Assimilation of Time-Averaged Pseudoproxies for Climate Reconstruction
Assimilation of Time-Averaged Pseudoproxies for Climate Reconstruction
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DOI：
10.1175/jcli-d-12-00693.1
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发表时间：
2014-01
影响因子：
4.9
通讯作者：
N. Steiger;G. Hakim;E. Steig;D. Battisti;G. Roe
中科院分区：
文献类型：
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作者：
N. Steiger;G. Hakim;E. Steig;D. Battisti;G. Roe
在地表温度的气候场重建（CFR）中检验了一种新型集合数据同化（DA）技术的有效性。采用了一种简约的、计算成本低的DA技术，该技术仅需要气候合理状态的静态集合。通过从有噪声的伪代理的稀疏网络重建地表温度场，利用大气环流模式（GCM）和20世纪再分析（20CR）数据进行了伪代理实验。在全球区域的实验中，将DA方法与基于主成分分析（PCA）的传统CFR方法进行了比较。在所有实验中，DA在重建全球平均温度方面均优于PCA，并且在不同实验中更具一致性，其时间序列相关性范围为0.69 - 0.94，而PCA方法为0.19 - 0.87。DA在空间重建技能方面的改进更为明显，特别是在伪代理采样稀疏的区域以及20CR实验中。据推测，DA改进了空间重建，是因为它依赖于连贯的、空间局部的温度模式，即使在使用冰川状态重建非冰川状态以及反之亦然时，这些模式仍然稳健。DA所利用的这些局部关系似乎比PCA所利用的正交变异模式更稳健。比较GCM和20CR数据的结果表明，仅依赖GCM数据的伪代理实验可能会对重建技能产生错误的印象。
The efficacy of a novel ensemble data assimilation (DA) technique is examined in the climate field reconstruction (CFR) of surface temperature. A minimalistic, computationally inexpensive DA technique is employed that requires only a static ensemble of climatologically plausible states. Pseudoproxy experiments are performed with both general circulation model (GCM) and Twentieth Century Reanalysis (20CR) data byreconstructingsurfacetemperaturefieldsfromasparsenetworkofnoisypseudoproxies.TheDAapproach is compared to a conventional CFR approach based on principal component analysis (PCA) for experiments on global domains. DA outperforms PCA in reconstructing global-mean temperature in all experiments and is more consistent across experiments, with a range of time series correlations of 0.69‐0.94 compared to 0.19‐ 0.87 for the PCA method. DA improvements are even more evident in spatial reconstruction skill, especially in sparsely sampled pseudoproxy regions and for 20CR experiments. It is hypothesized that DA improves spatialreconstructionsbecauseitreliesoncoherent,spatiallylocaltemperaturepatterns,whichremainrobust even when glacial states are used to reconstruct nonglacial states and vice versa. These local relationships, as utilized by DA, appear to be more robust than the orthogonal patterns of variability utilized by PCA. Comparing results for GCM and 20CR data indicates that pseudoproxy experiments that rely solely on GCM data may give a false impression of reconstruction skill.