Spatiotemporal Reconstruction of Antarctic Near-Surface Air Temperature from MODIS Observations

X. Zhang, X. Dong, J. Zeng, S. Hou, Paul Smeets, Carleen Reijmer, Y. Wang

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature measurements in combination with in situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05° × 0.05°, spanning from 2001 to 2018. Comparison with independent in situ air temperature measurements shows low uncertainty, with a mean bias of 0.09°C, a mean absolute error of 2.23°C, and a correlation coefficient of 97%. Furthermore, the performance of the reconstruction is better than ERA5 (the fifth-generation ECMWF reanalysis model) against in situ measurements. For the 2001–18 period, the MODIS-based near-surface air temperature product yields annual warming in the East Antarctica, but cooling in the Antarctic Peninsula and West Antarctica. However, they are not statistically significant. This product can also be used to investigate the impact of the Southern Hemisphere annual mode (SAM) on year-to-year variability of air temperature. The enhanced positive phase of SAM in recent decades in austral summer has a cooling effect on East and West Antarctica. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.
Original languageEnglish
Pages (from-to)5537-5553
Number of pages17
JournalJournal of Climate
Volume35
Issue number17
DOIs
Publication statusPublished - 1 Sept 2022

Keywords

  • Antarctica
  • Atmosphere
  • Neural networks

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