Human influence on the seasonal cycle of tropospheric temperature

Abstract

We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic "fingerprint" predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics. Simple physical mechanisms explain these features. The model fingerprint of seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. Our results suggest that attribution studies with the changing seasonal cycle provide powerful evidence for a significant human effect on Earth's climate.

Comparison and assessment of large-scale surface temperature in climate model simulations

“…Second, at the lowest frequencies, model spectral densities are higher than in NCEP-2 and ERA-I, and the 95 % posterior intervals of nearly all of the simulated spectra do not overlap with the reanalysis spectra. This difference in the amplitude of simulated and observed variability (which is most pronounced in the tropics) is consistent with findings obtained elsewhere for multi-model analyses of tropospheric temperature (Santer et al, 2018). A model bias in the opposite direction to that found here (i.e., a systematic underestimate of the amplitude of observed internal variability on multidecadal timescales) would be more concerning -such an error would spuriously inflate signal-to-noise ratios for anthropogenic signal detection (Santer et al, 2018).…”

“…This choice makes the DLM seasonal component analogous to calculating a constant climatology, which is a common practice in climate science. If small changes in the seasonal amplitudes exist (Santer et al, 2018) the changes are aliased in the DLM residuals, although we found no evidence of this in the data.…”

“…Control simulations provide estimates of "pure" internal variability, which is an integral component of climate change detection and attribution studies (Santer et al, 2018). In the MIROC5 pre-industrial control simulation analyzed here, there are no year-to-year changes in the atmospheric concentrations of greenhouse gases, particulate pollution, volcanic aerosols, or solar irradiance.…”

“…As expected based on the changes in incoming solar radiation as a function of latitude and season, the phasing of the annual cycle differs in the Northern and Southern hemispheres. A semiannual cycle is apparent in the tropics (Santer et al, 2018). Our statistical analyses focus on these area-averaged time series.…”

Past warming trend constrains future warming in CMIP6 models

“…The last box plot in (D) shows the IPCC AR5 likely (>66% probability; equivalent to 17 to 83% range) range. Each box plot shows 5 to 95% range, likely range, and median value, as illustrated in the legend.onMarch 20, 2020 http://advances.sciencemag.org/ Downloaded from model's 1981-2014 regional trend map based only on pattern covariance, similar to standard detection and attribution methods(34), and is independent of any global mean warming trends.This approach of "fingerprint of spatial trend variation" yields a correlation of each model's pattern covariance with TCR (R = 0.59 inFig. 4B, compared with R = 0.64 for the correlation of TCR with recent global mean warming inFig.…”