Evidence that global evapotranspiration makes a substantial contribution to the global atmospheric temperature slowdown

Original Paper


The difference between the time series trend for temperature expected from the increasing level of atmospheric CO2 and that for the (more slowly rising) observed temperature has been termed the global surface temperature slowdown. In this paper, we characterise the single time series made from the subtraction of these two time series as the ‘global surface temperature gap’. We also develop an analogous atmospheric CO2 gap series from the difference between the level of CO2 and first-difference CO2 (that is, the change in CO2 from one period to the next). This paper provides three further pieces of evidence concerning the global surface temperature slowdown. First, we find that the present size of both the global surface temperature gap and the CO2 gap is unprecedented over a period starting at least as far back as the 1860s. Second, ARDL and Granger causality analyses involving the global surface temperature gap against the major candidate physical drivers of the ocean heat sink and biosphere evapotranspiration are conducted. In each case where ocean heat data was available, it was significant in the models: however, evapotranspiration, or its argued surrogate precipitation, also remained significant in the models alongside ocean heat. In terms of relative scale, the standardised regression coefficient for evapotranspiration was repeatedly of the same order of magnitude as—typically as much as half that for—ocean heat. The foregoing is evidence that, alongside the ocean heat sink, evapotranspiration is also likely to be making a substantial contribution to the global atmospheric temperature outcome. Third, there is evidence that both the ocean heat sink and the evapotranspiration process might be able to continue into the future to keep the temperature lower than the level-of-CO2 models would suggest. It is shown that this means there can be benefit in using the first-difference CO2 to temperature relationship shown in Leggett and Ball (Atmos Chem Phys 15(20):11571–11592, 2015) to forecast future global surface temperature.



The authors would like to acknowledge with appreciation the support and advice of J. Gordon and C. Dawson and the helpful and constructive comments of an anonymous referee of the paper.


  1. Adams HD, Williams AP, Xu C, Rauscher SA, Jiang X, McDowell NG (2013) Empirical and process-based approaches to climate-induced forest mortality models. Front Plant Sci 4:438Google Scholar
  2. Ahmad N, Du L (2017) Effects of energy production and CO2 emissions on economic growth in Iran: ARDL approach. Energy 123:521–537. CrossRefGoogle Scholar
  3. Allen MP (1997) Understanding regression analysis. Plenum, New YorkGoogle Scholar
  4. Amblard P-O, Michel OJ (2013) The relation between Granger causality and directed information theory: a review. Entropy 15:113–143CrossRefGoogle Scholar
  5. Attanasio A, Pasini A, Triacca U (2012) A contribution to attribution of recent global warming by out-of-sample Granger causality analysis. Atmos Sci Lett 13(1):67–72. CrossRefGoogle Scholar
  6. Attanasio A, Pasini A, Triacca U (2013) Granger causality analyses for climatic attribution. Atmos Clim Sci 3:515–522Google Scholar
  7. Ban-Weiss GA, Bala G, Cao L, Pongratz J, Caldeira K (2011) Climate forcing and response to idealized changes in surface latent and sensible heat. Environ Res Lett 6:1–8CrossRefGoogle Scholar
  8. Bounoua L, Collatz GJ, Los SO, Sellers PJ, Dazlich DA, Tucker CJ, Randall DA (2000) Sensitivity of climate to changes in NDVI. J Clim 13(13):2277–2292.<2277:SOCTCI>2.0.CO;2 CrossRefGoogle Scholar
  9. Bounoua L, Hall FG, Sellers PJ, Kumar A, Collatz GJ, Tucker CJ, Imhoff ML (2010) Quantifying the negative feedback of vegetation to greenhouse warming: A modeling approach. Geophys Res Lett 37(23):n/a-n/aGoogle Scholar
  10. Chen X, Tung K-K (2014) Varying planetary heat sink led to global-warming slowdown and acceleration. Science 345(6199):897–903. CrossRefGoogle Scholar
  11. Cook ER, Peters K (1981) The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull 41:45–53Google Scholar
  12. Dai A (2016) Historical and future changes in streamflow and continental runoff: a review. Terrestrial water cycle and climate change: natural and human-induced impacts. Geophys Monogr 221:17–37CrossRefGoogle Scholar
  13. Enting IG (1987) A modelling spectrum for carbon cycle studies. Math Comput Simul 29(2):75–85. CrossRefGoogle Scholar
  14. Enting IG (2010) Inverse problems and complexity in earth system science. In: Dewar RL, Detering F (eds) Complex physical, biophysical and econophysical systems. Singapore, World ScientificGoogle Scholar
  15. Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W, Cox P, Driouech F, Emori S, Eyring V, Forest C, Gleckler P, Guilyardi E, Jakob C, Kattsov V, Reason C, Rummukainen M (2013) Evaluation of climate models. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  16. Fyfe JC, Gillett NP, Zwiers FW (2013) Overestimated global warming over the past 20 years. Nat Clim Chang 3(9):767–769. CrossRefGoogle Scholar
  17. Good SP, Noone D, Bowen G (2015) Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349(6244):175-177Google Scholar
  18. Granger CWJ (1969) Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37(3):424Google Scholar
  19. Granger CWJ (1980) Testing for causality: a personal viewpoint. J Econ Dyn Control 2:329–352. CrossRefGoogle Scholar
  20. Grassi S, Hillebrand E, Ventosa-Santaulària D (2013) The statistical relation of sea-level and temperature revisited. Dyn Atmos Oceans 64:1–9. CrossRefGoogle Scholar
  21. Greene WH (2012) Econometric analysis, 7th edn. Prentice Hall, BostonGoogle Scholar
  22. Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int J Climatol 34:623–642CrossRefGoogle Scholar
  23. IHS EViews: EViews 9.5, IHS Global Inc., Irvine, California, 2017. available at: (last accessed 20 July 2017)
  24. IPCC (2007) Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (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, CambridgeGoogle Scholar
  25. IPCC (2013) Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  26. Ishii M, Kimoto M (2009) Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J Oceanogr 65:287–299 Ocean heat data used available at (last accessed 12 October 2017)CrossRefGoogle Scholar
  27. Janjua PZ, Samad G, Khan N (2014) Climate change and wheat production in Pakistan: an autoregressive distributed lag approach. NJAS-Wagening J Life Sci 68:13–19. CrossRefGoogle Scholar
  28. Jasechko S, Sharp ZD, Gibson JJ, Birks SJ, Yi Y, Fawcett PJ (2013) Terrestrial water fluxes dominated by transpiration. Nature 496(7445):347-350Google Scholar
  29. Karplus WJ (1977) The spectrum of mathematical modelling and systems simulation. Math Comput Simul 19(1):3–10. CrossRefGoogle Scholar
  30. Karplus WJ (1992) The heavens are falling: the scientific prediction of catastrophes in our time. Plenum, New York. CrossRefGoogle Scholar
  31. Keeling RF, Piper SC, Bollenbacher AF, Walker SJ (2009) Carbon Dioxide Research Group, Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0444, available at: (last accessed 14 September 2017)
  32. Kleidon A, Fraedrich K, Heimann M (2000) A green planet versus a desert world: estimating the maximum effect of vegetation on the land surface climate. Clim Chang 44(4):471–493. CrossRefGoogle Scholar
  33. Leggett LMW, Ball DA (2015) Granger causality from changes in level of atmospheric CO2 to global surface temperature and the El Niño–Southern Oscillation, and a candidate mechanism in global photosynthesis. Atmos Chem Phys 15(20):11571–11592. CrossRefGoogle Scholar
  34. Levitus S et al (2012) World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys Res Lett 39:L10603 Ocean heat data used available at, last accessed 12 October 2017CrossRefGoogle Scholar
  35. Mazzocchi F, Pasini A (2017) Climate model pluralism beyond dynamical ensembles. WIREs Clim Change 8(6):e477. CrossRefGoogle Scholar
  36. McCabe MF, Ershadi A, Jiménez C, Miralles DG, Michel D, Wood EF (2016) The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data. Geosci Model Dev 9(1):283–305. CrossRefGoogle Scholar
  37. Meehl GA, Arblaster JM, Fasullo JT, Hu A, Trenberth KE (2011) Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat Clim Chang 1(7):360–364. CrossRefGoogle Scholar
  38. Montgomery DC, Jennings CL, Kulahci M (2008) Introduction to time series analysis and forecasting. Wiley, New YorkGoogle Scholar
  39. Moore JC, Grinsted A, Zwinger T, Jevrejeva S (2013) Semi-empirical and process-based global sea level projections. Rev Geophys 51(3):484–522. CrossRefGoogle Scholar
  40. Morice CP, Kennedy JJ, Rayner NA, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J Geophys Res: Atmos 117(D8):n/a-n/aGoogle Scholar
  41. Mudelsee M (2010) Climate time series analysis. Springer, Dordrecht. CrossRefGoogle Scholar
  42. Newman M (2013) An empirical benchmark for decadal forecasts of global surface temperature anomalies. J Clim 26(14):5260–5269. CrossRefGoogle Scholar
  43. Notaro M, Liu Z (2008) Statistical and dynamical assessment of vegetation feedbacks on climate over the boreal forest. Clim Dyn 31(6):691–712. CrossRefGoogle Scholar
  44. Pasini A, Mazzocchi F (2015) A multi-approach strategy in climate attribution studies: is it possible to apply a robustness framework? Environ Sci Pol 50:191–199. CrossRefGoogle Scholar
  45. Pasini A, Lore M, Ameli F (2006) Neural network modelling for the analysis of forcings/temperature relationships at different scales in the climate system. Ecol Model 191(1):58–67. CrossRefGoogle Scholar
  46. Pasini A, Triacca U, Attanasio A (2012) Evidence of recent causal decoupling between solar radiation and global temperature. Environ Res Lett 7:034020 (6pp)CrossRefGoogle Scholar
  47. Pasini A, Triacca U, Attanasio A (2016) Evidence for the role of the Atlantic multidecadal oscillation and the ocean heat uptake in hiatus prediction. Theor Appl Clim. (published online).
  48. Pasini A, Racca P, Amendola S, Cartocci G, Cassardo C (2017) Attribution of recent temperature behaviour reassessed by a neural-network method. Sci Rep 7(1):17681. CrossRefGoogle Scholar
  49. Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16(3):289–326. CrossRefGoogle Scholar
  50. Rahman MR, Lateh H (2015) Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theor Appl Climatol 128(1-2):27–41. CrossRefGoogle Scholar
  51. Rubino M, Etheridge DM, Trudinger CM, Allison CE, Battle MO, Langenfelds RL, Steele LP, Curran M, Bender M, White JWC, Jenk TM, Blunier T, Francey RJ (2013) A revised 1000 year atmospheric d13C-CO2 record from Law Dome and South Pole, Antarctica. J Geophys Res Atmos 118(15):8482–8499. CrossRefGoogle Scholar
  52. Sato M, Hansen JE, McCormick MP, Pollack JB (1993) Stratospheric aerosol optical depths, 1850–1990. J Geophys Res 98:22987–22994 Data used available at:, last accessed 10 August 2014CrossRefGoogle Scholar
  53. Schonwiese C-D, Walter A et al (2010) Statistical assessments of anthropogenic and natural global climate forcing. An update. Meteorol Z 19(1):3–10. CrossRefGoogle Scholar
  54. Shen M, Piao S, Jeong S-J, Zhou L, Zeng Z, Ciais P, Chen D, Huang M, Jin C-S, Li LZX, Li Y, Myneni RB, Yang K, Zhang G, Zhang Y, Yao T (2015) Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proc Nat Acad Sci 112(30):9299-9304Google Scholar
  55. Stern DI, Kaufmann RK (2014) Anthropogenic and natural causes of climate change. Clim Chang 122(1–2):257–269. CrossRefGoogle Scholar
  56. Swanson KL, Tsonis AA (2009) Has the climate recently shifted? Geophys Res Lett 36:L06711–L06714CrossRefGoogle Scholar
  57. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498 CMIP5 RCP 8.5 data used available at:, last accessed 3 October 2017CrossRefGoogle Scholar
  58. Toda HY, Yamamoto T (1995) Statistical inference in vector autoregressions with possibly integrated processes. J Econ 66(1–2):225–250. CrossRefGoogle Scholar
  59. Trenberth KE, Fasullo JT (2010) Tracking Earth's Energy. Science 328(5976):316-317Google Scholar
  60. Trenberth KE, Fasullo JT (2013) An apparent hiatus in global warming? Earth’s Futur 1(1):19–13. CrossRefGoogle Scholar
  61. Triacca U, Pasini A, Attanasio A, Giovannelli A, Lippi M (2014) Clarifying the roles of greenhouse gases and ENSO in recent global warming through their prediction performance. J Clim 27(20):7903–7910. CrossRefGoogle Scholar
  62. Verdes PF (2007) Global warming is driven by anthropogenic emissions: a time series analysis approach. Phys Rev Lett 90:048501CrossRefGoogle Scholar
  63. Walter MT, Wilks DS, Parlange J-Y, Schneider RL (2004) Increasing evapotranspiration from the conterminous United States. J Hydrometeorol 5(3):405–408.<0405:IEFTCU>2.0.CO;2 CrossRefGoogle Scholar
  64. Yan X-H, Boyer T, Trenberth K, Karl TR, Xie S-P, Nieves V, Tung K-K, Roemmich D (2016) The global warming hiatus: slowdown or redistribution? Earth’s Futur 4(11):472–482. CrossRefGoogle Scholar
  65. Zeng Z, Piao S, Li LZX, Zhou L, Ciais P, Wang T, Li Y, Xu L, Wood EF, Friedlingstein P, Mao J, Estes LD, Myneni RB, Peng S, Shi X, Seneviratne SI, Wang Y (2017) Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat Clim Chang 7(6):432–436. CrossRefGoogle Scholar
  66. Zhang K, Kimball JS, Nemani RR, Running SW, Hong Y, Gourley JJ et al (2015) Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Sci Rep 5:15956 (9pp)CrossRefGoogle Scholar
  67. Zhu J, Zeng X (2015) Comprehensive study on the influence of evapotranspiration and albedo on surface temperature related to changes in the leaf area index. Adv Atmos Sci 32(7):935–942. CrossRefGoogle Scholar
  68. Zhu J, Zeng X (2016) Influences of the interannual variability of vegetation LAI on surface temperature. Atmos Oceanic Sci Lett 9(4):292–297. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Global Risk Policy Group Pty LtdTownsvilleAustralia

Personalised recommendations