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At least 19 records

A Polarimetric Radar Analysis of Ice Microphysical Processes in Melting Layers of Winter Storms Using S-Band Quasi-Vertical Profiles

Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization Z H . QVP ML statistics are examined for two regimes in the ML data: Z H ≥ 20 dB Z and Z H < 20 dB Z . Regions of Z H ≥ 20 dB Z indicate locations of MLs collocated with enhanced differential reflectivity Z DR and reduced copolar correlation coefficient ρ hv , while for Z H < 20 dB Z a well-defined ML is difficult to discern using Z H alone. Evidence of large Z DR up to 4 dB, backscatter differential phase δ up to 8°, and low ρ hv down to 0.80 associated with lower Z H (from -10 to 20 dB Z ) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase K DP and maximum Z H in the ML; these are the first QVP observations of K DP in MLs documented at S band. Negative correlation occurs between minimum ρ hv in the ML and ML depth and between minimum ρ hv in the ML and the corresponding enhancement of Z H (Δ Z H = Z H max - Z H rain ).

Meteorology & Atmospheric Sciences↗

A radar view of ice microphysics and turbulence in Arctic cloud systems

Ice microphysical processes are inherently complex because of their sensitivity to temperature and humidity, the diversity of ice crystal habits, and their interaction with supercooled liquid water (SCL) and turbulence. Long-term surface-based radar observations have been systematically used to unravel the different processes that affect ice particle growth. In this study, we present a statistical analysis of 6.5 years of Ka-band radar observations in Arctic cloud systems, combined with thermodynamic profiles derived from radiosonde measurements. For the first time, ice particle growth and sublimation – diagnosed from vertical gradients of radar reflectivity and mean Doppler velocity – are systematically mapped across a broad range of temperature and moisture conditions. These vertical gradients correspond closely to saturation levels relative to ice and exhibit a strong temperature dependence in supersaturated regions. Notably, distinct signatures near −15 °C are indicative of dendritic growth. Turbulence, quantified via the eddy dissipation rate (EDR), is most frequently observed in regions containing SCL. The co-occurrence of SCL and elevated turbulence results in significantly enhanced ice particle growth compared to conditions in which either is present alone. This work provides new observational constraints that are critical for improving the representation of ice microphysics in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗

Numerical Errors in Ice Microphysics Parameterizations and their Effects on Simulated Regional Climate

The major characteristics of ice microphysics in Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) bulk-type cloud microphysics originate from the diagnosed ice number concentration, which is a function of the cloud-ice mixing ratio. In this study, we correct numerical errors in ice microphysics processes of the WDM6, in which the cloud-ice shape is assumed as single bullets and examine the impact on regional climate simulations. Here, by rederiving the relationships between cloud microphysics characteristics, including the one linking the cloud-ice mixing ratio and number concentration, we remove numerical errors intrinsic to the description of cloud-ice characteristics in the original WDM6 microphysics scheme. The revised WDM6 is tested using a WRF framework for regional climate simulations over the East Asian region. We find that our correction to the WDM6 improves the model’s performance in capturing the observed distribution of the monsoon rain band. A reduction in cloud ice is significant in the revised WDM6, which strengthens the Western North Pacific High. By conducting the additional sensitivity experiment in which the characteristics of cloud-ice shape are revised as the one for the column type, our study also finds out that the impacts of the existing numerical errors on the simulated monsoon is as large as the ones of the changes in cloud-ice shape.

54 ENVIRONMENTAL SCIENCES↗

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗

Improving parameterization of ice microphysical processes in Arctic clouds using a synergistic modeling and observational approach (Final Report)

The primary goals of the project were to: 1) Improve the fundamental quantitative models of ice growth processes using ARM radar observations. 2) Better understand the growth of ice particle populations through vapor deposition, riming, and aggregation in Arctic clouds using a new particle-property microphysical model. 3) Robustly estimate and constrain key unknown model parameters using ARM radar observations combined with the particle-property model to develop an improved parameterization of ice growth processes for traditional bulk parameterizations used in cloud and climate models.

58 GEOSCIENCES↗

Improved Convective Ice Microphysics Parameterization in the NCAR CAM Model

Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four-class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Further, comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.

54 ENVIRONMENTAL SCIENCES↗

Developing and Testing a Novel Stochastic Ice Microphysics Parameterization for Cloud and Climate Models Using ARM Field Campaign Data (Final Progress Report)

The major goals of this project were: 1) to use field campaign measurements from DOE’s Atmospheric Radiation Measurement (ARM) program to characterize variability of important parameters describing properties of ice particles in the atmosphere; 2) based on this observational analysis, to develop a parameterization scheme for weather and climate models that stochastically varies these parameters, and implement the new scheme into a weather model called the Weather Research and Forecasting model (WRF); 3) to use WRF coupled with the new stochastic scheme to simulate ARM field campaign thunderstorm cases and analyze how accounting for this parameter variability affects the model simulations. This work was performed jointly between the National Center for Atmospheric Research, University of Oklahoma, and University of Utah. To accomplish these goals, we extended an approach previously developed to characterize the variability in the size distribution of ice particles to parameters that are explicitly represented in models (i.e., relationships between ice particle mass and size, and between particle fall velocity and size). Our project was, to our knowledge, the first to apply observationally-constrained estimates of this parameter variability describing mass-size and fall velocity-size in a modeling framework. Our results showed efficacy of the approach, evaluated using ARM observations. Similarly, to our knowledge, work in this project was the first to propose and evaluate in detail a stochastic approach for unresolved turbulent mixing in high-resolution model simulations against detailed, benchmark large eddy simulations and ARM observations. Results showed some promising behavior, particularly with increased mixing and dilution of air in thunderstorm cores with surrounding environmental air, bringing the stochastic simulations closer to the benchmark large eddy simulations; however, results were somewhat degraded using stochastic mixing compared to observations from the AMIE/DYNAMO field campaign. This project also further refined and applied a modeling methodology called “piggybacking” that can robustly separate dynamical and thermodynamic impacts of model changes, and comparison studies of different models based on cases developed from ARM observations. Finally, this project directly supported three graduate students who completed their PhDs as well as a postdoctoral research fellow.

54 ENVIRONMENTAL SCIENCES↗

Characterizing the Small-Scale Dynamical, Ice Microphysical, and Residual Aerosol Properties of Mid-Latitude Cold Clouds: A Pilot Study (Final Report)

Our understanding of cirrus clouds has been advancing rapidly due to improvements in measurements made inside clouds, computer cloud models, and laboratory experimentation. This advancement in knowledge has made it clear that cirrus are complicated systems where the intricate interactions among dynamic air motions, solar and terrestrial radiation, ice crystal growth, and water and air chemistry can substantially influence cirrus structure and evolution. Measurements made inside clouds have shown that cirrus crystals are often complex with shapes that are often unclassifiable, especially at small sizes. Laboratory measurements have revealed that the ice crystal surface is complex, even at small sizes. Recent laboratory and satellite studies show that this surface complexity modifies the radiative properties of cirrus clouds in important ways. Moreover, observations and computer cloud modeling work show that the larger and smaller scale properties of cirrus are sensitive to the interplay between small-scale air motions and the aerosol articles upon which cirrus ice crystals form. Taken together, these studies indicate that accurately accounting for the radiative properties of cirrus clouds requires knowing the air motions and aerosol properties that influence the number concentration and crystal size, along with an understanding of the surface properties of vapor grown ice. Although this prior work has been illuminating, there have been few studies that provide detailed shape and surface complexity measurements for the small ice crystals that often compose cirrus clouds.

54 ENVIRONMENTAL SCIENCES↗

Improving GCM Predictability of Mixed-Phase Clouds and Aerosol Interactions at High Latitudes with ARM Observations

The overachieving goal of this project is to improve the predictability of mixed-phase clouds and aerosol interactions in the Community Atmosphere Model version 6 (CAM6) through comparison with the ARM observations. There are three main objectives of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Test the performance of ice microphysics in CESM-CAM6 with the ARM observations in northern and southern high latitudes; and (3) Examine mixed-phase cloud microphysics-aerosol-turbulence-radiation interactions in CESM-CAM6. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds in CESM-CAM6 by implementing the marine organic aerosol (MOA) and treating the ice nucleating particles (INPs) from MOA and its impacts on mixed-phase clouds. We improved the treatment of ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process by considering the subgrid heterogeneous distributions between liquid droplets and ice crystals in mixed-phase clouds; (2) Tested the performance of ice microphysics in CESM-CAM6 with the ARM observations at high latitudes. We compared the simulated INP concentrations with the ARM observations, e.g., from M-PACE, ISDAC, INPOP, and other data (Mace Head, Zeppelin, CAPRICORN). We examined the impact of improved WBF treatment on model simulated Arctic mixed-phase clouds observed in the M-PACE field campaign. Seasonal variations of modeled mixed-phase cloud properties (LWO, IWP) are compared with the ground-based remote sensing retrievals at the ARM’s NSA $Utqia\dot{g}vik$ site; and (3) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM6 that include the impacts of MOA INPs, and impacts of different model parameterizations (CLUBB versus UW turbulence & shallow convection schemes, MG2 versus MG1) on high-latitude mixed-phase cloud properties. Aerosol indirect effects of MOA through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) were investigated.

54 ENVIRONMENTAL SCIENCES↗

Improving GCM Predictability of Mixed-Phase Clouds and Aerosol Interactions at High Latitudes with ARM Observations

The goal of this project is to improve the predictability of mixed-phase clouds and aerosol interactions in the Community Atmosphere Model version 6 (CAM6) through comparison with the ARM observations. There are three main objectives of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Test the performance of ice microphysics in CESM-CAM6 with the ARM observations in northern and southern high latitudes; and (3) Examine mixed-phase cloud microphysics-aerosol-turbulence-radiation interactions in CESM-CAM6. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds in CESM-CAM6 by implementing the marine organic aerosol (MOA) and treating the ice nucleating particles (INPs) from MOA and its impacts on mixed-phase clouds. We made a first attempt to represent different secondary ice production (SIP) mechanisms in a GCM (CESM2-CAM6). We found that misrepresentation of these ice formation processes in global climate models (GCMs) leads to too weak negative cloud feedback over the Southern Ocean (SO) and too high climate sensitivity in the models. In addition to ice formation processes, we improved the treatment of ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process by considering the subgrid heterogeneous distributions between liquid droplets and ice crystals in mixed-phase clouds; (2) Tested the performance of our improved representations of ice microphysics with the ARM observations at high latitudes.

54 ENVIRONMENTAL SCIENCES↗

Improving Predictability of Mixed-Phase Clouds and Aerosol Interactions in the Community Earth System Model (CESM) with ARM Measurements

The objective of this project is to improve the simulation and predictability of mixed-phase clouds and aerosol interactions in the Community Earth System Model (CESM) through comparisons with the DOE ARM observations. There are four major goals of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Develop a long-term multi-sensor mixed-phase cloud dataset; (3) Test the performance of improved ice microphysics in CESM-CAM5 with the ARM data; and (4) Examine mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds that include ice nucleation, ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process, feedbacks between cloud microphysics, dynamics and radiation, and provides links to aerosol properties in the Community Earth System Model (CESM)-Community Atmosphere Model version 5 (CAM5); (2) Developed a long-term multi-sensor mixed-phase cloud dataset. The mixed-phase cloud retrieval algorithms are improved by refining the phase determinations and ice number concentration retrievals. A multi-year mixed-phase cloud observation dataset (1999-2004; October 2013-February 2017) is regenerated at the ARM Barrow site with the improved algorithms. The dataset has been used in the global model evaluation to improve the model parameterizations; (3) Tested the performance of improved ice microphysics in CESM-CAM5 with the ARM data. CESM-CAM5 is run in the DOE Cloud-Associated Parameterizations Testbed (CAPT) and in the single-column model (SCM) mode to facilitate comparison with ARM observations. Multi-year CAPT simulations of seasonal variations of cloud microphysical properties, cloud liquid water path (LWP) and ice water path (IWP), cloud longwave and shortwave forcings, cloud occurrence frequency are evaluated against the ARM multi-sensor mixed-phase cloud retrievals; and (4) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5 that include the impacts of different ice nucleation mechanisms, sensitivity to dust IN concentration, treatment of the WBF process on mixed-phase cloud properties. Aerosol effect on mixed-phase clouds through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) is investigated.

54 ENVIRONMENTAL SCIENCES↗

Modelling mixed-phase clouds with the large-eddy model UCLALES–SALSA

The large-eddy model UCLALES–SALSA, with an exceptionally detailed aerosol description for both aerosol number and chemical composition, has been extended for ice and mixed-phase clouds. Comparison to a previous mixed-phase cloud model intercomparison study confirmed the accuracy of newly implemented ice microphysics. A further simulation with a heterogeneous ice nucleation scheme, in which ice-nucleating particles (INPs) are also a prognostic variable, captured the typical layered structure of Arctic mid-altitude mixed-phase cloud: a liquid layer near cloud top and ice within and below the liquid layer. In addition, the simulation showed a realistic freezing rate of droplets within the vertical cloud structure. The represented detailed sectional ice microphysics with prognostic aerosols is crucially important in reproducing mixed-phase clouds.

54 ENVIRONMENTAL SCIENCES↗

What Controls Crystal Diversity and Microphysical Variability in Cirrus Clouds?

Abstract Variability of ice microphysical properties like crystal size and density in cirrus clouds is important for climate through its impact on radiative forcing, but challenging to represent in models. For the first time, recent laboratory experiments of particle growth (tied to crystal morphology via deposition density) are combined with a state‐of‐the‐art Lagrangian particle‐based microphysics model in large‐eddy simulations to examine sources of microphysical variability in cirrus. Simulated particle size distributions compare well against balloon‐borne observations. Overall, microphysical variability is dominated by variability in the particles' thermodynamic histories. However, diversity in crystal morphology notably increases spatial variability of mean particle size and density, especially at mid‐levels in the cloud. Little correlation between instantaneous crystal properties and supersaturation occurs even though the modeled particle morphology is directly tied to supersaturation based on laboratory measurements. Thus, the individual thermodynamic paths of each particle, not the instantaneous conditions, control the evolution of particle properties.

54 ENVIRONMENTAL SCIENCES↗

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

58 GEOSCIENCES↗

A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals From Cloud Particle Imagery

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a proof-of-concept framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generated synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we used synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_e$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. On unseen synthetic images, our ML models accurately predicted ice crystal properties. ResNet-18 performed best, achieving $R^2$ values of 0.99 and 0.98 for $ρ_e$ and $A_e$, respectively, and MAE of 0.10 for mathematical equation in single view tasks. Stereo view ResNet-18 further reduced RMSE by 40% for $ρ_e$ and $A_e$ and reduced MAE by 0.08 for $N_b$. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.

Ko, J. [Columbia Univ., New York, NY (United State↗

The Role of Ice Splintering on Microphysics of Deep Convective Clouds Forming Under Different Aerosol Conditions: Simulations Using the Model With Spectral Bin Microphysics

Observations during the Ice in Clouds Experiment-Tropical (ICE-T) field experiment show that the ice particles concentration in a developing deep convective clouds at the level of T = -15 °C reached about 500 L -1 , that is, many orders higher than that of ice-nucleating particle. To simulate microphysics of these clouds, the 2-D Hebrew University Cloud model (HUCM) with spectral bin microphysics was used in which two main types of ice multiplication mechanisms were included in addition to the Hallet-Mossop mechanism. In the first ice multiplication mechanism ice splinters form by drop freezing and drop-ice collisions. Ice multiplication of this type dominates during developing stage of cloud evolution, when liquid water content is significant. At later stage when clouds become nearly glaciated, ice crystals are produced largely by ice splintering during ice-ice collisions (the second ice multiplication mechanism). Simulations show that droplet size distributions, as well as size distributions of ice particles, agree well with the measurements during ICE-T. Simulations with different cloud condensation nuclei concentrations show the existence of the “optimum” cloud condensation nuclei concentration (or droplet concentration), at which concentration of ice splinters reaches maximum. In these simulations ice nucleation caused by the direct formation of ice crystals upon ice-nucleating particles, as well as the Hallett-Mossop process, has a negligible contribution to the ice crystal concentration.

54 ENVIRONMENTAL SCIENCES↗