SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: /NGEO1854 Anthropogenic aerosol forcing of Atlantic tropical storms N. J. Dunstone 1, D. S. Smith 1, B. B. B. Booth 1, L. Hermanson 1, R. Eade 1 Supplementary information Figure S1: Examining the evolution and consistency of ensemble members. Top plot of each panel shows individual ensemble members (colored lines), together with the ensemble mean (black line). The lower plots show the ensemble mean again (red/blue shading) together with the fraction of ensemble members at each point that share the same sign as the ensemble mean (green lines). There is good agreement between the ensemble members and the ensemble mean for all variables. NATURE GEOSCIENCE 1

2 Figure S2: North Atlantic anthropogenic aerosols driving historical multidecadal variability. Top three panels show the multi-decadal variability of North Atlantic anthropogenic aerosols and their impact on cloud properties; other panels show the downward shortwave radiation and surface temperature in absolute terms (middle) and relative to the tropical average (lower). a, The timeseries of combined optical depth of anthropogenic aerosols species (SO 4, soot, biomass burning, fossil fuel organic carbon) over the North Atlantic Ocean ( W and 0-60 N) in the HadGEM2ES ensemble mean (red/blue shading). Active and inactive periods of observed tropical storm activity (blue lines, as defined from Fig. 1a) coincide with multi-decadal variability in aerosol optical depth around the historical trend ( ). This variability is driven by socio-economic factors: increasing strongly at the start of the 20 th century during rapid early industrial expansion, then more slowly over the period (where economic slow down is linked to the two World Wars and the Great Depression), before a large increase reaching a peak in the 1970s, 2

3 followed by a decline thereafter (which is linked to the removal of Sulphur Dioxide from emissions in response to air-quality and acid rain impacts in both North America and Europe). Aerosol forcing through the direct aerosol effect will be proportional to these changes. b, The variability in aerosol concentrations modulates the cloud droplet effective radius via the aerosol first indirect effect which will lead to changes in cloud albedo. Given the non-linear relationship between aerosol concentration and cloud droplet radius, relatively large changes in effective radius are seen at low aerosol concentrations (i.e. earlier in the 20 th century). c, Total cloud fraction, which responds to both changes in the aerosol second indirect effect (changes in cloud lifetime) and also (likely more strongly) as a coupled feedback to SST changes resulting from the total aerosol forcing. d-e, The resulting changes in downward shortwave radiation (d) and surface temperature (e). These also show marked multi-decadal variability around the historical trend which is approximately in phase with observed tropical storm activity. However, there are some discrepancies, which we now investigate further by examining the tropic-wide (30S-30N) mean timeseries (orange lines). Some forcings are common to both the North Atlantic and to the entire tropics, such as large equatorial volcanic eruptions (e.g. Krakatoa in the 1880s) or strong greenhouse gas warming in the late 20 th century. f-g, The difference between North Atlantic and tropical means, for downward shortwave radiation (f) and surface temperature (g) plotted relative to 20 th century mean. The resulting distinct multi-decadal variability (in excellent agreement with the phases of observed tropical storm activity) drives the changes in the latitude of the ITCZ and hence the impacts we find in the MDR wind shear, precipitation and ultimately tropical storms that are shown in Fig. 1. For reference we show the 90% range of HadGEM2ES control variability as the grey shading. Finally, we overplot the evolution of the fixed aerosol ensemble (AERO 1860, green lines) to show that this both does not exhibit the pronounced phases of multidecadal variability seen in the all-forcings ensemble and mostly stays within the range of control run variability (grey shading). In summary, while the 20th century saw an overall increasing trend in aerosols over the North Atlantic, periods of either rapid economic expansion or stagnation and more recently the impact of air quality legislation, leads to multi-decadal variability around the change in aerosol amount (a). The aerosol changes have both a direct impact (a), and act indirectly via interactions with clouds (b and c), on the amount of SW reaching the surface (d) together with volcanoes (not shown). While the long term trend in North Atlantic temperatures (e) is a balance of greenhouse gas warming and aerosol cooling, it is the variability in the aerosol and volcano driven SW changes (d) that is strongly linked to the multi-decadal SST variability (e). The wider North Atlantic temperature trend and variability shown here, unsurprisingly matches that of the MDR variability, shown in Figure 1k. When we remove the SW and SST changes seen in the wider Tropics from that in the North Atlantic (f and g, respectively), we are excluding drivers of change which are common to both regions (such as the SW impact of Krakatoa in 1883, underlying drivers of warming) to get at the underlying mechanism (temperature differences between MDR and wider tropics). The resulting temporal pattern of variability is driven predominantly by multi-decadal changes that have a disproportionally large impact in the North Atlantic, which during this historical period have been anthropogenic aerosol changes. 3

4 20 Fig. S3: The impact of different forcing agents on simulated TS. Panel (a) is similar to Fig. 2a but shows the impact of additional forcings which mostly stay within the control ensemble variability during the historical period and extends the timeseries using RCP4.5 only. Panel (b) shows the results of a comprehensive set of experiments with a 17 member perturbed physics ensemble of the HadCM3 climate model variants S1. HadCM3 S2 is a previous Hadley Centre climate model, with a lower resolution than HadGEM2ES, a different dynamical core and different physical parameterisations. Although it does include aerosol indirect effects, their treatment is less sophisticated than in HadGEM2ES. HadCM3 requires less computing time enabling a comprehensive set of experiments to be performed in which each individual forcing agent was withheld in turn. From this suite of experiments we back out the implied impact of each forcing (shown in (b)). The MSLP tracking technique (see Methods) is again used on daily model data to count TS from HadCM3. Like HadGEM2ES (a) and the CMIP models (Fig. 2b) the HadCM3 all-forcings simulation shows a significant reduction in model TS over the historical period, with the 20 th century mean lying outside the 90% of variability of the corresponding control run ensemble mean. Also like HadGEM2ES, we find that anthropogenic aerosols are the key forcing responsible for this depression until about 1970, after which GHGs strongly reduce TS numbers, offsetting an increase caused by (reductions of) aerosols. 4

5 Figure S4: Timeseries of aerosol optical depth over the North Atlantic for different future emission scenarios. The timeseries of combined optical depth of anthropogenic aerosols species (SO 4, soot, biomass burning, fossil fuel organic carbon) over the North Atlantic Ocean ( W and 0-60 N) in the HadGEM2ES ensemble mean. Note the rapid decline in the coming decades simulated in the RCP2.6 aggressive mitigation scenario, relative to the RCP4.5 scenario S3. This large decrease in anthropogenic aerosols over the North Atlantic in RCP2.6 is sufficient to more than offset the additional greenhouse gas warming simulated in RCP4.5 over the MDR between (as shown in Fig. 1l). 5

6 Fig. S5. Investigation of the longer-term future ( ). As Fig. 4, but for the period and only the RCP4.5 scenario. Here we suggest possible mechanisms that might explain the far-future changes in TS simulated by HadGEM2ES, but note that additional model experiments are required to establish causality. Reducing TS frequency and MDR precipitation in the longer-term future (Fig. 1) are consistent with a general reduction in the strength of the tropical (including the Hadley) circulation (e) that is robustly simulated by climate models as greenhouse gases increase S4. In HadGEM2ES an anomalous local zonal (Walker) circulation may also be important (f). This might be a response to the large regional warming seen in the north-east tropical Pacific region (a) but is beyond the scope of this paper and requires further investigation. Stippling is not shown on the SAT map as all points have warmed beyond the control ensemble 90% variability. 6

7 Figure S6: A comparison of different SST indices against the timeseries of model tropical storms (top). Panel (a) shows model tropical storms (as Fig. 1b,c). (b) MDR minus tropics SST, as in Fig. 1k,l but now for the shorter August-October period so that it is consistent with the Vecchi hurricane index 9 which is plotted in (c). The latter is a modification to the SST (MDR-TROP) index based on a Poisson regression model in which the number of hurricanes (rather than named tropical storms) depends on the exponential of a weighted difference between the tropical Atlantic and tropic-wide SST. This index has been calibrated using a comparison with direct storm tracking performed in climate model experiments under a number of different climate scenarios. Here for HadGEM2ES future projections, it shows an improvement over the simpler SST (MDR-TROP) index as it captures more of the 21 st century declining trend. It is still however, an underestimate of the future decline shown in panel a. This is better captured by an index of MDR minus the north tropical Pacific (10-20N) motivated by projected Walker circulation changes (Fig. S3). However, none of the indices performs particularly well over the whole historical and future period, highlighting the need to understand the relevant physical processes, and to examine model storms directly rather than relying on surrogate indices. 7

8 Figure S7: Detailed breakdown of the SST (MDR-TROP) index for each individual model over the period. Model names in green belong to the INDIRECT ensemble (mainly CMIP5), names in red belong to the DIRECT (mainly CMIP3), names in grey are excluded as they are believed to use offline/prescribed representation of the first aerosol indirect effect. Of all 39 models, 34 of them (87%) show a reduced SST (MDR-TROP) index over the historical period compared to their control run. The INDIRECT ensemble has a much larger spread of values for SST MDR- TROP but is always negative. It includes a number of large reductions in SST MDR-TROP, with the mean of INDIRECT lying outside of the range of the DIRECT ensemble as shown by the box-and-whisker diagrams on the right (they show the full range, interquartile range, median and mean (stars) statistics). 8

9 Fig. S8: Further investigation of the strength of aerosol forcing in CMIP5 models. The 20 th century change in SST (MDR-TROP) simulated by the coupled models (as plotted in Fig. S5) is plotted against the strength of anthropogenic aerosol radiative forcing in the MDR (diagnosed using specialised fixed-sst CMIP5 forcing experiments, see Methods) for six CMIP5 models from the INDIRECT ensemble. Note that the NorESM and MIROC5 models show weak forcing due to aerosols thereby explaining their relatively small changes in SST MDR-TROP. The range of indirect aerosol forcing is likely due to model differences in aerosol parameterisations, aerosol transport and lifetime and the presence of climatological low-level cloud. The strong relationship between aerosol forcing and SST (MDR-TROP) (r=0.82) suggests that the spread in the INDIRECT ensemble can be explained primarily by aerosol forcing alone, providing further evidence of an impact of aerosols on SST (MDR-TROP) and hence TS. 9

10 Supplementary references S1 Collins, M., Booth, B. B. B., Harris, G. R., Murphy, J. M., Sexton, D. M. H. & Webb, M. J. Towards quantifying uncertainty in transient climate change. Clim Dyn , doi: /s (2006) S2 Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B., and Wood, R. A. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Climate Dynamics, vol 16, p (doi: /s ) (2000) S3 van Vuuren, D. P. The representative concentration pathways: an overview. Climatic Change 109,5 31, doi: /s z (2011) S4 Held, I. M. & Soden, B. J. Robust Responses of the Hydrological Cycle to Global Warming. J. Climate, 19, (2006) 1