تحقیقات کاربردی علوم جغرافیایی

تحقیقات کاربردی علوم جغرافیایی

ATTRIBUTION OF CLIMATE CHANGE TO THE EXTREME SNOW FALLS OF MAZANDARAN PROVINCE (stochastic climate modeling using Lorenz-63)

نویسندگان
zahra hejazizadeh
parviz Zeaiean Firouzabadi
Roya Arefyan
چکیده
The aim of this study was to do attribution of climate change to the extreme snow fall of Mazandaran province in the northern part of Iran and southern shores of the Caspian Sea in the vicinity of Alborz mountains. The area is not prone to extreme snow fall or even snow fall then this phenomenon has had great damages to the infrastructures of the region. The study is performed on the time interval of 1987-2017in winter time (DJF). Corresponding calculation is done for two parallel worlds of counterfactual i.e. without external forcing and factual with external forcing in the context of stochastic climate modeling. This is done by the chaotic dynamical 3-D model Lorenz-63. Then the two worlds of study are defined on the basis of LM-63 and SLM-63 as deterministic and stochastic climate models. Fakher- Planck equation had the role of implementing time evolution of the PDF into the modal. The conditional probability and Bayesian framework is the preliminaries of the method of this study. The model is belonging to the space state models and Bayesian recursive estimation. These all is the basis of the EnKF as nonlinear filtering approach to the nonlinear dynamical model of this study. It is tried to bring down all the related situation associated with the issue on the basis of sound mathematical, epistemological and physical foundations. All the computations are done on the environment of GIS, Matlab, Mathematica and Maple. Then causal theory of pearl (2000) is used as the evidence of verification for the whole process. The final results showed that the extreme snow of Mazandaran province is attributable to the climate forcing defined for the study 0.8978 in its PN causation, 0.1942 in its PS causation and 0.4519 in its PNS causation.
کلیدواژه‌ها
: Attribution
stochastic process
Lorenz-63
Ensemble Kalman Filter (EnKF )
causal (counterfactual) theory
1- Hannart, A., Pearl, J., Otto, F. E. L., Naveau, P., & Ghil, M. (2016). Causal Counterfactual Theory for the Attribution of Weather and Climate-Related Events. Bulletin of the American Meteorological Society, 97(1), 99-110. https://doi.org/10.1175/BAMS-D-14-00034.1
2- Mach, K.J., S. Planton and C. von Stechow, (2014): in Climate Change: 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, pp. 117–130.
3- D.G Andrews, (2010): An Introduction to Atmospheric Physics. eBook (net library), Cambridge university press 2nd Ed, ISBN- 13 978=0-511-72966-9,
4- Mehran Kardar, statistical Physics of Fields (2007): Cambridge university press.www.cambridge.org/ 9780521873413.

5- Friedrich Nietzsche, (1968) The will to power, vintage books, edited and translated by Walter Kaufmann, R.J. Hollingdale. ISBN 978-0-394-70437-1/.
6- Neuberg, L. G. (2003). CAUSALITY: MODELS, REASONING, AND INFERENCE, by Judea Pearl, Cambridge University Press, 2000. Econometric Theory, 19(4), 675–685. doi:10.1017/S0266466603004109.
7- John Wallace, Peter Hobbs (2006). an introductory survey to Atmospheric science447, Library of Congress Cataloging-in-Publication Data Wallace, John M. (John Michael),1940–: /. —2nd ed. p. cm. ISBN 0-12-732951-X 1.
8- Palmer, T. N. (1999). A Nonlinear Dynamical Perspective on Climate Prediction. Journal of Climate, 12(2), 575-591. https://doi.org/10.1175/1520-0442(1999)0122.0.CO;2 9- Werndl, Charlotte (2016) On defining climate and climate change. British Journal for the Philosophy of Science, 67. pp. 337-364. ISSN 0007-0882. https://nora.nerc.ac.uk/id/eprint/502420.
10- Williams, P. D., Cullen, M. J., Davey, M. K., & Huthnance, J. M. (2013). Mathematics applied to the climate system: outstanding challenges and recent progress. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 371(1991), 20120518. https://doi.org/10.1098/rsta.2012.0518.
11- Knutti Reto and Rugenstein Maria (2015).Feedbacks, climate sensitivity and the limits of linear modelsPhil. Trans. R. Soc. A.3732015014620150146.http://doi.org/10.1098/rsta.2015.0146.
12- Bindoff et al. (2013). Detection and attribution of climate change: From global to regional. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Doschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds., Cambridge University Press, pp. 867-952, http://doi:10.1017/CBO9781107415324.022.
13-Hegerl, G. C., O. Hoegh-Guldberg, G. Casassa, M. P. Hoerling, R. S. Kovats, C. Parmesan, D. W. Pierce and P. A. Stott. (2010): Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change. In Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change. T. F. Stocker, C. B. Field, D. Qin, V. Barros, G.-K. Plattner, M. Tignor, P. M. Midgley and K. L. Ebi, eds. Bern, Switzerland: IPCC Working Group I Technical Support Unit, University of Bern.
14- Stone, D. A., and M. R. Allen, (2005): The end-to-end attribution problem: From emissions to impacts. Climatic Change, 71, 303–318, http://doi:10.1007/s10584 -005-6778-2.
15- Katzav, J. K., & Parker, W. S. (2015). The future of climate modeling. Climatic Change, 132(4), 475-487. https://doi.org/10.1007/s10584-015-1435-x
16- Hayhoe, K. et al. (2017). Detection and attribution of climate change. https://doi.org/10.7930/J0WH2N5418-1
17- Trenberth, K. E., Fasullo, J. T. and Shepherd, T. G. (2015) Attribution of climate extreme events. Nature Climate Change, 5 (8). pp. 725-730. ISSN 1758-678X. https://doi.org/10.1038/nclimate2657.
18- Aglaé Jézéquel, Vivian Dépoues, Hélène Guillemot, Mélodie Trolliet, Jean-Paul Vanderlinden, (2018): Behind the veil of extreme event attribution. Climatic change, 2018-08, Vol.149 (3-4), p.367-383. https://doi.org10.1007/s10584-018-2252-9

19- Intergovernmental Panel on Climate Change (IPCC). (2014). Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
.20- Stephen Ornes (2018): how does climate change influence extreme weather? Impact attribution research seeks answers 8232–8235 PNAS August, vol. 115 no. 33 https//doi:10.1073/pnas.1811393115
21- T. G. Shepherd (2016): A Common Framework for Approaches to Extreme Event Attribution. Current Climate Change Reports, 2(1):28–38, https//doi: 10.1007/s40641-016-0033-y.
22- Altan Allawala1 and J. B. Marston1 (2016): Statistics of the stochastically-forced Lorenz attractor by the Fokker-Planck equation and cumulant expansions. ©2016 American Physical Society. http//doi.org/10.1103/PhysRevE.94.052218
23- Christian L.E Franzke, Kane, Berner, Williams and Lucarini (2014): stochastic climate theory and modelling, John Wiley and sons, ltd. WIREs climate change 2015,6:63-78. https://doi.org/10.1002/wcc.318
24- Hannart, A., Pearl, J., Otto, F. E. L., Naveau, P., & Ghil, M. (2016). Causal Counterfactual Theory for the Attribution of Weather and Climate-Related Events. Bulletin of the American Meteorological Society, 97(1), 99-110. https://doi.org/10.1175/BAMS-D-14-00034.1.
25- Way-back Machin (2008): Mazandaran, geography and history Archived. http://web.archive.org/
26- Encyclopedia Britannica (2007): An Entry for Mazandaran. https://www.britannica.com/place/Mazandaran
27- way-back Machine (2007): Iran daily Caspian region Archived. http://web.archive.org/.
28-Scheffers, A. M., Kelletat, D. H. (2016). Lakes of the World with Google Earth: Understanding Our Environment. Germany: Springer International Publishing. ISBN:9783319296173, 3319296175.
29- James Farley Nicholls and Ralf Toumi (2014): On the lake effects of the Caspian Sea (Space and Atmospheric Physics Group, Imperial College London, UK). Quarterly Journal of the Royal Meteorological Society Q.J.R. Meteorology. Soc. 140: 1399–1408.https://doi.org/10.1002/qj.2222

30- National Research Council. 2005. Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties. Washington, DC: The National Academies Press. https://doi.org/10.17226/11175.
31- Bullock, M.A., Harder, J.W., Simon-Miller, A.A., & Mackwell, S.J. (2014). Comparative Climatology of Terrestrial Planets. pp. 277–326. Tucson: University of Arizona Press. CY - Tucson SN - 9780816599752 UR.
32- L.D. Landau and E.m. Lifshitz (1970): statistical physics second and revised edition course of theoretical physics volume 5. pp. 2–30. Pergamon press. 68-18526.
33- Andrew J. Majda Ilya Timofeev and Eiic Vanden Eijnden (2001): A Mathematical Framework for Stochastic Climate Models, Courant Institute. Communications on Pure and Applied Mathematics, Vol. LIV, 0891–0974 (2001) c 2001 John Wiley & Sons, Inc. https://doi.org/10.1002/cpa.1014.
34- DA'ithi' A.Stone and Myles R.Allen (2005): The End-to-End Attribution problem; from emissions to impacts.university of oxford, springer climate change (2005) 71; 303-318.DOI: 10.1007/s 10584-005-6778-2.
35- Valerio Lucarini et al (2016): Extremes and Recurrence in Dynamical System, PURE AND APPLIED MATHEMATICS, John Wiley & Sons. DOI:10.1002/9781118632321
36- Lorenz E. L. (1968): Climatic determinism. Meteorology. Monogram. American Meteorology Society., 25, 1–3.
37- Roy, D. & Musielak, Z.E., 2007. "Generalized Lorenz models and their routes to chaos. I. Energy-conserving vertical mode truncations," Chaos, Solitons & Fractals, Elsevier, vol. 32(3), pages 1038-1052. DOI: 10.1016/j.chaos.2006.02.013
38-Lorenz, Edward Norton (1963). "Deterministic non-periodic flow". Journal of the Atmospheric Sciences. 20 (2): 130–141. Bibcode:1963JAtS...20.130L. doi:10.1175/1520- 0469(1963)0202.0.CO;2.
39- Gardiner C.W (1997): Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences. 2nd edition. springer-verlag Berlin Heibelberg New York ISBN 3-540-15607-0.
40-Etienne Ghy (2013): L’attracteur de Lorenz, paradigme du chaos s CNRS - ´ENS Lyon UMPA, 46 All´ee d’Italie 69364, Lyon, France
41- Mickaë lD.Chekrouna ,EricSimonnet, MichaelGhil (2011): Stochastic climate dynamics;Random attractors and time-dependent invariant measures. Physics-d (2011). doi: 10.1016/j.physd.2011.06.00
42- Eugenia Kalnay (2003): Atmospheric modeling, data assimilation and predictability. University of Maryland. © Cambridge university press (2003) ISBN 928-3-662-09711-5.
43- Wannes Van den Bossche (2013): Data assimilation toolbox for Matlab. Thesis for Master of Science in de ingenieurs wetenschappen: Academiejaar 2012 – 2013, Copyright KU Leuven
44- Geir Evensen (2009): Data Assimilation; The ensemble kalman filter second edition. Springer Dordrecht Heidelberg London New York. e-ISBN 978-3-642-03711-5, DOI 10.1007/978-3-642-03711-5
45- Valerio Lucarini, Richard Blender, Corentin Herbert, Salvatore Pascale, Francesco Ragone, Jeroen Wouters (2014): Mathematical and physical ideas for climate science. DOI: 10.1002/2013RG000446.
46-Mark Z. Jacobson (2005): Fundamentals of Atmospheric Modeling Second Edition. Stanford University (2005) © Cambridge University Press 2005. Online ISBN:9781139165389. DOI:
https://doi.org/10.1017/CBO9781139165389
47- Miller, R. N., Carter, E. F., & Blue, S. T. (1999). Data assimilation into nonlinear stochastic models. Tellus A: Dynamic Meteorology and Oceanography, 51(2), 167–194. https://doi.org/10.3402/tellusa.v51i2.12315
48-Pchelintsev, A.N. (2014): "Numerical and Physical Modeling of the Dynamics of the Lorenz System". Numerical Analysis and Applications. 7 (2): 159–167. doi:10.1134/S1995423914020098.
49-Hilborn, Robert C. (2000): Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers (second ed.). Oxford University Press. ISBN 978-0-19-850723-9.
تحقیقات کاربردی علوم جغرافیایی
دوره 25، شماره 0
زمستان 1404