Emergence thresholds for ultra-high exposure to climate extremes within the grid-scale and in the pre-industrial period: a comparative analysis
We subsequently quantify human exposure to climate extremes in a way that facilitates comparison and aggregation across extreme event categories. We consider all people in a grid cell exposed to a climate extreme in a particular year if the climate extreme occurs in that year. We thereby assume that if such a river flood or wildfire occurs somewhere in a 0.5° × 0.5° grid cell, this is sufficiently close to any person located in that grid cell to be considered affected by this extreme event. The demographic data we used converts this annual human exposure to lifetime exposure by summing annual grid fractions of individual event categories.
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. We went as far as possible with the selection of this percentile, because 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 achieved the limit of reliable information that could be obtained 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. Even if the sum of exposed grid cell fractions across a pre-industrial lifetime doesn’t cover the entire grid cell, we still get the birth cohort size associated with that grid cell. Although each birth cohort has a different life expectancy in each country, we sum the number of emerged people. To calculate the total cohort sizes for each birth cohort, we divide the number of people who have emerged globally by the total cohort size. Note that ULE, therefore, 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 in comparison with what they would face in a pre-industrial climate.
Every year beginning in 1960 to 2020, all demographic datasets are changed to represent lifetimes. The life expectancies for each country are first plotted on the basis of the original 5-year groups values, assuming they are representative of the middle of that group. Furthermore, we add 5 years to annual life expectancies to capture the life expectancy of each cohort since birth, 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. Population numbers for 2101 are the mean of the preceding 10 years of the data, taking each year beyond 2020 as the mean of the previous decade. We divide the age totals by 5 to maintain the original population size, and then calculate the estimated population size based on the cohort sizes in each country. This provides the absolute numbers of 0- to 100-year-olds for each year across 1960–2113.
The Inter-Sectoral Impact Model Intercomparison Project: an analysis of extreme event definitions and correlations with population totals
The Inter-Sectoral Impact Model Intercomparison Project gives a simulation protocol for projecting the impacts of climate change across sectors. Impact models representing these sectors are based on the atmospheric boundary conditions of bias-adjusted global climate models from phase 5 of the conjugate model intercomparison project and used in ISIMIP2b. Simulations are used for pre-industrial control, historical and future periods. Future simulations are based on Representative Concentration Pathways. Global projections of annual, grid-scale fractions of exposure to each extreme event category are calculated from ISIMIP2b impact simulations and GCM input data. For the full details of these computations, we refer to ref. 12, but we summarize extreme event definitions below.
We identify the quantile ranges for the lifetime GDP of each birth year and for the singular GRDI map, which is assumed to align with 2020 population totals. 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. 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. 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. The location of ULE can be masked thanks to the quantile range of each vulnerability indicator. With GRDI (Supplementary Fig. 11) and GDP (Supplementary Fig. 12), we compare the lowest and the highest 20% of each indicator by population.
The co-author told Nature that they didn’t stop reporting the findings because of their responsibilities to future generations. 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.
As an example, people who were born in 1960 and spend their lives in Brussels 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. The same cohort is projected to live through 26 heatwaves if the temperature increases by 3.5 C. (If all existing climate policies are implemented, global temperatures are expected to be 2.7 °C higher than in pre-industrial times by 2100.)
The researchers define this as a threshold of lifetime exposure to extreme weather that someone living in a world without climate change would have only a one in 10,000 chance of experiencing. It would be virtually impossible to experience many climate extremes if there hadn’t been climate change.
They then used demographic data to calculate, for a series of generations born between 1960 and 2020, worldwide, the fraction of each generation that would reach that limit across their lifetimes — and how that would vary with different global-warming scenarios.
