1. 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

2. 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

3. 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 CO 2 15-  m v 2 band  An interference filter allows only 15-  m band to pass through  A cell of CO 2 gas is placed in the instrument’s optical path with its pressure changed in a cyclic manner

4. Channels Channel Central Wave Cell pressure weighting function Number No. (cm -1 ) (pre-launch peak (wavelength) specific) 1 668 (15  m) 100 (hPa) 15mb (29km) 2 668 (15  m) 35 (hPa) 5mb (37km) 3 668 (15  m) 10 (hPa) 1.5mb (45km) P(peak)~P(cell)/[CO 2 ] 1/2,  Weighting function determined by the pressure values  Three different pressures to give three different weighting function

5. Scan and Calibration Cycle

6. SSU Satellites 6 Only 7 among the 9 NOAA TOVS satellites were equipped with SSU

7. Brightness Temperature Anomalies—From NOAA Operational Calibration 7 El Chicon Mt. Pinatubo 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

8. SSU Data Issues  Gas leaking problem in the CO 2 cell  cell pressure change  atmospheric CO 2 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

9. Cell Pressure Time Series from Gas Leak 9

10. 10 SSU cell pressure Weighting function Measured BT CO 2 cell pressure decreasing -> weighting function peaks higher -> because of increasing lapse rate, measured Tb increasing -> warm bias Effect of Cell Pressure Decreasing

11. SSU CDR Development Flow Chart SSU BTs with fixed cell pressures SSU BTs with removing CO2 increasing effects SSU BTs with limb adjustments SSU BTs with diurnal corrections Well-merged SSU gridded BTs Original SSU BTs Interpolated profiles along each SSU pixels Interpolated profiles along each SSU pixels SSU simulations with real and fixed cell pressures SSU simulations with fixed and varying CO2 amount SSU simulations at nadir and off- nadir Diurnal correction database SSU TCDR

12. Reanalysis Uncertainties Don’t have an Impact on Correction 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)  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

13. After instrument CO2 cell + atmospheric CO2 correction, the original downward trend became even more negative for ch1 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 Effect of Correction and Merging

14. The Trend Debate 14 Plot from Thompson et al. 2012 in Nature

15. Debate Example #1 15 NOAA-9, NOAA-11 NOAA-9 NOAA-11  UKMO did not correct Cell pressure effect for Channel 1  But NOAA did

16. Debate Example #2 16 SSU channels 2, 3, and MSU channel 4 all flat from 1985-1992 Only SSU channel 1 shows downward trend-- inconsistency

17. Debate Example #3 17 Is the large drop (0.3 K in 6 months) real or bad observations at the early stage of NOAA-14 Disconnection problem for channel 3

18. Debate Example #4 18 Channel 3 global mean trend is similar between NOAA and UKMO But zonal mean trends have very different patterns

19. 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?

20. 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

21. Thank You! 21