Introduction
The land surface influences the atmosphere at multiple spatial
and temporal scales (Pitman, 2003; Pielke et al., 2011; Williams and Maxwell,
2011). Land–atmosphere coupling strength is the degree to which land surface
anomalies (e.g., soil moisture, vegetation characteristics, temperature, snow
cover) lead to changes in atmospheric states and fluxes (e.g., rainfall, cloud
cover, moisture convergence) as well as how anomalies in the atmosphere
affect the land surface. The influence of land surface anomalies on
atmospheric anomalies (and vice versa) proceeds through a chain of non-linear
processes. The strength of these processes varies spatially and temporally
and depend, in part, on the background state of the system (Betts, 2004;
Betts et al., 1996; Koster and Suarez, 2003; Taylor and Ellis, 2006).
The chain of mechanisms between soil moisture (SM) and precipitation (P)
anomalies can be summarized following Santanello et al. (2011) as
ΔSM⇒ΔEFSM⇒ΔPBL⇒ΔEFATM⇒ΔCLD⇒ΔP,
where the changes in soil moisture (ΔSM) lead to changes in
evaporative fraction (ΔEFSM), which alters the properties of
the planetary boundary layer (ΔPBL) including the state (temperature,
humidity) and the entrainment rate. These three near-surface coupling
mechanisms (ΔSM, ΔEFSM, and ΔPBL) precede
changes away from the land surface that further the change evaporative fraction
(ΔEFATM), leading to changes in cloud development and growth
(ΔCLD), and ultimately forcing changes in precipitation (ΔP).
The chain cycles with ΔP driving ΔSM to varying degrees
depending on the region and season (Zhang et al., 2008). Equation (1) is a
conceptualization of complex and non-linear processes, such that the sign of
the ΔCLD response to a ΔSM forcing can vary (Westra et
al., 2012; Gentine et al., 2013b). Equation (1) is a simplification of the
short (less than a day) timescale coupling mechanisms and neglects large-scale circulation and moisture feedbacks (Lee et al., 2012; Lintner and
Neelin, 2009; Lintner et al., 2013). Additional feedbacks that operate on
short timescales not shown in Eq. (1), such as ΔEFSM or
ΔEFATM leading to ΔSM, may also be important
(Seneviratne et al., 2010; Meng et al., 2014a, b). Despite simplifications,
Eq. (1) highlights the primary control SM exerts on EF (evaporative fraction) as compared to
secondary factors such as entrainment (Gentine et al., 2011). In a convective
regime, ΔSM initiates a series of events that first alter the
atmosphere (ΔPBL) prior to changing P. The series of events
ΔSM–ΔPBL comprises the terrestrial portion of the coupling
mechanisms and is the focus of this study. The statistical relationship
between model-simulated ΔSM or ΔEF and the observed
ΔPBL is examined here. The ΔSM through ΔPBL sequence is a necessary,
but not sufficient, set of processes that determine how P responds to
changes in SM. Therefore, by demonstrating the limitations of various
statistical metrics in capturing the relationships between ΔSM,
ΔEF, and ΔPBL, this study highlights the periods and conditions
that coupling can be diagnosed using the aforementioned diagnostic metrics.
The sensitivity of atmospheric processes to ΔSM has been quantified
with observations (Koster et al., 2003; Taylor and
Ellis, 2006) and multiple model experiments (Dirmeyer et al., 2006; Guo et
al., 2006; Hirsch et al., 2014; Koster et al., 2000, 2006, 2011; Lee et
al., 2012). Ferguson et al. (2012) combined multiple sources of reanalysis
data with LCL (lifting condensation level) and SM observations to examine the relationship between early
morning surface layer SM (SM1) and both the
LCL and the EF in the afternoon during the convective season. The
relationship was quantified using the Kendall τ coefficient (Kτ), a
non-parametric rank correlation coefficient that measures the association
between two time series. Ferguson et al. (2012) found strong coupling
(Kτ) between SM1–EF, EF–LCL, and SM1–LCL over many regions
including monsoon regions such as northern Australia. These three coupling
mechanisms span the first three components in Eq. (1) (ΔSM,
ΔEFSM, ΔPBL). While these represent only part of
the processes involved in land–atmosphere coupling, they comprise a
fundamental pathway by which SM anomalies drive an atmospheric response.
Several regional analyses have investigated the importance of land–atmosphere
coupling in northern Australia (Evans et al., 2011). Koster et al. (2000)
showed land–atmosphere coupling increased the variance of P in both
northern and eastern Australia. In agreement, Ferguson et al. (2012) found
high correlations in SM1–EF, EF–LCL, and SM1–LCL during the convective
(monsoon) season over the northern savannas. These studies were limited in
scope and did not explicitly explore how the coupling behaves during periods
with different background climate states. Therefore, it is important to
evaluate whether the methods used to characterize land–atmosphere behavior
are valid during alternate periods with varying background states.
To examine statistical measures of land–atmosphere coupling strength we
explore the correspondence between the temporal variations in land-surface-model-derived soil moisture
and water flux estimates with the observation-based estimates of the variations in the boundary layer state. The
relationship between EF and shallow cumuli have been demonstrated by Gentine
et al. (2013a); however, here we examine the temporal co-evolution of SM as
it relates to the estimated LCL height during the onset through to the peak
of the monsoon season. Significant statistical association between soil
moisture or surface fluxes and the atmosphere provides a necessary but not
sufficient condition to demonstrate significant land–atmosphere coupling.
The lack of land–atmosphere feedbacks in offline simulations means we cannot
assess cause and effect, but by examining the statistical correspondence we
can determine if the co-evolution of the simulated states (SM and EF) are
consistent with observed LCL.
The statistical association is defined here such that the land surface
processes in Eq. (1) (ΔSM, ΔEFSM) are simulated and
evaluated in relation to the observationally estimated ΔPBL. The
dynamic progression represented in Eq. (1) is simulated for ΔSM and
ΔEFSM only. The physical mechanisms that drive ΔPBL
from ΔSM and ΔEF are not simulated, while the sequence of
events in the atmosphere (ΔEFATM, ΔCLD and ΔP) are neglected. This terrestrial-derived statistical association captures
how a model-simulated ΔSM relates to state changes in the afternoon
mixed layer (ΔPBL) by assuming that ΔPBL can be characterized
using near-surface atmospheric states. Strong association as defined here is
a necessary but not sufficient prerequisite for strong ΔSM–ΔPBL
or ΔSM–ΔP coupling because the full chain of events is not
simulated. An ensemble of offline simulations using two model configurations,
one of which neglects groundwater and therefore contains greatly reduced deep
soil moisture, are driven using four forcing data sets. The simulations
provide estimates of SM1 in addition to SM over the root zone
(SMrz), total ET and the ET components. Afternoon (14:00 LT) LCL is
derived using the near-surface atmospheric variables from the forcing
data sets, and the sensitivity of the ensemble median Kτ is examined for
the onset and peak of the monsoon season.
Observations of the (18–11∘ S, 120–150∘ E) domain-averaged mean annual cycle of precipitation (P in
mmday-1).
We focus on northern Australia to examine whether the relationship between
soil moisture and the boundary layer can be diagnosed from SM1 in regions
with a pronounced dry season, given the influence of groundwater on
transpiration and deep SM variability (Decker et al., 2013). Northern
Australia has a pronounced May to September dry season and a monsoon-driven
wet season from November through February (Fig. 1). The monsoonal climate
allows us to examine the SM1–LCL association as defined in Ferguson et
al. (2012) in sharply contrasting seasons (Fig. 1) that exhibit contrasting
background soil moisture states. By examining the differences between
correspondence during the onset (defined here as SON, September-October-November, to coincide with the
initial increase in rainfall) of the wet season when soil moisture will be
low and then through to the peak (defined as DJF, December-January-February, to coincide with the
precipitation maximum) of the wet season, we aim to determine the reliability
of diagnosing the terrestrial and near-surface stages of land–atmosphere
correspondence using Kτ derived from SM1 and LCL during periods
where total ET fluxes are dominated by either soil evaporation or
transpiration.
This manuscript is organized as follows. The model simulations, the SM1
and ET observations used for model evaluation, and the near-surface
atmospheric data sets are summarized in Sect. 2. Section 3 outlines the
statistical measure used to define the association between the different
states, the derivation of LCL from the atmospheric data, and the model
experiments used to estimate the evaporative fraction and soil moisture. The
Results section consists of the SM1–LCL- and EF–LCL-based association
strength; the impacts of defining association strength with SMrrz
(the root zone SM) are presented in Sect. 4. The results are explained in
terms of the governing physical processes and previous research in Sect. 5.
Model simulations and data
Near-surface atmospheric and forcing data
The LCL (see Sect. 3.2) over the entire study
region is computed from combinations of near-surface atmospheric data using
two reanalysis products. The LCL is also calculated at the two flux sites
using the tower observations. The model simulations (see Sect. 2.2) are
driven using a combination of atmospheric states and fluxes from reanalysis
products, a gauge-based daily precipitation data set, and a 3-hourly
satellite-based precipitation product. We follow Decker et al. (2014) and
utilize four forcing data sets to drive model simulations.
The two gridded sources of temperature, humidity, wind speed, pressure, and
radiative fluxes are the Global Land Data Assimilation System (GLDAS;
http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings; Rodell et
al., 2004) and the Modern-Era Retrospective Analysis for Research and
Applications (MERRA) product (Bosilovich et al., 2008). These two data sets
are utilized due to the high spatial resolution of GLDAS (0.25∘)
and high temporal resolution of MERRA (hourly). Two forcing data sets are
comprised of the uncorrected GLDAS and MERRA data interpolated to a common
0.25∘×0.25∘ grid. In addition, two precipitation-corrected data sets developed in Decker et al. (2014) are used. The
uncorrected atmospheric states and radiative fluxes from MERRA are combined
with P corrected via two algorithms. First, MERRA is corrected using the
Australian Water Availability Project (AWAP) daily gridded precipitation data
(Jones et al., 2009) to remove the monthly biases (labeled MERRA.B). Second,
the MERRA precipitation is replaced with precipitation derived from
disaggregating the daily AWAP data with the 3-hourly Tropical Rainfall
Measuring Mission (TRMM) 3B42 (Huffman et al., 2007) data (labeled
MERRA.BT). These two corrected data sets have identical monthly mean
precipitation but different distributions of submonthly precipitation.
Simulated estimates of soil moisture and evaporative fraction
We use the Community Land Model version 4 (CLM4; Oleson et al., 2010) to
simulate the states and fluxes of water and energy using configurations
documented in Decker et al. (2013, 2014). The land surface model simulations
and reanalysis products allow for the relationships within the terrestrial
leg (SM-PBL in Eq. 1) to be diagnosed without fully simulating the land
surface–atmosphere dynamics and feedbacks. A detailed description of the
groundwater configurations and modifications are given in Decker et
al. (2014).
The suite of simulations is utilized to address forcing data and model
configuration uncertainties in addition to exploring a large soil moisture
state space. Two different configurations of CLM4 are used. The first
consists of the default CLM4 (referred to as CTRL). The second (referred to
as DRY) uses a modified CLM4 that replaces the two-way soil moisture coupling
between the soil column and the aquifer with a free drainage bottom boundary
condition. The modifications significantly reduce the soil moisture at depths
below several centimeters and the ET flux during periods of low rainfall
while not imparting large differences on the changes in total column water
(Decker et al., 2014). The two model configurations thus enable the coupling
between the atmosphere and the land surface to be examined under two
differing background soil moisture states.
The CLM4 evapotranspiration is computed as the sum of the soil evaporation,
the canopy evaporation and the transpiration. Transpiration is determined
from the rate of photosynthesis and is, in part, a function of SM. The
dependence on SM is determined by the soil water potential in each soil
layer, the root distribution (prescribed by plant functional type, PFT), and
the PFT dependence on water stress. The spatial distribution and phenology of
PFTs are specified and identical across all simulations. The C3 grass PFT
sets approximately 99 % of the roots within 1m of the surface, while
approximately 90 % of the roots are within this depth for the broadleaf
evergreen forest PFT.
The experiment design follows the simulations outlined in Decker et
al. (2014) that have been shown to be in good agreement with observations
over parts of Australia. One control (CTRL) simulation and one dry simulation
are equilibrated for the period 1948–1979 using the corrected NCEP/NCAR data
(Qian et al., 2006) after interpolating to the same 0.25∘×0.25∘ grid as the other forcing data sets. The CTRL and DRY
simulations ending in 1979 provide initial conditions for the four CTRL and
four DRY simulations from 1979 to 2007. The model evaluation period spans the
5 years coincident with the SM and ET data from 2003 to 2007. The
associations are computed using the period 1990–2008. Both the CTRL and the
DRY simulations are forced with the four forcing data sets (see Sect. 2.1):
GLDAS, MERRA, MERRA.B, and MERRA.BT, generating a total of eight model
simulations. The SM (from all model layers) and turbulent energy fluxes are
output at 3-hourly intervals (coincident with the temporal resolution of
the GLDAS forcing), while the remaining CLM4 output is saved as monthly
means.
Validation data: soil moisture and evapotranspiration
The spatiotemporal behavior of the simulated surface soil moisture (SM1)
and evapotranspiration (ET) are validated against gridded observationally
based estimates. SM1 is evaluated against the daily Advanced Microwave
Scanning Radiometer – Earth Observing System (AMSR-E) L3 surface SM product.
The data are derived from passive microwave measurements and available for
the period 2002–2011 (Njoku et al., 2003). AMSR-E-based SM compares
favorably with in situ measurements over Australia (Draper et al., 2009) and
exhibits spatiotemporal variability consistent with land model simulations
(Liu et al., 2009). To simplify the comparison with the simulated SM, the
first model layer (∼ 0.7 cm deep) SM is assumed comparable to SM from
AMSR-E despite the uncertain effective measurement depth (approximately
1 cm) that varies with SM.
The simulated evapotranspiration is evaluated against three ET products.
Multiple ET data sets based on different methodologies are included due to the
uncertainty associated with deriving gridded moisture flux data (Jiménez et
al., 2011). The Global Land Evaporation Amsterdam Methodology (GLEAM; Miralles et al., 2011a, b), the model-tree ensemble-based
data set from MPI-Jena (J2010 hereafter) (Jung et al., 2010), and the Moderate
Resolution Imaging Spectrometer (MODIS) MOD16 data set (Mu et al., 2007,
2011) are used to estimate the observed mean seasonal ET fluxes.
The observed ET is estimated using the arithmetic mean of the three data sets
after the GLEAM and MOD16 data are aggregated to the coarse resolution
(0.5∘×0.5∘) of the J2010 data. The simulations are
subsequently compared to the mean observed ET separately for the wet season
(December–February) and the end of the dry season (September–November).
In addition to the gridded SM and ET data sets, the model is evaluated against
observations from two flux tower sites included in the OZ Flux network
(ozflux.org.au). The Adelaide River site (Beringer, 2013a) spans
November 2007 through May 2009 and is located at 13.08∘ S, 131.12∘ E. The Howard Springs site (Beringer, 2013b) spans from 2001
to present and is located at 12.48∘ S, 131.15∘ E. Both sites
provide air temperature, water vapor, surface pressure, radiation, turbulent
fluxes (including ET), and soil moisture measurements at 30 min intervals.
The level 3 (L3) quality controlled data were utilized in this study.
Adelaide River provides SM data at 5 cm depth while Howard Springs provides SM
at a depth from 10 cm. The simulations are validated against the observed ET
and SM at these two locations.
Methods
Kendall τ
We evaluate the relationships between variables involved in land–atmosphere
coupling processes using Kτ, a non-parametric, rank
correlation statistic (Press et al., 1992). Following Ferguson et al. (2012),
Kτ is used to indicate the correspondence between two states important
to land–atmosphere coupling. Kτ does not assume linearity between the
variables being compared and tests for statistical significance. Kτ
ranges from -1 to 1 (positive values indicate the temporal variations are
synchronized), with statistical significance depending on the sample size
(approximately 0.12 for the simulation-based results in this study). Kτ
is defined as
Kτ=No-Nd0.5nn-1,
where No is the number of ordinate pairs, Nd is the
number of disordinate pairs, and n is the number of observations. Ordinate
pairs are pairs of numbers for which the change between them have the same
sign, i.e., both are either positive or negative. The strong seasonal cycle in
northern Australia (Figs. 2, 3) necessitates that the seasonality be removed
from the data or it will likely control the statistical relationship. The
least squares linear trend is removed from the data by calculating the trend
over each season individually. The data are detrended instead of removing the
monthly mean annual cycle to ensure we do not create discontinuities within a
season. Removing the mean annual cycle could possibly subtract very different
mean values from points that are continuous in time, causing artificial
discontinuities between the data from the last day of a month and the first day
of the subsequent month. Detrending the data over a season ensures the
methods do not introduce artificial discontinuities between months within a
given season. The spatially distributed Kτ is calculated between the
seasonally detrended 3-hourly modeled SM1 during the morning and the
estimated 3-hourly LCL from the afternoon at each grid cell for each
month during both the wet and dry seasons. Kτ is additionally derived
with detrended data at two flux tower sites using measurements of SM and LCL
estimated from the tower data. The morning SM1 is utilized because SM will
be highest in the morning prior to decreasing during the day due to ET. The
local time of SM and LCL varies because the simulations and forcing data
utilize Greenwich Mean Time (GMT). The distributed Kτ is found separately for each of the
eight simulated (see Sect. 2.2) estimates of SM1 and the four estimates of
LCL (Sect. 3.2), generating a total of 32 estimates of Kτ for each month
in both the wet and dry seasons. The median Kτ is found separately for
the wet and dry seasons for the two different model configurations
(Sect. 3.3) to give the final estimation of the correspondence. The
association is also diagnosed using Kτ between the model-simulated
afternoon evaporative fraction and the afternoon LCL. A second definition of
association is found by calculating Kτ between the morning time root
zone SM (SMrz) and the afternoon LCL (SMrz–LCL).
SMrz is defined as the vertically averaged SM from the surface to a
depth of 1 m.
The physical meaning of a negative SM–LCL Kτ association is as follows.
A high value of SM will cause a larger ET flux, moistening the lower
atmosphere, causing a lower LCL. Thus, we hypothesize that in regions where the
land and atmosphere are coupled the SM–LCL Kτ should be negative. If SM has
no association with LCL, then Kτ is expected to be statistically
insignificant. Similarly, if ET is negatively associated with LCL (Kτ< 0), it means that high ET may be moistening the lower atmosphere again,
leading to a lower LCL.
Calculation of lifting condensation level
The state of the convective atmosphere is evaluated using the LCL, defined as the height (in pressure) at which a parcel
reaches saturation when ascending adiabatically from the surface. While a
lower LCL is favorable to convection, it is not a sufficient constraint to
guarantee it. For convection to occur a parcel must reach the level of free
convection (LFC), which may not occur even if a parcel reaches the LCL. The
height (in pressure) of the LCL is derived using only near-surface variables
under the assumption that the boundary layer is well developed and therefore
well mixed. Estimating the LCL from near-surface variables provides heights
comparable to direct observations (Ferguson and Wood, 2009). Under these
assumptions, the pressure at the LCL is given by
LCL=Psrf-PsrfTairTdew-cpR,
where Psrf is the surface pressure (Pa), Tair is the near-surface air temperature (K), Tdew is the near-surface dew point
temperature (K), R is the specific gas constant of dry air
(JK-1kg-1), and cp is the specific heat of dry
air at constant pressure (JK-1kg-1). Four spatially explicit
estimates of LCL are found by applying Eq. (4) to several combinations of
near-surface forcing data, and two point-wise estimated are derived from the
flux tower data. The atmospheric states Psrf and Tair are
directly provided by both reanalysis products and the tower measurements. The
measure of atmospheric moisture, Tdew, is calculated for GLDAS,
MERRA, and the tower sites separately using the respective near-surface
humidity, temperature, and pressure data from each data set. The four
distributed estimates of LCL are calculated with Eq. (4) by (1) using GLDAS
for pressure and both temperatures, (2) using MERRA for pressure and both
temperatures, (3) using pressure from MERRA and temperatures from GLDAS, and
(4) using pressure from GLDAS and temperatures from MERRA. The LCL is quality
controlled by limiting LCL to be less than the surface pressure.
Results
Validation of simulated soil moisture and evapotranspiration
The two model configurations are separately validated against the
observationally estimated soil moisture and evapotranspiration on monthly and
seasonal timescales, respectively. Figure 2a shows the time series of the
area-averaged (10–15∘ S, 120–150∘ E) normalized ensemble
mean first layer soil moisture from the CTRL and the DRY ensembles and the
AMSR-E observed data. The simulation dynamics are evaluated using the
normalized SM1 due to the difficulties in direct comparison of simulated
and observed soil moisture (Koster et al., 2009). The strong seasonal cycle
of soil moisture owing to the monsoonal climate is evident in both the
observationally based estimates and the simulations. CTRL and DRY are nearly
identical, aside from the dry season in 2005 where the soil moisture in CTRL
decreases more than that from DRY. The observed moistening of the soil
following the dry seasons in Fig. 2a occurs within a month of that of the
simulated moistening. The mean monthly soil moisture closely follows
the observationally based estimates and exhibits dynamic behavior independent
of the model configuration.
(a) The mean normalized (using the first two moments) first
layer soil moisture (SM1) from the CTRL and DRY simulations and the AMSR-E
observations. (b) The difference between the mean SM1 (from all
simulations over all months from 2004 to 2009) and the AMSR-E observations.
The bias of the ensemble-mean-time-averaged surface layer soil moisture from
the eight simulations against the AMSR-E product is shown in Fig. 2b. Over
large regions of northern Australia, the simulated SM1 is within
0.025 mm3mm-3 of AMSR-E. The difference in mean SM1 between
the two model configurations is similarly small (figure not shown). Figure 2
demonstrates that the temporal evolution (Fig. 2a) and mean state (Fig. 2b)
of the simulated SM1 are similar to the AMSR-E estimates.
The seasonal mean ET is validated against the arithmetic mean of the three
gridded ET products for both DJF (Fig. 3a, c, e) and SON (Fig. 3b, d, f). The
observed DJF ET (Fig. 3e) has a strong north–south gradient with a maxima
centered around 13∘ S, 130∘ E. The strong north–south
gradient is also present in the ensemble mean ET (Fig. 3a); however, the
simulations overestimate DJF ET over much of the domain. The observationally
based estimates show an ET of less than 50 Wm-2 south of
18∘ S while the simulations remain above 60 Wm-2 in this
region. The mean SON ET is markedly lower compared to DJF ET in both the
gridded data (Fig. 3f) and the simulations (Fig. 3b). Similar to DJF, both
the model and the ET product show a strong north–south gradient. The
simulations underestimate the ET in the York Peninsula (east of
140∘ E and north of 17∘ S) during SON and overestimate the
ET in this region during DJF. The overestimation of DJF ET compared to the
gridded product is much more pronounced for the CTRL simulations (Fig. 3a)
than for the DRY simulations (Fig. 3c). The underestimation of the SON ET in the
simulations is largely a result of including the DRY model configuration. The
CTRL simulations exhibit a 10–20 Wm-2 increase in SON ET over the
DRY model runs (Fig. 3b, d). Overall, the model exhibits spatiotemporal ET
in close agreement with this gridded ET product.
Point measurements of SM and ET at two locations show reasonable agreement
with the model simulations. The Howard Springs SM observations 10 cm depth
(Fig. 4a) typically increases from 0.05 to 0.2 mm3mm-3 from the
dry to the wet season. The observations are drier during the wet season and
have a smaller (by a factor of 2) seasonal cycle than both the DRY and CTRL
simulations. DRY is much drier (∼ 0.08 mm3mm-3) than CTRL
(∼ 0.18 mm3mm-3) during the dry season and in better
agreement with the measurements (∼ 0.05 mm3mm-3). This
contrasts with the agreement at the Adelaide River site (Fig. 4b) where the
measurements and CTRL peak at around 0.30 mm3mm-3 during the 2008
wet season. DRY (0.02–0.07 mm3mm-3) is again much drier than
CTRL (0.15 mm3mm-3) during the 2008 dry season but CTRL is in
better agreement with the data (0.15 mm3mm-3). The AMSR-E
estimate, CTRL, and DRY are similar in Fig. 4a and b (the y axis scale is
the same in both figures), while the SM observations at the two sites differ
drastically. The disagreement in the mean as well as the amplitude of the
seasonal variability is likely due to both the difference in scale between
the measurements and simulations and poor representation of soil properties
in the model. When the SM comparison is normalized using the first two
moments as in Fig. 2a (not shown) there is greater agreement between the
measurements, AMSR-E, and the simulations.
The ET data at Howard Springs (Fig. 4c) demonstrates that the CTRL simulation
always produces too little ET during the dry season. While the gridded ET
estimate in Fig. 4c falls within 10 Wm-2of the CTRL simulation
during the dry season, the tower data are nearly 20 Wm-2 greater
than during both the 2007 and 2008 dry seasons. The wet season peak in ET is
well simulated by both CTRL and DRY at Howard Springs. The model performance
is different at the Adelaide River as both CTRL and DRY have a wet season peak ET
of around 120 Wm-2 while the measurements peak closer to
150 Wm-2. Figure 4d further demonstrates that DRY has too little
dry season ET.
The mean ET (Wm-2) from the wet season (DJF shown in the
left hand column) and the transition between the dry and wet seasons (SON
shown in the right hand column). The ensemble mean ET from (a) CTRL
over DJF, (b) CTRL over SON, (c) DRY for DJF,
(d) DRY from SON, (e) OBS (the mean of three gridded ET
products) over DJF, and (f) OBS for SON.
The monthly soil moisture (SM in mm3mm-3) from the
ensemble mean from CTRL and DRY, AMSR-E, and flux tower measurements
(OBStower) from flux tower sites at (a) Howard Springs at
10 cm depth and (b) Adelaide River at 5 cm depth. The monthly
evapotranspiration (ET in Wm-2) from CTRL, DRY, the mean of three ET products
(OBSgridded) and the measurements at the (c) Howard
Springs and (d) Adelaide River flux tower sites.
The results from Figs. 2, 3, and 4 demonstrate that CLM4 simulates the
monthly and seasonal first layer soil moisture and evapotranspiration
reasonably. While the details of the model performance vary depending on
which site, season, and ensemble member are used for validation; overall, the
spatial and temporal patterns of ET and SM are generally captured by the
modeling system. The accuracy of the estimated land surface states and fluxes
therefore enables the use of the simulated variables in the diagnoses of the
land–atmosphere association strength during SON and DJF.
Spatiotemporal mean soil moisture (mm3mm-3) SM as a
function of depth (m) for (a) DJF and (b) SON.
Background SM state
The sharp contrast in background SM state can be illustrated by taking a
spatiotemporal average of SM as a function of depth for CTRL and DRY for
DJF (Fig. 5a) and SON (Fig. 5b). The soil moisture away from the surface is
markedly different between CTRL and DRY. During DJF, CTRL shows a slight
increase in soil moisture with depth, reaching a peak of
∼ 0.35 mm3mm-3 at depths near 3 m. In contrast, DRY has
a peak soil moisture of only ∼ 0.24 mm3mm-3 at the
surface and decreases with depth to near zero at 3 m. Similar patterns of SM
with depth are seen over SON; however, SM1 is considerably lower for both
CTRL and DRY compared to DJF.
Despite the similar mean and temporal behavior of SM1 shown in Fig. 2, SM
away from the surface differs substantially between the two model
configurations (Fig. 5). The mean DJF ET is similar between CTRL and DRY,
with differences between the two of only 10–20 Wm-2, corresponding
to roughly 10–20 % of the mean value. The fractional contribution of
transpiration to the total ET during DJF is roughly 10–30 % for both DRY
and CTRL (Fig. 6), indicating that the evaporation is the dominant ET
mechanism. The enhanced mean SM in CTRL causes the CTRL ET to be greater than
the DRY ET during DJF, yet both compare reasonably well to the
observationally based estimates (Fig. 3). However, the lack of SM at depths
below several centimeters for DRY during SON causes the reduced ET as
compared to CTRL during this period. The mean ET during SON is sensitive to
the mean SM away from the surface, indicating that transpiration
significantly contributes to the total ET during this period as can be seen
in Fig. 6. The large contribution of transpiration to the total ET in CTRL
(Fig. 6b) is facilitated by the moist subsurface soil moisture (Fig. 5b). The
reduced root zone SM in DRY leads to an increase in water stress and reduced
transpiration, causing both the lower mean ET and transpiration fraction in
DRY relative to CTRL. This reduction during SON is large relative to the mean
ET during the period (Fig. 3).
The mean transpiration fraction (fraction of total ET from
transpiration defined as the ratio of transpiration over total ET) from the
wet season (DJF shown in the left hand column) and the transition between the
dry and wet seasons (SON shown in the right hand column). The ensemble mean
transpiration fraction to total ET from (a) CTRL over DJF,
(b) CTRL over SON, (c) DRY over DJF, and (d) DRY
over SON.
The ensemble median Kτ correlation metric between
the afternoon time (local) EF and the afternoon
computed LCL from (a) CTRL over DJF,
(b) CTRL over SON, (c) DRY over DJF, and (d) DRY
over SON. The black outlined squares in (a–d) denote the values
from the flux tower sites. Only statistically significant (95 %
confidence level) results are shown in (a–d).
The ensemble median Kτ correlation metric between
the morning first layer soil moisture (SM1) and the afternoon computed
LCL from (a) CTRL over DJF,
(b) CTRL over SON, (c) DRY over DJF, and (d) DRY
over SON. The black outlined squares in (a–d) denote the values
from the flux tower sites. Only statistically significant (95 %
confidence level) results are shown in (a–d).
The ensemble median Kτ correlation metric between
the morning root zone soil moisture (SMrz) and the afternoon
computed LCL from (a) CTRL over DJF,
(b) CTRL over SON, (c) DRY over DJF, and (d) DRY
over SON. The black outlined squares in (a–d) denote the values
from the Howard Springs flux tower site. Only statistically significant
(95 % confidence level) results are shown in (a–d).
The standard deviation of the Kτ correlation metric
among the ensemble members between the afternoon computed LCL and either the morning root zone soil moisture
(SMrz) over (a) DJF and (b) SON or the morning
first layer soil moisture (SM1) over (c) DJF and
(d) SON.
Correspondence: EF–LCL and SM1–LCL
The statistical association between the evaporative fraction and the LCL is
shown in Fig. 7, with the results from the two flux towers shown in enclosed
squares around 13∘ N, 131∘E. The insignificant
associations are greyed out while the statistically significant results are
shown in color. During DJF, CTRL (Fig. 7a) and DRY (Fig. 7c) exhibit strong
surface flux–atmosphere correspondence, with the strongest association over
the Cape York Peninsula (east of 140∘ E and north of 17∘ S)
and the southwestern part of the domain. Similarly, the EF–LCL association is
significant during SON (Figs. 7b, d) over much of the domain, although
the magnitude is reduced relative to DJF. Both ensembles show strong
associations independent of the season; however, the differences between CTRL
and DRY vary with season. The DJF EF–LCL correspondence near 15∘ S,
132∘ E is statistically significant in DRY (Fig. 7c) but not in CTRL
(Fig. 7a), contrasting the similar SON EF–LCL association in this region
exhibited by both DRY (Fig. 7d) and CTRL (Fig. 7b). The flux towers (boxed
squares in Fig.7a–c) show statistically significant association between
EF and the LCL during both seasons. The EF–LCL correspondence from the tower
observations agree more closely with DRY in DJF as CTRL shows statistically
insignificant association in the region (13∘ S, 131∘ E). The
reduced deep layer soil moisture resulting from the removal of the
groundwater module enhances the DJF correspondence in agreement with the
tower data.
Figure 8 shows the median Kτ between SM1 and the LCL
(see Sect. 3.3) for CTRL and DRY separately during DJF (Fig. 8a, c) and SON
(Fig. 8b, d). Several important features are present in Fig. 8. The
SM1–LCL association during DJF and SON is largely similar between the two
model configurations. CTRL (Fig. 8a) and DRY (Fig. 8c) exhibit similar
spatial patterns and magnitudes of Kτ. Some regions (17∘ S,
126∘ E) exhibit increases in the magnitude of Kτ in CTRL
relative to DRY in DJF (Fig. 8a, c) although the differences are
statistically insignificant over most of the domain. Regardless of these
slight variations in Kτ, CTRL and DRY exhibit a strong association
between SM1 and the boundary layer during the peak of the wet season over
coincident parts of the domain. Both model configurations also show areas
(15∘ S, 131∘ E) with insignificant correspondence adjacent
to the strongly associated regions. In contrast, CTRL and DRY both contain
regions of significant positive Kτ demonstrating a negative
correspondence during SON, in disagreement with the results from the Adelaide
River tower site. The tower sites show statistically significant
negative SM–LCL association during DJF adjacent to a region (15∘ S,
131∘ E) of insignificant correspondence in both simulations. The
similarity in SM1–LCL correspondence between CTRL and DRY during both DJF
and SON implies a similar temporal variability of SM1 as related to the
LCL. From Fig. 3, the mean ET fluxes are considerably different during SON.
The similar temporal behavior relative to the LCL for both DRY and CTRL
indicates that the SM1 variability is physically independent of the
season's mean ET fluxes.
Contrasting Figs. 7 and 8 reveals that the surface fluxes (Fig. 7b, d) are
associated with the LCL despite the simulated surface layer soil moisture
(Fig. 8b, d) lacking similar correspondence. The regions of positive Kτ
in Fig. 8 contradict the strongly negative Kτ in Fig. 7 during SON. The
flux towers show negative association for both EF–LCL and SM–LCL during DJF
and SON in Figs. 7 and 8. The EF–LCL correspondence during DJF is much
stronger than the correlation from SM1, and DRY exhibits regions of
stronger EF–LCL correspondence than CTRL; however, the differences are not
statistically significant over much of the domain. A key difference between
the flux tower and model simulation estimated Kτ is the depth of the SM.
The measurement depth at the tower sites are 5 and 10 cm for Adelaide River
and Howard Springs respectively, while the model surface layer soil moisture
is taken from a depth of 0.7 cm. The change in sign of SM1–LCL Kτ
from SON (Fig. 8b, d) to DJF (Fig. 8a, c) demonstrates that applying Eq. (4)
to SM1 and the LCL does not always capture the co-evolution of the land
surface and the atmosphere during periods where deep SM and transpiration
dominate the ET flux.
In short, our results demonstrate that the simulated surface layer soil
moisture cannot adequately capture the SM–LCL association during both DJF and
SON. The significant contributions of transpiration to the total ET fluxes
(especially during SON) are responsive to perturbations in SMrz and
not SM1.
Proposed Association strength definition: SMrz–LCL
The definition of a statistical metric that captures the relationship between
land surface moisture states and fluxes must encompass the relevant physical
mechanisms. Previously, observationally derived values of Kτ were
limited to using SM1 because the AMSR-E (or other microwave) SM
measurements typically measure to depths of less than a few centimeters beneath
the soil surface (Ferguson et al., 2012). Computing Kτ between SM1
and the LCL incorporates the surface layer soil moisture that is important
for surface evaporation from the soil. Therefore, the relationship between the
temporal variations in SM and the LCL in DJF (or other periods where the ET
is largely comprised of soil evaporation) can be adequately defined using
SM1. Kτ computed from SM1 neglects SMrz variations that
drive transpiration during the initial increase in precipitation following
the dry season and therefore may not fully encompass the extent of
land–atmosphere associations. Acknowledging the importance of transpiration
during the northern Australian wet season, we further evaluate the
land–atmosphere association by computing Kτ between the vertically
averaged SMrz and the LCL. As opposed to remotely sensed SM from
AMSR-E (or other satellite products), the use of simulated SM facilitates the
estimation of SMrz. Applying Eq. (4) using SMrz imposes a
different set of problems, as the rooting depth is model dependent and
generally only approximately known. There is substantial evidence that
eucalypts have rooting depths exceeding 20 m (Schenk and Jackson 2002),
however neither CLM4 nor the direct observations in this study extend that
deep. Due to these limitations, SMrz is computed as the weighted
mean of the SM observations at 10, 40, and 100 cm for the Howard Springs
site. We assume that the SMrz consists of the soil layers between
the surface and a depth of 1m, as more than 90 % of the prescribed
roots in CLM4 are within 1m of the surface (Oleson et al., 2010). This
assumed rooting depth is consistent with the model formulation but not
realistic given the rooting depths of eucalypts.
Figure 9 shows the ensemble median Kτ diagnosed between SMrz
and the LCL. Comparing Figures 8 and 9 it is clear that including the portion
of SM that partially controls transpiration increases the magnitude of the
DJF SM–LCL associations and eliminates the region near 14∘ S,
131∘ E with statistically insignificant correspondence (Fig. 8a, c)
despite soil evaporation contributing significantly to the simulated ET.
Large differences between the SON SMrz–LCL and SM1–LCL Kτ
are seen south of 15∘ S and east of 130∘ E. Despite large
regions of statistically significant SON SMrz–LCL, Kτ for CTRL
(Fig. 9b) and DRY (Fig. 9d) regions of insignificant association are
prevalent near 13∘ S, 131∘ E. The flux-tower-derived SON
SMrz–LCL correspondence is insignificant in agreement with the DRY
and CTRL results near 13∘ S, 131∘ E. The similarity between
the DRY and CTRL SMrz–LCL Kτ highlights the negligible
groundwater impact (Fig. 9b, d). Comparing Fig. 9b and d with Fig. 3b and d
reveals that despite the impact of groundwater on the mean ET flux over SON,
the mean state of the deep SM imparts little influence on the temporal
dynamics of SMrz in relation to the LCL. Neglecting the SM beneath
the surface layer in the calculation of Kτ results in a weak diagnosis
of SM–LCL association during SON because transpiration is partly governed by the
water availability within the root zone. By defining the association using
SMrz, it is clear that the land is strongly linked to the LCL during
both DJF and SON. The DJF SM–LCL association in CTRL near flux tower sites is
stronger when defined in this manner, although both sets of simulations still
show SMrz to be statistically associated to the LCL.
The SM1–LCL and SMrz–LCL Kτ shown in Figs. 8 and 9 are the
median from ensembles with 32 estimates. The ensembles explicitly use
multiple constructions of LCL to sample the possible range of atmospheric states
given the near-surface MERRA and GLDAS estimates and may lead to uncertain
estimates of Kτ. The inter-ensemble uncertainty of the Kτ metric is
examined to demonstrate the robustness of the results. The standard deviation
of the association between SMrz and the LCL and between SM1 and
the LCL among the ensemble members is generally less than 0.15 (Fig. 10a–d).
The variation among the ensemble members is smaller than the median Kτ
shown in Figs. 8 and 9. The low standard deviation relative to the median
demonstrates that the association shown in Figs. 8 and 9 is robust, since more than 83 % of the Kτ estimates (assuming they are normally
distributed) have a correspondence of the same sign reported in the figures.
The correspondence using SM1 (Fig. 10c) shows larger ensemble uncertainty
near the coast centered around 135∘ E compared to the SMrz
association in DJF (Fig. 10a) and over the Cape York Peninsula in SON
(Fig. 10b, a). Aside from the region near 15∘ S, 130∘ E
during SON, the larger ensemble uncertainty is found when using SM1 to
define the correspondence.
Discussion
The seasonal ET from CTRL, DRY, and the gridded ET products from DJF through
SON provide insight into the mechanisms that limit the SON DRY ET. The ET
from CTRL and DRY are similar (within ±10 %) during the large DJF
precipitation forcing. The dry season commences between MAM (March-April-May) and JJA (June-July-August; Fig. 1)
resulting in increased vapor pressure deficit (VPD) between the vegetation
and the atmosphere and increased photosynthetically active radiation (PAR).
The changes in VPD and PAR promote increased transpiration from DJF through
MAM, although the actual transpiration is also governed by SMrz.
Comparing Figs. 3, 5, and 6 indicates that the DRY ET is relatively SM
limited and unable to maintain ET similar in magnitude to CTRL and the
observationally based estimates during SON. The SM limitation causes a
reduction in the total ET by limiting the amount of transpiration (Fig. 6d).
Within the model, the soil column–groundwater interactions parameterized in
CTRL inhibit the large, ET limiting SMrz reduction present in DRY.
In reality, the inability of DRY to maintain ET during SON may result from the
shallow rooting depths assumed in CLM4. The depths are substantially
shallower than the rooting depths of eucalypts. Realistic rooting depth
profiles reaching nearly 20 m in Australia (Schenk and Jackson, 2002) and
corresponding soil layer depths may negate the impact of the parameterized
soil column–groundwater impacts current in CLM4.
The EF–LCL association (Fig. 7) is similar for both model configurations
despite the mean ET (Fig. 3), SM (Fig. 5) and transpiration fraction
(Fig. 6) differing considerably between CTRL and DRY. The EF–LCL
similarity holds for both DJF and SON despite the differing background soil
moisture states between the two periods and differing contributions of
transpiration to the total ET (Fig. 6). The results indicate that while
the mean ET and transpiration fraction is a strong function of mean soil
moisture, the SM–LCL association diagnosed using offline simulations of SM
and EF with an observationally estimated LCL is insensitive to the
background state. The coincidence of the temporal variations in SM, EF, and
LCL are demonstrated by the large values of Kτ. These seemingly
counterintuitive results may be an artifact of using a rank correlation
coefficient to determine the strength of the correspondence. Kτ only
measures the temporal coincidence of the two time series while neglecting
the magnitude of these variations. Therefore, Kτ cannot distinguish
between a dry SM state with the small evaporative fluxes and a wet state
with large fluxes if the timing of the SM and flux variations are identical.
Although Kτ is largely independent to the background soil moisture
state, alternative definitions of association may not remain as invariant.
While association in Fig. 7 is largely unaffected by the mean SM state, the
mean ET flux is largely derived from deeper SM through transpiration during
the onset of the wet season prior to DJF. The statistical relationship
between soil moisture and the boundary layer under these conditions is poorly
defined using SM1. The consistent EF–LCL co-evolution during SON and DJF
highlights the inadequacy of characterizing land–atmosphere processes using
only SM1. Attempting to define the SM–LCL relationship with SM1 results
in a physically improbable conclusion where Kτ transitions from positive
(Fig. 7b) to negative (Fig. 7a) as the wet season is established (Fig. 1) and
directly contradicts the EF–LCL results. Despite the domain's mean
precipitation increasing from roughly zero to several millimeters per day
during SON, Kτ from SM1–LCL is both positive (i.e., 15∘ S,
126∘ E) and negative (i.e., 15∘ S, 134∘ E) over this
period. The transition from negligible (or positive) to strong statistical
rank correlation between the soil moisture and the atmosphere during the wet
season is an artifact resulting from the use of SM1. More consistent
correspondence in general agreement with the EF–LCL dynamics throughout the
wet season exists between SMrz and LCL because transpiration is
incorporated into the diagnostic. During SON, the dry surface layer SM is
responsible for little ET, so variations in ET are not associated with
variations in SM1. The SMrz–LCL Kτ eliminates the
insignificant association around 17∘ S, 128∘ E exhibited in
the SM1–LCL metric. Despite regions of significant SMrz–LCL
association in DRY and CTRL, the Howard Springs data show insignificant
SMrz–LCL correspondence during SON. The lack of observed
association is possibly related to the inability to sample SM at depths that
correspond to the physically relevant rooting depths. The necessity of using
SMRZ agrees with Lee et al. (2012), where transpiration was found to limit precipitation
variability over tropical regions. The importance of transpiration among the
various ET components is not limited to northern Australia or monsoon regions
(Coenders-Gerrits et al., 2014; Schlesinger and Jasechko, 2014), highlighting
the need to characterize land–atmosphere dynamics using SM well beneath the
surface.
Statistically determing the association using only near-surface variables
from land surface model simulations and atmospheric data as done in this study
(i.e. Ferguson et al., 2012; Betts, 2009) is limited due to only
examining a part of the full land–atmosphere coupling processes. While the
LCL is an important determinant in the formation of precipitation, moisture
convergence, upper level inversions, convective available potential energy,
wind shear, and many other factors play important roles in the formation of
convection. The correspondence diagnosed in this study with Eq. (4) is by
definition limited in scope to only part of the coupling continuum described
in Eq. (1). Therefore, an association defined using these methods provides a
necessary but not sufficient condition for strong land–atmosphere
interactions between soil moisture and precipitation.
Our results likely extend to monsoonal regions beyond northern Australia.
GLACE (Global Land–Atmosphere Coupling Experiment; Koster et al., 2006) revealed multiple areas of strong
land–atmosphere coupling coincide with major monsoon systems. The strong
co-evolution during the wet season (September–February) diagnosed using
SMrz and Kτ in our results qualitatively agrees with the
strong coupling in monsoon regions from GLACE. The dry season antecedent to
the large precipitation fluxes induces low evaporation while allowing deeply
rooted plants to transpire despite the prolonged dry period. These conditions
over northern Australia (Figs. 3, 4) lead to transpiration dominating the ET
flux during the onset of the wet season. The behavior of the land–atmosphere
system as diagnosed using Kτ under these conditions must be defined
using SMrz rather than SM1 to ensure the relevant pathways of
the moisture fluxes are not neglected.
Our results demonstrate the necessity of capturing the relevant physical
processes when designing a metric to evaluate the relationship between the
land and the atmosphere. The contradiction between the SON SM1–LCL and the
EF–LCL relationships in our study suggests that the methods of Ferguson et
al. (2012) will fail to find coupling during periods when the land surface
fluxes respond to SMrz but not SM1. Future research that
investigates SM–LCL using Kτ within a fully coupled land–atmosphere system
should not neglect SMrz in favor of SM1. Failure to incorporate
the relevant SM information would directly limit the situations for which the
diagnosed coupling is valid.
Conclusions
The feasibility of diagnosing the land–atmosphere relationship
using a rank correlation coefficient is analyzed utilizing ensembles of land
surface simulations and near-surface atmospheric data. Using four forcing
data sets, ensembles of CLM simulations over northern Australia are performed,
using configurations that intentionally span a range of mean SM states by
either including or neglecting soil column–groundwater interactions. The
seasonal dynamics of the simulated SM1 is insensitive to the mean soil
moisture state and all simulations compare favorably with the AMSR-E soil
moisture product. Furthermore, the simulated ET from December to February is
similar between the CTRL and DRY runs, with both configurations largely
consistent with the DJF ET estimated from three gridded ET products.
The strength of the temporal co-evolution of land and atmosphere states is
diagnosed between both SM1 and EF from the simulations and the LCL as
calculated from the near-surface atmospheric data. In line with the coupling
strength found in previous studies, during the peak wet season strong SM1–LCL
and EF–LCL associations are shown. The wet season onset (SON) shows large
rank correlation coefficients between EF and LCL that contrasts the lack of
correlation between SM1 and LCL. The contradicting correlations between
EF–LCL and SM1–LCL demonstrate that the SON land–atmosphere relationship is
not properly characterized with SM1. The land–atmosphere interactions during
periods with non-negligible transpiration necessitates the use of root zone
soil moisture instead of the surface soil moisture to properly capture the
physical processes. The correlation between SMrz and LCL differs
considerably from that between SM1 and LCL. The co-evolution of
SMrz and LCL is shown by strong statistical correspondence
throughout the wet season and is consistent with the co-evolution between EF
and LCL. During the peak of the wet season, SM1 is sufficient to explain
the SM–LCL association while during the monsoon season onset SMrz
is necessary. The results demonstrate that the root zone soil moisture must
be considered when diagnosing the relationship between SM and the LCL.
Our results show that the statistically diagnosed land–atmosphere
correspondence in offline simulations is insensitive to the mean vertical
profile of soil moisture. It is, however, sensitive to the depth of the soil
moisture considered. While the strong soil moisture-atmosphere associations
shown here are a necessary but not sufficient condition to diagnose full
land–atmosphere coupling, the results demonstrate the need to describe
SM–LCL coupling using the physically relevant soil moisture. Studies that
explore the behavior of the land–atmosphere system should use a statistical
measure which encapsulates the SM that is physically relevant to the dominant
processes. Future studies that evaluate land–atmosphere coupling using a
full land–atmosphere model environment risk not capturing regions of
land–atmosphere coupling if only SM1 is considered. In order to evaluate
coupling during periods when ET is dominated by transpiration, SMrz
should be considered. We recommend that future studies of land–atmosphere
coupling focus on root zone soil moisture rather than surface layer soil
moisture.