Projected changes in extreme precipitation events over Iran in the 21st century based on CMIP5 models

2020 ◽  
Vol 82 ◽  
pp. 75-95
Author(s):  
M Darand

Climate extremes have large impacts on human societies and natural ecosystems. Projection of changes in climate extremes is very important for long-term planning. The current study investigated future changes in extreme precipitation events over Iran based on 18 CMIP5 models for the period 2006-2100. National gridded data from the Asfazari database were used to evaluate climate model simulation. Results indicate that models with higher spatial resolution (CCSM4 and MRI-CGCM3) perform better than those with lower resolution in capturing the spatial features of extreme precipitation events. Bias correction was applied to the models and the projected changes were assessed with the nonparametric modified Mann-Kendal trend test and Sen slope estimator at a 95% confidence level. Annual total precipitation (PRPCTOT) and rainy days (RD) were projected to decrease but the intensity and frequency of precipitation extremes were predicted to increase significantly. The projected decreases were larger in northwestern parts than other regions, with PRPCTOT decreasing by 18 to 22 mm decade-1 and RD by 4 to 4.8 d decade-1. Although there were discrepancies in rates between the models, extreme precipitation events over Iran were generally projected to increase. An increase in consecutive dry days (CDD) was predicted for most regions by the end of the 21st century under RCP8.5, with the largest increase of 5 to 6.8 d decade-1 found for northwestern Iran. In eastern areas of Iran, where precipitation occurs extremely rarely, the number of days with daily precipitation exceeding 10 mm (R10) or even 20 mm (R20) were projected to increase significantly. In conclusion, these changes suggest an increased risk of flash floods in Iran from increased extreme precipitation under the RCP8.5 emission scenario.

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1089 ◽  
Author(s):  
Yifeng Peng ◽  
Xiang Zhao ◽  
Donghai Wu ◽  
Bijian Tang ◽  
Peipei Xu ◽  
...  

Extreme precipitation events, which have intensified with global warming over the past several decades, will become more intense in the future according to model projections. Although many studies have been performed, the occurrence patterns for extreme precipitation events in past and future periods in China remain unresolved. Additionally, few studies have explained how extreme precipitation events developed over the past 58 years and how they will evolve in the next 90 years as global warming becomes much more serious. In this paper, we evaluated the spatiotemporal characteristics of extreme precipitation events using indices for the frequency, quantity, intensity, and proportion of extreme precipitation, which were proposed by the World Meteorological Organization. We simultaneously analyzed the spatiotemporal characteristics of extreme precipitation in China from 2011 to 2100 using data obtained from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Despite the fixed threshold, 95th percentile precipitation values were also used as the extreme precipitation threshold to reduce the influence of various rainfall events caused by different geographic locations; then, eight extreme precipitation indices (EPIs) were calculated to evaluate extreme precipitation in China. We found that the spatial characteristics of the eight EPIs exhibited downward trends from south to north. In the periods 1960–2017 and 2011–2100, trends in the EPIs were positive, but there were differences between different regions. In the past 58 years, the extreme precipitation increased in the northwest, southeast, and the Tibet Plateau of China, while decreased in northern China. Almost all the trends of EPIs are positive in the next two periods (2011–2055 and 2056–2100) except for some EPIs, such as intensity of extreme precipitation, which decrease in southeastern China in the second period (2056–2100). This study suggests that the frequency of extreme precipitation events in China will progressively increase, which implies that a substantial burden will be placed on social economies and terrestrial ecological processes.


2015 ◽  
Vol 28 (2) ◽  
pp. 451-467 ◽  
Author(s):  
Kelly Lombardo ◽  
Brian A. Colle ◽  
Zhenhai Zhang

Abstract This study analyzed the contribution of cyclones to projected changes in cool season (1 November–31 March) precipitation over the eastern United States and western North Atlantic Ocean. First, global climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were compared to Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) precipitation analyses for the period 1979–2004. The CMIP5 ensemble mean realistically reproduced the historical distribution of regional precipitation with no discernable effect because of model spatial resolution. Subsequently, the projected changes in precipitation on cyclone and noncyclone days under the representative concentration pathway 8.5 (RCP8.5) scenario were quantified. While precipitation on both types of days was projected to increase, the increase on noncyclone days (23%) was greater than the increase on cyclone days (12%). The increase in precipitation on cyclone days occurred despite a decrease in the number of cyclone days. This increase can be attributed primarily to a shift toward more frequent extreme precipitation events coupled with a decline in light precipitation events.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ling Li ◽  
Ziniu Xiao ◽  
Shuxiang Luo ◽  
Aili Yang

Extreme precipitation events, which have intensified with global warming, will have a pernicious influence on society. It would be desirable to understand how they will evolve in the future as global warming becomes more serious with time. Thus, the primary objective of this study is to provide a comprehensive understanding of the changing characteristics of the precipitation extremes in the 21st century over Shaanxi Province, a climate-sensitive and environmentally fragile area located in the east of northwestern China, based on a consecutive simulation of the 21st century conducted by the regional climate model RegCM4 forced by the global climate model HadGEM2-ES at high resolution under middle emission scenario of the Representative Concentration Pathway 4.5 (RCP4.5). Basic validation of the model performance was carried out, and six extreme precipitation indices (EPIs) were used to assess the intensity and frequency of the extreme precipitation events over Shaanxi Province. The results show that RegCM4 reproduces the observed characteristics of extreme precipitation events over Shaanxi Province well. Overall for the domain, the EPIs excluding consecutive dry days (CDD) have a growing tendency during 1980–2098 although they exhibit spatial variability over Shaanxi Province. Some areas in the arid northern Shaanxi may have more heavy rainfalls by the middle of the 21st century but less wet extreme events by the end of the 21st century. And the humid central and southern regions would suffer more precipitation-related natural hazards in the future.


2016 ◽  
Vol 149 ◽  
pp. 216-221 ◽  
Author(s):  
Sutyajeet Soneja ◽  
Chengsheng Jiang ◽  
Crystal Romeo Upperman ◽  
Raghu Murtugudde ◽  
Clifford S. Mitchell ◽  
...  

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1167 ◽  
Author(s):  
Jintao Zhang ◽  
Fang Wang

To avoid more severe impacts from climate change, countries worldwide pledged to implement intended nationally determined contributions (INDCs) for emission reductions (as part of the Paris Agreement). However, it remains unclear what the resulting precipitation change in terms of regional extremes would be in response to the INDC scenarios. Here, we analyzed China’s extreme precipitation response of the next few decades to the updated INDC scenarios within the framework of the Paris Agreement. Our results indicate increases in the intensity and frequency of extreme precipitation (compared with the current level) in most regions in China. The maximum consecutive five-day precipitation over China is projected to increase ~16%, and the number of heavy precipitation days will increase as much as ~20% in some areas. The probability distributions of extreme precipitation events become wider, resulting in the occurrence of more record-breaking heavy precipitation in the future. We further considered the impacts of precipitation-related extremes and found that the projected population exposure to heavy precipitation events will significantly increase in almost all Chinese regions. For example, for heavy precipitation events that exceed the 20 year baseline return value, the population exposure over China increases from 5.7% (5.1–6.0%) to 15.9% (14.2–16.4%) in the INDC-pledge scenario compared with the present-day level. Limiting the warming to lower levels (e.g., 1.5 °C or 2.0 °C) would reduce the population exposure to heavy precipitation, thereby avoiding impacts associated with more intense precipitation events. These results contribute to an improved understanding of the future risk of climate extremes, which is paramount for the design of mitigation and adaptation policies in China.


2020 ◽  
Author(s):  
Timo Kelder ◽  
Malte Müller ◽  
Louise Slater ◽  
Rob Wilby ◽  
Patrik Bohlinger ◽  
...  

<p>Constraining the non-stationarity of climate extremes is a topical area of research that is complicated by the brevity and sparsity of observational records. For regions with available data, analyses typically focus on detecting century-long changes in the annual maxima. However, these are not necessarily impact-relevant events and hence, a potentially more pressing research challenge is the detection of changes in the 1-in-100-year event. Furthermore, recent decades have seen abrupt temperature increases and therefore detecting decadal, rather than centurial, trends may be more important. An alternative approach to the traditional analysis based on observations is to pool ensemble members from seasonal prediction systems into an UNprecedented Simulated Extreme ENsemble (UNSEEN). This method creates numerous alternative pathways of reality, thus increasing the sample size. Previous studies have shown promising results that improve design value estimates by this method. Here, we use the hindcast of the ECMWF seasonal prediction system SEAS5 and pool together four lead times and 25 ensemble members, resulting in an ensemble of 100. We assess the robustness of this method in terms of the ensemble member independence, model stability and fidelity and then use the UNSEEN ensemble to detect non-stationarities in 100-year precipitation estimates over the period 1981-2016. We justify the pooling of ensemble members and lead times through a case study of autumn 3-day extreme precipitation events across Norway and Svalbard, which shows that the ensemble members are independent and that the model is stable over lead times. Despite previously reported model biases in the sea-ice extent and the sea-surface temperature in SEAS5, validation measures indicate that the model reliably reproduces ‘visible extremes’, i.e. the seasonal maxima. Using extreme value statistics, we then compare estimated return values from observations with the UNSEEN ensemble. Results indicate that the UNSEEN approach provides significantly different extreme values for return periods above 35 years. Additionally, while it is problematic to detect trends in the 100-year values from observations, the UNSEEN approach finds a significant positive trend over Svalbard. Validating UNSEEN events and trends is a complex task, but our approach reproduces ‘visible’ extremes well, building confidence in the modeled extremes. Both Norway and Svalbard have experienced severe floods from extreme precipitation events and our UNSEEN-trends approach is the first to provide an indication of the changes in these rare events. Further application of this approach can 1) help estimating design values, especially relevant for data-scarce regions 2) detect trends in rare climate extremes, including other variables than precipitation and 3) improve our physical understanding of the non-stationarity of climate extremes, through the possible attribution of detected trends.</p>


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