Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach
Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach
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DOI：
10.1007/s00382-011-1023-3
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发表时间：
2011-03
期刊：
影响因子：
4.6
通讯作者：
E. Hawkins;J. Robson;R. Sutton;Doug M. Smith;N. Keenlyside
中科院分区：
文献类型：
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作者：
E. Hawkins;J. Robson;R. Sutton;Doug M. Smith;N. Keenlyside
我们探讨了在一个完美的模式分析中对海表温度（SST）进行统计年代际预测的潜力，重点是大西洋盆地。不同的统计方法（滞后相关，线性逆模型和构造的Ancestor）被发现有显着的技能，在预测大西洋SST的内部变化长达十年的控制集成的两个不同的全球气候模式（GCM），即HadCM 3和HadGEM 1。考虑非局部信息的统计方法往往表现最好，但哪种统计方法最成功取决于所考虑的区域、所使用的GCM数据和预测提前期。然而，构建锚定方法往往在较长的交货期内具有最高的技能。重要的是，预测技能最高的区域可能与从方差解释参数中确定为潜在可预测的区域非常不同。这一发现表明，显着的本地十年变化不一定是一个熟练的十年预测的先决条件，和统计方法捕捉低频SST演变的一些动态。特别是，使用HadGEM 1的数据，在6-10年的前置时间显着的技能被发现在热带北大西洋，一个区域相对较小的年代际变化相比，年际变化。这种技巧似乎来自于重建远北大西洋的SST，这表明北方纬度更高的地区是SST观测改善预测的最佳地点。我们还探讨了增加次表面温度数据是否可以改善这些十年统计预测，并发现，再次，它取决于该地区，预测的前置时间和GCM数据使用。总体而言，我们认为，估计的预测技巧，激励进一步发展的统计年代际预测的SST作为基准，为当前和未来的GCM为基础的年代际气候预测。
We explore the potential for making statistical decadal predictions of sea surface temperatures (SSTs) in a perfect model analysis, with a focus on the Atlantic basin. Various statistical methods (Lagged correlations, Linear Inverse Modelling and Constructed Analogue) are found to have significant skill in predicting the internal variability of Atlantic SSTs for up to a decade ahead in control integrations of two different global climate models (GCMs), namely HadCM3 and HadGEM1. Statistical methods which consider non-local information tend to perform best, but which is the most successful statistical method depends on the region considered, GCM data used and prediction lead time. However, the Constructed Analogue method tends to have the highest skill at longer lead times. Importantly, the regions of greatest prediction skill can be very different to regions identified as potentially predictable from variance explained arguments. This finding suggests that significant local decadal variability is not necessarily a prerequisite for skillful decadal predictions, and that the statistical methods are capturing some of the dynamics of low-frequency SST evolution. In particular, using data from HadGEM1, significant skill at lead times of 6–10 years is found in the tropical North Atlantic, a region with relatively little decadal variability compared to interannual variability. This skill appears to come from reconstructing the SSTs in the far north Atlantic, suggesting that the more northern latitudes are optimal for SST observations to improve predictions. We additionally explore whether adding sub-surface temperature data improves these decadal statistical predictions, and find that, again, it depends on the region, prediction lead time and GCM data used. Overall, we argue that the estimated prediction skill motivates the further development of statistical decadal predictions of SSTs as a benchmark for current and future GCM-based decadal climate predictions.