A lack of reliable estimates of cloud condensation nuclei (CCN) aerosols over oceans has severely limited our ability to quantify their effects on cloud properties and extent of cooling by reflecting solar radiation—a key uncertainty in anthropogenic climate forcing. We introduce a methodology for ascribing cloud properties to CCN and isolating the aerosol effects from meteorological effects. Its application showed that for a given meteorology, CCN explains three-fourths of the variability in the radiative cooling effect of clouds, mainly through affecting shallow cloud cover and water path. This reveals a much greater sensitivity of cloud radiative forcing to CCN than previously reported, which means too much cooling if incorporated into present climate models. This suggests the existence of compensating aerosol warming effects yet to be discovered, possibly through deep clouds

Goren, Tom

Rosenfeld, Daniel

Wang, Minghuai

Yu, Shaocai

Zheng, Youtong

Zhu, Yannian

Caltech Authors - Main

 
www.sciencemag.org/cgi/content/full/science.aav0566/DC1 
 
 
 
 
Supplementary Materials for 
 
Aerosol-driven droplet concentrations dominate coverage and water of 
oceanic low-level clouds 
Daniel Rosenfeld*, Yannian Zhu, Minghuai Wang*, Youtong Zheng, Tom Goren, 
Shaocai Yu* 
 
 
*Corresponding author. Email: daniel.rosenfeld@huji.ac.il (D.R.); minghuai.wang@nju.edu.cn (M.W.); 
shaocaiyu@zju.edu.cn (S.Y.) 
 
 
Published 17 January 2019 on Science First Release 
DOI: 10.1126/science.aav0566 
 
 
This PDF file includes: 
Supplementary Text 
Figs. S1 to S13 
Tables S1 to S3 
 1 
 
Supplementary Text 
Satellite-based estimation of cloud-top radiative cooling rate 
We calculate the CTRC by running the Santa Barbara DISORT Atmospheric Radiative 
Transfer (SBDART) model. The input data come from two sources: MODIS-derived cloud 
properties and reanalysis sounding from NCEP reanalysis. The satellite-retrieved cloud 
quantities include the CTT, CTH, CBH, τ, and re. We use CTT and CTH to revise the reanalysis 
temperature/moisture sounding. By assuming a relative humidity of 100% at the cloud top, we 
can compute the cloud-top water vapor density with the retrieved CTT. The in-cloud temperature 
and moisture soundings are adjusted assuming moist adiabatic profile. The cloud-base values, in 
combination with the reanalysis temperature and moisture at 2 m above the surface level, were 
then used to constrain the sub-cloud layer soundings. Then we assume an inversion-layer depth 
of 200 m and adjust the above-cloud soundings accordingly. Figure S13 shows an example 
illustrating the revision of raw reanalysis soundings and how the revised soundings are compared 
with the radiosonde measurements. The case was on 21 July, 2013, selected during the Marine 
ARM (Atmospheric Radiation Measurement) GPCI (Global Energy and Water Cycle 
Experiment-Cloud System Study-Pacific Cross-section Inter-comparison) Investigation of 
Clouds (MAGIC) field campaign. The upper and bottom panel represent the radiosonde and 
reanalysis data, respectively. From the Fig. S13d and e, the revision (solid lines) markedly 
improves the original reanalysis sounding (dashed lines) as seen from the much better agreement 
with the radiosonde sounding (Fig. 13a and b). As a result, the estimated CTRC from the revised 
reanalysis agrees well with the “ground truth” calculation (Fig. 13c and f) for this case. A 
systematic comparison between satellite-estimated CTRC and the “ground truth” measurements 
for a total of 58 cases during the MAGIC campaign shows a correlation coefficient of 0.91 with 
13% error (35). Such a good performance of this satellite estimation largely benefits from the 
fact that the CTRC is mostly sensitive to cloud-top properties and the free-tropospheric 
sounding, both of which are well constrained by satellite data.   
 
 2 
 
 
Fig. S1. Same as Fig. 3, but for the mean instead of the median values for each bin. 
 3 
 
 
Fig. S2. Same as Fig. S1, but for LTS >18K (A,D,G,J), 14K≤LTS≤18K (B,E,H,K), and 
LTS<14K (C,F,I,L). 
 4 
 
 
Fig. S3. The dependence of median Cf on Nd for intervals of (A) low tropospheric static 
stability; (B) cloud top radiative cooling rate, and (C) Inversion strength at cloud tops, ∆θ; Cloud 
top drop effective radius of each line (i.e., for a given LWP) decreases with increasing drop 
concentrations, and reaches 14 µm at the broken line. The box plot distributions for each bin are 
shown in Figures S8-S10.
 5 
 
 
Fig. S4. Same as Fig. S3, but for LTS >18K (A,D,G), 14K≤LTS≤18K (B,E,H), and LTS<14K 
(C,F,I). 
 6 
 
 
Fig. S5. The cloud fraction as a function of Nd bins for different LWPcore intervals. The box plot 
distributions and number of cases that their median constitute each of the points that compose 
Fig. 1A. The horizontal bars are the medians and the dots are the means. The number of 1x1 
degree areas composing each distribution are given at the top. 
 7 
 
 
Fig. S6. The cloud fraction as a function of Nd bins for different CGT intervals. The box plot 
distributions and number of cases that their median constitute each of the points that compose 
Fig. 1B. The horizontal bars are the medians and the dots are the means. The number of 1x1 
degree areas composing each distribution are given at the top. 
 8 
 
 
Fig. S7. The LWP as a function of Nd bins for different CGT intervals. The box plot distributions 
and number of cases that their median constitute each of the points that compose Fig. 1C. The 
horizontal bars are the medians and the dots are the means. The number of 1x1 degree areas 
composing each distribution are given at the top.
 9 
 
 
Fig. S8. The CRE as a function of Nd bins for different CGT intervals. The box plot distributions 
and number of cases that their median constitute each of the points that compose Fig. 1D. The 
horizontal bars are the medians and the dots are the means. The number of 1x1 degree areas 
composing each distribution are given at the top. 
 10 
 
 
Fig. S9. The cloud fraction as a function of Nd bins for different LTS intervals. The box plot 
distributions and number of cases that their median constitute each of the points that compose 
Fig. S2A. The horizontal bars are the medians and the dots are the means. The number of 1x1 
degree areas composing each distribution are given at the top. 
 11 
 
 
Fig. S10. The cloud fraction as a function of Nd bins for different CTRC intervals. The box plot 
distributions and number of cases that their median constitute each of the points that compose 
Fig. S2B. The horizontal bars are the medians and the dots are the means. The number of 1x1 
degree areas composing each distribution are given at the top. 
 12 
 
 
Fig. S11. The cloud fraction as a function of Nd bins for different ∆θ intervals. The box plot 
distributions and number of cases that their median constitute each of the points that compose 
Fig. S2C. The horizontal bars are the medians and the dots are the means. The number of 1x1 
degree areas composing each distribution are given at the top.
 13 
 
 
Fig. S12. Demonstration of the cloud fraction calculation for a given scene (Feb 19th, 2015, center at 
Latitude:-0.43o, Longitude:-169.18o). The green line represented the histogram of reflectance at 0.64 µm 
where has τ>0. Similarly, the red line represents the histogram of the pixels have 0.64 µm reflectance 
10 -2 10 -1 10 0
0.64 m reflectance
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
N
um
be
r o
f p
ix
el
s
Refl0.64 m
Refl0.64 m( >0)
Refl0.64 m>(Refl0.64 m
min( )
+0.02)
 14 
 
greater than the reflectance that is matched to the minimum detectable τ. For this case, the 
calculated Cf are 0.40 and 0.59 for green and red lines respectively. 
 
Figure S13: Profiles of temperature (a, d), water vapor mixing ratio (b, e), and heating rate (c,f) 
from radiosonde (upper panel) and reanalysis (bottom panel). In d, the vertical red line marks the 
satellite-retrieved cloud top temperature. The solid and dashed lines represent the revised and 
raw reanalysis sounding.  
(a) (c) (b) 
(d) 
(e) 
(f) 
 15 
 
Table S1. Same as Table 2, but for Nd. Multiple regression with polynomial fit of the second order for Cf 
with Log10(Nd), ∆θ and CTRC for different intervals of CGT.  The values in the 3 right columns are the 
partial R2, or fraction of explained variability of Cf by Nd, ∆θ and CTRC. Note that Nd explains nearly 
3/4 of the variability in CRE for a given CGT, as shown in Fig. 3D.Table S2. 
CGT [m] Total R2 RMS Error log (Nd) ∆θ CTRC 
Cf  [-]  K Wm-2 
0-150 0.932 0.035 0.719 0.163 0.050 
150-300 0.910 0.061 0.657 0.127 0.126 
300-450 0.925 0.055 0.629 0.160 0.136 
450-600 0.899 0.060 0.647 0.169 0.082 
600-800 0.873 0.060 0.623 0.145 0.105 
LWP  [g m-2]    
0-150 0.852 1.259 0.527 0.236 0.089 
150-300 0.865 4.390 0.283 0.173 0.408 
300-450 0.886 6.480 0.319 0.221 0.346 
450-600 0.867 10.841 0.546 0.124 0.197 
600-800 0.897 13.122 0.682 0.041 0.174 
CRE  [wm-2]    
0-150 0.943 3.468 0.763 0.149 0.030 
150-300 0.922 8.797 0.592 0.107 0.224 
300-450 0.943 9.167 0.650 0.110 0.183 
450-600 0.928 11.684 0.734 0.085 0.110 
600-800 0.936 11.148 0.764 0.052 0.120 
 16 
 
Table S2.  
Table S2.1, Coefficients of multiple regression with polynomial fit of the second order for Cf 
with log10(Nd), LTS and CTRC  
Coefficients  
Cf LWPaverage CRE 
CGT2 -6.74013E-01 1.76970E-04 3.66454E-05 
CGT×log10(Nd) 5.50474E-05 1.44208E-01 -1.20699E-01 
CGT×LTS 1.89463E-05 9.41140E-03 -6.08122E-03 
CGT×CTRC -1.15019E-06 2.88727E-04 1.17351E-04 
CGT 7.45851E-04 -3.87350E-01 1.65038E-01 
log10(Nd)2 2.11680E-02 -3.50530E+00 -2.04209E+01 
log10(Nd)×LTS 8.42938E-03 2.12819E+00 -3.99771E+00 
log10(Nd)×CTRC -9.01241E-03 -4.70276E-01 1.14618E+00 
log10(Nd) -2.31617E-01 -5.72627E+01 1.56830E+02 
LTS2 -6.86324E-04 -1.52631E-01 1.31675E-01 
LTS×CTRC 4.49587E-04 6.90503E-02 -5.64969E-02 
LTS 4.46546E-02 3.49598E+00 -1.01558E+00 
CTRC2 2.02720E-04 4.41370E-02 -4.23289E-02 
CTRC 2.30734E-02 2.65287E+00 -3.84016E+00 
constant -5.32511E-02 8.08247E+01 -1.55510E+02 
Table S2.2 Coefficients of multiple regression with polynomial fit of the second order for Cf 
with log10(Nd), ∆θ and CTRC  
Coefficients  
Cf LWPaverage CRE 
CGT2 -7.10431E-07 1.31485E-04 6.17390E-05 
CGT×log10(Nd) 1.46158E-04 1.57701E-01 -1.33537E-01 
CGT×∆θ 1.31106E-06 1.99350E-03 -1.50789E-03 
CGT×CTRC -5.45026E-06 -1.56801E-03 1.10417E-03 
CGT 6.97372E-04 -3.17076E-01 1.24522E-01 
log10(Nd)2 4.01239E-02 6.90955E-01 -2.56514E+01 
log10(Nd)×∆θ 9.51925E-03 7.82047E-01 -2.58789E+00 
log10(Nd)×CTRC -5.32763E-03 -2.92653E-01 8.44217E-01 
log10(Nd) -8.35930E-02 -3.50551E+01 1.12136E+02 
∆θ2 8.74063E-05 1.76373E-02 -2.51274E-02 
∆θ×CTRC -3.49189E-04 -6.54616E-02 7.15781E-02 
∆θ -1.83496E-02 -3.84826E+00 6.47132E+00 
CTRC2 5.66640E-05 9.71324E-03 -1.11179E-02 
CTRC 1.59196E-02 1.73881E+00 -2.55785E+00 
constant 2.71063E-01 8.96093E+01 -1.34376E+02 
 17 
 
Table S2.3 Coefficients of multiple regression with polynomial fit of the second order for Cf 
with log10(Nd/Wb0.5), and CTRC  
Coefficients  
Cf LWPaverage CRE 
CGT2 -4.95543E-07 1.41445E-04 2.11668E-05 
CGT×log10(Nd/Wb0.5) 1.91532E-04 1.47582E-01 -1.22848E-01 
CGT×LTS 2.31120E-05 8.24980E-03 -5.25877E-03 
CGT×CTRC -1.56665E-06 5.82819E-04 -1.18194E-04 
CGT 2.62163E-04 -3.68739E-01 1.84456E-01 
log10(Nd/Wb0.5)2 6.46651E-02 -9.96660E-01 -3.08719E+01 
log10(Nd/Wb0.5)×LTS 1.50697E-02 3.25238E+00 -4.51614E+00 
log10(Nd/Wb0.5)×CTRC -8.24803E-03 -2.78810E-01 8.43776E-01 
log10(Nd/Wb0.5) -5.24256E-01 -7.84239E+01 2.02349E+02 
LTS2 -1.45163E-04 -1.20300E-01 8.17321E-02 
LTS×CTRC 5.15651E-04 8.19169E-02 -6.64008E-02 
LTS 1.33540E-02 8.21935E-01 1.78916E+00 
CTRC2 1.84189E-04 4.55344E-02 -3.96966E-02 
CTRC 2.11852E-02 2.15325E+00 -3.03469E+00 
constant 4.91849E-01 1.12229E+02 -2.16206E+02 
 
Table S2.4 Coefficients of multiple regression with polynomial fit of the second order for Cf 
with log10(Nd/Wb0.5), ∆θ and CTRC  
Coefficients 
 Cf LWP_average CRE 
CGT2 -5.20412E-07 8.94761E-05 4.62832E-05 
CGT×log10(Nd/Wb0.5) 2.63277E-04 1.51284E-01 -1.26869E-01 
CGT×∆θ 6.28499E-06 1.55349E-03 -1.24522E-03 
CGT×CTRC -6.23672E-06 -1.52396E-03 1.11880E-03 
CGT 2.45838E-04 -3.05552E-01 1.52230E-01 
log10(Nd/Wb0.5)2 1.21903E-01 8.63985E+00 -4.11349E+01 
log10(Nd/Wb0.5)×∆θ 9.31700E-03 8.91438E-01 -2.17551E+00 
log10(Nd/Wb0.5)×CTRC -6.19273E-03 -4.57559E-01 8.50075E-01 
log10(Nd/Wb0.5) -4.60532E-01 -6.86739E+01 1.75006E+02 
∆θ2 1.47450E-04 1.83971E-02 -2.61888E-02 
∆θ×CTRC -3.02845E-04 -5.94642E-02 6.26179E-02 
∆θ -2.14331E-02 -3.89954E+00 5.92309E+00 
CTRC2 3.25041E-05 5.66408E-03 -4.72092E-03 
CTRC 1.67688E-02 1.71821E+00 -2.17631E+00 
constant 6.61140E-01 1.17774E+02 -1.90085E+02 
 
 18 
 
Table S3. Coefficients of multiple regression with polynomial fit of the second order for Cf, 
LWP and CRE with Log10(Nd/Wb0.5), LTS and CTRC for different intervals of CGT. 
 Coefficients 
 Cf 
CGT[m] 0-150 150-300 300-450 450-600 600-800 
log10(Nd/Wb0.5)2 1.18714E-01 1.55303E-01 5.69580E-02 -1.15867E-01 -2.11737E-01 
log10(Nd/Wb0.5)×LTS 1.76865E-02 1.53946E-02 9.48784E-03 1.28244E-02 1.80381E-02 
log10(Nd/Wb0.5)×CTRC -5.07513E-03 -9.17625E-03 -9.51958E-03 -9.04303E-03 -8.02936E-03 
log10(Nd/Wb0.5) -6.66649E-01 -8.11120E-01 -3.55468E-01 2.32741E-01 5.46725E-01 
LTS2 5.80331E-04 8.85080E-04 1.96935E-05 -1.13677E-04 -6.55903E-04 
LTS×CTRC 2.21470E-04 1.61253E-04 2.37620E-04 9.47426E-04 1.24853E-03 
LTS -3.38613E-02 -2.91508E-02 2.64914E-02 4.93126E-02 5.96270E-02 
CTRC2 7.27014E-06 9.80262E-05 1.77930E-04 2.94522E-04 2.62931E-04 
CTRC 8.56704E-03 2.16460E-02 2.65834E-02 2.26063E-02 1.40227E-02 
constant 9.87006E-01 1.18011E+00 3.99762E-01 -2.93046E-01 -6.77496E-01 
 LWP 
CGT[m] 0-150 150-300 300-450 450-600 600-800 
log10(Nd/Wb0.5)2 1.59374E+00 2.57911E+00 -5.51389E+00 -1.53407E+01 -2.49188E+01 
log10(Nd/Wb0.5)×LTS 4.22581E-01 1.04312E+00 2.47008E+00 4.33802E+00 5.71783E+00 
log10(Nd/Wb0.5)×CTRC -7.79838E-02 -3.79837E-01 -3.78152E-01 -5.38516E-01 -1.14255E+00 
log10(Nd/Wb0.5) -1.06518E+01 -2.73718E+01 -8.34143E+00 1.51644E+01 4.05042E+01 
LTS2 1.48647E-02 4.72932E-02 -7.81423E-03 -6.90974E-03 -2.28862E-01 
LTS×CTRC 5.85299E-03 -9.24063E-03 -2.49655E-02 1.03447E-01 3.89293E-01 
LTS -7.77659E-01 -2.53620E+00 -2.04041E+00 4.09783E-01 1.64870E+01 
CTRC2 1.40988E-03 1.39656E-02 2.96282E-02 6.70806E-02 1.09061E-01 
CTRC 2.07298E-01 1.70635E+00 3.02007E+00 3.94883E+00 4.09623E+00 
constant 1.76598E+01 6.06260E+01 4.83449E+01 7.54761E+00 -1.48116E+02 
 CRE 
CGT[m] 0-150 150-300 300-450 450-600 600-800 
log10(Nd/Wb0.5)2 -1.68639E+01 -3.41280E+01 -4.11857E+01 -3.82838E+01 -3.28584E+01 
log10(Nd/Wb0.5)×LTS -1.91048E+00 -3.36296E+00 -4.69972E+00 -5.29494E+00 -5.76827E+00 
log10(Nd/Wb0.5)×CTRC 1.87786E-01 9.48032E-01 1.03298E+00 1.27209E+00 1.66873E+00 
log10(Nd/Wb0.5) 7.62432E+01 1.69323E+02 2.01783E+02 1.95592E+02 1.88830E+02 
LTS2 -2.73027E-02 -5.18022E-02 4.61020E-02 2.26296E-02 1.71663E-01 
LTS×CTRC -1.14846E-02 1.24795E-02 5.36856E-03 -1.31197E-01 -2.62325E-01 
LTS 3.20229E+00 5.91427E+00 2.88859E+00 -6.54896E-01 -8.77226E+00 
CTRC2 -5.55033E-04 -1.94330E-02 -3.17978E-02 -6.04180E-02 -7.02555E-02 
CTRC -2.57491E-01 -3.00791E+00 -3.90748E+00 -4.26488E+00 -3.90019E+00 
constant -8.43720E+01 -2.09835E+02 -2.12928E+02 -1.81375E+02 -1.18694E+02 
 
 
 


Aerosol-driven droplet concentrations dominate coverage and water of oceanic low level clouds

https://authors.library.caltech.edu/92390/2/aav0566_Rosenfeld_SM.pdf

Aerosol-driven droplet concentrations dominate coverage and water of oceanic low level clouds

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