Impact of Vegetation Feedback on the Response of Precipitation to Antecedent Soil Moisture Anomalies over North America
|
|
- Willa Johnston
- 6 years ago
- Views:
Transcription
1 534 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 Impact of Vegetation Feedback on the Response of Precipitation to Antecedent Soil Moisture Anomalies over North America YEONJOO KIM AND GUILING WANG Department of Civil and Environmental Engineering, and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut (Manuscript received 6 July 2006, in final form 29 January 2007) 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 Corresponding author address: Dr. Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road, Storrs, CT gwang@engr.uconn.edu 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-thannormal 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. 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 evapo- DOI: /JHM American Meteorological Society
2 JUNE 2007 K I M A N D WANG 535 FIG. 1. Diagram for the impact of vegetation feedback on how precipitation responds to initial soil moisture anomalies. Here P indicates changes in precipitation due to vegetation feedback, and dashed lines indicate negative feedback. transpiration. 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)
3 536 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 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 CO 2 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 (LAI max ) with a predictive phenology factor (D): LAI daily LAI max D, where LAI daily is the PFT-specific daily LAI, and LAI max is derived from the monthly PFT-specific MODIS LAI at 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, 1 but their LAIs between plantation and harvest are predicted in response to hydrometeorological conditions in the same way as grasses are. 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 resistanceleaf 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 dif-
4 JUNE 2007 K I M A N D WANG 537 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 map. ferent 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
5 538 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 TABLE 1. Lists of simulations. 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 resolution (Bosilovich and Chern 2006). 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 Name of ensemble Start date Initial soil moisture (% of climatology) Control C_Apr 1 April 100, 99, 98, 97, C_May 1 May and 96 C_Jun 1 June C_Jul 1 July C_Aug 1 August SM SM_D80_Apr 1 April 20, 19, 18, 17, SM_D80_May 1 May and 16 SM_D80_Jun 1 June SM_D80_Jul 1 July SM_D80_Aug 1 August SM_W80_Apr 1 April 180, 179, 178, 177, SM_W80_May 1 May and 176 SM_W80_Jun 1 June SM_W80_Jul 1 July SM_W80_Aug 1 August SM_D30_Jun 1 June 70, 69, 68, 67, and 66 SM_W30_Jun 130, 129, 128, 127, and 126 SM_Veg SM_Veg_D80_Apr 1 April 20, 19, 18, 17, SM_Veg_D80_May 1 May and 16 SM_Veg_D80_Jun 1 June SM_Veg_D80_Jul 1 July SM_Veg_D80_Aug 1 August SM_Veg_W80_Apr 1 April 180, 179, 178, 177, SM_Veg_W80_May 1 May and 176 SM_Veg_W80 _Jun 1 June SM_Veg_W80_Jul 1 July SM_Veg_W80_Aug 1 August SM_Veg_D30_Jun 1 June 70, 69, 68, 67, and 66 SM_Veg_W30_Jun 130, 129, 128, 127, and 126 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. 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
6 JUNE 2007 K I M A N D WANG 539 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. 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). 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
7 540 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 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. 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. 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
8 JUNE 2007 K I M A N D WANG 541 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. 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). 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
9 542 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 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. 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). 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 [W th 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 thresh-
10 JUNE 2007 K I M A N D WANG 543 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. old whole plant water stress) do not enhance vegetation growth, but do enhance the wetting impact through precipitation. 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
11 544 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 FIG. 8. Seasonal change of phenology factor [D in Eq. (1)], D drought for drought-deciduousness, and D winter 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 Note that D is always set to be one for evergreen PFTs. (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 [W th 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 mmday 1, about 1 2 mm day 1 higher than the model climatology ( 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. 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
12 JUNE 2007 K I M A N D WANG 545 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. 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
13 546 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 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. 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
14 JUNE 2007 K I M A N D WANG 547 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. 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 1mofthesoil. 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 func-
15 548 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 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. tions 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
16 JUNE 2007 K I M A N D WANG 549 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. 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
17 550 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 8 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 (NA03OAR ). REFERENCES Bonan, G. B., and S. Levis, 2006: Evaluating aspects of the community land and atmosphere models (CLM3 and CAM3) using a Dynamic Global Vegetation Model. J. Climate, 19, Bosilovich, M. G., and W. Y. Sun, 1999: Numerical simulations of the 1993 Midwestern flood: Land atmosphere interactions. J. Climate, 12, , and J.-D. Chern, 2006: Simulation of water sources and precipitation recycling for the MacKenzie, Mississippi, and Amazon River basins. J. Hydrometeor., 7, Bounoua, L., G. J. Collatz, S. O. Los, P. J. Sellers, D. A. Dazlich, C. J. Tucker, and D. A. Randall, 2000: Sensitivity of climate to changes in NDVI. J. Climate, 13, Buermann, W., D. Jiarui, X. Zeng, R. B. Myneni, and R. E. Dickinson, 2001: Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations. J. Climate, 14, Chase, T. N., R. A. Pielke, T. G. F. Kittel, R. Nemani, and S. W. Running, 1996: Sensitivity of a general circulation model to global changes in leaf area index. J. Geophys. Res., 101 (D3), Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry, 1991: Physiological and environmental regulation of stomatal conductance, photosynthesis, and transpiration: A model that includes a laminar boundary layer. Agric. For. Meteor., 54, , M. Ribas-Carbo, and J. A. Berry, 1992: Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Aust. J. Plant Physiol., 19, Collins, W. D., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM3.0). NCAR Tech. Note NCAR/TN-464 STR, Boulder, CO, 226 pp. Dai, Y., and Coauthors, 2003: The common land model. Bull. Amer. Meteor. Soc., 84, Dickinson, R. E., M. Shaikh, R. Bryant, and L. Graumlich, 1998: Interactive canopies for a climate model. J. Climate, 11, Dirmeyer, P. A., 1994: Vegetation stress as a feedback mechanism in midlatitude drought. J. Climate, 7, Guillevic, P., R. D. Koster, M. J. Suarez, L. Bounoua, G. J. Collatz, S. O. Los, and S. P. P. Mahanama, 2002: Influence of the interannual variability of vegetation on the surface energy balance A global sensitivity study. J. Hydrometeor., 3, Hack, J. J., J. M. Caron, S. G. Yeager, K. W. Oleson, M. M. Holland, J. E. Truesdale, and P. J. Rasch, 2006: Simulation of the global hydrological cycle in the CCSM Community Atmosphere Model version 3 (CAM3): Mean features. J. Climate, 19, Kim, Y., and G. L. Wang, 2005: Modeling seasonal vegetation variation and its validation against Moderate Resolution Imaging Spectroradiometer (MODIS) observations over North America. J. Geophys. Res., 110, D04106, doi: / 2004JD , and G. Wang, 2007: Impact of initial soil moisture anomalies on subsequent precipitation over North America in the coupled land atmosphere model CAM3 CLM3. J. Hydrometeor., 8, Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, , and Coauthors, 2006: GLACE: The Global Land Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, Lin, S. J., 2004: A vertically Lagrangian finite-volume dynamical core for global models. Mon. Wea. Rev., 132, , and R. B. Rood, 1996: Multidimensional flux-form semi- Lagrangian transport schemes. Mon. Wea. Rev., 124, Lu, L., R. A. Pielke, G. E. Liston, W. J. Parton, D. Ojima, and M. Hartman, 2001: Implementation of a two-way interactive atmospheric and ecological model and its application to the central United States. J. Climate, 14, Notaro, M., Z. Liu, and J. W. Williams, 2006: Observed vegetation climate feedbacks in the United States. J. Climate, 19, Oglesby, R. J., and D. J. Erickson III, 1989: Soil moisture and the persistence of North American drought. J. Climate, 2, Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note NCAR/ TN-461 STR, 174 pp. Pal, J. S., and E. A. B. Eltahir, 2001: Pathways relating soil moisture conditions to future summer rainfall within a model of the land atmosphere system. J. Climate, 14, , and, 2003: A feedback mechanism between soil moisture distribution and storm tracks. Quart. J. Roy. Meteor. Soc., 129, Pielke, R. A., R. Avissar, M. Raupach, A. J. Dolman, X. Zeng, and A. S. Denning, 1998: Interactions between the atmosphere and terrestrial ecosystems: Influence on weather and climate. Global Change Biol., 4, Shukla, J., and Y. Minz, 1982: Influence of the land surface evapotranspiration on the earth s climate. Science, 215, Tian, Y., R. E. Dickinson, L. Zhou, R. Myneni, M. Friedl, C. Schaaf, M. Carroll, and F. Gao, 2004: Land boundary conditions from MODIS data and consequences for the albedo of a climate model. Geophys. Res. Lett., 31, L05504, doi: / 2003GL Tsvetsinskaya, E. A., L. O. Mearns, and W. E. Easterling, 2001: Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations. Part I: Simulation of surface fluxes over the growing season. J. Climate, 14, Wang, G. L., Y. Kim, and D. G. Wang, 2007: Quantifying the strength of soil moisture precipitation coupling and its sensitivity to surface water budget changes. J. Hydrometeor., 8, Wang, W., B. T. Anderson, N. Phillips, R. K. Kaufmann, C. Potter, and R. B. Myneni, 2006: Feedbacks of vegetation on summertime climate variability over the North American Grasslands. Part I: Statistical analysis. Earth Interactions, 10. [Available online at
Impact of Initial Soil Moisture Anomalies on Subsequent Precipitation over North America in the Coupled Land Atmosphere Model CAM3 CLM3
JUNE 2007 K I M A N D WANG 513 Impact of Initial Soil Moisture Anomalies on Subsequent Precipitation over North America in the Coupled Land Atmosphere Model CAM3 CLM3 YEONJOO KIM AND GUILING WANG Department
More informationEffects of sub-grid variability of precipitation and canopy water storage on climate model simulations of water cycle in Europe
Adv. Geosci., 17, 49 53, 2008 Author(s) 2008. This work is distributed under the Creative Commons Attribution 3.0 License. Advances in Geosciences Effects of sub-grid variability of precipitation and canopy
More informationThe role of soil moisture in influencing climate and terrestrial ecosystem processes
1of 18 The role of soil moisture in influencing climate and terrestrial ecosystem processes Vivek Arora Canadian Centre for Climate Modelling and Analysis Meteorological Service of Canada Outline 2of 18
More informationQuantifying the Strength of Soil Moisture Precipitation Coupling and Its Sensitivity to Changes in Surface Water Budget
JUNE 2007 W A N G E T A L. 551 Quantifying the Strength of Soil Moisture Precipitation Coupling and Its Sensitivity to Changes in Surface Water Budget GUILING WANG, YEONJOO KIM, AND DAGANG WANG Department
More information8.2 GLOBALLY DESCRIBING THE CURRENT DAY LAND SURFACE AND HISTORICAL LAND COVER CHANGE IN CCSM 3.0 USING AVHRR AND MODIS DATA AT FINE SCALES
8.2 GLOBALLY DESCRIBING THE CURRENT DAY LAND SURFACE AND HISTORICAL LAND COVER CHANGE IN CCSM 3.0 USING AVHRR AND MODIS DATA AT FINE SCALES Peter J. Lawrence * Cooperative Institute for Research in Environmental
More informationConsistent Parameterization of Roughness Length and Displacement Height for Sparse and Dense Canopies in Land Models
730 J O U R N A L O F H Y D R O M E T E O R O L O G Y S P E C I A L S E C T I O N VOLUME 8 Consistent Parameterization of Roughness Length and Displacement Height for Sparse and Dense Canopies in Land
More informationWhy Has the Land Memory Changed?
3236 JOURNAL OF CLIMATE VOLUME 17 Why Has the Land Memory Changed? QI HU ANDSONG FENG Climate and Bio-Atmospheric Sciences Group, School of Natural Resource Sciences, University of Nebraska at Lincoln,
More information1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report
1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report 2. Results and Accomplishments Output from multiple land surface schemes (LSS)
More informationEvaluation of the Utility of Satellite-Based Vegetation Leaf Area Index Data for Climate Simulations
3536 JOURNAL OF CLIMATE Evaluation of the Utility of Satellite-Based Vegetation Leaf Area Index Data for Climate Simulations WOLFGANG BUERMANN AND JIARUI DONG Department of Geography, Boston University,
More informationA Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 6, 325 329 A Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model YU En-Tao 1,2,3, WANG Hui-Jun 1,2, and SUN Jian-Qi
More informationImpact of vegetation cover estimates on regional climate forecasts
Impact of vegetation cover estimates on regional climate forecasts Phillip Stauffer*, William Capehart*, Christopher Wright**, Geoffery Henebry** *Institute of Atmospheric Sciences, South Dakota School
More informationJ1.7 SOIL MOISTURE ATMOSPHERE INTERACTIONS DURING THE 2003 EUROPEAN SUMMER HEATWAVE
J1.7 SOIL MOISTURE ATMOSPHERE INTERACTIONS DURING THE 2003 EUROPEAN SUMMER HEATWAVE E Fischer* (1), SI Seneviratne (1), D Lüthi (1), PL Vidale (2), and C Schär (1) 1 Institute for Atmospheric and Climate
More informationAn ENSO-Neutral Winter
An ENSO-Neutral Winter This issue of the Blue Water Outlook newsletter is devoted towards my thoughts on the long range outlook for winter. You will see that I take a comprehensive approach to this outlook
More informationDiagnosing the Climatology and Interannual Variability of North American Summer Climate with the Regional Atmospheric Modeling System (RAMS)
Diagnosing the Climatology and Interannual Variability of North American Summer Climate with the Regional Atmospheric Modeling System (RAMS) Christopher L. Castro and Roger A. Pielke, Sr. Department of
More informationModeling the Biosphere Atmosphere System: The Impact of the Subgrid Variability in Rainfall Interception
2887 Modeling the Biosphere Atmosphere System: The Impact of the Subgrid Variability in Rainfall Interception GUILING WANG AND ELFATIH A. B. ELTAHIR Ralph M. Parsons Laboratory, Department of Civil and
More informationDEVELOPMENT OF A LARGE-SCALE HYDROLOGIC PREDICTION SYSTEM
JP3.18 DEVELOPMENT OF A LARGE-SCALE HYDROLOGIC PREDICTION SYSTEM Ji Chen and John Roads University of California, San Diego, California ABSTRACT The Scripps ECPC (Experimental Climate Prediction Center)
More informationUse of Satellite-Based Precipitation Observation in Improving the Parameterization of Canopy Hydrological Processes in Land Surface Models
OCTOBER 2005 W A N G E T A L. 745 Use of Satellite-Based Precipitation Observation in Improving the Parameterization of Canopy Hydrological Processes in Land Surface Models DAGANG WANG, GUILING WANG, AND
More informationThe Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 1, 25 30 The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO HU Kai-Ming and HUANG Gang State Key
More informationInteractions between Vegetation and Climate: Radiative and Physiological Effects of Doubled Atmospheric CO 2
VOLUME 12 JOURNAL OF CLIMATE FEBRUARY 1999 Interactions between Vegetation and Climate: Radiative and Physiological Effects of Doubled Atmospheric CO 2 L. BOUNOUA,* G. J. COLLATZ, P. J. SELLERS,# D. A.
More informationEffects of Land Use on Climate and Water Resources
Effects of Land Use on Climate and Water Resources Principal Investigator: Gordon B. Bonan National Center for Atmospheric Research 1850 Table Mesa Drive, P.O. Box 3000 Boulder, CO 80307-3000 E-mail: bonan@ucar.edu
More informationAsymmetric response of maximum and minimum temperatures to soil emissivity change over the Northern African Sahel in a GCM
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L05402, doi:10.1029/2007gl032953, 2008 Asymmetric response of maximum and minimum temperatures to soil emissivity change over the Northern
More informationApplication and impacts of the GlobeLand30 land cover dataset on the Beijing Climate Center Climate Model
IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS Application and impacts of the GlobeLand30 land cover dataset on the Beijing Climate Center Climate Model To cite this article:
More informationRemote Sensing Data Assimilation for a Prognostic Phenology Model
June 2008 Remote Sensing Data Assimilation for a Prognostic Phenology Model How to define global-scale empirical parameters? Reto Stöckli 1,2 (stockli@atmos.colostate.edu) Lixin Lu 1, Scott Denning 1 and
More informationLand Surface Processes and Their Impact in Weather Forecasting
Land Surface Processes and Their Impact in Weather Forecasting Andrea Hahmann NCAR/RAL with thanks to P. Dirmeyer (COLA) and R. Koster (NASA/GSFC) Forecasters Conference Summer 2005 Andrea Hahmann ATEC
More informationMDA WEATHER SERVICES AG WEATHER OUTLOOK. Kyle Tapley-Senior Agricultural Meteorologist May 22, 2014 Chicago, IL
MDA WEATHER SERVICES AG WEATHER OUTLOOK Kyle Tapley-Senior Agricultural Meteorologist May 22, 2014 Chicago, IL GLOBAL GRAIN NORTH AMERICA 2014 Agenda Spring Recap North America Forecast El Niño Discussion
More informationChristopher L. Castro Department of Atmospheric Sciences University of Arizona
Spatiotemporal Variability and Covariability of Temperature, Precipitation, Soil Moisture, and Vegetation in North America for Regional Climate Model Applications Christopher L. Castro Department of Atmospheric
More informationSoil Moisture and Snow Cover: Active or Passive Elements of Climate?
Soil Moisture and Snow Cover: Active or Passive Elements of Climate? Robert J. Oglesby 1, Susan Marshall 2, Charlotte David J. Erickson III 3, Franklin R. Robertson 1, John O. Roads 4 1 NASA/MSFC, 2 University
More informationObservational validation of an extended mosaic technique for capturing subgrid scale heterogeneity in a GCM
Printed in Singapore. All rights reserved C 2007 The Authors Journal compilation C 2007 Blackwell Munksgaard TELLUS Observational validation of an extended mosaic technique for capturing subgrid scale
More information5. General Circulation Models
5. General Circulation Models I. 3-D Climate Models (General Circulation Models) To include the full three-dimensional aspect of climate, including the calculation of the dynamical transports, requires
More informationClimate Response to Irrigation in the American West. Benjamin J. Wauer
Climate Response to Irrigation in the American West Benjamin J. Wauer Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland ABSTRACT Significant changes in population
More informationArctic sea ice response to atmospheric forcings with varying levels of anthropogenic warming and climate variability
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044988, 2010 Arctic sea ice response to atmospheric forcings with varying levels of anthropogenic warming and climate variability Jinlun Zhang,
More informationClimate Roles of Land Surface
Lecture 5: Land Surface and Cryosphere (Outline) Climate Roles Surface Energy Balance Surface Water Balance Sea Ice Land Ice (from Our Changing Planet) Surface Albedo Climate Roles of Land Surface greenhouse
More informationPathways Relating Soil Moisture Conditions to Future Summer Rainfall within a Model of the Land Atmosphere System
15 MARCH 2001 PAL AND ELTAHIR 1227 Pathways Relating Soil Moisture Conditions to Future Summer Rainfall within a Model of the Land Atmosphere System JEREMY S. PAL AND ELFATIH A. B. ELTAHIR Ralph M. Parsons
More informationThe complexity of using a feedback parameter to quantify the soil moisture-precipitation relationship
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi:10.1029/2011jd017173, 2012 The complexity of using a feedback parameter to quantify the soil moisture-precipitation relationship Shanshan Sun 1 and Guiling
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 5 August 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
More informationImproving canopy processes in the Community Land Model using Fluxnet data: Assessing nitrogen limitation and canopy radiation
Improving canopy processes in the Community Land Model using Fluxnet data: Assessing nitrogen limitation and canopy radiation Gordon Bonan, Keith Oleson, and Rosie Fisher National Center for Atmospheric
More informationClimate Change Impact on Air Temperature, Daily Temperature Range, Growing Degree Days, and Spring and Fall Frost Dates In Nebraska
EXTENSION Know how. Know now. Climate Change Impact on Air Temperature, Daily Temperature Range, Growing Degree Days, and Spring and Fall Frost Dates In Nebraska EC715 Kari E. Skaggs, Research Associate
More informationDirection and range of change expected in the future
Direction and range of Air Temperature Over the past 30 years, air Across the greater PNW and temperature has been Columbia Basin, an ensemble increasing an average of forecast from ten of the best 0.13
More informationANNUAL CLIMATE REPORT 2016 SRI LANKA
ANNUAL CLIMATE REPORT 2016 SRI LANKA Foundation for Environment, Climate and Technology C/o Mahaweli Authority of Sri Lanka, Digana Village, Rajawella, Kandy, KY 20180, Sri Lanka Citation Lokuhetti, R.,
More informationDrought Monitoring with Hydrological Modelling
st Joint EARS/JRC International Drought Workshop, Ljubljana,.-5. September 009 Drought Monitoring with Hydrological Modelling Stefan Niemeyer IES - Institute for Environment and Sustainability Ispra -
More informationLand Surface: Snow Emanuel Dutra
Land Surface: Snow Emanuel Dutra emanuel.dutra@ecmwf.int Slide 1 Parameterizations training course 2015, Land-surface: Snow ECMWF Outline Snow in the climate system, an overview: Observations; Modeling;
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 23 April 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
More informationAssimilating terrestrial remote sensing data into carbon models: Some issues
University of Oklahoma Oct. 22-24, 2007 Assimilating terrestrial remote sensing data into carbon models: Some issues Shunlin Liang Department of Geography University of Maryland at College Park, USA Sliang@geog.umd.edu,
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 11 November 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
More informationSensitivity of climate models to seasonal variability of snow-free land surface albedo
Theor. Appl. Climatol. (2009) 95: 197 221 DOI 10.1007/s00704-007-0371-8 Printed in The Netherlands Max-Planck-Institute for Meteorology, Hamburg, Germany Sensitivity of climate models to seasonal variability
More informationAnticipated and Observed Trends in the Global Hydrological Cycle. Kevin E. Trenberth NCAR
Anticipated and Observed Trends in the Global Hydrological Cycle Kevin E. Trenberth NCAR The presence of moisture affects the disposition of incoming solar radiation: Evaporation (drying) versus temperature
More informationClimate Impacts of Agriculture Related Land Use Change in the US
Climate Impacts of Agriculture Related Land Use Change in the US Jimmy Adegoke 1, Roger Pielke Sr. 2, Andrew M. Carleton 3 1 Dept. Of Geosciences, University of Missouri-Kansas City 2 Dept. of Atmospheric
More informationGEOG415 Mid-term Exam 110 minute February 27, 2003
GEOG415 Mid-term Exam 110 minute February 27, 2003 1 Name: ID: 1. The graph shows the relationship between air temperature and saturation vapor pressure. (a) Estimate the relative humidity of an air parcel
More informationInfluence of variations in low-level moisture and soil moisture on the organization of summer convective systems in the US Midwest
Influence of variations in low-level moisture and soil moisture on the organization of summer convective systems in the US Midwest Jimmy O. Adegoke 1, Sajith Vezhapparambu 1, Christopher L. Castro 2, Roger
More informationHigh initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044119, 2010 High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming Yuhji Kuroda 1 Received 27 May
More informationAssimilation of satellite derived soil moisture for weather forecasting
Assimilation of satellite derived soil moisture for weather forecasting www.cawcr.gov.au Imtiaz Dharssi and Peter Steinle February 2011 SMOS/SMAP workshop, Monash University Summary In preparation of the
More informationImpact of Eurasian spring snow decrement on East Asian summer precipitation
Impact of Eurasian spring snow decrement on East Asian summer precipitation Renhe Zhang 1,2 Ruonan Zhang 2 Zhiyan Zuo 2 1 Institute of Atmospheric Sciences, Fudan University 2 Chinese Academy of Meteorological
More informationMARIAN MARTIN, ROBERT E. DICKINSON, AND ZONG-LIANG YANG
3359 Use of a Coupled Land Surface General Circulation Model to Examine the Impacts of Doubled Stomatal Resistance on the Water Resources of the American Southwest MARIAN MARTIN, ROBERT E. DICKINSON, AND
More informationNASA NNG06GC42G A Global, 1-km Vegetation Modeling System for NEWS February 1, January 31, Final Report
NASA NNG06GC42G A Global, 1-km Vegetation Modeling System for NEWS February 1, 2006- January 31, 2009 Final Report Scott Denning, Reto Stockli, Lixin Lu Department of Atmospheric Science, Colorado State
More informationInter- Annual Land Surface Variation NAGS 9329
Annual Report on NASA Grant 1 Inter- Annual Land Surface Variation NAGS 9329 PI Stephen D. Prince Co-I Yongkang Xue April 2001 Introduction This first period of operations has concentrated on establishing
More informationTerrestrial Snow Cover: Properties, Trends, and Feedbacks. Chris Derksen Climate Research Division, ECCC
Terrestrial Snow Cover: Properties, Trends, and Feedbacks Chris Derksen Climate Research Division, ECCC Outline Three Snow Lectures: 1. Why you should care about snow: Snow and the cryosphere Classes of
More informationThe Interdecadal Variation of the Western Pacific Subtropical High as Measured by 500 hpa Eddy Geopotential Height
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2015, VOL. 8, NO. 6, 371 375 The Interdecadal Variation of the Western Pacific Subtropical High as Measured by 500 hpa Eddy Geopotential Height HUANG Yan-Yan and
More informationNOTES AND CORRESPONDENCE. On the Radiative and Dynamical Feedbacks over the Equatorial Pacific Cold Tongue
15 JULY 2003 NOTES AND CORRESPONDENCE 2425 NOTES AND CORRESPONDENCE On the Radiative and Dynamical Feedbacks over the Equatorial Pacific Cold Tongue DE-ZHENG SUN NOAA CIRES Climate Diagnostics Center,
More informationSeptember 2018 Weather Summary West Central Research and Outreach Center Morris, MN
September 2018 Weather Summary The mean temperature for September was 60.6 F, which is 1.5 F above the average of 59.1 F (1886-2017). The high temperature for the month was 94 F on September 16 th. The
More informationStress Deciduous Phenology in the CLM
Stress Deciduous Phenology in the CLM Kyla Dahlin & Rosie Fisher February 25, 2014 image credit: Forrest Copeland talesfromthebigcountry.wordpress.com Can we accurately model seasonal changes in vegetation
More informationComparison of Land Precipitation Coupling Strength Using Observations and Models
AUGUST 2010 Z E N G E T A L. 979 Comparison of Land Precipitation Coupling Strength Using Observations and Models XUBIN ZENG Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona
More informationA Study of the Relations between Soil Moisture, Soil Temperatures and Surface Temperatures Using ARM Observations and Offline CLM4 Simulations
Climate 2014, 2, 279-295; doi:10.3390/cli2040279 Article OPEN ACCESS climate ISSN 2225-1154 www.mdpi.com/journal/climate A Study of the Relations between Soil Moisture, Soil Temperatures and Surface Temperatures
More informationArctic Climate Change. Glen Lesins Department of Physics and Atmospheric Science Dalhousie University Create Summer School, Alliston, July 2013
Arctic Climate Change Glen Lesins Department of Physics and Atmospheric Science Dalhousie University Create Summer School, Alliston, July 2013 When was this published? Observational Evidence for Arctic
More informationA Multidecadal Variation in Summer Season Diurnal Rainfall in the Central United States*
174 JOURNAL OF CLIMATE VOLUME 16 A Multidecadal Variation in Summer Season Diurnal Rainfall in the Central United States* QI HU Climate and Bio-Atmospheric Sciences Group, School of Natural Resource Sciences,
More informationP2.1 DIRECT OBSERVATION OF THE EVAPORATION OF INTERCEPTED WATER OVER AN OLD-GROWTH FOREST IN THE EASTERN AMAZON REGION
P2.1 DIRECT OBSERVATION OF THE EVAPORATION OF INTERCEPTED WATER OVER AN OLD-GROWTH FOREST IN THE EASTERN AMAZON REGION Matthew J. Czikowsky (1)*, David R. Fitzjarrald (1), Osvaldo L. L. Moraes (2), Ricardo
More informationLandscapes as patches of plant functional types: An integrating concept for climate and ecosystem models
GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 16, NO. 2, 1021, 10.1029/2000GB001360, 2002 Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models Gordon B. Bonan and
More informationFUTURE PROJECTIONS OF PRECIPITATION CHARACTERISTICS IN ASIA
FUTURE PROJECTIONS OF PRECIPITATION CHARACTERISTICS IN ASIA AKIO KITOH, MASAHIRO HOSAKA, YUKIMASA ADACHI, KENJI KAMIGUCHI Meteorological Research Institute Tsukuba, Ibaraki 305-0052, Japan It is anticipated
More informationGuiling Wang 1 Miao Yu 1,2 Yongkang Xue 3
Clim Dyn DOI 10.1007/s00382-015-2812-x Modeling the potential contribution of land cover changes to the late twentieth century Sahel drought using a regional climate model: impact of lateral boundary conditions
More informationEnergy Systems, Structures and Processes Essential Standard: Analyze patterns of global climate change over time Learning Objective: Differentiate
Energy Systems, Structures and Processes Essential Standard: Analyze patterns of global climate change over time Learning Objective: Differentiate between weather and climate Global Climate Focus Question
More informationChiang Rai Province CC Threat overview AAS1109 Mekong ARCC
Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC This threat overview relies on projections of future climate change in the Mekong Basin for the period 2045-2069 compared to a baseline of 1980-2005.
More informationModeling the Arctic Climate System
Modeling the Arctic Climate System General model types Single-column models: Processes in a single column Land Surface Models (LSMs): Interactions between the land surface, atmosphere and underlying surface
More informationPresentation Overview. Southwestern Climate: Past, present and future. Global Energy Balance. What is climate?
Southwestern Climate: Past, present and future Mike Crimmins Climate Science Extension Specialist Dept. of Soil, Water, & Env. Science & Arizona Cooperative Extension The University of Arizona Presentation
More informationImplementation of the NCEP operational GLDAS for the CFS land initialization
Implementation of the NCEP operational GLDAS for the CFS land initialization Jesse Meng, Mickael Ek, Rongqian Yang NOAA/NCEP/EMC July 2012 1 Improving the Global Land Surface Climatology via improved Global
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 25 February 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
More informationSeasonal and Spatial Patterns of Rainfall Trends on the Canadian Prairie
Seasonal and Spatial Patterns of Rainfall Trends on the Canadian Prairie H.W. Cutforth 1, O.O. Akinremi 2 and S.M. McGinn 3 1 SPARC, Box 1030, Swift Current, SK S9H 3X2 2 Department of Soil Science, University
More informationImpacts of vegetation and cold season processes on soil moisture and climate relationships over Eurasia
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi:10.1029/2006jd007774, 2007 Impacts of vegetation and cold season processes on soil moisture and climate relationships over Eurasia
More informationImpacts of the April 2013 Mean trough over central North America
Impacts of the April 2013 Mean trough over central North America By Richard H. Grumm National Weather Service State College, PA Abstract: The mean 500 hpa flow over North America featured a trough over
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 24 September 2012
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 24 September 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño
More informationFeedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part II: A Coupled Stochastic Model
Earth Interactions Volume 10 (2006) Paper No. 16 Page 1 Copyright 2006, Paper 10-016; 10,207 words, 11 Figures, 0 Animations, 2 Tables. http://earthinteractions.org Feedbacks of Vegetation on Summertime
More informationArctic System Reanalysis Provides Highresolution Accuracy for Arctic Studies
Arctic System Reanalysis Provides Highresolution Accuracy for Arctic Studies David H. Bromwich, Aaron Wilson, Lesheng Bai, Zhiquan Liu POLAR2018 Davos, Switzerland Arctic System Reanalysis Regional reanalysis
More informationREQUEST FOR A SPECIAL PROJECT
REQUEST FOR A SPECIAL PROJECT 2017 2019 MEMBER STATE: Sweden.... 1 Principal InvestigatorP0F P: Wilhelm May... Affiliation: Address: Centre for Environmental and Climate Research, Lund University Sölvegatan
More information4.4 EVALUATION OF AN IMPROVED CONVECTION TRIGGERING MECHANISM IN THE NCAR COMMUNITY ATMOSPHERE MODEL CAM2 UNDER CAPT FRAMEWORK
. EVALUATION OF AN IMPROVED CONVECTION TRIGGERING MECHANISM IN THE NCAR COMMUNITY ATMOSPHERE MODEL CAM UNDER CAPT FRAMEWORK Shaocheng Xie, James S. Boyle, Richard T. Cederwall, and Gerald L. Potter Atmospheric
More informationNovember 2018 Weather Summary West Central Research and Outreach Center Morris, MN
November 2018 Weather Summary Lower than normal temperatures occurred for the second month. The mean temperature for November was 22.7 F, which is 7.2 F below the average of 29.9 F (1886-2017). This November
More informationSeasonal Climate Transitions in New England
1 Seasonal Climate Transitions in New England Alan K Betts Atmospheric Research Pittsford, VT 05763 akbetts@aol.com Revised October 6, 2010 (submitted to Weather) Abstract For continental climates at northern
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 15 July 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
More informationA R C T E X Results of the Arctic Turbulence Experiments Long-term Monitoring of Heat Fluxes at a high Arctic Permafrost Site in Svalbard
A R C T E X Results of the Arctic Turbulence Experiments www.arctex.uni-bayreuth.de Long-term Monitoring of Heat Fluxes at a high Arctic Permafrost Site in Svalbard 1 A R C T E X Results of the Arctic
More informationImpacts of vegetation and groundwater dynamics on warm season precipitation over the Central United States
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi:10.1029/2008jd010756, 2009 Impacts of vegetation and groundwater dynamics on warm season precipitation over the Central United
More informationUnderstanding land-surfaceatmosphere. observations and models
Understanding land-surfaceatmosphere coupling in observations and models Alan K. Betts Atmospheric Research akbetts@aol.com MERRA Workshop AMS Conference, Phoenix January 11, 2009 Land-surface-atmosphere
More informationEcosystem-Climate Interactions
Ecosystem-Climate Interactions Dennis Baldocchi UC Berkeley 2/1/2013 Topics Climate and Vegetation Correspondence Holdredge Classification Plant Functional Types Plant-Climate Interactions Canopy Microclimate
More informationHow Patterns Far Away Can Influence Our Weather. Mark Shafer University of Oklahoma Norman, OK
Teleconnections How Patterns Far Away Can Influence Our Weather Mark Shafer University of Oklahoma Norman, OK Teleconnections Connectedness of large-scale weather patterns across the world If you poke
More informationHuman influence on terrestrial precipitation trends revealed by dynamical
1 2 3 Supplemental Information for Human influence on terrestrial precipitation trends revealed by dynamical adjustment 4 Ruixia Guo 1,2, Clara Deser 1,*, Laurent Terray 3 and Flavio Lehner 1 5 6 7 1 Climate
More informationSUPPLEMENTARY INFORMATION
Figure S1. Summary of the climatic responses to the Gulf Stream. On the offshore flank of the SST front (black dashed curve) of the Gulf Stream (green long arrow), surface wind convergence associated with
More informationSouth & South East Asian Region:
Issued: 15 th December 2017 Valid Period: January June 2018 South & South East Asian Region: Indonesia Tobacco Regions 1 A] Current conditions: 1] El Niño-Southern Oscillation (ENSO) ENSO Alert System
More informationObservation: predictable patterns of ecosystem distribution across Earth. Observation: predictable patterns of ecosystem distribution across Earth 1.
Climate Chap. 2 Introduction I. Forces that drive climate and their global patterns A. Solar Input Earth s energy budget B. Seasonal cycles C. Atmospheric circulation D. Oceanic circulation E. Landform
More informationSoil Moisture Feedbacks to Precipitation in Southern Africa
4198 J O U R N A L O F C L I M A T E VOLUME 19 Soil Moisture Feedbacks to Precipitation in Southern Africa BENJAMIN I. COOK Department of Environmental Sciences, University of Virginia, Charlottesville,
More informationThe Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 3, 219 224 The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times LU Ri-Yu 1, LI Chao-Fan 1,
More informationMETRIC tm. Mapping Evapotranspiration at high Resolution with Internalized Calibration. Shifa Dinesh
METRIC tm Mapping Evapotranspiration at high Resolution with Internalized Calibration Shifa Dinesh Outline Introduction Background of METRIC tm Surface Energy Balance Image Processing Estimation of Energy
More informationMay 3, :41 AOGS - AS 9in x 6in b951-v16-ch13 LAND SURFACE ENERGY BUDGET OVER THE TIBETAN PLATEAU BASED ON SATELLITE REMOTE SENSING DATA
Advances in Geosciences Vol. 16: Atmospheric Science (2008) Eds. Jai Ho Oh et al. c World Scientific Publishing Company LAND SURFACE ENERGY BUDGET OVER THE TIBETAN PLATEAU BASED ON SATELLITE REMOTE SENSING
More informationAGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land Atmosphere Feedback
656 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 6 AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land Atmosphere Feedback SARITH P. P. MAHANAMA Goddard Earth Sciences
More information16 Global Climate. Learning Goals. Summary. After studying this chapter, students should be able to:
16 Global Climate Learning Goals After studying this chapter, students should be able to: 1. associate the world s six major vegetation biomes to climate (pp. 406 408); 2. describe methods for classifying
More information