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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cloud Phase Simulation at High Latitudes in EAMv2: Evaluation using CALIPSO Observations and Comparison with EAMv1

This study performs a comprehensive evaluation of the simulated cloud phase in the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) atmosphere model version 2 (EAMv2) and version 1 (EAMv1). Enabled by the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) simulator, EAMv2 and EAMv1 predicted cloud phase is compared against the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) at high latitudes where mixed-phase clouds are prevalent. Our results indicate that the underestimation of cloud ice in simulated high-latitude mixed-phase clouds in EAMv1 has been significantly reduced in EAMv2. The increased ice clouds in the Arctic mainly result from the modification on the WBF (Wegner-Bergeron-Findeisen) process in EAMv2. The impact of the modified WBF process is moderately compensated by the low limit of cloud droplet number concentration (CDNC) in cloud microphysics and the new dCAPE_ULL trigger used in deep convection in EAMv2. Moreover, it is found that the new trigger largely contributes to the better cloud phase simulation over the Norwegian Sea and Barents Sea in the Arctic and the Southern Ocean where large errors are found in EAMv1. However, errors in simulated cloud phase in EAMv1, such as the overestimation of supercooled liquid clouds near the surface in both hemispheres and the underestimation of ice clouds over Antarctica, persist in EAMv2. In conclusion, this study highlights the impact of deep convection parameterizations, which has received little attention, on high-latitude mixed-phase clouds, and the importance of continuous improvement of cloud microphysics in climate models for accurately representing mixed-phase clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Climate Impacts of Ice Nucleation

Several different ice nucleation parameterizations in two different General Circulation Models (GCMs) are used to understand the effects of ice nucleation on the mean climate state, and the Aerosol Indirect Effects (AIE) of cirrus clouds on climate. Simulations have a range of ice microphysical states that are consistent with the spread of observations, but many simulations have higher present-day ice crystal number concentrations than in-situ observations. These different states result from different parameterizations of ice cloud nucleation processes, and feature different balances of homogeneous and heterogeneous nucleation. Black carbon aerosols have a small (0.06 Wm(exp-2) and not statistically significant AIE when included as ice nuclei, for nucleation efficiencies within the range of laboratory measurements. Indirect effects of anthropogenic aerosols on cirrus clouds occur as a consequence of increasing anthropogenic sulfur emissions with different mechanisms important in different models. In one model this is due to increases in homogeneous nucleation fraction, and in the other due to increases in heterogeneous nucleation with coated dust. The magnitude of the effect is the same however. The resulting ice AIE does not seem strongly dependent on the balance between homogeneous and heterogeneous ice nucleation. Regional effects can reach several Wm2. Indirect effects are slightly larger for those states with less homogeneous nucleation and lower ice number concentration in the base state. The total ice AIE is estimated at 0.27 +/- 0.10 Wm(exp-2) (1 sigma uncertainty). This represents a 20% offset of the simulated total shortwave AIE for ice and liquid clouds of 1.6 Wm(sup-2).

Gettelman, Andrew↗
DOE OSTI2020

Understanding cloud droplet spectral dispersion effect using empirical and semi-analytical parameterizations in NCAR CAM5.3

Five parameterizations of cloud droplet spectral shape are implemented in a global climate model to investigate the dispersion effect and aerosol indirect effect (AIE). We design a series of experiments by modifying the microphysical cloud scheme of NCAR CAM5.3 (Community Atmospheric Model Version 5.3). We employ four empirical (Martin94, RLiu03, PengL03, Liu08) and one semi-analytical (LiuLi15) expressions for cloud droplet spectral shape parameters. Analysis focuses on the instantaneous differences in the simulated cloud microphysical properties and the comparison between model output and satellite data. The results show that RLiu03, PengL03 and LiuLi15 produce wider droplet spectrum and faster autoconversion rate, but Liu08 has a narrower droplet spectrum and slower autoconversion rate than the default parameterization (Martin94) in CAM5.3. Global dispersion effects caused by the five parameterizations modify the aerosol indirect effect by -10% (counteract) to 13% (strengthen). The simulated AIEs and dispersion effects exhibit noticeably spatial inhomogeneity. In the sensitive regions of AIE (Southeast Asia, North Pacific and west coast of South America), we decompose the response of shortwave cloud forcing to the change in droplet number for analysis. The varying dispersion effects can be explained by different responses of cloud properties in different spectral parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM)

Fire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire-aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997-2016. Furthermore, we distributed fire emissions vertically using smoke plume heights from Multi-angle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg y -1 of primary particulate organic matter and 3.9 Tg y -1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14 ± 7% and contribute to a reduction in net shortwave radiation at the surface (-2.3 ± 0.5 W m -2 ). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17 ± 0.15°C, relative humidity to increase by 0.4 ± 0.3%, and diffuse light fraction to increase by 0.5 ± 0.3%. In response, GPP declines by 2.8 Pg C y -1 , as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light) nearly cancelling out large negative drivers (decreases shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Intercomparison of Large‐Eddy Simulations of a Convection Cloud Chamber Using Haze‐Capable Bin and Lagrangian Cloud Microphysics Schemes

Abstract Recent in situ observations show that haze particles exist in a convection cloud chamber. The microphysics schemes previously used for large‐eddy simulations of the cloud chamber could not fully resolve haze particles and the associated processes, including their activation and deactivation. Specifically, cloud droplet activation was modeled based on Twomey‐type parameterizations, wherein cloud droplets were formed when a critical supersaturation for the available cloud condensation nuclei (CCN) was exceeded and haze particles were not explicitly resolved. Here, we develop and adapt haze‐capable bin and Lagrangian microphysics schemes to properly resolve the activation and deactivation processes. Results are compared with the Twomey‐type CCN‐based bin microphysics scheme in which haze particles are not fully resolved. We find that results from the haze‐capable bin microphysics scheme agree well with those from the Lagrangian microphysics scheme. However, both schemes significantly differ from those from a CCN‐based bin microphysics scheme unless CCN recycling is considered. Haze particles from the recycling of deactivated cloud droplets can strongly enhance cloud droplet number concentration due to a positive feedback in haze‐cloud interactions in the cloud chamber. Haze particle size distributions are more realistic when considering solute and curvature effects that enable representing the complete physics of the activation process. Our study suggests that haze particles and their interactions with cloud droplets may have a strong impact on cloud properties when supersaturation fluctuations are comparable to mean supersaturation, as is the case in the cloud chamber and likely is the case in the atmosphere, especially in polluted conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating and Improving Convective Parameterization for GCMs Using ARM Observations. Final report

This technical report summarizes the achievements during the project period funded by the Atmospheric System Research (ASR) program. The general goal of the project is to improve the representation of atmospheric deep convection in global climate models. By using DOE Atmospheric Radiation Measurement (ARM) program observations and a hierarchy of models from cloud-resolving to global climate models, we evaluated and improved many aspects of convection parameterization schemes, such as the trigger function for convection and closure condition to determine the amount of convection. We also incorporated the stochasticity of convection into a convection scheme. These improvements, when implemented into the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) and the DOE Exascale Energy Earth System Model (E3SM), led to improved simulation of precipitation characteristics, including precipitation intensity, Intertropical Convergence Zone, and Madden-Julian oscillation. The project also improved the representation of microphysical processes in convective clouds. It enables the interaction of cloud microphysics with aerosols in weather and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Parameterizations of Entrainment-Mixing Mechanisms and their Effects on Cloud Droplet Spectral Width Based on Numerical Simulations

Entrainment-mixing mechanisms significantly affect cloud droplet number concentration, radius, and spectral shape. Quantitative examination of entrainment-mixing effects on cloud droplet spectral width is lacking. Here, we examine the effects of entrainment-mixing processes on cloud microphysics by 12,218 different setups, each simulated 10 times using the Explicit Mixing Parcel Model (EMPM) driven by the observational data from the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) campaign. Parameterizations of entrainment-mixing mechanisms are developed by relating homogeneous mixing degree to transition scale number that depends on the dissipation rate and droplet evaporation time scale. The correlation between relative dispersion of cloud droplet size distribution and homogeneous mixing degree changes from negative to positive with the decreasing homogeneous mixing degree. The different relationships are closely related to the competition between complete and partial droplet evaporation and the number concentration of small droplets, which are quantitatively described by two newly introduced dimensionless numbers. The competition is significantly affected by relative humidity and mixing fraction of entrained air as well as turbulence dissipation rate, but not much by cloud droplet number concentration. Especially, when relative humidity and dissipation rate are high, there is only a negative correlation. This study sheds new light on generalizing the homogeneous/inhomogeneous concept by considering relative dispersion, and also provides parameterizations of entrainment-mixing processes and relative dispersion for atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Metrics for evaluating tropical cyclones in climate data

This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Furthermore, upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assimilating Scanning Radar Data into Cloud-Scale Models

The aim of this project is to improve the initial conditions of large eddy simulations (LES) of deep convective storms by assimilating scanning radar reflectivity and radial velocity data as well as local environmental observations. Accomplishing this goal often results in more accurate numerical forecasts of clouds and precipitation than if the background initial meteorological conditions are solely relied upon for convection initiation and intensification, as is typical of most operational and research models. Conducted in a storm-scale LES framework, these more accurate numerical analyses can serve as tool to better understand and parameterize land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development of convection. Our work examined the sensitivity of the numerical representation of cloud-scale wind and microphysical structures to: i) the assimilation of radiosonde observations characterizing the near-storm inflow, ii) the assimilation of radar data of varying resolution for model simulations with grid spacings of 1-km and 0.25-km, and iii) to the assimilation of three-dimensional plan-projection-indicator (PPI) and range-height indicator (RHI) radar volumes. Few studies have explored deep convective storm analyses and forecasts produced by assimilation of radar observations to cloud-scale LES. Furthermore, to our knowledge, no storm-scale DA study has explored the value of assimilating high-resolution sector RHI volumes to determine if they can improve the retrieval of the vertical kinematic and microphysical storm structure aloft over the use of traditionally utilized PPI scans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Molthan, Andrew L.↗
NASA NTRS2009

A Multiscale Modeling System: Developments, Applications, and Critical Issues

The foremost challenge in parameterizing convective clouds and cloud systems in large-scale models are the many coupled dynamical and physical processes that interact over a wide range of scales, from microphysical scales to the synoptic and planetary scales. This makes the comprehension and representation of convective clouds and cloud systems one of the most complex scientific problems in Earth science. During the past decade, the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) has pioneered the use of single-column models (SCMs) and cloud-resolving models (CRMs) for the evaluation of the cloud and radiation parameterizations in general circulation models (GCMs; e.g., GEWEX Cloud System Science Team 1993). These activities have uncovered many systematic biases in the radiation, cloud and convection parameterizations of GCMs and have led to the development of new schemes (e.g., Zhang 2002; Pincus et al, 2003; Zhang and Wu 2003; Wu et al. 2003; Liang and Wu 2005; Wu and Liang 2005, and others). Comparisons between SCMs and CRMs using the same large-scale forcing derived from field campaigns have demonstrated that CRMs are superior to SCMs in the prediction of temperature and moisture tendencies (e.g., Das et al. 1999; Randall et al 2003b; Xie et al. 2005).

Tao, Wei-Kuo↗
NASA NTRS2015

Remote Sensing of Cloud Top Heights Using the Research Scanning Polarimeter

Clouds cover roughly two thirds of the globe and act as an important regulator of Earth's radiation budget. Of these, multilayered clouds occur about half of the time and are predominantly two-layered. Changes in cloud top height (CTH) have been predicted by models to have a globally averaged positive feedback, however observational changes in CTH have shown uncertain results. Additional CTH observations are necessary to better and quantify the effect. Improved CTH observations will also allow for improved sub-grid parameterizations in large-scale models and accurate CTH information is important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. RSP scans along the aircraft track and obtains measurements at 152 viewing angles at any aircraft location. The approach presented here aggregates measurements from multiple scans to a single location at cloud altitude using a correlation function designed to identify the location-distinct features in each scan. During NASAs SEAC4RS air campaign, the RSP was mounted on the ER-2 aircraft along with the Cloud Physics Lidar (CPL), which made simultaneous measurements of CTH. The RSPs unique method of determining CTH is presented. The capabilities of using single and combinations of channels within the approach are investigated. A detailed comparison of RSP retrieved CTHs with those of CPL reveal the accuracy of the approach. Results indicate a strong ability for the RSP to accurately identify cloud heights. Interestingly, the analysis reveals an ability for the approach to identify multiple cloud layers in a single scene and estimate the CTH of each layer. Capabilities and limitations of identifying single and multiple cloud layers heights are explored. Special focus is given to sources of error in the method including optically thin clouds, physically thick clouds, multi-layered clouds as well as cloud phase. When determining multi-layered CTHs, limits on the upper clouds opacity are assessed.

Cloud height indicators↗
DOE OSTI2022

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Abstract The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

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↗
NASA NTRS1997

Cloud Radiation Forcings and Feedbacks: General Circulation Model Tests and Observational Validation

Using an atmospheric general circulation model (the National Center for Atmospheric Research Community Climate Model: CCM2), the effects on climate sensitivity of several different cloud radiation parameterizations have been investigated. In addition to the original cloud radiation scheme of CCM2, four parameterizations incorporating prognostic cloud water were tested: one version with prescribed cloud radiative properties and three other versions with interactive cloud radiative properties. The authors' numerical experiments employ perpetual July integrations driven by globally constant sea surface temperature forcings of two degrees, both positive and negative. A diagnostic radiation calculation has been applied to investigate the partial contributions of high, middle, and low cloud to the total cloud radiative forcing, as well as the contributions of water vapor, temperature, and cloud to the net climate feedback. The high cloud net radiative forcing is positive, and the middle and low cloud net radiative forcings are negative. The total net cloud forcing is negative in all of the model versions. The effect of interactive cloud radiative properties on global climate sensitivity is significant. The net cloud radiative feedbacks consist of quite different shortwave and longwave components between the schemes with interactive cloud radiative properties and the schemes with specified properties. The increase in cloud water content in the warmer climate leads to optically thicker middle- and low-level clouds and in turn to negative shortwave feedbacks for the interactive radiative schemes, while the decrease in cloud amount simply produces a positive shortwave feedback for the schemes with a specified cloud water path. For the longwave feedbacks, the decrease in high effective cloudiness for the schemes without interactive radiative properties leads to a negative feedback, while for the other cases, the longwave feedback is positive. These cloud radiation parameterizations are empirically validated by using a single-column diagnostic model. together with measurements from the Atmospheric Radiation Measurement program and from the Tropical Ocean Global Atmosphere Combined Ocean-Atmosphere Response Experiment. The inclusion of prognostic cloud water produces a notable improvement in the realism of the parameterizations, as judged by these observations. Furthermore, the observational evidence suggests that deriving cloud radiative properties from cloud water content and microphysical characteristics is a promising route to further improvement.

Lee,Wan-Ho↗