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It has been a decade since changes in diurnal temperature range (DTR) globally have been assessed in a stand-alone data analysis. The present study takes advantage of substantively improved basic data holdings arising from the International Surface Temperature Initiative’s databank effort and applies the National Centers for Environmental Information’s automated pairwise homogeneity assessment algorithm to reassess DTR records. It is found that breakpoints are more prevalent in DTR than other temperature elements and that the resulting adjustments have a broader distribution. This strongly implies that there is an overarching tendency, across the global meteorological networks, for nonclimatic artifacts to impart either random or anticorrelated rather than correlated biases in maximum and minimum temperature series. Future homogenization efforts would likely benefit from simultaneous consideration of DTR and maximum and minimum temperatures, in addition to average temperatures. Estimates of change in DTR are relatively insensitive to whether adjustments are calculated directly or inferred from adjustments returned for the maximum and minimum temperature series. The homogenized series exhibit a reduction in DTR since the midtwentieth century globally (-0.044 K/decade). Adjustments serve to approximately halve the long-term global reduction in DTR in the basic “raw” data. Most of the estimated DTR reduction occurred over 1960–1980. In several regions DTR has apparently increased over 1979–2012, while globally it has exhibited very little change (-0.016 K/decade). Estimated changes in DTR are an order of magnitude smaller than in maximum and minimum temperatures, which have both been increasing rapidly on multidecadal timescales (0.186 K/decade and 0.236 K/decade, respectively, since the midtwentieth century).

[1] This analysis revisits detection and attribution of free atmosphere temperatures from radiosondes, almost a decade after previous studies. Since that time, data sets have not only become longer, but understanding of observational uncertainty has vastly improved. In addition, a coordinated set of experiments exploring the effects of human and natural forcings on past climate change has been made with a new generation of climate models. These advances allow a much more thorough analysis of the effects of modeling and observational uncertainty on attribution results than previously possible. Observational uncertainty is explored using multiple radiosonde reconstructions including those with ensembles of realizations exploring the effects of processing choices. Modeling uncertainty is explored by calculating multiple fingerprints of natural influence (from changes in solar irradiance and volcanic aerosols) and of human influence (due to greenhouse gases and due to the effects of combined anthropogenic forcings including stratospheric ozone depletion). With increased confidence over previous studies, human influences (both greenhouse gas and other anthropogenic forcings) have been detected in spatiotemporal changes in free atmosphere temperature from 1961 to 2010, irrespective of whether the full atmospheric column (30–850 hPa) is examined or purely the troposphere, with stratospheric ozone depletion dominating the cooling that has been observed in the lower stratosphere. Thus the advances of the last decade yield increased confidence that anthropogenic influences have made a substantial contribution to the evolution of free atmosphere temperatures.

[1] Multidecadal-scale changes in atmospheric temperature have been measured by both radiosondes and the satellite-borne microwave sounding unit (MSU). Both measurement systems exhibit substantial time varying biases that need to removed to the extent possible from the raw data before they can be used to assess climate trends. A number of methods have been developed for each measurement system, leading to the creation of several homogenized data sets. In this work, we evaluate the agreement between MSU and homogenized radiosonde data sets on multiyear (predominantly 5-year) time scales and find that MSU data sets are often more similar to each other than to radiosonde data sets and vice versa. Furthermore, on these times scales the differences between MSU data sets are often not larger than published internal uncertainty estimates for the RSS product alone and therefore may not be statistically significant when the internal uncertainty in each data set is taken into account. Given the data limitations it is concluded that using radiosondes to validate multidecadal-scale trends in MSU data, or vice versa, or trying to use such metrics alone to pick a ‘winner’ is an ill-conditioned approach and has limited utility without one or more of additional independent measurements, or methodological, or physical analysis.

[1] Changes in the circumstances behind in situ temperature measurements often lead to biases in individual station records that, collectively, can also bias regional temperature trends. Since these biases are comparable in magnitude to climate change signals, homogeneity “corrections” are necessary to make the records suitable for climate analysis. To quantify the effectiveness of U.S. surface temperature homogenization, a randomized perturbed ensemble of the USHCN pairwise homogenization algorithm was run against a suite of benchmark analogs to real monthly temperature data. Results indicate that all randomized versions of the algorithm consistently produce homogenized data closer to the true climate signal in the presence of widespread systematic errors. When applied to the real-world observations, the randomized ensemble reinforces previous understanding that the two dominant sources of bias in the U.S. temperature records are caused by changes to time of observation (spurious cooling in minimum and maximum) and conversion to electronic resistance thermometers (spurious cooling in maximum and warming in minimum). Error bounds defined by the ensemble output indicate that maximum temperature trends are positive for the past 30, 50 and 100 years, and that these maximums contain pervasive negative biases that cause the unhomogenized (raw) trends to fall below the lower limits of uncertainty. Moreover, because residual bias in the homogenized analogs is one-tailed under biased errors, it is likely that maximum temperature trends have been underestimated in the USHCN. Trends for minimum temperature are also positive over the three periods, but the ensemble error bounds encompass trends from the unhomogenized data.