scholarly journals Assessment of extreme precipitation indices over Indochina and South China in CMIP6 models

2021 ◽  
pp. 1-59
Author(s):  
Bin Tang ◽  
Wenting Hu ◽  
Anmin Duan
Keyword(s):  
Spatial Pattern ◽  
Interannual Variability ◽  
South China ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Global Climate Models ◽  
Wind Component ◽  
Realistic Simulation ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices

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.

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
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Eastern China ◽  
Northern China ◽  
Western China ◽  
Cmip5 Models ◽  
The West ◽  
Southeastern China ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices

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.

scholarly journals Evaluation of 21st Century CMIP5 GCM Outputs for Climate Change Impact Assessments in Shire River Basin in Malawi

2021 ◽  
Author(s):  
Petros Nandolo Zuzani ◽  
Cosmo Ngongondo ◽  
Faides Mwale ◽  
Patrick Willems
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Heavy Precipitation ◽  
Climate Impact ◽  
Global Climate Models ◽  
Daily Maximum ◽  
Maximum Precipitation ◽  
Study Results ◽  
Precipitation Indices

Abstract Data scarcity globally has impeded our understanding of hydrological processes. This study was aimed at evaluating skills of models in reproducing past climate in the Shire River Basin (SRB) in Malawi for future climate impact assessments. The study used data, simulated by Global Climate Models (GCMs), participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A total of 52 models were considered comprising a mixture of models in the Representative Concentration Pathways of RCP4.5 and RCP6.0. The mean annual bias, correlation, extreme precipitation indices obtained from the RClimdex package of R software program and frequency distributions were used to quantify the accuracy of the GCM simulations. On the precipitation indices, emphasis was placed on the frequency indices (number of heavy precipitation days (RR ≥ 10 mm), R10mm, number of very heavy precipitation days (RR ≥ 20 mm), R20mm, number of extremely heavy precipitation days (RR ≥ 25 mm), R25mm, Consecutive Dry Days (RR < 1 mm), CDD and Consecutive Wet Days (RR ≥ 1 mm), CWD and on the intensity indices (daily maximum precipitation, RX1day, 5-day maximum precipitation, RX5days, annual total wet-day precipitation, PRCPTOT and very wet days, (R95P). Study results have revealed that there is variation in the performances of individual models and that the overall performance of the models over the SRB is generally low. Some individual models perform better than the multi-model ensemble. Results have also shown the better performance of the following models: ACCESS1-3_rcp45_r1i1p1, BNU-ESM_rcp45_r1i1p1, CSIRO-Mk3-6-0_rcp45_r3i1p1, CSIRO-Mk3-6-0_rcp45_r8i1p1 and GFDL-ESM2G_rcp45_r1i1p1 of medium-low emission pathway, RCP4.5, in replicating the historical extreme precipitation for Shire River Basin.

scholarly journals Future projection of extreme precipitation indices over the Indochina Peninsula and South China in CMIP6 models

2021 ◽  
pp. 1-63
Author(s):  
Bin Tang ◽  
Wenting Hu ◽  
Anmin Duan
Keyword(s):  
South China ◽  
Extreme Precipitation ◽  
Coupled Model ◽  
Indochina Peninsula ◽  
Future Projection ◽  
Weighting Method ◽  
Extreme Rainfall Events ◽  
The Future ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices

AbstractA future projection of four extreme precipitation indices over the Indochina Peninsula and South China (INCSC) region with reference to the period 1958–2014 is conducted through the application of multimodel ensemble approach and rank-based weighting method. The weight of each model from phase 6 of the Coupled Model Intercomparison Project (CMIP6) is calculated depending on its historical simulation skill. Then, the weighted and unweighted ensembles are used for future projections. The results show that all four extreme precipitation indices are expected to increase over the INCSC region, both in the middle (2041–2060) and at the end (2081–2100) of the 21st century, under three Shared Socioeconomic Pathway (SSP) scenarios. The increases in total extreme precipitation (R95p), extreme precipitation days (R95d), and the fraction of total rainfall from events exceeding the extreme precipitation threshold (R95pT) in the Indochina Peninsula are more significant than those in South China. The occurrence of extreme rainfall events may become more frequent in the future over the INCSC region, since the probability that R95pT increases is larger than 0.7 in the whole INCSC region. A comparison between the weighted and unweighted ensemble means shows that the uncertainty over South China is almost always reduced after applying the weighted scheme to future probabilistic projection, while the reductions in uncertainty over the Indochina Peninsula may depend on SSPs. The more extreme precipitation over the INCSC region in the future may be related to the larger water vapor supply and the more unstable local atmospheric stratification.

Understanding Precipitation Characteristics of Afghanistan at Provincial Scale

2021 ◽  
Author(s):  
Shakti Suryavanshi ◽  
Nitin Joshi ◽  
Hardeep Kumar Maurya ◽  
Divya Gupta ◽  
Keshav Kumar Sharma
Keyword(s):  
Extreme Precipitation ◽  
Asian Country ◽  
Annual Precipitation ◽  
Precipitation Pattern ◽  
Precipitation Trend ◽  
Extreme Precipitation Events ◽  
Changing Patterns ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices ◽  
Precipitation Events

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.

scholarly journals Performance of CMIP3 and CMIP5 GCMs to Simulate Observed Rainfall Characteristics over the Western Himalayan Region

2017 ◽  
Vol 30 (19) ◽  
pp. 7777-7799 ◽  
Author(s):  
Jitendra Kumar Meher ◽  
Lalu Das ◽  
Javed Akhter ◽  
Rasmus E. Benestad ◽  
Abdelkader Mezghani
Keyword(s):  
Interannual Variability ◽  
Annual Cycle ◽  
Climate Models ◽  
Global Climate ◽  
India Meteorological Department ◽  
Rain Gauge ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Western Himalayan Region ◽  
Cmip5 Gcms

Abstract The western Himalayan region (WHR) was subject to a significant negative trend in the annual and monsoon rainfall during 1902–2005. Annual and seasonal rainfall change over the WHR of India was estimated using 22 rain gauge station rainfall data from the India Meteorological Department. The performance of 13 global climate models (GCMs) from phase 3 of the Coupled Model Intercomparison Project (CMIP3) and 42 GCMs from CMIP5 was evaluated through multiple analysis: the evaluation of the mean annual cycle, annual cycles of interannual variability, spatial patterns, trends, and signal-to-noise ratio. In general, CMIP5 GCMs were more skillful in terms of simulating the annual cycle of interannual variability compared to CMIP3 GCMs. The CMIP3 GCMs failed to reproduce the observed trend, whereas approximately 50% of the CMIP5 GCMs reproduced the statistical distribution of short-term (30 yr) trend estimates than for the longer-term (99 yr) trends from CMIP5 GCMs. GCMs from both CMIP3 and CMIP5 were able to simulate the spatial distribution of observed rainfall in premonsoon and winter months. Based on performance, each model of CMIP3 and CMIP5 was given an overall rank, which puts the high-resolution version of the MIROC3.2 model [MIROC3.2 (hires)] and MIROC5 at the top in CMIP3 and CMIP5, respectively. Robustness of the ranking was judged through a sensitivity analysis, which indicated that ranks were independent during the process of adding or removing any individual method. It also revealed that trend analysis was not a robust method of judging performances of the models as compared to other methods.

scholarly journals Historical Spatial and Temporal Climate Trends in Southern Ontario, Canada

2017 ◽  
Vol 56 (10) ◽  
pp. 2767-2787 ◽  
Author(s):  
Hussein Wazneh ◽  
M. Altaf Arain ◽  
Paulin Coulibaly
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Indices ◽  
Total Precipitation ◽  
Southern Ontario ◽  
Temperature And Precipitation ◽  
Annual Total ◽  
Precipitation Indices ◽  
Temperature Indices

AbstractSpatial and temporal trends in historical temperature and precipitation extreme events were evaluated for southern Ontario, Canada. A number of climate indices were computed using observed and regional and global climate datasets for the area of study over the 1951–2013 period. A decrease in the frequency of cold temperature extremes and an increase in the frequency of warm temperature extremes was observed in the region. Overall, the numbers of extremely cold days decreased and hot nights increased. Nighttime warming was greater than daytime warming. The annual total precipitation and the frequency of extreme precipitation also increased. Spatially, for the precipitation indices, no significant trends were observed for annual total precipitation and extremely wet days in the southwest and the central part of Ontario. For temperature indices, cool days and warm night have significant trends in more than 90% of the study area. In general, the spatial variability of precipitation indices is much higher than that of temperature indices. In terms of comparisons between observed and simulated data, results showed large differences for both temperature and precipitation indices. For this region, the regional climate model was able to reproduce historical observed trends in climate indices very well as compared with global climate models. The statistical bias-correction method generally improved the ability of the global climate models to accurately simulate observed trends in climate indices.

scholarly journals Trend of Extreme Precipitation over Sumatera Island for 1981-2010

Agromet ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 41-51
Author(s):  
. Misnawati ◽  
Mega Perdanawanti
Keyword(s):  
Extreme Precipitation ◽  
Ecosystem Health ◽  
The Other ◽  
Economic Losses ◽  
Positive Trend ◽  
Climate Extreme ◽  
Precipitation Data ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices ◽  
Climate Events

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).

Forecast and Uncertainty Analysis of Extreme Precipitation in China from Ensemble of Multiple Climate Models

2021 ◽  
Author(s):  
Peng Deng ◽  
Jianting Zhu
Keyword(s):  
Extreme Precipitation ◽  
Flood Control ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Model Averaging ◽  
Statistical Distribution ◽  
Global Climate Models ◽  
Effective Measure

Abstract Global climate change is expected to have major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various Global Climate Models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, Generalized Extreme Value (GEV) distribution and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian Model Averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

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