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  • 1
    Online Resource
    Online Resource
    Newark :American Geophysical Union,
    Keywords: Clouds. ; Climatic changes. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (368 pages)
    Edition: 1st ed.
    ISBN: 9781119700333
    Series Statement: Geophysical Monograph Series ; v.281
    DDC: 551.576
    Language: English
    Note: Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Preface -- Chapter 1 Science of Cloud and Climate Science: An Analysis of the Literature Over the Past 50 Years -- 1.1 Research on Clouds and Climate -- 1.1.1 Science of Science for Clouds and Climate -- 1.1.2 Publication Data and Methods -- 1.2 Publications on Clouds and Climate -- 1.2.1 Citation and Readability -- 1.3 The Role of Clouds in Radiation, Circulation, and Precipitation -- 1.4 Methodology in Clouds and Climate -- 1.4.1 Techniques -- 1.4.2 Authorship -- 1.5 Summary and Outlook -- Acknowledgments -- Availability Statement -- References -- Part I Clouds and Radiation -- Chapter 2 An Overview of Aerosol-Cloud Interactions -- 2.1 Introduction and Motivation -- 2.1.1 The Importance of Aerosol‐Cloud Interactions for Climate -- 2.1.2 Outline and Aims of this Review -- 2.2 How aerosols affect different cloud types -- 2.3 Aerosol Activation -- 2.4 Warm Cloud Albedo -- 2.5 Approaches to Determining Susceptibility -- 2.6 New Methodological Approaches -- 2.6.1 Gaussian‐Process Emulation to Address State Dependence -- 2.6.2 Tendency Emulation to Address Time Dependence of Adjustments -- 2.6.3 Causality of LWP Adjustments -- 2.6.4 Causality Inference from Transient Events -- 2.6.5 Ensemble Approaches to Uncertainty Quantification and Reduction in GCMs -- 2.6.6 Regime Classification -- 2.7 Aerosol Effects on Ice and Mixed‐Phase Clouds -- 2.8 Semi‐Direct Effects -- 2.9 Field Experiments -- 2.10 New Satellite Products -- 2.11 Outlooks -- Acknowledgments -- References -- Chapter 3 Ice Crystal Complexity and Link to the Cirrus Cloud Radiative Effect -- 3.1 Introduction -- 3.2 Ice Crystal Morphological Complexity Across Scales -- 3.2.1 Types of Morphological Complexity -- 3.2.2 Complexity Metrics -- 3.3 Observations of Complex Crystals. , 3.3.1 Microscopic Observations of Ice Morphology Associated with Complexity -- 3.3.2 Deriving Mesoscopic Complexity from High‐Resolution Angular Light Scattering Patterns -- 3.3.3 Remote Sensing of Ice Crystal Complexity -- 3.3.4 Synthesis of Ice Crystal Complexity Observations -- 3.4 Light Scattering by Complex Crystals -- 3.4.1 Computer Simulation of Angular Light Scattering by Ice Crystals -- 3.4.2 In situ Observations of Ice Crystal Angular Light Scattering Functions and Ice Cloud Asymmetry Parameter -- 3.4.3 Ice Cloud Asymmetry Parameter Inferred from Space‐Borne Remote Sensing Observations -- 3.5 The Impact of Crystal Complexity on Ice Cloud Radiative Effect -- 3.5.1 Estimating Ice Cloud CRE -- 3.5.2 Uncertainty in Cirrus CRE Estimations Caused by Crystal Complexity -- 3.6 Conclusions -- 3.7 Recommendations for Future Work -- Acknowledgments -- References -- Chapter 4 Cloud-Radiation Interactions and Cloud-Climate Feedbacks From an Active-Sensor Satellite Perspective -- 4.1 Introduction -- 4.2 Cloud‐Radiation Interactions -- 4.2.1 Cloud Radiative Heating Rates -- 4.2.2 Cloud Radiative Effects as a Function of the Cloud Phase -- 4.2.3 Multilayered Clouds -- 4.3 Constraining Cloud Feedbacks -- 4.3.1 Different Cloud Feedbacks -- 4.3.2 Interannual Cloud Feedbacks -- 4.3.3 Sc and Shallow Cu Clouds -- 4.3.4 Inferring Low‐Cloud Feedbacks from Observations -- 4.3.5 Extratropical (Opacity) Cloud Feedbacks -- 4.3.6 High‐Level Cloud Feedbacks -- 4.3.7 Radiative Kernel‐Based Cloud Feedbacks -- 4.4 Summary -- Acknowledgments -- References -- Part II Cloud Types -- Chapter 5 A Review of the Factors Influencing Arctic Mixed‐Phase Clouds: Progress and Outlook -- 5.1 Introduction -- 5.2 The formation and characteristics of Arctic mixed‐phase clouds -- 5.3 Factors that control Arctic clouds -- 5.3.1 Thermodynamic Structure of the Arctic Atmosphere. , 5.3.2 Temperature Inversions -- 5.3.3 Moisture Inversions -- 5.3.4 Warm and Moist Air Intrusions -- 5.3.5 Large‐Scale Subsidence -- 5.3.6 Aerosol Particles -- 5.4 A brief survey of Arctic field campaigns targeting cloud‐controlling factors -- 5.4.1 Overview -- 5.5 Insights on Arctic Cloud‐Controlling Factors Gained from ACLOUD, PASCAL, and AFLUX -- 5.6 Outlook -- Acknowledgments -- References -- Chapter 6 Extratropical Cloud Feedbacks -- 6.1 Introduction -- 6.2 Consistent Features of the Observational Record -- 6.3 GCM Responses to Warming -- 6.4 Processes Contributing to Extratropical SW Cloud Feedback -- 6.4.1 Summary of Processes -- 6.4.2 Boundary‐Layer Cloud Changes -- 6.4.3 Ice, Liquid, and Mixed‐Phase -- 6.4.4 Hydrological Cycle Changes -- 6.4.5 Aerosol‐Cloud Interactions -- 6.4.6 Radiative Response to Cloud Changes -- 6.5 Prospects -- 6.5.1 Precipitation Efficiency -- 6.5.2 Aerosol‐Cloud Interactions -- 6.5.3 Boundary‐Layer Clouds in Cold Air Outbreaks -- 6.5.4 Improving Comparisons Between Models and Observations -- Acknowledgments -- Appendix -- References -- Chapter 7 Tropical Marine Low Clouds: Feedbacks to Warming and on Climate Variability -- 7.1 Introduction -- 7.1.1 Climatological Characteristics of Tropical Low Clouds and Their Impacts on Earth's Radiation Budget -- 7.1.2 The Simplest Model of the Marine Boundary Layer -- 7.1.3 How do Cloud‐Controlling Factors Impact Tropical Low Clouds? -- 7.2 RESPONSE OF TRADE CUMULUS AND STRATOCUMULUS TO WARMING -- 7.2.1 Forcing‐Feedback Studies -- 7.2.2 Constraints from Observed Natural Variability -- 7.2.3 Why the Trade Cumulus Feedback likely Differs from that of Stratocumulus -- 7.2.4 Outlook -- 7.3 ROLE OF LOW CLOUDS IN VARIATIONS IN CLIMATE -- 7.3.1 Cloud‐Ocean Coupling -- 7.3.2 Low‐Cloud Feedbacks and Patterns of Sea Surface Temperature. , 7.4 MESOSCALE ORGANIZATION AND POSSIBLE IMPLICATIONS FOR CLOUD FEEDBACKS -- 7.4.1 Observed Mesoscale Organization of Low Clouds -- 7.4.2 Unknown Mesoscale Organization Feedbacks -- 7.5 RECENT ADVANCES IN AND PROSPECTS FOR HIGH‐RESOLUTION MODELING OF LOW CLOUDS -- 7.5.1 Limitations of GCMs -- 7.5.2 Current State of High‐Resolution Models -- 7.5.3 New Challenges -- Acknowledgments -- References -- Chapter 8 Mechanisms for the Self-Organization of Tropical Deep Convection -- 8.1 Introduction -- 8.2 Observing large‐scale tropical cloud clustering -- 8.3 Organizational mechanisms -- 8.3.1 Feedbacks Between Radiation, Moisture, and Circulation -- 8.3.2 Coarsening dynamics through local moisture feedbacks -- 8.3.3 Cold pools -- 8.3.4 Surface heat fluxes -- 8.3.5 Effects of cold pools on organization -- 8.3.6 Interaction of cold pools and system scale circulations -- 8.3.7 Interactive surface temperatures -- 8.4 Temporally Varying surface conditions and the diurnal cycle -- 8.4.1 The quest for the origin of observed convective clustering -- Acknowledgments -- References -- Chapter 9 An Overview of Mesoscale Convective Systems: Global Climatology, Satellite Observations, and Modeling Strategies -- 9.1 An Overview of Mesoscale Convective Systems -- 9.2 Mesoscale Convective Systems in the Tropics -- 9.2.1 Interaction of MCSs with the El Niño Southern Oscillation -- 9.2.2 Interactions of MCSs with Monsoons -- 9.2.3 Interactions of MCSs with the Madden‐Julian Oscillation -- 9.3 Mesoscale Convective Systems Over the Midlatitudes -- 9.4 Satellite Observations of Mesoscale Convective Systems -- 9.4.1 Remote Sensing of Mesoscale Convective System‐Microphysical Interactions -- 9.4.2 Remote Sensing of the Mesoscale Convective System Life Cycle -- 9.4.3 Remote Sensing of Mesoscale Convective System‐Land Surface Interactions. , 9.5 Modeling of Mesoscale Convective Systems -- 9.5.1 Parameterization and Resolution Improvements -- 9.5.2 Model Physics Improvements -- 9.5.3 Convection‐Permitting Models for Climate Simulations -- 9.6 Summary -- References -- Part III Clouds and Circulation -- Chapter 10 Interactions Between the Tropical Atmospheric Overturning Circulation and Clouds in Present and Future Climates -- 10.1 Introduction -- 10.2 Future Changes to the Tropical Overturning Circulation -- 10.3 Future Changes to Clouds -- 10.4 Observational Evidence of Changes to Clouds and the Tropical Overturning Circulation -- 10.5 Toward an Improved Mechanistic Understanding of Cloud‐Circulation Interactions -- 10.6 Discussion -- Acknowledgments -- Availability Statement -- References -- Chapter 11 Clouds and Radiatively Induced Circulations -- 11.1 Introduction -- 11.2 Clouds and Tropospheric Diabatic Circulations -- 11.3 Low clouds and shallow circulations -- 11.3.1 Mechanism of Shallow Circulations -- 11.3.2 Shallow Circulations and Aggregation of Convection -- 11.3.3 Observed Shallow Circulations -- 11.4 Responses of tropical high clouds to the CRE‐AH -- 11.4.1 TTL Cirrus -- 11.4.2 Anvil Clouds -- References -- Chapter 12 The Small-Scale Mixing of Clouds With Their Environment: Impacts on Micro- and Macroscale Cloud Properties -- 12.1 Introduction -- 12.2 Entrainment and Mixing in Warm Clouds -- 12.3 The Theory of Small‐Scale Mixing -- 12.3.1 The Damköhler Number -- 12.3.2 The Mixing Diagram -- 12.4 Investigating Small‐Scale Mixing -- 12.4.1 Observations -- 12.4.2 Numerical Modeling -- 12.5 Effects of Small‐Scale Mixing -- 12.5.1 Microscale Impacts -- 12.5.2 Macroscale Impacts -- 12.6 Conclusions -- Acknowledgments -- References -- Part IV Clouds and Precipitation -- Chapter 13 Precipitation Efficiency and Climate Sensitivity -- 13.1 Introduction. , 13.2 Defining Precipitation Efficiency.
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  • 2
    Keywords: Forschungsbericht ; Wolke ; Kondensationskeim ; Atmosphärisches Aerosol
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (8 Seiten, 1,04 MB) , Illustrationen, Diagramme
    Language: German
    Note: Förderkennzeichen BMBF 01LK1204B. - Verbund-Nummer 01126505 , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Mit deutscher und englischer Kurzfassung
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  • 3
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    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3Atmospheric Chemistry and Physics, COPERNICUS GESELLSCHAFT MBH, 19, pp. 5111-5126, ISSN: 1680-7316
    Publication Date: 2020-03-05
    Description: Multilayer clouds (MLCs) occur more often in theArctic than globally. In this study we present the results of a detection algorithm applied to radiosonde and radar datafrom an 1-year time period in Ny-Ålesund, Svalbard. Multi-layer cloud occurrence is found on 29 % of the investigated days. These multilayer cloud cases are further analysed regarding the possibility of ice crystal seeding, meaning that an ice crystal can survive sublimation in a subsaturated layer between two cloud layers when falling through this layer. For this we analyse profiles of relative humidity with respect to ice to identify super- and subsaturated air layers. Then the sublimation of an ice crystal of an assumed initial size of r=400 μm on its way through the subsaturated layer is calculated. If the ice crystal still exists when reaching a lower supersaturated layer, ice crystal seeding can potentially take place. Seeding cases are found often, in 23 % of the investigated days (100 % includes all days, as well as non-cloudy days). The identification of seeding cases is limited by the radar signal inside the subsaturated layer. Clearly separated multilayer clouds, defined by a clear interstice in the radar image, do not interact through seeding (9 % of the investigated days). There are various deviations between the relative humidity profiles and the radar images, e.g. due to the lack of ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). Additionally, horizontal wind drift of the radiosonde and time restriction when comparing radiosonde and radar data cause further deviations. In order to account for some of these deviations, an evaluation by manual visual inspection is done for the non-seeding cases.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 4
    Publication Date: 2021-10-27
    Description: We show that there is a strong sensitivity of cloud microphysics to model time step in idealized convection-permitting simulations using the COnsortium for Small-scale MOdeling model. Specifically, we found a 53% reduction in precipitation when the time step is increased from 1 to 15 s, changes to the location of precipitation and hail reaching the surface, and changes to the vertical distribution of hydrometeors. The effect of cloud condensation nuclei perturbations on precipitation also changes both magnitude and sign with the changing model time step. The sensitivity arises because of the numerical implementation of processes in the model, specifically the so-called “splitting” of the dynamics (e.g., advection and diffusion) and the parameterized physics (e.g., microphysics scheme). Calculating one step at a time (sequential-update splitting) gives a significant time step dependence because large supersaturation with respect to liquid is generated in updraft regions, which strongly affect parameterized microphysical process rates—in particular, ice nucleation. In comparison, calculating both dynamics and microphysics using the same inputs of temperature and water vapor (hybrid parallel splitting) or adding an additional saturation adjustment within the dynamics reduces the time step sensitivity of surface precipitation by limiting the supersaturation seen by the microphysics, although sensitivity to time step remains for some processes.
    Keywords: 551.5 ; convection permitting ; microphysics ; time step ; parallel splitting ; saturation adjustment ; physics-dynamics coupling
    Language: English
    Type: map
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  • 5
    Publication Date: 2023-11-02
    Description: The impact of cloud condensation nuclei (CCN) concentration on microphysical processes within thunderstorms and the resulting surface precipitation is not fully understood yet. In this work, an analysis of the microphysical pathways occurring in these clouds is proposed to systematically investigate and understand these sensitivities. Thunderstorms were simulated using convection‐permitting (1 km horizontal grid spacing) idealized simulations with the ICON model, which included a 2‐moment microphysics parameterization. Cloud condensation nuclei concentrations were increased from 100 to 3,200 CCN/cm3, in five different wind shear environments ranging from 18 to 50 m/s. Large and systematic decreases of surface precipitation (up to 35%) and hail (up to 90%) were found as CCN was increased. Wind shear changes the details, but not the sign, of the sensitivity to CCN. The microphysical process rates were tracked throughout each simulation, closing the mass budget for each hydrometeor class, and collected together into “microphysical pathways,” which quantify the different growth processes leading to surface precipitation. Almost all surface precipitation occurred through the mixed‐phase pathway, where graupel and hail grow by riming and later melt as they fall to the surface. The mixed‐phase pathway is sensitive to CCN concentration changes as a result of changes to the riming rate, which were systematically evaluated. Supercooled water content was almost insensitive to increasing CCN concentration, but decreased cloud drop size led to a large reduction in the riming efficiency (from 0.79 to 0.24) between supercooled cloud drops and graupel or hail, resulting in less surface precipitation.
    Description: Plain Language Summary: The amount of rain and hail from thunderstorms can be influenced by the amount of pollution in the form of aerosol particles, which determine how many cloud droplets form and how large they are. Unfortunately, different numerical models give different answers on whether rain and hail increase or decrease if pollution increases. In this article, we present a new analysis method helping to identify the small‐scale processes which are responsible for the increase or decrease in a specific numerical scheme. We apply it to simulations of thunderstorms and show that the decrease of rain and hail in the numerical model used here is mostly linked to the riming process. Riming is the collision of cloud droplets and frozen particles at temperatures below 0°C, such that the liquid water freezes to the surface of the ice particles and makes them bigger. Less riming occurs when pollution increases, because cloud droplets are smaller. This process is very important because nearly all rain reaching the surface consists of melted ice particles.
    Description: Key Points: Microphysical pathways are constructed by tracking microphysical processes rates and closing the hydrometeor mass budget. More cloud condensation nuclei lead to less surface precipitation and hail, due to smaller cloud drop sizes and reduced riming collection efficiency. Simulations with constant riming collection efficiency reveal two different hail formation pathways.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: HORIZON EUROPE European Research Council http://dx.doi.org/10.13039/100019180
    Description: https://doi.org/10.5445/IR/1000156063
    Keywords: ddc:551.5 ; convective clouds ; hail ; riming ; precipitation ; CCN ; convection‐permitting simulation
    Language: English
    Type: doc-type:article
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