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Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model

Richard A. Betts, Lorenzo Alfieri, Catherine Bradshaw, John Caesar, Luc Feyen, Pierre Friedlingstein, Laila Gohar, Aristeidis Koutroulis, Kirsty Lewis, Catherine Morfopoulos, Lamprini Papadimitriou, Katy J. Richardson, Ioannis Tsanis, Klaus Wyser
Published 2 April 2018.DOI: 10.1098/rsta.2016.0452

Article Figures & Data

Figures

  • Figure 1.
    Figure 1.

    Hunger and Climate Vulnerability Index for 1981–2010 climate (ensemble mean across the bias-corrected HadGEM3 ensemble).

  • Figure 2.
    Figure 2.

    Simulated changes in annual daily maximum temperature relative to 1981–2010 at 2°C global warming, for individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean. The labels above each panel identify the driving CMIP5 model (or ensemble mean).

  • Figure 3.
    Figure 3.

    Simulated changes in the percentage of days with daily temperature above the 90th percentile for 1981–2010 at 2°C global warming, for individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean. The labels above each panel identify the driving CMIP5 model (or ensemble mean).

  • Figure 4.
    Figure 4.

    Simulated changes in the number of consecutive dry days relative to 1981–2010, at 2°C global warming, for individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean. The labels above each panel identify the driving CMIP5 model (or ensemble mean).

  • Figure 5.
    Figure 5.

    Simulated changes in the annual maximum rainfall over 5 days relative to 1981–2010, at 2°C global warming, for individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean. The labels above each panel identify the driving CMIP5 model (or ensemble mean).

  • Figure 6.
    Figure 6.

    Simulated changes in the average length of flood events (number of days in which the cumulative daily rainfall excess is positive, compared with the 95th percentile in 1981–2010, at 2°C global warming, for individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean. The labels above each panel identify the driving CMIP5 model (or ensemble mean).

  • Figure 7.
    Figure 7.

    Hunger and Climate Vulnerability Index calculated for simulated climate states at 2°C global warming for five individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean.

  • Figure 8.
    Figure 8.

    Change in Hunger and Climate Vulnerability Index relative to baseline calculated for simulated climate states at 2°C global warming, for five individual HadGEM3 simulations driven by SSTs and SICs from different members of the CMIP5 ensemble, and the ensemble mean.

  • Figure 9.
    Figure 9.

    Changes in run-off for mean flows simulated by the JULES ecosystem–hydrology model under six climate simulations at 2°C global warming. (a) Ensemble mean and (b) percentage of models agreeing on increased flow.

  • Figure 10.
    Figure 10.

    Distributions of changes in run-off for mean flows simulated by the JULES ecosystem–hydrology model under the ensemble of six climate projections at 1.5°C (blue) and 2°C (orange) global warming. Boxes show the 25th and 75th percentile changes, whiskers show the range, circles show the four projections that do not define the ends of the range, and crosses show the ensemble means. Numbers in square brackets show the ensemble-mean flow in the baseline, in millimetres of rain equivalent.

  • Figure 11.
    Figure 11.

    Distributions of changes in run-off for low flows (flows for lowest 10% of time) simulated by the JULES ecosystem–hydrology model under the ensemble of six climate projections at 1.5°C (blue) and 2°C (orange) global warming. Boxes show the 25th and 75th percentile changes, whiskers show the range, circles show the four projections that do not define the ends of the range, and crosses show the ensemble means. Numbers in square brackets show the ensemble-mean flow in the baseline, in millimetres of rain equivalent.

  • Figure 12.
    Figure 12.

    Comparison of global mean changes in climate extremes indices relative to 1981–2010 at 2°C and 1.5°C global warming for individual ensemble members and ensemble mean. (a) Change in annual daily maximum temperature; (b) percentage of days with maximum temperature above 90th percentile for 1981–2010; (c) change in consecutive dry days; (d) change in annual maximum 5-day rainfall.

  • Figure 13.
    Figure 13.

    Global mean percentage changes relative to 1981–2010 in (a) precipitation over land, (b) mean run-off flows, (c) low run-off lows (10th percentile), at 2°C and 1.5°C global warming.

  • Figure 14.
    Figure 14.

    Difference in annual maximum daily maximum temperature between 2°C and 1.5°C global warming, for individual ensemble members and ensemble mean.

  • Figure 15.
    Figure 15.

    Difference between 2°C and 1.5°C global warming for percentage of days with maximum temperature above 90th percentile of baseline, for individual ensemble members and ensemble mean.

  • Figure 16.
    Figure 16.

    Difference in consecutive dry days between 2°C and 1.5°C global warming, for individual ensemble members and ensemble mean.

  • Figure 17.
    Figure 17.

    Difference in annual maximum 5 day rainfall between 2°C and 1.5°C global warming, for individual ensemble members and ensemble mean.

  • Figure 18.
    Figure 18.

    Hunger and Climate Vulnerability Index at 1.5°C global warming (ensemble mean).

  • Figure 19.
    Figure 19.

    Difference in Hunger and Climate Vulnerability Index between 2°C and 1.5°C global warming, for individual ensemble members and ensemble mean.

  • Figure 20.
    Figure 20.

    Difference between 2°C and 1.5°C global warming in percentage changes in mean (top) run-off in JULES simulations driven by the ensemble of HadGEM3 simulations. Note that the use of percentage changes emphasizes changes in regions where the baseline streamflow is small.

Tables

  • Table 1.

    ClimPACT weather extremes indices.

    IDdefinitionunitssector of relevance
    TXxannual maximum daily maximum temperature°Chealth, agriculture and food security
    TX90ppercentage of days above the 90th percentile of daily maximum temperature in the 1981–2010 average%health, agriculture and food security, water resources and hydrology
    CDDmaximum number of consecutive days with precipitation less than 1 mmdayshealth, agriculture and food security, water resources and hydrology
    RX5daymaximum consecutive 5 day precipitationmmhealth, agriculture and food security, water resources and hydrology
  • Table 2.

    Proxies for flood and drought events used in the HCVI.

    extreme weather eventdescription of proxy
    average length of flood eventsnumber of days in which the cumulative daily rainfall excess is positive, compared with the 95th percentile in the 1981–2010 average
    average length of drought eventsnumber of days in which the cumulative daily rainfall deficit is positive, compared with the 20th percentile in the 1981–2010 average
  • Table 3.

    Time of reaching GWLs of 1.5°C and 2°C in the raw output from the HadGEM3 climate simulations, driven by different sets of CMIP5 sea-surface temperatures. The dates are the centre year of a 20-year period for which the climate data are applied to the calculation of the ClimPACT indices.

    driving SSTs1.5°C2.0°C
    IPSL-CM5A-LR20152030
    GFDL-ESM2M20402055
    HadGEM2-ES20272039
    IPSL-CM5A-MR20202034
    MIROC-ESM-CHEM20232035
    ACCESS1–020342046
  • Table 4.

    Time of reaching GWLs of 1.5°C and 2°C in each bias-corrected output from the HadGEM3 climate simulations, driven by different sets of CMIP5 sea-surface temperatures. The dates are the centre year of a 20 year period for which the climate data is applied to the HCVI calculation and JULES simulations.

    driving SSTs1.5°C2.0°C
    IPSL-CM5A-LR20242035
    GFDL-ESM2M20362051
    HadGEM2-ES20192033
    IPSL-CM5A-MR20232036
    MIROC-ESM-CHEM20202032
    ACCESS1-020262040
  • Table 5.

    Global mean changes at 2°C global warming compared to present day for individual ensemble members, for the ClimPACT indices, the flood and drought proxies used as input to the HCVI calculations, and percentage change in mean precipitation (Pmean), mean run-off (Rmean) and low run-off (Rlow).

    IPSL-CM5A-LRGFDL-ESM2MHadGEM2-ESIPSL-CM5A-MRMIRC-ESM-CHEMACCESS1-0ensemble mean
    TXx (°C)2.12.82.52.92.42.82.6
    TX90p (% time)20.124.324.929.023.527.925.0
    CDD−3.00.9−3.4−5.7−2.0−5.5−2.9
    RX5day (mm)3.55.46.96.86.06.75.9
    drought proxy0.760.89n.a.0.380.380.660.61
    flood proxy0.830.82n.a.0.750.730.780.78
    Pmean (%)2.13.45.03.05.32.94.0
    Rmean (%)2.46.58.14.48.64.95.8
    Rlow (%)−2.03.811.28.09.45.15.9
  • Table 6.

    Global mean changes at 1.5°C global warming compared to present day for individual ensemble members, for the ClimPACT indices, the flood and drought proxies used as input to the HCVI calculations, and percentage change in mean precipitation (Pmean), mean run-off (Rmean) and low run-off (Rlow).

    IPSL-CM5A-LRGFDL-ESM2MHadGEM2-ESIPSL-CM5A-MRMIROC-ESM-CHEMACCESS1-0ensemble mean
    TXx (°C)1.21.91.72.01.51.91.7
    TX90p (% time)10.015.716.219.214.118.315.6
    CDD−1.20.7−1.3−5.40.0−3.8−1.6
    RX5day (mm)1.13.64.54.64.04.33.6
    drought proxy0.740.48n.a.0.390.160.310.42
    flood proxy0.750.73n.a.0.730.790.730.75
    Pmean (%)1.40.93.11.33.92.42.2
    Rmean (%)2.10.75.40.76.75.03.9
    Rlow (%)−3.40.35.92.25.94.92.6
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Keywords

1.5°C
Paris Agreement
2°C
global climate impacts
water resources
terrestrial ecosystems
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