scholarly journals Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

2015 ◽  
Vol 28 (21) ◽  
pp. 8603-8619 ◽  
Author(s):  
Zhihong Jiang ◽  
Wei Li ◽  
Jianjun Xu ◽  
Laurent Li

Abstract Compared to precipitation extremes calculated from a high-resolution daily observational dataset in China during 1960–2005, simulations in 31 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have been quantitatively assessed using skill-score metrics. Four extreme precipitation indices, including the total precipitation (PRCPTOT), maximum consecutive dry days (CDD), precipitation intensity (SDII), and fraction of total rainfall from heavy events (R95T) are analyzed. Results show that CMIP5 models still have wet biases in western and northern China. Especially in western China, the models’ median relative error is about 120% for PRCPTOT; the 25th and 75th percentile errors are of 70% and 220%, respectively. However, there are dry biases in southeastern China, where the underestimation of PRCPTOT reach 200 mm. The performance of CMIP5 models is quite different between western and eastern China. The simulations are more reliable in the east than in the west in terms of spatial pattern and interannual variability. In the east, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble constructed from a selection of the most skillful models shows improved behavior compared to the all-model ensemble. The wet bias in western and northern China and dry bias over southeastern China are all decreased. The median of errors for PRCPTOT has a decrease of 69% and 17% in the west and east, respectively. The good reproduction of the southwesterlies along the east coast of the Arabian Peninsula is revealed to be the main factor explaining the improvement of precipitation patterns and extreme events.

2020 ◽  
Author(s):  
Xi Lu

<p>This study evaluates 32 climate models from CMIP5 compared with a daily gridded<br>observation dataset of extreme precipitation indices including total extreme precipitation (R95p),<br>maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of<br>precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show<br>the majority models have good performance on spatial distribution but overestimate the amplitude of<br>precipitation over Northern China. Most models can also capture interannual variation of R95p and<br>RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best<br>multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p,<br>RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising<br>region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty<br>analysis shows the increase of extreme rainfall over Northwest China has a low confidence level.<br>The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer<br>extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian<br>Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which<br>result in more extreme precipitation.</p>


Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 691 ◽  
Author(s):  
Xiaoqiang Rao ◽  
Xi Lu ◽  
Wenjie Dong

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation.


2016 ◽  
Vol 29 (24) ◽  
pp. 8989-9004 ◽  
Author(s):  
Wei Li ◽  
Zhihong Jiang ◽  
Jianjun Xu ◽  
Laurent Li

Abstract The present article is the second part of a study on the extreme precipitation indices over China in CMIP5 models that perform a probabilistic projection of future precipitation indices with reference to the period 1986–2005. This is realized with a rank-based weighting method. The ranking of the 25 models is done according to their performance in simulating rainfall indices in present-day climate. Such weights are used to form a weighted ensemble for future climate projection. Results show that, compared to the unweighted raw ensemble, the projection with the weighted scheme is more credible, as the signal-to-noise ratio (SNR) of indices is larger from the weighted ensemble. From the beginning of the mid-twenty-first century, changes of wet indices with probability >0.5 increase significantly, especially over western China and the Yellow–Huai River basin, where the changes of all wet indices are in excess of 10%, the increase of total precipitation (PRCPTOT) can reach up to 20% over western China at the end of twenty-first century, and the SNR of PRCPTOT and precipitation intensity (SDII) is the highest at those two regions. This indicates that the precipitation in those regions has a high reliability to become more extreme. The maximum consecutive dry days (CDD) decreases throughout the north of 30°N, which shows that drought conditions in northern China would be reduced, and they are more likely to increase in southern China. However, the SNR for projection of CDD is less than 1.0 almost everywhere. Such a situation seems related to a strengthening of the East Asian summer monsoon and the associated northward shift of the monsoon front.


2021 ◽  
pp. 1-59
Author(s):  
Bin Tang ◽  
Wenting Hu ◽  
Anmin Duan

AbstractPrecipitation extremes over the Indochina and South China (INCSC) region simulated by 40 global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) were quantitatively assessed based on the skill score metrics of four extreme precipitation indices when compared with observational results from a high-resolution daily precipitation dataset for 1958–2014. The results show that it is difficult for most of the CMIP6 models to reproduce the observed spatial pattern of extreme precipitation indices in the INCSC region. The interannual variability of the extreme precipitation indices is relatively better simulated for South China than for Indochina. In general, most of the CMIP6 models perform better in South China compared with Indochina when taking both the simulations of spatial pattern and interannual variability into consideration. Only three models (EC-Earth3, EC-Earth3-Veg, and NorESM2-MM) can successfully reproduce both the spatial pattern and the interannual variability for the INCSC region. Through model ranking, the multi-model ensemble generated by a selection of the most skillful models leads to a more realistic simulation of the extreme precipitation indices both in South China and Indochina. Better simulation of the meridional wind component over South China and the water vapor convergence over Indochina can partly reduce the wet biases, resulting in a more realistic simulation of extreme precipitation indices over the INCSC region.


2021 ◽  
Author(s):  
Shakti Suryavanshi ◽  
Nitin Joshi ◽  
Hardeep Kumar Maurya ◽  
Divya Gupta ◽  
Keshav Kumar Sharma

Abstract This study examines the pattern and trend of seasonal and annual precipitation along with extreme precipitation events in a data scare, south Asian country, Afghanistan. Seven extreme precipitation indices were considered based upon intensity, duration and frequency of precipitation events. The study revealed that precipitation pattern of Afghanistan is unevenly distributed at seasonal and yearly scales. Southern and Southwestern provinces remain significantly dry whereas, the Northern and Northeastern provinces receive comparatively higher precipitation. Spring and winter seasons bring about 80% of yearly precipitation in Afghanistan. However, a notable declining precipitation trend was observed in these two seasons. An increasing trend in precipitation was observed for the summer and autumn seasons, however; these seasons are the lean periods for precipitation. A declining annual precipitation trend was also revealed in many provinces of Afghanistan. Analysis of extreme precipitation indices reveals a general drier condition in Afghanistan. Large spatial variability was found in precipitation indices. In many provinces of Afghanistan, a significantly declining trends were observed in intensity-based (Rx1-day, RX5-day, SDII and R95p) and frequency-based (R10) precipitation indices. The duration-based precipitation indices (CDD and CWD) also infer a general drier climatic condition in Afghanistan. This study will assist the agriculture and allied sectors to take well-planned adaptive measures in dealing with the changing patterns of precipitation, and additionally, facilitating future studies for Afghanistan.


Agromet ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 41-51
Author(s):  
. Misnawati ◽  
Mega Perdanawanti

Extreme climate events have significant impacts on various sectors such as agriculture, ecosystem, health and energy. The issue would lead to economic losses as well as social problems. This study aims to investigate the trend of extreme precipitation in Sumatera Island based on observed data during 30-year period, 1981–2010. There are ten indices of climate extreme as defined by ETCCDMI, which were tested in this study, including PRCPTOT, SDII, CDD, CWD, R10, R50, R95p, R99p, Rx1day and Rx5day. Then, the trend was analyzed based on the Mann-Kendall statistic, performed on the time series of precipitation data. The result shows that there was positive trend of extreme precipitation found in most stations over Sumatera, either statistically significant or insignificant. In each extreme precipitation indices, the number of observed stations indicating the insignificant change is higher than the significant one. This research also found that some indices including SDII, Rx1day, R50, R95p and R99p, showed a significantly-positive trend followed by a higher intensity of wetter and heavier events of extreme precipitation over Sumatera. On the other hand, the wet spell (CWD) index shows a negative trend (α=0.05).


2018 ◽  
Vol 32 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Sicheng He ◽  
Jing Yang ◽  
Qing Bao ◽  
Lei Wang ◽  
Bin Wang

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.


2019 ◽  
Vol 19 (22) ◽  
pp. 14107-14117 ◽  
Author(s):  
Deming Han ◽  
Yingge Ma ◽  
Cheng Huang ◽  
Xufeng Zhang ◽  
Hao Xu ◽  
...  

Abstract. Perfluoroalkyl acids (PFAAs) are a form of toxic pollutant that can be transported across the globe and accumulated in the bodies of wildlife and humans. A nationwide geographical investigation considering atmospheric PFAAs via a passive air sampler (PAS) based on XAD (a styrene–divinylbenzene copolymer) was conducted in 23 different provinces/municipalities/autonomous regions in China, which provides an excellent chance to investigate their occurrences, spatial trends, and potential sources. The total atmospheric concentrations of 13 PFAAs (n=268) were 6.19–292.57 pg m−3, with an average value of 39.84±28.08 pg m−3, which were higher than other urban levels but lower than point source measurements. Perfluorooctanoic acid (PFOA) was the dominant PFAA (20.6 %), followed by perfluorohexanoic acid (PFHxA), perfluorooctane sulfonate (PFOS), and perfluoroheptanoic acid (PFPeA). An increasing seasonal trend of PFAA concentrations was shown as summer < autumn < spring < winter, which may be initiated by stagnant meteorological conditions. Spatially, the content of PFAAs displayed a declining gradient trend of central China > northern China > eastern China > north-eastern China > south-western China > north-western China > southern China, and Henan contributed the largest proportion of PFAAs. Four sources of PFAAs were identified using a positive matrix factorization (PMF) model, including PFOS-based products (26.1 %), products based on PFOA and perfluorononanoic acid (PFNA; 36.6 %), degradation products of fluorotelomer-based products (15.5 %), and an unknown source (21.8 %).


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