Altitude Correction of GCM-Simulated Precipitation Isotopes in a Valley Topography of the Chinese Loess Plateau

Yanqiong Xiao, Gahong Yang, Kei Yoshimura, Deye Qu, Fenli Chen, Athanassios A. Argiriou and Shengjie Wang ()
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Yanqiong Xiao: Key Laboratory of Resource Environment and Sustainable Development of Oasis of Gansu Province, College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Gahong Yang: Key Laboratory of Resource Environment and Sustainable Development of Oasis of Gansu Province, College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Kei Yoshimura: Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo 169-8555, Japan
Deye Qu: Key Laboratory of Resource Environment and Sustainable Development of Oasis of Gansu Province, College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Fenli Chen: Key Laboratory of Resource Environment and Sustainable Development of Oasis of Gansu Province, College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Athanassios A. Argiriou: Laboratory of Atmospheric Physics, Department of Physics, University of Patras, GR-26500 Patras, Greece
Shengjie Wang: Key Laboratory of Resource Environment and Sustainable Development of Oasis of Gansu Province, College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

Sustainability, 2023, vol. 15, issue 17, 1-13

Abstract: Altitude is one of the important factors influencing the spatial distribution of precipitation, especially in a complex topography, and simulations of isotope-enabled climate models can be improved by altitude correlation. Here we compiled isotope observations at 12 sites in Lanzhou, and examined the relationship between isotope error and altitude in this valley in the Chinese Loess Plateau using isoGSM2 isotope simulations. Before altitude correction, the performance using the nearest four grid boxes to the target site is better than that using the nearest box; the root mean square error in δ 18 O using the nearest four grid boxes averagely decreases by 0.37‰ compared to that using the nearest grid boxes, and correlation coefficient increases by 0.05. The influences of altitude on precipitation isotope errors were examined, and the linear relationship between altitude error and isotope simulations was calculated. The strongest altitude isotopic gradient between δ 18 O mean bias error and altitude error is in summer, and the weakest is in winter. The regression relationships were used to correct the simulated isotope composition. After altitude correction, the root mean square error decreases by 1.21‰ or 0.86‰ using the nearest one or four grid boxes, respectively, and the correlation coefficient increases by 0.13 or 0.08, respectively. The differences between methods using the nearest one or four grids are also weakened, and the differences are 0.02‰ for root mean square error and −0.01 for the correlation coefficient. The altitude correction of precipitation isotopes should be considered to downscale the simulations of climate models, especially in complex topography.

Keywords: precipitation isotopes; interpolation; altitude correction; Chinese Loess Plateau (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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