1. Current Debate on Stratospheric Temperature Trends from SSU
Cheng-Zhi Zou
Wenhui Wang, Haifeng Qian, and Likun Wang
NOAA/NESDIS/Center For Satellite Applications and Research
2013 GSICS User’s Workshop
College Park, Maryland, 8 April 2013

2. Objectives A CDR story addressing GSICS requirements Accuracy
Stability
Continuity
Data Archiving
Metadata
Documentation
So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ……….
In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

3. Background
Stratospheric temperature trend is an important indicator of anthropogenic global warming
Stratospheric cooling:
Ozone depletion
Increasing carbon dioxide and
other greenhouse gases
Radiosonde observations difficult to reach to mid-upper stratospheres
Lidar observations are sparse
Rely on satellite observations
So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ……….
In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

4. The SSU Instrument
One of the NOAA TOVS instruments (MSU, HIRS, SSU) from
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
So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ……….
In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

5. Channels Weighting function determined by the pressure values
P(peak)~P(cell)/[CO2]1/2,
Channel Cell pressure weighting function Number (pre-launch peak specific) (hPa) 15mb (29km) 2 35 (hPa) 5mb (37km) 3 10 (hPa) 1.5mb (45km)
So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ……….
In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

6. Brightness Temperature Anomalies— NOAA Operational Calibration
7 SSU satellites; discontinuity problems
5-day and global
averaged Tb
anomaly time series
Include all 8
pixels per scan-
line
Global coverage
Cloud effect minimal ; include most observations
Global inter-satellite
differences between
NOAA-7 and NOAA-8
are as large as 4 K
SSU time series after removing seasonal cycle
1) Time series are not linked to each other due to the cell pressure problems
El Chicon
Mt. Pinatubo

7. SSU Data Issues
Gas leaking problem in the CO2 cell cell pressure change
atmospheric CO2 variations
diurnal drift effect semi-diurnal tides
Limb-effect
inter-satellite biases
No instruments on NOAA-10 and NOAA-12
No overlap between NOAA-9 and NOAA-11

8. Cell Pressure Time Series from Gas Leak
Plot from S.Kobayashi et al. 2009

9. 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
Weighting function
Measured BT
Plot from Wang et al. 2012)

10. 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

11. The Trend Debate
Plot from Thompson et al in Nature

12. Trend Decomposing

13. Debate Example #1 NOAA-9, NOAA-11 UKMO did not correct
Cell pressure effect for
Channel 1
But NOAA did
NOAA-9, NOAA-11
NOAA-9 NOAA-11

14. Debate Example #2 If we believe NOAA/STAR data is right,
SSU Channel 1
 STAR
 UKMO
 Models
If we believe NOAA/STAR data is right,
then nearly all model simulations underestimate
the cooling rate significantly!

15. Debate Example #3 SSU channel 3 Global mean trend Zonal mean trend
Channel 3 global mean trend is similar between NOAA and UKMO
But zonal mean trends have very different patterns

16. Debate Example #4
Discontinuity and bias trade off 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. Ongoing Activity for improvements
Level-1c calibration– NOAA operational calibration was used before. We are developing new calibration schemes to understand if it can make differences to build confidence on the raw brightness temperatures

18. Acknowledgment
Level-1c: Roger Saunders, John Nash, Jim Miller, Mike Chalfant, Tony Reale, Shinya Kobayashi, Laurie Rokke, ….
UKMO data: Dave Thompson, Craig Long, Dian Seidel, Roger Lin, Bill Randel….
CRTM: Yong Han, Mark Liu, Yong Chen

19. References
Wang, L., C.-Z. Zou, and H. Qian (2012), Construction of stratospheric temperature data records from Stratospheric Sounding Units. J. Climate, Vol 25,
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,   doi: /nature11579 
Zou, C.-Z., et al. (2013) On the differences of SSU datasets between the NOAA and UK Met Office versions, In preparation