Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown

Transcription

1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE3058 Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown Doug M. Smith, Ben B. B. Booth, Nick J. Dunstone, Rosie Eade, Leon Hermanson, Gareth S. Jones, Adam A. Scaife, Katy L. Sheen & Vikki Thompson Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK Supplementary Information Supplementary Table S1: CMIP5 model simulations used in this study Supplementary Table S2: Sensitivity of detection and attribution analysis Supplementary Figure S1: External influences on GMST trends of different lengths Supplementary Figure S2: Robust aerosol influence on GMST trends Supplementary Figure S3: Robust aerosol influence on pattern of surface temperature trends Supplementary Figure S4: Robustness of response amongst models Supplementary Figure S5: Further anthropogenic aerosol impacts on atmospheric circulation for the 15 year period 1998 to 2012 Supplementary Figure S6: Aerosol forcing of the Aleutian Low Supplementary Figure S7: Aleutian Low influences on the PDO Supplementary Figure S8: Pacific basin-wide variability associated with the Aleutian Low variations driven by anthropogenic aerosols Supplementary Figure S9: Anthropogenic aerosol impacts on Rossby wave source for the 15 year period 1998 to 2012 Supplementary Figure S10: Detection and attribution of global annual mean temperature Supplementary references NATURE CLIMATE CHANGE 1

2 Supplementary Table S1: CMIP5 model simulations with different combinations of external forcing factors. Numbers show the ensemble size. All: all forcings (using RCP4.5 from 2005). GHG: greenhouse gases only. Nat: natural forcings (volcanoes and solar). Anthro: all anthropogenic forcings (GHG, aerosols, land use and ozone). Aero: anthropogenic aerosols only. No aero: All minus aero. Land use: anthropogenic land use changes. Ozone: anthropogenic ozone. No ozone: All minus ozone. Note that some modelling centres performed simulations ending in 2005, but we only include those extending to 2012 in order to study the recent period most relevant to the slowdown in global mean surface temperature trends. Model ACCESS1-0 1 CCSM4 5 Forcing combination All GHG Nat Anthro Aero No aero Land use Ozone No ozone CNRM-CM CSIRO-Mk a 5 5 CanESM GFDL-CM3 1 GFDL- ESM2G 1 GFDL- ESM2M 1 GISS-E2- H(p1) GISS-E2- R(p1) HadGEM2-CC 1 HadGEM2-ES IPSL-CM5A- LR IPSL-CM5A- MR IPSL-CM5B- LR MIROC-ESM 1 MIROC4h 3 MIROC5 3 MPI-ESM-LR 3 MPI-ESM-MR 3 MRI-CGCM3 1 NorESM1-ME 1 NorESM1-M bcc-csm1-1-m 1 bcc-csm Total a zonal wind data were not available

3 Supplementary Table S2: Sensitivity of detection and attribution analysis. GHG Nat Aero Error Annual means 0.66 [0.52, 0.85] 0.58 [0.41, 0.74] 0.43 [0.15, 0.71] 0.60 [0.26, 0.94] 0.53 [0.15, 0.92] 0.15 [-0.27, 0.57] [3%] [1%] Raw data 10 year trends 15 year trends 0.85 [0.56, 1.18] 0.84 [0.53, 1.18] 0.78 [0.57, 0.99] 0.72 [0.51, 0.94] 0.43 [0.20, 0.66] 0.34 [0.01, 0.66] 0.39 [0.20, 0.58] 0.38 [0.07, 0.69] 1.20 [0.49, 1.99] 1.05 [0.29, 1.91] 0.94 [0.44, 1.48] 0.64 [0.11, 1.18] [27%] [21%] [19%] [19%] 20 year trends 0.89 [0.69, 1.14] 0.78 [0.59, 1.00] 0.36 [0.12, 0.59] 0.40 [0.04, 0.77] 1.24 [0.72, 1.91] 0.78 [0.31, 1.36] [21%] [24%] Annual means 0.69 [0.53, 0.86] 0.54 [0.38, 0.71] 0.53 [0.25, 0.81] 0.81 [0.48, 1.15] 0.53 [0.16, 0.92] 0.04 [-0.37, 0.45] [3%] [2%] El Niño removed 10 year trends 15 year trends 0.78 [0.49, 1.09] 0.77 [0.47, 1.10] 0.77 [0.57, 0.99] 0.71 [0.51, 0.93] 0.53 [0.31, 0.76] 0.53 [0.21, 0.85] 0.44 [0.26, 0.63] 0.52 [0.21, 0.83] 0.93 [0.25, 1.67] 0.83 [0.10, 1.64] 0.88 [0.38, 1.42] 0.59 [0.07, 1.14] [20%] [21%] [14%] [22%] 20 year trends 0.96 [0.74, [0.59,1.01] 0.38 [0.12, 0.62] 0.56 [0.19, 0.94] 1.42 [0.87, 2.18] 0.78 [0.29, 1.38] [11%] [26%] Scaling coefficients (β, see Methods, shown as central estimate with 5 to 95% range in brackets) for GHG, Nat and Aero (as in Fig. 1c) computed over different periods, for different trend lengths, and with or without the effects of El Niño in the observations. In each cell the upper row is for the period 1861 to 2012 and the lower row is for the period 1900 to 2012 in order to exclude the eruption of Krakatoa for which uncertainties could be particularly large (e.g. ref S17). We use data up to 2012 to obtain the most robust estimates, whereas in the main text we limit the data to 1998 to illustrate the potential to predict the slowdown. The right hand column shows the error computed as the average difference between scaled model simulations and observations ( o C per decade) over the period since the eruption of Mount Pinatubo in 1991, and also expressed as an absolute percentage of the raw (unscaled) model

4 error in brackets. El Niño is removed using data provided by ref S18 since 1900 (available from Before 1900 El Niño is removed by linear regression (after removing a quadratic trend, as in ref S19) using the extended multivariate El Niño index (MEI, ref S20, available from Note that because El Niño was not removed from the model simulations, uncertainties may be slightly overestimated, but this does not alter our conclusions. Models (ref S21) and paleoclimate data (ref S22) suggest an enhanced probability of El Niño following volcanic eruptions. However, the occurrence of El Niño following recent volcanic eruptions could be at least partly coincidental (e.g. ref S23), such that the scaling coefficients for Nat (β Nat ) could be underestimated (ref S19). We therefore repeat the detection and attribution analysis after statistically removing the effects of El Niño from the observations (which gives similar results to subsampling the model simulations based on their phase of El Niño as shown in ref S19). We also assess the sensitivity to the length of trends and to the period of analysis. The results in the table above show that β Nat increases by about 30% on average after removing El Niño. For annual means since 1900 β Nat also becomes consistent with unity, in agreement with ref S19. However, the central estimate of β Nat is less than unity for all cases considered, and significantly so in all cases apart from one, even after removing El Niño. Using data for the period 1956 to 2005 ref S19 also obtained a central estimate of β Nat that is less than one (0.75). Whether or not El Niño events are coincidental with volcanic eruptions, the evidence that surface temperature in models tend to cool too much following volcanic eruptions is therefore robust, for different periods and trend lengths. Further work is needed to understand the reason for this, and why other aspects, including brightness temperatures related to lower tropospheric temperatures (ref S23) and net incoming radiation (ref S24), appear to be simulated reasonably well. There are uncertainties in all of the scaling coefficients. For example, although often consistent with one, the central estimates for β GHG vary from 0.54 to 0.96 and β Aero vary from 0.04 to Nevertheless, in all cases the error is greatly reduced by scaling (by 73% to 99%), suggesting that model errors to external forcing factors could potentially explain the majority of the differences with observations. Minor volcanic eruptions that were not included in the model simulations may also play a role (e.g. ref S23) and the possibility that internal variability may have reinforced the external signal by coincidence cannot be ruled out. The detection and attribution approach used here underpins much of the evidence for anthropogenic influences on climate (e.g. ref S25), and has proved successful in predicting decadal temperatures (ref S26). However, as with all statistical analyses there is a possibility of over-fitting the observations, and the coefficients are uncertain. Forecasts based on this approach should therefore be interpreted with caution. Nevertheless, our results open up the possibility that recovery from Mount Pinatubo along with anthropogenic aerosols could be the dominant cause of the recent warming slowdown, and highlight the need for improved understanding of external influences on climate in order to gain further confidence in near-term predictions as well as longer term projections.

5 Supplementary Figure S1: External influences on GMST trends of different lengths. GMST trends ( o C per decade) ending in 2012 as a function of starting year of the trend. Trends are of different lengths, such that those starting in 1900 cover the 113 year period , whereas those starting in 2000 cover the 13 year period Trends are shown for observations (black) and the ensemble mean of CMIP5 models forced by different factors (as in Figure 1, see Methods). The recent reduction in GMST trends is seen more clearly by considering many different periods (see also Fig. 1) than simply comparing two periods as in some studies (ref. S1). Vertical grey lines indicate major volcanic eruptions. A reduction of trends since the eruption of Mount Pinatubo in 1991 is clearly captured by the Nat ensemble (green curve). The combined eruptions of Pinatubo, El Chichon (1982) and Agung (1963) have also influenced recent trends over longer periods up to 50 years. Supplementary Figure S2: Robust aerosol influence on GMST trends. As Figure 1b but with blue shading showing the range of aerosol impacts diagnosed in different ways (see Methods): Aerosol only, All-No_aerosol, All-Nat-GHG-LU-Oz, Anthro- GHG-LU-Oz. This demonstrates a robust reduction in GMST trends since about 2000 in response to anthropogenic aerosols.

6 Supplementary Figure S3: Robust aerosol influence on pattern of surface temperature trends. As Figure 2 but including aerosol impacts diagnosed in different ways (see Methods): (d) Aerosol only (e) All-No_aerosol (f) All-Nat-GHG- LU-Oz (g) Anthro-GHG-LU-Oz (h) Average of all methods. All aerosol estimates show a tongue of cooling in the tropical Pacific and extending along the western coasts of North and South America, together with warming in the central north Pacific. This pattern projects onto the PDO, which can be characterised by the difference between tropical and north Pacific temperatures in the regions shown by the boxes (ref. S2). This demonstrates that models simulate a robust response to anthropogenic aerosols over the period that would promote a negative phase of the PDO.

7 Supplementary Figure S4: Robustness of response amongst models. Trends for the 15 year period 1998 to 2012 for sea level pressure (left column) and near surface temperature (right column) for the ensemble mean of each of the model ensembles forced by anthropogenic aerosols (Table S1). Boxes show the NPI region (left column) and the northern and tropical PDO boxes (right column, ref. S2). All three models simulate a weakening of the Aleutian Low (increased pressure in the NPI region), demonstrating a robust signal. However, the magnitude of the response varies between the models, and the centre of the pressure anomaly is displaced eastwards in CSIRO-Mk3-6-0 relative to the other models and the observations (Fig. 3). Consequently, the surface temperature changes do not project strongly onto the negative PDO pattern in CSIRO-Mk3-6-0, whereas a clear negative PDO pattern is simulated by the other models. A weakening of the Aleutian Low is therefore robust amongst models, but further studies are needed to understand inter-model differences, both in the local effects of aerosols on the radiation budget and also in the remote dynamical response.

8 Supplementary Figure S5: Further anthropogenic aerosol impacts on atmospheric circulation for the 15 year period 1998 to As Figure 2 but for relative precipitation (a, b, precipitation divided by the climatological average, % per decade) and zonal wind at 200 hpa (c, d, U200, ms -1 decade -1 ). Note the different precipitation scale bars between observations and models. Precipitation observations are from the Global Precipitation Climatology Project v2.2. Model U200 is from the ensemble mean of HadGEM2-ES. Models simulate reduced precipitation in the tropical Pacific consistent with the negative PDO (ref. S8), and a southwards shift of the northern hemisphere jet (increased/decreased U200 on the equatorward/poleward flanks) in both the Pacific and Atlantic. As with other variables (Figure 3), the model response is weaker than the observations.

9 Supplementary Figure S6: Aerosol forcing of the Aleutian Low. Lead/lag correlation between 15-year trends in the Aleutian Low (measured by the NPI, region shown in Fig. 3a) and trends in SAOD over China (red), USA (green) and Europe (orange). Solid curves are for the ensemble mean of all aerosol only simulations, with shading showing the range obtained using the ensemble mean of the three models that provided ensemble simulations (CSIRO-Mk3-6-0, CanESM2 and HadGEM2-ES, Supplementary Table S1). Circles show where SAOD significantly leads the PDO at p=0.05 (Methods). Boxes for China ( o E, o N), USA ( o E, o N) and Europe (10 o W to 30 o E, o N) are shown in Fig. 4a. Correlations are computed using data from 1880 to Correlations are largest for aerosols leading the NPI consistent with an atmospheric response to SAOD, and suggesting that the atmospheric response to SAOD changes may involve changes in SSTs and land temperatures that evolve over several years. However, given the limited sample of observed events from which to compute correlations further model experiments, in which aerosol emissions from China, USA and Europe are varied independently, are needed to determine the lagged response more precisely.

10 Supplementary Figure S7: Aleutian Low influences on the PDO. Lead/lag correlation between 15-year trends in the Aleutian Low (measured by the NPI, region shown in Fig. 3a) and trends in the PDO index (orange) measured as the difference between surface temperatures in the northern and tropical Pacific regions (ref. S2, regions are shown in Fig. S3). Correlations are also shown for the northern (green, o E, o N) and tropical (red, o W, 10 o S-6 o N) regions separately. Solid curves are for the ensemble mean of all aerosol only simulations, with shading showing the range obtained using the ensemble mean of the three models that provided ensemble simulations (CSIRO-Mk3-6-0, CanESM2 and HadGEM2-ES, Supplementary Table S1). Correlations are computed using data from 1880 to Circles show where correlations are significantly greater than zero at p=0.05 (Methods). For a given absolute lead/lag (e.g. plus or minus one year) correlations are larger for NPI leading rather than lagging, suggesting that PDO changes are influenced by changes in the Aleutian Low in these model simulations. This is consistent with other studies which argue that the Aleutian Low plays a role in driving the PDO (e.g. refs. S3 and S4).

11 Supplementary Figure S8: Pacific basin-wide variability associated with the Aleutian Low variations driven by anthropogenic aerosols. Correlations from the ensemble mean aerosol only simulations between 15-year trends in the Aleutian Low (measured by the NPI, region shown in Fig. 3a) and trends in (a) sea level pressure (b) zonal winds and (c) near surface temperature. Arrows in (b) show the climatological near surface winds from ERA Interim. Correlations are computed using data from 1880 to Changes in the Aleutian Low impact the north-easterly trade winds in the north Pacific (see also ref. S4), leading to a coupled Pacific basin-wide response (ref. S5) including the PDO (which can be characterised by the difference between tropical and north Pacific temperatures in the small boxes in (c), ref. S2).

12 The key role played by the Pacific Ocean in the warming slowdown has been established by coupled model experiments constrained either by tropical surface temperatures (in the region shown by the large box in (c), ref. S6) or tropical zonal winds (refs. S5 and S7, latitudinal extent of regions is shown by the boxes in (b)). The Pacific Ocean can vary through natural internal variability leading to the prevailing view in the literature that the warming slowdown is predominantly caused by internal variability (refs. S6-S12). Our results challenge this view by showing that the key pattern of dynamical variability in the Pacific Ocean (a negative PDO, Figs. 2, 3, 4, S3) along with a reduction in GMST trends (Figs. 1, S1, S2) is robustly simulated by models driven only by changes in anthropogenic aerosols. This has fundamental implications both for understanding and attributing past climate change and for predicting both regional and global climate in the coming years to decades.

13 Supplementary Figure S9: Anthropogenic aerosol impacts on Rossby wave source for the 15 year period 1998 to (a, b) Interannual standard deviation of Rossby wave source (S, December to February, units are s -2 ) at 200 hpa from observations and a single HadGEM2-ES ensemble member. (c, d) Trends in S over the 15 year period 1998 to 2012 (10-11 s -2 per decade, smoothed with a 5x5 filter). Model S is from the ensemble mean of HadGEM2-ES since data were not available for the other models. S is calculated by (e.g. ref. S13): where ζ is the absolute vorticity and is the meridional divergent component of the horizontal wind. Calculated in this way, the Rossby wave source is the rate of change of vorticity due to vortex stretching (first term) and vorticity advection by the divergent part of the wind (second term). Variations in S therefore tend to occur in the jet regions where changes in divergent winds interact with vorticity (a, b). The largest simulated trends in S occur east of Japan, consistent with localised cooling upstream from increased aerosol emissions from China. We compute stationary Rossby ray paths (ref. S14) and show that long wavelength Rossby waves propagate northeastwards from this region, in qualitative agreement with trends in geopotential height and the Aleutian Low (Figure 3). Our results are consistent with aerosol impacts on Rossby waves shown in previous studies (refs. S15, S16). The importance of Rossby waves in the warming slowdown, including seasonal aspects, has been demonstrated previously (ref. S8). However, our results show that the anomalous Rossby waves were likely forced by anthropogenic aerosols rather than occurring through internal variability as previously thought.

14 Supplementary Figure S10: Detection and attribution of global annual mean temperature. As Figure 1 but for global annual mean surface temperature rather than 15-year trends. The Nat scaling factor (β Nat, green bar inset) is significantly less than unity, consistent with the analysis of 15-year trends in Fig. 1. This provides further evidence that models over-estimate GMST cooling following major volcanic eruptions. A rescaled forecast from 1998 (dotted red curve) is much closer to the observations than the original unscaled projection (dashed red curve), showing that recent differences between observations and model simulations are potentially largely explained by an over-estimation of GMST cooling in the models following the eruption of Mount Pinatubo in 1991.

15 Supplementary References S1. Karl, T. R. et al. Possible artifacts of data biases in the recent global surface warming hiatus, Science, 348, (2015). S2. Dong, L., Zhou, T. & Chen, X. Changes of Pacific decadal variability in the twentieth century driven by internal variability, greenhouse gases, and aerosols, Geophys. Res. Lett., 41, (2014). S3. Schneider, N. and B. D. Cornuelle, The Forcing of the Pacific Decadal Oscillation, J. Climate, 18, , doi: /jcli (2005) S4. Newman, M., M. A. Alexander, T. R. Ault, K. M. Cobb, C. Deser, E. Di Lorenzo, N. J. Mantua, A. J. Miller, S. Minobe, H. Nakamura, N. Schneider, D. J. Vimont, A. S. Phillips, J. D. Scott, and C. A. Smith, The Pacific Decadal Oscillation, Revisited, Submitted to J. Climate, in press, doi: /JCLI-D (2016) S5. England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus, Nature Climate Change, 4, (2014). S6. Kosaka, Y. & Xie, S.-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling, Nature, 501, (2013). S7. Watanabe, M. et al. Contribution of natural decadal variability to global warming acceleration and hiatus, Nature Climate Change, 4, (2014). S.8 Trenberth, K. E., Fasullo, J. T., Branstator, G. & Phillips, A. S. Seasonal aspects of the recent pause in surface warming, Nature Climate Change, 4, (2014). S9. Meehl, G. A., Arblaster, J. M., Fasullo, J. T., Hu, A. & Trenberth, K. E. Modelbased evidence of deep-ocean heat uptake during surface-temperature hiatus periods, Nature Climate Change, 1, (2011). S10. Risbey, J. S. et al. Well-estimated global surface warming in climate projections selected for ENSO phase, Nature Climate Change, 4, (2014). S11. Roberts, C. D., Palmer, M. D., McNeall, D. & Collins, M. Quantifying the likelihood of a continued hiatus in global warming, Nature Climate Change, 5, (2015). S12. Dai, A., Fyfe, J. C., Xie, S.-P. & Dai, X. Decadal modulation of global surface temperature by internal climate variability, Nature Climate Change, 5, (2015). S13. Sardeshmukh P. and B.J. Hoskins, The Generation of Global Rotational Flow by Steady Idealized Tropical Divergence, J. Atmos. Sci., 45, (1988)

16 S14. Scaife, A.A., R.E. Comer, N.J. Dunstone, J. Knight, D.M. Smith, C. MacLachlan, N. Martin, D. Peterson, D. Rowlands, E. Carrol, S. Belcher and J. Slingo, Tropical Rainfall, Rossby Waves and Regional Winter Climate Predictions, Quart. J. Roy. Met. Soc., submitted S15. Ming, Y., V. Ramaswamy, and G. Chen, A model investigation of aerosolinduced changes in boreal winter extratropical circulation, J. Climate, 24, (2011) S16. Lewinschal, A., A. M. L. Ekman, and H. Kornich, The role of precipitation in aerosol-induced changes in northern hemisphere wintertime stationary waves, Climate Dynamics, 41, (2013) S17. Gillett, N. P., V. K. Arora, D. Matthews and M.R. Allen, Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations, Journal of Climate, 26, , (2013) S18. Thompson, D. W. J., J. M. Wallace, P. D. Jones, and J. J. Kennedy, Identifying signatures of natural climate variability in time series of global-mean surface temperature: Methodology and insights, J. Clim., 22, , doi: /2009jcli (2009) S19. Lehner, F., A. P. Schurer, G. C. Hegerl, C. Deser, and T. L. Frölicher, The importance of ENSO phase during volcanic eruptions for detection and attribution, Geophys. Res. Lett., 43, doi: /2016gl (2016) S20. Wolter, K., and M. S. Timlin, El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Intl. J. Climatology, 31, , DOI: /joc.2336 (2011) S21. Maher, N., McGregor, S., England, M. H. & Gupta, A. S. Effects of volcanism on tropical variability, Geophys. Res. Lett. 42, (2015) S22. Li, J. et al., El Niño modulations over the past seven centuries, Nature Climate Change, 3, (2013) S23. Santer, B. D. et al. Volcanic contribution to decadal changes in tropospheric temperature, Nature Geosci. 7, (2014) S24. Smith, D.M., R.P. Allan, A.C. Coward, R. Eade, P. Hyder, C. Liu, N.G. Loeb, M.D. Palmer, C.D. Roberts and A.A. Scaife, Earth's energy imbalance since 1960 in observations and CMIP5 models, Geophys. Res. Letts., 42, doi: /2014gl (2015) S25. Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari and X. Zhang, Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working

17 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA (2013) S26. Allen, M. R., J. F. B. Mitchell and P. A. Stott, Test of a decadal climate forecast, Nature Geoscience, 6, p.243 doi: /ngeo1788 (2013)