Chance Rosie, Baker Alex R, Carpenter Lucy, Jickells Tim D
Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, YO10 5DD, UK.
Environ Sci Process Impacts. 2014 Aug;16(8):1841-59. doi: 10.1039/c4em00139g.
Recent studies have highlighted the impact of sea surface iodide concentrations on the deposition of ozone to the sea surface and the sea to air flux of reactive iodine. The use of models to predict this flux demands accurate, spatially distributed sea surface iodide concentrations, but to date, the observational data required to support this is sparse and mostly arises from independent studies conducted on small geographical and temporal scales. We have compiled the available measurements of sea surface iodide to produce a data set spanning latitudes from 69°S to 66°N, which reveals a coherent, large scale distribution pattern, with highest concentrations observed in tropical waters. Relationships between iodide concentration and more readily available parameters (chlorophyll, nitrate, sea surface temperature, salinity, mixed layer depth) are evaluated as tools to predict iodide concentration. Of the variables tested, sea surface temperature is the strongest predictor of iodide concentration. Nitrate was also strongly inversely associated with iodide concentration, but chlorophyll-a was not.
最近的研究强调了海表碘化物浓度对臭氧向海表沉降以及活性碘的海-气通量的影响。使用模型预测这种通量需要准确的、空间分布的海表碘化物浓度，但迄今为止，支持这一预测所需的观测数据稀少，且大多来自于在小地理范围和时间尺度上开展的独立研究。我们汇总了现有的海表碘化物测量数据，生成了一个涵盖南纬69°至北纬66°纬度范围的数据集，该数据集揭示了一种连贯的大规模分布模式，热带水域观测到的浓度最高。评估碘化物浓度与更容易获取的参数（叶绿素、硝酸盐、海表温度、盐度、混合层深度）之间的关系，作为预测碘化物浓度的工具。在所测试的变量中，海表温度是碘化物浓度最强的预测指标。硝酸盐也与碘化物浓度呈强烈负相关，但叶绿素a并非如此。
