Attribution and Causality Analyses of Regional Climate Variability
Danlu Cai (),
Klaus Fraedrich (),
Frank Sielmann,
Shoupeng Zhu and
Lijun Yu
Additional contact information
Danlu Cai: Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Klaus Fraedrich: Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Frank Sielmann: Meteorologisches Institut, Universität Hamburg, 20146 Hamburg, Germany
Shoupeng Zhu: Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Lijun Yu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Land, 2023, vol. 12, issue 4, 1-18
Abstract:
A two-step attribution and causality diagnostic is designed by employing singular spectrum analysis to unfold the attributed climate time series into a trajectory matrix and then subjected to an empirical orthogonal function analysis to identify the evolving driving forces, which can finally be related to major climate modes through their independent frequencies by wavelet analysis. Application results from the arid and drought-prone southern Intermountain region of North America are compared with the climate or larger scale forcing diagnosed from slow feature analysis using the sources of the water and energy flux balance. The following results are noted: (i) The changes between the subsequent four 20-year periods from 1930 to 2010 suggest predominantly climate-induced forcing by the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. (ii) Land cover influences on the changing land cover are of considerably smaller magnitude (in terms of area percentage cover) whose time evolution is well documented from forestation documents. (iii) The drivers of the climate-induced forcings within the last 20 years are identified as the quasi-biennial oscillation and the El Niño–Southern Oscillation by both the inter-annual two-step attribution and the causality diagnostics with monthly scale-based slow feature analysis.
Keywords: large-scale climate forces; causality diagnostics; slow feature analysis; water and energy flux balance (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/2073-445X/12/4/817/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/4/817/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:4:p:817-:d:1115110
Access Statistics for this article
Land is currently edited by Ms. Carol Ma
More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().