Sensitivity of Tropospheric and Stratospheric Temperature Trends to Radiosonde Data Quality

Abstract

Radiosonde data have been used, and will likely continue to be used, for the detection of temporal trends in tropospheric and lower-stratospheric temperature. However, the data are primarily operational observations, and it is not clear that they are of sufficient quality for precise monitoring of climate change. This paper explores the sensitivity of upper-air temperature trend estimates to several data quality issues. Many radiosonde stations do not have even moderately complete records of monthly mean data for the period 1959?95. In a network of 180 stations (the combined Global Climate Observing System Baseline Upper-Air Network and the network developed by J. K. Angell), only 74 stations meet the data availability requirement of at least 85% of nonmissing months of data for tropospheric levels (850?100 hPa). Extending into the lower stratosphere (up to 30 hPa), only 22 stations have data records meeting this requirement for the same period, and the 30-hPa monthly data are generally based on fewer daily observations than at 50 hPa and below. These networks show evidence of statistically significant tropospheric warming, particularly in the Tropics, and stratospheric cooling for the period 1959?95. However, the selection of different station networks can cause network-mean trend values to differ by up to 0.1 K decade?1. The choice of radiosonde dataset used to estimate trends influences the results. Trends at individual stations and pressure levels differ in two independently produced monthly mean temperature datasets. The differences are generally less than 0.1 K decade?1, but in a few cases they are larger and statistically significant at the 99% confidence level. These cases are due to periods of record when one dataset has a distinct bias with respect to the other. The statistical method used to estimate linear trends has a small influence on the result. The nonparametric median of pairwise slopes method and the parametric least squares linear regression method tend to yield very similar, but not identical, results with differences generally less than ±0.03 K decade?1 for the period 1959?95. However, in a few instances the differences in stratospheric trends for the period 1970?95 exceed 0.1 K decade?1. Instrument changes can lead to abrupt changes in the mean, or change-points, in radiosonde temperature data records, which influence trend estimates. Two approaches to removing change-points by adjusting radiosonde temperature data were attempted. One involves purely statistical examination of time series to objectively identify and remove multiple change-points. Methods of this type tend to yield similar results about the existence and timing of the largest change-points, but the magnitude of detected change-points is very sensitive to the particular scheme employed and its implementation. The overwhelming effect of adjusting time series using the purely statistical schemes is to remove the trends, probably because some of the detected change-points are not spurious signals but represent real atmospheric change. The second approach incorporates station history information to test specific dates of instrument changes as potential change-points, and to adjust time series only if there is agreement in the test results for multiple stations. This approach involved significantly fewer adjustments to the time series, and their effect was to reduce tropospheric warming trends (or enhance tropospheric cooling) during 1959?95 and (in the case of one type of instrument change) enhance stratospheric cooling during 1970?95. The trends based on the adjusted data were often statistically significantly different from the original trends at the 99% confidence level. The intent here was not to correct or improve the existing time series, but to determine the sensitivity of trend estimates to the adjustments. Adjustment for change-points can yield very different time series depending on the scheme used and the manner in which it is implemented, and trend estimates are extremely sensitive to the adjustments.Overall, trends are more sensitive to the treatment of potential change-points than to any of the other radiosonde data quality issues explored.