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Metric of the 2–6 day sea-surface temperature response to wind stress in the Tropical Pacific and its sensitivity to the K-Profile Parameterization of vertical mixing

机译:热带太平洋2–6天海表温度对风应力的响应及其对垂直混合K剖面参数化的敏感性

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摘要

Uncertainty in wind forcing has long hampered direct tests of ocean model output against observations for the purpose of refining the boundary layer K-Profile Parameterization (KPP) of oceanic vertical mixing. Considered here is a short-term metric that could be sensitive to the ways in which the KPP directly affects the adjustment of sea surface temperatures for a given change in wind stress. In particular a metric is developed based on the lagged correlation between the 2–6 day filtered wind stress and sea surface temperature. The metric is normalized by estimated observational and model uncertainties such that the significance of differences may be assessed. For this purpose multiple wind reanalysis products and their blended combinations were used to represent the range of forcing uncertainty, while perturbed KPP parameter model runs explore the sensitivity of the metric to the parameterization of vertical mixing. The correlation metric is sensitive to perturbations to most KPP parameters, in ways that accord with expectations, although only a few parameters show a sensitivity on the same order as the sensitivity to switching between wind products. This suggests that uncertainties in wind forcing continue to be a significant limitation for applying direct observational tests of KPP physics. Moreover, model correlations are biased high, suggesting that the model lacks or does not resolve sources of variability on the 2–6 day time scale.
机译:为了完善海洋垂直混合的边界层K剖面参数化(KPP),强迫风的不确定性长期以来阻碍了海洋模型输出与观测结果的直接测试。这里考虑的是一个短期指标,该指标可能对KPP直接影响给定风应力变化而直接影响海表温度调节的方式敏感。特别是基于2-6天过滤后的风应力和海面温度之间的滞后相关性,制定了一个度量标准。通过估计的观测不确定性和模型不确定性对度量进行标准化,以便可以评估差异的重要性。为此,使用了多个风力再分析产品及其混合组合来表示强迫不确定性的范围,而扰动的KPP参数模型运行则探索了指标对垂直混合参数化的敏感性。尽管只有少数几个参数显示出与风电切换之间的灵敏度相同数量级的灵敏度,但相关度量对大多数KPP参数的扰动非常敏感,符合期望的方式。这表明,强迫风的不确定性继续成为应用KPP物理直接观测测试的重要限制。此外,模型的相关性偏高,表明模型缺乏或不能解决2-6天时间尺度上的变异性来源。

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