پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 25 |
| تعداد شماره‌ها | 962 |
| تعداد مقالات | 7,975 |
| تعداد مشاهده مقاله | 13,507,757 |
| تعداد دریافت فایل اصل مقاله | 9,591,298 |
Fractal analysis of GISS Earth's surface temperature data | ||
| Journal of Interfaces, Thin Films, and Low dimensional systems | ||
| دوره 7، شماره 1، اسفند 2023، صفحه 721-729 اصل مقاله (942.89 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22051/jitl.2024.46341.1106 | ||
| نویسندگان | ||
| Maedeh Lak* 1؛ Sakineh Hosseinabadi* 2؛ Amir Ali Masoudi* 1 | ||
| 1Department of Condensed Matter Physics, Faculty of Physics, Alzahra University, Tehran, Iran | ||
| 2Department of Physics, East Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده | ||
| Rising temperature plays a significant role in global warming and has consequences on human health conditions, ecosystems, energy etc. Hence, studying and monitoring its states will help scientists seek solutions to prevent its harmful effects. In this study, we investigated the Earth's surface temperature anomaly fluctuations by fractal analysis. We gathered the temperature anomaly dataset including land and sea surface temperatures. The maximum, minimum, and average temperatures of each year were investigated. Furthermore, we used multifractal detrended fluctuation analysis (MF-DFA) to figure out whether these fluctuations appear randomly or follow a rule. By removing the trend and applying the MFDFA on data, the Hurst exponent obtained as H=0.83±0.02, which means a positive long-term correlation exists among data that causes the increasing trend. Besides, the scalability exponent, τ(q), and the singularity spectrum, f(α), were plotted, and both of them approved the multifractality for the temperature dataset. To discover what is the cause of multifractality, the main, shuffling, and surrogating data were evaluated. The results depicted both the long correlation for small and large fluctuations and the distribution deviation from Gaussian distribution have effects on the multifractal behavior of the data, but according to the graph, the long correlation is more effective. | ||
| کلیدواژه‌ها | ||
| Multifractal Detrended Fluctuation Analysis؛ Long Term Correlation؛ GISTEMP | ||
| عنوان مقاله [English] | ||
| آنالیز فراکتالی داده های GISS دمای سطح زمین | ||
| نویسندگان [English] | ||
| مائده لک1؛ سکینه حسین آبادی2؛ امیرعلی مسعودی1 | ||
| 1گروه فیزیک ماده چگال، دانشکده فیزیک، دانشگاه الزهرا، تهران، ایران | ||
| 2گروه فیزیک، دانشگاه آزاد اسلامی، واحد تهران شرق، تهران، ایران | ||
| مراجع | ||
|
[1] S. Hitz and J. Smith, “Estimating global impacts from climate change.”, Global Environmental Change, 14 (2004) 201.
[2] K. Abbass et al, “A review of the global climate change impacts, adaptation, and sustainable mitigation measures.”, Environmental Science Pollution Research, 29 (2022) 42539.
[3] U. Shahzad, “Global Warming: Causes, Effects and Solutions.”, Durreesamin Journal, 1 (2015) 2204.
[4] Md. Khademul Islam Molla et al, “Analysis of Temperature Change under Global Warming Impact using Empirical Mode Decomposition.”, International Journal of Information Technology, 3 (2006) 1.
[5] R.K. Pachauri et all, Climate change 2014 synthesis report. contribution of working groups I, II, and III to the fifth assessment report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland, 2014.
[6] AK. Verma and S. Prakash, “Impact of COVID-19 on Environment and Society.”, Journal of Global Biosciences, 9 (2020) 7352.
[dataset] [7] GISTEMP Team, GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies, 2024. https://data.giss.nasa.gov/gistemp/. - N. Lenssen et al, “Improvements in the GISTEMP uncertainty model. J. Geophysical Research Atmospheres, 124 (2019) 6307. https://doi.org/10.1029/2018JD029522.
[8] NASA GISS, Research features, Why So Many Global Temperature Records?. https, ://www.giss.nasa.gov/research/features/201501_gistemp/, 2015 (accessed 21 January 2015)
[9] Data GISS, GISTEMP, Frequently Asked Questions. https://data.giss.nasa.gov/gistemp/faq, 2016 (accessed 18 August 2016).
[10] C. Maji et al, “Impact of climate change on seismicity: a statistical approach.”, Arabian Journal Geosciences, 14 (2021) 2725.
[11] M.P. de Souza Echer et al, “On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity.”, Journal of Atmospheric and Solar-Terrestrial Physics, 74 (2012) 87.
[12] M. Hasan et al, “ARMA Model Development and Analysis for Global Temperature Uncertainty.”, Frontiers in Astronomy and Space Sciences, 10 (2023) 1098345.
[13] E. Myrvoll-Nilsen et al, “Warming Trends, and Long-Range Dependent Climate Variability Since The year 1900: A Bayesian Approach.”, Frontiers in Earth Science, 7 (2019) 214.
[14] F. Yasmeen, “Measuring Global Warming: Global and Hemisphere Mean Temperature Anomalies Predictions Using Sliced Functional Time Series (SFTS) Model.”, Open Journal of Applied Sciences, 9 (2019) 316.
[15] A.A. Tashilova et al, “Analysis of Climate Change in the Caucasus Region: End of the 20th–Beginning of the 21st Century.”, Climate, 7 (2019) 11.
[16] U. Triacca and A. Pasini, “Arctic amplification: evidence from a cluster analysis of temperature time series for eight latitude bands.”, Theoretical and Applied Climatology, 137 (2019) 505.
[17] P. Baranowski et al, “Multifractal analysis of meteorological time series to assess climate impacts.”, Theoretical and Applied Climatology, 137 (2015) 1811.
[18] R.E. Benestad, “Record-value, non-stationarity tests, and extreme value distributions.”, Global and Planetary Change, 44 (2004) 11.
[19] K. Rypdal et al, “Long-range memory in Earth's surface temperature on time scales from months to centuries.”, Journal of Geophysical Research: Atmospheres, 118 (2013) 7046.
[20] A. Phinyomark et al, “Fractal Analysis of Human Gait Variability via Stride Interval Time Series.”, Frontiers in Physiology 11 (2020) 333.
[21] B.B. Mandelbrot, “How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension.”, Science, 165 (1967) 636.
[22] I. Pilgrim, R.P. Taylor, Fractal Analysis of Time-Series Data Sets: Methods and Challenges. In Fractal Analysis, S. Ouadfeul (Ed.), IntechOpen, London, UK, 2019, pp. 5–30.
[23] A. Eke, “Pitfalls in fractal time series analysis: fMRI BOLD as an exemplary case.”, Frontiers in Physiology, 3 (2012) 417.
[24] J.W. Kantelhardt, Fractal and multifractal time series. In Mathematics of Complexity and Dynamical Systems, R. Meyers (Ed.), Springer, New York, 2012, In: Meyers, pp. 463–87.
[25] S. Kimiagar et al, “Fractal analysis of discharge current fluctuations.”, Journal of Statistical Mechanics: Theory and Experiment, 3 (2009) 03020.
[26] M.S. Movahed et al, “Multifractal detrended fluctuation analysis of sunspot time series.”, Journal of Statistical Mechanics: Theory and Experiment, 2 (2006) 02003. | ||
|
آمار تعداد مشاهده مقاله: 141 تعداد دریافت فایل اصل مقاله: 201 |
||