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Early onset of industrial-era warming across the oceans and continents

A Corrigendum to this article was published on 11 May 2017

Abstract

The evolution of industrial-era warming across the continents and oceans provides a context for future climate change and is important for determining climate sensitivity and the processes that control regional warming. Here we use post-ad 1500 palaeoclimate records to show that sustained industrial-era warming of the tropical oceans first developed during the mid-nineteenth century and was nearly synchronous with Northern Hemisphere continental warming. The early onset of sustained, significant warming in palaeoclimate records and model simulations suggests that greenhouse forcing of industrial-era warming commenced as early as the mid-nineteenth century and included an enhanced equatorial ocean response mechanism. The development of Southern Hemisphere warming is delayed in reconstructions, but this apparent delay is not reproduced in climate simulations. Our findings imply that instrumental records are too short to comprehensively assess anthropogenic climate change and that, in some regions, about 180 years of industrial-era warming has already caused surface temperatures to emerge above pre-industrial values, even when taking natural variability into account.

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Figure 1: Terrestrial and marine palaeoclimate reconstructions.
Figure 2: Onset and magnitude of industrial-era warming in regional temperature reconstructions.
Figure 3: Data–model comparison of the onset of industrial-era warming.
Figure 4: Latitudinal development of site-level temperature trends.

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Acknowledgements

We thank the many scientists who made their published palaeoclimate datasets available via public data repositories. This work developed out of the PAGES (Past Global Changes) Ocean2k working group; we are grateful to K. Anchukaitis, H. Wu, C. Giry, D. Oppo and V. Ersek for their contributions to the Ocean2k syntheses, to the more than 75 volunteers who constructed the Ocean2k phase 1 metadatabase14,15, and to K. Anchukaitis and V. Trouet for discussions. We thank P. Petrelli, F. Klein and A. Schurer for assistance in obtaining model datasets, and K. McGregor for editorial assistance. We acknowledge support from PAGES funded by the US and Swiss National Science Foundations (NSF) and NOAA, and thank T. Kiefer, M.-F. Loutre and the PAGES 2k Network Coordinators for organizational support. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. The US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support for CMIP and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. N.J.A. is supported by an Australian Research Council (ARC) QEII fellowship awarded under DP110101161 and this work contributes to ARC Discovery Project DP140102059 (N.J.A., M.A.J.C.) and the ARC Centre of Excellence for Climate System Science (N.J.A., S.J.P., J.G.). H.V.M. is supported by ARC Future Fellowship FT140100286 and acknowledges funding from ARC Discovery Project DP1092945 (H.M.V., S.J.P.). We acknowledge fellowship support from a CSIC-Ramón y Cajal post-doctoral programme RYC-2013-14073 (B.M.), a Clare Hall College Cambridge Shackleton Fellowship (B.M.), and an ARC DECRA fellowship DE130100668 (J.G.). We acknowledge research support from US NSF grant OCE1536249 (M.N.E.), the ARC Special Research Initiative for the Antarctic Gateway Partnership (Project ID SR140300001; S.J.P.), Red CONSOLIDER GRACCIE CTM2014-59111-REDC (B.M.), Swiss NSF grant PZ00P2_154802 (R.N.), the Danish Council for Independent Research, Natural Science OCEANHEAT project 12-126709/FNU (M.-S.S.), the National Natural Science Foundation of China (41273083; K.S.) and Shanghai Fund (2013SH012; K.S.). This is University of Maryland Center for Environmental Science contribution 5206.

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N.J.A. designed the study with input from H.V.M., J.E.T., M.N.E., N.P.M. and D.S.K. The palaeoclimate data and model analysis was led by N.J.A. with assistance provided by R.N., K.T., B.M., H.G., S.J.P. and E.J.S.; R.N. and J.G. produced the terrestrial Australasia 2k reconstruction; N.J.A., J.E.T., M.N.E., K.H.K., C.P.S. and J.Z. contributed expertise on the high-resolution marine database and reconstructions; H.V.M., M.N.E., B.M., K.T., G.L., J.A.A., P.G.M., M.-S.S., M.-A.S., K.S. and H.L.F. contributed expertise on the moderate-resolution marine database; H.G., S.J.P. and N.J.A. contributed expertise on climate model output, and N.P.M., D.S.K., R.N., E.J.S., J.G., M.A.J.C. and L.v.G. contributed expertise on the terrestrial databases and reconstructions. All authors contributed to discussions that shaped the study and the manuscript. N.J.A. led the writing with contributions from all authors.

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Correspondence to Nerilie J. Abram.

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The authors declare no competing financial interests.

Additional information

The data and code needed to reproduce the results are available at the World Data Service for Paleoclimatology (http://www.ncdc.noaa.gov/paleo/study/20083).

Reviewer Information Nature thanks Z. Liu, T. Watanabe and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Regional distributions of onset estimates for industrial-era warming and post-ad 1800 warming trends.

a, Median (black vertical bars; as in Fig. 2a) onset of sustained, significant warming in regional reconstructions, shown with uncertainty ranges in onset estimates based on available reconstruction ensembles (colours; Methods). Distributions of onset estimates related to reconstruction uncertainty denote median (vertical bars), 25%–75% range (boxes) and 5%–95% range (horizontal lines). Distributions for the onset of warming are also calculated for Northern Hemisphere and Southern Hemisphere reconstruction ensembles3,5, with additional details in Supplementary Fig. 1. The size of reconstruction ensembles is given (n). b, As in a, but for uncertainty ranges in onset estimates based on the SiZer change-point detection method. Distributions (grey) denote the uncertainty in detecting a known warming onset based on synthetic tests, for which 1,000 noise series with lag-1 autocorrelation and trend-to-variability characteristics matching the regional reconstructions are applied to an underlying trend (Methods, Extended Data Fig. 3). Crosses are used for regions for which low trend-to-variability characteristics (Antarctica) or high autocorrelation (Asia) limit the detection of a sustained, significant warming trend in the synthetic tests. c, Distribution of century-scale (100-yr) linear warming trends since ad 1800 (coloured bars), shown with reference to the 5%–95% range of century-scale trends beginning during the period ad 1500–1799 (grey shading). Values denote the percentage of century-scale trends since ad 1800 that lie below the 5% level (left) or above the 95% level (right) of trends beginning during the period ad 1500–1799.

Extended Data Figure 2 SiZer trend maps used to assess the onset of sustained, significant warming.

a, SiZer trend maps23,24 for each of the regional land and ocean temperature reconstructions. The timing of warming (red) and cooling (blue) trends are calculated at different levels of smoothing. Significant (P < 0.1) trends are shown by dark red and dark blue shading. Vertical lines indicate the median onset time for the most recent phase of sustained, significant warming calculated across 15–50-yr filter widths (as used in Fig. 2a; Table 1). b, As in a, but for the ensemble mean of regional surface air and surface ocean temperatures across CMIP5 last-millennium and historical model simulations. The SiZer analysis for the multi-model ensemble mean is shown for illustrative purposes only and removes the influence of unforced variability to highlight the multi-model thermodynamic response to climate forcings since ad 1500. The multi-model change-point distributions shown in Fig. 3a are based on SiZer analysis of individual experiments (Supplementary Fig. 2), not on the ensemble mean shown here. c, d, Radiative climate forcings from greenhouse (green), solar (orange) and volcanic (red) sources since ad 150017,52. Note that the magnitude of short-term forcing from large volcanic events17,22 exceeds the lower limit of the plot axis.

Extended Data Figure 3 Assessment of change-point detection methods using synthetic time series.

a, Example synthetic time series (grey), consisting of long-term trends with a change-point at year 0 (black) and AR(1) noise with a lag-1 autocorrelation of 0.1 and ratio of 100-yr trend to 2σ noise of 1:0.5. The synthetic trends represent: (i) no trend, then linear upward trend; (ii) small linear downward trend, then linear upward trend; (iii) small linear downward trend then accelerating upward trend (one-quarter of a period of a sine curve); (iv–vi) as in (iii), but with 10-year long downward excursions centred at 0 years (iv), −25 years (v) and −50 years (vi) relative to the onset of the accelerating upward trend. Synthetic trends are designed to capture known features of Earth’s climate evolution, namely, a long-term gradual pre-industrial cooling trend followed by accelerating industrial-era warming with superimposed episodic volcanic cooling events. The distributions of change-point results using the SiZer method (blue; Methods), the best-fit intersection of two straight lines53 (orange) and a Bayesian linear change-point method54 (purple; screening for one change-point and selecting the time of maximum probability) are shown for each experiment. Distributions show the median (thick vertical bars), 25%–75% range (boxes) and 5%–95% range (horizontal lines) of change-points returned across 1,000-member ensembles for each test. b, As in a, but testing the influence of different magnitudes of AR(1) noise on detecting the onset of warming in an underlying trend (using the small linear downward trend then accelerating upward trend, as in test (iii) in a). Tests use a ratio of the 100-yr trend to 2σ noise of 1:0.2 (vii), 1:0.5 (viii), 1:1 (ix) and 1:1.5 (x). c, As in b, but testing the influence of different AR(1) autocorrelation on detecting the onset of warming in an underlying trend. Tests use lag-1 autocorrelations of 0.1 (xi), 0.3 (xii), 0.5 (xiii) and 0.7 (xiv). See Methods for a detailed discussion of these change-point method tests and Extended Data Fig. 1b for application of the SiZer method tests using signal-to-noise parameters applicable to the regional reconstructions.

Extended Data Figure 4 Sensitivity of the onset of sustained, significant warming to seasonality.

Median onset of sustained, significant warming in regional reconstructions (grey vertical bars; as in Fig. 2a), shown against the median (blue vertical bars), 25%–75% range (boxes) and 5%–95% range (horizontal lines) of corresponding regional median warming onsets across ten multi-model last-millennium climate simulations with full radiative forcings. Dark blue distribution plots (as in Fig. 3a) show the onset of warming for regions for which regional temperature information from the models has been extracted for annual or season-specific intervals that match the climate representation of the regional reconstructions (as defined in refs 4, 14). For comparison, light blue distributions show the model results for the median onset of sustained, significant warming using annual average data for the reconstruction regions with a seasonal preference (Europe, Asia, Australasia and South America). The similarity between change-point results based on annual average and season-specific model data suggests that it is unlikely that seasonality plays a role in the regional characteristics of the onset of sustained, significant warming described here. JJA, June–August; SONDJF; September–February; DJF, December–February.

Extended Data Figure 5 Sensitivity of the onset of sustained, significant warming to filter width.

Distributions across 15–50-yr filter widths for the regional onset of sustained, significant warming (Extended Data Fig. 2a) showing the median (grey vertical bars), 25%–75% range (grey boxes) and 5%–95% range (grey horizontal lines). Coloured circles demonstrate the change-points determined at specific filter widths, with markers at ad 2015 indicating that a sustained recent warming is not detected. With the exception of Antarctica, for which significant warming is not observed at any filter width, the change-point analysis shows that shorter filter widths (less smoothing) yield more recent onset dates for sustained warming. This is because decadal-scale variability resets the time over which significant warming is determined to have been sustained in records with less smoothing. This effect accounts for the wide right-side tails produced in warming onset distributions for the Northern Hemisphere reconstruction regions for which decadal-scale variability is particularly strong (Fig. 2).

Extended Data Figure 6 Site-level onset of sustained, significant trends.

a, b, The same SiZer-based trend analysis performed on the regional reconstructions (Methods, Fig. 2a) was applied to individual temperature-sensitive marine and terrestrial records4,14,15,16 to determine the median time of onset of sustained, significant industrial-era warming (a) or cooling (b) trends. c, Number of records available in a 2° × 2° grid region (maximum n = 28). Where multiple site-level records are available in a 2° × 2° grid, the median-of-medians change-point is shown in a or b. Symbols are crossed in a and b if the interquartile range (25–75 percentile range) of change-points within a grid box exceeds 80 years. In all plots, terrestrial data are shown as squares and marine data are shown as circles. The onset of site-level warming and cooling trends is compiled by latitudinal band in Fig. 4.

Extended Data Figure 7 Moderately resolved marine records and their industrial-era trends.

a, Details of the moderate-resolution, SST-sensitive marine records37,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78 compiled by the Ocean2k working group. See ref. 15 for further details on these records. Acronyms under seasonality refer to months. bd, Cumulative distributions of the onset of sustained, significant warming (red; upward) or cooling (blue; downward) in subsets of the moderately resolved marine records. SiZer-based trend analysis was performed on the site-level records and is compiled using the same subsets examined in the ‘mini-bin’ analysis of ref. 15. Shading in a denotes records for which SiZer analysis detects recent significant cooling trends. The subsets of moderately resolved marine records produce the best differentiation between recent warming and cooling trends when the site-level change-point results are grouped according to non-upwelling (warming) and upwelling (cooling) sites. This suggests that enhanced ocean upwelling during the industrial era may provide a more robust mechanism to account for recent cooling trends in some localized parts of the world oceans, rather than differentiation based on latitude or geochemical analysis type. NH, Northern Hemisphere.

Extended Data Figure 8 Onset of sustained, significant warming trends in climate simulations.

The same SiZer-based change-point analysis performed on the regional reconstructions (Methods) was applied to climate simulations compiled at 5° × 5° grid resolution to determine the spatial fingerprint for the onset of industrial-era trends. a, b, The multi-model mean for the onset of sustained, significant warming in surface air temperature (a) and sea surface temperature (b) calculated for the first ensemble member of LOVECLIM, CSIRO Mk3L, CCSM4 and HadCM3 last-millennium experiments with full transient radiative forcings (‘All forcing’). c, d, As in a and b, but for the first ensemble member of greenhouse-only (‘GHG-only’) forced experiments of the same models. Crosses indicate grid boxes in which one or more models produce recent significant cooling rather than warming trends. See Supplementary Fig. 3 for results from individual models. In the subset of models examined here, the fingerprint of warming onset across the oceans is characterized by early warming of most tropical ocean regions except the equatorial eastern Pacific. Delayed warming—or cooling—occurs in the North Atlantic, Arctic and Southern Oceans. The onset of sustained warming is delayed at the grid level in many terrestrial parts of the Northern Hemisphere mid- to high latitudes, owing to large decadal-scale model variability over these regions, a feature that is reduced (but still evident; Extended Data Fig. 5) in regionally averaged climate data. The delayed onset of Antarctic warming is not evident in this grid-level assessment, consistent with Southern Hemisphere data–model disagreement observed in regionally averaged data.

Extended Data Table 1 High-resolution, SST-sensitive coral data compiled by the Ocean2k project
Extended Data Table 2 Details of the last-millennium and historical climate simulations

Supplementary information

Supplementary Figures

This file contains Supplementary Figures 1-3. (PDF 6198 kb)

Supplementary Tables

This file contains the data for Supplementary Table 1. (XLSX 207 kb)

Century-scale temperature trends for the continent and tropical ocean reconstruction regions since 1500CE

Trends represent 100-year linear trends, stepped by 1 year. Year in top label gives the starting year for the linear 100y trend and indicator bar below map shows the time-span of the 100-year trends. Trends that are non-significant (p>0.1) are masked in grey. The century-scale trends shown in the animation are used to examine the distribution of regional temperature trends before and after 1800CE (Fig. 2b, Table 1). (MOV 4650 kb)

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Abram, N., McGregor, H., Tierney, J. et al. Early onset of industrial-era warming across the oceans and continents. Nature 536, 411–418 (2016). https://doi.org/10.1038/nature19082

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