پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,232 |
| تعداد مقالات | 67,790 |
| تعداد مشاهده مقاله | 115,274,014 |
| تعداد دریافت فایل اصل مقاله | 90,018,418 |
Detection and Attribution of Precipitation Extremes to Human Influence in Iran | ||
| فیزیک زمین و فضا | ||
| مقاله 14، دوره 49، شماره 4، اسفند 1402، صفحه 193-210 اصل مقاله (1.5 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2024.364541.1007559 | ||
| نویسندگان | ||
| Masoud Shirazi؛ Banafsheh Zahraie؛ Mohsen Nasseri* | ||
| School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| Evaluating the susceptibility of regional climates to climate change provides a framework for realistically analyzing potential future climate changes. This paper investigates the impact of human activities on variations in extreme precipitation in Iran by evaluating data provided from 286 rain-gauge stations during 1967-2010 and general circulation simulation results of the CanESM2 model. This investigation was based on six forcing factors, including natural external factors (volcanic aerosols, solar radiation), anthropogenic and a combination of them, Green House Gases (GHGs), changes in land use, and anthropogenic aerosols. Seven precipitation indices, namely Rx1day (annual maximum 1-day precipitation), Rx5day (annual maximum 5-day precipitation), R10mm (annual count of days with daily precipitation exceeding 10 mm), R20mm (annual count of days with daily precipitation exceeding 20 mm), CDD (consecutive dry days), CWD (consecutive wet days), and PRCPTOT (annual total wet day precipitation), have been analyzed via the optimal fingerprint method. The results revealed that Rx1day, Rx5day and CWD increased, while R10mm, R20mm, CDD, and PRCPTOT decreased among which CDD and Rx1day indices showed significant variations, with values of 18.4% and 10.9%, respectively. Furthermore, the obtained results implied that only the impact of anthropogenic forcing, with a value of 1.4, was detected and attributed to variations in CDD. Additionally, anthropogenic forcing caused an increase in the return period of a 20-year event by 1.9 years for CDD. Although human-induced forcing factors presented marked trends in some cases, their effects were not generally detected and attributed to the change in the observations, apart from one exception. | ||
| کلیدواژه‌ها | ||
| Climate change؛ Detection and attribution؛ Precipitation Extremes؛ Iran | ||
| عنوان مقاله [English] | ||
| Detection and Attribution of Precipitation Extremes to Human Influence in Iran | ||
| نویسندگان [English] | ||
| Masoud Shirazi؛ Banafsheh Zahraie؛ Mohsen Nasseri | ||
| School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
| چکیده [English] | ||
| Evaluating the susceptibility of regional climates to climate change provides a framework for realistically analyzing potential future climate changes. This paper investigates the impact of human activities on variations in extreme precipitation in Iran by evaluating data provided from 286 rain-gauge stations during 1967-2010 and general circulation simulation results of the CanESM2 model. This investigation was based on six forcing factors, including natural external factors (volcanic aerosols, solar radiation), anthropogenic and a combination of them, Green House Gases (GHGs), changes in land use, and anthropogenic aerosols. Seven precipitation indices, namely Rx1day (annual maximum 1-day precipitation), Rx5day (annual maximum 5-day precipitation), R10mm (annual count of days with daily precipitation exceeding 10 mm), R20mm (annual count of days with daily precipitation exceeding 20 mm), CDD (consecutive dry days), CWD (consecutive wet days), and PRCPTOT (annual total wet day precipitation), have been analyzed via the optimal fingerprint method. The results revealed that Rx1day, Rx5day and CWD increased, while R10mm, R20mm, CDD, and PRCPTOT decreased among which CDD and Rx1day indices showed significant variations, with values of 18.4% and 10.9%, respectively. Furthermore, the obtained results implied that only the impact of anthropogenic forcing, with a value of 1.4, was detected and attributed to variations in CDD. Additionally, anthropogenic forcing caused an increase in the return period of a 20-year event by 1.9 years for CDD. Although human-induced forcing factors presented marked trends in some cases, their effects were not generally detected and attributed to the change in the observations, apart from one exception. | ||
| کلیدواژه‌ها [English] | ||
| Climate change, Detection and attribution, Precipitation Extremes, Iran | ||
| مراجع | ||
|
Allen, M.R., & Tett, S.F.B. (1999). Checking for model consistency in optimal fingerprinting. Clim. Dyn., 15(6), 419-434, https://doi.org/10.1007/s003820050291. Ávila, A., Justino, F., Wilson, A., Bromwich, D., & Amorim, M. (2016). Recent precipitation trends, flash floods and landslides in southern brazil. Environ. Res. Lett., 11(11), https://doi.org/10.1088/1748-9326/11/11/114029. Barnett, T., Hasselmann, K., Chelliah, M., Delworth, T., Hegerl, G., Jones, P., Rasmusson, E., Roeckner, E., Ropelewski, C., Santer, B., & Tett, S. (1999). Detection and attribution of recent climate change: A status report. Bull. Am. Meteorol. Soc., 80(12), 2631-2659. https://doi.org/10.1175/1520-0477(1999)080<2631:DAAORC>2.0.CO;2. Barnett, T.P., Pierce, D.W., & Schnur, R. (2001). Detection of anthropogenic climate change in the world's oceans. Science, 292(5515), 270-274. https://doi.org/10.1126/science.1058304. Barnett, T., Zwiers, F., Hengerl, G., Allen, M., Crowly, T., Gillett, N., Hasselmann, K., Jones, P., Santer, B., Schnur, R., Scott, P., Taylor, K., & Tett, S. (2005). Detecting and attributing external influences on the climate system: A review of recent advances. J. Climate, 18(9), 1291-1314. https://doi.org/10.1175/JCLI3329.1. Bindoff, N.L., Stott, P.A., AchutaRao, K.M., Allen, M.R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I.I., Overland, J., Perlwitz, J., Sebbari, R., & Zhang, X. (2013). Detection and attribution of climate change: from global to regional. In: 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 TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J. et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Braganza, K., Karoly, D.J., Hirst, A.C., Stott, P., Stouffer, R.J., & Tett, S.F.B. (2004). Simple indices of global climate variability and change part II: Attribution of climate change during the twentieth century. Clim. Dyn., 22(8), 823-838, https://doi.org/10.1007/s00382-004-0413-1. Chen, H., & Sun, J. (2017). Contribution of human influence to increased daily precipitation extremes over China. Geophys. Res. Lett., 44(5), 2436-2444. https://doi.org/ 10.1002/2016GL072439. Christidis, N., Stott, P.A., Brown, S., Hegerl, G.C., & Caesar, J. (2005). Detection of changes in temperature extremes during the second half of the 20th century. Geophys. Res. Lett., 32(20), 1-4. https://doi.org/10.1029/2005GL023885. Daly, C., Neilson, R.P., & Phillips, D.L. (1994). A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology and Climatology, 33(2), pp. 140-158. Darand, M., & Sohrabi, M.M. (2018). Identifying drought-and flood-prone areas based on significant changes in daily precipitation over Iran. Nat. Hazards, 90(3), 1427-1446, https://doi.org/ 10.1007/s11069-017-3107-9. Dong, T., Zhu, X., Deng, R., Ma, Y., & Dong, W, (2022). Detection and attribution of extreme precipitation events over the Asian monsoon region. Weather and Clim. Extremes, 38(100497), https://doi.org/10.1016/j.wace.2022.100497. Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., & Rummukainen, M. (2013). Evaluation of climate models. In: 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 TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J et al (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Frich, P., Alexander, L.V., Della-Marta, P., Gleason, B., Haylock, M., Tank Klein, A.M.G., & Peterson, T. (2002). Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19(3), 193-212, https://doi.org/10.3354/cr019193. Ghasemi, A.R. (2015). Changes and trends in maximum, minimum and mean temperature series in Iran. Atmos. Sci. Let., 16(3), 366-372, https://doi.org/10.1002/asl2.569. Goovaerts, Pierre. (2003). Using elevation to aid the geostatistical mapping of rainfall erosivity. CATENA., 34, 227-242, 10.1016/S0341-8162(98)00116-7. Hasselmann, K. (1993). Optimal fingerprints for the detection of time-dependent climate change. J. Clim., 6(10), 1957-1971, https://doi.org/10.1175/1520-0442(1993)006<1957:OFFTDO>2.0.CO;2. Hegerl, G.C., Hasselmann, K., Cubasch, U., Mitchell, J.F.B., Roeckner, E., Voss, R., & Waszkewitz, J. (1997). Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change. Clim. Dyn., 13(9), 613-634, https://doi.org/10.1007/s003820050186. Hegerl, G.C., Von Storch, H., Hasselmann, K., Santer, B.D., Cubasch, U., & Jones, P.D. (1996). Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. J. Clim., 9(10), 2281-2306. https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2. Hegerl, G.C., Zwiers, F.W., Stott, P.A., & Kharin, V.V. (2004). Detectability of anthropogenic changes in annual temperature and precipitation extremes. J. Clim., 17(19), 3683-3700, https://doi.org/10.1175/1520-0442(2004)017<3683:DOACIA>2.0.CO;2. Hegerl, G., & Zwiers, F. (2011) Use of models in detection and attribution of climate change. Wiley Interdiscip Rev Clim Change, 2(4), 570-591. https://doi.org/10.1002/wcc.121. Hegerl, G.C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M.P., Kovats, R.S., Parmesan, C., Pierce, D.W., & Stott, P.A. (2010). Good practice guidance paper on detection and attribution related to anthropogenic climate change. In: Meeting report of the intergovernmental panel on climate change expert meeting on detection and attribution of anthropogenic climate change [Stocker TF, Field CB, Qin D, Barros V, Plattner G-K, Tignor M et al (eds.)]. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland. Herring, S.C., Christidis, N., Hoell, A., Hoerling, M.P., & Stott, P. (2020). Explaining extreme events of 2018 from a climate perspective. Bull. Amer. Meteor. Soc., 101, 1-140, https://doi.org/10.1175/BAMS-ExplainingExtremeEvents2018.1. Kharin, V.V., Zwiers, F.W., Zhang, X., & Hegerl, G.C. (2007). Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Clim., 20(8), 1419-1444. https://doi.org/10.1175/JCLI4066.1. Kharin, V.V., Zwiers, F.W., Zhang, X., & Wehner, M. (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Change, 119(2), 345-357. https://doi.org/10.1007/s10584-013-0705-8. King, A.D., Donat, M.G., Fischer, E.M., Hawkins, E., Alexander, L.V., Karoly, D.J., Dittus, A.J., Lewis, S.C., & Perkins, S.E. (2015). The timing of anthropogenic emergence in simulated climate extremes. Environ. Res. Lett., 10(9), https://doi.org/10.1088/1748-9326/10/9/094015. Klein Tank, A.M.G., & Können, G.P. (2003). Trends in indices of daily temperature and precipitation extremes in Europe, 1946-99. J. Clim., 16(22), 3665-3680, https://doi.org/10.1175/1520-0442(2003)016<3665:TIIODT>2.0.CO;2. Lambert, F.H., Gillett, N.P., Stone, D.A., & Huntingford, C. (2005). Attribution studies of observed land precipitation changes with nine coupled models. Geophys. Res. Lett., 32(18), 1-4, https://doi.org/10.1029/2005GL023654. Lambert, F.H., Stott, P.A., Allen, M.R., & Palmer, M.A. (2004). Detection and attribution of changes in 20th century land precipitation. Geophys. Res. Lett., 31(10), L10203 1-4, https://doi.org/10.1029/2004GL019545. Li, H., Chen, H., & Wang, H. (2017). Effects of anthropogenic activity emerging as intensified extreme precipitation over china. J. Geophy. Res., 122(13), 6899-6914,. https://doi.org/10.1002/2016JD026251. Min, S., Zhang, X., Zwiers, F.W., & Hegerl, G.C. (2011). Human contribution to more-intense precipitation extremes. Nature, 470(7334), 378-381, https://doi.org/10.1038/nature09763. Mondal, A., & Mujumdar, P.P. (2015). On the detection of human influence in extreme precipitation over India. J. Hydrol., 529, 1161-1172, https://doi.org/10.1016/j.jhydrol.2015.09.030. Pahlavan, H., Zahraie, B., Nasseri M., & Mehdipour, M., (2018). Improvement of multiple linear regression method for statistical downscaling of monthly precipitation. International Journal of Environmental Science and Technology, 15(9), pp. 1897–1912, DOI: 10.1007/s13762-017-1511-z. 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. Rahimzadeh, F., Asgari, A., & Fattahi, E. (2009). Variability of extreme temperature and precipitation in Iran during recent decades. Int. J. Climatol., 29(3), 329-343, https://doi.org/10.1002/joc.1739. Ribes, A., Azaís, J., & Planton, S. (2009). Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate. Clim. Dyn., 33(5), 707-722. https://doi.org/10.1007/s00382-009-0561-4. Saadi, T., Alijani, B., Bavani, A.R.M., Akbary, M., Noury, M., & Saeidi, S. (2020). Detection and attribution of climate change in extreme precipitation using optimal fingerprinting (Case study: southwestern Iran). In 5th International Young Earth Scientists (YES) Congress “Rocking Earth’s Future”. German YES Chapter, GFZ German Research Centre for Geosciences. https://doi.org/10.2312/yes19.14. Saboohi, R., Soltani, S., & Khodagholi, M. (2012). Trend analysis of temperature parameters in Iran. Theor. Appl. Climatol., 109(3-4), 529-547, https://doi.org/10.1007/s00704-012-0590-5. Sarojini, B.B., Stott, P.A., & Black, E. (2016). Detection and attribution of human influence on regional precipitation. Nat. Clim. Change., 6(7), 669-675, https://doi.org/10.1038/nclimate2976. Spagnoli, B., Planton, S., Déqué, M., Mestre, O., & Moisselin, J. (2002). Detecting climate change at a regional scale: The case of France. Geophys. Res. Lett., 29(10), 90-1. Stott, P.A., Christidis, N., Otto, F.E.L., Sun, Y., Vanderlinden, J., van Oldenborgh, G.J., Vautard, R., von Storch, H., Walton, P., Yiou, P., & Zwiers, F.W. (2016). Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev. Clim. Change, 7(1), 23-41. https://doi.org/10.1002/wcc.380. Stott, P.A., Gillett, N.P., Hegerl, G.C., Karoly, D.J., Stone, D.A., Zhang, X., & Zwiers, F. (2010). Detection and attribution of climate change: A regional perspective. Wiley Interdiscip. Rev. Clim. Change, 1(2), 192-211, https://doi.org/10.1002/wcc.34. Stott, P.A., Tett, S.F.B., Jones, G.S., Allen, M.R., Mitchell, J.F.B., & Jenkins, G.J. (2000). External control of 20th century temperature by natural and anthropogenic forcings. Science, 290(5499), 2133-2137, https://doi.org/10.1126/science.290.5499.2133. Wang, H., Chen, Y., & Chen, Z. (2013). Spatial distribution and temporal trends of mean precipitation and extremes in the arid region, northwest of china, during 1960-2010. Hydrol. Processes, 27(12), 1807-1818, https://doi.org/10.1002/hyp.9339. Wang, Z., Jiang, Y., Wan, H., Yan, J., & Zhang, X. (2021). Toward optimal fingerprinting in detection and attribution of changes in climate extremes. J. Am. Stat. Assoc., 116(533), 1-13, https://doi.org/10.1080/01621459.2020.1730852. Wen, Q.H., Zhang, X., Xu, Y., & Wang, B. (2013). Detecting human influence on extreme temperatures in China. Geophys. Res. Lett., 40(6), 1171-1176, https://doi.org/10.1002/grl.50285. Zandi, O., Zahraie, B., Nasseri, M., & Behrangi, A. (2022). Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area. Atmospheric Research, 272, pp. 623–641, DOI: 10.1016/j.atmosres.2022.106159. Zhai, P., Zhou, B., & Chen, Y. (2018). A review of climate change attribution studies. J. Meteorol. Res., 32(5), 671-692, https://doi.org/ 10.1007/s13351-018-8041-6. Zhang, X., Aguilar, E., Sensoy, S., Melkonyan, H., Tagiyeva, U., Ahmed, N., Kutaladze, N., Rahimzadeh, F., Taghipour, A., Hantosh, T.H., & Albert, P., (2005). Trends in middle east climate extreme indices from 1950 to 2003. J. Geophys. Res. D. Atmos., 110(22), 1-12, https://doi.org/10.1029/2005JD006181. Zhang, X., Wan, H., Zwiers, F.W., Hegerl, G.C., & Min, S. (2013). Attributing intensification of precipitation extremes to human influence. Geophys. Res. Lett., 40(19), 5252-5257, https://doi.org/10.1002/grl.51010. Zhang, X., Zwiers, F.W., Hegerl, G.C., Lambert, F.H., Gillett, N.P., Solomon, S., Stott, P.A., & Nozawa, T., (2007). Detection of human influence on twentieth-century precipitation trends. Nature, 448(7152), 461-465, https://doi.org/10.1038/nature06025. Zohrabi, N., Goodarzi, E., Massah Bavani, A., & Najafi, H. (2017). Detection and attribution of climate change at regional scale: Case study of Karkheh river basin in the west of Iran. Theor. Appl. Climatol., 130(3-4), 1007-1020, https://doi.org/10.1007/s00704-016-1896-5. Zohrabi, N., Massah Bavani, A., Goodarzi, E., & Eslamian, S. (2014). Attribution of temperature and precipitation changes to greenhouse gases in northwest Iran. Quat. Int., 345, 130-137. https://doi.org/10.1016/j.quaint.2014.01.026. Zou, S., Abuduwaili, J., Duan, W., Ding, J., De Maeyer, P., Van De, Voorde, T., & Ma, L. (2021) Attribution of changes in the trend and temporal non-uniformity of extreme precipitation events in Central Asia. Sci Rep, 11(1), 15032. https://doi.org/10.1038/s41598-021-94486-w. | ||
|
آمار تعداد مشاهده مقاله: 279 تعداد دریافت فایل اصل مقاله: 286 |
||