On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates
On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates
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DOI：
10.1007/s00382-013-1725-9
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发表时间：
2013-03
期刊：
影响因子：
4.6
通讯作者：
J. Vial;J. Dufresne;S. Bony
中科院分区：
文献类型：
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作者：
J. Vial;J. Dufresne;S. Bony
本研究诊断的气候敏感性，辐射强迫和气候反馈估计11个大气环流模式参加第五阶段的耦合模式相互比较项目（CMIP 5），并分析模式间的差异。这是考虑到这样一个事实，即气候对二氧化碳（CO2）增加的反应不一定只是由地表温度变化介导的，但也可能是由于快速的陆地变暖和对流层对CO2辐射强迫的调整。通过考虑对流层调整CO2强迫的一部分，而不是作为反馈，并通过使用辐射内核的方法，我们分解气候敏感性估计的反馈和调整与水蒸气，温度直减率，表面反射和云。除了一个例外，云对CO2的调整通常是积极的，并且与云反馈的强度降低有关;多模式平均云反馈弱约33%。然而，与温度、水蒸气和湿度相关的非云调整似乎更好地理解为对地表变暖的反应。分离出对流层调整并不会显著影响气候敏感性估计的传播，这主要是由于不同的气候反馈。大约70%的传播来自云反馈，云反馈仍然是气候敏感性模型间传播的主要来源，热带地区的贡献很大。低灵敏度和高灵敏度模式之间的热带云反馈的差异发生在大范围的动力学制度，但主要是从与浅积云和层积云的优势相关的制度。水汽加直减率反馈的组合也有助于气候敏感性估计的传播，模型间的差异主要来自整个对流层的相对湿度响应。最后，本研究指出，在气候敏感性估计的模型间传播的解释的调整和反馈的计算非线性的实质性作用。我们表明，在气候模式模拟与大强迫（例如，4 × CO2），非线性不能假设为较小或忽略。话虽如此，这里提出的大多数结果与以前的一些反馈研究是一致的，尽管方法的性质非常不同，所有与之相关的不确定性。
This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO2) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO2radiative forcing. By considering tropospheric adjustments to CO2as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO2is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO2), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.