Downscaling and projection of summer rainfall in Eastern China using a nonhomogeneous hidden Markov model

Abstract

A nonhomogeneous hidden Markov model (NHMM) is used to stochastically simulate summer (June–August) daily precipitations in the middle and low reaches of the Yangtze River in Eastern China, with driving forcing from three global climate models (GCMs). Simulations cover the historical period from 1961 to 2005 and from 2006 to 2100 following the RCP4.5 scenario. The model is first evaluated against data from the regional observation network. The results show that NHMM effectively enhances the ability of GCMs in simulating summer daily rainfall in the region. For future projection at different time horizons of the 21st century, the spectral distribution of regional precipitations (in function of their intensity) shows consistent changes with a decrease of occurrence probability for light rain (<10 mm/day) and an increase for heavy rain (>10 mm/day). Among variables of interest, total precipitation (PRCPTOT), precipitation intensity, the number of rainy days for daily precipitation exceeding 10 mm (R10mm) and 95th percentile of precipitation (P95), all show a gradually increasing trend in the 21st century, and geographically an eastward gradient with smaller increase (or even weak decrease) for the west and larger increase for the east. It is noted that obvious changes occur in the eastern region with 95% significance level, and PRCPTOT or R10mm increases by 40–60% in the late 21st century. Further quantitative assessment is performed for global warming of 1.5 and 2°C. The half‐degree additional warming makes R10mm change by −3.7, 2.4 and 12.1% over western, central and eastern regions, respectively.

Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

“…Actually, our previous work also produced a future projection, but with CMIP5 models (Guo et al., 2019). The result there is close to the present work using CMIP6 models, that is, MLYRB exhibits a general wetter trend for its future precipitation.…”

“…Our previous work (Ding et al., 2016; Guo et al., 2019) shows that with four large‐scale predictors, including sea level pressure (12.5–35°N, 105–120°E), geopotential height at 500 hPa (10–25°N, 95–170°E), relative humidity at 500 hPa (27.5–32.5°N, 105–125°E), and zonal wind at 500 hPa (25–32.5°N, 95–140°E), we are able to establish a good NHMM simulating summer daily precipitation over MLYRB. In practice, we apply the principal component analysis on joint field of the four predictors to filter out noises.…”

“…Our previous paper (Guo et al, 2019) showed that NHMM effectively enhances the ability of CMIP5 GCMs in simulating the spectral distribution and spatial pattern of summer precipitation in MLYRB in eastern China. We also drew the same conclusion by conducting an independent validation of NHMM with CMIP6 GCMs during 1991-2010 (Figure S1 in Supporting Information S1).…”

“…In our recent study (Guo et al., 2019), we used the nonhomogeneous hidden Markov model (NHMM) to construct a relationship between large‐scale circulation patterns (predictor) and the probability distribution of regional precipitation (predictand) (Hughes & Guttorp, 1994). It continuously learns the evolution characteristics of synoptic weather situations over time, so that the statistical property of meteorological variables can be reproduced more comprehensively and accurately (Cioffi et al., 2017; Fu et al., 2013; Greene et al., 2011; Robertson et al., 2004; Tan et al., 2013).…”

Projection and possible causes of summer precipitation in eastern China using self-organizing map

“…In an accompanying paper that uses the same datasets, but a non-homogenous hidden Markov model as the statistical downscaling tool, Guo et al (2019) also obtained results approving probability transferring from light to strong rainfall for the whole basin domain.…”