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The aim of this paper is to investigate the teleconnection of large-scale climatic signals with river flow discharge in northern Iran. To this end, the Garmrud, Alishrud, and Babolrud rivers were selected: their monthly data set during the years 1993–2018 was received from the regional water company of the Mazandaran Province. Climate indices including the Southern Oscillation Index, extreme eastern tropical Pacific sea surface temperature (Niño 1 + 2), Global Mean Temperature Index, North Pacific pattern (NP), Pacific Decadal Oscillation, and North Atlantic Oscillation were investigated. The results of the cross-correlation, and the Spearman's test showed that there is a significant relationship at a 0.01 level between the indices and the river flow discharge. The most correlated indices were the NP and Niño 1 + 2. The NP index was inversely correlated and the Niño 1 + 2 was directly correlated with the rivers' flow variations, and the time lag of these two indices' maximum effectiveness was 2 months and 1 month, respectively. The adaptive neuro-fuzzy inference system and multilayer perceptron (MLP) models were used as the predictor models. The inputs were selected from the time lags of both river discharge and climatic indices. According to the normalized root mean square error (NRMSE) values (0.14–0.22), the predictions are evaluated as having moderate and appropriate qualities. The best performance of all models, R = 0.824 and RMSE = 0.350 CMS, was achieved for the MLP model. The results showed that consideration of the climatic indices as inputs may improve monthly flow predictability by an average of 24.1% (11.4–38.4% among the rivers and models). [ABSTRACT FROM AUTHOR]

This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran's monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran's precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5 ∘ S-5 ∘ N, 170 ∘ -120 ∘ W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran's precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. [ABSTRACT FROM AUTHOR]

The biggest known mass extinction in the history of animal life occurred at the Permian–Triassic boundary and has often been linked to global warming. Previous studies have suggested that a geologically rapid (<40 kyr) temperature increase of more than 10°C occurred simultaneously with the main extinction pulse. This hypothesis is challenged by geochemical and palaeontological data indicating profound environmental perturbations and a temperature rise prior to the main extinction. Using secondary ion mass spectrometry (SIMS), we measured oxygen isotope ratios from Changhsingian (late Permian) ostracods of north‐western Iran. Our data show that ambient seawater temperature began to rise at least 300 kyr prior to the main extinction event. Gradual warming by approximately 12°C was probably responsible for initial environmental degradation that eventually culminated in the global end‐Permian mass extinction. [ABSTRACT FROM AUTHOR]

This study investigated the correlation of thermodynamic parameters with the regional averaged lightning flash density in southwest Iran based on OTD/LIS observations and reanalysis datasets from 1996 to 2014. In addition, the most important parameters and their domains were identified for explaining the lightning density variance using a multiple linear regression model. The results showed that surface sensible heat flux over Syria and Jordan, CAPE over southwest Iran, sea surface temperature over the Arabian Sea, and latent heat flux over Northeastern Europe are the best parameters to predict the lightning density in winter. While in spring, CAPE over southwest Iran, the surface sensible heat flux over northeast Iran and the Arabian Sea, and air surface temperature over the Saudi Arabian Peninsula are the main factors for predicting lightning density. The regression models explain almost 90% and 93% of the lightning variability in winter and spring, respectively. The local effect of CAPE and low-level wind shear on lightning activity is more important in spring than in winter, indicating that the contribution of local lightning-generating convective activity is higher in spring. The correlation coefficient between humidity indices and lightning density showed that western moisture sources have a more important role in providing moisture to convection and lightning in winter than in spring. It is worth noting that the wind speed of jet streams at 300 hPa is positively correlated with lightning activity in winter, although their contribution to the multiple linear regression model is negligible. [ABSTRACT FROM AUTHOR]

In this paper, we present a new methodology for improving the results of seasonal drought forecasting by developing a Bayesian Maximum Entropy-based fusion (BMEF) model. The BMEF model combines the forecasts done by four individual (single-source) data-driven models to achieve better outcomes. Regional drought indices of Effective Drought Index (EDI) and Multiple Standard Precipitation Index (MSPI) are forecasted using the individual forecasting models of Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and M5tree. The outputs of the individual models with the best performances are selected to be fused using the BMEF model and the results are analyzed and compared. The effect of different large-scale climate signals on rainfall and drought forecasting is analyzed and the most effective climate variables are selected as predictors in the forecasting models. Next, the uncertainty analysis on the results of the individual models as well as those of the BMEF model is carried out by deriving the probability mass functions of the drought indices using a resampling technique and Monte Carlo analysis. Finally, the results of the uncertainty analysis are evaluated to compare the performance of individual models and the BME-based fusion model in decreasing the uncertainty of seasonal drought forecasting. The performance of the proposed methodology is evaluated by using it to forecast seasonal drought conditions in the southwest of Iran. Based on the results of the uncertainty analysis, the BMEF model provides more reliable forecasts particularly for severe drought events than the individual models. It is also inferred that adding the SST to the predictors, decreases the uncertainty of drought forecasts. • A new methodology is proposed to improve seasonal drought forecasting. • A BME-based fusion (BMEF) model is developed for combining the individual forecasts. • Drought indices of EDI and MSPI are forecasted under uncertainty. • Favorable effect of sea surface temperature (SST) on drought forecasting is shown. • The BMEF model reduces the uncertainty of the drought forecasts. [ABSTRACT FROM AUTHOR]

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1–G8). Synoptic data for the period 1995–2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020–2040), middle future (2040–2060), and far future (2060–2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995–2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by −74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by −4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate. [ABSTRACT FROM AUTHOR]

Daily values of precipitation in southwestern parts of Iran along with the corresponding Madden–Julian oscillation (MJO) phase are analysed to introduce some auxiliary indicators that can identify the wet and dry spells within a particular MJO phase. As a case study, the identification procedure focuses on phase 1 during the rainy season (Nov–Apr). The study shows that this phase contains two opposite spells, referred to as spells with pervasive precipitation (SWPP) and spells with widespread dryness (SWWD). Although the precipitation characteristics are essentially different between SWPP and SWWD, their associated MJO indices are found statistically identical. Therefore, the MJO–precipitation relationship fails to identify the wet/dry spells within an MJO phase. To resolve this deficiency, other climate indicators, which behave differently between these two spells, are introduced. The westward moisture flux over equatorial parts of the Indian Ocean and the northeastward moisture flux over the North African tropics are up to seven times stronger in the SWPP compared to SWWD. The dominance of a strong cyclonic (anticyclonic) circulation over the eastern side of the Mediterranean Sea and the negative (positive) west–east temperature gradient over southern parts of Iran are introduced as SWPP (SWWD) indicators. The study reveals that the SWPP (SWWD) coincides with a strong (weak) convection dipole that resembles the positive Indian Ocean dipole. The intensified (weakened) wind speed in the subtropical jet stream and persistence of the jet‐exit‐regions above Iran (Egypt and Saudi Arabia) are other SWPP (SWWD) indicators. Negative (positive) anomalies of sea surface temperature over the North Atlantic Ocean, the Black, Mediterranean, and Caspian Seas, as well as the Persian Gulf, signify the prevalence of the SWPP (SWWD). To the best of our knowledge, this is the first time that auxiliary indices are utilized to improve the MJO–precipitation relationship in southwestern Iran. [ABSTRACT FROM AUTHOR]

• An AWHP based combined cooling and desalination system is proposed. • Low-grade waste heat is recovered from AWHP to be used for desalination step. • A bio-fueled combined cycle is used for powering both AWHP and hospital equipment. • Use of biofuel and air to water heat pump leads to a significant reduction in CO footprints. • Sensitivity of GOR to dehumidifier/humidifier effectiveness, air humidity, top/bottom temperature is analyzed. This paper presents a trigeneration facility based on a biogas driven gas turbine cycle for electricity production and an air to water heat pump for simultaneous extraction of cooling and potable water, and evaluates its robustness and effectiveness under different climatic conditions and variable demands. The air to water heat pump, which consists of an inside evaporator fan, expansion device, condenser, evaporator, and a compressor, is installed for supplying the cooling load of a hospital building in summer. In the refrigeration cycle, R134a absorbs heat from the inside warm air for space cooling and releases it to the saline water for driving a desalination process. Maximum values of cooling and electrical demands of a standard hospital building, where is located in south of Iran, over the hottest day of 2018 are considered for the worst case implementation. Engineering equation solver and general algebraic mathematical modeling system are employed to develop the non-linear optimization strategy, find the best operating point of the air to water heat pump based trigeneration plant, and minimize its energy cost under the greatest load level. Moreover, the sensitivity of the gained output ratio of desalination cycle to the relative humidity of the air entering and leaving the humidifier, the dehumidifier and humidifier effectiveness, and the top and bottom temperatures are comprehensively analyzed. The relative humidity of air entering the humidifier and the larger dehumidifier effectiveness have the significant effects on increasing the gained output ratio of the desalination cycle. Due to intense solar irradiation in summer, the seawater temperature naturally increases, which results in larger gained output ratio and higher mass flow rate of pure water. [ABSTRACT FROM AUTHOR]

To answer the question "what has changed the surface temperatures of water bodies around Iran under climate change conditions," Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) data with 9.2-km spatial resolution and monthly time step were evaluated. A significant increasing trend was obtained using Pearson's correlation coefficient and Mann-Kendall trend test on data from the Caspian Sea (June) and the Southern Gulfs (September). High correlations of 0.69 and 0.97 between remotely sensed data and ERA-Interim reanalysis data were also obtained for June and September, respectively. To assess the impact of some climatic parameters on SST variability in those two months, zonal and meridional wind at 10 m, wind speed at 10 m, air temperature at 2 m, and mean sea level pressure were evaluated. High correlations between meridional wind and the Southern Gulf SST, and meridional wind and Caspian SST were −0.86 and 0.75, respectively. Moreover, the results showed an important role of air temperature (0.96) and sea level pressure (−0.77) in the rising temperature trends of the Southern Gulfs. Wind speed was also detected in both regions as a booster of the trend. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) has changed greatly across the world in the past decades. There are various drivers that may affect the changes in ET. Understanding the changes in ET and the potential drivers is of great importance to water resources management in the arid regions. In this study, we examine the trends in ET using satellite-based data in the Asian arid regions from 2003 to 2017. Trends in different ET components, i.e., the evaporation from bare soil and transpiration from vegetation, are estimated to examine their respective contributions to the changes in ET. The results show that the evaporation from bare soil is the main component of ET and has contributed larger to the changes in ET from 2003 to 2017. Statistically significant increasing trend is mostly found in Southwest Pakistan, Northwest India, Southeast Iran, many areas in Northwest China, and some parts in South Kazakhstan. The relations between ET and the global sea surface temperature (SST) are also examined. It suggests that the interannual variation of ET is closely related to annual SST anomalies in most areas, especially in Kazakhstan, Turkmenistan, Northwest China, Southwest Pakistan, and some parts of eastern Iran. This analysis provides useful information for water resources management and planning with respect to forest and agricultural development in the Asian arid regions. [ABSTRACT FROM AUTHOR]

Iran faces a water shortage crisis due to its long and frequent droughts and rapid population growth. In this study, moisture sources have been determined in 161 precipitation events from 12 stations using a Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Ten main moisture sources have been identified among which the Persian Gulf and the Persian Gulf with terrestrial sources are dominant. Backward trajectory outputs demonstrate that the precipitation events in the stations in the north of Iran originate mostly from the Caspian Sea and terrestrial sources. These precipitation events are mostly plotted on the Global Meteoric Water Line (GMWL) or between GMWL and Eastern Mediterranean Meteoric Water Line (EMMWL). Precipitation events in the middle and south of Iran stations receive moisture mostly from the Persian Gulf and terrestrial sources and they are mostly plotted on EMMWL. Linking backward trajectory models with precipitation events (δ 18O and δ 2H) determines the general isotope characteristics of each moisture source. Most of the precipitation events originate from high sea surface temperature (SST) water bodies and are plotted on EMMWL. However, precipitation events originating from lower SST water bodies are mostly plotted on GMWL and between GMWL and EMMWL. Plotting the average (δ 18O and δ 2H) in the studied stations on the meteoric water lines of the regions located on the dominant air masses trajectories toward Iran confirms the dominant moisture sources for the studied stations and also other obtained results by stable isotope technique and HYSPLIT model in this study. [ABSTRACT FROM AUTHOR]

Sudanese systems are low-pressure thermal s ystems which originate from the Red Sea and their low-pressure isoheights are detectable around south of the Red Sea, Sudan and Ethiopia. These systems when combined with Mediterranean troughs in cold seasons, and absorbing more humidity from the Red Sea, reach the south west of Iran and result in heavy rainfalls in western, southwestern and southern parts of Iran. In all these synoptic-dynamic systems, low-pressure isobars or low isoheights are considerable. 11 severe torrents which happened in southwest of Iran (1998-2008) are selected and using Japanese ReAnalysis data (JRA25), anomalies of dynamic and thermodynamic indices 2 days prior to the start of heavy rainfalls and during rainy days have been analyzed. The selected indices in this research are relative vorticity, convergence and divergence, vorticity advection, specific humidity and moisture advection. Pressure levels of 500 and 700 were analyzed to detect the location of troughs, vorticity and vertical wind velocity. Also level of 1000 hPa was investigated to determine the atmospheric convergence in near surface levels. It should be mentioned that to determine the advection of moisture, vertical profile of moisture advection (1000-250 hPa levels) was analyzed. According to the results of this research and analyzing 6-hourly recorded rainfall amounts, it is understood that in the reference events, before the start of the torrent, geopotential height values in the under-studied region decrease and at the time of maximum rainfalls, the geopotential height reaches to its lowest values and by the end of the torrent, the values start increasing; whereas parameters related to convergence and vertical movements, such as relative vorticity, moisture advection, convergence of moisture and specific humidity amounts correspond to the same trend of rainfall from the beginning to the end. [ABSTRACT FROM AUTHOR]