Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
State Dependence from Palaeo Data
References

Lessons on Climate Sensitivity From Past Climate Changes

Profile image of Joel KatzavJoel Katzav

2016

https://doi.org/10.1007/S40641-016-0049-3
visibility

…

description

11 pages

link

1 file

Abstract

Authors: von der Heydt A. S., Dijkstra H. A., van de Wal R. S. W., Caballero R., Crucifix M., Foster G. L., Huber M., Kohler P., Rohling E., Valdes P. J., Ashwin P., Bathiany S., Berends T., van Bree L. G. J., Ditlevsen P., Ghil M., Haywood A., Katzav J., Lohmann G., Lohmann J., Lucarini V., Marzocchi A., Palike H., Ruvalcaba Baroni I., Simon D., Sluijs A., Stap L. B., Tantet A., Viebahn J., Ziegler M. Over the last decade, our understanding of climate sensitivity has improved considerably. The climate system shows variability on many timescales, is subject to non-stationary forcing and it is most likely out of equilibrium with the changes in the radiative forcing. Slow and fast feedbacks complicate the interpretation of geological records as feedback strengths vary over time. In the geological past, the forcing timescales were different than at present, suggesting that the response may have behaved differently. Do these insights constrain the climate sensitivity relevant for the present day? In this paper, we review the progress made in theoretical understanding of climate sensitivity and on the estimation of climate sensitivity from proxy records. Particular focus lies on the background state dependence of feedback processes and on the impact of tipping points on the climate system. We suggest how to further use palaeo data to advance our understanding of the currently ongoing climate change.

Related papers

Rapid climatic warming: palaeo-data analysis and modeling
Jef Vandenberghe
View PDFchevron_right
Reply to Response by Summerhayes et al. to Marks (2025): Contemporary global warming versus climate change in the Holocene
Leszek Marks

Przegląd Geologiczny, 2025

The natural input of solar energy is transformed by various external and internal factors to modulate climate on the Earth and there is a close relationship between insolation changes and long-term hydroclimate trends. Latitudinal insolation in the Holocene depended on the Earth's orbital parameters (Milankoviè cycles). A train of thought of Summerhayes et al. (2025) is charged with the concept of the dominant role in climate change of the antrhopogenic emission of greenhouse gases, mostly CO 2 , and they reject the evidence on considerably higher temperatures than the current warming in the Early and Middle Holocene, Late and Middle Pleistocene interglacials and during the Early Pleistocene. Changes in incoming solar radiation during the Northern Hemisphere summers and a general weakening of summer monsoons in the Middle and Late Holocene resulted in a global disaster for ancient civilizations. Contrary to the hurray optimistic point of view of Summerhayes et al. (2025), much is to be done to understand the modern climate change, based on the multilateral analysis of geological proxies. It is a reasonable way not to promote the simplistic prognoses of climate change. The contribution of CO 2 to the modern warming is still an open question.

View PDFchevron_right
Understanding Earth's Deep Past: Lessons for Our Climate Future Committee on the Importance of Deep-Time Geologic Records for Understanding Climate Change Impacts; National Research Council of the National Academies
Dennis V Kent
View PDFchevron_right
Book Review: Battarbee, R.W. and Binney, H.A., 2008. Natural climate variability and global warming – a Holocene perspective
Witold Szczuciński

Geologos, 16 (1): 69–71, 2010

View PDFchevron_right
Making sense of palaeoclimate sensitivity
Andre Berger

Nature, 2012

View PDFchevron_right
Climate sensitivity constrained by CO2 concentrations over the past 420 million years
Dana Royer, Jeffrey Park

Nature, 2007

A firm understanding of the relationship between atmospheric carbon dioxide concentration and temperature is critical for interpreting past climate change and for predicting future climate change 1 . A recent synthesis 2 suggests that the increase in globalmean surface temperature in response to a doubling of the atmospheric carbon dioxide concentration, termed 'climate sensitivity', is between 1.5 and 6.2 6C (5-95 per cent likelihood range), but some evidence is inconsistent with this range 1-5 . Moreover, most estimates of climate sensitivity are based on records of climate change over the past few decades to thousands of years, when carbon dioxide concentrations and global temperatures were similar to or lower than today 1,6 , so such calculations tend to underestimate the magnitude of large climate-change events 7 and may not be applicable to climate change under warmer conditions in the future. Here we estimate long-term equilibrium climate sensitivity by modelling carbon dioxide concentrations over the past 420 million years and comparing our calculations with a proxy record. Our estimates are broadly consistent with estimates based on short-term climate records, and indicate that a weak radiative forcing by carbon dioxide is highly unlikely on multi-million-year timescales. We conclude that a climate sensitivity greater than 1.5 6C has probably been a robust feature of the Earth's climate system over the past 420 million years, regardless of temporal scaling.

View PDFchevron_right
Climate sensitivity constrained by temperature reconstructions over the past seven centuries
William Hyde, Thomas Crowley

Nature, 2006

The magnitude and impact of future global warming depends on the sensitivity of the climate system to changes in greenhouse gas concentrations. The commonly accepted range for the equilibrium global mean temperature change in response to a doubling of the atmospheric carbon dioxide concentration 1 , termed climate sensitivity, is 1. 5-4.5 K (ref. 2). A number of observational studies 3-10 , however, find a substantial probability of significantly higher sensitivities, yielding upper limits on climate sensitivity of 7.7 K to above 9 K (refs 3-8). Here we demonstrate that such observational estimates of climate sensitivity can be tightened if reconstructions of Northern Hemisphere temperature over the past several centuries are considered. We use large-ensemble energy balance modelling and simulate the temperature response to past solar, volcanic and greenhouse gas forcing to determine which climate sensitivities yield simulations that are in agreement with proxy reconstructions. After accounting for the uncertainty in reconstructions and estimates of past external forcing, we find an independent estimate of climate sensitivity that is very similar to those from instrumental data. If the latter are combined with the result from all proxy reconstructions, then the 5-95 per cent range shrinks to 1.5-6.2 K, thus substantially reducing the probability of very high climate sensitivity.

View PDFchevron_right
The effects of changing solar activity on climate: contributions from palaeoclimatological studies
Bas van Geel

Journal of Space Weather and Space Climate, 2012

Natural climate change currently acts in concert with human-induced changes in the climate system. To disentangle the natural variability in the climate system and the human-induced effects on the global climate, a critical analysis of climate change in the past may offer a better understanding of the processes that drive the global climate system. In this review paper, we present palaeoclimatological evidence for the past influence of solar variability on Earth's climate, highlighting the effects of solar forcing on a range of timescales. On a decadal timescale, instrumental measurements as well as historical records show the effects of the 11-year Schwabe cycle on climate. The variation in total solar irradiance that is associated with a Schwabe cycle is only~1 W m À2 between a solar minimum and a maximum, but winter and spring temperatures on the Northern Hemisphere show a response even to this small-scale variability. There is a large body of evidence from palaeoclimatic reconstructions that shows the influence of solar activity on a centennial to millennial timescale. We highlight a period of low solar activity starting at 2800 years before present when Europe experienced a shift to colder and wetter climate conditions. The spatial pattern of climate change that can be recognized in the palaeoclimatological data is in line with the suggested pattern of climate change as simulated by climate models. Millennial-scale climate oscillations can be recognized in sediment records from the Atlantic Ocean as well as in records of lake-level fluctuations in southeastern France. These oscillations coincide with variation in 14 C production as recognized in the atmospheric 14 C record (which is a proxy-record for solar activity), suggesting that Earth's climate is sensitive to changes in solar activity on a millennial timescale as well.

View PDFchevron_right
Assessing climate forcings of the Earth system for the past millennium
Eva Bauer

Geophysical Research Letters, 2003

View PDFchevron_right
What caused Earths temperature variations during the last 800,000 years? Data-based evidences on radiative forcing and constraints on climate sensitivity
Reto Knutti

European geosciences union general assembly, 2009

The temperature on Earth varied largely in the Pleistocene from cold glacials to interglacials of different warmths. To contribute to an understanding of the underlying causes of these changes we compile various environmental records (and model-based interpretations of some of them) in order to calculate the direct effect of various processes on Earth's radiative budget and, thus, on global annual mean surface temperature over the last 800,000 years. The importance of orbital variations, of the greenhouse gases CO 2 , CH 4 and N 2 O, of the albedo of land ice sheets, annual mean snow cover, sea ice area and vegetation, and of the radiative perturbation of mineral dust in the atmosphere are investigated. Altogether we can explain with these processes a global cooling of 3.9 AE 0.8 K in the equilibrium temperature for the Last Glacial Maximum (LGM) directly from the radiative budget using only the Planck feedback that parameterises the direct effect on the radiative balance, but neglecting other feedbacks such as water vapour, cloud cover, and lapse rate. The unaccounted feedbacks and related uncertainties would, if taken at present day feedback strengths, decrease the global temperature at the LGM by À8.0 AE 1.6 K. Increased Antarctic temperatures during the Marine Isotope Stages 5.5, 7.5, 9.3 and 11.3 are in our conceptual approach difficult to explain. If compared with other studies, such as PMIP2, this gives supporting evidence that the feedbacks themselves are not constant, but depend in their strength on the mean climate state. The best estimate and uncertainty for our reconstructed radiative forcing and LGM cooling support a present day equilibrium climate sensitivity (excluding the ice sheet and vegetation components) between 1.4 and 5.2 K, with a most likely value near 2.4 K, somewhat smaller than other methods but consistent with the consensus range of 2-4.5 K derived from other lines of evidence. Climate sensitivities above 6 K are difficult to reconcile with Last Glacial Maximum reconstructions.

View PDFchevron_right

References (109)

  1. Knutti R, Rugenstein MAA. Phil Trans R Soc A. 2015;373: 20150146. doi:10.1098/rsta.2015.0146.
  2. Schwartz SE. Surv Geophys. 2012;33(3):745. doi:10.1007/s10712-012-9180-4.
  3. IPCC. Climate change 2013: the physical science basis. Contri- bution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Uni- versity Press, Cambridge, United Kingdom and New York, NY, USA).
  4. Vial J, Dufresne JL, Bony S. Clim Dyn. 2013;41(11-12):3339. doi:10.1007/s00382-013-1725-9.
  5. Caldwell PM, Zelinka MD, Taylor KE, Marvel K. J Clim. 2016;29(2):513. doi:10.1175/JCLI-D-15-0352.1.
  6. Zhao M, Golaz JC, Held IM, Ramaswamy V, Lin SJ, Ming Y, Ginoux P, Wyman B, Donner LJ, Paynter D, Guo H. J Clim. 2016;29(2):543. doi:10.1175/JCLI-D-15-0191.1.
  7. Marvel K, Schmidt GA, Miller RL, Nazarenko LS. Nature Clim Change. 2016;6(4):386. doi:10.1038/nclimate2888.
  8. Tan I, Storelvmo T, Zelinka MD. Science. 2016;352(6282):224.
  9. Storelvmo T, Leirvik T, Lohmann U, Phillips PCB, Wild M. Nature Geosci. 2016;9(4):286. doi:10.1038/ngeo2670.
  10. Edwards TL, Crucifix M, Harrison SP. Prog Phys Geogr. 2007;31(5):481.
  11. McCarroll D. Int J Qual Sci. 2015;30(2):154. doi:10.1002/jqs.2775.
  12. Schneider von Deimling T, Held H, Ganopolski A, Rahmstorf S. Clim Dyn. 2006;27:149. doi:10.1007/s00382-006-0126-8.
  13. Annan J, Hargreaves JC. Geophys Res Lett. 2006;33:L06704. doi:10.1029/2005GL025259.
  14. Schmittner A, Urban NM, Shakun JD, Mahowald NM, Clark PU, Bartlein PJ, Mix AC, Rosell-Melé A. Science. 2011;334(6061):1385. doi:10.1126/science.1203513.
  15. Crucifix M. Geophys Res Lett. 2006;33(18):L18701. doi:10.1029/2006GL027137.
  16. Schmidt GA, Annan JD, Bartlein PJ, Cook BI, Guilyardi E, Hargreaves JC, Harrison SP, Kageyama M, LeGrande AN, Konecky B, Lovejoy S, Mann ME, Masson-Delmotte V, Risi C, Thompson D, Timmermann A, Tremblay LB, Yiou P. Clim Past. 2014;10(1):221. doi:10.5194/cp-10-221-2014.
  17. Hargreaves JC, Annan JD, Yoshimori M, Abe-Ouchi A. Geophysical Research Letters. 2012;39(24):L24702. doi:10.1029/2012GL053872.
  18. Owhadi H, Scovel C, Sullivan T. SIAM Rev. 2015;57(4):566. doi:10.1137/130938633.
  19. Brynjarsdóttir J, O'Hagan A. Inverse Problems. 2014;30(11):114007. doi:10.1088/0266-5611/30/11/114007. 20. von der Heydt AS, Köhler P, van de Wal RSW, Dijkstra HA. Geophys Res Lett. 2014;41(18):6484. doi:10.1002/2014GL061121.
  20. PALAEOSENS Project members. Nature. 2012;491(7426):683. doi:10.1038/nature11574.
  21. Charney JG. Carbon dioxide and climate: a scientific assessment (National Academy of Science, Washington, D.C., U.S.A). 1979.
  22. Gregory JM, Ingram WJ, Palmer MA, Jones GS, Stott PA, Thorpe RB, Lowe JA, Johns TC, Williams KD. Geophys Res Lett. 2004;31(3):L03205. doi:10.1029/2003GL018747.
  23. Lunt DJ, Haywood AM, Schmidt GA, Salzmann U, Valdes PJ, Dowsett HJ. Nat Geosci. 2010;3(1):60. doi:10.1038/ngeo706.
  24. Pagani M, Liu Z, LaRiviere J, Ravelo AC. Nat Geosci. 2010;3(1):27. doi:10.1038/ngeo724.
  25. Skinner L. Science. 2012;337(6097):917. doi:10.1126/sci- ence.1224011.
  26. Rohling EJ, Medina-Elizalde M, Shepherd JG, Siddall M, Stan- ford JD. J Clim. 2012;25:1635. doi:10.1175/2011JCLI4078.1.
  27. Senior CA, Mitchell JFB. Geophys Res Lett. 2000;27(17):2685. doi:10.1029/2000GL011373.
  28. Andrews T, Forster PM. Geophys Res Lett. 2008;35:L04802. doi:10.1029/2007GL032273.
  29. Andrews T, Gregory JM, Webb MJ. J Clim. 2015;28:1630. doi:10.1175/JCLI-D-14-00545.1.
  30. Yoshimori M, Hargreaves JC, Annan JD, Yokohata T, Abe- Ouchi A. J Clim. 2011;24:6440. doi:10.1175/2011JCLI3954.1.
  31. Köhler P, de Boer B, von der Heydt AS, Stap LB, van de Wal RSW. Clim Past. 2015;11:1801. doi:10.5194/cp-11-1801-2015.
  32. Ghil M. Climate change: multidecadal and beyond (World Scien- tific Publ. Co., 2015, pp. 31-51), chap. 2, A mathematical theory of climate sensitivity or, How to deal with both anthropogenic forcing and natural variability.
  33. Lucarini V, Blender R, Herbert C, Ragone F, Pas- cale S, Wouters J. Rev Geophys. 2014;52(4):809. doi:10.1002/2013RG000446.
  34. Roe G. Annu Rev Earth Planet Sci. 2009;37:93. doi:10.1146/annurev.earth.061008.134734.
  35. Zaliapin I, Ghil M. Nonlinear Process Geophys. 2010;17(2):113. doi:10.5194/npg-17-113-2010.
  36. Bloch-Johnson J, Pierrehumbert RT, Abbot DS. Geophys Res Lett. 2015;42(12):4973. doi:10.1002/2015GL064240.
  37. Ragone F, Lucarini V, Lunkeit F. Clim Dyn. 2016;46(5- 6):1459. doi:10.1007/s00382-015-2657-3.
  38. Lucarini V, Ragone F, Lunkeit F. J Stat Phys. 2016:1-29. doi:10.1007/s10955-016-1506-z.
  39. Li C, von Storch JS, Marotzke J. Clim Dyn. 2013;40(5):1071. doi:10.1007/s00382-012-1350-z.
  40. Andrews T, Gregory JM, Webb MJ, Taylor KE. Geophys Res Lett. 2012;39(9):L09712. doi:10.1029/2012GL051607.
  41. Anagnostou E, John EH, Edgar KM, Foster GL, Ridgwell A, Inglis GN, Pancost RD, Lunt DJ, Pearson PN. Nature. 2016;380(7603). doi:10.1038/nature17423.
  42. Caballero R, Huber M. Proc Natl Acad Sci. 2013;110:14162. doi:10.1073/pnas.1303365110.
  43. Martinez-Boti MA, Foster GL, Chalk TB, Rohling EJ, Sexton PF, Lunt DJ, Pancost RD, Badger MPS, Schmidt DN. Nature. 2015;518(7537):49. doi:10.1038/nature14145.
  44. Köhler P, Stap LB, von der Heydt AS, de Boer B, van de Wal RSW. Clim Past Discuss. 2016;2016:1. doi:10.5194/cp-2016-23.
  45. Shaffer G, Huber M, Rondanelli R, Pedersen JOP. Geophys Res Lett. 2016;43(12):6538. doi:10.1002/2016GL069243.
  46. Köhler P, Bintanja R, Fischer H, Joos F, Knutti R, Lohmann G, Masson-Delmotte V. Quat Sci Rev. 2010;29(1-2):129. doi:10.1016/j.quascirev.2009.09.026.
  47. Friedrich T, Timmermann A, Tigchelaar M, Timm OE, Ganopolski A. Science Advances in press. 2016.
  48. Hansen J, Sato M, Kharecha P, Beerling D, Berner R, Masson- Delmotte V, Pagani M, Raymo M, Royer DL, Zachos JC. Open Atmos Sci J. 2008;2(217). doi:10.2174/1874282300802010217.
  49. Haywood AM, Hill DJ, Dolan AM, Otto-Bliesner BL, Bragg F, Chan WL, Chandler MA, Contoux C, Dowsett HJ, Jost A, Kamae Y, Lohmann G, Lunt DJ, Abe-Ouchi A, Pickering SJ, Ramstein G, Rosenbloom NA, Salzmann U, Sohl L, Stepanek C, Ueda H, Yan Q, Zhang Z. Clim Past. 2013;9(1):191. doi:10.5194/cp-9-191-2013.
  50. Stap LB, de Boer B, Ziegler M, Bintanja R, Lourens LJ, van de Wal RSW. Earth and Planet Sci Lett. 2016;439(1). doi:10.1016/j.epsl.2016.01.022.
  51. Manabe S, Bryan K, Geophys J. Res Oceans. 1985;90: 11689.
  52. Kutzbach JE, He F, Vavrus SJ, Ruddiman WF. Geophys Res Lett. 2013;40:3721. doi:10.1002/grl.50724.
  53. Hansen J, Sato M, Ruedy R, Nazarenko L, Lacis A, Schmidt G, Russell G, Aleinov I, Bauer M, Bauer S, Bell N, Cairns B, Canuto V, Chandler M, Cheng Y, Del Genio A, Faluvegi G, Fleming E, Friend A, Hall T, Jackman C, Kelley M, Kiang N, Koch D, Lean J, Lerner J, Lo K, Menon S, Miller R, Minnis P, Novakov T, Oinas V, Perlwitz J, Perlwitz J, Rind D, Romanou A, Shindell D, Stone P, Sun S, Tausnev N, Thresher D, Wielicki B, Wong T, Yao M, Zhang S. J Geophys Res. 2005;110(D18):D18104. doi:10.1029/2005JD005776.
  54. Russell GL, Lacis AA, Rind DH, Colose C, Opstbaum RF. Geophys Res Lett. 2013;40:5787. doi:10.1002/2013GL056755.
  55. Colman R, McAvaney B. Geophys Res Lett. 2009;36:L01702. doi:10.1029/2008GL036268.
  56. Jonko AK, Shell KM, Sanderson BM, Danabasoglu G. J Clim. 2013;26:2784. doi:10.1175/JCLI-D-12-00479.1.
  57. Meraner K, Mauritsen T, Voigt A. Geophys Res Lett. 2013;40(22):5944. doi:10.1002/2013GL058118.
  58. Hargreaves JC, Abe-Ouchi A, Annan JD. Clim Past. 2007;3:77. doi:10.5194/cp-3-77-2007.
  59. Yoshimori M, Yokohata T, Abe-Ouchi A. J Climate. 2009;22:3374. doi:10.1175/2009JCLI2801.1.
  60. Sherwood SC, Bony S, Boucher O, Bretherton C, Forster PM, Gregory JM, Stevens B. Bull Am Meteorol Soc. 2015;96:217. doi:10.1175/BAMS-D-13-00167.1.
  61. Ghil M. J Atmos Sci. 1976;33:3.
  62. Ghil M, Childress S. Topics in geophysical fluid dynamics: atmospheric dynamics, dynamo theory, and climate dynamics. Berlin/Heidelberg/New York: Springer-Verlag; 1987.
  63. Ghil M. Nonlinear Process Geophys. 2001;8(4/5):211. doi:10.5194/npg-8-211-2001.
  64. Bódai T, Lucarini V, Lunkeit F, Boschi R. Clim Dyn. 2015;44:3361.
  65. Lenton TM, Held H, Kriegler E, Hall JW, Lucht W, Rahm- storf S, Schellnhuber HJ. Proc Nat Acad Sci. 2008;105(6):1786. doi:10.1073/pnas.0705414105.
  66. Drijfhout S, Bathiany S, Beaulieu C, Brovkin V, Claussen M, Huntingford C, Scheffer M, Sgubin G, Swingedouw D. Proc Natl Acad Sci. 2015;112(43):E5777.
  67. Dijkstra HA, Viebahn JP. Commun Nonlinear Sci Numer Simul. 2015;13(1-3). doi:10.1016/j.cnsns.2014.09.015.
  68. Pierrehumbert RT, Abbot D, Voigt A, Koll D. Annu Rev Earth Planet Sci. 2011;39:417.
  69. Rose BEJ, Ferreira D, Marshall J. J Clim. 2013;26(9):2862. doi:10.1175/JCLI-D-12-00175.1.
  70. Bhattacharya K, Ghil M, Vulis IL. J Atmos Sci. 1982;39:1747. 72. Zachos JC, Dickens GR, Zeebe RE. Nature. 2008;451(7176):279. doi:10.1038/nature06588.
  71. Kallen E, Crafoord C, Ghil M. J Atmos Sci. 1979;36(12):2292. doi:10.1175/1520-0469(1979)036¡2292:FOIACM¿2.0.CO;2.
  72. Ghil M, Le Treut H. J Geoph Res. 1981;86(C6):5262. 75. Maasch KA, Saltzman B. J Geophys Res Atmos. 1990;95(D2):1955. doi:10.1029/JD095iD02p01955.
  73. Saltzman B, Maasch KA. Earth and Environmental Sci- ence Transactions of the Royal Society of Edinburgh. 1990;81(04):315. doi:10.1017/S0263593300020824.
  74. Ghil M. Physica D. 1994;77:130.
  75. Paillard D, Parrenin F. Earth Planet Sc Lett. 2004;227:263. doi:10.1016/j.epsl.2004.08.023.
  76. Ashwin P, Ditlevsen P. Clim Dyn. 2015;45:2683-2695. doi:10.1007/s00382-015-2501-9.
  77. Gildor H, Tziperman E. J Geophys Res. 2001;106(C5):9117. doi:10.1029/1999JC000120.
  78. Saltzman B. Dynamical Palaeoclimatology, (Academic Press, New York, U.S.A.) 2001.
  79. Crucifix M. Philos Trans R Soc A Math Phys Eng Sci. 2012;370(1962):1140. doi:10.1098/rsta.2011.0315.
  80. von der Heydt AS, Ashwin P. 2016. arXiv:1604.03311. In review.
  81. Thuburn J. Quat J Roy Met Soc. 2005;131(605):73. doi:10.1256/qj.04.46.
  82. Huber M. Nature Geosci. 2013;6:15.
  83. Hewitt C, Mitchell J. Clim Dyn. 1997;13:821.
  84. Goldner A, Huber M, Caballero R. Clim Past. 2013;9:173. doi:10.5194/cp-9-173-2013.
  85. Scheffer M, Brovkin V, Cox PM. Geophys Res Lett. 2006;33(10):L10702. doi:10.1029/2005GL025044.
  86. Milankovitch M. Kanon der Erdbestrahlung und seine Anwen- dung auf das Eiszeiten-problem (Roy. Serbian Acad.) 1941.
  87. Hays JD, Imbrie J, Shackleton NJ. Science. 1976;194:1121. doi:10.1126/science.194.4270.1121.
  88. Imbrie J, Boyle EA, Clemens SC, Duffy A, Howard WR, Kukla G, Kutzbach J, Martinson DG, McIntyre A, Mix AC, Molfino B, Morley JJ, Peterson LC, Pisias NG, Prell WL, Raymo ME, Shackleton NJ, Toggweiler JR. Paleoceanogr. 1992;7(6):701. doi:10.1029/92PA02253.
  89. Huybers P, Tziperman E. Paleoceanography. 2008;23(1). doi:10.1029/2007PA001463.
  90. Kawamura K, Parrenin F, Lisiecki L, Uemura R, Vimeux F, Severinghaus JP, Hutterli MA, Nakazawa T, Aoki S, Jouzel J, Raymo ME, Matsumoto K, Nakata H, Motoyama H, Fujita S, Goto-Azuma K, Fujii Y, Watanabe O. Nature. 2007;448(7156):912. doi:10.1038/nature06015.
  91. Paillard D. Quat Sci Rev. 2015;107:11. doi:10.1016/j.quascirev.2014.10.002.
  92. Hansen J, Lacis A, Rind D, Russell G, Stone P, Fung I, Ruedy R, Lerner J. Geophys Monogr. 1984;29. doi:10.1029/GM029p0130. (Amer. Geophys. Union.)
  93. de Boer B, Stocchi P, van de Wal RSW. Geosci Model Dev. 2014;7(5):2141. doi:10.5194/gmd-7-2141-2014.
  94. Schmidt GA. J Quat Sci. 2010;25(1):79. doi:10.1002/jqs.1314.
  95. Haywood AM, Dowsett HJ, Dolan AM. Nature Commun. 2016;7:10646. doi:10.1038/ncomms10646.
  96. Lenton TM, Livina VN, Dakos V, van Nes EH, Scheffer M. Philos Trans R Soc A Math Phys Eng Sci. 2012;370(1962):1185. doi:10.1098/rsta.2011.0304.
  97. Mudelsee M, Bickert T, Lear CH, Lohmann G. Rev Geophys. 2014;52(3):333. doi:10.1002/2013RG000440.
  98. van der Mheen M, Dijkstra HA, Gozolchiani A, den Toom M, Feng Q, Kurths J, Hernandez-Garcia E. Geophys Res Lett. 2013;40(11):2714. doi:10.1002/grl.50515.
  99. Huber M, Caballero R. Clim Past. 2011;7(2):603. doi:10.5194/cp-7-603-2011.
  100. Jaramillo C, Ochoa D, Contreras L, Pagani M, Carvajal-Ortiz H, Pratt LM, Krishnan S, Cardona A, Romero M, Quiroz L, Rodriguez G, Rueda MJ, de la Parra F, Moron S, Green W, Bayona G, Montes C, Quintero O, Ramirez R, Mora G, Schouten S, Bermudez H, Navarrete R, Parra F, Alvaran M, Osorno J, Crowley JL, Valencia V, Vervoort J. Science. 2010;330(6006):957. doi:10.1126/science.1193833.
  101. Zhou J, Poulsen CJ, Rosenbloom N, Shields C, Briegleb B. Clim Past. 2012;8(2):565. doi:10.5194/cp-8-565-2012.
  102. Abbot DS, Huber M, Bousquet G, Walker CC. Geophys Res Lett. 2009;36:L05702. doi:10.1029/2008GL036703.
  103. Masson-Delmotte V, Kageyama M, Braconnot P, Charbit S, Krinner G, Ritz C, Guilyardi E, Jouzel J, Abe-Ouchi A, Cru- cifix M, Gladstone RM, Hewitt CD, Kitoh A, LeGrande AN, Marti O, Merkel U, Ohgaito TMR, Otto-Bliesner B, Peltier WR, Ross I, Valdes PJ, Vettoretti G, Weber SL, Wolk F, YU Y. Clim Dyn. 2006;26(5):513. doi:10.1007/s00382-005-0081-9.
  104. Singarayer JS, Valdes PJ. Quat Sci Rev. 2010;29(1-2):43. doi:10.1016/j.quascirev.2009.10.011.
  105. Tziperman E, Stone L, Cane M, Jarosh H. Science. 1994;264:72.
  106. Jin FF, Neelin J, Ghil M. Physica D: Nonlinear Phenomena. 1996;98(442):2-4. doi:10.1016/0167-2789(96)00111-X.
  107. Fedorov AV, Philander SG. Science. 2000;288(5473):1997. doi:10.1126/science.288.5473.1997.
  108. Collins M. Clim Dyn. 2005;24:89. doi:10.1007/s00382-004-0478-x.
  109. Chekroun MD, Simonnet E, Ghil M. Physica D: Nonlinear Phe- nomena. 2011;240(21):1685. doi:10.1016/j.physd.2011.06.005.

Related papers

Using the past to constrain the future: how the palaeorecord can improve estimates of global warming
Sandy P. Harrison

Progress in Physical Geography, 2007

Climate sensitivity is defi ned as the change in global mean equilibrium temperature after a doubling of atmospheric CO 2 concentration and provides a simple measure of global warming. An early estimate of climate sensitivity, 1.5-4.5°C, has changed little subsequently, including the latest assessment by the Intergovernmental Panel on Climate Change. The persistence of such large uncertainties in this simple measure casts doubt on our understanding of the mechanisms of climate change and our ability to predict the response of the climate system to future perturbations. This has motivated continued attempts to constrain the range with climate data, alone or in conjunction with models. The majority of studies use data from the instrumental period (post-1850), but recent work has made use of information about the large climate changes experienced in the geological past. In this review, we fi rst outline approaches that estimate climate sensitivity using instrumental climate observations and then summarize attempts to use the record of climate change on geological timescales. We examine the limitations of these studies and suggest ways in which the power of the palaeoclimate record could be better used to reduce uncertainties in our predictions of climate sensitivity.

View PDFchevron_right
Predicting future climate change: lessons from palaeoclimatology
Stephan Harrison

Sophisticated General Circulation Models (GCMs) are used to model the climate and are the principal tools used to predict future climate change. The outputs of these models have important policy implications but the uncertainties associated with the predictions and model inadequacy are not always appreciated by user-groups. This paper assesses some of the limitations associated with GCMs, including their limited ability in resolving rapid climate change in the past. The paper suggests that the variability of past climates gives us clues to the potential for rapid climate change in the future and outlines several locations in the Earth's natural systems where such rapid change might be identified and propagated.

View PDFchevron_right
On the state dependency of fast feedback processes in (paleo) climate sensitivity
Anna von der Heydt

Geophysical Research Letters, 2014

VON DER HEYDT ET AL.: STATE-DEPENDENT CLIMATE SENSITIVITY Palaeo data have been frequently used to determine the equilibrium (Charney) climate sensitivity S a , and-if slow feedback processes (e.g. land icealbedo) are adequately taken into account-they indicate a similar range as estimates based on instrumental data and climate model results. Most studies implicitly assume the (fast) feedback processes to be independent of the background climate state, e.g., equally strong during warm and cold periods. Here we assess the dependency of the fast feedback processes on the background climate state using data of the last 800 kyr and a conceptual climate model for interpretation. Applying a new method to account for background state dependency, we find S a = 0.61 ± 0.06 K (W m −2) −1 using the latest LGM temperature reconstruction and significantly lower climate sensitivity during glacial climates. Due to uncertainties in reconstructing the LGM temperature anomaly, S a is estimated in the range S a = 0.55-0.95 K (W m −2) −1 .

View PDFchevron_right
Rapid climate change: lessons from the recent geological past
Meric Srokosz

Global and Planetary Change, 2011

Rapid, or abrupt, climate change is regarded as a change in the climate system to a new state following the crossing of a threshold. It generally occurs at a rate exceeding that of the change in the underlying cause. Episodes of rapid climate change abound in the recent geological past (defined here as the interval between the last glacial maximum, dated to approximately 20,000 years ago, and the present). Rapid climate changes are known to have occurred over time periods equal to or even less than a human lifespan: moreover, their effects on the global system are sufficiently large to have had significant societal impacts. The potential for similar events to occur in the future provides an important impetus for investigating the nature and causes of rapid climate change. This paper provides a brief overview of rapid climate change and an introduction to this special issue, which presents results generated by the palaeoclimatic component of the UK Natural Environment Research Council's rapid climate change programme, called RAPID. The papers in the special issue employ palaeoclimatic proxy data-sets obtained from marine, ice core and terrestrial archives to reconstruct rapid climate change during the last glacial cycle, its subsequent termination and the ensuing Holocene interglacial; some papers also report new attempts to match the palaeoclimate data to hypothesised causes through numerical modelling. The results confirm the importance of freshwater forcing in triggering changes in Atlantic meridional overturning circulation (MOC) and the close links between MOC and rapid climate change. While advancing our understanding of these linkages, the RAPID research has highlighted the need for further research in order to elucidate more specific details of the mechanisms involved.

View PDFchevron_right
Palaeoclimate constraints on the impact of 2 °C anthropogenic warming and beyond
Max Holloway

Nature Geoscience, 2018

Over the past 3.5 million years, there have been several intervals when climate conditions were warmer than during the preindustrial Holocene. Although past intervals of warming were forced differently than future anthropogenic change, such periods can provide insights into potential future climate impacts and ecosystem feedbacks, especially over centennial to millennial timescales that are often not covered by climate model simulations. Our observation based synthesis of the understanding of past intervals with temperatures within the range of projected future warming suggests that there is a low risk of runaway greenhouse gas feedbacks for global warming of no more than 2°C. However, substantial regional environmental impacts can occur. A global average warming of 1-2°C with strong polar amplification has, in the past, been accompanied by significant shifts in climate zones and the spatial distribution of land and ocean ecosystems. Sustained warming at this level has also led to substantial reductions of the Greenland and Antarctic ice sheets, with sealevel increases of at least several meters on millennial time scales. Comparison of paleo observations with climate model results suggests that, due to the lack of certain feedback processes, model based climate projections may underestimate long-term warming in response to future radiative forcing by as much as a factor of two, and thus may also underestimate centennial to millennial-scale sea level rise.

View PDFchevron_right

Cited by

What can Palaeoclimate Modelling do for you?
Bette Otto-bliesner

Earth Systems and Environment

In modern environmental and climate science it is necessary to assimilate observational datasets collected over decades with outputs from numerical models, to enable a full understanding of natural systems and their sensitivities. During the twentieth and twenty-first centuries, numerical modelling became central to many areas of science from the Bohr model of the atom to the Lorenz model of the atmosphere. In modern science, a great deal of time and effort is devoted to developing, evaluating, comparing and modifying numerical models that help us synthesise our understanding of complex natural systems.

View PDFchevron_right
Extreme Sensitivity and Climate Tipping Points
Anna von der Heydt

Journal of Statistical Physics

A climate state close to a tipping point will have a degenerate linear response to perturbations, which can be associated with extreme values of the equilibrium climate sensitivity (ECS). In this paper we contrast linearized (‘instantaneous’) with fully nonlinear geometric (‘two-point’) notions of ECS, in both presence and absence of tipping points. For a stochastic energy balance model of the global mean surface temperature with two stable regimes, we confirm that tipping events cause the appearance of extremes in both notions of ECS. Moreover, multiple regimes with different mean sensitivities are visible in the two-point ECS. We confirm some of our findings in a physics-based multi-box model of the climate system.

View PDFchevron_right
Beyond equilibrium climate sensitivity
Reto Knutti

Nature Geoscience

Equilibrium climate sensitivity characterizes the Earth' long-term global temperature response to an increased atmospheric carbon dioxide concentration. It has reached almost iconic status as the single number that describes how severe climate change will be. The consensus 'likely' range for climate sensitivity of 1.5-4.5 o C today is the same as given by Jule Charney in 1979, but is now based on quantitative evidence from across the climate system, and through climate history. The quest to constrain climate sensitivity has revealed important insights into the timescales of the climate system response, natural variability, limitations in observations and climate models, but also concerns about the simple concepts underlying climate sensitivity and radiative forcing, which open avenues to better understand and constrain the climate response to forcing. Estimates of the transient climate response are better constrained by observed warming and are more relevant for predicting warming over the next decades. Newer metrics relating global warming directly to the total emitted CO2 show that in order to keep warming to within 2 o C, future CO2 emissions have to remain strongly limited, irrespective of climate sensitivity being at the high or low end. If we increase the amount of CO2 in the Earth's atmosphere and wait for climate to respond, how much warmer would surface temperatures eventually get? What seems like a simple but important question to ask given current and projected human-induced CO2 emissions, is an issue that scientists have struggled with since the first rough estimates were made more than a century ago 1,2. To answer that question, starting in the 1960s, scientists have used energy balance arguments combined with observed changes in the global energy budget, evaluated comprehensive climate models against observations, and analyzed the relationship between external forcing and climate change over different climate states in the past (see Methods for a list of early publications). The idea of using those different lines of evidence has not changed, but progress in simulating climate, a longer and more accurate observed record of past warming, and better constrained paleoclimate reconstructions now offer more possibilities to evaluate and constrain models. However, recent research has pointed out previously unknown limitations in some of the concepts and assumptions underlying a single constant climate sensitivity 3. While publications have appeared using various methods, arguably the most important conceptual recent insights is that feedbacks change with equilibration time, an insight that is based on studies in comprehensive climate models. Other recent insights show that the treatment of observations is important 4. Knowing, to first order, how the global climate will warm in response to increased CO2 is critical: for unabated emissions, it is the difference between a hot and an extremely hot future. The value of halving the uncertainty in that projection may be in the trillions of dollars 5. Here we update an earlier review 6 and report recent progress in this area. We discuss limitations, highlight the implications for climate science and policy, discuss new metrics that relate the climate response directly to emissions, and propose avenues for future research.

View PDFchevron_right
Palaeoclimate constraints on the impact of 2 °C anthropogenic warming and beyond
Max Holloway

Nature Geoscience, 2018

Over the past 3.5 million years, there have been several intervals when climate conditions were warmer than during the preindustrial Holocene. Although past intervals of warming were forced differently than future anthropogenic change, such periods can provide insights into potential future climate impacts and ecosystem feedbacks, especially over centennial to millennial timescales that are often not covered by climate model simulations. Our observation based synthesis of the understanding of past intervals with temperatures within the range of projected future warming suggests that there is a low risk of runaway greenhouse gas feedbacks for global warming of no more than 2°C. However, substantial regional environmental impacts can occur. A global average warming of 1-2°C with strong polar amplification has, in the past, been accompanied by significant shifts in climate zones and the spatial distribution of land and ocean ecosystems. Sustained warming at this level has also led to substantial reductions of the Greenland and Antarctic ice sheets, with sealevel increases of at least several meters on millennial time scales. Comparison of paleo observations with climate model results suggests that, due to the lack of certain feedback processes, model based climate projections may underestimate long-term warming in response to future radiative forcing by as much as a factor of two, and thus may also underestimate centennial to millennial-scale sea level rise.

View PDFchevron_right
Weak dependence of future global mean warming on the background climate state
Reto Knutti

Climate Dynamics, 2019

It is debated whether the global mean transient temperature response to forcing is largely independent of biases in the simulated base state climate. To test this, we run the Community Earth System Model (CESM1) into new quasi-equilibria by altering the solar constant by ± 25 Wm −2 and initialize idealized CO 2 climate change experiments from these climate states. The simulations branched off from the warm and control states, with increased and unaltered solar constant, respectively, show similar global mean feedback parameters, effective CO 2 radiative forcings (ERF) and ocean heat uptake efficiencies and therefore simulate indiscernibly different amounts of global warming. The experiments starting from the cold climate state behave differently: The CO 2 ERF is lower mostly because of rapid adjustments in the clouds, and the climate sensitivity is enhanced, mainly due to a large surface albedo feedback caused by the increased sea ice and snow coverage that extends to lower latitudes. The global heat uptake efficiency is reduced in the cold state. While taking up more heat in the Southern Ocean than the experiments from the warmer states much less heat is taken up by the North Atlantic. The less stabilizing net climate feedback parameter and the decreased ocean heat uptake efficiency dominate over the reduced CO 2 ERF, and the simulations initialized from the cold state show ~ 10% more global warming compared to those from the control state. This enhanced warming mainly occurs at higher-latitudes over sea ice and snow-covered areas and the North Atlantic. While the local climate response may depend strongly on the base state, the future global mean temperature increase simulated by CESM1 is remarkably independent on the initial climate state.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved