Marcel Crok tips me via Twitter that a new analysis of ‘the pause’ by Dr. Ross McKittrick has given some new numbers for the different temperature datasets. It was published in the Open Journal of Statistics
NOTE: (added) Some people saw the green line in the figure above as a trend line. It is not. It is a comparison line to show the similarity of global temperatures 19 years apart in relation to McKittrick’s paper. It simply shows the “plateau” of temperatures has not changed much since then. To see more about the pause in trends, this essay will be informative.
The RSS satellite dataset says:
McKitrick, R. (2014) HAC-Robust Measurement of the Duration of a Trendless Subsample in a Global Climate Time Series. Open Journal of Statistics, 4, 527-535. doi: 10.4236/ojs.2014.47050.
The IPCC has drawn attention to an apparent leveling-off of globally-averaged temperatures over the past 15 years or so. Measuring the duration of the hiatus has implications for determining if the underlying trend has changed, and for evaluating climate models. Here, I propose a method for estimating the duration of the hiatus that is robust to unknown forms of heteroskedasticity and autocorrelation (HAC) in the temperature series and to cherry-picking of endpoints.
For the specific case of global average temperatures I also add the requirement of spatial consistency between hemispheres. The method makes use of the Vogelsang-Franses (2005) HAC-robust trend variance estimator which is valid as long as the underlying series is trend stationary, which is the case for the data used herein. Application of the method shows that there is now a trendless interval of 19 years duration at the end of the HadCRUT4 surface temperature series, and of 16 – 26 years in the lower troposphere. Use of a simple AR1 trend model suggests a shorter hiatus of 14 – 20 years but is likely unreliable.
The IPCC does not estimate the duration of the hiatus, but it is typically regarded as having extended for 15 to 20 years. While the HadCRUT4 record clearly shows numerous pauses and dips amid the overall upward trend, the ending hiatus is of particular note because climate models project continuing warming over the period. Since 1990, atmospheric carbon dioxide levels rose from 354 ppm to just under 400 ppm, a 13% increase.  reported that of the 114 model simulations over the 15-year interval 1998 to 2012, 111 predicted warming.  showed a similar mismatch in comparisons over a twenty year time scale, with most models predicting 0.2˚C – 0.4˚C/decade warming. Hence there is a need to address two questions: 1) how should the duration of the hiatus be measured? 2) Is it long enough to indicate a potential inconsistency between observations and models? This paper focuses solely on the first question.
I propose a robust definition for the length of the pause in the warming trend over the closing subsample of surface and lower tropospheric data sets. The length term MAX J is defined as the maximum duration J for which a valid (HAC-robust) trend confidence interval contains zero for every subsample beginning at J and ending at T −m where m is the shortest duration of interest. This definition was applied to surface and lower tropospheric temperature series, adding in the requirement that the southern and northern hemispheric data must yield an identical or larger value of MAX J . In the surface data we compute a hiatus length of 19 years, and in the lower tropospheric data we compute a hiatus length of 16 years in the UAH series and 26 years in the RSS series. MAX J estimates based on an AR1 estimator are lower but likely incorrect since higher-order autocorrelation exists in the data. Overall this analysis confirms the point raised in the IPCC report  regarding the existence of the hiatus and adds more precision to the understanding of its length.
Dr. McKittrick writes on his website: http://www.rossmckitrick.com/index.html
I make the duration out to be 19 years at the surface and 16-26 years in the lower troposphere depending on the data set used. R Code to generate the graphs, tables and results is here.
The full paper is here: http://dx.doi.org/10.4236/ojs.2014.47050