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Volume 3, Issue 3 (10-2016)
Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small. Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.
This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI. Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions. The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.
Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.
Volume 7, Issue 2 (8-2020)
Identification and synoptic analysis of the highest precipitation linked to ARs in Iran
Abstract
Atmospheric rivers (ARs) are long-narrow, concentrated structures of water vapour flux associated with extreme rainfall and floods. Accordingly, the arid and semi-arid regions are more vulnerable to this phenomenon. Therefore, this study identifies and introduces the highest precipitation occurred during the presence of ARs from November to April (2007-2018). The study also attempted to demonstrate the importance of ARs in extreme precipitation, influenced areas and identifies the effective synoptic factors. To this end, integrated water vapour transport data were used to identify ARs, and documented thresholds applied. AR event dates were investigated by their daily precipitation, and eventually, ten of the highest precipitation events (equivalent to the 95th percentile of maximum precipitation) associated with ARs were introduced and analyzed. The results showed that most ARs associated with extreme precipitation directly or indirectly originated from the southern warm seas. So the Red Sea, the Gulf of Aden and the Horn of Africa were the major source of ARs at the time of maximum IVT occurred. Synoptically, seven AR events formed from the low-pressure Sudanese system and three events from integration systems. The subtropical jet was the dominant dynamic of the upper troposphere, which helped to develop and constant of ARs. Moreover, the predominant structure of jets had a meridional tendency in Sudanese systems, while it was a zonal orientation in integration systems. The intense convective flows have caused extreme precipitation due to the dominance of strong upstream flow besides having the highest moisture flux. The station had the highest precipitation has been located in the eastern and northwestern region of the negative omega field or upstream flows.
Keywords: Identification and synoptic analysis, highest precipitation, Ars, Iran.
Volume 7, Issue 4 (2-2021)
Mesoscale Convective Systems (MCSs) are the convective precipitation structure that is most frequently associated with floods at mid-latitudes, mainly due to the high degree of organisation, which allows the structure to be maintained for a longer period of time and to become more extensive. Moreover, MCSs are an important link between atmospheric convection and larger-scale atmospheric circulation. Based on the results of previous studies, it can be claimed that Sudanese low pressure systems in many cases are the cause of the formation of MCSs, especially in southwestern Iran. Although many studies have been done in Iran on these systems and how they are formed, but the role of some environmental components of their formation and intensification, such as vertical wind shear, High and Low Level Jets (HLJ and LLJ) has received less attention. Therefore, the purpose of this study is to investigate the role of these factors in addition of the known factors that cause the formation of these systems. For this purpose, the flood of 24 and 25 march 2019 in the south and southwest of Iran has been selected as a case study.
To track and investigate the spatial characteristics of MCSs in this study, IR channel of the second-generation Meteosat imagery (MSG) on March 24 and 25, 2019, with a spatial resolution of 3 km and a temporal resolution of 15 minutes from Eumetsat site was extracted. After calibration and georeference of the images, the brightness temperature was calculated. The exact choice of temperature threshold for the identification of convective systems is optional and depends on the spatial resolution and wavelength of imagery. The size distribution obtained from the 207 or 218 k thresholds are not very different, especially for larger convection systems. Therefore, in this study, a threshold of 218 degrees Kelvin was used. Also, there is no agreement among researchers on the criterion of minimum length or area in the definition of MCSs, and this criterion is mostly determined by the characteristics of the region and the selected temperature threshold. In this study we select a threshold of 10 thousand square kilometers. In other words, the system was identified as MCSs, which at some point in life had an area of more than 10,000 square kilometers. The daily precipitation data of GPCC database were used to investigate the scattering of precipitation produced by these systems. Also, to understand the synoptic and environmental conditions of occurrence of MCSs on studied days, first geopotential height data, zonal and meridional wind components, potential temperature, relative humidity, vertical velocity and CAPE from ECMWF database were extracted and then the required maps and diagrams were prepared to synoptic and environmental analyses.
In general, the results of this study showed that three MCSs on March 24 and 25, 2019 affected different parts of Iran. The maximum area of the cold core of the first system is about 73,000 square kilometers and has traveled from west to north of Iran. The second system, which affected Iran from the west to the northeast, had a maximum area of about 660 thousand square kilometers. The cold core of the third quasi-stable system with a linear extension (northeast-southwest) and a maximum area of about 440 thousand square kilometers, has moved slightly to the southeast.
The synoptic conditions of the formation of these systems have been the same as the common pattern of the formation of Sudanese low pressure systems and MCSs. In this pattern, Azores high pressure can bring the cold air of the high latitudes to the middle latitudes and hot and humid air is injected by the high pressure over the Oman Sea and the Arabian Sea, which activates the Red Sea convergence zone along with the Mediterranean system. These conditions have led to the formation of the minimum potential temperature zone in the eastern Mediterranean with significant temperature and pressure differences compared to its environment, resulting in the formation of LLJ. This LLJ has been very effective in transferring hot and humid air to western Iran. So that in the peak hours of convective activity in the center of Iran, a potential temperature difference of about 30 degrees Kelvin with the environment has created that has played an effective role in the formation of convective storms. The transfer of hot and humid air by the LLJ has led to the formation and continuation of convection and the release of latent heat to enhance the convergence and longer life of convection systems. On the other hand, the coupling of LLJ and HLJ, by strengthening the MCSs in the western part of Iran and strengthening the divergent flow at higher levels, has strengthened the HLJ, which in turn has led to strengthening the convective system. Vertical wind shear probably also led to the formation of new convective cells in areas far from the origin of the primary convective cells. During the peak hours, unstable convective activity was observed over a large part of Iran, especially the southern and western parts, and its maximum was observed from the southern half of the Red Sea along the convergence zone to the west of Iran.
Therefore, various components of the Sudanese low pressure system play an important role in the formation, continuity and development of mseoscale convective systems. It seems that low-level jet, vertical wind shear and its interaction with the Red Sea convergence zone and the outflow of primary convective cells have a very effective role in the occurrence of this phenomenon. Thus, more detailed studies of this issue using mesoscale numerical models will probably identify unknown aspects of Iran's climate.
Volume 9, Issue 3 (12-2022)
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