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Dataset Extreme sea level at different global warming levelsTebaldi, ClaudiaTebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746–751 (2021). https://doi.org/10.1038/s41558-021-01127-1 (as shown in Fig. 1 b,d,f and Fig. 2 top panel for 7,283 locations). Global warming levels reached by 2100 causing a present-day 100-year extreme sea level event to become at least an annual event (for central value and low and upper bounds), and extended mean value of the difference between current 100-yr and the 1-yr events. The numbers from 1 to 9 along the 3rd, 4th and 5th columns correspond to the warming levels in the legend of Figure 1: 1.5, 2, 2+, 3, 4, 5, none (The + sign associated with 2 and 5 °C indicates projections that include SEJ-derived estimates of ice-sheet contribution to RSLC.) See also the data in an interactive way at the Perry World House Global Climate Security AtlasDataset Return period (years) in future (2071–2100) for discharge corresponding to a 30-year flood in the past (1971–2000), for CMIP6 under the ssp585 scenarioHirabayashi, YukikoHirabayashi, Y., Tanoue, M., Sasaki, O. et al. Global exposure to flooding from the new CMIP6 climate model projections. Sci Rep 11, 3740 (2021). https://doi.org/10.1038/s41598-021-83279-w (Fig. 1) Projected change in river flood frequency under the ssp585 climate change scenario. Multi-model median return period (years) in future (2071–2100) for discharge corresponding to a 100-year flood in the past (1971–2000), for CMIP6 under the ssp585 (SSP5-RCP8.5) scenario. See also data in an interactive way at the Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Return period (years) in future (2071–2100) for discharge corresponding to a 10-year flood in the past (1971–2000), for CMIP6 under the ssp585 scenarioHirabayashi, YukikoHirabayashi, Y., Tanoue, M., Sasaki, O. et al. Global exposure to flooding from the new CMIP6 climate model projections. Sci Rep 11, 3740 (2021). https://doi.org/10.1038/s41598-021-83279-w (Fig. 1) Projected change in river flood frequency under the ssp585 climate change scenario. Multi-model median return period (years) in future (2071–2100) for discharge corresponding to a 100-year flood in the past (1971–2000), for CMIP6 under the ssp585 (SSP5-RCP8.5) scenario. See also data in an interactive way at the Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Historical trend in the frequency of occurrence of most intense tropical cyclonesMurakami, HiroyukiMurakami, Hiroyuki; Delworth, Thomas L.; Cooke, William F.; Zhao, Ming; Xiang, Baoqiang; Hsu, Pang-Ch, 2020, Detected climatic change in global distribution of tropical cyclones, PNAS 2020 117 (20) 10706-10714, https://doi.org/10.1073/pnas.1922500117 Historical trend in the frequency of occurrence of the most intense tropical cyclones (from 1980 to 2020) - An increase of 0.1 per year means one additional hurricane every 10 years. Here intense storms are defined as the same as major hurricanes (>=96 kt or >=111 mph in maximum wind speed). In the netCDF file, "slope" is the TCF trend in the units of "number per day." So, please multiply it by 365 when you plot the trend in the units of "number per year" as shown in the figure in the paper. The variable "pval" is the p-value for the trend. When pval is less than or equal to 0.05, the trend on the grid cell is statistically significant at the 95% level. The projected future change in frequency is shown in Fig. S3a for global and Fig. S3b for North Atlantic, respectively, in the Supplement of the paper. The model project decreasing number of storms in both global and North Atlantic toward the end of this century. Regarding storm intensity, although the results were not shown in the paper, the climate models project increasing storm intensity under a warmer climate. Further information in Carbon Brief https://www.carbonbrief.org/global-warming-has-changed-spread-of-tropical-cyclones-around-the-world/ See also data in an interactive way at Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Maximum sustainable fish yield now and projected in the future with climate changeFree, ChristopherFree CM, Mangin T, Molinos JG, Ojea E, Burden M, Costello C, et al. (2020) Realistic fisheries management reforms could mitigate the impacts of climate change in most countries. PLoS ONE 15(3): e0224347. https://doi.org/10.1371/journal.pone.0224347 Maximum sustainable yield (MSY in metric tons mt = 1000kg), historical (2012–2021) and future (2091–2100) for three climate change scenarios RCP4.5, RCP6.0, and RCP8.5, and percent change, in each exclusive economic zone EEZ. See Fig. 1 in paper for reference. "Maximum sustainable yield (MSY) of the evaluated stocks is forecast to decrease by 2.0%, 5.0%, and 18.5% from 2012–2021 to 2091–2100 under RCPs 4.5, 6.0, and 8.5, respectively. Across emissions scenarios, MSY is generally projected to decrease for equatorial countries and increase for poleward countries. Particularly dramatic reductions in MSY are predicted for the equatorial West African countries. Even under the least severe emissions scenario, nineteen countries, fifteen of which are in West Africa, are projected to experience reductions in MSY of 50–100%. The number of countries projected to experience dramatic losses in MSY, and the intensity of these losses, expands under the more severe emissions scenarios. In the most severe scenario, 51 countries are expected to experience reductions in MSY of 50–100%. All eighteen West African countries south of Senegal and north of Angola (including these two countries) are forecast to experience reductions in MSY greater than 85%. The equatorial Indo-Pacific and South America are also projected to experience considerable losses in MSY under the three emissions scenarios, with especially pronounced losses under RCP 8.5. Twenty-two countries are projected to experience increases in MSY under all three emissions scenarios with seven of these countries showing a 15% average increase in MSY across scenarios. The five most consistent and pronounced climate change “winners” are: Finland, Antarctica, Norway (4 EEZs: Norway plus Bouvet Island, Jan Mayen, and Svalbard), Portugal (3 EEZs: Portugal plus Azores and Madeira), and Fiji." See also data in an interactive way at Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Return period (years) in future (2071–2100) for discharge corresponding to a 100-year flood in the past (1971–2000), for CMIP6 under the ssp585 scenarioHirabayashi, YukikoHirabayashi, Y., Tanoue, M., Sasaki, O. et al. Global exposure to flooding from the new CMIP6 climate model projections. Sci Rep 11, 3740 (2021). https://doi.org/10.1038/s41598-021-83279-w (Fig. 1) Projected change in river flood frequency under the ssp585 climate change scenario. Multi-model median return period (years) in future (2071–2100) for discharge corresponding to a 100-year flood in the past (1971–2000), for CMIP6 under the ssp585 (SSP5-RCP8.5) scenario. See also data in an interactive way at the Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Hydro-political riskFarinosi, FabioF. Farinosi, C. Giupponi, A. Reynaud, G. Ceccherini, C. Carmona-Moreno, A. De Roo, D. Gonzalez-Sanchez, G. Bidoglio, 2018, An innovative approach to the assessment of hydro-political risk: A spatially explicit, data driven indicator of hydro-political issues, Global Environmental Change, Volume 52, Pages 286-313, https://doi.org/10.1016/j.gloenvcha.2018.07.001 In the folder you can find 5 different files: the baseline map was drawn by using the "likel_hp_risk" file. To exactly reproduce the map in the paper, you just have to display it in a stretched form (for instance, in ArcMap -> standard deviations type, n=2.5). The other files are the source of the scenario maps: each of them is basically the difference between the future scenario and the baseline map. The first number in the name indicates the year of projection (2050 and 2100), the second the RCP (4.5 and 8.5). The scenarios account for both population and climate change, as explained in the paper. In order to draw the maps you find in the paper, you have to set the minimum and maximum values of the scale identical for the 4 files (just picking min and max values from the 4). See also data in an interactive way at Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Historical trend in the frequency of occurrence of tropical cyclonesMurakami, HiroyukiMurakami, Hiroyuki; Delworth, Thomas L.; Cooke, William F.; Zhao, Ming; Xiang, Baoqiang; Hsu, Pang-Ch, 2020, Detected climatic change in global distribution of tropical cyclones, PNAS 2020 117 (20) 10706-10714, https://doi.org/10.1073/pnas.1922500117 Historical trend in the frequency of occurrence of tropical cyclones (from 1980 to 2020) - An increase of 0.1 per year means one additional hurricane every 10 years. In the netCDF file, "slope" is the TCF trend in the units of "number per day." So, please multiply it by 365 when you plot the trend in the units of "number per year" as shown in the figure in the paper. The variable "pval" is the p-value for the trend. When pval is less than or equal to 0.05, the trend on the grid cell is statistically significant at the 95% level. The projected future change in frequency is shown in Fig. S3a for global and Fig. S3b for North Atlantic, respectively, in the Supplement of the paper. The model project decreasing number of storms in both global and North Atlantic toward the end of this century. Regarding storm intensity, although the results were not shown in the paper, the climate models project increasing storm intensity under a warmer climate. Further information in Carbon Brief https://www.carbonbrief.org/global-warming-has-changed-spread-of-tropical-cyclones-around-the-world/ See also data in an interactive way at Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Historical multivariate climate departures (1958 to 2017)Abatzoglou, JohnAbatzoglou, J.T., Dobrowski, S.Z. & Parks, S.A. Multivariate climate departures have outpaced univariate changes across global lands. Sci Rep 10, 3891 (2020). https://doi.org/10.1038/s41598-020-60270-5 "We calculate annual multivariate climate departures during 1958–2017 relative to a baseline 1958–1987 period that account for covariance among four variables important to Earth’s biota and associated systems: annual climatic water deficit, annual evapotranspiration, average minimum temperature of the coldest month, and average maximum temperature of the warmest month. Results show positive trends in multivariate climate departures that were nearly three times that of univariate climate departures across global lands. Annual multivariate climate departures exceeded two standard deviations over the past decade for approximately 30% of global lands. Positive trends in climate departures over the last six decades were found to be primarily the result of changes in mean climate conditions consistent with the modeled effects of anthropogenic climate change rather than changes in variability. These results highlight the increasing novelty of annual climatic conditions viewed through a multivariate lens and suggest that changes in multivariate climate departures have generally outpaced univariate departures in recent decades. The largest positive σd trends occurred in regions of historically low variance (e.g., equatorial regions), in regions such as southern Europe that have seen large changes in climate trends in the variables considered, and in boreal regions that saw joint increases in AET (annual evapotranspiration) and D (annual climatic water deficit) that are orthogonal to the historical covariance of these variables.” See also data in an interactive way at Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlasDataset Percent difference in mean fisheries catch and profits in 2091–2100 relative to 2012–2021Free, ChristopherFree CM, Mangin T, Molinos JG, Ojea E, Burden M, Costello C, et al. (2020) Realistic fisheries management reforms could mitigate the impacts of climate change in most countries. PLoS ONE 15(3): e0224347. https://doi.org/10.1371/journal.pone.0224347 See Fig. 5. Percent difference in mean catch and profits in 2091–2100 relative to 2012–2021 (“today”) for 156 countries under realistic adaptation implementing management at 5-year intervals for three climate change scenarios RCP4.5, RCP6.0, and RCP8.5. "Maximum sustainable yield (MSY) of the evaluated stocks is forecast to decrease by 2.0%, 5.0%, and 18.5% from 2012–2021 to 2091–2100 under RCPs 4.5, 6.0, and 8.5, respectively. Across emissions scenarios, MSY is generally projected to decrease for equatorial countries and increase for poleward countries. Particularly dramatic reductions in MSY are predicted for the equatorial West African countries. Even under the least severe emissions scenario, nineteen countries, fifteen of which are in West Africa, are projected to experience reductions in MSY of 50–100%. The number of countries projected to experience dramatic losses in MSY, and the intensity of these losses, expands under the more severe emissions scenarios. In the most severe scenario, 51 countries are expected to experience reductions in MSY of 50–100%. All eighteen West African countries south of Senegal and north of Angola (including these two countries) are forecast to experience reductions in MSY greater than 85%. The equatorial Indo-Pacific and South America are also projected to experience considerable losses in MSY under the three emissions scenarios, with especially pronounced losses under RCP 8.5. Twenty-two countries are projected to experience increases in MSY under all three emissions scenarios with seven of these countries showing a 15% average increase in MSY across scenarios. The five most consistent and pronounced climate change “winners” are: Finland, Antarctica, Norway (4 EEZs: Norway plus Bouvet Island, Jan Mayen, and Svalbard), Portugal (3 EEZs: Portugal plus Azores and Madeira), and Fiji." See also data in an interactive way at Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlas