Multi-sensor diagnostics of mixed-phase
cloud microphysical processes
with implication for EarthCARE
Kentaroh Suzuki (University of Tokyo)
Thanks to: Takashi M. Nagao, Aya Murai, Rino Maki
EarthCARE Prelaunch Science & Validation Workshop @ESA-ESRIN, Frascati
November 13-17, 2023

Sensitivity of aerosol forcing to model cloud physics
AR50CMIP6Compatible with historicaltemperature record(Bellouin et al. ʼ20)

ERFari+aci [Wm-2]

-1
NICAM
CMIP5-2

AR6-3

Bellouin
MIROC5: Jing et al. (JCʼ19)
MIROC6: Michibata & Suzuki (GRLʼ20)
NICAM: Goto et al. (GMDʼ20)
CMIP6: Smith et al. (ACPʼ20)
CMIP5: Zelinka et al. (JGRʼ14)
Bellouin et al. (Rev. Geophysʼ20)-4-5
MIROC5

MIROC6-D

MIROC6-P

DIAG Precip PROG Precip

Obs. Infer.
TC80
BR68
BH94
KK00
LD04
SB06

Differing model physicsof precipitation

CONCLUDING COMMENTS. The two principal sources of uncertainty in climate projections aredue to uncertainties surrounding aerosol radiative
forcings and the uncertainties attached to cloudproperties to global clouds. Profile data on aerosols
now offer new insights into how they are transportedand mixed in the atmosphere and new insights into
aerosol radiative forcing, and they have directly
assisted in routine assimilation of aerosol informa-Satellite-based diagnostics of precipitation processes
Reff=5-10µm

Reff=20-25µm

al. (ʼ18)(see also
Nakajima
et al. ʼ10; Suzuki et al. ʼ10)
FIG. 7. Observed composite relations betweenStephens
optical depthet
measured
downward
from cloud
top and radarreflectivity derived from 4 years of data separate for (top) oceanic clouds and (bottom) continental clouds andfor (left) cloudy/nonraining cases and (right) drizzle/rain cases. The reflectivity range for clouds is approximatelyeeff
Z < −15 dBZ, for drizzle −15 < Z < 0 dBZ, and for rain Z > 0 dBZ; these profiles reveal information about cloudto-rain transitions in clouds. The schematics are for illustration and correspond to each composite profile
result. The color scale refers to reflectivity counts as a percentage of total reflectivity occurrence within eachgiven optical depth bin. Over land the drizzle mode is mostly missing (adapted from Takahashi et al. 2017).

n Precipitation process “probed” in multi-variate global composites (Z , COT, R ):Contoured Frequency by Optical Depth Diagram (CFODD)n Serves as a process-oriented model diagnostic tool for CRMs (Suzuki et al. ʼ11; ʼ13) &GCMs (e.g. Suzuki et al. ʼ15; Jing et al. ʼ17, ʼ19; Michibata & Suzuki ʼ20)
AMERICAN METEOROLOGICAL SOCIETY

MARCH 2018| 577

Geophysical R

Constraint
onLetters
model cloud physics
links to ACI forcing
10.1029/2018GL079956
GeophysicalResearchFor Reff=5-10µm
Satellite Obs

MIROC5 BR68

MIROC5 KK00
Wetscaveng

Reflectivity [dBZ]

Reflectivity [dBZ]

Reflectivity [dBZ]
Jing and Suzuki (GRL ʼ18)n Satellite-based process constraint on model
physicsaerosol
helps
identify
Figure 2.cloud(a) The anthropogenic
indirecteffect from estimate
those from the default simulationsof Model for Interdisciplinary Res
compensating model errors in aerosol-cloud-radiationinteraction
autoconversion schemes, as well as those from the ﬁxed-Nc wet-sca
n Limited to liquid-phase clouds with “static”
accumulations
globaldata
IPCC AR5
indicates the uncertainty ofrange,
while thosefor the model(b and c) The differences in the zonal annual mean AOD and LWP, res-> How to extend into mixed-phase clouds?
model conﬁgurations.
-> How to add “dynamical” context to process
diagnostics?

Use of cloud phase information from SWIR
[Assumption]- Plane-Parallel of Liquid+Ice- Deriving COTliq,ice and CERmix- Retrieving mixed-phase state

COTLIQ ←→COTICECERLIQ
CERICE

CER
COTICOTF

Nagao and Suzuki (ESS ʼ21)
Applicable to JAXA GCOM-C/SGLI & NASA Aqua/MODIS

𝑪𝑶𝑻𝑰𝑪𝑬𝑭𝑰𝑪𝑬 =𝑪𝑶𝑻𝑳𝑰𝑸 + 𝑪𝑶𝑻𝑰𝑪𝑬𝑪𝑬𝑹𝑴𝑰𝑿= 𝑪𝑬𝑹𝑳𝑰𝑸 𝟏 − 𝑭𝑰𝑪𝑬 + 𝑪𝑬𝑹𝑰𝑪𝑬𝑭𝑰𝑪𝑬n The liquid-ice light absorption difference at SWIR is exploited to retrieve ice COT fraction
ranging b/w liquid & ice in a temperature-independent manner, with (total) COT & CERn Measurement principle is different from CALIPSO lidar
n CALIOP “looks at” the vicinity of cloud top (COD <~ 3)n SWIR “penetrates” somewhat deeper inside the cloud layer (COD >~ 10)n Their combination can characterize vertical phase stratification (Nagaoʼs talk)Global occurrences of “phase stratification”
Liq
CALIPSO=Liq
MODIS=LiqLiq

Ice

CALIPSO=Liq
MODIS=Ice

Ice
CALIPSO=Ice
MODIS=Liq

Liq

Ice

How do different phase combinations relate to precipitation?

CALIPSO=Ice
MODIS=IceLinking the phase stratification to radar profile
LiquidIce

Ice

MODIS (~”Cloud column”)

LiquidIce

LiquidIce

5-10,Liq/Liq

5-10,Liq/Ice

5-10,Ice/Liq

5-10,Ice/Ice

Suzuki & Nagao (In prep.)

03.5

0

3.5

20

320

320

320

340

2.5

40

2.5

40

2.540

2.560

260

260

ICOD

3.5

ICOD0ICOD

3.5

260

2-20020
Reflectivity [dB]-20020
Reflectivity [dB]-20020Reflectivity [dB]-20020
Reflectivity [dB]15-25,Liq/Liq

15-25,Liq/Ice

15-25,Ice/Liq

15-25,Ice/Ice

3.5

0

3.5

0

3.5

0

3.5

20

320

320

320

340

2.540

2.540

2.540

2.560

260

260

260

2-20
020
Reflectivity [dB]-20020
Reflectivity [dB]-20020
Reflectivity [dB]

ICOD

0ICOD

15-25µm

0ICOD

5-10µmICOD
ICOD Depth
Cloud Optical
Depth Cloud Optical

LiquidEffective Radius (CERmix)

CALIPSO (~”Cloud top”)-20020Reflectivity [dB]n Precipitation characteristics vary with both cloud-top particle size & “cloud-column” phasen “Cloud-top” phase appears to have a weaker effect on precipitation

How does precipitation vary with cloud phase fraction?
0.4-0.6 (Mixed)

0.8-1 (Ice)

5-10,Ice/Liq

5-10,Ice/Mid

5-10,Ice/Ice

3.5

0

3.5

0

3.5

20

320

320

340

2.5

40

2.5

40

2.560

260

260

2ICOD

0ICOD

-20020Reflectivity [dB]-20020Reflectivity [dB]-20020Reflectivity [dB]15-25,Ice/Liq

15-25,Ice/Mid

15-25,Ice/Ice

03.50

3.503.520

320

320

340

2.540

2.540

2.560

260

260

2-20020
Reflectivity [dB]-20020Reflectivity [dB]n Precipitation continuously varies with column COT phase ration More “icy” clouds tend to produce precipitation more efficiently

ICOD

ICOD

0-0.2 (Liquid)

ICOD

15-25µm

ICOD

5-10µmTotal Optical Depth

Effective Radius (CERmix)

CALIPSO = ICE

Total Optical Depth

MODIS ice COT ratio:-20020
Reflectivity [dB]

Suzuki & Nagao (In prep.)Application to model evaluation - Preliminary
Ice COT ratio:

0.00-0.25

0.25-0.5

0.5-0.75

For Reff=12-18µm
0.75-1.0

MIROC6DIAG prcp
MIROC6PROG prcp

Satelliten “Phase dependence” of precipitation varies with model precipitation
physics (DIAG vs PROG precipitation)n PROG shows a larger suppression of precipitation in more liquid-containing
clouds, closer to satellite statistics: Implication for cloud-phase feedback?

60
–30

1812–2060
H.10Takahashi
et al.
–1010020–30
Reflectivity (dBZ)–20–10100
10Reflectivity (dBZ)

Perspective of EarthCARE: Adding dynamical context
02020

= 0.7
s–1
ww
= 0.0
mm
s–1
5–10 µm
10–15 µm
15-20 µm

4040(d) 0(b)

01.0
s–1
ww
==0.3
mm
s–1

Optical Depth (Td)
Optical Depth (Td)Bin-microphysics model

Optical Depth (Td)
Optical Depth (Td)(a)(c)020

2020
4040

6060
6060 in Warm Rain
Land-Ocean
Differences
inWarmRain –10–30
1000
1010 Differences
2020–30–20
101010–30 –20–10 Land-Ocean–20–20 –10–30–10 0 0Reflectivity(dBZ)
Reflectivity(dBZ)
Reflectivity(dBZ)
Reflectivity(dBZ)

2020

1820

Hypothetical schematic
simulator of Haynes et al. (2007) in a form that is adjusted

Optical Depth (Td)

Optical Depth (Td)–1
Figure 9. (a–d) Vertical(c)profiles of radar reflectivity
as ma functionof cloud opticalsolutions
to the size-resolving onw = 0.7
s–1w = 1.0 m s(d)depth obtained as steady-statemodel when four different magnitudes
of updraught velocities are assumed. Different colours
correspond to different ranges of cloud-top effective p00
re = 5–10 µm (red), 10–15 µm (green) and 15–20 µm (blue). [Colour figure can be viewed at wileyonlinelibrary.com].

20smaller (larger) particle sizes correspond to higher (lowto the spectral-bin microphysics
scheme to simulate the radar numberconcentration. Although the profiles somewh
4040
reflectivity profiles in the one-dimensional model. The model is differing assumptions of other parameters such as thenumerically integrated with the initial condition of zero mixing pressures/temperatures and the replenishment tim
ratios of liquid particles60
forthe entire
cloudlayer
until10the steadythe60–30
high/low
dropletnumberconcentration
is a prim
–30
–10
0–20
10–20
20
–10
020
state is achieved under assumptions ofdifferent values of number that controls theshift of the
profiles from non-prec
Reflectivity (dBZ)
Reflectivity(dBZ)
concentrations and updraught velocities. The former producesTakahashi,
precipitating
ones. &The
replenishment
time constan
SuzukiStephens(QJRMS
ʼ17)
Figure9.
(a–d)
Verticalprofilesof
radarreflectivityasafunctionof
cloud
opticaldepthobtainedassteady-statesolutions
tothesize-resolving
different values of the cloud-top effective radius that are used to the time-scale over which the size spectrum oneis
model
when
four
different
magnitudes
of
updraught
velocities
areassumed.
Different
colours
correspondtodifferent
rangesofcloud-top
effectiveparn How can EarthCARE/Doppler
help untangle
thefrom
dynamics-microphysics
coupling?
classify the radar reflectivity
profiles obtained
the model. condensation process
toanassumed
functionchara
re = 5–10 µm (red), 10–15 µm (green) and 15–20 µm (blue). [Colour figure can be viewed at wileyonlinelibrary.com].
The also
model needs
performedto
withthermodynamic
conditions
the assumed value of the droplet number concent
n “Time-dimension”
bevaryingaddedto process
diagnostics
within a reasonable range (see also the Appendix for details of the replenishment time constant has an effect of different

Adding time dimension: Radar+Geostationary satellites
Ongoing work by Rino Maki
CloudSat
Longitude
Match-up

Latituden Match-up defined as ±5km & ±5min
n Himawari-8 data of every 10min in
30min before/after the match-up areused for analysis
n Time derivative of cloud property islinked to vertical cloud profile

CER: Effective radius
COT: Optical thickness
CER, COT

CER, COT

CER, COT

CER, COT

CER, COT

CER, COT

CER, COT

Himawari-8

𝒅𝒍𝒏(𝑪𝑬𝑹)/𝒅𝒕
𝒅𝒍𝒏(𝑪𝑶𝑻)/𝒅𝒕

CloudSatTime-30min-20min-10minMatch-up+10min+20min+30min

Sensitivity of rain process to d(cloud property)/dt
CloudSat+Himawari-8
CER: 5-10μm

Maki et al. (In prep.)

10-15μm

15-20μm

𝚫𝐥𝐧 𝑪𝑶𝑻/𝚫𝒕

𝚫𝑵𝒄 ~ 𝟎Condensation
𝜼 >2

𝚫𝐥𝐧 𝑪𝑬𝑹/𝚫𝒕

𝚫𝑵𝒄 = 𝟎h=5.0

𝚫𝑳𝑾𝑷 ~ 𝟎Coalescence-2 < 𝜼 < -0.5No Rain𝜼≔Rain𝚫𝑳𝑾𝑷 = 𝟎h=-1.0𝚫𝐥𝐧 𝐂𝐎𝐓𝚫𝐭𝚫𝐥𝐧 𝐂𝐄𝐑𝚫𝐭n CER<15µm: Drizzle characteristic is sensitive to time-derivative of cloud propertyn CER>15µm: Drizzle forms independent of temporal change of cloud property

Summary
nTwo pieces of cloud phase information from CALIOP & MODIS
are combined with cloud radar profile to propose precipitation
process diagnostics for mixed-phase clouds
nThe precipitation characteristics are found to vary with bothcloud-top particle size and “cloud-column” phase fraction-> Precipitation occurs more efficiently in more “icy” clouds at given Reff

nApplication to climate model evaluation is tested with MIROC6
to compare the phase-dependence of precipitation process-> Prognostic precip tends to generate the statistics closer to satellite

nCombined use of radar and geostationary satellites enables tolink time-derivative of cloud properties to vertical
microphysical structure-> CFODD statistics are sensitive to d(cloud property)/dt