impacts of historic climate variability on seasonal soil frost in

Impacts of Historic Climate Variability on Seasonal Soil Frost in theMidwestern United States

TUSHAR SINHA

School of Life Sciences, Arizona State University, Tempe, Arizona

KEITH A. CHERKAUER AND VIMAL MISHRA

Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana

(Manuscript received 17 December 2008, in final form 30 November 2009)

ABSTRACT

The present study examines the effects of historic climate variability on cold-season processes, including

soil temperature, frost depth, and the number of frost days and freeze–thaw cycles. Considering the impor-

tance of spatial and temporal variability in cold-season processes, the study was conducted in the midwestern

United States using both observations and model simulations. Model simulations used the Variable In-

filtration Capacity (VIC) land surface model (LSM) to reconstruct and to analyze changes in the long-term

(i.e., 1917–2006) means of soil frost variables. The VIC model was calibrated using observed streamflow

records and near-surface soil temperatures and then evaluated for streamflow, soil temperature, frost depth,

and soil moisture before its application at the regional scale. Soil frost indicators—such as the number of frost

days and freeze–thaw cycles—were determined from observed records and were tested for the presence of

significant trends. Overall trends in extreme and mean seasonal soil temperature from 1967 onward indicated

a warming of soil temperatures at a depth of 10 cm—specifically in northwest Indiana, north-central Illinois,

and southeast Minnesota—leading to a reduction in the number of soil frost days. Model simulations in-

dicated that by the late-century period (1977–2006), soil frost duration decreased by as much as 36 days

compared to the midcentury period (1947–76). Spatial averages for the study area in warm years indicated

shallower frost penetration by 15 cm and greater soil temperatures by about 38C at 10-cm soil depth than in the

cold years.

1. Introduction

Many recent studies of observation records have sug-

gested that climate variability has increased and climate

change has accelerated in the recent years. For instance,

global and regional surface temperatures have increased

in the twentieth century, with the greatest warming oc-

curring in the last three decades (WMO 2006). The fre-

quency of extreme temperature and precipitation events

has also increased; specifically, the earth has experienced

11 of its top 12 warmest years in the last 12 yr (1995–2006)

based on the instrumental record available since 1850

(Alley et al. 2007). In the Northern Hemisphere, there

has been a significant reduction in mean snow cover

area (e.g., Dye and Tucker 2003; Mote et al. 2005), while

the maximum area covered by seasonally frozen ground

has decreased by about 7% since the 1900s, and spring

snowmelt has occurred earlier (Lemke et al. 2007; Stewart

et al. 2005; Hodgkins and Dudley 2006). Easterling (2002)

found that the number of days with subfreezing air tem-

perature has decreased by four days per year in the United

States during the period of 1948–99, while in recent

decades the frost-free season based on air temperature

has started 11 days earlier in the northeastern United

States (Cooter and Leduc 1995). The air temperature

frost-free season length has increased more in the west-

ern United States than in the eastern United States

(Easterling 2002; Kunkel et al. 2004). While most of the

studies were dedicated to studying the effect of climate

variability at larger scales (Easterling 2002; Kunkel

et al. 2004; Frauenfeld et al. 2007), fewer studies have

concentrated on regional scales (Cooter and Leduc 1995;

Kling et al. 2003). Cold-season processes are associated

Corresponding author address: Tushar Sinha, School of Life

Sciences, Arizona State University, P.O. Box 874501, Tempe, AZ

85287-4501.

E-mail: [email protected]

VOLUME 11 J O U R N A L O F H Y D R O M E T E O R O L O G Y APRIL 2010

DOI: 10.1175/2009JHM1141.1

� 2010 American Meteorological Society 229

with a high degree of spatial (e.g., northern Minnesota

versus southern Indiana) and temporal (e.g., onset–thaw

of soil frost) variability in the midwestern United States,

which underscores the need for a study dedicated to this

region.

Climate variability and climate change may directly

influence cold-season variables. For instance, winter soil

temperature is directly governed by the increase in

both air temperature and precipitation—mostly snow-

fall (Zhang et al. 2001, 2003). Hardy et al. (2001) sug-

gested that the shifting of regional climate toward lower

snow accumulations and shorter durations of snow on

the ground will result in deeper soil freezing and more

days with soil frost in temperate forests. Recently, an

observational study found that decreasing snow cover

has led to cooler soil temperatures in northern Indiana

(Sinha and Cherkauer 2008). However, increases in

frozen ground are short-term effects of limited spatial

extent (Zhang 2005), as increasing air temperatures

eventually overwhelm changes in insulation due to the

snowpack. For instance, although winter air tempera-

ture increased by 48–68C at Irkutsk, Russia, during the

past century, the soil temperature has increased by up to

98C because of an increase in early winter snowfall and

early snowmelt in spring (Zhang et al. 2003). Thus,

changes in cold-season soil temperatures and its derived

variables, such as the number of soil frost days, are influ-

enced by changes in air temperature and winter precipi-

tation as snow.

Historic observations in the midwestern United States

indicate that climate variability has increased during

the past century, which is likely to reinforce spatial–

temporal variability in regional cold-season processes.

The northern regions of the Midwest have warmed by

more than 28C, whereas the southern portions of the

Ohio River Valley have cooled by about 0.58C during

the past century (Easterling and Karl 2001). This region

has also experienced an increase in annual precipitation

by up to 20% with about double the frequency of heavy

precipitation events in the latter part of twentieth cen-

tury than in the early part of the century (Easterling and

Karl 2001; Kunkel et al. 1998). In addition, the mid-

western United States experiences uncertainties in the

frequency and severity of cold-season storms that affect

flooding in late winter and early spring, the number of

soil frost days and its time of occurrence, and soil tem-

peratures. Therefore, we addressed the following ques-

tions in the present study: (i) How significant were the

trends–changes associated with cold-season variables

(soil frost, number of frost days and freeze–thaw cycles)

in the midwestern United States? (ii) How has climate

variability influenced regional cold-season variables in

the past century? (iii) How sensitive were the responses

of cold-season variables during the extreme climatic

years (e.g., warm–cold and wet–dry)? We addressed

these questions by integrating observations and model-

ing forced with long-term meteorological observations.

2. Background

To determine how climate variability affects soil frost,

cold season descriptive variables were determined from

both observed and simulated records. The selected sim-

ulated soil frost variables were calibrated and evaluated

with observations before being used to extend the time

series for the past century and the entire study area using

the Variable Infiltration Capacity (VIC) model.

a. VIC model

The VIC model (Liang et al. 1994, 1996; Cherkauer

and Lettenmaier 1999) is a large-scale hydrologic model

that has been used for large watersheds (.10 000 km2)

and has performed well under different climatic re-

gimes (e.g., Liang et al. 1996; Wood et al. 1997; Lohmann

et al. 1998a,b; Nijssen et al. 2001a,b; Cherkauer and

Lettenmaier 2003; Bowling et al. 2003a,b). The details of

the VIC model are described in Liang et al. (1994, 1996)

and Cherkauer and Lettenmaier (1999), and only a brief

description of the details is provided here. Distinguish-

ing characteristics of the model include its represen-

tation of the spatial subgrid variability of infiltration

through the Variable Infiltration Capacity curve, and it

use of a nonlinear Arno parameterization for baseflow

recession (Abdulla et al. 1999). The horizontal land sur-

face is classified into any number of user-defined vege-

tation classes based upon the fraction of the grid cell it

occupies. The model can be driven by daily meteorologi-

cal variables, and it predicts surface temperature, evapo-

transpiration, runoff, snow water equivalent (SWE), and

other variables for each grid cell. Runoff and baseflow

can be routed to the basin outlet from each grid cell

using a stand-alone routing model developed by Lohmann

et al. (1998a,b).

Updates to the representation of cold-season pro-

cesses in the VIC model are detailed in Cherkauer and

Lettenmaier (1999) and Cherkauer et al. (2003), though a

brief description is provided here for clarity. Cherkauer

and Lettenmaier (1999) developed a frozen soil algo-

rithm that solves for thermal fluxes at a user-defined

number of soil nodes through the soil column and pre-

dicts soil water and ice content for each soil layer. In

addition, the moisture fluxes are adjusted by the pres-

ence of ice. Calculating fluxes using both ice and liquid

soil moisture makes the soil appear wet, which reduces

infiltration, while drainage is reduced when only liquid

soil moisture is used.

230 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 11

The model employs a two-layer snow algorithm, in

which a thin surface layer is used to estimate energy

exchange with atmosphere and the bottom layer acts as

storage to simulate deeper snowpacks (Cherkauer and

Lettenmaier 1999). The snow algorithm was originally

designed for use within the Distributed Hydrology Soil

Vegetation Model (DHSVM; Storck and Lettenmaier

1999). Cherkauer et al. (2003) added explicit represen-

tation of snow interception in the canopy to improve

representation of snow accumulation and ablation in

forested regions. Canopy intercepted snow is removed

through sublimation, mass release, and meltwater drip.

The model, as used for this study, assumed a spatially

uniform snow cover within each vegetation type, which

suggests an underestimation of snowmelt rates under

thin and discontinuous snow. It also assumes a new snow

density of 100 kg m23. Densification of older snowpacks

is based on Snow Thermal Model 89 (SNTHRM.89;

Jordan 1991). The snowpack surface albedo curve pa-

rameters are based on snow age and season (Bras 1990).

Maximum temperature when precipitation can occur as

only snow was set to 0.258C, whereas the minimum

temperature when precipitation can fall completely as

rain was defined as 20.258C.

The VIC model version 4.1.0 r3 was used in this study

with the finite difference soil thermal solution described

by Cherkauer and Lettenmaier (1999) using a constant

bottom boundary temperature. The thermal damping

depth was set to 10 m, and the model was allowed to spin

up for 2 yr.

b. Study area

The study area consists of the following six states in

the midwestern United States: Minnesota (MN), Iowa

(IA), Wisconsin (WI), Illinois (IL), Michigan (MI), and

Indiana (IN; Fig. 1). These regions were selected be-

cause the spatial and temporal variability in seasonal soil

frost is likely to vary with air temperature, which in-

creases from north to south, and precipitation, which

increases from the northwest to the southeast. This area

comprises parts of the upper Mississippi River basin, the

Ohio River basin, and the Great Lakes drainage basin.

The area consists of the rolling forested landscapes of

southern Illinois, and Indiana; a central region of the

relatively flat farmland that produces mostly corn and

soybean; and the evergreen and deciduous forests of

northern Wisconsin, Minnesota, and Michigan (Easterling

and Karl 2001). In winter, the region is influenced by cold

Arctic air masses, which move into the region, resulting in

frequent storms and windy conditions. The total annual

precipitation varies from as low as 640 mm in the western

Minnesota and Iowa to more than 1020 mm along the

Ohio River. Annual average temperatures vary from 2.758C

in the north to 158C in the south.

3. Methods and data

a. Observational data

1) DAILY SOIL TEMPERATURES

Data for soil temperatures were obtained from sites

that have collected at least 10 years of consistent soil

temperature at a depth of 10 cm. The longest period for

which consistent soil temperature data were available

was 1966 to 2006. Data since 1982 were obtained from

the National Climatic Data Center’s (NCDC) Summary

of the Day (SOD) dataset (available online at http://

www.ncdc.noaa.gov/oa/ncdc.html) and the Illinois State

Water Survey (WARM 2008). Data prior to 1982 were

available only as paper records, and they were digitized

from the Climatological Data for five of the six states

within the study domain (U.S. Department of Com-

merce 1966–1982). Maximum and minimum soil tem-

peratures at a depth of 10 cm were taken using a Palmer

dial-type temperature sensor, which has a precision of

FIG. 1. Map showing the locations of 1) selected sites that have

been collecting soil temperatures: (a) MOR, (b) WAS, and (c)

Lamberton (LAM) in MN; (d) Chatham (CHA) and (e) northwest

Michigan (NMI) in MI; (f) Hancock, WI (HAN); (g) DEC, (h)

AME, and (i) Burlington (BUR) in IA; ( j) Freeport (FRE), (k)

Urbana (URB), and (l) Orr (ORR) in IL; (m) WAN, (n) West

Lafayette (WLA), and (o) Dubois (DUB) in IN; and 2) calibrated

watersheds in the region: ANOKA–Mississippi River at Anoka,

MN; MINNR–Minnesota River at Jordan, MN; IOWAR–Iowa

River at Wapello, IA; CHIPR; GRAND–Grand River at Grand

Rapids, MI; ILLIR–Illinois River at Valley City, IL; and WABAS.

Stars represent streamflow gauging stations.

APRIL 2010 S I N H A E T A L . 231

0.58C (Schaal et al. 1981). All the measurements for

these sites were taken under a bare soil surface (refer to

the Appendix A). Data for the Illinois sites were col-

lected by the Illinois State Water Survey and represent

soil temperatures at 10 cm below a grass surface.

2) WEEKLY FROST DEPTH AND SNOW DEPTH

Weekly frost and snow depths were obtained from the

National Snow and Ice Data Center (NSIDC) for se-

lected sites in Minnesota during the two cold seasons

of 1975/76 and 1979/80 for comparison of observed and

simulated variables. The frost depths were measured us-

ing frost tubes (Haugen and King 1998).

3) MONTHLY STREAM FLOW DATA

Monthly stream flow data were obtained since 1982

for selected basins monitored by the U.S. Geological

Survey (USGS) based on criteria described in section 3e.

These data were useful for model calibration and eval-

uation. Several gauged watersheds within the study do-

main were selected for calibration (Fig. 1).

b. Model input data

1) SOIL DATA AND VEGETATION DATA

Soil parameters were processed from a multilayer soil

characteristics dataset at a 1-km resolution for the con-

terminous United States (CONUS-SOIL; Miller and

White 1998). The CONUS-SOIL data are based on the

original State Soil Geographic database (STATSGO).

Land use parameters were acquired from the Land Data

Assimilation System (LDAS) project (Mitchell et al.

2004; Maurer et al. 2002). Additional parameters re-

quired for the frozen soil algorithm were taken from

Mao and Cherkauer (2009). The land use map was pro-

cessed using vegetation classifications based on 1-km

resolution satellite data from 1992 to 1993 (Hansen et al.

2000). These data have been gridded to a resolution

of 1/8th-degree latitude and longitude for the contermi-

nous United States. Monthly leaf area index (LAI)

data for each vegetation type and each 1/88 grid cell have

been obtained from Myneni et al. (1997). Vegeta-

tion library parameters were obtained from Mao and

Cherkauer (2009).

2) EXTENSION OF DAILY GRIDDED

METEOROLOGICAL DATA

Long-term records of daily precipitation and maxi-

mum and minimum air temperature from 1915 to 2006

were obtained from the NCDC’s SOD dataset and

gridded at a spatial resolution of 1/88 latitude by longi-

tude (about 14 km 3 11 km) for the midwestern United

States using the methodology of Hamlet and Letten-

maier (2005). This gridding methodology is applicable

for long-term trend analysis, as it accounts for changes

in meteorological station location and temporal in-

consistencies, which otherwise can lead to spurious

trends in precipitation and temperature. Hamlet and

Lettenmaier (2005) found that despite acceptable stream-

flow simulations through the VIC model for recent de-

cades, streamflow prior to 1950 was strongly biased in

comparison with that after 1950 due to temporal hetero-

geneities in the driving data. Hamlet and Lettenmaier

(2005) suggested a methodology to make temporal cor-

rections based upon monthly U.S. Historical Climatology

Network (HCN) records and topographic corrections

based upon monthly precipitation maps produced by

the Parameter-elevation Regressions on Independent

Slopes Model (PRISM). This methodology has been

tested and verified for the state of California and has

now been applied to the midwestern United States start-

ing in 1915 for this study. Daily wind speeds since 1949

were obtained from the National Center for Atmospheric

Research–National Centers for Environment Prediction

(NCAR–NCEP) reanalysis project (Kalnay et al. 1996),

whereas the daily wind speed prior to 1948 was derived

from post-1949 wind climatology as described in Hamlet

and Lettenmaier (2005).

c. Data processing

1) SOIL TEMPERATURE

Daily observed soil temperature data from selected

stations were subjected to quality-checking procedures

to identify missing dates, inconsistencies, and unrea-

sonably high diurnal ranges in soil temperatures (Sinha

and Cherkauer 2008). The erroneous data were replaced

with ‘‘no data’’ values for making comparisons. Years

missing more than 10% of observed values during the

cold season were eliminated to avoid any biases in trend

analysis. Cold-season statistics were computed for a pe-

riod from September through May, which incorporates

the earliest observed occurrence of soil frost and the

latest observed thaw at all the sites in the region. The

following cold-season variables were estimated from

the daily time series of maximum and minimum soil

temperatures (Tsoil) at a depth of 10 cm as described in

Sinha and Cherkauer (2008):

(i) Seasonal mean maximum Tsoil is determined by

calculating the mean of daily maximum Tsoil data

series during the cold season.

(ii) Seasonal mean minimum Tsoil is determined by

calculating the mean of daily minimum Tsoil data

series during the cold season.


(iii) Extreme minimum temperature is determined by

calculating the extreme value of daily minimum Tsoil

data series during the cold season.

(iv) Annual soil frost days are estimated by counting

the number of days when the soils are frozen dur-

ing the cold season.

(v) Annual freeze–thaw cycles are estimated by count-

ing the number of times soil temperature changes

between frozen and thawed states during the cold

season. Details provided in Sinha and Cherkauer

(2008) are summarized for clarity. A freeze event

occurs when at least daily minimum Tsoil on the

first day is at or below 08C and both daily maximum

and minimum Tsoil on the subsequent day are also

at or below 08C. Similarly, a thaw event occurs

following a freeze event when both maximum and

at least one minimum Tsoil on two consecutive days

are greater than 08C. A freeze event, followed by

a thaw event at a given soil depth is defined as one

freeze–thaw cycle. A threshold value of 20.258C

(instead of 08C in the case of observed data) was used

to estimate freeze–thaw cycles from model-simulated

daily maximum and minimum soil temperatures.

This step is useful to make similar comparisons from

frost variables computed from observed soil tem-

peratures. The observed soil temperature sensor

has a precision of 0.58C, which implies that any

fluctuation of less than 0.258 around 08C will not be

measured by the sensor.

(vi) Onset day of soil frost is determined by computing

the first day since 1 September when soil is frozen.

(vii) Last thaw day is determined by computing the last

day of soil frost since 1 September.

2) FROST DEPTH

Among the stations measuring frost depth in the study

region, only data from the Minnesota sites passed quality

checks for consistency and continuity and were included

in this analysis.

3) CLIMATE VARIABILITY IN THE GRIDDED

METEOROLOGICAL FORCING DATA

Climate variability for the study area was assessed

by estimating extreme temperature and precipitation

events during the cold season derived from the daily

gridded meteorological forcing data for precipitation

and air temperature. Mean air temperatures were cal-

culated by averaging daily maximum and minimum

air temperature over the cold season, from September

through May, for the entire region. Seasonal anomalies

were normalized for both precipitation and average air

temperature (Fig. 2) using

Xnormalized

5(X

t�X

mean)

(Xmax�X

min)

, (1)

where Xnormalized is the normalized time series, Xt is the

time series for the cold season, Xmean is the mean, and

Xmax is the maximum value and Xmin is the minimum

value of the time series. A threshold value of 0.3 was

selected to define multiple extreme events that occurred

during the past century to distinguish into warm and wet

years based upon air temperature and precipitation,

respectively (Fig. 2). This means that the years with

values greater than 0.3 were considered as warm based

on average air temperature and considered as wet based

upon precipitation. Similarly, a threshold of 20.3 was

selected to identify cold and dry years. Only one year—

1917—met the criteria of a cold year, whereas other

extreme events were spread over multiple years. Sea-

sonal statistics were analyzed to study the effects of

climate variability on selected soil frost indicators.

d. Observed data analysis

Data analysis was performed in two stages: 1) data

series were tested to identify the presence of any sig-

nificant autocorrelations, which is the correlation of a

variable with itself over successive time intervals, and 2)

data series were tested to identify significant trends in

soil frost variables using the Mann–Kendall test. The

presence of autocorrelation increases the possibility of

detecting trends even if they are absent (Rao et al. 2003).

FIG. 2. Cold-season normalized anomalies of gridded meteoro-

logical forcing data from 1917 to 2006 averaged over the entire

study area for (a) precipitation (PRCP) and (b) mean air temper-

ature. The continuous line indicates a significant trend; the dashed

line indicates a nonsignificant trend. The Mann–Kendall slope of

trend line is indicated.


In the case of significant autocorrelation in a data series,

the effective number of independent observations de-

creases and thus accounting for it reduces the chances of

detecting spurious trends.

Data series with more than 10 years of observations

remaining, after data quality checks, were tested for

significant trends using the Mann–Kendall test, as it is

applicable for 10 or more observations. This test has

been widely used for trend analysis in hydrologic studies

and was applied in a similar form used by Hirsch et al.

(1992). The details of the test were described in Sinha

and Cherkauer (2008). In cases of significant autocor-

relation, a modified Mann–Kendall test (Hamed and

Rao 1998) was used that calculates the autocorrelation

between the ranks of the data after removing the ap-

parent trend, as described in Sinha and Cherkauer (2008).

The trend analysis was performed for the soil frost var-

iables described in section 3c for the following periods

to maximize the number of stations measuring consis-

tent soil temperature at a depth of 10 cm: (i) 1967–2006,

(ii) 1974–2006, (iii) 1983–2006, and (iv) 1991–2006.

e. Modeling Strategy

The VIC model was calibrated using observed stream-

flow from 1982 to 1992 and seasonal average daily soil

temperature at a depth of 10 cm. The model was evalu-

ated for streamflow from 1993 to 2003 as well as aver-

age seasonal soil temperature, soil moisture (three sites

in Illinois), and frost depth (three sites in Minnesota)

based upon data availability. Direct comparisons of

trends in model-derived soil frost variables (such as soil

frost days and extreme seasonal daily Tsoil) have not

been made with those estimated from the observed data

at selected sites, as trends are difficult to identify in model-

derived variables. However, the calibrated model was used

to compare the general patterns of selected soil frost

variables using Spearman rank correlation coefficient

r before it was used to quantify long-term changes in

cold-season parameters for the study domain.

1) CALIBRATION AND EVALUATION OF

STREAMFLOW

Seven major watersheds were selected that were min-

imally influenced by major dams and reservoirs (Fig. 1).

Since land use was based upon 1992/93 satellite data, the

10 years prior to this period (1982–91) were selected as

the calibration period, whereas the subsequent 10 years

(1994–2003) were selected as the evaluation period. This

minimized the effects on streamflow due to land use

change. The VIC model was calibrated on a watershed

basis using observed streamflow measurements. Cali-

bration was performed to improve the agreement of ob-

served and simulated discharge volumes and the shape of

hydrographs. A single set of model parameters that

control infiltration, runoff, and baseflow were obtained

for the entire study domain, to minimize the effect of

parameters change between watersheds on any spatial

analysis. Figure 3 shows the mean monthly hydrographs

for the seven watersheds for the entire length of the

calibration and validation periods.

The following accuracy indicators were used to eval-

uate the model performance:

d Nash–Sutcliffe efficiency (E) as proposed by Nash and

Sutcliffe (1970) was calculated as (Krause et al. 2005):

E 5 1��

n

i51(O

i� P

i)2

�n

i51(O

i�O)2

, (2)

where Oi and Pi are the observed and simulated

discharge at month i, respectively; O is the mean of

observed discharge, and n is the number of monthly

observations.

d Index of agreement (d) was proposed by Willmott

(1981) to overcome the insensitivity of E to differ-

ences in the observed and predicted means and vari-

ances (Legates and McCabe 1999). It was defined as

the ratio of the mean square error and the potential

error (Gaile and Willmott 1984) and was calculated as

(Krause et al. 2005)

d 5 1��

n

i51(O

i� P

i)2

�n

i51(jP

i�Oj1 jO

i�Oj)2

. (3)

The E and the d were applied to monthly streamflows for

the period 1982–2003 (Fig. 3). Values of E varied from

0.27 for the Chippewa River at Durand, WI (CHIPR), to

0.84 for the Wabash River at Mt. Carmel, IN (WABAS),

and the values of d varied between 0.81 and 0.96 for the

CHIPR and WABAS, respectively.

2) CALIBRATION AND EVALUATION OF SOIL

TEMPERATURES

Fifteen sites, with soil temperature observations at

10 cm soil depth, were selected to calibrate and evaluate

model performance. The key soil parameters—such as

Ds (which is the fraction of maximum velocity of base-

flow Dsmax from the lowest soil layer where nonlinear

baseflow begins), parameter describing the variation of

saturated hydraulic conductivity with soil moisture in

the three soil layers, and average soil temperature used


as the bottom boundary for soil heat flux solutions—were

adjusted during the calibration process. The Spearman

rank correlation coefficient r was selected to compare

relationships between simulated and observed soil frost

variables, because it does not require any assumption

about the frequency distribution of the variables; instead,

it uses the ranks of the data to estimate the correlation

between the variables. It is computed using

FIG. 3. Comparison of monthly observed (Obs) and simulated (Sim) streamflow from 1982 to

2003 at the seven calibrated watersheds. Shown are E (%) and d (%) between observed and

simulated streamflow over the entire period.


r 5

�n

i51R(x

i)R(y

i)� n

n 1 1

2

� �2

�n

i51R(x

i)2� n

n 1 1

2

� �2" #0.5

�n

i51R(y

i)2 � n

n 1 1

2

� �2" #0.5

,

(4)

where R(x) and R(y) are the ranks of a pair of variables

(x and y), with each containing n observations.

4. Results and discussion

a. Observational data analysis for soil frost variables

Trends in observed soil temperature records were

assessed for four periods to maximize the length of re-

cord, as well as the number of active stations, since the

majority of stations came online in the 1980s and 1990s.

Trend analysis indicated that a higher percentage of

stations show statistically significant trends in soil frost

variables for the period of 1967 to 2006 than for any

other period (Table 1). However, this period also had

the fewest available stations. The percentage of stations

showing significant trends in soil frost variables varied

over the different periods considered. Most of the sig-

nificant trends were observed in extreme minimum Tsoil

and seasonal mean maximum Tsoil for all the periods

under consideration. Interestingly, among significant

trends, the magnitude of the Mann–Kendall slope for

extreme minimum Tsoil was higher than the correspond-

ing slopes in mean seasonal Tsoil for all the periods

(Tables 2–5), indicating greater changes in extreme min-

imum Tsoil. The greatest increase in extreme minimum

Tsoil of 0.688C since 1983 as well as the largest decrease

in mean maximum Tsoil (20.528C since 1991) were ob-

served at Morris, MN (MOR; Tables 4 and 5), indicat-

ing that variability in cold-season soil temperatures has

decreased. This site also experienced the greatest rate

of increase in annual number of soil frost days from

1991 to 2006.

During the periods since 1967 and 1974, trends that

were statistically significant indicated a warming in ex-

treme and mean seasonal Tsoil—specifically in northwest

Indiana, north-central Illinois, and southeast Minnesota—

leading to a reduction in the number of soil frost days

[Figs. 4(1a)–4(4b)]. However, periods since 1983 and 1991

indicated a decrease in soil temperatures at a limited

number of sites, such as Morris, where soil frost days in-

creased with time. Significant negative trends in soil tem-

peratures were identified only for the shorter duration

records. For instance, Chatham, MI (CHA), and Farmland,

IN (FRM), have experienced a reduction in extreme mini-

mum Tsoil, with Farmland, IN, also experiencing a reduction

in seasonal mean Tsoil from 1983 to 2006. The mixture of

statistically significant positive and negative trends was

indicative of the dominance of localized controls over

climate signals on seasonal soil frost variables for shorter

periods (i.e., since 1983 and since 1991, respectively).

Higher slopes with significant trends since 1991 in-

dicate that for this shorter period, soil frost variables

have generally experienced more rapid change than in

the past (Table 5). In a few cases, when the trends were

not significant, the direction of change was sensitive

to the period in consideration. For example, at Waseca,

MN (WAS), the direction of change reversed for the

number of soil frost days when computed since 1974 to

that computed since 1983 [Figs. 4(4b) and 4(4c)]. The

1970s were characterized by cold winters in the mid-

western United States, which may have caused greater

soil frost days during early 1970s. Despite the increase

in soil frost days since 1983, the overall direction of

change since 1974 indicated a decrease at WAS, where

the changes were statistically insignificant.

b. Comparison of observations withsimulated variables

Calibration and evaluation of the VIC model typically

focuses on streamflow statistics; however, to use the

model for analysis of soil frost processes, additional

evaluations of soil temperature and frost depth were

performed. Fifteen sites were selected for evaluation of

observed and simulated cold-season variables (Fig. 1).

For the evaluations, all sites, except for the three in

Iowa, made use of daily maximum and minimum Tsoil

data to compute seasonal statistics. The Iowa sites re-

corded only a single daily temperature measurement

taken at the time of observation; hence, these sites were

unable to capture the daily extreme values observed by

the other sites. This implies that the seasonal extremes

and frost dynamics were underrepresented at those sites.

Cold-season mean daily Tsoil comparisons indicated

that the VIC model generally captured overall patterns

TABLE 1. Percentage of stations with statistically significant

trends in soil frost variables for different periods at a significance

level of 5%. The number of stations available for each period is

provided in parentheses.

Time period

1967–2006 1974–2006 1983–2006 1991–2006

Variable (5) (8) (21) (36)

Mean max Tsoil (8C) 80 25 29 08

Mean min Tsoil (8C) 60 25 19 14

Extreme min Tsoil

(8C)

60 25 29 25

Frost days 40 13 29 14

Frost–thaw cycles 40 25 19 19


of the observed daily Tsoil at most of the selected sites

(Fig. 5); however, there were differences in absolute

values. Here r between the observed and simulated

mean daily Tsoil ranged from 0.75 for Dubois, IN (DUB),

to 20.27 for MOR, with 7 out of the 12 sites having r $

0.5 (excluding the three IA sites because of different

observed Tsoil format). The general patterns in soil

temperature were captured by the model at most sites,

except at MOR and Decorah, IA (DEC), where the

Spearman rank correlations were negative. Since the

Iowa sites had a different format for the observed Tsoil,

the simulated mean daily Tsoil were expected to be

colder than observed Tsoil recorded at a specific time of

a day. The Morris, MN, site, on the other hand, expe-

rienced the highest variability in observed Tsoil among

all the sites, as described earlier, resulting in larger

differences in simulated and observed Tsoil. The dis-

crepancy in absolute values between observed and

simulated Tsoil may be due to differences in meteoro-

logical data and soil data at the selected sites. The

regional simulations were conducted using the 1/8th-

degree gridded meteorological forcing data, which

were spatially interpolated to the center of the grid cell

using neighboring meteorological stations and may

differ from the actual observations of daily precipitation

and air temperatures at selected point locations. The

model further disaggregated daily meteorological data to

subdaily scale, which affected snowfall timings. In ad-

dition, the model assumes a maximum (minimum)

temperature of 0.258 (20.258C) when precipitation falls

as snow (completely as rain). This may cause differences

between observed and simulated snow depth, resulting

in changes in cold-season daily average Tsoil. Further-

more, the VIC model simulations make use of average

soil conditions for the grid cell, which may not capture

site-specific conditions despite forcing the VIC grid cell

with the same vegetation type as the observational site.

Overall, the VIC-simulated daily Tsoil were deemed

acceptable, as the observed patterns were simulated at

more than half of the selected sites and thus the model

was used as an analysis tool to estimate long-term

changes in soil frost variables.

Simulated snow depths and soil frost depths were

evaluated with observations at three sites in Minnesota

using observational data for the cold seasons of 1975/76

and 1979/80 (Fig. 6). Snow depth and frost depth were

well simulated for 1979/80 at the Morris and Waseca sites,

whereas the underestimation of snow depth at Lamberton

resulted in deeper soil frost penetration [Figs. 6(1c)–6(3d)].

Overestimation of snow depth at Waseca for the 1975/76

season led to an underestimation of frost depth, because

of an increase in the insulation effect of snow at the

ground surface. A smaller overestimation of snow cover

at the Lamberton site during 1975/76 did not have a

significant effect on soil frost penetration, which was

slightly deeper than observed [Fig. 6(2b)]. Since the dis-

tribution of precipitation between rain and snow rela-

tive to air temperature is dependent on geographical

locations, snow depth is difficult to simulate (Fassnacht

and Soulis 2002). Additionally, the VIC model is re-

constructing subdaily precipitation and air tempera-

ture from daily values averaged for an 1/8th-degree grid

cell, which further affects the occurrence of snow. With

these caveats in place, the simulation of snow and soil

frost depths by the calibrated VIC model were deemed

satisfactory.

TABLE 2. Stations showing significant trends in soil frost variables from 1967 to 2006. The values indicate the Mann–Kendall slope.

Numbers in parentheses indicate the p value tested for significance; p , 0.05 is statistically significant and N indicates the absence of

significant trend at a 5% significance level.

Stations Mean max Tsoil Mean min Tsoil Extreme min Tsoil Frost days Frost–thaw cycles

CHA N N N N 0.12 (0.04)

WAS 0.06 (0.003) 0.10 (,0.001) 0.23 (0.004) 20.84 (0.005) N

URB 0.06 (0.003) 0.04 (0.003) 0.12 (0.003) N N

WLA 0.07 (,0.001) 0.09 (,0.001) 0.18 (0.05) 21.38 (,0.001) 20.04 (,0.001)

DUB 0.08 (,0.001) N N N N

TABLE 3. Stations showing significant trends in soil frost variables from 1974 to 2006. The values indicate the Mann–Kendall slope, and the

number in parentheses indicate the p value tested for significance; N same as in Table 2.


MOR N N 0.36 (0.006) N N

WAS N 0.04 (0.030) N N N

WLA 0.09 (,0.001) 0.12 (,0.001) 0.19 (0.003) 21.60 (0.001) 20.06 (0.044)

DUB 0.11 (,0.001) N N N 20.09 (0.004)


As a final evaluation, the calibrated model was used to

estimate the same soil frost indicators computed from

observed soil temperature records. Spearman correlation

coefficients between the annual numbers of soil frost

days estimated from observations and simulations ranged

from 20.05 at Wanatah, IN (WAN), to 0.86 at Ames, IA

(AME), with 5 out of 15 sites indicating r $ 0.50 (Fig. 7).

Three sites had r # 0, indicating significant differences

between observed and simulated soil frost days, most

likely related to the non-site-specific meteorology and

calibration mentioned previously. Generally, the model

predicted a higher number of soil frost days than ob-

servations. In contrast, there were fewer fluctuations

in freeze–thaw events estimated from simulated Tsoil

compared to those estimated from observed Tsoil. This

resulted in r # 0 for 4 out of 12 sites (excluding IA sites)

for the number of freeze–thaw cycles (Fig. 8). It is harder

to predict freeze–thaw cycles than the number of soil

frost days, as soil temperature sensors may not observe

small fluctuations around 08C because of their limited

precision and unknown accuracy, especially around 08C.

To study the effects of climate variability on temporal

and spatial variability in cold-season processes, simula-

tions for the entire period (since 1917) as well as ob-

served daily soil temperature were sorted into cold and

warm and wet and dry years as described in section 3c.

Because only one year (1917) met the criteria of cold

year, and it did not overlap with observations, years 1978

TABLE 4. Same as Table 3, but for 1983–2006.


MOR N N 0.68 (0.025) 2.18 (0.048) N

LAM N N N 2.88 (0.045) N

CHA N N 20.14 (0.040) N N

DEC 0.09 (0.032) N N N N

NAS N 0.13 (0.004) N 22.88 (0.033) N

CAS 0.16 (0.004) N N N N

AME N N 0.37 (0.009) N N

ATL N N N 20.22 (0.017) N

BUR N 0.21 (,0.001) 0.19 (,0.001) 20.08 (,0.001) 20.07 (,0.001)

WLA 0.08 (0.015) 0.10 (0.001) N N N

FRM 20.14 (0.002) 20.16 (,0.001) 20.20 (0.011) 2.00 (0.005) 0.14 (0.005)

OOL N 0.10 (0.003) N N N

DUB 0.13 (0.002) 20.06 (0.019) 0.32 (0.003) 21.00 (0.004) 20.21 (,0.001)

TABLE 5. Same as Table 3 but for 1991–2006.


MOR 20.52 (0.004) N N 9.00 (0.004) N

DEC N N N 7.8 (0.038) N

NAS N N N N 0.50 (0.011)

KAN N N N N 0.38 (0.021)

AME N N N N 20.60 (0.047)

ATL N N 0.44 (0.027) N 20.33 (0.035)

BUR N N 0.11 (,0.001) N N

FRE N N 0.25 (0.038) 20.25 (0.014) N

STC N 0.16 (0.006) 0.50 (0.020) N N

PEO 0.15 (0.020) N N N N

BON N N N 1.86 (0.033) N

ORR N 0.12 (0.019) N N N

SPR N 0.09 (0.031) N N N

BEL N N 0.14 (0.022) N N

FAI N 0.13 (0.011) N N N

INA 20.19 (0.006) N N N N

WAN N N N 23.00 (0.030) N

WIN N N 20.40 (0.022) N N

FRM N 20.15 (0.022) 20.35 (0.018) N 0.33 (0.018)

OOL N N 0.14 (0.031) 0.14 (0.027) N

DUB N N 20.43 (0.037) 0.33 (0.011) N


FIG. 4. Observed trends in the cold-season variables showing (1) mean maximum Tsoil (8C), (2) mean minimum Tsoil

(8C), (3) extreme minimum Tsoil (8C), (4) number of frost days, and (5) number of freeze–thaw (F/T) cycles for

the following periods: (a) 1967–2006, (b) 1974–2006, (c) 1983–2006, and (d) 1991–2006. Symbols represent station

locations. Filled triangles indicate that trends are significant at the 5% significance level and empty triangles indicate

nonsignificant trends. Triangles show the direction of trends.


and 1979 were used to represent cold years for com-

parisons. The differences between observed and simu-

lated Tsoil were within 15% for at least half the sites for

cold, wet, and dry climatic divisions (Table 6), whereas

the model did not performed as well in warm years.

Differences for warm years were still within 20% for five

out of eight sites; therefore, the model performance was

deemed acceptable to capture the long-term means in

daily Tsoil for all climatic divisions and was used as an

analysis tool to quantify climate variability effects.

c. Cold-season sensitivity to snow cover and airtemperature under extreme conditions

To understand the role of snow cover and air tem-

perature on soil temperature and soil frost development,

observations of air temperature and precipitation since

1917 were used to sort simulated variables into warm,

cold, wet, and dry years as described in section 3c. Time

series of a representative year for each climate type were

compared with air temperature for four locations across

the study domain (Fig. 9). For all sites and climate con-

ditions, once the soil was frozen, daily changes in soil

temperature were smaller than for air temperature; when

snow exceeded a threshold depth [;(5–10 cm)], changes

in daily near-surface soil temperature became very small.

This is in agreement with the findings of Nikol’skii et al.

(2002), who found that the top 10 cm of snow cover had

a strong insulation effect on soil surface temperature.

The minimum value of soil temperature is strongly cor-

related to the timing of snow accumulation. When snow

accumulates soon after the occurrence of freezing air

temperatures [e.g., Figs. 9(2c) and 9(2d)] and remains

throughout the winter, minimum soil temperatures are

close to 08C. If snow accumulation occurs later, then

FIG. 5. Cold-season time series of observed and simulated mean daily soil temperatures at a depth of 10 cm for the

following sites: (a) MOR, (b) LAM, (c) WAS, (d) CHA, (e) NMI, (f) HAN, (g) DEC, (h) AME, (i) BUR, ( j) FRE,

(k) URB, (l) ORR, (m) WAN, (n) WLA, and (o) DUB. Observations at the three sites in IA (DEC, AME, and

BUR), unlike the other states, are for a specific time during a day and do not provide information about daily

extremes.


FIG. 6. Daily time series of observed (red) and simulated (black) variables (cm) snow (snow

depth) and frost depth (F. Depth) and observed thaw depth (blue, in cm) at the following sites

in MN: (1) MOR, (2) LAM, and (3) WAS for the two cold seasons of 1975/76 and 1979/80.


soil temperatures will be colder even under warm [e.g.,

Fig. 9(2a)] or wet [Fig. 9(3c)] climate conditions. When

little to no snow is present during the winter, air tem-

perature is the dominant control on soil temperature

and freezing [e.g., Fig. 9(1d)]; however, snow cover clearly

is the dominant controlling factor, as the colder tem-

peratures during a wet winter [Fig. 9(1c)] do not result

in colder soils than a slightly warmer winter without

snow [Fig. 9(1d)].

d. Spatial and temporal analysis

Subsequent to calibration and evaluation, the VIC

model was used to extend the spatial and temporal

analysis of soil frost. Model simulations starting in 1917

indicated that the annual number of soil frost days de-

creased significantly from north to south in the region

[Fig. 10(1a)], with the average number of soil frost days

ranging from 160 in northern Minnesota to about 4 days

in southern Illinois and Indiana. The average number of

freeze–thaw cycles at a depth of 10 cm was highest in

northern Indiana and lower Michigan, the central parts of

Wisconsin and Illinois, and in southern Iowa [Fig. 10(2a)].

The central regions of Wisconsin and lower Michigan

were under agricultural or mixed use conditions based

upon the land use data used for this study. Agricultural

lands are more susceptible to soil frost than forests be-

cause without a canopy, agricultural fields are more ex-

posed to cold air and surface residues. The lack of a

canopy does lead to greater accumulation of snow in

agricultural fields; however, that snow is more likely to be

redistributed by wind, and it is fully exposed to the sun so

that it melts more quickly. Thus agricultural fields were

more exposed and left with less insulation for longer

periods of time than forested environments, which re-

sulted in higher numbers of freeze–thaw cycles. In con-

trast, western Minnesota and northern Wisconsin were

mostly forested, which reduced the depth of ground snow

but maintained it over longer periods of time. Snow cover

insulates the soil surface from changes in air temperature

and thus reduces the frequency of freeze–thaw cycles.

Furthermore, in the northern regions of the study do-

main, the presence of more persistent below-freezing

FIG. 7. Same as Fig. 5, but for annual time series of number of soil frost days estimated from observed and simulated

soil temperatures at a depth of 10 cm.


temperatures resulted in fewer freeze–thaw cycles. The

number of freeze–thaw cycles was also limited in south-

ern Illinois and Indiana because the warmer air temper-

atures resulted in fewer days with soil frost and thus fewer

opportunities to complete freeze–thaw cycles.

The timing of the onset of soil frost and last spring thaw

both varied across the study area. The earliest occurrence

of soil frost in the region, on an average, took place

around early November in northern Minnesota, whereas

in the southern regions frost occurred around early to

mid-December [Fig. 10(3a)]. The last thaw in spring

occurred around early February in the southern regions

and around the end of April in the northern regions

[Fig. 10(4a)].

To study temporal variability, changes in soil frost var-

iables were calculated between 30-yr groups represent-

ing early- (1917–46), mid- (1947–76) and late-century

(1977–2006) periods. By studying the differences during

early-century and midcentury periods with respect to the

late-century period, systematic biases—such as the effects

of annual variations that are not generally well repre-

sented in the model-simulated variables—were removed.

Changes in the number of soil frost days in the late

century with respect to the middle and early century in-

dicated a decrease in soil frost days in most of the

FIG. 8. Same as Fig. 5, but for annual time series of number of F/T cycles estimated from observed and simulated soil

temperatures at a depth of 10 cm.

TABLE 6. Obs and Sim average cold-season daily soil tempera-

ture during extreme climatic years (warm, cold, wet, and dry) for

sites measuring soil temperatures (8C) since 1970. Values in bold

indicate that the difference between Obs and Sim soil temperatures

were within 15% of each other.

Warm (8C) Cold (8C) Wet (8C) Dry (8C)

Site Obs Sim Obs Sim Obs Sim Obs Sim

MOR 3.06 3.99 20.58 3.88 2.19 3.04 2.97 4.02

WAS 6.80 7.36 5.3 4.44 5.13 4.75 5.34 5.63

LAM 6.99 4.60 2.56 4.85 5.64 4.84 6.77 5.57

CHA 1.53 3.80 4.90 5.16 4.64 4.02 4.74 4.73URB 10.63 9.22 3.88 7.62 7.72 8.78 9.02 8.52

WAN 9.91 8.23 8.17 7.91 6.39 7.97 8.96 7.03

WLA 10.11 8.63 6.78 6.55 8.0 8.06 7.56 7.37DUB 11.39 9.41 9.26 7.95 8.56 8.62 10.05 8.23


northern and central regions while an increase was ob-

served in southern Illinois [Fig. 10(1b)] and the upper

peninsula of Michigan [Fig. 10(1c)]. The decrease in soil

frost was by as much as 24 days in lower Michigan and 18

days in southern Minnesota, southern Wisconsin, and

the northern regions of Illinois and Indiana [Fig. 10(1c)].

Most of these regions are agricultural lands, which are

more exposed to changes in air temperature. Despite

these reductions, the number of freeze–thaw cycles was

unaffected in most of the region in the late century as

compared to the early-century period [Fig. 10(2b)]. In

comparison, by midcentury, regions in the north indi-

cated an increase or no change in freeze–thaw cycles

while regions in the south indicated a decrease [Fig. 10(2c)].

Increases in soil temperatures may have resulted in

more fluctuations around 08C in the north while pro-

viding fewer opportunities for soil frost development in

the south.

In the late-century period, fewer sites experienced an

earlier onset of frost in central and southern Illinois,

north-central Minnesota, and in the upper peninsula of

Michigan [Fig. 10(3c)]. During this period, both the first

occurrence of frost and the last thaw changed by as much

as 18 days [Fig. 10(4c)]. Interestingly, the area that dis-

played a delay in the onset day of soil frost between the

late- and early-century periods was larger than the re-

gion that experienced the same change between the late-

and midcentury periods [Figs. 10(3b) and 10(3c)]. These

sites were generally those where the number of soil frost

days decreased [Figs. 10(1b) and 10(1c)]. On the other

hand, the last day of thawing in the late-century period

occurred earlier in most of the region [Figs. 10(4b) and

FIG. 9. Daily values of observed air temperature (8C), simulated soil temperature (8C) and simulated snow depth (cm) in (1) northwest

MN (48.81258N, 96.18758W), (2) northern WI (46.06258N, 91.06258W), (3) southern IA (41.56258N, 94.06258W), and (4) southern IN

(38.81258N, 86.68758W), and the following cold seasons: (a) warm, (b) cold, (c) wet, and (d) dry. Simulated soil temperatures and snow

depth were used because of the lack of observations at the selected sites during extreme climatic years.


FIG. 10. Spatial plot indicating (a) annual average values since 1917, (b) late-century (1977–2006) minus early-

century conditions (1917–46), and (c) late-century minus midcentury conditions (1947–76) for the following

variables: (1) frost days (number), (2) F/T cycles (number), (3) onset day of soil frost, and (4) last thaw day.


10(4c)], by as much as 27 days in lower Michigan and

southern Illinois. This indicates a reduction in soil frost

duration between the mid- and late-century periods by

as much as 36 days—specifically in southeast Minnesota,

northeast Iowa, and north-central Indiana.

Simulations from 1917 indicate that average winter

soil temperatures at a depth of 10 cm for the months of

December, January, and February ranged from 288C in

the north to 128C in the south [Fig. 11(1a)]. In spring

(March–May), the monthly average temperature varied

from about 18 to about 128C [Fig. 11(2a)]. Frost depths

were greatest in the northern regions and increased to

their maximum values in the spring [Figs. 11(3a) and

11(4a)]. This may be due to the lower-than-average SWE

during spring in comparison to the winter season

[Figs. 11(5a) and 11(6a)]. In winter, higher average

monthly SWE was indicative of more days with snow

cover and deeper snow, which further insulated the soil

surface and decreased soil frost penetration. Furthermore,

the development of soil frost continued from winter into

spring, leading to increased frost depths.

Winter soil temperatures increased in the northern part

of the study domain with respect to the early-century

period, while the central part of the study domain expe-

rienced warming between the middle and late century

[Figs. 11(1b) and 11(1c)]. In contrast, northeastern Min-

nesota and the upper peninsula of Michigan underwent

a cooling trend in winter soil temperatures during the

latter part of the twentieth century. This may be due to

a decrease in average monthly winter SWE, which was

indicative of reduced snow cover in these regions of

Minnesota and Michigan [Fig. 11(5c)]. This also led to

increased frost depth during the winters of the late-century

period with respect to midcentury [Fig. 11(3c)]. During

spring, most of the northern regions experienced an in-

crease in soil temperature, leading to reductions in frost

depth in northern Minnesota and western Wisconsin

[Figs. 11(4b) and 11(4c)]. Winter SWE has increased in

southern-central Illinois and Indiana, southern Minne-

sota and northeast Iowa by up to 9% in the late cen-

tury in comparison to the midcentury while spring SWE

has decreased in the northern region, southeastern

Wisconsin, and northern Michigan by 9% [Figs. 11(5c)

and 11(6c)]. The decrease in spring SWE coincided with

the regions that were classified as agricultural and mixed

use. Agricultural regions have higher snow accumula-

tions on the ground surface than forested sites because

trees intercept snowfall, which reduces snow accumu-

lation. Similarly, the regions where SWE has increased

during winter were mostly under agricultural areas.

Cold-season soil temperatures in warm years varied

from 08C in the north to 128C in the south, while in the

cold years temperatures ranged from 258C in the north

up to 98C in the south, an average decrease of 38C (Fig. 12).

Warm years had shallower frost penetration, which may

even be shallower by up to 100 cm at a few sites in northern

Minnesota in comparison to the cold years [Figs. 12(2a)

and 12(2b)]. Cold-year soil frost was deeper, specifically

in most of Minnesota and northern Wisconsin, with av-

erage depths for the entire region exceeding by 15 cm

than those simulated for warm years. Cold years also

distinguished themselves from warm years through higher

snow accumulations. The variability in SWE during cold

years was higher, with a greater number of sites expe-

riencing extreme values of SWE, than in the warm years

(Fig. 13). However, total precipitation in the cold years

was lower than in warm years.

In wet years, soil temperatures were warmer than in

the dry years (Figs. 12c and 12d), but the spatial average

of soil temperatures over the entire study domain for

both wet and dry years were similar (5.068 and 4.988C,

respectively). Wet years experienced higher SWE than

dry years, resulting in reduced depth of frost penetra-

tion, as expected. The median precipitation in wet years

was about 30% higher than that for dry years (Fig. 13).

Although soil temperature changes generally followed

patterns similar to those of air temperature during wet

and dry years, regions with higher SWE, such as the

upper peninsula of Michigan, experienced warmer soil

temperatures than the corresponding air temperatures

because of the insulation effect of snow cover. The fre-

quency and severity of extreme temperature and pre-

cipitation events are likely to be enhanced even more in

the future than has occurred in the past, resulting in

more dramatic changes in cold-season processes.

5. Conclusions

This study focused on the effects of historic climate

variability on cold-season variables in the midwestern

United States, using both observations and model sim-

ulations. Soil frost indicators—such as the number of

frost days and freeze–thaw cycles—were determined

from observed records for different periods based upon

the availability of data and were tested for the presence

of significant trends. We used the VIC model to re-

construct long-term historic time series of cold-season

variables and then analyzed trends and associated spa-

tial and temporal variability. We also analyzed the hy-

drologic response to extreme warm, wet, cold, and dry

years to understand the effect of climate variability on

seasonal soil frost. The primary findings of this study are

summarized as follows:

d An analysis of observed data indicated that the per-

centage of stations showing significant trends and the


FIG. 11. Spatial plots indicating (a) seasonal average since 1917, (b) late-century

(1977–2006) minus early-century conditions (1917–46), and (c) late-century minus

midcentury conditions (1947–76) in Tsoil (8C), frost depth (cm), and SWE (cm). The

odd numbers represent winter (December–February) and even numbers represent

spring (March–May).


FIG. 12. Cold seasonal averages for the following selected soil frost variables: 1) Tsoil (8C), 2) frost depth (cm), and

3) SWE (mm), 4) PRCP (mm), and 5) monthly air temperature Tair (8C) with the following years: (a) warm, (b) cold,

(c) wet, and (d) dry.


direction of those trends varied for the different ob-

servational periods considered. A higher percentage

of stations experienced statistically significant trends

in soil frost variables since 1967 in comparison to the

shorter periods considered in this study. Overall

trends in extreme and mean seasonal Tsoil from 1967

and from 1974 to present have generally indicated

a warming in soil temperatures, specifically at north-

west Indiana, north-central Illinois, and southeast

Minnesota, leading to a reduction in the number of

soil frost days. The record is mixed on shorter time

scales (since 1983 and since 1991), indicating localized

effects dominate the regional climate signal in this

region.d Analysis of daily time series of simulated soil tem-

perature and snow depth at four sites indicated that

snow is the dominant control on soil temperatures.

When significant snow (depth . 5 cm) is present, soil

temperatures are relatively constant and do not reach

the extreme minimums of a soil directly exposed to

cold air temperatures. Actual soil temperatures under

snow are controlled by the timing of snow accumula-

tion, such that soil temperatures can be substantially

colder in years with late accumulation or discontinu-

ous snow cover even when winter air temperatures are

higher. With limited to no snow cover, soil tempera-

tures are highly correlated to air temperatures.d The duration of soil frost has decreased by as much as

36 days in the late-twentieth-century period (1977–

2006) as compared to the midcentury (1947–76) period

in southeast Minnesota, northeast Iowa and north-

central Indiana, increasing the length of growing sea-

son. In most of the central and southern regions, the

length of the growing season increased by about two

weeks in the late twentieth century as compared to the

midcentury period. These changes in soil frost dura-

tion suggest a shift in the time of sowing and fertilizer

applications earlier in the spring by two weeks. In

contrast, soil frost days have increased by two weeks

between the middle and end of the twentieth century

in the upper peninsula of Michigan, where the onset of

frost also now occurs two weeks earlier. The central

regions of Wisconsin and lower Michigan have experi-

enced increased freeze–thaw cycles in the late twentieth

FIG. 13. Box plots showing (top) frost depth, (middle) SWE, and (bottom) PRCP for cold seasons representing:

warm, cold, wet, and dry years. The tops and bottoms of the boxes indicate the 25th and 75th quartiles, respectively;

the lines in the middle represent the median value, the whiskers indicate the maximum and minimum values in the

distribution, and crosses indicate values that fell outside of 1.5 3 the interquartile range and were therefore classified

as outliers.


century relative to the early- and midcentury periods.

Therefore, these regions may experience increased po-

tential for soil erosion due to enhanced freezing and

thawing over winter months.d Historically, soil temperatures in warm years were

greater by up to 38C than those in cold years with

shallower frost penetration by 15 cm on average. Cold

years were typically influenced by deeper snowpacks

(higher SWE totals), which led to shallower penetra-

tion of soil frost, specifically in Minnesota and north-

ern Wisconsin. This indicates that snow accumulation

played a key role in soil frost formation and seasonal

dynamics. During the cold season, the extreme wet

years experienced especially high SWE, resulting in

further reduced penetration of soil frost and warmer

soil temperatures compared to dry years.

Long-term model simulations provided useful infor-

mation on spatial and temporal patterns of cold-season

variables for the entire six-state study domain. However,

there are some limitations of this study. This study did

not account for the feedback between vegetation and

climate, as vegetation parameters were based on 1992/93

land cover data. This implies that the model results are

not as adaptive to real-world changes in agricultural and

natural vegetation. The model was also not configured

to represent the effects of urbanization or the lakes and

wetlands that are common in the northern parts of the

study domain. Representation of the earlier-mentioned

processes may improve estimations of various water and

energy balance components in the model, reducing un-

certainties in estimation of freeze–thaw cycles and cold-

season processes.

Acknowledgments. We are thankful to NASA for

providing funding for this research through the Grant

NNG04GP13P. This manuscript is Purdue Climate

Change Research Center (PCCRC) paper 0901.

APPENDIX A

Location and Soil Types for Sites Collecting Soil Temperatures at 10-cm Depth

Stations Latitude (8N) Longitude (8E) Elevation (m) Soil Soil cover

Morris WC Exp (MN) 45.58 295.87 347.5 Forman clay loam Bare

Lamberton SW Exp (MN) 44.23 295.32 348.7 Nicollet silty clay loam Bare

Waseca Exp (MN) 44.07 293.53 351.4 Nicollet clay loam Bare

Chatham Exp Frm (MI) 46.35 286.93 268.2 Rocky loam Sod

Hancock Exp Frm (WI) 44.12 289.53 328.0 Plainfield loamy sand Bare

NW Michigan Res Frm (MI) 44.88 285.68 249.9 — Bare

Decorah 2 N (IA) 43.30 291.80 262.1 Fayette silt loam Bare

Nashua 2 SW (IA) 42.93 292.57 315.5 Kenyon loam Bare

Kanawha (IA) 42.93 293.80 361.2 Nicollet loam Bare

Oelwein 2 S (IA) 42.65 291.92 307.8 Bixby loam Cultivated

Castana (IA) 42.07 295.83 442.0 Ida silt loam Bare

Ames 8 WSW (IA) 42.02 293.77 335.0 Clarion loam Bare

Atlantic 1 NE (IA) 41.42 295.00 353.6 Marshal silt loam Bare

Burlington radio KBUR (IA) 40.82 291.17 214.3 Grundy silt loam Bare

Freeport (IL) 42.28 289.67 265.0 Dubuque Grass

Stcharles (IL) 41.90 288.37 226.0 — Grass

De Kalb (IL) 41.85 288.85 265.0 Flanagan/drummer Grass

Stelle (IL) 40.95 288.17 213.0 Monee Grass

Monmouth (IL) 40.92 290.73 229.0 — Grass

Peoria (IL) 40.70 289.52 207.0 Clinton Grass

Urbana (IL) 40.08 288.23 219.0 Flanagan silt loam Grass

Bondville (IL) 40.05 288.22 213.0 Flanagan/Elburn Grass

Orr (IL) 39.80 290.83 206.0 Clarksdale Grass

Springfield (IL) 39.52 289.62 177.0 Ipava Grass

Brownstown (IL) 38.95 288.95 177.0 Cisne Grass

Olney (IL) 38.73 288.10 134.0 Bluford Grass

Belleville (IL) 38.52 289.88 133.0 Wier Grass

Fairfield (IL) 38.38 288.38 136.0 Cisne Grass


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impacts of historic climate variability on seasonal soil frost in

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