بررسی کارایی نسخه‌های قطعی و احتمالاتی (چند عضوی همادی) مجموعه داده ERA5 در برآورد دمای ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

مقدمه: دما یکی از اصلی­ترین متغیرهای جوی است و تأثیری مستقیم بر سایر متغیرهای جوی از جمله بارش دارد. میانگین دمای کره زمین در دهه­های اخیر افزایش یافته است. افزایش دما باعث افزایش تبخیر- تعرق، افزایش فرین­های اقلیمی، خشکسالی و آتش‌سوزی جنگل­ها می­شود. بررسی کارایی نسخه­های مختلف REA5 می­تواند نقش مؤثری در انتخاب صحیح بهترین نسخه از ERA5 در مطالعات اقلیمی کشور داشته باشد. این پژوهش دمای ایران را براساس نسخه­های قطعی و احتمالاتی مجموعه داده باز تحلیل ECMWF-ERA5 که جایگزین بازتحلیل ERA-Interim است را مورد بررسی قرار می­دهد.
مواد و روش­ها: داده­های مورد استفاده در این مطالعه میانگین ماهانه دمای هوا در دو گروه قطعی و احتمالاتی است. برای بررسی تاثیر تفکیک افقی در برونداد داده­های ERA5، از دو تفکیک ۲۵/۰ و ۵/۰ درجه قوسی از نسخه­های قطعی و احتمالاتی ERA5 برای بررسی میانگین دمای هوا بر روی کشور ایران استفاده شده است. همچنین از نسخه EDA چند عضوی با تفکیک افقی ۵/۰ درجه قوسی نیز برای مقایسه نسخه مجموعه داده­های احتمالاتی ERA5 با نسخه قطعی آن استفاده شده است. پیش از درستی سنجی داده­های سه نسخه مختلف ERA5 از 10 عضو متفاوت نسخه احتمالاتی ERA5 که تحت عنوان Member0 تا Member9 ارائه می­شوند، یک مجموعه داده همادی چند عضوی تولید شد.
نتایج و بحث: نتایج نشان داد ERA5 توزیع فضایی میانگین دما را در ایران به‌ درستی برآورد می­کند. با این‌حال در عرض‌های جغرافیایی بالا و مناطقی با توپوگرافی پیچیده کارایی ERA5 در برآورد دما نسبت به مناطق خشک و نیمه‌خشک داخلی کمتر است. علاوه بر توپوگرافی پیچیده مناطق خاص جغرافیایی همانند سواحل جنوبی دریای خزر بر کارایی ERA5 تأثیر می­گذارند. کارایی پایین­تر دمای ERA5 در مناطق ساحلی شمال کشور و زاگرس ممکن است منعکس‌کننده برخی کاستی‌ها در نمایش دقیق ویژگی­های جغرافیایی این مناطق همانند همانند برهمکنش هوا- دریا و همزمان برهمکنش جریانات مرطوب محلی با خط ساحلی و توپوگرافی پیچیده البرز و زاگرس باشد.
نتیجه ­گیری: ارزیابی متغیر دمای نسخه­های مختلف ERA5 با داده‌های مشاهداتی نشان داد که نسخه قطعی ERA5 با تفکیک ۵/۰ درجه قوسی به‌طور سیستماتیک دارای کم­برآوردی برای دما است که این مقدار برای متوسط پهنه­ای کشور ۴۱/۷- درصد است. در مقابل نسخه قطعی ERA5 با تفکیک ۵/۰ درجه قوسی دمای ایران را در متوسط پهنه­ای کشور ۰۱/۱۲ درصد بیش­تر برآورد می­کند. بررسی سه نسخه متفاوت از مجموعه داده ERA5 برای دو نسخه قطعی و احتمالاتی نشان داد که نسخه قطعی ERA5 با تفکیک افقی 25/0 درجه قوسی با متوسط پهنه­ای اریبی ۰۷/۱ درجه سلسیوس و درصد اریبی ۳۶/۹ درصد بهترین برآورد از دمای ایران را ارائه می­دهد. پراکنش ماهانه دمای میانگین ایران براساس برونداد قطعی ERA5 با تفکیک ۲۵/۰ درجه قوسی نشان داد که کمینه دمای ایران با ۴۹/۱۰- درجه سلسیوس در ماه ژانویه و بیشینه آن با ۸۳/۳۹ درجه سلسیوس در ماه ژولای اتفاق می­افتد. دما در تمامی ماه­ها و میانگین سالانه از آرایش توپوگرافی در ایران پیروی می­کند. به­طوری که کمینه دمایی ایران در ۵ ماه سال (ژانویه، فوریه، مارس، نوامبر و دسامبر) منفی است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating the performance of the deterministic and probabilistic versions (multi-member ensemble) of the ERA5 dataset in estimating Iran's temperature

نویسندگان [English]

  • Aboulfazl Heydari
  • Azar Zarrin
  • Abbasali Dadashi-Roudbari
Department of Geography, Faculty of Literature and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

Introduction: Temperature is a key atmospheric variable and has a direct effect on other atmospheric variables including precipitation. The average global temperature has increased in recent decades, which causes an increase in evapotranspiration, an increase in climate extremes, drought, and wildfires. Examining the performance of different versions of ERA5 can help in choosing the best version of ERA5 in climate studies of the country. This research examines Iran's temperature based on deterministic and probabilistic versions of the ECMWF-ERA5 reanalysis, which replaces the previous releases.
Materials and methods: The data used in this study is the average monthly air temperature which we examined in two ERA5 deterministic and probabilistic datasets. To investigate the impact of horizontal resolution on temperature estimation, two horizontal resolutions of 0.25o and 0.5o from the deterministic and probabilistic versions of ERA5 have been used. Also, the multi-member Ensemble of Data Assimilations (EDA) version with a horizontal resolution of 0.5o has been used to compare ERA5 probabilistic dataset versus its deterministic version. Before evaluating the data of three different versions of ERA5, a multi-member ensemble dataset was generated from 10 different members of the probabilistic version of ERA5, which are presented as Member0 to Member9.
Results and discussion: The results showed that ERA5 correctly estimates the spatial distribution of average temperature in Iran. However, in Iran's higher latitudes and regions with complex topography, the performance of ERA5 in temperature estimation is worse than in arid and semi-arid interior regions. In addition to the complex topography, the ERA5 performance is affected by certain complex geographical features such as the southern coast of the Caspian Sea. The worse performance of ERA5 temperature in the coastal areas of the north of the country and Zagros may reflect some shortcomings in the accurate representation of the geographical features of these areas, such as the air-sea interaction and the simultaneous interaction of local moist currents with the coastline and the complex topography of Alborz and Zagros.
Conclusion: Comparing different versions of ERA5 with observational data showed that the deterministic version of ERA5 with a resolution of 0.5o systematically underestimates the temperature in Iran, which is -7.41% for the area-averaged of the country. On the other hand, the deterministic version with a resolution of 0.5o overestimates the temperature of Iran by 12.01% in the area-averaged of the countryExamining three different versions of the ERA5 dataset for two deterministic and probabilistic versions showed that the deterministic version of ERA5 with a horizontal resolution of 0.25o with an area-averaged bias of 1.07 oC and a percentage of bias of 9.36% shows the best estimate of Iran's temperature. The monthly distribution of Iran's average temperature based on the ERA5 deterministic version with a resolution of 0.25o showed that the minimum temperature of Iran is -10.49 oC in January and the maximum temperature is 39.83 oC in July. The temperature in all months and the annual average follow the topography in Iran. The minimum temperature is negative in 5 months of the year (January, February, March, November, and December) in Iran.

کلیدواژه‌ها [English]

  • Iran
  • Temperature
  • ERA5 dataset
  • Multi-member ensemble

مراجع

 
-Ahmadi, M., Dadashi Roudbari, A., Ahmadi, H. and Alibakhshi, Z., 2018. Analysis of Iran Temperature Structure Based on ECMWF, ERA Interim Version. Physical Geography Research Quarterly, v. 50(2), p. 353-372. Doi: 10.22059/jphgr.2018.238512.1007092 (in Persian).
-Akhlaghi-Hosseiny, S.F., Zarrin, A. and Dadashi-Roudbari, A., 2023. Examining the Diurnal Temperature Range (DTR) in Iran using the AgERA5 dataset, Journal of Geography and Environmental Hazards, v. 12, p. 189-208 (in Persian).
-Asadi Rahim-Begi, N., Zarrin, A., Modfidi, A. and Dadashi-Roudbari, A., 2022. Seasonal Distribution Analysis of Extreme Precipitation in Iran using AgERA5 dataset. Iranian Journal of Soil and Water Research, v. 52, p. 2723-2737 (in Persian).
-Azizi mobaser, J., Rasoulzadeh, A., rahmati, A., shayeghi, A. and Bakhtar, A., 2021. Evaluating the Performance of Era-5 Re-Analysis Data in Estimating Daily and Monthly Precipitation, Case Study; Ardabil Province. Iranian Journal of Soil and Water Research, v. 51, p. 2937-2951 (in Persian).
-Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Baserud, L., Izsak, B. and Frei, C., 2022. Evaluation of daily precipitation analyses in E‐OBS (v19. 0e) and ERA5 by comparison to regional high‐resolution datasets in European regions. International Journal of Climatology, v. 42, p. 27-747.
-Barlow, M., Zaitchik, B., Paz, S., Black, E., Evans, J. and Hoell, A., 2016. A review of drought in the Middle East and southwest Asia. Journal of climate, v. 29(23), p. 8547-8574.
-Carvalho, D., Rafael, S., Monteiro, A., Rodrigues, V., Lopes, M. and Rocha, A., 2022. How well have CMIP3, CMIP5 and CMIP6 future climate projections portrayed the recently observed warming. Scientific Reports, v. 12(1), p. 1-7.
-Freville, H., Brun, E., Picard, G., Tatarinova, N., Arnaud, L., Lanconelli, C. and Van den Broeke, M., 2014. Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica, The Cryosphere, v. 8(4), p. 1361-1373.
-Gao, L. and Hao, L., 2014. Verification of ERA-Interim reanalysis data over China. J. Subtrop. Resour. Environ, v. 9, p. 75-81.
-Gualtieri, G., 2021. Reliability of era5 reanalysis data for wind resource assessment: A comparison against tall towers. Energies, v. 14(14), p. 4169.
-Gualtieri, G., 2021. Reliability of ERA5 reanalysis data for wind resource assessment: a comparison against tall towers. Energies, v. 14(14), p. 41-69.
-Hamill, T.M., 2021. Comparing and Combining Deterministic Surface Temperature Postprocessing Methods over the United States. Monthly Weather Review, v. 149(10), p. 3289-3298.
-Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz‐Sabater, J. and Thepaut, J.N., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, v. 146(730), p. 1999-2049.
-IPCC., 2021. Summary for policymakers Climate Change 2021: The Physical Science Basis. Contribu- tion of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press (2021).
-Kadkhoda, E., Omidvar, K., Zarrin, A. and Mazidi, A., 2023. Seasonal analysis and trend of heat stress in Iran using ERA5 data. Journal of the Earth and Space Physics, Articles in Press, Accepted (in Persian).
-Karaman, C.H. and Akyurek, Z., 2023. Evaluation of near-surface air temperature reanalysis datasets and downscaling with machine learning based Random Forest method for complex terrain of Turkey. Advances in Space Research, v. 71(12), p. 5256-5281.
-Mohammadi Ghaleni, M. and Sharafi, S., 2022. Evaluation of CRU TS4.05 and ERA5 Datasets Accuracy to Precipitation, Temperature and Potential Evapotranspiration in Different Climates across Iran. Iranian Journal of Irrigation & Drainage, v. 16, p. 879-890 (in Persian).
-Munoz-Sabater, J., Dutra, E., Agusti-Panareda, A., Albergel, C., Arduini, G., Balsamo, G. and Thepaut, J.N., 2021. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth system science data, v. 13(9), p. 4349-4383.
-Ravand, A., khaledi, S. and hasanabadi, D., 2022 Forecasting the effects of climate change on the climatic of the Miahne-city using climate models (SDSM), Journal of Applied researches in Geographical Sciences, v. 21, p. 251-270 (in Persian).
-Rahimzadeh, F., Asgari, A. and Fattahi, E., 2009. Variability of extreme temperature and precipitation in Iran during recent decades. International Journal of Climatology: A Journal of the Royal Meteorological Society, v. 29(3), p. 329-343.
-Sam Khaniani, A. and Mohammadi, A., 2022. Comparison of ERA5-Land reanalysis data with surface observations over Iran, Iranian Journal of Geophysics, v. 16, p. 195-212 (in Persian).
-Sheridan, S.C., Lee, C.C. and Smith, E.T., 2020. A comparison between station observations and reanalysis data in the identification of extreme temperature events, Geophysical Research Letters, v. 47(15), p. e2020GL088120.
-Stefanidis, K., Varlas, G., Papaioannou, G., Papadopoulos, A. and Dimitriou, E., 2022. Trends of lake temperature, mixing depth, and ice cover thickness of European lakes during the last four decades. Science of the Total Environment, v. 830, p. 154709.
-Syed, Z., Ahmad, S., Dahri, Z.H., Azmat, M., Shoaib, M., Inam, A. and Ahmad, S., 2022. Hydroclimatology of the Chitral River in the Indus Basin under changing climate, Atmosphere, v. 13, p. 295.
-Tarek, M., Brissette, F.P. and Arsenault, R., 2020. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America, Hydrology and Earth System Sciences, v. 24(5), p. 2527-2544.
-Tetzner, D., Thomas, E. and Allen, C., 2019. A validation of ERA5 reanalysis data in the Southern Antarctic Peninsula—Ellsworth land region, and its implications for ice core studies. Geosciences, v. 9(7), p. 289.
-Trenberth, K.E., 2011. Changes in precipitation with climate change. Climate research, v. 47(1-2), p. 123-138.
-Vanella, D., Longo-Minnolo, G., Belfiore, O.R., Ramirez-Cuesta, J.M., Pappalardo, S., Consoli, S. and Gandolfi, C., 2022. Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy, Journal of Hydrology: Regional Studies, v. 42, p. 101182.
-Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Liberta, G. and Krzeminski, B., 2020. ERA5-based global meteorological wildfire danger maps, Scientific data, v. 7, p. 216.
-Yao, X., Fu, B., Lu, Y., Sun, F., Wang, S. and Liu, M., 2013. Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment, PloS one, v. 8(1), p. e54660.
-Yilmaz, M., 2023. Accuracy assessment of temperature trends from ERA5 and ERA5-Land. Science of The Total Environment, v. 856, p. 159182.
-Zarrin, A. and Dadashi-Roudbari, A., 2022. Evaluation of reanalysis-based, satellite-based, and bias-correction-based datasets for capturing extreme precipitation in Iran, Meteorology and Atmospheric Physics, v. 134(4), p. 67.
-Zarrin, A., Dadashi-Roudbari, A. and Hassani, S., 2021. Historical variability and future changes in seasonal extreme temperature over Iran. Theoretical and Applied Climatology, v. 146, p. 1227-1248.
-Zhu, J., Xie, A., Qin, X., Wang, Y., Xu, B. and Wang, Y., 2021. An assessment of ERA5 reanalysis for Antarctic near-surface air temperature. Atmosphere, v. 12(2), p. 217.
-Zou, J., Lu, N., Jiang, H., Qin, J., Yao, L., Xin, Y. and Su, F., 2022. Performance of air temperature from ERA5-Land reanalysis in coastal urban agglomeration of Southeast China. Science of The Total Environment, v. 828, p. 154459.
 

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