Cloud Microphysics in Global Cloud Resolving Models

Figures & data

Table 1. List of acronyms of models, satellites, and instruments.

Acronym	Long name
AMSR-E	Advanced Microwave Scanning Radiometer-EOS
AROME	Applications of Research to Operations at Mesoscale
ARPEGE-NH	Action de Recherche Petite Echelle Grande Echelle-NonHydrostatic version
CALIPSO/CALIOP	Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations/Cloud-Aerosol LIdar with Orthogonal Polarization
CAM	Community Atmosphere Model
COSP	The cloud feedback model intercomparison project Observation Simulator Package
CPR	Cloud Profiling Radar
CRTM	Community Radiative Transfer Model
DFSM	Double Fourier Series Model
EarthCARE	Earth Clouds, Aerosols and Radiation Explorer
ECHAM	European Centre Hamburg model
ECSIM	EarthCARE Simulator
FV3	Finite-Volume Cubed-Sphere Dynamical Core
GCOM-C/SGLI	Global Change Observation Mission-Climate/ Second-generation Global Imager
GEOS	Goddard Earth Observing System
GOES	Geostationary Operational Environmental Satellite
GPM/DPR/GMI	Global Precipitation Measurement mission/Dual-frequency Precipitation Radar/GPM Microwave Imager
GRIST	Global-to-Regional Integrated forecast SysTem
ICON	ICOsahedral Non-hydrostatic atmospheric model
IFS	Integrated Forecasting System
MIROC	Model for Interdisciplinary Research on Climate
MODIS	Moderate Resolution Imaging Spectroradiometer
MSSG	Multi-Scale Simulator for the Geoenvironment
MPAS	Model for Prediction Across Scales
MTSAT	Multi-functional Transport Satellite
NICAM	Nonhydrostatic ICosahedral Atmospheric Model
NOAA/AVHRR	National Oceanic and Atmospheric Administration /Advanced Very High Resolution Radiometer
POLARRIS	POLArimetric Radar Retrieval and Instrument Simulator
RTTOV	Radiative Transfer for the TIROS Operational Vertical Sounder
SAM	System for Atmospheric Modeling
SCALE	Scalable Computing for Advanced Library and Environment
SDSU	Satellite Data Simulator Unit
TRMM/PR/VIRS/TMI	Tropical Rainfall Measuring Mission/Precipitation Radar/ Visible Infrared Scanner/ TRMM Microwave Imager
UM	Unified Model
WRF	Weather Research and Forecasting Model

Fig. 1 Typical spatiotemporal scales of some examples of weather and climate phenomena. The numerical weather prediction model (NWP), general circulation model (GCM), and global cloud resolving model (GCRM) cover different but partially overlapping scales.

Fig. 2 Cloud images calculated by the GCRMs in the DYAMOND project and from the geostationary satellite Himawari-8 (After Fig. 2 in Stevens et al., 2019). From left to right: IFS with a horizontal resolution of 4, 9 km, and NICAM (top row); ARPEGE-NH, Himawari-8, and ICON (second row); FV3, GEOS5, and UM (third row); and SAM and MPAS (bottom row).

Table 2. List of GCRMs. Short names and references to the model-frameworks and recent settings are summarized. Cloud microphysics schemes in the DYAMOND GCRMs refer to the schemes used for the DYAMOND project. The subscripts v, c, r, i, s, and g indicate vapor, cloud water, rain, cloud ice, snow, and graupel categories, respectively.

Model name	References	Cloud microphysics schemes
ARPEGE-NH	Bubnová et al. (1995) Voldoire et al. (2017)	SMB with five categories (v, c, r, i, and s) Roehrig et al. (2020)
FV3	Lin and Rood (1997), Lin (2004), Zhou et al. (2019)	SMB with six categories (v, c, r, i, s, and g) Lin et al. (1983), Zhou et al. (2019)
GEOS5	Putman and Lin (2007), Putman and Suarez (2011), Arnold et al. (2020)	SMB with six categories (v, c, r, i, s, and g) Lin et al. (1983), Zhou et al. (2019)
GRIST	Zhang et al. (2019b; 2020b)	SMB with six categories (v, c, r, i, s, and g) Lin et al. (1983), Zhou et al. (2020)
ICON	Zängl et al. (2015), Hohenegger et al. (2020)	SMB with six categories (v, c, r, i, s, and g) Lin et al. (1983), Baldauf et al. (2011)
IFS	Malardel et al. (2016), Dueben et al. (2020), Wedi et al. (2020)	SMB with five categories (v, c, r, i, and s) Forbes et al. (2011)
MSSG	Takahashi et al. (2008), Sasaki et al. (2016), Onishi et al. (2019)	Bin microphysics for liquid particles and SMB for ice categories (v, liquid, i, s, g) Onishi and Takahashi (2012)
MPAS	Skamarock et al. (2012)	Hybrid of SMB and DMB with six categories [v, c, r, i, s, and g (the number concentrations of rain and cloud ice are predicted with prescribed aerosols)] Thompson et al. (2008) modified for WRF 3.8.1. (WRF User's Guide, n.d.)
NICAM	Satoh et al. (2014), Kodama et al. (2021)	SMB with six categories (v, c, r, i, s, and g) Lin et al. (1983), Tomita (2008b), Roh and Satoh (2014)
SAM	Khairoutdinov and Randall (2003), Satoh et al. (2019)	SMB with six categories [v, c, r, i, s, and g (ice categories are diagnosed)] Khairoutdinov and Randall (2003)
UM	Wood et al. (2014), Walters et al. (2019)	SMB with four categories [v, c, r, and i (cloud number concentration is diagnosed with climatological distribution of aerosols)] Wilson and Ballard (1999), Boutle et al. (2014), Walters et al. (2019)

Fig. 3 Cloud images calculated by the NICAM with horizontal resolutions of 14, 3.5 and 0.87 km (after Kajikawa et al., 2016). Zoom images of a tropical cyclone are shown in the right column.

Fig. 4 A schematic image of the comparison between models and satellites using satellite simulators. From Masunaga et al. (2010), © American Meteorological Society. Used with permission.

Fig. 5 Sample images of (a) the default NICAM with the global quasi-uniform icosahedral grid system, (b) stretched NICAM, and (c) diamond NICAM (after Uchida et al., 2017, © American Meteorological Society. Used with permission.).

Fig. 6 Comparison of the vertical profiles of annual mean ice water content (IWC) [mg m-3] from (a) CloudSat satellite observations [2B-CWC-RO product (Austin et al., 2009; Austin & Stephens, 2001)], (b) NSW6 in NICAM.12, (c) NSW6 in NICAM.16, and NDW6 in NICAM.16. The solid lines represent 273-K isotherms. The global simulation data with horizontal resolution of 14 km and 38 vertical layers were provided by courtesy of C. Kodama and A. T. Noda.

Fig. 7 Comparison of outgoing longwave radiation at the top of the atmosphere (OLR) from CERES satellite observations (solid lines with points) and NICAM simulations with various versions of NDW6 (solid lines) (after Satoh et al., 2018).

Fig. 8 The vertical profiles of (a) downward shortwave radiation and (b) upward shortwave radiation (after Seiki et al., 2014). Thick lines indicate observations by radiometer sonde, thin solid lines indicate NICAM simulations using NDW6 with spherical SSPs, and thin dashed lines indicate NICAM simulations using NDW6 with nonspherical SSPs. The dashed rectangles indicate the location of a cirrus layer.

Fig. 9 Comparison of (a) OLR and (b) outgoing shortwave radiation at the top of the atmosphere (OSR) from CERES satellite observations (solid lines) and NICAM.16 simulations with various settings of cloud and radiation coupling. NSW6 uses full coupling between cloud and radiation (dashed lines), NSW6-Mie assumes variable effective radii but spherical SSPs (long dashed short dashed lines), and NSW6-Mie-ReFIX assumes constant effective radii and spherical SSPs (dotted lines). The global simulation data were provided by courtesy of C. Kodama.

Fig. 10 Cloud microphysical processes and cloud interaction among water classes (after Satoh et al., 2018). Solid lines indicate the processes that change the number and mass concentration of hydrometeors, while dashed lines indicate the processes that change only the mass concentration of hydrometeors. Hom/het indicates homogeneous/heterogeneous, respectively.

Table 3. Dependences of cloud-related processes on bulk cloud microphysical parameters.

Processes	Dependences	References	Assumptions
Auto-conversion (c → r)	$\rho \frac{\mathrm{\partial }{q}_c}{\mathrm{\partial }t}\propto (\rho q_c{)}^2{\overline{D_c}}^6$	Seifert and Beheng (2001)	Long’s kernel (Long, 1974) is used for the collection efficiency.
Auto-conversion (i → s)	$\rho \frac{\mathrm{\partial }{q}_i}{\mathrm{\partial }t}\propto \rho q_i{n}_i{\overline{D_i}}^4$	Seiki et al. (2015a)	Cloud ice is assumed to be spherical and sufficiently small to satisfy Stoke’s law. $v_{t,i}\propto D^2$
Riming (c → j) (j = i, s, g)	$\rho \frac{\mathrm{\partial }{q}_j}{\mathrm{\partial }t}\propto \rho q_c{n}_j{\overline{D_j}}^{2+d}$	Seiki and Roh (2020)	Power law is assumed between vt and D as follows: $v_t\propto D^d$with d typically ranging from 0.1–0.5 (e.g. Locatelli & Hobbs, 1974).
Condensation Evaporation Vapor Deposition Sublimation Melting	$\rho \frac{\mathrm{\partial }{q}_j}{\mathrm{\partial }t}\propto n_j\overline{D_j}$	Igel et al. (2015) Seiki and Roh (2020)	Spherical shape is assumed for ice particles.
Immersion freezing	$\rho \frac{\mathrm{\partial }{q}_r}{\mathrm{\partial }t}\propto \rho q_r{\overline{D_r}}^3$	Bigg (1953)	The equation is derived from experiment.
Cloud optical thickness τc	${\tau }_c\propto n_c{\overline{D_c}}^2$	Liou (2002)	Extinction efficiency is assumed to be approximately constant (2) for most particles.
Visible cloud albedo Rc	$R_c=\frac{\sqrt{3}\left(1-g\right){\tau }_c}{2+\sqrt{3}\left(1-g\right){\tau }_c}$	Liou (2002) Fu (2007)	Two-stream approximation is used with the asymmetry factor g typically ranging from 0.75–0.9 (e.g. Fu, 2007)
Infrared emissivity of cirrus εIR	${ϵ}_{IR}=1-\mathrm{e}\mathrm{x}\mathrm{p}\left(-0.79{\tau }_c\right)$	Fu and Liou (1993)	The function is derived by fitting to results from a numerical model with experimental data.

Table 4. Optical sensors and corresponding sensitive physical parameters.

Optical sensors	Satellites/instruments	Physical parameters
Visible/near-infrared imager (0.3–4 μm)	Geostationary meteorological satellites (e.g. GOES, Himawari, Meteosat), NOAA/AVHRR, Aqua, Terra/MODIS, GCOM-C/SGLI, TRMM-VIRS	Cloud optical thickness, Cloud-top effective radius
Infrared imager (8–12 μm)		Cloud-top temperature, Ice cloud optical thickness Ice cloud-top effective radius
Microwave imager (10–20 GHz)	Aqua/AMSR-E, GCOM-W/AMSR2, TRMM/TMI, GPM/GMI	Liquid water path, Precipitation flux,
Microwave imager (80–90 GHz)		Ice water path
Lidar (532, 1064 nm)	CALIPSO/CALIOP	Thermodynamic phase, Ice shape, Optical thickness
Cloud radar (94 GHz)	CloudSat/CPR	Ice water content, Liquid water content
Precipitation radar (13.6, 35.5 GHz)	TRMM/PR, GPM/DPR	Precipitation flux, Hydrometeor class

Fig. 11 Breakdown of OLR change under global warming simulated by the NICAM (after Noda et al., 2016). An OLR change ΔF is attributed to a change in emissivity Δε, a change in cloud-top temperature ΔT_CT, and a change in clear sky radiation ΔF^CLR. The breakdown was calculated by cloud size. Sum shows the summation of the contributions and the difference between ΔF and Sum indicates the error of the analysis.

Fig. 12 The cloud-top and rain-top diagram using Ku-band radar echo and infrared brightness temperature from (a) satellite observations, (c) NSW6 in NICAM.12, and (e) NSW6 in NICAM.16. CFAD using Ku-band radar echo from (b) satellite observations, (d) NSW6 in NICAM.12, and (f) NSW6 in NICAM.16. Here, simulated results were processed by a Joint-Simulator. From Roh et al. (2017), © American Meteorological Society. Used with permission.

Fig. 13 Joint probability density function of the depolarization ratio δ and the ratio of attenuated backscattering coefficients for successive layers x from (a) satellite observations, (b) NSW6 in NICAM.16, and (c) NDW6. Low-level clouds behind a frontal cloud system were sampled over the Southern Ocean. Signals of supercooled liquid water are highlighted by circles. From Roh et al. (2020), © American Meteorological Society. Used with permission.

Fig. 14 Observation geometry of the EarthCARE satellite (from https://www.eorc.jaxa.jp/EARTHCARE/museum/poster_gallary.html, ©JAXA).

Fig. 15 Overview of the ground observations used in the ULTIMATE project around the Tokyo metropolitan area.

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