Interactive comment on “ Diagnosing the seasonal land – atmosphere coupling strength over Northern Australia : dependence on soil moisture state and coupling strength definition

Figure 3. The ensemble median Kendall-tau correlation metric (K) between the morning time (local) evaporative fraction (EF) and the afternoon computed lifting condensation level (LCL) at from (a) CTRL over DJF, (b) CTRL from SON, (c) DRY over DJF, and (d) DRY from SON. Only statistically significant (95% confidence level) results are shown in (a)-(d). Figure 5. Same as Figure 3 but for K between SMRZ-LCL.


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 Figures 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;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 where the changes in soil moisture (∆SM) lead to changes in evaporative fraction (∆EF SM ), which alters the properties of the planetary boundary layer (∆PBL) including the state (temperature, humidity) and the entrainment rate.These three near surface coupling mechanisms (∆SM, ∆EF SM , and ∆PBL) precede changes away from the land surface that further change evaporative fraction (∆EF ATM ), leading to changes in cloud development and growth (∆CLD), and ultimately forcing changes in precipitation (∆P ).
The chain cycles with ∆P driving ∆SM to varying degrees depending on the region and season (Zhang et al., 2008).Equation ( 1) is a conceptualization of complex and nonlinear processes, such that the sign of the ∆CLD response to a ∆SM forcing can vary (Westra et al., 2012;Gentine et al., 2013).Equation ( 1) is a simplification of the short (less than a day) timescale coupling mechanisms.Large or decadal scale processes, such as the response of vegetation to P (or P to vegetation) (Betts et al., 1996), or large scale circulation and moisture feedbacks (Lee et al., 2012;Lintner and Neelin, 2009;Lintner et al., 2013) are neglected.Additional feedbacks that operate on short timescales not shown in Eq. ( 1), such as ∆EF SM or ∆EF ATM leading to ∆SM, may also be important (Seneviratne et al., 2010;Meng et al., 2014).Despite simplifications, Eq. (1) highlights the primary control SM exerts on EF as compared to secondary factors such as entrainment (Gentine et al., 2011).In a convective regime ∆SM initiates a series of events that first alter the atmosphere (∆PBL) prior to changing P .
The series of events from ∆SM-∆PBL comprises the terrestrial portion of the coupling mechanisms is the focus of this study, with coupling examined here limited to these Introduction

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Full processes.The ∆SM through ∆PBL sequence is a necessary, but not sufficient, set of processes that determine how P responds to changes in SM.
The sensitivity of ∆P to ∆SM has been quantified with observations (Koster et al., 2003;Taylor and Ellis, 2006) and multiple model experiments.The first phase of the Global Land Atmosphere Coupling Experiment (GLACE) identified several regional "hot spots" during boreal summer where SM was strongly coupled to P with increased SM leading to increased P (Guo et al., 2006;Koster et al., 2006).The realism of these "hot spots" has not been demonstrated using observations, although the models used to simulate these "hot spots" have been validated with station measurements from several locations (Dirmeyer et al., 2006).Simulations indicate that land-atmosphere coupling can amplify P variance when the near surface humidity is high and the evaporative fraction depends on SM (Koster et al., 2000).The second phase of GLACE quantified the ability of SM anomalies to affect the temperature and P in sub-seasonal forecasts.These experiments demonstrated the asymmetric response of changes in forecast skill to the sign of the SM anomalies (Koster et al., 2011).GLACE-2 like experiments over Australia also exhibit asymmetry in the impact of coupling strength on maximum and minimum temperatures (Hirsch et al., 2013).
Alternate efforts have examined only a subset of the dynamics processes conceptualized in Eq. (1). Lee et al. (2012) turned off transpiration in a coupled land-atmosphere model to show that extreme precipitation is reduced owing to transpiration over tropical regions.In contrast to examining how moisture fluxes from the root zone alter coupling, Ferguson et al. (2012) combined multiple sources of reanalysis data with lifting condensation level (LCL) and SM observations to examine the relationship between early morning surface layer SM (SM 1 ), and both the LCL and the EF in the afternoon during the convective season.The relationship was quantified using the Kendall tau coefficient (K τ), a non-parametric rank correlation coefficient that measures the association between two time series.Ferguson et al. (2012) found strong coupling (K τ) between SM 1 -EF, EF-LCL, and SM 1 -LCL over many regions including monsoon regions such as Northern Australia.These three coupling mechanisms span the first Introduction

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Full (1) (∆SM, ∆EF SM and ∆PBL).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 landatmosphere coupling increased the variance of P in both Northern and Eastern Australia.In agreement, Ferguson et al. (2012) found high correlations in SM 1 -EF, EF-LCL, and SM 1 -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.
We focus on Northern Australia to examine whether coupling strength can be diagnosed from SM 1 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. 1a).The monsoonal climate allows us to examine the SM 1 -LCL coupling as defined in Ferguson et al. (2012) in sharply contrasting seasons (Fig. 1a-d) that exhibit contrasting background soil moisture states.By examining the differences between coupling strength during the onset (defined here as SON 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 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 coupling using K τ derived from SM 1 and LCL during periods where total ET fluxes are dominated by either soil evaporation or transpiration.The coupling strength is defined here such that the land surface processes in Eq. (1) (∆SM, ∆EF SM and ∆PBL) are examined, while the sequence of events in the atmosphere (∆CLD and ∆P ) are neglected.This terrestrial derived coupling captures how ∆SM relates to state changes in the mixed layer (∆PBL).Strong coupling as defined here is a necessary but not sufficient prerequisite for strong ∆SM-∆P coupling.An ensemble of offline simulations using two model configurations, one Introduction

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Full of which neglects groundwater and therefore contains greatly reduced deep soil moisture, are driven using four forcing datasets.The simulations provide estimates of SM 1 in addition to SM over the root zone (SM rz ), total ET and the ET components.Afternoon (14:00 LT) LCL is derived using the near surface atmospheric variables from the forcing datasets, and the sensitivity of the ensemble median K τ is examined for the onset and peak of the monsoon season.This manuscript is organized as follows.The SM 1 and ET observations used for model validation and the near surface atmospheric datasets are summarized in Sect. 2. Section 3 outlines the statistical measure of association used for coupling strength, 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 SM 1 -LCL and EF-LCL based coupling strength, the impact of defining coupling strength with SM rrz (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.

Validation data: soil moisture and evapotranspiration
The simulated surface soil moisture (SM 1 ) 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 to 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.Introduction

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Full The simulated evapotranspiration is evaluated against three ET products.Multiple ET datasets based on different methodologies are included due to the uncertainty associated with deriving gridded moisture flux data (Jimenez et al., 2011).The Global Land Evaporation Amsterdam Methodology (GLEAM) (Miralles et al., 2011a, b), the modeltree ensemble based dataset from MPI-Jena (J2010 hereafter) (Jung et al., 2010), and the Moderate Resolution Imaging Spectrometer (MODIS) MOD16 dataset (Mu et al., 2007(Mu et al., , 2011) ) are used to estimate the observed mean seasonal ET fluxes.The observed ET is estimated using the arithmetic mean of the three datasets 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 (December-February) and the end of the dry (September-November) seasons.

Near surface atmospheric state and flux data
The lifting condensation level (LCL see Sect.3.2) is computed from combinations of near surface atmospheric data using two reanalysis products.Similarly, the model simulations (see Sect. 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.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.These two corrected datasets have identical monthly mean precipitation but different distributions of sub-monthly precipitation.

Kendall τ
We evaluate the land-atmosphere coupling strength using Kendall tau (K τ), a nonparametric, rank correlation statistic (Press et al., 1992).Following Ferguson et al. (2012), K τ is used to indicate the correspondence between two states important to land-atmosphere coupling.K τ does not assume linearity between the variables being compared and tests for statistical significance.K τ ranges from −1 to 1 (positive values indicate the temporal variations are synchronized), with statistical significance depending on the sample size (approximately 0.12 in this study).K τ is defined as where N o is the number of ordinate pairs, N d 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.K τ is calculated between the detrended three hourly modeled SM 1 during the morning and the estimated three hourly LCL from the afternoon at each grid cell for each season.The local time of SM and LCL varies because the simulations and forcing data utilize GMT.K τ is found separately for each of the eight simulated (see Sect. and the four estimates of LCL (Sect.3.2), generating a total of 32 estimates of K τ.The median K τ 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 coupling strength.The coupling is also diagnosed using K τ between the model simulated afternoon evaporative fraction and the afternoon LCL.A second definition of coupling is found by calculating K τ between the morning time root zone SM (SM rz ) and the afternoon LCL (SM rz -LCL).SM rz is defined as the vertically averaged SM from the surface to a depth of 1 m.

Calculation of lifting condensation level
The state of the convective atmosphere is evaluated using the lifting condensation level (LCL), defined as the height (in pressure) that 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 where P srf is the surface pressure (Pa), T air is the near surface air temperature (K), T dew is the near surface dew point temperature (K), R is the specific gas constant of dry air (J K −1 kg −1 ), and c p is the specific heat of dry air at constant pressure (J K −1 kg −1 ).
Four estimates of LCL are found by applying Eq. ( 4) to several combinations of near surface forcing data.While P srf and T air are directly provided by both reanalysis products, T dew is calculated using the available near surface atmospheric states.The four Introduction

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Full 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.

Simulated estimates of soil moisture and evaporative fraction
Owing to the need to evaluate the soil moisture and evaporative fraction at sub-daily timescales in computing K τ, 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. (2013Decker et al. ( , 2014)).The land surface model simulations and reanalysis products allow for the terrestrial leg (DSM-DPBL in Eq. 1) to be explored explicitly.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 configuration 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 twoway 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 changes 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 10440 Introduction

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Full and phenology of PFTs are specified and identical across all simulations.The C3 grass PFT sets approximately 99 % of the roots within 1 m 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 The SM (from all model layers), and turbulent energy fluxes are output at three hourly intervals (coincident with the temporal resolution of the GLDAS forcing), while the remaining CLM4 output is saved as monthly means.

Validation of simulated soil moisture and evapotranspiration
The two model configurations are separately validated against the observed soil moisture and evapotranspiration on monthly and seasonal timescales, respectively.to the monsoonal climate is evident in both the observations and the simulations.CTRL and DRY are nearly identical aside from the dry season in 2003 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 to that of the simulated moistening.The mean monthly soil moisture closely follows that of the observations and exhibits dynamic behavior independent of the model configuration.
The bias of the ensemble mean time averaged surface layer soil moisture from the eight simulations against the AMSR-E data is shown in Fig. 2b.Over large regions of Northern Australia, the simulated SM 1 is within 0.025 mm 3 mm −3 of AMSR-E.The difference in mean SM 1 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 SM 1 are similar to the AMSR-E observations.The seasonal mean ET is validated against the arithmetic mean of the three gridded ET products for both DJF (Fig. 3a, c, and e) and SON (Fig. 3b, d, and 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 observations show an ET of less than 50 W m −2 south of 18 • S while the simulations remain above 60 W m −2 in this region.The mean SON ET is markedly lower compared to DJF ET in both the observations (Fig. 3f) and the simulations (Fig. 3b). the estimated land surface states and fluxes therefore enables the use of the simulated variables in the diagnoses of the land-atmosphere coupling strength.

Background SM state
The sharp contrast in background SM state can be illustrated by taking a spatialtemporal average of SM as a function of depth for CTRL and DRY for DJF (Fig. 4a) and SON (Fig. 4b).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 mm 3 mm −3 at depths near 3 m.In contrast, DRY has a peak soil moisture of only ∼ 0.24 mm 3 mm −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 SM 1 is considerably lower for both CTRL and DRY compared to DJF.Despite the similar mean and temporal behavior of SM 1 shown in Fig. 2, SM away from the surface differs substantially between the two model configurations.The mean DJF ET is similar between CTRL and DRY, indicating that the surface 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, however both compare reasonable well to the observations (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.The reduced root zone SM in DRY leads to an increase in water stress and reduced transpiration.This reduction during SON is large relative to the mean ET during the period (Fig. 3).

Coupling strength: EF-LCL and SM 1 -LCL
The statistical association between the evaporative fraction and the LCL is shown in Fig. 5.During DJF, CTRL (Fig. 5a) and DRY (Fig. 5c) exhibit strong surface fluxatmosphere coupling, with the strongest coupling over the Cape York Peninsula (East Introduction

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Full of 140 • E and North of 17 • S) and the Southeastern part of the domain.Similarly, the EF-LCL coupling is significant during SON (Fig. 5b and d) over much of the domain, although the magnitude is reduced relative to DJF.Both ensembles show strong coupling independent of the season, however the differences between CTRL and DRY vary with season.DRY is generally more strongly coupled than CTRL during DJF (Fig. 5a     and c), contrasting the similar coupling strength exhibited by both DRY and CTRL in SON (Fig. 5b and d).The reduced deep layer soil moisture resulting from the removal of the groundwater module enhances the DJF coupling while suppressing the SON coupling.
Figure 6 shows the median Kendall tau (K τ) between SM 1 and the LCL (see Sect. 3.3) for CTRL and DRY separately during DJF (Fig. 6a and c) and SON (Fig. 6b  and d).Several important features are present in Fig. 6.The SM 1 -LCL coupling during DJF and SON is largely similar between the two model configurations.CTRL (Fig. 6a) and DRY (Fig. 6c) exhibit similar spatial patterns and magnitudes of K τ.Small regions exhibit increases in the magnitude of K τ in CTRL relative to DRY in DJF (Fig. 6a and c) although the differences are statistically insignificant over most of the domain.
Regardless of these slight variations in K τ, CTRL and DRY exhibit a strongly coupled relationship between SM 1 and the boundary layer during the peak of the wet season over coincident parts of the domain.Both model configurations also show areas with insignificant coupling adjacent to the strongly coupled regions.In contrast, CTRL and DRY both contain regions of significant positive K τ demonstrating a negative coupling during SON.The similarity in SM 1 -LCL coupling between CTRL and DRY during both DJF and SON implies that the temporal variability of SM 1 is physically independent of the season mean ET fluxes (Fig. 3).Contrasting Figs. 5 and 6 reveals that the surface fluxes (Fig. 5b and d) are coupled despite the surface layer soil moisture (Fig. 6b and d) lacking similar coupling.The regions of positive K τ in Fig. 6 contradict the strongly negative K τ in Fig. 5 during SON.
Similarly, the EF-LCL coupling during DJF is much stronger that the SM 1 coupling, and DRY generally exhibits stronger coupling than CTRL.In contrast, during SON, CTRL Introduction

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Full generally shows slightly higher coupling strength than DRY (Fig. 6b and d), however the difference is not statistically significant over much of the domain.The change in sign of K τ from SON to DJF (Fig. 6b and d) demonstrates that applying Eq. ( 4) to SM 1 and the LCL does not always capture the coupling between the land and the atmosphere during periods where deep SM and transpiration dominate the ET flux.
In short, our results demonstrate that the surface layer soil moisture cannot adequately capture the SM-LCL coupling during both DJF and SON.The significant contributions of transpiration to the total ET fluxes (especially during SON) are responsive to perturbations in SM rz and not SM 1 .

Proposed coupling strength definition: SM rz -LCL
The definition of land-atmosphere coupling using land surface moisture states and fluxes must encompass the relevant physical mechanisms.Previously, Ferguson et al. (2012) was limited to using SM 1 in deriving K τ because the AMSR-E (or other microwave) SM measurements only typically measure to depths less than a few centimeters beneath the soil surface.Computing K τ between SM 1 and the LCL incorporates the surface layer soil moisture that is important for surface evaporation from the soil.Therefore the DJF coupling (or other periods where the ET is mostly comprised of soil evaporation) should be adequately defined using SM 1 .K τ computed from SM 1 neglects SM rz 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 coupling.Acknowledging the importance of transpiration during the Northern Australian wet season, we further evaluate the land-atmosphere coupling by computing K τ between the vertically averaged SM rz 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 SM rz .Applying Eq. (4) using SM rz imposes a different set of problems, as the rooting depth is model dependent and generally only approximately known.We assume that the SM rz consists of the soil layers between the surface and Figures

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Full a depth of 1 m, as greater than 90 % of the prescribed roots in CLM4 are within 1 m of the surface (Oleson et al., 2010).
Figure 7 shows the ensemble median coupling strength diagnosed between SM rz and the LCL.Comparing Figs. 5 and 7 it is clear that including the portion of SM that partially controls transpiration increases the magnitude of the DJF coupling strength and eliminates the regions with statistically insignificant coupling (Fig. 6) despite soil evaporation dominating the simulated ET.Large differences between SM rz -LCL and SM 1 -LCL coupling also occurs during SON.Both CTRL and DRY show statistically significant SON coupling, with groundwater exerting no significant impact (Fig. 7b and d).Comparing Fig. 7b 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 SM rz in relation to the LCL.Neglecting the SM beneath the surface layer in the calculation of K τ results in a weak diagnosed coupling strength during DJF and SON because transpiration is partly governed by the water availability within the root zone.By defining coupling strength using SM rz it is clear that the land is strongly coupled to the LCL during both DJF and SON.The coupling strength for the CTRL simulations is stronger when defined in this manner, although both sets of simulations still show SM rz to be statistically associated to the LCL.
The standard deviation of the coupling strength between SM rz and the LCL and between SM 1 and the LCL among the ensemble members is generally less than 0.15 (Fig. 8a-d).The variation among the ensemble members is smaller than the median coupling strength shown in Figs. 6 and 7.The coupling using SM 1 (Fig. 8c) show larger ensemble uncertainty near the coast centered around 135 • E compared to the SM rz coupling in DJF (Fig. 8a) and over the Cape York Peninsula in SON (Fig. 8a and b).Aside from the region near 15 • S and 130 • E during SON, the larger ensemble uncertainty is found when using SM 1 to define the coupling strength.Figures

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Discussion
The seasonal ET from CTRL, DRY, and the observations 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 and JJA (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 SM rz .Comparing Figs. 3 and 4 indicates that the DRY ET is relatively SM limited and unable to maintain ET similar in magnitude to CTRL and the observations during SON.
Physically, the soil column-groundwater interactions parameterized in CTRL inhibit the large, ET limiting SM rz reduction present in DRY.
The coupling between EF-LCL (Fig. 5) is similar for both model configurations despite the mean ET (Fig. 3) and SM (Fig. 4) differing considerably between CTRL and DRY.The similarity holds for both DJF and SON despite the differing background soil moisture states between the two periods.The results indicate that while the mean ET is a strong function of mean soil moisture, the SM-LCL coupling as diagnosed here is insensitive to the background state.The coincidence of the temporal variations in SM, EF, and LCL are demonstrated by the large values of K τ.These seemingly counterintuitive results may be an artifact of using a rank correlation coefficient to determine the coupling strength.K τ only measures the temporal coincidence of the two time-series while neglecting the magnitude of these variations.Although K τ is largely independent to the background soil moisture state, alternative definitions of coupling may not remain as invariant.
While coupling in Fig. diagnosed with SM 1 .The physically improbable SM 1 -LCL coupling varies from positive (Fig. 5b) to negative (Fig. 5a) as the wet season is established (Fig. 1).Despite the domain mean precipitation increasing from roughly zero to several mm day −1 during SON, K τ from SM 1 -LCL exhibits both positive and negative coupling over this period.The transition from negligible (or positive) to strong land-atmosphere coupling during the wet season is an artifact resulting from the use of SM 1 .Consistent coupling in general agreement with the EF-LCL dynamics throughout the wet season exists between SM rz -LCL because transpiration is incorporated into the coupling diagnostic.The necessity of using SM rz agrees with Lee et al. (2012) where transpiration was found to limit precipitation variability over tropical regions.The dominance of transpiration among the various ET components is not limited to Northern Australia or monsoon regions (Jasechko et al., 2013) highlighting the need to characterize land-atmosphere dynamics using SM well beneath the surface.
Statistically determining the coupling strength using only near surface variables from land surface model simulations of SM observations (i.e.Ferguson et al., 2013;Betts, 2004) 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 coupling 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 coupling defined using these methods provides a necessary but not sufficient condition for strong landatmosphere interactions between soil moisture and precipitation.
Our results likely extend to monsoonal regions beyond Northern Australia.GLACE (Koster et al., 2006) revealed multiple areas of strong land-atmosphere coupling coincide with major monsoon systems.The strong coupling during the wet season (September-February) diagnosed using SM rz and K τ 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 Introduction

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Full plants to transpire despite the prolonged dry period.These conditions over Northern Australia (Figs. 3 and 4) lead to transpiration dominating the ET flux during the onset of the wet season.Coupling diagnosed using K τ under these conditions must be defined using SM rz rather than SM 1 to ensure the relevant pathways of the moisture fluxes are not neglected.

Conclusions
The land-atmosphere coupling strength is analyzed utilizing ensembles of land surface simulations and near surface atmospheric data.Using four forcing datasets, ensembles of CLM simulations over Northern Australia are performed, using configurations that either include or neglect soil column-groundwater interactions.The seasonal dynamics of the simulated SM 1 is insensitive to the mean soil moisture state and all simulations compare favorably with the AMSR-E soil moisture product.Further, 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 land-atmosphere coupling is diagnosed between both SM 1 and EF from the simulations and the LCL as calculated from the near surface atmospheric state.During the peak wet season strong SM 1 -LCL and EF-LCL coupling are shown.The wet season onset (SON) shows strong EF-LCL coupling that contrasts the weak SM 1 -LCL coupling demonstrating the SON coupling is not properly characterized with SM 1 .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.Properly defining the coupling with SM rz differs considerably from the SM 1 diagnosed coupling and shows strong coupling throughout the wet season.The SM rz -LCL coupling is consistent with that from EF-LCL.Our results also show that the diagnosed land-atmosphere coupling is insensitive to the mean vertical profile of soil moisture.It is however, sensitive to the depth of the soil column considered.The implication of our findings therefore indicates a need to include the root zone in order capture periods when the ET is dominated by transpiration.We recommend that future studies of land-atmosphere coupling should include groundwater and focus on root zone soil moisture rather than surface layer soil moisture.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | three components in Eq.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3.3) are driven using a combination of atmospheric states and fluxes from reanalysis products, a gauge based daily precipitation dataset, and a 3 hourly satellite-based precipitation product.We followDecker et al. (2014) and utilize four forcing datasets to drive model simulations.The two 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 at al., 2004) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product (Bosilovich et al., 2008).These two datasets are utilized due to the high spatial resolution of GLDAS (0.25 • ) and high temporal resolution of MERRA (hourly).Two forcing datasets are comprised of the uncorrected GLDAS and MERRA data interpolated to a common 0.25 • × 0.25 • grid.In addition two precipitation corrected datasets developed in Decker et al. (2014) are used.The uncorrected atmospheric states and radiative fluxes from MERRA are combined 10437 Discussion Paper | Discussion Paper | Discussion Paper | 3.3) estimates of SM 1 Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Figure 2a shows the timeseries of the area averaged (10-15 • S to 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 SM 1 due to the difficulties in direct comparison of simulated and observed soil moisture (Reichle and Koster, 2004).The strong seasonal cycle of soil moisture owing Discussion Paper | Discussion Paper | Discussion Paper | Similar to DJF, both the model and the observations show a strong north-south gradient.The simulations under estimate 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 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 W m −2 increase in SON ET over the DRY model runs (Fig. 3b and d).Overall, the model exhibits spatial-temporal ET in close agreement with this gridded ET product.The results from Figs. 2 and 3 demonstrate that CLM4 simulates the monthly and seasonal first layer soil moisture and evapotranspiration reasonably.The accuracy of Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 5 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.Coupling under these conditions is poorly defined using SM 1 .The strong EF-LCL coupling during SON and DJF highlights the inadequacy of coupling Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | • × 0.25 • grid as the other forcing datasets.The CTRL and DRY simulations ending in 1979 provide initial conditions for the four CTRL and four DRY simulations from 1979-2007.The model evaluation period spans the five years coincident with the SM and ET data from 2003-2007.The coupling is computed using the period 1990-2008.Both the CTRL and the DRY simulations are forced with the four forcing datasets (see Sect. 2.2): GLDAS, MERRA, MERRA.B, and MERRA.BT, generating a total of eight model simulations.