Reconstructing East Asian Temperatures from 1368 to 1911 Using Historical Documents, Climate Models, and Data Assimilation
结合历史文献、气候模型与数据同化重建1368—1911年东亚气温AI 解读 ↓
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结合历史文献、气候模型与数据同化重建1368—1911年东亚气温
- 研究问题
- 明清历史文献偏重异常天气且缺乏仪器观测时，如何重建东亚年度摄氏气温，并为没有直接历史记录的地点提供温度估计？
- 方法与思路
- 作者依次采用克里金插值、分位数映射和贝叶斯数据同化，并以带 fused lasso 惩罚的非平稳自回归模型及卡尔曼滤波整合模拟与记录。
- 材料陈述的贡献
- 摘要称，该框架将历史文献的有序温度指标与气候模拟整合，改善历史气温重建，并为缺乏直接记录的地点提供后验温度估计。
- 阅读关注点
- 应核对缺失记录的零均值处理、温标校准与空间插值假设，检查非平稳模型、模拟先验及同化步骤的验证方式和不确定性传播。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收。摘要未提供精度比较数值，也未充分说明极端天气记录偏差的可检验性。
原文摘要
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 REACHES digitizes historical documents from the Ming and Qing dynasties of China, converting qualitative descriptions into a four-level ordinal temperature scale.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.