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Climate Change Indicators

Climate Change Indicators: West Nile Virus

This indicator tracks the rate of reported West Nile virus disease cases across the United States.

Figure 1. Reported Neuroinvasive Cases of West Nile Virus Disease in the United States, 2002-2014

This figure shows the annual incidence of West Nile virus neuroinvasive disease, which is calculated as the number of new cases per 100,000 people. The graph is based on cases that local and state health departments report to CDC’s national disease tracking system. Neuroinvasive cases, which occur for less than 1 percent of people infected with West Nile virus, are those that affect the brain or cause neurologic dysfunction.

Data source: CDC, 20168
Web update: August 2016

Figure 2. Reported Neuroinvasive Cases of West Nile Virus Disease by State, 2002-2014

This map shows the average annual incidence of West Nile virus neuroinvasive disease in each state, which is calculated as the average number of new cases per 100,000 people per year from 2002 to 2014. The map is based on cases that local and state health departments report to CDC’s national disease tracking system. Neuroinvasive cases, which occur for less than 1 percent of people infected with West Nile virus, are those that affect the brain or cause neurologic dysfunction.

Data source: CDC, 20169
Web update: August 2016

Figure 1. Reported Neuroinvasive Cases of West Nile Virus Disease in the United States, 2002-2014

Figure 2. Reported Neuroinvasive Cases of West Nile Virus Disease by State, 2002–2014

Key Points

Background

About the
Indicator

Indicator Notes

Data Sources

Technical
Documentation

Key Points

The incidence of West Nile virus neuroinvasive disease in the United States has varied widely from year to year (see Figure 1). No obvious long-term trend can be detected yet through this limited data set.

The years 2002, 2003, and 2012 had the highest reported incidence rates, around one case per 100,000 people (see Figure 1).

West Nile virus occurs throughout the contiguous 48 states. Average annual incidence is highest in parts of the Southwest, the Mississippi Delta region, the Great Plains, and the Rocky Mountain region (see Figure 2).

Background

Climate change is expected to affect the geographic and seasonal patterns of vector-borne diseases (that is, diseases caused by pathogens transmitted by mosquitoes, ticks, fleas, or other arthropods) in the United States.1 West Nile virus was first detected in the United States in 1999 and is now the most common cause of mosquito-borne disease in the United States in most years. While many infected people feel no symptoms, others can experience symptoms such as headache, body aches, joint pains, vomiting, diarrhea, and rash, as well as more severe damage to the central nervous system in some patients, causing encephalitis, meningitis, and occasionally death.2 From 1999 to 2014, a total of 41,762 cases of West Nile virus disease were reported in to the U.S. Centers for Disease Control and Prevention (CDC). Nearly half of the reported cases of people infected with West Nile virus were neuroinvasive—that is, affecting the brain or causing neurologic dysfunction.3 Mosquitoes acquire the virus by biting infected birds, which are the main hosts of the virus. People are then infected when they are bitten by these virus-carrying mosquitoes.

Climate change may raise the risk of human exposure to West Nile virus. Studies show that warmer temperatures associated with climate change can speed up mosquito development, biting rates, and the incubation of the disease within a mosquito.4 Climate change’s effects on birds may also contribute to changes in long-range virus movement, as the timing of migration and breeding patterns are driven by climate. Mild winters and drought have been associated with West Nile virus disease outbreaks,5,6 while rainfall can also contribute by creating breeding sites for mosquitoes.7

Climate is just one of many important factors that influence the transmission, distribution, and incidence of West Nile virus disease. Human exposure to infected mosquitoes is also influenced by multiple factors, including changes in the proximity of human populations to mosquitoes and host bird species, increased awareness of West Nile virus, and modified behaviors, such as spending less time outdoors during peak mosquito-biting times and taking precautions to avoid being bitten.

About the Indicator

This indicator looks at the incidence of human cases of West Nile virus disease, which reflects the rate of new cases contracted in a given geographic area and time period. Incidence is typically calculated as the number of cases per 100,000 people per year. This indicator focuses on neuroinvasive cases, as the symptoms are noticeable and typically require medical care. The indicator summarizes surveillance data reported to the CDC. West Nile is a nationally notifiable disease, which means health care providers are required to report confirmed cases to their local or state health departments. CDC compiles these reported data and calculates national totals and rates. Figure 1 shows national incidence by year since 2002, when West Nile first became notifiable. Figure 2 shows the incidence for each state, averaged over the period from 2002 to 2014.

Indicator Notes

This indicator depends on clinicians making the correct diagnosis and reporting laboratory-confirmed cases to public health authorities. Changes in diagnosing practices, awareness of the virus, and mosquito control efforts over time can affect trends. Cases are reported based on the patient’s state of residence, which is not necessarily the place where they were infected. Non-neuroinvasive cases of West Nile virus disease have been tracked since 2005, but because the symptoms may be mild or undetected, non-neuroinvasive cases are significantly underreported.10 Thus this indicator only focuses on neuroinvasive cases.

Because of the many factors affecting mosquito populations and reporting of West Nile virus disease and the fact that data have only been collected for a little more than a decade, this indicator does not provide sufficient information to determine what proportion of the observed changes in incidence is directly driven by climate change. Further study is critical to improving the usefulness of this indicator and informing decisions affecting public health. For information on prevention, symptoms, and treatment of West Nile virus disease, see: www.cdc.gov/westnile/index.html.

Data Sources

This indicator is based on publicly available West Nile virus disease data compiled by CDC at: www.cdc.gov/westnile/statsmaps/cummapsdata.html.

Technical Documentation

Download related technical information PDF

References

1 Beard, C.B., R.J. Eisen, C.M. Barker, J.F. Garofalo, M. Hahn, M. Hayden, A.J. Monaghan, N.H. Ogden, and P.J. Schramm. 2016. Chapter 5: Vector-borne diseases. The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

2 CDC (Centers for Disease Control and Prevention). 2016. West Nile virus symptoms and treatment. www.cdc.gov/westnile/symptoms/index.html. Accessed January 2016.

3 CDC (Centers for Disease Control and Prevention). 2016. West Nile virus statistics and maps. www.cdc.gov/westnile/statsmaps/index.html. Accessed January 2016.

4 Beard, C.B., R.J. Eisen, C.M. Barker, J.F. Garofalo, M. Hahn, M. Hayden, A.J. Monaghan, N.H. Ogden, and P.J. Schramm. 2016. Chapter 5: Vector-borne diseases. The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

5 Beard, C.B., R.J. Eisen, C.M. Barker, J.F. Garofalo, M. Hahn, M. Hayden, A.J. Monaghan, N.H. Ogden, and P.J. Schramm. 2016. Chapter 5: Vector-borne diseases. The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

6 Hahn, M.B., A.J. Monaghan, M.H. Hayden, R.J. Eisen, M.J. Delorey, N.P. Lindsey, R.S. Nasci, and M. Fischer. 2015. Meteorological conditions associated with increased incidence of West Nile virus disease in the United States, 2004–2012. Am. J. Trop. Med. Hyg. 92(5):1013–1022.

7 Beard, C.B., R.J. Eisen, C.M. Barker, J.F. Garofalo, M. Hahn, M. Hayden, A.J. Monaghan, N.H. Ogden, and P.J. Schramm. 2016. Chapter 5: Vector-borne diseases. The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

8 CDC (Centers for Disease Control and Prevention). 2016. West Nile virus statistics and maps. www.cdc.gov/westnile/statsmaps/index.html. Accessed January 2016.

9 CDC (Centers for Disease Control and Prevention). 2016. West Nile virus statistics and maps. www.cdc.gov/westnile/statsmaps/index.html. Accessed January 2016.

10 CDC (Centers for Disease Control and Prevention). 2016. West Nile virus surveillance resources. www.cdc.gov/westnile/resourcepages/survresources.html. Accessed January 2016.

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Learn about other indicators in this section

Climate Indicators

Climate Change Indicators

Greenhouse Gases

Weather and Climate

Oceans

Snow and Ice

Health and Society

Heat-Related Deaths

Heat-Related Illnesses

Heating and Cooling Degree Days

Lyme Disease

West Nile Virus

Length of Growing Season

Ragweed Pollen Season

Ecosystems

Frequent Questions


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Climate Change Indicators

Climate Change Indicators: Ragweed Pollen Season

This indicator depicts changes in the length of ragweed pollen season in the United States and Canada.

Figure 1. Change in Ragweed Pollen Season, 1995–2015

This figure shows how the length of ragweed pollen season changed at 11 locations in the central United States and Canada between 1995 and 2015. Red circles represent a longer pollen season; the blue circle represents a shorter season. Larger circles indicate larger changes.

Data source: Ziska et al., 20168
Web update: August 2016

Figure 1. Change in Ragweed Pollen Season, 1995–2015

			Key Points

			Background

About the

			Indicator

			Indicator Notes

			Data Sources

Technical

			Documentation

Key Points

Since 1995, ragweed pollen season has grown longer at 10 of the 11 locations studied (see Figure 1).

The increase in ragweed season length generally becomes more pronounced from south to north. Ragweed season increased by 25 days in Winnipeg, Manitoba; 24 days in Saskatoon, Saskatchewan; 21 days in Fargo, North Dakota; and 18 days in Minneapolis, Minnesota (see Figure 1). This trend is consistent with many other observations showing that climate is changing more rapidly at higher latitudes.6

The trends in Figure 1 are strongly related to changes in the length of the frost-free season and the timing of the first fall frost. Northern areas have seen fall frosts happening later than they used to, with the delay in first frost closely matching the increase in pollen season. Meanwhile, some southern stations have experienced only a modest change in frost-free season length since 1995.7

Background

Allergies are a major public health concern, with hay fever (congestion, runny nose, itchy eyes) accounting for more than 13 million visits to physicians’ offices and other medical facilities every year.1 One of the most common environmental allergens is ragweed, which can cause hay fever and trigger asthma attacks, especially in children and the elderly.2 An estimated 15.5 percent of all Americans are sensitive to ragweed.3

Ragweed plants mature in mid-summer and produce small flowers that generate pollen. Ragweed pollen season usually peaks in late summer and early fall, but these plants often continue to produce pollen until the first frost. A single ragweed plant can produce up to a billion pollen grains in one season, and these grains can be carried long distances by the wind.4

Climate change can affect pollen allergies in several ways. Warmer spring temperatures cause some plants to start producing pollen earlier (see the Leaf and Bloom Dates indicator), while warmer fall temperatures extend the growing season for other plants, such as ragweed (see the Length of Growing Season indicator). Warmer temperatures and increased carbon dioxide concentrations also enable ragweed and other plants to produce more allergenic pollen, in larger quantities. This means that many locations could experience longer allergy seasons and higher pollen counts as a result of climate change.5

About the Indicator

This indicator shows changes in the length of the ragweed pollen season in 11 cities in the central United States and Canada. These locations were selected as part of a study that looked at trends in pollen season at sites similar in elevation, but across a range of latitudes from south to north. At each location, air samples have been collected and examined since at least the 1990s as part of a national allergy monitoring network. Pollen spores are counted and identified using microscopes.

Pollen counts from each station have been analyzed to determine the start and end dates of each year’s ragweed pollen season. Because the length of ragweed season naturally varies from year to year, statistical techniques have been used to determine the average rate of change over time. This indicator shows the total change in season length from 1995 to 2015, which was determined by multiplying the average annual rate of change by the number of years in the period.

Indicator Notes

This indicator is based on data from a limited number of cities in the central states and provinces. These cities cover a broad range from north to south, however, which allows researchers to establish a clear connection between pollen season changes and latitude.

Many factors can influence year-to-year changes in pollen season, including typical local and regional variations in temperature and precipitation, extreme events such as floods and droughts, and changes in plant diversity. In addition, seasonal pollen production may differ between stations located in densely urbanized areas and stations in outlying areas. Adding more years of data would provide a better picture of long-term trends, but widespread data were not available prior to 1995.

This indicator does not show how the intensity of ragweed pollen season (pollen counts) might also be changing.

Data Sources

Data for this indicator come from the National Allergy Bureau, which is part of the American Academy of Allergy, Asthma, and Immunology’s Aeroallergen Network. Data were compiled and analyzed by a team of researchers that published a more detailed version of this analysis in a scientific journal with data through 2009.9

Technical Documentation

Download related technical information PDF

References

1 Schappert, S.M., and E.A. Rechtsteiner. 2011. Ambulatory medical care utilization estimates for 2007. National Center for Health Statistics. Vital and Health Statistics 13(169). www.cdc.gov/nchs/data/series/sr_13/sr13_169.pdf.

2 Fann, N., T. Brennan, P. Dolwick, J.L. Gamble, V. Ilacqua, L. Kolb, C.G. Nolte, T.L. Spero, and L. Ziska, 2016. Chapter 3: Air quality impacts. The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

3  Salo, P.M., S.J. Arbes, Jr., R. Jaramillo, A. Calatroni, C.H. Weir, M.L. Sever, J.A. Hoppin, K.M. Rose, A.H. Liu, P.J. Gergen, H.E. Mitchell, and D.C. Zeldin. 2014. Prevalence of allergic sensitization in the United States: Results from the National Health and Nutrition Examination Survey (NHANES) 2005–2006. J. Allergy Clin. Immun. 134(2):350–359. See online data repository.

4 National Institute of Allergy and Infectious Diseases. 2011. Pollen allergy. www.niaid.nih.gov/topics/allergicdiseases/documents/pollenallergyfactsheet.pdf.

5 Fann, N., T. Brennan, P. Dolwick, J.L. Gamble, V. Ilacqua, L. Kolb, C.G. Nolte, T.L. Spero, and L. Ziska, 2016. Chapter 3: Air quality impacts. The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

6 IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg1.

7 Ziska, L., K. Knowlton, C. Rogers, D. Dalan, N. Tierney, M. Elder, W. Filley, J. Shropshire, L.B. Ford, C. Hedberg, P. Fleetwood, K.T. Hovanky, T. Kavanaugh, G. Fulford, R.F. Vrtis, J.A. Patz, J. Portnoy, F. Coates, L. Bielory, and D. Frenz. 2011. Recent warming by latitude associated with increased length of ragweed pollen season in central North America. P Natl. Acad. Sci. USA 108:4248–4251.

8 Ziska, L., K. Knowlton, C. Rogers, National Allergy Bureau, Aerobiology Research Laboratories, Canada. 2016 update to data originally published in: Ziska, L., K. Knowlton, C. Rogers, D. Dalan, N. Tierney, M. Elder, W. Filley, J. Shropshire, L.B. Ford, C. Hedberg, P. Fleetwood, K.T. Hovanky, T. Kavanaugh, G. Fulford, R.F. Vrtis, J.A. Patz, J. Portnoy, F. Coates, L. Bielory, and D. Frenz. 2011. Recent warming by latitude associated with increased length of ragweed pollen season in central North America. P Natl. Acad. Sci. USA 108:4248–4251.

9 Ziska, L., K. Knowlton, C. Rogers, D. Dalan, N. Tierney, M. Elder, W. Filley, J. Shropshire, L.B. Ford, C. Hedberg, P. Fleetwood, K.T. Hovanky, T. Kavanaugh, G. Fulford, R.F. Vrtis, J.A. Patz, J. Portnoy, F. Coates, L. Bielory, and D. Frenz. 2011. Recent warming by latitude associated with increased length of ragweed pollen season in central North America. P Natl. Acad. Sci. USA 108:4248–4251.

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Learn about other indicators in this section

Climate Indicators

Climate Change Indicators

Greenhouse Gases

Weather and Climate

Oceans

Snow and Ice

Health and Society

Heat-Related Deaths

Heat-Related Illnesses

Heating and Cooling Degree Days

Lyme Disease

West Nile Virus

Length of Growing Season

Ragweed Pollen Season

Ecosystems

Frequent Questions

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Climate Change Indicators

Climate Change Indicators: Glaciers

This indicator examines the balance between snow accumulation and melting in glaciers, and it describes how glaciers in the United States and around the world have changed over time.

Figure 1. Average Cumulative Mass Balance of "Reference" Glaciers Worldwide, 1945â€“2015

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This figure shows the cumulative change in mass balance of a set of â€œreferenceâ€? glaciers worldwide beginning in 1945. The line on the upper graph represents the average of all the glaciers that were measured. Negative values indicate a net loss of ice and snow compared with the base year of 1945. For consistency, measurements are in meters of water equivalent, which represent changes in the average thickness of a glacier. The small chart below shows how many glaciers were measured in each year. Some glacier measurements have not yet been finalized for the last few years, hence the smaller number of sites.

Data sources: WGMS, 20163
Web update: AugustÂ 2016

Figure 2. Cumulative Mass Balance of Three U.S. Glaciers, 1958â€“2014

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This figure shows the cumulative mass balance of the three U.S. Geological Survey â€œbenchmarkâ€? glaciers since measurements began in the 1950s or 1960s. For each glacier, the mass balance is set at zero for the base year of 1965. Negative values indicate a net loss of ice and snow compared with the base year. For consistency, measurements are in meters of water equivalent, which represent changes in the average thickness of a glacier.

Data sources: Oâ€™Neel et al., 2014;4Â USGS, 20155
Web update: JuneÂ 2015

Photographs of McCall Glacier, Alaska, 1958 and 2003

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Sources: Post, 1958;6Â Nolan, 20037
Web update: AugustÂ 2016

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Figure 1. Average Cumulative Mass Balance of "Reference" Glaciers Worldwide, 1945â€“2015

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Figure 2. Cumulative Mass Balance of Three U.S. Glaciers, 1958â€“2014

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Photographs of McCall Glacier, Alaska, 1958 and 2003

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Key Points

Â
Background

About the
Indicator

Â
Indicator Notes

Â
Data Sources

Technical
Documentation

Key Points

On average, glaciers worldwide have been losing mass since at least the 1970s (see Figure 1), which in turn has contributed to observed changes in sea level (see the Sea Level indicator). A longer measurement record from a smaller number of glaciers suggests that they have been shrinking since the 1940s. The rate at which glaciers are losing mass appears to have accelerated over roughly the last decade.

All three U.S. benchmark glaciers have shown an overall decline in mass balance since the 1950s and 1960s and an accelerated rate of decline in recent years (see Figure 2). Year-to-year trends vary, with some glaciers gaining mass in certain years (for example, Wolverine Glacier during the 1980s), but the measurements clearly indicate a loss of glacier mass over time.

Trends for the three benchmark glaciers are consistent with the retreat of glaciers observed throughout the western United States, Alaska, and other parts of the world.2 Observations of glaciers losing mass are also consistent with warming trends in U.S. and global temperatures during this time period (see the U.S. and Global Temperature indicator).

Background

A glacier is a large mass of snow and ice that has accumulated over many years and is present year-round. In the United States, glaciers can be found in the Rocky Mountains, the Sierra Nevada, the Cascades, and throughout Alaska. A glacier flows naturally like a river, only much more slowly. At higher elevations, glaciers accumulate snow, which eventually becomes compressed into ice. At lower elevations, the â€œriverâ€? of ice naturally loses mass because of melting and ice breaking off and floating away (iceberg calving) if the glacier ends in a lake or the ocean. When melting and calving are exactly balanced by new snow accumulation, a glacier is in equilibrium and its mass will neither increase nor decrease.

In many areas, glaciers provide communities and ecosystems with a reliable source of streamflow and drinking water, particularly in times of extended drought and late in the summer, when seasonal snowpack has melted away. Freshwater runoff from glaciers also influences ocean ecosystems. Glaciers are important as an indicator of climate change because physical changes in glaciersâ€”whether they are growing or shrinking, advancing or recedingâ€”provide visible evidence of changes in temperature and precipitation. If glaciers lose more ice than they can accumulate through new snowfall, they ultimately add more water to the oceans, leading to a rise in sea level (see the Sea Level indicator). The same kinds of changes occur on a much larger scale within the giant ice sheets that cover Greenland and Antarctica, potentially leading to even bigger implications for sea level. Small glaciers tend to respond more quickly to climate change than the giant ice sheets. Altogether, the worldâ€™s small glaciers are adding roughly the same amount of water to the oceans per year as the ice sheets of Greenland and Antarctica. During the last two decades, they added more water overall to the oceans than the ice sheets did.1Â

About the Indicator

This indicator is based on long-term monitoring data collected at selected glaciers around the world. Scientists collect detailed measurements to determine glacier mass balance, which is the net gain or loss of snow and ice over the course of the year. A negative mass balance indicates that a glacier has lost ice or snow. If cumulative mass balance becomes more negative over time, it means glaciers are losing mass more quickly than they can accumulate new snow.

Figure 1 shows trends in mass balance for a set of 40 reference glaciers around the world that have been measured consistently since the 1970s, including a few that have been measured since the 1940s. Data from these reference glaciers have been averaged together to depict changes over time. Figure 2 shows trends for three â€œbenchmarkâ€? glaciers: South Cascade Glacier in Washington state, Wolverine Glacier near Alaskaâ€™s southern coast, and Gulkana Glacier in Alaskaâ€™s interior. These three glaciers were chosen because they have been studied extensively by the U.S. Geological Survey for many years and because they are thought to be representative of other glaciers nearby.

This indicator describes the change in glacier mass balance, which is measured as the average change in thickness across the surface of a glacier. The change in ice or snow has been converted to the equivalent amount of liquid water.

Indicator Notes

The relationship between climate change and glacier mass balance is complex, and the observed changes at specific reference or benchmark glaciers might reflect a combination of global and local variations in temperature and precipitation. Individual glaciers also vary in their structure, flow, and response to climate. Slightly different measurement and analysis methods have been used at different glaciers, but overall trends appear to be similar.

Long-term measurements are available for only a relatively small percentage of the worldâ€™s glaciers. This indicator does not include the Greenland and Antarctic ice sheets, although two decades of satellite data suggest that these ice sheets are also experiencing a net loss of ice.8 Continued satellite data collection will allow scientists to evaluate long-term trends in the future.

Data Sources

The World Glacier Monitoring Service compiled data for Figure 1, based on measurements collected by a variety of organizations around the world. The U.S. Geological Survey Benchmark Glacier Program provided the data for Figure 2. Historical data, as well as periodic reports and measurements of the benchmark glaciers, are available on the programâ€™s website at: www2.usgs.gov/climate_landuse/clu_rd/glacierstudies/default.asp.

Technical Documentation

Download related technical information PDF

References

1. IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg1.

2. IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg1.

3. WGMS (World Glacier Monitoring Service). 2016 update to data originally published in: WGMS. 2015. Global glacier change bulletin no. 1 (2012â€“2013). Zemp, M., I. GÃ¤rtner-Roer, S.U. Nussbaumer, F. HÃ¼sler, H. Machguth, N. MÃ¶lg, F. Paul, and M. Hoelzle (eds.). ICSU (WDS)/IUGG (IACS)/UNEP/UNESCO/WMO. Zurich, Switzerland: World Glacier Monitoring Service. http://wgms.ch/downloads/WGMS_GGCB_01.pdf.

4. Oâ€™Neel, S., E. Hood, A. Arendt, and L. Sass. 2014. Assessing streamflow sensitivity to variations in glacier mass balance. Climatic Change 123(2):329â€“341.

5. USGS (U.S. Geological Survey). 2015. Water resources of Alaskaâ€”glacier and snow program, benchmark glaciers. www2.usgs.gov/climate_landuse/clu_rd/glacierstudies/default.asp.

6. Post, A. 1958. McCall Glacier. Glacier photograph collection. Boulder, Colorado: National Snow and Ice Data Center/World Data Center for Glaciology. http://nsidc.org/data/g00472.html.

7. Nolan, M. 2003. McCall Glacier. Glacier photograph collection. Boulder, Colorado: National Snow and Ice Data Center/World Data Center for Glaciology. http://nsidc.org/data/g00472.html.

8. Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program. http://nca2014.globalchange.gov.Â

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Covering about 70 percent of the Earth’s surface, the world’s oceans have a two-way relationship with weather and climate. The oceans influence the weather on local to global scales, while changes in climate can fundamentally alter many properties of the oceans. This chapter examines how some of these important characteristics of the oceans have changed over time.

Why does it matter?

As greenhouse gases trap more energy from the sun, the oceans are absorbing more heat, resulting in an increase in sea surface temperatures and rising sea level. Changes in ocean temperatures and currents brought about by climate change will lead to alterations in climate patterns around the world. For example, warmer waters may promote the development of stronger storms in the tropics, which can cause property damage and loss of life. The impacts associated with sea level rise and stronger storms are especially relevant to coastal communities.

Although the oceans help reduce climate change by storing large amounts of carbon dioxide, increasing levels of dissolved carbon are changing the chemistry of seawater and making it more acidic. Increased ocean acidity makes it more difficult for certain organisms, such as corals and shellfish, to build their skeletons and shells. These effects, in turn, could substantially alter the biodiversity and productivity of ocean ecosystems.

Changes in ocean systems generally occur over much longer time periods than in the atmosphere, where storms can form and dissipate in a single day. Interactions between the oceans and atmosphere occur slowly over many months to years, and so does the movement of water within the oceans, including the mixing of deep and shallow waters. Thus, trends can persist for decades, centuries, or longer. For this reason, even if greenhouse gas emissions were stabilized tomorrow, it would take many more years—decades to centuries—for the oceans to adjust to changes in the atmosphere and the climate that have already occurred.

Summary of Key Points

Ocean Heat. Three independent analyses show that the amount of heat stored in the ocean has increased substantially since the 1950s. Ocean heat content not only determines sea surface temperature, but also affects sea level and currents.

Sea Surface Temperature. Ocean surface temperatures increased around the world during the 20th century. Even with some year-to-year variation, the overall increase is clear, and sea surface temperatures have been consistently higher during the past three decades than at any other time since reliable observations began in the late 1800s.

Sea Level. When averaged over all of the world’s oceans, sea level has risen at a rate of roughly six-tenths of an inch per decade since 1880. The rate of increase has accelerated in recent years to more than an inch per decade. Changes in sea level relative to the land vary by region. Along the U.S. coastline, sea level has risen the most along the Mid-Atlantic coast and parts of the Gulf coast, where some stations registered increases of more than 8 inches between 1960 and 2015. Sea level has decreased relative to the land in parts of Alaska and the Pacific Northwest.

A Closer Look: Land Loss Along the Atlantic Coast. As sea level rises, dry land and wetlands can turn into open water. Along many parts of the Atlantic coast, this problem is made worse by low elevations and land that is already sinking. Between 1996 and 2011, the coastline from Florida to New York lost more land than it gained.

Coastal Flooding. Flooding is becoming more frequent along the U.S. coastline as sea level rises. Nearly every site measured has experienced an increase in coastal flooding since the 1950s. The rate is accelerating in many locations along the East and Gulf coasts. The Mid-Atlantic region suffers the highest number of coastal flood days and has also experienced the largest increases in flooding.

Ocean Acidity. The ocean has become more acidic over the past few decades because of increased levels of atmospheric carbon dioxide, which dissolves in the water. Higher acidity affects the balance of minerals in the water, which can make it more difficult for certain marine animals to build their protective skeletons or shells.

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Climate Change Indicators: Leaf and Bloom Dates

This indicator examines the timing of leaf growth and flower blooms for two widely distributed plants in the United States.

Figure 1. First Leaf and Bloom Dates in the Contiguous 48 States, 1900â€“2015

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This figure shows modeled trends in lilac and honeysuckle first leaf dates and first bloom dates across the contiguous 48 states, using the 1981 to 2010 average as a baseline. Positive values indicate that leaf growth and blooming began later in the year, and negative values indicate that leafing and blooming occurred earlier. The thicker lines were smoothed using a nine-year weighted average. Choosing a different long-term average for comparison would not change the shape of the data over time.

Data source: Schwartz, 20165
Web update: AugustÂ 2016

Figure 2. Change in First Leaf Date Between 1951â€“1960 and 2006â€“2015

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This figure shows modeled trends in lilac and honeysuckle first leaf dates at weather stations across the contiguous 48 states. This map compares the average first leaf date during two 10-year periods.

Data source: Schwartz, 20166
Web update: AugustÂ 2016

Figure 3. Change in First Bloom Date Between 1951â€“1960 and 2006â€“2015

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This figure shows modeled trends in lilac and honeysuckle first bloom dates at weather stations across the contiguous 48 states. This map compares the average first bloom date during two 10-year periods.

Data source: Schwartz, 20167
Web update: AugustÂ 2016

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Figure 1. First Leaf and Bloom Dates in the Contiguous 48 States, 1900â€“2015

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Figure 2. Change in First Leaf Date Between 1951â€“1960 and 2006â€“2015

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Figure 3. Change in First Bloom Date Between 1951â€“1960 and 2006â€“2015

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Key Points

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Background

About the
Indicator

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Indicator Notes

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Data Sources

Technical
Documentation

Key Points

First leaf and bloom dates in lilacs and honeysuckles in the contiguous 48 states show a great deal of year-to-year variability, which makes it difficult to determine whether a statistically meaningful change has taken place. Earlier dates appear more prevalent in the last few decades, however (see Figure 1).

Leaf and bloom events are generally happening earlier throughout the North and West but later in much of the South (see Figures 2 and 3). This observation is generally consistent with regional differences in temperature change (see the U.S. and Global Temperature indicator).

Other studies have looked at trends in leaf and bloom dates across all of North America and the entire Northern Hemisphere. These studies have also found a trend toward earlier spring eventsâ€”some more pronounced than the trends seen in just the contiguous 48 states.4

Background

The timing of natural events, such as flower blooms and animal migration, can be influenced by changes in climate. Phenology is the study of such important seasonal events. Phenological events are influenced by a combination of environmental factors, including temperature, light, rainfall, and humidity. Different plant and animal species respond to different cues.

Scientists have high confidence that the earlier arrival of spring events is linked to recent warming trends in global climate.1 Disruptions in the timing of these events can have a variety of impacts on ecosystems and human society. For example, an earlier spring might lead to longer growing seasons (see the Length of Growing Season indicator), more abundant invasive species and pests, and earlier and longer allergy seasons. Unusually warm weather in late winter can create a â€œfalse springâ€? that triggers the new growth of plants to begin too early, leaving them vulnerable to any subsequent frosts.

Because of their close connection with climate, the timing of phenological events can be used as an indicator of the sensitivity of ecological processes to climate change. Two particularly useful indicators of the timing of spring events are the first leaf dates and the first bloom dates of lilacs and honeysuckles, which have an easily monitored flowering season, a relatively high survival rate, and a large geographic distribution. The first leaf date in these plants relates to the timing of events that occur in early spring, while the first bloom date is consistent with the timing of later spring events, such as the start of growth in forest vegetation.2

About the Indicator

This indicator shows trends in the timing of first leaf dates and first bloom dates in lilacs and honeysuckles across the contiguous 48 states. Because many of the phenological observation records in the United States are less than 40 years long, and because these records may have gaps in time or space, computer models have been used to provide a more complete understanding of long-term trends nationwide.

The models for this indicator were developed using data from the USA National Phenology Network, which collects ground observations from a network of federal agencies, field stations, educational institutions, and citizens who have been trained to log observations of leaf and bloom dates. For consistency, observations were limited to a few specific types of lilacs and honeysuckles. Next, models were created to relate actual leaf and bloom observations with records from nearby weather stations. Once scientists were able to determine the relationship between climate factors (particularly temperatures) and leaf and bloom dates, they used this knowledge to estimate leaf and bloom dates for earlier years based on historical weather records. They also used the models to estimate how leaf and bloom dates would have changed in a few areas (mostly in the far South) where lilacs and honeysuckles are not widespread.

This indicator uses data from nearly 3,000 weather stations throughout the contiguous 48 states. The exact number of stations varies from year to year. For each year, the timing of first leaf and first bloom at each station was compared with the 1981 to 2010 average to determine the number of daysâ€™ â€œdeviation from normal.â€? This indicator presents the average deviation across all stations, along with maps that compare the most recent 10-year period (2006â€“2015) with a mid-20th-century baseline (1951â€“1960) at individual stations. These time periods were chosen to match published studies.3

Indicator Notes

Plant phenological events are studied using several data collection methods, including satellite images, models, and direct observations. Locational differences, the use of varying data collection methods, and different phenological indicators (such as leaf or bloom dates for different types of plants) can lead to a range of estimates of the arrival of spring.

Climate is not the only factor that can affect phenology. Observed variations can also reflect plant genetics, changes in the surrounding ecosystem, and other factors. This indicator minimizes the influence of genetic variations by relying on cloned plants (that is, plants with no genetic differences).

Data Sources

Leaf and bloom observations were compiled by the USA National Phenology Network and are available at: www.usanpn.org. This indicator is also based on temperature data from the National Oceanic and Atmospheric Administrationâ€™s National Centers for Environmental Information, which maintains a large collection of climate data online at: www.ncei.noaa.gov. Data for this indicator were analyzed using methods described by Schwartz et al. (2013).8

Technical Documentation

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References

1 IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014: Impacts, adaptation, and vulnerability. Working Group II contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg2.

2 Schwartz, M.D., R. Ahas, and A. Aasa. 2006. Onset of spring starting earlier across the Northern Hemisphere. Glob. Chang. Biol. 12:343â€“351.

3 Schwartz, M.D., T.R. Ault, and J.L. Betancourt. 2013. Spring onset variations and trends in the continental United States: Past and regional assessment using temperature-based indices. Int. J. Climatol. 33:2917â€“2922.

4 For example, see: Schwartz, M.D., R. Ahas, and A. Aasa. 2006. Onset of spring starting earlier across the Northern Hemisphere. Glob. Chang. Biol. 12:343â€“351.

5 Schwartz, M.D. 2016 update to data originally published in: Schwartz, M.D., T.R. Ault, and J.L. Betancourt. 2013. Spring onset variations and trends in the continental United States: Past and regional assessment using temperature-based indices. Int. J. Climatol. 33:2917â€“2922.

6 Schwartz, M.D. 2016 update to data originally published in: Schwartz, M.D., T.R. Ault, and J.L. Betancourt. 2013. Spring onset variations and trends in the continental United States: Past and regional assessment using temperature-based indices. Int. J. Climatol. 33:2917â€“2922.

7 Schwartz, M.D. 2016 update to data originally published in: Schwartz, M.D., T.R. Ault, and J.L. Betancourt. 2013. Spring onset variations and trends in the continental United States: Past and regional assessment using temperature-based indices. Int. J. Climatol. 33:2917â€“2922.

8 Schwartz, M.D., T.R. Ault, and J.L. Betancourt. 2013. Spring onset variations and trends in the continental United States: Past and regional assessment using temperature-based indices. Int. J. Climatol. 33:2917â€“2922.

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Stream Temperature

Tribal Connection: Trends in Stream Temperature in the Snake River

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Marine Species Distribution

Leaf and Bloom Dates

Community Connection: Cherry Blossom Bloom Dates in Washington, D.C.

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Climate Change Indicators: Snowpack

This indicator measures trends in mountain snowpack in the western United States.

Figure 1. Trends in April Snowpack in the Western United States, 1955–2016

This map shows trends in April snowpack in the western United States, measured in terms of snow water equivalent. Blue circles represent increased snowpack; red circles represent a decrease.

Data source: Mote and Sharp, 20162
Web update: August 2016

Figure 1. Trends in April Snowpack in the Western United States, 1955–2016

Key Points

Background

About the
Indicator

Indicator Notes

Data Sources

Technical
Documentation

Key Points

From 1955 to 2016, April snowpack declined at more than 90 percent of the sites measured (see Figure 1). The average change across all sites amounts to about a 23-percent decline.

Large and consistent decreases have been observed throughout the western United States. Decreases have been especially prominent in Washington, Oregon, and the northern Rockies.

While some stations have seen increases in snowpack, all 11 states included in this indicator have experienced a decrease in snowpack on average over the time period. In the Northwest (Idaho, Oregon, Washington), all but three stations saw decreases in snowpack over the period of record.

Background

Temperature and precipitation are key factors affecting snowpack, which is the amount or thickness of snow that accumulates on the ground. In a warming climate, more precipitation will be expected to fall as rain rather than snow in most areas—reducing the extent and depth of snowpack. Higher temperatures in the spring can cause snow to melt earlier.

Mountain snowpack plays a key role in the water cycle in western North America, storing water in the winter when the snow falls and releasing it as runoff in spring and summer when the snow melts. Millions of people in the West depend on the melting of mountain snowpack for power, irrigation, and drinking water. In most western river basins, snowpack is a larger component of water storage than human-constructed reservoirs.1

Changes in mountain snowpack can affect agriculture, winter recreation, and tourism in some areas, as well as plants and wildlife. For example, certain types of trees rely on snow for insulation from freezing temperatures, as do some animal species. In addition, fish spawning could be disrupted if changes in snowpack or snowmelt alter the timing and abundance of streamflows.

About the Indicator

This indicator uses a measurement called snow water equivalent to determine trends in snowpack. Snow water equivalent is the amount of water contained within the snowpack at a particular location. It can be thought of as the depth of water that would result if the entire snowpack were to melt.

The U.S. Department of Agriculture and other collaborators have measured snowpack since the early 1900s. In the early years of data collection, researchers measured snow water equivalent manually, but since 1980, measurements at some locations have been collected with automated instruments. This indicator is based on data from approximately 700 permanent measurement sites in the western United States. The indicator shows long-term rates of change for April 1, the most frequent observation date, because it could reflect changes in snowfall, and it is extensively used for spring streamflow forecasting.

Indicator Notes

EPA selected 1955 as a starting point for this analysis because many measurement sites in the Southwest were established in the early 1950s. Natural variability in the Earth’s climate means that snowpack trends may vary slightly when measured over different time periods. For example, the period from 1945 to 1955 was unusually snowy in the Northwest, so if this indicator were to start in 1945 or 1950, the Northwest would appear to show larger decreases over time. However, the general direction of the trend is the same regardless of the start date.

Although most parts of the West have seen reductions in snowpack―consistent with overall warming trends shown in the U.S. and Global Temperature indicator―snowfall trends may be partially influenced by non-climatic factors such as observation methods, land-use changes, and forest canopy changes.

Data Sources

Data for this indicator came from the U.S. Department of Agriculture’s Natural Resources Conservation Service Water and Climate Center and the California Department of Water Resources. The map was constructed using methods described by Mote et al. (2005).3 The U.S. Department of Agriculture data are available at: www.wcc.nrcs.usda.gov. The California Department of Water Resources data are available at: http://cdec.water.ca.gov/snow/current/snow/index.html.

Technical Documentation

Download related technical information PDF

References

1. Mote, P.W., A.F. Hamlet, M.P. Clark, and D.P. Lettenmaier. 2005. Declining mountain snowpack in Western North America. Bull. Amer. Meteor. Soc. 86(1):39–49.

2. Mote, P.W., and D. Sharp. 2016 update to data originally published in: Mote, P.W., A.F. Hamlet, M.P. Clark, and D.P. Lettenmaier. 2005. Declining mountain snowpack in Western North America. B. Am. Meteorol. Soc. 86(1):39–49.

3. Mote, P.W., A.F. Hamlet, M.P. Clark, and D.P. Lettenmaier. 2005. Declining mountain snowpack in Western North America. B. Am. Meteorol. Soc. 86(1):39–49.

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Climate Indicators

Climate Change Indicators

Greenhouse Gases

Weather and Climate

Oceans

Snow and Ice

Arctic Sea Ice

Antarctic Sea Ice

Glaciers

Lake Ice

Community Connection: Ice Breakup in Two Alaskan Rivers

Snowfall

Snow Cover

Snowpack

Health and Society

Ecosystems

Frequent Questions


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Climate Change Indicators: Length of Growing Season

This indicator measures the length of the growing season in the contiguous 48 states.

Figure 1. Length of Growing Season in the Contiguous 48 States, 1895–2015

This figure shows the length of the growing season in the contiguous 48 states compared with a long-term average. For each year, the line represents the number of days shorter or longer than average. The line was smoothed using an 11-year moving average. Choosing a different long-term average for comparison would not change the shape of the data over time.

Data source: Kunkel, 20162
Web update: August 2016

Figure 2. Length of Growing Season in the Contiguous 48 States, 1895–2015: West Versus East

This figure shows the length of the growing season in the western and eastern United States compared with a long-term average. For each year, the line represents the number of days shorter or longer than average. The lines were smoothed using an 11-year moving average. Choosing a different long-term average for comparison would not change the shape of the data over time.

Data source: Kunkel, 20163
Web update: August 2016

Figure 3. Change in Length of Growing Season by State, 1895–2015

This map shows the total change in length of the growing season from 1895 to 2015 for each of the contiguous 48 states.

Data source: Kunkel, 20164
Web update: August 2016

Figure 4. Timing of Last Spring Frost and First Fall Frost in the Contiguous 48 States, 1895–2015

This figure shows the timing of the last spring frost and the first fall frost in the contiguous 48 states compared with a long-term average. Positive values indicate that the frost occurred later in the year, and negative values indicate that the frost occurred earlier in the year. The lines were smoothed using an 11-year moving average. Choosing a different long-term average for comparison would not change the shape of the data over time.

Data source: Kunkel, 20165
Web update: August 2016

Figure 5. Change in Timing of Last Spring Frost by State, 1895–2015

This map shows the total change in last spring frost date from 1895 to 2015 for each of the contiguous 48 states.

Data source: Kunkel, 20166
Web update: August 2016

Figure 6. Change in Timing of First Fall Frost by State, 1895–2015

This map shows the total change in first fall frost date from 1895 to 2015 for each of the contiguous 48 states.

Data source: Kunkel, 20167
Web update: August 2016

Figure 1. Length of Growing Season in the Contiguous 48 States, 1895–2015

Figure 2. Length of Growing Season in the Contiguous 48 States, 1895–2015: West Versus East

Figure 3. Change in Length of Growing Season by State, 1895–2015

Figure 4. Timing of Last Spring Frost and First Fall Frost in the Contiguous 48 States, 1895–2015

Figure 5. Change in Timing of Last Spring Frost by State, 1895–2015

Figure 6. Change in Timing of First Fall Frost by State, 1895–2015

Key Points

Background

About the
Indicator

Indicator Notes

Data Sources

Technical
Documentation

Key Points

The average length of the growing season in the contiguous 48 states has increased by nearly two weeks since the beginning of the 20th century. A particularly large and steady increase occurred over the last 30 years (see Figure 1).

The length of the growing season has increased more rapidly in the West than in the East. In the West, the length of the growing season has increased at an average rate of about 2.2 days per decade since 1895, compared with a rate of nearly one day per decade in the East (see Figure 2).

The length of the growing season has increased in almost every state. States in the Southwest (e.g., Arizona and California) have seen the most dramatic increase. In contrast, the growing season has actually become shorter in a few southeastern states (Figure 3).

In recent years, the final spring frost has been occurring earlier than at any point since 1895, and the first fall frost has been arriving later. Since 1980, the last spring frost has occurred an average of three days earlier than the long-term average, and the first fall frost has occurred about three days later (see Figure 4).

Patterns in the timing of spring and fall frost (Figures 5 and 6) largely mirror the overall patterns in the length of growing season (Figure 3). States that saw an increased length of growing season had comparable changes in frost-free days for both the spring and fall.

Background

The length of the growing season in any given region refers to the number of days when plant growth takes place. The growing season often determines which crops can be grown in an area, as some crops require long growing seasons, while others mature rapidly. Growing season length is limited by many different factors. Depending on the region and the climate, the growing season is influenced by air temperatures, frost days, rainfall, or daylight hours.

Changes in the length of the growing season can have both positive and negative effects on the yield and prices of particular crops. Overall, warming is expected to have negative effects on yields of major crops, but crops in some individual locations may benefit.1 A longer growing season could allow farmers to diversify crops or have multiple harvests from the same plot. However, it could also limit the types of crops grown, encourage invasive species or weed growth, or increase demand for irrigation. A longer growing season could also disrupt the function and structure of a region’s ecosystems and could, for example, alter the range and types of animal species in the area.

About the Indicator

This indicator looks at the impact of temperature on the length of the growing season in the contiguous 48 states, as well as trends in the timing of spring and fall frosts. For this indicator, the length of the growing season is defined as the period of time between the last frost of spring and the first frost of fall, when the air temperature drops below the freezing point of 32°F. This is referred to as the frost-free season.

Changes in growing season have been calculated using temperature data from 750 weather stations throughout the contiguous 48 states. These data were compiled by the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information. Growing season length and the timing of spring and fall frosts were averaged across the nation, then compared with long-term average numbers (1895–2015) to determine how each year differed from the long-term average.

Indicator Notes

Changes in measurement techniques and instruments over time can affect trends. This indicator includes only data from weather stations with a consistent record of data points for the time period. Methods for producing state, regional, and national values by year were designed to provide spatial representativeness regardless of station density.

Data Sources

All six figures are based on temperature data compiled by the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information, and these data are available online at: www.ncei.noaa.gov. Analysis of frost timing and growing season length was provided by Kunkel (2016).8

Technical Documentation

Download related technical information PDF

References

1 IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014: Impacts, adaptation, and vulnerability. Working Group II contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg2.

2 Kunkel, K.E. 2016 update to data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

3 Kunkel, K.E. 2016 update to data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

4 Kunkel, K.E. 2016 expanded analysis of data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

5 Kunkel, K.E. 2016 update to data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

6 Kunkel, K.E. 2016 expanded analysis of data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

7 Kunkel, K.E. 2016 expanded analysis of data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

8 Kunkel, K.E. 2016 update to data originally published in: Kunkel, K.E., D.R. Easterling, K. Hubbard, and K. Redmond. 2004. Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett. 31:L03201.

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Climate Indicators

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Greenhouse Gases

Weather and Climate

Oceans

Snow and Ice

Health and Society

Heat-Related Deaths

Heat-Related Illnesses

Heating and Cooling Degree Days

Lyme Disease

West Nile Virus

Length of Growing Season

Ragweed Pollen Season

Ecosystems

Frequent Questions


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Climate Change Indicators: Heat-Related Illnesses

This indicator tracks how often people are hospitalized because of exposure to heat.

Figure 1. Heat-Related Hospitalizations in 20 States, 2001â€“2010

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This graph shows the annual rate of hospitalizations classified as â€œheat-relatedâ€? by medical professionals in 20 states that participate in CDCâ€™s hospitalization tracking program, from 2001 to 2010. The rate is based on hospital discharge records for May 1 to September 30 of every year. The rate has been age-adjusted to account for the effects of population change over timeâ€”for example, if the proportion of older adults has increased.

Data source: Choudhary and Vaidyanathan, 20149
Web update: AugustÂ 2016

Figure 2. Average Rate of Heat-Related Hospitalizations in 23 States, 2001â€“2010

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This map shows the 2001â€“2010 average rate of hospitalizations classified as â€œheat-relatedâ€? by medical professionals in 23 states that participate in CDCâ€™s hospitalization tracking program. Rates are based on hospital discharge records for May 1 to September 30 of every year. Rates have been age-adjusted to account for differences in the population distribution over time and between statesâ€”for example, if one state has a higher proportion of older adults than another.

Data source: CDC, 201610
Web update: AugustÂ 2016

Figure 3. Heat-Related Hospitalizations in 20 States by Sex and Age, 2001â€“2010

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This graph shows the total number of hospitalizations classified as â€œheat-relatedâ€? by medical professionals in 20 states that participate in CDCâ€™s hospitalization tracking program, from 2001 to 2010. Totals are broken out by sex and by age group. The graph shows 10-year totals based on hospital discharge records for May 1 to September 30 of every year.

Data source: Choudhary and Vaidyanathan, 201411
Web update: AugustÂ 2016

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Figure 1. Heat-Related Hospitalizations in 20 States, 2001â€“2010

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Figure 2. Average Rate of Heat-Related Hospitalizations in 23 States, 2001â€“2010

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Figure 3. Heat-Related Hospitalizations in 20 States by Sex and Age, 2001â€“2010

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Key Points

Â
Background

About the
Indicator

Â
Indicator Notes

Â
Data Sources

Technical
Documentation

Key Points

From 2001 to 2010, the 20 states covered in Figure 1 recorded a total of about 28,000 heat-related hospitalizations.6Â The resulting annual rates ranged from 1.1 cases per 100,000 people in 2004 to 2.5 cases per 100,000 people in 2006, with a 10-year average rate of 1.8 cases per 100,000 people (see Figure 1).

The pattern in Figure 1 largely matches the pattern in heat-related deaths during the same period (see the Heat-Related Deaths indicator), including a low value in 2004 and a peak in 2006. Considerable year-to-year variability makes it difficult to determine whether heat-related illnesses have increased or decreased to a meaningful degree since 2001.

Heat-related hospitalization rates vary widely among the 23 states studied (see Figure 2). Average rates from 2001 to 2010 ranged from fewer than one case per 100,000 people in some states to nearly four cases per 100,000 people in others. The highest rates occurred in Kansas, Louisiana, Missouri, South Carolina, and Tennessee. Relatively high hospitalization rates in the Southeast and Midwest suggest a connection between hotter and more humid summers and increased rates of heat-related illness, compared with other regions.7

People aged 65+ accounted for more heat-related hospitalizations than any other age group from 2001 to 2010, and males were hospitalized for heat-related illnesses more than twice as often as females (see Figure 3). Men tend to have a higher risk of heat-related illness than women because they are more likely to work in outdoor occupations such as construction.8

This indicator could overlook illnesses that were not diagnosed as heat-related, did not result in a hospitalization, or were not fully documented or reported.

Background

Heat-related illnesses can occur when a person is exposed to high temperatures, such that their body cannot cool itself sufficiently through sweating. Symptoms range from mild swelling, rashes, or cramps to potentially deadly heat exhaustion and heat stroke. Heat is the leading weather-related killer in the United States (see the Heat-Related Deaths indicator), and it causes many people to visit the emergency room or be admitted to the hospital.

Any person can suffer from heat stress, regardless of age, sex, or health status. Older adults and children, however, have a higher-than-average risk of becoming ill due to exposure to extreme heat.1 People working outdoors, the socially isolated and economically disadvantaged, those with chronic illnesses, and some communities of color are also especially vulnerable to heat.2

Unusually hot summer temperatures have become more common across the contiguous 48 states in recent decades3 (see the High and Low Temperatures indicator), and extreme heat events (heat waves) are expected to become longer, more frequent, and more intense in the future.4 As a result, the risk of heat-related illness is expected to increase.5 Hospitalization rates can also change, however, as people acclimate to higher temperatures and as communities strengthen their heat response plans and take other steps to continue to adapt.

About the Indicator

This indicator shows hospitalization rates for â€œheat-relatedâ€? illnesses such as heat exhaustion, heat cramps, mild heat edema (swelling in the legs and hands), heat syncope (fainting), and heat stroke. It is based on hospital discharge records for patients who are admitted to the hospital for 23 hours or more. Every discharge record includes a diagnosis determined by a physician or other medical professional.

This indicator covers states across a wide range of regions and climate zones that have participated in a national hospital data tracking program since at least 2001. All of these states require hospitals to submit discharge data to a state organization, which then compiles and reports the data to the U.S. Centers for Disease Control and Prevention (CDC). Dividing the annual number of hospitalizations by the corresponding state population, then multiplying by 100,000, gives the hospitalization rates (per 100,000 people) shown in this indicator.

Figure 1 shows annual hospitalization rates, averaged over a group of 20 states. Figure 2 shows state-specific rates for 23 states, averaged over the period from 2001 to 2010. Figure 3 shows the total number of hospitalizations across the same 20 states shown in Figure 1, broken out by sex and by age group. Because excessive heat events are associated with summer months, this indicator is limited to hospitalizations occurring in May through September. Reporting of illnesses and processing of records can involve a time lag of several years, so this indicator currently only shows data through 2010.

Indicator Notes

Several factors influence this indicatorâ€™s ability to estimate the true number of hospitalizations associated with extreme heat events: the indicator is limited to a subset of states; it uses records that have only been collected comprehensively since 2001; it relies on the judgment of thousands of individual medical professionals to accurately diagnose and explicitly document heat-related illnesses; and it assumes that hospitals fulfill their state-mandated reporting obligations. Nonetheless, this data set represents the best available source of observedÂ data for tracking heat-related hospitalizations across multiple states. Focusing exclusively on the months from May 1 to September 30 may help to exclude incorrect reports, but it could also exclude legitimate heat-related illnesses that occur in other months.

Hospitalsâ€™ practice of recording heat stress as a diagnosis does not signify that high temperatures are the only factor that causes or contributes to a hospitalization. Pre-existing medical conditions can significantly increase an individualâ€™s vulnerability to heat. Other important factors, such as the overall vulnerability of the population, the extent to which people have adapted and acclimated to higher temperatures, and the local climate and topography, can affect trends in â€œheat-relatedâ€? hospitalizations. Heat response measures, such as early warning and surveillance systems, air conditioning, health care, public education, cooling centers, infrastructure standards, and air quality management, can also make a big difference in heat-related hospitalization rates.Â

Data Sources

Data for this indicator were provided by CDC. This analysis was developed by CDCâ€™s Environmental Public Health Tracking Program, which publishes state-level hospitalization data online at: www.cdc.gov/nceh/tracking.

Technical Documentation

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References

1 Sarofim, M.C., S. Saha, M.D. Hawkins, D.M. Mills, J. Hess, R. Horton, P. Kinney, J. Schwartz, and A. St. Juliana. 2016. Chapter 2: Temperature-related death and illness.Â The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

2 Sarofim, M.C., S. Saha, M.D. Hawkins, D.M. Mills, J. Hess, R. Horton, P. Kinney, J. Schwartz, and A. St. Juliana. 2016. Chapter 2: Temperature-related death and illness.Â The impacts of climate change on human health in the United States: A scientific assessment. U.S. Global Change Research Program. https://health2016.globalchange.gov.

3 Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program. http://nca2014.globalchange.gov.

4 Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program. http://nca2014.globalchange.gov.

5 IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014: Impacts, adaptation, and vulnerability. Working Group II contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg2.

6 Choudhary, E. and A. Vaidyanathan. 2014. Heat stress illness hospitalizationsâ€”Environmental public health tracking program, 20 states, 2001-2010. Surveillance Summaries 63(SS13):1-10. www.cdc.gov/mmwr/preview/mmwrhtml/ss6313a1.htm.

7 Choudhary, E. and A. Vaidyanathan. 2014. Heat stress illness hospitalizationsâ€”Environmental public health tracking program, 20 states, 2001-2010. Surveillance Summaries 63(SS13):1-10. www.cdc.gov/mmwr/preview/mmwrhtml/ss6313a1.htm.

8 Choudhary, E. and A. Vaidyanathan. 2014. Heat stress illness hospitalizationsâ€”Environmental public health tracking program, 20 states, 2001-2010. Surveillance Summaries 63(SS13):1-10. www.cdc.gov/mmwr/preview/mmwrhtml/ss6313a1.htm.

9 Choudhary, E. and A. Vaidyanathan. 2014. Heat stress illness hospitalizationsâ€”Environmental public health tracking program, 20 states, 2001-2010. Surveillance Summaries 63(SS13):1-10. www.cdc.gov/mmwr/preview/mmwrhtml/ss6313a1.htm.

10 CDC (U.S. Centers for Disease Control and Prevention). 2016. Environmental Public Health Tracking Program: Heat stress hospitalizations indicator data. Accessed March 2016. http://ephtracking.cdc.gov/showHome.action.

11 Choudhary, E. and A. Vaidyanathan. 2014. Heat stress illness hospitalizationsâ€”Environmental public health tracking program, 20 states, 2001-2010. Surveillance Summaries 63(SS13):1-10. www.cdc.gov/mmwr/preview/mmwrhtml/ss6313a1.htm.

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Learn about other indicators in this section

Â Â Â Â Â Â

Climate Indicators

Climate Change Indicators

Greenhouse Gases

Weather and Climate

Oceans

Snow and Ice

Health and Society

Heat-Related Deaths

Heat-Related Illnesses

Heating and Cooling Degree Days

Lyme Disease

West Nile Virus

Length of Growing Season

Ragweed Pollen Season

Ecosystems

Frequent Questions