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

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.

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.

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 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.

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) 1 100 (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.

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

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

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

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)

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

Trend Decomposing

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

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!

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

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

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

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

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