Global Climate Monitoring with Microwave Measurements

  • Costas A. Varotsos
  • Vladimir F. Krapivin


The rising cost of global climate change is partly due to natural disasters that will lead to potential negative impacts for many areas. There are problems encountered by many experts whose forecasts vary considerably. Maarten (2006) provides an overview of current knowledge about climate change and its effects on climate variability and extreme weather conditions that could lead to natural disasters, with particular attention to the applicability of information on disaster risk reduction. Extreme events may accompany a gradual rise in average global temperatures due to rising CO2 concentrations. First, the variability of the global climate appears in extreme weather. It is sufficient here to cite such extreme events of November 2018 as multiple wildfires in northern and southern California with tragic consequences. The phenomenon of extreme weather has occurred in the summer of May and June 2018 in almost all European countries, when nature provided events from flood droughts. Table 9.1 lists some of them.


  1. Abrahamson DE (1989) Challenge of global warming. Island Press, WashingtonGoogle Scholar
  2. Bilham R (2005) A flying start, then a slow slip. Science 308:1126–1127CrossRefGoogle Scholar
  3. Blowers A, Hinchliffe S (2003) Environmental responses. John Wiley & Sons, LondonGoogle Scholar
  4. Burkhart HE, Tomê M (2012) Modeling forest trees and stands. Springer Netherlands.CrossRefGoogle Scholar
  5. Butler JH, Montzka SA (2016) The NOAA annual greenhouse gas index (AGGI). NOAA Earth System Research Laboratory, Boulder. Scholar
  6. Campbell JL, Rustad L, Boyer EW, Christopher S (2009) Consequences of climate change for biogeochemical cycling in forests of northeastern North America. Can J For Res 39:264–284CrossRefGoogle Scholar
  7. Chen X, Zhang X (2016) How environmental uncertainty moderated of relative advantage and perceived credibility on the adoption of mobile health services by Chinese organizations in the big data era. Int J Telemed Appl 2016:1–11. Scholar
  8. Chernavsky DS (ed) (2004) Recognition, autodiagnostics, thinking: synergetics and human science. Radiotekhnika Publ, Moscow. [in Russian]Google Scholar
  9. Condie KC (2005) Earth as an evolving planetary system. Elsevier Academic, Burlington, MAGoogle Scholar
  10. Debertin DL (2012) Agricultural production economics. Macmillan Publishing Company, LondonGoogle Scholar
  11. Degermendzhi AG (2009) New directions in biophysical ecology. In: Cracknell AP, Krapivin VF, Varotsos CA (eds) Global climatology and Ecodynamics. Springer/Praxis, Chichester, pp 379–396CrossRefGoogle Scholar
  12. FAO (2001) Global forest resources assessment 2000. Main Report. FAO Forestry Paper, Rome, 140Google Scholar
  13. Field CB, Raupach MR (eds) (2004) Global carbon cycle: integrating humans, climate, and the natural world. Island Press, WashingtonGoogle Scholar
  14. Field JG, Hempel G, Summerhayer CP (eds) (2002) Oceans 2020: science trends and the challenge of sustainability. Island Press, WashingtonGoogle Scholar
  15. Forrester JW (1971) World dynamics. Wright - Allen Press, Cambridge, MA.Google Scholar
  16. Gardner JS (2002) Natural hazards risk in the Kullu District, Himachal Pradesh, India. Geogr Rev 92:172–177CrossRefGoogle Scholar
  17. Goldner J (2002) Messages from space. Michael Wiese Production, SuiteGoogle Scholar
  18. Grigoryev AA, Kondratyev KYA (2001) Ecological catastrophes. The St.-Petersburg Scientific Centre of RAS, St.-Petersburg. [in Russian]Google Scholar
  19. Gyde LH (2012) Definitions of Forest, deforestation, afforestation, and reforestation. Forest Information Services, GainesvilleGoogle Scholar
  20. Hales B, Takahashi T, Bandstra L (2005) Atmospheric CO2 uptake by a coastal upwelling system. Glob Biogeochem Cycles 19(GB1009):1–11Google Scholar
  21. Hardy JT (2003) Climate change. Wiley, WashingtonGoogle Scholar
  22. Hasenauer H (2006) Sustainable Forest management: growth models for Europe. Springer, BerlinCrossRefGoogle Scholar
  23. Hinchliffe S, Blowers A, Freeland J (2002) Understanding environmental issues. Wiley, LondonGoogle Scholar
  24. Ji Y, Stocker E (2002) Seasonal, intraseasonal, and interannual variability of global land fires and their effects on atmospheric aerosol distribution. J Geophys Res 107(D23):4697CrossRefGoogle Scholar
  25. Jönsson AM, Linderson M-L, Stjernquist I, Scglyter P, Bärring L (2004) Climate change and the effect of temperature backlashes causing frost damage in Picea abies. Glob Planet Chang 44(1–4):195–207CrossRefGoogle Scholar
  26. Kendrick TD (1957) The Lisbon earthquake. Lippincott, PhiladelphiaGoogle Scholar
  27. Kondratyev KYA, Krapivin VF, Phillips GW (2002) Global environmental change: modelling and monitoring. Springer, BerlinCrossRefGoogle Scholar
  28. Kondratyev KYA, Krapivin VF, Savinykh VP, Varotsos CA (2004) Global ecodynamics: a multidimensional analysis. Springer-Praxis, ChichesterCrossRefGoogle Scholar
  29. Kondratyev KYA, Krapivin VF, Varotsos CA (2006) Natural disasters as interactive components of global ecodynamics. Springer/Praxis, ChichesterGoogle Scholar
  30. Krapivin VF (1993) Mathematical model for global ecological investigations. Ecol Model 67(204):103–127CrossRefGoogle Scholar
  31. Krapivin VF, Kelley JJ (2009) Model-based method for the assessment of global change in a nature-society system. In: Cracknell AP, Krapivin VF, Varotsos CA (eds) Problems of global climatology and ecodynamics. Springer/Praxis, Chichester, pp 133–184CrossRefGoogle Scholar
  32. Krapivin VF, Shutko AM (2012) Information technologies for remote monitoring of the environment. Springer/Praxis, ChichesterCrossRefGoogle Scholar
  33. Krapivin VF, Varotsos CA (2007) Globalization and sustainable development. Springer/Praxis, ChichesterGoogle Scholar
  34. Krapivin VF, Varotsos CA (2008) Biogeochemical cycles in globalization and sustainable development. Springer/Praxis, ChichesterGoogle Scholar
  35. Krapivin VF, Mkrtchyan FA, Ivanov MV (2005) Expert system for the operative environmental diagnostics. The 26th Asian conference on remote sensing (ACRS2005). Hanoi. 7–11 November 2005. Vietnam. Asian Association on Remote Sensing – 26th Asian conference on remote sensing and 2nd Asian space conference, ACRS 2005, 3, pp 1967–1975.Google Scholar
  36. Krapivin VF, Varotsos CA, Soldatov VY (2015a) New ecoinformatics tools in environmental science: applications and decision-making. Springer, LondonCrossRefGoogle Scholar
  37. Krapivin VF, Varotsos CA, Soldatov VY (2015b) New ecoinformatics tools in environmental science: applications and decision-making. Springer, LondonCrossRefGoogle Scholar
  38. Krapivin VF, Varotsos CA, Soldatov VY (2017a) The Earth’s population can reach 14 billion in the 23rd century without significant adverse effects on survivability. Int J Environ Res Public Health 14(8):3–18CrossRefGoogle Scholar
  39. Krapivin VF, Varotsos CA, Nghia BQ (2017b) A modeling system for monitoring water quality in lagoons. Water Air Soil Pollut 228(397):1–12Google Scholar
  40. Krapivin VF, Nitu C, Varotsos CA (2019) Microwave remote sensing tools and ecoinformatics. Matrix Rom, BucharestGoogle Scholar
  41. Li J, Mao J (2016) Changes in the boreal summer intraseasonal oscillation projected by the CNRM-CM5 model under the RCP 8.5 scenario. Clim Dyn 47(12):3713–3736CrossRefGoogle Scholar
  42. Lindenmayer DB, Foster DR, Franklin JF, Hunter ML, Noss RF, Schmiegelow FA, Perry D (2004) Salvage harvesting policies after natural disturbance. Science 303(5662):1303CrossRefGoogle Scholar
  43. Lomborg B (2001) The skeptical environmentalist – measuring the real state of the world. Cambridge University Press, CambridgeGoogle Scholar
  44. Lucas JS, Southgate PC (2012) Aquaculture: farming aquatic animals and plants. Wiley, New YorkCrossRefGoogle Scholar
  45. Maarten KA (2006) The impacts of climate change on the risk of natural disasters. Disasters 30(1):5–18CrossRefGoogle Scholar
  46. McConnel WJ (2004) Forest cover change – tales of the unexpected. Global Change Newsl 57:8–11Google Scholar
  47. McNulty SG (2002) Hurricane impacts on US forest carbon sequestration. Environ Pollut 116:S17–S25CrossRefGoogle Scholar
  48. Milne A (2004) Doomsday: the science of catastrophic events. Praeger Publisher, WestportGoogle Scholar
  49. Moisseev NN (1979) Mathematics produces an experiment. Science Publ, Moscow. [in Russian]Google Scholar
  50. Monin AS, Shishkov YA (1991) The warming dilemmas in the 20th century. In: Monin AS (ed) Man and Chaos. Hydrometeoizdat, St. Petersburg, pp 47–49. [in Russian]Google Scholar
  51. Morris D, Freeland J, Hinchcliffe S, Smith S (2003) Changing environments. Wiley, LondonGoogle Scholar
  52. Nilsson K, Sangster M, Gallis C, Hartig T, de Vries S, Seeland K, Schipperijn J (eds) (2011) Forests, trees and human health. Springer, DordrechtGoogle Scholar
  53. Nitu C, Krapivin VF, Bruno A (2000a) Intelligent techniques in ecology. Printech, BucharestGoogle Scholar
  54. Nitu C, Krapivin VF, Bruno A (2000b) System modelling in ecology. Printech, BucharestGoogle Scholar
  55. Nitu C, Krapivin VF, Pruteanu E (2004) Ecoinformatics: intelligent systems in ecology. – magic print. Onesti, BucharestGoogle Scholar
  56. Nitu C, Krapivin VF, Soldatov VY (2013) Information-modeling technology for environmental investigations. Matrix ROM, BucharestGoogle Scholar
  57. Oppenheimer C (1996) Volcanism. Geography 81(1):65–81Google Scholar
  58. Perrie W, Ren X, Zhang W, Long Z (2004) Simulation of extratropical Hurricane Gustav using a coupled atmosphere-ocean-sea spray model. Geophys Res Lett 31(3):L03110CrossRefGoogle Scholar
  59. Riahi K, Krey V, Rao S, Chirkov V, Fischer G, Kolp P, Kindermann G, Nakicenovic N, Rafai P (2011) RCP-8.5: exploring the consequence of high emission trajectories. Clim Chang 109(33).
  60. Richter CF (1969) Earthquakes. Nat Hist 78:37–45Google Scholar
  61. Saavedra-Rivano N (1979) A critical analysis of the Mesarovic-Pestel world model. Appl Math Model 3(5):384–390CrossRefGoogle Scholar
  62. Sarlis NV, Skordas ES, Varotsos PA, Ramírez-Rojas A, Flores-Márquez EL (2018) Natural time analysis: on the deadly Mexico M8.2 earthquake on 7 September 2017. Physicol A 506:625–634CrossRefGoogle Scholar
  63. Sarlis NV, Skordas ES, Varotsos PA, Ramírez-Rojas A, Flores-Márquez EL (2019) Investigation of the temporal correlations between earthquake magnitudes before the Mexico M8.2 earthquake on 7 September 2017. Physicol A 517:475–483CrossRefGoogle Scholar
  64. Sellers PJ, Randall DA, Collotz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Bounoua L (1996) A revised land surface parametrization (SiB2) for atmospheric GCMs. Part 1: model formulation. J Clim 9(4):676–705CrossRefGoogle Scholar
  65. Shi W, Zhang A, Zhou X, Min Z (2018) Challenges and prospects of uncertainties in spatial big data analysis. Ann Am Assoc Geogr 108(6):1513–1520Google Scholar
  66. Silver D (1998) Tropical rain Forest. McGraw-Hill, New YorkGoogle Scholar
  67. Sudarshana P, Nageswara M, Soneji JR (2012) Tropical forests. InTech, New YorkCrossRefGoogle Scholar
  68. Tarko AM (2003) Analysis of global and regional changes in biogeochemical carbon cycle: a spatially distributed model. Interim report, IR-03-041. IIASA, LaxenburgGoogle Scholar
  69. Varotsos P (2005) The physics of seismic electric signals. TerraPub, TokyoGoogle Scholar
  70. Varotsos P, Alexopoulos K (1984a) Physical properties of the variations of the electric field of the earth preceding earthquakes, I. Tectonophysics 110:73–98CrossRefGoogle Scholar
  71. Varotsos P, Alexopoulos K (1984b) Physical properties of the variations of the electric field of the earth preceding earthquakes. II. Determination of epicenter and magnitude. Tectonophysics 110:99–125CrossRefGoogle Scholar
  72. Varotsos CA, Krapivin VF (2017) A new big data approach based on geoecological information-modeling system. Big Earth Data 1(1–2):47–63CrossRefGoogle Scholar
  73. Varotsos PA, Sarlis NV, Skordas ES (2002) Long-range correlations in the electric signals that precede rupture. Phys Rev E 66:011902CrossRefGoogle Scholar
  74. Varotsos PA, Sarlis NV, Skordas ES (2011) Natural time analysis: the new view of time. Precursory seismic electric signals, earthquakes and other complex time series. Springer, Berlin/HeidelbergCrossRefGoogle Scholar
  75. Varotsos PA, Sarlis NV, Skordas ES (2019) Phenomena preceding major earthquakes interconnected through a physical model. Ann Geophys 37:315–324CrossRefGoogle Scholar
  76. Vernadsky VI (1944) Several words about the noosphere. Successes Present Biol 18(2):49–93. [in Russian]Google Scholar
  77. Victor DG (2001) The collapse of the Kyoto Protokol and the struggle to slow global warming. Princeton Univ. Press, PrincetonGoogle Scholar
  78. Victor DG (2004) Climate change. debating America’s policy options. The Council of Foreign Relations, New YorkGoogle Scholar
  79. Walker G (2003) Snowball earth: the story of the great global catastrophe that spawned life as we know it. Crown Publishers, New YorkGoogle Scholar
  80. Waring R, Running S (2007) Forest ecosystems. Analysis at multiple scales. Academic Press, OrlandoGoogle Scholar
  81. Wayne GP (2013) The beginner’s guide to representative concentration pathways. Sceptical Science, p 24Google Scholar
  82. Wright EL, Erickson JD (2003) Incorporating catastrophes into integrated assessment: science, impacts, and adaptation. Clim Chang 57:265–286CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Costas A. Varotsos
    • 1
  • Vladimir F. Krapivin
    • 2
  1. 1.National and Kapodistrian University of Athens (NKUA)AthensGreece
  2. 2.Institute of Radio-Engineering and ElectronicsFryazinoRussia

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