Evaluation of Mixed-Phase Cloud Microphysics Parameterizations with the NCAR Single Column Climate Model (SCAM) and ARM Observations PDF Download
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Languages : en
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Mixed-phase stratus clouds are ubiquitous in the Arctic and play an important role in climate in this region. However, climate models have generally proven unsuccessful at simulating the partitioning of condensed water into liquid droplets and ice crystals in these Arctic clouds, which affect modeled cloud phase, cloud lifetime and radiative properties. An ice nucleation parameterization and a vapor deposition scheme were developed that together provide a physically-consistent treatment of mixed-phase clouds in global climate models. These schemes have been implemented in the National Center for Atmospheric Research (NCAR) Community Atmospheric Model Version 3 (CAM3). This report documents the performance of these schemes against ARM Mixed-phase Arctic Cloud Experiment (M-PACE) observations using the CAM single column model version (SCAM). SCAM with our new schemes has a more realistic simulation of the cloud phase structure and the partitioning of condensed water into liquid droplets against observations during the M-PACE than the standard CAM simulations.
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Languages : en
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Book Description
Mixed-phase stratus clouds are ubiquitous in the Arctic and play an important role in climate in this region. However, climate models have generally proven unsuccessful at simulating the partitioning of condensed water into liquid droplets and ice crystals in these Arctic clouds, which affect modeled cloud phase, cloud lifetime and radiative properties. An ice nucleation parameterization and a vapor deposition scheme were developed that together provide a physically-consistent treatment of mixed-phase clouds in global climate models. These schemes have been implemented in the National Center for Atmospheric Research (NCAR) Community Atmospheric Model Version 3 (CAM3). This report documents the performance of these schemes against ARM Mixed-phase Arctic Cloud Experiment (M-PACE) observations using the CAM single column model version (SCAM). SCAM with our new schemes has a more realistic simulation of the cloud phase structure and the partitioning of condensed water into liquid droplets against observations during the M-PACE than the standard CAM simulations.
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Languages : en
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Mixed-phase clouds are composed of a mixture of cloud droplets and ice crystals. The cloud microphysics in mixed-phase clouds can significantly impact cloud optical depth, cloud radiative forcing, and cloud coverage. However, the treatment of mixed-phase clouds in most current climate models is crude and the partitioning of condensed water into liquid droplets and ice crystals is prescribed as temperature dependent functions. In our previous 2007 ARM metric reports a new mixed-phase cloud microphysics parameterization (for ice nucleation and water vapor deposition) was documented and implemented in the NCAR Community Atmospheric Model Version 3 (CAM3). The new scheme was tested against the Atmospheric Radiation Measurement (ARM) Mixed-phase Arctic Cloud Experiment (M-PACE) observations using the single column modeling and short-range weather forecast approaches. In this report this new parameterization is further tested with CAM3 in its climate simulations. It is shown that the predicted ice water content from CAM3 with the new parameterization is in better agreement with the ARM measurements at the Southern Great Plain (SGP) site for the mixed-phase clouds.
Author: Meng Zhang
Publisher:
ISBN: 9780355325027
Category : Arctic regions
Languages : en
Pages : 52
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Mixed-phase clouds are persistently observed in the Arctic and the phase partition of cloud liquid and ice in mixed-phase clouds has important impacts on the surface energy budget and Arctic climate. In this study, we test the NCAR Community Atmosphere Model Version 5 (CAM5) in the single-column and weather forecast modes and evaluate the model performance against observation data obtained during the DOE Atmospheric Radiation Measurement (ARM) Program’s M-PACE field campaign in October 2004 and long-term ground-based multi-sensor measurements. We find that CAM5, like other global climate models, poorly simulates the phase partition in mixed-phase clouds by significantly underestimating the cloud liquid water content. An assumption of the pocket structure in the distribution of cloud liquid and ice based on in situ observations inside mixed-phase clouds has provided a possible solution to improve the model performance by reducing the Wegner-Bergeron-Findeisen (WBF) process rate. In this study, the modification of the WBF process in the CAM5 model has been achieved with applying a stochastic perturbation to the time scale of the WBF process relevant to both ice and snow to account for the heterogeneous mixture of cloud liquid and ice. Our results show that the modification of the WBF process improves the modeled phase partition in mixed-phase clouds. The seasonality of mixed-phase cloud properties is also better captured in the model compared with long-term ground-based remote sensing observations. Furthermore, the phase partitioning is insensitive to the reassignment time step of perturbations.
Author: Constantin Andronache
Publisher: Elsevier
ISBN: 012810550X
Category : Science
Languages : en
Pages : 302
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Book Description
Mixed-Phase Clouds: Observations and Modeling presents advanced research topics on mixed-phase clouds. As the societal impacts of extreme weather and its forecasting grow, there is a continuous need to refine atmospheric observations, techniques and numerical models. Understanding the role of clouds in the atmosphere is increasingly vital for current applications, such as prediction and prevention of aircraft icing, weather modification, and the assessment of the effects of cloud phase partition in climate models. This book provides the essential information needed to address these problems with a focus on current observations, simulations and applications. - Provides in-depth knowledge and simulation of mixed-phase clouds over many regions of Earth, explaining their role in weather and climate - Features current research examples and case studies, including those on advanced research methods from authors with experience in both academia and the industry - Discusses the latest advances in this subject area, providing the reader with access to best practices for remote sensing and numerical modeling
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Languages : en
Pages : 15
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Mixed-phase cloud microphysical and dynamical processes are still poorly understood, and their representation in GCMs is a major source of uncertainties in overall cloud feedback in GCMs. Thus improving mixed-phase cloud parameterizations in climate models is critical to reducing the climate forecast uncertainties. This study aims at providing improved knowledge of mixed-phase cloud properties from the long-term ACRF observations and improving mixed-phase clouds simulations in the NCAR Community Atmosphere Model version 5 (CAM5). The key accomplishments are: 1) An improved retrieval algorithm was developed to provide liquid droplet concentration for drizzling or mixed-phase stratiform clouds. 2) A new ice concentration retrieval algorithm for stratiform mixed-phase clouds was developed. 3) A strong seasonal aerosol impact on ice generation in Arctic mixed-phase clouds was identified, which is mainly attributed to the high dust occurrence during the spring season. 4) A suite of multi-senor algorithms was applied to long-term ARM observations at the Barrow site to provide a complete dataset (LWC and effective radius profile for liquid phase, and IWC, Dge profiles and ice concentration for ice phase) to characterize Arctic stratiform mixed-phase clouds. This multi-year stratiform mixed-phase cloud dataset provides necessary information to study related processes, evaluate model stratiform mixed-phase cloud simulations, and improve model stratiform mixed-phase cloud parameterization. 5). A new in situ data analysis method was developed to quantify liquid mass partition in convective mixed-phase clouds. For the first time, we reliably compared liquid mass partitions in stratiform and convective mixed-phase clouds. Due to the different dynamics in stratiform and convective mixed-phase clouds, the temperature dependencies of liquid mass partitions are significantly different due to much higher ice concentrations in convective mixed phase clouds. 6) Systematic evaluations of mixed-phase cloud simulations by CAM5 were performed. Measurement results indicate that ice concentrations control stratiform mixed-phase cloud properties. The improvement of ice concentration parameterization in the CAM5 was done in close collaboration with Dr. Xiaohong Liu, PNNL (now at University of Wyoming).
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Languages : en
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By making use of the in-situ data collected from the recent Atmospheric Radiation Measurement Mixed-Phase Arctic Cloud Experiment, we have tested the mixed-phase cloud parameterizations used in the two major U.S. climate models, the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory climate model (AM2), under both the single-column modeling framework and the U.S. Department of Energy Climate Change Prediction Program-Atmospheric Radiation Measurement Parameterization Testbed. An improved and more physically based cloud microphysical scheme for CAM3 has been also tested. The single-column modeling tests were summarized in the second quarter 2007 Atmospheric Radiation Measurement metric report. In the current report, we document the performance of these microphysical schemes in short-range weather forecasts using the Climate Chagne Prediction Program Atmospheric Radiation Measurement Parameterizaiton Testbest strategy, in which we initialize CAM3 and AM2 with realistic atmospheric states from numerical weather prediction analyses for the period when Mixed-Phase Arctic Cloud Experiment was conducted.
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Languages : en
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Our overall goal is the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data at all three ARM sites, and the implementation and testing of these parameterizations in global models. To test recently developed prognostic parameterizations based on detailed cloud microphysics, we have compared SCM (single-column model) output with ARM observations at the SGP, NSA and TWP sites. We focus on the predicted cloud amounts and on a suite of radiative quantities strongly dependent on clouds, such as downwelling surface shortwave radiation. Our results demonstrate the superiority of parameterizations based on comprehensive treatments of cloud microphysics and cloud-radiative interactions. At the SGP and NSA sites, the SCM results simulate the ARM measurements well and are demonstrably more realistic than typical parameterizations found in conventional operational forecasting models. At the TWP site, the model performance depends strongly on details of the scheme, and the results of our diagnostic tests suggest ways to develop improved parameterizations better suited to simulating cloud-radiation interactions in the tropics generally. These advances have made it possible to take the next step and build on this progress, by incorporating our parameterization schemes in state-of-the-art three-dimensional atmospheric models, and diagnosing and evaluating the results using independent data. Because the improved cloud-radiation results have been obtained largely via implementing detailed and physically comprehensive cloud microphysics, we anticipate that improved predictions of hydrologic cycle components, and hence of precipitation, may also be achievable.
Author: Jerry M. Straka
Publisher: Cambridge University Press
ISBN: 1139478834
Category : Science
Languages : en
Pages : 407
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Book Description
This book focuses specifically on bin and bulk parameterizations for the prediction of cloud and precipitation at various scales - the cloud scale, mesoscale, synoptic scale, and the global climate scale. It provides a background to the fundamental principles of parameterization physics, including processes involved in the production of clouds, ice particles, liquid water, snow aggregate, graupel and hail. It presents full derivations of the parameterizations, allowing readers to build parameterization packages, with varying levels of complexity based on information in the book. Architectures for a range of dynamical models are given, in which parameterizations form a significant tool for investigating large non-linear numerical systems. Model codes are available online at www.cambridge.org/9780521883382. Written for researchers and advanced students of cloud and precipitation microphysics, this book is also a valuable reference for all atmospheric scientists involved in models of numerical weather prediction.
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Languages : en
Pages : 80
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Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) program's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud top temperature of -15 C. The observed liquid water path of around 160 g m−2 was about two-thirds of the adiabatic value and much greater than the mass of ice crystal precipitation which when integrated from the surface to cloud top was around 15 g m−2. The simulations were performed by seventeen single-column models (SCMs) and nine cloud-resolving models (CRMs). While the simulated ice water path is generally consistent with the observed values, the median SCM and CRM liquid water path is a factor of three smaller than observed. Results from a sensitivity study in which models removed ice microphysics indicate that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path. Despite this general underestimate, the simulated liquid and ice water paths of several models are consistent with the observed values. Furthermore, there is some evidence that models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter is also present. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics. This case study, which has been well observed from both aircraft and ground-based remote sensors, could be a benchmark for model simulations of mixed-phase clouds.
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Languages : en
Pages : 33
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We proposed to analyze in-situ cloud data collected during ARM/ASR field campaigns to create databases of cloud microphysical properties and their uncertainties as needed for the development of improved cloud parameterizations for models and remote sensing retrievals, and for evaluation of model simulations and retrievals. In particular, we proposed to analyze data collected over the Southern Great Plains (SGP) during the Mid-latitude Continental Convective Clouds Experiment (MC3E), the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX), the Small Particles in Cirrus (SPARTICUS) Experiment and the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign, over the North Slope of Alaska during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE), and over the Tropical Western Pacific (TWP) during The Tropical Warm Pool International Cloud Experiment (TWP-ICE), to meet the following 3 objectives; derive statistical databases of single ice particle properties (aspect ratio AR, dominant habit, mass, projected area) and distributions of ice crystals (size distributions SDs, mass-dimension m-D, area-dimension A-D relations, mass-weighted fall speeds, single-scattering properties, total concentrations N, ice mass contents IWC), complete with uncertainty estimates; assess processes by which aerosols modulate cloud properties in arctic stratus and mid-latitude cumuli, and quantify aerosol's influence in context of varying meteorological and surface conditions; and determine how ice cloud microphysical, single-scattering and fall-out properties and contributions of small ice crystals to such properties vary according to location, environment, surface, meteorological and aerosol conditions, and develop parameterizations of such effects. In this report we describe the accomplishments that we made on all 3 research objectives.
