Given the complexity of the climate system and the non-linear relationships between the ocean and atmosphere within this system, it is imperative to comprehend and consider the uncertainties that stem from different sources. Understanding and accounting for uncertainties play a crucial role in predicting climatic variables and facilitating a comprehensive evaluation of greenhouse gas mitigation and adaptation policies. The objective of this study is to quantify the uncertainties in historical and future average monthly precipitation by employing various General Circulation Models (GCMs), bias correction methods, Shared Socioeconomic Pathways (SSPs) scenarios, and seven projection periods. To achieve this, the outputs of ten GCMs were adjusted using nine quantile mapping bias correction methods for the Rafsanjan study area, and a suitable method was chosen to analyze the uncertainties of SSPs and projection periods. Two statistical criteria, namely the standard deviation and interquartile range, were utilized to measure the uncertainties. The results revealed that the standard deviation and interquartile range of average monthly precipitation were lower during the historical period compared to the projection period. This difference was determined based on the selection of bias correction methods and GCMs. Furthermore, for both the historical and future periods, the STDEVs and IQRs of average monthly precipitation were lower depending on the type of bias correction methods rather than the type of GCMs. In general, the uncertainties associated with projection periods and the type of GCMs are higher during future periods compared to other sources of uncertainties such as bias correction methods and SSP scenarios. This highlights the necessity for a more accurate analysis. This study contributes to an enhanced understanding of the inherent uncertainties in climate change projections that arise from various sources.

Due to inherent limitations of global climate models, their outputs are significantly biased in comparison to observed values which could provide unreliable climate projections. This study evaluates the performance of 10 global climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating precipitation in the Rafsanjan study area over calibration (1986-2005) and validation (2006-2014) period. For correcting simulated precipitation, various quantile mapping-based bias correction methods applied in these two periods. Evaluating the performance of various climate models and quantile mapping-based bias correction methods and approaches is carried out through multiple statistical metrics including NSE, PBIAS, MAE, and KGE as well as Taylor's diagram. Finally, simulated precipitation of selected model extracted for projection period under SSP1-2.6, SSP2-4.5 and SSP3-7.0 scenarios and corrected by suitable bias correction method. Results showed that the MPI-ESM1-2-LR model has better performance in simulating precipitation over calibration and validation periods compared to other climate models. The results of evaluating the performance of quantile mapping-based bias correction methods in both periods also showed that bernlnorm method performs better than others for the correction of simulated precipitation by climate models. In addition, the evaluation results of quantile mapping approaches including NTP, PT, and DDT in these periods demonstrated that NTP and PT have an acceptable performance compared to the DDT approach. Present study can help to improve the credibility of future climate projections using CMIP6 climate models.

The aim of this study is to evaluate the quantile mapping methods for the bia correction of reanalysis data of AgMERRA and ERA5 daily precipitation and air temperature data. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson's correlation coefficient (r) were used to assess the performance of the correction methods and corresponding Taylor diagrams were drawn for comparative assessment. Daily observed data of maximum temperature, minimum temperature, and precipitation during the period of 1980-2010 from seven synoptic stations in Khorasan-e-Razavi Province were used. In addition, the Mann-Kendall test and Sen's slope were used to determine the trend and its magnitude in the data. The results indicated that both minimum and maximum temperatures exhibited a significant increasing trend, such that the slope of the minimum temperature increase in all three data sets is higher than that of the maximum temperature data. Also, the precipitation data have a decreasing trend, but this decreasing trend is not significant at most stations. In addition, the error evaluation metrics of the two data sets, ERA5 and AgMERRA, compared to the observational data, showed that both data sets have made a good estimate of the maximum and minimum temperatures, such that the MAE and RMSE indices have low and good values. The correlation of the maximum and minimum temperature data also varies between 0.7 and 0.9, with the highest correlations related to ERA5 data. However, in the case of precipitation, the correlation values were low, especially for AgMERRA data. Among the quantile mapping correction methods, the PTF: Scale method has better efficiency than other methods in correcting the reanalysis data, as it has reduced the RMSE and MAE measures in both data sets. The Pearson correlation coefficient has increased at all stations compared to before correction.

The general circulation models (GCMs) are state-of-art tools available to investigate the response of climate system to external and internal forcing. They are used to predict/project climate in seasonal to decadal time scales. The general circulation models (GCMs) have more or less biases, and bias correction methods are the techniques used to correct their biases. The Coupled model intercomparison project phase 6 (CMIP6) has been widely used to simulate the historical period and project the future climate. However, due to the uncertainty of the models and their coarse resolution, GCMs are not directly used to assess the impacts of climate change. Therefore, to reduce the uncertainty in CMIP6 models, bias correction is necessary in the first step. This study evaluates three methods of bias correction including, Linear Scaling, Variance Scaling of Temperature, Empirical Quantile Mapping, Quantile mapping using a smoothing spline and Empirical Robust Quantile Mapping for two variables of minimum and maximum temperature against 46 synoptic stations in Iran during 1980-2014 using the EC-Earth3-CC. To evaluate direct model output (DMO) and bias correction methods, we used three metrics including root-mean-square error (RMSE), percent bias (PBIAS), index of agreement (d), and interannual variability skill score (IVS). The results showed that the direct model output of the EC-Earth3-CC model has a cold bias (underestimation) for both minimum and maximum temperature in all climate zones of Iran, as well as the area-averaged values of the country. The bias correction methods examined in this research have significantly reduced the bias in Iran. It is found that the bias decreased to 51.8 percent for the minimum temperature in the highlands of Azerbaijan, northeastern Iran, as well as parts of the Alborz and Zagros mountains. In general, the results of the three methods are close and they are not much different from each other. Based on the analysis of RMSE values, bias correction methods significantly reduced RMSE in comparison with DMO. So that the value of this metric in DMO has been more than 2 oC in most of Iran's climate zones, while the use of bias correction methods has reduced the error value to less than 1 oC. Also, bias correction methods have increased the index of agreement (d) by more than two times in average climate zones. Since the EC-Earth3-CC DMO has a good performance in depicting interannual climate variability (IVS) and is close to the observations, this has caused the DMO not to differ greatly from the results of using bias correction methods such as linear scaling. Finally, the bias correction methods used in this research estimate the maximum temperature with higher accuracy than that for the minimum temperature. There is no single bias correction method that provides the best performance in all regions. Therefore, each of these methods has its own advantages and limitations, which are caused by spatiotemporal differences and local geographical features.

Global space coverage of the TRMM satellite imagery has provided a good opportunity to use the precipitation data estimated by this satellite in the country. Various studies have been conducted in the country to evaluate the accuracy of the above data in comparison to the measured data at ground stations. However, few studies have examined the efficacy of postprocessing methods in correcting TRMM precipitation data. The purpose of this study was to evaluate the performance of quantile mapping methods in improving TRMM precipitation data compared to ground data. For this purpose, 10 quantile mapping methods were applied to the gridded TRMM precipitation data in Kermanshah province on a monthly time scale (Apr-Oct) from 2005 to 2012. The ground precipitation data for the same time periods were collected from 13 synoptic weather stations and 82 rain gauges. The results showed that non-corrected estimations of TRMM precipitation data in the elevated (lowland) areas higher (lower) than the ground data. In addition, it was found that the difference between satellite- and ground-based estimates of precipitation in high-precipitation months was much greater than low-precipitation ones. The Parametric transformation of scale method with the least error, among the others, was introduced as the most appropriate quantile correction method. The results of data post-processing showed that the mentioned method could improve the accuracy of TRMM precipitation data. In addition, the correlation coefficient between ground measurements and satellite precipitation data varied in the range of 0.73 to 0.94 (significant at the 5% level), with the highest correlations obtained compared to the precipitation of synoptic and TRMM stations.

Precipitation is one of the main components of flood, drought and water resources warning studies, hence, its quantitative prediction is of the great importance. The increasing development of computing and satellite technologies and remote sensing in recent years has led to the development of several meteorological forecasting models, of which the TIGGE database with a large number of powerful forecasting models, is the most important. The aim of this study was to evaluate the performance of all available numerical models in the database to predict daily precipitation in 38 synoptic stations located in different climates of Iran. In addition, removing biases from raw datasets using Quantile Mapping (QM) method is another objective of this study. Results showed that in humid, semi-humid, Mediterranean and Arid climate zones (mostly includes the southwest, northwest and northeast parts of Iran), most of the prediction models are highly correlated with ground observations, while in semi-arid and extra-arid regions the correlation coefficient (CC) between the forecasted and observed datasets is very low. For example, the CC and RMSE values obtained from ECMWF and METEO centers in most parts of the country are higher than 0. 6 and lower than 4 mm/day, respectively, while the performance of CMA and CPTEC models is not remarkable and leads to the weak results. Also, evaluation of the corrected precipitation values by QM method indicates that there is a significant improvement in the performance of most prediction systems. Findings in extra-arid, arid, and Mediterranean zones demonstrate an increase in CC value, averagely about 20%. Moreover, the results depicted that by removing biases from the raw datasets, the performance of numerical weather prediction (NWP) models in estimating the low and high precipitation events is improved and this issue further increases the applicability of precipitation forecasting systems in flood warning systems and water resources management.

Given the economic dependence of OPEC member countries, including Iran, on crude oil prices and export earnings, and considering the fact that one of the factors affecting crude oil prices is OPEC decisions, in this study, we investigate the impact of OPEC Summit Statements (increase, decrease and stabilization of production levels) focused on Iran's oil export earnings. Therefore, from the monthly data for the period 2018-1986 in 9the form of three quantities: West Texas crude oil prices are below $ 40 (Q-reg1), between $ 40 and $ 70 (Q-reg2) and above $ 70 (Q-). reg3) and Structural Vector Autoregressive Model (SVAR) were used in the quantile method. The results of the estimation of the instantaneous reaction functions (IRF) in the quintiles showed that the impact of Iranian oil export earnings on oil prices increased from $ 40 to $ 40 and then $ 70. Decreases. On the other hand, in quintiles of crude oil prices below $ 40, between $ 40 and $ 70 and above $ 70, first the shock caused by the statement declines, then the shock caused by the statement increases and finally, the shock caused by the level stability statement OPEC production affects Iran's oil export earnings. Also, as crude oil prices rise, the impact of these statements on Iran's oil earnings is diminished.

The Time–Area method is a suitable technique for watershed routing and can be potentially used as a distributed model.  As an advantage, it can also be used as a GIS-based method. The performance of the existing methods for deriving isochrone locations is compared in this study with that of the kinematic wave theorem. In most methods, travel time is proportional to the distance-to-outlet of any point raised to a power. Investigating the numerical value of powers, it is shown that exponent 1.5 of Laurenson's method yields the closest time-area hydrograph to that of the kinematic wave solution. Therefore, this paper showed that certain empirical isochrone delineation methods could be applicable provided that the travel length is measured towards the outlet.