Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations
Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations
复制标题
将强迫响应与气候模型集合和观测中的内部变异分开的模式识别方法
DOI：
10.1175/jcli-d-19-0855.1
复制
发表时间：
2020
影响因子：
4.9
通讯作者：
Deser, C
中科院分区：
文献类型：
--
作者：
Wills, RCJ;Battisti, DS;Armour, KC;Schneider, T;Deser, C
气候模型模拟的集合通常用于将外部强迫的气候变化与内部变率分开。然而，从运行大型集成中获得的大部分信息在传统的数据缩减方法（例如线性趋势分析或大规模空间平均）中丢失了。本文演示了模式识别方法（信噪比最大化模式过滤）如何从大型集合中提取外部强迫气候变化模式，并以比简单集合平均少 10 倍的集合成员来识别强迫气候响应。它在过滤掉内部变异的空间相干模式（例如厄尔尼诺、北大西洋涛动）方面特别有效，否则这些模式会混入区域对强迫响应的估计中。该方法用于识别由 40 个成员组成的社区地球系统模型 (CESM) 大型集合中的强迫气候响应，包括对火山喷发的类似厄尔尼诺现象的响应以及北大西洋涛动的强迫趋势。基于集合的受迫响应估计用于测试将受迫响应与单个实现（即各个集合成员）隔离的统计方法。低频模式过滤被发现可以巧妙地识别个体系综成员内的受迫响应，并应用于观测温度的 HadCRUT4 重建，从而识别观测到的温度变化的缓慢成分，这些成分与人为温室气体和气溶胶强迫的预期影响一致。
Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.