Rainfall statistics, stationarity, and climate change

Proceedings of the National Academy of Sciences of the United States of America
Fubao SunGraham D Farquhar

Abstract

There is a growing research interest in the detection of changes in hydrologic and climatic time series. Stationarity can be assessed using the autocorrelation function, but this is not yet common practice in hydrology and climate. Here, we use a global land-based gridded annual precipitation (hereafter P) database (1940-2009) and find that the lag 1 autocorrelation coefficient is statistically significant at around 14% of the global land surface, implying nonstationary behavior (90% confidence). In contrast, around 76% of the global land surface shows little or no change, implying stationary behavior. We use these results to assess change in the observed P over the most recent decade of the database. We find that the changes for most (84%) grid boxes are within the plausible bounds of no significant change at the 90% CI. The results emphasize the importance of adequately accounting for natural variability when assessing change.

References

Aug 25, 2004·Science·Randal D KosterUNKNOWN GLACE Team
Feb 2, 2008·Science·P C D MillyRonald J Stouffer
Mar 4, 2015·Proceedings of the National Academy of Sciences of the United States of America·Noah S DiffenbaughDanielle Touma

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Citations

Nov 15, 2018·Scientific Reports·Anne M Lausier, Shaleen Jain
Feb 28, 2020·Scientific Reports·Zhi LiYang Wang
Jun 27, 2019·Scientific Reports·Udit Bhatia, Auroop Ratan Ganguly
Nov 24, 2020·The Science of the Total Environment·Hong Wang, Fubao Sun
Apr 2, 2021·Environmental Science and Pollution Research International·Yun Hao, Jun Lu
Jun 10, 2019·The Science of the Total Environment·Piero Di CarloWilliam H Brune

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