Abstract
What changes may the future bring to climate time series analysis? First, we outline (Sects. 9.1–9.3) more short-term objectives of “normal science” (Kuhn, The Structure of Scientific Revolutions, 2nd edn. University of Chicago Press, Chicago, 210 pp, 1970), extensions of previous material (Chaps. 1–8). Then we take a chance (Sects. 9.4 and 9.5) and look on paradigm changes in climate data analysis that may be effected by virtue of strongly increased computing power (and storage capacity). Whether this technological achievement comes in the form of grid computing (Allen, Nature 401(6754):642, 1999; Allen et al., Nature 407(6804):617–620, 2000; Stainforth et al., Philos Trans R Soc Lond Ser A 365(1857):2145–2161, 2007) or quantum computing (Nielsen, Chuang, Quantum Computation and Quantum Information. Cambridge University Press, Cambridge, 676pp, 2000; DiCarlo et al., Nature 460(7252):240–244, 2009; Lanyon et al., Nat Phys 5(2):134–140, 2009; Rieffel and Polak, Quantum Computing: A Gentle Introduction. MIT Press, Cambridge, MA, 372pp, 2011)—the assumption here is the availability of machines that are faster by a factor of 10 to the power of, say, 12, by a midterm period of, say, less than a few decades.
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References
Berkowitz J, Kilian L (2000) Recent developments in bootstrapping time series. Econometric Reviews 19(1): 1–48
Blaauw M (2010) Methods and code for ‘classical’ age-modelling of radiocarbon sequences. Quaternary Geochronology 5(5): 512–518
Blaauw M, Heegaard E (2012) Estimation of age–depth relationships. In: Birks HJB, Lotter AF, Juggins S, Smol JP (Eds) Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques. Springer, Dordrecht, pp 379–413
Buck CE, Millard AR (Eds) (2004) Tools for Constructing Chronologies: Crossing Disciplinary Boundaries. Springer, London, 257pp
Candolo C, Davison AC, Demétrio CGB (2003) A note on model uncertainty in linear regression. The Statistician 52(2): 165–177
Chatfield C (1995) Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society, Series A 158(3): 419–466
Chatfield C (2004) The Analysis of Time Series: An Introduction. Sixth edition. Chapman and Hall, Boca Raton, FL, 333pp
Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Magaña Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Marquis M, Averyt K, Tignor MMB, Miller Jr HL, Chen Z (Eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 847–940
Draper D (1995) Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society, Series B 57(1): 45–97
Easterling DR, Alexander LV, Mokssit A, Detemmerman V (2003) CCI/CLIVAR workshop to develop priority climate indices. Bulletin of the American Meteorological Society 84(10): 1403–1407
Fohlmeister J (2012) A statistical approach to construct composite climate records of dated archives. Quaternary Geochronology 14: 48–56
Fraver S, Bradford JB, Palik BJ (2011) Improving tree age estimates derived from increment cores: A case study of red pine. Forest Science 57(2): 164–170
Goodess CM, Jacob D, Déqué M, Guttiérrez JM, Huth R, Kendon E, Leckebusch GC, Lorenz P, Pavan V (2009) Downscaling methods, data and tools for input to impacts assessments. In: van der Linden P, Mitchell JFB (Eds) ENSEMBLES: Climate change and its impacts at seasonal, decadal and centennial timescales. Met Office Hadley Centre, Exeter, pp 59–78
Hargreaves JC, Annan JD (2002) Assimilation of paleo-data in a simple Earth system model. Climate Dynamics 19(5–6): 371–381
Hendy EJ, Tomiak PJ, Collins MJ, Hellstrom J, Tudhope AW, Lough JM, Penkman KEH (2012) Assessing amino acid racemization variability in coral intra-crystalline protein for geochronological applications. Geochimica et Cosmochimica Acta 86: 338–353
Hercman H, Pawlak J (2012) MOD-AGE: An age–depth model construction algorithm. Quaternary Geochronology 12: 1–10
Jun M, Knutti R, Nychka DW (2008) Spatial analysis to quantify numerical model bias and dependence: How many climate models are there? Journal of the American Statistical Association 103(483): 934–947
Klauenberg K, Blackwell PG, Buck CE, Mulvaney R, Röthlisberger R, Wolff EW (2011) Bayesian glaciological modelling to quantify uncertainties in ice core chronologies. Quaternary Science Reviews 30(21–22): 2961–2975
Le Quéré C, Andres RJ, Boden T, Conway T, Houghton RA, House JI, Marland G, Peters GP, van der Werf GR, Ahlström A, Andrew RM, Bopp L, Canadell JG, Ciais P, Doney SC, Enright C, Friedlingstein P, Huntingford C, Jain AK, Jourdain C, Kato E, Keeling RF, Klein Goldewijk K, Levis S, Levy P, Lomas M, Poulter B, Raupach MR, Schwinger J, Sitch S, Stocker BD, Viovy N, Zaehle S, Zeng N (2013) The global carbon budget 1959–2011. Earth System Science Data 5(1): 165–185
Leith NA, Chandler RE (2010) A framework for interpreting climate model outputs. Applied Statistics 59(2): 279–296
Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao Z-C (2007) Global climate projections. In: Solomon S, Qin D, Manning M, Marquis M, Averyt K, Tignor MMB, Miller Jr HL, Chen Z (Eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 747–845
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282): 747–756
Mudelsee M, Fohlmeister J, Scholz D (2012) Effects of dating errors on nonparametric trend analyses of speleothem time series. Climate of the Past 8(5): 1637–1648
Murray JW, Hansen J (2013) Peak oil and energy independence: Myth and reality. Eos, Transactions of the American Geophysical Union 94(28): 245–246
Parrenin F, Barnola J-M, Beer J, Blunier T, Castellano E, Chappellaz J, Dreyfus G, Fischer H, Fujita S, Jouzel J, Kawamura K, Lemieux-Dudon B, Loulergue L, Masson-Delmotte V, Narcisi B, Petit J-R, Raisbeck G, Raynaud D, Ruth U, Schwander J, Severi M, Spahni R, Steffensen JP, Svensson A, Udisti R, Waelbroeck C, Wolff E (2007) The EDC3 chronology for the EPICA Dome C ice core. Climate of the Past 3(3): 485–497
Paul A, Schäfer-Neth C (2005) How to combine sparse proxy data and coupled climate models. Quaternary Science Reviews 24(7–9): 1095–1107
Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S, Qin D, Manning M, Marquis M, Averyt K, Tignor MMB, Miller Jr HL, Chen Z (Eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 589–662
Scholz D, Hoffmann DL (2011) StalAge – an algorithm designed for construction of speleothem age models. Quaternary Geochronology 6(3–4): 369–382
Scholz D, Hoffmann DL, Hellstrom J, Ramsey CB (2012) A comparison of different methods for speleothem age modelling. Quaternary Geochronology 14: 94–104
Smith RL, Tebaldi C, Nychka D, Mearns LO (2009) Bayesian modeling of uncertainty in ensembles of climate models. Journal of the American Statistical Association 104(485): 97–116
Stensrud DJ (2007) Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, Cambridge, 459pp
Tans P (2009) An accounting of the observed increase in oceanic and atmospheric CO2 and an outlook for the future. Oceanography 22(4): 26–35
Tebaldi C, Sansó B (2009) Joint projections of temperature and precipitation change from multiple climate models: A hierarchical Bayesian approach. Journal of the Royal Statistical Society, Series A 172(1): 83–106
van der Linden P, Mitchell JFB (Eds) (2009) ENSEMBLES: Climate change and its impacts at seasonal, decadal and centennial timescales. Met Office Hadley Centre, Exeter, 160pp
van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: An overview. Climatic Change 109(1–2): 5–31
von Storch H, Zwiers F (2013) Testing ensembles of climate change scenarios for “statistical significance”. Climatic Change 117(1–2): 1–9
von Storch H, Zwiers FW (1999) Statistical Analysis in Climate Research. Cambridge University Press, Cambridge, 484pp
Wheatley JJ, Blackwell PG, Abram NJ, McConnell JR, Thomas ER, Wolff EW (2012) Automated ice-core layer-counting with strong univariate signals. Climate of the Past 8(6): 1869–1879
Wunsch C (2006) Discrete Inverse and State Estimation Problems. Cambridge University Press, Cambridge, 371pp
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Mudelsee, M. (2014). Future Directions. In: Climate Time Series Analysis. Atmospheric and Oceanographic Sciences Library, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-319-04450-7_9
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DOI: https://doi.org/10.1007/978-3-319-04450-7_9
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