A CALL FOR NEW APPROACHES TO QUANTIFYING BIASES IN OBSERVATIONS OF SEA SURFACE TEMPERATURE
A CALL FOR NEW APPROACHES TO QUANTIFYING BIASES IN OBSERVATIONS OF SEA SURFACE TEMPERATURE
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DOI：
10.1175/bams-d-15-00251.1
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发表时间：
2017-08-01
影响因子：
8
通讯作者：
Zhang, Huai-Min
中科院分区：
文献类型：
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作者：
Kent, Elizabeth C.;Kennedy, John J.;Zhang, Huai-Min
全球地表温度变化是气候变化的基本表现。最近，观测到的表面温度变化率的变化引起了广泛的争论，这突出表明了对海表温度测量进行调整时不确定性的重要性。这些调整用于补偿系统偏差和观测协议的变化。更好地量化调整及其不确定性将增加对估计的表面温度变化的信心，并提供更高质量的网格SST场，供许多应用程序使用，偏差调整是基于观测过程的物理模型或SST与参考数据集（如夜间海洋空气温度）之间关系不变的假设。这些方法在最大的空间和时间尺度上产生类似的SST偏差估计，但区域差异可能超过估计的不确定性。我们描述的挑战，以提高我们的理解SST的偏见。克服这些问题需要澄清过去的观测方法，改进与每种观测方法相关的偏差建模，并开发对缺乏关于观测方法的元数据不太敏感的统计偏差估计，需要新的方法，将特定于每种观测类型的偏差模型嵌入稳健的统计框架中。移动的平台和观测类型的快速变化要求对各个历史和当今平台的偏差进行评估（即，船或浮标）或平台组。缺乏用于验证和偏差模型开发的观测元数据和高质量观测可能仍然是主要挑战。
Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method.New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.