Spatial and seasonal isotope variability in precipitation across China: monthly isoscapes based on regionalized fuzzy clustering

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Date
2022-06-01
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Publisher
American Meteorological Society
Abstract
The spatial patterns of stable hydrogen and oxygen isotopes in precipitation (precipitation isoscapes) provide a geographic perspective to understand the atmospheric processes in modern environment and paleoclimate records. Here we compiled stable isotope data in modern precipitation at 223 sites across China and 48 in surrounding countries, and used regionalized fuzzy clustering to create monthly precipitation isoscapes for China (C-Isoscape). Based on regressions using spatial and climatic parameters for 12 months, the best-fitting equations were chosen for four climate clusters, and then the four layers were weighted using fuzzy membership. The moisture transportation path, controlled by the westerlies and the monsoon, results in different spatial and seasonal diversity of precipitation isotopes. Based on C-Isoscape, we determined a nationwide meteoric water line asδ2H = 7.4δ18O + 5.5 using least squares regression orδ2H = 8.0δ18O + 10.2 using precipitation weighted reduced major axis regression. Compared with previous global products, the C-Isoscape usually shows precipitation more enriched in18O and2H in summer and more depleted in winter for northwest China, while the C-Isoscape values are more enriched in heavy isotopes in most months for southwest China. The new monthly precipitation isoscapes provide an accurate and high-resolution mapping for Chinese precipitation isotopes, allowing for future intra-annual atmospheric process diagnostics using stable hydrogen and oxygen isotope in precipitation in the region. Ó 2022 American Meteorological Society.
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Keywords
Isotopes, China, Fuzzy logic, Precipitation, Seasons, Hydrogen isotopes, Oxygen isotopes
Citation
Wang, S., S. Lei, M. Zhang, C. Hughes, J. Crawford, Z. Liu, & D. Qu, (2022). Spatial and seasonal isotope variability in precipitation across China: monthly isoscapes based on regionalized fuzzy clustering. Journal of Climate, 35, 3411–3425. doi:10.1175/JCLI-D-21-0451.1.
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