--- - attributes: ~ caption: 'Top: Illustration of eastern North American topography in a resolution of 68 miles x 68 miles (110 x 110 km). Bottom: Illustration of eastern North America at a resolution of 19 miles x 19 miles (30 x 30 km). Global climate models are constantly being enhanced as scientific understanding of climate improves and as computational power increases. For example, in 1990, the average model divided up the world into grid cells measuring more than 300 miles per side. Today, most models divide the world up into grid cells of about 60 to 100 miles per side, and some of the most recent models are able to run short simulations with grid cells of only 15 miles per side. Supercomputer capabilities are the primary limitation on grid cell size. Newer models also incorporate more of the physical processes and components that make up the Earth’s climate system. (Figure source: Melillo et al. 2014)dd5b893d-4462-4bb3-9205-67b532919566' chapter_identifier: appendix-1--technical-support-document create_dt: 2014-05-01T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/example-increasing-spatial-resolution-of-climate-models.yaml identifier: example-increasing-spatial-resolution-of-climate-models lat_max: 52 lat_min: 30 lon_max: 106 lon_min: 80 ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2011-12-31T23:59:59 time_start: 1995-01-01T00:00:00 title: Example of Increasing Spatial Resolution of Climate Models uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/example-increasing-spatial-resolution-of-climate-models url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'This figure shows the projected increase in deaths due to warming in the summer months (hot season, April–September), the projected decrease in deaths due to warming in the winter months (cold season, October–March), and the projected net change in deaths compared to a 1990 baseline period for the 209 U.S. cities examined, using the GFDL–CM3 and MIROC5 climate models (see Ch. 2: Temperature-Related Deaths and Illness). (Figure source: adapted from Schwartz et al. 2015)e805bfdc-c4c2-43a0-b2e5-5a66945c74e4' chapter_identifier: executive-summary create_dt: 2014-10-31T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-projected-changes-in-deaths-in-us-cities-by-season.yaml identifier: es-projected-changes-in-deaths-in-us-cities-by-season lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2100-12-31T23:59:59 time_start: 2030-01-01T00:00:00 title: Projected Changes in Deaths in U.S Cities by Season uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-projected-changes-in-deaths-in-us-cities-by-season url: ~ usage_limits: ~ - attributes: ~ caption: 'A sensitivity analysis was conducted to test for potential impacts of differences in the modeling approaches (use of different historical reference periods and use of different sets of CMIP5 models) in the research studies highlighted in this assessment (see Research Highlights in Chapters 2, 3, 5, and 6). The values in the first column are temperature changes for three different reference periods used in this assessment, relative to the 1971–2000 reference period used in the 2014 NCA. The sets of values in the second column show future temperature changes for individual climate models for 2050–2059, relative to 1971–2000, for those studies that used the RCP6.0 scenario. From left to right, the vertical sets of values represent (a) 21 models used in the Vibrio/Alexandrium bacteria study (red), (b) 11 models used in the Gambierdiscus study (green), (c) the 5 models used in the Lyme disease study (purple), (d) the 2 models used in the extreme temperature study (blue), and (e) the single model used in the air quality study (orange). Each “×” represents a single model. The filled-in circle is the mean temperature change for all models in the column. (Figure source: NOAA NCEI / CICS-NC)' chapter_identifier: appendix-1--technical-support-document create_dt: 2015-08-07T11:40:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/sensitivity-analysis-of-differences-in-modeling-approaches.yaml identifier: sensitivity-analysis-of-differences-in-modeling-approaches lat_max: 90.00 lat_min: -90.00 lon_max: 180.00 lon_min: -180.00 ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2099-12-31T23:59:59 time_start: 1971-01-01T00:00:00 title: Sensitivity Analysis of Differences in Modeling Approaches uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/sensitivity-analysis-of-differences-in-modeling-approaches url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'The air quality response to climate change can vary substantially by region across scenarios. Two downscaled global climate model projections using two greenhouse gas concentration pathways estimate increases in average daily maximum temperatures of 1.8°F to 7.2°F (1°C to 4°C) and increases of 1 to 5 parts per billion (ppb) in daily 8-hour maximum ozone in the year 2030 relative to the year 2000 throughout the continental United States. Unless reductions in ozone precursor emissions offset the influence of climate change, this “climate penalty” of increased ozone concentrations due to climate change would result in tens to thousands of additional ozone-related premature deaths per year, shown here as incidences per year by county (see Ch. 3: Air Quality Impacts). (Figure source: adapted from Fann et al. 2015)54a66159-1675-43bb-b5d3-a9b7f283e4de' chapter_identifier: executive-summary create_dt: 2014-10-31T12:34:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-projected-change-in-temperature-ozone-and-ozone-related-premature-deaths-in-2030.yaml identifier: es-projected-change-in-temperature-ozone-and-ozone-related-premature-deaths-in-2030 lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2030-12-31T23:59:59 time_start: 2030-01-01T00:00:00 title: 'Projected Change in Temperature, Ozone, and Ozone-Related Premature Deaths in 2030' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-projected-change-in-temperature-ozone-and-ozone-related-premature-deaths-in-2030 url: ~ usage_limits: ~ - attributes: ~ caption: Examples of sources of uncertainty in projecting impacts of climate change on human health. The left column illustrates the exposure pathway through which climate change can affect human health. The right column lists examples of key sources of uncertainty surrounding effects of climate change at each stage along the exposure pathway. chapter_identifier: appendix-1--technical-support-document create_dt: 2015-08-24T11:20:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/tsd-sources-of-uncertainty.yaml identifier: tsd-sources-of-uncertainty lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Sources of Uncertainty uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/tsd-sources-of-uncertainty url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'This figure provides 10-year estimates of fatalities related to extreme events from 2004 to 2013,a1b08f2f-e94c-4628-b82a-a646e71116ec as well as estimated economic damages from 58 weather and climate disaster events with losses exceeding $1 billion (see Smith and Katz 2013 to understand how total losses were calculated).4fe32146-a968-4dde-8a2b-df2aa2eabdd4 These statistics are indicative of the human and economic costs of extreme weather events over this time period. Climate change will alter the frequency, intensity, and geographic distribution of some of these extremes,dd5b893d-4462-4bb3-9205-67b532919566 which has consequences for exposure to health risks from extreme events. Trends and future projections for some extremes, including tornadoes, lightning, and wind storms are still uncertain (see Ch. 4: Extreme Events).' chapter_identifier: executive-summary create_dt: 2015-02-23T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-estimated-deaths-and-billion-dollar-losses-from-extreme-weather-events-in-the-u-s-2004-2013.yaml identifier: es-estimated-deaths-and-billion-dollar-losses-from-extreme-weather-events-in-the-u-s-2004-2013 lat_max: 49.28 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 5 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2013-12-31T23:59:59 time_start: 2004-01-01T00:00:00 title: 'Estimated Deaths and Billion Dollar Losses from Extreme Events in the United States, 2004-2013' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-estimated-deaths-and-billion-dollar-losses-from-extreme-weather-events-in-the-u-s-2004-2013 url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Maps show the reported cases of Lyme disease in 2001 and 2014 for the areas of the country where Lyme disease is most common (the Northeast and Upper Midwest). Both the distribution and the numbers of cases have increased (see Ch. 5: Vector-Borne Diseases). (Figure source: adapted from CDC 2015)6066212c-7cfd-46af-8255-e6c75647167a' chapter_identifier: executive-summary create_dt: 2014-08-27T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-changes-in-lyme-disease-case-report-distribution.yaml identifier: es-changes-in-lyme-disease-case-report-distribution lat_max: -67.0 lat_min: -98.0 lon_max: 48.0 lon_min: 36.0 ordinal: 6 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2014-12-31T23:59:59 time_start: 1996-01-01T00:00:00 title: Changes in Lyme Disease Case Report Distribution uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-changes-in-lyme-disease-case-report-distribution url: ~ usage_limits: ~ - attributes: ~ caption: 'Precipitation and temperature changes affect fresh and marine water quantity and quality primarily through urban, rural, and agriculture runoff. This runoff in turn affects human exposure to water-related illnesses primarily through contamination of drinking water, recreational water, and fish or shellfish (see Ch. 6: Water-Related Illness).' chapter_identifier: executive-summary create_dt: 2014-10-30T14:42:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness.yaml identifier: es-links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 7 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: 'Links between Climate Change, Water Quantity and Quality, and Human Exposure to Water-Related Illness' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'The food system involves a network of interactions with our physical and biological environments as food moves from production to consumption, or from “farm to table.” Rising CO2 and climate change will affect the quality and distribution of food, with subsequent effects on food safety and nutrition (see Ch. 7: Food Safety).' chapter_identifier: executive-summary create_dt: 2014-11-20T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-farm-to-table.yaml identifier: es-farm-to-table lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 8 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Farm to Table uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-farm-to-table url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'At the center of the diagram are human figures representing adults, children, older adults, and people with disabilities. The left circle depicts climate impacts including air quality, wildfire, sea level rise and storm surge, heat, storms, and drought. The right circle shows the three interconnected health domains that will be affected by climate impacts: Medical and Physical Health, Mental Health, and Community Health (see Ch. 8: Mental Health). (Figure source: adapted from Clayton et al. 2014)f66b946f-c672-4a4b-8f71-1b05738e029e' chapter_identifier: executive-summary create_dt: 2014-12-05T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-the-impact-of-climate-change-on-physical-mental-and-community-health.yaml identifier: es-the-impact-of-climate-change-on-physical-mental-and-community-health lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 9 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: 'Impact of Climate Change on Physical, Mental, and Community Health' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-the-impact-of-climate-change-on-physical-mental-and-community-health url: ~ usage_limits: Free to use with credit to the original figure source.