Extreme weather events in Iran under a changing climate

Alizadeh, Omid; Najafi, Mohammad Saeed

doi:10.1007/s00382-017-3602-4

Cited by 111 publications

(50 citation statements)

References 41 publications

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“…However, no study was found in the literature related to the assessment of the trends in the bioclimate of Iran, and therefore, it was not possible to compare the findings of the present study. However, the trend in temperatures in Iran was assessed in a number of previous studies [47,[85][86][87][88][89][90].…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2 shows the climate zones of Iran. Alizadeh-Choobari and Najafi [47] classified the climate of Iran into six zones based on rainfall, namely, extremely dry in the central, eastern and south-east regions; dry in an extended region from the centre to the east; semi-dry in the northwest, northeast, southwest and west; semi-wet in the west and north; and wet and extremely wet in the northern coastal region. Overall, the semi-dry or dry climate dominates the most parts of Iran.…”

Section: Topography and Climate Of Iranmentioning

confidence: 99%

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Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

et al. 2019

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Changes in bioclimatic indicators can provide valuable information on how global warming induced climate change can affect humans, ecology and the environment. Trends in thermal bioclimatic indicators over the diverse climate of Iran were assessed in this study to comprehend their spatio-temporal changes in different climates. The gridded temperature data of Princeton Global Meteorological Forcing with a spatial resolution of 0.25° and temporal extent of 1948–2010 was used for this purpose. Autocorrelation and wavelets analyses were conducted to assess the presence of self-similarity and cycles in the data series. The modified version of the Mann–Kendall (MMK) test was employed to estimate unidirectional trends in 11 thermal bioclimatic indicators through removing the influence of natural cycles on trend significance. A large decrease in the number of grid points showing significant trends was noticed for the MMK in respect to the classical Mann–Kendall (MK) test which indicates that the natural variability of the climate should be taken into consideration in bioclimatic trend analyses in Iran. The unidirectional trends obtained using the MMK test revealed changes in almost all of the bioclimatic indicators in different parts of Iran, which indicates rising temperature have significantly affected the bioclimate of the country. The semi-dry region along the Persian Gulf in the south and mountainous region in the northeast were found to be more affected in terms of the changes in a number of bioclimatic indicators.

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Section: Discussionmentioning

confidence: 99%

Section: Topography and Climate Of Iranmentioning

confidence: 99%

Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

et al. 2019

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“…Iran encircles a variety of climates, which range from extremely to subtropical dry in the central to eastern parts of the country, wet to extremely wet in the coastal plains of the Caspian Sea, relatively wet in some western areas, and arid to semiarid zones in the rest of the country [34]. The Mediterranean Synoptic System is the dominant weather system in the country, where the rainy season is from October to April, with a peak in December.…”

Section: Study Areamentioning

confidence: 99%

Ground Validation of GPM IMERG Precipitation Products over Iran

et al. 2019

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Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014)(2015)(2016)(2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product's performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to -Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product's performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.Remote Sens. 2020, 12, 48 2 of 23 subject to different errors and uncertainties, such as ground clutter, anomalous propagation, signal attenuation, beam blockage, and bright band contamination [6].Rain gauges are limited in describing the spatial distribution of precipitation depending on the arrangement and density of the rain gauge network [7,8]. In order to spatially characterize precipitation, gauge measurements are transformed to a gridded precipitation dataset. This is carried out through interpolation of rain gauge measurements, using spatial interpolation and geo-statistical methods [9]. These may be prone to missing values, wind effects, insufficient numbers of rain gauges, and a sparse network, especially in less accessible mountainous and oceanic areas [4].In view of the above, the spatial limitations, resolution, and coverage of ground-based measurements highlight the importance of satellite-based precipitation estimates at both the regional and global scale. Satellite-based precipitation estimates are also subject to uncertainties through cloud top reflectance, thermal radiance, infrequent satellite overpasses, and retrieval algorithm related to the nature of indirect measurement [10]. Therefore, a thorough validation of satellite precipitation data in any given area is necessary to achieve insight regarding is accuracy...

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“…As warmer air can hold more water vapour, precipitation will increase in the future period in the cold months of the year when the dynamic conditions favour cloud formation and precipitation. Conversely, the decrease of precipitation in the warm months of the year is due to an increase in the height of the lifting condensation level, the feature that is caused by a substantial increase in the saturation moisture content due to warming (Alizadeh-Choobari and Najafi, 2018b).…”

Section: Climate Change In Iranmentioning

confidence: 99%

“…Many aspects of the water cycle have undergone changes in a warming climate, including more intense droughts in some areas (Trenberth et al, 2014) and more frequent floods in others (Alfieri et al, 2018). Reduction of precipitation in subtropical semi-arid to arid regions under global warming is noted in most previous studies (e.g., Alizadeh-Choobari and Najafi, 2018b), and this certainly has exacerbated semi-arid to arid conditions over these regions. In addition, an earlier arrival of spring-like conditions under the warmer climate leads to earlier snowmelt and river flow peaks in spring, but causing available fresh water to be significantly reduced in summer and autumn (Stone et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Impacts of global warming on the surface water balance components in Iran as simulated by RegCM4

Moslemzadeh

Irannejad

Alizadeh

2019

Intl Journal of Climatology

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The impacts of future global warming on the climate of semi‐arid to arid regions of Iran have remained mostly unexplored. Using Regional Climate Model version 4.4 (RegCM4) under the RCP4.5 and RCP8.5 scenarios, changes in surface water balance components in Iran are investigated for the future period 2070 to 2099 relative to the base period 1970 to 1999. The initial and boundary conditions for RegCM4 were derived from the CSIRO‐Mk3.6 general circulation model (GCM). Model results during the base period are compared against the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis Interim (ERA‐Interim) and the Climatic Research Unit (CRU) datasets. Analyses of the ERA‐Interim and CRU datasets and model simulations during the base period indicated that due to the large latitudinal extent of Iran and its complex topography, different regions of the country experience a diverse range of climate, with monthly precipitation ranging from 0.1 mm day−1 over arid regions of southeastern Iran to more than 2.5 mm day−1 over wet coastal regions of northern Iran. In the future period, near‐surface temperatures over all regions of Iran will increase under the two scenarios, with more temperature rises under the RCP8.5 scenario. Under the two scenarios, precipitation over most regions of Iran will increase in winter and decrease in summer. As winter will be warmer in the future under the two scenarios, and a warmer air has a higher water vapour storage capacity, precipitation will increase in the future winter climate. On the other hand, the decrease of precipitation in summer is due to an increase of the altitude of the lifting condensation level under the warmer climate. It is shown that increases in the surface temperature and precipitation in the future winter are associated with an increase of evaporatranspiration in nearly all regions of Iran.

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Extreme weather events in Iran under a changing climate

Cited by 111 publications

References 41 publications

Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

Ground Validation of GPM IMERG Precipitation Products over Iran

Impacts of global warming on the surface water balance components in Iran as simulated by RegCM4

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