Clouds and Their Climatic Impact E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Geophysical Monograph Series
- Sprache: Englisch
Exploring how clouds influence radiation, circulation, and precipitation
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.
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Veröffentlichungsjahr: 2023
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Table of Contents
COVER
TABLE OF CONTENTS
TITLE PAGE
COPYRIGHT
DEDICATION
LIST OF CONTRIBUTORS
PREFACE
REFERENCES
1 Science of Cloud and Climate Science: An Analysis of the Literature Over the Past 50 Years
1.1 RESEARCH ON CLOUDS AND CLIMATE
1.2 PUBLICATIONS ON CLOUDS AND CLIMATE
1.3 THE ROLE OF CLOUDS IN RADIATION, CIRCULATION, AND PRECIPITATION
1.4 METHODOLOGY IN CLOUDS AND CLIMATE
1.5 SUMMARY AND OUTLOOK
ACKNOWLEDGMENTS
AVAILABILITY STATEMENT
REFERENCES
Part I: Clouds and Radiation
2 An Overview of Aerosol-Cloud Interactions
2.1 INTRODUCTION AND MOTIVATION
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
2.7 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
3 Ice Crystal Complexity and Link to the Cirrus Cloud Radiative Effect
3.1 INTRODUCTION
3.2 ICE CRYSTAL MORPHOLOGICAL COMPLEXITY ACROSS SCALES
3.3 OBSERVATIONS OF COMPLEX CRYSTALS
3.4 LIGHT SCATTERING BY COMPLEX CRYSTALS
3.5 THE IMPACT OF CRYSTAL COMPLEXITY ON ICE CLOUD RADIATIVE EFFECT
3.6 CONCLUSIONS
3.7 RECOMMENDATIONS FOR FUTURE WORK
ACKNOWLEDGMENTS
REFERENCES
4 Cloud-Radiation Interactions and Cloud-Climate Feedbacks From an Active-Sensor Satellite Perspective
4.1 INTRODUCTION
4.2 CLOUD-RADIATION INTERACTIONS
4.3 CONSTRAINING CLOUD FEEDBACKS
4.4 SUMMARY
ACKNOWLEDGMENTS
REFERENCES
Part II: Cloud Types
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
5.4 A BRIEF SURVEY OF ARCTIC FIELD CAMPAIGNS TARGETING CLOUD-CONTROLLING FACTORS
5.5 INSIGHTS ON ARCTIC CLOUD-CONTROLLING FACTORS GAINED FROM ACLOUD, PASCAL, AND AFLUX
5.6 OUTLOOK
ACKNOWLEDGMENTS
REFERENCES
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
6.5 PROSPECTS
ACKNOWLEDGMENTS
APPENDIX
REFERENCES
7 Tropical Marine Low Clouds: Feedbacks to Warming and on Climate Variability
7.1 INTRODUCTION
7.2 RESPONSE OF TRADE CUMULUS AND STRATOCUMULUS TO WARMING
7.3 ROLE OF LOW CLOUDS IN VARIATIONS IN CLIMATE
7.4 MESOSCALE ORGANIZATION AND POSSIBLE IMPLICATIONS FOR CLOUD FEEDBACKS
7.5 RECENT ADVANCES IN AND PROSPECTS FOR HIGH-RESOLUTION MODELING OF LOW CLOUDS
ACKNOWLEDGMENTS
REFERENCES
NOTE
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
8.4 TEMPORALLY VARYING SURFACE CONDITIONS AND THE DIURNAL CYCLE
ACKNOWLEDGMENTS
REFERENCES
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
9.3 MESOSCALE CONVECTIVE SYSTEMS OVER THE MIDLATITUDES
9.4 SATELLITE OBSERVATIONS OF MESOSCALE CONVECTIVE SYSTEMS
9.5 MODELING OF MESOSCALE CONVECTIVE SYSTEMS
9.6 SUMMARY
REFERENCES
Part III: Clouds and Circulation
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
11 Clouds and Radiatively Induced Circulations
11.1 INTRODUCTION
11.2 CLOUDS AND TROPOSPHERIC DIABATIC CIRCULATIONS
11.3 LOW CLOUDS AND SHALLOW CIRCULATIONS
11.4 RESPONSES OF TROPICAL HIGH CLOUDS TO THE CRE-AH
REFERENCES
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
12.4 INVESTIGATING SMALL-SCALE MIXING
12.5 EFFECTS OF SMALL-SCALE MIXING
12.6 CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
Part IV: Clouds and Precipitation
13 Precipitation Efficiency and Climate Sensitivity
13.1 INTRODUCTION
13.2 DEFINING PRECIPITATION EFFICIENCY
13.3 CHANGES IN PRECIPITATION EFFICIENCY AND CLIMATE SENSITIVITY
13.4 PRESENT-DAY PRECIPITATION EFFICIENCY AND CLIMATE SENSITIVITY
13.5 PRECIPITATION EFFICIENCY OF EXTRATROPICAL CLOUDS
13.6 CONCLUDING REMARKS
ACKNOWLEDGMENTS
REFERENCES
14 Observed Scaling of Precipitation Extremes With Surface Temperature and Convective Available Potential Energy
14.1 INTRODUCTION
14.2 DEFINITION OF PRECIPITATION EXTREMES AND SCALING METHODOLOGY
14.3 OBSERVATIONAL DATASET
14.4 SCALING OF PRECIPITATION EXTREMES WITH SURFACE TEMPERATURE
14.5 SCALING OF PRECIPITATION EXTREMES WITH CAPE
14.6 CONCLUDING REMARKS
REFERENCES
15 Satellite Precipitation Measurements: What Have We Learnt About Cloud-Precipitation Processes From Space?
15.1 INTRODUCTION
15.2 SATELLITE REMOTE SENSING OF PRECIPITATION – PHYSICAL BASIS
15.3 HISTORICAL OVERVIEW OF SATELLITE PRECIPITATION REMOTE SENSING
15.4 UNDERSTANDING CLOUD-PRECIPITATION PROCESSES
15.5 FUTURE SATELLITE MISSIONS FOR CLOUD-PRECIPITATION PROCESS STUDIES
15.6 SUMMARY
REFERENCES
Part V: Outlook
16 Machine Learning for Clouds and Climate
16.1 INTRODUCTION
16.2 MACHINE LEARNING
16.3 APPLICATION TO CLOUDS AND CLIMATE
16.4 GETTING STARTED WITH MACHINE LEARNING
16.5 OUTLOOK
ACKNOWLEDGMENTS
REFERENCES
INDEX
END USER LICENSE AGREEMENT
List of Tables
Chapter 1
Table 1.1 Boolean syntax for Scopus database queries. We filter for English-...
Chapter 2
Table 2.1 Cloud response to aerosol perturbations by cloud and aerosol type....
Chapter 3
Table 3.1 Summary of different complexity types, the typical temperature (T)...
Table 3.2 Values of asymmetry parameter (g) and extinction coefficient () e...
Chapter 4
Table 4.1 Description of the datasets used in Figure 4.4.
Chapter 5
Table 5.1 List of Arctic field campaigns and the cloud-controlling factors t...
Chapter 6
Table 6.1 Summary of findings of observational constraints on changes in sho...
Table 6.2 As in Table 6.1, but with additional explanation of inferred feedb...
Table S1 List of GCMs
Chapter 14
Table 14.1 Summary of selected previous studies of the
P_e–Ta
scaling ...
Chapter 16
Table 16.1 Acronyms used in this chapter.
List of Illustrations
Chapter 1
Figure 1.1 The monograph chapters are organized around climatic impacts of c...
Figure 1.2 Atmospheric science publications, particularly open-access ones, ...
Figure 1.3 Publication and citation number are positively correlated, but no...
Figure 1.4 Citation rates in clouds and climate research have slowed relativ...
Figure 1.5 Articles have become only slightly less readable with page number...
Figure 1.6 While publications on clouds and radiation have been most numerou...
Figure 1.7 Recent growth in machine learning publication has been dramatic. ...
Figure 1.8 About 40% of studies use a combination of modeling and/or satelli...
Figure 1.9 Over a decade, author number increased by a factor of 14 and the ...
Figure 1.10 Work across cloud and climate research is typically characterize...
Chapter 2
Figure 2.1 Schematic depicting some of the possible aerosol effects on cloud...
Figure 2.2 Cloud optical depth () and albedo () as a function of . Solid ...
Figure 2.3 Scene albedo as a function of cloud droplet number concentratio...
Figure 2.4 Liquid water path tendencies as function of and LWP. The LWP ...
Figure 2.5 A schematic of causal links between cloud, aerosol, precipitation...
Figure 2.6 MODIS Aqua imagery of the 2008 eruption of Klauea, Hawaii. MODIS...
Figure 2.7 MODIS Terra imagery of open and closed cellular convection over t...
Chapter 3
Figure 3.1 Overview of different types of ice crystal morphological complexi...
Figure 3.2 Examples of different types of morphological complexity seen on i...
Figure 3.3 Approximately 500 cirrus ice particles captured from near the top...
Figure 3.4 Roughened hexagonal columns and Gaussian random spheres with diff...
Figure 3.5 Examples of ice crystal replicas sampled during three crystal gro...
Figure 3.6 Statistical analysis of ice crystal complexity values retrieved f...
Figure 3.7 Observed number of thick ice clouds with a given combination of c...
Figure 3.8 Mean complexity parameter as a function of effective radius for...
Figure 3.9 Computer-generated shapes for ice crystals. Source: Adapted from ...
Figure 3.10 Panel (a) shows the phase functions of a hexagonal columnar pris...
Figure 3.11 Columnar ice crystal (crystal number 2844) captured by PHIPS dur...
Figure 3.12 Measured and modeled differential scattering cross-sections of a...
Figure 3.13 Asymmetry parameter () as a function of temperature (a) and rel...
Figure 3.14 Observed global average of cloud top ice asymmetry parameters as...
Figure 3.15 Shortwave (a), longwave (b), and total (shortwave + longwave) (c...
Figure 3.16 Instantaneous daytime net CRE of cirrus clouds as a function of ...
Figure 3.17 The bulk optical properties , SSA, and g as a function of effec...
Figure 3.18 Difference in the global SW CRE after changing the current param...
Chapter 4
Figure 4.1 Cloud radiative heating rate biases in climate models. Zonal prof...
Figure 4.2 Cloud radiative effect as a function of the cloud phase regime. M...
Figure 4.3 Illustration of the main cloud feedbacks and their impacts on cli...
Figure 4.4 Low-cloud sensitivities of Sc- and shallow Cu-dominated to cloud-...
Figure 4.5 Short-term LW cloud feedback for (a) CALIPSO-GOCCP OPAQ (2007–201...
Chapter 5
Figure 5.1 Schematic of typical thermodynamic structures of the Arctic atmos...
Figure 5.2 Locations of selected Arctic campaigns and datasets, including (a...
Chapter 6
Figure 6.1 Himawari-8 image of the North Pacific 31 December 2020. Cloud str...
Figure 6.2 SW (top) and LW (bottom) cloud feedback calculated following Zeli...
Figure 6.3 LWP (top), IWP (middle), and P-E (bottom) in piControl simulation...
Figure 6.4 Two-meter temperature for the 40–85 °S region versus global mean ...
Figure 6.5 As in Figure 6.4, but for LWP (top), IWP (middle), and P-E (botto...
Figure 6.6 As in Figure 6.3, but showing the difference between piControl an...
Figure 6.7 Mechanisms from the literature relevant to extratropical cloud fe...
Figure 6.8 Extratropical ice and liquid water path response averaged over 40...
Chapter 7
Figure 7.1 Long-term annual mean (a) net CRE at TOA from all cloud types, (b...
Figure 7.2 Schematic summary of how subtropical stratocumulus and trade cumu...
Figure 7.3 Observed sensitivity of low-cloud (a) amount-induced and (b) opti...
Figure 7.4 Effect of cloud feedback on SST variance in models, taken from Mi...
Figure 7.5 Radar retrievals, from the cloud radar at the Barbados Cloud Obse...
Chapter 8
Figure 8.1
A typical final state of convective self-aggregation.
Precipitati...
Figure 8.2
Defining clusters in satellite imagery.
(top) Snapshot of brightn...
Figure 8.3
A simplified two-layer model for radiative-convective instability
...
Figure 8.4
Internal structure of a simulated cold pool.
Panels show the radi...
Figure 8.5
Aggregation with interactive SST.
Panels show the column relative...
Figure 8.6
Diurnal self-aggregation.
Comparison of two cloud-resolving simul...
Chapter 9
Figure 9.1 Climatology of annual MCS occurrence as in Feng et al. (2021a) (t...
Figure 9.2 Climatology of the MCS contribution to total rainfall. Top panel ...
Figure 9.3 A schematic of the ENSO effects on MCS, as well as their feedback...
Figure 9.4 Relative changes in the probability of precipitation intensities ...
Figure 9.5 Infrared images of organized convection taken by the METEOSAT-5 s...
Figure 9.6 Examples of global MCSs (bright white shading) and precipitable w...
Chapter 10
Figure 10.1 (a) The observed mean cloud water content (liquid + ice, black c...
Figure 10.2 (a) CMIP5 multi-model mean fractional change in mean upward moti...
Figure 10.3 The CMIP6 multi-model mean change in pressure velocity (
ω
; ...
Figure 10.4 Change in annual-mean spatial extent of the tropical ascent regi...
Figure 10.5 The mean precipitation from 30S-30N from six different aquaplane...
Chapter 11
Figure 11.1 Illustration of the radiatively induced circulations discussed i...
Figure 11.2 One-day average of a convection-permitting simulation: (a) map o...
Figure 11.3 Radiative cold pool in a GCM simulation; left panel: temperature...
Figure 11.4 Cloud fraction (shadings, dataset GOCCP, Chepfer et al., 2010) a...
Figure 11.5 Radiative heating rates for a TTL cirrus assuming typical tropic...
Figure 11.6 Radiative heating rates for (a) thick, (b) intermediately thick,...
Figure 11.7 Air motions induced by the CRE-AH in tropical high clouds.
Figure 11.8 CRE-AH and wind vectors at (a) the initialization and (b) and (c...
Figure 11.9 Mechanisms of the CRE-AH influencing the morphology and life cyc...
Chapter 12
Figure 12.1 This figure illustrates the two limiting scenarios of homogeneou...
Figure 12.2 This idealized mixing diagram illustrates how the normalized mea...
Chapter 13
Figure 13.1 (a) Cloud microphysics precipitation efficiency is defined as pr...
Figure 13.2 (a) Precipitation efficiency as a function of SST for the thre...
Figure 13.3 Contributions to the global-mean cloud feedbacks in the Iris sim...
Figure 13.4 Scatterplot of the change in precipitation efficiency with warmi...
Figure 13.5 Schematic illustration of the six mechanisms by which changes in...
Chapter 14
Figure 14.1 Climatological distributions of precipitation (
upper left
), surf...
Figure 14.2 Global distribution of mean
P_e–Ta
scaling rate (
upper lef
...
Figure 14.3 Similar as Figure 14.2, but for the
P_e–Td
scaling relatio...
Figure 14.4 Similar as Figure 14.2, but for the dependence of natural logari...
Figure 14.5 Schematic plot for the different types of
P_e–TS
scaling (
Chapter 15
Figure 15.1 Scattering and absorption at 166 GHz for raindrop, hail, dendrit...
Figure 15.2 Timeline of active cloud and precipitation radar missions with, ...
Figure 15.3 (Left) Statistical representations of radar reflectivity profile...
Figure 15.4 (a) The conditional profile latent heating averaged over the tro...
Figure 15.5 (a) Mean latitude-pressure cross-section of latent heating (K da...
Figure 15.6 Vertical profiles of radar reflectivity statistically represente...
Figure 15.7 (Left) Schematic illustrations of the temporal composite analysi...
Chapter 16
Figure 16.1 ML algorithms (orange boxes), corresponding interpretation metho...
Figure 16.2 Use of an attribution method, namely, Layer-Wise Relevance Propa...
Figure 16.3 Lower tropospheric subgrid moistening and heating tendencies (le...
Figure 16.4 SR of east-west (U) and north-south (V) wind velocity from low-r...
Figure 16.5 Cloud classification by applying hierarchical clustering to the ...
Figure 16.6 Workflow for a machine learning project in physical sciences.
Guide
Cover
Title Page
Copyright
Dedication
List of Contributors
Preface
Table of Contents
Begin Reading
Index
End User License Agreement
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Elizabeth J. Catlos and Ibrahim Çemen (Eds.)
278
Extensional Tectonics: Continental Breakup to Formation of Oceanic Basins (Tectonic Processes: A Global View, Volume 2)
Ibrahim Çemen and Elizabeth J. Catlos (Eds.)
279
Strike-Slip Tectonics: Oceanic Transform Faults to Continental Plate Boundaries (Tectonic Processes: A Global View, Volume 3)
Ibrahim Çemen and Elizabeth J. Catlos (Eds.)
280
Landscape Fire, Smoke, and Health: Linking Biomass Burning Emissions to Human Well-Being
Tatiana V. Loboda, Nancy H. F. French, and Robin C. Puett (Eds.)
281
Clouds and Their Climatic Impacts: Radiation, Circulation, and Precipitation
Sylvia C. Sullivan and Corinna Hoose (Eds.)
Geophysical Monograph 281
Clouds and Their Climatic Impacts
RADIATION, CIRCULATION, AND PRECIPITATION
Sylvia C. Sullivan
Corinna Hoose
Editors
This Work is a co-publication of the American Geophysical Union and John Wiley and Sons, Inc.
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Library of Congress Cataloging-in-Publication Data
Names: Sullivan, Sylvia C., editor. | Hoose, Corinna, editor.
Title: Clouds and their climatic impacts : radiation, circulation, and precipitation / editors Sylvia C. Sullivan, Corinna Hoose.
Description: Hoboken, NJ : Wiley-American Geophysical Union, 2024. | Series: Geophysical monograph series | Includes index.
Identifiers: LCCN 2023027576 (print) | LCCN 2023027577 (ebook) | ISBN 9781119700319 (cloth) | ISBN 9781119700333 (adobe pdf) | ISBN 9781119700340 (epub)
Subjects: LCSH: Clouds. | Climatic changes.
Classification: LCC QC921 .C567 2024 (print) | LCC QC921 (ebook) | DDC 551.57/6–dc23/eng/20230815
LC record available at https://lccn.loc.gov/2023027576
LC ebook record available at https://lccn.loc.gov/2023027577
Cover image: Monsoon clouds over the Bay of Bengal from the International Space Station. Credit: NASA
Cover design: Wiley
Dedicated in memory of our colleague, mentee, and friend, Marco Paukert.
LIST OF CONTRIBUTORS
Andrew S. AckermanNASA Goddard Institute for Space StudiesNew York, NY, USA
Alessandro BattagliaDepartment of Environment, Land and Infrastructure EngineeringPolytechnic University of TurinTurin, ItalyandDepartment of Physics and AstronomyUniversity of LeicesterLeicester, UK
Gilles BellonDepartment of PhysicsThe University of AucklandAuckland, New ZealandandCentre National de Recherches MétéorologiquesUniversité de Toulouse, Météo France, CNRSToulouse, France
Tom BeuclerInstitute of Earth Surface DynamicsUniversity of LausanneLausanne, SwitzerlandandDepartment of Earth System ScienceUniversity of California IrvineIrvine, CA, USAandDepartment of Earth and Environmental EngineeringColumbia UniversityNew York, NY, USA
Scott BraunNASA Goddard Space Flight CenterGreenbelt, MD, USA
Florent BrientLaboratoire de Météorologie DynamiqueInstitut Pierre Simon LaplaceSorbonne University, CNRSParis, France
Grégory V. CesanaCenter for Climate Systems ResearchColumbia UniversityNew York, NY, USAandNASA Goddard Institute for Space StudiesNew York, NY, USA
Sudip ChakrabortyInstitute for Harnessing Data and Model Revolution in the Polar RegionsDepartment of Information SystemsUniversity of Maryland, Baltimore CountyBaltimore, MD, USAandNASA Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, CA, USA
David DeleneDepartment of Atmospheric SciencesUniversity of North DakotaGrand Forks, ND, USA
Bastiaan van DiedenhovenSRON Netherlands Institute for Space ResearchLeiden, The NetherlandsandCenter for Climate System ResearchColumbia UniversityNew York, NY, USA
Tra DinhDepartment of PhysicsThe University of AucklandAuckland, New Zealand
Wenhao DongCooperative Programs for the Advancement of Earth System ScienceUniversity Corporation for Atmospheric ResearchBoulder, CO, USAandNOAA Geophysical Fluid Dynamics LaboratoryPrinceton, NJ, USAandDepartment of Earth System ScienceMinistry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing, China
Imme Ebert-UphoffCooperative Institute for Research in the AtmosphereColorado State UniversityFort Collins, CO, USAandDepartment of Electrical and Computer EngineeringColorado State UniversityFort Collins, CO, USA
Zhe FengAtmospheric Sciences and Global Change DivisionPacific Northwest National LaboratoryRichland, WA, USA
Michelle E. FrazerDepartment of GeosciencesPennsylvania State UniversityUniversity Park, PA, USAandProgram in Atmospheric and Oceanic SciencesPrinceton UniversityPrinceton, NJ, USA
Blaž GaspariniDepartment of Meteorology and GeophysicsUniversity of ViennaVienna, Austria
Pierre GentineDepartment of Earth and Environmental Engineering, and Columbia Climate SchoolColumbia UniversityNew York, NY, USA
Franziska GlassmeierDepartment of Geoscience and Remote SensingDelft University of TechnologyDelft, The Netherlands
Hamish GordonDepartment of Chemical Engineering, and Center for Atmospheric Particle StudiesCarnegie Mellon UniversityPittsburgh, PA, USA
Jan O. HaerterComplexity and ClimateLeibniz Centre for Tropical Marine ResearchBremen, GermanyandConstructor UniversityBremen, GermanyandNiels Bohr InstituteCopenhagen UniversityCopenhagen, Denmark
David S. HendersonSpace Science and Engineering CenterUniversity of Wisconsin–MadisonMadison, WI, USA
Fabian HoffmannMeteorological InstituteFaculty for PhysicsLudwig Maximilian University of MunichMunich, Germany
Corinna HooseInstitute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruhe, Germany
Emma JärvinenInstitute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruhe, Germany
Olivier JourdanLaboratoire de Météorologie PhysiqueUniversité Clermont Auvergne/OPGC/CNRSClermont-Ferrand, France
Seiji KatoNASA Langley Research CenterHampton, VA, USA
Maki KikuchiEarth Observation Research CenterJapan Aerospace Exploration AgencyIbaraki, Japan
Yanluan LinDepartment of Earth System ScienceMinistry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing, China
Guosheng LiuDepartment of Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahassee, FL, USA
Simone LolliInstitute of Methodologies for Environmental AnalysisNational Research Council of ItalyTito, ItalyandCommSensLabDepartment of Signal Theory and CommunicationsUniversitat Politècnica de CatalunyaBarcelona, Spain
Nicholas J. LutskoScripps Institution of OceanographyUniversity of California San DiegoLa Jolla, CA, USA
Nathan MageeDepartment of PhysicsThe College of New JerseyEwing, NJ, USA
Daniel T. McCoyDepartment of Atmospheric ScienceUniversity of WyomingLaramie, WY, USA
Caroline MullerInformation and System SciencesInstitute of Science and Technology AustriaKlosterneuburg, AustriaandLaboratoire de Météorologie DynamiqueParis Sciences Lettres/Institut Pierre Simon LaplaceParis, France
Johannes MülmenstädtDepartment of Atmospheric Sciences and Global ChangePacific Northwest National LaboratoryRichland, WA, USA
Timothy A. MyersCooperative Institute for Research in Environmental SciencesUniversity of ColoradoBoulder, CO, USAandPhysical Science LaboratoryNational Oceanic and Atmospheric AdministrationBoulder, CO, USAandLawrence Livermore National LaboratoryLivermore, CA, USA
Steven NeshybaDepartment of ChemistryUniversity of Puget SoundTacoma, WA, USA
Hossein ParishaniAnsys, Inc.Irvine, CA, USAandDepartment of Earth System ScienceUniversity of California IrvineIrvine, CA, USA
Michael PritchardDepartment of Earth System ScienceUniversity of California IrvineIrvine, CA, USA
Stephan RaspGoogle ResearchSan Francisco, CA, USAandClimateAiSan Francisco, CA, USAandMeteorological InstituteFaculty for PhysicsLudwig Maximilian University of MunichMunich, Germany
Kathleen A. SchiroDepartment of Environmental SciencesUniversity of VirginiaCharlottesville, VA, USA
Martin SchnaiterInstitute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruhe, Germany
Ryan C. ScottNASA Langley Research CenterHampton, VA, USA
Steven C. SherwoodClimate Change Research Centre and ARC Centre of Excellence for Climate ExtremesUniversity of New South WalesSydney, Australia
Georgia SotiropoulouLaboratory of Atmospheric Processes and their ImpactsSchool of Architecture, Civil & Environmental EngineeringEcole Polytechnique Fédérale de LausanneLausanne, SwitzerlandandDepartment of Meteorology, andBolin Center for Climate ResearchStockholm UniversityStockholm, Swedenandcurrently at the Department of PhysicsNational and Kapodistrian University of AthensAthens, Greece
Hui SuDepartment of Civil and Environmental EngineeringThe Hong Kong University of Science and TechnologyHong Kong SARandDepartment of Atmospheric and Oceanic SciencesUniversity of California Los AngelesLos Angeles, CA, USA
Sylvia C. SullivanDepartment of Chemical & Environmental Engineering, andDepartment of Hydrology & Atmospheric ScienceUniversity of ArizonaTucson, AZ, USAandInstitute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruhe, Germany
Kentaroh SuzukiAtmosphere and Ocean Research InstituteThe University of TokyoChiba, Japan
Ivy TanDepartment of Atmospheric and Oceanic SciencesMcGill UniversityMontreal, Canadaandformerly atNASA Goddard Space Flight CenterGreenbelt, MD, USAand affiliated withUniversity of Maryland Baltimore CountyBaltimore, MD, USA
Patrick C. TaylorClimate Science BranchNASA Langley Research CenterHampton, VA, USA
Christopher R. TeraiAtmospheric, Earth, and Energy DivisionLawrence Livermore National LaboratoryLivermore, CA, USA
Thibault Vaillant de GuélisScience Systems and Applications, Inc.Hampton, VA, USAandNASA Langley Research CenterHampton, VA, USA
Raphaela VogelMeteorological InstituteDepartment of Earth System SciencesUniversity of HamburgHamburg, GermanyandLaboratoire de Météorologie DynamiqueInstitut Pierre Simon LaplaceSorbonne University, CNRSParis, France
Fritz WaitzInstitute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruhe, Germany
Manfred WendischLeipzig Institute for MeteorologyUniversity of LeipzigLeipzig, Germany
Guanglang XuInstitute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruhe, Germany
Lauren ZamoraEarth System Science Interdisciplinary CenterUniversity of MarylandCollege Park, MD, USAandNASA Goddard Space Flight CenterGreenbelt, MD, USA
Mark D. ZelinkaAtmospheric, Earth, and Energy DivisionLawrence Livermore National LaboratoryLivermore, CA, USA
Ming ZhaoNOAA Geophysical Fluid Dynamics LaboratoryPrinceton, NJ, USA
PREFACE
Understanding and adjusting to climate under anthropogenic influence is now one of our greatest challenges. In our various emission-dependent projections of climate, clouds are–and have been for quite some time–the primary source of uncertainty. In 1979, Jule Charney and coauthors assembled a report for the National Research Council explaining how much surface temperature should increase in response to a doubling of atmospheric carbon dioxide concentrations, a value called the equilibrium climate sensitivity. In their analysis, they described the net effect of clouds as “an extremely difficult question to answer” (National Academy of Sciences, 1979). Thirty-five years later, the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) stated that “clouds and aerosols continue to contribute the largest uncertainty to estimates and interpretations of the Earth's changing energy budget” (Boucher et al., 2013). In the past five years, the World Climate Research Programme has declared the coupling of clouds to circulation and their influence on the equilibrium climate sensitivity a Grand Challenge, meaning a transformative and high-priority topic of research.
The intractability of the net climate impact of clouds is first a problem of scale. Clouds evolve rapidly relative to other components of the Earth system. High-frequency observation or model output is necessary to capture their life cycle. Clouds also exist at spatial scales smaller than the mesh of a typical global climate model and thus require parameterization. Even in cloud-resolving models, the microphysics of cloud droplets and ice crystals must be parameterized. Second, the climate impacts of clouds are tied to cloud type and location. Clouds come in a variety of flavors: ice, liquid, or mixed phase; geometrically or optically thin or deep; and isolated or organized in larger fields. Climate impacts depend on which of these types exists and where it exists, be it in the tropics or in the Arctic, over land or over ocean. Finally, the climate impacts of clouds are multifaceted. If we understand the Earth as a control system, a large range of cloud feedbacks can act to amplify or diminish direct warming. Clouds are embedded in the large-scale atmospheric circulation, but they also determine it, changing temperature gradients by means of their absorption or remission of radiation. And clouds are the source of precipitation, perhaps the climate variable of greatest relevance to building socioeconomic resilience.
Despite these challenges, we are making progress. The Technical Summary of the sixth IPCC Assessment Report notes that uncertainties in the equilibrium climate sensitivity, as well as in the transient climate response, have been lessened in the new generation of climate models, in part thanks to “a 50% reduction in the uncertainty range of cloud feedbacks” and an accounting for aerosol-cloud interactions (Arias et al., 2021). Multi-decade satellite observations, global coverage of ground-based instrument networks, and model resolutions sufficient to resolve atmospheric convection are all fueling this progress. Indeed, a strength of cloud and climate research is the variety of techniques brought to bear on different questions. The momentum and technical diversity of this field are primary motivators for this monograph.
The research attention that has been devoted to clouds and climate is not only impressive, but also intimidating. In the introductory chapter of this volume, we discuss the exponential growth in publications on clouds and climate over the past five decades. Since 1970, the literature on clouds and climate has doubled every 8 years, a rate twice that for scientific literature in general. More than 600 articles were published per year throughout the 2010s, and by 2019, unique authors studying clouds and climate had increased by a factor of 70 relative to 1989. To extract meaning from this overwhelming body of work, review efforts like this monograph are increasingly necessary. Many existing books address a subset of cloud types or climate impacts, and a few recent ones tackle the whole range of these types and impacts. What makes this volume unique is lead authorship by early career scientists. Those who will push the field forward over the coming decades have assessed the state of knowledge in their specialties and posed what they believe will be the most pressing questions for the years ahead.
We have structured this book with the diversity of cloud types and impacts in mind. We begin with three chapters discussing liquid droplet and ice crystal formation on aerosol particles and the feedbacks associated with absorption and emission by these cloud hydrometeors. Five chapters present radiatively influential cloud types: Arctic mixed-phase, extratropical, tropical low, and tropical deep organized. Three chapters are devoted to cloud-circulation coupling at the global, meso, and individual cloud scales; and an additional three chapters discuss precipitation – its efficiency, extremes, and measurement by satellites. We close with an outlook on the role of machine learning for understanding cloud physics and dynamic and their importance to climate.
Our goal is to provide a “lay of the land” for new graduate students in this diverse and rapidly evolving field of research. For those gaining independence as researchers, our hope is to direct their time and energy toward high-impact problems. And for those even farther along their career trajectory, we wish to facilitate collaborations across subfields and highlight the foci of younger researchers.
The dedication of our lead authors has made this project possible. Thanks go to AGU Publications Director, Dr. Jenny Lunn, the AGU Books Editorial Board, and the editorial staff at Wiley – Dr. Rituparna Bose, Lesley Fenske, Noel McGlinchey, Vaishali Rajasekar, Sangaprabha Mohan, and Layla Harden – for their efforts in preparing the volume. We would also like to acknowledge the many reviewers, whose time and expertise have ensured the quality of all chapters. And finally, to our readers, thank you for your interest in and support of this field.
Sylvia C. Sullivan
Department of Chemical and Environmental Engineering University of Arizona, USA
Institute of Meteorology and Climate Research
Karlsruhe Institute of Technology, Germany
Corinna Hoose
Institute of Meteorology and Climate Research
Karlsruhe Institute of Technology, Germany
REFERENCES
Arias, P. A., Bellouin, N., Coppola, E., et al. (2021). Technical Summary.
In
Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
(pp. 35–141). Cambridge University Press, Cambridge, UK.
Boucher, O., Randall, D., Artaxo, P., et al. (2013).
Clouds and aerosols
.
In
Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
(pp. 571–657). Cambridge University Press, Cambridge, UK.
Charney, J. G., Arakawa, A., Baker, D. J., et al. (1979).
Carbon dioxide and climate: A scientific assessment
. National Academy of Sciences, Woods Hole, MA.
1Science of Cloud and Climate Science: An Analysis of the Literature Over the Past 50 Years
Sylvia C. Sullivan1,2 and Corinna Hoose2
1Department of Chemical & Environmental Engineering, and Department of Hydrology & Atmospheric Science, University of Arizona, Tucson, AZ, USA
2Institute of Meteorology & Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
ABSTRACT
Clouds pose a particularly difficult challenge within Earth's climate system. They are relatively small in spatiotemporal scale but still have a strong influence on radiative fluxes, global circulation, and precipitation patterns. Increasing research attention has been devoted to them over the past 50 years, and we give a summary of the resulting body of scientific literature in this introductory chapter. Articles on clouds and climate are doubling every 8 years, a rate about twice that of scientific publications generally. This expanding number of publications correlates with more citations, but citation rates have also slowed in the most recent decade, despite a growing number of atmospheric science students. We show some basic “science of science” (SciSci) analyses of the clouds and climate literature, such as authorship networks or abstract text mining for techniques, and suggest that further SciSci analyses may help us to process the proliferation of results on clouds and climate and optimize how we do research in the crucial years ahead.
1.1 RESEARCH ON CLOUDS AND CLIMATE
Clouds have a multifaceted impact on the climate. They affect the terrestrial radiative balance by reflecting visible and ultraviolet radiation from the Sun and absorbing infrared radiation from the Earth surface. They are also coupled to the large-scale circulation. Cloud formation is determined by where circulation patterns bring moisture aloft, while the reflection and absorption of radiation by clouds also feeds back on circulation. Finally, precipitation is generated from clouds. The intensity, frequency, and duration of precipitation are determined by cloud structure and dynamics.
Climatic impacts of clouds are also multiscale, involving a huge range of processes, from new particle formation at the nanometer scale up to atmospheric wave propagation over thousands of kilometers. Simulating or measuring these 15 orders of magnitude challenges our computational and observational tools. And with phenomena as diverse as turbulence and nucleation, and forms as varied as cumulonimbus towers and stratocumulus decks, clouds challenge our ability to simplify or generalize.
This combination of impact and complexity has piqued the interest of ever more researchers and funding agencies and given rise to an imposing body of scientific literature. As in other scientific fields, this rapid growth in publication has motivated systematic review and meta-analysis, in this case through entities like the Intergovernmental Panel on Climate Change (IPCC) or the Climate Model Intercomparison Projects (CMIP). The fifth assessment report of the IPCC contained a chapter dedicated to clouds and climate (Boucher et al., 2013), and the most recent report devotes sections to cloud feedbacks and water cycle changes with warming (Douville et al., 2021; Forster et al., 2021). An endorsed model intercomparison project focused exclusively on cloud feedbacks (CFMIP) exists within the experimental design of the most recent CMIP (Webb et al., 2017).
This monograph represents one of such increasingly important community-wide review efforts. With primarily early-career lead authors, each chapter provides a review of the existing scientific literature and open questions in a given subfield. The volume is intended to act as a resource for graduate students, both to orient in their new subfield and gain fluency in related ones. For young scientists, it may direct their energy toward high-impact questions or help them formalize their future research program, and for more established scientists, it may generate ideas for collaboration.
Chapters have been organized around the climatic impacts of clouds on radiation, circulation, and precipitation described in Figure 1.1. Chapters 2 through 4 focus on the formation of liquid droplets and ice crystals on particulates and how this cloud formation can affect radiative fluxes, both at the surface and in the atmosphere. Chapters 5 through 9 review research on the most climatically relevant cloud types including Arctic mixed-phase, extratropical, tropical marine low, and tropical organized deep. Cloud-circulation coupling at the global, meso, and micro scales are covered in Chapters 10 through 12. The final part discusses precipitation efficiency, extremes, and measurements (Chapters 13–15).
1.1.1 Science of Science for Clouds and Climate
As an imposing body of scientific literature might already suggest, clouds and climate research at the institute or university level has tended to answer questions by performing new simulations or analyses, rather than by synthesizing output from existing studies. There could be many possible reasons for this focus on generating new output, for example, limited code and data documentation and accessibility in the past or cost and difficulty of storing and analyzing large volumes of data. In any case, systematic review at the community level is fueled by this very large number of individual studies. In this introductory chapter, we posit that analyzing how these individual studies are produced in a kind of “science of science” (SciSci) is another meta-exercise that would be useful for the clouds and climate community.
SciSci is an emerging area of study that combines scientometrics and the sociology of science to understand and optimize how science is done, from the emergence of new paradigms to the career path of students. As Fortunato et al. (2018) note in their review, SciSci has been driven in recent years by increasingly quantitative and accessible data on publications in databases like Scopus or Web of Science and by collaboration of natural, computational, and social scientists. Although some SciSci findings are domain- or culture-specific, a number of generalizable statements can be made. For example, networks of scientific concepts, tools, and authors tend to densify over time, indicating risk aversion and the tendency to select questions and collaborations conservatively (Foster et al., 2015; Fortunato et al., 2018). Such densification can be dangerous, as a small subset of authors cite one another and reinforce established hypotheses in an echo chamber effect.
SciSci analyses have not yet been done for clouds and climate research to our knowledge. Two decades ago, Geerts (1999) aggregated data on atmospheric science publications and found an increasing number of journals, pages per journal, and words per page in articles published between 1965 and 1995. But we have not found studies building upon this one or studies focused exclusively on climatic impacts of clouds. As a field with burgeoning student interest and the pressure to inform climate policy reliably and rapidly, clouds and climate research could benefit from such meta-study. To catalyze these efforts, we perform preliminary SciSci analyses in this introductory chapter.
Figure 1.1 The monograph chapters are organized around climatic impacts of clouds on radiation, circulation, and precipitation.
1.1.2 Publication Data and Methods
We draw our publication data from the Scopus database of Elsevier, which contains abstracts and citations of peer-reviewed academic literature. A set of Boolean keywords define the Scopus query for publications on clouds and climate, as well as those filtered by theme (Table 1.1). For example, for all publications in the field, we require that the title, abstract, or keywords contain both the term “clouds” and the term “climate” (TITLE-ABS-KEY(“clouds” AND “climate”)). We also require that the document be an English language journal article past the review stage (LIMIT TO(PUBSTAGE(“final”)) AND LIMIT-TO(DOCTYPE,“ar”) AND LIMIT-TO(LANGUAGE,“English”)) and that the journal domain be Earth or environmental sciences (LIMIT-TO(SUBJAREA(“EART”) OR LIMIT-TO(SUBJAREA(“ENVI”)). We find a non-negligible number of publications on Mars for some queries and additionally omit these (NOT TITLE-ABS-KEY(“Mars”)).
Scopus queries are also used to classify techniques by searching all clouds and climate abstracts for keywords. For example, we classify abstracts that contain the strings “model,” “parameterization,” “simulation,” “GCM,” “trajector,” or “radiative transfer” as modeling work. Abstracts can be classified as employing multiple techniques if they satisfy multiple queries. The anonymous Author ID field of Scopus queries is used with the Python networkx package to generate authorship networks. Finally, the titles associated with some queries are used to generate word clouds, using the Python WordCloud package. WordCloud takes the title text and fills space with individual words, sized by their frequency. A set of default “stop words” is omitted (listed in https://github.com/amueller/wordcloud/blob/master/wordcloud/stopwords) to which we add the following words: cloud, climate, using, change, comparison, evaluation, effect, global, study, effects, atmospheric, changes, response, based, part, characteristic, and influence. By eliminating these words, concrete article themes and techniques emerge more clearly from the title text.
The queries in Table 1.1 are denoted Clouds and Climate for all publications on clouds and climate; Impacts for publications decomposed into cloud impacts on radiation, circulation, and precipitation corresponding to the monograph sections; Chapters for publications decomposed according to the monograph chapters; and Techniques for identification of the techniques used in the abstracts of the clouds and climate publications. With the queries in Table 1.1, advanced searches can be reproduced at www.scopus.com/search/form.uri?display=advanced. Publication data for the figures have been downloaded on 3 September 2020 and are shown only through the end of 2019. The only non-Scopus data used concerns doctorates granted and funding awarded for the United States, which we take from the National Center for Science and Engineering Statistics (NCSES) Survey Data at https://ncsesdata.nsf.gov/home. For US doctorate degrees granted in geosciences, we look at the Survey of Earned Doctorates at https://ncsesdata.nsf.gov/builder/sed with Doctorate Recipients as our measure and Academic Discipline: Geosciences, atmospheric sciences, and ocean sciences as our dimension. For US annual funding, we look at the Survey of Federal Funds for Research and Development at https://ncsesdata.nsf.gov/builder/ffs with Research Obligations as our measure and Fields of Study: Atmospheric Science as our dimension.
1.2 PUBLICATIONS ON CLOUDS AND CLIMATE
We quantify the rapid growth of interest in clouds and climate with the output of the Scopus Clouds and Climate query. We find that the number of articles published on clouds and climate has been doubling every 8 years since 1970 (Fig. 1.2a). While an average of 27 articles per year were published in the 1980s, this rate had increased more than tenfold by the 2000s. In the 2010s, an average of 660 articles per year were published. By comparison, Price (1963) and Fortunato et al. (2018) find a 15-year doubling period for scientific articles more generally, while Milojević (2015) cites 16- and 19-year doubling periods for physics and biomedical articles, respectively.
We could also consider how this growth compares to that of other subfields in climate science. Searching instead for publications on ocean and climate or air pollution and climate, these have doubling times of 7.9 and 8.5 years, respectively, comparable to that of clouds and climate (not shown). Publications on the biosphere and climate are growing somewhat more gradually with a doubling time of 8.9 years, while publications on the land surface, the cryosphere, or the carbon cycle have stronger recent growth relative to cloud research, doubling in only 6.6 years, 5.1 years, and 6.0 years, respectively. These doubling rates show that publication growth for climate research is about twice as fast as that for general scientific research.
Table 1.1 Boolean syntax for Scopus database queries. We filter for English-language journal articles (“ar”) in the finalized publication stage (“final”), classified as Earth and planetary sciences (“EART”) or environmental sciences (“ENVI”). Filters other than TITLE-ABS-KEY are held constant for all searches and denoted other filters.
Queries
Clouds and Climate
TITLE-ABS-KEY(“clouds” AND “climate”) AND NOT TITLE-ABS-KEY(“Mars”) AND LIMIT-TO(PUBSTAGE(“final”)) AND LIMIT-TO(DOCTYPE,“ar”) AND LIMIT-TO(SUBJAREA,“EART”) OR LIMIT-TO(SUBJAREA,“ENVI”) AND LIMIT-TO(LANGUAGE,“English”)
Impacts
+
other filters
TITLE-ABS-KEY(“clouds” AND “climate” AND “circulation”) TITLE-ABS-KEY(“clouds” AND “climate” AND “radiati*”) TITLE-ABS-KEY(“clouds” AND “climate” AND “precipitation”)
Chapters
+
other filters
TITLE-ABS-KEY(“equilibrium climate sensitivity” OR “cloud feedback*”) TITLE-ABS-KEY((“radiative transfer” AND “climate” AND “cloud”) OR “cloud radiative effect*”) TITLE-ABS-KEY(“cloud classification”) TITLE-ABS-KEY(“cloud microphysics” OR (“cloud” AND “microphysics”) OR “microphysics parameterization”) TITLE-ABS-KEY(“aerosol indirect effect” OR “aerosol-cloud interaction”) TITLE-ABS-KEY(“atmospher*” AND (“dynamical core” OR “primitive equations”)) TITLE-ABS-KEY(“radiative-convective equilibrium” OR “convective organization” OR “convective aggregation” OR “organized convection”) TITLE-ABS-KEY(“cloud-circulation coupling” OR (“cloud” AND “large-scale circulation”)) TITLE-ABS-KEY(“cloud” AND “field campaign”) TITLE-ABS-KEY(“cloud” AND “ground-based measurement”) TITLE-ABS-KEY((“cloud” AND “machine learning”) OR (“cloud” AND “causal inference”))
Techniques
ABS(“in-situ” OR “flight” OR “campaign” OR “aircraft” OR “rocket” OR “drone”) ABS(“model” OR “parameterization” OR “simulation” OR “GCM” OR “trajector” OR “radiative transfer”) ABS(“reanalys” OR “emission”) ABS(“satellite” OR “CERES” OR “TRMM” OR “ISCCP” OR “remote sens” OR “retrieval” OR “imager” OR “CALIPSO” OR “CloudSat” OR “MODIS” OR “mission”) ABS(“ground-based” OR “station” OR “meteorological observator” OR “rain gauge” OR “site” OR “SHEBA” OR “flux tower”) ABS(“laboratory” OR “chamber” OR “chemical characteriz”) ABS(“observation”)
Not only is publication number on clouds and climate rapidly rising, the number of unique journals has also increased over time, doubling roughly every 11 years (Fig. 1.2b). In the 1980s, with the focus more often on meteorology, the Journal of Atmospheric Sciences published the most articles. By the 1990s, spurred by the release of the Charney Report and the launch of early geostationary satellites, interest broadened to the role of clouds in climate with more publications now in the Journal of Climate and Journal of Geophysical Research. Although only initiated in 2001, Atmospheric Chemistry and Physics (ACP) already published the most articles by the 2010s. As an open-access publisher, ACP output has also driven the large increase in open-access publication percentage over the past four decades from less than 10% in the 1980s to more than 50% in the late 2010s (Fig. 1.2c).
1.2.1 Citation and Readability
Does this expanding body of literature correspond to higher readership and, hence, citation? For the five most prolific journals publishing on clouds and climate, higher annual publication counts do correlate with higher annual citation counts from 1970 through 2019, but not always very strongly (Fig. 1.3). In spite of – or perhaps because of – its open-access policy, high publication rates in ACP correlate least strongly with high citation rates. From linear fits to these publication-citation scatter plots, annual citation count per journal increases by 33 for each additional article published. This annual citation increase per publication is largest for the Journal of Atmospheric Sciences at almost 60.
Figure 1.2 Atmospheric science publications, particularly open-access ones, and their unique sources have all increased dramatically over the past 50 years. Total number of publications per year from 1970 through 2019 with the Clouds and Climate query in Table 1.1 and their exponential fit (panel a). Number of unique journals for these publications per year from 1970 through 2019 and their exponential fit (panel b). Percentage of publications that have been open-access over time (panel c).
Without filtering for journal, cumulative distributions show that cloud and climate citation peaks for articles written between 1995 and 2005 (Fig. 1.4a,b). We may expect decreasing citation rates for more recent years, as the time since publication shortens, but the decreasing citation rates prior to 1995 indicate that older literature has been cited less than more recent literature. In the late 1990s, almost 20% of articles have more than a hundred citations, and only 10% are cited five times or fewer, counter to the highly skewed nature of most citation distributions in other fields in which many articles are never cited and a very small number are very highly cited (Price, 1965).
To further quantify citation trends, we employ the Kullback-Leibler divergence:
Figure 1.3 Publication and citation number are positively correlated, but not always strongly. Annual publication counts from 1970 through 2019 scattered against annual citation counts over the same period for the five most prolific atmospheric science publications; Journal of Climate and Journal of the Atmospheric Sciences are shown as solid traces, Geophysical Research Letters as a dashed trace, Atmospheric Chemistry and Physics as a dotted-dashed trace, and Journal of Geophysical Research as a dotted trace. Correlation coefficients are given next to the publication name in the legend.
Figure 1.4 Citation rates in clouds and climate research have slowed relative to publication rate in the most recent decade. Total number of citations per article per year from 1980 to 1999 (panel a) and from 1999 to 2019 (panel b). Time series of the Kullback-Leibler divergence between the cumulative density of citations in a given year and the proceeding year (panel c).
where P and Q are two probability distribution functions of annual citation numbers, xi. The larger the value of DKL, the more different the distributions P and Q are. Stated more formally, the larger the value of DKL, the more information (in nats when DKL is evaluated with the natural logarithm) would be lost in replacing P by Q. DKL is not symmetric, and we always take the preceding year as the reference distribution (Q). Calculating a 5-year running mean of this pairwise DKL yields relatively stable values of between 10 and 15 until 2007; thereafter, DKL increases monotonically up to a most recent value of 140 (Fig. 1.4c). These increasing DKL values indicate that citation growth in recent years is not maintaining pace with publication growth.
Decreased readability could contribute to this seeming drop in readership in recent years. The assessment of atmospheric science literature in Geerts (1999) found an increasing number of journals, pages per journal, and words per page in articles published between 1965 and 1995. While we reproduce this increasing trend in article length, page number per article, as a crude metric of readability, has been growing far less rapidly than publication or journal numbers, at an average of only a page per decade since 1970 (Fig. 1.5a). Geerts (1999) also proposed that lagging US federal funding and the plateauing number of PhD students could slow these publication rates in coming years. We find instead that, while US funding has converged or even dropped in recent years, the number of US students in geosciences has continued to grow (Fig. 1.5b). Equivalent data are not as readily available for other regions, however, and these US trends may not be representative globally.
1.3 THE ROLE OF CLOUDS IN RADIATION, CIRCULATION, AND PRECIPITATION
To understand how research effort has been devoted across the climatic impacts of clouds, we next decompose the publication trends from section 1.2, using the Impacts queries from Table 1.1
