ESA-JAXA Pre-Launch EarthCARE Science and Validation Workshop, 13–17 November 2023, Rome, Italy

Zonal Cloud Trends Obtained fromPassive MODIS andActive CALIOP and CPR Sensors
Seung-Hee Ham,a Norman Loeb,b Seiji Kato,b Tyler J. Thorsen,bAiko Voigt,c William L. Smith Jr.,b and David Winkerb
Analytical Mechanics Associates (AMA), Hampton, Virginiaa NASA Langley Research Center, Hampton, Virginia
cDepartment of Meteorology and Geophysics, University of Vienna, Vienna, AustriabObjectives• Filter CALIPSO and CloudSat clouds to minimize the impact of sensor
degradations and to have more comparable sensitivity to clouds as in MODISobservations• Compare zonal cloud trends obtained from MODIS and CALIPSO-CloudSat
combined dataset• Separate the cloud trends into ENSO and non-ENSO components

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Cloud Selections in CALIPSO and CloudSat Measurements
Clouds are selected to minimize impacts of sensor degradation and for more comparable sensitivity to MODIS
measurements. Only daytime observations are used since CloudSat daytime is only available after 2012.

q CloudSat Clouds• Clouds with radar reflectivity (Z) ≥ –25 dBZ
Clouds with Z < -25 dBZ are excluded since these clouds could be detected in earlier mission due to the change in minimum
detectable radar reflectivity .

q CALIPSO clouds• Horizontal averaging scale for cloud detections (HOR) ≤ 20 km
CALIPSO clouds are detected either from a single beam (1/3 km) or spatial averaging of several beams (1, 5, 20, or 80 km). The 80km clouds are excluded since these are mostly appeared over the Arctic and a strong decreasing trend is noted.

•CAD (cloud-aerosol-discrimination) score ≥ 20
The CAD score represents a confidence level of the detected clouds. When CAD < 20, there is no confidence due to highly
attenuated CALIPSO signals.

•Cloud optical depth (𝜏) ≥ 0.3
MODIS misses clouds with 𝜏 < 0.3 (Kato et al. 2019). Therefore, for more consistent comparison, clouds with 𝜏 < 0.3 are also
excluded in CALIPSO measurements. Also those thin clouds are more affected by the sensor degradation issues.

q MODIS clouds: MODIS cloud parameters from CERES Single Scanner Footprint (SSF) Ed4
product3

Calculating Cloud Volume Fraction
Referencevertical levels(0 to 20 km with a0.16-km interval)

1.28 km50%
1.12 km25%
0.96 km0%0.80 km50%
0.64 km100%
0.48 km0%0.32 km0%0.16 km

0%0 km• CALCS cloud volume fraction:
The cloud volume fraction profile is computed at
a CALIPSO pixel resolution using merged cloud
top and base heights of multiple cloud layers.
• MODIS cloud volume fraction:
The cloud volume fraction profile is computed ata CERES footprint using cloud information in
SSF product. Note that a single-layer cloud
assumption is used for MODIS cloud retrieval. In
the MODIS retrieval, cloud effective height (zeff)
is first derived and then cloud top and baseheights are inferred using the geometrical depth(Δz) of the cloud layer. The value of Δz isinferred from the parametrized equation using
cloud optical depth, phase, and altitude.

Zonal Distributions of Cloud Volume Fraction from MODIS, CloudSat, CALIPSO, andCALIPSO+CloudSat (CALCS)(Z ≥ –25 dBZ)

2008, Daytime(CAD≥20, HOR≤20km, 𝜏≥0.3)

o CloudSat misses thin cirrus and lowclouds.
o CALIPSO misses mid/low clouds when theCALIPSO signal is fully attenuated.
o Combining CALIPSO+CloudSat (CALCS)
gives more complete picture of clouds.
o The differences in cloud amounts fromMODIS and CALCS seem to be mostly
due to different cloud heights. Differencesin high, mid, and low clouds arecompensating.
5

Cloud Volume (%)

Zonal Cloud Trends from CALIPSO+CloudSat (CALCS) and MODIS for 2008-2017
CALCS

MODIS

• Consistent features:
Both CALCS and MODIS indicate anincrease of uppermost clouds and adecrease underlying clouds over 60°S–60°N.

2008-2017 Trend(% dec-1)

Cloud Volume (%)• Inconsistent features:
Cloud altitudes in CACS and MODIS trends
over the Antarctic are different: Probably due
to the limitations of the MODIS cloud height
retrievals in temperature inversion or skintemperature issues. Also, it can be related tothe chemical composition of polar
stratospheric clouds (i.e., nonhydrometeors), which cannot be detected byMODIS.

Trend of Cloud Vol Ano (% dec-1)

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ENSO Time Series versus Cloud Volume Fraction Anomalies (%) over 60°S–60°S
Multivariate ENSO Index (MEI) V2

MODIS Cloud Volume Fraction Anomalies (%)

CALCS Cloud Volume Fraction Anomalies (%)

Decomposition of Cloud Trends into ENSO and non-ENSO Components
Monthly Cloud Volume Fraction
Anomaly (%) Time series

ΔC=Total Cloud Trend

Third-order regression model using Multivariate ENSO Index (MEI)

c0 + c1 MEI + c2 MEI2 + c3 MEI3 +ENSO Component!" ) of time sies(∆𝐶non-ENSO Componentof time series (εi)

Cloud trend ofENSO component

Cloud trend ofnon-ENSO component

Monthly cloud anomaly predictedfrom the regression model!" = 𝑐# + 𝑐$ 𝑀𝐸𝐼" + 𝑐% 𝑀𝐸𝐼" % + 𝑐& 𝑀𝐸𝐼" &∆𝐶

Total sum of squares (TSS)𝑇𝑆𝑆 = , ∆𝐶" − ∆𝐶 %("'$(Residual sum of squares (RSS)
Coefficient of determination

ε!"
𝑅𝑆𝑆 = , ∆𝐶" − ∆𝐶"'$

R2 = 1 – RSS/TSS(%= , 𝜀" %"'$

Using the regression model,
cloud anomaly time series areare separated into ENSO andnon-ENSO components. Thenthe trends of them can beobtained.
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Decomposing 2008-2017 Cloud Trends into the ENSO and non-ENSO Components
CALCS

MODIS

Coefficient of Determination (R2)CALCSMODISTotal cloudtrends(% dec-1)

Trends (% dec-1)of the ENSO
Component

Trends (% dec-1)of the non-ENSO
Component

Less than 30% of total variation of cloud anomalies
are explained by the regression model using MEI(R2 < 0.3).
Non-ENSO Component
1) Arctic cloud increase related to the sea ice loss (Kayand Gettleman 2009; Wu and Lee 2012; Sato et al.
2012)2) Non-ENSO signals in the cloud trends are positive inthe uppermost cloud layer and negative in theunderlying (mid, low) clouds. This indicates risinghigh clouds as the response to temperature warming(Wetherald and Manabe 1988; Zelinka and Hartmann2010; Voigt et al. 2019; Aerenson et al. 2022;
Richardson et al. 2022)
9Non-ENSO Component of MODIS Aqua Cloud Trends (2003–2017)
2003-2007

2003-20132003-20092003-2015

2003-2011•The 10-year period may not belong enough for detecting risinghigh cloud features (Takahashi etal., 2019; Davies et al. 2017;
Chepfer et al. 2018).

•Therefore, a longer periods are
considered using MODIS
measurements.

•All periods generally show positive
trends in uppermost clouds andnegative trends in the underlying
clouds.

2003-201710

Trends of Cloud Top Height for High Clouds (10-18 km)
from MODIS Using a Longer Record (2003-2022)
Non-ENSO Component
MEI30°S–30°N Domain AveragedCloudTop
HeightAnomaly•••

6-month running means

Aqua Drifting

The domain averaged CTH anomalies show slight increasing trend, but the magnitude largely differ by the period.
A longer record will reduce the uncertainty of the trend estimate.
The magnitude of CTH trends seems to be comparable in earlier studies (Aerenson et al. 2022; Richardson et al. 2022).

Summaryo When CloudSat+CALIPSO clouds are filtered for known sensitivity differences,trends agree well with MODIS cloud trends for 2008–2017.
o When the clouds are decomposed into ENSO and non-ENSO components, thenon-ENSO component is related to 1) the cloud increase over the Arctic and 2)
rising high clouds over 60°S–60°N. MODIS and CALCS consistently capture
these features.
o When a longer MODIS record is used, rising high clouds are observed, but themagnitude varies depending on periods.

12Please contact to seung-hee.ham@nasa.gov if you have any questions.