An evaluation of the time of observation bias adjustment in the U.S.

Historical Climatology Network

Russell S. Vose, Claude N. Williams Jr., Thomas C. Peterson, Thomas R. Karl,

and David R. Easterling

National Climatic Data Center, Asheville, North Carolina, USA

Received 7 July 2003; revised 8 September 2003; accepted 10 September 2003; published 23 October 2003.

[1] The U.S. Historical Climatology Network (HCN)

database contains statistical adjustments that address

historical changes in observation time at each observing

station in the network. A paper in 2002 suggested that these

adjustments cause HCN temperature trends to be

‘‘spuriously’’ warm relative to other datasets for the United

States. To test this hypothesis, this paper evaluates the

reliability of these ‘‘time of observation bias’’ adjustments in

HCN. The results indicate that HCN station history

information is reasonably complete and that the bias

adjustment models have low residual errors. In short, the

time of observation bias adjustments in HCN appear to be

robust. INDEX TERMS: 3309 Meteorology and Atmospheric

Dynamics: Climatology (1620); 1610 Global Change: Atmosphere

(0315, 0325); 3399 Meteorology and Atmospheric Dynamics:

General or miscellaneous. Citation: Vose, R. S., C. N. Williams

Jr., T. C. Peterson, T. R. Karl, and D. R. Easterling, An evaluation

of the time of observation bias adjustment in the U.S. Historical

Climatology Network, Geophys. Res. Lett., 30(20), 2046,

doi:10.1029/2003GL018111, 2003.

1. Introduction

[2] The U.S. Historical Climatology Network (HCN)

database [Easterling et al., 1996] is commonly used to

quantify climate change because it contains adjustments

that account for historical variations in observation time

[Karl et al., 1986], station location/instrumentation [Karl

and Williams, 1987], and population growth [Karl et al.,

1988]. In a comparative analysis of the conterminous

United States, Balling and Idso [2002] determined that

adjusted HCN temperature trends for the past 30 years were

slightly more positive than those derived from other datasets

[e.g., Jones, 1994; Christy et al., 2000]. This led to the

hypothesis that the HCN contained a ‘‘spurious’’ warming

that likely resulted from the adjustments for historical

variations in observation time. Given this supposition, the

purpose of this paper is to demonstrate the reliability of the

adjustments for this ‘‘time of observation’’ bias in HCN.

The analysis consists of three parts. First, the accuracy of

HCN station history information is verified using an independent source of metadata. Second, the predictive skill of

the adjustment approach is evaluated using data from 500

hourly stations over the period 1965 – 2001 (the approach

was originally developed using data from 79 stations over

the period 1957 –64). Finally, HCN temperature trends are

shown to be consistent with those in Jones [1994].

2. Background

[3] The majority of weather stations in the U.S. Cooperative Observing Network (and therefore in HCN) are staffed

by volunteers. Consequently, the network has no mandatory

time at which daily measurements must be taken. Most

individuals prefer observing times other than midnight,

resulting in an observation day that differs from the standard

calendar day. For example, at a station where the volunteer

reads the thermometers at 0800 LST, the observation day

extends from 0800 LST the previous day to 0800 LST on

the current day. At a station where the volunteer reads the

thermometers at 1700 LST, the observation day starts and

ends 9 hours later. Nevertheless, the observations at both

stations are recorded for the same calendar day.

[4] When the observation day differs from the calendar

day, a ‘‘carry over’’ bias of up to 2.0C is introduced into

monthly mean temperatures. This bias occurs when atmospheric conditions cause a temperature from one day to be

ascribed to the following day. For instance, suppose an

observer reads the maximum and minimum thermometers at

1700 LST on April 1, then a cold front passes through the

area overnight. If the temperature on April 2 never exceeds

the value at 1700 LST on April 1 (when the thermometers

were last reset), then the recorded maximum will actually be

the temperature at 1700 LST on April 1. This temperature

will be higher than if the 24-hour measurement ended at

midnight, and because the monthly mean is computed by

averaging the daily maximums and minimums, the mean for

April will likewise be artificially high. In general, this carryover phenomenon results in a warm bias for observation

days ending in the afternoon and a cool bias for those

ending in the morning.

[5] Non-calendar day observations also result in a ‘‘drift’’

bias of up to 1.5C in monthly means. This bias results from

the inclusion of data from the end of the previous month, as

well as the exclusion of data from the end of the current

month, in the computation of a monthly mean. For example,

at a station where the observation day ends at 0700 LST, the

average for April will be based upon data from the last

17 hours of March 31 through the first 7 hours of April 30.

Because the end of March is usually cooler than the end of

April, the average for April will typically be lower than if

the station’s observation day ended at midnight. In general,

drift bias is most pronounced during transition seasons and

for morning observation times.

[6] There has been a systematic change in the preferred

observation time in the U.S. Cooperative Observing NetGEOPHYSICAL RESEARCH LETTERS, VOL. 30, NO. 20, 2046, doi:10.1029/2003GL018111, 2003

This paper is not subject to U.S. copyright.

Published in 2003 by the American Geophysical Union.

CLM 3 -- 1
work over the past century. Prior to the 1940s most

observers recorded near sunset in accordance with U.S.

Weather Bureau instructions, and thus the U.S. climate

record as a whole contains a slight warm bias during the

first half of the century. A switch to morning observation

times has steadily occurred during the latter half of the

century to support operational hydrological requirements,

resulting in a broad-scale nonclimatic cooling effect. In

other words, the systematic change in the time of observation in the United States in the past 50 years has artificially

reduced the temperature trend in the U.S. climate record

[Hansen et al., 2001].

3. HCN Metadata

[7] This time of observation bias is addressed in the

adjusted HCN using the method described in Karl et al.

[1986]. This adjustment approach requires as input an a

priori knowledge of the actual observation time in each year

at each HCN station. If this information is inaccurate for a

significant number of stations, then the resulting adjustments would be erroneous for a large portion of the

network, thus impacting national-scale trend analyses. Given

this possibility, the accuracy of HCN station history

information is verified here using an independent source

of metadata.

[8] The verification consists of a simple comparison of

HCN metadata with time of observation metadata that were

‘‘inferred’’ using the method described in DeGaetano

[1999]. This technique infers the observation time at annual

resolution on a station-by-station basis using daily maximum and minimum temperature data. The estimated observation time is morning (0600 – 0800 LST), afternoon

(1600 – 1900 LST), or midnight (0000 LST); for comparative purposes HCN metadata were thus aggregated in a

similar fashion. According to DeGaetano [1999], the procedure correctly classified the observation schedule in 90%

of the station-years tested in a network of over 1000 U.S.

stations during a 40-year period, suggesting that the inferred

metadata are reasonably accurate. The daily data for the

analysis were extracted from the U.S. HCN daily database

[Easterling et al., 1996], and the comparison focused on the

last three decades because that was the period in which

Balling and Idso [2002] noted a difference in trends

between the adjusted HCN and other datasets.

[9] Figure 1 depicts the percentage of HCN stations

with morning and afternoon observing times according to

inferred metadata and actual metadata (midnight is not

depicted because it accounts for less than 10% of the total).

In general, the inferred metadata closely agree with the

actual metadata on the national scale. Both sources indicate

that the percentage of afternoon readers decreased over the

past half century while the percentage of morning observers

increased, a change which has been widely documented for

some time. As described earlier, this systematic change

would impart a distinct cold bias to the HCN database if

not properly treated.

4. Bias Adjustments

[10] The analysis described here examines the ability of

the HCN adjustments to account for the time of observation

bias at the national scale. The first step entailed computing a

suite of unbiased, biased, and bias-adjusted temperature

time series for all hourly observing stations in the country.

These series were then used to estimate (a) the time of

observation bias at each hour at each station and (b) the

residual bias remaining at each hour after the application of

the adjustment. Finally, the station-based biases and residuals were areally averaged into hourly means for the

conterminous United States, and the importance of the

residual biases was assessed by computing the number of

HCN stations using each observation time.

[11] Data for the analysis were extracted from the Surface

Airways Hourly database [Steurer and Bodosky, 2000]

archived at the National Climatic Data Center. The analysis

employed data from 1965 –2001 because the adjustment

approach itself was developed using data from 1957 –64.

The Surface Airways Hourly database contains data for 500

stations during the study period; the locations of these

stations are depicted in Figure 2. The period of record

varies from station to station, and no minimum record

length was required for inclusion in this analysis.

[12] The first step entailed calculating a time series of

mean monthly temperatures for each station. Each monthly

mean in each series was the average of the daily mean

temperatures in the month, and each daily mean was the

average of the maximum and minimum temperatures during

the calendar day (i.e., the ‘‘observation time’’ was midnight). The daily maximum was the highest of the 24 hourly

temperatures, and the minimum was the lowest of the

hourly values. The use of hourly observations rather than

true daily extremes does slightly impact the monthly mean

[Baker, 1975], but the corresponding effect on the estimate

of the time of observation bias in any month is small [i.e.,

usually less than .03C; Karl et al., 1986].

[13] A time series of biased mean monthly temperatures

was then developed for each station for each hour of the

day. Bias was introduced by simply changing the ‘‘observation time’’ from midnight to another hour before computing the monthly means. For example, the first biased

series for each station had 0100 LST as its observation time,

the second had 0200 LST as its observation time, and so on

Figure 1. Percent of HCN stations with morning and

afternoon observation times according to HCN metadata

and metadata inferred using the method of DeGaetano

[1999].

CLM 3 - 2 VOSE ET AL.: EVALUATION OF OBSERVATION BIAS ADJUSTMENT
through 2300 LST. Each monthly mean in each biased

series was the average of the daily mean temperatures in

the month, and each daily mean was the average of the

maximum and minimum temperatures during the 24-hr

period ending at the specified observation time.

[14] Finally, a time series of bias-adjusted mean monthly

temperatures was derived from each biased series using the

Karl et al. [1986] technique. In this approach, drift bias for

each month in each series was quantified using a weighted

average of the current month’s temperature, the previous

month’s temperature, and the observation hour. Carry-over

bias in each month was predicted using multilinear regression equations that require as input the station’s coordinates,

observation hour, average daily diurnal temperature range

(r), and average daily day-to-day temperature difference (d).

Rather than using daily data, which are not available in

HCN prior to 1948, the model interpolates r and d from

empirical estimates derived from hourly data for the period

1957 – 64 at four neighboring first-order stations.

[15] The next step involved estimating (a) the time of

observation bias at each hour at each station and (b) the

residual bias remaining at each hour after the application of

the Karl et al. [1986] adjustment model. The time of

observation bias (TOB) at a specific hour was defined as

the average difference between the unbiased series for that

hour (i.e., midnight) and the biased series:

1

ð Þ n  12

Xn

i1⁄41

X12

j1⁄41

Bij  Uij ;

where Uij is the temperature in the unbiased series in month

j in year i, Bij is the corresponding temperature in the biased

series for that hour, and n is the number of years with data

for that station. Similarly, the residual bias (RTOB) was

defined as the average difference between the biased series

and the bias-adjusted series for that hour:

1

ð Þ n  12

Xn

i1⁄41

X12

j1⁄41

Bij  Aij ;

where Ajk is the temperature in the bias-adjusted series in

month j in year i for that hour.

[16] The TOB and RTOB values at all stations were

averaged into a spatial mean for the conterminous United

States for each hour of the day. Spatial averaging was

accomplished by interpolating station-based values to the

nodes of a 0.25  0.25 latitude-longitude grid, then

computing the cosine-weighted average of the gridded

values. Interpolation was performed using the inverse

distance weighting model of Willmott et al. [1985], which

has been widely used to grid temperature data in the United

States [e.g., Robeson, 1997]. The model explicitly accounts

for the sphericity of the earth when computing the distance

between each station and grid point, and it permits extrapolation beyond the range of data values in the neighboring

stations.

[17] Figure 3 depicts the spatial mean TOB and RTOB for

each hour of the day over the conterminous United States

during the period 1965 –2001. In general, morning observation schedules result in a cool TOB in mean annual

temperature whereas afternoon observations result in a

warm TOB. The largest positive bias occurs at roughly the

same time as the daily maximum temperature, and the

greatest negative bias occurs near the time of the daily

minimum temperature. The sinusoidal pattern of TOB, the

magnitude of the biases, and the timing of the extremes are

consistent with previous results [e.g., Winkler et al., 1981;

Karl et al., 1986]. The magnitude of RTOB is less than

0.05C for most hours and is always less than TOB,

suggesting that the Karl et al. [1986] adjustment results in

an improved estimate of calendar-day mean monthly temperatures. The magnitude of RTOB exceeds 0.05C between

1000 and 1500 LST, with the worst residuals (0.20C)

occurring around noon LST.

[18] Time of observation metadata suggest that the

potential impact of RTOB is minimal in HCN (Figure 4).

For instance, over the period 1965 – 2001, only 4% of all

HCN stations had an observing hour between 1000 and

1500 LST, indicating that the somewhat larger RTOB during

that period should be of little consequence in national-scale

analyses. More than a third of the stations observed at either

0700 or 0800 LST, when the average RTOB was only

0.003C (versus an average TOB of 0.15C). Another

third observed at either 1700 or 1800 LST, when RTOB was

0.04C (versus an average TOB of 0.51C). Aside from

midnight, no other hour accounted for more than about 5%

Figure 2. Locations of the 500 stations used to evaluate

the Karl et al. [1986] adjustment.

Figure 3. Time of observation temperature bias in the

conterminous United States before and after the application

of the Karl et al. [1986] adjustment.

VOSE ET AL.: EVALUATION OF OBSERVATION BIAS ADJUSTMENT CLM 3 - 3
of the total record, and none had an RTOB with a magnitude

in excess of 0.05C.

5. Temperature Trends

[19] Balling and Idso [2002] found that adjusted HCN

temperature trends for the past 30 years were slightly more

positive than those calculated using an updated version of

the Jones [1994] dataset. This is surprising because the

latter contains a nearly complete version of HCN that

includes adjustments for the time of observation bias. To

resolve this discrepancy, temperature trends derived from

the fully adjusted HCN database are compared here with

those derived from two subsets of Jones. The first subset

consists of all U.S. stations in Jones (1578 in total). The

second consists of 248 stations that are not in HCN and that

require no adjustments for variations in observation time

because they always have an observation hour of midnight.

[20] The same technique was used to compute temperature trends for HCN and the two subsets of Jones. The first

step involved removing stations that had less than 20 years

of data during the 1961 – 90 base period. Next, each annual

value at each station was converted to an anomaly from its

mean during the base period. The annual anomalies were

then interpolated to the nodes of a 0.25  0.25 latitudelongitude grid using the method of Willmott et al. [1985],

and the grid point values were area weighted into a mean

anomaly for the conterminous United States for each year.

Finally, least squares regression was applied to compute

trends over the period 1970– 2000. Consistent with Balling

and Idso [2002], HCN has a larger trend during that period

(0.29C dec1

) than either subset of Jones (each with a

trend of 0.23C dec1

). However, the difference in trend

results from a drastic change in the size of the Jones dataset

in 1996; prior to that point the network contains at least

1000 U.S. stations per year (the majority being HCN

stations), whereas thereafter it contains no more than 150.

When trends are computed for the period 1970 –95, HCN

and both subsets of Jones exhibit the same rate of warming

(0.25C dec1

). The fact that the non-HCN subset of Jones

has the same trend as HCN suggests that the time of

observation bias has been properly treated in the latter.

6. Summary and Conclusions

[21] Given the documented change from afternoon to

morning observation schedules over the past half century

and the known cool (warm) bias for morning (afternoon)

observers, it is clear that the U.S. climate record contains a

nonclimatic cooling effect that must be addressed when

estimating the magnitude of a temperature trend. Consequently, this paper reviewed the efficacy the approach used

to address this bias in HCN. First, the accuracy of HCN

station history information was verified with an independent

source of metadata inferred using the method of DeGaetano

[1999]. The predictive skill of the adjustment approach was

then examined using data from a network of 500 stations

over the period 1965 –2001. While nontrivial bias-adjustment error was apparent from 1000 – 1500 LST, those

observation hours were found to be used by very few

stations. Finally, adjusted HCN temperature trends were

shown to be consistent with those derived from Jones

[1994]. In short, the adjustment for the time of observation

bias in HCN appears to be robust.

[22] Acknowledgments. Partial support for this work was provided

by the Office of Biological and Environmental Research, U.S. Department

of Energy, and the NOAA Office of Global Programs.

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R. S. Vose, C. N. Williams Jr., T. C. Peterson, T. R. Karl, and D. R.

Easterling, National Climatic Data Center, 151 Patton Avenue, Asheville,

NC 28801, USA. (russell.vose@noaa.gov)

Figure 4. Percent of HCN stations using various observation times from 1965 – 2001 as documented by HCN

metadata.

CLM 3 - 4 VOSE ET AL.: EVALUATION OF OBSERVATION BIAS ADJUSTMENT