Fast Processes in Large-Scale Atmospheric Models E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Geophysical Monograph Series
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
1 Progress in Understanding and Parameterizing Fast Physics in Large‐Scale Atmospheric Models
ABSTRACT
1.1 FAST PHYSICS AND PROGRESS OF PARAMETERIZATION DEVELOPMENT
1.2 OBJECTIVES AND SCOPE OF THE BOOK
1.3 BOOK STRUCTURE AND SUMMARY OF CHAPTERS
1.4 HOW TO APPROACH THE CONTENT IN THIS BOOK
ACKNOWLEDGMENTS
REFERENCES
Part I: Processes and Parameterizations
2 Radiative Transfer and Atmospheric Interactions
ABSTRACT
2.1 BACKGROUND AND INTRODUCTION
2.2 FUNDAMENTALS AND EQUATION GOVERNING RADIATIVE TRANSFER FOR PLANE‐PARALLEL ATMOSPHERE
2.3 GASEOUS ABSORPTION
2.4 COMMONLY USED APPROXIMATIONS OF RADIATIVE TRANSFER
2.5 RADIATION‐CLOUD INTERACTIONS IN LARGE‐SCALE ATMOSPHERIC MODELS
2.6 3D RADIATIVE TRANSFER
2.7 RADIATIVE TRANSFER SCHEMES IN NUMERICAL MODELS
2.8 SUMMARY AND OUTLOOK
REFERENCES
3 Aerosols and Climate Effects
ABSTRACT
3.1 AEROSOL PROCESSES
3.2 AEROSOL PROPERTIES
3.3 AEROSOL DIRECT EFFECTS
3.4 AEROSOL INDIRECT EFFECTS
3.5 SUMMARY AND OUTLOOK
ACKNOWLEDGMENTS
REFERENCES
4 Entrainment, Mixing, and Their Microphysical Influences
ABSTRACT
4.1 INTRODUCTION
4.2 IMPORTANCE OF CUMULUS AND STRATOCUMULUS
4.3 ENTRAINMENT‐MIXING MECHANISMS
4.4 ENTRAINMENT RATE
4.5 SUMMARY AND OUTLOOK
APPENDIX
LIST OF SYMBOLS
REFERENCES
5 Deep Convection and Convective Clouds
ABSTRACT
5.1 INTRODUCTION
5.2 BASIC STRUCTURE OF PROBLEM AND RELATIONSHIP TO OTHER PROCESSES
5.3 STRATEGIES FOR SOLUTION
5.4 MOMENTUM, CHEMISTRY, MICROPHYSICS, AND AEROSOL INTERACTIONS
5.5 CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
6 Stratus, Stratocumulus, and Remote Sensing
ABSTRACT
6.1 INTRODUCTION
6.2 OVERVIEW
6.3 FORMATION AND MAINTENANCE, AND CLOUD MORPHOLOGY
6.4 DIURNAL AND SEASONAL VARIATIONS
6.5 AEROSOL IMPACTS ON STRATUS AND STRATOCUMULUS
6.6 REMOTE SENSING
6.7 CONCLUDING REMARKS
ACKNOWLEDGMENTS
LIST OF ACRONYMS
REFERENCES
7 Planetary Boundary Layer and Processes
ABSTRACT
7.1 BACKGROUND AND INTRODUCTION
7.2 DIURNAL CYCLE OF SUMMERTIME BOUNDARY LAYER OVER LAND
7.3 THEORETICAL REPRESENTATIONS OF THE BOUNDARY LAYER
7.4 IDEALIZED MODELS OF THE BOUNDARY LAYER
7.5 SURFACE LAYER
7.6 DISCUSSION AND FUTURE OUTLOOK
ACKNOWLEDGMENTS
REFERENCES
8 Human Impacts on Land Surface‐Atmosphere Interactions
ABSTRACT
8.1 INTRODUCTION
8.2 CROSS‐SCALE INTERACTIONS OF CITIES WITH THE ATMOSPHERE
8.3 CROSS‐SCALE INTERACTIONS OF AGRICULTURE WITH THE ATMOSPHERE
ACKNOWLEDGMENTS
REFERENCES
9 Gravity Wave Drag Parameterizations for Earth's Atmosphere
ABSTRACT
9.1 INTRODUCTION AND BASIC EQUATIONS
9.2 REPRESENTATION IN LARGE‐SCALE MODELS
ACKNOWLEDGMENTS
AVAILABILITY STATEMENT
REFERENCES
Part II: Unifying Efforts
10 Higher‐Order Equations Closed by the Assumed PDF Method: Suitability for Parameterizing Cumulus Convection
ABSTRACT
10.1 INTRODUCTION
10.2 HISTORICAL REVIEW
10.3 OVERVIEW OF HIGHER‐ORDER CLOSURE
10.4 EXAMPLE: PROGNOSIS OF VERTICAL TURBULENT FLUX OF TOTAL WATER
10.5 HOW DO MASS‐FLUX SCHEMES, HIGHER‐ORDER CLOSURES, AND LOW‐ORDER CLOSURES REPRESENT NONLOCAL VERTICAL TRANSPORT OF MOISTURE?
10.6 EXAMPLE: PROGNOSIS OF THE VERTICAL COMPONENT OF TURBULENCE KINETIC ENERGY,
10.7 HOW DO MASS‐FLUX SCHEMES, HIGHER‐ORDER CLOSURE, AND LOW‐ORDER CLOSURE REPRESENT BUOYANCY?
10.8 MUST HIGHER‐ORDER CLOSURE MODELS OF CUMULUS LAYERS INCLUDE VERTICAL COHERENCE AND ICE?
10.9 GLOBAL SIMULATIONS USING HIGHER‐ORDER CLOSURE TO REPRESENT DEEP CONVECTION
10.10 CONCLUSIONS
APPENDIX A PROGNOSTIC EQUATIONS IN CLUBB
APPENDIX B ASSUMED PDF CLOSURE IN CLUBB
ACKNOWLEDGMENTS
REFERENCES
11 An Introduction to the Eddy–Diffusivity/Mass–Flux (EDMF) Approach: A Unified Turbulence and Convection Parameterization
ABSTRACT
11.1 INTRODUCTION AND MOTIVATION
11.2 EDDY–DIFFUSIVITY/MASS–FLUX APPROACH: INTRODUCTION
11.3 EDMF FORMULATION
11.4 EDMF AND THE DRY CONVECTIVE BOUNDARY LAYER
11.5 EDMF AND SHALLOW MOIST CONVECTION
11.6 THE MULTIPLUME EDMF PARAMETERIZATION
11.7 MULTIPLUME MOIST EDMF RESULTS
11.8 THE FULLY UNIFIED (SHALLOW AND DEEP) EDMF PARAMETERIZATION
11.9 RESULTS FROM THE FULLY UNIFIED MULTIPLUME EDMF
11.10 SUMMARY
ACKNOWLEDGMENTS
REFERENCES
12 Application of Machine Learning to Parameterization Emulation and Development
ABSTRACT
12.1 INTRODUCTION
12.2 PARAMETERIZATIONS AS MAPPINGS
12.3 MACHINE LEARNING TOOLS TO APPROXIMATE PARAMETERIZATION MAPPINGS
12.4 ML EMULATIONS TO SPEED UP CALCULATION OF PARAMETERIZATIONS
12.5 USING ML TO DEVELOP NEW PARAMETERIZATIONS: TRAINING COARSE‐GRID PARAMETERIZATION USING DATA FROM FINE‐GRID SIMULATIONS
12.6 USING ENSEMBLE OF ML TOOLS
12.7 ENSURING PHYSICAL CONSTRAINTS
12.8 GOING BEYOND THE TRADITIONAL PARADIGM
12.9 SUMMARY AND DISCUSSION
REFERENCES
13 Top‐Down Approaches to the Study of Cloud Systems
ABSTRACT
13.1 INTRODUCTION
13.2 A BRIEF HISTORICAL PERSPECTIVE
13.3 ORGANIZING PRINCIPLES AND TERMINOLOGY
13.4 EXAMPLES OF TOP‐DOWN APPROACHES IN ATMOSPHERIC PHYSICS
13.5 INTERPRETATIVE AND DIAGNOSTIC METHODS IN THE WORLD OF THE TOP‐DOWN APPROACH TO ATMOSPHERIC SYSTEMS
13.6 GUIDELINES FOR TOP‐DOWN APPROACHES AND CONCLUDING REMARKS
ACKNOWLEDGMENTS
REFERENCES
Notes
Part III: Measurements, Model Evaluation, and Model‐Measurement Integration
14 Ground‐Based Remote‐Sensing of Key Properties
ABSTRACT
14.1 THE EMERGENCE OF GROUND‐BASED OBSERVATORIES
14.2 CORNERSTONE GROUND‐BASED REMOTE SENSORS
14.3 RETRIEVING GEOPHYSICAL PROPERTIES FROM REMOTE‐SENSING MEASUREMENTS
14.4 FORWARD SIMULATORS
14.5 USING OBSERVATIONS TO INFORM MODELS
14.6 THE NEXT GENERATION OF GROUND‐BASED OBSERVATORIES
REFERENCES
15 Satellite and Airborne Remote Sensing of Clouds and Aerosols
ABSTRACT
15.1 INTRODUCTION AND OUTLINE
15.2 PASSIVE SENSING OF COLUMN‐INTEGRATED CLOUD PROPERTIES
15.3 PASSIVE SENSING OF COLUMN‐INTEGRATED AEROSOL PROPERTIES
15.4 VERTICAL STRUCTURE OF CLOUDS AND AEROSOLS
15.5 REMOTE SENSING OF CLOUDS FROM ABOVE AT HIGH SPATIAL RESOLUTION
15.6 RETRIEVAL UNCERTAINTY QUANTIFICATION
15.7 SUMMARY AND OUTLOOK
ACKNOWLEDGMENTS
LIST OF ABBREVIATIONS AND ACRONYMS
REFERENCES
16 In Situ and Laboratory Measurements of Cloud Microphysical Properties
ABSTRACT
16.1 INTRODUCTION
16.2 WHAT DO MODELS NEED TO KNOW ABOUT CLOUDS?
16.3 A SAMPLE OF NEW MEASUREMENT CAPABILITIES AND RECENT RESULTS FROM FIELD STUDIES AND LABORATORY EXPERIMENTS
16.4 SUMMARY AND FUTURE DIRECTIONS
ACKNOWLEDGMENTS
REFERENCES
17 Frameworks for Testing and Evaluating Fast Physics Parameterizations in Climate and Weather Forecasting Models
ABSTRACT
17.1 OVERVIEW
17.2 SCM, CRM, AND LES—AN INTEGRATED DEVELOPMENT AND EVALUATION FRAMEWORK
17.3 NWP HINDCAST APPROACH FOR CLIMATE MODEL EVALUATION
17.4 EVALUATION METRICS AND DIAGNOSTICS
17.5 CONCLUDING REMARKS AND FUTURE OUTLOOK
ACKNOWLEDGMENTS
REFERENCES
18 Future Research Outlook: Challenges and Opportunities
ABSTRACT
18.1 RECENT ADVANCES
18.2 CHALLENGES AND OPPORTUNITIES FOR FUTURE RESEARCH
ACKNOWLEDGMENTS
REFERENCES
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Gaussian points and weights.
Table 2.2 Coefficients in two‐stream approximations.
Table 2.3 Shortwave (SW) and longwave (LW) schemes in the WRF model.
Chapter 3
Table 3.1 CMIP6 Emissions of aerosols and chemically reactive gases from dif...
Table 3.2 Comparison of the three dust emission schemes used in GCMs.
Table 3.3 Sea‐Salt emission parameterizations.
Table 3.4 Aerosol dry deposition scheme in global climate models.
Table 3.5 In‐Cloud aerosol scavenging schemes in global climate models.
Table 3.6 Aerosol treatments in global climate models.
Chapter 6
Table 6.1 ARM instruments and their corresponding measurements and uncertain...
Chapter 10
Table A.1 List of Prognosed Moments in CLUBB.
Chapter 12
Table 12.1 Statistics for estimating the accuracy of the heating rate calcul...
Chapter 16
Table 16.1 List of airborne instruments for measuring hydrometeor size distr...
List of Illustrations
Chapter 1
Figure 1.1 Schematic of the approximate timelines of development of fast phy...
Figure 1.2 Historical values of equilibrium climate sensitivity (ECS) and tr...
Figure 1.3 Schematic to illustrate the atmospheric scale hierarchy and invol...
Chapter 2
Figure 2.1 Estimate of the Earth's annual and global mean energy balance for...
Figure 2.2 The radiation intensity as a function of wavelength for the Sun (...
Figure 2.3 Diagram illustrating the scattering and absorption of sunlight by...
Figure 2.4 Definitions of the zenith angle
θ
and the azimuthal angle
φ
...
Figure 2.5 Top panel: Spectral irradiances for the solar (red curve) and the...
Figure 2.6 Relative accuracy of the reflectance and transmittance computed f...
Figure 2.7 Same as Figure 2.6, except for absorptance. The left and right pa...
Figure 2.8 Ice crystal size and shape as a function of height and relative h...
Figure 2.9 Solar, thermal IR, and net radiative forcings for cirrus clouds a...
Figure 2.10 Mean correlation curves with standard deviations (vertical bars)...
Figure 2.11 An example of possible cloud configurations.
Figure 2.12 A 3D cirrus observation from Lidar (private communication with E...
Figure 2.13 Annual mean inhomogeneity factor for high‐level clouds derived f...
Figure 2.14 (a) Visualization of a checkboard medium and (b) the scene albed...
Figure 2.15 (a) 3D ice water content (IWC, g/m
3
) and mean effective ice crys...
Figure 2.16 A schematic representation of flux components received by the ta...
Figure 2.17 The upper panels display the spatial distribution of deviations ...
Figure 2.18 Differences between the domain‐averaged net radiative flux on mo...
Figure 2.19 Comparison of the deviations of the five flux components compute...
Figure 2.20 The monthly mean SWE map for April 2008: (a) simulated from WRF ...
Figure 2.21 The monthly mean (a) SWE, (b) precipitation, and (c) cumulative ...
Figure 2.22 The effect of 3D mountains over WUS on the precipitation, runoff...
Figure 2.23 Flowchart of the radiation program in a numerical model.
Chapter 3
Figure 3.1 Schematic of aerosol processes in the atmosphere.
Figure 3.2 Emission rates of SO
2
, BC, POM, and NO due to fossil fuel use in ...
Figure 3.3 Dry deposition velocities on (a) water surfaces and (b) snow and ...
Figure 3.4 Aerosol water burden (Tg) in 12 AeroCom II models. From Kai Zhang...
Figure 3.5 Shortwave all‐sky DREs of biomass burning aerosols during the fir...
Figure 3.6 Supersaturation as a function of equilibrium wet diameter for par...
Chapter 4
Figure 4.1 Schematic for the evolution of cloud microphysics in adiabatic an...
Figure 4.2 Mixing diagram of cubic volume‐mean radius over cubic adiabatic v...
Figure 4.3 Homogeneous mixing degree (
ψ
) as a function of timescales in...
Figure 4.4 (a) Parameterization of cloud entrainment‐mixing mechanisms by re...
Figure 4.5 Schematic illustration of the filament structure of a 1 Hz sample...
Figure 4.6 Homogeneous mixing degree (
ψ
) vs. averaging time window (
t
) ...
Figure 4.7 Examples of homogeneous mixing degree (
ψ
) as a function of a...
Figure 4.8 Relationships between entrainment rate (
λ
) and (a) vertical ...
Figure 4.9 Regression based on equation (4.35) between entrainment rate (λ) ...
Figure 4.10 Schematic for combined studies on entrainment‐mixing mechanisms ...
Chapter 5
Figure 5.1 A cumulus ensemble idealized as an ensemble of laterally entraini...
Figure 5.2 A plume with both entrainment and detrainment, from Grell and Fre...
Figure 5.3 Parameterized convective system including mesoscale processes (Be...
Figure 5.4 Vertical velocities (top) with retrieval uncertainties (bottom) (...
Figure 5.5 Mass fluxes for a number of deep convective parameterizations, de...
Figure 5.6 Change of CAPE by mean advection and PBL processes (horizontal ax...
Figure 5.7 CAPE for tropical convection simulated by a CRM (Donner et al., 1...
Chapter 6
Figure 6.1 Examples of (a) stratus generated around a low‐pressure system, (...
Figure 6.2 Combined annual mean coverage of stratus and stratocumulus clouds...
Figure 6.3 Merged true color corrected reflectance images from 22 September ...
Figure 6.4 Mean 2018 cloud properties from CERES Aqua MODIS retrievals. (a) ...
Figure 6.5 Same as Figure 6.4, but for daytime only and for low (a) cloud‐to...
Figure 6.6 Mean cloud radiative effects from CERES measurements (2000–2010) ...
Figure 6.7 Net radiative cloud effect from CERES, 2000–2015, from the NASA H...
Figure 6.8 Monthly means of single‐layered stratus cloud (a) base height CBH...
Figure 6.9 Same as Figure 6.8, except for single‐layered stratus clouds (a) ...
Figure 6.10 Schematic diagram of variables affecting marine boundary‐layer (...
Figure 6.11 Liquid water potential temperature (
θ
L
, red lines) and tota...
Figure 6.12 Schematic showing cloud and boundary‐layer evolution from Califo...
Figure 6.13 Visible satellite imagery showing stratocumulus clouds in the (a...
Figure 6.14 Examples of four mesoscale structure types occurring in marine s...
Figure 6.15 Optically thin veil clouds, ultraclean layers, and drizzling Sc ...
Figure 6.16 Cloud properties retrieved from GOES‐16 imagery corresponding to...
Figure 6.17 Plot of 94‐GHz cloud radar reflectivity (both downward and upwar...
Figure 6.18 Occurrence frequency of positive MCAO index in (a) Northern Hemi...
Figure 6.19 Horizontal roll convection example in a cold‐air outbreak. (a) N...
Figure 6.20 Multichannel pseudocolor two‐hourly GOES‐16 images of SE Pacific...
Figure 6.21 Mean November 1978 low cloud fraction in percent from GOES‐2 vis...
Figure 6.22 Example of daytime variations in cloud microphysics along 21°S l...
Figure 6.23 Daily average (left) and relative amplitude (middle) and phase (...
Figure 6.24 Mean drizzle occurrence during July 2006–June 2007 determined fr...
Figure 6.25 ERF
ACI
during industrial era computed using MODIS COD and CF, CE...
Figure 6.26 Schematic illustration of using observations to validate and imp...
Figure 6.27 October 2008 day + night cloud‐top pressure. Regional means from...
Figure 6.28 Dependence of spectral reflectance on cloud optical depth and dr...
Figure 6.29 Simulated reflectances at 0.86 and 2.11 μm for a range COD and C...
Figure 6.30 (a) Theoretically retrieved CER values from MODIS 3.7‐, 2.1‐, an...
Figure 6.31 Annual mean difference between MODIS 1.6‐ and 3.7‐μm CER retriev...
Figure 6.32 (a) LWP binned as a function of ceilometer CF and CERES MODIS CF...
Figure 6.33 (a) Probability density functions (PDFs, solid lines) and cumula...
Figure 6.34 (a) Temporal variation of the profiles of decomposed cloud refle...
Figure 6.35 Boxplot of 1‐s aircraft in situ measured (blue) and 1‐min surfac...
Figure 6.36 Profiles of cloud (top row) and drizzle (bottom row) microphysic...
Chapter 7
Figure 7.1 Average planetary boundary layer (PBL) depths as reported by the ...
Figure 7.2 Top: Diurnal cycle of downwelling and upwelling shortwave and lon...
Figure 7.3 An example of the diurnal cycle of updraft velocity within the bo...
Figure 7.4 Conceptual schematic of the evolution of summertime planetary bou...
Figure 7.5 Top: High‐resolution (10 Hz) vertical velocity (
w
) at 2 m (gray) ...
Chapter 8
Figure 8.1 Anthropogenic sensible heat fluxes (W m
−2
) in the Beijing M...
Figure 8.2 Doppler LiDAR Beam Swinging (DBS) scan 30‐min mean vertical‐veloc...
Figure 8.3 Paved surfaces in the Greater Houston Metro area keep the nightti...
Figure 8.4 A schematic of the SLUCM (on the left‐hand side) and the multilay...
Figure 8.5 A schematic of modeling crop‐growth processes in the Noah‐MP‐Crop...
Chapter 9
Figure 9.1 Depiction of three dominant sources of GWs: (a, b) orographic, (c...
Figure 9.2 Simulated propagation of a gravity wave packet through two differ...
Figure 9.3 Schematic depiction of flow over and around a large mountain. Sou...
Figure 9.4 Total eastward (left panels) and westward (right panels) momentum...
Chapter 10
Figure 10.1 Simulation of surface precipitation rate by CAM‐CLUBB averaged o...
Figure 10.2 Simulation of zonally averaged absolute temperature by CAM‐CLUBB...
Chapter 11
Figure 11.1 Schematic illustrating the thermodynamic structure of a typical ...
Figure 11.2 Hourly mean potential temperature profiles (4 and 8 hours of sim...
Figure 11.3 BOMEX cumulus case: profiles of updraft fraction area, updraft v...
Figure 11.4 Joint distribution of total‐water mixing ratio and liquid‐water ...
Figure 11.5 Mean liquid water potential temperature, total water, and liquid...
Figure 11.6 Profiles of the difference (excess) of moist conserved variables...
Figure 11.7 EDMF and the LBA case: (left) cloud base and top height, (right)...
Chapter 12
Figure 12.1 Various types of ML tools. New ML tools emerge very often.
Figure 12.2 (a) Nonlinear neuron, (b) linear neuron (equation (12.4)), and (...
Figure 12.3 Relationship between complexity of approximating function and un...
Figure 12.4 The 10‐day average of the total cloudiness (left), the U‐compone...
Figure 12.5 The process of preparation for training of an ML convection para...
Figure 12.6 A compound parameterization design.
Chapter 13
Figure 13.1 From top left in a clockwise direction: Examples of pattern and ...
Figure 13.2 Overview of subdisciplines within the study of complex systems. ...
Figure 13.3 Marine stratocumulus clouds undergoing alternating precipitating...
Chapter 14
Figure 14.1 (a) Radars transmit pulses of electromagnetic waves using a tran...
Figure 14.2 On 28 January 2013 at the Southern Great Planes (SGP) observator...
Figure 14.3 On 5 March 2020 at the Jülich Observatory for Cloud Evolution (J...
Figure 14.4 On 11 and 12 April 2018 at the PANGEA observatory in Greece a ze...
Figure 14.5 On 22 October 2018 at the Jülich Observatory for Cloud Evolution...
Figure 14.6 On 2 July 2018 at the Eastern North Atlantic (ENA) observatory, ...
Figure 14.7 General circulation models (GCMs) commonly represent the mass mi...
Figure 14.8 On 16 January 2018 at the Eastern North Atlantic (ENA) observato...
Chapter 15
Figure 15.1 Look‐up tables (LUTs) obtained from RT calculations show the rel...
Figure 15.2 Sensitivity of polarized reflectance in the rainbow angular rang...
Figure 15.3 Comparison of radiance images, expressed in brightness temperatu...
Figure 15.4 Results of global MAIAC AOT validation against AERONET (Holben e...
Figure 15.5 Left panel: MISR true color image, full swath, and nadir view. R...
Figure 15.6 Left two panels: Comparison of AOT retrieved from AirMSPI data c...
Figure 15.7 CloudSat transect of radar reflectivity through Typhoon Yutu‐Wes...
Figure 15.8 MISR red image (672 nm) overlaid with retrieved vector wind, col...
Figure 15.9 Left panel: The spectral responses of MODIS/Aqua's channels 33–3...
Figure 15.10 Earth‐and‐New‐Moon EPIC images captured on 19 November 2017 at ...
Figure 15.11 True‐color channel combination of an MTI image of Los Alamos (3...
Figure 15.12 Upper left: NASA ER‐2 pilot prepared for takeoff.
Source:
Court...
Figure 15.13 Cumulative histogram of cloud‐free atmospheric columns over oce...
Figure 15.14 Geographic distribution of MODIS/Aqua bispectral cloud property...
Figure 15.15 The ellipses in this schematic roughly represent the level sets...
Chapter 16
Figure 16.1 Cloud droplet size distributions measured in a large cloud chamb...
Figure 16.2 A profile of water vapor supersaturation and cloud liquid water ...
Figure 16.3 Cloud droplet size distribution measured using a Forward Scatter...
Figure 16.4 Buoyancy perturbation of a mixture of air from stratocumulus c...
Figure 16.5 In situ measurements of cloud microphysical properties for the t...
Figure 16.6 Cloud droplet size distributions, weighted by number concentrati...
Figure 16.7 Mixing diagrams generated from HOLODEC data obtained in small, l...
Figure 16.8 Ice crystal size distributions from a cirrus cloud as measured b...
Figure 16.9 High‐resolution measurements of temperature and liquid water con...
Figure 16.10 High‐resolution measurements through a single cumulus cloud fro...
Figure 16.11 Examples of hydrometeors sampled in a mixed‐phase cloud at the ...
Figure 16.12 Power spectral densities of liquid water content for two cloud ...
Figure 16.13 The average number of ice‐nucleating macromolecules per droplet...
Figure 16.14 Steady‐state moist and cloudy conditions: measurements of verti...
Chapter 17
Figure 17.1 A schematic diagram for the SCM‐CRM‐LES modeling framework and h...
Figure 17.2 A schematic diagram that integrates Trans‐AMIP into the SCM‐CRM‐...
Chapter 18
Figure 18.1 Schematic to illustrate the three levels of fast physics paramet...
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
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Geophysical Monograph Series
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Fast Processes in Large‐Scale Atmospheric Models: Progress, Challenges, and Opportunities
Yangang Liu and Pavlos Kollias (Eds.)
Geophysical Monograph 282
Fast Processes in Large-Scale Atmospheric Models
Progress, Challenges, and Opportunities
Yangang LiuPavlos KolliasEditors
Leo J. DonnerAdvisor
This Work is a co‐publication of the American Geophysical Union and John Wiley and Sons, Inc.
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Names: Liu, Yangang, editor. | Kollias, Pavlos, editor. | Donner, Leo Joseph, 1956‐ other.Title: Fast processes in large scale atmospheric models : progress, challenges, and opportunities / Yangang Liu, Pavlos Kollias, editors ; Leo J. Donner, advisor.Description: Hoboken, NJ : Wiley‐American Geophysical Union, 2024. | Series: Geophysical monograph series | Includes index.Identifiers: LCCN 2023015913 (print) | LCCN 2023015914 (ebook) | ISBN 9781119528999 (cloth) | ISBN 9781119528968 (adobe pdf) | ISBN 9781119528944 (epub)Subjects: LCSH: Atmospheric models.Classification: LCC QC861.3 .F378 2024 (print) | LCC QC861.3 (ebook) | DDC 551.501/13–dc23/eng/20231012LC record available at https://lccn.loc.gov/2023015913LC ebook record available at https://lccn.loc.gov/2023015914
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LIST OF CONTRIBUTORS
M. Joan Alexander
NorthWest Research Associates
Boulder, CO, USA
Vassilis Amiridis
Institute for Astronomy, Astrophysics, Space Applications
and Remote Sensing
National Observatory of Athens
Athens, Greece
Julio T. Bacmeister
Climate and Global Dynamics Laboratory
National Center for Atmospheric Research
Boulder, CO, USA
Michael Barlage
National Center for Atmospheric Research
Boulder, CO, USA
Alexei Belochitski
(†)
I. M. Systems Group
Environmental Modeling Center
College Park, MD, USA
Kamal Kant Chandrakar
National Center for Atmospheric Research
Boulder, CO, USA
Fei Chen
National Center for Atmospheric Research
Boulder, CO, USA
Anthony B. Davis
NASA Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA, USA
Xiquan Dong
Department of Hydrology and Atmospheric Sciences
University of Arizona
Tucson, AZ, USA
Leo J. Donner
NOAA Geophysical Fluid Dynamics Laboratory
Princeton University
Princeton, NJ, USA
Graham Feingold
Chemical Sciences Laboratory
NOAA Earth System Research Laboratories
Boulder, CO, USA
Sinan Gao
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters / Key Laboratory for Aerosol‐Cloud‐Precipitation of China Meteorological Administration
Nanjing University of Information Science and Technology
Nanjing, China
Virendra P. Ghate
Environmental Science Division
Argonne National Laboratory
Lemont, IL, USA
Yu Gu
Joint Institute for Regional Earth System Science and Engineering, and Department of Atmospheric and Oceanic Sciences
University of California Los Angeles
Los Angeles, CA, USA
Christopher Heale
Department of Physical Sciences
Embry–Riddle Aeronautical University
Daytona Beach, FL, USA
Pavlos Kollias
Environmental and Climate Sciences Department
Brookhaven National Laboratory
Upton, NY, USA
and
School of Marine and Atmospheric Science
Stony Brook University
Stony Brook, NY, USA
Ilan Koren
Department of Earth and Planetary Sciences
Weizmann Institute
Rehovot, Israel
Vladimir Krasnopolsky
Environmental Modeling Center/National Centers for Environmental Predictions/National Weather Service
NOAA Center for Weather and Climate Prediction
College Park, MD, USA
Christopher G. Kruse
NorthWest Research Associates
Boulder, CO, USA
Marcin J. Kurowski
NASA Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA, USA
Katia Lamer
Department of Environmental and Climate Sciences
Brookhaven National Laboratory
Upton, NY, USA
Vincent E. Larson
Department of Mathematical Sciences
University of Wisconsin–Milwaukee
Milwaukee, WI, USA
and
Pacific Northwest National Laboratory
Richland, WA, USA
Wuyin Lin
Department of Environmental and Climate Sciences
Brookhaven National Laboratory
Upton, NY, USA
Alexander Marshak
NASA Goddard Space Flight Center
Greenbelt, MD, USA
Kuo‐Nan Liou
(†)
Joint Institute for Regional Earth System Science and Engineering, and Department of Atmospheric and Oceanic Sciences
University of California Los Angeles
Los Angeles, CA, USA
Xiaohong Liu
Department of Atmospheric Sciences
Texas A&M University
College Station, TX, USA
Yangang Liu
Environmental and Climate Sciences Department
Brookhaven National Laboratory
Upton, NY, USA
Ulrich Loehnert
Institute of Geophysics and Meteorology
University of Cologne
Cologne, Germany
Chunsong Lu
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters / Key Laboratory for Aerosol‐Cloud‐Precipitation of China Meteorological Administration
Nanjing University of Information Science and Technology
Nanjing, China
Eleni Marinou
Institute for Astronomy, Astrophysics, Space Applications
and Remote Sensing
National Observatory of Athens
Athens, Greece
Allison McComiskey
Department of Environmental and Climate Sciences
Brookhaven National Laboratory
Upton, NY, USA
David B. Mechem
Department of Geography and Atmospheric Science
University of Kansas
Lawrence, KS, USA
Patrick Minnis
Analytical Mechanics Associates, Inc.
Hampton, VA, USA
Jadwiga H. Richter
Climate and Global Dynamics Laboratory
National Center for Atmospheric Research
Boulder, CO, USA
Sabrina Schnitt
Institute of Geophysics and Meteorology
University of Cologne
Cologne, Germany
Raymond A. Shaw
Atmospheric Sciences Program
Michigan Technological University
Houghton, MI, USA
Cheng Sun
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters / Key Laboratory for Aerosol‐Cloud‐Precipitation of China Meteorological Administration
Nanjing University of Information Science and Technology
Nanjing, China
Kay Suselj
NASA Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA, USA
and
Joint Institute for Regional Earth System Science and Engineering
University of California Los Angeles
Los Angeles, CA, USA
João Teixeira
NASA Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA, USA
and
Joint Institute for Regional Earth System Science and Engineering
University of California Los Angeles
Los Angeles, CA, USA
Junhong Wei
School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies
Sun Yat‐sen University
Guangzhou, China
and
Southern Marine Science and Engineering Guangdong Laboratory
Zhuhai, China
Shaocheng Xie
Atmospheric, Earth and Energy Division
Lawrence Livermore National Laboratory
Livermore, CA, USA
Xiaoqi Xu
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters / Key Laboratory for Aerosol‐Cloud‐Precipitation of China Meteorological Administration
Nanjing University of Information Science and Technology
Nanjing, China
PREFACE
Computer models are essential tools for understanding atmospheric phenomena and for making accurate predictions of any changes in the Earth's climate, weather, and resources of renewable energy resulting from anthropogenic activities that generate greenhouse gases and particulates into the atmosphere. Many physical processes that influence Earth's climate and weather occur on spatial (temporal) scales smaller (shorter) than typical grid sizes (time steps) of general circulation models, and thus must be parameterized.
This book focuses on the atmospheric subgrid processes—collectively called fast physics—by reviewing and synthesizing relevant physical understanding, parameterization developments, various measurement technologies, and model evaluation framework. The book contains 18 chapters and is divided into three parts to reflect and synthesize the multiple aspects involved.
The first chapter briefly introduces the historical development of fast physics parameterizations and the involved complexities. Part I is devoted to discussing major subgrid processes, with eight chapters (Chapters 2–9) each covering different processes more or less in the conventional compartmentalized format that emphasizes individual processes. Topics covered include, but not limited to, radiative transfer, aerosols, and aerosol direct and indirect effects; entrainment‐mixing processes and their microphysical influences; convection and convective clouds; stratiform clouds such as stratus and stratocumulus; planetary boundary layer processes; land surface and its interactions with the atmosphere; and gravity waves. On top of the conventional treatments, some promising ideas/approaches are described that have recently emerged to unify the treatment of individual processes and thus allow for consideration of process interactions.
Part II is devoted to such unifying efforts, with four chapters (Chapters 10–13) covering four different endeavors: the unifying parameterizations based on assumed probability density functions; the EDMF approach that combines the eddy–diffusivity and mass–flux approaches to unify turbulence and convection; application of machine learning techniques; and innovative top‐down attempts that consider the involved totality by borrowing ideas from systems theory, statistical physics, and nonlinear sciences.
Part III (Chapters 14–17) is devoted to assessments, model evaluation, and model‐measurement integration, with four chapters that focus on satellite and airborne remote‐sensing measurements; surface‐based remote‐sensing measurements; in situ and laboratory measurements; and model evaluation and model‐measurement integration, respectively. The final chapter of the book summarizes emerging challenges, new opportunities, and future research directions.
The development of the book happened around two noteworthy events. The first was that the 2021 Nobel Prize in Physics was awarded to three pioneers in modeling climate and weather and studying complex systems (Syukuro Manabe of Princeton University, USA; Klaus Hasselmann of the Max Planck Institute for Meteorology, Germany; and Giorgio Parisi of Sapienza University of Rome, Italy). This exciting choice accentuates not only the critical importance of the subject but also the outstanding challenges of the topics discussed in this book.
The second event was the COVID‐19 pandemic, which unfortunately overlapped with the writing of most of the chapters in this book and affected the lives of many of our contributors. We would like to express our special thanks to all the authors and reviewers, as well as to the staff at Wiley and AGU for their hard work and patience as we completed this book under these circumstances.
This book is dedicated to two of our dear colleagues and contributing authors who passed away during this period, Kuo‐Nan Liou and Alexei Belochitski. The book is also dedicated to Yangang Liu's mother, Chunlan Sun, who was hospitalized during the pandemic and passed away in China recently without his company.
Yangang Liu
Brookhaven National Laboratory, USA
Pavlos Kollias
Brookhaven National Laboratory, USA
and Stony Brook University, USA
1Progress in Understanding and Parameterizing Fast Physics in Large‐Scale Atmospheric Models
Yangang Liu1 and Pavlos Kollias1,2
1Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
2School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
ABSTRACT
This introductory chapter discusses the atmospheric subgrid processes – collectively called “fast physics” or “fast processes” – and their parameterizations in large‐scale atmospheric models. It presents a brief historical progression of the parameterization of fast processes in numerical models. Despite great efforts and notable advances in understanding, progress in improving fast physics parameterizations has been frustratingly slow, the underlying reasons for which are explored. To guide readers, this chapter describes the main objectives and scope of this book and summarizes each chapter.
1.1 FAST PHYSICS AND PROGRESS OF PARAMETERIZATION DEVELOPMENT
Large‐scale atmospheric models are integral components of weather and climate models. Ongoing developments in high‐resolution modeling (i.e., global storm‐resolving models (GSRMs; Stevens et al., 2019); Energy Exascale Earth System Model (E3SM; Rasch et al., 2019); and large‐eddy simulations (LES; Gustafson et al., 2020)) have resulted in ultra high‐resolution numerical simulations of atmospheric systems. Despite these advancements, coarser resolution large‐scale models remain our main modeling capability for future climate predictions. Many atmospheric processes and phenomena that influence Earth's weather and climate occur at spatiotemporal scales that are too small to be resolved in these large‐scale atmospheric models and must be parameterized – approximately represented by the variables that can be resolved by the model grids. In this book, we refer to this array of parameterized subgrid processes and phenomena collectively as “fast physics” or “fast processes” for convenience, including radiative transfer, aerosol/cloud physics, convection, boundary layer processes, gravity wave (GW), and land‐atmosphere interactions.
While early parameterizations of fast physics used simple and often empirical or ad hoc relationships (e.g., the Kessler bulk parameterization for representing cloud microphysical processes; Kessler, 1969), later parameterization development was concerned about building conceptual models with increasingly detailed physical processes by leveraging theoretical analysis, observations, and/or detailed process modeling studies.
Furthermore, parallel to the continuing improvement/development of parameterizations for individual fast processes, there has been growing interest in studying and understanding interactions/couplings among different processes. Significant progress has been made and several promising approaches have emerged since late 1900s and early 2000s. Figure 1.1 illustrates the approximate timelines in developing fast physis parameterizations in context of the conventional parameterizations that target individual processes as well as several unifying efforts that addresses multiple processes together.
Figure 1.1 Schematic of the approximate timelines of development of fast physics parameterizations. Conventional parameterizations are focused on individual fast processes. The four lines of unifying efforts (PDF‐Based High Order Closure, Eddy Diffusivity and Mass Flux, Super/Ultra Parameterization, and Machine Learning Parameterization) aim to unify the representation of more than two physical processes. Top‐down approaches borrow holistic ideas that have been scattered in various disciplines (e.g., nonlinear systems dynamics, statistical physics, information theory, self‐organization, networks, and pattern formation).
Despite remarkable efforts and increasing recognition of the importance of these fast processes over the past few decades, progress remains frustratingly slow in improving their representation in models. As a result, their impacts on future climate predictions remain poorly understood and highly uncertain. The slow progress is perhaps best attested by the historical lack of change in the ranges of climate sensitivity across models from the celebrated 1979 US National Research Council report (Charney et al., 1979) to the latest (6th) Coupled Model Intercomparison Project (CMIP6) results used in the Intergovernmental Panel on Climate Change (IPCC) report (Figure 1.2). Deficient fast physics parameterizations, especially those related to clouds, have been thought to be primarily responsible for the stubborn large spread of model climate sensitivity (Meehl et al., 2020; Zelinka et al., 2020). Aerosol climate forcing in climate models has been fraught with similarly unchanged uncertainty (for details refer to Chapter 3 of this book).
Figure 1.2 Historical values of equilibrium climate sensitivity (ECS) and transient climate response (TCR). Source: Adapted from Meehl et al. (2020), which can be consulted for details on the data sources and definitions.
The slow progress can be attributed to two overarching types of complexities (also see Jakob, 2010; Randall, 2013). The first lies in the “4M‐2N complexities” inherently accompanying the atmosphere and associated physical processes (Figure 1.3). Briefly, fast processes and especially those cloud‐related ones involve multibody (sub)systems with numerous particles of different sizes and shapes, in which multiple physical processes (multiphysics) occur over a wide range of spatiotemporal scales (multiscale) and interact with one another, and manifest themselves in a variety of cloud types such as cumulus and stratiform clouds (multitype). The equations describing these processes are often highly nonlinear and exhibit non‐Gaussian statistics (Lovejoy & Schertzer, 2010).
Figure 1.3 Schematic to illustrate the atmospheric scale hierarchy and involved “4M‐2N complexities.” Together with the “operational complexity” discussed in the text, these science complexities have posed and will continue to pose challenges to model development in general and fast physics parameterizations in particular. Source: Leonardo da Vinci/Wikimedia Commons and NASA/Wikimedia Commons/Public Domain.
The other inherent complexity lies with that model development involves an iterative cycle of developing parameterizations, implementing and evaluating parameterizations against observations to identify potential parameterization deficiencies and further improvement. This iterative procedure calls for an organic integration of the key components involved ranging from modeling to measurements, which in turn demands effective coordination of expertise in distinct areas. However, effective coordination and collaboration across different disciplines and institutions are not trivial, and such an “operational complexity” adds another layer of technological and social challenges in virtually every step of model development. The issue will become more acute as the field is moving toward more emphasis on process interactions with ever‐increasing data volumes and model resolutions. To echo Jakob (2010), “… acceleration in model development can only be achieved by significantly strengthening these weak links through additional research and better coordination across existing programs.”
1.2 OBJECTIVES AND SCOPE OF THE BOOK
The objectives of this book are threefold. First, to survey advances in understanding of key fast processes and their parameterization developments (Part I). In particular, Part II of this book is uniquely devoted to unifying efforts. Second, unlike most review articles or the book by Stensrud (2007) on fast physics parameterizations, this book includes discussions on measurement techniques and studies that use observations for model evaluation and thus covers approaches to addressing the weak link in the iterative loop of model development. Third, by surveying the recent advances in key areas, we hope to reveal new challenges, opportunities, and directions for future research.
It is worth noting that the related literature is enormous and that the selection of the material in this text is nonexhaustive and likely biased to the authors' own research interests. On the other hand, books focusing on fast physics parameterizations are rare; the only one we are aware of is Stensrud (2007), which is primarily on conventional parameterizations of individual fast processes in numerical weather prediction (NWP) models. Bringing together modeling and measurements with a common goal of parameterization development and evaluation and including multiple unifying efforts are unique to this book.
1.3 BOOK STRUCTURE AND SUMMARY OF CHAPTERS
Fast physics in large‐scale atmospheric models involves multiple processes that occur over a wide range of spatiotemporal scales. Progress has been made on many fronts and new promising directions of research are emerging. To reflect and synthesize the multiple facets involved, this book is divided into three parts. Part I deals with the major subgrid processes, with eight chapters (Chapters 2–9) covering different fast processes. Beyond conventional treatments, some promising approaches have recently emerged to unify the treatment of (some) processes and thus allows for consideration of process interactions. Part II is devoted to such unifying efforts, with four chapters (Chapters 10–13) that each cover a different endeavor. Part III is devoted to measurements, model evaluation, and model‐measurement integration, with four chapters (Chapters 14–17) that focus on satellite and airborne remote sensing measurements, surface‐based remote sensing measurements, in situ and laboratory measurements, and model evaluation and model‐measurement integration, respectively.
1.3.1 Process Studies and Parameterizations
Essential to the Earth's climate and weather and understanding climate change is the understanding and representation of the solar (shortwave) and terrestrial (longwave) radiation and of radiative transfer processes such as absorption, and scattering. In Chapter 2, Gu and Liou present the fundamentals of radiative transfer and its interactions with the atmosphere, and summarize the commonly used radiative transfer parameterization schemes in atmospheric models. Also discussed are several more advanced topics in the study of the atmospheric radiation, including cloud vertical overlapping, cloud horizontal inhomogeneity, and 3D radiative transfer in both the cloudy atmosphere and over complex rugged land surfaces such as mountainous terrains. In particular, the chapter highlights that the current commonly used radiation schemes normally represent 1D transport in the vertical direction, although radiative transfer in 3D atmosphere and surfaces could play an important role in determining the radiation budget and radiative heating at the top of the atmosphere, at the surface, and within the atmosphere. Both horizontal and vertical subgrid scale inhomogeneities and 3D radiative transfer may substantially influence the radiative transfer within clouds and cloud‐radiation interactions, suggesting the need for further investigation and for improving their representations in models.
Atmospheric aerosols are suspensions of solid particles or liquid droplets in the air. Aerosols contain multiple compositions, exhibit various morphologies, and span a few orders of magnitude in sizes from a few nanometers to tens of micrometers. Aerosol radiative effects constitute one of the largest uncertainties in climate projection, and the large spread of simulated values among general circulation models (GCMs) can be traced to different representations of aerosol processes, including emissions, transport, formation and removal, and aerosol‐cloud interactions. In Chapter 3, Liu provides an overview of atmospheric aerosols and their climatic impacts through both aerosol direct effects on radiation (aerosol‐radiation interactions) and aerosol indirect effects (aerosol‐cloud interactions). The authors focus on addressing topics related to three aerosol‐related questions: (1) How are aerosol properties and processes as well as aerosol‐cloud interactions represented and compared in current GCMs? (2) What are the major assumptions, simplifications, and weaknesses of the current representations? (3) Why are there large uncertainties in the aerosol climate effects from GCMs? Several future directions are highlighted.
Although entrainment of surrounding dry air into clouds, subsequent turbulent mixing processes, and their microphysical influences have been known to be essential in determining cloud microphysical and related properties for some time, theoretical understanding of these processes is still far from complete, and their parameterizations in atmospheric models are in their infancy. In Chapter 4, Lu, Liu, Xu, Gao, and Sun discuss these issues in shallow clouds (cumulus and stratocumulus clouds), focusing on two critical yet understudied aspects: entrainment‐mixing mechanisms and entrainment rate. Different conceptual models of entrainment‐mixing mechanisms are reviewed, and latest studies on unifying microphysical measures to quantify different entrainment‐mixing mechanisms are presented. Approaches for estimating fractional entrainment rate in cumulus clouds are summarized; relationships of entrainment rate to internal cloud properties (e.g., vertical velocity) or external properties (e.g., relative humidity in environment) are discussed as plausible parameterizations. Three approaches for estimating entrainment velocity in stratocumulus clouds are also discussed. Several topics are highlighted for future research, e.g., the connection between entrainment rate, entrainment‐mixing mechanisms, and relationships to other factors (e.g., rain initiation, detrainment, spectral shape of cloud droplet size distributions, entrained aerosols, and environmental relative humidity).
Following the discussion on entrainment in shallow cumulus clouds and its role in shallow convection parameterization, Donner turns to deep convection from the perspective of large‐scale flows in Chapter 5. The chapter begins with discussing the effects of convection on large‐scale flows in which it is embedded, follows with strategies for solving the problem of cumulus parameterization, and concludes with a brief overview of interactions between convection and momentum, chemistry, tracers, cloud microphysics, and aerosols. Emphasized are the roles of convective vertical velocities in treating aerosol‐cloud interactions and cloud microphysics related to cloud feedbacks. Major deficiencies in existing parameterizations are discussed, including interactions between deep convection and aerosols, convection‐chemistry interactions, understanding and representation of convective organization, and knowledge of convective‐scale pressure‐gradient forces in treating effects of convection on momentum fluxes. Limitations of mean‐state perspectives and the widely used quasi‐equilibrium assumption are discussed. Also touched on are connections with other topics (e.g., scale awareness, higher‐order closure, multiscale modeling frameworks and high‐resolution models without conventional deep convection parameterizations, shallow convection, boundary‐layer processes, and gravity waves) detailed in other chapters.
Besides convective clouds, stratiform clouds including stratus and stratocumulus clouds constitute another critical component of the atmospheric system that significantly affects climate and has long been the subject of active research from many perspectives. In Chapter 6, Dong and Minnus provide an overview of such clouds, with a focus on what we have learned from observational studies in terms of improving their parameterization in atmospheric models. Stratus and stratocumulus cloud properties and their importance are discussed based on measurements from trained surface observers, satellite and ground‐based remote sensors, and aircraft field campaigns. The processes that determine the variations in stratocumulus properties and govern where and when they occur are discussed, along with such factors as aerosols, radiation, and humidity. Retrieval methods used for extracting information about stratus and stratocumulus clouds from satellite‐ and ground‐based sensors are also briefly reviewed, with an emphasis on the knowledge learned for improving understanding and parameterizations of such clouds in large‐scale models. Unique consistency between the early trained observers and the state‐of‐the‐art technologies is demonstrated; synergy of different observational platforms is highlighted for future investigation. Emerging but understudied phenomena are summarized, including the impact of low‐level temperature advection, veil clouds developing at the top of the marine boundary layer in areas of open‐cell and unorganized cellular convection, the role of gravity waves in the subtropical jet stream in initiating Pocket of Cells in some closed‐cell stratocumulus over the southeast Pacific, and effects of land‐sea breezes. Outstanding issues in profiling marine boundary layer cloud and drizzle microphysical properties are highlighted, including the need for incorporating cloud‐top entrainment, drizzle, and vertical and horizontal inhomogeneities to address the issue of nonadiabatic multispectral retrievals.
As a layer between the ground surface and the free troposphere, the planetary boundary layer (PBL) is often turbulent and particularly important, because the majority of biota (including humans) and climatically important low clouds like stratocumulus and shallow cumulus reside. Even deep convection is highly related to the properties of the plumes or thermals originating in the PBL. In Chapter 7, Ghate and Mechem introduce the PBL structure and the commonly used theoretical approaches for investigating the PBL. A hierarchy of models for representing the boundary layer is presented, including mixed‐layer models, first‐order closure, 1.5‐order TKE closure, and higher‐order closure approaches. Challenges for evaluating the emerging advanced schemes (high order, PDF‐based, or EDMF) are also discussed in context of the inherent needs for observations of joint PDFs of vertical air motion and thermodynamic variables. The discussion emphasizes the buoyancy‐driven convective boundary layer but briefly mentions impacts of shear and clouds. The chapter concludes with a brief historical context and future outlook for representing the boundary layer in large‐scale atmospheric models. To some extent, this chapter can be viewed as an introduction to Chapters 13 and 14 where the PDF‐based and EDMF schemes are detailed.
Although the focus of this book is on atmospheric processes, the weather and climate system consists of other subsystems that strongly interact with the atmosphere over a wide range of spatiotemporal scales. In particular, various surface processes are fundamental to the exchange of heat, water, and momentum between the surface and the atmosphere through PBL. Thus, modeling land‐surface processes has been an integral component of atmospheric models. In Chapter 8
