirectly, and the influences vary in the two cloud categories. Less CLcr (especially), ML, and VDrv content, caused by stronger ACR, lead to less raindrop production in the LD04 deep-convective clouds, which is different from stratiform clouds, and finally correct the overestimated rainfall center to better match the observations.

The numerical study on the sensitivity of different auto-conversion parameterization to CCN concentration

The auto-conversion from cloud droplet to raindrop is a process whereby rain drops formed by collision-coalescence of cloud droplets. As an essential link connecting aerosol-cloud interaction, it significantly influences the changes in cloud morphology and precipitation. In order to explore the sensitivity of auto-conversion schemes to cloud condensation nuclei (CCN) concentration, using the auto-conversion scheme in the Thompson scheme (TH-AU) and Milbrandt-Yau scheme (MY-AU), we set four groups of CCN concentrations to simulate a strong convection process in Ningxia region of China. The results show that: The sensitivity of different auto-conversion schemes to changes in CCN concentrations varies significantly, and the aerosol-induced changes in precipitation and convection strongly depend on the auto-conversion scheme. With the increase of CCN concentration, the mixing ratio of cloud droplets increases, and the particle size decreases, resulting in a decrease in the auto-conversion intensity for the two schemes, which makes more supercooled water participate in the ice phase process. Compared with the TH-AU, the MY-AU has lower auto-conversion intensity at the same CCN concentration, the proportion of supercooled cloud droplets participating in the ice phase process is higher than that in the TH-AU, which leads to the raindrop mixing ratio of 4000–6000 m in MY-AU is lower than that in TH-AU at the same CCN concentration, and the mixing ratio of ice phase particles in MY-AU scheme is higher in the convective mature stage, especially snow and graupel particles, and the graupel particle generation height of MY-AU is lower than that of TH-AU. In terms of dynamic structure, with the increase of CCN concentration, more cloud droplets are activated and frozen which makes the enhancement of updraft mainly occur in the upper layer in both schemes, but the stronger gravitational drag caused by graupel particles in MY-AU may enhance the downdraft in the middle and lower layers, which makes the convection of MY-AU decay early at higher CCN concentration. In addition, changes in microphysical processes also lead to differences in cumulative precipitation and accumulated ground graupel-fall of the two schemes. The cumulative precipitation and the accumulated ground graupel-fall of the MY-AU decrease strongly with the increase of CCN concentration because the warm rain process of MY-AU is strongly inhibited. Compared with MY-AU, the warm rain process of TH-AU is not significantly inhibited, which leads to the cumulative precipitation and the accumulated ground graupel-fall of the TH-AU scheme increases when the CCN concentration is 50–200 cm−3 and slightly decreases when the CCN concentration is 200–10000 cm−3.

A new warm-cloud collection and breakup parameterization scheme for weather and climate models

Microphysical process rates in bulk microphysics schemes have been parameterized in simple forms which may inadequately represent their complicated dependencies on hydrometeor quantities. In this study, we develop a bulk warm rain microphysics scheme that includes physically based parameterizations of collection and breakup. The parameterizations are derived from the stochastic collection and breakup equations by approximating the collection and breakup kernels in elaborate forms and analytically evaluating the integrals in those equations. Although the new scheme performs relatively complicated calculations, it is computationally not too expensive to be used in weather and climate models. We first evaluate the raindrop self-collection and collisional breakup (RSCB) parameterizations developed in this study using a box model. Under the intrinsic limitation of bulk schemes, they generally yield the equilibrium raindrop size distributions closest to those from a bin-based solver. We then evaluate the new scheme through the simulations of shallow cumuli field and a single warm convective cloud. In the simulation of shallow cumuli field, the new scheme quite well reproduces the observed cloud properties. Compared to two other bulk schemes, the new scheme yields somewhat different rainwater mass and raindrop number concentration, which is largely attributable to the differences in autoconversion parameterizations. In the simulation of a single warm convective cloud where RSCB plays a major role, the rate of raindrop number depletion by RSCB and the time of precipitation onset predicted by the new scheme are close to those predicted by a bin scheme used as the reference.

Subgrid-scale horizontal and vertical variation of cloud water in stratocumulus clouds: a case study based on LES and comparisons with in situ observations

Abstract. Stratocumulus clouds in the marine boundary layer cover a large fraction of ocean surface and play an important role in the radiative energy balance of the Earth system. Simulating these clouds in Earth system models (ESMs) has proven to be extremely challenging, in part because cloud microphysical processes such as the autoconversion of cloud water into precipitation occur at scales much smaller than typical ESM grid sizes. An accurate autoconversion parameterization needs to account for not only the local microphysical process (e.g., the dependence on cloud water content qc and cloud droplet number concentration Nc) but also the subgrid-scale variability of the cloud properties that determine the process rate. Accounting for subgrid-scale variability is often achieved by the introduction of a so-called enhancement factor E. Previous studies of E for autoconversion have focused more on its dependence on cloud regime and ESM grid size, but they have largely overlooked the vertical dependence of E within the cloud. In this study, we use a large-eddy simulation (LES) model, initialized and constrained with in situ and surface-based measurements from a recent airborne field campaign, to characterize the vertical dependence of the horizontal variation of qc in stratocumulus clouds and the implications for E. Similar to our recent observational study (Zhang et al., 2021), we found that the inverse relative variance of qc, an index of horizontal homogeneity, generally increases from cloud base upward through the lower two-thirds of the cloud and then decreases in the uppermost one-third of the cloud. As a result, E decreases from cloud base upward and then increases towards the cloud top. We apply a decomposition analysis to the LES cloud water field to understand the relative roles of the mean and variances of qc in determining the vertical dependence of E. Our analysis reveals that the vertical dependence of the horizontal qc variability and enhancement factor E is a combined result of condensational growth throughout the lower portion of the cloud and entrainment mixing at cloud top. The findings of this study indicate that a vertically dependent E should be used in ESM autoconversion parameterizations.

On the Relation among Satellite Observed Liquid Water Path, Cloud Droplet Number Concentration and Cloud Base Rain Rate and Its Implication to the Auto-Conversion Parameterization in Stratocumulus Clouds

AbstractPrecipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path (LWP), cloud droplet number concentration (Nd) and cloud base rain rate (Rcb) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. Rcb has a positive correlation with LWP/Nd and the increase of Rcb becomes larger as LWP/Nd increases. However, the increase of Rcb with respect to LWP/Nd becomes more gradual in regions with larger Nd, which indicates the relation is moderated by Nd. These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and Nd. The sensitivity of the auto-conversion rate to Nd is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on Rcb. The upper limit of the dependency of auto-conversion rate on Nd is assessed from the relation between Rcb and Nd, since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.

Multilayer cloud conditions in trade wind shallow cumulus – confronting two ICON model derivatives with airborne observations

Abstract. Airborne remote sensing observations over the tropical Atlantic Ocean upstream of Barbados are used to characterize trade wind shallow cumulus clouds and to benchmark two cloud-resolving ICON (ICOsahedral Nonhydrostatic) model simulations at kilometer and hectometer scales. The clouds were observed by an airborne nadir-pointing backscatter lidar, a cloud radar, and a microwave radiometer in the tropical dry winter season during daytime. For the model benchmark, forward operators convert the model output into the observational space for considering instrument-specific cloud detection thresholds. The forward simulations reveal the different detection limits of the lidar and radar observations, i.e., most clouds with cloud liquid water content greater than 10−7 kg kg−1 are detectable by the lidar, whereas the radar is primarily sensitive to the “rain” category hydrometeors in the models and can detect even low amounts of rain. The observations reveal two prominent modes of cumulus cloud top heights separating the clouds into two layers. The lower mode relates to boundary layer convection with tops closely above the lifting condensation level, which is at about 700 m above sea level. The upper mode is driven by shallow moist convection, also contains shallow stratiform outflow anvils, and is closely related to the trade inversion at about 2.3 km above sea level. The two cumulus modes are sensed differently by the lidar and the radar observations and under different liquid water path (LWP) conditions. The storm-resolving model (SRM) at a kilometer scale barely reproduces the cloud modes and shows most cloud tops being slightly above the observed lower mode. The large-eddy model (LEM) at hectometer scale reproduces better the observed cloudiness distribution with a clear bimodal separation. We hypothesize that slight differences in the autoconversion parameterizations could have caused the different cloud development in the models. Neither model seems to account for in-cloud drizzle particles that do not precipitate down to the surface but generate a stronger radar signal even in scenes with low LWP. Our findings suggest that even if the SRM is a step forward for better cloud representation in climate research, the LEM can better reproduce the observed shallow cumulus convection and should therefore in principle better represent cloud radiative effects and water cycle.

A two-moment machine learning parameterization of the autoconversion process

Autoconversion is the mass transfer from cloud to precipitation water in an early stage of cloud development, and is the dominant process in the formation of embryonic droplets that trigger precipitation formation. The accurate parameterization of this process is key, in order to improve the interaction between cloud microphysics and cloud dynamics for models from cloud scale to the global climate scale. For model based parameterizations of the auto-conversion process, the usual approach to develop an autoconversion parameterization is by curve fitting the autoconversion rates obtained from simulations or numerical solutions of the kinetic collection equation under a wide range of initial conditions. However, in this case, the autoconversion is modeled by a function that is a nonlinear product of liquid water content and droplet concentration and depends on a small number of parameters. As a result, a large amount of scatter around the actual values can be obtained, indicating a weak relationship between actual and fitted autoconversion rates.The purpose of this paper is to analyze whether neural networks are better than traditional curve fitting or regression to obtain parameterizations of autoconversion. Then, a deep neural network was trained from an autconversion rates dataset generated by solving the kinetic collection equation for a wide range of droplet concentrations and liquid water contents. The obtained machine learned parameterization shows a very good match with actual rates calculated from the kinetic collection equation.

Impact of aerosols and turbulence on cloud droplet growth: an in-cloud seeding case study using a parcel–DNS (direct numerical simulation) approach

Abstract. This paper investigates the relative importance of turbulence and aerosol effects on the broadening of the droplet size distribution (DSD) during the early stage of cloud and raindrop formation. A parcel–DNS (direct numerical simulation) hybrid approach is developed to seamlessly simulate the evolution of cloud droplets in an ascending cloud parcel. The results show that turbulence and cloud condensation nuclei (CCN) hygroscopicity are key to the efficient formation of large droplets. The ultragiant aerosols can quickly form embryonic drizzle drops and thus determine the onset time of autoconversion. However, due to their scarcity in natural clouds, their contribution to the total mass of drizzle drops is insignificant. In the meantime, turbulence sustains the formation of large droplets by effectively accelerating the collisions of small droplets. The DSD broadening through turbulent collisions is significant and therefore yields a higher autoconversion rate compared to that in a nonturbulent case. It is argued that the level of autoconversion is heavily determined by turbulence intensity. This paper also presents an in-cloud seeding scenario designed to scrutinize the effect of aerosols in terms of number concentration and size. It is found that seeding more aerosols leads to higher competition for water vapor, reduces the mean droplet radius, and therefore slows down the autoconversion rate. On the other hand, increasing the seeding particle size can buffer such a negative feedback. Despite the fact that the autoconversion rate is prominently altered by turbulence and seeding, bulk variables such as liquid water content (LWC) stays nearly identical among all cases. Additionally, the lowest autoconversion rate is not co-located with the smallest mean droplet radius. The finding indicates that the traditional Kessler-type or Sundqvist-type autoconversion parameterizations, which depend on the LWC or mean radius, cannot capture the drizzle formation process very well. Properties related to the width or the shape of the DSD are also needed, suggesting that the scheme of Berry and Reinhardt (1974) is conceptually better. It is also suggested that a turbulence-dependent relative-dispersion parameter should be considered.

Formulation of Autoconversion and Drop Spectra Shape in Shallow Cumulus Clouds

Abstract Two-moment autoconversion parameterizations as compared to accretion parameterizations exhibit significant errors suggesting that additional moments are needed to increase their accuracy. We develop a three-moment autoconversion parameterization using output from an LES model with size-resolved microphysics. Adding the third moment decreases the errors of parameterization and improves precipitation prediction. However, the errors are still significantly larger than errors of accretion rate. An analysis of the cloud drop size distributions (DSDs) in the simulated tropical convective cloud system reveals that most DSDs have a significant fraction of cloud liquid water content qc in the midsize droplet range (radii from 20 to 40 μm). Our data indicate that more than 30% of DSDs have over half of qc contained in the midsize range and about 60% of spectra have, at least, one-third of qc in this range. Even when the rain/drizzle mode is small (radar reflectivity Z < −10 dBZ), there is a significant number of spectra in which fraction of qc in the midsize range is as large as 60%. These DSDs are more complex than the frequently used gamma or lognormal distributions, which exhibit a single mode and can be defined by three microphysical moments. The need to define DSDs by more than three moments explains the large errors in the three-moment autoconversion parameterization. The limitation of three-parameter gamma or lognormal distributions should be kept in mind when applying them in precipitating shallow cumulus clouds.

Effect of Aerosol Particles on Orographic Clouds: Sensitivity to Autoconversion Schemes

Aerosol particles can serve as cloud condensation nuclei (CCN) to influence orographic clouds. Autoconversion, which describes the initial formation of raindrops from the collision of cloud droplets, is an important process for aerosol-cloud-precipitation systems. In this study, seven autoconversion schemes are used to investigate the impact of CCN on orographic warm-phase clouds. As the initial cloud droplet concentration is increased from 100 cm−3 to 1000 cm−3 (to represent an increase in CCN), the cloud water increases and then the rainwater is suppressed due to a decrease in the autoconversion rate, leading to a spatial shift in surface precipitation. Intercomparison of the results from the autoconversion schemes show that the sensitivity of cloud water, rainwater, and surface precipitation to a change in the concentration of CCN is different from scheme to scheme. In particular, the decrease in orographic precipitation due to increasing CCN is found to range from −87% to −10% depending on the autoconversion scheme. Moreover, the surface precipitation distribution also changes significantly by scheme or CCN concentration, and the increase in the spillover (ratio of precipitation on the leeward side to total precipitation) induced by increased CCN ranges from 10% to 55% under different autoconversion schemes. The simulations suggest that autoconversion parameterization schemes should not be ignored in the interaction of aerosol and orographic cloud.

A New Criterion to Improve Operational Drizzle Detection with Ground-Based Remote Sensing

AbstractLight shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol–cloud–precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Jülich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better.

The Weather Research and Forecasting Model with Aerosol-Cloud Interactions (WRF-ACI): Development, Evaluation, and Initial Application.

The Weather Research and Forecasting Model with Aerosol-Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models' results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m-2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol-cloud interactions.

Study of aerosol direct and indirect effects and auto-conversion processes over the West African monsoon region using a regional climate model

This study assesses the direct and indirect effects of natural and anthropogenic aerosols (e.g., black carbon and sulfate) over West and Central Africa during the West African monsoon (WAM) period (June–July–August). We investigate the impacts of aerosols on the amount of cloudiness, the influences on the precipitation efficiency of clouds, and the associated radiative forcing (direct and indirect). Our study includes the implementation of three new formulations of auto-conversion parameterization [namely, the Beheng (BH), Tripoli and Cotton (TC) and Liu and Daum (R6) schemes] in RegCM4.4.1, besides the default model’s auto-conversion scheme (Kessler). Among the new schemes, BH reduces the precipitation wet bias by more than 50% over West Africa and achieves a bias reduction of around 25% over Central Africa. Results from detailed sensitivity experiments suggest a significant path forward in terms of addressing the long-standing issue of the characteristic wet bias in RegCM. In terms of aerosol-induced radiative forcing, the impact of the various schemes is found to vary considerably (ranging from −5 to −25 W m−2).

A Physically Based Autoconversion Parameterization

Abstract A physically based parameterization for the autoconversion is derived by solving the stochastic collection equation (SCE) with an approximated collection kernel. The collection kernel is constructed using the terminal velocity of cloud droplets and the collision efficiency between cloud droplets that is obtained using a particle trajectory model. The new parameterization proposed in this study is validated through comparison with results obtained by a bin-based direct SCE solver and other autoconversion parameterizations using a box model. The autoconversion-related time scale and drop number concentration are employed for the validation. The results of the new parameterization are shown to most closely match those of the direct SCE solver. It is also shown that the dependency of the autoconversion rate on drop number concentration in the new parameterization is similar to that in the direct SCE solver, which is partially caused by the shape of drop size distribution. The new parameterization and other parameterizations are implemented into a cloud-resolving model, and idealized shallow warm clouds are simulated. The autoconversion parameterizations that yield the small (large) autoconversion rate tend to predict large (small) cloud optical thickness, small (large) cloud fraction, and small (large) surface precipitation amount. Cloud optical thickness and cloud fraction are changed by up to ~45% and ~20% by autoconversion parameterizations, respectively. The new parameterization tends to yield the moderate autoconversion rate among the autoconversion parameterizations. Moreover, it predicts cloud optical thickness, cloud fraction, and surface precipitation amount that are generally the closest to those of the bin microphysics scheme.

Evaluation of the Warm Rain Formation Process in Global Models with Satellite Observations

Abstract This study examines the warm rain formation process over the global ocean in global climate models. Methodologies developed to analyze CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations are employed to investigate the cloud-to-precipitation process of warm clouds and are applied to the model results to examine how the models represent the process for warm stratiform clouds. Despite a limitation of the present study that compares the statistics for stratiform clouds in climate models with those from satellite observations, including both stratiform and (shallow) convective clouds, the statistics constructed with the methodologies are compared between the models and satellite observations to expose their similarities and differences. A problem common to some models is that they tend to produce rain at a faster rate than is observed. These model characteristics are further examined in the context of cloud microphysics parameterizations using a simplified one-dimensional model of warm rain formation that isolates key microphysical processes from full interactions with other processes in global climate models. The one-dimensional model equivalent statistics reproduce key characteristics of the global model statistics when corresponding autoconversion schemes are assumed in the one-dimensional model. The global model characteristics depicted by the statistics are then interpreted as reflecting behaviors of the autoconversion parameterizations adopted in the models. Comparisons of the one-dimensional model with satellite observations hint at improvements to the formulation of the parameterization scheme, thus offering a novel way of constraining key parameters in autoconversion schemes of global models.

Aerosol-cloud-precipitation interactions in WRF model: Sensitivity to autoconversion parameterization

Cloud-to-rain autoconversion process is an important player in aerosol loading, cloud morphology, and precipitation variations because it can modulate cloud microphysical characteristics depending on the participation of aerosols, and affects the spatio-temporal distribution and total amount of precipitation. By applying the Kessler, the Khairoutdinov-Kogan (KK), and the Dispersion autoconversion parameterization schemes in a set of sensitivity experiments, the indirect effects of aerosols on clouds and precipitation are investigated for a deep convective cloud system in Beijing under various aerosol concentration backgrounds from 50 to 10000 cm−3. Numerical experiments show that aerosol-induced precipitation change is strongly dependent on autoconversion parameterization schemes. For the Kessler scheme, the average cumulative precipitation is enhanced slightly with increasing aerosols, whereas surface precipitation is reduced significantly with increasing aerosols for the KK scheme. Moreover, precipitation varies non-monotonically for the Dispersion scheme, increasing with aerosols at lower concentrations and decreasing at higher concentrations. These different trends of aerosol-induced precipitation change are mainly ascribed to differences in rain water content under these three autoconversion parameterization schemes. Therefore, this study suggests that accurate parameterization of cloud microphysical processes, particularly the cloud-to-rain autoconversion process, is needed for improving the scientific understanding of aerosol-cloud-precipitation interactions.

Evaluation of clouds in ACCESS using the satellite simulator package COSP: Regime‐sorted tropical cloud properties

This study uses a regime sorting technique to explore the relationships that ACCESS1.3 clouds have with the large‐scale environment. Satellite simulator output is used to demonstrate that the modeled clouds have similar sensitivity to the large‐scale dynamic and thermodynamic conditions as shown by CloudSat and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The high cloud cover and longwave cloud radiative effect is represented very well in the model across all regimes. The cloud types that the model simulates the most poorly are stratocumulus over cool sea surface temperatures (SSTs) and the deep convective regimes associated with strong upward midtropospheric vertical velocity and weak lower tropospheric stabilities. The reflectance of the deep convective regimes shows a stronger sensitivity to SST and less dependence on the large‐scale dynamics than the observations. Many of the model errors identified occur across all regimes, such as the underestimate of clouds with large scattering ratios (SR) and the too frequent occurrence of drizzle and rain. A sensitivity test in which a different warm rain scheme was used shows that the modelled frequency of occurrence of nonprecipitating low cloud is quite sensitive to the autoconversion parameterization. The new scheme produced more cloud with large SR and higher cloud tops in better agreement with the observations. The thermodynamic regime analysis shows that the transition of shallow to deeper convection in the model requires a warmer SST and weaker LTS than the observations. The significant underestimate of cumulus congestus is likely to contribute to this delay due to the role these clouds have in preconditioning the midtroposphere for the onset of deep convection.

Improving bulk microphysics parameterizations in simulations of aerosol effects

AbstractTo improve the microphysical parameterizations for simulations of the aerosol effects in regional and global climate models, the Morrison double‐moment bulk microphysical scheme presently implemented in the Weather Research and Forecasting model is modified by replacing the prescribed aerosols in the original bulk scheme (Bulk‐OR) with a prognostic double‐moment aerosol representation to predict both aerosol number concentration and mass mixing ratio (Bulk‐2M). Sensitivity modeling experiments are performed for two distinct cloud regimes: maritime warm stratocumulus clouds (Sc) over southeast Pacific Ocean from the VOCALS project and continental deep convective clouds in the southeast of China. The results from Bulk‐OR and Bulk‐2M are compared against atmospheric observations and simulations produced by a spectral bin microphysical scheme (SBM). The prescribed aerosol approach (Bulk‐OR) produces unreliable aerosol and cloud properties throughout the simulation period, when compared to the results from those using Bulk‐2M and SBM, although all of the model simulations are initiated by the same initial aerosol concentration on the basis of the field observations. The impacts of the parameterizations of diffusional growth and autoconversion of cloud droplets and the selection of the embryonic raindrop radius on the performance of the bulk microphysical scheme are also evaluated by comparing the results from the modified Bulk‐2M with those from SBM simulations. Sensitivity experiments using four different types of autoconversion schemes reveal that the autoconversion parameterization is crucial in determining the raindrop number, mass concentration, and drizzle formation for warm stratocumulus clouds. An embryonic raindrop size of 40 µm is determined as a more realistic setting in the autoconversion parameterization. The saturation adjustment employed in calculating condensation/evaporation in the bulk scheme is identified as the main factor responsible for the large discrepancies in predicting cloud water in the Sc case, suggesting that an explicit calculation of diffusion growth with predicted supersaturation is necessary to improve the bulk microphysics scheme. Lastly, a larger rain evaporation rate below clouds is found in the bulk scheme in comparison to the SBM simulation, which may contribute to a lower surface precipitation in the bulk scheme.

Incorporating the subgrid‐scale variability of clouds in the autoconversion parameterization using a PDF‐scheme

An investigation of the impact of the subgrid‐scale variability of cloud liquid water on the autoconversion process as parameterized in a general circulation model is presented in this paper. For this purpose, a prognostic statistical probability density distribution (PDF) of the subgrid scale variability of cloud water is incorporated in a continuous autoconversion parameterization. Thus, the revised autoconversion rate is calculated by an integral of the autoconversion equation over the PDF of total water mixing ratio from the saturation vapor mixing ratio to the maximum of total water mixing ratio. An evaluation of the new autoconversion parameterization is carried out by means of one year simulations with the ECHAM5 climate model. The results indicate that the new autoconversion scheme causes an increase of the frequency of occurrence of high autoconversion rates and a decrease of low ones compared to the original scheme. This expected result is due to the emphasis on areas of high cloud liquid water in the new approach, and the non‐linearity of the autoconversion with respect to liquid water mixing ratio. A similar trend as in the autoconversion is observed in the accretion process resulting from the coupling of both processes. As a consequence of the altered autoconversion, large‐scale surface precipitation also shows a shift of occurrence from lower to higher rates. The vertically integrated cloud liquid water estimated by the model shows slight improvements compared to satellite data. Most importantly, the artificial tuning factor for autoconversion in the continuous parameterization could be reduced by almost an order of magnitude using the revised parameterization.