Hindcasting Ocean Climate Variability Using Time-Dependent Surface Data to Drive a Model: An Idealized Study
Hindcasting Ocean Climate Variability Using Time-Dependent Surface Data to Drive a Model: An Idealized Study
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使用随时间变化的表面数据驱动模型来预测海洋气候变化：理想化研究
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
Shengpan P. Zhang
中科院分区：
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作者：
R. Greatbatch;Guoqing Li;Shengpan P. Zhang
摘要本文利用海洋环流模式，通过不同的时变表面通量、海表温度（SST）和海表盐度（SSS）数据的组合，研究了年代际气候事件的后报。从对照运行中生成数据，与随后的模型实验进行比较。最强大的结果得到使用通量边界条件的表面温度和盐度。对于这些边界条件，模型结果对表面数据中的噪声相对不敏感，并且需要大约20年的时间来克服施加的不正确的初始条件。当使用SST和SSS数据运行时，模型结果对噪音输入更加敏感。要获得有意义的结果，单靠SST数据是不够的;还需要SSS数据。这与众所周知的海洋气候模式在切换到混合边界条件时的不稳定性有关。时变的SSS数据不能被气候学取代;使用最佳拟合的T-S关系...
Abstract This paper investigates the hindcasting of interdecadal climate events using an ocean circulation model driven by different combinations of time-varying surface flux, sea surface temperature (SST), and sea surface salinity (SSS) data. Data are generated from a control run, against which the subsequent model experiments are compared. The most robust results are obtained using flux boundary conditions on both surface temperature and salinity. For these boundary conditions, model results am relatively insensitive to noise in the surface data and take about 20 years to overcome the imposition of an incorrect initial condition. Model results are much more sensitive to noisy inputs when run using SST and SSS data. To obtain meaningful results, SST data alone are not sufficient; SSS data are also required. This is related to the well-known instability of ocean climate models upon a switch to mixed boundary conditions. Time-varying SSS data cannot be replaced by climatology; using a best-fit T–S relation...