JOURNAL ARTICLE

Quantifying spatiotemporal variations and driving factors of the energy budget in the Loess Plateau.

  • Published In: International Journal of Climatology, 2023, v. 43, n. 5. P. 2062 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Fen, Gou; Wei, Liang; Jianwu, Yan; Zhigang, Chen; Shaobo, Sun; Zhao, Jin; Weibin, Zhang 3 of 3

Abstract

The land surface energy exchange indirectly describes the energy forcing effect of solar radiation on the atmospheric system. Exploring the exchange process is of great significance to understand the formation and change of weather and climate. Based on the ERA5 reanalysis data and a process‐based land surface model (the Ecosystem‐Atmosphere Simulation Scheme), this study analysed the spatiotemporal variations and influencing factors in the energy budget in the Loess Plateau (LP). The results showed that from 1990 to 2017, the average annual surface net radiation (Rn) and latent heat (LE) in the LP showed a decreasing trend. The Rn and LE presented an increased spatial pattern from northwest to southeast. On a monthly scale, the Grain for Green (GFG) project amplified the negative effect in the period of November to February and September, but diminished the negative effect in other months. Climate change contributed more to energy exchange than land cover change during the study period. Our results provide useful information for developing adaptive strategies for the region to adapt to global climate change. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Climatology. 2023/04, Vol. 43, Issue 5, p2062
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2023
  • ISSN:0899-8418
  • DOI:10.1002/joc.7963
  • Accession Number:162842418
  • Copyright Statement:Copyright of International Journal of Climatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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