Relationships between Large-Scale Climate Signals and Winter Precipitation Amounts and Patterns over Iran

Ghamghami, Mahdi; Bazrafshan, Javad

doi:10.1061/(asce)he.1943-5584.0002066

Cited by 14 publications

(6 citation statements)

References 54 publications

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“…Relevant candidate predictors for the NHMM mainly include the geopotential height (GP), sea level pressure, wind fields, air temperature, humidity and their combinations (Ghamghami & Bazrafshan, 2021; Neykov et al, 2012; Zha et al, 2022). Given the high dimensionality of the original datasets, the inputs have to undergo a principal component analysis to find the most dominant behaviours of the large‐scale atmospheric predictors (Ghamghami et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Jiang,

Cioffi,

Conticello

et al. 2023

Hydrological Processes

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Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.

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Section: Introductionmentioning

confidence: 99%

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Jiang,

Cioffi,

Conticello

et al. 2023

Hydrological Processes

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“…Also, there had been some studies relating precipitation variability to large-scale climate anomalies for Iran. Ghamghami and Bazrafshan (2021) investigated the relationships between sixteen large-scale climate signals with weather types and winter precipitation amounts and patterns over Iran. They employed daily precipitation data during the period 1991-2010 from 130 weather stations across the country.…”

Section: Introductionmentioning

confidence: 99%

Influence of large-scale climate signals on theprecipitation variability over Iran

Javanshiri¹,

Babaeian²,

Pakdaman³

2022

Preprint

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This study investigates relationships between the twenty large-scale climatesignals and the precipitation variability during 1960–2018 in Iran.The twenty large-scale climate indicators include atmosphere-ocean teleconnectionsas well as El Ni˜no and Southern Oscillation (ENSO) signals,Pacific and Atlantic ocean Sea Surface Temperature (SST). WaveletCoherence Analysis (WCA) was applied to detect the impact of climatesignals on Iran’s monthly precipitation. The results revealed that (a) thesignificant wavelet coherence and the phase difference between monthlyprecipitation and climate signals were highly variable in time and periodicity,(b) at inter-annual scale, Iran’s precipitation had been linked moreto Extreme Eastern Tropical Pacific SST(0◦-10◦S, 90◦-80◦W) (Ni˜no1.2), Oceanic Ni˜no Index (ONI), and Western Hemisphere Warm Pool(WHWP), respectively, (c) decadal and inter-decadal precipitation variabilityare mainly associated with variability in Atlantic Meridional Mode(AMM), Western Pacific Index (WP) and Arctic Oscillation (AO), and(d) in the most recent decade, the coherence between precipitation andlarge-scale climate signals has been declined in decade and inter-decadescales, and an unstable coherence has been emerged in annual scale.

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“…Iran is located in the global arid region and its average precipitation is 250 mm, so that 25% of Iran's soil is in the arid regions, 40% in the arid regions and 25% in the semi-arid regions [1]. Water resources are located in a small part of the country's area and should be transferred from these areas to two thirds of Iran's area, which is one of the water-scarce areas.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal analysis of Iran precipitation

Sorkhabi

2021

Preprint

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In this study, spatial and temporal analysis of precipitation in Iran in 2019 and 2020 has been studied. The average annual precipitation in 2019 is about 345 mm and in 2020 285 mm. In 2019, the maximum precipitation was in the western and northwestern regions, and the minimum precipitation was in the central and eastern regions. In 2020, the maximum precipitation was in the northwestern areas and the minimum precipitation was in the central, eastern and southwestern areas. Northwestern regions of Iran have a positive precipitation difference, i.e. in 2020 it had more precipitation. The southern and western regions have a negative difference, i.e. in 2020, there was less rain.

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Relationships between Large-Scale Climate Signals and Winter Precipitation Amounts and Patterns over Iran

Cited by 14 publications

References 54 publications

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Influence of large-scale climate signals on theprecipitation variability over Iran

Spatial and temporal analysis of Iran precipitation

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