AirSeaFluxCode: Open-source software for calculating turbulent air-sea fluxes from meteorological parameters
AirSeaFluxCode: Open-source software for calculating turbulent air-sea fluxes from meteorological parameters
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DOI：
10.3389/fmars.2022.1049168
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发表时间：
2023-02-06
影响因子：
3.7
通讯作者：
Yelland, Margaret J.
中科院分区：
文献类型：
--
作者：
Biri, Stavroula;Cornes, Richard C.;Yelland, Margaret J.
大气和海洋之间的热量、水分和动量的湍流交换或通量在地球气候系统中起着至关重要的作用。湍流通量的直接测量是非常具有挑战性和稀疏的，并没有跨越整个海洋的环境条件。这意味着，将直接通量观测与平均气象和海面变量的同时测量相联系的经验“批量公式”参数化具有相当大的不确定性。在本文中，我们提出了一个Python 3.6（或更高版本）的开源软件包“AirSeaFluxCode”的热量（潜热和显热）和动量通量的计算。包括十种不同的参数化，每种参数化都基于已发表的描述或代码，并且每种参数化都来自不同的观测集，或者关于湍流交换过程的不同假设。它们代表了一系列当前专家关于通量如何取决于平均属性的意见，并可用于探索计算通量的不确定性。AirSeaFluxCode还允许将平均气象输入参数（气温、湿度和风速）从获得它们的高度调整到用户定义的输出高度。这种高度调整使得能够比较在海平面以上不同高度处进行的测量或模型导出的值。参数化使用相对容易测量或可作为模型输出的输入参数计算通量：风速、气温、海面温度、大气压力和湿度。在原始代码可用的地方，我们将其输出与AirSeaFluxCode进行了比较。通过标准化计算方法或计算气象变量来提高算法一致性的任何变化都进行了讨论，并量化了影响：除了少数极端情况外，这些变化微不足道，AirSeaFluxCode表现出强大的鲁棒性。我们还调查了不同的假设交换过程中所造成的通量的影响，或固有的参数化的实施中的选择。例如，海面温度通常是指通常在1至10米深度获得的数据。然而，由于一些参数化需要一个“皮肤”的海面温度，代码调整温度在深度皮肤温度包括在内：这有一个非常显着的影响通量。选择一个适合现有海表温度的参数化方法，将避免调整海表温度数据的需要和与这种调整有关的不确定性，还将避免由于使用“错误”的温度测量而产生的偏差。关于海水表面湿度减少的规模的假设也导致了重大差异，以说明盐度的影响：减少系数的不确定性需要在今后的分析中加以量化。极端条件下的通量特别不确定，因为不同参数化中的传递系数在非常高和非常低的风速下变化最大。低风速也是具有挑战性的数值实现，因为选择必须作出有关：迭代计算的收敛标准，包括对流阵风的参数化，或应用专案限制的各种参数。所有这些选择都能显著影响轻风的通量估计。
The turbulent exchanges, or fluxes, of heat, moisture and momentum between the atmosphere and the ocean play a crucial role in the Earth's climate system. Direct measurements of turbulent fluxes are very challenging and sparse, and do not span the full range of environmental conditions that exist over the ocean. This means that empirical "bulk formulae" parameterizations that relate direct flux observations to concurrent measurements of the mean meteorological and sea surface variables contain considerable uncertainty. In this paper, we present a Python 3.6 (or higher) open-source software package "AirSeaFluxCode" for the computation of the heat (latent and sensible) and momentum fluxes. Ten different parameterizations are included, each based on published descriptions or code and each derived from a different set of observations, or different assumptions about the turbulent exchange processes. They represent a range of current expert opinion on how the fluxes depend on mean properties and can be used to explore uncertainty in calculated fluxes. AirSeaFluxCode also allows the adjustment of the mean meteorological input parameters (air temperature, humidity and wind speed) from the height at which they are obtained to a user-defined output height. This height adjustment enables the comparison of measurements, or model-derived values, made at different heights above sea-level. The parameterizations calculate the fluxes using input parameters that are relatively easily to measure, or are available as model output: wind speed, air temperature, sea surface temperature, atmospheric pressure and humidity. Where original code is available we have compared its output with that of AirSeaFluxCode. Any changes made to increase consistency across algorithms by standardizing computational methods or calculation of meteorological variables, for example, are discussed and the impacts quantified: these are shown to be insignificant except for a few cases where conditions were extreme, and AirSeaFluxCode is shown to be robust. We also investigate the impact on the fluxes caused by different assumptions about the exchange processes, or the choices inherent in the implementation of the parameterizations. For example, sea surface temperature usually refers to data typically obtained at depths of between 1 and 10 m. However, since some parameterizations require a "skin" sea surface temperature, code that adjusts temperature at depth to skin temperature is included: this has a very significant impact on the fluxes. Selecting a parameterization that is appropriate for the available sea surface temperature will avoid the need to adjust the sea temperature data and the uncertainties associated with that adjustment, and will also avoid the biases due to use of the "wrong" measure of temperature. Significant differences also resulted from assumptions about the size of reduction in sea surface humidity to account for salinity effects: the uncertainty in the reduction factor needs to be quantified in future analyses. Fluxes in extreme conditions are particularly uncertain since the transfer coefficients in the different parameterizations vary most at very high and very low wind speeds. Low wind speeds are also challenging for numerical implementation since choices have to be made regarding: convergence criteria for the iterative calculation, inclusion of a parameterization for convective gustiness, or application of ad hoc limits to various parameters. All of these choices can significantly affect the flux estimates for light winds.