Perry World House

Perry World House is a center for global policy engagement on the University of Pennsylvania's campus. It drives policy-oriented education and convenings; hosts distinguished leaders and scholars from an array of countries and institutional contexts; and fosters interdisciplinary research on complex global challenges. In doing so, Perry World House ensures that the knowledge and networks developed at Penn have a positive impact around the world.

 

Search results

Now showing 1 - 10 of 18
  • Dataset
    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 scenario
    Hirabayashi, Yukiko
    Hirabayashi, 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-atlas
  • Dataset
    Maximum sustainable fish yield now and projected in the future with climate change
    Free, Christopher
    Free 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-atlas
  • Dataset
    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 scenario
    Hirabayashi, Yukiko
    Hirabayashi, 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-atlas
  • Dataset
    Extreme sea level at different global warming levels
    Tebaldi, Claudia
    Tebaldi, 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 Atlas
  • Dataset
    Percent difference in mean fisheries catch and profits in 2091–2100 relative to 2012–2021
    Free, Christopher
    Free 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
  • Dataset
    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 scenario
    Hirabayashi, Yukiko
    Hirabayashi, 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-atlas
  • Dataset
    Historical trend in the frequency of occurrence of most intense tropical cyclones
    Murakami, Hiroyuki
    Murakami, 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-atlas
  • Dataset
    Difference between historical (year 2006) and projected (year 2100) marine cell species richness for climate change scenario RCP8.5 (+4.5C)
    Molinos, Jorge Garcia
    Data needed to reproduce the climate change scenario RCP8.5 in Fig. 1 in the paper 'Climate velocity and the future global redistribution of marine biodiversity', García Molinos et al. 2016 (https://www.nature.com/articles/nclimate2769) "Here, we use climate velocity trajectories, together with information on thermal tolerances and habitat preferences, to project changes in global patterns of marine species richness and community composition under IPCC Representative Concentration Pathways (RCPs) 4.5 and 8.5.” See the map displayed in the Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlas
  • Dataset
    Difference between historical (year 2006) and projected (year 2100) marine cell species richness for climate change scenario RCP4.5 (+2.7C)
    Molinos, Jorge Garcia
    Data needed to reproduce the climate change scenario RCP4.5 in Fig. 1 in the paper 'Climate velocity and the future global redistribution of marine biodiversity', García Molinos et al. 2016 (https://www.nature.com/articles/nclimate2769) "Here, we use climate velocity trajectories, together with information on thermal tolerances and habitat preferences, to project changes in global patterns of marine species richness and community composition under IPCC Representative Concentration Pathways (RCPs) 4.5 and 8.5.” See the map displayed in the Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlas
  • Dataset
    Drought risk global map for the period 2000–2014
    Carrao, Hugo; Naumann, Gustavo; Barbosa, Paulo
    See Fig. 9 in ‘Mapping global patterns of drought risk: An empirical framework based on sub-national estimates of hazard, exposure and vulnerability’, Carrao, Naumann, Barbosa, (2016), https://doi.org/10.1016/j.gloenvcha.2016.04.012 Citations related: As a reference/acknowledgement to their work, Global Drought Observatory GDO (https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2001) and the paper http://www.sciencedirect.com/science/article/pii/S0959378016300565. See also the data in an interactive way at the Perry World House Global Climate Security Atlas https://global.upenn.edu/perryworldhouse/global-climate-security-atlas