Data-Adaptive Harmonic Decomposition and Stochastic Modeling of Arctic Sea Ice
Data-Adaptive Harmonic Decomposition and Stochastic Modeling of Arctic Sea Ice
复制标题
北极海冰的数据自适应谐波分解和随机建模
DOI：
10.1007/978-3-319-58895-7_10
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
M. Ghil
中科院分区：
文献类型：
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作者：
D. Kondrashov;M. Chekroun;X. Yuan;M. Ghil
我们提出并应用了一种新的方法来描述和建模复杂的多变量数据集在地球科学和其他地方。数据自适应谐波（DAH）分解识别窄带，时空模式（DAHM），其频率不一定是彼此的整数倍。这些模式的DAH系数（DAHC）随时间的演化可以使用一组耦合的Stuart-Landau随机微分方程来建模，该方程捕获模式在时间和空间中的频率和幅度调制。这种方法首先应用于一个具有挑战性的合成数据集，然后从美国国家冰雪数据中心（NSIDC）的北极海冰浓度（SIC）数据。我们的DAHM对36年（1979-2014）数据集进行了简洁而准确的描述。初步结果表明，模拟使用我们的多层Stuart-Landau模型（MSLM）的SICs是稳定的更长的时间间隔，超过了21世纪世纪末，并表现出年代际变化与过去的历史记录。初步结果表明，该模式对9月海冰范围有较好的预报能力。
We present and apply a novel method of describing and modeling complex multivariate datasets in the geosciences and elsewhere. Data-adaptive harmonic (DAH) decomposition identifies narrow-banded, spatio-temporal modes (DAHMs) whose frequencies are not necessarily integer multiples of each other. The evolution in time of the DAH coefficients (DAHCs) of these modes can be modeled using a set of coupled Stuart-Landau stochastic differential equations that capture the modes’ frequencies and amplitude modulation in time and space. This methodology is applied first to a challenging synthetic dataset and then to Arctic sea ice concentration (SIC) data from the US National Snow and Ice Data Center (NSIDC). The 36-year (1979–2014) dataset is parsimoniously and accurately described by our DAHMs. Preliminary results indicate that simulations using our multilayer Stuart-Landau model (MSLM) of SICs are stable for much longer time intervals, beyond the end of the twenty-first century, and exhibit interdecadal variability consistent with past historical records. Preliminary results indicate that this MSLM is quite skillful in predicting September sea ice extent.
北极海冰范围的数据自适应调和分解与预测
DOI：
10.1093/climsys/dzy001
发表时间：
2018
期刊：
Dynamics and Statistics of the Climate System
影响因子：
--
作者：
Kondrashov, Dmitri;Chekroun, Mickaël D;Ghil, Michael
通讯作者：
Ghil, Michael