**Impact of Agricultural Practice on Regional Climate in a Coupled Land
Surface Mesoscale Model**

**H.S. Cooley**

Energy and Resources Group, University of California at Berkeley,

Berkeley, CA

Earth Sciences Division, Lawrence Berkeley National Laboratory,

Berkeley, CA

**W.J. Riley and M.S. Torn**

Earth Sciences Division, Lawrence Berkeley National Laboratory,

Berkeley, CA

**Y. He**

Computational Research Division, Lawrence Berkeley National Laboratory,

Berkeley, CA

#  Abstract

The land surface has been shown to form strong feedbacks with climate
due to linkages between atmospheric conditions and terrestrial ecosystem
exchanges of energy, momentum, water, and trace gases. Although often
ignored in modeling studies, land management itself may form significant
feedbacks. Because crops are harvested earlier under drier conditions,
regional air temperature, precipitation, and soil moisture, for example,
affect harvest timing, particularly of rain-fed crops. This removal of
vegetation alters the land surface characteristics and may, in turn,
affect regional climate. We applied a coupled climate (MM5) and
land-surface (LSM1) model to examine the effects of early and late
winter wheat harvest on regional climate in the Department of Energy
Atmospheric Radiation Measurement (ARM) Climate Research Facility in the
Southern Great Plains, where winter wheat accounts for 20% of the land
area. Within the winter wheat region, simulated 2 m air temperature was
1.3^o^C warmer in the Early Harvest scenario at mid-day averaged over
the two weeks following harvest. Soils in the harvested area were drier
and warmer in the top 10 cm and wetter in the 10-20 cm layer. Midday
soils were 2.5^o^C warmer in the harvested area at mid-day averaged over
the two weeks following harvest. Harvest also dramatically altered
latent and sensible heat fluxes. Although differences between scenarios
diminished once both scenarios were harvested, the short-term impacts of
land management on climate were comparable to those from land cover
change demonstrated in other studies.

# Introduction

One of the most significant ways in which humans have modified
ecological systems is via land use and land cover change *\[Vitous*ek,
1994\]. Impacts occur from local to global scales and include loss of
biodiversity, disruption of biogeochemical cycles, erosion, disruption
of the fire regime, and climate change *\[Vitous*ek, 1994; Ostlie, 1997
#56\].

The Great Plains of the United States have experienced extensive land
cover modification. Due to rich soil and favorable climate, vast tracts
have been converted from prairie to managed agriculture. Historically,
the prairie in this region extended over 2.6 million km^2^, from Canada
to Mexico and the foothills of the Rockies to Indiana *\[Ostl*ie,
1997\]. The extent of land-surface modification increases from west to
east. Between 82 and 99% of the tallgrass prairie, predominately found
in the eastern section of the Great Plains, has been converted *\[Samson
and Kno*pf, 1994\]. Using Advanced Very High Resolution Radiospectometry
(AVHRR) 1-km satellite data, Loveland and Hutcheson \[1995\] estimate
that 25% and 90% of historical short- and tallgrass prairie,
respectively, were under cultivation by 1990.

Vegetation influences climate through land surface properities, e.g.,
albedo, surface roughness, rooting depth, leaf area index, and canopy
resistance *\[Pielke and Aviss*ar, 1990\] over a range of spatial and
temporal scales *\[Piel*ke, 1993*; Pielke et a*l., 1998\]. These
land-surface characteristics vary by land-cover type. Thus anthropogenic
modification of vegetation alters these characteristics and consequently
climate. The specific effect of conversion to cropland depends on the
crop type and its properties in comparison with those of the land cover
type that it replaces. In addition, human modification affects landscape
heterogeneity, which observational and modeling studies have shown may
induce or modify mesoscale circulations *\[Segal et a*l., 1998\].

Throughout sections of the Great Plains, the dominant agricultural crop
is winter wheat, which is planted in early fall and harvested in June or
July. This pattern contrasts sharply with the seasonal cycle of prairie
grasses, which are most active from June to September \[cite\]. We
expect that the change from prairie to winter wheat shifts the magnitude
and seasonal timing of energy, momentum, water, and carbon fluxes
between the atmosphere and ecosystem. McPherson et al. \[2004\] showed a
striking discontinuity in visual greenness between the winter wheat
region in Oklahoma and the surrounding vegetation, and they argued that
these changes impact maximum daily near-surface air temperature and
dewpoint. Using data from automated Mesonet sites throughout Oklahoma,
they detected a summer warming and winter cooling in the winter wheat
region relative to the surrounding area. In a study combining modeled
and observational data of July conditions in Oklahoma, Weaver and
Avissar \[2001\] demonstrated that landscape heterogeneity, such as that
associated with intermixed winter wheat and other crops, was sufficient
to induce vertical velocities of 1-2 m s^-1^. Their simulated vertical
velocity patterns coincided with satellite observations of cloud
formation, implying that these enhanced vertical velocities were
sufficient to induce convective cloud formation.

Modeling sensitivity experiments provide a means to evaluate the effect
of land-surface characteristics on regional and global climate. In
traditional force-response sensitivity experiments, the value of one
variable of the model environment is altered and results are compared to
a control scenario, providing an indication of the forcing potential of
a single land-surface characteristic on climate. As reviewed by Pielke
et al. \[1998\] and Garratt \[1993\], these sensitivity studies indicate
that land-surface characteristics can affect regional and global
climate. The single factor analysis, however, cannot resolve the more
realistic case of multiple factors changing simultaneously, which often
occurs with land cover modification.

Climate and land-cover modifications result in simultaneous changes in
many system state variables, and thus evaluating the net positive or
negative feedback requires an integrated analysis. Over the past ten
years, coupled atmosphere and land surface models have been developed,
allowing a quantitative exploration of these interactions. The effect of
land cover change on the climate of the United States has been studied
by comparing current, historical, and in some cases, future land cover
scenarios *\[Bon*an, 1997*; Bon*an, 1999*; Bounoua et a*l., 2002*;
Copeland et a*l., 1996*; DeFries et a*l., 2002*; Pan et a*l., 1999\].
These studies reported regional and seasonal differences in response to
land cover change. Bounoua et al. \[2002\] demonstrated that conversion
from forest to cropland produced a cooling in temperate latitudes and a
warming in the tropics. DeFries et al. \[2002\] projected that future
land cover conversion will be concentrated in the tropics and
subtropics, and conversion will warm these regions. Studies centered on
the United States have found that historical land cover change,
particularly the conversion of forest to cropland, has produced a
cooling in the central and eastern United States and a warming in the
western United States *\[Bon*an, 1997*; Bon*an, 1999*; Pan et a*l.,
1999\]. Bonan \[1997; 1999\] showed that modeled response was greatest
during the summer months and diminished in fall when crops have been
harvested and trees have lost their leaves. Pan et al. \[1999\] further
demonstrated that the land surface-climate system response was not
sensitive to synoptic regime (i.e., normal, flood, or drought
conditions) for any climate parameters considered (i.e., latent and
sensible heat fluxes and surface air temperature) except precipitation.

Within a particular land cover type, land management may also affect
land surface-climate interactions. Regional modeling studies are
required to address land management because the spatial heterogeneity of
agricultural practices cannot be captured at the scale of GCM grid
cells. In a regional modeling study, Segal et al. \[1998\] found that
irrigation resulted in an increase in precipitation in non-irrigated
areas, but did not produce any new rainfall areas. Similarly, Chase et
al. \[1999\], in a study corroborated by historical data, found that
irrigation in the northern Colorado plains impacted climate in the
plains and in the adjacent mountains. Other management factors, such as
nitrogen addition and harvest timing, may also be important but have not
yet been explicitly addressed. Harvest should influence climate because
removal of vegetation decreases albedo, surface roughness, leaf area
index, and canopy resistance.

The potential for feedback between atmospheric conditions and land
management exists because some of the climate-forcing practices are
themselves responses to climate. Air temperature, precipitation, and
soil moisture, for example, affect harvest timing, particularly for
rain-fed crops. Crop growth and grain development depend on season-long
environmental conditions, such as cumulative degree days, cumulative and
episodic precipitation, and cumulative photosynthetically active
radiation. During dry conditions, crops mature earlier, thereby forcing
an earlier harvest. Due to economic and technological constraints,
harvest tends to be temporally coherent.

In this paper we examine the extent to which an early harvest affects
regional climate by applying a coupled climate (MM5) and land surface
model (LSM1). We apply the coupled model in the Department of Energy's
Atmosphere and Radiation Measurement (ARM) Climate Research Facility
(ACRF; previously named the Cloud and Radiation Testbed - CART) in the
Southern Great Plains (hereafter ARM-SGP). We focus here on winter wheat
because it is found in a nearly contiguous belt and is the dominant
agricultural crop in the region. Two two-month long simulations are
performed: the first with an early winter wheat harvest in June, and the
second with a late harvest in July. Spatial and temporal patterns of
land-surface conditions and exchanges and near-surface atmospheric
conditions are compared between the two scenarios.

# Methods

## Coupling LSM1 to MM5 and Model Testing

We have integrated the land-surface model LSM1 *\[Bon*an, 1996\] into
the meteorological model MM5 *\[Grell et a*l., 1995\] to simulate the
coupled interactions of water, energy, and CO~2~ exchanges between
plants and the atmosphere. LSM1 has several advantages over the most
sophisticated land-surface model (OSULSM *\[Chen and Dudh*ia, 2001a;
*Chen and Dudh*ia, 2001b\]) currently implemented in the publicly
available version of MM5. The OSULSM uses a Penman potential evaporation
approach, a relatively simple canopy resistance model, and has no
ability to simulated changing plant leaf area.

In contrast, LSM1 is a "big-leaf" *\[Dickinson et a*l., 1986*; Sellers
et a*l., 1996\], single-canopy land surface model that simulates CO~2~,
H~2~O, and energy fluxes between ecosystems and the atmosphere. Modules
are included that simulate aboveground fluxes of radiation, momentum,
sensible heat, and latent heat; energy and water fluxes below ground,
and coupled CO~2~ and H~2~O exchange between plants and the atmosphere.
Twenty-eight surface types, comprising varying fractional land covers of
thirteen plant types, are simulated in the model. Soil hydraulic
characteristics are determined from sand, silt, and clay content. LSM1
has been tested in a range of ecosystems at the site level (e.g.,
*\[Bonan et a*l., 1995*; Bonan et a*l., 1997*; Riley et a*l., 2003\])
and accurately predicts carbon, latent heat, and sensible heat fluxes
under a variety of conditions.

LSM1 requires as input above-canopy air temperature, wind speed, CO~2~
concentration, vapor pressure, downward diffuse and direct shortwave
radiation, downward longwave radiation, and precipitation or irrigation
amount. These variables are available in the interface between the
atmosphere and current land-surface submodels in MM5, with the exception
of the partitioning of incoming solar radiation between direct and
diffuse and visible and near-infrared components. We applied the CCM2
version of the atmospheric radiation code currently in MM5 to produce
these radiation inputs. In the interface between the land surface and
atmospheric models we converted vegetation and soil types from the USGS
vegetation classes as used in MM5 to those used in LSM1.

We tested the coupled MM5-LSM1 model by comparing simulation results to
data collected during the FIFE campaign *\[Betts and Ba*ll, 1998\] and
to simulations using the OSULSM. These comparisons are similar to those
made by Chen and Dudhia \[2001b\] for the OSULSM. The FIFE experiments
were conducted over a 15×15 km area of the Konza prairie in Kansas
during 1987-1989. In the comparisons we applied the 30-minute spatial
averages over the study area for three months each in 1987, 1988, and
1989. We present results for sensible, latent, and ground heat fluxes
and 2 m air and surface skin temperature predictions.

## MM5/LSM1 Simulations

We used the standard initialization procedure for MM5, which applies
first-guess and boundary condition fields interpolated from NCEP
reanalysis data to the outer computational grid. Simulations were
performed with a coarse grid of 100×100 km horizontal resolution (54×68
grid points) spanning the 48 contiguous states and one-way nesting to
10×10 km horizontal resolution (41×41 grid points) for the approximate
3°×3° area of the ARM--SGP region. The fine grid was centered over the
First International Satellite Land Surface Climatology Project Field
Experiment (FIFE) area for testing and centered over the ARM-SGP region
for the June and July harvest simulations. In the vertical direction we
used 18 σ‑layers between the 100 mb level and earth's surface. The
following physics packages were used in the simulations: Grell
convective scheme, simple ice microphysics, MRF PBL scheme, and the CCM2
radiation package.

Because this study used one-way nesting between the 100 and 10 km
horizontal resolutions, changes resulting from harvest in the ARM-SGP 10
km grid area were not communicated back to the surrounding continental
area. Although Lu et al. \[2001\] discussed the importance of two-way
nesting in climate models, we feel that our results would not be
significantly changed for any climate variables considered in this
study, except possibly precipitation, which we discuss in more detail in
section 4.2.

## Harvest Simulation

For the harvest simulations, the 10×10 km resolution model grid was
centered over the ARM-SGP. Two scenarios were run over the study period,
which extended from June 1 to July 31, 1987. In the first scenario,
referred to as Early Harvest, all winter wheat in the domain was
harvested on June 4, 1987 (Julian Day 155). In the second scenario,
referred to as Late Harvest, all winter wheat in the domain was
harvested on July 5, 1987 (Julian Day 185). These dates represent the
extremes of winter wheat harvest in Oklahoma *\[US*DA, 1997b\]. Winter
wheat is widespread in the region, accounting for 20% of the land area
in the ARM-SGP region. We simulated harvest by setting the land cover
type to bare soil in winter wheat grid cells. We did not consider the
impact of remnant stems on albedo or water retention capacity of the
soil.

Land cover at the 10×10 km resolution model grid was assigned based on
the USGS 1-km AVHRR data obtained from satellite images taken between
April 1992 and March 1993. We divided the area that the USGS designated
as agricultural land into two categories: winter wheat and other crops,
based on data from the 1997 Census of Agriculture *\[US*DA, 1997a\]. In
some counties, the USGS data did not record the presence of crops even
though the census data indicated that these counties had substantial
crop coverage. The scaling of surface characterization data from the 1
to 10 km scale caused this discrepancy by assigning the most prevalent
land surface type among the one hundred 1×1 km grid cells to the 10×10
km grid cell. Thus, specificity of land cover characterization was lost
when scaling up to the 10 km scale. To address this problem, we forced
the percent crop and winter wheat in each county to match values
calculated from the 1997 census data, thereby creating a new land cover
map (Figure 1).

LAI values prescribed by LSM1 look-up tables were used for all land
cover types except winter wheat. For winter wheat, we averaged LAI
measurements taken between 1998 and 2001 at the AmeriFlux winter wheat
site in Ponca City, Oklahoma (Verma, personal communication) (Figure 2).

# Results 

## Coupled Model Testing

To evaluate the coupling between MM5 and LSM1 we performed comparisons
with data from FIFE and to simulations using the OSULSM for three months
each in 1987, 1988, and 1989. For example, comparisons between
measurements and predictions from MM5 coupled with the LSM1 and OSULSM
land-surface models during June, 1987 are shown in Figure 3 for: (a)
latent heat flux; (b) sensible heat flux; and (c) ground heat flux. The
predictions are sensitive to the initial soil moisture, so that
comparisons between modeled and measured values were made after a model
spin-up of several days. Generally, the LSM1 predictions of surface
energy fluxes are as or more accurate than those of the OSULSM
land-surface model.

For both models surface skin temperature and 2 m air temperature were
less accurately simulated than the surface energy fluxes (Figure 4). In
the first two weeks of June both land-surface models under-predicted
peak surface skin temperatures by up to 4°C. Air temperature at 2 m was
consistently underestimated by up to 3°C in both models during the first
two weeks after spin-up.

Comparisons of this kind between land-surface models coupled to
regional-scale meteorological models can be problematic. For example,
errors in simulation of atmospheric processes (e.g., vapor transport,
cloud parameterization, radiation dynamics, or PBL dynamics) will
propagate to the land-surface model and may distinctly impact the energy
exchange predictions of each land-surface model. A second limitation to
this test is that the FIFE dataset is calculated as a spatial average
over 225 km^2^ assembled from data from a limited number of stations (22
in 1987, 10 in 1988, and 14 in 1989). Thus the dataset may not
accurately capture the spatial heterogeneity in fluxes present in the
area at all times. Still, the FIFE study provides a high-quality dataset
to evaluate distributed land-surface models. Comparisons such as those
performed here over several seasons and years with varying climate give
some confidence that the coupled model can accurately simulate the
mechanisms important in energy partitioning important for land-use
change analyses.

## Harvest Simulations

We report results as the difference between the Early and Late Harvest
scenarios. Therefore comparisons during June represent the difference
between a harvested and a non-harvested scenario, and comparisons during
July represent the difference between a June harvest and a recently
harvested scenario. If, for example, on June 10 at noon, air temperature
was warmer in the Early Harvest scenario then in the Late Harvest
scenario, the difference between the simulations was positive. Percent
change was calculated relative to the Late Harvest scenario.

Figures 5-8 depict Late Harvest and differences between Early and Late
Harvest for the following variables: latent and sensible heat fluxes, 2
m air temperature, soil temperature, soil moisture in 0-10 cm and 10-20
cm depth layers, and cumulative precipitation. Differences between
scenarios were spatially heterogeneous and were largest in the center of
the domain, where winter wheat is planted in a nearly contiguous belt.
Because results varied according to land cover type, we divided the
results into two categories: winter wheat and non-winter wheat.
Differences between scenarios were thus averaged over the winter wheat
and the non-winter wheat land cover types.

Four distinct phases in the system's response were identified based on
abrupt changes in energy partitioning. Phases 1, 2, 3, and 4 extended
from Julian Days 155-158 (0-3 days after the June harvest), 158-170
(3-15 days after the June harvest), 170-185 (15-30 days after the June
harvest), and 185-210 (days 30-55 after the June harvest, or days 0-25
after the July harvest), respectively. We discuss each phase in detail
below.

In both scenarios, latent heat flux accounted for the largest portion of
net radiation flux from the surface to the atmosphere during Phase 1
(Figure 5). In comparing the effect of early harvest relative to late
harvest, midday latent heat flux increased by 99 W m^-2^ (22%), and
sensible heat flux decreased by 44 W m^-2^ (-28%) across the winter
wheat region. In the non-winter wheat region, midday sensible heat flux
increased by 3 W m^‑2^ (2%), and there was no change in midday latent
heat flux. The increase in latent heat flux in the winter wheat region
was largest the first day after harvest and declined over the following
three days, reflecting a concurrent decline in soil moisture.

Large latent heat fluxes in both scenarios, combined with a lack of
precipitation during Phase 1 (Figure 6), resulted in a decline in
volumetric soil moisture content, *θ* (m^3^ water m^-3^ soil), in the
0-10 (*θ*~0-10~) and 10-20 (*θ*~0-20~) cm soil layers in both harvest
scenarios (Figure 7). In the winter wheat region, *θ*~0-10~ declined by
0.029 (-11%) and *θ*~10-20~ increased slightly (0.0006, 0.2%) relative
to the Late Harvest scenario. There was no difference in soil moisture
in the non-winter wheat region.

In the Early Harvest scenario, the vegetation removal eliminated soil
surface shading, removed roots that access deeper water, and eliminated
transpiration. As a result, all latent heat flux in the harvested region
was due to soil evaporation. We calculated the reduction in soil water
in each soil layer as a percentage of the integrated latent heat flux
over the first three days. Soil water losses as a percentage of
integrated latent heat flux in the 0‑10 cm layer were substantially
larger in the harvested scenario than in the Late Harvest scenario (55%
versus 33%). As the soils in the harvested region dried, water was
wicked from the 10-20 cm layer to the surface layer and lost as soil
evaporation. Harvest shifted the source of evaporated water to the
surface soil, resulting in a relatively drier surface layer and a wetter
deep layer.

The diurnal 2 m air temperature range declined in the winter wheat
region, as the midday temperature decreased by 0.9°C and nighttime
temperature increased by 0.3°C. By contrast, the diurnal temperature
range of the soil in the winter wheat region increased slightly, with
greater cooling at night (1.1°C) than during midday (0.3°C) (Figure 8).
There was no change in the non-winter wheat region. Although albedo
declined as a result of harvest, the change in latent heat flux was
substantial enough to cause a net cooling in midday soil and 2 m air
temperatures.

In Phase 2, large differences in all ecosystem stocks and fluxes were
evident between the two scenarios. In the winter wheat region, midday
latent heat flux declined by 173 W m^-2^ (-45%), and sensible heat flux
increased by 124 W m^-2^ (121%) relative to the Late Harvest scenario.
In the non-winter wheat region, midday latent heat flux increased by 9 W
m^-2^ (3%), and sensible heat flux increased by 1 W m^-2^ (1%) relative
to the Late Harvest scenario.

Precipitation occurred in both scenarios during Phase 2, with slightly
more (0.04 cm) falling in the Early Harvest scenario. The increase in
precipitation was likely due to the excess latent heat flux and
subsequent transport of moisture to the atmosphere from the soils in the
harvested region during Phase 1.

Soil moisture continued to decline in both simulations, but the
difference in *θ*~0‑10~ and *θ*~10-20~ reached a maximum during this
period. Large reductions occurred in the winter wheat region, where
*θ*~0-10~ layer declined by 0.05 (-24%). Although *θ*~10-20~ declined in
both scenarios during this period, *θ*~10-20~ in the winter wheat region
increased by 0.02 (11%) in the Early Harvest scenario relative to the
Late Harvest scenario. There was no difference between scenarios in the
non-winter wheat region.

The Early Harvest scenario 2 m air temperature continued to warm
throughout Phase 2 relative to the Late Harvest scenario. Diurnal
temperature range increased, as the increase in midday temperature was
greater than the increase in nighttime temperature. In the winter wheat
region, midday soil and 2 m air temperatures increased by 2.5°C and
1.3°C, respectively. There was no difference between scenarios in the
non-winter wheat region.

Phase 3 was characterized by moderate differences between scenarios in
ecosystem stocks and fluxes, but of the opposite sign of those in Phase
2. The latent heat flux increased in both scenarios throughout Phase 3.
In the winter wheat region, midday latent heat flux increased by 74 W
m^-2^ (20%), and sensible heat flux decreased by 17 W m^-2^ (-17%) in
the Early Harvest relative to the Late Harvest scenario. At midday in
the non-winter wheat region, both latent and sensible heat fluxes
increased by 4 W m^-2^. The increase in latent heat flux was due to
enhanced precipitation in both scenarios during Phase 3. Slightly more
precipitation occurred in the Early Harvest scenario (0.3 cm), resulting
in greater latent heat flux. The additional precipitation in both
scenarios reduced differences in *θ*~0-10~ and *θ*~10-20~.

In Phase 3, there was a slight cooling (0.2°C) in 2 m air temperature in
the winter wheat region due to increased latent heat flux. Midday soil
temperature was slightly warmer (0.8°C) in the winter wheat region. The
reduction in the difference between scenarios in soil and air
temperature was associated with an increase in precipitation (Figure 9).
Trends in the air temperature differences between scenarios mimicked
those in soil temperature throughout the simulation. As precipitation
increased, differences in soil and air temperature decreased.

Differences between the two scenarios in Phase 4 were small. Midday
latent and sensible heat fluxes were slightly higher in the Early
Harvest scenario. Differences in soil and 2 m air temperatures
approached zero. There was no additional difference in precipitation
between the two scenarios after Julian Day 200, as indicated by the
horizontal line (Figure 6). An additional 0.3 cm of precipitation
occurred in the Early Harvest scenario relative to the Late Harvest
scenario over the two-month simulation. Because we forced the domain
boundaries with atmospheric liquid and vapor contents, we may have
minimized differences in precipitation between scenarios. Enhanced
exchange of soil moisture to the atmosphere resulted in all of the
difference in cumulative precipitation. The rapid return to normal
conditions for all the ecosystem water pools and fluxes indicates that
the system had little or no memory of the previous month's harvest.

## Edge Effect and Spatial Variation

There was considerable spatial heterogeneity in net ecosystem latent and
sensible heat fluxes, soil moisture and temperature, and 2 m air
temperature in the response to harvest. As an example, consider
differences in midday sensible heat flux on Julian Day 163, eight days
after the June harvest. As shown in Figure 10, little or no change
occurred in half of the domain, but the winter wheat region experienced
a very large average increase of 250 W m^-2^ in midday sensible heat
flux. Changes in the center of the winter wheat region, with sensible
heat flux reaching a peak difference of 475 W m^-2^, were greater than
on the edges.

Although changes were largest within the winter wheat region, there was
an edge effect, in which the impact of the harvest was felt in areas
adjacent to the harvested, winter wheat region. In the example shown in
Figure 10, increases in sensible heat flux in the winter wheat area
resulted in the advection of warmer, drier air to the surrounding
non-winter wheat area. The warm, dry air resulted in increases in latent
heat fluxes in the surrounding non-winter wheat area. In the adjacent
area, midday sensible heat flux declined by up to 50 W m^-2^ and latent
heat flux increased. This effect was more pronounced to the East of the
harvested region, which was downwind on this particular day. The
surrounding non-harvested region, extending up to 60 km from the
harvested region, absorbed the majority of the changes resulting from
harvest that were communicated to the atmosphere. Within the harvested
area itself, there was also a gradient in impacts, between the center
and edge of the harvested region, such that the largest differences are
found in the center of the harvested area. This gradient is established
because of the ameliorating impacts of the surrounding non-harvest area
on the edges inside the harvested area.

# Discussion

Harvest dramatically alters energy, momentum, and water fluxes between
the surface and atmosphere. These effects are comparable in magnitude to
those predicted for land cover conversion by previous studies. We found
a 1.3°C warming in midday air temperature during the first two weeks
following harvest. In a ten-year climate simulation, Bonan \[1999\]
found that conversion from forest to crop resulted in a decline in
midday temperature of 0.6-1°C and 1-3°C in the East and Midwest,
respectively. In a similar study, Bounoua et al. \[2002\] reported a
0.7-1.1°C decline in temperate regions due to land cover conversion. Our
findings suggest that realistic harvest scenarios should be an integral
part of climate model studies.

There were qualitative shifts in the sign of the response to harvest
over time. Although harvest initially caused an increase in latent heat
flux and a cooling of soil and air temperatures, these effects were
reversed after three days. Transitions between the four temporal phases
following harvest result from changes in water availability and
subsequent effects on energy partitioning. The transition between Phases
1 and 2, for example, was caused by a decline in soil moisture. As
precipitation increased, sensible heat flux declined and latent heat
flux became more pronounced, causing a transition to Phase 3. Harvest
thus caused dramatic shifts in energy partitioning, which led to changes
in soil moisture and air and soil temperatures. Changes in soil moisture
then caused changes in energy partitioning. When precipitation fell in
both scenarios, however, the differences between these scenarios
declined. These results suggest that harvest should be examined under
different synoptic regimes, particularly during dry periods. The
variability in system response through time also highlights the
importance of running longer simulations. If we had only examined a
two-week simulation, we would not have observed the qualitative shifts
in energy partitioning or the re-equilibration of the system after the
second harvest.

Although the model predicted that harvest has little long-term impact on
the system, harvest likely causes secondary effects not explored here.
The substantial drying of the 0-10 cm soil layer during Phase 1 and
changes in soil moisture and air and soil temperatures throughout the
simulations influence microbial processes, decomposition, and trace-gas
fluxes. These processes influence the isotopic composition of evaporated
water and thus the interpretation of ^18^O measurements in H~2~O and
CO~2~ as a tracer for ecosystem-atmosphere exchanges and atmospheric
dynamics *\[Riley et a*l., 2002\]. In addition, a dry soil surface may
promote aeolian erosion and thereby impact air quality.

# Conclusions

Our findings improve the understanding of the impacts of temporally
coherent harvesting and subsequent soil and atmospheric conditions
within and outside of the harvested area. We note that changes
identified in this study are analogous to changes that may occur as
crops shift to an earlier growing season in response to global warming.

We observed that an early harvest, which is prompted by hot, dry
conditions, could substantially exacerbate these conditions within the
harvested area. Farmers adjacent to the harvested area can also be
adversely impacted. Collective decision-making and land management
techniques, such as mulching, may mitigate the adverse impacts observed
here. Agricultural decision-making is dependent upon a number of
factors, including expected returns on investments, labor and machinery
availability, current market conditions, and uncertainty estimates. Thus
while we have shown that an early harvest exacerbates hot, dry
conditions, it is important to realize that the decision of harvest
timing is highly constrained.

We have shown that land management has an impact on climate comparable
to that reported for land use and land cover change. To the extent that
management is influenced by short-term climatic or weather conditions,
these practices form important feedbacks with regional climate.

# Acknowledgments

This work was supported by the Laboratory Directed Research and
Development

Program at Lawrence Berkeley National Laboratory and the Atmospheric
Radiation Measurement Program, Office of Science, U.S. Department of
Energy under Contract No. DE-AC03-76SF00098. The leaf area index (LAI)
data used here were obtained in a field study supported by the Office of
Biological and Environmental Research (BER), U.S. Department of Energy,
through the Great Plains Regional Center of the National Institute for
Global Environmental Change (NIGEC) under Cooperative Agreement No.
DE-FC03-90ER61010.

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![](media/image1.png){width="4.916666666666667in"
height="2.604861111111111in"}

Figure 1. Dominant land cover types across the ARM-CART determined from
1992 USGS 1-km AVHRR and 1997 USDA agricultural census data. The
dominant land cover types are represented as the following: orange =
winter wheat, purple = other crops, yellow = pasture/grassland, blue =
savannah. Note that winter wheat occurs in a nearly contiguous belt in
the center of the region.

![](media/image2.wmf){width="5.498611111111111in"
height="1.9027777777777777in"}

Figure 2. Green and total leaf area index for (a) winter wheat and (b)
other crops. The values for winter wheat are from the Ameriflux site in
Ponca City, OK and those for the other crops are prescribed by LSM1.

![](media/image3.wmf){width="5.497222222222222in"
height="5.722916666666666in"}

Figure 3. Comparison between FIFE measurements and the MM5/ISOLSM and
MM5/OSULSM predictions for June 1987. Shown are the (a) latent, (b)
sensible, and (c) ground heat fluxes.

![](media/image4.wmf){width="5.497222222222222in"
height="4.771527777777778in"}

Figure 4. Comparison between FIFE measurements and the MM5/ISOLSM and
MM5/OSULSM predictions for June 1987. Shown are the surface skin and 2 m
air temperatures.

![](media/image5.wmf){width="5.498611111111111in"
height="3.786111111111111in"}

Figure 5. Predicted latent and sensible heat fluxes over the simulation
period. Panels show the (a) latent and (c) sensible heat fluxes for the
late harvest scenario and the average difference (early - late harvest
scenario) in (b) latent and (d) sensible heat fluxes for the winter
wheat and non-winter wheat land cover types. Patterns in sensible heat
flux mirror those in latent heat flux.

![](media/image6.wmf){width="5.498611111111111in"
height="1.9027777777777777in"}

Figure 6. Cumulative precipitation over the simulation period. Panels
show the (a) cumulative precipitation for the late harvest scenario and
(b) the regional average of the difference (early-late harvest scenario)
in cumulative precipitation. Note that there was an initial dry period,
followed by a wet period.

![](media/image7.wmf){width="5.498611111111111in"
height="3.786111111111111in"}

Figure 7. Soil moisture in the 0-10 and 10-20 cm layer over the
simulation period. Panels show the (a and c) soil moisture in the late
harvest scenario and the (b and d) average difference (early - late
harvest scenario) in soil moisture for the winter wheat and non-winter
wheat land cover types. Initial changes in the 0-10 cm layer are
opposite those in the 10-20 cm layer.

![](media/image8.wmf){width="5.498611111111111in"
height="3.786111111111111in"}

Figure 8. 2 m air temperature and soil temperature in the 0-10 cm layer
over the simulation period. Panels show the (a) air and (c) soil
temperature for the late harvest scenario and the average difference
(early - late harvest scenario) in (b) air and (d) soil temperature for
the winter wheat and non-winter wheat land cover types. For both
variables, there was an initial cooling, followed by a prolonged warm
period. This warming is interrupted on Julian Day 170, corresponding to
an increase in precipitation.

![](media/image9.wmf){width="3.334722222222222in" height="2.3in"}

Figure 9. Average cumulative precipitation across the region and the
difference in soil and air temperatures over the simulation period.
Differences in soil and air temperature are averaged for the winter
wheat land cover type. System response was closely tied to precipitation
patterns. System response was greatest during the two-week dry period
following harvest. After the onset of rainfall, differences between
scenarios approached zero.

![](media/image10.png){width="4.0777777777777775in"
height="2.5868055555555554in"}

Figure 10. Changes in sensible heat flux (W m^-2^) across the region on
Julian Day 163. Little or no change occurred in half of the domain, but
the winter wheat region experienced very large average increases of 250
W m^-2^ in midday sensible heat fluxes. Changes were largest in the
center of the winter wheat region, with sensible heat flux reaching a
peak difference of 475 W m^-2^. Changes were also evident along the
edges of the harvested area, indicating an edge effect. This effect was
more pronounced to the East in accordance with the prevailing winds
during this period.