It’s time for adults to act like grown ups on climate change
Population totals, cohort sizes, and life expectancy forecasts for the United Nations World Population Prospects (UNWPP) from 1960 to 2020
The projection of the population totals, cohort sizes and life expectancy allows us to forecast the ULE to these six extremes. Population totals at the grid scale come from the ISIMIP database (Fig. The estimates for the historical period are from the History Database of the Global Environment. 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. Estimates of country-level population totals for each 5-year age group can be found in the cohort sizes of the global human capital 43. Life expectancy data come from the United Nations World Population Prospects (UNWPP; ref. 44) and describe the life expectancy of 5-year-olds at the country level for 5-year blocks (1950–1955 to 2015–2020). In this dataset, life expectancy is reported for 5-year-olds to exclude biases from infant mortality. This study requires countries to have cohort and life expectancies estimates in these datasets if they can be located at the ISIMIP grid scale. We refer to the supplementary material of ref. We need to explain our applications in this analysis but we don’t know how to do a broader discussion.
Beginning in 1960 to 2020, the demographic datasets can be changed to represent lifetimes. Life expectancies for each country are first linearly interpolated to annual values by assuming that the values of the original 5-year groups are representative of the middle of that group. We add 5 years to every cohort’s life expectancies to capture the life expectancy as the original data begin at age 5. As the maximum UNWPP life expectancy for people born in 2020 prescribes the final year in this analysis (2113), annual population totals must be extrapolated to reach this year. For population totals, we take each year beyond 2100 as the mean of the preceding 10 years of the dataset, such that population numbers for 2101 are the mean of 2091–2100. For cohort sizes in each country, we interpolate annual cohort sizes and age groups from the original 5-year age groups and divide age totals by 5 to maintain original population sizes in this dataset and linearly extrapolate these estimates to 2113. The total number of 100 year-olds for each year across the 1960–2113 period is provided.
Source: Global emergence of unprecedented lifetime exposure to climate extremes
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) – Vulnerabilities, Lifetimes and Population Ranks
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a simulation protocol for projecting the impacts of climate change across sectors such as biomes, agriculture, lakes, water, fisheries, marine ecosystems and permafrost (www.isimip.org). The impact models represent these sectors using a set of biases-adjusted global climate models that were selected from the 5th phase of the CMIP5 for their availability of daily data. Impact simulations are run for pre-industrial control (286 ppm CO2; 1666–2099), historical (1861–2005) and future (2006–2099) periods. The simulations are based on the GCM input datasets. Global projections of annual, grid-scale fractions of exposure to each extreme event category are calculated from ISIMIP2b impact simulations and GCM input data. The details of these computations are described in ref. 12.
The lifetime GDP of each birth year is identified by 20% of the quantile ranges and the singular GRDI map is identified by 20%. To this end, we rank the vulnerability indicators and apply these ranks to our birth cohort totals on the same grid and for the matching year. The 2020 birth cohort’s ranks taken from the lifetime mean GDP are aligned with population totals of newborns in 2020. Finally, we bin the ranked vulnerability indicators by their associated population totals into five groups of nearly equal population (as it is not possible to achieve perfect bin sizes given the sums of grid-scale population totals). This groups the richest and poorest and least and most deprived into the aforementioned quantile ranges. A map that is used to mask the locations of ULE can be found under the vulnerability indicator’s quantile range. With GRDI (Supplementary Fig. 11) and GDP (Supplementary Fig. 12), we compare the lowest and the highest 20% of each indicator by population.
Climate change is happening right before the eyes of children in the United States and around the world, and that’s a big part of the kids’ lives
The authors of the study collaborated with researchers from Save the Children and wrote a report that incorporated the findings of the Nature study but also heard the voices of children. There are people that claim that the climate crisis is going to be a major problem in the future. “What they don’t realize is that it is happening right before their eyes.”
The researchers did this analysis for each country and the results were devastating. 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. They say that represents a lot of the birth cohort. Even with 1.5 C of warming, 62 million children will still be exposed to an unprecedented heatwave in 2020.
