5rsta.royalsocietypublishing.orgP h i l .T r a n s .R .S o c .A376:20160452 ........................................................
Table1. ClimPACTweatherextremesindices.
ID definition units sectorofrelevance
TXx annualmaximumdailymaximumtemperature °C health,agricultureandfoodsecurity
......................................... ............................................ .......................................... ..................................... ......................................
TX90p percentageofdaysabovethe90thpercentile
ofdailymaximumtemperatureinthe
1981–2010average
% health,agricultureandfoodsecurity,
waterresourcesandhydrology
......................................... ............................................ .......................................... ..................................... ......................................
CDD maximumnumberofconsecutivedayswith
precipitationlessthan1mm
days health,agricultureandfoodsecurity,
waterresourcesandhydrology
......................................... ............................................ .......................................... ..................................... ......................................
RX5day maximumconsecutive5dayprecipitation mm health,agricultureandfoodsecurity,
waterresourcesandhydrology
......................................... ............................................ .......................................... ..................................... ......................................
members at any given date. Since speciﬁc levels of global warming such as 1.5°C or 2°C were
reached at different times in the different ensemble members, according to the SST forcings used,
any given level of global warming could be associated with different radiative forcings in different
ensemble members. In any given ensemble member at any speciﬁc level of global warming, the
CO2 concentration and SSTs were the same as in the driving CMIP5 model at that GWL. Land
cover was ﬁxed in this simulation—there was no dynamic vegetation nor any time-dependent
anthropogenic land use change.
Some comparison of the higher-resolution atmospheric simulations with the original CMIP5
simulations, is provided by Wyser et al.[20].
(b) Temperatureandprecipitationextremes:theClimPACTindices
To quantify changes in weather extremes projected in our climate simulations, we calculated
a number of indices designed to be relevant to sector-speciﬁc impacts using an established
methodology, ClimPACT [21]( table 1)
(c) Foodsecurity:theHungerandClimateVulnerabilityIndex
To assess implications of climate change for vulnerability to food insecurity, we used an
adaptation of the Hunger and Climate Vulnerability Index (HCVI) [22]. The HCVI was developed
by the United Nations World Food Programme to provide a country-level assessment of
vulnerability to food insecurity as a result of climate-related events. We used a new iteration of the
HCVI which makes use of gridded climate model projections to understand the impact of climate
change on vulnerability to food insecurity, and the beneﬁts that adaptation can bring via scenarios
of adaptation investment [ 23]. This iteration of the HCVI only considers in-country production
of food and does not account for food trade. For this reason, the HCVI is only calculated for
122 developing and least-developed countries (deﬁned here as countries not in the OECD or EU
which can be resolved by the scale of the climate model; i.e. larger than 500 km2).
The index provides quantiﬁcation at the national level across the globe of the scale and
direction of impact of climate change on food insecurity. As such, it aims to provide the following:
(i) information to help policy-makers understand the level of challenge to global food security that
climate change presents; (ii) information on the geography of the impacts and help to evaluate the
relative beneﬁts of mitigation and adaptation responses.