The Early Eocene Climate Optimum (EECO, which occurred about 51 to 53 million years ago)1, was the warmest interval of the past 65 million years, with mean annual surface air temperature over ten degrees Celsius warmer than during the pre-industrial period2,3,4. Subsequent global cooling in the middle and late Eocene epoch, especially at high latitudes, eventually led to continental ice sheet development in Antarctica in the early Oligocene epoch (about 33.6 million years ago). However, existing estimates place atmospheric carbon dioxide (CO2) levels during the Eocene at 500–3,000 parts per million5,6,7, and in the absence of tighter constraints carbon–climate interactions over this interval remain uncertain. Here we use recent analytical and methodological developments8,9,10,11 to generate a new high-fidelity record of CO2 concentrations using the boron isotope (δ11B) composition of well preserved planktonic foraminifera from the Tanzania Drilling Project, revising previous estimates6. Although species-level uncertainties make absolute values difficult to constrain, CO2 concentrations during the EECO were around 1,400 parts per million. The relative decline in CO2 concentration through the Eocene is more robustly constrained at about fifty per cent, with a further decline into the Oligocene12. Provided the latitudinal dependency of sea surface temperature change for a given climate forcing in the Eocene was similar to that of the late Quaternary period13, this CO2 decline was sufficient to drive the well documented high- and low-latitude cooling that occurred through the Eocene14. Once the change in global temperature between the pre-industrial period and the Eocene caused by the action of all known slow feedbacks (apart from those associated with the carbon cycle) is removed2,3,4, both the EECO and the late Eocene exhibit an equilibrium climate sensitivity relative to the pre-industrial period of 2.1 to 4.6 degrees Celsius per CO2 doubling (66 per cent confidence), which is similar to the canonical range (1.5 to 4.5 degrees Celsius15), indicating that a large fraction of the warmth of the early Eocene greenhouse was driven by increased CO2 concentrations, and that climate sensitivity was relatively constant throughout this period.
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Financial support was provided by NERC grants (NE/H017356/1 and NE/I005595/1 to G.L.F. and P.N.P.) and by a NERC Post Doctoral Research Fellowship (NE/H016457/1 to K.M.E.). G.N.I. thanks the UK NERC for supporting his PhD studentship (via NE/I005595/1) and R.D.P. acknowledges the Royal Society Wolfson Research Merit Award. R.D.P. and G.N.I. also acknowledge the Advanced ERC Grant T-GRES (340923). We thank the Tanzania Petroleum Development Corporation, the Tanzania Commission for Science and Technology and the Tanzania Drilling Project field team for support. We also acknowledge A. Milton and S. Nederbraght for technical assistance, and we are grateful to M. Huber for discussions on drivers of Eocene warmth.
The authors declare no competing financial interests.
Extended data figures and tables
Extended Data Figure 1 Palaeogeography and δ18O-derived temperature against foraminiferal calcification depth.
a, Approximate palaeoposition of Tanzanian Drilling Project (TDP) sites studied here (map generated from www.odsn.de). b, Reconstructed temperature (TEocene) and relative depth of each foraminifera within each time slice. The pale blue line represents the output of a General Circulation Model simulation run with Eocene boundary conditions89,90, whereas the other coloured lines show the General Circulation Model output offset to intersect with the warmest temperature at depth zero for each time slice. Note that depth assignments are approximate (see Methods). Also shown are the reconstructed temperatures from core-top (Holocene) planktonic foraminiferal δ18O (Supplementary Table 1), where G. ruber is assigned a depth of zero and the rest of the planktonic foraminifera are offset to reproduce the measured temperature profile of the GLOW15 cruise (light blue/green line)51,66. For Holocene temperature reconstructions we used the modern site latitude of 9° S, and δ18Osw (using the Standard Mean Ocean Water (SMOW) standard) of 0‰.
Increasing δ11Bsw, as indicated for each line in units of per mil, results in lower pH for the same δ11B. However, for the same δ11B range, the reconstructed pH range is larger for higher δ11Bsw (see brown-shaded regions).
Comparison of calcite saturation Ωcalc for pre-industrial times (PI; blue) and 55 Myr ago (red) at different latitudes.
Extended Data Figure 4 Compilation of several CO2 records for the Eocene in comparison to this study.
Data are from refs 5, 11, 12, 18 and 19. The contribution of different parameters to the uncertainty on our CO2 reconstructions is colour-coded; sequentially from bottom to top, red is from δ11Bsw, blue is from the Ωcalc uncertainty, green is from the δ11Bc error, black is the 40 p.p.m. uncertainty in the event of disequilibrium with the atmosphere. Other parameters contribute <10% uncertainty to the CO2 calculations and are not shown. Note that the data from Pearson et al.12 (orange circles) are corrected as in Fig. 3, and there are two scenarios included for the Tanzania records of ref. 12 and this study: one with δ11Bc = δ11Bborate (closed blue and orange symbols) and the other applying T. sacculifer (open blue and orange symbols) corrections to the shallowest symbiotic planktonic foraminifera (Methods). The y axis is in log-scale. Error bars are representative of each proxy’s reconstruction uncertainty (typically at 95% confidence). For the δ11Β reconstructions in this study, the errors are based on Monte Carlo propagation of relevant errors (Methods).
Extended Data Figure 5 CO2 as a driver of latitudinal cooling in the Eocene, and ECS analyses of the EECO and late Eocene time slices.
The case for the T. sacculifer calibration applied to shallowest planktonic foraminifera. a, Evolving relationship between SST14 for high (blue) and low (red) latitudes and the CO2 forcing of each of our time slices relative to the EECO. Linear regression fits and coefficients of determination (R2) are also shown, with the 95% confidence interval (shaded bands). b, Apparent latitudinal SST sensitivity for the last 520 kyr (ref. 13). The dashed line is a second-order polynomial through the SST sensitivity data (grey crosses) of ref. 13, and the grey lines show the 95% confidence interval. A red rectangle surrounds the SST sensitivity estimates averaged as a low-latitude mean, and the blue line indicates the high-latitude mean (see text and Methods). c, Reconstructed (lines) and estimated (symbols) SST relative to 53.2 Myr ago. Symbols show each of our time slices, calculated using the respective CO2 reconstructions and the average low- (red) and high- (blue) latitude SST sensitivities of b. Bold lines and shaded uncertainty band (at 95% confidence) show the reconstructed long-term mean SST estimates using the TEX86 proxy at high (blue colour >55°) and low (red colour <30°) latitudes14 relative to the SST ~53 Myr ago. Error bars represent full propagation of errors at 95% confidence14. d, Range in mean surface temperature change for early (green) and late (black) Eocene corrected for changes due to slow feedbacks2,4,21,29. e, Forcing compared to the pre-industrial period, calculated using our CO2 reconstructions for the time slices 53.2 Myr ago (early Eocene) and 36.9 Myr ago (late Eocene) (see Methods). f, Probability density functions of ECS for the early (green) and late (black) Eocene compared to IPCC estimates (dashed lines show the 95% confidence interval (solid pink line)).
The colour scale shows the difference between pCO2 in air and pCO2 in sea water. The model uses Eocene boundary conditions and positive values mean that seawater is a source of CO2 (in parts per million); the star shows the palaeo-location of Tanzania.
Extended Data Figure 7 Comparison of Eocene and modern planktonic foraminiferal δ11B and δ13C with δ18O.
a and b show analyses from the time slices 53.2 Myr ago and 40.3 Myr ago, respectively (as in Fig. 2 and Supplementary Table 1). Cibicidoides species (Cibs) are shaded in blue. c, Core-top (Holocene) offshore Tanzania foraminiferal measurements. Seawater δ11Bborate and δ18O was calculated from temperature, alkalinity and dissolved inorganic carbon measurements (from GLODAP cruises 18 and 23, stations 17742 and 23037, and 53.96° E to 7.04° S and 52.37° E to 6.33° S, respectively), correcting for anthropogenic carbon input. The black line in c represents seawater-derived δ18O and δ11Bborate data (Methods). The symbols for the time slices 53.2 and 40.3 Myr ago are as in Fig. 2. Note the change in scale for the x axis between the Eocene and Holocene panels. Errors in δ11B represent 2 s.d. of long-term precision (Methods).
Extended Data Figure 8 Reconstructed pH using different combinations of published symbiont-bearing and non-symbiotic foraminiferal vital-effect calibrations for the two most complete with depth time slices.
a shows the case where no vital effect corrections were applied for comparison. The vital-effect corrections for deeper asymbiotic planktonic foraminifera are based on either Globigerina bulloides26 (b and d) and Neogloboquadrina pachyderma65 (c and e) calibrations. For shallow symbiont-bearing planktonics we used calibrations specific to modern shallow, symbiont-bearing Trilobatus sacculifer22 (b and c) and Orbulina universa62 (d and e) (as recalculated by ref. 64 and offset by −3‰ to account for analytical differences between negative thermal ionization mass spectrometry and MC-ICPMS instrumentation8,9). For comparison we also show the pH reconstructions for the case where we assume δ11Bc = δ11Bborate.
Extended Data Figure 10 The cGENIE output of seawater pH versus δ13C for the top ~300 m of the ocean.
The scenarios explored are from offshore Tanzania (triangles), Walvis Ridge in the South Atlantic Ocean (crosses), and the global ocean (circles) at three different atmospheric CO2 concentrations (modern, 3× pre-industrial and 16× pre-industrial, PI). An additional scenario showing 3× pre-industrial CO2 but considering the temperature effect on remineralization (indicated as ‘Tremin’) is also shown as blue squares and triangles.
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Anagnostou, E., John, E., Edgar, K. et al. Changing atmospheric CO2 concentration was the primary driver of early Cenozoic climate. Nature 533, 380–384 (2016). https://doi.org/10.1038/nature17423
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