Simulated Versus Observed Variability in Tropospheric Temperature
The objective of the analysis was to explore whether climate models (from both CMIP5 and CMIP6 ensembles) underestimate observed low-frequency variability of mid- to upper tropospheric temperature (TMT). Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets. To do so, we developed a statistical framework to compare the spectral features of TMT variability in climate model ensembles and satellite data under different analyst choices (the satellite and climate model TMT data sets, the method for separating signal and noise, the frequency range considered, and the statistical model to represent observed variability). We included both short-term and long-term memory statistical models and applied objective criteria to select the best fitting models to the observed time series. We also highlighted the differences and implications of four different signal removal strategies. We used the average power over specific frequency bands to compare climate model forced and unforced simulations and observations.
We find that on timescales of 5-20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.
Our focus here is on assessing how comparisons of simulated and observed natural variability spectra are affected by uncertainties in data, climate models, and the separation of signal and noise. We also explore the impact of using different statistical models to characterize observed natural variability. We are particularly interested in determining whether current climate models systematically underestimate the amplitude of observed natural variability of global-mean mid- to upper-tropospheric temperature (TMT) on time scales of 1–2 decades. If such a bias existed, it would imply that signal-to-noise (S/N) ratios had been spuriously inflated in previous anthropogenic signal detection studies with TMT. We rely on tropospheric temperature for multiple reasons. First, considerable scientific and political attention has been focused on the question of whether satellite TMT datasets show statistically significant warming. Answering this question requires information on the credibility of model estimates of natural TMT variability. Second, unlike surface temperature records obtained from land thermometers, ships of opportunity, and ocean buoys, satellite TMT measurements have time-invariant near-global coverage. This is advantageous for reliable estimation of variability. Third, structural uncertainties in observed and modeled TMT variability can be well characterized: information on global-scale TMT changes is available from three satellite research groups and “synthetic” TMT has been calculated from over three dozen climate models.