Current Debate on Stratospheric Temperature
Trends from SSU
Cheng-Zhi Zou
NOAA/NESDIS/Center For Satellite Applications and Research
With Help from Haifeng Qian, Wenhui Wang, Likun Wang
2013 GSICS Annual Meeting
Williamsburg, Virginia, 4-8 March 2013
Introduction



Stratospheric temperature trend
is an important indicator of
anthropogenic global warming
Stratospheric cooling:
Ozone depletion
Increasing carbon dioxide and
other greenhouse gases

Radiosonde observations can’t
reach to mid-upper stratospheres

Lidar observations are sparse

Rely on satellite observations
The SSU Instrument

One of the NOAA TOVS
instruments (MSU, HIRS, SSU)
from 1978-2007

Infrared radiometer use pressure
modulation technique to measure
atmospheric radiation from CO2
15-mm v2 band

An interference filter allows only
15-mm band to pass through

A cell of CO2 gas is placed in the
instrument’s optical path with its
pressure changed in a cyclic
manner
Channels
 Weighting function determined by the
pressure values
 Three different pressures to give three
different weighting function
Channel
Number
Central Wave Cell pressure weighting function
No. (cm-1)
(pre-launch
peak
(wavelength)
specific)
1
668 (15mm)
100 (hPa)
15mb (29km)
2
668 (15mm)
35 (hPa)
5mb (37km)
3
668 (15mm)
10 (hPa)
1.5mb (45km)
P(peak)~P(cell)/[CO2]1/2,
Scan and Calibration Cycle
SSU Satellites
Only 7 among the 9 NOAA TOVS satellites were equipped with SSU
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Brightness Temperature Anomalies—From
NOAA Operational Calibration
• 5-day and global
averaged Tb
anomaly time series
• Include all 8
pixels per scanline
• Global coverage
• Cloud effect minimal ;
include most observations
• Global inter-satellite
differences between
NOAA-7 and NOAA-8
are as large as 4 K
El Chicon
Mt. Pinatubo
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SSU Data Issues
 Gas leaking problem in the CO2 cell cell pressure change
 atmospheric CO2 variations
 limb-effect
 diurnal drift effect semi-diurnal tides
 inter-satellite biases
 No instruments on NOAA-10 and NOAA-12
 No overlap between NOAA-9 and NOAA-11
Cell Pressure Time Series from Gas Leak
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Effect of Cell Pressure Decreasing
CO2 cell pressure decreasing -> weighting function peaks higher
-> because of increasing lapse rate, measured Tb increasing
-> warm bias
SSU cell pressure
Measured BT
10
Weighting function
SSU CDR Development Flow Chart
Original SSU BTs
Interpolated profiles along
each SSU pixels
SSU BTs with
fixed cell pressures
SSU BTs with removing CO2
increasing effects
SSU BTs with limb
adjustments
SSU BTs with diurnal
corrections
SSU simulations with real and
fixed cell pressures
SSU simulations with fixed and
varying CO2 amount
SSU simulations at nadir and offnadir
Diurnal correction database
Well-merged SSU gridded
BTs
SSU TCDR
Reanalysis Uncertainties Don’t have an
Impact on Correction

Bias
corrections
are
sensitive
to
the
layer
temperature differences, not the
temperature profile itself

The spurious temperature
jumps near 1996 and 2000 are
apparently due to model errors

These jumps in MERRA
temperatures did not show up
in the simulated correction time
series (bottom panel)

Demonstrating reanalysis
uncertainties may not be critical
to the accuracies of the
corrections
No big jumps between satellites and
streams in MERRA
Top: CRTM simulated channel 3 global mean time
series for NOAA11 (red) and NOAA14 (blue) with
varying CO2 cell pressure (color) and fixed CO2 cell
pressure (grey).
Bottom: Their differences (color minus grey)
Effect of Correction and Merging
After instrument CO2 cell + atmospheric CO2 correction, the
original upward trend became flat for ch2 and ch3
NOAA -7
biases were
reduced after
CO2 cell
correction
After instrument CO2 cell + atmospheric CO2 correction, the original
downward trend became even more negative for ch1
The Trend Debate
Plot from Thompson et al. 2012 in Nature
14
Debate Example #1
1
STAR
0.5
UKMO
0
 UKMO did not correct
Cell pressure effect for
Channel 1
-0.5
 But NOAA did
-1
-1.5
-2
-2.5
NOAA-9, NOAA-11
15
NOAA-9 NOAA-11
Debate Example #2
SSU channels 2, 3, and MSU channel 4 all flat from 1985-1992
Only SSU channel 1 shows downward trend-- inconsistency
16
Debate Example #3
Disconnection problem for channel 3
Is the large drop (0.3 K in 6 months) real or bad observations
at the early stage of NOAA-14
17
Debate Example #4
Channel 3 global mean trend is similar between NOAA and UKMO
18
But zonal mean trends have very different patterns
Ongoing Activity for improvements

Collect more information from other scientists: Roger Saunders, John Nash, Jim Miller, Mike
Chalfant, Tony Reale, Laurie Rokke, Shinya Kobayashi, Mitch Goldberg….


Modeling climate Community is joining the debate: Adrian Simmons, Dick Dee …
Level-1c calibration– NOAA operational calibration was used before. We are trying to
develop new calibration schemes to understand if it can make differences; Especially for
inconsistencies between channels
(Reprocessing level-1c is time consuming and need more support)

CRTM – not sure if inconsistency between channels are related to CRTM, but check with
CRTM teams (Mark Liu, Yong Chen) to see if there are potential problems in CRTM

Checking diurnal drift corrections– maybe related to channel 3 zonal mean trend pattern?
References

Wang, L., C.-Z. Zou, and H. Qian (2012), Construction of stratospheric
temperature data records from Stratospheric Sounding Units. J. Climate, Vol 25,
2931-2946

Thompson, D. W. J., D. J. Seidel, W. J. Randel, C.Z. Zou, A.H. Butler, C. Mears,
A. Osso, C. Long, R. Lin, (2012): The mystery of recent stratospheric temperature
trends. Nature, 491, 692-697. doi:10.1038/nature11579

Zou, C.-Z., et al. (2013) On the differences of SSU datasets between the NOAA and UK Met
Office versions, In preparation
Thank You!
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