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Siyan Dong, Ying Sun, and Chao Li

Abstract

This paper examines the possible influence of external forcings on observed changes in precipitation extremes in the mid-to-high latitudes of Asia during 1958–2012 and attempts to identify particular extreme precipitation indices on which there are better chances to detect the influence of external forcings. We compare a recently compiled dataset of observed extreme indices with those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations using an optimal fingerprinting method. We consider six indices that characterize different aspects of extreme precipitation, including annual maximum amount of precipitation falling in 1 day (Rx1day) or 5 days (Rx5day), the total amount of precipitation from the top 5% or top 1% daily amount on wet days, and the fraction of the annual total precipitation from these events. For single-signal analysis, the fingerprints of external forcings including anthropogenic agents are robustly detected in most studied extreme indices over all Asia and for midlatitude Asia but not for high-latitude Asia. For two-signal analysis, anthropogenic influence is detectable in these indices over Asia at 5% or slightly less than 5% significance level, whereas natural influence is not detectable. In high-latitude Asia, anthropogenic influence is detected only in a fractional index, representing a stark contrast to the midlatitude and full Asia results. We find relatively smaller internal variability and thus higher signal-to-noise ratio in the fractional indices when compared with the other ones. Our results point to the need for studying precipitation extreme indices that are less affected by internal variability while still representing the relevant nature of precipitation extremes to improve the possibility of detecting a forced signal if one is present in the data.

Open access
Ying Sun, Siyan Dong, Ting Hu, Xuebin Zhang, and Peter Stott
Open access
Ying Sun, Siyan Dong, Ting Hu, Xuebin Zhang, and Peter Stott
Free access
Siyan Dong, Ying Sun, Chao Li, Xuebin Zhang, Seung-Ki Min, and Yeon-Hee Kim

Abstract

While the IPCC Fifth Assessment Working Group I report assessed observed changes in extreme precipitation on the basis of both absolute and percentile-based extreme indices, human influence on extreme precipitation has rarely been evaluated on the basis of percentile-based extreme indices. Here we conduct a formal detection and attribution analysis on changes in four percentile-based precipitation extreme indices. The indices include annual precipitation totals from days with precipitation exceeding the 99th and 95th percentiles of wet-day precipitation in 1961–90 (R99p and R95p) and their contributions to annual total precipitation (R99pTOT and R95pTOT). We compare these indices from a set of newly compiled observations during 1951–2014 with simulations from models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). We show that most land areas with observations experienced increases in these extreme indices with global warming during the historical period 1951–2014. The new CMIP6 models are able to reproduce these overall increases, although with considerable over- or underestimations in some regions. An optimal fingerprinting analysis reveals detectable anthropogenic signals in the observations of these indices averaged over the globe and over most continents. Furthermore, signals of greenhouse gases can be separately detected, taking other forcing into account, over the globe and over Asia in these indices except for R95p. In contrast, signals of anthropogenic aerosols and natural forcings cannot be detected in any of these indices at either global or continental scales.

Open access
Lianchun Song, Siyan Dong, Ying Sun, Guoyu Ren, Botao Zhou, and Peter A. Stott
Full access
Cheng Qian, Jun Wang, Siyan Dong, Hong Yin, Claire Burke, Andrew Ciavarella, Buwen Dong, Nicolas Freychet, Fraser C. Lott, and Simon F. B. Tett
Open access
Wenxia Zhang, Wei Li, Lianhua Zhu, Yuanyuan Ma, Linyun Yang, Fraser C Lott, Chunxiang Li, Siyan Dong, Simon F B Tett, Buwen Dong, and Ying Sun
Free access