利用历史文献、气候模型和数据同化方法重建1368年至1911年东亚地区温度
Reconstructing East Asian Temperatures from 1368 to 1911 Using Historical Documents, Climate Models, and Data Assimilation
AI总结：
本研究针对1368-1911年东亚缺乏仪器温度数据的问题，依托REACHES历史文献数据集，结合克里金插值、分位数映射与新型贝叶斯数据同化方法，融合LME模型模拟，提升了历史温度重建精度，为气候研究提供关键资源。
摘要：
我们提出了一种重建1368年至1911年东亚地区年度温度的新方法，所依托的数据集为Reconstructed East Asian Climate Historical Encoded Series（REACHES，东亚气候历史编码重建序列）。这一时期缺乏仪器观测数据，给了解过去的气候状况带来了重大挑战。REACHES将中国明清时期的历史文献进行数字化处理，把定性描述转换为四级有序温度等级。然而，这类基于指数的数据偏向于异常或极端天气现象，导致数据空缺很可能对应着正常气候状况。为解决这一偏差问题，并重建东亚地区任意地点（包括没有直接历史数据的地点）的历史温度，我们采用了一个三层统计框架。首先，我们执行克里金插值法对东亚地区的温度数据进行插值，采用零均值假设来处理缺失信息。其次，我们利用Last Millennium Ensemble（LME，过去千年集合）再分析数据，并应用分位数映射法将克里金插值后的REACHES数据校准为摄氏度温度标度。最后，我们提出了一种新颖的贝叶斯数据同化方法，将克里金插值后的摄氏度数据与LME模拟结果相结合，以提升重建精度。我们使用灵活的非平稳自回归时间序列模型对每个地理位置的LME数据建模，并采用带融合套索惩罚项的正则化最大似然估计。由此得到的动态分布作为先验信息，通过卡尔曼滤波纳入克里金插值后的摄氏度REACHES数据进行修正，最终得到温度后验估计值。这种将历史文献、当代气候模型与先进统计方法的全面整合，提升了历史温度重建的准确性，为未来的环境与气候研究提供了关键资源。
英文摘要：
We propose a novel approach for reconstructing annual temperatures in East Asia from 1368 to 1911, leveraging the Reconstructed East Asian Climate Historical Encoded Series (REACHES). The lack of instrumental data during this period poses significant challenges to understanding past climate conditions. REACHES digitizes historical documents from the Ming and Qing dynasties of China, converting qualitative descriptions into a four-level ordinal temperature scale. However, these index-based data are biased toward abnormal or extreme weather phenomena, leading to data gaps that likely correspond to normal conditions. To address this bias and reconstruct historical temperatures at any point within East Asia, including locations without direct historical data, we employ a three-tiered statistical framework. First, we perform kriging to interpolate temperature data across East Asia, adopting a zero-mean assumption to handle missing information. Next, we utilize the Last Millennium Ensemble (LME) reanalysis data and apply quantile mapping to calibrate the kriged REACHES data to Celsius temperature scales. Finally, we introduce a novel Bayesian data assimilation method that integrates the kriged Celsius data with LME simulations to enhance reconstruction accuracy. We model the LME data at each geographic location using a flexible nonstationary autoregressive time series model and employ regularized maximum likelihood estimation with a fused lasso penalty. The resulting dynamic distribution serves as a prior, which is refined via Kalman filtering by incorporating the kriged Celsius REACHES data to yield posterior temperature estimates. This comprehensive integration of historical documentation, contemporary climate models, and advanced statistical methods improves the accuracy of historical temperature reconstructions and provides a crucial resource for future environmental and climate studies.
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