Observed and simulated estimates of the meridional overturning circulation at 26.5° N in the Atlantic
Observed and simulated estimates of the meridional overturning circulation at 26.5° N in the Atlantic
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大西洋北纬 26.5° 经向翻转环流的观测和模拟估计
DOI：
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发表时间：
2009
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通讯作者：
J. Marotzke
中科院分区：
文献类型：
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作者：
J. Baehr;S. Cunningham;H. Haak;P. Heimbach;T. Kanzow;J. Marotzke
利用英国/美国快速/MOCHA阵列估算的大西洋26.5°N经向翻转环流(MOC)的逐日时间序列，对两个全球环流模式模拟的MOC进行了评估：(I)耦合气候模式ECHAM5/MPI-OM的8成员集合，以及(Ii)ECCO-GODAE状态估计。在ECHAM5/MPI-OM中，我们发现观测到的MOC和模拟的MOC在99%的可信区间内具有相似的变率和时间平均值。在ECCO-GODAE中，我们发现观测到的MOC与模拟的MOC在99%的可信区间内显示出显著的相关性。为了研究不同输送分量的贡献，将MOC分解为佛罗里达洋流输送、埃克曼输送和大洋中部输送。在这两个模式中，海洋中部的输送都是用MOC减去佛罗里达洋流和埃克曼输送的残差来近似的。由于这些模型根据定义保存了运量，今后应将快速/莫查中洋运输与模型中的剩余运量进行比较。RAPID/MOCHA与ECHAM5/MPI-OM和ECCO-GODAE MOC估计在26.5°N的方差和相关性的相似性，在估计气候模拟的(自然)可变性及其在气候变化信噪比检测分析中的使用方面令人鼓舞。增强对模拟水文和运输可变性的信心将需要更长的观测时间序列。
Daily timeseries of the meridional overturning circulation (MOC) estimated from the UK/US RAPID/MOCHA array at 26.5° N in the Atlantic are used to evaluate the MOC as simulated in two global circulation models: (I) an 8-member ensemble of the coupled climate model ECHAM5/MPI-OM, and (II) the ECCO-GODAE state estimate. In ECHAM5/MPI-OM, we find that the observed and simulated MOC have a similar variability and time-mean within the 99% confidence interval. In ECCO-GODAE, we find that the observed and simulated MOC show a significant correlation within the 99% confidence interval. To investigate the contribution of the different transport components, the MOC is decomposed into Florida Current, Ekman and mid-ocean transports. In both models, the mid-ocean transport is closely approximated by the residual of the MOC minus Florida Current and Ekman transports. As the models conserve volume by definition, future comparisons of the RAPID/MOCHA mid-ocean transport should be done against the residual transport in the models. The similarity in the variance and the correlation between the RAPID/MOCHA, and respectively ECHAM5/MPI-OM and ECCO-GODAE MOC estimates at 26.5° N is encouraging in the context of estimating (natural) variability in climate simulations and its use in climate change signal-to-noise detection analyses. Enhanced confidence in simulated hydrographic and transport variability will require longer observational time series.