A simple scaling approach to produce climate scenarios of local precipitation extremes for the Netherlands

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1 Supplementary Material to A simple scaling approach to produce climate scenarios of local precipitation extremes for the Netherlands G. Lenderink and J. Attema

2 Extreme precipitation during 26/27 th August 2010 Supplementary Figure 1. Rain radar images running from 1200 local time (LT) 26 August 2010, with steps of one hour (left-to-right, top to bottom) until 0200 LT 27 August. A relatively thin line of high precipitation intensity (red colors) oriented in the southwest-northeast direction, and slowly moving in the northeast direction, is embedded in a larger area with lighter precipitation (white and grey colours). During this event more than 130 mm of precipitation fell in the eastern part of the Netherlands. Supplementary Figure 1 shows the rain radar images during 15 hours from an event that caused more than 100 mm of rain and severe flooding, including disruption of traffic on a major highway, in the eastern part of the Netherlands (Brauer et al 2011). Supplementary Figure 2 illustrates the supply of moisture (partly from the subtropics) that played a big role in this event. Thus, in this event both small scale processes related to atmospheric convection, and large scale processes acting to supply moisture play a role.

3 Supplementary Figure 2. Left: Tracking of air parcels that produced the heaviest rain on the 26 th. Moist parcels are red, whereas drier parcels are cyan. It is shown that a large part of the moist parcels originated from the sub-tropics. Right: vertically integrated water vapor content of the atmosphere (in units of kg/m 2, equivalent to mm s of rain when all water vapor is converted to rain). A small band an atmospheric river transports very moist air from the subtropics to the Netherlands (thanks to Sarah Kew).

4 Observed scaling of hourly precipitation for winter and summer Supplementary Figure 3. Dependency of hourly precipitation extremes on dew point temperature derived from 26 stations in the Netherlands; left: winter DJF, right: summer JJA. Dew point temperatures are taken 4 hours before the event. Shown are results for the 90, 99 and 99.9th percentile of wet events. Solid line: raw data, stippled lines: estimated from a GPD fit, including uncertainty estimates (grey shading). Red and black linear lines show (exponential) dependencies of 7 and 14 % per degree.

5 Changes in precipitation extremes: example of the differences between the RACMO ensemble members Supplementary Figure 4. Changes in 99th percentile daily precipitation for wet days in winter (upper 8 panels) and summer (lower eight panels). Differences (in percentage) are between and The eight panels show the results of the 8 different ensemble members.

6 Supplementary Figure 5. As Supplementary Figure 4, but now daily maximum of hourly precipitation

7 Comparison changes in means and extremes Supplementary Figure 6. Summer and winter changes from compared to averaged over all ensemble members; upper panels the mean precipitation, lower panels 99 th percentile of daily precipitation on wet days.

8 Normaled changes in hourly and daily precipitation extremes Supplementary Figure 7. Changes in daily precipitation in winter and summer per degree rise in local dew point temperature (derived for days with rain). Changes (in % per degree) are derived comparing (left) and (right) to the reference period The average dew point temperature change over the W-EUR box shown is given by DT in the caption. Supplementary Figures 7 and 8 show the precipitation response scaled with the local dew point temperature change for daily and hourly precipitation change, similar to Figure 4 (upper panels) of the main paper. For all plots, the reference period is , whereas the future period is shown in the plot title. Pattern correlations between these normalized responses are quite high, ranging from 84 % (daily precipitation in winter) to 76 % (hourly precipitation in summer) (see black numbers in the left column in Table 1). We note that the common reference period, however, slightly biases these estimates. Comparing the with , and with , in order to assure independent data, the pattern correlation are slightly lower, with values between 64 and 73 %. In the latter case, the pattern correlation of the response derived from a single model simulation drops to below 24% (red numbers in Table 1).

9 Supplementary Figure 8. Changes in daily maximum of hourly precipitation in winter and summer per degree rise in local dew point temperature (derived for days with rain). Changes (in % per degree) are derived comparing (left) and (right) to the reference period The average dew point temperature change over the W-EUR box shown is given by DT in the caption w.r.t vs w.r.t w.r.t vs w.r.t DJF daily 0.84 / / 0.13 DJF hourly 0.83 / / 0.24 JJA daily 0.80 / / 0.12 JJA hourly 0.76 / / 0.11 Table 1. Pattern correlation between the normalized response in daily in hourly precipitation extremes derived from comparing two change signals (with periods prevised in the upper row). In black: correlation derived from the 16 member ensemble, and in red: correlation derived from member 1 of the ensemble.

10 Different indices of daily precipitation extremes. Supplementary Figure 9. Comparison between different indices of extreme daily precipitation for winter. Upper panels, the 95, 99, and 99.9 th percentile of daily precipitation (left to right); lower panels, same percentiles but then for wet days only. Supplementary Figure 10. Same as previous figure, but then for summer.

11 Changes in convective available potential energy (CAPE) Supplementary Figure 11 Probability of exceedance of convective available potential energy (CAPE) derived from the first 8 ensemble members of the RACMO simulations (hourly output of 7 locations across the Netherlands). Results of individual members are stippled, and the results derived from the pooled data of the first 8 members are given by the thick solid lines. Supplementary Figure 12. Different percentiles (99, 99.9, and th ) of Convective Available Potential Energy (CAPE, in m 2 s -2 ) derived from the RACMO2 simulations; left winter, right summer. Thin lines are results of single members, the thick line the result averaged over 8 members. The grey lines denote a 30% increase from 1995 to 2050, expected based on the two degrees warming. Results are derived from hourly data from 7 stations locations distributed equally over the Netherlands.

12 Comparison dew point temperature changes Supplementary Figure 13. Changes in dew point temperature derived from compared to for winter (upper panels) and summer (lower panels). From left to right: dew point temperature changes conditioned on days with more than 0.1 mm (left), 1 mm (middle) and 5 mm (right).

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