Journal
ATMOSPHERIC RESEARCH
Volume 198, Issue -, Pages 97-107Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.atmosres.2017.08.011
Keywords
Trend analysis; Long-term persistence; Modified Man-Kendall test; Temperature extremes; Iraq
Categories
Funding
- Ministry of Education Malaysia and Universiti Teknologi Malaysia [Q.J130000.2522.10H36]
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The existence of long-term persistence (LTP) in hydro-climatic time series can lead to considerable change in significance of trends. Therefore, past findings of climatic trend studies that did not consider LTP became a disputable issue. A study has been conducted to assess the trends in temperature and temperature extremes in Iraq in recent years (1965-2015) using both ordinary Mann-Kendal (MK) test; and the modified Mann-Kendall (m-MK) test, which can differentiate the multi-decadal oscillatory variations from secular trends. Trends in annual and seasonal minimum and maximum temperatures, diurnal temperature range (DTR), and 14 temperature -related extremes were assessed. MK test detected the significant increases in minimum and maximum temperature at all stations, where m-MK test detected at 86% and 80% of all stations, respectively. The temperature in Iraq is increasing 2 to 7 times faster than global temperature rise. The minimum temperature is increasing more (0.48-1.17 C/decade) than maximum temperature (0.25-1.01 C/decade). Temperature rise is higher in northern Iraq and in summer. The hot extremes particularly warm nights are increasing all over Iraq at a rate of 2.92-10.69 days/decade, respectively. On the other hand, numbers of cold days are decreasing at some stations at a rate of 2.65 to 8.40 days/decade. The use of m-MK test along with MK test confirms the significant increase in temperature and some of the temperature extremes in Iraq. This study suggests that trends in many temperature extremes in the region estimated in previous studies using MK test may be due to natural variability of climate, which empathizes the need for validation of the trends by considering LTP in time series.
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