The summertime atmospheric hydrologic cycle over the southwestern US

Bruce T. AndersonDepartment of Geography, Boston University

675 Commonwealth Ave.Boston, MA 02215-1401

brucea@bu.edu

Hideki Kanamaru and John, O. RoadsScripps Institution of Oceanography

UCSD-0224La Jolla, CA 92093-0224

2

ABSTRACT

In this paper we examine the large-scale summertime hydrologic cycle associated with the

northwestern branch of the North American monsoon, centered on the southwestern United

States, using a suite of surface and upper-air based observations, reanalysis products, and

regional model simulations. In general, it is found that on an area-averaged basis, seasonal

precipitation is balanced predominantly by evaporation; in addition, this evaporation also

supports a net vertically integrated moisture flux divergence from the region of the same

magnitude as the precipitation itself. This vertically-integrated large-scale moisture flux

divergence is the result of an offsetting balance between convergence of low-level moisture and

divergence of moisture aloft (<750mb). Over the western portion of the domain, most of this

low-level moisture convergence is related to advection from the Gulf of California and eastern

Pacific; over the eastern portion of the domain, low-level moisture convergence is related to

advection from the Gulf of Mexico. The low-level moisture, supplied both by evaporation and

advection, is carried aloft primarily by convection (as opposed to large-scale vertical velocities),

which then feeds both the precipitation and large-scale divergence fields. The large-scale

divergence augments the anti-cyclonic circulation of moisture aloft, resulting in enhanced exiting

fluxes over the Great Plains. A new metric for measuring recycling of moisture in convective

semi-arid areas is introduced; this metric is designed to better capture the importance of

evaporative processes for supporting regional precipitation in these types of environments.

Using this metric, it is shown that about 70-90% of the area-averaged precipitation is the result

of evaporative processes, while the remaining 10-30% is related to low level convergence of

moisture.

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1. Introduction

Much effort has been dedicated to investigating the climatological, interannual, and

intraseasonal characteristics of the northwestern branch of the North American monsoon system,

situated over the southwestern United States and northwestern portion of Mexico. In general, the

region shows strong interactions between the land-surface, ocean, and atmosphere, resulting in

complex coupling of the momentum and moisture fields. Included among these coupling

processes are: the maintenance and modulation of low-level moisture flux advection from the

Gulf of California, the Gulf of Mexico, and the eastern Pacific (Brenner, 1974; Hales, 1974;

Douglas et al., 1993; Stensrud et al., 1995; Schmitz and Mullen, 1996; Berbery and Fox-

Rabinovitz, 2003); the lofting and divergence of moisture associated with deep convection over

the Sierra Madres and Sierra Nevadas (Berbery, 2001; Fawcett et al., 2002); instabilities related

to the passage of mid-latitude, mid-troposphere weather systems (Carleton, 1986; Stensrud et al.,

1995; Maddox et al., 1995; Kiladis, 1999; Mo, 2000; Anderson and Roads, 2002); the passage of

subtropical low-pressure systems over Mexico (Farfan and Zehnder, 1994; Anderson et al., 2000;

Englehart and Douglas, 2001; Douglas and Leal, 2003); and the establishment of large-scale

upper-air teleconnection patterns (Carleton et al., 1990; Comrie and Glenn, 1998; Higgins et al.,

1998; Higgins and Shi, 2001; Cavazos et al., 2002). An excellent summary of many of these

influences can be found in Douglas et al. (1993), and Higgins et al. (2003).

Despite these efforts, however, it does not appear that there is a description of the explicit

large-scale hydrodynamic balances that support the summertime precipitation field in this region.

For instance, is the climatological precipitation field the result of a net, vertically-integrated

convergence of moisture? If so, does this convergence of moisture occur aloft, indicating the

4

possible influence from remote source regions, or at lower levels, suggesting more local dynamic

forcing? And how do the dynamic-based convergence fields compare with atmospheric moisture

convergence related to local evaporation? Roads et al. (1994), Roads and Betts (2000) and Roads

and Chen (2000) have looked at these balances for the United States as a whole using Reanalysis

data as well as fine-scale regional modeling data. Schmitz and Mullen (1996) looked at these

balances for the southwestern United States but were hampered by the coarse-scale resolution of

the atmospheric analysis under consideration. Berbery (2001) considered these balances over the

core monsoon region in western Mexico but the analysis only extended through the northern

portion of the Gulf of California. Here we attempt to address similar issues by explicitly

quantifying the terms that comprise the atmospheric moisture tendency equation, using as many

data sources as possible. Using the results, we then hope to provide a better description of the

important processes influencing the climatological hydrologic cycle for the summertime

southwestern US. Section 2 describes these datasets. Section 3 then investigates the relevant

hydrodynamic terms found in each. Section 4 summarizes the findings and discusses their

implications for moisture recycling within the region.

2. Datasets

To perform this analysis, we use a global to regional spectral modeling system developed at

the National Centers for Environmental Prediction (NCEP) to produce regional simulations of

the summertime atmospheric hydrologic cycle over the southwestern US (Juang and Kanamitsu,

1994; Juang et al., 1997). The modeling system nests the regional spectral model (RSM) within

daily forecasts from NCEP's global spectral model (GSM); these GSM forecasts are initialized

using NCEP's operational analysis data. The resolution of the GSM is triangular-62

5

(approximately 200 km); the resolution for the RSM is 25 km (Figure 1). Vertically, the

prognostic equations are solved on 18 levels represented in sigma coordinates (where s=p/ps).

The RSM domain extends from northern California to central Mexico and includes the Sierra

Nevada and Rocky Mountain ranges in the United States, the Baja and Sierra Madre mountain

ranges in Mexico, along with the Gulf of California. Also shown in Figure 1 are selected

radiosonde observing sites (see below) used for evaluation of the simulated hydrodynamic fields.

In total, five 92-day continuous GSM/RSM forecast simulations of the summertime (July-

September) southwestern US atmosphere have been performed for the period 1998-2002. In

order to produce the continuous simulations of the RSM (as opposed to a set of ninety 24-hour

weather forecasts), the coarse-scale GSM and fine-scale RSM short-term forecast fields at 2400

UTC (and not the reinitialized 0000 UTC Reanalysis field) were used as the initial conditions for

the next RSM nesting period. Numerically, this technique allows the RSM forecast, which

inherently contains corrections for anomalies associated with enhanced orography and

resolution, to carry these corrections forward from one nesting period to the next, thereby

reducing spin-up effects. Additional details about the full modeling system are found in the

Appendix and in Anderson and Roads (2002), which also includes a detailed evaluation of the

model’s simulation of precipitation for the summer of 1999.

For this research, we will principally use the RSM’s explicitly-calculated sigma-level time-

integrated diagnostic budget terms for the water vapor tendency equation (Anderson, 2002):

†

∂q∂t

+ v ⋅ —q + ˙ s ∂q∂s

Ê

Ë Á

ˆ

¯ ˜ + P - g∂E

∂s

Ê

Ë Á

ˆ

¯ ˜ = F

tendency + (large - scale divergence) + (precipitation - vertical diffusion) = Residual(1)

6

†

q - Moisture content (kg)v - Horizontal velocity vector— - Two - dimensional divergence operators - Vertical (sigma) coordinate (equal to p ps)E -Vertical eddy diffusionP -PrecipitationF - Residual Forcings

Although these terms are output at 6-hour intervals, they are calculated at each model time-

step (approximately 90 seconds) before being averaged over the 6-hour period; hence they

capture interactions associated with both the mean and fluctuating fields. Here, the residual

forcings include horizontal diffusion, semi-implicit model adjustment and boundary forcing. In

general the horizontal diffusion term serves to smooth fine-scale noise seen in the horizontal

convergence term over regions of complex topography. The boundary forcing term can be large

at the edges of the domain, however this term is damped to zero within approximately ten grid

points of the edge. It should be noted that for this paper, moisture divergence associated with the

explicitly-resolved vertical and horizontal advective fluxes of moisture will be referred to

alternatively as “large-scale divergence”, “advective divergence”, or simply “divergence”; the

moisture divergence associated with vertical diffusion processes, related to the time-rate of

change of moisture due to parameterized sub-grid scale vertical motions in the model, will be

referred to either explicitly as “vertical diffusion” or alternatively as “convection”. In addition,

because of fine-scale variations related to the spectral method, all diagnostic budget terms from

the RSM are smoothed using a Lanczos smoothing filter of order 1.0, which is consistent with

the smoothing performed on the terrain data within the model simulation itself.

In addition to the RSM data, we also use diagnostic terms related to the atmospheric

hydrologic cycle derived from both reanalysis and observational data products. From the

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reanalysis, we archive the 4-times daily pressure level data for winds, temperature and humidity.

At the surface, we archive the 4-times daily data for surface pressure, precipitation, and

evaporation. Extensive documentation of the reanalysis product can be found in Kalnay et al.

(1996). Evaluation of the reanalysis hydrologic fields during the summertime monsoon season

can be found in Higgins et al. (1997); Roads et al., (1999); and Roads and Chen (2000).

Observationally, we have archived the 1x1-degree GPCC monthly precipitation dataset

available through NASA’s Distributed Active Archive Center, which spans the period from

1986-2002. This dataset uses rain-gauge measurements archived by the World Meteorological

Organization, as well as other meteorological and hydrological services/institutions; a subset of

about 6700 meteorological stations are selected and data from these stations are analyzed using a

spatial objective analysis method (Rudolf, 1996; see Acknowledgements for data availability). In

addition, we use the Climate Prediction Center’s (CPC) Unified precipitation data set, which is a

daily precipitation data set spanning 55 years (1948-2002) for the continental United States

(Higgins et al., 1996). This data set takes quality-controlled data from daily co-op stations, first-

order stations, and hourly observing sites and grids it to 0.25-degree resolution (approximately

25km). We also archived the 2-times daily radiosonde profiles, taken from NOAA’s FSL

Radiosonde Database (see Acknowledgments for data availability). The radiosonde profiles are

used to provide estimates of large-scale moisture flux divergence values for the region

(radiosonde locations shown in Figure 1). Finally, we archived the Land Data Assimilation

System (LDAS) evaporation estimates to compare with the vertically-integrated convective

terms taken from the RSM. This dataset is produced by forcing an offline land surface model

(Mosaic - Koster and Suarez, 1994) with estimates of atmospheric (precipitation and radiation)

and surface (soil, vegetation, elevation) parameters derived from observations and regional

8

model assimilations (Mitchell, 2003; Luo et al., 2003). The output from this land data

assimilation scheme has been evaluated by Robock et al., (2003), although only for the southern

Great Plains during the warm season. In general it was found that the latent heat flux (associated

with evaporation) was too large, however, this product represents one of the few alternative

evaporation estimates available; hence, the data are used here simply as a qualitative check on

the RSM and reanalysis output.

3. Results

To begin to evaluate the climatological characteristics of the analyzed and simulated

precipitation fields, Figure 2 shows the summertime (July-September) mean precipitation taken

from the observed (CMAP and Unified) datasets, along with the Reanalysis and regional model

estimates for the 5-year period of overlap (1998-2002). These results indicate that the RSM

summertime climatological precipitation for this region is in good agreement with that seen in

the analyzed observations. In particular, the RSM captures the increased precipitation over the

eastern portion of the domain; in addition it captures the fine-scale, orographic precipitation

found over northern Arizona, along the Mogollon rim, and through central Utah. The RSM,

however, fails to capture the local maximums found along the Arizona-Mexico border. In

general, though, the large-scale area-average for the RSM and observations are very similar

(1.3mm/day for both products; see Table 1 for all area-average values calculated in this paper).

In contrast, the coarse-scale NCEP Reanalysis precipitation data show an eastward shift relative

to the GPCC estimates and are generally weaker throughout the domain.

To see how representative of the long-term climatology this 5-year period is, Figure 3 shows

the difference between the 5-year climatology (1998-2002) and the full climatology for the two

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observed data products. Overall, the 5-year period under consideration has negative anomalies

over the southeastern corner of the domain, with some regions showing a decrease in

precipitation of up to 40%. However, over the rest of the domain, precipitation during this

period is similar to that seen in the longer-term averages, with an area average percent change of

only 16% for the CPC Unified dataset (18% for the GPCC data). As such, these results suggest

that studies of the hydrologic cycle during this period may be representative of the overall

climatology.

To further evaluate the reanalysis and model-based results, and to help determine whether the

domain-wide precipitation may be related to large-scale moisture convergence advected from

outside the domain region, we next calculate large-scale estimates of horizontal moisture

divergence using wind and moisture profiles from the selected radiosonde stations (see Figure 1

for station locations). The station locations are selected based upon consistency of record and

incorporation of twice-daily soundings. Unfortunately, this prevents the use of stations in

Mexico, which would have given a better representation of moisture divergence profiles

associated with the intense monsoon precipitation found over the Sierra Madre Occidental;

however, these locations do provide excellent coverage for the southwestern portion of the US.

The moisture-divergence estimates are next derived using a new objective line-integral technique

developed by Kanamaru and Salvucci (2003) designed to correct for the spurious mass

divergence from the observed wind fields, which subsequently causes errors in the estimated

moisture flux divergence. The method adjusts the spatial weights applied to each radiosonde

station in the calculation of divergence. The adjustment is done in a least squares sense to

minimize the modification to the weights under the constraint of mass conservation. In this

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paper, we chose zero mass divergence over the 5-year mean at each observation time (0000 UTC

and 1200 UTC) as the constraint.

Similar large-scale moisture divergence estimates are also calculated using simulated 2-times

daily profiles (interpolated to the observed station locations) taken from the RSM and Reanalysis

output to better compare with observations, as well as 4-times daily profiles to determine if there

are systematic biases associated with sampling frequency; in addition the RSM estimates are

calculated using simulated profiles only at the standard reporting levels, as well as at higher-

vertical resolution (every 50mb through the bottom 500mb; every 100mb above 500mb).

In order to handle the varying surface pressures and the practice of reporting at fixed pressure

levels from radiosondes, the observed and simulated values of moisture flux, mass flux, and

specific humidity (needed to account for mass balance adjustment) are vertically integrated

through the lowest few pressure levels (below 925mb for the observations and below 850mb for

the simulations and the Reanalysis) at each sounding and then averaged over the climatological

mean. For the purpose of illustration, the moisture divergence value is plotted as if it were

uniformly distributed over the first few pressure levels (which may not be the case), however the

overall computation represents the correct estimate for the integrated flux divergence near the

surface.

Vertical profiles of the 2xdaily moisture divergence estimates are shown in Figure 4. All

three standard reporting level estimates indicate large-scale moisture divergence above 750mb,

in agreement with previous work (Anderson and Roads, 2001). In addition, all three estimates

indicate moisture convergence from the surface to approximately 850mb. The apparent

difference in magnitude between the low-level RSM and Reanalysis profiles compared with the

observed profile is due to the difference in the level to which the low-level convergence is

11

integrated (see above); the integrated moisture convergence over this low-level region (surface to

850mb) for the RSM, reanalysis and observed profiles is actually very similar (0.4-0.5mm/day –

see below). The full, vertically-integrated moisture divergence estimates from the surface to the

top of the atmosphere (TOA) are found in Table 1. Profiles from the radiosonde data indicate net

moisture divergence over the region of approximately 1.9mm/day. For the coarse-scale RSM

estimate, the net divergence is also 1.9mm/day while for the Reanalysis the net divergence is

0.8mm/day, principally due to the significant underestimation of divergence between 800-

600mb. This discrepancy seems to indicate the need for fine-scale horizontal resolution in

simulations of the moisture and momentum fields, even when calculating large-scale averages of

these non-linear quantities. In addition, it can be seen that the higher vertical resolution profiles

taken from the RSM differ significantly from the coarse-resolution profiles, principally due to

the maximum moisture divergence values found at 650mb and 600mb. When the full vertical

integral is calculated using the higher-resolution profiles, the net divergence increases to

2.3mm/day, or about 20%. This result suggests an additional sensitivity to the vertical resolution

of the moisture and momentum fields, even at the upper levels where the absolute fields may be

smaller than at the surface. Although not shown, the RSM and reanalysis profiles are also

calculated using 4xdaily values. For the reanalysis estimates, the net divergence is essentially

the same as that found using the 2xdaily profiles (0.9mm/day). In addition, the RSM profiles

also closely match their 2xdaily counterparts (net divergence is 2.1mm/day and 2.4mm/day for

the coarse- and high-resolution vertical profiles respectively).

Previous research identified the large-scale, upper-air (<700mb) moisture divergence seen in

the three profiles and suggested that the presence of this moisture divergence discounted the

hypothesis that climatological summertime precipitation is supported by moisture convergence

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from remote source regions (Anderson and Roads, 2001). In addition to this upper-air

divergence, the profile-based estimates presented above also suggest that there is a net area-

average moisture divergence through the full atmosphere. To better characterize the geographic

structure of this moisture divergence, and compare it with the other terms in the moisture

tendency equation, we look at the vertically-integrated RSM moisture budget terms, calculated

from the surface to the top of the atmosphere (Figure 5). These fields indicate that summertime

precipitation (panel c) for this region, which is shown to be in good agreement with observed

values (Figure 2), is balanced solely by vertical diffusion of moisture supplied by evaporation

(panel b). The largest evaporative flux is found over the Gulf of California, however there is

also significant local evaporation throughout the domain. The grid-point evaporation over the

southwestern US in fact exceeds precipitation, resulting in net, vertically-integrated large-scale

divergence of moisture over most of the region (panel a). The area-averaged value of grid-point

moisture divergence over the southwestern US is approximately 1.3mm/day, which is lower than

the large-scale estimates derived from the observed and simulated radiosonde profiles (see Table

1). Possible reasons for this discrepancy will be discussed in a bit. Still, this divergence

estimate, along with the radiosonde-based estimates, are larger than previous model estimates for

this region (see Figure 6c from Berbery and Fox-Rabinovitz, 2003), which suggest fluctuating

moisture divergence and convergence through the months of July and August, but no apparent

net seasonal mean. Although this discrepancy may arise due to the fact that we are looking at

seasonal mean values whereas the previous study looked at the onset of the monsoon from June

through August, monthly mean divergence estimates for July and August, taken from the RSM

as well as the radiosonde profiles, continue to indicate net divergence for each month. Instead,

the discrepancy may arise due to the fact that the year under consideration in the previous study

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(1993) had a particularly late onset date, followed by significant rainfall through August

(Berbery and Fox-Rabinovitz, 2003), which may have resulted in a modification of the

climatological values.

To further evaluate the grid-point RSM tendency terms, we next compare the vertically-

integrated vertical diffusion term (which is equivalent to surface evaporation) with evaporation

estimates taken from LDAS (Figure 6). Overall, the two fields show good geographic

agreement, with a slight gradient running from west to east, along with maximum values over the

Sierra Madre Occidental in Mexico and a local maximum over the elevated regions of Colorado

and the Arizona/New Mexico border. However, the estimates from the RSM are larger than the

LDAS estimates by about 1mm/day throughout the domain (the area-average difference is

1.3mm/day – see Table 1). Although this result suggests that the regional model simulation

over-estimates surface evaporation, it is important to note that the grid-point precipitation field

from the RSM is very similar to that derived from observations; at the same time, the vertically-

integrated grid-point moisture divergence is actually less than the large-scale estimate derived

from the radiosonde profiles, suggesting that the grid-point evaporation field from the RSM may

be underestimating the actual values. To see whether the LDAS estimate of evaporation is

consistent with these same observational fields, the LDAS data is smoothed to 25km resolution

and is then subtracted from the analyzed observed precipitation (Figure 7). Results indicate that

there is near-zero difference throughout the southwestern US (the area average difference for the

region of interest is approximately 0.05mm/day). This result contrasts with the significant

moisture divergence estimates derived from the large-scale radiosonde network, suggesting that

in fact the LDAS estimates for this region may be significantly less than the actual evaporation

rates.

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At this point, it is difficult to determine the actual evaporation rates; grid-point estimates

from the RSM suggest it is about 2.6mm/day while the observed estimates, derived from the

difference between the area-averaged analyzed precipitation (taken from the Unified dataset) and

the radiosonde-derived divergence fields, suggest it is closer to 3.2mm/day, resulting in a

difference between the two estimates of about 0.6mm/day (see Table 1). To investigate where

the discrepancy may lay, we subdivide the net vertically-integrated flux divergence into its low-

level convergent component and its upper-level divergent component. As mentioned, the large-

scale vertically-integrated moisture divergence is dominated by moisture divergence above

850mb; below this level there is weaker moisture convergence. If the vertically-integrated net

divergence is calculated from the surface to 850mb, both the simulated and observed radiosonde-

based estimates suggest convergence of 0.4 mm/day (Table 1). In addition, if the grid-point

based estimates of the horizontal divergence, integrated from the surface to 850mb, is computed

using the RSM tendency terms, the area-average also shows a convergence of 0.4mm/day (Table

1); results are quantitatively similar if the grid-point values are averaged along sigma levels as

opposed to pressure levels in order to account for strong variations in the fine-scale orography.

Hence, it appears the low-level dynamic component of the moisture divergence term is properly

captured by the grid-point tendency estimates. However, when the horizontal moisture

divergence term is integrated from 850mb to the top of the atmosphere, the simulated and

observed radiosonde-based estimates both indicate a net divergence of 2.3mm/day while the

grid-point based estimates derived from the RSM indicate a net divergence of 1.6mm/day (again,

results are the same if the area-average is done along sigma levels as opposed to pressure levels);

the resulting difference between these two estimates is 0.7mm/day, very similar to the difference

in the surface evaporation estimates described above, which suggests that the difference in area-

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average evaporation is translating into a difference in the area-average large-scale divergence

aloft.

One way to test whether the difference in the upper-air divergence estimates arises from an

error in the large-scale estimates derived from the radiosonde profiles (both observed and

simulated) or from the RSM grid-point simulations is to extend the averaging domain for the

RSM grid-point values to the northern and eastern edge of the model domain (i.e. extend the top

boundary of the averaging region to the top of the RSM domain and the eastern boundary to the

right-hand side of the domain); in these regions, there are large boundary-forcing and offsetting

moisture divergence terms that bring the RSM moisture and momentum fields into agreement

with the larger-scale fields provided by the GSM. When these fields are included, the area-

average moisture divergence integrated from 850mb to the TOA is 2.2mm/day, which is in better

agreement with the values derived from the radiosonde-based profiles (see Table 1). This result

suggests that the model is underestimating surface evaporation over the center of the domain;

this under-estimation translates into an under-estimation of upper-level divergence over the

interior of the domain, which is then compensated by a numerically-prescribed enhancement of

divergence near the boundaries in order to preserve mass and moisture continuity with the larger-

scale fields. Despite these apparent discrepancies between the RSM grid-point estimates and the

area-averaged estimates of evaporation and upper-air divergence, both the explicit grid-point

fields and the derived large-scale fields indicate a balance in which low-level moisture

convergence and evaporation support seasonal-mean precipitation and a net divergence of

moisture aloft.

One issue that has yet to be dealt with is the process whereby the low-level moisture is re-

distributed to the upper-levels. Using the RSM grid-point tendency terms, it appears that the

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vertical diffusion of moisture, integrated from 850mb-TOA, results in a net convergence of

moisture of 3.0mm/day, which is larger than the full vertical integral of 2.6mm/day; the extra

0.4mm/day is of course due to the additional moisture supplied by low-level convergence, which

is subsequently lofted via convection. Hence, at approximately 850mb, the large-scale vertical

circulation does not significantly contribute to the vertical redistribution of moisture within the

column. As one moves higher in the atmosphere, however, this contribution increases such that

at approximately 650mb, the large-scale vertical convergence from the lower atmosphere to the

upper-atmosphere is about 0.7mm/day and reaches a maximum of approximately 1mm/day at

500mb. In comparison, the vertical diffusion term indicates convergence of approximately

1.7mm/day and 0.1mm/day above these levels. Unfortunately, these values are extremely

difficult to evaluate using observational products, however they do suggest that convective

lofting is the primary mechanism responsible for vertically redistributing low-level moisture to

the mid-troposphere, at which point the large-scale vertical circulation becomes important.

Finally, to see how the moisture flux fields are related to the large-scale upper-level

divergence and low-level convergence of moisture described above, the vertically-integrated

moisture flux vectors are calculated using the 4xdaily RSM grid-point moisture and momentum

fields, along with the 2xdaily observed profiles; the vertical integral is taken from the surface to

800mb and separately from 800mb-TOA in order to capture the fluxes associated with the

vertical heterogeneity in the divergence profiles. Both the simulated and observed moisture flux

fields agree well with results in Berbery (2001) (see Figure 12) and therefore are not shown here.

However, we will summarize two important results. First, seasonal mean horizontal moisture

fluxes calculated from 4-times (2-times) daily data taken from the RSM (radiosondes) and

integrated from the surface to 800mb indicate that, for the western portion of the domain, large-

17

scale low-level fluxes into the southwestern US originate from both the eastern Pacific and the

Gulf of California, in agreement with the 800 mbar moisture fluxes shown in Berbery (2001). In

addition, for the upper-level moisture fluxes, integrated from 800mb to the top of the

atmosphere, there is a large, anti-cyclonic circulation of moisture over the entire domain. It is

important to note, however, that along most of the trajectory, these fluxes are being augmented

by the large-scale divergence of water vapor at these levels; this feature is captured by increases

in the magnitude of the moisture flux vectors as seen in Figure 12d from Berbery (2001),

indicating an enhancement of the moisture fluxes exiting over the Great Plains.

4. Summary and Discussion

a. Summary

In this paper, we investigated the large-scale summertime hydrologic cycle associated with

the southwestern branch of the North American monsoon. Using analyzed, simulated and

observed estimates of precipitation, as well as estimates of the large-scale diagnostic budget

terms related to the moisture tendency equation, we attempted to characterize and quantify the

various dynamic and hydrodynamic processes important for producing summertime rainfall in

this region, shown schematically in Figure 8 and quantified in Table 1. In general, low-level

moisture convergence (~0.4 mm/day), associated with advection of moisture from the Gulf of

California and the Pacific Ocean to the western portion of the domain, and with the advection of

moisture from the Gulf of Mexico to the eastern portion of the domain (Berberry, 2001),

augments the local convergence due to evaporation (~2.9 mm/day, representing the average

value between the RSM grid-point estimate of 2.6mm/day and the observationally-based

estimate of 3.2mm/day). Both of these processes supply low-level moisture to the region, which

18

is then carried aloft by convection (~3.0mm/day) and large-scale vertical advection (~0.3

mm/day, representing the average value between the surface and 700mb); this lofted moisture

feeds both the precipitation (~1.3 mm/day) and upper-air divergence fields (~2.0mm/day, again

representing the average value between the RSM grid-point estimate of 1.6mm/day and the

observationally-based estimate of 2.3mm/day). The upper-air divergence subsequently results in

enhanced exiting moisture fluxes over the Great Plains (Schmitz and Mullen, 1996; Berberry,

2001). In addition, the upper-air divergence exceeds the low-level convergence, resulting in a

net, vertically-integrated divergence of moisture from the region, making the southwestern US an

effective seasonal source of moisture to the atmosphere.

It should be emphasized that these values represent the climatological cycling of moisture in

the atmosphere and that both intraseasonal and interannual variability in this cycling may have

very different balances. For instance, the hydrologic balance associated with “synoptic”-type

events in this region has been described previously (Anderson, 2002). Results indicate that

precipitation events themselves are predicated upon a weakening of the upper-level advection of

water vapor from the precipitating region, such that the quasi-stationary vertical diffusion of

moisture into the atmosphere balances rainfall as opposed to large-scale moisture divergence. In

contrast, controlled model simulations have shown that interannual variability in precipitation in

this region is the result of enhanced low level moisture convergence associated with changes in

the low-level temperature, pressure and surface circulation patterns produced by anomalous

evaporation, indicating a very subtle interaction between the momentum, moisture, and energy

fields (Kanamitsu and Mo., 2003). Further characterization and description of the dominant

large-scale hydrologic budget terms associated with this interannual variability in precipitation

over the southwestern United States is being investigated presently.

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b. Discussion

Based upon the balances found in this convectively-active semi-arid region, it appears that

the precipitation rate is strongly dependent upon the rate of evaporation of local moisture. One

way to quantify this relationship is by estimating the “recycling” of precipitation, which is

defined as the ratio of locally-derived precipitation (Pl) to total precipitation (P). Using a

traditional method for estimating the recycling rate, in which advective fluxes of moisture (Fin)

along a parcel trajectory length (L) are compared with the evaporative fluxes (E), gives

(Trenberth, 1999):

†

Pl

P=

ELEL + 2Fin

(2)

Given this estimation technique, we find that for a typical trajectory of 1000km, evaporative

fluxes of approximately 2.9mm/day (representing the average value between the RSM- and

observationally-based estimates) and advective fluxes of approximately 50kg/ms (which is the

average orthogonal component along the southern and western boundaries) the recycling rate is

about 0.25 (the recycling rate is about 0.14 if a 500km path trajectory is assumed). The low

numbers in this region are due principally to the low evaporative rate compared with advective

fluxes passing through the region. However, this recycling rate masks an important feature for

the hydrologic cycle described here, namely that although the evaporative fluxes are small

compared with the advective fluxes, they are relatively large compared with the large-scale

moisture convergence itself.

To better estimate the importance of evaporative fluxes to the rate of precipitation in this

region, we decide to utilize knowledge about the cycling of moisture found here. To start, we

look at the control volume delineated by the radiosonde network. Within this region we compute

the area-average evaporation through the bottom of the atmospheric column. We then compute

20

the area-average low-level horizontal moisture convergence. We then assume that both the

evaporative moisture and the convergent moisture are convected aloft by turbulent processes.

Some of this moisture precipitates out as rainfall while some is advected away, resulting in

upper-air divergence from the region. The main assumption here is that the low-level moisture,

supplied both by evaporation and large-scale dynamic convergence, is well mixed during the

convective process. This allows us to assume that the proportion of locally-generated rainfall

(Pl) and advectively-generated rainfall (Pa) are in the same ratio as the area-averaged evaporative

convergence and the low-level moisture convergence. Making the assumptions above allows us

to write:

†

P = Pl + Pa =E

E + -r—r u q low( )

P +-r—

r u q low

E + -r—r u q low( )

P (3)

The recycling ratio, Pl/P, then simply becomes:

†

Pl

P=

EE + -r—

r u q low( )(4)

For the region under consideration, we estimated the evaporative convergence to be

2.9mm/day, which is approximately the mean of the values provided by the radiosonde profiles

and the grid-point RSM tendency term. The low-level convergence is more difficult to estimate

because it is reliant on a definition of “low level”. One option is to integrate from the surface to

the level of precipitation. Another is to integrate from the surface to some fixed pressure height.

A final option is to integrate from the surface to the sigma level at which the moisture

convergence goes to zero. For the region under consideration, both the radiosonde-based area-

average estimates and the RSM grid-point estimates of the low-level moisture divergence,

integrated from the surface to 850mb, indicate a net moisture convergence of 0.4mm/day; in

addition, the RSM sigma-level grid-point moisture divergence values, integrated from the

21

surface to the level of zero divergence, also indicate a convergence of 0.4mm/day. Therefore,

for this region, we will use an estimate of 0.4mm/day, although for other regions the two

methods may not necessarily produce the same estimate. Using these values for the area-

averaged evaporation and low-level moisture convergence gives a recycling ratio of 0.88. In

other words about 85-90% of the area-average precipitation is due to recycling of moisture

within the column while about 10-15% is due to introduction of moisture via advective

convergence. Calculating similar recycling ratios for individual grid-points suggests that values

range from 0.7-0.9 (not shown). Overall, these numbers suggest a much greater importance of

evaporative fluxes in generating regional precipitation than does the traditional estimate.

It should be emphasized, however, that the two estimates are based upon different

assumptions. The first ratio assumes that all moisture entering the control volume (either through

evaporation or advection) is well-mixed within the control volume; the second assumes that only

convergent sources of moisture (i.e. evaporation and low-level convergence) are well-mixed and

that these in turn supply divergent sinks of moisture (i.e. precipitation and upper-air divergence).

Which estimate one chooses to emphasize depends in large part upon what question one wishes

to answer, i.e. the question of how often moisture is cycled within the system before it is

advected out of the control volume (which is fairly quickly given the relatively large moisture

flux aloft) or the question of whether the overall precipitation rate is related to the rate of cycling

of moisture within the control volume or the rate of large-scale moisture convergence.

It is also important to note that the method, as constructed here, is only applicable for

convectively-active regions in which there is large-scale low-level moisture convergence and

upper-level moisture divergence. As an example, in a region where moisture convergence

occurs aloft (at the precipitating level itself for instance), with little convergence at lower levels,

22

this method would show a large evaporative component to the precipitation when in fact much of

the precipitation may be related to larger-scale dynamic processes. Here we introduce the metric

as a better means of estimating recycling for this particular region given the hydrodynamic

balances identified in the research. For the index to be more generally applicable it would need

to be modified in order to incorporate a broader range of balances (for instance ones in which

there is large-scale divergence of moisture at low levels or convergence aloft). In addition, a

better index should also account for the correlations between the anomalous dynamic

convergence of moisture (either on interannual or intraseasonal time-scales) and the anomalous

evaporation and precipitation on these time-scales. However, since most recycling estimates are

based upon monthly mean values (Brubaker et al., 1993; Eltahir and Bras, 1996; Trenberth

1999), we decided to construct one which could be estimated from similar fields. Further

evaluation of this index as a diagnostic metric, as well as alternative indices based upon

intraseasonal variations in the various hydrodynamic terms, will be introduced in a subsequent

manuscript.

23

Acknowledgements. The authors wish to thank the Distributed Active Archive Center (Code

902), and Brian Cosgrove in particular, at the Goddard Space Flight Center, Greenbelt, MD,

20771 for distributing the evaporation data; and the science investigator, Dr. Bruno Rudolf at the

GPCC, Deutscher Wetterdienst, Germany, for producing the GPCC precipitation data products.

Goddard's DAAC is sponsored by NASA's Mission to Planet Earth program. Overviews of the

Goddard DAAC can be found at:

http://eosdata.gsfc.nasa.gov/CAMPAIGN_DOCS/FTP_SITE/INT_DIS/readmes/gpcc.html#400.

In addition, FSL Radiosonde data can be found at: http://raob.fsl.noaa.gov/

This research was funded by a cooperative agreement from NOAA-NA17RJ1231, NOAA-

NA16GP1622 and the NSF SAHARA project. The views expressed herein are those of the

authors and do not necessarily reflect the views of NOAA or NSF.

24

APPENDIX

Regional Spectral Modeling System

The NCEP global to regional modeling system used in this study contains two components

- a low-resolution global spectral model (GSM) and a regional spectral model with a single nest

(RSM). The nesting method is a one-way, non-interactive procedure that is designed to calculate

regional responses (or adjustments) of the RSM to the large-scale background fields provided by

the coarser-resolution GSM. This nesting procedure is performed through the entire domain, not

only at the lateral boundary zones and is therefore referred to as a perturbation method (see

Juang and Kanamitsu, 1994; Juang et al., 1997 for details).

The GSM and RSM use the same primitive hydrostatic sigma-coordinate equations

expressed in slightly different forms (Juang and Kanamitsu, 1994; hereafter JK). The GSM

equations consist of the vorticity, divergence, virtual temperature, and conservation equation for

water vapor on 18 sigma-layer coordinates and a mass continuity equation for surface pressure

(Kanamitsu, 1989). The regional model differs slightly in that it utilizes the momentum

equations instead of the vorticity/divergence equations (JK). The model physics were described

in Kalnay et al. (1996). Below are some of the important parameterizations:

Deep convection - Deep convection is modeled using a simplified Arakawa-Schubert

parameterization (Kalnay et al., 1996). This scheme assumes a quasi-equilibrium available

buoyant energy; changes in buoyant energy associated with convective activity are provided by

large-scale changes associated with the environmental stability of the atmosphere. The

instability is removed by relaxing the temperature and moisture profiles towards equilibrium

values using a prescribed time interval (Grell, 1993). In the simplified version, cloud size is

effectively prescribed as opposed to being determined from a parameterization-dependent

25

spectrum. In addition Hong and Pan (1996) detail modifications to this scheme. In particular, in

the subcloud layers, the level of maximum moist static energy represents the level of origination

for updraft-air; however, if the depth between this level and the level of free convection is

greater than a certain threshold, convection is suppressed. Convection can also be produced in

regions with large convective available potential energy (Hong and Pan, 1996). Importantly, the

scheme accounts for downdrafts associated with convection; in previous modeling attempts over

the southwest US it was shown that the parameterization scheme needs to consider evaporation

of precipitation below the cloud base in order to create outflow and increased convection in the

southwestern United States (Dunn and Horel, 1994b).

Large-scale Precipitation - A vertical iteration starting from the top sigma level checks for

supersaturation at each level. Supersaturated layers are set to a near-saturated state using an

approximate wet-bulb process; all excess liquid water goes into the form of precipitation. As the

precipitation moves downward through the column, it is either augmented (by passage through

additional supersaturated layers) or reduced (by passage through unsaturated layers). If it is

reduced due to passage through unsaturated layers, evaporation in that layer occurs where the

rate of evaporation is dependent upon the drop-size distribution (NMC, 1988).

Boundary Layer Processes - Boundary layer processes (Hong and Pan, 1996) are based upon a

nonlocal boundary layer vertical diffusion in which the surface layer and boundary layer are

coupled using a prescribed profile with similarity-based scale parameters. Included in this

scheme are also countergradient diffusion processes, which are shown to be important for mixing

low-level moisture upward more efficiently (Hong and Pan, 1996). Importantly, the scheme

strongly couples the boundary layer physics and the convective processes described earlier,

improving the precipitation forecasts for heavy rain events (Hong and Pan, 1996). Boundary

26

layer height is computed iteratively by first computing the boundary height without accounting

for virtual temperature instability near the surface; from this estimate, calculations of vertical

velocities can be obtained, which then allow estimation of the virtual temperature instability and

subsequently modified PBL heights (Hong and Pan, 1996). The diffusion parameters for

momentum and mass can then be determined from the boundary layer height using the

prescribed profile shape. Above the boundary layer, vertical eddy transfer uses a more-

traditional Richardson number-dependent diffusion process.

Soil Model Processes - The soil model in the RSM is based upon the Oregon State University

scheme as described in Chen et al. (1996). The scheme includes an explicit vegetation canopy,

soil hydrology and soil thermodynamics. For the soil scheme, it uses a 2-level (0.1 and 1.9m

thick) prognostic soil model for both moisture and temperature, with parameterized diffusion

between the two levels and a spatial distribution of soil type and soil properties as described in

Betts et al. (1997). Also included in the prognostic scheme are equations for moisture contained

in vegetation and snow (Chen et al., 1996). Unlike the atmospheric fields, the prognostic soil

moisture and temperature fields are not updated from the GSM/Reanalysis output but are instead

continuously evolving fields based solely upon the RSM physics. Evaluation and analysis of the

soil model performance has been done both within the NCEP Eta (Chen et al., 1996; Betts et al.,

1997) and RSM (Roads and Chen, 2000) modeling systems. To initialize the soil model, soil

moisture and temperature values are derived from the Reanalysis data at the start of the summer

season (July 1). As described by Roads et al. (1999, 2000, 2003), Kanamitsu et al. (2003), and

Chen and Roads (2004), long term simulations with the GSM/RSM can result in unrealistic soil

moisture, due to coupled land-atmosphere feedbacks that can sometimes result in excessive

drying. A number of solutions have been developed to counter this feedback for not only global

27

and regional simulations but also for analysis models (e.g. Kalnay et al. 1996, Kanamitsu et al.

2003). For this study, we decided to initialize the soil moisture from NCEP's Global Data

Assimilation System output at the beginning of each summer run in order that at least the upper

layers of the soil moisture would come into quasi- equilibrium with the RSM atmosphere and at

the same time the lower layers would not adversely affect the simulations. This re-initialization

once every 3 months did appear to produce more realistic RSM simulations than a preliminary

run that ran continuously for several years as well as another run that was initialized every day

from the global analysis. Again, a number of methodologies, including precipitation assimilation,

are still underway to further improve these kinds of regional simulation studies dependent upon

land-atmosphere feedbacks.

Sea Surface Temperatures - Sea-surface temperatures in the RSM are re-initialized daily using

the coarse-scale Reanalysis data. The one exception is in the Gulf of California, which is absent

from the global data. For the Gulf, SSTs are assigned a constant value equal to the temperature

at the mouth of the Gulf (approximately 29° C). This assignment is based upon results of

previous modeling studies indicating the importance of proper SST fields in the Gulf (Dunn and

Horel, 1994b); sensitivity studies indicate that finer-scale SST resolution does not quantitatively

affect the dynamic fields in the region (Anderson, 1998).

28

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33

TABLE LEGENDS

Table 1 Atmospheric water vapor tendency equation estimates and their data sources. Signconvention such that positive values equivalent to atmospheric moisture divergence

FIGURE LEGENDS

Fig. 1 RSM (• - 25 km resolution) and GSM (+ - T62) grid points and RSM orography contours.Orography contour interval is 250 meters; shading interval is 750 meters with maximumshaded elevation of 3000 meters. Location of important geographic and selected upper-airobserving stations (O) also shown (NKX - Miramar AFB, San Diego; TUS - Tucson; EPZ -Santa Teresa; MID - Midland; AMA - Amarillo; DDC - Dodge City; LBF - North Platte;SLC - Salt Lake City; DRA - Desert Rock). Box indicates region over which area-averagevalues are calculated - see text. Latitude-longitude lines every 5-degrees.

Fig. 2 a) Mean daily summertime (JAS) precipitation from the CPC UNIFIED dataset. Shadinginterval is 0.5 mm day-1; minimum shading is 0.5 mm day-1. Average taken over the time-period 1998-2002 b) Same as a) except for the GPCC data. c) same as a) except for theRSM data. d) same as a) except for the NCEP Reanalysis data.

Fig.3 a) Daily summertime (JAS) precipitation anomaly for the period 1998-2002, as seen in theClimate Prediction Center’s (CPC) UNIFIED dataset. Shading interval is 0.25 mm day-1;minimum shading is +/-0.25 mm day-1. Positive values are shaded; negative values aredashed. Anomaly taken against the time-period 1986-2002. b) same as (a) except for theGPCC data-set.

Fig. 4 Area-averaged seasonal-mean moisture divergence profiles calculated using wind andhumidity data taken from selected 2xdaily radiosonde locations (see Figure 1). Divergenceprofiles calculated for observed data (solid line), simulated data interpolated to the standardreporting levels (closed squares), simulated data interpolated to high resolution pressurelevels (every 50mbar below 500mbar; every 100mbar above 500mbar; open squares), andReanalysis profiles (triangles).

Fig. 5 Mean daily summertime (JAS) vertically-integrated RSM diagnostic moisture tendencyterms. Shading interval is 1 mm day-1; minimum shading is +/-1 mm day-1. Positive valuesare shaded; negative values are dashed. (a) large-scale total moisture divergence; (b) verticaldiffusion moisture divergence (equivalent to evaporation but of opposite sign); (c) totalprecipitation (same as Figure 2c but with different scaling); (d) overall moisture tendency.

Fig. 6 (a) Mean daily summertime (JAS) evaporation estimates, taken from the MOSAIC/LDASanalysis. Values averaged over the period 1998-2002. Shading interval is 1 mm day-1;minimum shading is +/-1 mm day-1. (b) same as (a) except for evaporation estimates takenfrom the RSM data product

Fig. 7 Mean daily summertime (JAS) difference between precipitation and evaporation.Precipitation estimates taken from the CPC Unified dataset; evaporation estimates taken

34

from the MOSAIC/LDAS analysis and extrapolated to the CPC 25km gridpoints. Valuesaveraged over the period 1998-2002. Shading interval is 0.5 mm day-1; minimum contour is+/- 0.5 mm day-1. Positive values are shaded; negative values are dashed.

Fig. 8 Schematic diagram showing 3-dimensional moisture cycling over the southwestern UnitedStates. Small blue arrows indicate low-level moisture fluxes; Large arrow indicates upper-level (<700mb) moisture fluxes. Black ovals represent convective activity. Shown in colorare the RSM daily precipitation estimates overlying the model orography, shown on the z-axis.

35

Table 1 Atmospheric water vapor tendency equation estimates and their data sources. Signconvention such that positive values equivalent to atmospheric moisture divergence

Precipitation Evaporation Vertically-integratedDivergence

Upper-levelDivergence850mb-TOA)

Low-levelDivergence(Sfc-850mb)

RSMgrid-point

1.3mm/d -2.6mm/d 1.3mm/d 1.6mm/d -0.4mm/d

ObservedRadiosonde

— — 1.9mm/d 2.3mm/d -0.4mm/d

Reanalysis-estimatedRadiosonde

— — 0.8mm/d — —

RSM-estimatedRadiosonde:Standard levels

— — 1.9mm/d 2.3mm/d -0.4mm/d

RSM-estimatedRadiosonde: Full

— — 2.3mm/d — —

CPC Precipitation 1.3mm/d — — — —

LDAS — -1.3mm/d — — —

Net Obs. Div. +Precipitation

— -3.2mm/d — — —

Fig. 1 RSM (

dots

- 25 km resolution) and GSM (crosses - T62) grid points and RSM orographycontours. Orography contour interval is 250 meters; shading interval is 750 meters with maxi-mum shaded elevation of 3000 meters. Location of important geographic and selected upper-airobserving stations (O) also shown (NKX - Miramar AFB, San Diego; TUS - Tucson; EPZ - SantaTeresa; MID - Midland; AMA - Amarillo; DDC - Dodge City; LBF - North Platte; SLC - SaltLake City; DRA - Desert Rock). Box indicates region over which area-average values are calcu-lated - see text. Latitude-longitude lines every 5-degrees.

CPC UNIFIED Dataset: 1998-2002

Fig. 2 a) Mean daily summertime (JAS) precipitation from the CPC UNIFIED dataset. Shading inter-

val is 0.5 mm day

-1

; minimum shading is 0.5 mm day

-1

. Average taken over the time-period 1998-2002 b) Same as a) except for the GPCC data. c) same as a) except for the RSM data. d) same as a)except for the NCEP Reanalysis data.

Daily Mean Summertime PrecipitationFrom Observational and Simulated Data Products

NCEP Reanalysis Dataset: 1998-2002RSM Dataset: 1998-2002

GPCC Dataset: 1998-2002

(c)

(b)

(d)

(a)

Fig.3 a) Daily summertime (JAS) precipitation anomaly for the period 1998-2002, as seen in the Cli-

mate Prediction Center s (CPC) UNIFIED dataset. Shading interval is 0.25 mm day

-1

; minimum

shading is +/-0.25 mm day

-1

. Positive values are shaded; negative values are dashed. Anomaly takenagainst the time-period 1986-2002 b) same as (a) except for the GPCC data-set.

GPCC Dataset: Anomaly: 1998-2002(b)CPC UNIFIED Anomaly: 1998-2002(a)

Daily Mean Observed Summertime Precipitation Anomaly

−0.015 −0.01 −0.005 0 0.005 0.01 0.015

0

200

400

600

800

1000

Moisture Divergence (10−6 kg kg−1 s−1)

Pre

ssur

e (m

b)RadiosondeRSM − Standard levelsRSM − every 50mbReanalysis

Fig. 4 Area-averaged seasonal-mean moisturedivergence profiles calculated using wind andhumidity data taken from selected 2xdaily radio-sonde locations (see Figure 1). Divergence profilescalculated for observed data (solid line), simulateddata interpolated to the standard reporting levels(closed squares), simulated data interpolated tohigh resolution pressure levels (every 50mbarbelow 500mbar; every 100mbar above 500mbar;open squares), and Reanalysis profiles (triangles).

Precipitation

Vertical DiffusionLarge-scale Divergence

Moisture Tendency(c) (d)

(b)(a)

Fig. 5 Mean daily summertime (JAS) vertically-integrated RSM diagnostic moisture tendency terms. Shading

interval is 1 mm day

-1

; minimum shading is +/-1mm day

-1

. Positive values are shaded; negative values aredashed. (a) large-scale total moisture divergence; (b) vertical diffusion moisture divergence (equivalent toevaporation, but of opposite sign); (c) total precipitation (same as Figure 2c but with different scaling); (d)overall moisture tendency.

Evaporation Estimate from LDAS: 1998-2002 Evaporation Estimate from RSM: 1998-2002(a) (b)

Fig. 6 (a) Mean daily summertime (JAS) evaporation estimates, taken from the MOSAIC/LDAS analysis.

Values averaged over the period 1998-2002. Shading interval is 1 mm day

-1

; minimum shading is +/-1mm

day

-1

. (b) same as (a) except for evaporation estimates taken from the RSM data product

CPC Precip - LDAS Evap.

Fig. 7 Mean daily summertime (JAS) differencebetween precipitation and evaporation. Precipita-tion estimates taken from the CPC Unified dataset;evaporation estimates taken from the MOSAIC/LDAS analysis and extrapolated to the CPC 25kmgridpoints. Values averaged over the period 1998-

2002. Shading interval is 0.5 mm day

-1

; minimum

contour is +/- 0.5 mm day

-1

. Positive values areshaded; negative values are dashed.

Fig. 8 Schematic diagram showing 3-dimensional moisture cycling over the southwesternUnited States. Small blue arrows indicate low-level moisture fluxes; Large arrowindicates upper-level (<700mb) moisture fluxes. Black ovals represent convectiveactivity. Shown in color are the RSM daily precipitation estimates overlying the modelorography, shown on the z-axis.