Cloud Processes and Cloud Feedbacks in Large-scale Models PDF Download

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Languages : en
Pages : 0

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Category : Atmospheric circulation
Languages : en
Pages : 506

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Author: Global Energy and Water Cycle Experiment. Cloud Systems Study
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Languages : en
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Author: WMO. World Climate Research Programme
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Languages : en
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Author: Robert Pincus
Publisher: Springer
ISBN: 3319772732
Category : Science
Languages : en
Pages : 396

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This volume presents a series of overview articles arising from a workshop exploring the links among shallow clouds, water vapor, circulation, and climate sensitivity. It provides a state-of-the art synthesis of understanding about the coupling of clouds and water vapor to the large-scale circulation. The emphasis is on two phenomena, namely the self-aggregation of deep convection and interactions between low clouds and the large-scale environment, with direct links to the sensitivity of climate to radiative perturbations. Each subject is approached using simulations, observations, and synthesizing theory; particular attention is paid to opportunities offered by new remote-sensing technologies, some still prospective. The collection provides a thorough grounding in topics representing one of the World Climate Research Program’s Grand Challenges. Previously published in Surveys in Geophysics, Volume 38, Issue 6, 2017 The aritcles “Observing Convective Aggregation”, “An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles”, “Implications of Warm Rain in Shallow Cumulus and Congestus Clouds for Large-Scale Circulations”, “A Survey of Precipitation-Induced Atmospheric Cold Pools over Oceans and Their Interactions with the Larger-Scale Environment”, “Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review”, “Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review”, “Structure and Dynamical Influence of Water Vapor in the Lower Tropical Troposphere”, “Emerging Technologies and Synergies for Airborne and Space-Based Measurements of Water Vapor Profiles”, “Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors”, and “EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation” are available as open access articles under a CC BY 4.0 license at link.springer.com.

Author: Yangang Liu
Publisher: John Wiley & Sons
ISBN: 1119528941
Category : Science
Languages : en
Pages : 483

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Improving weather and climate prediction with better representation of fast processes in atmospheric models Many atmospheric processes that influence Earth’s weather and climate occur at spatiotemporal scales that are too small to be resolved in large scale models. They must be parameterized, which means approximately representing them by variables that can be resolved by model grids. Fast Processes in Large-Scale Atmospheric Models: Progress, Challenges and Opportunities explores ways to better investigate and represent multiple parameterized processes in models and thus improve their ability to make accurate climate and weather predictions. Volume highlights include: Historical development of the parameterization of fast processes in numerical models Different types of major sub-grid processes and their parameterizations Efforts to unify the treatment of individual processes and their interactions Top-down versus bottom-up approaches across multiple scales Measurement techniques, observational studies, and frameworks for model evaluation Emerging challenges, new opportunities, and future research directions The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Author: Xiaofan Li
Publisher: Springer
ISBN: 3319263609
Category : Science
Languages : en
Pages : 364

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This is an updated and revised second edition of the book presenting new developments in the field of cloud-resolving modeling. The first edition of the book introduces the framework of cloud-resolving model, methodologies for analysis of modeling outputs, and validation of simulations with observations. It details important scientific findings in the aspects of surface rainfall processes, precipitation efficiency, dynamic and thermodynamic processes associated with tropical convection, diurnal variations, radiative and cloud microphysical processes associated with development of cloud clusters, air-sea coupling on convective scales, climate equilibrium states, and remote sensing applications. In additional to the content from the first edition of the book, the second edition of the book contains the new scientific results in the development of convective-stratiform rainfall separation scheme, the analysis of structures of precipitation systems, the thermal effects of doubled carbon dioxide on rainfall, precipitation predictability, and modeling depositional growth of ice crystal. The book will be beneficial both to graduate students and to researchers who do cloud, mesoscale and global modeling.

Author: Sylvia Sullivan
Publisher: John Wiley & Sons
ISBN: 1119700310
Category : Science
Languages : en
Pages : 371

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Clouds and Their Climatic Impacts Clouds are an influential and complex element of Earth’s climate system. They evolve rapidly in time and exist over small spatial scales, but also affect global radiative balance and large-scale circulations. With more powerful models and extensive observations now at our disposal, the climate impact of clouds is receiving ever more research attention. Clouds and Their Climatic Impacts: Radiation, Circulation, and Precipitation presents an overview of our current understanding on various types of clouds and cloud systems and their multifaceted role in the radiative budget, circulation patterns, and rainfall. Volume highlights include: Interactions of aerosol with both liquid and ice clouds Surface and atmospheric cloud radiative feedbacks and effects Arctic, extratropical, and tropical clouds Cloud-circulation coupling at global, meso, and micro scales Precipitation efficiency, phase, and measurements The role of machine learning in understanding clouds and climate The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Author: Katinka Bellomo
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Languages : en
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One of the greatest challenges in projections of future climate change is narrowing the uncertainty in the magnitude and sign of cloud feedback. The main limitations are that cloud processes need to be parameterized in climate models, and long-term observations of cloud cover are limited. In this dissertation we address this problem by studying the interplay of cloud feedbacks with atmospheric circulation and Sea Surface Temperature (SST). We first investigate the response of clouds to external radiative forcing by examining changes in cloud cover and their radiative impact in multiple and independent surface and satellite cloud cover datasets. Observed changes in cloud cover and estimated cloud amount feedback from 1954 to 2008 over the Indo-Pacific Ocean are found to be consistent in sign but significantly smaller in amplitude than changes simulated by an ensemble of historical simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive over the same period of time. However, climate models are capable of simulating changes in cloud cover of the same strength and pattern as observed, when they are forced with a greater increase in SST. This suggests that observed changes in cloud cover are at least in part forced by anthropogenic emissions. It remains unclear whether observations exhibit unrealistically large trends in cloud cover, or clouds are not sensitive enough to changes in surface temperature in climate models. However, climate models underestimate changes in cloud cover also on shorter and better constrained timescales. The implications of underestimating the strength of a positive cloud feedback is explored using idealized model experiments in the context of internal climate variability. It is found that a positive feedback among cloud cover, SST, and large-scale atmospheric circulation over the subtropical stratocumulus regions affects basin-wide pattern and persistence of SST anomalies in both the Atlantic and Pacific Oceans. Collectively, these findings suggest that climate models underestimate the impacts of cloud feedbacks on the persistence of regional and global SST anomalies, thus potentially underestimating climate sensitivity to future climate change.

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Languages : en
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The representation of clouds in Global Climate Models (GCMs) remains a major source of uncertainty in climate change simulations. Cloud climatologies have been widely used to either evaluate climate model cloud fields or examine, in combination with other data sets, climate-scale relationships between cloud properties and dynamical or microphysical parameters. Major cloud climatologies have been based either on satellite observations of cloud properties or on surface observers views of cloud type and amount. Such data sets provide either the top-down view of column-integrated cloud properties (satellites) or the bottom-up view of the cloud field morphology (surface observers). Both satellite-based and surface cloud climatologies have been successfully used to examine cloud properties, to support process studies, and to evaluate climate and weather models. However, they also present certain limitations, since the satellite cloud types are defined using radiative cloud boundaries and surface observations are based on cloud boundaries visible to human observers. As a result, these data sets do not resolve the vertical distribution of cloud layers, an issue that is important in calculating both the radiative and the hydrologic effects of the cloud field. Ground-based cloud radar observations, on the other hand, resolve with good accuracy the vertical distribution of cloud layers and could be used to produce cloud type climatologies with vertical layering information. However, these observations provide point measurements only and it is not immediately clear to what extent they are representative of larger regimes. There are different methods that can be applied to minimize this problem and to produce cloud layering climatologies useful for both cloud process and model evaluation studies. If a radar system is run continuously over a number of years, it eventually samples a large number of dynamical and microphysical regimes. If additional data sets are used to put the cloud layering information into the context of large-scale dynamical regimes, such information can be used to study interactions among cloud vertical distributions and dynamical and microphysical processes and to evaluate the ability of models to simulate those interactions. The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has established several Climate Research Facilities (ACRF) that provide continuous, long-term observations of clouds and radiation. ARM, with its overall goal of improving the treatment of radiation and clouds in climate models has provided unique observing systems for accelerating progress on the representation of cloud processes. In this project, six and a half years (January 1998 to June 2004) of cloud observations collected at the Southern Great Plains (SGP) Oklahoma ACRF were used to produce a cloud-type climatology. The climatology provides cloud amounts for seven different cloud types as well as information on the detailed structure of multi-layer cloud occurrences. Furthermore, the European Centre for Medium-Range Weather Forecasts (ECMWF) model output was used to define the dynamic regimes present during the observations of the cloud conditions by the vertically pointing radars at the SGP ACRF. The cloud-type climatology and the ECMWF SGP data set were then analyzed to examine and map dynamical conditions that favor the creation of single-layer versus multi-layer cloud structures as well as dynamical conditions that favor the occurrence of drizzle in continental stratus clouds. In addition, output from the ECMWF weather model forecasts was analyzed with the objective to compare model and radar derived cloud type statistics, in order to identify the major model deficiencies in cloud vertical distribution and map their seasonal variations. The project included two primary goals. The first was to create a cloud type climatology over the Southern Great Planes site that will show how cloud vertical distribution varies with dynamic and thermodynamic regime and how these variations would affect cloud climate feedbacks. The second was to compare this climatology to clouds derived by a numerical model in order to identify the major model deficiencies in cloud vertical distribution and map their seasonal variations.