Geophysical Research Letters
RESEARCH LETTER
10.1002/2015GL066305
Key Points:
• Evaporative fraction is often better
correlated with vegetation phenology
than with soil moisture
• Vegetation controls on evaporative
fraction can be separated from
atmospheric forcing
• Vegetation metrics imply stronger
land-atmosphere coupling than soil
moisture metrics
Supporting Information:
• Supporting Information S1
Correspondence to:
I. N. Williams,
[email protected]
Citation:
Williams, I. N., and M. S. Torn
(2015), Vegetation controls on surface heat flux partitioning, and
land-atmosphere coupling, Geophys. Res. Lett., 42, 9416–9424,
doi:10.1002/2015GL066305.
Received 23 SEP 2015
Accepted 16 OCT 2015
Accepted article online 19 OCT 2015
Published online 6 NOV 2015
Published 2015. American Geophysical
Union. This article is a US Government
work and is in the public domain in the
United States of America.
WILLIAMS AND TORN
Vegetation controls on surface heat flux partitioning,
and land-atmosphere coupling
Ian N. Williams1 and Margaret S. Torn1
1 Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, California, USA
Abstract
We provide observational evidence that land-atmosphere coupling is underestimated by a
conventional metric defined by the correlation between soil moisture and surface evaporative fraction
(latent heat flux normalized by the sum of sensible and latent heat flux). Land-atmosphere coupling is
3 times stronger when using leaf area index as a correlate of evaporative fraction instead of soil moisture,
in the Southern Great Plains. The role of vegetation was confirmed using adjacent flux measurement sites
having identical atmospheric forcing but different vegetation phenology. Transpiration makes the
relationship between evaporative fraction and soil moisture nonlinear and gives the appearance of weak
coupling when using linear soil moisture metrics. Regions of substantial coupling extend to semiarid and
humid continental climates across the United States, in terms of correlations between vegetation metrics
and evaporative fraction. The hydrological cycle is more tightly constrained by the land surface than
previously inferred from soil moisture.
1. Introduction
The land surface is a source of predictability in the climate system [Shukla and Mintz, 1982; Viterbo and Beljaars,
1995; Betts et al., 1996; Beljaars et al., 1996; Xue et al., 1996; Koster et al., 2010; Guo et al., 2011; Yang et al., 2011;
Boussetta et al., 2013]. Feedbacks between the land and atmosphere can amplify climate extremes such as
heat waves and drought [Trenberth and Guillemot, 1996; Beljaars et al., 1996; Bonan and Stillwell-Soller, 1998;
Pal and Eltahir, 2003; Guo et al., 2006; Fischer et al., 2007; Seneviratne et al., 2013]. Climate modeling studies
point to strong coupling between soil moisture and precipitation in “hot spot” regions (e.g., across the Great
Plains of North America) where soil moisture is variable enough to influence evaporation and precipitation
[Koster et al., 2004, 2006; Dirmeyer, 2011]. On the other hand, observations indicate weak coupling in these
and other regions [Ferguson et al., 2012; Phillips and Klein, 2014]. Such assessments are based on observed
correlations, particularly between soil moisture and the surface evaporative fraction (the ratio of latent to
total sensible plus latent heating). Implicit in these soil moisture metrics is the assumption that available soil
moisture is readily evaporated given sufficient surface radiative heating and is neither restricted nor aided
by vegetation. Here we test these assumptions using observations and show that vegetation-based metrics
yield greater correlations with evaporative fraction (hereafter EF) relative to soil moisture metrics, implying
stronger land-atmosphere coupling.
Land-atmosphere coupling can be divided into two segments describing the relationships between the land
state and surface fluxes, and between surface fluxes and the atmospheric state [Guo et al., 2006; Santanello
et al., 2011]. Past work has examined the role of soil moisture in the land segment [Dirmeyer, 2011], but vegetation is also an important factor that is not yet well understood [Puma et al., 2013]. Transpiration is a significant
component of evapotranspiration over land [Schlesinger and Jasechko, 2014; Lawrence et al., 2007] and is a key
pathway by which plants influence climate [Bonan, 2008; Seneviratne et al., 2010]. Living plants maintain an
unbroken chain of water from roots to leaves, through which water flows by a combination of capillary force,
and by water pressure gradients across the soil-root and leaf-air interfaces. The latter gradient can be regulated by stomata (the pores on plant leaves), which close when conditions are unfavorable for photosynthesis
[Ball et al., 1987; Sellers et al., 1996]. Furthermore, factors that limit photosynthesis can also limit transpiration, since both water vapor and carbon dioxide are exchanged through stomata. Such limiting factors are
not exclusive to soil moisture and also include temperature, light, and nutrients, among others. As a result
of this coupling between photosynthesis and transpiration, variations in the uptake of carbon dioxide by
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vegetation are associated with variations in stomatal conductance of water vapor and are indicative of
vegetation controls on surface latent heat flux.
Here we use canopy-scale photosynthetic uptake of CO2 (known as gross primary productivity or GPP) as a
proxy for stomatal conductance [e.g., Baldocchi et al., 2001]. GPP is calculated from measured net ecosystem
exchange of CO2 (NEE) by subtracting the respiratory component of this flux [e.g., Reichstein et al., 2005].
Green leaf area index (hereafter LAI) is another useful proxy for vegetation controls on transpiration [Baldocchi
and Meyers, 1998]. LAI is predicted by land surface models and can be inferred from in situ measurements or
remote sensing, facilitating model-observation comparisons. Apart from transpiration, other effects of vegetation include the interception of precipitation and shading of the soil surface by leaves, which tend to reduce
the direct evaporation of soil moisture. Both of these effects are increasing functions of LAI.
We focus on establishing a relationship between land surface variables and the surface EF, which is a critical
first step in linking land surface forcing to clouds and precipitation [Santanello et al., 2011; Guo et al., 2006].
The EF is tightly coupled to the characteristics of clouds in atmospheric models, including base height and
diurnal timing [Schar et al., 1999; Findell and Eltahir, 2003; Gentine et al., 2013]. Previous work has shown that
the EF can influence precipitation both directly, through the well-known precipitation recycling mechanism
[Brubaker et al., 1993], and also indirectly through the effects of EF on planetary boundary layer height, lifted
condensation level, level of free convection, and convective triggering [Eltahir, 1998; Schar et al., 1999; Findell
and Eltahir, 2003; Betts, 2007; Berg et al., 2013]. Whether precipitation occurrence is favored by high or low
EF depends on thermodynamic profiles across the boundary layer top and into the free troposphere [Ek and
Holtslag, 2004; Santanello et al., 2011; Gentine et al., 2013].
2. Sites
We quantified EF using measurements collected at four sites (Southern Great Plains (SGP)-crop, SGP-grass,
Morgan-Monroe, and Corral Pocket). SGP-crop and SGP-grass are adjacent (separated by about 250 m) and
located at the Central Facility of the U.S. Southern Great Plains (SGP) Atmospheric Radiation Measurement
Climate Research Facility, near Lamont, Oklahoma. SGP-crop is typically planted with winter wheat from
October to May and left uncultivated or planted with corn (maize) from June to September. SGP-grass is a
grassland that is periodically cut and bailed [Cook, 2011]. These two (unirrigated) sites together represent the
dominant vegetation types in the SGP [Fischer et al., 2007]. Morgan-Monroe is a temperate deciduous forest
located in the Morgan-Monroe State Forest near Bloomington, Indiana [Schmid et al., 2000]; Corral Pocket is a
grazed, semiarid grassland in southeastern Utah [Bowling et al., 2010].
Corral Pocket is the driest site (216 mm annually) [Bowling et al., 2010] and is often sparsely vegetated.
The Morgan-Monroe site is wetter and experiences more evenly distributed precipitation across seasons
(1105 mm annually) [Wayson et al., 2006]. The SGP sites experience intermediate precipitation (750 mm annually) and are located in a previously identified hot spot for land-atmosphere coupling [Koster et al., 2004].
Additional site characteristics and instrumentation details are given in the supporting information.
3. Role of Vegetation
We confirmed that correlations between EF and soil moisture (volumetric water content or VWC) suggest
weak land-atmosphere coupling in the SGP. Previous observational studies found weak correlations between
soil moisture and evaporative fraction in this region, using a variety of soil moisture metrics. In those studies,
Pearson’s correlation (R2 ) is 0.21 [Phillips and Klein, 2014], and Kendall rank correlation coefficient (𝜏 ) is 0.10
[Ferguson et al., 2012], based on in situ and remote sensing measurements, respectively. Another study found
correlations ranging from R2 = 0.01 to as high as R2 = 0.55 [Ford et al., 2014], but the latter was obtained by
conditionally sampling during dry conditions when such correlations are greatest. All of these studies used
upper layer soil moisture from 2 to 10 cm below the surface.
We correlated daily daytime EF (calculated from daytime averages of latent and sensible heat fluxes), with
daily averaged soil moisture volumetric water content (at 10 cm depth) during the growing season for grasses
at the SGP-grass site (May–September). The relationship between soil moisture and EF is shown as a scatter
diagram (Figure 1a). There is a statistically significant correlation between EF and soil moisture, but it is small
(R2 = 0.21), suggesting that most of the variance in EF is explained by factors other than soil moisture.
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Figure 1. Comparison between soil moisture and vegetation metrics of land-atmosphere coupling. Evaporative fraction
(EF) (a) is weakly correlated with 10 cm soil moisture volumetric fraction and (b) is more strongly correlated with leaf
area index, at the SGP-grass site. (c and d) Similar results were obtained for the SGP-crop sites. (e) The importance of
vegetation is further evidenced by the strong correlation between EF and gross primary productivity (GPP). (f ) Despite
sharing the same atmospheric forcing, the SGP-grass and SGP-crop sites (adjacent fields) exhibit large differences in EF,
due to differences in LAI between these sites.
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Next, we used LAI as a correlate of EF instead of soil moisture, to quantify the influence of vegetation on EF. LAI
was inferred from the normalized difference vegetation index calculated from visible (red) and near-infrared
shortwave reflectance, measured continuously with multifilter radiometers located at both SGP sites, following established methods (see Text S1 in the supporting information) [Baret and Guyot, 1991]. The correlation
of EF with LAI is much larger (R2 = 0.65), and likewise the scatter in the relationship between EF and LAI is
much reduced (Figure 1b) compared to the relationship between EF and soil moisture. Similar results were
found after filtering for precipitation-free days (R2 = 0.67). We also used the Kendall rank correlation coefficient
(hereafter 𝜏 ), which is robust to outliers and can be applied to nonlinear, monotonic relationships. The relationship between EF and LAI (𝜏 = 0.65 ) is nearly twice that between EF and soil moisture (𝜏 = 0.34). Together
with Pearson’s correlation, these results suggest that land-atmosphere coupling is 2 to 3 times stronger when
using vegetation as opposed to soil moisture metrics, for grasses in the SGP.
We repeated the scatter plot of EF and soil moisture for the SGP-crop site (Figure 1c), to test the robustness
of our results. We focused on the period from April to August. Winter wheat peaks in April (in terms of CO2
uptake and LAI), is harvested in early June, and is sometimes followed by summer cereal crops (maize) from
June to September. The larger range in soil moisture at the SGP-crop site compared to the SGP-grass site
(cf. Figures 1c and 1a) reflects differences in soil moisture sensors between the SGP-crop and SGP-grass
sites, in addition to differences in vegetation type and field management between these sites. Despite these
differences, we obtained the same overall result that EF is poorly correlated with soil moisture (R2 = 0.11) and
is much more strongly correlated with LAI (R2 = 0.53). Higher LAI indicates higher rates of photosynthesis,
greater canopy conductance of water vapor, and therefore higher rates of transpiration, all else being equal.
Although a denser canopy also presents a greater surface area for evaporation, the effects of surface area on
transpiration are limited by self-shading within plant canopies [e.g., Sellers et al., 1992].
To further explore the vegetation control on EF, we repeated the above analyses but used gross primary productivity (GPP) as a correlate of EF. As discussed above, variations in GPP tend to track variations in stomatal
conductance because both water and CO2 are exchanged when the stomata are open to take up CO2 for photosynthesis. We focused only on times when the crop field at the SGP-crop site was planted (GPP was not
available for the SGP-grass site). We found a substantial correlation between EF and GPP (Figure 1e) (R2 = 0.67),
which supports our interpretation that vegetation influences EF through stomatal controls on transpiration.
From this result, we conclude that EF can be tightly coupled to vegetation at the same time that it is decoupled
from upper layer soil moisture (10 cm depth).
We checked the soil depth dependence of the correlation metrics, for the same time periods as shown in
Figure 1a, at the SGP-grass site. The correlation between EF and soil moisture increases slightly, from R2 = 0.21
at 10 cm depth to R2 = 0.27 at 30 cm and R2 = 0.31 at 60 cm. Although deep soil moisture is better correlated
with EF than upper layer soil moisture, it is not as strongly correlated as LAI.
Our results suggest that dryness in the upper soil layer does not guarantee low EF, because vegetation can
access root zone soil moisture for transpiration (from soil layers deeper than 10 cm) as needed for photosynthesis. Similarly, soil wetness does not guarantee high EF because stomatal conductance may be curtailed if
other factors are limiting for photosynthesis. Additionally, transpiration is affected by vegetation phenology,
including timing of leaf-out and root growth, discussed later.
4. Atmospheric Versus Land Controls on Evaporative Fraction
It is of interest to know whether correlations between LAI and EF are due to vegetation controls on EF, or if
they are both driven by a third, atmospheric factor, such as seasonal variations in net radiation and specific
humidity deficit. We took advantage of the juxtaposition of the SGP-crop and SGP-grass sites (separated by
approximately 250 m), which results in nearly identical atmospheric forcing at both sites. Therefore, the large
differences in EF between these two sites (up to 0.8 in daily averages and 0.4 in monthly averages; see supporting information Figures S1 and S2) should be attributable to the differences in the land surface. In fact,
the difference in EF (SGP-grass minus SGP-crop) is well correlated with the difference in LAI between the sites
(R2 = 0.63), as shown in Figure 1f. Both LAI and EF are lower at the SGP-grass site than at the SGP-crop site
in April, and likewise LAI and EF are higher at the SGP-grass site in June, as expected from the differences in
the timing of vegetation leaf-out and senescence at these sites (see supporting information Figure S2). Other
studies used another approach to separate the atmospheric and vegetation influences on EF at a different site,
by inverting the Penman-Monteith equation to infer surface conductance (reflecting the combined effects of
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Figure 2. Land-atmosphere coupling in a relatively wet climate at the Morgan-Monroe site, shown as daytime daily
averages. (a) EF is a strong function of GPP during both dry (red circles) and wet (blue circles) periods. (b) EF increases
rapidly following spring leaf-out (days 120–150) and is followed by a decline in soil moisture during summer months.
The nonlinear relationship between EF and soil moisture (shown in Figure 2b) would falsely imply weak coupling when
using the (linear) correlation between EF and soil moisture as a metric of land-atmosphere coupling.
stomatal and aerodynamic conductance across the surface layer). That method yields similar results, in that
variations in EF are in large part explained by variations in stomatal conductance, although atmospheric vapor
pressure deficit also plays a role [Wilson and Baldocchi, 2000]. We conclude that correlations between EF and
LAI are indicative of vegetation controls on EF.
5. Coupling Strength in Wet Climates
Evaporation shifts from a soil moisture-limited regime to an energetically limited regime as soil moisture
approaches saturation [e.g., Seneviratne et al., 2010], such that evapotranspiration in wetter climates is not
expected to be sensitive to soil moisture. This hypothesis was put forth to explain why land-atmosphere
coupling is relatively weak across much of the eastern United States (e.g., compared to the Southern Great
Plains), when estimated from soil moisture correlations [Koster et al., 2004]. However, vegetation could further
limit evapotranspiration in these wetter climates, which would yield stronger coupling in terms of
vegetation metrics.
To explore land-atmosphere coupling in wetter climates, we extended our analyses to the Morgan-Monroe
site. We included observations from April to September, to capture the state before and after leaf-out, which
typically occurs in April. We focused on precipitation-free times (1 day after daily precipitation exceeding
1 mm) to remove confounding influences of canopy interception of rainfall, leaving 63% of the time series
intact. We confirmed that EF is well correlated with GPP (R2 = 0.63) at Morgan-Monroe (Figure 2a), thus indicating that coupling can be strong in wetter climates when vegetation metrics are used. Including days
with precipitation slightly decreased the correlation to R2 = 0.58. Correlations between soil moisture and
evaporative fraction are often stronger during dry periods, e.g., when VWC < 0.3 [Ford et al., 2014]. Therefore, we further separated the data into the upper and lower 50th percentiles of soil moisture (blue and red
points in Figure 2a, respectively), to check whether our results are dependent on occasionally dry periods at
Morgan-Monroe. A strong relationship between EF and GPP can be seen even during the wettest periods at
Morgan-Monroe, suggesting that the importance of vegetation is not limited to the drier periods at this site.
The relationship between EF and soil moisture (Figure 2b) is nonmonotonic when including the leaf-out
period in April (the day of the year is indicated by colors in Figure 2b). EF increases from about 0.2 to 0.8 in
April and May (days 90–150) while soil moisture decreases. This pattern suggests that spring leaf-out induces
a switch from low evapotranspiration to high evapotranspiration. Energetic limitations are typically invoked
to explain why soil moisture and evapotranspiration are sometimes inversely related over the eastern United
States and other relatively wet climates [Dirmeyer, 2011]. Our results suggest that evapotranspiration can be
further limited by vegetation growth, as demonstrated by the low values of EF at saturating soil moisture in
Figure 2b. Other deciduous forest sites exhibited similar switching between low and high EF following leaf-out
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Figure 3. The role of deep soil moisture in land-atmosphere coupling. (a) Deep soil moisture is almost continuously
depleted over the growing season at the Corral Pocket site, but EF increases early and decreases late in the growing
season, resulting in a nonlinear relationship between EF and soil moisture. (b) Daytime net ecosystem exchange of CO2
(NEE) follows a pattern similar to EF and suggests that vegetation growth induces the nonlinear relationship between EF
and deep soil moisture. The much shorter period of high EF following a dry winter (circles) relative to a wet winter
(squares) illustrates the importance of seasonal timescale land-atmosphere coupling at Corral Pocket. (c) The SGP-grass
site exhibits a similar nonlinear pattern of EF and soil moisture, but where soil moisture is occasionally replenished by
summer rainfall (as indicated by arrows in d). Soil wet-up precedes increases in leaf area index by 1–2 weeks (LAI was
used in place of NEE, which was not available for the SGP site).
(results not shown). Similar conclusions have been drawn at longer (annual) timescales. For example, river
basins with larger vegetation coverage have higher evaporation ratios at a given dryness index [Li et al., 2013].
6. The Role of Deep Soil Moisture
Vegetation integrates many factors that indirectly influence surface heat fluxes through their impacts on transpiration, including root zone soil moisture. The role of this deeper soil moisture is particularly evident at
the Corral Pocket site. The grassland at this semiarid site depends heavily on winter precipitation (stored as
deep soil moisture) to support vegetation growth in the following spring [Bowling et al., 2010]. We defined
the growing season at Corral Pocket as the period having negative weekly averaged daytime net ecosystem
exchange of CO2 (NEE is defined such that negative values indicate surface uptake of CO2 ). Daytime NEE is
used in place of GPP, which was not available for the Corral Pocket site. The seasonal pattern of NEE is similar
to that of estimated GPP at this site, for the periods considered here [Bowling et al., 2010, their Figure 6].
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The time series plotted in the EF-soil moisture phase space follows a nonmonotonic, curved trajectory when
including the start of the growing season. EF increases rapidly at the start of the growing season (Figure 3a;
colors indicate days of the year) and is initially negatively correlated with soil moisture (the average of 20
and 60 cm measurements). After a few weeks into the growing season, both EF and soil moisture begin to
decline, exhibiting a positive correlation. Deep soil moisture decreases after the first few weeks of CO2 uptake
by vegetation (Figure 3b), which is consistent with transpiration of root zone soil moisture.
The contrast between a dry year (2006; circles in Figure 3) and a wet year (2007; squares in Figure 3) demonstrates seasonal timescale coupling between precipitation and EF, with initially higher (early spring) soil
moisture leading to higher EF and a longer period of carbon uptake by vegetation during the growing season,
as shown previously [Bowling et al., 2010]. Similarly to the other sites, the EF at Corral Pocket is approximately
linearly related to GPP, here approximated by daytime NEE (R2 = 0.63; supporting information Figure S3). This
result again illustrates the utility of surface CO2 exchanges as metrics of land-atmosphere coupling.
The pattern of vegetation growth and soil dry-down at the Corral Pocket site is a simpler version of that at
wetter sites that see replenishment of deep soil moisture in summer. The SGP-grass site (Figure 3d) exhibits a
similar temporal pattern of vegetation growth (in terms of LAI) accompanied by soil drying. Wet-up, followed
by vegetation growth and dry-down, is repeated after a major rain event and tends to produce a negative
correlation between soil moisture and EF for each period of vegetation growth following rainfall (Figure 3c).
These results do not preclude a role of direct evaporation of near-surface soil moisture, as a component of
evapotranspiration along with transpiration and canopy interception [e.g., Wilson et al., 2000; Gu et al., 2006].
In fact, 5 cm soil moisture is moderately correlated (R2 = 0.53) with EF at the Corral Pocket site (supporting
information Figure S4) after the growing season, when vegetation is no longer taking up CO2 for photosynthesis (approximately July–September). EF when nonvegetated (or senescent) is about half that when vegetated
(cf. Figures S3 and S4). Similar correlations were found at the SGP-crop site when the crop field was bare
(R2 = 0.55).
7. Conclusions
These results make a case for including vegetation metrics in assessments of land-atmosphere coupling
strength. The observed coupling between soil moisture and EF appears to be weak when using soil moisture as a correlate of EF but is strong when using vegetation leaf area index or gross primary productivity as
a correlate. In terms of surface EF, we found relatively strong coupling in a wetter climate (Morgan-Monroe
Forest) outside of coupling “hot spots.” Although we did not examine the atmospheric branch of this coupling, another study found a comparatively strong influence of EF on convective triggering over the eastern
United States in a high-resolution reanalysis data set [Findell et al., 2011]. Our results suggest that this coupling
extends to the land surface, with transpiration being a link between soil moisture and EF.
Three factors explain why correlations between soil moisture and EF falsely imply a weak land segment of
land-atmosphere coupling. First, EF is strongly influenced by transpiration, which extracts water from a deeper
soil layer than is typically considered in assessments of land-atmosphere coupling strength. Second, transpiration is coupled to photosynthesis and therefore responds to physical and biogeochemical factors that
influence photosynthesis apart from soil moisture. Third, the relationship between soil moisture and EF is
highly nonlinear and nonmonotonic, as a result of the tendency for vegetation growth following soil wet-up,
and senescence following soil dry-down. It is interesting to consider statistical methods that might better
quantify this complex nonlinear relationship. However, nature has already provided a solution. The surface
CO2 uptake by vegetation, in terms of either daytime NEE or GPP, is monotonically related to EF. Leaf area index
(LAI) is also an improvement over soil moisture as a measure of the land surface state relevant to surface heat
flux partitioning. These findings suggest that uncertainties and biases in predicted land-atmosphere coupling
[Dirmeyer et al., 2006] are related to the representation of vegetation in weather and climate models.
We have not addressed questions of the relative effects of land surface forcing and large-scale atmospheric
dynamics on precipitation. Previous work has emphasized the dominant role of horizontal moisture transports
from an atmospheric water balance perspective [e.g., Ruiz-Barradas and Nigam, 2013]. However, the processes
requiring improvements in climate models may or may not be the dominant processes at a given spatiotemporal scale. There remain outstanding problems in representing the hydrological cycle and its response to
climate forcing in climate models [Klein et al., 2006; Dai, 2006; Taylor et al., 2012; Yin et al., 2013], toward
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which research on land-atmosphere coupling has been directed [e.g., Betts et al., 1996; Betts, 2004; Hohenegger
et al., 2009]. Improved model representation of vegetation may yield untapped potential for seasonal climate
prediction [Koster and Walker, 2015]. Likewise, the vegetation control of evapotranspiration is a source of
uncertainty in climate models [Lawrence et al., 2007] and has not been fully explored for purposes of reducing
prediction biases in climate and weather forecast models.
Acknowledgments
The level 2 Ameriflux data used
in this study were obtained from
ameriflux.ornl.gov. Data for the
SGP sites were obtained from
arm.gov, from the following data
streams: sgp30co2flx4mmetC1.b1,
sgp30baebbrE13.c1, sgpswatsE13.b1,
sgpmfrsrC1.b1, and sgpmfrsrE13.b1.
This material is based upon work
supported by the U.S. Department
of Energy, Office of Science, Office of
Biological and Environmental
Research, Atmospheric System
Research, and Atmospheric Radiation
Management Programs under contract
DE-AC02-05CH11231. We acknowledge
the following AmeriFlux sites for their
data records: US-Cop and US-MMS.
In addition, funding for AmeriFlux
data resources was provided by
the U.S. Department of Energy
Office of Science. Thanks to Dave
Bowling for sharing Corral Pocket
data and to Kim Novick for sharing
Morgan-Monroe data.
WILLIAMS AND TORN
References
Baldocchi, D., and T. Meyers (1998), On using eco-physiological, micrometeorological and biogeochemical theory to evaluate
carbon dioxide, water vapor and trace gas fluxes over vegetation: A perspective, Agric. Forest Meteorol., 90(1–2), 1–25,
doi:10.1016/s0168-1923(97)00072-5.
Baldocchi, D., et al. (2001), Fluxnet: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor,
and energy flux densities, Bull. Am. Meteorol. Soc., 82(11), 2415–2434, doi:10.1175/1520-0477(2001)082<2415:fantts>2.3.co;2.
Ball, T. J., I. E. Woodrow, and J. A. Berry (1987), A model predicting stomatal conductance and its contribution to the control of
photosynthesis under different environmental conditions, in Progress in Photosynthesis Research, edited by T. J. Ball, I. E. Woodrow,
and J. A. Berry, pp. 221–224, Springer, Netherlands.
Baret, F., and G. Guyot (1991), Potentials and limits of vegetation indexes for LAI and APAR assessment, Remote Sens. Environ., 35(2–3),
161–173, doi:10.1016/0034-4257(91)90009-u.
Beljaars, A. C. M., P. Viterbo, M. J. Miller, and A. K. Betts (1996), The anomalous rainfall over the United States during
July 1993: Sensitivity to land surface parameterization and soil moisture, Mon. Weather Rev., 124(3), 362–383,
doi:10.1175/1520-0493(1996)124<0362:tarotu>2.0.co;2.
Berg, A., K. Findell, B. R. Lintner, P. Gentine, and C. Kerr (2013), Precipitation sensitivity to surface heat fluxes over North America in reanalysis
and model data, J. Hydrometeorol., 14(3), 722–743, doi:10.1175/jhm-d-12-0111.1.
Betts, A. K. (2004), Understanding hydrometeorology using global models, Bull. Am. Meteorol. Soc., 85(11), 1673–1688,
doi:10.1175/bams-85-11-1673.
Betts, A. K. (2007), Coupling of water vapor convergence, clouds, precipitation, and land-surface processes, J. Geophys. Res., 112, D10108,
doi:10.1029/2006JD008191.
Betts, A. K., J. H. Ball, A. C. M. Beljaars, M. J. Miller, and P. A. Viterbo (1996), The land surface-atmosphere interaction: A review based on
observational and global modeling perspectives, J. Geophys. Res., 101(D3), 7209–7225, doi:10.1029/95JD02135.
Bonan, G. B. (2008), Forests and climate change: Forcings, feedbacks, and the climate benefits of forests, Science, 320(5882), 1444–1449,
doi:10.1126/science.1155121.
Bonan, G. B., and L. M. Stillwell-Soller (1998), Soil water and the persistence of floods and droughts in the Mississippi River Basin,
Water Resour. Res., 34(10), 2693–2701, doi:10.1029/98WR02073.
Boussetta, S., G. Balsamo, A. Beljaars, T. Kral, and L. Jarlan (2013), Impact of a satellite-derived leaf area index monthly climatology in a global
numerical weather prediction model, Int. J. Remote Sens., 34(9–10), 3520–42, doi:10.1080/01431161.2012.716543.
Bowling, D. R., S. Bethers-Marchetti, C. K. Lunch, E. E. Grote, and J. Belnap (2010), Carbon, water, and energy fluxes in a semiarid cold desert
grassland during and following multiyear drought, J. Geophys. Res., 115, G04026, doi:10.1029/2010JG001322.
Brubaker, K. L., D. Entekhabi, and P. S. Eagleson (1993), Estimation of continental precipitation recycling, J. Clim., 6(6), 1077–1089,
doi:10.1175/1520-0442(1993)006<1077:eocpr>2.0.co;2.
Cook, D. R. (2011), Energy Balance Bowen Ratio handbook, ARM Tech. Rep. TR-037, USDOE Office of Science, Washington, D. C.
Dai, A. (2006), Precipitation characteristics in eighteen coupled climate models, J. Clim., 19(18), 4605–4630, doi:10.1175/jcli3884.1.
Dirmeyer, P. A. (2011), The terrestrial segment of soil moisture-climate coupling, Geophys. Res. Lett., 38, L16702, doi:10.1029/2011GL048268.
Dirmeyer, P. A., R. D. Koster, and Z. Guo (2006), Do global models properly represent the feedback between land and atmosphere?,
J. Hydrometeorol., 7(6), 1177–1198, doi:10.1175/jhm532.1.
Ek, M. B., and A. A. M. Holtslag (2004), Influence of soil moisture on boundary layer cloud development, J. Hydrometeorol., 5(1), 86–99,
doi:10.1175/1525-7541(2004)005<0086:iosmob>2.0.co;2.
Eltahir, E. A. B. (1998), A soil moisture rainfall feedback mechanism: 1. Theory and observations, Water Resour. Res., 34(4), 765–776,
doi:10.1029/97WR03499.
Ferguson, C. R., E. F. Wood, and R. K. Vinukollu (2012), A global intercomparison of modeled and observed land-atmosphere coupling,
J. Hydrometeorol., 13(3), 749–784, doi:10.1175/jhm-d-11-0119.1.
Findell, K. L., and E. A. B. Eltahir (2003), Atmospheric controls on soil moisture-boundary layer interactions. Part I: Framework development,
J. Hydrometeorol., 4(3), 552–569, doi:10.1175/1525-7541(2003)004<0552:acosml>2.0.co;2.
Findell, K. L., P. Gentine, B. R. Lintner, and C. Kerr (2011), Probability of afternoon precipitation in Eastern United States and Mexico enhanced
by high evaporation, Nat. Geosci., 4(7), 434–439, doi:10.1038/ngeo1174.
Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Luethi, and C. Schaer (2007), Soil moisture-atmosphere interactions during the 2003 European
summer heat wave, J. Clim., 20(20), 5081–5099, doi:10.1175/jcli4288.1.
Ford, T. W., C. O. Wulff, and S. M. Quiring (2014), Assessment of observed and model-derived soil moisture-evaporative fraction relationships
over the United States Southern Great Plains, J. Geophys. Res. Atmos., 119, 6279–6291, doi:10.1002/2014JD021490.
Gentine, P., A. A. M. Holtslag, F. D’Andrea, and M. Ek (2013), Surface and atmospheric controls on the onset of moist convection over land,
J. Hydrometeorol., 14(5), 1443–1462, doi:10.1175/jhm-d-12-0137.1.
Gu, L., T. Meyers, S. G. Pallardy, P. J. Hanson, B. Yang, M. Heuer, K. P. Hosman, J. S. Riggs, D. Sluss, and S. D. Wullschleger (2006), Direct and
indirect effects of atmospheric conditions and soil moisture on surface energy partitioning revealed by a prolonged drought at a
temperate forest site, J. Geophys. Res., 111, D16102, doi:10.1029/2006JD007161.
Guo, Z., et al. (2006), GLACE: The global land-atmosphere coupling experiment. Part II: Analysis, J. Hydrometeorol., 7(4), 611–625,
doi:10.1175/jhm511.1.
Guo, Z., P. A. Dirmeyer, and T. DelSole (2011), Land surface impacts on subseasonal and seasonal predictability, Geophys. Res. Lett., 38,
L24812, doi:10.1029/2011GL049945.
Hohenegger, C., P. Brockhaus, C. S. Bretherton, and C. Schaer (2009), The soil moisture-precipitation feedback in simulations with explicit
and parameterized convection, J. Clim., 22(19), 5003–5020, doi:10.1175/2009jcli2604.1.
Klein, S. A., X. Jiang, J. Boyle, S. Malyshev, and S. Xie (2006), Diagnosis of the summertime warm and dry bias over the U.S. Southern Great
Plains in the GFDL climate model using a weather forecasting approach, Geophys. Res. Lett., 33, L18805, doi:10.1029/2006GL027567.
VEGETATION AND LAND-ATMOSPHERE COUPLING
9423
Geophysical Research Letters
10.1002/2015GL066305
Koster, R. D., and G. K. Walker (2015), Interactive vegetation phenology, soil moisture, and monthly temperature forecasts, J. Hydrometeorol.,
16, 1456–1465, doi:10.1175/JHM-D-14-0205.1.
Koster, R. D., et al. (2004), Regions of strong coupling between soil moisture and precipitation, Science, 305(5687), 1138–1140,
doi:10.1126/science.1100217.
Koster, R. D., et al. (2006), GLACE: The global land-atmosphere coupling experiment. Part I: Overview, J. Hydrometeorol., 7(4), 590–610,
doi:10.1175/jhm510.1.
Koster, R. D., et al. (2010), Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment,
Geophys. Res. Lett., 37, L02402, doi:10.1029/2009GL041677.
Lawrence, D. M., P. E. Thornton, K. W. Oleson, and G. B. Bonan (2007), The partitioning of evapotranspiration into transpiration,
soil evaporation, and canopy evaporation in a GCM: Impacts on land-atmosphere interaction, J. Hydrometeorol., 8(4), 862–880,
doi:10.1175/jhm596.1.
Li, D., M. Pan, Z. Cong, L. Zhang, and E. Wood (2013), Vegetation control on water and energy balance within the Budyko framework,
Water Resour. Res., 49(2), 969–976, doi:10.1002/wrcr.20107.
Pal, J. S., and E. A. B. Eltahir (2003), A feedback mechanism between soil-moisture distribution and storm tracks, Q. J. R. Meteorol. Soc.,
129(592), 2279–2297, doi:10.1256/qj.01.201.
Phillips, T. J., and S. A. Klein (2014), Land-atmosphere coupling manifested in warm-season observations on the U.S. Southern Great Plains,
J. Geophys. Res. Atmos., 119, 509–528, doi:10.1002/2013JD020492.
Puma, M. J., R. D. Koster, and B. I. Cook (2013), Phenological versus meteorological controls on land-atmosphere water and carbon fluxes,
J. Geophys. Res. Biogeosci., 118, 14–29, doi:10.1029/2012JG002088.
Reichstein, M., et al. (2005), On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and
improved algorithm, Global Change Biol., 11(9), 1424–1439, doi:10.1111/j.1365-2486.2005.001002.x.
Ruiz-Barradas, A., and S. Nigam (2013), Atmosphere-land surface interactions over the Southern Great Plains: Characterization from pentad
analysis of doe arm field observations and NARR, J. Clim., 26(3), 875–886, doi:10.1175/jcli-d-11-00380.1.
Santanello, J., A. Joseph, C. D. Peters-Lidard, and S. V. Kumar (2011), Diagnosing the sensitivity of local land-atmosphere coupling via the soil
moisture-boundary layer interaction, J. Hydrometeorol., 12(5), 766–786, doi:10.1175/jhm-d-10-05014.1.
Schar, C., D. Luthi, U. Beyerle, and E. Heise (1999), The soil-precipitation feedback: A process study with a regional climate model, J. Clim.,
12(3), 722–741, doi:10.1175/1520-0442(1999)012<0722:tspfap>2.0.co;2.
Schlesinger, W. H., and S. Jasechko (2014), Transpiration in the global water cycle, Agric. Forest Meteorol., 189, 115–117,
doi:10.1016/j.agrformet.2014.01.011.
Schmid, H. P., C. S. B. Grimmond, F. Cropley, B. Offerle, and H. B. Su (2000), Measurements of CO2 and energy fluxes over a mixed hardwood
forest in the Mid-Western United States, Agric. Forest Meteorol., 103(4), 357–374, doi:10.1016/s0168-1923(00)00140-4.
Sellers, P. J., J. A. Berry, G. J. Collatz, C. B. Field, and F. G. Hall (1992), Canopy reflectance, photosynthesis, and transpiration.
3. A Reanalysis using improved leaf models and a new canopy integration scheme, Remote Sens. Environ., 42(3), 187–216,
doi:10.1016/0034-4257(92)90102-p.
Sellers, P. J., D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, G. D. Collelo, and L. Bounoua (1996),
A revised Land Surface parameterization (SIB2) for atmospheric GCMs. Part 1: Model formulation, J. Clim., 9(4), 676–705,
doi:10.1175/1520-0442(1996)009<0676:arlspf>2.0.co;2.
Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling (2010), Investigating soil
moisture-climate interactions in a changing climate: A review, Earth Sci. Rev., 99(3-4), 125–161, doi:10.1016/j.earscirev.2010.02.004.
Seneviratne, S. I., et al. (2013), Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5
experiment, Geophys. Res. Lett., 40, 5212–5217, doi:10.1002/grl.50956.
Shukla, J., and Y. Mintz (1982), Influence of land-surface evapo-transpiration on the Earth’s climate, Science, 215(4539), 1498–1501,
doi:10.1126/science.215.4539.1498.
Taylor, C. M., R. A. M. de Jeu, F. Guichard, P. P. Harris, and W. A. Dorigo (2012), Afternoon rain more likely over drier soils, Nature, 489(7416),
423–426, doi:10.1038/nature11377.
Trenberth, K. E., and C. J. Guillemot (1996), Physical processes involved in the 1988 drought and 1993 floods in North America, J. Clim., 9(6),
1288–1298, doi:10.1175/1520-0442(1996)009<1288:ppiitd>2.0.co;2.
Viterbo, P., and A. C. M. Beljaars (1995), An improved land-surface parameterization scheme in the ECMWF model and its validation, J. Clim.,
8(11), 2716–2748, doi:10.1175/1520-0442(1995)008<2716:ailsps>2.0.co;2.
Wayson, C. A., J. C. Randolph, P. J. Hanson, C. S. B. Grimmond, and H. P. Schmid (2006), Comparison of soil respiration methods in a
mid-latitude deciduous forest, Biogeochemistry, 80(2), 173–189, doi:10.1007/s10533-006-9016-8.
Wilson, K. B., and D. D. Baldocchi (2000), Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous
forest in North America, Agric. Forest Meteorol., 100(1), 1–18, doi:10.1016/s0168-1923(99)00088-x.
Wilson, K. B., P. J. Hanson, and D. D. Baldocchi (2000), Factors controlling evaporation and energy partitioning beneath a deciduous forest
over an annual cycle, Agric. Forest Meteorol., 102(2-3), 83–103, doi:10.1016/s0168-1923(00)00124-6.
Xue, Y., M. J. Fennessy, and P. J. Sellers (1996), Impact of vegetation properties on U.S. summer weather prediction, J. Geophys. Res., 101(D3),
7419–7430.
Yang, R., K. Mitchell, J. Meng, and M. Ek (2011), Summer-season forecast experiments with the NCEP climate forecast system using different
land models and different initial land states, J. Clim., 24(9), 2319–2334, doi:10.1175/2010jcli3797.1.
Yin, L., R. Fu, E. Shevliakova, and R. E. Dickinson (2013), How well can CMIP5 simulate precipitation and its controlling processes over tropical
South America?, Clim. Dyn., 41(11–12), 3127–3143, doi:10.1007/s00382-012-1582-y.
WILLIAMS AND TORN
VEGETATION AND LAND-ATMOSPHERE COUPLING
9424