| Peer-Reviewed

Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures

Received: 24 May 2021    Accepted: 7 June 2021    Published: 16 June 2021
Views:       Downloads:
Abstract

The validity and interpretation of differing representations of proxy temperature profiles from the past 2,000 years for the northern hemisphere remains controversial. One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the “hockey stick” exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation. In both cases, the resultant sine waves from spectral analysis up to 1880 AD can be used to train a an artificial neural network using oscillatory data corresponding to the pre-industrial era, then forecasting temperatures into the 20th century, enabling an estimation of natural and anthropogenic contributions to recent warming. The limitations of highly complex general circulation models that do not to adequately incorporate oscillatory patterns in temperatures may be a compelling reason to promote more extensive use of forecasting with established machine learning techniques such as ANNs.

Published in Earth Sciences (Volume 10, Issue 3)
DOI 10.11648/j.earth.20211003.14
Page(s) 95-117
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Climate Change, Temperature, Oscillation, Natural, Anthropogenic, Forecast, Artificial Neural Network

References
[1] Jones, P. D., Early European Instrumental Records, in: Jones, P. D., Ogilvie, A. E. J., Davies, T. D., Briffa, K. R. (Eds.), History and Climate: Memories of the Future? Springer US, Boston, MA, 2001, 55-77.
[2] Luterbacher, J., Werner, J. P., Smerdon, J. E., Fernández-Donado, L., González-Rouco, F. J., Barriopedro, D., Ljungqvist, F. C., Büntgen, U., Zorita, E., Wagner, S., Esper, J., McCarroll, D., Toreti, A., Frank, D., Jungclaus, J. H., Barriendos, M., Bertolin, C., Bothe, O., Brázdil, R., Camuffo, D., Dobrovolný, P., Gagen, M., García-Bustamante, E., Ge, Q., Gómez-Navarro, J. J., Guiot, J., Hao, Z., Hegerl, G. C., Holmgren, K., Klimenko, V. V., Martín-Chivelet, J., Pfister, C., Roberts, N., Schindler, A., Schurer, A., Solomina, O., Gunten, L., von, Wahl, E., Wanner, H., Wetter, O., Xoplaki, E., Yuan, N., Zanchettin, D., Zhang, H., Zerefos, C., European summer temperatures since Roman times. Environ. Res. Lett. 2016, 11.
[3] Parker, D. E., Legg, T. P., Folland, C. K., A new daily central England temperature series, 1772-1991. International Journal of Climatology 1992, 12, 317-342.
[4] Hernández, A., Martin-Puertas, C., Moffa-Sánchez, P., Moreno-Chamarro, E., Ortega, P., Blockley, S., Cobb, K. M., Comas-Bru, L., Giralt, S., Goosse, H., Luterbacher, J., Martrat, B., Muscheler, R., Parnell, A., Pla-Rabes, S., Sjolte, J, Scaife, A. A., Swingedouw, D., Wise, E., Xu, G., Modes of climate variability: Synthesis and review of proxy-based reconstructions through the Holocene. Earth-Science Reviews 2020, 209, 103286.
[5] Trachsel, M.. Grosjean, M., Larocque-Tobler, I., Schwikowski, M., Blass, A., Sturm, M., Quantitative summer temperature reconstruction derived from a combined biogenic Si and chironomid record from varved sediments of Lake Silvaplana (south-eastern Swiss Alps) back to AD 1177. Quaternary Science Reviews 2010, 29, 2719-2730.
[6] Ljungqvist, F. C., A new reconstruction of temperature variability in the extra-tropical Northern Hemisphere during the last two millennia. Geografiska Annaler: Physical Geography 2010, 92 A (3), 339-351.
[7] Folland, C. K., Karl, T., Vinnikov, K. YA., IPCC First Assessment Report. Chapter 7. Observed Climate Variations and Change, 1990, 195-238.
[8] Mann, M. E., Bradley, R S., Hughes, M. K., Global-scale temperature patterns and climate forcing over the past six centuries. Nature 1998, 392 (6678), 779-787.
[9] Mann, M. E., Bradley, R S., Hughes, M. K., Northern hemisphere temperatures during the past millennium: Inferences, uncertainties, and limitations. Geophysical Research Letters 1999, 26 (6), 759-762.
[10] IPCC Third Assessment Report, Summary for policymakers 2001.
[11] Masson-Delmotte, V., Schulz, M. Abe-Ouchi, A., Beer, J., Ganopolski, A., González Rouco J. F., Jansen, E., Lambeck, K., Luterbacher, J., Naish,. T., Osborn, T., Otto-Bliesner, B., Quinn, T., Ramesh, R., Rojas, M., Shao X., Timmermann, A., 2013. Information from Paleoclimate Archives. 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 [Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex V., Midgley P. M. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
[12] Zorita, E., González-Rouco, F., Legutke, S., Testing the Mann et al. (1998) approach to paleoclimate reconstructions in the contextof a 1000-year control simulation with the ECHO-G coupled climate model, Journal of Climate 2003, 16, 1378-1390.
[13] von Storch, H., Zorita, E., Jones, J. M., Dimitriev, Y., González-Rouco, F., Tett, S. F. B., Reconstructing Past Climate from Noisy Data", Science 2004, 306, 679-882.
[14] Bürger, G., Fast, I., Cubasch U., Climate reconstruction by regression—32 variations on a theme, Tellus A 2006, 58, 227-235.
[15] Zorita, E., González-Rouco, F., von Storch, H., Comments on “Testing the fidelity of methods used in proxy-base reconstructions of past climate”. Journal of Climate 2007, 20, 3693-3698.
[16] Wahl, E. R., Ammann, C. M., Robustness of the Mann, Bradley, Hughes reconstruction of Northern Hemisphere surface temperatures: Examination of criticisms based on the nature and processing of proxy climate evidence. Climatic Change 2007, 85 (1-2), 33-69.
[17] North, G. R., Apportioning natural and forced components in climate change. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109 (36), 14285-14286.
[18] Lüdecke, H. J., Weiss, C. O., Harmonic Analysis of Worldwide Temperature Proxies for 2000 Years. The Open Atmospheric Science Journal 2017, 11, 44-53.
[19] Abbot, J., Marohasy, J., The application of machine learning for evaluating anthropogenic versus natural climate change, GeoResJ, 2017, 14, 36-46.
[20] Wilson, R., Wiles, G., D’Arrigo, R., Zweck, C., Cycles and shifts: 1,300 years of multi-decadal temperature variability in the Gulf of Alaska. Climate Dynamics 2007, 28, 425-440.
[21] Wan, H., Zhang, X., Zwiers, F., Human influence on Canadian temperatures, Climate Dynamics 2019, 52, 479-494.
[22] Mann, M. E., Steinman, B. A., Miller, S. K., Absence of internal multidecadal and interdecadal oscillations in climate model simulations. Nature Communications 2020, 11, 49.
[23] Ge, Q., Hao, Z., Zheng, J., Shao, X., Temperature changes over the past 2000 years in China and comparison with the Northern Hemisphere. Climate of the Past 2013, 9, 1153-1160.
[24] van der Bilt, W. G. M., D'Andrea, W. J., Werner, J. P., Bakke, J., Early Holocene temperature oscillations exceed amplitude of observed and projected warming in Svalbard lakes. Geophysical Research Letters 2019, 46, 14,732-14,741.
[25] Scafetta, N., Discussion on climate oscillations: CMIP5 general circulation models versus a semi-empirical harmonic model based on astronomical cycles. Earth-Science Reviews 2013, 126, 321-357.
[26] Scafetta, N., On the reliability of computer-based climate models. Italian Journal of Engineering Geology and Environment 2019, 1, 49-70.
[27] de Larminat. P., Earth climate identification vs. anthropic global warming attribution. Annual Reviews in Control 2016, 42, 114-125.
[28] Haigh, J. D., The impact of solar variability on climate. Science 1996, 272, 981-985.
[29] Haigh, J. D., The Sun and the Earth’s Climate. Living Reviews in Solar Physics 2007, 4, 1-63.
[30] Gray, L. J., Beer, J., Geller, M., Geller, M., Haigh, J. D., Lockwood, M., Matthes, K., Cubasch, U., Fleitmann, D., Harrison, G., et al.. Solar influences on climate. Reviews of Geophysics 2010, 48, RG4001.
[31] Kern, A. K., Harzhauser, M., Piller, W. E., Mandic, O., Soliman, A., Strong evidence for the influence of solar cycles on a Late Miocene lake system revealed by biotic and abiotic proxies, Palaeogeography. Palaeoclimatology, Palaeoecology 2012, 329-330, 124-136.
[32] Ramos-Román, M, J., Jiménez-Moreno G., Camuera J., García-Alix, A,, Anderson R. S., Jiménez-Espejo, F. J., Sachse, D., Toney, J., Carrión, J. S., Webster, C. Y., Yanes, C., Millennial-scale cyclical environment and climate variability during the Holocene in the western Mediterranean region deduced from a new multiproxy analysis from the Padul record (Sierra Nevada, Spain). Global and Planetary Change 2018, 168, 35-53.
[33] Zhao, X. H., Feng, X. S., Correlation between solar activity and the local temperature of Antarctica during the past 11,000 years. Journal of Atmospheric and Solar-Terrestrial Physics 2015, 122, 26-33.
[34] Raspopov, O. M., Dergachev V. A., Esper J., Kozyreva, O. V., Frank, D., Ogurtsov, M., Kolström, T., Shao, X., The influence of the de Vries (~200-year) solar cycle on climate variations: Results from the Central Asian Mountains and their global link. Palaeogeography, Palaeoclimatology, Palaeoecology 2008, 259, 6-16.
[35] De Vries, H., Variation in concentration of radiocarbon with time and location on Earth, K. Ned. Akad. Van. Wet.-B, 1958, 61, 94-102.
[36] Suess, H. E., The radiocarbon record in tree rings of the last 8000 years. Radiocarbon 1980, 22, 200-209, 280.
[37] Christiansen, B., Ljungqvist, F. C., Challenges and perspectives for large-scale temperature reconstructions of the past two millennia. Reviews of Geophysics 2017, 55, 40-96.
[38] National Oceanic and Oceanic Administration, National Centres for Environmental Information. Paleoclimatology Data https://www.ncdc.noaa.gov/data-access/paleoclimatology-data
[39] Darji, M. P., Dabhi, V., Harshadkumar B. P. Rainfall forecasting using neural network: a survey. International conference on advances in computer engineering and applications (ICACEA). 2015.
[40] Nayak, D. R., Mahapatra, A., Mishra, P., A survey on rainfall prediction using artificial neural network. International Journal of Computer Applications 2013, 72 (16), 32-40.
[41] Yadu, A. K., Shrivastava, G., Application of Neural Network in Drought Forecasting; An Intense Literature Review, International Journal of Computer Engineering and Technology 2019, 10 (2), 180-195.
[42] Abbot, J, Marohasy J., Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Advances in Atmospheric Science 2012, 29 (4), 717-730.
[43] Abbot, J., Marohasy J., Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research 2014, 138, 166-178.
[44] Specht, D. F., A general regression neural network. IEEE Transactions on Neural Networks and Learning Systems 1991, 2 (6), 568-76.
[45] Moberg, A., Sonechkin, D. M., Holmgren, K., Datsenko, N. M., Karlen, W., Lauritzen, S. E., Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data. Nature 2005, 433 (7026), 613-617.
[46] Christiansen, B., Ljungqvist F. C., The extra-tropical Northern Hemisphere temperature in the last two millennia: reconstructions of low-frequency variability. Climate of the Past 2012, 8, 765-786.
[47] Crowley, T. J., Lowery T., How warm was the Medieval Warm Period? Ambio 2000, 29, 51-54.
[48] Jones, P. D., Briffa, K. R., Barnett, T. P., Tett, S. F. B., High-resolution Palaeoclimatic Records for the last Millennium: Interpretation, Integration and Comparison with General Circulation Model Control-run Temperatures. The Holocene 1998, 8, 455-471.
[49] Esper, J., Cook, E. R., Schweingruber, F. H., Low-Frequency Signals in Long Tree-Ring Chronologies for Reconstructing Past Temperature Variability. Science 2002, 295, 5563.
[50] Schneider, L, Smerdon, J. E., Buntgen, U., Wilson, R. J. S., Myglan, V. S., Kirdyanov, A. V., Esper, J., Revising mid-latitude summer temperatures back to A. D. 600 based on a wood density network. Geophysical Research Letters 2015, 42, 4556-4562.
[51] Humlum, O, Solheim, J-E, Stordahl, K., Identifying natural contributions to late Holocene climate change. Global and Planetary Change 2011, 79, 145-156.
[52] Easterbrook, D. J.,. in Evidence-Based Climate Science (Second Edition), Using Patterns of Recurring Climate Cycles to Predict Future Climate Changes 2016.
[53] Jain A., Kumar A. M., Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing 2007, 7, 585-592.
[54] Zhang G. P., Neural Networks for Time-Series Forecasting. In: Rozenberg G., Bäck T., Kok J. N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg, 2012.
[55] Remus, W., O’Connor M., Neural networks for time series forecasting. In: Armstrong, J. S. (ed) Principles of forecasting: a handbook for researchers and practitioners. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. 2001.
[56] Abbot, J., Marohasy J., Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization. Atmospheric Research 2017, 197, 289-299.
[57] Abbot, J., Marohasy J., Forecasting Monthly Rainfall in the Western Australian Wheat-belt up to 18 months in Advance Using Artificial Neural networks. Lecture Notes in Artificial Intelligence 2016, 9992, 71-87.
[58] Bathiany, S., Scheffer, M., van Nes, E. H., Williamson M. S., T. M. Lenton, T. M.. Abrupt Climate Change in an Oscillating World. Scientific Reports 2018, 8, 5040.
[59] Loehle, C., Singer F., Holocene temperature records show millennial-scale periodicity. Canadian Journal of Earth Sciences 2010, 47, 1327-1336.
[60] Loehle, C., A 2000-year global temperature reconstruction on non-tree ring proxies. Energy and Environment 2007, 18 (7), 1049-1058.
[61] Sicre, M.-A., Jacob, J., Ezat, U., Rousse, S., Kissel, C., Yiou, P., Eiríksson, J., Knudsen, K. L., Jansen E., Turon J. L., Decadal variability of sea surface temperatures off North Iceland over the last 2000 years. Earth and Planetary Science Letters 2008, 268 (1-2), 137-142.
[62] Mangini, A., Verdes, P., Spotl, C., Scholz, D., Vollweiler, N., Kromer, B., Persistent influence of the North Atlantic hydrography on central European winter temperature during the last 9000 years. Geophysical Research Letters 2007, 34 (2), L02704.
[63] Kitagawa, H., Matsumoto, E. Climatic implication of d13C variations in a Japanese cedar (Cryptomeria japonica) during the last two millennia. Geophysical Research Letters 1995, 22 (16), 2155-2158.
[64] Thornalley, D. J. R., Elderfield, H., McCave, I. N., Holocene oscillations in temperature and salinity of the surface subpolar North Atlantic. Nature 2009, 457 (7230), 711-714.
[65] Came, R. E., Oppo, D. W., McManus, J. F., Amplitude and timing of temperature and salinity variability in the subpolar North Atlantic over the past 10 ky. Geology 2007, 35 (4), 315-318.
[66] Oppo, D. W., Rosenthal, Y., Linsley, B. K., 2,000-year long temperature and hydrology reconstructions from the Indo-Pacific warm pool. Nature 2009, 460 (7259), 1113-1116.
[67] Bond, G. C., Showers, W., Elliot, M., Evans, M., Lotti, R., Hajdas, I., Bonani, G., Johnson, S., The north atlantic’s 1-2 kyr climate rhythm: relation to Heinrich events, Dansgaard/Oeschger cycles and the little ice age, in: Mechanisms of Global Climate Change at Millennial Time Scales, American Geophysical Union, 2013.
[68] Braun, H., Christl, M., Rahmstorf, S., Ganopolski, A., Mangini, A., Kubatzki, C., Roth, K., Kromer, B., Possible solar origin of the 1470 year glacial climate cycle demonstrated in a coupled model. Nature 2005, 438, 208-211.
[69] Schulz, M., On the 1470 year pacing of Dansgaard-Oeschger warm events, Paleoceanography, 2002, 17 (2), 1014.
[70] Turney, C S. M., Kershaw, A. P., Clemens, S. C., Branch, N., Moss, P. T., Keith Fifield, L., Millennial and orbital variations of El Nino/Southern Oscillation and high-latitude climate in the last glacial period. Nature, 2004, 428, 306-310.
[71] De Menocal, P., Ortiz, J., Guilderson, T., Sarnthein, M., Coherent high- and low-latitude climate variability during the Holocene warm period, Science 2000, 288, 2198-2202.
[72] Kelsey, A. M., Menk, F. W., Moss P. T., An astronomical correspondence to the 1470 year cycle of abrupt climate change. Climate of the Past Discussions 2015, 11, 4895-4915.
[73] Liu, Y., Cai, Q., Song, H., An, Z., Linderholm, H. W., Amplitudes, rates, periodicities and causes of temperature variations in the past 2485 years and future trends over the central eastern Tibetan Plateau, Chinese Science Bulletin 2011, 56, 2986-2994.
[74] Lüdecke, H. J, Weiss, C. O., Hempelmann, A., Paleoclimate forcing by the solar de Vries / Suess cycle. Climate of the Past Discussions 2015, 11, 279-305.
[75] Büntgen, U., Tegel, W., Nicolussi, K., McCormick, M., Frank, D., Trouet, V., Kaplan, J. O., Herzig, F., Heussner, K.-U., Wanner, H., Luterbacher, J., Esper, J.,. 2500 years of European climate variability and human susceptibility. Science 2011, 331, 578-582.
[76] Lüdecke, J. J., Weiss, C. O., Zhao X., Feng X. Centennial Cycles Observed in Temperature Data from Antarctica to Central Europe. Polarforschung 2015, 85 (2), 179-181.
[77] Cook, E. R., Buckley, B. M., D’Arrigo, R. D., Peterson, M. J., Warm-season temperatures since 1600 BC reconstructed from Tasmanian tree rings and their relationship to large-scale sea surface temperature anomalies. Climate Dynamics 2000, 16, 79-91.
[78] Semenov, V. A., Latif, M., Dommenget, D., Keenlyside, N. S., Strehz, A., Martin, T, Park W., The impact of North Atlantic-Arctic multidecadal variability on Northern Hemisphere surface air temperature. Journal of Climate 2010, 23, 5668-5677.
[79] Dai, A., Fyfe, J. C., Xie, S. P., Dai, X., Decadal modulation of global surface temperature by internal climate variability. Nature Climate Change 2015, 5, 555-559.
[80] Delworth, T. L., Zeng, F., Vecchi, G. A., Yang, X., Zhang L., Zhang, R., The North Atlantic Oscillation as a driver of rapid climate change in the Northern Hemisphere. Nature Geoscience 2016, 9, 509-513.
[81] Ortega, P. et al. Swingedouw, D., Masson-Delmotte, V., Raible, C. C., Casado, M., Yiou, P., A model-tested North Atlantic Oscillation reconstruction for the past millennium. Nature 2015, 523, 71-74.
[82] Miller, D. R., Habicht, M. H., Keisling, B. A., Castañeda I. S., Bradley, R. S.,. A 900-year New England temperature reconstruction from in situ seasonally produced branched glycerol dialkyl glycerol tetraethers (brGDGTs). Climate of the Past 2018, 14, 1653-1667.
[83] Broecker, W. S.,. Paleoclimate: Was the Medieval Warm Period Global? Science 2001, 291 (5508), 1497-1499.
[84] Soon, W., Baliunas, S.,. Proxy climatic and environmental changes of the past 1000 years, Climate Research 2003, 23, 89-110.
[85] Dergachev, V. A., Raspopov. O. M., Reconstruction of the Earth’s Surface Temperature Based on Data of Deep Boreholes, Global Warming in the Last Millennium, and Long Term Solar Cyclicity. Part 2. Experimental Data Analysis. Geomagnetism and Aeronomy 2010, 50 (3), 393-402.
[86] Dergachev, V. A., Raspopov, O. M., Reconstruction of the Earth’s Surface Temperature Based on Data of Deep Boreholes, Global Warming in the Last Millennium, and Long Term Solar Cyclicity. Part 1. Experimental Data. Geomagnetism and Aeronomy 2010, 50 (3), 383-392.
[87] McKay, N. P., Kaufman, D. S., An extended Arctic proxy temperature database for the past 2,000 years. Scientific Data 2014, 1, 140026.
[88] Auger, J. D., Mayewski, P. A., Maasch, K. A., Schuenemann, K. C., Carleton, A. M,, Birkel, S. D., Saros, J. E., 2000 years of North Atlantic-Arctic climate. Quaternary Science Reviews 2019, 216, 1-17.
[89] Pei, Q., Zhang, D. D., Li J., Fei, J., Proxy-based temperature reconstruction in China for the Holocene. Quaternary International 2019, 521, 168-174.
[90] Deng, W., Liu, X., Chen, X., Wei, G., Zeng, T., Xie, L., Zhao, J. X.,. A comparison of the climates of the Medieval Climate Anomaly, Little Ice Age, and Current Warm Period reconstructed using coral records from the northern South China Sea. Journal of Geophysical Research: Oceans 2017, 122, 264-275.
[91] Dahl-Jensen, D., Mosegaard, K., Gundestrup, N., Clow, G D., Johnsen, S J., Hansen A. W., Balling, N., Past Temperatures Directly from the Greenland Ice Sheet, Science 1998, 282, 268-271.
[92] Margaritelli, G., Cisneros M., Cacho I., Capotondi L., Vallefuoco M., Rettori R., Lirer F., Climatic variability over the last 3000 years in the central - western Mediterranean Sea (Menorca Basin) detected by planktonic foraminifera and stable isotope records. Global and Planetary Change 2018, 169, 179-187.
[93] Demezhko, Y. D., Golovanova, I. V., Climatic Changes in the Urals over the Past Millennium—an Analysis of Geothermal and Meteorological Data. Climate of the Past 2007, 3, 237-242.
[94] Lüning, S., Schulte, L., Garcés-Pastor, S., Danladi, I. B., Gałka, M., The Medieval Climate Anomaly in the Mediterranean region. Paleoceanography and Paleoclimatology 2019, 34 (10), 1625-1649.
[95] Lüning, S., Gałka, M., Vahrenholt, F., Warming and cooling: The Medieval Climate Anomaly in Africa and Arabia. Paleoceanography 2017, 32, 1219-1235.
[96] Young, N E., Schweinsberg, A D., Briner, J P., Schaefer, J M., Glacier maxima in Baffin Bay during the Medieval Warm Period coeval with Norse settlement. Science Advances 2015, 1: e1500806.
[97] Huang, J. B., Wang, S. W., Luo, Y., Zhao Z. C., Wen, X-Y,, Debates on the causes of global warming. Advances in Climate Change Research 2012, 3 (1), 38-44.
[98] Usoskin, I. G., Kovaltsov, G. A., Cosmic rays and climate of the Earth: Possible connection. Comptes Rendus Geoscience 2008, 340, 441-450.
[99] Kikby, J., Cosmic rays and climate. Surveys in Geophysics 2007, 28, 333-375.
[100] Dergachev, V. A., Volobuev D. M., Solar Radiation Change and Climatic Effects on Decennial-Centennial Scales Geomagnetism and Aeronomy 2018, 58 (8), 1042-1049.
[101] Georgieva, K., Nagovitsyn, Yu, Kirov. B., Reconstruction of the Long Term Variations of the Total Solar Irradiance from Geomagnetic Data. Geomagnetism and Aeronomy 2015, 55 (8), 1026-1032.
[102] Lüdecke, H. J., Cina, R., Dammschneider, H-J., Lüning, S., Decadal and multidecadal natural variability in European temperature. Journal of Atmospheric and Solar-Terrestrial Physics 2020, 205, 105294.
[103] van Geel B., Ziegler P. A., 2013. IPCC Underestimates the Sun’s role in Climate Change. Energy & Environment 24 (3&4), 432-453.
[104] Zhao, X., Soon W., Velasco Herrera, V. M., Evidence for Solar Modulation on the Millennial-Scale Climate Change of Earth. Universe 2020, 6, 153.
[105] Eddy, J. A., The Maunder minimum. Science 1976, 192, 1189-1202.
[106] Vitale D., Bilancia, M., Role of the natural and anthropogenic radiative forcings on global warming: evidence from cointegration-VECM analysis. Environmental and Ecological Statistics 2013, 20, 413-444.
[107] Joos, F. and Spahni. R., Rates of Change in Natural and Anthropogenic Radiative Forcing over the Past 20,000 Years. Proceedings of the National Academy of Sciences of the United States of America 2008, 105, 1425-1430.
[108] Swanson, K. L., Sugihara, G., Tsonis A. A., May. R., Long-Term Natural Variability and 20th Century Climate Change. Proceedings of the National Academy of Sciences of the United States of America 2009, 106 (38), 16120-16123.
[109] Imbers, J., Lopez, A., Huntingford, C., Allen, M. R., Testing the robustness of the anthropogenic climate change detection statements using different empirical models. Journal of Geophysical Research: Atmospheres 2013, 118, 3192-3199.
[110] Mokhova I. I., Smirnov D. A., Estimating the Contributions of the Atlantic Multidecadal Oscillation and Variations in the Atmospheric Concentration of Greenhouse Gases to Surface Air Temperature Trends from Observations. Doklady Earth Sciences 2018, 480 (1), 602-606.
[111] Gervais F., Anthropogenic CO2 warming challenged by 60-year cycle. Earth-Science Reviews, Earth-Science Reviews 2016, 155, 129-135.
[112] Egorova, T., Rozanov, E., Arsenovic, P., Peter, T. and Schmutz, W., Contributions of Natural and Anthropogenic Forcing Agents to the Early 20th Century Warming, Frontiers in Earth Science, 2018, 6, Article 206.
[113] Florides, G., Christodoulides P., Messaritis V., Global Warming: CO2 vs Sun, Global Warming, Stuart Arthur Harris (Ed.), 2010, ISBN: 978-953-307-149-7, InTech, http://www.intechopen.com/books/global-warming/global-warming-co2-vs-sun
[114] Soon, W., Connolly, R., Connolly, M., Re-evaluating the role of solar variability on Northern Hemisphere temperature trends since the 19th Century. Earth Science Reviews 2015, 150, 409-452.
[115] Nozawa, T., Nagashima, T., Shiogama, H., Crooks, S. A., Detecting natural influence on surface air temperature change in the early twentieth century. Geophysical Research Letters 2005, 32, L20719.
[116] Wen, Q. H., Zhang, X., Xu, Y., Wang B., Detecting human influence on extreme temperatures in China Geophysical Research Letters 2013, 40, 1171-1176.
[117] Tett, S. F. B., Betts, R., Crowley, T. J. Gregory, J., Johns, T. C., Jones, A., Osborn, T. J., Ostrom, E., Roberts, D. L., Woodage, M. J., The impact of natural and anthropogenic forcings on climate and hydrology since 1550. Climate Dynamics 2007, 28, 3-34.
[118] Hegerl, G., Luterbacher, J., González-Rouco, F., Tett, S. F. B., Crowley, T., Xoplaki, E., Influence of human and natural forcing on European seasonal temperatures. Nature Geoscience 2011, 4, 99-103.
[119] Santer, B. D., Painter, J. F., Bonfils, C., Mears, C. A., Solomon, S., Wigley, T. M. L., Gleckler, P. J., Schmidt, G. A,. Doutriaux, C., Gillett, N. P., Taylor, K. E., Thorne, P. W., Wentz, F. J., Human and natural influences on the changing thermal structure of the atmosphere. Proceedings of the National Academy of Sciences of the United States of America 2013, 110 (43), 17235-17240.
[120] Zorita, E., Gonzalez-Rouco, J. F., von Storch, H., Montavez, J. P., Valero. F., Natural and anthropogenic modes of surface temperature variations in the last thousand years Geophysical Research Letters 2005, 32, L08707.
[121] Shiogama, H., Nagashima, T., Yokohata, T., Crooks, S. A., Nozawa, T., Influence of volcanic activity and changes in solar irradiance on surface air temperatures in the early twentieth century. Geophysical Research Letters 2006, 33, L09702.
[122] Zhou, L., Dickinson, R. E., Dai, A., Dirmeyer, P., Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: comparing multi-model simulations with observations. Climate Dynamics 2010, 35, 1289-130.
[123] Li, L., Wang B., Zhou T., Contributions of natural and anthropogenic forcings to the summer cooling over eastern China: An AGCM study Geophysical Research Letters 2007, 34, L18807.
[124] Li, C., Zhao, T., Ying, K., Quantifying the contributions of anthropogenic and natural forcings to climate changes over arid-semiarid areas during 1946-2005. Climatic Change 2017, 144, 505-517.
[125] Barkhordaria, A., von Storch, H., Zorita, E., Loikith, P. C., Mechoso, C. R., Observed warming over northern South America has an anthropogenic origin. Climate Dynamics 2018, 51, 1901-1914.
[126] Allen M. R., Gillett, N. P., Kettleborough, J. A., Hegerl, G., Schnur, R., Stott P. A., Boer, G, Covey, C., Delworth, T. L., Jones, G. S., Mitchell, J. F. B., Barnett, T. P., Quantifying anthropogenic influence on recent near-surface temperature change. Surveys in Geophysics 2006, 27, 491-544.
[127] Chylek, P., Klett, J. D., Dubey, M. K, Hengartner, N., The role of Atlantic Multi-decadal Oscillation in the global mean temperature variability. Climate Dynamics 2016, 47, 3271-3279.
[128] Folland, C. K., Boucher, O., Colman, A., Parker, D. E. Causes of irregularities in trends of global mean surface temperature since the late 19th century. Science Advances 2018, 4 (6), 5297.
[129] McIntyre, S., McKitrick, R., Corrections to the Mann et. al. (1998) Proxy Data Base and Northern Hemispheric Average Temperature Series. Energy & Environment 2003, 14 (6), 751-771.
[130] Mann, M. E., Zhang, Z. H., Hughes, M. K., Bradley, R. S., Miller, S. K., Rutherford, S., Ni, F. B., Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. Proceedings of the National Academy of Sciences of the United States of America 2008, 105, 13252-13257.
[131] Black, R., Environment correspondent, BBC News. Climate 'hockey stick' is revived websitehttp://news.bbc.co.uk/2/hi/science/nature/7592575.stm
[132] Esper, F, D., Zorita, J., Wilson, R, "A noodle, hockey stick, and spaghetti plate: A perspective on high-resolution paleoclimatology", Wiley Interdisciplinary Reviews: Climate Change 2010, 1 (4), 507-516.
Cite This Article
  • APA Style

    John Abbot. (2021). Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures. Earth Sciences, 10(3), 95-117. https://doi.org/10.11648/j.earth.20211003.14

    Copy | Download

    ACS Style

    John Abbot. Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures. Earth Sci. 2021, 10(3), 95-117. doi: 10.11648/j.earth.20211003.14

    Copy | Download

    AMA Style

    John Abbot. Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures. Earth Sci. 2021;10(3):95-117. doi: 10.11648/j.earth.20211003.14

    Copy | Download

  • @article{10.11648/j.earth.20211003.14,
      author = {John Abbot},
      title = {Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures},
      journal = {Earth Sciences},
      volume = {10},
      number = {3},
      pages = {95-117},
      doi = {10.11648/j.earth.20211003.14},
      url = {https://doi.org/10.11648/j.earth.20211003.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20211003.14},
      abstract = {The validity and interpretation of differing representations of proxy temperature profiles from the past 2,000 years for the northern hemisphere remains controversial. One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the “hockey stick” exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation. In both cases, the resultant sine waves from spectral analysis up to 1880 AD can be used to train a an artificial neural network using oscillatory data corresponding to the pre-industrial era, then forecasting temperatures into the 20th century, enabling an estimation of natural and anthropogenic contributions to recent warming. The limitations of highly complex general circulation models that do not to adequately incorporate oscillatory patterns in temperatures may be a compelling reason to promote more extensive use of forecasting with established machine learning techniques such as ANNs.},
     year = {2021}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures
    AU  - John Abbot
    Y1  - 2021/06/16
    PY  - 2021
    N1  - https://doi.org/10.11648/j.earth.20211003.14
    DO  - 10.11648/j.earth.20211003.14
    T2  - Earth Sciences
    JF  - Earth Sciences
    JO  - Earth Sciences
    SP  - 95
    EP  - 117
    PB  - Science Publishing Group
    SN  - 2328-5982
    UR  - https://doi.org/10.11648/j.earth.20211003.14
    AB  - The validity and interpretation of differing representations of proxy temperature profiles from the past 2,000 years for the northern hemisphere remains controversial. One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the “hockey stick” exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation. In both cases, the resultant sine waves from spectral analysis up to 1880 AD can be used to train a an artificial neural network using oscillatory data corresponding to the pre-industrial era, then forecasting temperatures into the 20th century, enabling an estimation of natural and anthropogenic contributions to recent warming. The limitations of highly complex general circulation models that do not to adequately incorporate oscillatory patterns in temperatures may be a compelling reason to promote more extensive use of forecasting with established machine learning techniques such as ANNs.
    VL  - 10
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • Institute of Public Affairs, Melbourne, Australia

  • Sections