Early benefits of mitigation in risk of regional climate extremes

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1 In the format provided by the authors and unedited. DOI: /NCLIMATE3259 Early benefits of mitigation in risk of regional climate extremes Andrew Ciavarella 1 *, Peter Stott 1,2 and Jason Lowe 1,3 Large differences in climate outcomes are projected by the signal, however. Broadly speaking, temperature studies concerning 1 Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK. 2 College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, Streatham Campus, North Park Road, Exeter EX4 4QF, UK. 3 Priestley International Centre for Climate, University of Leeds, Leeds LS2 9JT, UK. * andrew.ciavarella@metoffice.gov.uk Sensitivity to smoothing of percentile time series The HOPE Dates used in Table 1 of the main article were derived from 15-year rolling box-car means of the raw percentile time series calculated from the empirical MME distribution of seasonal values. The finite size of the MME (58 members for RCP 2.6 and 64 members for RCP 8.5) means that the location of a percentile series over time is subject to artificial noise due solely to the finite size of the ensemble. The 15-year smoothing of the raw percentiles attempts to remove this noise, to some degree mimicking the use of an ensemble 15 times the size (870 and 960 members respectively). Note that this is different from calculating percentiles from the distribution of 15-year means of member time series which would remove the inter-annual variability that is responsible for generating extreme values. In SI Figure 2 we present warm season HOPE Dates calculated as per Table 1 but instead derived also from percentile time series which have been smoothed using rolling 10-year and 5-year means as well as the results using raw percentiles. From 15 to 10 year smoothing the results are broadly stable with respect to the smoothing period, with earliest, latest or best estimates mostly changing by a few years at worst for the SREX regions and ranging from 2 to 5 years at worst for the continents. The story of which regions are earlier and later remains the same. The bias introduced by removing the smoothing is clear in most regions. HOPE Dates using unsmoothed percentiles are often significantly later, by around a decade. However we believe these dates to be biased artificially late and the relative NATURE CLIMATE CHANGE 1

2 stability of the results once smoothing of only 5 years is introduced supports this. Additional sensitivities of the study Small differences in spatial coverage exist between model members and the observations along coastal fringes which result from differences in model resolution and land fraction masks upon regridding to lower resolution of CRUTEM4. We have examined versions of the regional observational series that result from mutually masking CRUTEM4 coverage with each of the regridded model masks and the differences are in general very small over the period of interest and should not significantly affect the results of the validation. NATURE CLIMATE CHANGE 2

3 Figure 1: Map of the SREX regions used in the study as represented by the common 5 5 resolution grid onto which all model data is transformed. Region codes used in the main article are inset. Cells coloured (arbitrarily) in red, green, blue and beige are assigned to a region, while numbered orange boxes depict the regions as defined in the SREX. 1 HadCRUT4 2 land fraction values appear as percentages rounded to the nearest integer in cells that are neither all land (100%) nor all sea (0%). Cells that are all ocean are coloured pale blue. 3 NATURE CLIMATE CHANGE 3

4 Figure 2: HOPE Date results derived from 15, 10, 5 year smoothed and unsmoothed percentiles (left to right in each region column). The results for the range of HOPE Dates are broadly very similar with 15 to 10 year smoothing. Introducing even 5 year smoothing tends to remove much of the late bias present when using unsmoothed percentiles. For season colour coding see caption to SI Figure 5. Region names are coloured red for validation failure. 4 NATURE CLIMATE CHANGE 4

5 Figure 3: Calculation of HOPE Dates from the multi-model ensemble for all regional warm seasons, details and colour legend as per caption to Figure 2 of main article. Regional warm seasons failing at least one of the validation tests are labelled in red. 5 NATURE CLIMATE CHANGE 5

DOI: 10.1038/NCLIMATE3259 Figure 4: Latest possible HOPE Dates achievable under emission scenarios which do not exceed peak RCP 2.6 temperatures.

6 DOI: /NCLIMATE3259 Figure 4: Latest possible HOPE Dates achievable under emission scenarios which do not exceed peak RCP 2.6 temperatures. Best estimate latest possible date is the vertical red line (with boot strap range in pale red) defined by the intersection of the 15-year smoothed RCP8.5 90% (dark red line) with peak RCP2.6 95% temperatures (dotted line). The value in brackets is the number of years between best estimates of the HOPE Dates and the latest possible HOPE Date, giving an indication of the lapse in time possible between the start of rapid emissions reductions in the RCP 2.6 scenario and the real world. 6 NATURE CLIMATE CHANGE 6

7 Figure 5: Comparison of warm season HOPE Dates calculated from the multi-model ensemble (colours) with those from individual models (black) for (a) the SREX regions and (b) global land and continental regions. For individual model dates whiskers indicate full date range, boxes the 2 nd to penultimately ranked date, tick marks the individual other model dates and the central bar the mean across the individual model dates. Seasons are indicated by colour coding of the multi-model HOPE Dates as DJF = blues, MAM = greens, JJA = reds, SON = oranges with best estimates as dark colours and bootstrapped ranges as pale colours. Regions 4. (CNA), 7. (AMZ), 15. (WAF), 20. (CAS), 23. (SAS) and 26. (SAU) failed at least one of the validation tests on the multi-model ensemble in their warm season. 7 NATURE CLIMATE CHANGE 7

8 Figure 6: Time series of regional warm season near surface air temperatures ( ) from the historical multi-model ensemble (percentiles filled with orange colours) versus observed temperatures from CRUTEM4. Decimal values in each panel show the value of r from validation test 1 with significance value p given in parentheses, coloured red if a region fails this test (p > 5%). Regional warm seasons that pass the validation tests have observed series that are not easily distinguishable from members of the multi-model ensemble. 8 NATURE CLIMATE CHANGE 8

9 Figure 7: Power spectra of CMIP5 historical experiment time series for warm season mean temperatures over the period , with vertical axis units in K 2. Equivalent observational time series in black from CRUTEM4. The full range of total variances over all CMIP5 ensemble members used is indicated by a vertical bar while observed total variance is depicted with a diamond and these are coloured red if observed total variance lies outside of the model range (7. AMZ and 23. SAS hence fail validation test 2). These spectra are also used to generate the test ensemble against which the significance of correlations is assessed in validation test 1. 9 NATURE CLIMATE CHANGE 9

10 Model Centre Historical RCP 2.6 RCP 8.5 HadGEM2-ES MOHC CNRM-CM5 CNRM-CERFACS bcc-csm1-1 BCC bcc-csm1-1-m BCC BNU-ESM BNU CanESM2 CCCma CSIRO-Mk3-6-0 CSIRO-QCCCE FIO-ESM FIO IPSL-CM5A-LR IPSL IPSL-CM5A-MR IPSL FGOALS-g2 LASG-CESS MIROC-ESM MIROC MIROC-ESM-CHEM MIROC MIROC5 MIROC MPI-ESM-LR MPI-M MPI-ESM-MR MPI-M MRI-CGCM3 MRI GISS-E2-H NASA-GISS GISS-E2-R NASA-GISS CCSM4 NCAR NorESM1-M NCC NorESM1-ME NCC HadGEM2-AO NIMR-KMA GFDL-CM3 NOAA-GFDL GFDL-ESM2G NOAA-GFDL GFDL-ESM2M NOAA-GFDL CESM1-CAM5 NSF-DOE-NCAR Total: Table 1: Content of the multi-model ensemble broken down by experiment and number of ensemble members contributed per model. We use 27 CMIP5 models being those for which both RCP scenarios and historical data were available. References 1 Christopher B Field. Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press, Colin P Morice, John J Kennedy, Nick A Rayner, and Phil D Jones. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The hadcrut4 data set. Journal of Geophysical Research: Atmospheres ( ), 117(D8), NATURE CLIMATE CHANGE 10

Understanding the regional pattern of projected future changes in extreme precipitation

In the format provided by the authors and unedited. Understanding the regional pattern of projected future changes in extreme precipitation S. Pfahl 1 *,P.A.O Gorman 2 and E. M. Fischer 1 Changes in extreme