Introduction
Background information
The future of tropical forests is at risk in a warmer, more populous
21st-century world . Forests cover approximately
42 millionkm2 in tropical, temperate and boreal regions, which is
approximately 30 % of the Earth's land surface. Land use change (LUC)
occurs on local scales, with real-world social and economic benefits, but can
potentially cause ecological degradation across local, regional, and global
scales . A large portion, almost 35 %, of the Earth's
surface has already been modified for urban and industrial development,
agriculture, and pasture land . Worldwide changes to
forests, woodlands, grasslands and wetlands are being driven by the need to
provide food, fiber, water, and shelter . LUC has the
potential to have a significant impact on land–atmosphere interactions and
modify local climate conditions e.g.,.
Loss of natural forests worldwide in the tropics during the 1990s was as high
as 152 000 km2year-1, and Amazonian forests were cleared at a
rate of approximately 25 000 km2year-1 . By
1991, 426 000 km2 of the Amazon forest had already been removed,
approximately 10.5 % of the original forest area . More
recent estimates suggest that by 2006, 663 177 km2 of the Amazon
forest had been removed , with approximately an additional
60 000 km2 deforested since 2006 .
note that trends in Amazon economies, forests and climate
could lead to the replacement or severe degradation of more than half of the
closed-canopy forests of the Amazon Basin by the year 2030, even without
including the impacts of fire or global warming.
acknowledge that wide-scale vegetation removal is unrealistic for most
biomes, with the tropical forests being the lone exception.
It is clear that LUC in the Amazon region can have drastic consequences
because of the role forests have in mediating the climate. Forests influence
the climate through exchanges of energy, water, carbon dioxide, and other
chemical species with the atmosphere . LUC has played a role
in changing the global carbon cycle and, possibly, the global climate
.
One of the most important roles that forests have in the climate system is
their function in the carbon cycle. Forests sequester large amounts of
carbon, storing approximately 45 % of all terrestrial carbon and
contributing approximately 50 % of terrestrial net primary production
. also notes that carbon uptake by forests
contributed to a residual 2.6 PgCyear-1 terrestrial carbon sink
during the 1990s, offsetting approximately 33 % of anthropogenic carbon
emissions from fossil fuels and LUC, with deforestation releasing
1.6 PgCyear-1 during the 1990s. The trees of the Amazon contain
90–140 billion tons of carbon, equivalent to approximately 9–14 decades of
current global human-induced carbon emissions .
Design of previous modeling studies
Early total deforestation studies used coarse-resolution climate models that
did not resolve the local features of deforestation, but may have given a
reasonable representation of regional-scale changes. More recent experiments
tend to have increased resolution and duration, a feature to be expected as
computational resources have increased. The increased resolution and the
associated ability to resolve small-scale features is desired to represent
better the local dynamics involved with deforestation. With increased length
of integration, the capability to reach a new equilibrium climate is greatly
enhanced, and greater confidence in the significance of the results is
obtained. Shorter simulations are likely missing some global features
associated with Amazon deforestation that have not had a chance to develop in
the model integration, particularly when ocean dynamics are not modeled.
A noticeable inconsistency among the simulations is the replacement
vegetation used. The difference in using grassland, savanna, shrubs or bare
soil as a substitute for tropical forests is not known, although some
inherent differences may arise. Only one simulation, , used
a crop as replacement vegetation. Agricultural land cover should be the most
realistic replacement vegetation from a socioeconomic standpoint, and may
have different impacts than the aforementioned unmanaged replacement
vegetation.
Results from previous modeling studies
Previous studies have reported a change in annual surface temperature from
-1 to +3 ∘C. Several studies note that the change in temperature
is statistically significant
.
add that while surface air temperature increases by
1–3 ∘C, the soil-surface temperature increased by 2–5 ∘C.
A common feature of previous studies is decreases in precipitation, although
they are of varying intensity. Decreases in annual precipitation are
typically found to be significant
.
points out a difference in precipitation change in
simulations coupled with the ocean; the coupled model produced a rainfall
reduction that is nearly 60 % larger than was obtained by use of an AGCM
uncoupled from the ocean. As previously noted, the effect of different
replacement vegetation may also play a role. found that
changes in precipitation for 25, 50, and 75 % deforestation,
respectively, were -6.2, -11.6, and -15.7 % for soybean land cover,
which was significantly different than the +1.4, -0.8, and -3.9 %
changes for pasture. Both and note that
the seasonality of the precipitation did not change significantly, with the
rainy season and dry season remaining in the same periods.
Evapotranspiration decrease is a common finding of Amazon deforestation
studies
.
found that the differences in evapotranspiration are
statistically significant in all months. The decrease in transpiration of
53 % was much larger than the decrease in total evapotranspiration of
16 %; this indicates that evaporation from the surface can compensate for
the drop in transpiration .
noted that as the evaporation decreases, the near-surface specific humidity
decreases. This result is of particular interest in the response of planetary
boundary layer (PBL) growth.
Subsequent sections will describe the model of choice and associated
simulations used in this study to analyze the local response to Amazon
deforestation, along with a description of tropical crops incorporated into
the model. Results detail the mean climate changes in temperature,
precipitation, surface fluxes and modifications to the land–atmosphere
coupling. The possible impacts and causes of these changes are discussed, as
well as the role that irrigation plays in altering land–atmosphere coupling.
Methods
Model description
The model for this study is the Community Earth System Model (CESM) version
1.2.0 developed at the National Center for Atmospheric Research (NCAR). CESM
is a coupled model system for simulating the Earth's climate and is composed
of separate models simulating the Earth's atmosphere, ocean, land, land ice
and sea ice . Of the components available in CESM, the
following were run in their default settings: the Community Atmosphere Model
(CAM4), the Parallel Ocean Program (POP2), the Community Ice CodE (CICE4),
and the River Transport Model (RTM) (see model documentation for full
details).
The Community Land Model 4.5 (CLM4.5) incorporates recent scientific advances
in the understanding and representation of land surface processes relevant to
climate simulation . CLM4.5 is a model developer's release
that provides incremental improvements to CLM4.0 prior to the public release
of CLM version 5. Land surface heterogeneity in CLM4.5 is accomplished with a
nested sub-grid hierarchy in which grid cells are comprised of multiple land
units, soil columns, snow columns, and plant functional types (PFTs)
. The PFT level, which also includes bare ground, is
intended to capture the biogeophysical and biogeochemical differences between
broad categories of plants in terms of their functional characteristics.
Fluxes to and from the surface are defined at the PFT level, as well as the
vegetation state variables, such as vegetation temperature and canopy water
storage.
Each PFT is characterized by parameters that differ in leaf and stem optical
properties to determine the reflection, transmittance and absorption of solar
radiation . Each PFT also has a specific root distribution
to allow for root uptake of water from the soil. Different PFTs have
aerodynamic parameters that determine heat, moisture and momentum transfers,
and photosynthetic parameters that determine stomatal resistance,
photosynthesis and transpiration. These parameterizations are used to
represent optimally the behaviors of each PFT.
CLM4.5 includes a fully prognostic treatment of the terrestrial carbon and
nitrogen cycles . The model is fully prognostic for all
carbon and nitrogen state variables in the vegetation, litter, and soil
organic matter. The seasonal timing of new vegetation growth and litterfall
for each PFT is also prognostic, responding to soil and air temperature, soil
water availability, and day length. PFTs are classified into three distinct
phenological types that are represented by independent algorithms: an
evergreen type that has some fraction of annual leaf growth displayed for
longer than 1 year; a seasonal-deciduous type with a single growing season
per year controlled mainly by temperature and day length; and a
stress-deciduous type with the potential for multiple growing seasons per
year, controlled by temperature and soil moisture conditions.
CLM's default list of PFTs includes an unmanaged crop, essentially treated as
a second C3 grass PFT . In CLM4.5, a crop model
based on the AgroIBIS crop phenology algorithm has been
added, consisting of three distinct phases. Phase 1 starts at planting and
ends with leaf emergence; phase 2 continues from leaf emergence to the
beginning of grain fill; and phase 3 starts from the beginning of grain fill
and ends with physiological maturity and harvest.
CLM4.5 introduces three new agricultural PFTs: corn (CLM's only C4 crop),
soybean, and temperate cereals, i.e., spring wheat and winter wheat
. Temperate cereals represent wheat, barley, and rye,
assuming that these three crops have similar characteristics and can be
treated as one PFT. The changing of several PFT parameter values following
AgroIBIS further distinguishes corn (a C4 crop), soybean, and temperate
cereals from the existing unmanaged crop. The most notable difference in the
model between C3 and C4 photosynthesis is that the C4 photosynthetic pathway
allows for stomata to close more often, thus transpiring less, allowing for
higher water-use efficiency in C4 plants. With the crop model active in
CLM4.5, the vegetated land unit is split into unmanaged and managed parts.
PFTs in the unmanaged land unit all share the same below-ground properties
per grid cell, including water and nutrients, while PFTs in the managed land
unit occupy separate soil columns and do not interact with each other below
the ground, and thus do not compete for water and nutrients. Having PFTs in
separate managed land units allows for different management practices, such
as irrigation and fertilization, for each crop PFT.
CLM4.5 simulates the application of irrigation as a dynamic response to
simulated soil moisture conditions . When irrigation is
enabled, the crop area of each grid cell is divided into irrigated and
rainfed fractions according to a gridded data set of areas equipped for
irrigation. Irrigated and rainfed crops are placed on separate soil columns,
so that irrigation is only applied to the soil beneath irrigated crops. In
irrigated croplands, a check is made once per day to determine whether
irrigation is required; this check is made in the first time step after
06:00 LT. Irrigation is required if crop leaf area is greater than zero, and
water is the limiting factor for photosynthesis.
Key parameters used in developing CLM4.5 tropical crops. Planting
dates are in the format of month-day (example: 4-15 is 15 April). “–”
denotes a parameter that is not specified.
Spring
Winter
Tropical
Parameters
C3 Crop
Corn
Wheat
Soybean
Soybean
Corn
Corn (2)
Sugarcane
Rice
Cotton
Photosynthesis
C3
C4
C3
C3
C3
C3
C4
C4
C4
C3
C3
Max LAI
–
5
7
7
6
6
5
5
5
7
6
Max canopy top (m)
–
2.5
1.2
1.2
0.75
1
2.5
2.5
4
1.8
1.5
Last NH planting date
–
6-15
6-15
11-30
6-15
12-31
10-15
2-28
3-31
2-28
5-31
Last SH planting date
–
12-15
12-15
5-30
12-15
12-31
10-15
2-28
10-31
12-31
11-30
First NH planting date
–
4-01
4-01
9-01
5-01
10-15
9-20
2-01
1-01
1-01
4-01
First SH planting date
–
10-01
10-01
3-01
11-01
10-15
9-20
2-01
8-01
10-15
9-01
Min planting temp. (K)
–
279.15
272.15
278.15
279.15
283.15
283.15
283.15
283.15
283.15
283.15
Planting temp. (K)
–
283.15
280.15
–
286.15
294.15
294.15
294.15
294.15
294.15
294.15
GDD
–
1700
1700
1700
1900
2100
1800
1900
4300
2100
1700
Base temperature (∘C)
0
8
0
0
10
10
10
10
10
10
10
Max day to maturity
–
165
150
265
150
150
160
180
300
150
160
Maximum fertilizer (kgNm-2)
0
0.015
0.008
0.008
0.0025
0.05
0.03
0.03
0.04
0.02
0.02
Leaf albedo – near IR
0.35
0.35
0.35
0.35
0.35
0.58
0.58
0.58
0.58
0.58
0.58
Leaf transmittance – near IR
0.34
0.34
0.34
0.34
0.34
0.25
0.25
0.25
0.25
0.25
0.25
Leaf transmittance – visible
0.05
0.05
0.05
0.05
0.05
0.07
0.07
0.07
0.07
0.07
0.07
Tropical crops
In performing offline CLM4 simulations, the need to develop more realistic
PFTs for the tropics became apparent. The tropical broadleaf evergreen tree
PFT was initially replaced with the unmanaged crop PFT and C3 grass PFT. It
was thought that there would be a reduction in leaf area index (LAI) when
replacing the broadleaf evergreen trees; however, it was found that there was
a drastic basin-wide increase in LAI. It was determined that the crop and C3
grass PFTs were parameterized solely for the mid-latitude conditions. The
winter season temperature in the Amazon does not get cold enough to trigger
senescence; the survival temperature for C3 grass is -17 ∘C and
the establishment temperature for C3 grass is 15.5 ∘C, while the
planting temperature for managed crops ranges from 7 to 13 ∘C. The
Amazon has an annual average temperature of approximately 27 ∘C,
meaning minimum temperature thresholds for each PFT are always met. Another
aspect is the greater moisture availability in most of the Amazon; plants are
rarely stressed over most of the year by a lack of available moisture.
Using the and data sets of global crop
distribution, it was determined that the most prevalent crops in and around
the Amazon Basin are soybean, corn, cotton, rice and sugarcane. These crops
were then selected as tropical crops to be added to CLM4.5. Two separate corn
PFTs were added to simulate the two separate corn harvests that occur in the
region. Given the long growing season in the tropics, after the first corn
harvest of the year a second crop of corn is typically planted and harvested
later in the year. For each crop added, a rainfed and an irrigated PFT were
constructed based on irrigation data.
The new tropical crops are based on existing crops in CLM4.5, with
adjustments to physiology parameters to get realistic behavior. Tropical
soybean was based on the existing soybean PFT and tropical corn based on the
existing corn PFT. Tropical sugarcane is derived from the existing corn PFT,
tropical rice is a variation on the existing spring wheat PFT, and tropical
cotton is similar to soybean. Sugarcane was based on corn because both are C4
plants and corn is the only C4 crop in CLM4.5. Rice was based on the existing
spring wheat because they are both cereal grain crops. Cotton uses soybean as
a basis because they are both bushy C3 crops, with neither being a cereal
grain crop, as are the other C3 crops in CLM4.5. It is of note that sugarcane
is a multi-year perennial crop, while all the other crops are annual; CLM4.5
does not currently have the capability to simulate perennial crops. Thus,
sugarcane was modeled to have a planting date just after the previous
harvest, with the intention of simulating perennial coverage with a decrease
once a year when a portion of the sugarcane is typically harvested or
replaced.
The data were used to determine planting dates, growing
degree days, maximum LAI and maximum number of days to plant maturity for the
tropical crops being added; Table shows original crop PFT and
tropical crop PFT parameters that were modified. In addition to changing
those physiology parameters, the albedo and radiative transmissivity of crop
leaves were changed to match those of . The amount of
fertilizer applied to each crop was modified to allow for a more realistic
seasonal cycle. The goal of these new tropical crops is to provide a
realistic physical seasonal cycle of planting, crop height, crop LAI and
harvest time; compared to , the timing of
planting and harvest
are achieved, plant heights fall within the expected range
and LAI falls within the expected range of previous documentation.
The 5 min spatial resolution data were regridded for
use in CLM4.5. In the specified domain (85–35∘ W,
30∘ S–13∘ N), each CLM grid box having a total area of
tree PFT (tropical broadleaf evergreen and tropical broadleaf deciduous)
percentage (see Fig. for the default PFT distribution) greater
than zero was deforested; all existing PFTs in that grid box were removed.
Each respective deforested grid box is checked for the presence of crops in
the regridded Portmann data. If any crops are present in a deforested grid
box, the acreage for each crop is used to determine the percent coverage,
preserving the percentages in the deforested case. There is a maximum of five
crops allowed in each CLM grid box. If all six crops are present, the lowest
acreage crop is omitted. For deforested grid boxes with no crops present, a
Cressman analysis is used to interpolate crop coverage from neighboring grid
boxes. The calculated distribution of the tropical crops in the deforested
case can be seen in Fig. , with 12.82 % of the area being
soybean, 21.09 % for each corn crop, 14.77 % for sugarcane,
25.18 % for irrigated rice, and 5.04 % for cotton.
Distributions of the indicated plant functional types (PFTs) in the
control simulation as a percentage of each grid box. “Other vegetation”
includes C3 alpine grasses and bare soil.
The initial seasonal mean changes to the land surface can be seen in
Fig. . There is a basin-wide increase in surface albedo across the
deforested region. In the closed canopy region where the highest percentages
of broadleaf evergreen trees are located, there is a large reduction in both
LAI and canopy height across all seasons. To the southeast of that region, an
area where C4 grass was predominant, there is an increase in both LAI and
canopy height in NDJFM, the main growing season of the dominant crops,
soybean and rice, in that region. The other months show a general decrease in
LAI and canopy height in that region.
The choice of these irregular seasons is based on the growing season of the
tropical crops used as replacement vegetation. NDJFM largely coincides with
crop growth in the region south of the Equator and planting north of the
Equator. AMJ is the main growing season north of the Equator. JASO is
predominantly a period after harvest has occurred and planting south of the
Equator is taking place in the last month. Additionally, these seasons
correspond to the seasons of peak precipitation, as NDJFM has precipitation
predominantly south of the Equator, AMJ precipitation is centered on the
Equator and extends into northern South America, and JASO is the driest
period for the majority of the region, with precipitation centered over the
northwestern portion of South America.
Distribution of each tropical crop as replacement vegetation in the
Amazon region, with the color bar indicating the percentage of each grid
box.
Changes to surface properties after deforestation in NDJFM, AMJ and
JASO; albedo (top row), leaf area index (middle row) and canopy height (m)
(bottom row). Shading indicates significance at the 95 % confidence
level.
Model simulations
CESM with active components of CAM4, CLM4.5, POP2, CICE4 and RTM is used for
the model simulations in this study. The simulations are run at an
atmospheric model resolution of 0.9∘×1.25∘ and a
nominal 1∘ ocean resolution grid with a displaced pole over Greenland
for present-day (year 2000) initial conditions for greenhouse gas
concentrations. Before starting the coupled runs, a spin-up simulation for
the land surface was implemented to achieve a steady state for the carbon and
nitrogen processes of the interactive phenology. The CLM4.5 spin-up procedure
consists of a 650-year offline simulation with present-day atmospheric
forcing, achieved by repeatedly cycling through the input
data set, years 1–600 are forced with years 1951–1990 and years 601–650
are forced with years 1951–2000; the last land state from the offline
simulations is then used as the land initial condition in the coupled
simulations. A separate spin-up simulation is done for each coupled
experiment with matching PFT distributions. In the simulation utilizing
tropical crops, the crop model and irrigation models are active. Each of the
fully coupled simulations has a length of 250 years, in which only the last
125 years of monthly data are used for analysis. The control simulation uses
the default PFT distribution (Fig. ) and the deforested simulation
used the crop PFT distribution in Fig. .
In all simulations, the fire module is turned off. When coupling CLM4.5 with
CAM, specific humidity has been found to be too low over the Amazon region
(W. Sacks and D. Lawrence, personal communications, 2013). Fires in CLM4.5
are invoked as a function of relative humidity, soil wetness, temperature and
precipitation . With low specific humidity, the relative
humidity triggers the fire model in vast areas of the Amazon region,
predominantly regions neighboring the closed canopy forests (grid boxes with
greater than 60 % tree PFT). Along with a reduction in humidity, there is
a decrease in precipitation that is enough to invoke fire in the closed
canopy as well. From short coupled simulations, it was seen that fire occurs
in year 1 along the edge of the closed canopy and LAI is reduced. LAI becomes
significantly small in the northeast by year 4 and large reductions in LAI
propagate westward into the closed canopy in subsequent years.
CLM4.5 was tested in short coupled simulations with the fire module both
active and inactive. The results showed that canopy height was no longer
decreasing with the fire module inactive, although the LAI was reduced by
approximately 30 % from offline simulations. The LAI reduction is much
more severe to both the canopy height and LAI with fire active. Reduced LAI
in the coupled model presumably results from the low humidity and
precipitation impacting the phenology algorithms previously discussed. Thus,
it has been determined that the simulations used in this study should have
the fire module turned off. The LAI impacts due to deforestation are still
large and capable of producing a significant signal. In addition, the large
changes exhibited in surface roughness also provide a boundary condition to
the atmosphere capable of demonstrating the impacts of large-scale land
use change.
Results
Temperature
As can be seen in Fig. , in the initially dense forest region
there is an increase in surface temperature in all seasons; the majority of
the region warms by 1–3 K, with the central region warming by more
than 7 K. To the southeast, there is a region of temperature
decrease, typically less than 3 K. This temperature decrease is
largely over the region that was predominantly C4 grass.
noted that changes in surface temperature over the deforested region are
dipolar: an increase over the central and eastern Amazon and a decrease to
the southwest of the deforestation. The region of decrease is shifted
eastward in these findings, but such a dipolar change has a precedent.
Additionally, note a dipolar temperature change with a
decrease in the east and an increase towards the west, which is noted as
being directly related to the initial land–atmosphere coupling strength.
Despite the region of cooling, the areal average for each season shows an
increase: +0.8 K in NDJFM, +1.6 K in AMJ, and
+2.1 K in JASO.
Change in surface temperature (K) for NDJFM, AMJ and JASO. Shading
indicates significance at the 95 % confidence level.
The contrast in temperature change between the densely forested and C4 grass
areas becomes more apparent in the change in maximum monthly surface
temperature. The forested region experiences an increase in all months,
typically between 2 and 6 K. In the C4 grass area, the maximum
monthly surface temperature decreases from August to January by
4–6 K, with the remaining months having a mixed change between -2
and 2 K. The same pattern tends to hold up for minimum monthly
temperature, with the changes about half the magnitude. The overall range in
extremes for the densely forested area increases by 2–4 K, while in
the C4 grass area, the range of extremes is reduced by 2–4 K from
August to January and increases by less than 2 K in the remaining
months. It is worth noting that C4 grass in CLM can behave unrealistically at
times by dying off and then regrowing a couple months later
, which can affect surface temperature drastically.
The annual areal average increase in surface temperature of 1.4 K is
consistent with previous modeling studies; found a
1.4 K increase, found a 1.5 K increase and
found a 1.2 K increase. However, some studies
found smaller or larger temperature increases: 0.6 K
, 0.3 K ,
0.3 K , 2.5 K and
2.5 K . The results in this study lie within the
range of previous findings.
Precipitation
There is a significant decrease of at least 1 mmday-1 in
precipitation over the originally densely forested region throughout the
year, with some areas experiencing decreases larger than
4 mmday-1; see Fig. . The majority of this region
sees decreases of more than 50 %. During NDJFM, when the majority of the
Amazon region experiences at least 8 mmday-1 in precipitation in
the control simulation, there is a largely statistically significant decrease
in precipitation for the deforested region. An area of increase is present in
a region that is mainly irrigated rice. During AMJ when precipitation is
largely occurring within a few degrees of the Equator, there is a significant
decrease across this region of the Equator, while a significant increase is
present to the south. The driest season in the control simulation, JASO, has
a significant decrease in precipitation over much of the deforested region.
All seasons experience a decrease in the areal average:
-0.27 mmday-1 in NDJFM, -0.37 mmday-1 in AMJ,
and -0.44 mmday-1 in JASO.
Most of the precipitation changes can be explained by changes to convective
precipitation, which decreases in all seasons (not shown), with the only
exception being the region with irrigated rice. The reduction in convective
precipitation suggests changes in flux partitioning at the surface may modify
the properties and growth of the planetary boundary layer, as well as the
land–atmosphere coupling in the region.
The decreases exhibited in this study are consistent with previous modeling
studies; however, the magnitude of the decrease is smaller. This study found
an annual areal average decrease of 0.35 mmday-1, while previous
studies found decreases of 0.7 mmday-1 ,
0.4–0.7 mmday-1 , 1.6 mmday-1
, 1.2 mmday-1 ,
1.4 mmday-1 , and
0.8 mmday-1 . The smaller decrease in
precipitation may be due to previously mentioned model shortcomings with low
humidity and less climatological precipitation in the region.
Change in precipitation (mmday-1) for NDJFM, AMJ and
JASO. Shading indicates significance at the 95 % confidence
level.
Changes in surface energy fluxes in NDJFM, AMJ and JASO; net
radiation (Wm-2) (top row), latent heat flux (Wm-2)
(middle row) and sensible heat flux (Wm-2) (bottom row). Shading
indicates significance at the 95 % confidence level.
Radiation and fluxes
Net radiation is shown (Fig. ) to be significantly reduced
over the densely forest region in all seasons, typically by
30–50 Wm-2. To the southeast over the C4 grass area, an
increase is shown during NDJFM, changes between -10 and
10 Wm-2 are present in AMJ, and decreases of
10 Wm-2 exist in JASO. These changes are driven by changes to
albedo (seen in Fig. ) and impacts the partitioning of latent and
sensible heat flux.
Latent heat flux is primarily reduced across the region in all seasons; the
major exception is an increase during NDJFM in the former C4 grass area.
Sensible heat flux increases in the formerly densely forested area in all
seasons and is surrounded by a region of decrease in sensible heat flux.
There is an increase in sensible heat flux in the southeast during both NDJFM
and AMJ, while JASO has a mix of both increases and decreases, with most of
the area not experiencing a significant change. The annual areal averages of
latent heat flux and sensible heat flux both decrease, -8.1 and
-1.7 Wm-2, respectively. This change in the fluxes has reduced
the evaporative fraction in the region and indicates that the Amazon would
shift to a drier climate.
Evaporative fraction (Fig. ) is the ratio of latent heat flux to
the sum of latent and sensible heat fluxes. After deforestation, nearly the
entirety of the deforested region in AMJ and JASO have significant decreases
in evaporative fraction, indicating a drier climate in the region. NDJFM
experiences an increase in the evaporative fraction over a large portion of
the area; this is due to it being the season of main crop growth over that
area. The formerly densely forested region in NDJFM experiences a decrease in
evaporative fraction; this is probably due to the deeper root profile of tree
PFTs that would have access to a larger soil moisture reservoir.
Changes to evaporative fraction in NDJFM, AMJ and JASO; shading
indicates significance at the 95 % confidence level.
Land–atmosphere coupling
A novel aspect of this study is an assessment of the impact on
land–atmosphere coupling strength. A two-legged coupling metric
uses correlations between a land surface state
variable (soil moisture) and surface flux (latent heat) as a means to assess
terrestrial climate feedbacks, or a surface flux (sensible heat) and an
atmospheric property (PBL height) for the atmospheric climate feedback. It is
used here to describe the feedbacks present in the system and how they have
changed after deforestation. Positive values in these two instances would
imply that the land surface is controlling the feedback. We multiply these
correlations by the standard deviation (SD) of the response variable (latent
heat and PBL height, respectively) to determine the magnitude of the feedback
. The significance of the control simulation coupling strength
is based only on the correlation component, and the significance of the
change in coupling strength is based only on the change in correlation
.
In the terrestrial leg of the coupling (Fig. ) for the control
simulation, a large band of negative values during NDJFM corresponds to the
heavy rains during that season when soil moisture is not a limiting factor
for surface fluxes. As the rains shift throughout the year, this region
shifts accordingly. During the drier seasons in the south, the sign switches
to positive, an indication that soil moisture is controlling the latent heat
flux cf..
Terrestrial leg of coupling strength (Wm-2) between soil
moisture and latent heat flux for the control simulation (top row) and change
due to deforestation (bottom row) for NDJFM, AMJ and JASO. Shading indicates
significance of the correlation component at the 95 % confidence level.
After deforestation, the previously densely forested areas become more
strongly coupled throughout the year (Fig. ). This is probably due
to the shallower roots of crops, which have access to a smaller soil moisture
reservoir. There are also large areas of decreased coupling, particularly
over the southeast in JASO and south of the densely forested area in NDJFM.
During AMJ, nearly the whole region sees an increase in coupling.
The changes in coupling can occur due to changes in the correlation,
variability, or both. In NDJFM, the correlation increases in 54.8 % of
the region and flux variability decreases in 56.4 % of the region.
Neither component appears to be the leading agent of the changes; the changes
in NDJFM (the rainy season) are largely atmospherically driven due to changes
in precipitation. Areas with the largest reduction in precipitation have
correlation increases; they also have increases in variability and are
becoming more strongly coupled.
In AMJ and JASO, the changes in correlation are much larger: 69.5 and
76.6 % of the region have an increase in correlation, respectively.
Increases in correlation alone do not necessarily imply increased coupling,
as the combination of correlation and variance of the fluxes determines
coupling strength . While the majority of
the region in AMJ has stronger coupling, JASO has the majority of the region
showing a decrease in coupling. JASO has a decrease in variability for
62.7 % of the region, with 46.2 % of the region having an increase in
correlation and decrease in variability, largely taking place in the
southeast, where there was lower initial tree cover. In contrast, the more
densely forested regions largely experience an increase in correlation and an
increase in variability.
For the atmospheric leg of the coupling, in the control run, the entire
region is positively coupled based on the spatiotemporal correspondence
between the two (Fig. ). The areas of strongest coupling occur in
locations that were initially less tree-covered, as the dense canopy acts to
dampen the coupling between surface sensible heat flux and PBL height.
In all seasons, the densely forested areas have an increase in coupling after
deforestation (Fig. ). The southeastern region largely experiences
a decrease in coupling during all seasons. The largest contrast between the
densely forested area and the southeast occurs in JASO, which is after most
of the crops have been harvested and LAI is low.
For the atmospheric leg, the majority of the region either experiences an
increase in both correlation and variability or a decrease in both. There are
co-located correlation and variability increases over 31.0, 41.6, and
33.3 % of the region for NDJFM, AMJ and JASO, respectively. These regions
are predominantly along the southeastern coast, where increased temperature
and decreased precipitation occur, and in the previously forested areas.
Regions experiencing decreases in both were 36.9, 29.7, and 40.2 % for
those same seasons. These changes largely occurred in the southeastern area,
where lower initial tree cover is located.
Atmospheric leg of coupling strength (m) between sensible heat flux
and planetary boundary layer height for the control simulation (top row) and
change due to deforestation (bottom row) for NDJFM, AMJ and JASO. Shading
indicates significance of the correlation component at the 95 %
confidence level.
Top row: change in the terrestrial leg of coupling strength
(Wm-2) versus irrigation water added (mmday-1) for
irrigated grid boxes in NDJFM, AMJ and JASO. Bottom row: change in the
atmospheric leg of coupling strength (m) versus initial tree cover percentage
for NDJFM, AMJ and JASO. Shaded dots represent irrigated grid boxes, with the
shading being equivalent to the shading for irrigation water added
(mmday-1) in the top row.
Discussion and conclusions
Replacement of natural vegetation
with crops typical of tropical agriculture over the Amazon results in an
albedo increase, lowering net radiation, which in turn modifies the surface
fluxes. Latent heat flux is largely reduced across the domain, with the
exception being the former C4 grass region in NDJFM; sensible heat flux has a
more detailed spatial change with decreases in all seasons over the former
densely forested area and a seasonality to the changes in the surrounding
regions. The areal averages for latent heat flux and sensible heat flux are
reduced, but the evaporative fraction decreases, modifying the region toward
a drier climate. Combining the surface temperature increase with the surface
flux changes, a warmer, drier and deeper PBL results. There is a decrease in
precipitation, largely due to decreased convection, which further alters flux
partitioning due to reduced soil moisture. By modifying PBL properties and
PBL growth, modified interaction between the PBL and the free atmosphere
decreases vertical moisture transport and increases vertical heat transport.
These changes in vertical transport provide a mechanism that can impact the
circulation and may affect remote regions, with large-scale circulation
changes enhancing the precipitation changes.
An added level of complexity that previous studies did not consider is
irrigation. The irrigation impact is difficult to isolate, due to the grid
boxes with irrigated rice also having other crops present. Irrigation adds
water to the surface when water is a limiting factor for photosynthesis and
can have an impact on land–atmosphere interactions. Irrigation does appear
to have an impact on the coupling between land and atmosphere
(Fig. ). Irrigation is active in 8 months (ONDJFM in the
Southern Hemisphere and JFMAM in the Northern Hemisphere) when rice is widely
grown. In the months when irrigation is added, there is a negative
correlation between irrigation water added and the change in the terrestrial
leg of the coupling. The more irrigation water that is added, the less
coupled the soil moisture becomes to the latent heat flux.
By affecting the surface coupling, irrigation can also impact the atmospheric
leg of the coupling (Fig. ). A negative relationship between
irrigation water added and the change in SH-PBLH (atmospheric leg) coupling
further shows that irrigation is modifying land–atmosphere interactions.
Although irrigation is shown to have an impact on the atmospheric leg of the
coupling, the larger contributor appears to be the percentage of tree cover
lost (Fig. ). The coupling changes are largely the same for
non-irrigated grid boxes with original tree percentage less than 80 %,
typically between -50 and 50 m. JASO, the driest season, does have
a larger spread, but comparable magnitudes of increases and decreases. When
the initial tree cover is greater than 80 %, the coupling strength is
predominantly increasing and has a greater magnitude of the change. This
signal is also common in climate change scenarios driven by greenhouse gas
increases , suggesting land use change could further
amplify sensitivity to land surface anomalies in the tropics.
Irrigation largely decreases the coupling strength when the initial tree
cover is less than 80 % and increases the magnitude of the change. When
the initial tree cover is greater than 80 %, the grid boxes that
experience a decrease in coupling are typically irrigated, with the more
strongly irrigated grid boxes showing the largest decreases and less
irrigated grid boxes showing an increase in coupling that is comparable to
non-irrigated grid boxes. Just as with the terrestrial leg, more irrigation
water added decreases the coupling strength of the atmospheric leg of the
coupling.
Even using a realistic heterogeneous crop distribution in the Amazon region,
there is still general agreement with previous modeling studies. The higher
resolution and heterogeneity of the land cover show smaller-scale features
and regions of opposite change, particularly in the southeastern Amazon,
where the region has higher coverage of C4 grass. With crops being planted in
different regions at different times of the year, a level of complexity not
present in previous Amazon deforestation studies, and seasonality to land
surface changes that were not previously modeled, are now seen.
A warming and drying of the region has impacted on how the land surface and
atmosphere interact. By modifying the flux partitioning between latent and
sensible heat fluxes, the region shifts to a drier climate with a warmer,
drier and deeper PBL. By altering how the PBL grows, interaction with the
free atmosphere is altered; this can lead to a warmer and drier atmospheric
column above the region and may cause impacts to remote regions by modifying
the general circulation and transports of moisture and heat. There is
evidence that mesoscale responses of the atmosphere to land surface
perturbations at low latitudes may not be well represented in climate models
e.g.,; it would be worthwhile to repeat tropical
deforestation studies with cloud-resolving models in the future.
Remote impacts, such as modification to the African easterly waves and
increased precipitation over the southwestern United States, have been found
in these experiments, and will be discussed in a future paper
. By employing a coupled ocean model, changes to sea
surface temperature and the El Niño–Southern Oscillation have also been
found and will be discussed in a later paper.