Abstract

Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The modified CLM includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation, and precipitation at the seasonal time scale. The focus of this study is on how the impact of vegetation feedback varies with the timing and direction of initial soil moisture anomalies. During summer, wet soil moisture anomalies lead to increase in leaf area index and, consequently, increase in evapotranspiration and surface heating. Such increases tend to favor precipitation. Therefore, under wet summer soil moisture anomalies, the soil moisture–induced precipitation increase is reinforced when predictive phenology is included. That is, the vegetation feedback to precipitation is positive. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due probably to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, the leaf area index (LAI) response is delayed since LAI is still limited by cold temperature at that time of the year. During the summer following wet spring soil moisture anomalies, vegetation feedback is negative; that is, it tends to suppress the response of precipitation through the depletion of soil moisture by vegetation.

1. Introduction

Soil moisture–precipitation coupling may result in persistence of climate anomalies, making soil moisture a potentially useful predictor in seasonal predictions. The slowly varying soil moisture records past and present precipitation anomalies; as the resulting soil moisture feeds back to influence precipitation, this may lead to the persistence of soil moisture and precipitation anomalies. Where vegetation growth is limited by water, this soil moisture–precipitation coupling is modified by vegetation feedback, with uncertain impact on the land-induced precipitation persistence. For example, wetter-than-normal soil tends to promote precipitation and through the soil moisture–precipitation feedback may lead to persistence of higher-than-normal precipitation. As a result of the wetness, vegetation grows denser, which has two consequences: first, the increase of vegetation favors more precipitation through its impact on albedo and Bowen ratio, which enhances the wetness persistence, leading to a positive feedback (e.g., Bounoua et al. 2000; Buermann et al. 2001); second, the enhanced transpiration leads to faster depletion of soil moisture, which may reduce the persistence of wet anomalies, leading to a negative feedback (e.g., Pielke et al. 1998; Wang et al. 2006). Whether the net impact is positive or negative is uncertain. Such competing mechanisms or feedbacks are further elaborated using the diagram in Fig. 1.

Fig. 1.

Diagram for the impact of vegetation feedback on how precipitation responds to initial soil moisture anomalies. Here ΔP′ indicates changes in precipitation due to vegetation feedback, and dashed lines indicate negative feedback.

Fig. 1.

Diagram for the impact of vegetation feedback on how precipitation responds to initial soil moisture anomalies. Here ΔP′ indicates changes in precipitation due to vegetation feedback, and dashed lines indicate negative feedback.

Numerous studies have tackled the issue of how initial soil moisture anomalies impact climate conditions (e.g., Shukla and Minz 1982; Oglesby and Erickson 1989; Bosilovich and Sun 1999; Pal and Eltahir 2001; Kim and Wang 2007). Most of these studies agreed upon a positive feedback between soil moisture and precipitation: wet (dry) soil tends to enhance (suppress) precipitation through soil moisture’s impact on evapotranspiration. However, none of these studies considered the impact of the feedback from the dynamically varying vegetation, although several studies examined the impact of different prescribed vegetation on seasonal and interannual climate (e.g., Dirmeyer 1994). Recently, remotely sensed vegetation indices such as normalized difference vegetation index (NDVI) and NDVI-derived leaf area index (LAI) have been used to prescribe vegetation conditions in land models and to study the impact of vegetation on climate (e.g., Chase et al. 1996; Bounoua et al. 2000; Buermann et al. 2001; Guillevic et al. 2002). Bounoua et al. (2000) found that, as a result of global vegetation increase, both evapotranspiration and precipitation increase, and evapotranspiration increases more than precipitation does. Guillevic et al. (2002), however, found that the interannually varying vegetation influences evapotranspiration, but its influence on large-scale climate dynamics is very weak. Because of the prescribed vegetation variations in the models used, these studies did not directly tackle the issue of soil moisture–vegetation–precipitation coupling.

Recently, vegetation phenology schemes simulating the response of vegetation at the seasonal time scale to hydrometeorological and other environmental conditions have been incorporated into land surface and climate models (Dickinson et al. 1998; Lu et al. 2001; Tsvetsinskayaet al. 2001; Kim and Wang 2005). These models provide useful tools for studying seasonal vegetation–climate interactions. For example, Lu et al. (2001) coupled the CENTURY ecosystem model with the Regional Atmospheric Modeling System (RAMS), and performed simulations with both the offline and coupled models over the United States. Based on spatial averages over the central United States, lower simulated LAI in the coupled model than prescribed in the offline RAMS leads to more precipitation due to larger vegetation transmissivity, resulting in greater radiation at the land surface, and finally more convective precipitation in the coupled model. However, at one grid cell where winter wheat is the dominant vegetation, lower LAI in the coupled model than in the offline RAMS due to harvest leads to less precipitation in the coupled model.

In this study we use the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The model has been modified to include the predictive vegetation phenology scheme of Kim and Wang (2005), which allows us to study soil moisture–vegetation–precipitation feedbacks at the seasonal time scale. We focus on North America, a region of strong land–atmosphere coupling (Koster et al. 2004; Wang et al. 2007) identified by many GCMs including the CAM–CLM model. In our previous study (Kim and Wang 2007), we investigated the impact of soil moisture anomalies on subsequent precipitation using the coupled CAM–CLM with prescribed vegetation phenology. The present study focuses on how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions.

2. Model and methodology

a. Model description

The model used in this study is version 3 of the National Center for Atmospheric Research (NCAR) CAM (CAM3) (Collins et al. 2004) coupled with version 3 of CLM (CLM3) (Dai et al. 2003; Oleson et al. 2004). Oceanic boundary conditions in this coupled land–atmosphere model are prescribed with the climatological monthly varying sea surface temperature and sea ice coverage. The level of atmospheric CO2 is assumed to be 355 ppm. Among the three dynamics schemes available in CAM [Eulerian spectral, semi-Lagrangian dynamics, and finite volume (FV) dynamics], we choose the FV dynamical core (Lin and Rood, 1996; Lin 2004) with a horizontal resolution of 2° latitude by 2.5° longitude and a total of 26 levels in the vertical direction. The land model CLM3 has 10 unevenly spaced soil layers, up to 5 snow layers, and 1 vegetation layer. Land surface within each grid cell is represented by the fractional coverage of four types of patches (glacier, lake, wetland, and vegetated), and the vegetation portion of the grid cell is represented by the fractional coverage of up to 4 out of 16 different plant functional types (PFTs) available in the model. In this study, the default leaf phenology scheme in CLM3 is replaced with a predictive scheme that has been validated against the latest Moderate Resolution Imaging Spectroradiometer (MODIS) observational data over North America (Kim and Wang 2005).

In the predictive phenology scheme, the PFT-specific leaf area index is updated daily by scaling down the annual maximum leaf area index (LAImax) with a predictive phenology factor (D):

 
formula

where LAIdaily is the PFT-specific daily LAI, and LAImax is derived from the monthly PFT-specific MODIS LAI at 0.5° × 0.5° resolution (Tian et al. 2004; see Fig. 2). In Fig. 2, only information for the five primary PFTs that exist in North America is presented. Over this region, needleleaf trees (Fig. 2a) are mostly temperate evergreen trees; broadleaf trees (Fig. 2b) are mostly temperate deciduous trees, which are cold-deciduous; shrubs (Fig. 2c) consist of winter deciduous and evergreen shrubs; and grasses and crops (Figs. 2d and 2e) are both cold- and drought-deciduous, and are further divided into C3 and C4 types in the model according to the photosynthetic pathway. The phenology factor (D), ranging from zero to one, is simulated for cold-deciduous plants and drought-deciduous plants separately. For plants responding to both coldness and drought (e.g., grasses and crops), the phenology factor is determined based on the multiplicative effect of cold and drought stresses. For evergreen trees, their LAI seasonality is prescribed based on the MODIS LAI observations. For crops, their climatological plantation and harvest times are derived from the MODIS NDVI, but their LAIs between plantation and harvest are predicted in response to hydrometeorological conditions in the same way as grasses are.

Fig. 2.

Percentage of the grid cell occupied by and maximum LAI of (a) needleleaf trees, (b) broadleaf trees, (c) shrubs, (d) grasses, and (e) crops on 0.5° × 0.5° map.

Fig. 2.

Percentage of the grid cell occupied by and maximum LAI of (a) needleleaf trees, (b) broadleaf trees, (c) shrubs, (d) grasses, and (e) crops on 0.5° × 0.5° map.

In predicting leaf green-up, development, and senescence, the winter deciduous phenology scheme considers the impact of 10-day average air temperature, accumulated growing degree-days (AGDD), soil temperature, and photoperiod. The base temperatures for AGDD are 0°C for trees and −5°C for grass as grass can survive under colder temperature than trees. Once the criteria for leaf green up or senescence are met, it is assumed that the full leaf display in the beginning of the growing season or complete leaf offset at the end of the growing season takes 15 days. The drought deciduousness is predicted based on the whole plant water stress factor, which depends on soil water potential in different soil layers and the plant rooting profile. It ranges from zero at the permanent wilting point to one at saturation. The drought-deciduous phenology scheme predicts leaf shedding and growing based on the 10-day running mean of plant water stress. Further details about the phenology scheme can be found in Kim and Wang (2005).

In the land model, changes in LAI influence land surface properties such as albedo, surface roughness, and stomata resistance. In particular, stomata resistance, which is coupled with photosynthesis and transpiration, is important in determining the amount of soil moisture transpired to the overlying atmosphere (Bounoua et al. 2000). CLM uses a stomata resistance-leaf photosynthesis model similar to Collatz et al. (1991, 1992. The inverse of stomata resistance (i.e., stomata conductance) is linearly related to the leaf photosynthesis, which is limited by temperature and soil wetness and estimated with PFT-specific parameters. Further details about CLM3 can be found in Dai et al. (2003) and Oleson et al. (2004).

b. Methodology

Primary simulations using the coupled CAM3–CLM3 model include an initial integration and a large number of ensemble simulations with different initial soil moisture conditions and different vegetation treatments. Driven with the climatological SST, the initial integration is carried out for 12 yr. Data from the first 2 yr are discarded as the model spinup, and the last 10 yr of data, although it may be short, are used to derive the model climatology of soil moisture on the first day of each month. This soil moisture climatology is used to initialize subsequent experimental ensemble simulations, integrated from the first day of a given month until the end of the year.

Each ensemble includes five members, which are different from each other only in the initial soil moisture condition. For an ensemble without initial soil moisture anomalies, for example, its five members are initialized with 100%, 99%, 98%, 97%, and 96% of the soil moisture climatology. For an ensemble with 80% dry (or wet) anomalies of soil moisture climatology, its five members are initialized with 20%, 19%, 18%, 17%, and 16% (or 180%, 179%, 178%, 177%, and 176%) of the soil moisture climatology. In the experimental simulations, an increase or decrease of soil moisture equivalent of 80% and 30% of its climatology is applied. However, to distinguish signal from noise, our result analysis in section 3 will mostly focus on an extremely large magnitude (i.e., 80%) of soil moisture anomalies, although results from ensembles with a smaller magnitude (i.e., 30%) of soil moisture anomalies are also presented for comparison purpose. Note that in CAM3–CLM3 over much of North America, more than 80% increase of climatology is required to reach the field capacity; about 20% decrease of climatology is needed to reach the wilting point (Fig. 4 of Kim and Wang 2007). Further, the Illinois State Water Survey observed that soil moisture ranges from about 90% below to about 50% above its mean value at the most variable station, and ranges from about 40% below to about 30% above at the least variable station. This indicates that 80% increase and decrease of soil moisture climatology may be beyond the natural variability in some places, although the observed soil moisture is not directly comparable to the model soil moisture due to their discrepancies in the spatial and temporal resolutions (section 4b of Kim and Wang 2007).

Initial soil moisture anomalies are applied across much of North America (the lined box in Fig. 3) throughout the whole soil depth in the model (∼3.4 m). While Kim and Wang (2007) examined the impact of spatial coverage and depth of soil moisture anomalies on subsequent precipitation in details, this study focuses on vegetation feedback by applying soil moisture anomalies over a same spatial coverage and soil depth. Our results analysis will focus on the Mississippi River basin (shaded in Fig. 3) where precipitation is most sensitive to initial soil moisture anomalies (Kim and Wang 2007). This region also includes most of the North American areas of strong coupling between soil moisture and precipitation in CAM3–CLM3 (Koster et al. 2004; Wang et al. 2007). In addition, the dominant vegetation in this region includes grasses and crops (Fig. 2a), both of which respond to soil water stress. Therefore, vegetation–soil moisture–precipitation coupling is expected to be strong in this region.

Fig. 3.

Map of the study domain. A lined box presents North America (“NA”), the domain of initial soil moisture anomalies in the numerical experiments. The shaded area defines the Mississippi River basin on a 2° × 2.5° resolution (Bosilovich and Chern 2006).

Fig. 3.

Map of the study domain. A lined box presents North America (“NA”), the domain of initial soil moisture anomalies in the numerical experiments. The shaded area defines the Mississippi River basin on a 2° × 2.5° resolution (Bosilovich and Chern 2006).

Three different types of ensembles are designed: the Control, SM Anomaly, and SM_Veg Anomaly. The Control ensemble is initialized with the soil moisture climatology, and the SM Anomaly and SM_Veg Anomaly ensembles are initialized with certain soil moisture anomalies imposed to the soil moisture climatology. Table 1 lists all ensemble simulations carried out in this study. Vegetation seasonality in the Control and SM_Veg Anomaly ensembles is predicted by the predictive phenology scheme, and is prescribed in each of the SM Anomaly simulation using model output from the corresponding Control simulation. Therefore, climate differences between the SM Anomaly ensemble and the Control ensemble are attributed to the impact of soil moisture initialization through soil moisture–precipitation interactions; climate differences between the SM_Veg Anomaly ensemble and the Control ensemble are attributed to the impact of soil moisture initialization and vegetation feedbacks; and climate differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble represent the impact of vegetation feedback. The focus in this study is on the role of vegetation in modifying the impact of initial soil moisture anomalies.

Table 1.

Lists of simulations.

Lists of simulations.
Lists of simulations.

Apart from the 12-yr initial simulation with climatological SST, a 20-yr simulation driven with interannually varying SST from 1979 to 1998 is available from our previous study (Kim and Wang 2007). Based on this 20-yr integration, the t statistics are estimated to evaluate the statistical significance of simulated climate differences between two different types of ensembles (e.g., difference between SM_Veg and SM ensembles) in section 3. For each grid cell, monthly output from this 20-yr simulation is used to derive the 90% confidence interval in the significance tests of monthly results over the 2D spatial domain. Daily output is used to derive the 90% confidence interval in the significance tests of the daily and 10-day running averaged results over the Mississippi River basin. Here the simulation with interannually varying SST is used to get a more realistic estimate of the interannual variability of climate over our study domain. The simulation with climatological SST underestimates the interannual variability of climate over land, which if used would cause the statistical significance to be spuriously overestimated.

3. Result analysis

Our previous study (Kim and Wang 2007) showed that characteristics of soil moisture anomalies, including their timing and direction, influence the resulting precipitation response. Since vegetation is limited by different factors (soil moisture, temperature, and/or photoperiod) during different seasons, the timing of soil moisture anomalies will influence the vegetation response. Moreover, the processes and mechanisms giving rise to soil moisture–precipitation feedback are similar to those underlying the vegetation–precipitation feedback, leading to the expectation that the impact of vegetation anomalies on precipitation depends on the timing and magnitude of such soil moisture anomalies as well. Together these point to the potential dependence of the soil moisture–vegetation–precipitation feedback on the characteristics of soil moisture anomalies. In this study, we first examine how the impact of vegetation feedback on the response of precipitation to soil moisture initialization differs between dry and wet anomalies, and how it varies with the timing of soil moisture anomalies (see Table 1 for the list of simulations). We will then analyze the results in greater detail to develop some process-based understanding.

Vegetation responds to changes induced by initial soil moisture anomalies in the SM_Veg Anomaly, but such response is absent in the SM Anomaly. In Fig. 4, initial wet/dry anomalies in the soil and subsequent rainfall anomalies lead to an increase/decrease in LAI. Vegetation responds to initial wet soil moisture anomalies relatively slowly in ensembles starting from mid- or late spring such as 1 April and 1 May and much faster in ensembles starting after 1 June. This may result from the cold temperature stress on vegetation during spring and the high sensitivity of rainfall to wet soil moisture anomalies applied in the beginning of June, July, and August as evident in Fig. 5 (see section 3b for details).

Fig. 4.

Daily LAI anomalies as a response to an 80% increase (gray) and an 80% decrease (black) of soil moisture climatology applied on (a) 1 April, (b) 1 May, (c) 1 June, (d) 1 July, and (e) 1 August (SM_Veg-Control or SM_Veg-SM). Each line presents the ensemble mean of five members averaged over the Mississippi River basin. The shaded area presents the 90% confidence interval for the daily average of LAI.

Fig. 4.

Daily LAI anomalies as a response to an 80% increase (gray) and an 80% decrease (black) of soil moisture climatology applied on (a) 1 April, (b) 1 May, (c) 1 June, (d) 1 July, and (e) 1 August (SM_Veg-Control or SM_Veg-SM). Each line presents the ensemble mean of five members averaged over the Mississippi River basin. The shaded area presents the 90% confidence interval for the daily average of LAI.

Fig. 5.

Ten-day running averages of precipitation anomalies as a response to an 80% increase (gray) and an 80% decrease (black) of soil moisture climatology applied on (a) 1 April, (b) 1 May, (c) 1 June, (d) 1 July, and (e) 1 August. Solid/dash lines represent the differences between the SM_Veg/SM Anomaly ensemble and the Control ensemble. Each line presents the ensemble mean of five members averaged over the Mississippi River basin. The shaded area presents the 90% confidence interval for the 10-day average of precipitation.

Fig. 5.

Ten-day running averages of precipitation anomalies as a response to an 80% increase (gray) and an 80% decrease (black) of soil moisture climatology applied on (a) 1 April, (b) 1 May, (c) 1 June, (d) 1 July, and (e) 1 August. Solid/dash lines represent the differences between the SM_Veg/SM Anomaly ensemble and the Control ensemble. Each line presents the ensemble mean of five members averaged over the Mississippi River basin. The shaded area presents the 90% confidence interval for the 10-day average of precipitation.

In the case of dry anomalies, regardless of when soil moisture anomalies are applied, the impact of vegetation is small, and there seems to be some oscillation between positive feedback (vegetation feedback reinforcing the impact of initial soil moisture) and negative feedback (vegetation feedback suppressing the impact of initial soil moisture). That is, compared with the 90% confidence interval of precipitation differences, the magnitude of precipitation anomalies in the SM_Veg ensemble is sometimes larger than that in the SM ensemble, and sometimes smaller. However, overall, the difference between the two ensembles is small following dry soil moisture anomalies (Figs. 4 and 5). This lack of strong response to vegetation feedback may be attributed to dry biases of the model, as detailed in section 3a. The dry bias in the model causes such a severe water stress in vegetation that vegetation has little room to further decrease in response to dry soil moisture anomalies.

As shown in Fig. 5, in the case of wet anomalies in May through July, vegetation feedback reinforces the impact of initial soil moisture on precipitation; that is, a positive feedback occurs. However, in case of wet anomalies in April, negative feedback is dominant; that is, vegetation damps the impact of initial soil moisture (Fig. 5). Changes in precipitation due to vegetation feedback are considerable relative to those due to soil moisture feedback especially during June, July, and August (Fig. 6). While Fig. 6 presents spatial and temporal averages, the following analysis examines spatial details about the relative contribution of soil moisture feedback, vegetation feedback, as well as the detailed pathways of soil moisture–vegetation–precipitation interactions. And we use ensembles starting on 1 June as an example for the summer months and ensembles starting on 1 April as an example for spring.

Fig. 6.

Changes in precipitation due to soil moisture feedback (white; SM-Control), vegetation feedback (light gray; SM_Veg-SM), and both soil moisture and vegetation feedback (dark gray; SM_Veg-Control), averaged over the Mississippi River basin throughout the second and third months (as indicated inside parentheses) following the 80% wet soil moisture anomalies applied on 1 April, 1 May, 1 June, 1 July, and 1 August. The error bar presents a standard deviation among five ensemble members.

Fig. 6.

Changes in precipitation due to soil moisture feedback (white; SM-Control), vegetation feedback (light gray; SM_Veg-SM), and both soil moisture and vegetation feedback (dark gray; SM_Veg-Control), averaged over the Mississippi River basin throughout the second and third months (as indicated inside parentheses) following the 80% wet soil moisture anomalies applied on 1 April, 1 May, 1 June, 1 July, and 1 August. The error bar presents a standard deviation among five ensemble members.

a. Summer

How vegetation feedback modifies the response of precipitation to summer soil moisture anomalies is investigated with the ensembles starting on 1 June. From Fig. 7b, first we observe that initial wet soil moisture anomalies over North America increase LAI particularly over the Mississippi River basin. This is a region where precipitation is sensitive to initial soil moisture conditions, causing persistence of anomalies in water availability. These persistent anomalies of water availability (in precipitation and/or soil moisture) eventually lead to the response of vegetation since vegetation response is a fairly slow process. Over places where precipitation is not responsive, initial soil moisture anomalies will not cause persistent water availability anomalies, thus no lasting response from vegetation is found. During summer, water availability is the only factor limiting the LAI (Fig. 8). Wet soil moisture anomalies in the SM_Veg Anomaly ensembles therefore cause LAI to increase over the Mississippi River basin. Second, the increase in LAI lasts throughout the growing season (longer than four months), as a result of rainfall increase (see Fig. 9) in response to initial wet soil moisture anomalies through the positive soil moisture–vegetation–precipitation feedback. Relative to the SM Anomaly, changes in LAI and the resulting changes in precipitation following the initial soil moisture anomalies in the SM_Veg Anomaly further influence soil moisture as shown in Fig. 7c. On the one hand, the increase in LAI due to initial wet soil moisture anomalies leads to increase in water consumption by vegetation through transpiration and reduces soil water replenishment through interception loss (not shown), which tends to reduce soil moisture. On the other hand, the increase of LAI enhances evapotranspiration, which favors more precipitation and therefore tends to increase soil moisture. Whether soil is wetter or drier in the SM_Veg Anomaly (compared with the SM Anomaly) depends on the competition between the two mechanisms. There is no definitive winner, even though the direct drying impact seems to dominate over vast areas of vegetation increase (Fig. 7c).

Fig. 7.

(a) LAI in the Control ensemble (or the SM Anomaly ensemble), and (b) LAI differences and (c) soil water differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble in June, July, August, and September. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

Fig. 7.

(a) LAI in the Control ensemble (or the SM Anomaly ensemble), and (b) LAI differences and (c) soil water differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble in June, July, August, and September. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

Fig. 8.

Seasonal change of phenology factor [D in Eq. (1)], Ddrought for drought-deciduousness, and Dwinter for cold-deciduousness, averaged over the Mississppi River basin. The shaded area presents one standard deviation of monthly D. These are derived from the 20-yr CAM integration driven with interannually varying SST from 1979 to 1998. Note that D is always set to be one for evergreen PFTs.

Fig. 8.

Seasonal change of phenology factor [D in Eq. (1)], Ddrought for drought-deciduousness, and Dwinter for cold-deciduousness, averaged over the Mississppi River basin. The shaded area presents one standard deviation of monthly D. These are derived from the 20-yr CAM integration driven with interannually varying SST from 1979 to 1998. Note that D is always set to be one for evergreen PFTs.

Fig. 9.

(a) Precipitation in the Control ensemble, (b) precipitation differences between the SM Anomaly ensemble and the Control ensemble, and (c) precipitation differences between the SM_Veg Anomaly and the SM Anomaly ensemble in June, July, August, and September. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

Fig. 9.

(a) Precipitation in the Control ensemble, (b) precipitation differences between the SM Anomaly ensemble and the Control ensemble, and (c) precipitation differences between the SM_Veg Anomaly and the SM Anomaly ensemble in June, July, August, and September. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

The drying effect of vegetation on soil moisture complicates the response of precipitation to initial soil moisture anomalies, competing with the positive impact of LAI increase on precipitation (Fig. 1). Between the two, the impact of increased vegetation on precipitation seems to dominate the impact of vegetation-induced soil drying, leading to increase in precipitation as shown in Fig. 9. The statistically significant increases in precipitation suggest that vegetation feedbacks reinforce the impact of initial wet soil moisture anomalies on subsequent precipitation in this example. Furthermore, comparison between Figs. 9b and 9c suggests that increases in precipitation induced by vegetation feedback (Fig. 9c) are comparable in magnitude with those by soil moisture feedback (Fig. 9b) especially over the Mississippi River basin in July and August.

Albedo decreases as soil moisture increases in the SM relative to the Control as expected (Fig. 10b). Generally, albedo is expected to decrease with the increases of LAI. However, our results show increases of albedo (Fig. 10c) as LAI increases (Fig. 7b). This can happen when vegetation is brighter than the ground surface (Bounoua et al. 2000). In this specific case, over the Mississippi River basin, vegetation with relatively high albedo (i.e., grasses and crops) exists on the dark (prescribed in the model) and wet soil background (due to wet soil moisture anomalies). Therefore, such increases in albedo, together with the increased cloudiness that accompanies the precipitation increase, reduce the total net shortwave radiation (Fig. 11b). However, the increased cloudiness results in more downward longwave radiation, and enhanced evapotranspiration cools down the ground surface, leading to less upward longwave radiation. These imply an increase in net longwave radiation at the land surface (Fig. 11c). The increase of longwave radiation outcompetes the shortwave impact of albedo and clouds, resulting in an increase of net radiation (Fig. 11a). A similar effect was found by Pal and Eltahir (2003) who showed an increase in net radiation as a result of a soil moisture increase, with the longwave radiation impact dominant over the shortwave radiation impact. In addition, the LAI increase leads to a low Bowen ratio in the SM_Veg Anomaly ensembles, favoring the increase of latent heat at the expense of sensible heat (Figs. 11d and 11e).

Fig. 10.

(a) Albedo in the Control ensemble, (b) albedo differences between the SM Anomaly ensemble and the Control ensemble, and (c) albedo differences between the SM_Veg Anomaly and the SM Anomaly ensembles in June and July. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June.

Fig. 10.

(a) Albedo in the Control ensemble, (b) albedo differences between the SM Anomaly ensemble and the Control ensemble, and (c) albedo differences between the SM_Veg Anomaly and the SM Anomaly ensembles in June and July. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June.

Fig. 11.

Differences in (a) net radiation, (b) net shortwave radiation, (c) net longwave radiation, (d) latent heat, and (e) sensible heat between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble, averaged through the JJA season. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June.

Fig. 11.

Differences in (a) net radiation, (b) net shortwave radiation, (c) net longwave radiation, (d) latent heat, and (e) sensible heat between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble, averaged through the JJA season. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 June.

The large magnitude of soil moisture anomalies (i.e., 80% increase or decrease of the soil moisture climatology) may be beyond the range of natural variability (see section 2b). We therefore add another set of ensemble experiments with a smaller magnitude of soil moisture anomalies. Increases in LAI due to a 30% increase of initial soil moisture in Fig. 12a are as large as that in Fig. 7b, indicating that even a 30% increase of soil moisture climatology is enough for vegetation to reach its full leaf display. Unless cold stress exists, vegetation reaches its full leaf display once the whole plant water stress, ranging from zero at the permanent wilting point to one at saturation, is above a certain threshold [Wth = 0.4 in Eq.(6) of Kim and Wang (2005)]. Increases in LAI lead to increases in evapotranspiration, and therefore decreases in soil moisture (negative feedback from vegetation to soil moisture), which may eventually lead to a decrease in precipitation (negative feedback from vegetation to precipitation); the increased evapotranspiration, however, favors precipitation (positive feedback from vegetation to precipitation), which tends to increase soil moisture (positive feedback from vegetation to soil moisture). Comparison between Fig. 12b and Fig. 7c suggests that the negative feedback from vegetation to soil moisture is dominant in both the 30% and 80% anomaly cases, but it is more so with the 30% wet anomalies. Between the two cases, the direct drying impact of vegetation does not differ much; the wetting impact through precipitation increases with the magnitude of initial wet soil moisture anomalies. This is because the extra soil moisture anomalies beyond a certain threshold (i.e., the threshold whole plant water stress) do not enhance vegetation growth, but do enhance the wetting impact through precipitation.

Fig. 12.

(a) LAI differences, (b) soil water differences, and (c) precipitation differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble in June, July, August, and September. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with a 30% increase of soil moisture climatology on 1 June. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

Fig. 12.

(a) LAI differences, (b) soil water differences, and (c) precipitation differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble in June, July, August, and September. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with a 30% increase of soil moisture climatology on 1 June. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

In contrast to the dominant negative feedback from vegetation to soil moisture, positive feedback from vegetation to precipitation is dominant in both the 30% and 80% anomaly cases, and it is more so with the 80% wet anomalies (Fig. 12c versus Fig. 9c). The fact that the impact of vegetation on precipitation is smaller in the 30% anomaly case than in the 80% anomaly case is consistent with the stronger negative feedback from vegetation to soil moisture in the 30% anomaly case (Fig. 12b versus Fig. 7c). Note that the sensitivity of vegetation to soil moisture anomalies depends on a tunable parameter, the threshold whole plant water stress [Wth = 0.4 in Eq. (6) of Kim and Wang (2005)]. If this parameter increases (e.g., from the current value 0.4 to 0.6), the difference in the strength of vegetation feedback between the 30% and 80% anomaly cases will be smaller.

Initial dry soil moisture anomalies cause LAI to decrease, as expected, but this reduced LAI does not seem to significantly reduce precipitation (not shown). As a result, such LAI decrease does not last long, and is much smaller in magnitude than the LAI increase in the wet case (Fig. 7a). This insensitivity is likely related to a dry bias in the coupled model CAM3–CLM3 over the Mississippi River basin (Bonan and Levis 2006; Hack et al. 2006). Kim and Wang (2007) also compared the precipitation and soil moisture between the CAM3–CLM3 and the North American Regional Reanalysis (NARR) data, showing a dry bias of the model. For example, over this region, the Global Precipitation Climatology Project (GPCP) precipitation during June–August (JJA) is about 2–4 mm day−1, about 1–2 mm day−1 higher than the model climatology (http://www.ccsm.ucar.edu/models/atm-cam/sims/cam3.0). The dry bias in the Control ensembles leads to severe water stress in vegetation to such an extent that there is not much room for further LAI decrease in the SM_Veg relative to the SM (and the Control). Also, changes in albedo due to dry soil moisture anomalies are very minimal (not shown).

b. Spring

The impact of vegetation feedback during spring is examined using the SM_Veg Anomaly ensembles starting on 1 April as an example. Without considering vegetation feedback, Kim and Wang (2007) found that the impact of spring soil moisture anomalies on precipitation is not evident until early summer although the impact of anomalies on the large-scale circulation leads to slight changes in precipitation during spring. This is because the convective rainfall that responds to land surface condition changes does not become the dominant type of rain over North America until May or June. A similar delay in vegetation response exists (Fig. 13a), but for different reasons. The dominant land cover (grass and crops) in the Mississippi River basin responds to both cold stress and water stress (see Fig. 8). During spring, vegetation growth is still limited by low temperature. Vegetation in April, therefore, cannot take advantage of the increased soil moisture. Instead, the increase in LAI becomes obvious in May and reaches its peak in June. Evapotranspiration during April, however, is enhanced as a result of the wet soil (based on the comparison between SM and Control; not shown), but the response of precipitation does not occur until May or early June. Therefore, soil moisture is on its way back to normal in April and May in the SM ensembles, while the enhanced vegetation in the SM_Veg speeds up this process and may even lead to dry anomalies in the soil. As a result, precipitation may decrease, and vegetation feedback may weaken the impact of initial soil moisture, leading to a negative feedback.

Fig. 13.

(a) LAI differences, (b) soil water differences, and (c) precipitation differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble in April, May, June, and July. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 April. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

Fig. 13.

(a) LAI differences, (b) soil water differences, and (c) precipitation differences between the SM_Veg Anomaly ensemble and the SM Anomaly ensemble in April, May, June, and July. Only differences exceeding the 90% confidence level are shaded. The Anomaly ensembles are initialized with an 80% increase of soil moisture climatology on 1 April. The numbers in the left bottom of each panel indicate averages over the Mississippi River basin.

Differences in soil water and precipitation between the SM_Veg Anomaly and the SM Anomaly (Figs. 13b and 13c) are insignificant during the first two months (i.e., April and May) as a result of little change in LAI. In June and July, shaded (statistically significant based on a t test) negative anomalies suggest that vegetation feedback tends to weaken the impact of wet spring soil moisture anomalies, and therefore weaken the summer precipitation anomalies. Similar negative feedback by vegetation is also found in simulations initialized with dry soil moisture anomalies (not shown).

4. Conclusions and discussion

We carried out ensemble simulations using the coupled CAM–CLM model to examine how vegetation feedback modifies the impact of initial soil moisture anomalies on subsequent precipitation over North America. Vegetation feedback may reinforce or suppress the soil moisture–induced persistence of seasonal climate anomalies through water, energy, and momentum exchanges, depending on timing and direction of soil moisture anomalies. During summer months, wet soil moisture anomalies increased LAI, leading to increased precipitation via increased evapotranspiration and surface heating. That is, vegetation feedback reinforces the impact of initial soil moisture on precipitation. Dry soil moisture anomalies in the summer months, however, did not show significant impact on subsequent vegetation and precipitation, which may be attributed to the dry bias in the coupled CAM–CLM model. For wet soil moisture anomalies in spring, vegetation showed delayed response and the vegetation feedback is negative—during the summer following spring wet soil moisture anomalies, vegetation feedback tends to suppress the impact of soil moisture on precipitation.

Vegetation feedback in the coupled soil moisture–vegetation–precipitation system has been discussed in recent studies based on observational data analysis (Notaro et al. 2006; Wang et al. 2006). Note that these studies are different from ours since they do not specifically examine initial soil moisture anomalies. Rather, they directly relate vegetation anomalies to subsequent precipitation without considering how or why anomalies in vegetation take place. Using remotely sensed FPAR for vegetation data, Notaro et al. (2006) estimated vegetation feedback parameter for precipitation in the United States for every season. They showed that the impact of vegetation on precipitation is spatially inhomogeneous—positive over the corn and soybean belt and negative over the winter wheat belt, while our present study shows the feedback can be positive or negative depending on season. Further, Wang et al. (2006) analyzed the NDVI data over the North American Grasslands during the growing season using Granger causality test and found that above-average NDVI leads to lower rainfall during the growing season. Their EOF analysis in the frequency domain further showed that interaction between vegetation and precipitation tends to suppress each other at short time scales (less than two months), enhance each other at long time scales (interannual time scales), and oscillate at intermediate time scale (four to eight months). Their finding of negative feedback at the short time scales is consistent with our results with spring soil moisture anomalies, while other GCM studies generally disagree on negative feedback (see the reviews in Notaro et al. 2006). The oscillatory vegetation feedback was detected in our simulated LAI (Fig. 5) as well, although the magnitude is rather small and the time scale is shorter than what Wang et al. (2006) found.

The coupling between soil moisture and precipitation is strong under moderate soil moisture conditions, and weaker under dry and wet soil moisture conditions in general (Koster et al. 2006). In other words, model sensitivity depends to a certain degree on the model’s mean climate. Given the dry bias of CAM3, the response of precipitation to soil moisture feedback and vegetation feedback, therefore, may change if the model bias is reduced (Koster et al. 2006).

The impact of vegetation feedback is studied under soil moisture anomalies that are applied throughout the whole soil depth in the model (∼3.4 m). Given that different types of vegetation have different rooting depth, therefore respond selectively to soil moisture anomalies at different depth, theoretically the magnitude of the impact of vegetation feedback may vary with the depth of initial soil moisture or with the dominant vegetation type. However, over the Mississippi River basin, which is the part of our model domain where precipitation and vegetation are most responsive to initial soil moisture anomalies, the land cover is dominated by grass and crops. Their root system is fairly shallow, mostly residing in the top 1 m of the soil. As shown in Kim and Wang (2007), reducing the depth of soil moisture anomalies to about 0.83 m (the top seven layers in the model) does not significantly influence the precipitation response. These two together imply that reducing the depth of soil moisture anomalies to 0.83 m will not significantly influence the strength of vegetation feedback. Over places where trees dominate (e.g., the U.S. East), the hydrological regime is probably too wet to support a strong coupling between soil moisture and precipitation; therefore, the applied soil moisture anomalies will not persist, causing the lack of persistent response in vegetation.

Our model simulates the seasonal variation of LAI in response to natural hydrometeorological conditions. The impact of other dynamic processes operating at the seasonal time scale such as fire and irrigation were not considered. Fire tends to take place more frequently under a drought condition, which if considered would reinforce the vegetation response to the hydrological anomalies, and therefore reinforce the significance of vegetation feedback. Irrigation, which can be important for Midwest croplands, can wipe out a dry anomaly applied to the system and will also reduce the difference between wet and normal conditions as farmers are likely to irrigate more during normal years than during wet years. Irrigation therefore would reduce the response of precipitation to natural soil moisture anomalies and reduce the significance of vegetation feedback.

The phenology scheme used in this study predicts LAI based on environmental stress factors and the annual maximum leaf area index. The latter is a spatially varying, PFT-specific parameter, and represents the idealized peak growing-season LAI that would occur in absence of environmental stress, or the “potential LAI.” In our study the annual maximum LAIs are derived from MODIS LAI data and stay constant regardless of what soil moisture anomalies are considered. In reality, for deciduous woody plants and perennial grass, this “potential LAI” depends largely on nonstructural carbon reserves in perennial tissues at the beginning of the growing season, which results from carbon dynamics of the previous year. The level of environmental stresses during the current growing season do influence the “potential LAI,” but to a lesser degree. Ideally, one can combine a vegetation dynamics model (that functions at the interannual time scale or longer) and a phenology model (that functions at the seasonal time scale) to get a more accurate estimate of the annual maximum LAI. Specifiying this “potential LAI” based on observations may overestimate or underestimate LAI throughout the whole simulation period. However, its impact on the seasonality of LAI and on the relative comparison between, for example, SM and SM_Veg ensembles in this study may be small. It is also less problematic in this specific study as our focus is on the general mechanism involved in soil moisture–vegetation–precipitation interactions. For studies that focus on the role of vegetation feedback in specific historical climate events, such as the 1988 drought or 1993 flood in the United States, it will be more important that the “potential LAI” be estimated using a dynamic vegetation model driven with the climate forcing from the year preceding the event of interest before a phenology model is used to predict the seasonality of LAI. These issues will be tackled in future research.

Acknowledgments

The authors thank Dr. Michael G. Bosilovich at the NASA Global Modeling and Assimilation Office for helpful input and for providing the mask data of the Mississippi River basin. The authors also thank Dr. Samuel Levis at NCAR, Dr. Michael Notaro at the University of Wisconsin, and two anonymous reviewers for their constructive comments on earlier versions of the manuscript. This work is supported by the NOAA GEWEX Americas Prediction Project program (NA03OAR4310080).

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Footnotes

Corresponding author address: Dr. Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road, Storrs, CT 06269. Email: gwang@engr.uconn.edu