Towards seasonal Arctic shipping route predictions
Towards seasonal Arctic shipping route predictions
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DOI：
10.1088/1748-9326/aa7a60
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发表时间：
2017-07
影响因子：
6.7
通讯作者：
N. Melia;K. Haines;E. Hawkins;J. Day
中科院分区：
文献类型：
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作者：
N. Melia;K. Haines;E. Hawkins;J. Day
北极海冰的持续减少可能会导致该地区人类活动和航运机会增加，这表明对航线开放的季节性预测将变得更加重要。在这里，我们展示了一组“完美模型”实验的结果，以评估北极航线开放的可预测性特征。我们发现，早在一月份就可以对即将到来的夏季航运季节做出熟练的预测，尽管通常在五月“可预测性障碍”之前预测显示技能较低。我们证明，在从一月份开始的预测中，航线开通日期的预测不确定性是关闭日期的两倍，而且由于气候变化，北极航运季节变得更长，而关闭日期较晚是主要原因。我们发现，预测技能与状态相关，对高冰年或低冰年的预测比中冰年表现出更高的技能。从 7 月份开始预测，穿越北极最快的开放水域航线的准确度在 200 公里以内，与去年 11 月份的预测相比，准确度提高了六倍，而这通常并不比气候学更好。最后，我们发现准确的夏季海冰厚度信息的初始化对于获得熟练的预测至关重要，从而进一步激励对海冰厚度观测、气候模型和同化系统的投资。
The continuing decline in Arctic sea-ice will likely lead to increased human activity and opportunities for shipping in the region, suggesting that seasonal predictions of route openings will become ever more important. Here we present results from a set of ‘perfect model’ experiments to assess the predictability characteristics of the opening of Arctic sea routes. We find skilful predictions of the upcoming summer shipping season can be made from as early as January, although typically forecasts show lower skill before a May ‘predictability barrier’. We demonstrate that in forecasts started from January, predictions of route opening date are twice as uncertain as predicting the closing date and that the Arctic shipping season is becoming longer due to climate change, with later closing dates mostly responsible. We find that predictive skill is state dependent with predictions for high or low ice years exhibiting greater skill than medium ice years. Forecasting the fastest open water route through the Arctic is accurate to within 200 km when predicted from July, a six-fold increase in accuracy compared to forecasts initialised from the previous November, which are typically no better than climatology. Finally we find that initialisation of accurate summer sea-ice thickness information is crucial to obtain skilful forecasts, further motivating investment into sea-ice thickness observations, climate models, and assimilation systems.