Seasonal and decadal forecasts of Atlantic Sea surface temperatures using a linear inverse model
Seasonal and decadal forecasts of Atlantic Sea surface temperatures using a linear inverse model
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DOI：
10.1007/s00382-016-3375-1
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发表时间：
2017-09-01
期刊：
影响因子：
4.6
通讯作者：
Palmer, Tim
中科院分区：
文献类型：
--
作者：
Huddart, Benjamin;Subramanian, Aneesh;Palmer, Tim
利用一套统计线性反演模式（LIM）研究了大西洋海表温度（SST）在季节和年代际时间尺度上的可预报性。在大西洋部门（22 S和66 N之间）观测到的月SST异常被用来构建季节和年代际预测的LIM。的LIM的预测技能，然后比较，从目前的业务预报系统。结果表明，LIM在3-4个月的季节时间尺度上有很好的预报能力，在春季的可预报性增强。在年代际时间尺度上，还研究了年际和年内变化对可预报性的影响。结果表明，当我们只使用年代际变率构造LIM时，LIM套件对大部分区域具有约3-4年的预报能力。包括更高的频率变率有助于提高预报技巧，并保持LIM预测与观测到的SST异常的相关性更长的时间。这些结果表明，时间尺度的相互作用，在提高可预报性的年代际时间尺度上的重要性。因此，LIMs不仅可以用作估计统计技能的基准，而且还可以从不同的时间尺度、空间尺度甚至模型组件中分离出对预测技能的贡献。
Predictability of Atlantic Ocean sea surface temperatures (SST) on seasonal and decadal timescales is investigated using a suite of statistical linear inverse models (LIM). Observed monthly SST anomalies in the Atlantic sector (between 22S and 66N) are used to construct the LIMs for seasonal and decadal prediction. The forecast skills of the LIMs are then compared to that from two current operational forecast systems. Results indicate that the LIM has good forecast skill for time periods of 3-4 months on the seasonal timescale with enhanced predictability in the spring season. On decadal timescales, the impact of inter-annual and intra-annual variability on the predictability is also investigated. The results show that the suite of LIMs have forecast skill for about 3-4 years over most of the domain when we use only the decadal variability for the construction of the LIM. Including higher frequency variability helps improve the forecast skill and maintains the correlation of LIM predictions with the observed SST anomalies for longer periods. These results indicate the importance of temporal scale interactions in improving predictability on decadal timescales. Hence, LIMs can not only be used as benchmarks for estimates of statistical skill but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components.