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Benjamin D. Santer
,
Stephen Po-Chedley
,
Nicole Feldl
,
John C. Fyfe
,
Qiang Fu
,
Susan Solomon
,
Mark England
,
Keith B. Rodgers
,
Malte F. Stuecker
,
Carl Mears
,
Cheng-Zhi Zou
,
Céline J. W. Bonfils
,
Giuliana Pallotta
,
Mark D. Zelinka
,
Nan Rosenbloom
, and
Jim Edwards

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) intermodel 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 El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution 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.

Free access
Ryohei Yamaguchi
,
Ji-Eun Kim
,
Keith B. Rodgers
,
Karl Stein
,
Axel Timmermann
,
Sun-Seon Lee
,
Lei Huang
,
Malte F. Stuecker
,
John T. Fasullo
,
Gokhan Danabasoglu
,
Clara Deser
,
Jean-Francois Lamarque
,
Nan A. Rosenbloom
, and
Jim Edwards

Abstract

Biomass burning aerosol (BBA) emissions in the Coupled Model Intercomparison Project phase 6 (CMIP6) historical forcing fields have enhanced temporal variability during the years 1997–2014 compared to earlier periods. Recent studies document that the corresponding inhomogeneous shortwave forcing over this period can cause changes in clouds, permafrost, and soil moisture, which contribute to a net terrestrial Northern Hemisphere warming relative to earlier periods. Here, we investigate the ocean response to the hemispherically asymmetric warming, using a 100-member ensemble of the Community Earth System Model version 2 Large Ensemble forced by two different BBA emissions (CMIP6 default and temporally smoothed over 1990–2020). Differences between the two subensemble means show that ocean temperature anomalies occur during periods of high BBA variability and subsequently persist over multiple decades. In the North Atlantic, surface warming is efficiently compensated for by decreased northward oceanic heat transport due to a slowdown of the Atlantic meridional overturning circulation. In the North Pacific, surface warming is compensated for by an anomalous cross-equatorial cell (CEC) that reduces northward oceanic heat transport. The heat that converges in the South Pacific through the anomalous CEC is shunted into the subsurface and contributes to formation of long-lasting ocean temperature anomalies. The anomalous CEC is maintained through latitude-dependent contributions from narrow western boundary currents and basinwide near-surface Ekman transport. These results indicate that interannual variability in forcing fields may significantly change the background climate state over long time scales, presenting a potential uncertainty in CMIP6-class climate projections forced without interannual variability.

Open access