Preferences help
enabled [disable] Abstract
Number of results
2020 | 144 | 208-225
Article title

Numerical Predictions for Global Climate Changes

Title variants
Languages of publication
Solar irradiation is the major source of energy delivered to Earth’s atmosphere. Satellite measurements of total solar irradiation are proportional in time to sunspot numbers. Sunspot numbers have a well-organized periodic nature that is predictable in time. At the present time solar activity is in a very low phase and will be increasing over the next few decades. The atmosphere’s consumption of solar energy strongly depends upon the percentages of carbon dioxide and water vapor in it, which can cause greenhouse effects. The recent increases in the percentage of water vapor and carbon dioxide in the atmosphere have caused global warming. Water levels are affected by melting glaciers. The purpose of this paper is to separate the influence of solar activity and greenhouse effects on global warming and to forecast these influences for several decades.
  • Department of Mathematics, Syracuse University, 215 Carnegie Building, Syracuse, NY 13244, USA
  • Department of Epidemiology & Biostatistics, University at Albany, 1 University Place, Rensselaer, NY 12144, USA
  • [1] Active Cavity Radiometer Irradiance Monitor (ACRIM) Accessed 2018.
  • [2] Arndorfer S, Zurbenko I (2017). Time Series Analysis on Diabetes Mortality in the United States, 1999-2015 by Kolmogorov-Zurbenko Filter. Journal of Biometrics & Biostatistics Vol 8 (6). DOI: 10.4172/2155-6180.1000384
  • [3] Close B, Zurbenko I, Sun M (2018). kza: Kolmogorov-Zurbenko Adaptive Filters. R package version 4.1.0.
  • [4] Frohlich C, Crommelynck D, Wehrli C, Anklin M, Dewitte S, Fichot A, Finsterle W, Jiménez A, Chevalier A, Roth HJ (1997). In-flight performances of VIRGO solar irradiance instruments on SOHO. Solar Physics Vol 175 (2): 267-286. doi:10.1023/A:1004929108864
  • [5] Gray LJ, Beer J, Geller M, Haigh JD, Lockwood M, Matthew K, Cubasch U, Fleitmann D, Harrison G, Hood L, Luterbacher J, Meehl GA, Shindell D, van Geel B, White W (2010). Solar influences on climate. Reviews of Geophysics 48 RG4001. doi:10.1029/2009RG000282
  • [6] Hathaway, D (2015). The Solar Cycle. Living Reviews in Solar Physics 12 (4). DOI:10.1007/lrsp-2015-4
  • [7] Kossobokov V, Le Mouël JL, Courtillot V (2010). A statistically significant signature of multi-decadal solar activity changes in atmospheric temperatures at three European stations. Journal of Atmospheric and Solar-Terrestrial Physics 72: 595-606. doi:10.1016/j.jastp.2010.02.016
  • [8] National Climatic Data Center. National Oceanic and Atmospheric Administration. Global Historical Climatology Network Monthly Version 3. and
  • [9] Potrzeba-Macrina AL & Zurbenko IG (2017). Computational Aspects of Spectral Estimations and Periodicities in Irregularly Observed Data. Journal of Probability and Statistical Science 15 (2), 233-246
  • [10] Potrzeba-Macrina AL & Zurbenko IG (2019). Periods in Solar Activity. Advances in Astrophysics Vol 4, No 2, 47-60.
  • [11] Soon W, Legates DR (2013). Solar irradiance modulation of Equator-to-Pole (Artic) temperature gradients: Empirical evidence for climate variation on multi-decadal timescales. Journal of Atmospheric and Solar-Terrestrial Physics 93, 45-56.
  • [12] Stephenson, FR & Clark, DH (1978). Applications of Early Astronomical Records. Monographs on Astronomical Subjects: 4. Oxford University Press, New York.
  • [13] Sun Z, Wang Q, Batkhishig O, Ouyang Z (2016). Relationship between Evapotranspiration and Land Surface Temperature under Energy- and Water-Limited conditions in dry and cold climates. Advances in Meteorology Article ID 1835487.
  • [14] Tiwari RK, Rajesh R, Padmavathi, B (2016). Evidence of Higher-Order Solar Periodicities in China Temperature Record. Pure and Applied Geophysics 173: 2511-2520. DOI 10.1007/s00024-016-1287-y
  • [15] Trenberth KE, Fasullo, JT, Balmaseda, MA (2014). Earth’s Energy Imbalance. Journal of Climate Vol 27 (9), 3129-3144.
  • [16] Tsakiri K, Zurbenko I (2010). Prediction of Ozone Concentrations using Atmospheric Variables. Journal Air Quality, Atmosphere & Health. Vol 4 (2), 111-120. DOI:10.1007/s11869-010-0084-5
  • [17] WDC-SILSO, Royal Observatory of Belgium, Brussels. Sunspot Numbers:
  • [18] Valachovic E, Zurbenko I (2014). Skin Cancer, Irradiation, and Sunspots: The Solar Cycle Effect. Biomedical Research International. Vol 2014.
  • [19] Valachovic E, Zurbenko I (2017). Multivariate Analysis of Spatial-Temporal Scales in Melanoma Prevalence. Cancer Causes & Control. Springer Vol 28 (7), 733-743. doi:10.1007/s10552-017-0895-x
  • [20] Yang W, & Zurbenko I (2010). Kolmogorov-Zurbenko filters. WIREs Computational Statistics Vol 2(3), 340-351.
  • [21] Yang W, Zurbenko I (2012). KZFT package version 0.17, R-software first published version (19-Sept-2007) and latest published version (2012), Package Sources
  • [22] Zurbenko IG, Cyr DD (2011). Climate fluctuations in time and space. Climate Research Vol (46): 67-76.
  • [23] Zurbenko IG, Cyr DD (2013). Climate fluctuations in time and space. Climate Research Vol (57), 93–94. doi:10.3354/cr01168
  • [24] Zurbenko I, Luo M (2015). Surface Humidity Changes in Different Temporal Scales. American Journal of Climate Change 4, 226-238
  • [25] Zurbenko, IG & Potrzeba, AL (2009). Tidal Waves in Atmosphere and Their Effects. Acta Geophysica, Vol 58 (2), 356-373. DOI:10.2478/s11600-009-0049-y
  • [26] Zurbenko, IG & Potrzeba, AL (2013a). Tides in the Atmosphere. Air Quality, Atmosphere, & Health, 6 (1), 39-46. DOI:10.1007/s11869-011-0143-6
  • [27] Zurbenko, IG & Potrzeba, AL (2013b). Periods of Excess Energy in Extreme Weather Events. Journal of Climatology, Vol. 2013. Article ID 410898.
  • [28] Zurbenko IG & Potrzeba-Macrina AL (2019a). Solar Energy Supply Fluctuations to Earth and Climate Effects. World Scientific News 120 (2) 111-131
  • [29] Zurbenko, IG & Potrzeba-Macrina, AL (2019b). Analysis of Regional Global Climate Changes due to Human Influences. World Scientific News 132 (2019) 1-15
  • [30] Zurbenko IG, Smith D (2017). Kolmogorov-Zurbenko filters in spatiotemporal analysis. WIREs Computational Statistics, e1419. DOI:10.1002/wics.1419
  • [31] Zurbenko I, Sowizral, M (1999) Resolution of the destructive effect on noise on linear regression of two time series. Far East Journal of Theoretical Statistics Vol 3 (1), 139-157.
  • [32] Zurbenko, I.G. & Sun, M (2014). Periods of High Risk in Tornado Outbreaks in Central USA. Advances in Research 2(8). 426-440. DOI:10.9734/AIR/2014/10247
  • [33] Zurbenko, I.G. & Sun, M (2015). Associations of Jet Streams with Tornado Outbreaks in the North America. Atmospheric and Climate Sciences, Vol. 5 (3), 336-344.
  • [34] Zurbenko, I.G. & Sun, M. (2016). Jet Stream as a major factor of tornados in USA. Atmospheric and Climate Sciences, Vol. 6 (2), 236-253. DOI:10.4236/acs.2016.62020
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.