Population totals, cohort sizes, and life expectancy: Implications for ULE, GRDI, and population size data at the ISIMIP grid scale

Climate models were used to set a threshold for different types of weather, including floods and wildfires. For example, it was thought to be an historic threshold for heat in the city, but only six times a century would such an event occur without climate change.

Demographic data for population totals, cohort sizes and life expectancy allow us to project ULE to six extremes. Population totals at the grid scale come from a database. The estimates come from the History Database of the Global Environment and are for the 1860–00 period. We note that these datasets at present do not account for the impact of climate on population dynamics, for example, through changes in migration, fertility and mortality, although these feedbacks may substantially alter the demographic data. Cohort sizes from the Wittgenstein Centre for Demography and Global Human Capital43 provide estimates of country-level population totals every 5 years (between 1950 and 2100) for each 5-year age group (0- to 4-year-olds, 5- to 9-year-olds, and so on, until 95- to 99-year-olds and a final age group for those 100 years and older). The life-expectancy data comes from the United Nations World Population Prospects and describes the life expectancy of kids in their 5th year of life. In this dataset, life expectancy is reported for 5-year-olds to exclude biases from infant mortality. The study requires countries that are resolved at the ISimip grid scale and have life expectancy estimates in their datasets to be included. We refer to the supplementary material of ref. For a broader discussion, explain our application of the data in the analysis below.

We pre process GDP and GRDI products to make sure they compare well with our ULE estimates. The GDP series was extended to 2113 in order to accommodate the longest life expectancy of the 2020 birth cohort. GDP per capita is calculated at the grid scale using ISIMI population totals. The GDP per capita metric is used to calculate lifetime mean GDP per capita. We refer to lifetime mean GDP per capita as simply GDP. For GRDI, we conservatively regrid the original grid cells of  about 1 km to the 0.5° ISIMIP grid. Although GRDI is a map composed of data spanning 2010–2020, we assume this to be representative of 2020, but nonetheless compare it with the 1960–2020 birth cohort range, similar to the rest of the analysis.

The Inter-Sectoral Impact Model Intercomparison Project has a simulation protocol that can be used for projecting the impacts of climate change across sectors. The impact models that are used to represent these sectors are run using atmospheric boundaries from a set of bias-adjusted global climate models from phase 5 of the Coupled Model Intercomparison Project. Impact simulations are run for pre-industrial control (286 ppm CO2; 1666–2099), historical (1861–2005) and future (2006–2099) periods. Future simulations are based on Representative Concentration Pathways (RCPs) 2.6, 6.0 and 8.5 of GCM input datasets. The projections for global exposure to each category are calculated from impact simulations and input data. Extreme event definitions are summarized below, but we refer to ref-12 for full details.

We use two grid-scale indicators of vulnerability to compare with our estimates of ULE. The first is a GDP input dataset using the SSP2 and historical time series. Spatial interactions among cities, land cover, and road network information were used to disaggregate the country to grid level. The Global Gridded Deprivation Index v.1 shows the relative levels of deprivation and poverty for most of the population. The deprivation score uses six input components. The child dependency ratio is a ratio between the population of children and the working-age population. This can indicate vulnerability, for which high ratios indicate a dependency of supposed consumers and non-producers on the working-age (producing) population47. It is a long-term goal of the United Nations to have a stable infant mortality rate that is less than 1 year old per 1,000 live births. Third, the Subnational Human Development Index (SHDI), an assessment of human well-being across education, health and standard of living, originates from the Human Development Index, the latter of which is considered one of the most popular indices to assess country-level well-being. The SHDI improves on the HDI in terms of spatial scale and in representing 161 countries across all world regions and development levels49. multidimensional poverty50 and low values in the ratio of built-up to non-built-up area signals high deprivation, as rural populations are prone to it. The mean and slope of nighttime light intensity are used as indicators of deprivation for areas with low nighttime light intensity. These input components range from 30 arc seconds (roughly 1 km) resolution to subnational regions and are harmonized in an ArcGIS Fishnet feature class for aggregation onto a 0–100 range representing low to high deprivation. The IMR and SHDI components are half of the rest’s weight for the final aggregation. Generations face deprivation and vulnerability to climate extremes through the different dimensions of the GRDI. Our approach to vulnerability does not take into account the actual potential for local adaptation to climate change, but the approach provides relevant information about the potential for local adaptation.

The majority of those affected live in tropical countries, where the effects of warming will be worse than in other regions and where there is less infrastructure for climate resilience. The number of children living through floods, crop failures and fires is distressing. The authors say that the younger a person is, the higher the likelihood of exposure to climate extremes.

The researchers have done an analysis for every country. Some 111 million children born in 2020 will have what the authors call “an unprecedented life” of heatwave exposure in a world where temperatures increase to 3.5 °C. The researchers say that represents 92% of that birth cohort. Unprecedented heatwave exposure will still be experienced by around 62 million children born in 2020 even with 1.5 °C of warming.

The threshold of lifetime exposure to weather that one can only experience once is defined by the researchers as a one in 10,000 chance. “If you are beyond that limit, it’s virtually impossible to experience that many climate extremes if there would not have been climate change,” says Thiery.

They used demographic data to calculate the fraction of the generation that would reach that limit across their lifetimes, and how that would vary with different global-warming scenarios.