Data for The missing risks of climate change

<p>This repository contains the data behind the quantitative figures (figures 1, 3, and 4) in&nbsp;Rising, James, et al. &quot;The missing risks of climate change.&quot;&nbsp;<em>Nature</em>&nbsp;610.7933 (2022): 643-651.&nbsp;https://www.nature.com/articles/s41586-022-05243-6.</p> <p>The figures directory contains the figures (in their accepted paper form). The data directory contains CSV files with the associated data. The rows and columns are defined as follows:</p> <p>&nbsp;-&nbsp;fig1-mc.csv: Rows describe Monte Carlo draws describing the uncertainty for each scenario (SSP1-2.6 and SSP3-7.0) and outcome variable (in the &quot;variable&quot; column). The &quot;run&quot;&nbsp;column describes the basis for each draw of the uncertainty (e.g., model used). The &quot;unit&quot; column provides the units for the &quot;value&quot; column. The &quot;compound&quot; column is TRUE if the rows report compounded uncertainty, and false if the uncertainty is only from the individual analysis stage.</p> <p>&nbsp;- fig3-zscores.csv: Each row is a grid cell across the globe, reporting z-scores for various hazards and the population from GPW v4.0 (https://sedac.ciesin.columbia.edu/data/collection/gpw-v4). The z-scores are calculated compared to the recent history (1980-2010) from either longer historical data from CRU TS,&nbsp;with the column prefix &quot;hist.&quot;, or from SSP3-7.0 estimates from WorldClim bioclimatic variables for 2050, with the column prefix &quot;ssp370.&quot;. Column suffixes describe various hazards: &quot;wet&quot; is average precipitation in the wettest month, &quot;dry&quot; is annual precipitation, &quot;logwet&quot; is as &quot;wet&quot; but evaluated in logs, &quot;logdry&quot; is as &quot;dry&quot; but evaluated in logs, &quot;pcv&quot; is precipitation seasonality (coefficient of variation), &quot;hot&quot; is the maximum temperature of the warmest month, and &quot;cld&quot; is the minimum temperature of the coldest month. &quot;topcol&quot; reports the column with the most extreme z-score (with the z-score in &quot;topscore&quot; and a label in &quot;toplabel&quot;).</p> <p>&nbsp;- fig4-dmgfunc.csv: Monte Carlo draws of the uncertainty in number of people affected across 16 impacts, reported in &quot;affected&quot; as a fraction of the global population, for each temperature change from preindustrial, reported in &quot;temp&quot;.</p> <p>&nbsp;- fig4-pdfs.csv: Monte Carlo draws of the uncertainty in the number of people affected for each of 16 impacts and four aggregates. The fraction of the global population affected in reported in &quot;affected&quot; for the temperature change from preindustrial reported in &quot;temp&quot;. The impact is labeled in &quot;name&quot; and the aggregate category is reported in &quot;rname&quot;.</p> <p>Additional details on the generation of these data are included in the SI of the paper.</p>