Simulatiing ExtremeTemperature Indicators Based on RCP Scenarios: The Case of Khuzestan Province

Document Type : Research Article

Authors

• Amena Dashtbozorgi ¹
• Bohloul Alijani ²
• Zeinolabedin Jafarpour ³
• Alireza Shakiba ⁴

¹ Azad University, Science and Research Branch, Tehran

² Kharazmi University

³ Kharazmi University, Tehran, IRAN

⁴ Shahid Beheshti University

10.22067/geo.v4i4.47304

Abstract

1. Introduction
One of the six great challenges recognized by the World Climate Researches Program (WCRP) is the prediction and the characteristics of extreme events. The outcomes of various sources indicate that the extreme climate events have significantly increased over the last decades. Trend analysis of extreme temperature indicators is of crucial importance in estimating the trend of global warming. Temperature rise plays a decisive role in drought intensity leading to a more frequent occurrence of extreme drought events. Temperature rise also results in desertification, decline of water resources, and a decrease in agricultural products following the loss and decay of the products. Following the establishment of the Intergovernmental Panel on Climate Change (IPCC) in 1988 and the extensive research on it at a global scale, researchers began to realize that the average monthly temperature measure cannot indicate the trend changes of global warming. The results of the analysis of minimum and maximum average temperatures in the early 90's show that the minimum average temperatures have been rapidly increasing in relation to minimum average temperatures.
At the beginning of the 2000s, IPCC published a special report on emissions scenarios (SRES) in the Third Assessment Report (TAR) and used them for the Assessment Report Four (AR4). With the beginning of the 2010s, the Coupled Model Intercomparison Project Phase 5 (CMIP5) suggested new scenarios called Representative Concentration Pathways (RCP), identified with values of 2.6, 4.5, 6.0, and 8.5 W/m2. The 4.5, 6.0 and 8.5 RCP scenarios roughly correspond to B1, A1B, and A2 scenarios. After the development of RCP scenarios, some researchers studied the effects of such scenarios on diverse issues such as water resources, agriculture, and so forth through the temperature change trend analysis.

2. Study Area
This research is an attempt to analyze the future climate conditions of Khuzestan Province till the 2050s according to the RCP scenarios. Located in the southwest of Iran, Khuzestan plays a vital role in Iran’s economy in terms of agriculture and industry.

3. Material and methods
The method of the study on Khuzestan’s future climate conditions is based on RCP scenarios, dealing with both current and future conditions different stages. To analyze the future climate conditions, the following seven indices were selected out of the indices introduced by the Expert Team on Climate Change Detection and Indices ETCCDI: DTR (diurnal temperature range), TMAX mean (mean maximum temperature), T MIN mean (mean minimum temperature), TN10p (cold nights), TX10p (cold days), TN90p (warm nights), and TX90p (warm days to analyze the future conditions from 2013 to 2050, the 2.6, 4.5, and 6.0 RCP scenarios were chosen through the four proposed scenarios of the fifth assessment report of IPCC. The goal of all these scenarios is to predict the highest, lowest, and the mid-range of future climate changes.
MarkSim model(Jones, 2012) was selected as the downscaled model of the study.First, to assess the accuracy of the simulation model of regional temperature changes, the following models were selected for the years from 2013 to 2015: GCM, HadGEM2-ES (1.2414× 1.875), MIROC5 (1.4063×1.4063) and MRI-CGCM3 (1.125×1.125). Then, downscaling and temperature indices modeling were performed and the output of different models was compared. Regarding the variance analysis, the model of MIROC5 was considered as an appropriate model for the study and the level of significance for temperature indices trend was analyzed (p=0.05).

4. Results and Discussion
In this paper, the spatial change pattern of temperature extreme indices of Khuzestan was compared and analyzed within the two statistical periods of 1983-2012 and 2013-2050 using RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. The results indicate that under different scenarios, maximum and minimum temperature indices, particularly those in percentile would depict different trends in different parts of Khuzestan. On the basis of the present situation, the northern, central and southern parts of Khuzestan represent a significantly increasing trend, whereas other areas of the province have just an increasing trend. The time series of the maximum and minimum temperature means in the present situation indicate that in reference to the present situation, the temperature minimums (+2.72°C) are increasing ata roughly more rapid pace in relation to temperature maximums (+1.2°C), which explains the increase in DTR index. In relation to other scenarios, on average the TMIN mean index will show 1°C increase under RCP 4.5 scenario which will be witnessed in most parts of Khuzestan. In relation to other scenarios, the trend of this index under RCP 6.0 is more homogenous throughout the province and barely southern parts of Khuzestan will show an increase of trend in relation to other areas. The TMAX mean index under RCP 2.6 will show a more increasing trend as compared with the two other scenarios. The highest increase occurs in western parts of Khuzestan while other parts represent a similar trend.
In like manners, the analysis of percentile-based temperature indices such as the indices of cold events (TX10p, TN10p) and warm events (TN90p, TX90p) under miscellaneous scenarios indicate that the cold events indices depict a declining trend, whereas their warm equivalents depict an increasing trend. Of all the future scenarios, the RCP 4.5 and RCP 6.0 scenarios represent the highest spatial changes of the TN10p and TN90p indices, respectively. Under different scenarios, the TN90p index represents an increasing trend with almost the same linear slope that cold nights index curve declines. Under the RCP 2/6 scenario, the TN90p index indicates that if appropriate policies are taken to adjust to climate changes and bring them under control, the trend of warm nights will be controlled.

5. Conclusion
With a spatial-temporal analysis of temperature extreme indices under the present situation (from 1982-2012) and the RCP 2.6, RCP 4.5 and RCP 6.0 scenarios (2013-2050), This article simulates the temperature changes of Khuzestan based on the obtained data, GCM and MIROC5 models. However, the results indicate that in the present situation, the temperature minimums are roughly increasing more rapidly than the temperature maximums to such an extent that they lead to the declining trend of the DTR index. The simulation of climate change trend based on the RCPs suggests that the increase in temperature trend is likely to maintain in the future. All in all, the results show that, the trend of cold and warm nights indices (TN90p, TN10p) are compatible with the TMIN mean index trend as well as the compatibility of the trend of cold and warm days indices (TX90p, TX10p) and the TMAX mean index trend in different parts of Khuzestan. In this sense, the indices of the cold period (cold days and nights) and the indices of the warm period (warm days and nights) will represent a declining and an increasing trend, respectively.

Keywords

• Temperature extreme indicators
• Khuzestan
• RCP scenarios

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References

شکیبا، علیرضا، خلیلی، عین‌الله و دشت بزرگی، آمنه؛ 1388. تحلیل روند تغییرات دمایی شهرستان اهواز بر اساس شاخص‌های حدی. مجله چشم‌انداز جغرافیایی. علمی – پژوهشی دانشگاه آزاد اسلامی واحد رشت. سال چهارم. شماره 8. بهار و تابستان.

Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G. & Tagipour, A. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5),

Bassu, S., Brisson, N., Durand, J. L., Boote, K., Lizaso, J., Jones, J. W& Basso, B. (2014). How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 20(7), 2301-2320.

Bell, J. L., Sloan, L. C., & Snyder, M. A. (2004). Regional changes in extreme climatic events: A future climate scenario. Journal of Climate, 17(1), 81-87

Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A., Frei, C., Goyette, S.,& Woth, K. (2007). Future extreme events in European climate: An exploration of regional climate model projections. Climatic Change, 81(1), 71-95.

Bokwa, A., &Limanowka, D. (2014). Effect of relief and land use on heat stress in Krakow, Poland. DIE ERDE–Journal of the Geographical Society of Berlin, 145(1-2), 34-48.

Brunetti, M., Buffoni, L., Mangianti, F., Maugeri, M., &Nanni, T. (2004). Temperature, precipitation and extreme events during the last century in Italy. Global and Planetary Change, 40(1), 141-149.

Chamchati, H., &Bahir, M. (2011). Contribution of climate change on water resources in semi-arid areas: Example of the Essaouita Basin (Morocco). Am. J. Sci. Ind. Res, 2(2), 209-215.

Collins, M., Knutti, R., Arblaster, J. M., Dufresne, J. L., Fichefet, T., Friedlingstein, P., ...&Wehner, M. (2013). Long-term climate change: Projections, commitments and irreversibility.

Deryng, D., Conway, D., Ramankutty, N., Price, J., & Warren, R. (2014). Global crop yield response to extreme heat stress under multiple climate change futures. Environmental Research Letters, 9(3), 034011.

Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., &Mearns, L. O. (2000). Climate extremes: Observations, modeling, and impacts. Science, 289(5487), 2068-2074.

Fan, L., &Xiong, Z. (2015). Using quantile regression to detect relationships between large-scale predictors and local precipitation over northern China.Advances in Atmospheric Sciences, 32(4), 541-552.

Fernandez‐Long, M. E., Müller, G. V., Beltran‐Przekurat, A., &Scarpati, O. E. (2013). Long‐term and recent changes in temperature‐based agroclimatic indices in Argentina. International Journal of Climatology, 33(7), 1673-1686.

Folland, C. K., Karl, T. R., & Jim Salinger, M. (2002). Observed climate variability and change. Weather, 57(8), 269-278.

Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Klein Tank, A. M., & Peterson, T. (2002). Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research, 19(3), 193-212.

Frumkin, H., Hess, J., Luber, G., Malilay, J., &McGeehin, M. (2008). Climate change: The public health response. American Journal of Public Health, 98(3), 435-445.

Furio, D., &Meneu, V. (2011). Analysis of extreme temperatures for four sites across Peninsular Spain. Theoretical and Applied Climatology, 104(1-2), 83-99.

Ha, K. J., & Yun, K. S. (2012). Climate change effects on tropical night days in Seoul, Korea. Theoretical and Applied Climatology, 109(1-2), 191-203.

Hansen, J., &Lebedeff, S. (1987). Global trends of measured surface air temperature. Journal of Geophysical Research, 92(13), 345-13.

Hansen, J., Sato, M., & Ruedy, R. (2013). Global temperature update through 2012. National Aeronautics and Space Administration, Goddard Institute for Space Studies. http://www. nasa. gov/pdf/719139main_2012_GISTEMP_summary. pdf.

Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea, D. W., & Medina-Elizade, M. (2006). Global temperature change. Proceedings of the National Academy of Sciences, 103(39), 14288-14293.

Haylock, M. R., Peterson, T. C., Alves, L. M., Ambrizzi, T., Anunciação, Y. M. T., Baez, J., ...& Vincent, L. A. (2006). Trends in total and extreme South American rainfall in 1960-2000 and links with sea surface temperature. Journal of Climate, 19(8), 1490-1512.

ILUNGA, L., & TSHENDA, A. (2004). Facteurs physiques du ruissellement à Kigali (Rwanda). Geo-Eco-Trop, 28, 1-2.

IPCC, 2001: climate change 2001: the scientific basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell and C. A. Johnson (eds). Cambridge University Press, Cambridge, UK, and New York, USA, 2001. No. of pages: 881. Price £34.95, US$ 49.95, ISBN 0-521-01495-6 (paperback). £90.00, US$ 130.00, ISBN 0-521-80767-0 (hardback).

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., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

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., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp, doi:10.1017/CBO9781107415324

Jones, P. D., Wigley, T. M. L., & Kelly, P. M. (1982). Variations in surface air temperatures: Part 1. Northern Hemisphere, 1881-1980. Monthly Weather Review, 110(2), 59-70.

Jones, P.G. (2012). MarkSim_standalone for DSSAT users. Availableat: http://www.ccafs-climate.org/pattern_scaling/

Karl, T. R., &Trenberth, K. E. (2003). Modern global climate change. Science, 302(5651), 1719-1723.

Karl, T. R., Knight, R. W., & Plummer, N. (1995). Trends in high-frequency climate variability in the 20th-century. Nature, 377(6546), 217-220.

Karl, T. R., Knight, R. W., Gallo, K. P., Peterson, T. C., Jones, P. D., Kukla, G., ... &Charlson, R. J. (1993). A new perspective on recent global warming: asymmetric trends of daily maximum and minimum temperature. Bulletin of the American Meteorological Society, 74(6), 1007-1023.

Kennett, E. J., & Buonomo, E. (2006). Methodologies of pattern scaling across the full range of RT2A GCM ensemble members. Met Office Hadley Centre for Climate Prediction and Research: Exeter, UK.

Kharin, V. V., Zwiers, F. W., Zhang, X., &Wehner, M. (2013). Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119(2), 345-357.

Klein Tank, A. M. G., Wijngaard, J. B., Können, G. P., Böhm, R., Demaree, G., Gocheva, A., ... & Heino, R. (2002). Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International journal of climatology, 22(12), 1441-1453.

Kremser, S., Bodeker, G. E., & Lewis, J. (2014). Methodological aspects of a pattern-scaling approach to produce global fields of monthly means of daily maximum and minimum temperature. Geoscientific Model Development, 7, 249-266.

LaDochy, S., Medina, R., &Patzert, W. (2007). Recent California climate variability: Spatial and temporal patterns in temperature trends. Climate Research, 33(2), 159-169.

Li, J., Zhang, Q., Chen, Y. D., Xu, C. Y., & Singh, V. P. (2013). Changing spatiotemporal patterns of precipitation extremes in China during 2071–2100 based on Earth System Models. Journal of Geophysical Research: Atmospheres, 118(22).

Liu, B., Xu, M., Henderson, M., Qi, Y., & Li, Y. (2004). Taking China's temperature: Daily range, warming trends, and regional variations, 1955-2000. Journal of Climate, 17(22), 4453-4462.

Liu, J., Fritz, S., Van Wesenbeeck, C. F. A., Fuchs, M., You, L., Obersteiner, M., & Yang, H. (2008). A spatially explicit assessment of current and future hotspots of hunger in Sub-Saharan Africa in the context of global change. Global and Planetary Change, 64(3), 222-235.

Liu, S. C., Fu, C., Shiu, C. J., Chen, J. P., & Wu, F. (2009). Temperature dependence of global precipitation extremes. Geophysical Research Letters, 36(17).

Lopez, A., Smith, L. A., & Suckling, E. (2011). Pattern scaled climate change scenarios: Are these useful for adaptation? Centre for Climate Change Economics and Policy Working Paper, December.

Marengo, J. A., Chou, S. C., Torres, R. R., Giarolla, A., Alves, L. M., &Lyra, A. (2014). Climate change in central and South America: Recent trends, future projections, and impacts on regional agriculture. Working Paper No 73.

McFadden, J., & Miranowski, J. (2014, May). Climate Change Impacts on the Intensive and Extensive Margins of US Agricultural Land. In 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota (No. 170512). Agricultural and Applied Economics Association.

Mearns, L. O., Katz, R. W., & Schneider, S. H. (1984). Extreme high-temperature events: Changes in their probabilities with changes in mean temperature. Journal of Climate and Applied Meteorology, 23(12), 1601-1613.

Meehl, G. A., Karl, T., Easterling, D. R., Changnon, S., Pielke Jr, R., Changnon, D., ...&Zwiers, F. (2000). An introduction to trends in extreme weather and climate events: Observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bulletin of the American Meteorological Society, 81(3), 413-416.

Meehl, G. A., Zwiers, F., Evans, J., Knutson, T., Mearns, L., &Whetton, P. (2000). Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bulletin of the American Meteorological Society, 81(3), 427-436.

Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P., ... &Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-756.

Muhire, I., & Ahmed, F. (2016). Spatiotemporal trends in mean temperatures and aridity index over Rwanda. Theoretical and Applied Climatology, 123(1-2), 399-414.

Nandintsetseg, B., Greene, J. S., &Goulden, C. E. (2007). Trends in extreme daily precipitation and temperature near Lake Hövsgöl, Mongolia. International Journal of Climatology, 27(3), 341-347.

Naveed, S., Aslam, M., Maqbool, M. A., Bano, S., Zaman, Q. U., & Ahmad, R. M. (2014). Physiology of high temperature stress tolerance at reproductive stages in maize. J. Anim. Plant Sci, 24(4), 1141-1145.

Peterson, T. C., Taylor, M. A., Demeritte, R., Duncombe, D. L., Burton, S., Thompson, F., ...& Klein Tank, A. (2002). Recent changes in climate extremes in the Caribbean region. Journal of Geophysical Research: Atmospheres, 107(D21).

Peterson, T., Folland, C., Gruza, G., Hogg, W., Mokssit, A., & Plummer, N. (2001). Report on the activities of the working group on climate change detection and related rapporteurs. Geneva: World Meteorological Organization.

Plattner, G. K. & Stocker, T. F. (2010). From AR4 to AR5: New Scenarios in the IPCC Process. Workshop Report

Rahimzadeh, F., Asgari, A., &Fattahi, E. (2009). Variability of extreme temperature and precipitation in Iran during recent decades. International Journal of Climatology, 29(3), 329-343.

Seneviratne, S. I., Donat, M. G., Mueller, B., & Alexander, L. V. (2014). No pause in the increase of hot temperature extremes. Nature Climate Change,4(3), 161-163.

Seo, Y. A., Lee, Y., Park, J. S., Kim, M. K., Cho, C., &Baek, H. J. (2015). Assessing changes in observed and future projected precipitation extremes in South Korea. International Journal of Climatology, 35(6), 1069-1078.

Taylor, K. E., Stouffer, R. J., &Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498.

Tebaldi, C., &Arblaster, J. M. (2014). Pattern scaling: Its strengths and limitations, and an update on the latest model simulations. Climatic Change, 122(3), 459-471.

Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., ...& Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109, 5-31.

Yan, Z., Jones, P. D., Davies, T. D., Moberg, A., Bergström, H., Camuffo, D., ... & Thoen, E. (2002). Trends of extreme temperatures in Europe and China based on daily observations. In Improved Understanding of Past Climatic Variability from Early Daily European Instrumental Sources (pp. 355-392). Springer Netherlands.

Zhang, X., Hogg, W. D., &Bonsal, B. R. (2001). A cautionary note on the use of seasonally varying thresholds to assess temperature extremes: Comments on the use of indices to identify changes in climatic extremes'. Climatic Change, 50(4), 505-507.

Zhou, L., Dai, A., Dai, Y., Vose, R. S., Zou, C. Z., Tian, Y., & Chen, H. (2009). Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004. Climate Dynamics, 32(2-3), 429-440.