Robust anthropogenic signal identified in the seasonal cycle of tropospheric temperature
Robust anthropogenic signal identified in the seasonal cycle of tropospheric temperature
复制标题
对流层温度季节周期中确定的强大人为信号
DOI：
10.1175/jcli-d-21-0766.1
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发表时间：
2022
影响因子：
4.9
通讯作者：
Mears, C.
中科院分区：
文献类型：
--
作者：
Santer, B. D.;Po-Chedley, S.;Feldl, N.;Fyfe, J. C.;Fu, Q.;Solomon, S.;England, M.;Rodgers, K. B.;Stuecker, M. F.;Mears, C.
先前的工作在TAC（x，t）（对流层中层至上层温度（TMT）季节循环的振幅）中发现了人为指纹模式，但没有明确考虑卫星TAC（x，t）数据中的指纹识别是否可能受到影响真实世界的多年代际内部变率（MIV）。我们在这里解决这个问题，使用大型合奏（LE）与五个气候模式进行。LE提供叠加在基础强迫信号上的许多不同序列的内部可变性噪声。尽管五个模型在历史外部强迫、气候敏感性和MIV特性方面存在差异，但在240个LE实现的历史气候变化中，有239个模型的TAC（x，t）指纹是相似的，并且在统计上是可识别的。比较模拟和观察到的变异性光谱表明，一致的指纹识别是不太可能被模型低估所观察到的MIV的偏见。即使在MIV振幅存在较大（3-4倍）的模型间和实现间差异的情况下，季节性周期变化的人为指纹在模型和卫星数据中也是可以确定的。这主要是由于这样一个事实，即独特的，全球规模的指纹模式是空间上不同的较小规模的模式internalTAC（x，t）的变化与大西洋的几十年振荡和厄尔尼诺南方涛动。这里显示的季节性周期检测和归因结果的鲁棒性，以及理想化的水行星模拟的证据表明，基本的物理过程决定了观测和五个LE中强迫TAC（x，t）变化的共同模式。所涉及的关键过程包括温室气体引起的热带扩张，lapse-rate的变化，陆地表面干燥，海冰减少。
Previous work identified an anthropogenic fingerprint pattern inTAC(x,t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satelliteTAC(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, theirTAC(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 internalTAC(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 forcedTAC(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.