A Daily Soil Temperature Dataset and Soil Temperature Climatology

of the Contiguous United States

##### 

##### Qi S. Hu and Song Feng

Climate and Bio-Atmospheric Sciences Group

School of Natural Resource Sciences

University of Nebraska-Lincoln

Lincoln, Nebraska 68583-0728

September 2002

[Corresponding author's address:]{.underline} Dr. Qi Hu, School of
Natural Resource Sciences, 237 L.W. Chase Hall, University of
Nebraska-Lincoln, Lincoln, NE 68583-0728. E-mail: <qhu2@unl.edu>

Submitted to ***Journal of Applied Meteorology***, September 23, 2002

## [Abstract]{.underline}

## Although affected by atmospheric circulation, variations in soil temperature result primarily from the radiation and sensible and latent heat exchanges at the surface and heat transfer in the soils of different thermal properties. Thus soil temperature and its variation at various depths are unique parameters useful in understanding both the surface energy processes and regional environmental and climate conditions. Yet, despite the importance, long-term quality data of soil temperatures are not available for the United States. The goal of this study is to fill this data gap and develop a soil temperature dataset from the historical data of U.S. cooperative stations. Cooperative stations' soil temperatures at various depths from 1967 to 2002 are collected and examined by a set of quality checks, and erroneous data of extended periods are estimated using methods constructed in this study. After the quality control, the data are used to describe the soil temperature climatology as well as soil temperature change in the continuous United States. The 35-year climatology shows that the annual soil temperature at 10cm depth, where most stations have soil temperature measurements, decreases gradually from 297˚K in the coastal areas along the Gulf of Mexico to below 281˚K in the U.S.-Canadian boarder. In seasonal variation, the largest seasonal change occurs from spring to summer when soil temperatures are adjusted from the cold season to the warm season particularly in snow cover regions. Mild changes are observed from autumn to winter when the soil heat storage still dominates the soil temperature variations. An analysis of the soil temperature variation reveals a warming trend in soil temperatures in most of the stations in the north and northwestern United States and a large cooling trend in some stations in the southeast United States. Significant warming is found in the winter and spring season. Potential effects of these trends on regional agriculture and environment are discussed. 

## 1. Introduction

Among the processes affecting regional weather and climate in land areas
are exchanges of energy and water between the atmosphere and the Earth's
surface. The rates of these exchanges are dependent on several factors
including soil temperature and moisture. Variations in soil temperature
and moisture alter the partitioning of sensible and latent heat from the
surface and affect atmospheric boundary-layer processes and regional
circulation (Pan and Mahrt 1987; Peters-Lidard et al. 1998). Although
influenced by atmospheric circulation anomalies, variations in soil
temperature result primarily from the radiation and sensible and latent
heat exchanges at the surface and heat transfer in the soils of
different thermal properties in the vertical direction. Thus soil
temperatures at various depths are unique parameters useful to describe
both the surface energy processes and regional environmental and climate
conditions.

Soil temperature affects the surface heat flux at various time scales.
Because heat conduction in soils is a very slow process (Hillel 1980),
soil heat anomalies of daily or weekly time scales in shallow layers
near the surface are released to the atmosphere before being distributed
to the deeper layers. Only persistent long-term, such as interannual and
decadal scale, anomalies in surface heat budget can propagate to deep
soil layers and affect temperature variations in those layers
(Lachenbruch and Marshall 1986; Beltrami and Harris 2001; Beltrami
2002). This is shown by the relationship *z~m~=cּτ^1/2^*, which is
obtained by solving the heat transfer equation in soils for the depth,
*z~m~*, at which a specific time scale variation in the surface heat
process, e.g., annual solar cycle, τ, has the largest amplitude in the
soil temperature variation (Tang 1989). The parameter *c* is a constant
and proportional to the soil thermal conductivity. As this relationship
indicates, soil temperature at different depths records surface
temperature variations of different time scales, and effects of longer
time-scale temperature and heat variations at the surface and atmosphere
are eventually shown in temperature records of deeper soil layers.
Because heat conduction in soil is always down-gradient, a positive heat
anomaly in deep soil layers after a persistent warm period, for example,
would gradually release the extra heat to shallow layers, where the heat
anomaly would produce anomalies in surface heat as well as moisture
fluxes through effects on landcover and vegetation conditions. By
affecting the surface heat and moisture fluxes at interannual and
decadal time-scales, deep soil temperature anomalies could be one of the
sources affecting regional climate (Tang et al. 1982; Tang and Reiter
1986; Retnakumari et al. 2000). Thus, the potential use of soil
temperature is attractive for prediction of regional circulation and
climate, if interactions of soil temperature and atmospheric variations
are understood.

In additional to affecting the atmospheric circulation, soil temperature
anomalies at various depths also directly affect growth and yield of
agricultural crops. For example, cool spring season soil temperature in
shallow layers delays corn development and, on the other hand, warm
spring season soil temperature contributes to increase in corn yield
(Bollero et al. 1996; Hu and Buyanovsky 2002). Warm soil temperature
encourages plant leaf growth, which further affects the
evapotranspiration and soil water budget in crop fields (Bollero et al.
1996; Wraith and Ferguson 1994). Warm soil temperature in early spring
also allows planting at earlier dates, and an extended growing season
would open the possibility to grow high-yield varieties that usually
take a longer time to mature. Furthermore, soil temperature anomalies
affect the crop root growth; cool soil temperatures limit root expansion
(Kaspar and Bland 1992) and significantly affect the root system and
hence yield (McMichael and Burke 1998). Because of these effects of soil
temperature on agriculture and economy, it is important to understand
soil temperature variations so that both farming technology and
management strategies can be improved to reduce climate risks and
sustain grain production and agriculture in the changing environment.

In order to understand these roles of soil temperature in land surface
processes affecting weather and climate and in production agriculture, a
reliable dataset of soil temperatures is required. Existing pieces of
short-term soil temperature data series are in different archives with
various formats. Because of their variable quality they are difficult to
use in research and application. This situation imposes a strong need
for developing a quality dataset of soil temperatures from the existing
sources. A part of this effort was recently completed and resulted in a
high quality dataset of hourly soil temperatures at multiple depths
using measurements from 21 automated stations in the USDA NRCS SM-ST
Network (Hu et al. 2002). As a continuation of that previous work, this
study extends the quality assurance analysis to the U.S. National
Weather Service (NWS) cooperative stations daily soil temperature data
and produces a quality dataset for the contiguous United States from
1967 to early 2002. This quality dataset can be used, as demonstrated in
some of the previously cited studies, in analysis and understanding of
the soil temperature variations and their interactions with regional
circulation and their impacts on agriculture and natural resources.

In the next section, various data sources for soil temperatures from
cooperative stations in the contiguous United States are discussed.
Major sources of error in individual station's soil temperature data
series are discussed in section 2 along with methods to detect the
erroneous data and calculate their estimates. Because the data were
measured at various time schedules, e.g., some stations measured daily
maximum and minimum at midnight and some measured soil temperatures two
times each day, one at the seventh hour local time and the other at
nineteenth hour local time, and still the others measured once on a day,
a method is proposed in section 3 to calculate the daily average soil
temperatures which can be used for comparison and research purposes. In
section 4, the new dataset of daily average soil temperatures at
multiple depths from 1967 to early 2002 are used to describe the
climatology of soil temperatures in the contiguous United States.
Additional features, such as trend and major changes over the 36 years
of record for the soil temperatures at various depths, are examined and
the potential impact of these changes on agriculture are discussed in
section 5. Section 6 contains a summary of this study and remarks on the
significance of major results.

# 2. Daily soil temperature data and quality control

**a. Daily soil temperature data sources**

Daily soil temperature data for the contiguous United States are from
three major sources: i) the TD-9639 soil temperature data archive for
the period from January 1967 to December 1981, ii) the TD-3200 daily
data archive from January 1982 to December 1993, and iii) stations daily
soil temperature data from January 1994 to March 2002 available online
at <http://cdo.ncdc.noaa.gov/plclimprod/plsql/poemain.poe>. All three
datasets were produced at the National Climatic Data Center (NCDC) from
reports of individual stations. Additional soil temperatures from
cooperative stations not contained in these datasets, for example, some
stations in individual states, were found from archives at state climate
offices and included in this analysis. Soil temperatures in all these
archives were not previously subjected to quality control although the
air temperature and precipitation data in TD-3200 and the online
archives were quality controlled using the method developed at NCDC
(Reek et al. 1992).

According to these data sources, there were 337 stations in the
contiguous United States with soil temperature measurements between 1967
and 2002. Of these 337, only 292 stations continued their measurements
for longer than 5 years for at least one depth. The spatial distribution
of these 292 stations is shown in Fig. 1a. The spatial distribution of
the stations is quite irregular; more stations were in the Ohio and
Mississippi River valleys and the states of Iowa and the Dakotas, and
only a few in the southwestern and northeastern U.S. and none in Nevada
and Wisconsin. Among these stations, the number of stations in service
also changed over the years. As shown in Fig. 1b, more stations were
installed after 1978, after a dry period in the central United States,
and the number of stations peaked in the early 1980s but has since been
decreasing. Figure 1b also shows that most of the stations measured soil
temperature at 10cm depth, and only about 50 stations measured
temperature at 100cm depth. The number of stations in service also shows
an "annual cycle" in Fig. 1b because some stations, particularly in the
northern states, only measured soil temperature in warm season or during
the growing season for agricultural crops from mid April to late
September.

At these individual stations, soil temperatures were often measured
under different ground covers. A total of seven different covers were
found in the records: bare ground, fallow, grass, brome grass, sod,
straw muck, grass muck, and bare muck. Except for some stations not
specifying their cover types, most stations have measurements under bare
ground, and these measurements often have the longest record length. The
measurements under other cover types at a station were sometimes made
for specific experimental purposes, such as soil temperature effect on
vegetation health and crop growth, and continued only for certain
seasons or short time periods. So, in developing the dataset, we used
the soil temperatures measured under bare ground cover, and for stations
without measurements under bare ground, we used the soil temperatures
under the specific ground cover that had the longest record.

At most stations, soil temperatures were measured at multiple depths,
and for various reasons both English and metric units were used to
define the depths at the stations. Stations using the English units
measured soil temperatures at depths of 2, 4, 8, 20, and 40 inches below
the surface, and stations using the metric units measured the
temperature at depths of 5, 10, 20, 50, and 100 centimeters below the
surface (only a few stations had measurements at 80 inches or 200cm).
Although each pair of these English and metric units, e.g., 2in vs. 5cm,
4in vs. 10cm, etc, is not identical, they are very close to one another.
The largest difference is 1.6cm between 40in and 100cm. Temperature
differences between comparable depths are small because depth
differences are small, particularly in shallow depths, and the slightly
bigger differences at deeper depths will have little effect on
temperature because of small temperature gradient across them.
Therefore, we accepted the temperatures at depths in English unit as
comparable to temperatures at the corresponding depths in metric unit
and obtained soil temperatures at five metric depths: 5, 10, 20, 50, and
100cm, at each station.

Soil temperatures at different stations were measured at three different
"times-of-observations" (Karl et al. 1986). Some stations measured the
maximum and minimum soil temperatures at various depths at midnight;
some stations measured soil temperatures twice a day, one in the early
morning hours at either 7:00am or 8:00am local time and the other in the
late afternoon hours at local time either 5:00pm or 6:00pm; and the
remaining stations measured soil temperature only once a day fixed at
one of these local hours: 7:00am, 8:00am, 12:00noon, 5:00pm, and 6:00pm.

These considerable irregularities among stations data in observation
period, ground cover, depths where data were taken, and
time-of-observations make it difficult, if not possible, to develop a
soil temperature dataset with a consistent format for all the stations.
A practical approach is to apply quality control methods to individual
stations and their various time series of soil temperatures at specific
time-of-observation, depths, and ground cover, and obtain a mosaic yet
useful dataset of the soil temperatures. A quality control method to
obtain such a dataset is described in the next subsection.

**b. Data quality control**

Although many methods have been developed to identify erroneous air
temperatures (Hanssen-Bauer and Forland 1994; Menne and Duchon 2001;
Peterson et al. 1998a, b; Vincent et al. 2002), little attention has
been given to quality control of soil temperature data. Hu et al. (2002)
developed a quality control method to examine the soil temperature data
from the USDA NRCS SM-ST Network. In that approach, a soil temperature
model was established to calculate annual and diurnal reference soil
temperatures at various depths for each station. These reference
temperatures are used in evaluation of observed soil temperatures to
identify erroneous data and provide their estimates. In developing
quality control method for the cooperative stations, we use some of the
tools developed in Hu et al. (2002) and also expand their method to
include additional evaluation tools. Several evaluation methods
introduced in quality control for air temperatures also are used.
Details of each of these evaluation tools and their application
procedure to the soil temperatures are presented below.

***b.1. Internal consistency check***

Reek et al. (1992) outlined eight rules to identify erroneous data of
all meteorological variables resulting from data reporting and
digitizing, from typos, unit differences, and use of different based
values in data reporting (see their Table 1). We used three of their
eight rules for temperatures to check the soil temperatures for a)
*internal inconsistency*, which includes cases with daily maximum
temperature smaller than daily minimum, and unrealistically large or
small temperature values, b) *excessive diurnal range*, which includes
cases with extraordinarily large daily temperature range (T~max~-T~min~)
even though T~max~ and T~min~ may be within their reasonable ranges, and
c) *spike in temperature series*, which are defined "as the smallest
absolute result from the comparison of singular differences of three
consecutive days, centered on the day in question" (Reek et al. 1992).
Spikes greater than 28˚C are flagged for deletion, and spikes smaller
than 28˚C but greater than 22˚C are marked as suspect. Applications of
these rules to individual station soil temperature series singled out
the suspected data totaling 0.10% of the entire soil temperature data.
These suspected data values were flagged.

***b.2.** **Variation range check***

The above internal consistency checks identified some obvious outliers
in the data series, but they cannot detect erroneous data that have the
following problems: the data values are much larger than neighboring
values but not larger than the threshold for being detected by the
consistency check in b1, data that create substantially large changes in
soil temperature from the previous day or days, and data that show
absence of any change over some extended period. To identify these
erroneous data, we used two methods based on the two rules proposed and
used by O'Brien and Keefer (1985) in their identification and
elimination of erroneous data in real-time hydrological data. These
methods define a) high/low bounds for soil temperature at given depths
(LIM), and b) the limit for the rate of change of soil temperature at
given depths (ROC). By defining the high/low bounds, the LIM method can
help identify the data outside the variation range of soil temperatures
at each depth. Similarly, defining the bounds for the rate of change of
soil temperatures, the ROC detects data inconsistent with temporal
variation in soil temperatures. Similar methods derived from these rules
also were used in quality control of daily air temperatures in Meek and
Hatfield (1994; 2001).

To derive the high/low bounds in LIM for a given soil temperature data
series, for example, the daily maximum soil temperature or soil
temperature measured at an early morning hour at a depth, we first
identified both the highest and the lowest values of soil temperatures
on each calendar day of a year in the data history of up to 36 years.
After getting these pairs of "extreme" values for each day, we used
functions of form, ![](media/image1.wmf), to describe the envelope of
annual variation of these extreme temperatures. In this function, *t* is
the day of year, ω is the frequency (=2π/365), *z* is depth, and
![](media/image2.wmf) and ![](media/image3.wmf) are annual mean of these
"extreme" soil temperatures and their amplitude of variations,
respectively. Because of the potential for some of these extreme values
to be erroneous, this envelope was determined by visual inspection and
embraced 98% of those daily extreme values.

After the envelope functions are derived at each station and for each
observation time series, they were denoted as the upper and lower bounds
of soil temperature variations, *T~U~* and *T~L~*, respectively, for
those series and applied to compare with the individual data values.
Data satisfying, ![](media/image4.wmf), passed the LIM test, and data
that failed it were flagged as suspicious. A typical result is shown in
Fig. 2a for minimum soil temperature at 20cm from 1994 to 1997 at
Auburn, Alabama. The two thin lines are *T~U~* and *T~L~* and the thick
line is the soil temperature. The open circles indicate erroneous data
identified by this LIM test.

To check the daily rate of soil temperature change, we used a method in
Meek and Hatfield (1994; 2001) to determine the bound of the changes,
similar to finding the bounds in LIM. At each station and for each time
series, the soil temperature change between two consecutive days was
calculated from *ΔT(z,t)* =$\left| T(z,t) - T(z,t - 1) \right|$ and then
the maximum daily rate of change was identified among all the values for
the same day from the data of up to 36 years. Again, a fitting function
was constructed to describe the annual variation in those "extreme"
values of the rate of daily soil temperature change. However, because
the daily rate of change for soil temperatures is very small in winter,
particularly when a station has snow cover (Beltrami 2001), and large
during spring, it was difficult to describe the variation of the extreme
values except for a step function. For simplicity, we took a different
approach and used a constant value as the ROC bound (no bound for
minimum change). In deriving this bound, we first calculated the
standard deviation (SD) of the daily soil temperature change for a data
series and denoted the values larger than 8 SD as suspect. The time
series of soil temperature change without the suspect values was
examined and used to recalculate the SD, and the new 8 SD value was used
as the ROC bound. In this method, the first SD analysis was necessary
because a few large outliers in a data series can inflate the SD of the
series large enough to cause acceptance of some erroneous data. Such
erroneous data can be detected by the ROC criterion determined in the
second SD analysis on the new data series, which has the large outliers
removed.

An example of the ROC test is shown in Fig. 2b for the minimum soil
temperature at 20cm from 1994 to 1997 at Auburn, Alabama. The dashed
line is the ROC bound. Many of the erroneous data identified by the ROC
also were found erroneous by LIM.

Both the ROC and LIM checks were applied to the entire soil temperature
dataset and caused flags on 0.17% of the data (see Table 1).

***b.3. Check drift in soil temperatures***

After these consistency and variation range checks, the remaining
possible errors in a soil temperature data series would be those
embedded in the variations of the series, including changes in "average"
and variance (amplitude) of soil temperatures in a period of a few
months or years. In this subsection, we use "drift" to refer to such
changes of short-term average soil temperatures. Sources causing such
drifts are unclear due to lack of documentation. In this and the next
subsection, we describe the methods that identify these two kinds of
errors in the data and also provide estimates of soil temperatures in
those erroneous periods.

To identify the drift in a soil temperature series, we displayed each
station's data and compared the variation of the target time series with
time series at different depths. When there was no observation at other
depths at the same station, the comparison was made between observations
at the station and two to three neighboring stations within 200km
radius. From this comparison, we identified the segment in the data
series with sudden changes of the trend. A similar inspection method
also was used in examining soil moisture data (Robock et al., 2000) and
in automated weather station data quality control (Ashcroft et al.,
1990). A result of this inspection is shown in Fig. 3a for the data at
the station in Brawley, California. From 1989 to 1992, a drift of the
station's soil temperature at 20cm is clearly shown. No similar decrease
was observed at the other depths of the station. Another example is
shown in Fig. 3b for Estherville, Iowa. In this case, an unknown source
caused the station's soil temperature at 100cm to decrease continuously
from 1979 to 1986. This drift of the soil temperature was inconsistent
with temperature variations at other depths for this station (see upper
panel of Fig. 3b). These and similar erroneous data identified using
this method were flagged to indicate their error type. This procedure
flagged 1.48% of the data with this drift error.

Because data with the drift usually continued for a period of a few
months or years, as in the previous two examples, it is desirable to
provide estimates of soil temperatures for some of those drift periods.
Such an effort is justifiable for those periods when only the average of
soil temperatures was incorrect but the temperature variance was similar
to that of the neighboring periods, such as in the case at Estherville,
Iowa from 1979 to 1986 (Fig. 3b). For period of drift resulting from
sensor problems when both drift and error in variance occurred, such as
in the case at Brawley, California from 1989 to 1992 (Fig. 3a), no
estimate was attempted.

There are several ways to correct the drift. For example, we could use
soil temperatures at the same depth for the period excluding the drift
segment to get a mean temperature and use it as an estimate of the
average temperature in the drift segment. This method may miss
fluctuations in the mean temperatures in the segment, however,
especially if the segment contains several years. Another way is to use
average from soil temperatures at different depths observed at the same
time. As showed in Hu et al. (2002, see their figure 3), the trend in
soil temperatures is consistent between the shallow layers. In addition,
Zhang et al. (2001) used 100-year soil temperature records from a
station in Irkutsk, Russia and showed that the long-term soil
temperature trend at 40cm and 80cm are nearly identical. These results
suggest that the soil temperatures of different depths at the same
station vary with the same trend. Thus the trend at one depth can be
used as a good reference to estimate the temperatures in a drift segment
at a different depth. Based on these results, we constructed the
following equation to estimate the soil temperatures in drift segments:

> $T_{e} = T_{c} - (a_{c} + b_{c}t) + (a_{r} + b_{r}t) - {\Delta T}_{\text{rc}}$.
> (1)

In the above, $T_{e}$ and $T_{c}$ are the estimated and candidate soil
temperatures in the drift segment, respectively, *t* is time in day, and
$a_{c} + b_{c}t$ and $a_{r} + b_{r}t$ are linear regressions of the
candidate and reference average soil temperature for the drift segment,
with $a_{c}$, $b_{c}$, $a_{r}$ and $b_{r}$ as the regression
coefficients. The reference temperatures are from a different depth. The
difference of average soil temperatures between candidate and reference
series excluding the drift segment, ${\Delta T}_{\text{rc}}$, is
included in (1) to account for soil temperature variation with depth.

This method was used at 16 stations where drift of soil temperatures at
some depths was identified. As an example, the estimated soil
temperatures for the station at Estherville, Iowa are shown in Fig. 3b.
The estimated values at 100cm from 1979 to 1986 describe a consistent
variation as the observed in the other depths at the station.

***b.4. Check changes in soil temperature variance***

A problem parallel to the drift is the change or drift of variance or
amplitude of the soil temperatures. In detecting this kind of error, we
used the method of "scale cumulative sum" (SCS) proposed by Peterson et
al. (1998b) to measure changes in amplitude (variance) of a soil
temperature time series. An example of this error and SCS test result is
shown in Fig. 4 for the station in Salt Lake City, Utah. In this case,
the SCS revealed significant changes in variance of the soil
temperatures at 10 and 20cm depths after December 1995. Applying the SCS
to the entire dataset, we found about 0.54% of the data with such
errors.

Again, because such errors often continued for a period of a few months
or years (Fig. 4), it is useful to provide estimated soil temperatures
for those erroneous data periods. To estimate the soil temperatures with
the variance error, we took an approach similar to that in b3 and used
the soil temperatures at a different depth of the same station as the
reference temperatures. The candidate soil temperature series at one
depth was compared to the soil temperature series at other depths, or
from neighboring stations if data at only one depth was observed at the
target station, for their variance in the periods *excluding* the
erroneous segment to select the soil temperature series that yielded the
least difference in both the variance and average with the candidate
station series. After identifying the reference time series, the
following was used to calculate the estimate of soil temperatures in the
erroneous segment:

> $T_{e} = \lbrack(T_{c} - {\overline{T}}_{c}\frac{)}{\sigma_{c}}\rbrack \times (\frac{\sigma_{r} \times \sigma_{c1}}{\sigma_{r1}}) + {\overline{T}}_{c} - {\Delta T}_{\text{rc}}$,
> (2)

where, $T_{c}$ and $T_{e}$ are candidate (erroneous) soil temperature
and its estimate, respectively, ${\overline{T}}_{c}$ is the average
candidate soil temperature and $\sigma_{c}$ and $\sigma_{r}$ are
standard deviations of the candidate and reference temperature series,
respectively, for the erroneous data segment, and $\sigma_{c1}$ and
$\sigma_{r1}$ are standard deviation of candidate and reference
temperature series for the data period excluding the erroneous data
segment(s). The difference of the average soil temperatures between
candidate and reference series excluding the erroneous data segment,
${\Delta T}_{\text{rc}}$, is included in (2) to account for soil
temperature variation with depth. The estimate temperatures from (2)
will have a consistent variance with the rest of the data series, as
shown in Fig. 4 by the result from applying this method to the data at
the station in Salt Lake City, Utah.

***b.5. Procedure of the quality control***

These previously described individual procedures are used in the
following sequence to perform the quality control of a station's soil
temperature data. The internal consistency check (b1) was used to flag
inconsistent data. Then, variation range check (b2) is used to identify
data outliers. Finally, errors from drift and inconsistent variance are
examined (b3 and b4) in sequence to assure consistent variations in soil
temperatures at a depth and observation time. This procedure has been
applied to the soil temperature dataset from the NWS cooperative
stations in the contiguous United States. Erroneous data are flagged and
their estimates also are calculated. The total percentage of erroneous
data detected is 2.29%, and those for individual error categories are
listed in Table 1. In Table 1, the percentage for ROC error was small
because ROC check was applied after LIM check which flagged many
erroneous data.

**3. A daily mean soil temperature dataset**

**a. Why use a daily mean soil temperature?**

In this "quality-controlled" dataset of soil temperatures, nearly all
the stations' data are for bare ground cover and at a depth from 5 to
100cm under the surface. But the stations still have different
times-of-observations, i.e., daily maximum and minimum, observations at
morning *and* afternoon hours, and observation at a single hour on a
day. For the purpose of data quality control, it is necessary for each
station to keep its original data series. For research purposes,
however, these various stations data series are least desirable because
they offer little common ground for direct comparison and analysis of
soil temperature variation between stations and regions. On the other
hand, although the station data were measured in different formats,
e.g., daily maximum vs. observation at a specific time, and at different
time on a day, it may still be possible to derive one measure of "daily
soil temperature." From analysis of the stations' data series, it was
apparent that a "daily mean soil temperature" at each depth is a
reasonable way to "synchronize" the data. Accordingly, we defined a
daily mean soil temperature at each depth and developed a method to
calculate this mean temperature using stations data series.

**b. Calculate daily mean soil temperatures**

In this method, for stations measuring the daily maximum and minimum
soil temperatures, the daily mean soil temperature is obtained as an
arithmetic average of the maximum and minimum temperatures. In fact,
this has been the definition for daily means (Karl et al. 1995). For
stations measuring temperatures two times a day, one in an early morning
hour and the other in afternoon hour, the daily mean is defined as the
arithmetic average of the two temperatures. For stations measuring
temperatures only once a day, no similar mean can be defined. However,
because soil temperatures at depths below 50cm have very small amplitude
in daily variation, these single daily measurements at some particular
hours can be reasonable representations of daily mean temperatures at
those depths. In the following, we present the analysis results that
verify the use of the second and third definitions.

We examined the differences between the "standard" daily mean, i.e., the
arithmetic average of daily maximum and minimum temperatures (T~mm~),
and the averages calculated from the second and third definitions. The
data used in the evaluation were quality-controlled hourly soil
temperatures from 1994-2002 at the 21 automated soil temperature
stations in the USDA NRCS SM-ST Network (Hu et al. 2002). At each
station, the data are at depths from 5 to 100cm under the surface. After
calculating T~mm~ and the average from the two observations in the
morning and afternoon hours (T~ap~) at each depth, we examined the
average difference between T~ap~ and T~mm~, ![](media/image5.wmf), and
the average root mean square error (RMSE) of the difference,
![](media/image6.wmf), where *m* is the number of observations at a
depth and *N* is the total number of stations. Two results of these
differences, the largest one at 5cm and a small one at 50cm depth, are
shown in Fig. 5. The two dashed lines in each panel mark the one
standard deviations of variations in the differences among individual
stations. Figure 5 shows that T~ap~ calculated from temperatures at
those specific morning and afternoon hours is a reasonably accurate
measure of T~mm~ in both the shallow and deep depths. The largest
average RMSE between T~ap~ and T~mm~ is less than 0.25˚C. At depths
below 50cm the difference reduces to a few tenth of one degree of
Celsius. Another result in Fig. 5 is that the T~ap~, calculated from
temperatures at those different morning and afternoon hours,
approximates T~mm~ with a similar and small difference, showing a
steadiness of T~ap~ in representing the daily mean temperature.

Comparisons of T~mm~ and the single measurement of soil temperature at
various hours (T~sm~) are shown in Fig. 6. Figures 6c and 6d show that
at 5cm and other shallow depths at 10 and 20cm, the differences of T~sm~
and T~mm~ are large, and the largest average RMSE at 5cm is about 1.3˚C.
These large differences clearly indicate that T~sm~ cannot be used to
represent daily mean at those shallow depths. However, at deep depths
below 50cm, because daily temperature cycle has very small amplitudes
(Hu et al., 2002), T~sm~, particularly at the two morning hours, can
represent T~mm~. For example, at 50cm depth the largest average
difference between T~mm~ and T~sm~ is 0.12˚C, and the difference further
reduces to a few tenths of one degree Celsius at 100cm.

In summary, these results indicate that T~ap~ calculated from the pair
of morning and afternoon hour observations at the stations consistently
describe the daily mean soil temperature, T~mm~, at all depths with the
maximum average error of 0.25˚C at 5cm. A single daily measurement,
T~sm~, can only represent T~mm~ at depths below 50cm with an error
smaller than 0.12˚C. An explanation for T~ap~ and deep depths T~sm~ to
describe T~mm~ is the soil's thermal damping on temperature variations
so that the amplitude of diurnal cycle of soil temperature is small
particularly in deep soil layers. In addition, the phase shift of
diurnal cycle with the depth (e.g., Hillel 1980) also results in shift
of the daily maximum and minimum temperatures and helps reduce the
difference between T~ap~ and T~mm~. These methods were applied to the
soil temperatures at all the stations and a summary of their application
is in Table 2.

**4. Soil temperature climatology**

From the calculated daily mean soil temperatures, we derived the annual
and seasonal soil temperatures at various depths averaged over the
period 1980-2002, because of more stations in service after 1980 (see
Fig. 1b), and analyzed seasonal variations of soil temperatures in the
contiguous United States. The average and standard deviation of annual
soil temperatures at five depths, 5, 10, 20, 50, and 100cm, are shown in
Fig. 7 at individual stations and also by contour lines, because of the
low density and highly irregular distribution of the stations. The
number of stations is highest at 10cm and reduced considerably at the
other depths (see Fig. 1b).

Figure 7 shows that the annual average soil temperature decreases
northward as the climate changes from subtropical humid or dry in the
southern U.S. to temperate continental climate in the northern
territories. At 5 and 10cm below the surface, the average soil
temperature and temperature distribution are similar; the mean
temperature ranges from 24˚C in the coastal areas along the Gulf of
Mexico to below 8˚C in the U.S.-Canadian boarder. At 20cm, the annual
soil temperature is the coolest among temperatures at all five depths.
The coolest annual temperature at 20cm depth results from a combination
of the warm and cold season temperature variations in the soil column.
As shown in Fig. 8, the vertical profile of cold season temperature has
a minimum at 20cm. In the warm season, the temperature is warmer in the
shallow layers and cooler below 20cm than that at 20cm, and the decrease
of temperature below 20cm is at a rate smaller than the increase of soil
temperature in those depths in the cold season. A collective result of
these variations yields the minimum temperature at 20cm. At 50 and 100cm
depths, the warmer soil temperature in the cold season contributed to
annual average soil temperatures warmer than those at 5 and 10cm depths.

Variations in the soil temperature are shown by the standard deviation
in Fig. 7. Primarily because of soil's thermal damping property, the
annual fluctuations are smaller than 4˚C at all five depths, with
relatively large fluctuations in the central and north-central United
States.

Seasonal average and standard deviation of soil temperatures are shown
in Fig. 9, and they describe seasonal changes in the soil temperatures.
At 5 and 10cm, freezing occurs in winter season, from December through
January, in northern plains north of the 40˚N parallel from west Montana
to the west of the Great Lakes and also in the northeastern U.S.
Freezing disappears at most stations in central and southern U.S. at
depths below 100cm (figure not shown). The warmest soil temperature
occurs in summer when the soil temperatures at 10cm are as high as 34˚C
in southern part of Texas. Seasonal changes in soil temperatures show
the largest from spring to summer, when the 20˚C contour line "jumped"
over more than 20 degrees of latitude from the southern U.S. to the
U.S.-Canadian boarder. In contrast, the soil temperature change in the
transition from summer to fall and from fall to winter is mild.

The standard deviations of seasonal soil temperatures in Fig. 9 show the
largest standard deviations in spring and the smallest standard
deviations in fall. In spring, the large standard deviations, indicating
large variance of soil temperatures, has been related to fluctuations of
winter snow amount in high latitude regions of the north-central U.S.
but affected by alternations of contrasting weather regimes in the
central U.S. Our analyses showed that winters with more snow
accumulation in the northern U.S. caused cooler soil temperatures in
springs due to spring melting, whereas considerably warmer soil
temperatures were observed in springs following winters with less snow
and snow accumulation. This snow effect on soil temperatures also
contributed to the large winter season soil temperature variations in
the same region (Fig. 9). In the central U.S., late snowstorms in April
and early May during the warming course of temperatures caused large
soil temperature variations and large soil temperature deviations in
that region. In contrast, lack of similar processes in autumns leaves
small temperature fluctuations in that season.

**5. Discussions**

In addition to be useful to describing the average conditions and
spatial and seasonal variations of the soil temperatures, the developed
soil temperature dataset can be examined to reveal soil temperature
variations and understand changes in land surface processes as well as
possible effects of these changes in agriculture and environment. Two of
these features are discussed in this section.

**a. Soil temperature** **trend**

Although data length for most stations is only 35 years (1967-2002), a
trend analysis of the data could still provide insight into the soil
temperature variation and its relationship with changes in air
temperature and precipitation. We show in Fig. 10a the linear trend of
soil temperature at 10cm for 38 stations that have more than 30 years of
data, and Fig. 10b the temporal variation in the temperature averaged
over the 38 stations in Fig. 10a. Because the number of stations with
35-year temperature records at 100cm depth is much fewer than that at
10cm, we plotted the station average 100cm soil temperature variation
and trend in Fig. 10c.

These results show that the soil temperature at 10cm depth has been
warming at most stations over the 35 years with an average rate of
0.31˚C(10yr)^-1^. This rate, though larger than the average of
0.10˚C(10yr)^-1^ for the air temperature, is comparable to the rate of
change in air temperatures in the recent decades (Karl et al. 1986).
Among the stations with warming soil temperatures, the ones in the
northern half of the U.S. have the largest rates. The warming trend at
those stations is similar to the warming trend of their air temperature.
On the other hand, among the few stations with cooling trend, the ones
with largest cooling rate are in the southeast, and the cooling of soil
temperatures at those stations also was consistent with decrease of the
air temperature at the stations (see Fig. 4 in Karl et al., 1996). At
100cm depth, the few stations in the north-central United States all
showed a warming trend at an average rate of 0.30˚C(10yr)^-1^.

Superimposed on the trend of the soil temperatures in both the shallow
and deep soil layers are interannual variations in the temperatures. The
station-averaged variations of temperature deviation are shown in Figs.
10b and 10c (solid lines). The variations have similar phase and large
amplitude from 5 to 100cm depths over the years. These variations could
result from various sources including air temperature and precipitation
variations, particularly winter snow anomalies and snow melting in
spring. Details of these effects on interannual soil temperature
variations are currently examined in a separate study.

**b. Soil temperature change and agriculture**

Soil temperature under bare ground is often used as a measure of the
thermal condition in cropland before planting and is one of the
indicators for spring planting time, because warm soil temperature is
essential to seed germination and early development (Bollero et al.
1996). As shown in Figs. 11a and 11b, both the averaged winter and
spring soil temperature at 5 and 10cm depths increased in the last 35
years in the contiguous United States (significant above the 95%
confidence level). Such a warming trend in spring soil temperature would
favor planting in earlier dates, especially in the northern half of the
U.S. (e.g., Meyer and Dutcher 1998), and allow growth of high yield
varieties of certain crops, e.g., corn, which require a longer growing
season. Warmer soil temperature in early spring season also would
encourage both leaf and root growth of crops and higher yield (Bollero
et al. 1996; McMichael and Burke 1998). The soil temperature dataset
developed in this work can be used to quantify changes of average
planting dates for various crops and additional statistical features of
these new planting dates, e.g., probability of temperature deviations
from their mean. Moreover, analysis of the soil temperature data with
local air temperature and precipitation could be used to evaluate the
feasibility of growing new variety of crop as well as different crops.

Warming soil temperatures also can have negative impacts on agriculture.
The rising winter soil temperatures would be favorable for insects to
survive the winters and thus large insect populations in the following
growing season. Additionally, warmer soil and air temperatures in winter
and spring in the mid- and higher-latitude regions could assist fast
melting of snow to reduce winter season snowpack and hence stream flow
and water resources in growing season. The soil temperature dataset
produced from this study can be used to analyze these various effects of
soil temperature on agriculture and assist in development of practical
methods to maintain and improve agricultural production.

**6. Summary**

A dataset of soil temperatures is developed for the continuous United
States. The soil temperature data were from the U.S. NWS cooperative
stations. Each station has soil temperatures at up to five depths of 5,
10, 20, 50, and 100cm below the surface and up to 35 years from 1967 to
2002. These station data were examined for consistency in both temporal
variations and variations at various depths. In addition, the erroneous
data continuing for extended periods were provided with estimates using
methods developed in this study. This dataset not only extends the
climatic data array to include the soil temperatures but also opens
opportunities for acquiring knowledge of soil temperature variations and
their relationship with changes in air temperature and precipitation and
effect of soil temperature change on regional agriculture and
environment.

Taking this advantage, we explored soil temperature climatology using a
daily mean soil temperature derived from the "quality-controlled"
dataset. The climatology shows both the soil temperatures at various
depths average over each station's record history (up to 35 years) and
the dynamic aspects of the temperatures during transitions between
seasons. The former described a north-south gradient across the
contiguous U.S., and the latter revealed a large change of soil
temperature from spring to summer. In the winter months from December to
February, soil temperature is below freezing in the north-central U.S.
and the northern Great Plains in shallow layers above 100cm. At 100cm
depth, the winter temperature is below freezing only at a few stations
in high latitude of the northern U.S. In the vertical direction across
the soil column at each station, the annual average soil temperature is
coolest at 20cm below the surface as a result of different annual soil
temperature variations in the shallow and deep soil layers. Soil
temperatures at depths below 20cm are as warm as and at many stations
warmer than that above 20cm because of the heat storage capacity and
slow heat release at the deeper layers.

Soil temperature at most stations has shown a trend of warming in the
last 35 years, a result similar to the observed in the air temperature
changes. The average warming rate is 0.3˚C (10yr)^-1^ and comparable to
the warming rate of air temperature. A few stations in southeastern U.S.
show a large trend of cooling. These trends in soil temperatures are
significant in both the shallow and deep layers. Potential impacts of
the changing soil temperatures on agriculture are outlined based on
relationships of warming soil temperature in spring and earlier planting
dates of various crops.

In addition to these applications, the developed soil temperature
dataset can be used in studies leading to improved understanding of, for
example, the thermodynamic process in soils and the relationship of soil
temperature and surface heat flux variations. This relationship could be
used to examine the soil temperature variations described by regional
circulation or climate models and validate and also improve models'
ability in describing the surface energy processes.

### Acknowledgements

We thank Dr. Kenneth G. Hubbard for useful discussions during the work.
This work was partially supported by the U.S. National Climatic Data
Center and by USDA Cooperative Research Project NEB-40-008.

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## Tables

# Table 1: Percentages of erroneous data by categories.

  ----------------------------------- -----------------------------------
  Inconsistency data                  0.10%

  Violating LIM rule                  0.15%

  Violating ROC rule                  0.02%

  Drift in average soil temperature   1.48%

  Drift in soil temperature variance  0.54%

  Total                               2.29%
  ----------------------------------- -----------------------------------

## 

## 

## 

**Table 2**: Total number of daily mean soil temperatures at five depths
and percentages of the daily mean soil temperatures calculated using
T~mm~, T~ap~, and T~sm~.

<table>
<colgroup>
<col style="width: 8%" />
<col style="width: 13%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 10%" />
<col style="width: 7%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td>Depth</td>
<td><p>Number of observations</p>
<p>(daily mean × stations)</p></td>
<td><p>T<sub>mm</sub></p>
<p>(%)</p></td>
<td>T<sub>ap</sub> (7am+ 5pm) (%)</td>
<td><p>T<sub>ap</sub> (7am+</p>
<p>6pm) (%)</p></td>
<td><p>T<sub>ap</sub> (8am+</p>
<p>5pm) (%)</p></td>
<td><p>T<sub>ap</sub> (8am+</p>
<p>6pm) (%)</p></td>
<td><p>T<sub>sm</sub> (7am)</p>
<p>(%)</p></td>
<td><p>T<sub>sm</sub> (8am)</p>
<p>(%)</p></td>
<td>T<sub>sm</sub> (12Noon) (%)</td>
<td><p>T<sub>sm</sub> (5pm)</p>
<p>(%)</p></td>
<td><p>T<sub>sm</sub> (6pm)</p>
<p>(%)</p></td>
</tr>
<tr class="even">
<td>5cm</td>
<td>692074</td>
<td>66.16</td>
<td>2.86</td>
<td>4.48</td>
<td>22.59</td>
<td>4.92</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
</tr>
<tr class="odd">
<td>10cm</td>
<td>1991490</td>
<td>87.73</td>
<td>1.04</td>
<td>1.89</td>
<td>7.64</td>
<td>1.71</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
</tr>
<tr class="even">
<td>20cm</td>
<td>824914</td>
<td>71.15</td>
<td>2.40</td>
<td>4.21</td>
<td>18.10</td>
<td>4.14</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
<td>--</td>
</tr>
<tr class="odd">
<td>50cm</td>
<td>634032</td>
<td>2.16</td>
<td>1.06</td>
<td>0.81</td>
<td>2.94</td>
<td>0.06</td>
<td>4.59</td>
<td>34.57</td>
<td>0.01</td>
<td>33.51</td>
<td>14.19</td>
</tr>
<tr class="even">
<td>100cm</td>
<td>515452</td>
<td>1.69</td>
<td>1.30</td>
<td>0.99</td>
<td>3.60</td>
<td>0.07</td>
<td>5.65</td>
<td>37.97</td>
<td>0.00</td>
<td>31.86</td>
<td>11.86</td>
</tr>
</tbody>
</table>

## Figure Legends

**Figure 1**: (a) Distribution of the 292 U.S. cooperative stations
(dots). Open cycles are USDA SM-ST Network soil temperature stations
whose data are used in this study. (b) Variations of number of
cooperative stations measuring soil temperatures.

**Figure 2**: (a) Minimum daily soil temperature variation at 20cm
(solid line) and its *T~U~* and *T~L~* derived from the LIM method (gray
thin lines), and (b) daily rate of change of the daily minimum
temperature (solid line) and the ROC bound (dashed line). The open
circles in (a) and (b) are the suspected data points. The data are from
Auburn Agronomy Farm station (32.6˚N, -85.5˚W) in Auburn, Alabama.

**Figure 3**: (a) Soil temperatures at 10 and 20cm depths at the station
(33.0˚N, -115.5˚W) in Brawley, California. Compared to the temperature
at 10cm, the temperature at 20cm shows a drift from 1989 to 1992. (b)
Soil temperatures at 50 and 100cm depths at the station (43.5˚N,
-94.8˚W) in Estherville, Iowa. A drift of soil temperature is shown in
100cm data from 1979 to 1987. The estimated values of soil temperature
for the drift period are shown by the dashed-line.

**Figure 4**: Soil temperatures at 10 and 20cm from a station (40.8˚N,
-112.0˚W) in Salt Lake City, Utah, showing a change in variance or
amplitude of the soil temperatures after 1995. The estimated values of
soil temperature for the drift period are shown by the dashed-line.

**Figure 5**: (a) Stations average difference between 5cm T~mm~ and
T~ap~, which is calculated as arithmetic average from observations at
the two local hours indicated in the legend of the abscissa. The two
dashed-lines show one standard deviation from the average. (b) The same
as in (a) but for average RMSE (see text for details). (c) and (d) are
the same as (a) and (b), respectively, but for temperature at 50cm
depth.

**Figure 6**: Same as in corresponding panels in Fig. 5 but for
difference between T~mm~ and T~sm~ measured at different local time
indicated in the legend of the abscissa.

**Figure 7**: Annual mean and standard deviation of soil temperatures at
five depths. Contour interval is 2˚C for annual mean temperature and
0.5˚C for standard deviation. Individual stations' values also are shown
by the scales marked in the lower-left corner of each panel.

**Figure 8**: Vertical soil temperature profiles for February, August,
and annual average.

**Figure 9**: Monthly mean and standard deviations of 10cm soil
temperature for January, April, July, and October. Individual stations'
values also are shown by the scales marked in the lower-left corner of
each panel.

**Figure 10**: (a) Trend in soil temperatures at 10cm depth, in
˚C(10yr)^-1^. The scales are marked in the lower-left corner of the
figure. (b) Temporal variations of 10cm soil temperature averaged over
the stations in (a). (c) Temporal variations of 100cm soil temperature
averaged over stations with record length longer than 30 years. The
dashed-lines in (b) and (c) show the trend of the variations.

**Figure 11**: Trend of average 10cm soil temperature variation for
1967-2002 (see text for details).