Abstract

Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments.

中文翻译：

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使用机器学习减少极地表面湍流通量的参数化误差

摘要

众所周知，海冰和大气之间的湍流交换会影响海冰的融化速度、大气环流异常的发展，并可能影响极地和非极地地区之间的遥相关。气候模型中海冰上的湍流热通量的参数化仍然存在较大的模型误差，导致未来气候的预测存在很大的不确定性。通量通常使用基于莫宁-奥布霍夫相似理论的体积公式计算，该理论在极地地区表现出特殊的局限性。专门针对极地条件开发的参数化（例如，表示海冰上山脊或融化池塘的形式阻力）依赖于稀疏观测，因此可能不普遍适用。在这项研究中，针对北极的动量、显热和潜热的表面湍流通量开发了新的数据驱动参数化。机器学习已经在极地以外的地区使用，以提供准确且计算成本低廉的表面湍流通量估计。为了研究这种方法在北极的可行性，我们将神经网络模型拟合到参考数据集 (SHEBA)。预测性能已使用其他观测活动的数据进行了测试。对于动量和显热，神经网络的性能与最先进的散装配方相当，在某些情况下甚至更好。这些结果为传统批量方法失败的情况提供了一种有效的替代方案，并且可以为进一步的基于物理的开发提供信息。