Human influence on the seasonal cycle of tropospheric temperature

INTRODUCTION Fingerprint studies use pattern information to separate human and natural influences on climate. Most fingerprint research relies on patterns of climate change that are averaged over years or decades. Few studies probe shorter time scales. We consider here whether human influences are identifiable in the changing seasonal cycle. We focus on Earth’s troposphere, which extends from the surface to roughly 16 km at the tropics and 13 km at the poles. Our interest is in T AC , the geographical pattern of the amplitude of the annual cycle of tropospheric temperature. Information on how T AC has changed over time is available from satellite retrievals and from large multimodel ensembles of simulations. RATIONALE At least three lines of evidence suggest that human activities have affected the seasonal cycle. First, there are seasonal signals in certain human-caused external forcings, such as stratospheric ozone depletion and particulate pollution. Second, there is seasonality in some of the climate feedbacks triggered by external forcings. Third, there are widespread signals of seasonal changes in the distributions and abundances of plant and animal species. These biological signals are in part mediated by seasonal climate changes arising from global warming. All three lines of evidence provide scientific justification for performing fingerprint studies with the seasonal cycle. RESULTS The simulated response of the seasonal cycle to historical changes in human and natural factors has prominent mid-latitude increases in the amplitude of T AC . These features arise from larger mid-latitude warming in the summer hemisphere, which appears to be partly attributable to continental drying. Because of land-ocean differences in heat capacity and hemispheric asymmetry in land fraction, mid-latitude increases in T AC are greater in the Northern Hemisphere than in the Southern Hemisphere. Qualitatively similar large-scale patterns of annual cycle change occur in satellite tropospheric temperature data. We applied a standard fingerprint method to determine (i) whether the pattern similarity between the model “human influence” fingerprint and satellite temperature data increases with time, and (ii) whether such an increase is significant relative to random changes in similarity between the fingerprint and patterns of natural internal variability. This method yields signal-to-noise (S/N) ratios as a function of increasing satellite record length. Fingerprint detection occurs when S/N exceeds and remains above the 1% significance threshold. We find that the model fingerprint of externally forced seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. In these five datasets, S/N ratios for the 38-year satellite record vary from 2.7 to 5.8. Our positive fingerprint detection results are unaffected by the removal of all global mean information and by the exclusion of sea ice regions. On time scales for which meaningful tests are possible (one to two decades), there is no evidence that S/N ratios are spuriously inflated by a systematic model underestimate of the amplitude of observed tropospheric temperature variability. CONCLUSION Our results suggest that attribution studies with the seasonal cycle of tropospheric temperature provide powerful and novel evidence for a statistically significant human effect on Earth’s climate. We hope that this finding will stimulate more detailed exploration of the seasonal signals caused by anthropogenic forcing.