Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ∼9 % and ∼7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (∼39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ∼66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr⁻¹) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

中文翻译：

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通过观测和人工神经网络估算的全球海洋二甲基硫醚气候

船用二甲基硫醚（DMS）对气候非常重要，因为DMS能够改变地球的辐射预算。为了更好地了解大气中的硫，气溶胶/云动力学和反照率，需要了解DMS的全球规模分布，季节变化和海空通量。在这里，我们研究了使用人工神经网络（ANN）将可用的DMS测量值外推到全球海洋，并产生具有每月时间分辨率的全球气候。使用了一个全球数据库，该数据库包含地表水中82 996个船载DMS测量值以及一套环境参数，包括纬度-经度坐标，一天中的时间，一年中的时间，太阳辐射，混合层深度，海面温度，盐度，硝酸盐，磷酸盐和硅酸盐。DMS对环境参数的线性回归表明，在全球范围内，混合层深度和太阳辐射是DMS的最强预测指标。这些参数捕获〜9  ％和~7 原始DMS数据方差分别％。多线性回归可以捕获更多的原始数据方差（约39  ％），但严重低估了高浓度区域中的DMS。相反，人工神经网络 在我们的数据库中捕获了约66％的原始数据差异。像以前的气候一样，我们的结果表明，在高纬度夏季海洋中，地表海洋DMS具有强烈的季节性周期，具有最高的浓度和海空通量。我们估计全球海对空DMS通量较低（20.12±0.43  Tg S yr ^-1当使用相同的气体传输速度参数设置时，将比基于地图插值方法的先前估算值高。我们的敏感性测试结果表明，DMS浓度不会随每个环境参数单向变化，这强调了这些参数之间的相互作用。人工神经网络模型表明，DMS从海洋到大气的通量将随着全球变暖而增加。考虑到较大的DMS通量会引起较大的云反射率，这对应于负气候反馈。