A threshold for the emergence of ultra-violet exposure to extreme events in a pre-industrial environment and population sizes from 1960 to 2020
We define an emergence threshold for ULE to extreme events as the 99.99th percentile of our grid-scale samples of pre-industrial lifetime exposure. When it comes to the selection of this percentile, we went as extreme as possible given the bootstrapping of the pre-industrial control runs. This choice was based on a sensitivity analysis for different percentile values that showed a levelling off of lifetime exposure for percentiles more extreme than 99.99%. The 99.99th percentile was indicated as achieving the limit of reliable information from the empirical distribution. For each extreme event, birth year, GMT pathway and grid cell, we assess if lifetime exposure emerges or passes this threshold of extreme exposure in a pre-industrial climate. If this threshold is passed, we consider the whole birth cohort in this grid cell to have emerged, tallying its size among a global pool of the same birth cohort and GMT trajectory of people projected to live ULE. If the sum of exposed grid cell fractions does not cover the whole grid cell, we still extract the birth cohort size associated with that grid cell. The number of emerged people in each birth cohort is summed up to show how long they live in each country. Each cohort size is compared to the number of people who have emerged with the goal of estimating per birth cohort. ULE does not refer to unprecedented in terms of the magnitude of assets or people exposed, but rather in terms of the number of events accumulated across an average person’s lifespan.
All demographic datasets are modified to represent lifetimes annually, beginning from 1960 to 2020. 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. The life expectancy of a cohort when the data starts at age 5 is captured by adding 5 years to annual life expectancies. 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. In order to maintain original population sizes in this Dataset, we interpolate annual cohort sizes from the original 5-year age groups, and divide age totals by 5, which is the same as for the original population sizes. This provides the absolute numbers of 0- to 100-year-olds for each year across 1960–2113.
IsIMIP: Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) – A simulation protocol for quantifying the impacts of climate change across sectors
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 representing these sectors are run using atmospheric boundary conditions from a consistency of bias-adjusted global climate models that were selected based on their availability of daily data and ability. 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 GCM input datasets. The projections of exposure to each extreme event category are calculated by using impact simulations and GCM input data. The full details of these computations can be found in ref. 12.
The lifetime GDP of each birth year and for the singular GRDI map are the quantile ranges we identify. We apply the vulnerability indicators’ ranks to the totals from our birth cohort and for the matching year. For example, the ranks taken from the lifetime mean GDP of the 2020 birth cohort are aligned with the population totals of newborns in 2020. It is not possible to have perfect bin sizes given the sums of grid-scale population totals, so we bin the vulnerable indicators into five groups of nearly equal population. This groups the richest and poorest and least and most deprived into the aforementioned quantile ranges. A map can be created to hide the locations of ULE, such as birth years and all GMT pathways. We compare the lowest and highest 20% of GDP by population.
They were aware of their responsibilities and did not stop at reporting the findings, according to the co-author. The scientists took their research to the charity Save the Children, which advocates for and supports vulnerable children all over the world. Martina Bogado Duffner, a senior adviser on climate with the charity, says UN member states must now take more urgent and ambitious action to achieve the 1.5 C target, and allocate US$300 billion annually in climate finance, which was agreed at last year’s UN climate conference COP29, held in Baku.
People who were born in the year 1960 are projected to experience three heatwaves in their lifetimes. Those born in 2020 and who live in Brussels will experience an average of 11 heatwaves, assuming warming can be kept to 1.5 °C by 2100. If the temperature goes up to 2.5 C and there are 26 heatwaves for 3.5 C, that same group will live through 18 of them. (If all existing climate policies are implemented, global temperatures are expected to be 2.7 °C higher than in pre-industrial times by 2100.)
