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Future climate forcing potentially without precedent in the last 420 million years

Foster, Gavin L., Royer, Dana L., Lunt, Daniel J.·2017·Nature Communications
DOI 10.1038/ncomms14845PMC PMC5382278
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Abstract

The evolution of Earth’s climate on geological timescales is largely driven by variations in the magnitude of total solar irradiance (TSI) and changes in the greenhouse gas content of the atmosphere. Here we show that the slow ∼50 Wm −2 increase in TSI over the last ∼420 million years (an increase of ∼9 Wm −2 of radiative forcing) was almost completely negated by a long-term decline in atmospheric CO 2 . This was likely due to the silicate weathering-negative feedback and the expansion of land plants that together ensured Earth’s long-term habitability. Humanity’s fossil-fuel use, if unabated, risks taking us, by the middle of the twenty-first century, to values of CO 2 not seen since the early Eocene (50 million years ago). If CO 2 continues to rise further into the twenty-third century, then the associated large increase in radiative forcing, and how the Earth system would respond, would likely be without geological precedent in the last half a billion years.

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Figure 1 Temporal evolution of climate and atmospheric CO2. Latitudinal extent of continental ice deposits23 (blue bars) and multi-proxy atmospheric CO2 (in p.p.m.) compiled from the literature (data found in Supplementary Data 1; symbols). CO2 from leaf stomata shown in blue circles, pedogenic carbonate δ13C as pink crosses, boron isotopes in foraminifera as green triangles, liverwort δ13C as dark blue filled circles and δ13C of alkenones as dark blue crosses. The most likely LOESS fit through the data, taking into account X- and Y- uncertainty is shown as the blue line (data found in Supplementary Data 2). 68 and 95% confidence intervals are shown as dark and light grey bands. Red line is a linear best fit (curved due to log-scale for y axis) and 95% confidence interval for least squares regression through the CO2 data (m=3.4±0.17 1 s.e.m., R2=0.26, P<0.0005). Black line is least squares fit through the LOESS best fit (blue line) resampled to original data density (m=3.5±0.12 1 s.e.m., R2=0.44, P<0.0005). Dashed line is pre-industrial CO2 (278 p.p.m.). Icehouse time intervals are indicated by a black band and greenhouse intervals by a white band.
Figure 1 Temporal evolution of climate and atmospheric CO2. Latitudinal extent of continental ice deposits23 (blue bars) and multi-proxy atmospheric CO2 (in p.p.m.) compiled from the literature (data found in Supplementary Data 1; symbols). CO2 from leaf stomata shown in blue circles, pedogenic carbonate δ13C as pink crosses, boron isotopes in foraminifera as green triangles, liverwort δ13C as dark blue filled circles and δ13C of alkenones as dark blue crosses. The most likely LOESS fit through the data, taking into account X- and Y- uncertainty is shown as the blue line (data found in Supplementary Data 2). 68 and 95% confidence intervals are shown as dark and light grey bands. Red line is a linear best fit (curved due to log-scale for y axis) and 95% confidence interval for least squares regression through the CO2 data (m=3.4±0.17 1 s.e.m., R2=0.26, P<0.0005). Black line is least squares fit through the LOESS best fit (blue line) resampled to original data density (m=3.5±0.12 1 s.e.m., R2=0.44, P<0.0005). Dashed line is pre-industrial CO2 (278 p.p.m.). Icehouse time intervals are indicated by a black band and greenhouse intervals by a white band.CC BY · PubMed Central
Figure 2 Temporal evolution of climate forcing. (a) CO2 data (blue circles) and LOESS best fit (blue line; Supplementary Data 2) with ΔF for CO2 calculated as in equation (6). The change in solar forcing (ΔFsol) calculated using equations (2 and 3). (b) ΔFCO2,sol for data (blue circles) and LOESS best fit (blue line). Red line is a linear best fit and 95% confidence interval for least squares regression through the ΔFCO2,sol data (blue circles; m=−0.004±0.001 1 s.e.m., R2=0.01, P=0.0003). Black line is least squares fit through the LOESS best fit resampled to original data density (blue line; m=0.008±0.001 1 s.e.m., R2=0.06, P<0.0001). Icehouse time intervals are indicated by a black band and greenhouse intervals by a white band.
Figure 2 Temporal evolution of climate forcing. (a) CO2 data (blue circles) and LOESS best fit (blue line; Supplementary Data 2) with ΔF for CO2 calculated as in equation (6). The change in solar forcing (ΔFsol) calculated using equations (2 and 3). (b) ΔFCO2,sol for data (blue circles) and LOESS best fit (blue line). Red line is a linear best fit and 95% confidence interval for least squares regression through the ΔFCO2,sol data (blue circles; m=−0.004±0.001 1 s.e.m., R2=0.01, P=0.0003). Black line is least squares fit through the LOESS best fit resampled to original data density (blue line; m=0.008±0.001 1 s.e.m., R2=0.06, P<0.0001). Icehouse time intervals are indicated by a black band and greenhouse intervals by a white band.CC BY · PubMed Central
Figure 3 Probability density functions of climate forcing. (a) Atmospheric CO2 with proxy data in blue (Supplementary Data 1) and LOESS best fit (Supplementary Data 2) in red. Vertical lines show maximum atmospheric CO2 from relevant representative concentration pathways (RCP) (colours)39 and from a 12,000 Pg C scenario (from ref. 41; Wink12K, black dashed line). (c) Proxy-based ΔFCO2,sol in blue and LOESS best fit in red along with relevant RCP scenarios (colours)39 and 12,000 Pg C scenario (black dashed line)41.
Figure 3 Probability density functions of climate forcing. (a) Atmospheric CO2 with proxy data in blue (Supplementary Data 1) and LOESS best fit (Supplementary Data 2) in red. Vertical lines show maximum atmospheric CO2 from relevant representative concentration pathways (RCP) (colours)39 and from a 12,000 Pg C scenario (from ref. 41; Wink12K, black dashed line). (c) Proxy-based ΔFCO2,sol in blue and LOESS best fit in red along with relevant RCP scenarios (colours)39 and 12,000 Pg C scenario (black dashed line)41.CC BY · PubMed Central
Figure 4 Temporal evolution of CO2 and climate forcing. (a,b) Proxy-based atmospheric CO2 (Supplementary Data 1) on a log timescale with best fit LOESS and associated uncertainty envelope (Supplementary Data 2). (c) Ice core atmospheric CO2 from ref. 82 on log timescale. (d) Atmospheric CO2 on line timescale from ice core and observation record82 and future RCP8 and other41 scenarios (RCP3PD—grey, RCP4.5—orange, RCP6—red, RCP8.5—brown, Wink12k -black)3840. (e–h) ΔFCO2,sol calculated from data shown in a–d as described in text. In (c) ΔFCO2,sol is calculated from ice core CO2 estimates assuming no change in solar output82. No change in solar output is also applied to the records in h.
Figure 4 Temporal evolution of CO2 and climate forcing. (a,b) Proxy-based atmospheric CO2 (Supplementary Data 1) on a log timescale with best fit LOESS and associated uncertainty envelope (Supplementary Data 2). (c) Ice core atmospheric CO2 from ref. 82 on log timescale. (d) Atmospheric CO2 on line timescale from ice core and observation record82 and future RCP8 and other41 scenarios (RCP3PD—grey, RCP4.5—orange, RCP6—red, RCP8.5—brown, Wink12k -black)3840. (e–h) ΔFCO2,sol calculated from data shown in a–d as described in text. In (c) ΔFCO2,sol is calculated from ice core CO2 estimates assuming no change in solar output82. No change in solar output is also applied to the records in h.CC BY · PubMed Central

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Metadata

Authors
Foster, G. L., Royer, D. L., & Lunt, D. J.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2017-04-04
Volume
8
Issue
1
ISSN
2041-1723
DOI
10.1038/ncomms14845
PMC
PMC5382278
Cited by
774
References
6
Language
en
Licence

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