The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

北极海冰面积的缩小是气候变化的一个关键指标，也是未来全球变暖的加速器。自 1978 年以来，北极海冰一直使用基于卫星的微波传感进行测量；然而，基于原始卫星数据的不同算法转换，已经提供了北极海冰范围的不同测量值。我们提出并估计了一个动态因子模型，该模型以最佳方式组合了其中四个度量，并考虑了它们不同的波动性和互相关性。然后我们使用卡尔曼平滑器来提取北极海冰范围的最佳组合度量。事实证明，几乎所有的权重都放在 NSIDC 海冰指数上，证实并增强了对海冰指数及其所基于的 NASA 团队算法的信心。