The importance of including variability in climate

Transcription

1 D. M. H. Sexton and G. R. Harris SUPPLEMENTARY INFORMATION The importance of including variability in climate change projections used for adaptation Modification to time scaling to allow for short-term volcanic effects On 1-year time-scales the global mean annual temperature responds to rapid changes in radiative forcing associated with volcanic eruptions (e.g. in Fig. 1). Time scaling (see Methods), which assumes the regional vary linearly with global mean annual temperature change, therefore produces on 1-year time-scales a volcanoes in our regional variables which is not necessarily obtained in the GCM response at these spatial scales. In other words, we do not expect the regional 1-year volcanic forcing to be well represented by time scaling of the well-mixed greenhouse gases and other radiatively-active species. Rather than erroneously imposing a volcanic effect on these variables through the scaling, we modify the method 15 to exclude volcanic effects from the scaled response, and instead allow any volcanic forcing to emerge from the ensemble used to calibrate the time scaling. The first step in this modified method is the creation of a time series of changes in annual global mean temperature excluding the volcanic response. We interpolate across each period of major volcanic eruptions ( , , , , , , and ), smoothing from the year previous to volcanic eruption to four years after the eruption to remove the fast volcanic response. Four years is chosen based on sensitivity tests; too short a period misses some of the response due to volcanic effects whereas too long a period and we start to incorrectly infer positive temperature anomalies due to volcanic forcing. Four years also fits well with the period identified in another study S1. The modified global temperature changes are then used to perform the scaling. We recall that the time scaled estimates for the response of the coupled model runs of Ensemble 2 (Supplementary Table 1) are not exact, giving residuals that are used to estimate an error variance. A bias term is estimated from the ensemble mean of the residuals (Supplementary Figs. 1c and 2c). If the volcanic eruptions have an effect on the climate variable being time scaled, then their effect will appear as a response in volcanic years which will be of a consistent sign. If the mean of the residuals in the volcanic years (Supplementary Figs. 1d and 2d) is statistically significant at the 5% level, then volcanoes are considered important and the bias is smoothed on 30-year time scales excluding the volcanic years, leaving the bias untouched in volcanic years (Supplementary Fig. 1e). If there is no statistical significance, then the bias is smoothed on 30-year time scales across all years (Supplementary Fig. 2e). The residuals are then re-centred (Supplementary Figs. 1f and 2f) about the smoothed bias and then used to re-estimate an error variance. The inter-annual variation in the time series of error variance is large but this is just due to sampling from runs, since a residual sampled in one particular year could have equally been sampled in any nearby year. Recognising this, we smooth the high frequency error variance on 30-year time scales (Supplementary Figs. 1g and 2g). In Fig. 1, the 1-year plumes of annual global mean temperature show clear responses to volcanoes, whereas for the other four England-Wales variables, no effect from volcanoes is apparent. NATURE CLIMATE CHANGE 1

2 SUPPLEMENTARY INFORMATION Supplementary Fig 1. Diagnostic plot of the time scaling procedure 15, which has been adapted for 1-year time scales, for annual global mean temperature (K). a) anomalies relative to for each of the ensemble members with historical to 1990 and A1B 2 NATURE CLIMATE CHANGE

3 SUPPLEMENTARY INFORMATION forcing 27 ; b) response predicted by time scaling for each of the ensemble members; c) residuals, defined as the difference between the anomalies in a) and the prediction in b), for each of the ensemble members with the initial bias (black), estimated as the ensemble mean of the residuals; d) the bias from c) with values set to zero for years that are not affected by a major volcanic eruption; e) smoothed (30-year low pass filtered) bias with non-zero values from d) included if the bias in volcano years is statistically significant (which they are in this case); f) residuals re-centred about smoothed bias in e); and g) unsmoothed and smoothed (30-year low pass filtered) error variance, estimated as the variance of the re-centred residuals in f);h) as f) but the values at each time point have been normalised by removing their mean and dividing by their standard deviation. Sampled data Each realisation in Fig. 1 consists of a sample of a plausible climate change signal and a sample of the noise about this signal. The climate change component samples a plausible value for drivers of uncertainty in future global climate change under the given emissions scenario (aerosol forcing, climate sensitivity, ocean heat uptake and carbon cycle feedback), and also surface and atmospheric drivers of uncertainty in regional change per unit global temperature rise. The noise component captures time-dependent evolutions at the seasonal time scale and is sampled from the uncertainties associated with the time scaling algorithm (see Methods). These are randomly generated from the 240x240 correlation matrix of the 1-year errors for the period , estimated from the time scaling residuals, standardised (e.g. see Supplementary Figs. 1f and 2f) across the member ensemble at each time point (e.g. see Supplementary Figs. 1h and 2h). Standardisation here means dividing the residuals by the square root of the unsmoothed variance in Supplementary Figs. 1g and 2g). Once new samples of the standardised error have been sampled, they are rescaled by the smoothed annual error variance and added to the sampled time scaled climate change signal (which are similar in nature to Supplementary Figs. 1b and 2b). In this way, temporal correlations as simulated by the climate model are captured. Note that such temporal correlations may change with future development of the climate models if improved modelling of processes affects the climate variability. NATURE CLIMATE CHANGE 3

4 SUPPLEMENTARY INFORMATION Supplementary Fig 2. As Supplementary Fig. 1, but for percentage anomalies in winter precipitation over England and Wales with combined historical and A1B forcing. This is an example where the biases in volcano years (panel d) are not statistically significant, and therefore do not appear in the smoothed bias (panel e). 4 NATURE CLIMATE CHANGE

5 SUPPLEMENTARY INFORMATION Sources of uncertainty Two thousand sampled plausible realisations have been used in Supplementary Fig. 3 to calculate the contributions to variance in sampled seasonal anomalies at each time point relative to Firstly, each sampled realisation is decomposed into a signal and noise using a low-pass filter at 30 year time scales. An ensemble mean signal is then estimated and removed from each of 2000 signals to produce a set of deviations in the signal about the ensemble mean. Supplementary Fig. 3 shows the two main contributions to the overall variance across the sampled realisations (blue and orange lines) plotted in the context of the square of the ensemble mean signal (red lines). The blue line is the signal uncertainty, representing the variance of the deviations about the ensemble mean signal as a function of time. The orange lines show the variance in the time scaling noise as a function of time (the solid orange line is a smoothed version of the raw estimate shown by the noisy faded orange line). The time scaling noise variance has two sources of uncertainty: (i) climate variability, and (ii) lack of fit in the regional signal associated with scaling by annual global mean temperature. These two sources of uncertainty cannot be easily separated. However, an indication of the size of the climate variability is provided by plotting the variance across the GCM time series available from Ensemble 2 (see Supplementary Table 1) at each time point, following high-pass filtering to remove variability on 30-year time scales or more. This provides a noisy estimate (faded black lines) and a smoothed version (solid black lines). Supplementary Fig. 3 shows the climate change signal in annual global mean temperature is much larger than the uncertainty about that climate change signal and time scaling noise for the whole of the 21st century. At the scale of England and Wales, time scaling noise (orange) contributes more than signal uncertainty for temperature and precipitation for the first part of the 21 st century. For temperature, the time scaling noise is due to internal climate variability. However, for summer precipitation changes about 30% of the time scaling noise is lack of fit to the signal, and the rest is climate variability, which shows a small decrease over the 21st century. For winter precipitation changes, the lack of fit explains 20% increasing to 30% of the time scaling noise over the 21st century. The climate variability also increases during the 21st century. Figs. 1 and 2 and Supplementary Figs. 2a and 2c show that whilst the winters become wetter over the 21st century, even by the end of the 21st century it is still possible to obtain dry winters that are similar in magnitude to earlier dry winters. A possible explanation is that although we see greater moisture convergence in the winter under a warmer climate, the amount that becomes precipitation over the UK depends on the circulation patterns in a particular season. For winter precipitation, the signal uncertainty increases over time but does not reach the level of the overall time scaling noise. For summer temperature signal uncertainty becomes similar in magnitude to climate variability by 2050s and overtakes it by For winter temperature and summer precipitation changes, the signal uncertainty only becomes similar by NATURE CLIMATE CHANGE 5

6 SUPPLEMENTARY INFORMATION Supplementary Fig 3. Evolution of different contributions to the variance in sampled seasonal anomalies relative to for each variable, estimated from the 1-year sampled data relative to average. For a given year, two sources of variance are shown and compared to the variance of the ensemble mean climate change signal (red): a) uncertainty in climate change signal (blue); and b) time scaling noise (faded orange line shows raw estimate, solid orange line shows a smoothed version). A component of time scaling noise comes from climate variability and can be estimated by the ensemble variance of high-pass filtered GCM anomalies at each time point (faded black lines, with solid black lines showing a smoothed version). 6 NATURE CLIMATE CHANGE

7 SUPPLEMENTARY INFORMATION Supplementary Table 1. Summary of ensembles used in construction of UKCP09 6 A1B projections at GCM scale*. Ensemble Number (and references) Model configuration Uncertainty explored by perturbing model parameters Forcing scenario and nature of response Ensemble size 1 S2, S3, S4,14 Atmosphere thermodynamic slab ocean 2 15 Atmosphere Land/atmosphere/sea-ice Equilibrium doubled CO 2 concentrations Land/atmosphere/sea-ice Transient A1B S5, S6 Atmosphere Ocean Transient A1B 4 S7 Atmosphere Sulphate aerosol chemistry Transient A1B 5 15 Atmosphere Land/atmosphere/sea-ice Transient A1B but with no changes in greenhouse gas concentrations 6 S8 Atmosphere and dynamic vegetation model Terrestrial carbon cycle Transient A1B *UKCP09 also ran the equivalent of ensemble 2 for SRES emission scenarios B1 and A1FI, although these are not used in this paper and therefore omitted from the main text and this table. An additional 11 member ensemble of 25km-resolution regional climate model runs, driven by a subset of the -member coupled model atmosphere PPE (ensemble 2 in Supplementary Table 1) was also created. This was used in UKCP09 to provide PDFs at the 25km scale. NATURE CLIMATE CHANGE 7

8 SUPPLEMENTARY INFORMATION References S1. Soden B. J., Wetherald, R. T., Stenchikov, G. L., & Robock, A. Global cooling after the eruption of Mount Pinatubo: a test of climate feedback by water vapor. Science 296, (2008) DOI: /science S2. Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M. & Stainforth, D. A. Quantification of modelling uncertainties in a large ensemble of climate change simulations Nature 430, (2004). S3. Webb, M. J., Senior, C. A., Sexton, D. M. H., Ingram, W. J., Williams, K. D., Ringer, M. A., McAvaney, B. J., Colman, R., Soden, B. J., Gudgel, R., Knutson, T., Emori, S., Ogura, T., Tsushima, Y., Andronova, N., Li, B., Musat, I., Bony, S., & Taylor, K. E. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim. Dyn. 27, 38 (2006). DOI: /s S4. Rougier, J., Sexton, D. M. H, Murphy, J. M., & Stainforth, D. A. Analyzing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J. Climate (2009) doi: S5. Collins, M., Brierley, C. M., MacVean, M., Booth, B. B. B. & Harris, G. R. The sensitivity of the rate of transient climate change to ocean physics perturbations J. Climate 20, (2007) doi: S6. Brierley C. M., Collins M., & Thorpe, A. J. The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model. Clim. Dyn., 34, (2010) DOI /s S7. Ackerley D., Booth B. B. B., Knight S. H. E., Highwood E. J., Frame D. J., Allen M. R., & Rowell D. P.. Sensitivity of Twentieth-Century Sahel Rainfall to Sulfate Aerosol and CO 2 Forcing. J. Climate, 24, (2011) doi: D S8. Booth, B. B. B., Jones C. D., Collins, M., Totterdell, I. J., Cox, P. M., Sitch, S., Huntingford, C., Betts, R. A., Harris, G. R., Lloyd, J. High sensitivity of future global warming to land carbon processes. Environ. Res. Lett., 7, (2012) doi: / /7/2/ NATURE CLIMATE CHANGE