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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Challenging and Improving the Simulation of Mid-Level Mixed-Phase Clouds Over the High-Latitude Southern Ocean

Climate models exhibit major radiative biases over the Southern Ocean owing to a poor representation of mixed-phase clouds. In this study, we used the remote-sensing dataset from the Measurements of Aerosols, Radiation and Clouds over the Southern Ocean (MARCUS) campaign to assess the ability of the Weather Research and Forecasting (WRF) model to reproduce frontal clouds off Antarctica. It focuses on the modeling of thin mid-level supercooled liquid water layers which precipitate ice. The standard version of WRF produces almost fully glaciated clouds and cannot reproduce cloud top turbulence. Our work demonstrates the importance of adapting the ice nucleation parameterization to the pristine austral atmosphere to reproduce the supercooled liquid layers. Once simulated, droplets significantly impact the cloud radiative effect by increasing downwelling longwave fluxes and decreasing downwelling shortwave fluxes at the surface. The net radiative effect is a warming of snow and ice covered surfaces and a cooling of the ocean. Despite improvements in our simulations, the local turbulent circulation related to cloud-top radiative cooling is not properly reproduced, advocating for the need to develop a parameterization for top-down convection to capture the turbulence-microphysics interplay at cloud top.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Larger Cloud Liquid Water Enhances Both Aerosol Indirect Forcing and Cloud Radiative Feedback in Two Earth System Models

Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol-cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter-balanced in simulations of the historical period. However, reasons for this anti-correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean-state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean-state LWP on effective radiative forcing due to ACI (ERF ACI ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. Furthermore, the combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impacts of long-range-transported mineral dust on summertime convective cloud and precipitation: a case study over the Taiwan region

As one of the most abundant atmospheric aerosols and effective ice nuclei, mineral dust affects clouds and precipitation in the Earth system. Here numerical experiments are carried out to investigate the impacts of dust aerosols on summertime convective clouds and precipitation over the mountainous region of Taiwan by acting as ice-nucleating particles. We run the Weather Research and Forecasting model (WRF) with the Morrison two-moment and spectral-bin microphysics (SBM) schemes at 3 km resolution, using dust number concentrations from a global chemical transport model (GEOS-Chem-APM). The case study indicates that the long-range-transported mineral dust, with relatively low number concentrations, can notably affect the properties of convective clouds (ice and liquid water contents, cloud top height, and cloud coverage) and precipitation (spatial pattern and intensity). The effects of dust are evident during strong convective periods, with significantly increased ice water contents in the mixed-phase regime via the enhanced heterogeneous freezing. With both the Morrison and SBM schemes, we see the invigoration effects of dust aerosols on the convective intensity through enhanced condensation and deposition latent heating. The low-altitude dust particles are uplifted to the freezing level by updrafts, which, in turn, enhance the convective cloud development through immersion freezing and convective invigoration. Compared to the Morrison scheme, the SBM scheme predicts more realistic precipitation and different invigoration effects of dust. The differences are partially attributed to the saturation adjustment approach utilized in the bulk scheme, which leads to a stronger enhancement of condensation at midlatitudes to low altitudes and a weaker deposition increase at the upper level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2020

Multiple Environmental Influences on the Lightning of Cold-Based Continental Cumulonimbus Clouds. Part I: Description and Validation of Model

In this two-part paper, influences from environmental factors on lightning in a convective storm are assessed with a model. In Part I, an electrical component is described and applied in the Aerosol–Cloud model (AC). AC treats many types of secondary (e.g., breakup in ice–ice collisions, raindrop-freezing fragmentation, rime splintering) and primary (heterogeneous, homogeneous freezing) ice initiation. AC represents lightning flashes with a statistical treatment of branching from a fractal law constrained by video imagery. The storm simulated is from the Severe Thunderstorm Electrification and Precipitation Study (STEPS; 19/20 June 2000). The simulation was validated microphysically [e.g., ice/droplet concentrations and mean sizes, liquid water content (LWC), reflectivity, surface precipitation] and dynamically (e.g., ascent) in our 2017 paper. Predicted ice concentrations (~10 L -1 ) agreed—to within a factor of about 2—with aircraft data at flight levels (-10° to -15°C). Here, electrical statistics of the same simulation are compared with observations. Flash rates (to within a factor of 2), triggering altitudes and polarity of flashes, and electric fields, all agree with the coincident STEPS observations. The “normal” tripole of charge structure observed during an electrical balloon sounding is reproduced by AC. It is related to reversal of polarity of noninductive charging in ice–ice collisions seen in laboratory experiments when temperature or LWC are varied. Positively charged graupel and negatively charged snow at most midlevels, charged away from the fastest updrafts, is predicted to cause the normal tripole. Total charge separated in the simulated storm is dominated by collisions involving secondary ice from fragmentation in graupel–snow collisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic

To investigate the influence of sea ice openings like leads on wintertime Arctic clouds, the air mass transport is exploited as a heat and humidity feeding mechanism which can modify Arctic cloud properties. Cloud microphysical properties in the central Arctic are analysed as a function of sea ice conditions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020. The Cloudnet classification algorithm is used to characterize the clouds based on remote sensing observations and the atmospheric thermodynamic state from the observatory on board the research vessel (RV) Polarstern. To link the sea ice conditions around the observational site with the cloud observations, the water vapour transport (WVT) being conveyed towards RV Polarstern has been utilized as a mechanism to associate upwind sea ice conditions with the measured cloud properties. This novel methodology is used to classify the observed clouds as coupled or decoupled to the WVT based on the location of the maximum vertical gradient of WVT height relative to the cloud-driven mixing layer. Only a conical sub-sector of sea ice concentration (SIC) and the lead fraction (LF) centred on the RV Polarstern location and extending up to 50 km in radius and with an azimuth angle governed by the time-dependent wind direction measured at the maximum WVT is related to the observed clouds. We found significant asymmetries for cases when the clouds are coupled or decoupled to the WVT and selected by LF regimes. Liquid water path of low-level clouds is found to increase as a function of LF, while the ice water path does so only for deep precipitating systems. Clouds coupled to WVT are found to generally have a lower cloud base and larger thickness than decoupled clouds. Thermodynamically, for coupled cases the cloud-top temperature is warmer and accompanied by a temperature inversion at the cloud top, whereas the decoupled cases are found to be closely compliant with the moist adiabatic temperature lapse rate. The ice water fraction within the cloud layer has been found to present a noticeable asymmetry when comparing coupled versus decoupled cases. This novel approach of coupling sea ice to cloud properties via the WVT mechanism unfolds a new tool to study Arctic surface–atmosphere processes. With this formulation, long-term observations can be analysed to enforce the statistical significance of the asymmetries. Furthermore, our results serve as an opportunity to better understand the dynamic linkage between clouds and sea ice and to evaluate its representation in numerical climate models for the Arctic system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing the Robustness of Arctic Sea Ice Bi‐Stability in the Presence of Atmospheric Feedbacks

Abstract Arctic sea‐ice loss is influenced by multiple positive feedbacks, sparking concerns of accelerated loss in the coming years or even a tipping point, where a sea‐ice equilibrium disappears at a given CO 2 value and sea ice rapidly evolves to a new steady state. Such a tipping point would imply a bi‐stability of the Arctic climate—where multiple steady‐state Arctic climates are possible at the same CO 2 value. Previous works have sought to establish the existence of bi‐stability using a range of models, from zero‐dimensional sea ice thermodynamic models to fully coupled global climate models, with conflicting results. Here, we present a new model of the Arctic that includes both sea‐ice thermodynamics and key atmospheric feedbacks in a simple framework. We exploit the model's simplicity to identify physical mechanisms that control the timing and extent of sea‐ice bi‐stability, and the abruptness of ice loss. We show that longwave radiation feedbacks can have a strong influence on Arctic surface climate from atmospheric temperature increases alone, even without major contributions from clear‐sky moisture or convective clouds suggested previously. While winter sea‐ice bi‐stability is robust to changes in uncertain model parameters in this study, summer sea ice is more sensitive. Finally, our model indicates that positive feedbacks may modulate the CO 2 threshold of sea‐ice loss and the width of bi‐stability much more strongly than the abruptness of loss. These results lead to a comprehensive understanding of the conditions that favor Arctic sea‐ice bi‐stability, particularly the role of atmospheric feedbacks, in both future and past climates.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

Relationships between Immersion Freezing and Crystal Habit for Arctic Mixed-Phase Clouds—A Numerical Study

Abstract The number concentration of ice particles in Arctic mixed-phase clouds is a major controlling factor of cloud lifetime. The relationships between ice nucleation mode and ice crystal habit development are not yet constrained by observations. This study uses a habit-predicting microphysical scheme within a 3D large-eddy simulation model to evaluate the relationship between immersion freezing and ice habit in a simulated Arctic mixed-phase cloud case. Three immersion freezing parameterizations are considered: a volume-dependent freezing scheme (VF), a parameterization limited to activated droplets (C-AC), and a parameterization limited to coarse aerosol particles (C-CM). Both C-AC and C-CM are based on classical nucleation theory. The freezing rate with VF is found to be greater in downdraft regions than in updraft regions due to the downdraft having a higher number concentration of large droplets. The C-AC cases show active freezing of small droplets near cloud top, whereas in the C-CM cases, mainly the 8–32- μ m-sized droplets freeze in updraft regions near the cloud base. Because the initial crystal size is assumed to affect the axis ratio of hexagonal plates, the VF cases produce crystals with larger axis ratios, resulting in smaller mode radii than the C-AC cases. In all cases, irregular polycrystals dominate near cloud top and a band-like structure develops within the cloud, which qualitatively agrees with previous observations. In the VF and C-CM cases, unactivated large droplets arising from coarse-mode aerosol particles contributed significantly to the freezing rate, producing an important influence on crystal habit.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Here we improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. Furthermore, we evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSat–CALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm‐Resolving Simulations

Abstract This study examines marine boundary layer cloud regime transition during a cold air outbreak (CAO) over the Norwegian Sea, simulated by a global storm‐resolving model (GSRM) known as the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAM). By selecting observational references based on a combination of large‐scale conditions rather than strict time‐matched comparisons, this study finds that SCREAM qualitatively captures the CAO cloud transition, including boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also accurately locates the greatest ice and liquid in the mesoscale updrafts, however, underestimates supercooled liquid water in cumulus clouds. The model evaluation approach adopted by this study takes advantages of the existing computational‐expensive global simulations of GSRM and the available observations to understand model performance and can be applied to assessments of other cloud regimes in different regions. Such practice provides valuable guidance on the future effort to correct and improve biased model behaviors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties During the M-PACE Field Campaign

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly-continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This dataset provides a set of 25 samples from the M-PACE field campaign, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this dataset include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this dataset as well. The retrieval algorithm and analysis of this dataset are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Impacts of Single-Scattering and Microphysical Properties of Ice Particles Smaller Than 100 µm on the Bulk Radiative Properties of Tropical Cirrus

There are large uncertainties in the single-scattering (i.e., morphologies) and microphysical (i.e., concentrations) properties of ice particles whose size are less than ~100 µm. Insufficient resolutions of the most advanced cloud probes (e.g., cloud particle imager) cannot resolve the micrometer-scale morphologies of small ice particles. Further, the shattering of large ice particles on probes’ inlets or tips causes uncertainties in the measurement of the concentrations of small ice particles. These uncertainties have large impacts on the single-scattering and microphysical properties of small ice particles that are utilized to quantify the bulk radiative properties of cirrus. In this study, the impacts of uncertainties in the morphologies and concentrations of small ice particles on the bulk radiative properties of tropical cirrus were calculated using measurements acquired during the Tropical Warm Pool-International Cloud Experiment. Five different models (i.e., budding Buckyball, Chebyshev particle, droxtal, Gaussian random sphere, and sphere) that represent the shapes of small ice particles were used to calculate the single-scattering properties. The bulk radiative properties, average phase-function ($\overline{P_{11}}$), and average asymmetry parameter ($\overline{g}$) were computed by combining the measured size/habit distributions and the calculated single-scattering properties of ice particles. The impacts of the selection of varying morphologies of small particles on the bulk radiative properties were quantified. For these calculations, the possible range of the concentrations of small ice particles which depend on the degree of shattered large particles were also used. The impacts of varying the single-scattering properties of small ice particles on the bulk radiative properties were the largest in the upper parts of cirrus (T < –60 °C), while they were the smallest in the lower parts of cirrus (–45 < T < –30 °C). The impacts of uncertainties in the concentrations of small ice particles on the bulk radiative properties were largest in the lower parts of cirrus (–45 < T < –30 °C), whereas they were smallest in the upper parts of cirrus (T < –60 °C). The effect of shattering was maximum in the lower parts of cirrus, whilst it was minimum in the upper parts of cirrus. The combined impacts of uncertainties in the single-scattering (i.e., morphologies) and microphysical (i.e., concentrations) properties of small ice particles revealed variations of up to 11.2% (127.1%; 67.3%) of the integrated intensity in the forward (sideward; backward) angles in $\overline{P_{11}}$ and a corresponding change in $\overline{g}$ by up to 12.61%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying and Modeling the Impact of Phase State on the Ice Nucleation Abilities of 2-Methyltetrols as a Key Component of Secondary Organic Aerosol Derived from Isoprene Epoxydiols

Organic aerosols (OAs) may serve as ice-nucleating particles (INPs), impacting the formation and properties of cirrus clouds when their phase state and viscosity are in the semisolid to glassy range. However, there is a lack of direct parameterization between aerosol viscosity and their ice nucleation capabilities. In this study, we experimentally measured the ice nucleation rate of 2-methyltetrols (2-MT) aerosols, a key component of isoprene-epoxydiol-derived secondary organic aerosols (IEPOX-SOA), at different viscosities. These results demonstrate that the phase state has a significant impact on the ice nucleation abilities of OA under typical cirrus cloud conditions, with the ice nucleation rate increasing by 2 to 3 orders of magnitude when the phase state changes from liquid to semisolid. An innovative parametric model based on classical nucleation theory was developed to directly quantify the impact of viscosity on the heterogeneous nucleation rate. This model accurately represents our laboratory measurement and can be implemented into climate models due to its simple, equation-based form. Based on data collected from the ACRIDICON-CHUVA field campaign, our model predicts that the INP concentration from IEPOX-SOA can reach the magnitude of 1 to tens per liter in the cirrus cloud region impacted by the Amazon rainforest, consistent with recent field observations and estimations. This novel parameterization framework can also be applied in regional and global climate models to further improve representations of cirrus cloud formation and associated climate impacts.

2-methyltetrol↗
DOE OSTI2025

Decomposing Cloud Radiative Feedbacks by Cloud-Top Phase

Changes in cloud scattering properties and emissivity that arise from atmospheric warming cause substantial radiative feedbacks in model projections of anthropogenic climate change, and the relative importance of the underlying mechanisms is poorly understood. One leading hypothesis is that ice-to-liquid conversions cause clouds to optically thicken, producing a major negative feedback. We test this hypothesis by developing a method to decompose cloud radiative feedbacks by cloud-top phase. The method is applied to an ensemble of six state-of-the-art global climate models run with prescribed sea surface temperature. In these simulations, the global mean of the net cloud scattering and emissivity feedback from cloud-phase conversions ranges from −0.17 to −0.01 W m −2 K −1 , while the overall net cloud feedback ranges from 0.02 to 0.91 W m −2 K −1 . The multimodel mean of the cloud scattering and emissivity feedback from cloud-phase conversions is approximately 19% of the magnitude of the multimodel mean of the overall cloud feedback (−0.10 vs 0.52 W m −2 K −1 ). These results indicate that cloud-phase conversions cause a robust negative feedback by changing cloud scattering and emissivity, but this mechanism makes a modest contribution to the overall cloud feedback at the global scale.

Climate change↗
DOE OSTI2021

Comment on “Review of experimental studies of secondary ice production” by Korolev and Leisner (2020)

Abstract. This is a comment on the review by Korolev and Leisner (2020, hereafter KL2020). The only two laboratory/field studies ever to measure the breakup in ice–ice collisions for in-cloud conditions were negatively criticised by KL2020, as were our subsequent theoretical and modelling studies informed by both studies. First, hypothetically, even without any further laboratory experiments, such theoretical and modelling studies would continue to be possible, based on classical mechanics and statistical physics. They are not sensitive to the accuracy of lab data for typical situations, partly because the nonlinear explosive growth of ice concentrations continues until some maximum concentration is reached. To a degree, the same final concentration is expected regardless of the fragment number per collision. Second, there is no evidence that both lab/field observational studies characterising fragmentation in ice–ice collisions are either mutually conflicting or erroneous such that they cannot be used to represent this breakup in numerical models, contrary to the review. The fact that the ice spheres of one experiment were hail sized (2 cm) is not a problem if a universal theoretical formulation, such as ours, with fundamental dependencies, is informed by it. Although both lab/field studies involved head-on collisions, rotational kinetic energy for all collisions generally is only a small fraction of the initial collision kinetic energy (CKE) anyway. Although both lab/field experiments involved fixed targets, that is not a problem since the fixing of the target is represented via CKE in any energy-based formulation such as ours. Finally, scaling analysis suggests that the breakup of ice during sublimation can make a significant contribution to ice enhancement in clouds, again contrary to the impression given by the review.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation

Abstract Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.

54 ENVIRONMENTAL SCIENCES↗