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# Climate Change is Real

This repository contains copies of the climate change websites, data, and other information that was unceremoniously removed from the Environmental Protection Agency. While the EPA removed the websites, it does not eliminate the risk the climate change poses to people. These pages provide scientifically-based information about climate change, the risk it poses, and what we can do to combat it.

This repository allows others to republish information on climate change on their own website, such as [cityofchicago.org/climatechangeisreal](https://www.cityofchicago.org/climatechangeisreal), so people can still access this vital information. Republishing this information across multiple organizations helps raise the profile of climate change risks even higher and makes a statement that the science that demonstrates the rise in global temperatures cannot simply be deleted.

A list of cities who republished these websites are available [here](cities.md).

## Contributing Changes

We invite others to help maintain this repository. If you notice any broken links, missing pages, or if more pages were removed, please [open an issue](/issues) or [submit a pull request](/pulls).

## Hosting climatechangeisreal

There are two ways the website can be hosted in your own domain by either embedding an existing website or hosting the files on your own web servers.

### Embed Website

An unbranded, static copy of the website is hosted at http://climatechangeisreal.s3-website.us-east-2.amazonaws.com/. This website can be embedded using an `<iframe>` on your domain. These files are updated once this repository is updated, ensuring minimal long-term maintenance.

Please note, this is hosted on an "http" URL, so it will result in mixed-content warnings if embedded on "https" sites.

### Host Your Own

To begin, clone the repository to your preferred web server with `git clone https://github.com/Chicago/climatechangeisreal.git`.

Configure your web server using the following instructions. We also recommend that you automatically update the repository on a regular basis with a cron job or scheduled task with `git pull origin master`.

If git is not a viable solution, you can also simply [download the entire contents](https://github.com/Chicago/climatechangeisreal/archive/master.zip) as a ZIP file.

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#### IIS

Use the [web.config](web.config) file included in the repo.

### Custom Branding

This repository is designed to allow custom branding to match your site's name and logo. The `/branding` folder contains customizable HTML stubs to integrate into your own website.

  * `logo.png` - Logo used in the site header
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  * `menu.html` - Contains a simple "home button" so users can always navigate back to the `/climatechangeisreal` root. This file likely does not need to be changed.

After cloning the repository or pulling in new updates, simply copy your preferred branding to this folder using the above naming conventions.


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Climate Change Indicators

Climate Change Indicators: High and Low Temperatures

This indicator describes trends in unusually hot and cold temperatures across the United States.

Figure 1.Â U.S. Annual Heat Wave Index, 1895â€“2015

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This figure shows the annual values of the U.S. Heat Wave Index from 1895 to 2015. These data cover the contiguous 48 states. Interpretation: An index value of 0.2 (for example) could mean that 20 percent of the country experienced one heat wave, 10 percent of the country experienced two heat waves, or some other combination of frequency and area resulted in this value.

Data source: Kunkel, 20166
Web update: AugustÂ 2016

Figure 2. Area of Contiguous 48 States With Unusually Hot Summer Temperatures, 1910â€“2015

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This graph shows the percentage of the land area of the contiguous 48 states with unusually hot daily high and low temperatures during the months of June, July, and August. The thin lines represent individual years, while the thick lines show a nine-year weighted average. Red lines represent daily highs, while orange lines represent daily lows. The term â€œunusualâ€? in this case is based on the long-term average conditions at each location.

Data source: NOAA, 20157
Web update: AugustÂ 2016

Figure 3. Area of the Contiguous 48 States With Unusually Cold Winter Temperatures, 1911â€“2016

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This graph shows the percentage of the land area of the contiguous 48 states with unusually cold daily high and low temperatures during the months of December, January, and February. The thin lines represent individual years, while the thick lines show a nine-year weighted average. Blue lines represent daily highs, while purple lines represent daily lows. The term â€œunusualâ€? in this case is based on the long-term average conditions at each location.

Data source: NOAA, 20168
Web update: AugustÂ 2016

Figure 4. Change in Unusually Hot Temperatures in the Contiguous 48 States, 1948â€“2015

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This map shows trends in unusually hot temperatures at individual weather stations that have operated consistently since 1948. In this case, the term â€œunusually hotâ€? refers to a daily maximum temperature that is hotter than the 95th percentile temperature during the 1948â€“2015 period. Thus, the maximum temperature on a particular day at a particular station would be considered â€œunusually hotâ€? if it falls within the warmest 5 percent of measurements at that station during the 1948â€“2015 period. The map shows changes in the total number of days per year that were hotter than the 95th percentile. Red upward-pointing symbols show where these unusually hot days are becoming more common. Blue downward-pointing symbols show where unusually hot days are becoming less common.

Data source: NOAA, 20169
Web update: AugustÂ 2016

Figure 5. Change in Unusually Cold Temperatures in the Contiguous 48 States, 1948â€“2015

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This map shows trends in unusually cold temperatures at individual weather stations that have operated consistently since 1948. In this case, the term â€œunusually coldâ€? refers to a daily minimum temperature that is colder than the 5th percentile temperature during the 1948â€“2015 period. Thus, the minimum temperature on a particular day at a particular station would be considered â€œunusually coldâ€? if it falls within the coldest 5 percent of measurements at that station during the 1948â€“2015 period. The map shows changes in the total number of days per year that were colder than the 5th percentile. Blue upward-pointing symbols show where these unusually cold days are becoming more common. Red downward-pointing symbols show where unusually cold days are becoming less common.

Data source: NOAA, 201610
Web update: AugustÂ 2016

Figure 6. Record Daily High and Low Temperatures in the Contiguous 48 States, 1950â€“2009

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This figure shows the percentage of daily temperature records set at weather stations across the contiguous 48 states by decade. Record highs (red) are compared with record lows (blue).

Data source: Meehl et al., 200911
Web update: MayÂ 2014

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Figure 1. U.S. Annual Heat Wave Index, 1895â€“2015

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Figure 2. Area of Contiguous 48 States With Unusually Hot Summer Temperatures, 1910â€“2015

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Figure 3. Area of the Contiguous 48 States With Unusually Cold Winter Temperatures, 1911â€“2016

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Figure 4. Change in Unusually Hot Temperatures in the Contiguous 48 States, 1948â€“2015

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Figure 5. Change in Unusually Cold Temperatures in the Contiguous 48 States, 1948â€“2015

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Figure 6. Record Daily High and Low Temperatures in the Contiguous 48 States, 1950â€“2009

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Key Points

Â
Background

About the
Indicator

Â
Indicator Notes

Â
Data Sources

Technical
Documentation

Key Points

Heat waves in the 1930s remain the most severe heat waves in the U.S. historical record (see Figure 1). The spike in Figure 1 reflects extreme, persistent heat waves in the Great Plains region during a period known as the â€œDust Bowl.â€? Poor land use practices and many years of intense drought contributed to these heat waves by depleting soil moisture and reducing the moderating effects of evaporation.5

Nationwide, unusually hot summer days (highs) have become more common over the last few decades (see Figure 2). The occurrence of unusually hot summer nights (lows) has increased at an even faster rate. This trend indicates less â€œcooling offâ€? at night.

The 20th century had many winters with widespread patterns of unusually low temperatures, including a particularly large spike in the late 1970s (see Figure 3). Since the 1980s, though, unusually cold winter temperatures have become less commonâ€”particularly very cold nights (lows).

The two maps show where changes in the number of days with unusually hot (above the 95th percentile) and cold (below the 5th percentile) days have occurred since 1948. Unusually high temperatures have increased in the western United States and in several areas along the Gulf and Atlantic coasts, but decreased in much of the middle of the country (see Figure 4). The number of unusually cold days has generally decreased throughout the country, particularly in the western United States (see Figure 5).

If the climate were completely stable, one might expect to see highs and lows each accounting for about 50 percent of the records set. Since the 1970s, however, record-setting daily high temperatures have become more common than record lows across the United States (see Figure 6). The most recent decade had twice as many record highs as record lows.

Background

Unusually hot or cold temperatures can result in prolonged extreme weather events like summer heat waves or winter cold spells. Heat waves can lead to illness and death, particularly among older adults, the very young, and other vulnerable populations (see the Heat-Related Deaths and Heat-Related Illnesses indicators).1 People can also die from exposure to extreme cold (hypothermia). In addition, prolonged exposure to excessive heat and cold can damage crops and injure or kill livestock. Extreme heat can lead to power outages as heavy demands for air conditioning strain the power grid, while extremely cold weather increases the need for heating fuel.

Record-setting daily temperatures, heat waves, and cold spells are a natural part of day-to-day variation in weather. As the Earthâ€™s climate warms overall, however, heat waves are expected to become more frequent, longer, and more intense.2,3Â Higher heat index values (which combine temperature and humidity to describe perceived temperature) are expected to increase discomfort and aggravate health issues. Conversely, cold spells are expected to decrease. In most locations, scientists expect daily minimum temperaturesâ€”which typically occur at nightâ€”to become warmer at a faster rate than daily maximum temperatures.4 This change will provide less opportunity to cool off and recover from daytime heat.

About the Indicator

This indicator examines trends in unusual temperatures from several perspectives:

The size and frequency of prolonged heat wave events (Figure 1).

Unusually hot summer temperatures and cold winter temperatures nationwide (Figures 2 and 3).

The change in the number of days with unusually hot and cold temperatures at individual weather stations (Figures 4 and 5).

Changes in record high and low temperatures (Figure 6).Â

The data come from thousands of weather stations across the United States. National patterns can be determined by dividing the country into a grid and examining the data for one station in each cell of the grid. This method ensures that the results are not biased toward regions that happen to have many stations close together.

Figure 1 shows the U.S. Annual Heat Wave Index, which tracks the occurrence of heat wave conditions across the contiguous 48 states from 1895 to 2015. While there is no universal definition of a heat wave, this index defines a heat wave as a period lasting at least four days with an average temperature that would only be expected to occur once every 10 years, based on the historical record. The index value for a given year depends on how often heat waves occur and how widespread they are.

Figures 2 and 3 show trends in the percentage of the countryâ€™s area experiencing unusually hot temperatures in the summer and unusually cold temperatures in the winter. These graphs are based on daily maximum temperatures, which usually occur during the day, and daily minimum temperatures, which usually occur at night. At each station, the recorded highs and lows are compared with the full set of historical records. After averaging over a particular month or season of interest, the coldest 10 percent of years are considered â€œunusually coldâ€? and the warmest 10 percent are â€œunusually hot.â€? For example, if last yearâ€™s summer highs were the 10th warmest on record for a particular location with more than 100 years of data, that yearâ€™s summer highs would be considered unusually warm. Data are available from 1910 to 2015 for summer (June through August) and from 1911 to 2016 for winter (December of the previous year through February).

Figures 4 and 5 show how trends in unusually hot and cold daily temperatures throughout the year vary by location. These maps cover 1,100 weather stations that have operated since 1948. Figure 4 was created by reviewing all daily maximum temperatures from 1948 to 2015 and identifying the 95th percentile temperature (a temperature that one would only expect to exceed in five days out of every 100) at each station. Next, for each year, the total number of days with maximum temperatures higher than the 95th percentile (that is, unusually hot days) was determined. The map shows how the total number of unusually hot days per year at each station has changed over time. Figure 5 is similar except that it looks at unusually cold days, based on the 5th percentile of daily minimum temperatures.

Many people are familiar with record daily high and low temperatures, which are frequently mentioned in weather reports. Figure 6 depicts trends in these records by comparing the number of record-setting highs with the number of record-setting lows by decade. These data come from a set of weather stations that have collected data consistently since 1950.

Indicator Notes

Temperature data are less certain for the early part of the 20th century because fewer stations were operating at that time. In addition, measuring devices and methods have changed over time, and some stations have moved. The data have been adjusted to the extent possible to account for some of these influences and biases, however, and these uncertainties are not sufficient to change the fundamental trends shown in the figures.

Data Sources

The data for this indicator are based on measurements from weather stations managed by the National Oceanic and Atmospheric Administration. Figure 1 uses data from the National Weather Service Cooperative Observer Network. Figures 2 and 3 come from the U.S. Climate Extremes Index, which is based on a smaller group of long-term weather stations that are tracked by the National Centers for Environmental Information and referred to as the U.S. Historical Climatology Network. Figures 4 and 5 use data from a somewhat larger set of stations tracked by the National Centers for Environmental Information, known as the Global Historical Climatology Network. Figure 6 uses National Weather Service data processed by Meehl et al. (2009).12Â  All of these weather station records are available online at: www.ncdc.noaa.gov, and information about the Climate Extremes Index can be found at: www.ncdc.noaa.gov/extremes/cei.

Technical Documentation

Download related technical information PDF

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.

2Â 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.

3 National Research Council. 2011. Climate stabilization targets: Emissions, concentrations, and impacts over decades to millennia. Washington, DC: National Academies Press.

4 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.

5Â CCSP (U.S. Climate Change Science Program). 2008. Synthesis and Assessment Product 3.3: Weather and climate extremes in a changing climate. www.globalchange.gov/browse/reports/sap-33-weather-and-climate-extremes-changing-climate.

6 Kunkel, K. 2016. Updated version of Figure 2.3 in: CCSP (U.S. Climate Change Science Program). 2008. Synthesis and Assessment Product 3.3: Weather and climate extremes in a changing climate. www.globalchange.gov/browse/reports/sap-33-weather-and-climate-extremes-changing-climate.

7Â NOAA (National Oceanic and Atmospheric Administration). 2015. U.S. Climate Extremes Index. Accessed December 2015. www.ncdc.noaa.gov/extremes/cei.

8 NOAA (National Oceanic and Atmospheric Administration). 2016. U.S. Climate Extremes Index. Accessed May 2016. www.ncdc.noaa.gov/extremes/cei.

9Â  NOAA (National Oceanic and Atmospheric Administration). 2016. National Centers for Environmental Information. Accessed May 2016. www.ncdc.noaa.gov.

10 NOAA (National Oceanic and Atmospheric Administration). 2016. National Centers for Environmental Information. Accessed May 2016. www.ncdc.noaa.gov.

11Â Meehl, G. A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel. 2009. Relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S. Geophys. Res. Lett. 36:L23701.

12Â Meehl, G. A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel. 2009. Relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S. Geophys. Res. Lett. 36:L23701.

Printer-friendly PDF of all text and figures

Learn about other indicators in this section

Â Â Â Â Â Â Â

Climate Indicators

Climate Change Indicators

Greenhouse Gases

Weather and Climate

U.S. and Global Temperature

High and Low Temperatures

U.S. and Global Precipitation

Heavy Precipitation

Tropical Cyclone Activity

River Flooding

Drought

A Closer Look: Temperature and Drought in the Southwest

Oceans

Snow and Ice

Health and Society

Ecosystems

Frequent Questions


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Climate Change Indicators

Climate Change Indicators: Tropical Cyclone Activity

This indicator examines the frequency, intensity, and duration of hurricanes and other tropical storms in the Atlantic Ocean, Caribbean, and Gulf of Mexico.

Figure 1. Number of Hurricanes in the North Atlantic, 1878â€“2015

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This graph shows the number of hurricanes that formed in the North Atlantic Ocean each year from 1878 to 2015, along with the number that made landfall in the United States. The orange curve shows how the total count in the green curve can be adjusted to attempt to account for the lack of aircraft and satellite observations in early years. All three curves have been smoothed using a five-year average, plotted at the middle year. The most recent average (2011â€“2015) is plotted at 2013.

Data source: NOAA, 20164; Â Vecchi and Knutson, 20115
Web update: AugustÂ 2016

Figure 2. North Atlantic Tropical Cyclone Activity According to the Accumulated Cyclone Energy Index, 1950â€“2015

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This figure shows total annual Accumulated Cyclone Energy (ACE) Index values, which account for cyclone strength, duration, and frequency, from 1950 through 2015. The National Oceanic and Atmospheric Administration has defined â€œnear normal,â€? â€œabove normal,â€? and â€œbelow normalâ€? ranges based on the distribution of ACE Index values over the 30 years from 1981 to 2010.

Data source: NOAA, 20166
Web update: AugustÂ 2016

Figure 3. North Atlantic Tropical Cyclone Activity According to the Power Dissipation Index, 1949â€“2015

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This figure presents annual values of the Power Dissipation Index (PDI), which accounts for cyclone strength, duration, and frequency. Tropical North Atlantic sea surface temperature trends are provided for reference. Note that sea surface temperature is measured in different units, but the values have been plotted alongside the PDI to show how they compare. The lines have been smoothed using a five-year weighted average, plotted at the middle year. The most recent average (2011â€“2015) is plotted at 2013.

Data source: Emanuel, 20167
Web update: AugustÂ 2016

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Figure 1. Number of Hurricanes in the North Atlantic, 1878â€“2015

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Figure 2. North Atlantic Tropical Cyclone Activity According to the Accumulated Cyclone Energy Index, 1950â€“2015

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Figure 3. North Atlantic Tropical Cyclone Activity According to the Power Dissipation Index, 1949â€“2015

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Key Points

Â
Background

About the
Indicator

Â
Indicator Notes

Â
Data Sources

Technical
Documentation

Key Points

Since 1878, about six to seven hurricanes have formed in the North Atlantic every year. Roughly two per year make landfall in the United States. The total number of hurricanes (particularly after being adjusted for improvements in observation methods) and the number reaching the United States do not indicate a clear overall trend since 1878 (see Figure 1).

Â According to the total annual ACE Index, cyclone intensity has risen noticeably over the past 20 years, and six of the 10 most active years since 1950 have occurred since the mid-1990s (see Figure 2). Relatively high levels of cyclone activity were also seen during the 1950s and 1960s.

The PDI (see Figure 3) shows fluctuating cyclone intensity for most of the mid- to late 20th century, followed by a noticeable increase since 1995 (similar to the ACE Index). These trends are shown with associated variations in sea surface temperature in the tropical North Atlantic for comparison (see Figure 3).

Despite the apparent increases in tropical cyclone activity in recent years, shown in Figures 2 and 3, changes in observation methods over time make it difficult to know whether tropical storm activity has actually shown an increase over time.3

Background

Hurricanes, tropical storms, and other intense rotating storms fall into a general category called cyclones. There are two main types of cyclones: tropical and extratropical (those that form outside the tropics). Tropical cyclones get their energy from warm tropical oceans. Extratropical cyclones get their energy from the jet stream and from temperature differences between cold, dry air masses from higher latitudes and warm, moist air masses from lower latitudes.

This indicator focuses on tropical cyclones in the Atlantic Ocean, Caribbean, and Gulf of Mexico. Tropical cyclones are most common during the â€œhurricane season,â€? which runs from June through November. The effects of tropical cyclones are numerous and well known. At sea, storms disrupt and endanger shipping traffic. When cyclones encounter land, their intense rains and high winds can cause severe property damage, loss of life, soil erosion, and flooding. The associated storm surgeâ€”the large volume of ocean water pushed toward shore by the cycloneâ€™s strong windsâ€”can cause severe flooding and destruction.

Climate change is expected to affect tropical cyclones by increasing sea surface temperatures, a key factor that influences cyclone formation and behavior. The U.S. Global Change Research Program and the Intergovernmental Panel on Climate Change project that, more likely than not, tropical cyclones will become more intense over the 21st century, with higher wind speeds and heavier rains.1,2

About the Indicator

Records of tropical cyclones in the Atlantic Ocean have been collected since the 1800s. The most reliable long-term records focus on hurricanes, which are the strongest category of tropical cyclones in the Atlantic, with wind speeds of at least 74 miles per hour. This indicator uses historical data from the National Oceanic and Atmospheric Administration to track the number of hurricanes per year in the North Atlantic (north of the equator) and the number reaching the United States since 1878. Some hurricanes over the ocean might have been missed before the start of aircraft and satellite observation, so scientists have used other evidence, such as ship traffic records, to estimate the actual number of hurricanes that might have formed in earlier years.

This indicator also looks at the Accumulated Cyclone Energy (ACE) Index and the Power Dissipation Index (PDI), which are two ways of monitoring the frequency, strength, and duration of tropical cyclones based on wind speed measurements.

Every cyclone has an ACE Index value, which is a number based on the maximum wind speed measured at six-hour intervals over the entire time that the cyclone is classified as at least a tropical storm (wind speed of at least 39 miles per hour). Therefore, a stormâ€™s ACE Index value accounts for both strength and duration. The National Oceanic and Atmospheric Administration calculates the total ACE Index value for an entire hurricane season by adding the values for all named storms, including subtropical storms, tropical storms, and hurricanes. The resulting annual total accounts for cyclone strength, duration, and frequency. For this indicator, the index has been converted to a scale where 100 equals the median value (the midpoint) over a base period from 1981 to 2010. The thresholds in Figure 2 define whether the ACE Index for a given year is close to normal, significantly above normal, or significantly below.

Like the ACE Index, the PDI is based on measurements of wind speed, but it uses a different calculation method that places more emphasis on storm intensity. This indicator shows the annual PDI value, which represents the sum of PDI values for all named storms during the year.

Indicator Notes

Over time, data collection methods have changed as technology has improved. For example, wind speed collection methods have evolved substantially over the past 60 years, while aircraft reconnaissance began in 1944 and satellite tracking around 1966. Figure 1 shows how older hurricane counts have been adjusted to attempt to account for the lack of aircraft and satellite observations. Changes in data gathering technologies could substantially influence the overall patterns in Figures 2 and 3. The effects of these changes on data consistency over the life of the indicator would benefit from additional research.

While Figures 2 and 3 cover several different aspects of tropical cyclones, there are other important factors not covered here, including the size of each storm, the amount of rain, and the height of the storm surge. The reason for the recent divergence between cyclone activity and sea surface temperature in Figure 3 has not been identified conclusively, but it may relate to other factors that influence the formation of storms, such as the difference in wind speeds at different levels in the atmosphere (called vertical wind shear).8

Data Sources

Hurricane counts were compiled using methods described in Knutson et al.Â (2010).9Â Data for Figures 1 and 2 came from the National Oceanic and Atmospheric Administrationâ€™s Hurricane Research Division, and are available online at: www.aoml.noaa.gov/hrd/hurdat/comparison_table.html. Values for Figure 3 have been calculated by Kerry Emanuel at the Massachusetts Institute of Technology. Both the ACE Index and the PDI are based on wind speed measurements compiled by the National Oceanic and Atmospheric Administration.

Technical Documentation

Download related technical information PDF

References

1Â Â 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.

2Â 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.

3Â IPCC (Intergovernmental Panel on Climate Change). 2012. Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge, United Kingdom: Cambridge University Press. http://ipcc-wg2.gov/SREX.

4Â NOAA (National Oceanic and Atmospheric Administration). 2016. The Atlantic Hurricane Database Re-analysis Project. www.aoml.noaa.gov/hrd/hurdat/comparison_table.html.

5Â Vecchi, G.A., and T.R. Knutson. 2011. Estimating annual numbers of Atlantic hurricanes missing from the HURDAT database (1878â€“1965) using ship track density. J. Climate 24(6):1736â€“1746. www.gfdl.noaa.gov/bibliography/related_files/gav_2010JCLI3810.pdf.

6Â NOAA (National Oceanic and Atmospheric Administration). 2016. The Atlantic Hurricane Database Re-analysis Project. www.aoml.noaa.gov/hrd/hurdat/comparison_table.html.

7Â Emanuel, K.A. 2016 update to data originally published in: Emanuel, K.A. 2007. Environmental factors affecting tropical cyclone power dissipation. J. Climate 20(22):5497â€“5509.

8Â IPCC (Intergovernmental Panel on Climate Change). 2012. Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge, United Kingdom: Cambridge University Press. http://ipcc-wg2.gov/SREX.

9Â Â Knutson, T.R., J.L. McBride, J. Chan, K. Emanuel, G. Holland, C. Landsea, I. Held, J.P. Kossin, A.K. Srivastava, and M. Sugi. 2010. Tropical cyclones and climate change. Nature Geosci. 3:157â€“163.

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Community Connection: Ice Breakup in Two Alaskan Rivers

This feature examines the timing of spring river ice breakup in two communities that have kept detailed records for more than 100 years.

Figure 1. Ice Breakup Dates for Two Alaskan Rivers, 1896–2016

This figure shows the date each year when ice breaks up at two locations: the town of Nenana on the Tanana River and Dawson City on the Yukon River.

Data sources: Nenana Ice Classic, 20164; Yukon River Breakup, 20165
Web update: August 2016

Figure 1. Ice Breakup Dates for Two Alaskan Rivers, 1896–2016

Key Points

Background

Notes

Data Sources

Technical Documentation

Key Points

The Tanana and Yukon rivers both demonstrate long-term trends toward earlier ice breakup in the spring. The ice breakup dates for both the Tanana and Yukon rivers have shifted earlier by approximately seven days over their respective periods of record.

The 2016 breakup date recorded at Dawson City was the earliest on record.

Despite the overall trend toward earlier breakup, recent breakup dates for both rivers are largely within the range of historical variation.

Background

Regions in the far north are warming more quickly than other parts of the world, and this pattern is expected to continue1,2 (see the Arctic Sea Ice indicator; for more information about regional temperature changes, see the U.S. and Global Temperature indicator). The Tanana and Yukon rivers in Alaska provide a particularly noteworthy record of northern climate because, for a century or more, local citizens have recorded the date when the ice on these rivers starts to move or break up each spring. In fact, some towns have annual competitions to guess when ice breakup will occur.

Since 1917, the Nenana Ice Classic competition on the Tanana River in central Alaska has paid several million dollars in winnings to the people who come closest to guessing the exact date and time of day when the river ice will break up. A similar tradition exists in Dawson City on the Yukon River, just across the border in Canada, where breakup dates have been recorded since 1896.

River ice breakup is more than just a friendly competition, though. Ice breakup is an important time of transition for communities that rely on these relatively remote and free-flowing wild rivers for transportation, subsistence hunting and fishing, and other needs. In addition, early thawing can lead to severe ice movement, jamming, damage to infrastructure, and destructive floods.3

The data collected by these communities highlights how the river ice breakup dates in Nenana and Dawson City have changed over time. Both towns use the same method to measure the exact time of river ice breakup. Residents place a tripod on the ice in the center of the river. This tripod is attached by a cable to a clock on the shore. When the ice under the tripod breaks or starts to move, the tripod moves, pulling the cable and stopping the clock.

Notes

Besides climate change, natural year-to-year variations and other factors such as local development and land-use patterns can influence ice breakup dates. The two locations featured here are relatively remote and undeveloped, so the ice breakup dates are more likely to reflect natural changes in weather and climate conditions. However, corresponding measurements of water conditions and air temperature would be useful to help understand the connection between changes in river ice breakup and changes in climate.

Data Sources

Ice breakup dates for the Tanana River at Nenana and the Yukon River at Dawson City have been recorded and made publicly available as part of two long-running, community competitions: the Nenana Ice Classic and the Yukon River Breakup. The data shown here and other information can be found online at: www.nenanaakiceclassic.com and: www.yukonriverbreakup.com/statistics. Data records of ice breakup dates for these two rivers are also archived by the National Snow and Ice Data Center at: http://nsidc.org/data/lake_river_ice.

Technical Documentation

Download related technical information PDF

References

1. 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.

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. Beltaos, S., and B.C. Burrell. 2003. Climatic change and river ice breakup. Can. J. Civil Eng. 30:145–155.

4. Nenana Ice Classic. 2016. Accessed May 2016. www.nenanaakiceclassic.com.

5. Yukon River Breakup. 2016. Accessed May 2016. www.yukonriverbreakup.com.

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Lake Ice

Community Connection: Ice Breakup in Two Alaskan Rivers

Snowfall

Snow Cover

Snowpack

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Climate Change Indicators: Snow Cover

This indicator measures the amount of land in North America that is covered by snow.

Figure 1. Snow-Covered Area in North America, 1972–2015

This graph shows the average area covered by snow in a given calendar year, based on an analysis of weekly maps. The area is measured in square miles. These data cover all of North America (not including Greenland).

Data source: Rutgers University Global Snow Lab, 20162
Web update: August 2016

Figure 2. Snow-Covered Area in North America by Season, 1972–2015

This graph shows the average area covered by snow during spring (March–May), summer (June–August), fall (September–November), and winter (December–February), based on an analysis of weekly maps. The area is measured in square miles. These data cover all of North America (not including Greenland).

Data source: Rutgers University Global Snow Lab, 20163
Web update: August 2016

Figure 3. Snow Cover Season in the United States, 1972–2013

This figure shows the timing of each year’s snow cover season in the contiguous 48 states and Alaska, based on an average of all parts of the country that receive snow every year. The shaded band spans from the first date of snow cover until the last date of snow cover.

Data source: NOAA, 20154
Web update: August 2016

Figure 1. Snow-Covered Area in North America, 1972–2015

Figure 2. Snow-Covered Area in North America by Season, 1972–2015

Figure 3. Snow Cover Season in the United States, 1972–2013

			Key Points

			Background

About the

			Indicator

			Indicator Notes

			Data Sources

Technical

			Documentation

Key Points

When averaged over the entire year, snow covered an average of 3.24 million square miles of North America during the period from 1972 to 2015 (see Figure 1).

The extent of snow cover has varied from year to year. The average area covered by snow has ranged from 3.0 million to 3.6 million square miles, with the minimum value occurring in 1998 and the maximum in 1978 (see Figure 1).

Between 1972 and 2015, the average extent of North American snow cover decreased at a rate of about 3,300 square miles per year. The average area covered by snow during the most recent decade (2006–2015) was 3.21 million square miles, which is about 4 percent smaller than the average extent during the first 10 years of measurement (1972–1981)—a difference of 122,000 square miles, or approximately an area the size of New Mexico (see Figure 1).

Decreases in snow cover have largely occurred in spring and summer, whereas fall and winter snow cover have remained fairly steady over the time period studied (see Figure 2). Spring and summer snow cover can have a particularly important influence on water supplies.

Since 1972, the U.S. snow cover season has become shorter by nearly two weeks, on average (see Figure 3). By far the largest change has taken place in the spring, with the last day of snow shifting earlier by 19 days since 1972. In contrast, the first date of snow cover in the fall has remained relatively unchanged.

Background

Snow cover refers to the amount of land covered by snow at any given time. Naturally, it is influenced by the amount of precipitation that falls as snow. Air temperature also plays a role because it determines whether precipitation falls as snow or rain, and it affects the rate at which snow on the ground melts. As temperature and precipitation patterns change, so can the overall area covered by snow.

Snow cover is not just something that is affected by climate change; it also exerts an influence on climate. Because snow is white, it absorbs only a small portion of the sunlight that hits it (10 to 20 percent in the case of fresh snow), and it reflects the rest back to space. In contrast, darker surfaces such as bare ground and open water absorb the majority of the energy they receive and heat up more quickly. In this way, the overall amount of snow cover affects patterns of heating and cooling over the Earth’s surface. More snow means more energy reflects back to space, resulting in cooling, while less snow cover means more energy is absorbed at the Earth’s surface, resulting in warming.

On a more local scale, snow cover is important for many plants and animals. For example, some plants and animals rely on a protective blanket of snow to insulate them from sub-freezing winter temperatures. Humans and ecosystems also rely on snowmelt to replenish streams and groundwater. Snow cover also keeps the soil moist, so if the snow melts away earlier in the spring, the soil may dry out sooner, which can stress plants and increase the risk of wildfire.1

About the Indicator

This indicator tracks the area covered by snow since 1972, based on maps generated by analyzing satellite images collected by the National Oceanic and Atmospheric Administration. Figure 1 was created by analyzing weekly maps to determine the total extent of snow cover, then averaging the weekly observations together to get a value for each year. Figure 2 uses the same method, but with average snow cover calculated for each season: spring (defined as March–May), summer (June–August), fall (September–November), and winter (December–February). These two figures cover all of North America, not including Greenland. Figure 3 focuses on the contiguous 48 states plus Alaska. It shows the average date when snow first starts to cover the ground in the fall, the average last date of snow cover in the spring, and the length of time between them. These snow cover season dates have been averaged over all parts of the country that regularly receive snow.

Indicator Notes

Although satellite-based snow cover maps are available starting in the mid-1960s, some of the early years are missing data from several weeks during the summer, which would lead to an inaccurate annual average. Thus, the indicator is restricted to 1972 and later, with all years having a full set of data.

Data Sources

The data for this indicator were provided by the Rutgers University Global Snow Lab, which posts data online at: http://climate.rutgers.edu/snowcover, and the National Oceanic and Atmospheric Administration’s National Environmental Satellite, Data, and Information Service, which collects satellite measurements and compiles maps at: www.nesdis.noaa.gov.

Technical Documentation

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References

1 Harpold, A.A., and N.P. Molotch. 2015. Sensitivity of soil water availability to changing snowmelt timing in the western U.S. Geophysical Research Letters 42(19):8011–8020.

2 Rutgers University Global Snow Lab. 2016. Area of extent data: North America (no Greenland). Accessed January 2016. http://climate.rutgers.edu/snowcover.

3 Rutgers University Global Snow Lab. 2016. Area of extent data: North America (no Greenland). Accessed January 2016. http://climate.rutgers.edu/snowcover.

4 NOAA (National Oceanic and Atmospheric Administration). 2015. Snow cover maps. Accessed November 2015. ftp://eclipse.ncdc.noaa.gov/pub/cdr/snowcover.

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Lake Ice

Community Connection: Ice Breakup in Two Alaskan Rivers

Snowfall

Snow Cover

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Climate Change Indicators

Climate Change Indicators: Lyme Disease

This indicator tracks the rate of reported Lyme disease cases across the United States.

Figure 1. Reported Cases of Lyme Disease in the United States, 1991â€“2014

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This figure shows the annual incidence of Lyme 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 CDCâ€™s national disease tracking system.

Data source: CDC, 20158
Web update: AugustÂ 2016

Figure 2. Change in Reported Lyme Disease Incidence in the Northeast and Upper Midwest, 1991â€“2014

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This map shows how reported Lyme disease incidence has changed by state since 1991, based on the number of new cases per 100,000 people. The total change has been estimated from the average annual rate of change in each state. This map is limited to the 14 states where Lyme disease is most common, where annual rates are consistently above 10 cases per 100,000 people. Connecticut, New York, and Rhode Island had too much year-to-year variation in reporting practices to allow trend calculation.

Data source: CDC, 20159
Web update: AugustÂ 2016

Reported Lyme Disease Cases in 1996 and 2014

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These maps show the distribution of reported cases of Lyme disease in 1996 and 2014. Each dot represents an individual case placed according to the patientâ€™s county of residence, which may be different than the county of exposure. The year 1996 was chosen as a reasonable starting point for comparison with recent years. These maps focus on the parts of the United States where Lyme disease is most common.

Data source: CDC, 201510
Web update: AugustÂ 2016

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Figure 1. Reported Cases of Lyme Disease in the United States, 1991â€“2014

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Figure 2. Change in Reported Lyme Disease Incidence in the Northeast and Upper Midwest, 1991â€“2014

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Reported Lyme Disease Cases in 1996 and 2014

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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

The incidence of Lyme disease in the United States has approximately doubled since 1991, from 3.74 reported cases per 100,000 people to 7.95 reported cases per 100,000 people in 2014 (see Figure 1).

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Among the states where Lyme disease is most common, New Hampshire, Maine, and Vermont have experienced the largest increases in reported case rates since 1991, followed by Delaware and Massachusetts (see Figure 2). On average, these five states now report 50 to 100 more cases per 100,000 people than they did in 1991.

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Driven by multiple factors, the number and distribution of reported cases of Lyme disease have increased over time (see 1996 and 2014 maps).Â

Background

Lyme disease is a bacterial illness that can cause fever, fatigue, joint pain, and skin rash, as well as more serious joint and nervous system complications. Lyme disease is the most common vector-borne disease (that is, a disease transmitted by mosquitoes, ticks, or fleas) in the United States. In recent years, approximately 20,000â€“30,000 confirmed cases of Lyme disease per year have been reported to the Centers for Disease Control and Prevention.1Â However, the actual number of illnesses is likely greater than what is reported to health officials.2 Lyme disease is transmitted through the bite of certain species of infected ticks (referred to commonly as deer ticks) that carry the bacteria that cause Lyme disease. These ticks live not only on deer, but also on rodents, birds, and other host animals. Deer do not harbor the bacteria that cause Lyme disease, but certain other hosts such as white-footed mice do, and ticks pick up the bacteria by feeding on these infected hosts.

Climate is just one of many important factors that influence the transmission, distribution, and incidence of Lyme disease; however, studies provide evidence that climate change has contributed to the expanded range of ticks,3 increasing the potential risk of Lyme disease, such as in areas of Canada where the ticks were previously unable to survive. The life cycle and prevalence of deer ticks are strongly influenced by temperature.4,5 For example, deer ticks are mostly active when temperatures are above 45ËšF, and they thrive in areas with at least 85-percent humidity. Thus, warming temperatures associated with climate change are projected to increase the range of suitable tick habitat and are therefore one of multiple factors driving the observed spread of Lyme disease.6 Because tick activity depends on temperatures being above a certain minimum, shorter winters could also extend the period when ticks are active each year, increasing the time that humans could be exposed to Lyme disease. Unlike some other vector-borne diseases, tick-borne disease patterns are generally less influenced by short-term changes in weather (weeks to months) than by longer-term climate change.

Other factors that affect the number of Lyme disease cases include changes in the populations of host species (particularly deer), which affect tick population size. The percentage of ticks that are infected depends on the prevalence and infection rates of white-footed mice and certain other hosts. Host species populations and habitats can be affected by climate change and other ecosystem disturbances. Human exposure to infected ticks is also influenced by multiple factors, including changes in the proximity of human populations to ticks and other hosts, increased awareness of Lyme disease, and modified behaviors, such as spending less time outdoors, taking precautions against being bitten, and checking more carefully for ticks. People who work outdoors, like farmers and landscapers, may be especially at risk.7

About the Indicator

This indicator looks at the incidence of Lyme 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. Annual Lyme disease totals and rates for each state were provided by the Centers for Disease Control and Prevention. The original data were collected by state and local health departments, which track confirmed cases of Lyme disease that are diagnosed by health care providers and report these cases to the National Notifiable Diseases Surveillance System. Nationwide reporting of Lyme disease began in 1991.

Figure 1 shows the national incidence of Lyme disease since 1991, and Figure 2 shows trends in incidence over time in 14 states that collectively account for about 95 percent of the nationâ€™s reported cases. To illustrate changes in the distribution of reported cases over time, maps of the years 1996 and 2014 are presented side by side.

Indicator Notes

For consistency, this indicator includes only data for confirmed cases of Lyme disease that are reported to CDC, not cases that are considered â€œprobable.â€? Changes in diagnosing practices and awareness of the disease over time can affect trends, especially considering that Lyme disease can be difficult to diagnose. Cases are reported based on the patientâ€™s county of residence, which is not necessarily the place where they were infected. Risk of infection is focused in certain regions of the country, and confirmed reports from low-incidence states are often the result of travel to an area of higher incidence. Evidence suggests that expanding ranges of ticks in certain northern states may be more related to a warming climate than expanding ranges in southern states.11,12Â Because of the many factors affecting tick populations and reporting of Lyme disease, however, this indicator does not provide sufficient information to determine what proportion of the observed changes in Lyme disease 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 Lyme disease, see: www.cdc.gov/lyme.

Data Sources

All three figures are based on publicly available Lyme disease data compiled by the Centers for Disease Control and Prevention at: www.cdc.gov/lyme/stats/index.html. Incidence was calculated using mid-year population estimates from the U.S. Census Bureau.13,14

Technical Documentation

Download related technical information PDF

References

1 CDC (Centers for Disease Control and Prevention). 2015. Lyme disease data and statistics. www.cdc.gov/lyme/stats/index.html. Accessed December 2015.

2 CDC (Centers for Disease Control and Prevention). 2013. CDC provides estimate of Americans diagnosed with Lyme disease each year. www.cdc.gov/media/releases/2013/p0819-lyme-disease.html.

3 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.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.

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.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.

5 Leighton, P.A., J.K. Koffi, Y. Pelcat, L.R. Lindsay, and N.H. Ogden. 2012. Predicting the speed of tick invasion: An empirical model of range expansion for the Lyme disease vector Ixodes scapularis in Canada. J. Appl. Ecol. 49(2): 457-464.

6 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.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.

7 Gamble, J.L., J. Balbus, M. Berger, K. Bouye, V. Campbell, K. Chief, K. Conlon, A. Crimmins, B. Flanagan, C. Gonzalez-Maddux, E. Hallisey, S. Hutchins, L. Jantarasami, S. Khoury, M. Kiefer, J. Kolling, K. Lynn, A. Manangan, M. McDonald, R. Morello-Frosch, M.H. Redsteer, P. Sheffield, K. Thigpen Tart, J. Watson, K.P. Whyte, and A.F. Wolkin. 2016. Chapter 9: Populations of concern. 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). 2015. Lyme disease data and statistics. www.cdc.gov/lyme/stats/index.html. Accessed December 2015.

9 CDC (Centers for Disease Control and Prevention). 2015. Lyme disease data and statistics. www.cdc.gov/lyme/stats/index.html. Accessed December 2015.

10 CDC (Centers for Disease Control and Prevention). 2015. Lyme disease data and statistics. www.cdc.gov/lyme/stats/index.html. Accessed December 2015.

11 Diuk-Wasser, M.A., A.G. Hoen, P. Cislo, R. Brinkerhoff, S.A. Hamer, M. Rowland, R. Cortinas, G. Vourc'h, F. Melton, G.J. Hickling, J.I. Tsao, J. Bunikis, A.G. Barbour, U. Kitron, J. Piesman, and D. Fish. 2012. Human risk of infection with Borrelia burgdorferi, the Lyme disease agent, in eastern United States. Am. J. Trop. Med. Hyg. 86(2):320â€“327.

12 Stromdahl, E.Y., and G.J. Hickling. 2012. Beyond Lyme: Aetiology of tick-borne human diseases with emphasis on the south-eastern United States. Zoonoses Public Hlth. 59(Supplement 2):48â€“64.

13 U.S. Census Bureau. 2015. Population estimates: Intercensal estimates. Accessed December 3, 2015. www.census.gov/popest/data/intercensal/index.html.

14 U.S. Census Bureau. 2015. Population estimates: Current estimates data. Accessed December 3, 2015. www.census.gov/popest/data/index.html.Â

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From this page, you can download and view a printer-friendly PDF version of each indicator presented on this website. Each PDF contains all the figures and text associated with a given indicator.

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      Antarctic Sea Ice (PDF)(5 pp, 471 K,
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      Arctic Sea Ice (PDF)(7 pp, 766 K,
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      Atmospheric Concentrations of Greenhouse Gases (PDF)(13 pp, 820 K,
  August 2016)

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  August 2016)

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      Cherry Blossom Bloom Dates in Washington, D.C. (PDF)(3 pp, 302 K,
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      Ice Breakup in Two Alaskan Rivers (PDF)(3 pp, 353 K,
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      Leaf and Bloom Dates (PDF)(6 pp, 995 K,
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      Length of Growing Season (PDF)(8 pp, 907 K,
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      U.S. and Global Precipitation (PDF)(5 pp, 547 K,
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      U.S. Greenhouse Gas Emissions (PDF)(6 pp, 469 K,
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Climate Change Indicators: Ecosystems

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Tribal Connection:

Community Connection:

Ecosystems provide humans with food, clean water, and a variety of other services that can be affected by climate change. This chapter looks at some of the ways that climate change affects ecosystems, including changes in wildfires, streams and lakes, bird migration patterns, fish and shellfish populations, and plant growth.

Why does it matter?

Changes in the Earth’s climate can affect ecosystems by altering the water cycle, habitats, animal behavior—such as nesting and migration patterns—and the timing of natural processes such as flower blooms. Changes that disrupt the functioning of ecosystems may increase the risk of harm or even extinction for some species. While wildfires occur naturally, more frequent and more intense fires can significantly disrupt ecosystems, damage property, put people and communities at risk, and create air pollution problems even far away from the source.

While plants and animals have adapted to environmental change for millions of years, the climate changes being experienced now could require adaptation on larger and faster scales than current species have successfully achieved in the past, thus increasing the risk of extinction or severe disruption for many species.

Summary of Key Points

Wildfires. Since 1983, the United States has had an average of 72,000 recorded wildfires per year. Of the 10 years with the largest acreage burned since 1983, nine have occurred since 2000 with many of the largest increases occurring in western states. The proportion of burned land suffering severe damage each year has ranged from 5 to 21 percent.

Streamflow. Changes in temperature, precipitation, snowpack, and glaciers can affect the rate of streamflow and the timing of peak flow. Over the last 75 years, minimum, maximum, and average flows have changed in many parts of the country—some higher, some lower. Most of the rivers and streams measured show peak winter-spring runoff happening at least five days earlier than it did in the mid-20th century.

Stream Temperature. Stream temperatures have risen throughout the Chesapeake Bay region—the area of focus for this indicator. From 1960 through 2014, water temperature increased at 79 percent of the stream sites measured in the region. Temperature has risen by an average of 1.2°F across all sites and 2.2°F at the sites where trends were statistically significant.

Tribal Connection: Trends in Stream Temperature in the Snake River. Between 1960 and 2015, water temperatures increased by 1.4°F in the Snake River at a site in eastern Washington. Several species of salmon use the Snake River to migrate and spawn, and these salmon play an important role in the diet, culture, religion, and economy of the region’s Native Americans.

Great Lakes Water Levels. Water levels in most of the Great Lakes appear to have declined in the last few decades. However, the most recent levels are all within the range of historical variation. Water levels in lakes are influenced by water temperature, which affects evaporation rates and ice formation. Since 1995, average surface water temperatures have increased slightly for each of the Great Lakes.

Bird Wintering Ranges. Some birds shift their range or alter their migration habits to adapt to changes in temperature or other environmental conditions. Long-term studies have found that bird species in North America have shifted their wintering grounds northward by an average of more than 40 miles since 1966, with several species shifting by hundreds of miles. On average, bird species have also moved their wintering grounds farther from the coast, consistent with inland winter temperatures becoming less severe.

Marine Species Distribution. The average center of biomass for 105 marine fish and invertebrate species along U.S. coasts shifted northward by about 10 miles between 1982 and 2015. These species also moved an average of 20 feet deeper. Shifts have occurred among several economically important fish and shellfish species. For example, American lobster, black sea bass, and red hake in the Northeast have moved northward by an average of 119 miles.

Leaf and Bloom Dates. Leaf growth and flower blooms are examples of natural events whose timing can be influenced by climate change. Observations of lilacs and honeysuckles in the contiguous 48 states suggest that first leaf dates and bloom dates show a great deal of year-to-year variability. Leaf and bloom events are generally happening earlier throughout the North and West but later in much of the South.

Community Connection: Cherry Blossom Bloom Dates in Washington, D.C. Peak bloom dates of the iconic cherry trees in Washington, D.C., recorded since the 1920s, indicate that cherry trees are blooming slightly earlier than in the past. Bloom dates are key to planning the Cherry Blossom Festival, one of the region’s most popular spring attractions.

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Climate Indicators

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Weather and Climate

Oceans

Snow and Ice

Health and Society

Ecosystems

Wildfires

Streamflow

Stream Temperature

Tribal Connection: Trends in Stream Temperature in the Snake River

Great Lakes Water Levels and Temperatures

Bird Wintering Ranges

Marine Species Distribution

Leaf and Bloom Dates

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

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            US EPA

United States Environmental Protection Agency

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Climate Change Indicators

Downloads for Indicators Technical Documentation

EPA has developed comprehensive technical documentation that describes the data sources and analytical methods for every indicator presented in the Climate Change Indicators in the United States report. A PDF version of the technical documentation is provided below for each indicator, along with an overview that describes EPA's process for selecting and evaluating indicators.

Additional files you can download from other pages:

PDF versions of EPA's 2016 report and supporting elements

Printer-friendly PDF versions of each indicator (containing all text and figures)

PDF versions of previous editions of this report

High-resolution figures and the numerical data underlying the figures (on each indicator page)

      Technical documentation overview (PDF)(15 pp, 339 K,
  August 2016)

      Antarctic Sea Ice technical documentation (PDF)(7 pp, 302 K,
  August 2016)

      Arctic Sea Ice technical documentation (PDF)(11 pp, 382 K,
  November 2016)

      Atmospheric Concentrations of Greenhouse Gases technical documentation (PDF)(16 pp, 431 K,
  August 2016)

      Bird Wintering Ranges technical documentation (PDF)(5 pp, 223 K,
  May 2014)

      Climate Forcing technical documentation (PDF)(7 pp, 248 K,
  August 2016)

      Cherry Blossom Bloom Dates in Washington, D.C. technical documentation (PDF)(3 pp, 173 K,
  April 2016)

      Coastal Flooding Documentation (PDF)(6 pp, 202 K)

      Drought technical documentation (PDF)(9 pp, 273 K,
  August 2016)

      Glaciers technical documentation (PDF)(8 pp, 260 K,
  August 2016)

      Global Greenhouse Gas Emissions technical documentation (PDF)(8 pp, 226 K,
  August 2016)

      Great Lakes Water Levels and Temperatures technical documentation (PDF)(7 pp, 268 K,
  August 2016)

      Heating and Cooling Degree Days technical documentation (PDF)(6 pp, 222 K,
  August 2016)

      Heat-Related Deaths technical documentation (PDF)(9 pp, 229 K,
  August 2016)

      Heat-Related Illnesses technical documentation (PDF)(7 pp, 209 K,
  August 2016)

      Heavy Precipitation technical documentation (PDF)(7 pp, 195 K,
  August 2016)

      High and Low Temperatures technical documentation (PDF)(10 pp, 244 K,
  August 2016)

      Ice Breakup in Two Alaskan Rivers technical documentation (PDF)(4 pp, 178 K,
  August 2016)

      Lake Ice technical documentation (PDF)(9 pp, 270 K,
  August 2016)

      Land Loss Along the Atlantic Coast technical documentation (PDF)(21 pp, 1 MB,
  May 2014)

      Leaf and Bloom Dates technical documentation (PDF)(7 pp, 216 K,
  August 2016)

      Length of Growing Season technical documentation (PDF)(5 pp, 233 K,
  August 2016)

      Lyme Disease technical documentation (PDF)(9 pp, 309 K,
  August 2016)

      Marine Species Distribution technical documentation (PDF)(6 pp, 255 K,
  August 2016)

      Ocean Acidity technical documentation (PDF)(6 pp, 295 K,
  August 2016)

      Ocean Heat technical documentation (PDF)(5 pp, 195 K,
  August 2016)

      Ragweed Pollen Season technical documentation (PDF)(5 pp, 208 K,
  August 2016)

      River Flooding technical documentation (PDF)(5 pp, 184 K,
  August 2016)

      Sea Level technical documentation (PDF)(11 pp, 301 K,
  August 2016)

      Sea Surface Temperature technical documentation (PDF)(6 pp, 184 K,
  August 2016)

      Snow Cover technical documentation (PDF)(6 pp, 210 K,
  August 2016)

      Snowfall technical documentation (PDF)(7 pp, 212 K,
  August 2016)

      Snowpack technical documentation (PDF)(6 pp, 223 K,
  August 2016)

      Stream Temperature technical documentation (PDF)(4 pp, 197 K,
  August 2016)

      Streamflow technical documentation (PDF)(7 pp, 215 K,
  August 2016)

      Temperature and Drought in the Southwest technical documentation (PDF)(10 pp, 248 K,
  August 2016)

      Trends in Stream Temperature in the Snake River technical documentation (PDF)(4 pp, 171 K,
  August 2016)

      Tropical Cyclone Activity technical documentation (PDF)(8 pp, 223 K,
  August 2016)

      U.S. and Global Precipitation technical documentation (PDF)(6 pp, 183 K,
  August 2016)

      U.S. and Global Temperature technical documentation (PDF)(9 pp, 242 K,
  August 2016)

      U.S. Greenhouse Gas Emissions technical documentation (PDF)(6 pp, 207 K,
  August 2016)

      West Nile Virus technical documentation (PDF)(6 pp, 206 K,
  August 2016)

      Wildfires technical documentation (PDF)(13 pp, 548 K,
  August 2016)

Climate Indicators

Climate Change Indicators

Greenhouse Gases

Weather and Climate

Oceans

Snow and Ice

Health and Society

Ecosystems

Frequent Questions