Trend Analysis of Climate Compound Extreme Indices in Iran

جغرافیا و مخاطرات محیطی

Article 6, Volume 7, Issue 4 - Serial Number 28, January 2019, Pages 89-110   

Document Type: Research Article

DOI: 10.22067/geo.v0i0.67984

Authors

Hossain Esmaiili¹; Ahmad Roshani^{* 1}; Alireza Shakiba²; Fatemeh Parak¹

¹University of Kharazmi

²University of Shahid Beheshti

Abstract

Introduction

Although climate change is a global challenge, its effects occur locally and differ from  region to region (Filho et al., 2016; Leonard et al., 2014). Over the past few years, the large positive departures of temperatures from their mean values have become commonplace in many parts of the world. Surface temperature over land regions have warmed at a faster rate than over the oceans in both hemispheres (IPCC, 2007). Each of the last three decades has been successively warmer at the Earth’s surface than any preceding decades since 1850. The warmth of the period from 1983 to 2012 is very likely higher than any 30-year period in last 1,400 years in the Northern Hemisphere. The globally averaged temperature that results from combined land and ocean surface data shows a warming of 0.85 °C during the period 1880 to 2012, (IPCC, 2013). Indices for climate variability and extremes have been used for a long time, often by assessing daily temperature or precipitation observation above or below specific thresholds (Zhang et al., 2011). The dependence and thermodynamic relations between the precipitation and temperature have been addressed in numerous studies (Liu et al., 2012). Changes in extreme weather and climate events have significant impacts and are among the most serious challenges to society in coping with a changing climate (CCSP, 2008). The warming global climate has increased the concurrent climatic extremes and the intensity of extreme weather events over different regions of the Earth, such as drought, heat waves, tropical cyclones, floods, and fires (AghaKouchak et al., 2014; Alexander et al., 2005; Leonard et al., 2014). The projections of extreme weather phenomena on the basis of temperature and precipitation indices in AR5 show a probable increase in the number and intensity of dry and hot periods in the summer time (Filho et al., 2016; Hao et al., 2013). Many practical problems require the knowledge of the behavior of extreme values. In particular, the infrastructures we depend upon for food, water, energy, shelter and transportation are sensitive to the high or low values of meteorological variables (WMO, 2009). The impact of these events can be due to a single variable in an extreme state, but more often it is the result of a combination of variables which are not all necessarily extreme (Leonard et al., 2014). The combination of variables leading to an extreme impact is referred to a compound event (Beniston, 2011). Recent studies of joint quantities of precipitation and temperature are often described in terms of warm/wet, warm/dry, cool/wet and cool/dry climate combinations (e.g., Arsenovic et al., 2013; Beniston et al., 2009; Estralla & Menzel, 2012; Hao et al., 2013; Lopez-Moreno et al., 2011) and/or based on copula theory (see Miao et al., 2016). The combination of warm/wet, warm/dry, cool/wet and cool/dry modes reveals a systematic change at all the locations investigated with significant declines in the frequency of occurrence of the cool modes and a sharp rise in that of the warm modes. This article investigates the trends of combined temperature, precipitation, humidity, wind speed and sunshine statistic in spatial domain in Iran. Our investigation aims to use the joint quantities of temperature and precipitation and other variables of weather to offer some insights into the behavior of particular modes of heat and moisture which cannot be achieved by the analysis of the statistics of each individual variable. Over the last years, several extreme precipitation and temperature indices have been explained and analyzed in the literature (e.g., Parak et al., 2015; Rahimzadeh et al., 2009; Tabari et al., 2011); however,  the climate change has not been concerned with the use of joint extremes indices.

Materials and Methods

The study area in Iran lies approximately between 25oN and 40oN in latitude and between44oE and 64oE in longitude (see Fig. 1). Based on the Koppen climate classification, most parts of Iran are categorized under arid (BW) and semi-arid (BS) climates. Alborz and Zagros are the important mountains of Iran which play an important role in non-uniform spatiotemporal distribution of temperature and precipitation in Iran (Dinpashoh, 2006).
The examination of climate changes needs long and high quality records of climatic variables. In the present study, the dataset of daily maximum, the minimum and mean air temperatures and precipitation (P) for the period 1981-2015 from 47 synoptic stations from different geographic locations of Iran (Fig. 1) were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO) and were then analyzed. The homogeneity of the dataset was also assessed and approved by IRIMO. Most of the regions were covered by the corresponding data and the geographical location of the stations.

Results and Discussion

One solution for the better detection of climate change is the use of compound indices; therefore, a set of compound indices derived using daily resolution climatic time series data with a major focus on extreme events were computed and analyzed to assess climate changes in Iran. The compound indices consisted of cool/dry, cool/wet, warm/dry, warm/wet, TCI and UTCI which were examined for a 47 synoptic meteorological stations during 1981-2015. The main results of the research showed a substantial change in the behavior of the joint extremes of temperature and precipitation quantities associated with warming has occurred in the past three decades. More than 80 percent of Iran has experienced a decrease in the annual occurrence of the cold modes and an increase in the annual occurrence of the warm modes. Universal thermal climate index (UTCI) change showed a widespread and significant increase in the annual occurrence of the strong heat stress (32–38 °C) and a significant decrease in the annual occurrence of the no thermal stress class (9-26 °C); in fact, the changes are also spatially coherent as compared with joint extremes of temperature and precipitation changes. Trends in tourism climatic index (TCI), including the number of days with TCI≥60, and the number of days with TCI≥80 showed similar changes but with weak spatial coherence. The results of this study also allow researchers to preview the condition that may occur with greater frequency in the future throughout Iran.

Conclusion

This paper attempts to present a suite of compound extreme indices with a primary focus on extreme events. These indices were calculated and analyzed for a number of sites between the years of 1981-2015 to provide a general overview of climate change in Iran. The results from non-parametric test showed statistically significant and spatially coherent trends in the compound extreme indices. It was found that in more than 80 percent of Iran the frequency of the warm modes has increased, while the frequency of cold modes has decreased but with smaller magnitudes. More than 97% of the country exhibited a positive trend for the annual WD index. The significant increasing trends of the annual WD varied from (+) 3.7 to (+) 14.5 days per decade respectively in Chabahar and Kish Island stations. Based on the results of the analysis, apart from a few stations and more specifically in Shahrekord station, joint quantities of temperature and precipitation indicates almost the same trend and responding to these differences definitely requires further investigation. At the same time, the occurrence of TCI and UTCI in the range strong heat stress (32-38 °C) events has increased almost over the whole country, which is consistent with the increase of the frequency of warm modes. The distribution of the annual compound indices trends indicated that the negative and positive significant trends have mainly occurred in the northwest of Iran. The results also suggested the need for further investigation on local factors intervention in the environment, which could be one of the major causes of climate change.

Keywords

Climate change; Compound indices; Trend; Iran

References

AghaKouchak A, Cheng L, Mazdiyasni O, Farahmand A., 2014. Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought.Geophys. Res. Lett41: 8847–8852, doi:10.1002/2014GL062308.

Alexander L, Yang H, Perkins S., 2013. ClimPACT Indices and software. A document prepared on behalf of The Commission for Climatology (CCl) Expert Team on Climate Risk and Sector-Specific Climate Indices (ET CRSCI).

Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahim F, Tagipour A, Kumar Kolli R, Revadekar JV, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez Aguirre JL,. 2006. Global observedchanges in daily climate extremes of temperature and precipitation. Journal of Geophysical Research-Atmospheres 111: D05109. DOI: 10.1029/2005JD006290.

Alijani, B, Roshani, A, Parak, F and Heidari, R., 2013. Trends in extreme daily temperature using climate change indices in Iran. Geography and environmental hazards, 1 (2), 17-28. doi: 10.22067 / geo.v1i2.18617

Allen G.R, Pereira S.P, Raes D, Smith M., 1998. Crop evapotranspiration-Guideline for computing crop water requirements. FAO - Food and Agriculture Organization of the United Natuins.

Arsenovic P, Tosic I, Unkasevic M., 2013. SEASONAL ANALYSIS OF WARM DAYS IN BELGRADE AND NIS. J. Geogr. Inst. Cvijic. 63(4):(1 10). DOI: 10.2298/IJGI1304001A.

Beniston M. 2011. Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100. GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L07707, doi:10.1029/2008GL037119, 2009

Broede P, Fiala D, Blazejczyk K, Holmer I, Jendritzky G, Kampmann B, Tinz B, Havenith G., 2012. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int J Biometeorol.56(3):481–494. doi:10.1007/s00484-011-0454-1

CCSP. 2008. Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. [Thomas R. Karl, Gerald A. Meehl, Christopher D. Miller, Susan J. Hassol, Anne M. Waple and William L. Murray (eds.)]. Department of Commerce, NOAA’s National Climatic Data Center, Washington, D.C., USA, 164 pp.

Estrella N, Menzel A., 2012. Recent and future climate extremesarising from changes to the bivariate distribution of temperature and precipitation in Bavaria, Germany. Int. J. Climatol. 33 1687–95.

Fan L, Chen D., 2016. Trends in extreme precipitation indices across China detected using quantile regression. Atmos. Sci. Let. 17: 400–406.

Filho W.L, Musa H, Cavan G, O'Hare P, Seixas J. 2016. Climate Change Adaptation, Resilience and Hazards. Springer. DOI 10.1007/978-3-319-39880-8.

Hao Z, AghaKouchak A, Phillips T. J., 2013. Changes in concurrent monthly precipitation and temperature extremes. Environ Res Lett. doi:10.1088/17489326/8/3/034014.

IPCC, 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 966 pp.

IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 966 pp.

Kanji G.K., 2006. 100 STATISTICAL TESTS. SAGE Publications Ltd, ISBN-13 978 1 4129 2376 7 (Pbk).

Lemke B, Kjellstrom T., 2012. Calculating Workplace WBGT from Meteorological Data: A Tool for Climate Change Assessment. IndustrialHealth.50: 267–278.

Leonard M, Westra S, Phatak A, Lambert M, Hurk V.D B, McInnes K, Risbey J, Schuster S, Jakob D, Stafford-Smith M., 2014. A compound event framework for understanding extreme impacts. WIREs Clim Change.5:113–128. doi: 10.1002/wcc.252.

Leonard M, Westra S, Phatak A, Lambert M, Hurk, B.V.D, McInnes K, Risbey J, Schuster S, Jakob D, Stafford-Smith M., 2014. A compound event framework for understanding extreme impacts. WIREs Clim Change. 5:113–128. doi: 10.1002/wcc.252.

Liu C, Allan R P, Huffman G J., 2012. Co-variation of temperature and precipitation in CMIP5 models and satellite observations. Geophysics. Res. Lett.39 : L13803.

Lopez-Moreno g J.I, Vicente-Serrano S.M, Moran-Tejeda E, Lorenzo-Lacruz J, KenawyA, Beniston M., 2011. Effects of the North Atlantic Oscillation (NAO) on combined temperature and precipitation winter modes in the Mediterranean mountains: Observed relationshi-ps and projections for the 21st century. Global and Planetary Change. 77. 62–76.

Miao C, Sun Q, Duan Q, Wang Y., 2016. Joint analysis of changes in temperature and precipitation on the Loess Plateau during the period 1961–2011. Climate Dynamics.47: 3221. doi:10.1007/s00382-016-3022-x

Mieczkowski Z., 1985. The Tourism Climatic Index: a method of evaluating world climate fortourism. Can Geogr29:220–233.

Pappenberger P, Jendritzky G, Staiger H, Dutra E, Giuseppe F. Di, Richardson D. S, Cloke H. L., 2015. Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI). Int J Biometeorol.59:311–323.

Parak F, Roshani A, BodaghJamali J., 2015. Trends and Anomalies in Daily Climate Extremes over Iran during 1961–2010. Journal of Environmental and Agricultural Sciences. ISSN: 2313-8629.

Project team ECA&D, Royal Netherlands Meteorological Institute KNMI., 2013. EUMETNET/ECSN optional programme: ’European Climate Assessment & Dataset (ECA&D)’ Algorithm Theoretical Basis Document (ATBD), Version : 10.7.

Rahimzadeh F, Asgari A, Fattahi E., 2009. Variability of extreme temperature and precipitation in Iran during recent decades. Int. J. Climatol. 29:329–343.

Romm., 2015. CLIMATE CHANGE (WHAT EVERYONE NEEDS TO KNOW). Oxford University Press, ISBN-13 978-0190250171.

Tabari H, ShiftehSomee B, RezaeianZadeh M. 2011. Testing for long-term trends in climatic variables in Iran. Atmosph. Res. 100:132 –140.

Vincent L.A, Wang X.L, Milewska E.J, Wan H, Feng Y, Swail V., 2012. A Second Generation of Homogenized Canadian Monthly Surface Air Temperature for Climate Trend Analysis. JGR Atmospheres. 117: D18110, do: 10.1029/2012JD017859.

Wang X. L. L., 2008. Accounting for autocorrelation in detecting mean-shifts in climate data series using the penalized maximal t or F test. J. App. Met. Climatol. 47: 2423-2444.

Wang X.L, Wen Q, Wu Y., 2007. Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAM2504.1.

Wang X.L, Y. Feng, published online July., 2013: RHtestsV4 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, science and Technology Branch, Environment Canada. 28 pp. [Available online at http:etccdi. Pacific climate. Org/software.shtml, http://etccdi.pacificclimate.org/software.shtml.].

World Meteorological Organization, Geneva., 2011. Guide to Climatological Practices. WMO-No. 100.

World Meteorological Organization., 2009. Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation. Climate Data and Monitoring WCDMP-No. 72.

Yue S, Pilon P. and Cavadias G., 2002. Power of teh Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology. 259: 254-271.

Zhang X, Alexander L, Hegerl G.C, Jones P, Tank A.K, Peterson T.C, Trewin B, Zwiers F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Change.doi: 10.1002/wcc.147.