The data of the global annual mean near-surface air temperature of three data sets, HadCRUT4, GISSTEMP and NCDC is now out for the whole 2013. It is thus time for a brief update about the status of the recent temperature hiatus.
The global mean temperature was on average in 2013 0.02 K warmer than 2012 according to HadCRUT4, 0.03 C warmer according to GISTEMP and 0.05 C according to NCDC. The central estimates of the 15-year 1999-2013 trends were 0.074 C/decade , 0.093 C/decade and 0.066 C/decade for HadCRUT4, GISSTEMP and NCDC (compared to the 1998-2012 trends of 0.042 C/decade, 0.063 C/decade and 0.037 C/decade), and the 16 years trends since 1998 of 0.043 C/decade, 0.064 C/decade and 0.0410 C/decade, respectively.
With one more data point, an update of the recent temperature trends of the last 15 years can be constructed in two ways: by computing the trend over the now last 15 years (1999-2013) or by including the last year (2013) in the previous 15-year segment, yielding a 16-year segment. The difference is important because we would like to test whether or not the temperature trends are compatible with the IPCC model ensemble. In our last assessment we used all 15-year temperature trends produced by the IPCC models driven by scenario RCP4.5 over the period 2005-2060. Over this period the external forcing is very close to linear and the ensemble of all 15-year trends should encompass the expected trend due to the external forcing plus the spread caused by internal model variability plus the inter-model structural uncertainty. The total spread should thus represent the total uncertainty, from the models point of view, of the 15-year trend. The spread depends on the length of the time segment. Longer segments yield narrower modelled spreads of temperature trends , as these trends are become increasing dominated by the deterministic external forcing and decreasingly affected by internal and inter-model variability. This is why the agreement between modelled and observe trends should increase with longer time segments.
The results of both types of update are summarized below. In brief, the hiatus is still with us. Considering now the 16-year trends, the observed trends have been become even less compatible with the model spread than they were before.
In the left panel we have displayed the gliding 15-year temperature trends derived from the three data sets. The trends in the period 1999-2013 are somewhat higher than the 1998-2012 trends. This reflect both that 2013 has a bit a bit warmer than 2012 but also that the warm year 1998 is now not included the in the last 15-year segment 1999-2013. The compatibility with the 15-year trends produced by the IPCC models (here, only CMIP5 have been considered) has marginally increased. For instance, whereas less than 2% of all model trends lied below the HadCRUT4 trend for 1998-2012, this percentage is now 3%.
Comparing the observed and modelled 16-year trends (right panel), the observed trend in the last 16 year period is smaller, in all data sets, that the previous 16-year segment. For this segment length, therefore, the observed trend differs more clearly from the model ensemble. Since the ensemble of trends for 16-year segments gets narrower, the compatibility between observed and model trends for 16-year segments has worsened: about 1% of the model trends are as small or smaller than the observed HadCRUT4 and NCDC trends in 1998-2013, and about 2% of the model trends are below the GISS trend in 1998-2013.
A look at the temperatures of the lower-troposphere derived from satellite data is also instructive (see figure below for trends derived from the monthly means, not annual means). Both trends, near-surface and lower troposphere, are predicted by models to be different, the lower-troposphere trends caused by the external forcing should be warmer than at the surface. There are two 'competing' satellite data sets, one from the University of Alabama in Huntsville and another one from Remote Sensing Systems. Although both groups used the same raw satellite data, the corrections applied to take into account satellite drifts and to stitch together different satellite missions are different. Thus the final result also differ. What is quite remarkable here is that the trends computed from the RSS data become even negative and clearly differ from the UAH trends, illustrating the quite large uncertainties that still exist. For the satellite data, the comparison with the modelled trends is not as straight forward, since the satellite-derived temperatures represent a weighted averaged over several tropospheric layers and not directly the temperature at a prescribed height