The latest outputs of meteorological models suggest that global warming has caused an increase in evaporation from land surface and surface water bodies, which is anticipated to have a serious impact over time on water resources management and the global population Moazenzadeh et al. Meanwhile, global warming has caused increasing and several climate events such as drought, heat waves, floods, and fires in different parts of the world Alexander et al.
Predicting climate extremes based on temperature and precipitation index in the Fifth Report of the IPCC highlights a significant growth in the number and magnitude of warm and dry periods Hao et al. The daily index consists of temperature or precipitation extremes which have been used for assessing long-time variability and frequency of days above or below specific physically based thresholds Zhang et al. Mainly, due to the interdependence and thermodynamics, relations between precipitation and temperature have been addressed in numerous studies Liu et al.
The main goal of analyzing extremes is to describe the balance and changes of climate to find the most desirable balance in macro plans for applying high safety standards and preventing great loss to communities and systems with regard to severe events.
Sometimes, one variable may be in an extreme state, but more often, these events can be considered as a combination of variables, not all of which are necessarily extreme Leonard et al. An extreme impact may be a combination of one or more variables leading to a severe change in climate, referred to as a compound event. According to copula theory Miao et al. These compound indices reveal the change at all spatial or temporal scales with significant trends in the frequency of the cool and warm modes.
Most parts of Iran are located in the desert belt, which is affected by extreme climate change and trends. Therefore, considering trends and climate change and their effects is essential and unavoidable for planning risk management in relation to communities, systems, and infrastructures through adaptation and the bad effects of climate change.
Several national studies have investigated trends of climatic variables in Iran. In recent studies, several extreme precipitation and temperature indices have been analyzed Rahimzadeh et al.
In this study, the composition of more different climatic elements is considered and several statistical tests were used for trend detection. In this regard, the trends of combined temperature, precipitation, humidity, wind speed, and sunshine statistics in a spatial domain in Iran are investigated as the main research objectives.
The aim of the present research was to understand the variations in compound values of temperature and precipitation and other variables. Compared to previous studies, this can provide a comprehensive view of the behavior of the cool and warm modes of heat and moisture, of which analysis of the statistics of each variable is taken individually.
Iran is the spatial domain used in this study latitude 25—40 and longitude 44— The total area is 1,, km 2 and it includes a population of about 75 million, according to the latest National Census of Iran.
The climate of Iran varies due to differences in latitude, altitude, and a range of geographic features, including mountains and deserts. Currently, daily data are available for more than synoptic stations in Iran, but there are long-term records for only a few stations. Most of the stations, especially in the earlier years, contain inhomogeneities and uncertainties in their data sets. Our study was limited to only 45 stations, due to the problems mentioned above, or to the presence of wide data gaps, and also to the shortness of record length.
On the other hand, the data after have either been unverified or are not available. The selected stations take into account the length and completeness of records.
In order to interpret the climate change compound index, the data were collected from 45 synoptic stations in Iran for the period — These stations have long-time data and are well distributed in different climate regions and elevation levels.
One of the most important factors of data inhomogeneity is the change in observation, including time of observation and tools, change of station location, formula, and change in data processing WMO Studies of extreme index in climate change have been carried out based on meteorological observation in Iran.
As they have errors and uncertainty Rahimzadeh et al. The objective of quality control is to verify whether a reported data value is representative of what was intended to be measured and has not been contaminated by unrelated factors.
First, the ClimPACT quality control QC revealed unreasonable values of temperature and precipitation data, such as daily precipitation amounts less than zero and daily maximum temperature less than daily minimum temperature. In addition, the QC also identified outliers in daily maximum and minimum temperatures. It is worth mentioning that all potential data can be evaluated by information on the next and previous day of the event with specialized knowledge about local conditions.
The QC test of other climatic variables such as humidity, sunshine, and wind speed was done using simple statistical methods in SPSS software.
There are several methods for the assessment of homogeneity in time series data including relative and absolute methods. The software method is based on the PMF or F-test and can identify, and adjust for, multiple change points in a time series Wang In the case of a lack of reference station, the F -test is used for inhomogeneity detection in time series Wang et al.
PMF requires the use of reference stations for the homogeneity analysis, but PMF can be used as an absolute method i.
According to the diagrams, if any heterogeneous factors are identified, the time series is assumed to be inhomogeneous, otherwise the time series is homogeneous. The aim of the indicators is to illustrate the temporal and spatial distribution of climate change. It is important to develop a set of compound indices that are statistically robust to cover a wide range of climates and detect changes in climate extremes.
Combined indices and heat wave indices are a set of statistical indices that can cover a wide range of climate characteristics and detect variability and changes in climate. In other words, most extreme weather events are the result of combining climate variables. Therefore, following annual study, a specified compound extreme index consisting of seven indices was considered as specified in Table 2.
Another compound index, which was used in this study, is the UTCI. The scale of the index is able to express even slight differences in the intensity of meteorological stimulus. The assessment of the thermo-physiological effects of the atmospheric environment is one of the key issues in human biometeorology.
The main aim of the index is to present an environmental-physiological evaluation model to increase the plans related to health and welfare in public climate service, public health systems, prevention designs, and climate effects research Broede et al.
In order to calculate UTCI , the data of average wind speed, relative humidity, and sunshine duration and mean radiant temperature MRT were used. MRTs were calculated from air temperature, global temperature, and wind speed. Finally, UTCI is computed by the regression equation found at www. UTCI equivalent temperature and stress category Pappenberger et al.
Classification scheme for the tourism climatic index Mieczkowski A positive value of Z indicates an increasing trend and a negative value indicates a decreasing trend. Most extreme climate events result from combined climate variables. For instance, high temperature with low rainfall may cause heat waves and drought. The changes in threshold ranges for four precipitations CD , CW , WD , and WW at Ramsar meteorological station during — show that the possibility of occurrence is different and dry modes have the highest possibilities since Figure 2.
There is a clear correspondence between temperature and precipitation compound index. There are long-term changes in temperature and rainfall annual series based on field observations in Figures 3 — 7. The warming of our planet due to the emission of greenhouse gases is now unquestionable; and over the last century, the CO 2 atmospheric concentration has increased significantly and has, in turn, induced the average global temperature to increase by 0.
The high temperature in urban areas affects mostly health, economy, leisure activities, and wellbeing of urban residents. Thermal stress caused by warming highly affects the health of vulnerable peoples Tan et al. Developing countries are mostly affected by climate change, and Ethiopia is an example of the most vulnerable countries Cherie and Fentaw The intensity and frequency of extremes can be easily changed by climate change and the changes in climate extremes and their impacts on a variety of physical and biological systems examined by the Intergovernmental Panel on Climate Change IPCC and their effects can also contribute to global warming IPCC Many factors such as the expansion of cities, and fast population growth rate along with migration from rural to urban areas pose a major challenge for city planners and also contributes to increasing climate change WHO and UNICEF ; Alemu and Dioha Using various General Circulation Models, Feyissa et al.
Some environmental harms such as high temperature and extreme rainfall, which results in flooding in Addis Ababa, could be signals of climate change Birhanu et al. Also, the city temperature is mostly affected by anthropogenic activities along with climate change. The Mann—Kendall MK non-parametric test is usually used to detect an upward trend or downward i.
The null hypothesis for this test indicates no trend, whereas the alternative hypothesis indicates a trend in the two-sided test or a one-sided test as an upward trend or downward trend Pohlert The Sen's estimator is another non-parametric method used for the trend analysis of hydroclimate data set. It is also used to identify the trend magnitude.
Hence, this test computes the linear rate of change slope and the intercept as shown in Sen's method Sen The MK test is widely documented in various literature, as a powerful trend test for effective analysis of seasonal and annual trends in environmental data, hydrological data climate data , and this test is preferred over other tests because of its applicability in time-series data, which does not follow the statistical distribution.
There are numerous examples of MK trend test applications such as Asfaw et al. Another study also employed a non-parametric MK test and Sen's slope estimates to test the trend of each extreme temperature and rainfall indices as well as their statistical significance in the Western Tigray, Ethiopia Berhane et al. Similarly, the trend analysis of temperature in Gombe state, Nigeria was analyzed using the MK trend test and Sen's estimator to decide the nature of the temperature trend and significance level.
The study found that average and maximum temperatures revealed positive Kendall's statistics Z Alhaji et al. In a different study, Yadav et al. The results indicated that in all thirteen areas of Uttarakhand India , the trends of temperature and precipitation are increasing in some months, whereas in some other months the trends were decreasing.
Getachew used the MK trend test for the analysis of rainfall and temperature trends in the south Gonder zone Ethiopia.
The study found that a statistically significant increase in Nefas Mocha and Addis Zemen for mean annual temperature. Kuriqi et al. Furthermore, the MK test and the Sen's estimator test has been applied to examine the significant trend of rainfall, temperature, and runoff in the Rangoon watershed in Dadeldhura district of Nepal. The result revealed that there were warming trends in the study area Pal et al. In contrast, Machida et al. But, the MK test result shows it is not a powerful trend test.
The authors' experimental study showed that the use of MK trend test in detecting software aging is highly exposed in creating false positives Machida et al. Despite the various application of the MK trend test in different parts of the world, studies analyzing the non-parametric MK test is commonly employed to detect monotonic trends in a series of environmental data, climate data or hydrological data. However, the MK test is a non-parametric distribution-free test which is used to analyze time-series data for consistently monotonic trends.
Nevertheless, the major disadvantage of the method is the influence of autocorrelation in data on its test significance. Several modifications in the MK test have been proposed by different authors to remove the influence of autocorrelation done with varied techniques and one of the most common tests is corrected for bias before pre-whitening Malik et al. The MK test is mostly chosen for the analysis of climatic data since its measurement does not follow the normal distribution.
Thus, the present study has employed the MK trend test and Sen's slope estimate to understand the nature of the temperature trend and significance level in the study area. Hence, the current study is conducted based on the temperature variation in the city of Addis Ababa over two stations—Bole and Entoto.
The historic temperature used for Bole station is from to and the Entoto station from to In addition to this, the selection of this station is also classified based on geographical variation and the altitude differences. The result of this study i.
In terms of contribution to the existing literature, this study introduces one of the earliest case studies in this subject matter for Ethiopia and the findings will be useful in mitigating the adverse impacts of climate change in the country. Also, the analytical framework presented here can be employed by other researchers to study temperature variations in other regions of the world.
While this paper is crafted with a local case study, the results will also be useful for international literature. The rest of this paper is structured thus: Sect. Section 3 describes the results and brief discussion, while the general study conclusions are presented in Sect.
We employed the Mann—Kendall MK trend test and Sen's slope estimate to examine the nature of the temperature trend and significance level in the study area.
Figure 1 shows the general study methodological framework. According to the census, the city have a total population of 2,, inhabitants. Addis Ababa comprises 6 zones and 28 woredas. Addis Ababa covers an area of about Km 2 and it lies between 2, to 2, m above sea level. Furthermore, the lowest and the highest annual average temperature of the city is between 9. Figure 2 shows a map of the study area.
The quality of the data was visually and statistically assessed. Whereas, the MK test method was checked and tested statistically with the trend free pre-whiting process and the variance correction approaches before applying the test.
The trend free pre-whiting process was proposed to remove the serial correlation from the data before applying the trend test Yue et al. Likewise, to overcome the limitation of the occurrence of serial autocorrelation in time series, the variance correction procedure was applied as proposed by Hamed and Rao MK trend test is a non-parametric test used to identify a trend in a series. It is also used to determine whether a time series has a monotonic upward or downward trend. The non-parametric MK test is commonly employed to detect monotonic trends in a series of environmental data, hydrological data, or climate data.
The null hypothesis H0 shows no trend in the series and the data which come from an independent population are identically distributed. The alternative hypothesis, Ha, indicates that the data follow a monotonic trend i.
There are two benefits of using this test. First, it does not require the data to be normally distributed since the test is non-parametric distribution-free test and second, the test has low sensitivity to abrupt breaks due to inhomogeneous time series. Outside of the cold years in the early s, the temperatures in the eastern and middle part of the country were fairly warm compared to the 20th century average. This illustrates two important points to consider when looking at linear trends:.
In some cases if you compute a linear trend from a completely random set of data, the mathematical calculation will still produce a value that implies and up or down trend. When we apply this significance testing to the year trend on February mean temperatures, almost all of the trends disappear with the exception of cooling in small areas in southwest Georgia and central West Virginia. When the same significant testing is applied to the entire period of record trends, some of the smaller trends do disappear, but the warming over much of the nation, especially in the West and North do show up as statistically significant.
For the entire year period of the contiguous U. The pattern of warming minimum temperatures and maximum temperatures is quite different when we examine the two maps. No warming trend, and even slight cooling, has been observed in parts of the Southwest and Southeast. When examining the maximum temperature trends we once again see that much of the West and northern tier have experienced warming, but a large part of the country from the Great Plains to interior Southeast have experienced cooling maximum temperatures.
Trying to determine why there might be cooling maximum temperatures in the Southeast, the precipitation trends maps become extremely useful. While multiple factors are likely contributing to the cooling, locations from the Southern Plains, through the Southeast, and into the Northeast have experienced increasing precipitation.
Increased precipitation is often associated with cooler maximum temperatures due to a decrease in incoming energy from the sun during the daytime. Also, the rate of warming changes depending on the starting date used in that time series; 6 As noted above, a series on N observations does not necessarily mean these observations are independent.
Often, there is some temporal correlation. This should be taken into account for example when computing the degrees of freedom of the t-test. Last modified 05 Sep Skip to main content. Contact Us Search. You are here Home » Analysis Tools and Methods.
The following is by Dennis Shea NCAR : The detection, estimation and prediction of trends and associated statistical and physical significance are important aspects of climate research.
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