Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting
复制标题
使用核模拟预测预测区域和泛北极海冰异常
DOI：
10.1007/s00382-018-4459-x
复制
发表时间：
2017
期刊：
影响因子：
4.6
通讯作者：
A. Majda
中科院分区：
文献类型：
--
作者：
Darin Comeau;D. Giannakis;Zhizhen Zhao;A. Majda
预测北极海冰的范围是一个众所周知的困难预测问题，即使提前时间短至一个月。受北极年内变化现象，如重新出现的海面温度和海冰异常，我们使用的海冰异常的预测方法的基础上模拟预测。传统的模拟预测依赖于识别历史记录中的单个模拟，通常通过最小化欧几里得距离，并根据模拟的历史轨迹形成预测。在这里，一个合奏的类似物是用来进行预测，其中合奏权重是由一个动态适应的相似性内核，它考虑到了底层数据流形上的非线性几何形状。我们应用这种方法来预测泛北极和区域海冰面积和体积异常的多世纪气候模式数据，并在许多情况下发现改进的基准阻尼持久性预测。成功的例子包括北极海冰面积的3-6个月提前期预测，一些边缘冰区海的冬季海冰面积预测，以及许多北极中部盆地海冰量异常的3-12个月提前期预测。我们讨论了KAF的成功和海冰重新出现之间可能存在的联系，并发现KAF是成功的地区和季节表现出较高的年际变化。
Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. Here an ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. We discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability.
影响因子：
5.2
作者：
Goessling, H. F.;Tietsche, S.;Jung, T.
通讯作者：
Jung, T.
影响因子：
5.2
作者：
Tietsche, S.;Day, J. J.;Hawkins, E.
通讯作者：
Hawkins, E.