A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series
A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series
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使用更新的观测值和详细的辐射强迫时间序列对气候敏感性进行较低且更受限的估计
DOI：
10.5194/esd-5-139-2014
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
G. Myhre
中科院分区：
文献类型：
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作者：
R. Skeie;T. Berntsen;M. Aldrin;M. Holden;G. Myhre
抽象的。平衡气候敏感性（ECS）是基于观测到的近地表温度变化，海洋热含量（OHC）的变化和详细的辐射强迫（RF）的时间序列，从工业化前的时间到2010年的所有主要的人为和自然强迫机制的约束。RF时间序列通过能量平衡模型（EBM）和随机模型与OHC和温度变化的观测相关联，使用贝叶斯方法从数据中估计ECS和其他未知参数。对于净人为RF，2010年的后验均值为2.0 Wm−2，置信区间为90%（C.I.）1.3至2.8 Wm−2，不包括目前总的气溶胶效应（直接+间接）强于-1.7 Wm−2。ECS的后验平均值为1.8 °C，90% CI。范围从0.9到3.2 °C，这比以前公布的大多数估计都要严格。我们发现，同时使用三个OHC数据集和全球平均温度和OHC数据到2010年大幅缩小了ECS的范围相比，使用更新较少的数据和只有一个OHC数据集。仅使用一个OHC集和2000年以前的数据，就可以产生与以前发表的使用20世纪观测结果的估计相当的结果，包括概率函数中的厚尾。分析表明，多年代尺度的内部变率对全球平均温度变化的贡献很大。如果我们不明确考虑长期的内部变异性，90%CI。比主要分析窄40%，平均ECS略低，这表明如果方法过于简单，ECS的不确定性可能会被严重低估。除了通过估计的概率密度函数表示的不确定性之外，由于RF的时间发展和EBM中的结构不确定性的处理的限制，可能存在不确定性。
Abstract. Equilibrium climate sensitivity (ECS) is constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model (EBM) and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.0 Wm−2, with a 90% credible interval (C.I.) of 1.3 to 2.8 Wm−2, excluding present-day total aerosol effects (direct + indirect) stronger than −1.7 Wm−2. The posterior mean of the ECS is 1.8 °C, with 90% C.I. ranging from 0.9 to 3.2 °C, which is tighter than most previously published estimates. We find that using three OHC data sets simultaneously and data for global mean temperature and OHC up to 2010 substantially narrows the range in ECS compared to using less updated data and only one OHC data set. Using only one OHC set and data up to 2000 can produce comparable results as previously published estimates using observations in the 20th century, including the heavy tail in the probability function. The analyses show a significant contribution of internal variability on a multi-decadal scale to the global mean temperature change. If we do not explicitly account for long-term internal variability, the 90% C.I. is 40% narrower than in the main analysis and the mean ECS becomes slightly lower, which demonstrates that the uncertainty in ECS may be severely underestimated if the method is too simple. In addition to the uncertainties represented through the estimated probability density functions, there may be uncertainties due to limitations in the treatment of the temporal development in RF and structural uncertainties in the EBM.