A Gaussian process state-space model for atmospheric CO2 and sea surface temperature index reconstruction from boron isotope and planktonic δ18O proxies
A Gaussian process state-space model for atmospheric CO2 and sea surface temperature index reconstruction from boron isotope and planktonic δ18O proxies
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DOI：
10.1145/3429309.3429316
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发表时间：
2020-09
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影响因子：
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通讯作者：
Taehee Lee;C. Lawrence
中科院分区：
文献类型：
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作者：
Taehee Lee;C. Lawrence
在古气候学研究中，经常出现只有少量观测资料可用于重建过去气候事件的情况。状态空间模型可以通过为这些隐藏状态提供先验信息，使它们相互关联来克服这种稀缺性。从不同的代理同时推断多个事件以利用它们的相互依赖性是另一种选择。本文提出了一个高斯过程状态空间模型，利用硼同位素和碳酸盐δ18O替代数据重建大气CO2和海表温度指数。
It often occurs in practice that only a small number of observations are given for reconstructing past climate events in the field of paleoclimatology. State-space models can overcome such scarcity by giving priors to those hidden states to make them correlated to one another. Inferring multiple events simultaneously from various proxies to exploit their mutual dependency is another option. Here we present a Gaussian process state-space model to reconstruct both atmospheric CO2 and sea surface temperature index from boron isotope and planktonic δ18O proxies.