发表机构
University of Washington; University of Southern California（华盛顿大学; 南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出新的数据同化框架LMR4D-Var，整合异质古气候信息重建气候轨迹，经验证其技能优于此前方法，可同化多尺度记录并支持更久远时间的古气候应用。
AI 中文摘要
古气候档案拓展了仪器观测时代之外的气候知识，记录了不同季节、变量、时间平均及记忆长度的信息。长期以来的难题是如何将这些异质信息源整合到统一方法中。本文提出一种新的数据同化框架——Last Millennium Reanalysis 4D-Var（LMR4D-Var，末次千年再分析四维变分），该框架可从异质数据集重建气候轨迹，同时平衡模型、观测及初始条件的误差。我们对比了使用LMR4D-Var同化PAGES2k、Temp12k及钻孔温度剖面代用指标的结果，未将这些指标视为瞬时等价物。仪器验证显示，LMR4D-Var相较此前的重建结果达到最高技能；钻孔同化在保留年分辨记录的技能的同时，提升了重建的300-2000米海洋热含量与独立估算值的一致性，还得到了更冷的小冰期海洋重建结果；Temp12k的结果表明，该框架可同化年代际至千年尺度的记录，且结合合适的模拟器后，具备用于全新世及更久远时间应用的潜力。
英文摘要
Paleoclimate archives extend climate knowledge beyond the instrumental era, registering different seasons, variables, time averages, and memory lengths. A longstanding problem is to integrate these heterogeneous sources of information within a unified methodology. Here we present a new data-assimilation framework, Last Millennium Reanalysis 4D-Var (LMR4D-Var), which reconstructs climate trajectories from these heterogeneous datasets while balancing errors in the model, observations, and initial conditions. We compare results using LMR4D-Var to assimilate proxies from PAGES2k, Temp12k, and borehole temperature profiles without treating them as instantaneous equivalents. Instrumental verification shows that LMR4D-Var achieves the highest skill compared with previous reconstructions. Borehole assimilation preserves skill against withheld annually resolved records, increases agreement between reconstructed 300--2000-m ocean heat content and independent estimates, and yields a cooler reconstructed Little Ice Age ocean. Results for Temp12k demonstrate assimilation of decadal-to-millennial records and the potential for Holocene and deeper-time applications with suitable emulators.
