ISSN 1006-9895

CN 11-1768/O4

The Driving Force of Land Surface Air Temperature Variability Studied Based on the Slow Feature Analysis Method
Author:
Affiliation:

1.State Key Laboratory of Earth Surface Processes and Resource Ecology and College of Global Change and Earth System Science,Beijing Normal University,Beijing;2.Frontiers Science Center for Deep Ocean Multispheres and Earth System FDOMES/Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies,Ocean University of China

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
    Abstract:

    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 field of climate change research to explore the potential driving forces of climate change and related dynamic mechanisms. Based on the slow feature analysis method, this paper extracts the slowly varying external forcing information of the global land surface air temperature (LSAT) and studies 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 global sea surface temperature (SST) (Atlantic Multidecadal Oscillation AMO, tropical Pacific ENSO variability, and Interdecadal Pacific Oscillation IPO), indicating that the variability of LSAT in most parts of the world is significantly affected by GRF and the three SST modes. The influence of GRF on LSAT variability has the characteristic of global consistency, while the influence of the three SST modes on LSAT variability has obvious regional characteristics. In addition, because the SFA method can effectively reduce the interference of random noise in the original LSAT sequence, the interpretation variance of the LSAT variability of the GRF and SST modes is significantly improved, which further shows 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 (AGCM) test which is also named as Atmospheric Model Intercomparison Project (AMIP) test, used to verify the significant influence of the three SST modes on the regional LSAT variability.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:October 16,2020
  • Revised:June 23,2021
  • Adopted:September 06,2021
  • Online:
  • Published: