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It is necessary to evaluate the response of extreme events to global warming across different climates and geographical regions. This study aims to examine the trend changes of 13 temperature extreme indices over a 30-year statistical period from 1990 to 2020, using daily maximum and minimum temperature data from 37 synoptic stations in Iran. The Mann–Kendall trend test was employed to analyze the data trends. The results indicate that, except for the two indices, frost days (FDs) and ice days (IDs), the temperature extreme indices show an increasing trend across the country. The forward and backward graphs of the Mann–Kendall test reveal that the trend of the indices is significant at the 0.05 significance level, with the two indices TNn and TXn intersecting in 2009. This indicates that a mutation occurred in that year, where the increasing slope of the trend after the mutation is greater than the slope of the trend before the mutation. Moreover, the decadal changes of the indices in the three decades 1990–2000, 2000–2010, and 2010–2020 demonstrate that the highest increasing trend in temperature occurred in the second and third decades.

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC > 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 1995. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management.

Estimating the probable maximum precipitation (PMP) is necessary to calculate the probable maximum flood (PMF). It is of high importance in checking the adequacy of dam overflow capacity and other development and water transfer plans of a given area. In this research, using the for an annual 24-hour maximum rainfall data set of 45 synoptic stations throughout the country, the PMP values were calculated through the original Hershfield method and then the Hershfield-Desa method. By comparing the obtained results of 24-hour PMP estimation through the two mentioned methods, it is found that the estimation of PMP values in the original/first Hershfield method is well higher than the expected value (2.54 to 4.03). While in the modified method (Desa method), PMP values are significantly reduced and seem more reasonable (1.02 to 1.3). Meanwhile, the calculated variability and skewness coefficients also indicated more variability of PMP values in the southern stations of the country compared to rainy regions, which makes the estimation of PMP in the southern regions of the country considerably unreliable.

In recent years, air pollution has become a significant issue for megacities. This study analyzed the air pollution levels in Tehran and the relationship between pollutant concentrations and atmospheric quantities during 2023. The correlation coefficients between wind speed, temperature, mean sea level pressure (MSLP), and relative humidity (RH) were calculated against the concentrations of NO2, NOx, PM10, and PM2.5. Additionally, one case study was conducted for each pollutant. Approximately 72% of haze phenomena in Tehran were recorded in November, December, and January. The monthly pattern of PM10 concentration indicated higher levels in the southern and western parts of Tehran. For PM2.5, in addition to these areas, significant concentrations were also observed in the central and eastern parts. NO2 concentrations were found to be higher in the northeast and northern areas. An inverse relationship was found between wind speed and temperature with pollutant concentrations. Positive correlations between MSLP and pollutant concentrations suggested that the pollutant levels also increased as air pressure rose. RH showed a significant direct relationship with PM2.5 and NOx. Synoptic analysis revealed that PM10 case studies often occurred during the warm season, with a thermal low pressure situated over the Iranian plateau. During PM2.5 and NO2 pollution events, Tehran was influenced by high pressure, and 10 m wind speeds were weak. Finally, verification of the 24 h forecast of the CAMS model showed that, while the model accurately predicted the spatial distribution of pollutants in most cases, it consistently underestimated the concentration levels.

The living conditions in the Urmia Basin (northwestern Iran) face significant challenges due to dust events. This study investigates the spatial and temporal characteristics of dust phenomena in the Urmia Basin using MERRA-2 data and observational data from Tabriz, Urmia, Sarab, and Mahabad over a 30-year period (1990–2019). The findings reveal that despite several fluctuations, the annual number of dusty days increased from the 1990s to the 2010s in the Urmia Basin. The maximum number of dusty days was found to predominantly occur in May (spring) and October (autumn), driven by two distinct mechanisms. In early autumn, developing synoptic systems associated with increased wind speeds can cause dust emission from dry land sources. Consequently, an increase in dust wet deposition, precipitation, dust surface concentration, and the number of dusty days occurs in October. In contrast, a sharp decrease in precipitation from April to May leads to drying soil and dust emission in May. Among the studied cities, Tabriz experienced the highest number of dusty days (728) due to the combined effects of cross-border and local dust sources. The highest dust column density and dust dry deposition in the south and east of Urmia Lake indicate the impact of declining water levels, which resulted in a dry lakebed as the primary local dust source. The MERRA-2 spatial distribution reveals that dust surface concentration, and the number of dusty days decrease similarly from the southwest to the northeast of the Urmia Basin as the distance from cross-border dust sources increases. A positive correlation is observed between the number of dusty days and MEERA-2 data, including dust surface concentration, dust dry deposition, column mass dust, and total aerosol extinction, with coefficients of 0.74, 0.71, 0.69, and 0.68, respectively.

This study examines the relationship between relative vorticity, a key variable in mid-latitude synoptic motions, and precipitation in Iran. Using the S-mode PCA, activity centers of relative vorticity and precipitation were identified. Canonical correlation analysis (CCA) was applied to the factor scores of these centers to reveal coupled patterns of relative vorticity and precipitation. The analysis is based on 500- and 850-hPa relative vorticity fields at 2.5° grid points (10°–70° E and 10°–70° N) and uses monthly relative vorticity values from NCEP-DOE reanalysis databases (1981–2020) along with standardized rainfall data from 97 Iranian synoptic stations. Three main CCA patterns reveal connections: 500-hPa relative vorticity changes in the eastern Mediterranean, Middle East, and Iran relate to eastern Iran's precipitation. Relative vorticity over Eastern Europe inversely correlates with southern Caspian Sea coast precipitation. Changes over Turkey and Cyprus can affect northwestern Iran's rainfall. The changes in 850-hPa relative vorticity over the Arabian Sea inversely link to eastern Iran's precipitation, while those over the eastern Mediterranean directly connect to western Iran's precipitation. Relative vorticity changes in Eastern Europe negatively correlate with southwestern Caspian Sea coast precipitation.

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.

Analyzing and classifying atmospheric circulation patterns (CPs) is useful for studying climate variability. These classifications can effectively identify the links between large-scale and regional-local scale processes. This work uses the historical (1975–2014) and projected (2015–2054) simulations of the MPI-ESM1-2-HR model to reproduce the CPs over the Middle East and Iran. Eighteen CPs were identified based on the geopotential height (GPH) of 500 hPa data from Coupled Model Intercomparison Project Phase 6 (CMIP6) in SSP1-2.6, SSP3-7.0, and SSP5-8.5. The method of principal component analysis (PCA) and k-means clustering was used. Then, the possible variability of each pattern in the surface and mid-level of the atmosphere and their expected changes in the frequency of CPs in global warming scenarios were investigated. This research showed that CPs 3, 6, and 11 happen during warm months of the year. The surface thermal low pressure is associated with the subtropical high in the atmosphere mid-level. According to the intensity of the low and the northward development, or the orbital expansion of the subtropical high, this pattern has an increasing (CPs 3 and 6) or decreasing (CP11) trend in the future period. CPs 1 and 12 occur during cold months. In CP1, dynamic high pressure prevails over Iran. However, in CP12, Iran is affected by high pressure from southeastern Europe. They will decrease in future projections. CPs 7 and 16, which often occur in the transition season (spring), show an increase in the projected patterns. CP 18 occurs throughout the year, but its highest frequency is in autumn, and the frequency of occurrence decreases. An increase in 500 hPa geopotential height over the Arabian Sea in all 18 classes and all three SSPs is predicted for future periods. Analysis of the obtained weather types indicates the identification of all effective atmospheric circulation patterns in the study area so that the behavior and frequency of each pattern explain the prevailing atmospheric phenomena in this region.

This study examines the spatiotemporal characteristics of extreme precipitation indices in Iran. It analyzes data from 38 synoptic stations across the country, covering the period from 1990 to 2020, focusing on the 11 most common extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis employs the Mann–Kendall (M–K) trend test. The findings indicate that the indices PRCPTOT (annual total precipitation), R20 mm (very heavy precipitation days), R10 mm (heavy precipitation days), R25 mm (number of wet days), Rx1 day (maximum 1-day precipitation), Rx5 day (maximum 5-day precipitation), SDII (simple daily intensity index), R95p (very wet day precipitation), R99p (extremely wet day precipitation), and CWDs (consecutive wet days) showed the highest values in the northern and western regions of the country, particularly at stations like Ramsar, Hamedan, Ilam, Kermanshah, and Yasouj. Conversely, the eastern and southeastern parts of the country showed the lowest values for these indices. The Consecutive Dry Day (CDD) index exhibited the highest values at Zabol station (228 days) and Abadan station (193 days) in the southern region of the country. Generally, precipitation extremes in the western, northwestern, and Caspian Sea coasts showed an increasing trend, while the eastern, southeastern, and central parts of the country demonstrated a decreasing trend. The trend test results indicate significant mutations in all precipitation indices, except for SDII, with mutation points primarily occurring during the decade from 2000 to 2010. The magnitude of mutation for each index post-mutation is generally greater than before. This study provides valuable information for decision-makers in agriculture, food security, water, and the environment. It also serves as a resource for natural disaster prevention and mitigation.

Analyzing atmospheric circulation patterns characterize prevailing weather in a region. The method of principal component analysis and clustering was used to classify daily atmospheric circulation patterns. The average daily geopotential height of 500 hPa with 0.5° resolution of the ECMWF (1990–2019) were extracted from the Middle East. The S array was used to identify air types, and k-means clustering was used to classify daily air types. All days were divided into eighteen groups. Then, the surface maps and moisture flux divergence at the 850-hPa level of each pattern were studied. The, the connection between circulation patterns and precipitation occurrence is investigated by the PI index. The existence of a variety of precipitation and temperature regimes and consequent dry/wet periods is related to the type and frequency of the circulation patterns. In patterns with south to southwesterly currents, the low-pressure surface center extends from the south of the Red Sea to southern Turkey and is associated with the mid-level trough, where the moisture fluxes converge in the south of the Red Sea, southwest/south of Iran, and east of the Mediterranean Sea. Therefore, according to the intensity of the patterns, the most precipitation falls in the country’s western half, and the Zagros Mountain’s wind side. With the eastward movement of the Cyclonic patterns, the rainfall area extends to the eastern half of the country. With the pattern that the thermal low surface pressure extends to 35 °N latitude and is associated with the mid-level subtropical high, almost no rain occurs in the country.