Climate Change Indicators in the United States Technical Documentation U S and Global Temperature

U.S. and Global Temperature

Identification

1. Indicator Description

This indicator describes changes in average air temperature for the United States and the world from
1901 to 2015. In this indicator, temperature data are presented as trends in anomalies. Air temperature
is an important component of climate, and changes in temperature can have wide-ranging direct and
indirect effects on the environment and society.

Components of this indicator include:

Changes in temperature in the contiguous 48 states over time (Figure 1).

Changes in temperature worldwide over time (Figure 2).

A map showing rates of temperature change across the contiguous 48 states and Alaska (Figure
3).

2. Revision History

April 2010: Indicator published.

December 2012: Updated indicator with data through 2011.

August 2013: Updated indicator on EPA's website with data through 2012.

May 2014: Updated Figures 1 and 2 with data through 2013.

June 2015: Updated indicator on EPA's website with data through 2014.

August 2016: Updated indicator with data through 2015.

Data Sources

3. Data Sources

This indicator is based on temperature anomaly data provided by the National Oceanic and Atmospheric
Administration's (NOAA's) National Centers for Environmental Information (NCEI), formerly the National
Climatic Data Center (NCDC). Specifically, this indicator uses the following NCEI data sets:

• Figure 1, contiguous 48 states surface temperature; Figure 3, surface temperature map:
nClimDiv

• Figure 2, global surface temperature: Global Historical Climatology Network-Monthly (GHCN-M)
Version 3.2.0

• Figures 1 and 2, contiguous 48 states and global satellite-based temperature: analyses of
satellite data conducted by the Global Hydrology and Climate Center at the University of
Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS), maintained by NCEI

nClimDiv is itself based on data from the daily version of GHCN (GHCN-Daily). These data undergo more
extensive processing by NCEI on a monthly basis for inclusion in nClimDiv.

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4. Data Availability

All of the underlying data sets can be accessed online, along with descriptions and metadata. Specific
data sets were obtained as follows.

Contiguous 48 States Surface Time Series

Surface temperature time series data for the contiguous 48 states (Figure 1) are based on nClimDiv data
that were obtained from NCEI's "Climate at a Glance" web interface (www.ncdc.noaa.gov/cag). For
access to underlying nClimDiv data and documentation, see: www.ncdc.noaa.gov/monitoring-
references/maps/us-climate-divisions.php.

Global Surface Time Series

GHCN global surface temperature (Figure 2) were obtained from NCEI's "Climate at a Glance" web
interface (www.ncdc.noaa.gov/cag). For access to underlying GHCN-M Version 3.2.0 data and
documentation, see: www.ncdc.noaa.gov/ghcnm/v3.php.

Contiguous 48 States and Alaska Map

The map in this indicator (Figure 3) is based on nClimDiv monthly data by climate division, which are
publicly available from NCEI at: www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp.

Satellite-Based Time Series

EPA obtained the satellite analyses (Figures 1 and 2) from NCEI's public website at:
www.ncdc.noaa.gov/temp-and-precip/msu/overview.

Methodology

5. Data Collection

This indicator is based on temperature measurements. The global portion of this indicator presents
temperatures measured over land and sea, while the portion for the contiguous 48 states and Alaska
shows temperatures measured over land only.

Surface data for this indicator were compiled from thousands of weather stations throughout the United
States and worldwide using standard meteorological instruments. Data for the contiguous 48 states and
Alaska were compiled in the nClimDiv data set. Data for the rest of the world were taken from GHCN
data sets. All of the networks of stations cited here are overseen by NOAA, and their methods of site
selection and quality control have been extensively peer reviewed. As such, they represent the most
complete long-term instrumental data sets for analyzing recent climate trends. More information on
these networks can be found below.

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Contiguous 48 States Surface Time Series; Contiguous 48 States and Alaska Map

The nClimDiv divisional data set incorporates temperature data from GHCN-Daily stations in the
contiguous 48 states and Alaska. This data set includes stations that were previously part of the U.S.
Historical Climatology Network (USHCN), as well as additional stations that were able to be added to
nClimDiv as a result of quality-control adjustments and digitization of paper records. Altogether,
nClimDiv incorporates data from more than 10,000 stations. These stations are spread among 357
climate divisions in the contiguous 48 states and Alaska.

In addition to incorporating more stations, the nClimDiv data set differs from the USHCN because it
incorporates a grid-based computational approach known as climatologically-aided interpolation
(Willmott and Robeson, 1995), which helps to address topographic variability. Data from individual
stations are combined in a grid that covers the entire contiguous 48 states and Alaska with 5-kilometer
resolution. These improvements have led to a new data set that maintains the strengths of its
predecessor data sets while providing more robust estimates of area averages and long-term trends.
The nClimDiv data set is NOAA's official temperature data set for the contiguous 48 states and Alaska,
replacing USHCN.

To learn more about nClimDiv, see: www.ncdc.noaa.gov/news/ncdc-introduces-national-temperature-
index-page and: www.ncdc.noaa.gov/monitoring-references/maps/us-climate-divisions.php.

Global Surface Time Series

GHCN-M Version 3.2.0 contains monthly temperature data from weather stations worldwide—including
stations within the contiguous 48 states and Alaska. Monthly mean temperature data are available for
7,280 stations, with homogeneity-adjusted data available for a subset (5,206 mean temperature
stations). Data were obtained from many types of stations. For the global component of this indicator,
the GHCN land-based data were merged with an additional set of long-term sea surface temperature
data. This merged product is called the extended reconstructed sea surface temperature (ERSST) data
set, Version #3b (Smith et al., 2008).

NCEI has published documentation for the GHCN. For more information, including data sources,
methods, and recent improvements, see: www.ncdc.noaa.gov/ghcnm/v3.php and the sources listed
therein. Additional background on the merged land-sea temperature data set can be found at:
www.ncdc.noaa.gov/monitoring-references/faq.

Satellite-Based Time Series

In Figures 1 and 2, surface measurements have been supplemented with satellite-based measurements
for the period from 1979 to 2015. These satellite data were collected by NOAA's polar-orbiting satellites,
which take measurements across the entire globe. Satellites equipped with the necessary measuring
equipment have orbited the Earth continuously since 1978, but 1979 was the first year with complete
data. This indicator uses measurements that represent the lower troposphere, which is defined here as
the layer of the atmosphere extending from the Earth's surface to an altitude of about 8 kilometers.

NOAA's satellites use the Microwave Sounding Unit (MSU) to measure the intensity of microwave
radiation given off by various layers of the Earth's atmosphere. The intensity of radiation is proportional
to temperature, which can therefore be determined through correlations and calculations. NOAA uses

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different MSU channels to characterize different parts of the atmosphere. Note that since 1998, NOAA
has used a newer version of the instrument called the Advanced MSU.

For more information about the methods used to collect satellite measurements, see:
www.ncdc.noaa.gov/temp-and-precip/msu/overview and the references cited therein.

6. Indicator Derivation

Contiguous 48 States and Global Surface Time Series

NOAA calculated monthly temperature means for each site. In populating the GHCN and nClimDiv,
NOAA adjusted the data to remove biases introduced by differences in the time of observation. NOAA
also employed a homogenization algorithm to identify and correct for substantial shifts in local-scale
data that might reflect changes in instrumentation, station moves, or urbanization effects. These
adjustments were performed according to published, peer-reviewed methods. For more information on
these quality assurance and error correction procedures, see Section 7.

In this indicator, temperature data are presented as trends in anomalies. An anomaly represents the
difference between an observed value and the corresponding value from a baseline period. This
indicator uses a baseline period of 1901 to 2000 for the contiguous 48 states and global data, and a
baseline period of 1925 to 2000 for Alaska data due to sparse data prior to 1925. The choice of baseline
period will not affect the shape or the statistical significance of the overall trend in anomalies. For
absolute anomalies in degrees, it only moves the trend up or down on the graph in relation to the point
defined as "zero."

To generate the temperature time series, NOAA converted measurements into monthly anomalies in
degrees Fahrenheit. The monthly anomalies then were averaged to determine an annual temperature
anomaly for each year.

To achieve uniform spatial coverage (i.e., not biased toward areas with a higher concentration of
measuring stations), NOAA calculated area-weighted averages of grid-point estimates interpolated from
station data. The surface time series for the contiguous 48 states (Figure 1) is based on the nClimDiv
gridded data set, which reflects a high-resolution (5-kilometer) interpolated grid that accounts for
station density and topography. See: ftp://ftp.ncdc.noaa.gov/pub/data/cirs/climdiv/divisional-
readme.txt for more information. The global graph (Figure 2) comes from an analysis of grid cells
measuring 5 degrees by 5 degrees. See: www.ncdc.noaa.gov/temp-and-precip/ghcn-gridded-products
for more information.

Figures 1 and 2 show trends from 1901 to 2015, based on NOAA's gridded data sets. Although earlier
data are available for some stations, 1901 was selected as a consistent starting point.

Contiguous 48 States and Alaska Map

The map in Figure 3 shows long-term rates of change in temperature over the United States for the
period from 1901 to 2015, except for Alaska, for which widespread and reliable data collection did not
begin until 1925 (therefore the map shows 1925-2015 for Alaska). Hawaii and U.S. territories are not
included in this figure, due to insufficient data completeness or length of the measurement record. This
map is based on NOAA's nClimDiv gridded analysis, with results averaged within each climate division.

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The slope of each temperature trend was calculated from the annual climate division anomalies by
ordinary least-squares regression and then multiplied by 100 to obtain a rate of change per century.

Satellite-Based Time Series

NOAA's satellites measure microwave radiation at various frequencies, which must be converted to
temperature and adjusted for time-dependent biases using a set of algorithms. Various experts
recommend slightly different algorithms. Accordingly, Figure 1 and Figure 2 show globally averaged
trends that have been calculated by two different organizations: the Global Hydrology and Climate
Center at the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). For more
information about the methods used to convert satellite measurements to temperature readings for
various layers of the atmosphere, see: www.ncdc.noaa.gov/temp-and-precip/msu/overview and the
references cited therein. Both the UAH and RSS data sets are based on updated versions of analyses that
have been published in the scientific literature. For example, see Christy et al. (2000, 2003), Mears et al.
(2003), and Schabel et al. (2002).

NOAA provided data in the form of monthly anomalies. EPA calculated annual anomalies, then shifted
the entire curves vertically in order to display the anomalies side-by-side with surface anomalies.

Shifting the curves vertically does not change the shape or magnitude of the trends; it simply results in a
new baseline. No attempt has been made to portray satellite-based data beyond the time and space in
which measurements were made. The satellite data in Figure 1 are restricted to the atmosphere above
the contiguous 48 states.

Indicator Development

NOAA released the nClimDiv data set in 2014, which allowed this indicator to use climate divisions in
Figure 3 and a high-resolution climate division-based gridded analysis for Figure 1. Previous versions of
EPA's indicator presented a contiguous 48 states surface time series and a United States map based on a
coarse grid analysis, which was the best analysis available from NOAA at the time.

NOAA is continually refining historical data points in the GHCN and nClimDiv, often as a result of
improved methods to reduce bias and exclude erroneous measurements. As EPA updates this indicator
to reflect these upgrades, slight changes to some historical data points may become apparent. No
attempt has been made to portray data beyond the time and space in which measurements were made.

7. Quality Assurance and Quality Control

NCEI's databases have undergone extensive quality assurance procedures to identify errors and biases in
the data and either remove these stations from the time series or apply correction factors.

Contiguous 48 States Surface Time Series; Contiguous 48 States and Alaska Map

The nClimDiv data set follows the USHCN's methods to detect and correct station biases brought on by
changes to the station network over time. The transition to a grid-based calculation did not significantly
change national averages and totals, but it has led to improved historical temperature values in certain
regions, particularly regions with extensive topography above the average station elevation-
topography that is now being more thoroughly accounted for. An assessment of the major impacts of

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the transition to nClimDiv can be found at: www.ncdc.noaa.gov/monitoring-references/docs/GrDD-
Transition.pdf.

Global Surface Time Series

QA/QC procedures for GHCN temperature data are described in detail in Peterson et al. (1998) and
Menne and Williams (2009), and at: www.ncdc.noaa.gov/ghcnm/v3.php. GHCN data undergo rigorous
QA reviews, which include pre-processing checks on source data; removal of duplicates, isolated values,
and suspicious streaks; time series checks to identify spurious changes in the mean and variance via
pairwise comparisons; spatial comparisons to verify the accuracy of the climatological mean and the
seasonal cycle; and neighbor checks to identify outliers from both a serial and a spatial perspective.
Satellite-Based Time Series

NOAA follows documented procedures for QA/QC of data from the MSU satellite instruments. For
example, see NOAA's discussion of MSU calibration at:
www.star.nesdis.noaa.gov/smcd/emb/mscat/algorithm.php.

Analysis

8. Comparability Over Time and Space

Both nClimDiv and the GHCN have undergone extensive testing to identify errors and biases in the data
and either remove these stations from the time series or apply scientifically appropriate correction
factors to improve the utility of the data. In particular, these corrections address changes in the time-of-
day of observation, advances in instrumentation, and station location changes. See Section 7 for
documentation.

Contiguous 48 States Surface Time Series; Contiguous 48 States and Alaska Map

All GHCN-Daily stations are routinely processed through a suite of logical, serial, and spatial quality
assurance reviews to identify erroneous observations. For nClimDiv, all such observations were set to
"missing" before computing monthly values, which in turn were subjected to additional serial and
spatial checks to eliminate residual outliers. Stations having at least 10 years of valid monthly data since
1950 were used in nClimDiv.

For temperature, bias adjustments were computed to account for historical changes in observation
time, station location, temperature instrumentation, and siting conditions. As with USHCN, the method
of Karl et al. (1986) was applied to remove the observation time bias from the COOP network, and the
pairwise method of Menne and Williams (2009) was used to address changes in station location and
instrumentation in all networks. Because the pairwise method also largely accounts for local,
unrepresentative trends that arise from changes in siting conditions, nClimDiv contains no separate
adjustment in that regard.

For more documentation about nClimDiv, see: ftp://ftp.ncdc.noaa.gov/pub/data/cirs/climdiv/divisional-
readme.txt.

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Global Surface Time Series

The GHCN applied stringent criteria for data homogeneity in order to reduce bias. In acquiring data sets,
the original observations were sought, and in many cases where bias was identified, the stations in
question were removed from the data set. See Section 7 for documentation.

For data collected over the ocean, continuous improvement and greater spatial resolution can be
expected in the coming years, with corresponding updates to the historical data. For example, there is a
known bias during the World War II years (1941-1945), when almost all ocean temperature
measurements were collected by U.S. Navy ships that recorded ocean intake temperatures, which can
give warmer results than the techniques used in other years. Future efforts will aim to adjust the data
more fully to account for this bias.

Satellite-Based Time Series

NOAA's satellites cover the entire Earth with consistent measurement methods. Procedures to calibrate
the results and correct for any biases over time are described in the references cited under Section 7.

9. Data Limitations

Factors that may impact the confidence, application, or conclusions drawn from this indicator are as
follows:

1. Biases in surface measurements may have occurred as a result of changes over time in
instrumentation, measuring procedures (e.g., time of day), and the exposure and location of the
instruments. Where possible, data have been adjusted to account for changes in these variables.
For more information on these corrections, see Section 8. Some scientists believe that the
empirical debiasing models used to adjust the data might themselves introduce non-climatic
biases (e.g., Pielke et al., 2007).

2. Uncertainties in surface temperature data increase as one goes back in time, as there are fewer
stations early in the record. These uncertainties are not sufficient, however, to mislead the user
about fundamental trends in the data.

10. Sources of Uncertainty

Surface Time Series and Maps

Uncertainties in temperature data increase as one goes back in time, as there are fewer stations early in
the record. These uncertainties are not sufficient, however, to undermine the fundamental trends in the
data.

Error estimates are not readily available for U.S. temperature, but they are available for the global
temperature time series. See the error bars in NOAA's graphic online at:

www.ncdc.noaa.gov/sotc/service/global/global-land-ocean-mntp-anom/201001-201Q12.gif. In general,
Vose and Menne (2004) suggest that the station density in the U.S. climate network is sufficient to
produce a robust spatial average.

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Satellite-Based Time Series

Methods of inferring tropospheric temperature from satellite data have been developed and refined
over time. Several independent analyses have produced largely similar curves, suggesting fairly strong
agreement and confidence in the results.

Error estimates for the UAH analysis have previously been published in Christy et al. (2000, 2003). Error
estimates for the RSS analysis have previously been published in Schabel et al. (2002) and Mears et al.
(2003). Error estimates are not readily available, however, for the updated version of each analysis that
EPA obtained in 2016.

11. Sources of Variability

Annual temperature anomalies naturally vary from location to location and from year to year as a result
of normal variations in weather patterns, multi-year climate cycles such as the El Nino-Southern
Oscillation and Pacific Decadal Oscillation, and other factors. This indicator accounts for these factors by
presenting a long-term record (more than a century of data) and averaging consistently over time and
space.

12. Statistical/Trend Analysis

This indicator uses ordinary least-squares regression to calculate the slope of the observed trends in
temperature. A simple t-test indicates that the following observed trends are significant at the 95-
percent confidence level:

• Contiguous 48 states temperature, 1901-2015: +0.014 °F/year (p < 0.001)

• Contiguous 48 states temperature, 1979-2015, surface: +0.046 °F/year (p < 0.001)

• Contiguous 48 states temperature, 1979-2015, UAH satellite method: +0.041 °F/year (p < 0.001)

• Contiguous 48 states temperature, 1979-2015, RSS satellite method: +0.029 °F/year (p = 0.005)

• Global temperature, 1901-2015: +0.015 °F/year (p < 0.001)

• Global temperature, 1979-2015, surface: +0.028 °F/year (p < 0.001)

• Global temperature, 1979-2015, UAH satellite method: +0.026 °F/year (p < 0.001)

• Global temperature, 1979-2015, RSS satellite method: +0.022 °F/year (p < 0.001)

Among the individual climate divisions shown in Figure 3, 75% of divisions have statistically significant
temperature trends based on ordinary least-squares linear regression and a 95-percent confidence
threshold.

References

Christy, J.R., R.W. Spencer, and W.D. Braswell. 2000. MSU tropospheric temperatures: Dataset
construction and radiosonde comparisons. J. Atmos. Ocean. Tech. 17:1153-1170.
www.ncdc.noaa.gov/temp-and-precip/msu/uah-msu.pdf.

Christy, J.R., R.W. Spencer, W.B. Norris, W.D. Braswell, and D.E. Parker. 2003. Error estimates of version
5.0 of MSU/AMSU bulk atmospheric temperatures. J. Atmos. Ocean. Tech. 20:613-629.

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Karl, T.R., C. N. Williams Jr., P. J. Young, and W. M. Wendland. 1986. A model to estimate the time of
observation bias associated with monthly mean maximum, minimum, and mean temperature for the
United States. J. Climate Appl. Meteor. 25:145-160.

Mears, C.A., M.C. Schabel, and F.J. Wentz. 2003. A reanalysis of the MSU channel 2 tropospheric
temperature record. J. Climate 16:3650-3664. http://iournals.ametsoc.org/doi/pdf/10.1175/1520-
0442%282003%29016%3C3650%3AAROTMC%3E2.0.CQ%3B2.

Menne, M.J., and C.N. Williams, Jr. 2009. Homogenization of temperature series via pairwise
comparisons. J. Climate 22(7):1700-1717.

Peterson, T.C., R. Vose, R. Schmoyer, and V. Razuvaev. 1998. Global Historical Climatology Network
(GHCN) quality control of monthly temperature data. Int. J. Climatol. 18(11):1169-1179.

Pielke, R., J. Nielsen-Gammon, C. Davey, J. Angel, O. Bliss, N. Doesken, M. Cai, S. Fall, D. Niyogi, K. Gallo,
R. Hale, K.G. Hubbard, X. Lin, H. Li, and S. Raman. 2007. Documentation of uncertainties and biases
associated with surface temperature measurement sites for climate change assessment. B. Am.
Meteorol. Soc. 88:913-928.

Schabel, M.C., C.A. Mears, and F.J. Wentz. 2002. Stable long-term retrieval of tropospheric temperature
time series from the Microwave Sounding Unit. P. Int. Geophys. Remote Sens. Symposium III: 1845—
1847.

Smith, T.M., R.W. Reynolds, T.C. Peterson, and J. Lawrimore. 2008. Improvements to NOAA's historical
merged land-ocean surface temperature analysis (1880-2006). J. Climate 21:2283-2296.
www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-
ersst.

Vose, R.S., and M.J. Menne. 2004. A method to determine station density requirements for climate
observing networks. J. Climate 17(15):2961-2971.

Willmott, C.J., and S.M. Robeson. 1995. Climatologically aided interpolation (CAI) of terrestrial air
temperature. Int. J. Climatol. 15(2):221-229.

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