Improved seasonal forecast skill of pan-Arctic and regional sea ice extent in CanSIPS version 2

IF 3 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Forecasting Pub Date : 2023-10-01 DOI:10.1175/waf-d-22-0193.1

Joseph Martin, Adam Monahan, Michael Sigmond

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引用次数: 1

Abstract

Abstract This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.

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改进了CanSIPS版本2中泛北极和区域海冰范围的季节预报技巧

摘要本研究评估了加拿大季节-年际预测系统(CanSIPS)第2版在泛北极和区域尺度上对北极海冰范围的预测能力。此外，还将预测技能与CanSIPS版本1进行了比较。总的来说，由于CanSIPSv2开发过程中所做的更改，在考虑非趋势数据时，预测技能有了净增长。最显著的改进是在4月和5月初始化的夏末和秋季目标月份的预测，在以前的研究中，这些月份与春季可预测性障碍有关。通过将CanSIPSv1和CanSIPSv2的技能与中间版本CanSIPSv1b的技能进行比较，我们可以将CanSIPSv1和CanSIPSv2的技能差异归因于两个主要来源。首先，改进的海冰初始条件初始化程序显著提高了泛北极尺度以及北极中部、拉普捷夫海、鄂霍次克海和巴伦支海的区域预报技能。海冰体积初始化场的预测能力分析进一步支持了这一结论。其次，模式组合从CanSIPSv1到CanSIPSv2的变化(将组成canm3模式替换为GEM-NEMO)提高了白令海、卡拉海、楚科奇海、波弗特海、东西伯利亚海、巴伦支海和格陵兰-冰岛-挪威海(GIN)的预报技能。在哈德逊和巴芬湾，以及拉布拉多海，与CanSIPSv1相比，CanSIPSv2的预报有有限的和非系统的改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Weather and Forecasting 地学-气象与大气科学

CiteScore

5.20

自引率

17.20%

发文量

131

审稿时长

6-12 weeks

期刊介绍： Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.