Decadal prediction is a general term that encompasses predictions for annual, interannual, and decadal periods in which significant progress has been made over the years. Decadal climate prediction is made using a hindcast and the latest generation of climate models. It provides two categories of hindcast and prediction data. The purpose of this study is to evaluate the temperature from the DCPP and its prediction in Iran based on the available models of the DCPP Project contribution to the CMIP6 Project. The study area of this research is Iran. As mentioned, the purpose of this study is to predict the near-term temperature based on the output of the DCPP Project. For this purpose, daily temperature from 42 synoptic stations was used as observation to evaluate the available models of the DCPP Project. Unlike general circulation models (GCMs), the DCPP Project has an initialization that includes a three-month time step for implementation of each year. Air temperature of two models BCC-CSM2-MR and MPI-ESM1-2-HR with a horizontal resolution of 100 km is available for the DCPP Project from the CMIP6 series. Three statistics, Pearson correlation coefficient (PCC), root mean square error (RMSE) and mean bias error (MBE), were used to evaluate the selected models of the DCPP Project using observational data (synoptic stations). In the study of the relationship between observation and hindcast of the two selected models, it is found that the BCC-CSM2-MR model shows a high correlation (0. 99) in the mountainous areas of Zagros and Alborz and arid and semi-arid regions of the inland and east of Iran. However, the northern and southern coasts show a weak correlation (between 0. 92 and 0. 97). Examination of RMSE statistics for the BCC-CSM2-MR model also shows the maximum error between 1. 2 to 2. 2o in the coastal areas of the country (the Caspian Sea and the Oman Sea). The western and northern mountains of Iran show the minimum RMSE. The BCC-CSM2-MR model shows more bias than the MPI-ESM1-2-HR model in the northern regions of the country. Examination of the average monthly temperature anomaly across Iran in the predicted period compared to the hindcast period (1980-2019) showed that the monthly temperature anomaly is positive across the country compared to the normal period in all months of the year. This value is 1. 03 degrees Celsius for the country-wide average. In other words, the temperature in Iran will increase by one degree for the bear term period (2021-2028) compared to the long-term period of the last 40 years (1980-2019). In this study, for the first time, a decadal climate prediction of Iran's monthly temperature is assessed using the output of two available models BCC-CSM2-MR and MPI-ESM1-2-HR from the DCPP contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The evaluation of the models using three statistical measures RMSE, MBE and PCC showed that the BCC-CSM2-MR model has the lowest performance in the coastal areas of Iran (the Caspian and the Oman Sea) and the highest performance in the highlands of Iran. The output of the MPI-ESM1-2-HR model during the hindcast period (1980-2019) show good performance of this model in determining the temperature patterns of the country. The minimum temperature is based on the output of this model in January with a value of-6. 28o. Examination of the predicted temperature anomaly (2021-2028) compared to the hindcast period (1980-2019) shows that the average anomaly across the country for different months of the year during 2021-2028 compared to the hindcast period is 0. 99o.

In recent years, the importance of climate prediction has increased as a scientific source for understanding climate change and evaluating its consequences in political and economic decisions. Providing predictions with less uncertainty, especially for precipitation and temperature is of considerable importance for policymakers in time periods from several months to several decades. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability and variability. The DCPP consists of three components (A, B, and C). Component A comprises of the production and analysis of an extensive archive of retrospective forecasts. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts, and Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (Boer et al. 2016). The aim of this study is to predict precipitation extremes using the decadal Climate Prediction Project contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 2021 to 2028 over Iran. For this purpose, two types of data including 77 synoptic stations and three DCPP models (BCC-CSM2-MR, MPI-ESM1-2-HR, and MRI-ESM2-0) with a horizontal resolution of 100 km were used. The precipitation output of DCPP models, each with nine variants (27 members) were used for two time periods, including Hindcast (1981-2019) and Forecast (2021-2028). To evaluate DCPP models, we used the Root Mean squared error (RMSE), the Pearson correlation coefficient (PCC), the Mean Bias Error (MBE), the Percent bias (PBIAS), and the Taylor diagram methods. In addition, Direct Model Output (DMO) was corrected by the Delta Change Factor (DCF) method, and the Independent Weighted Mean (IWM) was used to generate a multi-model ensemble from 27 members. In this study, the ETCCDI indices including days with Heavy precipitation (R10mm), days with Very heavy precipitation days (R20mm), Simple daily intensity (SDII), The maximum 1-day precipitation amounts (Rx1day), The maximum 3-day precipitation amounts (Rx3day), The maximum 5-day precipitation amounts (Rx5day) were calculated to analyze precipitation extremes for all regions of Iran. Furthermore, the evaluation of the DCPP models showed that the output of mentioned models is acceptable for all regions of Iran. Also, the performance of CMIP6-DCPP-MME is higher than the individual models. The result of the prediction of precipitation extremes showed that the six studied extreme precipitation indices will increase for the next decade. The Southwest and Northeast are the two hotspots of positive anomaly. In contrast, the southern coast of the Caspian Sea for the R10mm index will experience a negative anomaly for the next decade. The findings show that the southeastern region of Iran, from the eastern borders to the north of the Strait of Hormuz, will be the main area of negative precipition anomalies in the country in the next decade. So that the indices of days with heavy (R10mm) and very heavy (R20mm) precipition will decrease by 2.7 and 0.3 days, and daily precipition intensity (SDII) will decrease by 2.6 mm/day.

The maximum range of long-term operational predictions was limited to one year until 2018, while with the implementation of the Decadal Climate Prediction Project (DCPP), their range was increased to a decade. These predictions are made by initializing global climate models using observational data. The current study aims to predict the precipitation and air temperature of Iran and neighboring countries for the next 5 years (2022 to 2026) in three time scales of seasonal, annual and five-year, using the output of DCPP models initialized in November 2021. For this purpose, the precipitation and temperature coarse data of the MPI-ESM1.2-LR (Max Plank Institute in Germany), MIROC6 (Japan Agency for Marine-Earth Science and Technology) and CNRM-ESM2-1 (Centre National de Recherches Meteorologiques, France) models were used. The horizontal resolution of the MPI-ESM1.2-LR, MIROC6 and CNRM-ESM2-1 models are 200×200, 250×250 and 250×250 km, respectively. The correction of the output of the climate models was done based on the standard methodology proposed by the WCRP (World Climate Research Program) working group of World Meteorological Organization (WMO). To correct the raw output of precipitation and temperature of the DCPP models, the GPCC and ERA5 datasets were used for precipitation and temperature, respectively. The results showed that Iran's precipitation is unlikely to be more than normal in any of the next five years. The highest decrease in precipitation will likely occur in 2022 and 2025, and the precipitation of the 2023 will most likely be normal. The high predictability of ENSO and the expectation of El Niño occurrence in 2023 confirm that the precipitation of Iran and neighboring countries is within the normal range for 2023. It is more likely that, in none of the next 5 years, the average temperature of the Iran will be below normal, and the temperature anomaly is at least in the range of 0.3-0.5 degree Celsius, and the largest increase is expected in the western half of Iran and the northeast region under study. The minimum and maximum temperature increase will most likely occur in 2022 and 2026 over Iran. In the studied period, the precipitation of West Asia, especially the areas adjacent to the Arabian Sea and the Red Sea, is most likely more than normal and other countries are estimated to be within the normal range. Also, the average air temperature of the next five years in West Asia will be between 0.3 and 1.2 degree of Celsius above normal, with the largest increase of 1 to 1.2 degrees occurring in eastern Turkmenistan, Tajikistan and Kyrgyzstan. It is expected that the air temperature anomaly in the Arabian Peninsula will be in the range of 0.3 to 0.5 degrees, which will be about 0.5 degrees lower than other countries in the region.

In this paper, we address a new problem in the context of Project payment scheduling when Project activities are allowed to be crashed with the purpose of maximizing the contractors net present value (NPV). We assume that the contractor is paid at some pre-specified points of time according to the volume of work performed. Upon completion of activities, the cost of their execution is paid. Two different approaches are used to determine the volume of work performed at so called review points. In the first approach, only completed activities are considered. In the second approach, any portions of the activities that are executed are considered. To increase the volume of work performed at the review points, the contractor may decide to crash some activities and as such possibly increases his NPV. As activity crashing costs the contractor money, a compromise needs to be made. Two mathematical models are developed to study each approach and hence help the contractor to make the best decision. These models offer a means of investigating whether it is advisable to crash some activities and are therefore of practical importance. It is shown that the contractor may increase his NPV, even when he pays for the activity crashing costs. The performance of the mathematical models is illustrated using a numerical example.