پژوهش‌های تغییرات آب و هوایی

پژوهش‌های تغییرات آب و هوایی

پیش‌نگری تغییر در تاریخ آخرین سرمازدگی بهاره و تعداد روزهای سرمازدگی در ایستگاه‌های پسته‌خیز استان کرمان با استفاده از برونداد مدل‎های اقلیمی 6CMIP

نوع مقاله : مقاله پژوهشی

نویسندگان
آمنه میان آبادی 1
مریم سلاجقه 2
1 گروه اکولوژی، پژوهشکده علوم محیطی، پژوهشگاه علوم و تکنولوژی پیشرفته و علوم محیطی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران
2 دانشجوی دکترای هواشناسی کشاورزی، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران
10.30488/ccr.2024.435000.1194
چکیده
در این مطالعه از خروجی مدل‌های اقلیمی CMIP6 برای پیش‌نگری تغییر در تاریخ آخرین سرمازدگی بهاره و تعداد روزهای سرمازدگی در طول فصل رشد پسته استفاده شد. به این منظور ابتدا 5 مدل اقلیمی شامل BCC-CSM2-MR، CNRM-CM6-1، CMCC-CM2-SR5،GFDL-ESM4  و MRI-ESM2-0 انتخاب شده و داده‌های دمای حداقل این مدل‌ها با مقادیر مشاهداتی در 6 ایستگاه انار، کرمان، رفسنجان، شهربابک، سیرجان و زرند در مناطق پسته‌خیز کرمان و برای دوره پایه (1990-2014) مقایسه گردید. معیارهای خطاسنجی نشان داد که از بین مدل‌های اقلیمی مدل CNRM-CM6-1 در ایستگاه‌های انار (r=0.74، ME=-0.89)، کرمان (r=0.73، ME=-1.19)، رفسنجان (r=0.81، ME=-1.88)، سیرجان (r=0.75، ME=-0.43) و زرند (r=0.74، ME=-2.71) و مدل GFDL-ESM4 در ایستگاه شهربابک (r=0.75، ME=1.42) بهتر از بقیه مدل‌ها دمای حداقل را تخمین می‌زنند. پس از تعیین بهترین مدل‌ برای هر ایستگاه، از خروجی آن برای تعیین تعداد روزهای سرمازدگی و آخرین سرمازدگی بهاره (بر مبنای آستانه 4 درجه) در دوره پایه و آینده (نزدیک (2026-2050)، میانی (2051-2075) و دور (2076-2100)) و برای 4 سناریوی SSP1-2.6، SSP2-4.5، SSP3-7.0 و SSP5-8.5 و ارزیابی تغییرات رخ داده استفاده شد. نتایج نشان داد که آخرین سرمازدگی بهاره در دوره‌های آینده نسبت به دوره پایه زودتر اتفاق خواهد افتاد و تعداد روزهای سرمازدگی نیز کاهش خواهد یافت. بر اساس آزمون مقایسه میانگین، تغییر در تاریخ وقوع آخرین سرمازدگی بهاره در تمام ایستگاه‌ها در دوره‌های آینده میانی و دور و در سناریوهای SSP3-7.0 و SSP5-8.5 از نظر آماری معنی‌دار است (P_value<0.05). تغییر در تعداد روزهای سرمازدگی نیز در تمام ایستگاه‌ها و تمام سناریوها معنی‌دار است. بر این اساس باید اثرات این تغییرات بر مراحل فنولوژی پسته را در ارائه برنامه‌های مدیریتی مورد توجه قرار داد.
کلیدواژه‌ها
سرمازدگی
پسته
کرمان
تغییر اقلیم
CMIP6

موضوعات

عنوان مقاله English

Projection of the change in the last spring frost and the number of frost days for the pistachio in Kerman province using the CMIP6 climate models

نویسندگان English

Ameneh Mianabadi 1
Maryam Salajegheh 2
1 Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
2 PhD Student in Agrometeorology, Water Sciences and Engineering Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده English

In this study, the CMIP6 climate models were used to predict the change in the date of the last spring chilling and the number of chilling days during the pistachio growing season. For this purpose, 5 climate models including BCC-CSM2-MR, CNRM-CM6-1, CMCC-CM2-SR5, GFDL-ESM4 and MRI-ESM2-0 were selected and the minimum temperature of these models was compared to observed values in 6 stations of Anar, Kerman, Rafsanjan, Shahrbabak, Sirjan and Zarand in the pistachio growing areas of Kerman for the base period (1990-2014). The evaluation criteria showed that among the climate models, CNRM-CM6-1 in Anar (r=0.74, ME=-0.89), Kerman (r=0.73, ME=-1.19), Rafsanjan (r=0.81, ME=- 1.88), Sirjan (r=0.75, ME=-0.43) and Zarand (r=0.74, ME=-2.71) and the GFDL-ESM4 in Shahrbabak (r=0.75, ME=1.42) performed better than the others. After determining the best model for each station, the minimum temperature was used to determine the number of chilling days and the last spring chilling (based on the 4°C threshold) in the base and future periods (near (2050-2026), middle (2075-2051), and far (2100-2076)) and for 4 scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 and to evaluate the changes. The results showed that the last spring chilling will occur earlier in future than the base period and the number of chilling days will also decrease. Based on the compare means test, the change in the last spring chilling at all stations in the middle and far future and for the SSP3-7.0 and SSP5-8.5 scenarios is statistically significant (P_value<0.05). The change in the number of chilling days is also significant in all stations and all scenarios. Based on the results, the effects of these changes on pistachio phenological stages should be taken into consideration in providing management plans.

کلیدواژه‌ها English

Chilling
Pistachio
Kerman
Climate Change
CMIP6
  1. افشاری، ح.، حکم‌آبادی، ح.، عبادی، ع.، عرب، ح.، قربانیان، ع.، 1388، بررسی مقاومت به سرمای بهاره برخی ارقام تجاری پسته منطقه دامغان، فصلنامه علمی پژوهشی گیاه و زیست بوم، شماره 18، 60-76.
  2. زاده پاریزی، ر.، تاج آبادی پور، ع.، 1397، بررسی وضعیت سطح زیرکشت ارقام پسته دراستان کرمان، دومین همایش ملی پسته ایران، رفسنجان، 21 و 22 شهریور 1397.
  3. میان‌آبادی، آ.، موسوی بایگی، م.، ثنایی‌نژاد، ح.، نظامی، ا.، 1388، بررسی و پهنه‌بندی یخبندان‌های زودهنگام پاییزه، دیرهنگام بهاره و زمستانه با استفاده از GIS در استان خراسان رضوی، مجله آب و خاک، جلد 23، شماره 1، 79-90.
  4. Ahmadi H, Rostami N, Dadashi-Roudbari A (2023) The impact of climate change on snowfall in Iran Basins using the satellite-derived snow products and CMIP6 Bias Corrected model. Theor Appl Climatol 151:603–618. https://doi.org/10.1007/s00704-022-04302-2
  5. Alexander L.V., Zhang X., Peterson T.C., et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:1–22. https://doi.org/10.1029/2005JD006290
  6. Alidoost F, Su Z, Stein A (2019) Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application. Weather Clim Extrem 26:100227. https://doi.org /10.1016/j.wace.2019.100227
  7. Almagro A, Oliveira PTS, Rosolem R, et al (2020) Performance evaluation of Eta/HadGEM2-ES and Eta/MIROC5 precipitation simulations over Brazil. Atmos Res 244:105053. https://doi.org /10.1016/j.atmosres.2020.105053
  8. Alvares CA, Sentelhas PC, Stape JL (2018) Modeling monthly meteorological and agronomic frost days, based on minimum air temperature, in Center-Southern Brazil. Theor Appl Climatol 134:177–191. https://doi.org/10.1007/s00704-017-2267-6
  9. Anandhi A, Perumal S, Gowda PH, et al (2013) Long-term spatial and temporal trends in frost indices in Kansas, USA. Clim Change 120:169–181. https://doi.org/10.1007/s10584-013-0794-4
  10. Augspurger CK (2013) Reconstructing patterns of temperature, phenology, and frost damage over 124 years: Spring damage risk is increasing. Ecology 94:41–50. https://doi.org/10.1890/12-0200.1
  11. Bal SK, Dhakar R, Kumar PV, et al (2021) Temporal trends in frost occurrence and their prediction models using multivariate statistical techniques for two diverse locations of Northern India. Theor Appl Climatol 146:1097–1110. https://doi.org/10.1007/s00704-021-03786-8
  12. Biazar SM, Ferdosi FB (2020) An investigation on spatial and temporal trends in frost indices in Northern Iran. Theor Appl Climatol 141:907–920. https://doi.org/10.1007/s00704-020-03248-7
  13. Bozkurt D, Rojas M, Boisier JP, Valdivieso J. (2018) Projected hydroclimate changes over Andean basins in central Chile from downscaled CMIP5 models under the low and high emission scenarios. Clim Change 150:131–147. https://doi.org /10.1007/s10584-018-2246-7
  14. Cai L, Alexeev VA, Walsh JE, Bhatt US (2018) Patterns, Impacts, and Future Projections of Summer Variability in the Arctic from CMIP5 Models. J Clim 31:9815–9833. https://doi.org /10.1175/JCLI-D-18-0119.1
  15. Chamberlain CJ, Cook BI, Morales‐Castilla I, Wolkovich EM (2021) Climate change reshapes the drivers of false spring risk across European trees. New Phytol 229:323–334. https://doi.org/10.1111/nph.16851
  16. Chmielewski F-M, Götz K-P, Weber KC, Moryson S (2018) Climate change and spring frost damages for sweet cherries in Germany. Int J Biometeorol 62:217–228. https://doi.org /10.1007 /s00484-017-1443-9
  17. Dunne JP, John JG, Shevliakova E, et al (2013) GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon System Formulation and Baseline Simulation Characteristics*. J Clim 26:2247–2267. https://doi.org /10.1175/JCLI-D-12-00150.1
  18. Eccel E, Rea R, Caffarra A, Crisci A (2009) Risk of spring frost to apple production under future climate scenarios: The role of phenological acclimation. Int J Biometeorol 53:273–286. https://doi.org/10.1007/s00484-009-0213-8
  19. Eyring V, Bony S, Meehl GA, et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/ 10.5194 /gmd-9-1937-2016
  20. Ghazi B, Jeihouni E (2022) Projection of temperature and precipitation under climate change in Tabriz, Iran. Arab J Geosci 15:621. https://doi.org/ 10.1007/s12517-022-09848-z
  21. Guan Y, Zheng F, Zhang P, Qin C (2015) Spatial and temporal changes of meteorological disasters in China during 1950–2013. Nat Hazards 75:2607–2623. https://doi.org/10.1007/s11069-014-1446-3
  22. Guo J, Yan Y, Chen D, et al (2020) The response of warm-season precipitation extremes in China to global warming: an observational perspective from radiosonde measurements. Clim Dyn 54:3977–3989. https://doi.org/10.1007/s00382-020-05216-3
  23. Gusain A, Ghosh S, Karmakar S (2020) Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos Res 232:104680. https://doi.org/10.1016/j.atmosres.2019.104680
  24. Hamed MM, Nashwan MS, Shahid S, et al (2022) Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmos Res 265:105927. https://doi.org/10.1016/j.atmosres.2021.105927
  25. Helali J, Oskouei EA, Hosseini SA, et al (2022) Projection of changes in late spring frost based on CMIP6 models and SSP scenarios over cold regions of Iran. Theor Appl Climatol 149:1405–1418. https://doi.org/10.1007/s00704-022-04124-2
  26. Hosseini SM, Karbalaee A, Hosseini SA (2021) Spatiotemporal changes of early fall and late spring frost and its trend based an daily minimum temperature in Iran. Arab J Geosci 14:. https://doi.org/10.1007/s12517-021-06608-3
  27. Kamyar A, Yazdanpanah H, Movahedi S, Morimoto D (2020) Assessment of the impacts of climate change on agro-climatic indices in Iran. Theor Appl Climatol 142:1359–1367. https://doi.org/10.1007/s00704-020-03385-z
  28. Kartschall T, Wodinski M, von Bloh W, et al (2015) Changes in phenology and frost risks of Vitis vinifera (cv Riesling). Meteorol Zeitschrift 24:189–200. https://doi.org/10.1127/metz/2015/0534
  29. Kataoka T, Tatebe H, Koyama H, et al (2020) Seasonal to Decadal Predictions With MIROC6: Description and Basic Evaluation. J Adv Model Earth Syst 12:. https://doi.org/10.1029/2019MS002035
  30. Kaukoranta T, Tahvonen R, Ylämäki A (2010) Climatic potential and risks for apple growing by 2040. Agric Food Sci 19:144–159. https://doi.org/10.2137/145960610791542352
  31. Kendall MG (1975) Rank Correlation Methods. Charles Griffin, London, UK
  32. Kramer K (1994) A modelling analysis of the effects of climatic warming on the probability of spring frost damage to tree species in The Netherlands and Germany. Plant, Cell Environ 17:367–377. https://doi.org/10.1111/j.1365-3040.1994.tb00305.x
  33. Lou W, Sun K, Sun S, et al (2013) Changes in pick beginning date and frost damage risk of tea tree in Longjing tea-producing area. Theor Appl Climatol 114:115–123. https://doi.org/10.1007/s00704-012-0825-5
  34. Mann HB (1945) Nonparametric Tests Against Trend. Econometrica 13:245–259
  35. Masaki Y (2021) First and last frost date determinations based on meteorological observations in Japan: trend analysis and estimation scheme construction. Theor Appl Climatol 145:411–426. https://doi.org/10.1007/s00704-021-03637-6
  36. Masaki Y (2022) Future frost risks in the Tohoku region of Japan under a warming climate—interpretation of regional diversity in terms of seasonal warming. Theor Appl Climatol 147:473–485. https://doi.org/10.1007/s00704-021-03834-3
  37. Maxino CC, McAvaney BJ, Pitman AJ, Perkins SE (2008) Ranking the AR4 climate models over the Murray-Darling Basin using simulated maximum temperature, minimum temperature and precipitation. Int J Climatol 28:1097–1112. https://doi.org/10.1002/joc.1612
  38. Menzel A, Sparks TH, Estrella N, et al (2006) European phenological response to climate change matches the warming pattern. Glob Chang Biol 12:1969–1976. https://doi.org/10.1111/j.1365-2486.2006.01193.x
  39. Modaresi F, Araghi A (2023) Projecting future reference evapotranspiration in Iran based on CMIP6 multi-model ensemble. Theor Appl Climatol 153:101–112. https://doi.org/10.1007/s00704-023-04465-6
  40. Mukherjee S, Aadhar S, Stone D, Mishra V (2018) Increase in extreme precipitation events under anthropogenic warming in India. Weather Clim Extrem 20:45–53. https://doi.org/10.1016/j.wace.2018.03.005
  41. Papalexiou SM, Montanari A (2019) Global and Regional Increase of Precipitation Extremes Under Global Warming. Water Resour Res 55:4901–4914. https://doi.org/10.1029/2018WR024067
  42. Pegahfar N (2023) Future precipitation and near surface air-temperature projection using CMIP6 models based on TOPSIS method: case study, Sistan-and-Baluchestan Province of Iran. Environ Monit Assess 195:1548. https://doi.org/10.1007/s10661-023-12084-x
  43. Poling EB (2008) Spring cold injury to winegrapes and protection strategies and methods. HortScience 43:1652–1662. https://doi.org/10.21273/hortsci.43.6.1652
  44. Qian B, Zhang X, Chen K, et al (2010) Observed long-term trends for agroclimatic conditions in Canada. J Appl Meteorol Climatol 49:604–618. https://doi.org/10.1175/2009JAMC2275.1
  45. Ragno E, AghaKouchak A, Love CA, et al (2018) Quantifying Changes in Future Intensity‐Duration‐Frequency Curves Using Multimodel Ensemble Simulations. Water Resour Res 54:1751–1764. https://doi.org/10.1002/2017WR021975
  46. Rana A, Madan S, Bengtsson L (2014) Performance evaluation of regional climate models (RCMs) in determining precipitation characteristics for Gothenburg, Sweden. Hydrol Res 45:703–714. https://doi.org/10.2166/nh.2013.160
  47. Rivera JA, Arnould G (2020) Evaluation of the ability of CMIP6 models to simulate precipitation over Southwestern South America: Climatic features and long-term trends (1901–2014). Atmos Res 241:104953. https://doi.org/10.1016/j.atmosres.2020.104953
  48. Rochette P, Bélanger G, Castonguay Y, et al (2004) Climate change and winter damage to fruit trees in eastern Canada. Can J Plant Sci 84:1113–1125. https://doi.org/10.4141/P03-177
  49. Scheifinger H, Menzel A, Koch E, Peter C (2003) Trends of spring time frost events and phenological dates in Central Europe. Theor Appl Climatol 74:41–51. https://doi.org/10.1007/s00704-002-0704-6
  50. Thrasher B, Wang W, Michaelis A, et al (2022) NASA Global Daily Downscaled Projections, CMIP6. Sci Data 9:262. https://doi.org/10.1038/s41597-022-01393-4
  51. Usta DFB, Teymouri M, Chatterjee U (2022) Assessment of temperature changes over Iran during the twenty-first century using CMIP6 models under SSP1-26, SSP2-4.5, and SSP5-8.5 scenarios. Arab J Geosci 15:416. https://doi.org/10.1007/s12517-022-09709-9
  52. Voldoire A, Saint‐Martin D, Sénési S, et al (2019) Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1. J Adv Model Earth Syst 11:2177–2213. https://doi.org/10.1029/2019MS001683
  53. Wu T, Lu Y, Fang Y, et al (2019) The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosci Model Dev 12:1573–1600. https://doi.org/10.5194/gmd-12-1573-2019
  54. Xiao L, Liu L, Asseng S, et al (2018) Estimating spring frost and its impact on yield across winter wheat in China. Agric For Meteorol 260–261:154–164. https://doi.org/10.1016/j.agrformet.2018.06.006
  55. Zhang D, Xu W, Li J, et al (2014) Frost-free season lengthening and its potential cause in the Tibetan Plateau from 1960
    to 2010. Theor Appl Climatol 115:441–450. https://doi.org/10.1007/s00704-013-0898-9

Zinoni F, Antolini G, Campisi T, et al (2002) Characterisation of Emilia-Romagna region in relation with late frost risk. Phys Chem Earth 27:1091–1101. https://doi.org/10.1016/S1474-7065(02)00145-6