Near term (2021-2028) climate prediction of monthly temperature in Iran using Decadal Climate Prediction Project (DCPP)

Document Type : Research Paper

Authors

1 Associate Professor, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

2 Post-Doc Researcher, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

3 M.Sc. Graduated, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Decadal prediction is a general term that encompasses predictions for annual, interannual, and decadal periods in which significant progress has been made over the years. Decadal climate prediction is made using a hindcast and the latest generation of climate models. It provides two categories of hindcast and prediction data. The purpose of this study is to evaluate the temperature from the DCPP and its prediction in Iran based on the available models of the DCPP project contribution to the CMIP6 project.
The study area of this research is Iran. As mentioned, the purpose of this study is to predict the near-term temperature based on the output of the DCPP project. For this purpose, daily temperature from 42 synoptic stations was used as observation to evaluate the available models of the DCPP project. Unlike general circulation models (GCMs), the DCPP project has an initialization that includes a three-month time step for implementation of each year. Air temperature of two models BCC-CSM2-MR and MPI-ESM1-2-HR with a horizontal resolution of 100 km is available for the DCPP project from the CMIP6 series. Three statistics, Pearson correlation coefficient (PCC), root mean square error (RMSE) and mean bias error (MBE), were used to evaluate the selected models of the DCPP project using observational data (synoptic stations).
In the study of the relationship between observation and hindcast of the two selected models, it is found that the BCC-CSM2-MR model shows a high correlation (0.99) in the mountainous areas of Zagros and Alborz and arid and semi-arid regions of the inland and east of Iran. However, the northern and southern coasts show a weak correlation (between 0.92 and 0.97). Examination of RMSE statistics for the BCC-CSM2-MR model also shows the maximum error between 1.2 to 2.2o in the coastal areas of the country (the Caspian Sea and the Oman Sea). The western and northern mountains of Iran show the minimum RMSE.
The BCC-CSM2-MR model shows more bias than the MPI-ESM1-2-HR model in the northern regions of the country. Examination of the average monthly temperature anomaly across Iran in the predicted period compared to the hindcast period (1980-2019) showed that the monthly temperature anomaly is positive across the country compared to the normal period in all months of the year. This value is 1.03 degrees Celsius for the country-wide average. In other words, the temperature in Iran will increase by one degree for the bear term period (2021-2028) compared to the long-term period of the last 40 years (1980-2019).
In this study, for the first time, a decadal climate prediction of Iran's monthly temperature is assessed using the output of two available models BCC-CSM2-MR and MPI-ESM1-2-HR from the DCPP contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The evaluation of the models using three statistical measures RMSE, MBE and PCC showed that the BCC-CSM2-MR model has the lowest performance in the coastal areas of Iran (the Caspian and the Oman Sea) and the highest performance in the highlands of Iran. The output of the MPI-ESM1-2-HR model during the hindcast period (1980-2019) show good performance of this model in determining the temperature patterns of the country. The minimum temperature is based on the output of this model in January with a value of -6.28o. Examination of the predicted temperature anomaly (2021-2028) compared to the hindcast period (1980-2019) shows that the average anomaly across the country for different months of the year during 2021-2028 compared to the hindcast period is 0.99o.

Keywords

Main Subjects


احمدی، م.، داداشی رودباری، ع.، اکبری ازیرانی، ط. و کرمی، ج.، 1398، کارایی مدل HadGEM2-ES در ارزیابی نابهنجاری فصلی دمای ایران تحت سناریوهای واداشت تابشی. فیزیک زمین و فضا، 45(3)، 625-644.
بابائیان، ا.، مدیریان، ر.، کریمیان، م. و جوانشیری، ز.، 1398، پیش‌بینی احتمالاتی ماهانه بارش و دمای کشور برای دوره 2024-2020 بر اساس پروژه DCPP سازمان جهانی هواشناسی. گزارش تحقیقاتی پژوهشکده اقلیم‌شناسی (مشهد)، گروه پژوهشی مدل‌‌سازی و پیش آگاهی اقلیمی، سازمان هواشناسی کشور.
بابائیان، ا.، مدیریان، ر.، کریمیان، م. و جوانشیری، ز.، 1399، یافته‌های پروژه جدید پیش‌بینی چند سالانه سازمان جهانی هواشناسی DCPP برای پیش‌بینی بارش ایران در دوره 2020-2024. هشتمین کنفرانس ملی مدیریت منابع آب ایران، 27 و 28 بهمن، دانشگاه فردوسی مشهد. مشهد.
بابائیان، ا.، مدیریان، ر.، کریمیان، م. و جوانشیری، ز.، 1400، پیش‌بینی چندسالانه بارش ایران با مقیاس‌کاهی برونداد مدل‌های DCPP، مطالعه موردی: دوره 2023-2019. پژوهشهای تغییرات آب و هوایی. پذیرفته شده برای انتشار.
زرین، آ. و داداشی رودباری، ع.، 1399، پیش‌نگری چشم‌انداز بلندمدت دمای آینده ایران مبتنی بر برونداد پروژه مقایسه مدل‌های جفت‌شده فاز ششم (CMIP6). فیزیک زمین و فضا، 46(3)، 583-602.
زرین، آ. و داداشی رودباری، ع.، 1400الف، پیش‌نگری دمای ایران در آینده نزدیک (2040-2021) بر اساس رویکرد همادی چند مدلی CMIP6. پژوهش‌های جغرافیای طبیعی، 53(1)، 75-90.
زرین، آ. و داداشی رودباری، ع.، 1400ب، پیش‌نگری دوره‌های خشک و مرطوب متوالی در ایران مبتنی بر برونداد همادی مدل‌های تصحیح شده اریبی CMIP6. فیزیک زمین و فضا، پذیرفته شده برای چاپ.
زرین، آ.، داداشی رودباری، ع. و صالح آبادی، ن، 1400، بررسی بی‌هنجاری و روند دمای ایران در پهنه‌های مختلف اقلیمی با استفاده از مدل‌های جفت شده پروژه مقایسه متقابل مرحله ششم (CMIP6). مجله ژئوفیزیک ایران، 15(1)، 35-54.
عالم‌زاده، ش.، احمدی گیوی، ف، محب الحجه، ع. و یازجی، د.، 1396، ساختار هندسی جت آفریقا-آسیا در وردسپهر زبرین و پاسخ آن به گرمایش زمین در مدل‌های CMIP5. مجله ژئوفیزیک ایران، 11(3)، 1-21.
عباسی، ف.، و اثمری، م. 1390، پیش‌بینی و ارزیابی تغییرات دما و بارش ایران در دهه‌های آینده با الگوی MAGICC - SCENGEN. آب و خاک (علوم و صنایع کشاورزی)، 25(1)، 70-83.
قهرمان، ن.، بابائیان، ا. و طباطبایی، م.، 1395، ارزیابی پس‌پردازش برون‌دادهای دینامیکی‌مدل‌های اقلیمی در برآورد تغییرات تبخیر ‌تعرق پتانسیل تحت سناریوهای واداشت تابشی (بررسی موردی: دشت مشهد). فیزیک زمین و فضا، 42(3)، 687-696.
معصوم پورسماکوش، ج.، میری، م. و پورکمر، ف.، 1396، ارزیابی داده‌های مدل‌های اقلیمی CMIP5 در مقابل داده‌های مشاهده‌ای ایران. مجله ژئوفیزیک ایران، 11(4)، 40-53.
Alexandersson, H., 1986, A homogeneity test applied to precipitation data. Journal of climatology, 6(6), 661-675.
Boer, G.J., Smith, D.M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., Kushnir, Y., Kimoto, M., Meehl, G.A., Msadek, R. and Mueller, W.A., 2016, The decadal climate prediction project (DCPP) contribution to CMIP6. Geoscientific Model Development, 9(10), 3751-3777.
Borchert, L. F., Koul, V., Menary, M. B., Befort, D. J., Swingedouw, D., Sgubin, G. and Mignot, J., 2021, Skillful decadal prediction of unforced southern European summer temperature variations.
Dadashi-Roudbari, A. and Ahmadi, M., 2020, Evaluating temporal and spatial variability and trend of aerosol optical depth (550 nm) over Iran using data from MODIS on board the Terra and Aqua satellites. Arabian Journal of Geosciences, 13(6), 1-23.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K. and Liebert, J., 2012, HESS Opinions" Should we apply bias correction to global and regional climate model data?". Hydrology & Earth System Sciences Discussions, 9(4).
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J. and Taylor, K. E., 2016, Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958.
Fallah-Ghalhari, G., Shakeri, F. and Dadashi-Roudbari, A., 2019, Impacts of climate changes on the maximum and minimum temperature in Iran. Theoretical and Applied Climatology, 138(3), 1539-1562.
He, S., Yang, J., Bao, Q., Wang, L. and Wang, B., 2019, Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China. Journal of Climate, 32(1), 195-212.
Hsiung, W. and Sunstein, C. R., 2006, Climate change and animals. U. Pa. L. Rev., 155, 1695.
Keenlyside, N. S., Latif, M., Jungclaus, J., Kornblueh, L. and Roeckner, E., 2008, Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 453(7191), 84-88.
Kim, H. M., Webster, P. J. and Curry, J. A., 2012, Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts. Geophysical Research Letters, 39(10).
Konisky, D. M., Hughes, L. and Kaylor, C. H., 2016, Extreme weather events and climate change concern. Climatic change, 134(4), 533-547.
Meehl, G. A., Goddard, L., Boer, G., Burgman, R., Branstator, G., Cassou, C., Corti, S., Danabasoglu, G., Doblas-Reyes, F., Hawkins, E. and Karspeck, A., 2014, Decadal climate prediction: an update from the trenches. Bulletin of the American Meteorological Society, 95(2), 243-267.
Meehl, G. A., Hu, A. and Tebaldi, C., 2010, Decadal prediction in the Pacific region. Journal of Climate, 23(11), 2959-2973.
Mochizuki, T., Ishii, M., Kimoto, M., Chikamoto, Y., Watanabe, M., Nozawa, T., Sakamoto, T.T., Shiogama, H., Awaji, T., Sugiura, N. and Toyoda, T., 2010, Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proceedings of the National Academy of Sciences, 107(5), 1833-1837.
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H. and Ilyina, T., 2018, A higher‐resolution version of the Max Planck institute earth system model (MPI‐ESM1. 2‐HR). Journal of Advances in Modeling Earth Systems, 10(7), 1383-1413.
Phung, M. L., Truong, D. T. and Pham, T. T. T., 2021, The Impact of Extreme Events and Climate Change on Agricultural and Fishery Enterprises in Central Vietnam. Sustainability, 13(13), 1-17.
Pohlmann, H., Jungclaus, J. H., Köhl, A., Stammer, D. and Marotzke, J., 2009, Initializing decadal climate predictions with the GECCO oceanic synthesis: Effects on the North Atlantic. Journal of Climate, 22(14), 3926-3938.
Screen, J. A. and Simmonds, I., 2011, Erroneous Arctic temperature trends in the ERA-40 reanalysis: A closer look. Journal of Climate, 24(10), 2620-2627.
Sharafati, A., Nabaei, S. and Shahid, S., 2020, Spatial assessment of meteorological drought features over different climate regions in Iran. International Journal of Climatology, 40(3), 1864-1884.
Smith, D. M., Cusack, S., Colman, A. W., Folland, C. K., Harris, G. R. and Murphy, J., M. 2007, Improved surface temperature prediction for the coming decade from a global climate model. science, 317(5839), 796-799.
Sospedra‐Alfonso, R. and Boer, G. J., 2020, Assessing the impact of initialization on decadal prediction skill. Geophysical Research Letters, 47(4), e2019GL086361.
Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H. and Abbaspour, K. C., 2019, The future of extreme climate in Iran. Scientific reports, 9(1), 1-11.
Wei, L., Xin, X., Xiao, C., Li, Y., Wu, Y. and Tang, H., 2019, Performance of BCC-CSM models with different horizontal resolutions in simulating extreme climate events in China. Journal of Meteorological Research, 33(4), 720-733.
Weiskopf, S. R., Rubenstein, M. A., Crozier, L. G., Gaichas, S., Griffis, R., Halofsky, J. E., Hyde, K. J., Morelli, T. L., Morisette, J. T., Muñoz, R. C. and Pershing, A. J., 2020, Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Science of the Total Environment, 733, 137782.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L. and Zhang, F., 2019, The Beijing Climate Center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12(4), 1573-1600.
Yang, X., Wood, E. F., Sheffield, J., Ren, L., Zhang, M. and Wang, Y., 2018, Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models. Journal of Hydrometeorology, 19(3), 609-623.
Zarrin, A. and Dadashi-Roudbari, A., 2021, Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144(1), 643-660.
Zarrin, A., Ghaemi, H., Azadi, M. and Farajzadeh, M., 2010, The spatial pattern of summertime subtropical anticyclones over Asia and Africa: A climatological review. International Journal of Climatology: A Journal of the Royal Meteorological Society, 30(2), 159-173.
Zhu, E., Yuan, X. and Wu, P., 2020, Skillful decadal prediction of droughts over large‐scale river basins across the globe. Geophysical Research Letters, 47(17), e2020GL089738.
Zi-Chen, T. A. N. G., Qing-Quan, L. I., Li-Juan, W. A. N. G. and Li-Quan, W. U., 2020, Evaluation of CanESM5 and MIROC6 models’ ability in predicting air temperature over China based on CMIP6 decadal experiment. Advances in Climate Change Research, 1, 147-153.
Zolina, O., Simmer, C., Kapala, A. and Gulev, S., 2005, On the robustness of the estimates of centennial‐scale variability in heavy precipitation from station data over Europe. Geophysical Research Letters, 32(14).