Testing Cloud Microphysics Parameterizations and Improving the Representation of the Wegner-Bergeron-Findeisen Process in Mixed-phase Clouds in NCAR CAM5 PDF Download
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Author: Meng Zhang Publisher: ISBN: 9780355325027 Category : Arctic regions Languages : en Pages : 52
Book Description
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: Meng Zhang Publisher: ISBN: 9780355325027 Category : Arctic regions Languages : en Pages : 52
Book Description
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
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
Author: Publisher: ISBN: Category : Languages : en Pages : 15
Book Description
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).
Author: Publisher: ISBN: Category : Languages : en Pages :
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.
Author: Jerry M. Straka Publisher: Cambridge University Press ISBN: 1139478834 Category : Science Languages : en Pages : 407
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.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
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: Publisher: ISBN: Category : Languages : en Pages :
Book Description
In our research we proposed to use data collected during the 2004 Mixed-Phase Arctic Cloud Experiment (MPACE) and the 2006 Tropical Warm Pool International Cloud Experiment (TWP-ICE) to improve retrievals of ice and mixed-phase clouds, to improve our understanding of how cloud and radiative processes affect cloud life cycles, and to develop and test methods for using ARM data more effectively in model. In particular, we proposed to: 1) use MPACE in-situ data to determine how liquid water fraction and cloud ice and liquid effective radius (r{sub ei} and r{sub ew}) vary with temperature, normalized cloud altitude and other variables for Arctic mixed-phase clouds, and to use these data to evaluate the performance of model parameterization schemes and remote sensing retrieval algorithms; 2) calculate rei and size/shape distributions using TWP-ICE in-situ data, investigate their dependence on cirrus type (oceanic or continental anvils or cirrus not directly traced to convection), and develop and test representations for MICROBASE; 3) conduct fundamental research enhancing our understanding of cloud/radiative interactions, concentrating on effects of small crystals and particle shapes and sizes on radiation; and 4) improve representations of microphysical processes for models (fall-out, effective density, mean scattering properties, rei and rew) and provide them to ARM PIs. In the course of our research, we made substantial progress on all four goals.
Author: Publisher: ISBN: Category : Languages : en Pages : 45
Book Description
[1] Simulations of mixed-phase clouds in forecasts with the NCAR Atmosphere Model version 3 (CAM3) and the GFDL Atmospheric Model version 2 (AM2) for the Mixed-Phase Arctic Cloud Experiment (M-PACE) are performed using analysis data from numerical weather prediction centers. CAM3 significantly underestimates the observed boundary layer mixed-phase cloud fraction and cannot realistically simulate the variations of liquid water fraction with temperature and cloud height due to its oversimplified cloud microphysical scheme. In contrast, AM2 reasonably reproduces the observed boundary layer cloud fraction while its clouds contain much less cloud condensate than CAM3 and the observations. The simulation of the boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when a new physically based cloud microphysical scheme is used (CAM3LIU). The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes. Sensitivity tests show that these results are not sensitive to the analysis data used for model initialization. Increasing model horizontal resolution helps capture the subgrid-scale features in Arctic frontal clouds but does not help improve the simulation of the single-layer boundary layer clouds. AM2 simulated cloud fraction and LWP are sensitive to the change in cloud ice number concentrations used in the Wegener-Bergeron-Findeisen process while CAM3LIU only shows moderate sensitivity in its cloud fields to this change. Furthermore, this paper shows that the Wegener-Bergeron-Findeisen process is important for these models to correctly simulate the observed features of mixed-phase clouds.
Author: Publisher: ISBN: Category : Languages : en Pages : 52
Book Description
Sensitivity of Arctic clouds and radiation in the Community Atmospheric Model, version 5, to the ice nucleation process is examined by testing a new physically based ice nucleation scheme that links the variation of ice nuclei (IN) number concentration to aerosol properties. The default scheme parameterizes the IN concentration simply as a function of ice supersaturation. The new scheme leads to a significant reduction in simulated IN concentration at all latitudes while changes in cloud amounts and properties are mainly seen at high- and midlatitude storm tracks. In the Arctic, there is a considerable increase in midlevel clouds and a decrease in low-level clouds, which result from the complex interaction among the cloud macrophysics, microphysics, and large-scale environment. The smaller IN concentrations result in an increase in liquid water path and a decrease in ice water path caused by the slowdown of the Bergeron-Findeisen process in mixed-phase clouds. Overall, there is an increase in the optical depth of Arctic clouds, which leads to a stronger cloud radiative forcing (net cooling) at the top of the atmosphere. The comparison with satellite data shows that the new scheme slightly improves low-level cloud simulations over most of the Arctic but produces too many midlevel clouds. Considerable improvements are seen in the simulated low-level clouds and their properties when compared with Arctic ground-based measurements. As a result, issues with the observations and the model-observation comparison in the Arctic region are discussed.
Author: Meng Zhang
Author: Meng Zhang
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Author: Jerry M. Straka
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