Do Multi‐Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?
Do Multi‐Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?
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DOI：
10.1029/2020ea001467
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发表时间：
2021-03
影响因子：
3.1
通讯作者：
L. Parsons;D. Amrhein;S. Sanchez;R. Tardif;M. K. Brennan;G. Hakim
中科院分区：
文献类型：
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作者：
L. Parsons;D. Amrhein;S. Sanchez;R. Tardif;M. K. Brennan;G. Hakim
重建过去的气候仍然是一项艰巨的任务，因为仪器前观测网络由地理稀疏和噪声的古气候代理记录组成，需要统计技术来告知完整的气候场。传统上，工具或气候模型统计关系用于将信息从替代测量传播到其他位置和其他气候变量。在这里，从单一气候模型和从多个气候模型的组合中提取的集合被用来在理想的实验中重建过去一千年的温度变率。我们发现，当重建独立的模式和仪器数据时，来自多模式集成的重建比来自单模式集成的重建产生更低的误差。具体地说，我们发现，在远离代理位置的区域，例如中高纬度海洋和海冰区域，误差下降幅度最大，这些区域往往与模式物理中的大不确定性有关。此外，我们发现多模型集成重构的性能优于使用协方差局部化的单模型重构。我们建议，多模式集合可以用于改善过去千年以后的时间段的古气候重建，以及除气温以外的气候变量，如干旱指标或海冰变量。
Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi‐model ensembles produce lower error than reconstructions from single‐model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid‐ and high‐latitude ocean and sea‐ice regions. Furthermore, we find that multi‐model ensemble reconstructions outperform single‐model reconstructions that use covariance localization. We propose that multi‐model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables.