Observed Stratospheric Temperature Changes during the Satellite Era

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Tuesday, 6 January 2015: 8:30 AM
212A West Building (Phoenix Convention Center - West and North Buildings)
Dian J. Seidel, NOAA, College Park, MD; and I. Moradi, C. A. Mears, J. Nash, W. J. Randel, R. Saunders, D. W. J. Thompson, and C. Z. Zou

Previous studies of stratospheric temperature variability and long-term change have been seriously hampered by observational deficiencies:

sparse sampling of the lower stratosphere, and no sampling of the middle and upper stratosphere, by radiosondes;

differences among homogeneity-adjusted radiosonde and Microwave Sounding Unit (MSU) lower-stratospheric temperature datasets created by different research teams; until recently, limited spatial analysis (zonal-means only) of lower, middle, and upper-stratospheric temperature from Stratospheric Sounding Unit (SSU) observations;

differences between SSU stratospheric layer-mean temperature datasets created by two teams;

lack of datasets that merge SSU (which ended in 2005) with ongoing Advanced MSU (AMSU) data.

For all these reasons, our understanding of the four-dimensional structure of stratospheric temperature change, and particularly of long-term trends, has been incomplete. However, the passage of time (and lengthening of the observational record), and new work on SSU, AMSU and merged MSU/AMSU data products, offer an opportunity to better characterize temperature changes throughout the stratosphere during the satellite era (1979-present). Moreover, the lack of major stratospheric injections of volcanic aerosol since the 1991 Mt. Pinatubo eruption, which have a pronounced short-term stratospheric warming effect, enable more subtle signals of temperature change to be characterized.

This study takes advantage all available peer-reviewed SSU, MSU and AMSU stratospheric temperature climate data records to identify signals of temperature change in the lower, middle and upper stratosphere. We use empirical orthogonal function analysis (without a priori assumptions about climate signals) and multiple linear regression analysis (using predictor time series) to identify and characterize temperature changes on time scales ranging from the annual cycle to long-term (>30 yr) trends, including the quasi-biennial oscillation, the El Nino-Southern Oscillation, response to volcanic aerosols, and the solar cycle. Whenever possible, we use multiple versions of data records to capture structural uncertainty in the observations. The results provide a more detailed basis for evaluating model simulations of stratospheric temperature during the satellite era.