Introduction

The U.S. Historical Climatology Network (USHCN, Karl et al. 1990) is a high-quality moderate sized data set of monthly averaged maximum, minimum, and mean temperature and total monthly precipitation developed to assist in the detection of regional climate change. The USHCN is comprised of 1221 high-quality stations from the U.S. Cooperative Observing Network within the 48 contiguous United States. An additional data set containing 46 stations for Alaska is also available; however, data for these stations are not adjusted for inhomogeneities as outlined below for the USHCN. The period of record varies for each station but generally includes the period 1900-1995. The stations were chosen using a number of criteria including length of period of record, percent missing data, number of station moves and other station changes that may affect the data homogeneity, and spatial coverage. Included with the data set are metadata files that contain station history information about station moves, instrumentation, observing times, and elevation.

The USHCN was developed and is maintained at the National Climatic Data Center (NCDC) and the Carbon Dioxide Information and Analysis Center (CDIAC) of Oak Ridge National Laboratory through a cooperative agreement between the NCDC and the U.S. Department of Energy. Currently it is distributed by NCDC and by CDIAC on various computer media including anonymous ftp.

Quality Control, Homogeneity Testing, and Adjustment Procedures

The data for each station in the USHCN are subjected to the following quality control and homogeneity testing and adjustment procedures.
1. A quality control procedure is performed that uses trimmed means and standard deviations in comparison with surrounding stations to identify suspects (> 3.5 standard deviations away from the mean) and outliers (> 5.0 standard deviations). Until recently these suspects and outliers were hand-verified with the original records. However, with the development at the NCDC of more sophisticated QC procedures this has been found to be unnecessary.

2. Next, the temperature data are adjusted for the time-of-observation bias (Karl, et al. 1986) which occurs when observing times are changed from midnight to some time earlier in the day. The TOB is the first of several adjustments. The ending time of the 24 hour climatological day varies from station to station and/or over a period of years at a given station. The TOB introduces a non climatic bias into the monthly means. The TOB software is an empirical model used to estimate the time of observation biases associated with different observation schedules and the routine computes the TOB with respect to daily readings taken at midnight. Details on the procedure are given in, “A Model to Estimate the Time of Observation Bias Associated with Monthly Mean Maximum, Minimum, and Mean Temperatures.” by Karl, Williams, et al.1986, Journal of Climate and Applied Meteorology 15: 145-160.

3. Temperature data at stations that have the Maximum/Minimum Temperature System (MMTS) are adjusted for the bias introduced when the liquid-in-glass thermometers were replaced with the MMTS (Quayle, et al. 1991). The TOB debiased data are input into the MMTS program and is the second adjustment. The MMTS program debiases the data obtained from stations with MMTS sensors. The NWS has replaced a majority of the liquid-in-glass thermometers in wooden Cotton-Region shelters with thermistor based maximum-minimum temperature systems (MMTS) housed in smaller plastic shelters. This adjustment removes the MMTS bias for stations so equipped with this type of sensor. The adjustment factors are most appropriate for use when time series of states or larger areas are required. Specific details on the procedures used are given in, “Effects of Recent Thermometer Changes in the Cooperative Network” by Quayle, Easterling, et al. 1991, Bulletin of the American Meteorological Society 72:1718-1724.

4. The homogeneity adjustment scheme described in Karl and Williams (1987) is performed using the station history metadata file to account for time series discontinuities due to random station moves and other station changes. The debiased data from the second adjustment are then entered into the Station History Adjustment Program or SHAP. The SHAP allows a climatological time series of temperature and precipitation adjustment for station inhomogeneities using station history information and is the third adjustment. The adjusted data retains its original scale and is not an anomaly series. The methodology uses the concepts of relative homogeneity and standard parametric (temperature) and non parametric (precipitation) statistics to adjust the data. In addition, this technique provides an estimate of the confidence interval associated with each adjustment. The SHAP program debiases the data with respect to changes other than the MMTS conversion to produced the “adjusted data”. Specific details on the procedures used are given in, “An Approach to Adjusting Climatological Time Series for Discontinuous Inhomogeneities” by Karl, and Williams, Jr. 1987, Journal of Climate and Applied Meteorology 26:1744-1763.

5. Estimates for missing data are provided using a procedure similar to that used in the homogeneity adjustment scheme in step three. This fourth adjustment uses the debiased data from the third adjustment (SHAP) and fills in missing original data when needed (i.e. calculates estimated data) based on a “network” of the best correlated nearby stations. The FILNET program also completed the data adjustment process for stations that moved too often for the SHAP program to estimate the adjustments needed to debias the data.

   Each of the above adjustments is done is a sequential manner. The areal edits are preformed first and then the data are passed through the following programs (TOBS, MMTS, SHAP and FILNET). At the end of each program, a dataset is produced and the graphs below show the annual temperature departures for each of the adjusted values.

6. The final adjustment is for an urban warming bias which uses the regression approach outlined in Karl, et al. (1988). The result of this adjustment is the “final” version of the data. Details on the urban warming adjustment are available in “Urbanization: Its Detection and Effect in the United States Climate Record” by Karl. T.R., et al., 1988, Journal of Climate 1:1099-1123.

Currently all data adjustments in the USHCN are based on the use of metadata. However station histories are often incomplete or changes that can cause a time series discontinuity, such as replacing a broken thermometer with one that is calibrated differently, are not routinely entered into station history files. Because of this we are developing another step in the processing that will apply a time series discontinuity adjustment scheme described in Peterson and Easterling (1994) and Easterling and Peterson (1995). This methodology does not use station histories and identifies discontinuities in a station's time series using a homogeneous reference series developed from surrounding stations.

To illustrate the effects of each adjustment scheme, we produced annual time series from each data set using the Climate Analysis System (CAS), a software package developed at NCDC that provides a wide-array of analysis options. Although various grid sizes can be used in the analyis of USHCN data, we determined the optimum grid size to be 2.5 degrees X 3.5 degrees. We calculated all anomalies with respect to the base period 1961-1990 and adjusted the time series to the period 1900 - 1910 to simplify the comparison of the different data sets. The following graphs depict the time series resulting from each USHCN data set.

The adjacent plot shows the annual time series calculated from each of the six USHCN data sets. The USHCN adjustment procedures are applied in stepwise fashion so that the effects from each adjustment have a cumulative effect. The data set containing the final adjustment procedure (urbanization adjustments) also contains all of the previous adjustments. Each series contains data from 1900-1999.

larger image

larger image

It is much easier to evaluate the effects of each adjustment by plotting stepwise differences between USHCN time series. The effect of each successive adjustment is clearly evident in the adjacent plot that shows the differences in the above time series.

Applying the Time of Observation adjustment (black line) resulted in approximately a 0.3F warming from the late 1960's to the 1990's. The shift from Cotton Region Shelters to the Maximum/Minimum Thermometer System in the mid-1980's is clearly evident in the difference between the TOBS and the MMTS time series (red line). This adjustment created a small warming in the US annual time series during the mid to late 1980's. Application of the Station History Adjustment Procedure (yellow line) resulted in an average increase in US temperatures, especially from 1950 to 1980. During this time, many sites were relocated from city locations to airports and from roof tops to grassy areas. This often resulted in cooler readings than were observed at the previous sites. When adjustments were applied to correct for these artificial changes, average US temperature anomalies were cooler in the first half of the 20th century and effectively warmed throughout the later half. Filling in missing data (blue line) produced cooler temperatures prior to 1915. Adjustments to account for warming due to the effects of urbanization (purple line) cooled the time series an average of 0.1F throughout the period of record.

larger image

The adjacent graph shows how the annual raw (areal edited) mean temperature anomalies compare with the anomalies from the data set containing all adjustments (final). The difference of these two time series is shown below.

The cumulative effect of all adjustments is approximately a one-half degree Fahrenheit warming in the annual time series over a 50-year period from the 1940's until the last decade of the century.

larger image

Data Package Description

The data package contains the following four versions of the data set.
1. Raw: the data in this version have been through all quality control but have no data adjustments.

2. TOB: these data have also been subjected to the time-of-observation bias adjustment.

3. Adjusted: these data have been adjusted for the time-of-observation bias, MMTS bias, and station moves, etc.

4. Urban: these data have all adjustments including the urban heat adjustments.

Updated Data Status

The four data sets listed above contain monthly quality checked data up to and including November 1995, and preliminary data for December 1995.

For additional information contact:

Sam McCown
National Climatic Data Center
Federal Building
151 Patton Avenue
Asheville, NC 28801
Phone: (828) 271-4800
FAX: (828) 271-4876
Internet: sam.mccown@noaa.gov
URL:


or:

Carbon Dioxide Information and Analysis Center
Oak Ridge National Laboratory
PO Box 2008
Oak Ridge, TN 37831-6335
Phone: (615) 574-0390
FAX: (615) 574-2232
Internet: cdp@ornl.gov
ftp: cdiac.esd.ornl.gov (128.219.24.36)
URL: http://cdiac.esd.ornl.gov/cdiac

A Brief Comparison of the USHCN and the U.S. Climate Division Dataset

Stengths of the U.S. Climate Division Dataset: The U.S. Climate Division Dataset consists of monthly mean temperature and precipitation for all 344 climate divisions in the contiguous U.S. back to January 1895. Many ancillary variables are available in the Division Dataset including Palmer drought indices, standardized z-scores of temperature and precipitation, percent area warm/cold, wet/dry variables, degree days, and intermediate Palmer model variables including evapotransporation and the SPI. Information for many geographical sub-regions are routinely available including climate regions, river basins, and agricultural regions. Estimated values are available near real-time and an operational processing system and extraction software is operational.

Weaknesses of the U.S. Climate Division Dataset: The U.S. Climate Division Dataset does not contain monthly maximum or minimum temperature or any variables/indices derivable from daily data. Temperature data is adjusted for time of observation bias, however no other adjustments are made for inhomogeneities. These inhomogeneities include changes in instrumentation, observer, and observation practices, station and instrumentation moves, and changes in station composition resulting from stations closing and opening over time within a division.

Strengths of the U.S. Historical Climate Network: The USHCN is a high-quality network of COOP stations with maximum, minimum, and mean temperature and precipitation, specially selected for analyzing long-term variability and change in the 48 contiguous United States. The stations in the network were chosen based on length of record, spatial distribution, and to minimize the number of station changes that can affect the homogeneity of the record. A methodology has been developed and is applied to test known station changes for their impact on the homogeneity, and data are adjusted if the change causes a statistically significant change in the time series. An urban warming correction based on population is also applied. The data set is a consistent network through time, which minimizes any biasing due to network changes through time.

Weaknesses of the U.S. Historical Climate Network: The start date for stations in the USHCN vary so that the stations used to compute the national value may change from year to year, especially for the earliest years. At present, ancillary variables are not available in the USHCN. Information for geographical sub-regions such as climate regions, river basins, and agricultural regions are currently not available, although they could be computed eventually. Data for the USHCN are not available in near real-time status.

Our Recommendations: We recommend using USHCN whenever possible for long-term climate analyses. The careful selection of each station and the series of adjustments applied to the data make the USHCN database the best long-term monthly temperature and precipitation data set available for the contiguous United States. It provides an accurate, serially complete, modern historical climate record that is suitable for detecting and monitoring long-term climatic changes. Other data sets, such as the Climate Division Dataset, may produce misleading trends due to artificial station changes. When performing analyses on scales smaller than regional, we recommend a review of the metadata in order to identify the stations most suitable for specific research needs.

References

Easterling, D.R., and T.C. Peterson, 1995: A new method of detecting undocumented discontinuities in climatological time series, Int. J. of Climatol., 15, 369-377.

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.

Karl, T.R., and C.W. Williams, Jr., 1987: An approach to adjusting climatological time series for discontinuous inhomogeneities, J. Climate Appl. Meteor., 26, 1744-1763.

Karl, T.R., H.F. Diaz, and G. Kukla, 1988: Urbanization: its detection and effect in the United States climate record, J. Climate, 1, 1099-1123.

Karl, T.R., C.N. Williams, Jr., F.T. Quinlan, and T.A. Boden, 1990: United States Historical Climatology Network (HCN) Serial Temperature and Precipitation Data, Environmental Science Division, Publication No. 3404, Carbon Dioxide Information and Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, 389 pp.

Peterson, T.C., and D.R. Easterling, 1994: Creation of homogeneous composite climatological reference series, Int. J. Climatol., 14, 671-680.

Quayle, R.G., D.R. Easterling, T.R. Karl, and P.Y. Hughes, 1991: Effects of recent thermometer changes in the cooperative station network, Bull. Am. Meteorol. Soc., 72, 1718-1724.

Logo Image Information

Normalized Difference Vegetation Index (NDVI) composite image derived from NOAA-AVHRR satellite images. The composite is over the period 10 July to 23 July, 1992. Low values of the NDVI are indicated in blue-gray and higher values by green tones. The image was provided by:

USGS
EROS Data Center
Sioux Falls, SD


A larger version of the image is also available.

Top of Page