基于慢特征分析方法研究陆地表面气温变率的驱动力

慢特征分析（SFA）方法可以从非平稳时间序列中提取出慢变的外强迫信息。近年来，SFA方法被应用于气候变化研究领域，用于探究气候变化的潜在驱动力及相关的动力学机制。本文基于SFA方法，提取全球陆地表面气温（LSAT）的慢变外强迫信息，研究全球LSAT慢变驱动力的空间结构特征及低频变率的主要驱动因子。SFA方法提取的LSAT慢变驱动力与历史时期全球辐射强迫（GRF）和全球海表温度（SST）的主模态（大西洋多年代际振荡AMO、热带太平洋ENSO变率和太平洋年代际振荡PDO）有显著的相关关系，表明全球大部分地区LSAT的变率受到GRF和三个SST模态的显著影响。GRF对LSAT变率的影响有全球一致性的特征，而三个SST模态对LSAT变率的影响则呈现出明显的区域特点。此外，由于SFA方法可以有效降低原始LSAT序列中随机噪声的干扰，GRF和SST模态对LSAT变率的解释方差显著提高，进一步表明GRF和SST模态是全球LSAT低频变率主要的驱动因子。最后，利用历史海温驱动AGCM试验（即AMIP试验）的结果，验证了三个SST模态对区域LSAT变率的显著影响。

Driving Force of Land Surface Air Temperature Variability Based on the Slow Feature Analysis Method

The slow feature analysis (SFA) can extract slowly varying external forcing information from non-stationary time series. In recent years, the SFA method has been applied to the climate change field to explore the potential driving forces of climate change and related dynamic mechanisms. This study extracts the slowly varying external forcing information of the global land surface air temperature (LSAT) based on the SFA method. It investigates the spatial structure characteristics of the global LSAT slow varying driving force and the main driving factors of low-frequency variability. The LSAT slowly varying driving force extracted by the SFA method has a significant correlation with global radiative forcing (GRF) and the main modes of the global sea surface temperature (SST) (i.e., Atlantic Multidecadal Oscillation, tropical Pacific El Ni?o–Southern Oscillation variability, and Interdecadal Pacific Oscillation), indicating that the LSAT variability in most parts of the world is significantly affected by GRF and the three SST modes. The influence of GRF on the LSAT variability has global consistency, while that of the three SST modes on the LSAT variability has obvious regional characteristics. In addition, the interpretation variance of the LSAT variability of the GRF and SST modes significantly improved because the SFA method can effectively reduce the explanatory random noise in the original LSAT sequence, further showing that the GRF and SST modes are the main driving factors of the global LSAT low-frequency variability. Finally, the results of the historical sea surface temperature-driven Atmospheric General Circulation Model test, which is also known as the Atmospheric Model Intercomparison Project (AMIP)test, verify the significant influence of the three SST modes on the regional LSAT variability.