改进冬季NAO分季节预测的潜力是什么?

IF 2 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Atmospheric Science Letters Pub Date : 2022-12-02 DOI:10.1002/asl.1146
Chris Kent, Adam A. Scaife, Nick Dunstone
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引用次数: 2

摘要

北大西洋涛动(NAO)是整个大西洋地区的主要变化模式,也是温带预报性能的关键指标。在中等范围(1-2 周)和季节性时间尺度。然而,在领先的动态预测系统中,我们发现次季节性预测(1个月的NAO,提前期为20-30 天)在统计上并不显著,并且表示预测技能上的差距。在这项研究中,我们研究了使用大型动态后向模型改进预测的潜力。首先,我们发现NAO的月度预测与中期预测误差的相关性很弱。这意味着,中期预测业绩的改善不太可能在更长的交付周期内带来显著改善。其次,麦登-朱利安振荡(MJO)是热带次季节变化的主要模式,并以10-15的滞后性投射到NAO上 天,但其遥相关在当前的预测系统中仅部分表示。因此,我们评估MJO‐NAO遥相关的改善是否可能导致每月NAO预测的改善。我们发现,即使是完美的MJO预测和遥相关,也只能在NAO预测技能上取得微小的改进。这项工作表明,每月的时间尺度可能代表了NAO的可预测性差距,因此也代表了欧洲-大西洋冬季气候,在这种气候下,很难实现真正的技能提高。这一领域的潜在进展可能源于目前未知的技能来源,大规模初始化的气候集合将是研究这些问题的重要工具。
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What potential for improving sub-seasonal predictions of the winter NAO?
The North Atlantic Oscillation (NAO) is the leading mode of variability across the Atlantic sector and is a key metric of extratropical forecast performance. Skilful predictions of the NAO are possible at medium‐range (1–2 weeks) and seasonal time scales. However, in a leading dynamical prediction system, we find that sub‐seasonal predictions (1 month NAO with a lead time of 20–30 days) are not statistically significant and represent a gap in forecast skill. In this study, we have investigated the potential for improving predictions using a large ensemble of dynamical hindcasts. First, we find that monthly predictions of the NAO are only weakly related to forecast errors at the medium‐range. This implies that improving medium‐range forecast performance is unlikely to drive significant improvements at longer lead times. Second, the Madden‐Julian Oscillation (MJO) is the leading mode of sub‐seasonal variability in the Tropics and projects onto the NAO with a lag of 10–15 days, but its teleconnection is only partially represented in current forecast systems. We, therefore, assess whether improved MJO‐NAO teleconnections are likely to lead to improved monthly NAO predictions. We find that even perfect MJO forecasts and teleconnections lead to only small improvements in NAO prediction skills. This work indicates that monthly timescales may represent a predictability gap for the NAO and hence the Euro‐Atlantic winter climate in which genuine skill improvements are difficult to achieve. Potential progress in this area could stem from currently unknown sources of skill and large initialised climate ensembles will be a vital tool for investigating these.
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来源期刊
Atmospheric Science Letters
Atmospheric Science Letters METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.90
自引率
3.30%
发文量
73
审稿时长
>12 weeks
期刊介绍: Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques. We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.
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