Interannual variability of top-ofatmosphere albedo observed by
CERES instruments
Seiji Kato
NASA Langley Research Center
Hampton, VA
SORCE Science team meeting, Sedona, Arizona,
Sep. 13-16, 2011
TOA irradiance derived from CERES
CERES: Clouds and Earth’s Radiant Energy System
Broadband instruments
•Measure shortwave, longwave (total-sw), and window
radiances in a cross-track mode
•Currently, CERES instruments are on Terra and Aqua.
•Terra data start from March 2000
•Aqua data start from July 2002
•One instrument is on NPP
•Footprint size of instruments on Terra and Aqua is ~20 km
•TOA irradiance stability is 0.5 Wm-2 per decade
•More information is on http://ceres.larc.nasa.gov/
TOA SW Irradiance (W m-2)
TOA irradiances are derived from angular distribution models
Annual mean derived from 4-years of data (from March 2000 Feb. 2004)
TOA LW Irradiance (W m-2)
Annual mean derived from 4-years of data (from March 2000 Feb. 2004)
Outline of this talk
• CERES instruments
• Top-of-atmosphere irradiance derived from
CERES radiance measurements
• Surface irradiance computation
• De-seasonalized anomalies (defined in later
slide) of irradiances
• Relationship between TOA albedo and cloud
fraction de-seasonalized anomalies
Surface irradiances
• Surface shortwave and longwave irradiances
are modeled by a radiative transfer model
• Inputs
– Temperature and humidity: Reanalysis
– Cloud and aerosol properties: MODIS,
Geostationary satellites
• Consistency check
– Comparison with CERES derived irradiance (TOA)
– Comparison with surface observations
– CloudSat, CALIPSO, AIRS
Surface downward and upward irradiance
(March 2000)
Temperature and humidity: Reanalysis
Cloud and aerosol properties: MODIS
Modeled irradiances with a 2-stream model
Zonal vertical heating rate profile
(200807)
Atmospheric absorption = (TOA down – TOA up) – (Surface down – Surface up)
Modeled heating rates at Aqua overpass time
Some definitions to analyze
variability
• Deseasonalized anomalies ΔF (e.g. deviation
from a monthly mean value)
F
F
F
1
n
F
n
Fi
i 1
• Investigate the variability of radiation budget
Standard deviation of deseasonalized anomalies at TOA
(March 2000 to Feb 2004)
Standard deviations computed from 1o X 1o monthly anomalies
Standard deviation of NET (SW+LW)
anomalies
(March 2000 to Feb 2004)
Thick high clouds: Negative SW anomalies and positive LW anomalies partially cancel
Low clouds: LW anomalies are small compared to SW anomalies
MODIS cloud fraction anomaly
Shortwave TOA Flux anomaly (Wm-2)
TOA albedo anomalies over tropics are well
correlated with cloud fraction anomalies
Loeb et al. GRL 2007
Why are albedo variability and cloud fraction variability so small ?
Correlation between TOA shortwave irradiance
and cloud cover anomalies
Correlation coefficients in polar regions are smaller
Global mean reflected shortwave and cloud
fraction anomalies
Cloud cover
anomalies
monthly
12month
average
Reflected
Shortwave
anomalies
When anomalies are averaged, global mean interannual variability is small
TOA irradiance and cloud cover anomalies
• The annual and global mean TOA reflected
shortwave is 96.7 W m-2, with maximum and
minimum value of 97.1 and 96.4 W m-2. The
difference between the maximum and minimum
values is 0.8% of the mean value.
• The annual and global mean OLR is 239 W m-2,
and the difference between maximum and
minimum values is 0.3% of the mean value.
• The annual and global mean cloud cover is 61.7%,
and the difference between maximum and
minimum values is 0.2% of the mean value.
Questions
• Why is global mean interannual variability of
albedo so small compared to its mean value?
• Or what prevents the albedo perturbation by
clouds to intensify with time?
• Investigate the reason from atmospheric
energy perspective
Net shortwave irradiance anomalies
Tropics 30°N to 30°S
From March 2000
Shortwave irradiance anomalies caused by clouds is
predominately deposited to the surface
Regional variability
Standard deviation of 1°×1° anomalies
Similar to TOA anomalies, large variability is due to ENSO
Regional variability (Longwave)
Standard deviation of 1°×1° anomalies
Wm-2
Wm-2
Surface SW net versus LW net
Correlation between
Δ surface SW net and Δ surface LW net
net
FSW
(sfc)
cp
w he
d T
dt
FL
Fs
net
FLW
(sfc) cp
hv
w e
Energy deposited by shortwave anomalies are used for
Surface temperature change
Latent heat flux
Sensible heat flux
Longwave emission
Horizontal advection by ocean currents
T
Negative correlation
More SW net gives
Less LW net
Where positive net
Is downward net flux
Correlation coefficients between
atmospheric net irradiance and cloud cover
Atmospheric net irradiance (SW + LW, energy deposited to the atmosphere
by radiation) anomalies is well correlated with cloud fraction anomalies, indicating
A close connection between cloud fraction variability and atmospheric energy
Assumption
Albedo
(Insolation)
Outgoing
longwave
radiation
Clouds
Meridional
temperature
gradient
Large scale
dynamics
Assumption:
Large-scale dynamics, which is driven by temperature gradient, controls global
mean cloud cover i.e. If large scale dynamics is constant, cloud cover is also constant
Zonal temperature anomalies
Zonal mean thermodynamic equation
Separate zonal mean temperature into climatological mean and anomaly
T
Assumptions:
Vertical velocity anomalies =
C T
2
Horizontal temperature advection anomalies =
Diabatic heating anomalies =
T
t
T
2
D
T
y2
J (t)
cp
a T
(a C) T
(a C D
T0e
Tc
2
)t i y
e
D
T
y2
J'
cp
where a < 0, C <0, and D>0
Kato, J Climate, 2009
T
Atmospheric temperature vs. Cloud cover
Correlation coefficient between 300 hPa – 1000 hPa geopotential height difference
and cloud cover
Cloud cover decorrelation time
Month
Derived from 48 months of CALIPSO and CloudSat data
Decorrelation time = (1+Φ)/(1-Φ), where Φ is autocorrelation with 1 month lag
Summary
• Interannual variability of global mean albedo is 0.8%
of the mean value and interannual variability of
longwave is 0.3% of the mean value.
• A large part of albedo variability is caused by cloud
cover variability.
• Proposed hypothesis
– Temperature anomalies caused by albedo variability is
dumped by dynamics and longwave emission. The time
scale of the decay is smaller than a year. As a consequence,
temperature anomalies decay exponentially with time,
which provides small interannual variability of
temperature and cloud cover.
Back-ups
Standard Deviation (W m-2)
Zonal variability
Latitude (o)
Latitude (o)
Latitude (o)
Solid line: Mean standard deviation of 1 X 1 region in a latitudinal zone
Dashed line: Standard deviation of zonal anomalies
Cloud cover versus albedo
Interannual variability of cloud cover is less than 1% of the mean value
Regions with 0.8 or greater correlation coefficient are colored
Zonal atmospheric SW, LW NET and temperature
variability
Atmospheric SW, LW, and NET
300 hPa 1000 hPa thickness
Temporal variability of zonal mean computed with
48 monthly zonal deseasonalized anomalies
Temperature variability in polar regions is caused by dynamics variabilities
A large LW net or Upward longwave irradiance anomalies do not appear very often
over ocean
Clear-sky, All-sky variability
Open circles: Clear-sky
Closed circles: All-sky
Correlation between TOA shortwave irradiance
and cloud cover anomalies
Cloud fraction