There have been several recent reports in the media and on social media comparing long-term trends in the lower troposphere from different datasets. The problem is that many of the trends reported were for different periods (i.e 1979-1995 vs. 1979-2016), so we would expect them to be different. The figure below is an attempt to clear this up.
The figure shows the "running trends". Each point on a given line represents the linear trend for that dataset, starting in January 1979, and ending at the time on the X-axis. Obviously, many of the older versions of the datasets are not updated to the present. The most recent versions are shown using thicker lines.
Some things to note:
- RSS V2.1 did not use AMSU data -- only MSU data.
- RSS V3.0 only used MSU and one AMSU (NOAA-15)
- UAH and RSS agree better than they ever have, but only through about 2000. After that, they diverge fairly rapidly.
- Sometime in 2009, the previous versions of the RSS and UAH datasets agreed exactly (for global TLT trends).
- As I mentioned on the previous post, the various merging parameters and the diurnal optimization are recalculated each month, so we do not expect the trend on this plot for 1979-2016 (0.182 C/decade) to exactly match the one reported in the paper (0.174 C/decade).
- In 2011, we calculated the 2-sigma error bar for TLT trends (1979-2010) using a monte-carlo method. The uncertianty value was +/- 0.044C/decade. This almost, but not quite, encompasses the new value for the TLT trend ending in 2010. One might ask, why not? Most of the uncertainty in the 2011 paper came from the differences between diurnal adjustments obtained from different models. Based on the new diurnal cycle estimates obtained using the measurements themselves (the subject of our last two papers on MSU/AMSU), all of the models we studied (CCM3, HADGEM, and MERRA) have average diurnal cycles that are wrong in the same direction when used to adjust the MSU and AMSU satellites. Thus the error ensemble from 2011 did not encompass the results found with the new diurnal adjustment. We plan to update the error analysis sometime in the future using better methods (this is a lot of work, so it might take a while).
- All of the RSS data, including all these previous versions, are available on our FTP site, but you do have sign up to get it. It's free.
Mears, C. A. and F. J. Wentz, (in press) A satellite-derived lower tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects , J. Climate.
Mears, C. A. and F. J. Wentz, (2016) Sensitivity of satellite-derived tropospheric temperature trends to the diurnal cycle adjustment, Journal of Climate 29(10) 3629-3646.
Mears, C. A., F. J. Wentz, P. Thorne and D. Bernie, (2011) Assessing Uncertainty in Estimates of Atmospheric Temperature Changes From MSU and AMSU Using a Monte-Carlo Estimation Technique, J. Geophys. Res., 116, D08112, doi:10.1029/2010JD014954.
Mears, C. A. and F. J. Wentz, (2009) Construction of the RSS V3.2 Lower Tropospheric Dataset From the MSU and AMSU Microwave Sounders, Journal of Atmospheric and Oceanic Technology, 26, 1493-1509.
Mears, C. A. and F. J. Wentz, (2009) Construction of the Remote Sensing Systems V3.2 Atmospheric Temperature Records From the MSU and AMSU Microwave Sounders, Journal of Atmospheric and Oceanic Technology, 26, 1040-1056.
Mears, C. A. and F. J. Wentz, (2005) The Effect of Drifting Measurement Time on Satellite-Derived Lower Tropospheric Temperature, Science, 309, 1548-1551.
Mears, C. A., M. C. Schabel and F. J. Wentz, (2003) A Reanalysis of the MSU Channel 2 Tropospheric Temperature Record, Journal of Climate, 16(22), 3650-3664..
J. R. Christy, R. W. Spencer, W. D. Braswell. "MSU Tropospheric Temperatures: Dataset Construction and Radiosonde Comparisons" Journal of Atmospheric and Oceanic Technology, vol. 17, pp. 1153-1170, 2000.