Global Mean Surface Temperature Response to Large‐Scale Patterns of Variability in Observations and CMIP5
Global Mean Surface Temperature Response to Large‐Scale Patterns of Variability in Observations and CMIP5
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DOI：
10.1029/2018gl081462
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发表时间：
2019-02
影响因子：
5.2
通讯作者：
J. Kajtar;M. Collins;L. Frankcombe;M. England;T. Osborn;M. Juniper
中科院分区：
文献类型：
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作者：
J. Kajtar;M. Collins;L. Frankcombe;M. England;T. Osborn;M. Juniper
全球平均地表温度（GMST）在十年到几十年的时间尺度上波动。内部变异的模式是部分原因，但这种关系可能会被非遗传性强迫信号所混淆。在这里，我们采用了一种基于物理的方法，将内部变率与强迫响应分离开来，以研究大尺度模式的趋势，特别是年代际太平洋涛动（IPO）和大西洋年代际变率（AMV），如何影响GMST。去除强迫响应后，观测到的GMST变异性接近耦合模型相互比较项目第5阶段模拟的中心估计值，但模型往往低估了时间尺度>10年的IPO变异性和时间尺度>20年的AMV。GMST趋势与这些模式之间的相关性也未得到充分代表，在10年和35年的时间尺度上，IPO和AMV的相关性最强。引人注目的是，模拟IPO和AMV更强变异性的模型也表现出这些模式与GMST之间更强的关系，主要分别在10年和35年的时间尺度上。
Global mean surface temperature (GMST) fluctuates over decadal to multidecadal time scales. Patterns of internal variability are partly responsible, but the relationships can be conflated by anthropogenically forced signals. Here we adopt a physically based method of separating internal variability from forced responses to examine how trends in large‐scale patterns, specifically the Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Variability (AMV), influence GMST. After removing the forced responses, observed variability of GMST is close to the central estimates of Coupled Model Intercomparison Project phase 5 simulations, but models tend to underestimate IPO variability at time scales >10 years, and AMV at time scales >20 years. Correlations between GMST trends and these patterns are also underrepresented, most strongly at 10‐ and 35‐year time scales, for IPO and AMV, respectively. Strikingly, models that simulate stronger variability of IPO and AMV also exhibit stronger relationships between these patterns and GMST, predominately at the 10‐ and 35‐year time scales, respectively.