Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

Zarrin, Azar; Dadashi-Roudbari, Abbasali

doi:10.1007/s00704-021-03568-2

Cited by 42 publications

(13 citation statements)

References 86 publications

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“…Zarrin and Dadashi-Roudbari [29] concluded that the trend and slope of the intensity of extreme precipitation are increasing by the year 2100 in all zones in Iran except for some areas (BWh, BWk, and Cfa zones) for SSP1-2.6 and SSP3-7.0 scenarios. Our result shows that the increasing trend of the AMP1 is projected in the western and southwestern parts of Iran.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A few studies including [29] have analyzed the future projections of extreme rainfall over Iran using an ensemble of CMIP6 models. Zarrin and Dadashi-Roudbari [29] projected the intensity of extreme precipitation based on an ensemble of bias-corrected five CMIP6 models. In this study, we update the previous studies based on 24 CMIP6 models under the three shared socioeconomic pathway (SSP) scenarios: SSP2-4.5, SSP3-7.0, and SSP5-8.5 [30].…”

Section: Introductionmentioning

confidence: 99%

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Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

et al. 2021

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Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

et al. 2021

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“…Global climate models (GCMs) developed by many organizations with Coupled Model Intercomparison Project (CMIP) are the progressive and advanced data to assess future challenges [9]. The current version of CMIP is CMIP6 is an ensemble of CMIP-endorsed Model Intercomparison Projects (MIPs) that provides an extensive version compared to CMIP5 in terms of the number of models and releases scenarios [10]. GCMs under CMIP6 differ from CMIP5 in three general ways such as higher horizontal resolution, more complexity of physical scheme of models, and new shared socio pathways (SSPs) scenarios replacing representative concentration pathways (RCPs) [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Average conditions of hydro-meteorological parameters are always considered in crop cultivation [8,17], unlike when dealing with flood assessment where the extreme event is crucial to be analyzed [10]. Therefore, the selection of downscaling technique plays a vital role in the analysis considering the different focus areas and time scales of climate change impact assessment.…”

Section: Introductionmentioning

confidence: 99%

Long-term rainfall projection based on CMIP6 scenarios for Kurau River Basin of rice-growing irrigation scheme, Malaysia

2022

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Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051–2080 show a significant increasing trend during main (wet) season compared to the near future period (2021–2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector. Article highlights The rainfall generator performs well in simulating future rainfall based on an ensemble of five different GCMs considering three different scenarios emission (low, medium, and high) caused by greenhouse gas and radiative forcing. The future rainfall projection predicted that off (dry) season would become wet, and main (wet) season would become wetter due increase in monthly and annual rainfall. The outcomes of this paper are beneficial for rice farmers and water managers of the study area to manage their rice cultivation and water release from the reservoir schedules to avoid losses due to flood and drought.

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Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

Ayugi

Jiang

Zhu

et al. 2021

Intl Journal of Climatology

143

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This study examines the improvement in Coupled Model Intercomparison Project Phase Six (CMIP6) models against the predecessor CMIP5 in simulating mean and extreme precipitation over the East Africa region. The study compares the climatology of the precipitation indices simulated by the CMIP models with the CHIRPS data set using robust statistical techniques for 1981-2005. The results display the varying performance of the general circulation models (GCMs) in the simulation of annual and seasonal precipitation climatology over the study domain. CMIP6 multi-model ensemble mean (hereafter MME) shows improved performance in the local annual mean cycle simulation with a better representation of the rainfall within the two peaks, especially the MAM rainfall relative to their predecessor. Moreover, simulation of extreme indices is well captured in CMIP6 models relative to CMIP5. The CMIP6-MME performed better than the CMIP5-MME with lesser biases in simulating Simple Daily Intensity Index (SDII), consecutive dry days (CDD), and very heavy precipitation days >20 mm (R20mm) over East Africa. Remarkably, most CMIP6 models are unable to simulate extremely wet days (R95p). Some CMIP6 models (e.g., NorESM2-MM and CNRM-CM6-1) depict robust performance in reproducing the observed indices across all analyses. OND season shows wet biases for some indices (i.e., R95p, PRCPTOT), except for SDII, CDD, and R20mm in CMIP6 models. Consistent with other studies, the mean ensemble performance for both CMIP5/6 shows better performance as compared with individual models due to the cancellation of some systematic errors in the

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Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

Cited by 42 publications

References 86 publications

Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

Long-term rainfall projection based on CMIP6 scenarios for Kurau River Basin of rice-growing irrigation scheme, Malaysia

Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

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