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Robust anthropogenic signal identified in the seasonal cycle of tropospheric temperature

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  • 1 aProgram for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California
  • | 2 bJoint Institute for Regional Earth System Science & Engineering, University of California at Los Angeles, Los Angeles, California
  • | 3 cDepartment of Earth and Planetary Sciences, University of California at Santa Cruz, Santa Cruz, California
  • | 4 dCanadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
  • | 5 eDept. of Atmospheric Sciences, University of Washington, Seattle, Washington
  • | 6 fMassachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, Massachusetts
  • | 7 gCenter for Climate Physics, Institute for Basic Science, Busan, South Korea
  • | 8 hPusan National University, Busan, South Korea
  • | 9 iDepartment of Oceanography and International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu
  • | 10 jRemote Sensing Systems, Santa Rosa, California
  • | 11 kCenter for Satellite Applications and Research, NOAA/NESDIS, Camp Springs, Maryland
  • | 12 lNational Center for Atmospheric Research, Boulder, Colorado.
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Abstract

Previous work identified an anthropogenic fingerprint pattern in T AC(x,t), the amplitude of the seasonal cycle of mid- to upper tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T AC(x,t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T AC(x,t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3-4) inter-model and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T AC(x,t) variability associated with the Atlantic Multidecadal Oscillation and the El Niño~Southern Oscillation. The robustness of the seasonal cycle D&A results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T AC(x,t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

Corresponding author: bensanter1289@gmail.com. Date: May 17, 2022

Abstract

Previous work identified an anthropogenic fingerprint pattern in T AC(x,t), the amplitude of the seasonal cycle of mid- to upper tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T AC(x,t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T AC(x,t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3-4) inter-model and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T AC(x,t) variability associated with the Atlantic Multidecadal Oscillation and the El Niño~Southern Oscillation. The robustness of the seasonal cycle D&A results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T AC(x,t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

Corresponding author: bensanter1289@gmail.com. Date: May 17, 2022
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