Modeling the Relationship between teleconnection indexes with warm season temperature anomalies in Iran Using Multivariate Regression

جغرافیا و مخاطرات محیطی

Article 1, Volume 6, Issue 3 - Serial Number 23, January 2018, Pages 47-66   

Document Type: Research Article

DOI: 10.22067/geo.v6i3.57628

Authors

Mohammad Amin Heidari^* ; Faramarz Khoshaghlagh

University of Tehran

Abstract

. Introduction
Climatic variation is one of the inherent features of the climate system. The components of the climate system are diverse and complex, so that these components interact with each other in a Interweaving way, so that the change in each component eventually changes other components as well. The climate indicators are defined to describe the status of the climate system and its changes. Each climatic index describes some aspects of the climate based on certain parameters. Therefore, various climate indicators have been proposed and used in many studies.
Climatic indices are measurable and computable and correlate with some of the elements of the climate in different regions. Some atmospheric variables such as pressure, temperature, precipitation and radiation, as well as non-atmospheric parameters such as sea surface temperature (SST) or ice cover, are among the factors to be considered for climate forcing in different parts of the world. The large water resources, such as seas and oceans, are among the most important climatic operators. These resources are capable of storing a large part of the solar energy and also, due to their fluid nature, are capable of transporting energy to other parts of the planet in various ways (surface flow, subsurface flow, convection, and moisture advection). Changes in ocean behavior, therefore, cause changes in atmospheric patterns, which can further change the short and long-term climatic conditions in different regions. For this reason, ocean surface temperature can be considered as one of the important indicators affecting climatic abnormalities.
All patterns of teleconnection as natural phenomena's, are resulting from the turbulent nature of the atmosphere and its internal energy resources. These patterns represent macro-scale variations in atmospheric wave patterns and jetstream flows, and affect the distribution of temperature, precipitation, storm paths, and the status and pattern and speed of the jetstream in large areas. For this reason, the patterns of teleconnection lead to abnormalities that occur simultaneously in very distant areas (Asakere, 2007; 48). In fact, the variability of the behavior of the atmosphere is a result of the set of behaviors and interactions between the ocean and the atmosphere. Hence, indicators that explain the abnormal behaviors of the ocean and therefore the atmosphere can help to identify the causes and nature of the occurrence of short and long-term climate abnormalities in a region. The study of air temperature anomalies in the warm season in Iran in relation to the most important oceanographic and atmospheric indices is the main aim of this research.
2. Material and Methods
In this study, two different databases were used including the data of the IRIMO stations and indexes data of oceanographic and atmospheric teleconnection of the NOAA Data Center, affiliated to the U.S. Center for Oceanography Studies. The data of the IRIMO stations consist of 30 synoptic stations with a period of 50 years of data (1961-2010). In the first step, the standardized temperature of each station was calculated per each month during the warm period of the year (from May to September). Then, for the purpose of detecting anomalies, a function was defined in Excel macro as; -0.5 >‌x> +0.5, and from among the 250 months examined the anomalies (at least 20 stations from 30 stations), 57 cases with anomalies among whole months were selected in the study period, and then by the Pearson correlation method, a relation was calculated between the 17 selected atmospheric‌-oceanic indicators and the air temperature. The indicators used in this study are the most important indicators introduced in international studies. Also, by using multivariate regression, optimal parameters and regression functions are presented in order to explain and predict the relationship between indices and temperature anomalies in the warm season in the whole of Iran.
3. Results and Discussion
The air temperature of Iran shows a significant relationship with the teleconnection indexes. According to the tests performed in selective stations, in general, NINO3, NINO1‌+‌2, NINO3.4, NINO4, GBI, CAR, PACEFIC WARM POOL and GLOBAL MEAN TEMP indexes were have a significant correlation in 90% confidence level. In terms of time in calculations with monthly synchronous steps at selected stations, the best indexes are GBI, NINO1 + 2, NINO3 and NINO3.4, with correlations of 0.8, -0.8, -0.57 and -0.4, respectively. In terms of a previous step, the GBI, NINO1‌+‌2 and NINO3 indexes had the highest correlation values of 0.8, -0.8 and -0.5, respectively. The temporal pattern of the impact of some indicators, such as NINO, which was mostly strong and inversely in the same month, was directly and significantly in the two and three months earlier. Based on the results obtained from the multivariate modeling, the correlation between the selected teleconnection indexes such as GLOBAL MEAN TEMP, GBI, NINO 1‌‌+‌2 with thermal anomalies in the warm season of Iran are 0.94; as the best temperature predictions, and at the same time a month earlier, the NINO3 index was added to the above-‌mentioned indexes. In general, the indexes of NINO3-4, NINO3, NINO1‌+‌2, NINO4, and GBI are the best atmospheric and oceanographic indicators that predict Iran's temperature anomalies.
4. Conclusion
According to numerical correlation analysis between the selective indexes and the temperature anomalies of the selective stations in the warm season in Iran showed that NINO3, NINO1 + 2, NINO3.4, NINO4, GBI and GLOBAL MEAN TEMPERATURE indexes are the most important oceanic-atmospheric predictors. Also, in this paper, linear regression functions for the relationship between indices and monthly temperature anomalies are presented, which can explain and predict the temperature changes in Iran. The correctness of these functions is confirmed by using the actual and modeled data (estimating R correlation values, RMSE and MBE values) with an acceptable error rate. It should be noted as long as the intervals of predicting are prolonged, apparently the importance of atmospheric indexes is reduced and contradictory the number and reliability of ocean indexes are increased. In total, using the above mentioned indices and using multivariate regression method in each step of time (simultaneously, one, two and three months earlier), the linear regression function for the relationship between indexes and monthly temperature anomalies of Iran has been presented, which by using it the Iran's temperature changes can be predicted finally. It should be noted that the functions obtained here are to predict the average temperature of selected stations in Iran, and therefore for each station the calculations must be made individually.

Keywords

Climate Perdition; Modeling; Multivariate Regression; Temperature Anomaly; climatic teleonnetion Index; Iran

References

اکبری، طیبه؛ 1384. شناسایی نقش الگوهای پیوند از دور نیمکره شمالی بر دمای ایران. پایان نامه دوره کارشناسی ارشد اقلیم شناسی. دانشکده ادبیات و علوم انسانی دانشگاه اصفهان.

چهرآرا ضیابری، تهمینه؛ 1391. بررسی مراکز فشار مؤثر بر اقلیم ایران طی فازهای NAO و SOI، پایان نامه کارشناسی ارشد اقلیم‌شناسی. دانشکده جغرافیا، دانشگاه تهران.

حلبیان، امیرحسین؛ محمدی، بختیار؛ 1390. ارتباط دمای ماهانه چند ایستگاه نمونه ایران با شاخص‌های مختلف انسو. فصلنامه علمی و پژوهشی فضای جغرافیایی. شماره 38،1-39.

خسروی، محمود؛ 1381. پدیده انسو (ENSO) و تغییرپذیری اقلیم جنوب شرق ایران. پایان نامه دوره دکتری اقلیم شناسی. دانشکده ادبیات و علوم انسانی دانشگاه اصفهان.

خورشید دوست، علی محمد؛ قویدل‌رحیمی، یوسف؛ 1383. ارزیابی اثر پدیده انسو بر تغییرپذیری بارش‌های فصلی استان آذربایجان شرقی با استفاده از شاخص چند متغیره انسو. مجله پژوهش‌های جغرافیایی. شماره 55. 26-15.

خوش اخلاق، فرامرز؛ 1377. پدیده انسو و تأثیر آن بر رژیم بارش ایران، فصلنامه تحقیقات جغرافیایی. سال سیزدهم. شماره51. 121-139.

عساکره، حسین؛ 1386. بررسی آماری روند دمای سالانه تبریز، مجله اندیشه جغرافیایی. شماره اول. 9-21

علیزاده، امین؛ عرفانیان، مریم و حسین انصاری؛ 1390. بررسی الگوهای پیوند از دور مؤثر بر پارامترهای بارش و دما (مطالعه موردی: ایستگاه سینوپتیک مشهد). نشریه آبیاری و زهکشی ایران، شماره 2، 176-185.

غیور، حسینعلی؛ عساکره، حسین؛ 1380. مطالعه اثر پیوند از دور بر اقلیم ایران مطالعه موردی: اثر نوسانات اطلس شمالی و نوسانات جنوبی بر تغییرات میانگین ماهانه دمای جاسک. فصلنامه تحقیقات جغرافیایی. شماره 69، 113-93.

قویدل رحیمی، یوسف؛ حاتمی، داریوش؛ رضایی، محمد؛ 1392. نقش الگوی پیوند از دور جو بالای دریای شمال-خزر در تغییرات زمانی بارش سواحل جنوبی دریای خزر. تحقیقات کاربردی علوم جغرافیایی. شماره 31. 46-29.

قویدل رحیمی، یوسف؛ فرج زاده اصل، منوچهر؛ حاتمی زرنه، داریوش؛ 1395. نقش اثرات الگوی پیوند از دور دریای شمال-خزر در نوسان پذیری دماهای بیشینه ایران، جغرافیا و برنامه ریزی، دوره 20، شماره 56، 239-255.

قویدل رحیمی، یوسف؛ فرج زاده، منوچهر؛ حاتمی زرنه، داریوش؛ 1394. تحلیل رابطه پیوند از دور بین الگوی دریای شمال-خزر و دماهای حداقل ایران، فضای جغرافیایی، شماره 52، 159-137 .

قویدل رحیمی، یوسف؛ فرج زاده، منوچهر؛ کاکاپور، سعید؛ 1393. نقش الگوی پیوند از دور دریای شمال-خزر در نوسانات بارش­های پاییزی مناطق شمال غرب و غرب ایران، مجله جغرافیا و برنامه­ریزی، شماره 49، 230-217

مسعودیان، ابوالفضل؛ 1384. ارتباط بارش ایران با انسو. مجله جغرافیا و توسعه ناحیه‌ای. شماره4.

ناظم السادات، محمدجعفر؛ قاسمی، احمدرضا؛ 1382. بارندگی شش ماهه سرد مناطق مرکزی و جنوب غربی ایران و ارتباط آن با پدیده النینو نوسانات جنوبی، مجله علوم و فنون کشاورزی و منابع طبیعی. سال هفتم. شماره سوم. 1-13.

Ajay, K., Ahmad, S., & Nayak, A., 2013. Forcing of hydroclimatic variability in the northwestern Great Plains since AD 1406. Quaternary International, 310, 47-61.

Alheit, J., & Bakun, A.., 2013. Effects of El Niño Southern Oscillation on the space–time variability of Agricultural Reference Index for Drought in midlatitudes. Agricultural and Forest Meteorology, 174, 110-128.

Cai, W., Whetton, P.H., & Pittock, A.B., 2001. Fluctuations of the relat ionship between ENSO and northeast Australian rainfall. Climate Dynamics, 17, 421-432.

Gelcer, E., Fraisse, C., Dzotsi, K., Hu, Z., Mendes, R., L., & Zotarelli, L., 2013. Increasing streamflow forecast lead time for snowmelt-driven catchment based on large-scale climate patterns. Advances in Water Resources, 53, 150-162.

Hans W., Andrea-Seim, L., Ou, T., Jeong, J., Liu, Y., Wang, X., Bao, G., & Folland, C., 2013. Exploring teleconnections between the summer NAO (SNAO) and climate in East Asia over the last four centuries – A tree-ring perspective. Dendrochronologia, 31, 4, 297-310.

Huber, S., & Fensholt, R., 2012. The role of the Atlantic Multidecadal Oscillation on medieval drought in North America: Synthesizing results from proxy data and climate models. Global and Planetary Change, 85, 56-65.

Jiang P., Yu, Z., & Gautam, R., 2013. Pacific and Atlantic Ocean influence on the spatiotemporal variability of heavy precipitation in the western United States. Global and Planetary Change, 109, 38-45.

john C., & Chiang, H., 2002. Tropical tropospheric temperature variat ions caused by ENSO and their influence on the remote tropical climate. Journal of Climate, 105, 16-26.

Kelly A., Paul, S., & Ruscher, H., 2014. Large Scale Climate Oscillations and Mesoscale Surface Meteorological Variability in the Apalachicola-Chattahoochee-Flint River. Journal of Hydrology, 25, 55-67.

Kutiel, H., Maheras, P., Tulkes M., & Paz, S., 2002. North Sea-Caspian Pattern(NCP)an Upper Level Atmospheric Teleconection Affecting the Eastern Mediterranean Implications on the Regional Climate. Theoretical and Applied Climatology. 52, 101-115.

Lazar, B., & Williams, M., 2008. Climate Change in Western Ski Areas: Potential Changes in the Timing of Wet Avalanches and Snow Quality for the Aspen Ski area in the years 2030 and 2100. Cold Regions Science and Technology 51, 219-228.

Michael J., 2011. Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel. Remote Sensing of Environment, 115, 12, 3276-3285.

Oglesby, R., Feng, S., Hu, Q., & Rowe, C., 2012. Spatial and Time Analysis of Rainfall in the Tiber River Basin (Central Italy) in relation to Discharge Measurements (1920-2010. Procedia Environmental Sciences, 7, 258-263.

Restrepo, J., Carlos-Ortiz, J., Pierini, J., Schrottke, K., Maza, M., Otero, L., & Aguirre, J., 2013. Data-driven modeling of surface temperature anomaly and solar activity trends. Environmental Modelling & Software, 37, 217-232.

Robert H., & Niebauer, H.J., 1999. Causes of Interannual Variability in the Sea Ice Core of the Eastern Bring Sea. Institute of Marine Science, 22, 250-263.

Romano, E., Bruna-Petrangeli, A., & Preziosi, E., 2011. Our current understanding of lake ecosystem response to climate change: What have we really learned from the north temperate deep lakes. Journal of Great Lakes Research, 37, 173-193.

Shabbar A., 2006. The impact of El Ni˜no-Southern oscillat ion on the Canadian climate. Advances in Geosciences, 6, 149–153.

Shimoda, Y., Ekram, M., Gurbir, A., Perhar, M., Melissa A., Sadraddini, S., Gudimov, A., & George B., 2013. Impact of Late Holocene climate variability and anthropogenic activities on Biscayne Bay (Florida, U.S.A.): Evidence from diatoms. Palaeoclimatology, 371, 80-92.

Suzan L. L., Jeannine-Marie S., Jacques, D., Sauchyn, J., & Vanstone, J. R., 2011. Teleconnections and interannual variability in Canadian groundwater levels. Journal of Hydrology, 410, 178-188.

Tang B.H., & Neelin, J.D., 2004. ENSO influence on Atlantic hurricanes via tropospheric warming. Geophysical Research Letters, 31, 204-230.

Tremblay, L., Larocque, M., Anctil, F., & Rivard, C., 2013. Freshwater discharge into the Caribbean Sea from the rivers of Northwestern South America (Colombia): Magnitude, variability and recent changes. Journal of Hydrology, 509, 266-281.

Victor M., Berta O., Rene-Garduño, E., Villanueva, E., & Adem, J., 2014. Simulation of the PDO effect on the North America summer climate with emphasis on Mexico. Atmospheric Research, 137, 228-244.

Wachnicka, A., Gaiser, E., Wingard, L., Briceño, H., & Harlem, P., 2010. Population synchronies within and between ocean basins: Apparent teleconnections and implications as to physical–biological linkage mechanisms. Journal of Marine Systems, 79, 267-285.

www.chaharmahalmet.ir/iranarchive.asp

www.esrl.noaa.gov/psd/data/climateindices/index.html