Fig. 7. Temporal evolution of annual outdoor days derived from CMIP6
models. Time series of annual outdoor days (day) derived from 29 CMIP6
GCMs under the historical and SSP5-8.5 scenarios. Thick solid blue line
indicates an ensemble mean of CMIP6 models. Horizontal black and blue
lines denote the 1976-2005 mean and the 2071-2100 mean, respectively.
Difference (2071–2100 minus 1976–2005) in the number of outdoor days
is represented in each plot. The background image was obtained from NASA
Visible Earth.
Underlying mechanisms responsible for the north-south disparity in
climate hazard have been investigated by studying changes in the
probability distribution of temperature over six selected countries
across all climate zones (Fig. 8). Modeling results show an evident
shift of the probability distribution of temperature toward warmer
temperature across the countries (Fig. 8a). The warming shift of the
probability distribution of temperature induces a significant increase
in outdoor days in the European Union and to a less degree in the United
States. The latter enjoys a broader range of climatic zones than the
former. In both regions, climate change causes fewer outdoor days during
the warm season. However, an increase in outdoor days in the cold season
compensates for this decreasing trend (Fig. 9). A large fraction of the
population in the northern high-latitude regions, such as Russia and
Canada, will likely see large increases in outdoor days, benefiting
significantly from climate change. In contrast, the projected
probability distribution of temperature in Brazil, Nigeria, and India,
are likely to move away from the conditions of thermal comfort, limiting
outdoor activities significantly in these large population centers of
the South (Fig. 8). The results of Figure 8 show clearly that the
north-south disparity is rooted in the background climatology of
temperature, mainly the position of the probability distribution of
temperature relative to the range of values used to define an outdoor
day. Similar results and conclusions are obtained by analyzing
projections by CMIP5 models (Fig. S5).