Research ArticleCLIMATE CHANGE

Nonlinear climate sensitivity and its implications for future greenhouse warming

See allHide authors and affiliations

Science Advances  09 Nov 2016:
Vol. 2, no. 11, e1501923
DOI: 10.1126/sciadv.1501923
  • Fig. 1 Paleo-SST proxy data locations.

    Locations of the 63 paleorecords of SST used in our study. Refer to tables S1 and S2 for details regarding the records.

  • Fig. 2 Reconstructed global mean temperatures.

    Globally averaged SAT (K) and radiative forcing anomalies (W/m2) for the last 784 ka and for the RCP8.5 scenario. (A) Global mean SAT anomalies (K) reconstructed from 14 long-term paleoproxies of SST (blue line) and from the transient model simulation (red line). Anomalies were calculated with respect to PI times. (B) Averaged, reconstructed global mean SAT anomaly (K, black line). Shading denotes uncertainty of ±2.12 K (see Materials and Methods). (C) CMIP5 ensemble mean projection for globally averaged SAT increase (K) with respect to PI mean state using RCP8.5 (black line) (6). Shading denotes ensemble SD. Dashed horizontal lines in (B) and (C) denote reconstructed maximum global mean SAT during the last 784,000 years (B) and its exceedance (C). (D) Radiative forcing anomalies (W/m2) with respect to PI mean state. Cyan, dust forcing; red, greenhouse gas forcing; black, ice sheet forcing; magenta, orbital forcing; brown, sea-level forcing; blue, sum of radiative forcings (see also fig. S7). (E) Radiative forcing anomalies (W/m2) with respect to PI mean state used for the CMIP5 RCP8.5 simulations (6).

  • Fig. 3 Sensitivity of global mean SAT anomalies to radiative forcing anomalies.

    Scatter diagram (circles) of reconstructed global mean SAT anomalies (K) (Fig. 2B) versus net radiative forcing anomalies (W/m2) (Fig. 2D) for the last 784,000 years. Anomalies are calculated with respect to PI values. Two-dimensional kernel density estimate of paleo-SAT/radiative forcing data (blue shading). The thick dashed yellow curve represents nonlinear regression of paleo-SAT/radiative forcing data, along with uncertainty ranges (dashed black curves; see Materials and Methods). The thick cyan line represents linear regression for cold phases. The slope represents Scold. The thick red line represents linear regression for warm phases. The slope represents Swarm. Dashed horizontal lines denote warm (orange) and cold (blue) phases using 1 SD of the reconstructed global mean SAT anomalies as a separator. Cold (warm) phases are defined by SAT anomalies of <−5.12 K (>−1.66 K). The CMIP5 transient model projections using the RCP8.5 forcing scenario are presented by purple circles. Using Swarm (orange shading) and taking into account the ocean heat uptake efficiency, we can calculate the transient response to the RCP8.5 radiative forcing. The resulting paleo-based projection with the corresponding uncertainty ranges is represented by cyan shading (see Materials and Methods).

  • Fig. 4 Future greenhouse warming projections.

    Ensemble mean simulated global mean surface temperature (K) evolution using all models of the CMIP5 multimodel ensemble in their historical and RCP8.5 simulations (thick red line) and corresponding uncertainty range (orange shading), along with estimates (blue) based on Swarm, an estimate of ocean heat uptake efficiency and the RCP8.5 radiative forcing time series. The corresponding uncertainty range is depicted as cyan shading (see Materials and Methods).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/11/e1501923/DC1

    table S1. Long-term (784 to 10 ka B.P.) paleorecords of SST.

    table S2. All paleorecords of SST used in this study.

    table S3. Warm-phase specific equilibrium climate sensitivities and corresponding global mean surface temperature change at year 2100.

    fig. S1. Underestimation factor for SST-proxy network.

    fig. S2. Model-data comparison of SST-anomalies for 14 SST proxy locations.

    fig. S3. Ratios of standard deviations of proxy-based and simulated SST-anomalies.

    fig. S4. Pattern and temporal evolution of leading EOF1 of reconstructed and simulated SST.

    fig. S5. Comparison of long-term temperature estimates.

    fig. S6. Model-data comparison of glacial-interglacial SST-anomalies.

    fig. S7. Radiative forcing estimates.

    References (5398)

  • Supplementary Materials

    This PDF file includes:

    • table S1. Long-term (784 to 10 ka B.P.) paleorecords of SST.
    • table S2. All paleorecords of SST used in this study.
    • table S3. Warm-phase specific equilibrium climate sensitivities and corresponding global mean surface temperature change at year 2100.
    • fig. S1. Underestimation factor for SST-proxy network.
    • fig. S2. Model-data comparison of SST-anomalies for 14 SST proxy locations.
    • fig. S3. Ratios of standard deviations of proxy-based and simulated SST-anomalies.
    • fig. S4. Pattern and temporal evolution of leading EOF1 of reconstructed and simulated SST.
    • fig. S5. Comparison of long-term temperature estimates.
    • fig. S6. Model-data comparison of glacial-interglacial SST-anomalies.
    • fig. S7. Radiative forcing estimates.
    • References (5398)

    Download PDF

    Files in this Data Supplement: