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  • View in gallery

    Geopotential height (m; shaded) and streamlines (dark gray) at (top) 200 and (bottom) 1000 hPa from the NCEP-FNL for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. The black contours represent 1000-m topography.

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    Simulation nests at 18- (D01), 6- (D02), and 1.2-km (D03) grid spacing, with areas higher than 1000 m shaded in dark gray. The black mesh denotes the eastern flanks of the Andes.

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    Residual of daily precipitation (mm day−1) between the WRFCTL and STRICT simulations (STRICT − WRFCTL) for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY. The solid black lines denote the border of the eastern flanks of the Andes. They are from the innermost domain (Δh = 1.2 km).

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    Rain rate (mm h−1) from WRFCTL (thick gray) and STRICT (thin dashed black) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY. They are averaged over the EADS (bordered by the black lines in Fig. 3a) and from the innermost grid (Δh = 1.2 km).

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    Daytime-averaged cloud content (g kg−1; cloud water, ice, snow, and graupel) along the cross section denoted by the dashed line in Fig. 3a for (a)–(e) the WRFCTL simulations and (f)–(j) the STRICT experiments, with the black lines representing terrain height (m). They are from the innermost nest (Δh = 1.2 km).

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    Hydrostatic surface pressure tendency (hPa h−1) associated with precipitation (; dashed–dotted), convergence (; dashed), and ET (; dotted), as well as the surface pressure tendency (hPa h−1) diagnosed from the WRF Model (; solid). They are averaged over the EADS and from the innermost grid (Δh = 1.2 km), with the thick gray (thin black) for the WRFCTL (STRICT) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY.

  • View in gallery

    Moisture budget (mm h−1) associated with precipitation (; dashed–dotted), convergence [; dashed], and ET (; dotted), as well as the precipitable water tendency (mm h−1) diagnosed from the WRF Model (; solid). They are averaged over the EADS and from the innermost grid (Δh = 1.2 km), with the thick gray (thin black) for the WRFCTL (STRICT) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY.

  • View in gallery

    Surface latent heat flux removed from the atmosphere (W m−2; dotted thin black) in the STRICT experiments, as well as the maximum CAPE (J kg−1; solid) and LCL (m; dashed) from the WRFCTL (thick gray) and STRICT (thin black) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY. They are averaged over the EADS and from the innermost domain (Δh = 1.2 km).

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    Daytime-averaged vertical profiles of (a)–(e) water vapor mixing ratio (g kg−1; double dotted), (f)–(j) equivalent potential temperature (K; solid) and saturation equivalent potential temperature (K; dashed), (k)–(o) latent heating (K h−1; dashed–dotted), and (p)–(t) latent and turbulent heating (K h−1; dotted) from the WRFCTL (thick gray) and STRICT (thin black) simulations for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. They are averaged over the EADS and from the innermost domain (Δh = 1.2 km).

  • View in gallery

    Daytime maximum vertical velocity (m s−1) along the cross section denoted by the dashed line in Fig. 3a for (a)–(e) the WRFCTL simulations and (f)–(j) the STRICT experiments, with the black lines representing terrain height (m). They are from the innermost grid (Δh = 1.2 km).

  • View in gallery

    Daytime-averaged (a)–(e) (solid), (f)–(j) (dashed), and (k)–(o) (dashed–dotted) (K h−1) from the WRFCTL (thick gray) and STRICT (thin black) experiments for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. The spatial average is conducted over the EADS within the innermost nest (Δh = 1.2 km).

  • View in gallery

    Daytime-averaged vertical eddy flux for (a)–(e) sensible (solid), (f)–(j) latent (dashed), and (k)–(o) total (dashed–dotted) heat (W m−2) from the WRFCTL (thick gray) and STRICT (thin black) experiments for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. The spatial average is conducted over the EADS within the innermost domain (Δh = 1.2 km).

  • View in gallery

    (a)–(e) The vertical profiles of the residual of daytime-averaged temperature (K; dotted gray) between the WRFCTL and STRICT simulations (STRICT − WRFCTL) for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. (f)–(j) As in (a)–(e), but for moist Brunt–Väisälä frequency (s−2; solid gray). Also shown are the daytime-averaged vertical profiles of (k)–(o) mass convergence (hPa h−1; solid) and (p)–(t) moisture convergence (mm h−1; dashed) from the WRFCTL (thick gray) and STRICT (thin black) simulations. They are averaged over the EADS and from the innermost grid (Δh = 1.2 km).

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Isolating the Role of Surface Evapotranspiration on Moist Convection along the Eastern Flanks of the Tropical Andes Using a Quasi-Idealized Approach

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  • 1 Duke University, Durham, North Carolina
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Abstract

The contribution of surface evapotranspiration (ET) to moist convection, cloudiness, and precipitation along the eastern flanks of the tropical Andes (EADS) was investigated using the Weather Research and Forecasting (WRF) Model with nested simulations of selected weather conditions down to 1.2-km grid spacing. To isolate the role of surface ET, numerical experiments were conducted using a quasi-idealized approach whereby, at every time step, the surface sensible heat effects are exactly the same as in the reference simulations, whereas the surface latent heat fluxes are prevented from entering the atmosphere. Energy balance analysis indicates that surface ET influences moist convection primarily through its impact on conditional instability, because it acts as an important source of moist entropy in this region. The energy available for convection decreases by up to approximately 60% when the ET contribution is withdrawn. In contrast, when convective motion is not thermally driven or under conditionally stable conditions, the role of latent heating from the land surface becomes secondary. At the scale of the Andes proper, removal of surface ET weakens upslope flows by increasing static stability of the lower troposphere, as the vertical gradient of water vapor mixing ratio tends to be less negative. Consequently, moisture convergence is reduced over the EADS. In the absence of surface ET, this process operates in concert with damped convective energy, suppressing cloudiness and decreasing daily precipitation by up to around 50% in the simulations presented here.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAS-D-14-0048.s1.

Corresponding author address: Ana P. Barros, Duke University, Box 90287, 2457 CIEMAS Fitzpatrick Bldg., Durham, NC 27708. E-mail: ana.barros@duke.edu

Abstract

The contribution of surface evapotranspiration (ET) to moist convection, cloudiness, and precipitation along the eastern flanks of the tropical Andes (EADS) was investigated using the Weather Research and Forecasting (WRF) Model with nested simulations of selected weather conditions down to 1.2-km grid spacing. To isolate the role of surface ET, numerical experiments were conducted using a quasi-idealized approach whereby, at every time step, the surface sensible heat effects are exactly the same as in the reference simulations, whereas the surface latent heat fluxes are prevented from entering the atmosphere. Energy balance analysis indicates that surface ET influences moist convection primarily through its impact on conditional instability, because it acts as an important source of moist entropy in this region. The energy available for convection decreases by up to approximately 60% when the ET contribution is withdrawn. In contrast, when convective motion is not thermally driven or under conditionally stable conditions, the role of latent heating from the land surface becomes secondary. At the scale of the Andes proper, removal of surface ET weakens upslope flows by increasing static stability of the lower troposphere, as the vertical gradient of water vapor mixing ratio tends to be less negative. Consequently, moisture convergence is reduced over the EADS. In the absence of surface ET, this process operates in concert with damped convective energy, suppressing cloudiness and decreasing daily precipitation by up to around 50% in the simulations presented here.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAS-D-14-0048.s1.

Corresponding author address: Ana P. Barros, Duke University, Box 90287, 2457 CIEMAS Fitzpatrick Bldg., Durham, NC 27708. E-mail: ana.barros@duke.edu

1. Introduction

Surface sensible and latent heat fluxes, redistributed aloft mainly through turbulent mixing in the boundary layer and by moist convection in the free atmosphere, function as the heat source needed to balance the net radiative cooling of the troposphere. Because the source is located at a higher pressure than the sink, this process can be regarded as a heat engine (Rennó and Ingersoll 1996). Following this framework, Rennó and Ingersoll [1996, their Eq. (42)] showed that the convective strength in equilibrium, as measured by the convective velocity scale , depends explicitly on the heat input at Earth’s surface.

From the standpoint of parcel dynamics, as a subcloud-layer air sample is lifted adiabatically, the energy available for convection depends on the parcel’s entropy, which increases with surface evapotranspiration (ET), and processes adjusting the temperature of the atmosphere above the air parcel (the colder the upper-level air, the larger the CAPE), including radiative cooling, horizontal advection, and adiabatic cooling associated with large-scale ascent [Emanuel 1994, his Eq. (14.2.13)]. In the tropics and per scaling analysis, Emanuel (1994) demonstrated that the contribution from the surface ET is around 1.5 times greater than the effects due to large-scale motion and more than triple that of the radiative forcing. Thus, surface ET influences convective overturning through its impact on conditional instability, a central concept of moist convection for more than a century (Emanuel et al. 1994). This interaction between surface ET and convection was also noted by Barros and Hwu (2002), who identified a positive feedback of surface ET on local rainfall in the phase space of the surface Bowen ratio and tropospheric relative humidity using results from numerical simulations at the meso-γ scale and observations from the Oklahoma Atmospheric Radiation Measurement Program (ARM) Cloud and Radiation Testbed (CART) Southern Great Plains research site.

Over mountainous regions, the role of surface ET on rainfall processes has been also investigated previously through diagnostic studies. Based on the observations from the Monsoon Himalayan Precipitation Experiment (MOHPREX) and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis, Barros and Lang (2003) found that about 15%–35% of daily surface ET is recycled at the ridge–valley scale in the central Himalayas. This estimate is consistent with the results of Iwasaki (2004), who attributed a daily increase of precipitable water on the order of 2–5 mm to surface ET and ridge–valley circulations alone around Mount Tanigawa in Japan, and similar analysis by Bhushan and Barros (2007) using numerical simulations in the inner region of the Sierra Madre mountains in Mexico. At seasonal scales and using the Regional Climate Model, version 3 (RegCM3), Grimm et al. (2007) demonstrated that antecedent dry conditions over the Brazilian Highlands in the spring can induce cyclonic circulations and enhance low-level moisture convergence, establishing favorable conditions for excessive rainfall in the subsequent summer. Focusing on southwestern Germany and eastern France (a mountainous region), Barthlott and Kalthoff (2011) conducted a set of simulations with varied initial soil moisture conditions. They concluded that, when the soil wetness is low and surface ET is limited by soil moisture availability, a positive relationship between daily rainfall and soil moisture exists; in contrast, for wet soils, where surface ET is controlled by net radiation, the influence of increasing soil moisture is tenuous and a systematic feedback is lacking. One limitation of previous modeling studies is that sensible and latent heat fluxes vary jointly and cannot be decoupled.

In the Southern Hemisphere, the most prominent mountain range is the Andes, running across seven countries from the northern to the southern tip of South America. A salient climatic feature of this continent is the year-round South American low-level jet (SALLJ), carrying warm and moist air from the Amazon basin southward to 35°S (Garreaud et al. 2009). With its core ranging from 1 to 1.5 km in height, this low-level jet can be an important moisture source for the eastern slopes of the Andes (Giovannettone and Barros 2009). At higher elevations, the Bolivian high (BH), a southwestward-propagating Rossby wave forced by the condensational heating over the Amazon (Gill 1980; Lenters and Cook 1997), may also play a role in cloud formation and precipitation. For instance, Garreaud (1999) showed that stronger than average easterly winds aloft over the central Andes, associated with the reinforcement and southward displacement of the Bolivian high, can promote upslope flows and enhance low-level easterlies within the Altiplano boundary layer, leading to increased moisture transport from the continental lowlands that feeds deep convection. In contrast with the central Himalayas, where land-use and land-cover change (LULCC) has severely reduced the density and connectedness of vegetation, the eastern slopes of the Andes are densely forested; therefore, it is expected that the ET contribution to the atmospheric moisture supply should be significant. This local effect was highlighted by Wei and Dirmeyer (2012), who conducted a diagnostic study to characterize the local versus remote impacts of soil moisture on precipitation [Eq. (1) of Wei and Dirmeyer (2012)]. Relying on a back-trajectory method, their results suggest that, during the austral summer, surface ET over the eastern Andes outweighs the contribution from remote regions (their Fig. S4d), but the opposite occurs at higher altitudes (their Fig. S4f).

The goal of this study is to understand the impact of surface ET on moist convection along the eastern flanks of the Andes (EADS), in particular its contribution to cloud formation and precipitation processes. In contrast with earlier studies over mountainous regions (e.g., Barros and Lang 2003; Iwasaki 2004; Bhushan and Barros 2007), where the contribution of surface ET was primarily inferred as the residual in the atmospheric moisture budget, here the focus is on energetics. In section 2, the moderate-complex Noah land surface model (Chen and Dudhia 2001) and the revised fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) surface layer (Jiménez et al. 2012) are simplified and an assembled land surface for the Advanced Research Weather Research and Forecasting (WRF) Model, version 3.4.1 (ARW3.4.1; Skamarock et al. 2008), is formulated, which provides a framework to isolate the role of surface ET as illustrated in section 3. The methodologies employed to quantify this effect are briefly reviewed in section 4, with the apparent heat source and apparent moisture sink in σ coordinates derived in appendix B. Analyses and interpretation of the results are presented in section 5. Conclusions and discussion are documented in section 6. The evaluation of model performance against observations could be found in the online supplemental materials.

2. The land surface sensible heat effect in ARW3.4.1

In ARW3.4.1 (Skamarock et al. 2008) with the configurations described in section 3,1 the feedback from the land surface to the atmosphere can be described as
e1
where is albedo; is emissivity; is surface skin temperature; and denote surface sensible and moisture fluxes, respectively (note that surface latent heat flux can be derived from ); is the surface roughness length; () is a function of stability function for momentum (heat/moisture ); represents bulk Richardson number; and are the 10-m wind speed; is the frictional velocity; and represents the adjusted lowest-model-level wind speed to adapt to free-convection conditions (Beljaars 1995) with subgrid-velocity-scale considerations (Mahrt and Sun 1995). Among them, , and are calculated in the Noah land surface model (Noah; Chen and Dudhia 2001) and impact atmospheric radiation; and are from Noah or the revised MM5 surface layer (RMM5; Jiménez et al. 2012), depending on over land or over water; can be time variant when snowfall is present or externally specified and updated in Noah; and the remainder of the variables are evaluated in RMM5 thereafter, along with , , and , entering into the Yonsei University (YSU) boundary layer scheme [Hong et al. 2006; see section 2.1 of Sun and Barros (2014) for the key processes of this parameterization].

The variables in Eq. (1) are referred to as the “connectors” from the land surface to the atmosphere, in the sense that, provided their values are exactly the same at every time step, any simulation shall produce identical atmosphere no matter how Noah and/or RMM5 are modified. In other words, the connectors in Eq. (1) constitute an assembled land surface model, including the surface layer as well as the land surface scheme. To verify the diagnosed connectors and the validity of this approach, a group of 1-h experiments were conducted. One experiment is a typical real-data case simulation (VERF_CNTL) with the connectors outputted at every time step. In another simulation (VERF_CONC), various variables in the Noah and RMM5 are multiplied by arbitrary numbers (artificially crashing the Noah and RMM5), but with the connectors from VERF_CNTL imposed at each time step. The remaining experiments are replicates of VERF_CONC, except for specifying additional variables (e.g., soil moisture and soil temperature) besides the diagnosed connectors (VERF_MORE) or only a subset of the 13 connectors (VERF_LESS). Using the same workstation and identical number of computer cores, VERF_CONC and VERF_MORE reproduced exactly the same atmosphere as in VERF_CNTL, whereas any member of VERF_LESS failed in this respect, suggesting that the 13 connectors in Eq. (1) are necessary and sufficient.

The assembled land surface model described above offers the chance to define the land surface sensible heat effects, including the impacts on radiation, diffusion, and stability. Obviously, , , , and belong to the surface sensible heat variables, but not and . For the remaining connectors, detailed examinations of ARW3.4.1 reveal that , soil temperature , and bottom soil temperature are indispensable to obtain connectors , , , , , , and [see section 2 of Sun (2014) for details]. As the 13 connectors are the only path from the land surface to the atmosphere, the land surface sensible heat effects are therefore represented by , , , , , and .

3. Experiment design: A quasi-idealized approach

As the moisture budget along the eastern slopes of the Andes is closely related to SALLJ (e.g., Fig. 7), two simulations were conducted for events during the SALLJ Experiment (SALLJEX; 15 November 2002–15 February 2003; Vera et al. 2006) to take the advantage of existing observations. The first simulation is for a weak LLJ event (WLLJ; 15 January 2003) and the second is for a strong case (SLLJ; 6 February 2003). In the former, the BH, potentially influential to cloud formation and precipitation over the high Andes (Garreaud 1999, 2000a), is more prominent (cf. Figs. 1a,b). The Chaco low (~25°S) is present in the latter (Fig. 1g), resembling the typical circulation features accompanied by intense low-level jets described by Salio et al. (2002). These two simulations represent atmospheric conditions that are observed frequently during the monsoon. Another three experiments were carried out in the dry season: a precipitation event with accumulations around the climatological mean (NDRY; 28 June 2003); a relatively wet day in the winter (CDRY; 9 August 2003); and an extremely dry case (EDRY; 20 July 2003). In these three dry-season simulations, characteristic conditions of the austral winter in South America occur, including the absence of the BH (Figs. 1c–e) associated with suppressed latent heating in the Amazon (Lenters and Cook 1997, their Fig. 1) and moist air masses that mainly originate from the subtropical Atlantic high (Figs. 1h–j), rather than from the tropical Atlantic as in the wet season (Figs. 1f,g) (Rao et al. 1996). Specifically for CDRY, the anticyclone centered around 35°S, as well as the southerly low-level flows to the east of the Andes (Fig. 1i), suggest the intrusion of Southern Hemisphere midlatitude systems into the subtropics and tropics [i.e., the so-called cold surge; see also Figs. 4b, 5b, and 10 in Garreaud (2000b)]. These five cases thus cover a wide range of weather conditions prevalent in South America.

Fig. 1.
Fig. 1.

Geopotential height (m; shaded) and streamlines (dark gray) at (top) 200 and (bottom) 1000 hPa from the NCEP-FNL for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. The black contours represent 1000-m topography.

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

Using ARW3.4.1 and focusing on the central Andes in Peru, the simulations were implemented on three nests (316 × 496, 433 × 604, and 756 × 726 grid cells) at 18-, 6-, and 1.2-km grid spacing (Fig. 2), with the initial and lateral boundary conditions derived from the NCEP Final Operational Global Analysis (NCEP-FNL). In the vertical direction, each grid consists of 60 sigma levels, and nearly 14 layers are within the lowest 1 km. The physics options applied are the Dudhia shortwave, Rapid Radiative Transfer Model (RRTM) longwave, Lin et al. microphysics, Kain–Fritsch cumulus parameterization (for the two outer domains only), YSU boundary layer, revised MM5 surface layer, and Noah land surface [Skamarock et al. (2008) and references therein]. One-way nesting was employed.

Fig. 2.
Fig. 2.

Simulation nests at 18- (D01), 6- (D02), and 1.2-km (D03) grid spacing, with areas higher than 1000 m shaded in dark gray. The black mesh denotes the eastern flanks of the Andes.

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

The outermost grid was initialized 1 day earlier than the period of interest at 0600 UTC (around midnight in the Andes) to provide lateral boundary conditions more consistent with model physics. For the two inner domains, the model ran for 36 h from 0000 UTC on the same day of each case, with the first 6-h model integration deemed as the spinup of the atmosphere (e.g., Sun and Barros 2012). The land surface conditions were initialized using the NCAR High-Resolution Land Data Assimilation System (HRLDAS; Chen et al. 2007), forced by the product from the Global Land Data Assimilation System (GLDAS; Rodell et al. 2004). HRLDAS was run on the same grid as the ARW3.4.1 simulations with five cycles (each cycle lasts for 1 yr) for a sufficient spinup of soil moisture, one of the fundamental variables determining surface evapotranspiration.

To isolate the role of surface evapotranspiration, quasi-idealized experiments were conducted, where at each time step the surface sensible heat variables, including , , , , , and ,2 are imposed exactly the same as in the runs described earlier, but with and thus , specified as zero when they represent a source to the atmosphere along the eastern flanks of the Andes. The eastern flanks of the Andes are approximated as the elevation band between 500 and 3500 m and for the eastern side only (Fig. 2). Over this region, the total energy available to the atmosphere is therefore substantially changed, as the latent heat transferred by surface ET is removed. These experiments are labeled as WLLJ_ADS, SLLJ_ADS, NDRY_ADS, CDRY_ADS, and EDRY_ADS, with their corresponding simulations (WRFCTL runs) detailed earlier in this section named as WLLJ_CTL, SLLJ_CTL, NDRY_CTL, CDRY_CTL, and EDRY_CTL, respectively. In the remainder of this article, these quasi-idealized simulations are also referred as STRICT experiments, consistent with the fact that the surface sensible heat effect in these simulations are strictly identical to their WRFCTL runs. A summary overview of the experiments is presented in Table 1.

Table 1.

Simulation description: In each experiment, the Dudhia shortwave, RRTM longwave, Lin et al. microphysics, Kain–Fritsch cumulus (for the two outer domains only), YSU boundary layer, revised MM5 surface layer, and Noah land surface schemes (Skamarock et al. 2008, and references therein) are employed. They are implemented on three one-way nested domains at 18-, 6-, and 1.2-km grid spacing, with the outermost domain initialized 18 h earlier than the two inner ones.

Table 1.

4. Methodology

As detailed in appendix A, the surface pressure tendency is given by
e2
where is time, stands for the hydrostatic surface pressure, represents the horizontal component of the total velocity vector, () is the density of air (liquid water), and denotes precipitation, with the integration spanning from the land surface to the top of the atmosphere . The overbar in Eq. (2) indicates average over a specified region (e.g., the EADS). For heavily precipitating systems (e.g., tropical cyclones), Lackmann and Yablonsky (2004) demonstrated that the component associated with precipitation is not negligible [up to −1.56 hPa h−1 for Hurricane Lili (2002)], though not dominant. In this study, each term of Eq. (2) was evaluated to examine the magnitude of the mass removed by turning off surface ET over the EADS.
The moisture budget equation can be written as
e3
where is specific humidity and represents precipitable water vapor (e.g., Trenberth and Guillemot 1995). At time scales greater than 10 days, the column moisture convergence nearly balances the difference between precipitation and surface evaporation (e.g., Li et al. 2013), indicating only a small fraction of the large-scale forcing of water vapor can be absorbed by the atmospheric water vapor storage (Emanuel 1994). At shorter time scales, is nonnegligible and its value can deviate appreciably from the residual of the rest, because this term, as well as , are typically diagnosed via finite differences and limited by model output frequency, while the other two are instantaneous. Unless this discrepancy is within a reasonable range (e.g., less than about 25%), which is generally not the case here (~10%–100%), comparison should be constrained among quantities sharing consistent calculation procedures.
The results discussed later in section 5 suggest that convection over the EADS can be dramatically suppressed in the STRICT experiments. To quantify the relevant impacts to the atmospheric environment, the apparent heat source and apparent moisture sink (Yanai et al. 1973; Nitta 1977) are examined. Ignoring eddy horizontal transport terms, these two measures can be obtained from mass continuity, heat energy, and moisture continuity equations,
e4
e5
where stands for potential temperature, is a reference pressure, represents water vapor mixing ratio, is the latent heat of vaporization, is the vertical velocity in pressure coordinate, represents radiative heating, and denotes latent heating, with as the specific heat of air at constant pressure. The overbar indicates spatial average over an area sufficiently large to contain ensembles of clouds but small enough to be regarded as a fraction of the large-scale system, and the prime indicates deviations from the horizontal average. Because and would be zero if there were no convective clouds, any nonzero values may be attributed to convection (Emanuel 1994), except in the subcloud layer, where turbulent mixing can be dominant. The “apparent” literally means besides true sources and sinks, the unresolved eddy fluxes are also accounted for (Yanai and Johnson 1993).
Vertical fluxes of sensible (), latent (), and total () heat associated with subcloud-layer turbulent eddies and cumulus clouds in the free atmosphere can be defined as
e6
e7
e8
where denotes the top of the atmosphere. The integral term represents the required vertical eddy flux at level to close the energy balance [see also Thompson et al. (1979)].

5. Results3

Upon removal of surface ET, daily precipitation over the EADS is considerably reduced in the WLLJ_ADS (~52%; Figs. 3a and 4a) and SLLJ_ADS (~46%; Figs. 3b and 4b) simulations, along with dramatically decreased cloud content (cf. Figs. 5a,b with Figs. 5f,g). Among the three remaining cases, although no influence is expected for EDRY (Fig. 3e; cf. Figs. 5e,j) based on the lack of clouds in the control simulation, the impacts on rainfall are marginal for both NDRY (Fig. 3c) and CDRY (Fig. 3d). Cloudiness changes are very slight in the CDRY (cf. Figs. 5d,i), whereas shallow clouds over the EADS in NDRY_CTL nearly disappear in NDRY_ADS (cf. Figs. 5c,h). Further, the feedback is not constrained to daytime when the surface ET is strong but also weakens nocturnal precipitation, especially in the cases of WLLJ and SLLJ (Fig. 4).

Fig. 3.
Fig. 3.

Residual of daily precipitation (mm day−1) between the WRFCTL and STRICT simulations (STRICT − WRFCTL) for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY. The solid black lines denote the border of the eastern flanks of the Andes. They are from the innermost domain (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

Fig. 4.
Fig. 4.

Rain rate (mm h−1) from WRFCTL (thick gray) and STRICT (thin dashed black) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY. They are averaged over the EADS (bordered by the black lines in Fig. 3a) and from the innermost grid (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

Fig. 5.
Fig. 5.

Daytime-averaged cloud content (g kg−1; cloud water, ice, snow, and graupel) along the cross section denoted by the dashed line in Fig. 3a for (a)–(e) the WRFCTL simulations and (f)–(j) the STRICT experiments, with the black lines representing terrain height (m). They are from the innermost nest (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

a. Mass balance, moisture budget, and CAPE

Analysis of the hydrostatic surface pressure tendency (Fig. 6) indicates that surface ET and precipitation-induced atmospheric mass modifications are trivial compared to convergence, which nearly balances mass changes within the air column over the EADS. The implication is that variations in clouds and precipitation in the STRICT experiments should not be directly related to air mass adjustment associated with the shutting down of surface moisture supplies to the atmosphere.

Fig. 6.
Fig. 6.

Hydrostatic surface pressure tendency (hPa h−1) associated with precipitation (; dashed–dotted), convergence (; dashed), and ET (; dotted), as well as the surface pressure tendency (hPa h−1) diagnosed from the WRF Model (; solid). They are averaged over the EADS and from the innermost grid (Δh = 1.2 km), with the thick gray (thin black) for the WRFCTL (STRICT) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY.

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

The contribution from the land surface to the atmospheric moisture budget is not negligible in the two monsoon events (WLLJ and SLLJ; see right panels of Figs. 7a and 7b) but varies during the dry season: negligible in the CDRY (Fig. 7d, right) while of the same order of magnitude as convergence in NDRY (Fig. 7c, right) and EDRY (Fig. 7e, right). Compared with the WRFCTL runs, the reduction of moisture convergence over the EADS is obvious in the WLLJ_ADS and SLLJ_ADS but not so in the NDRY_ADS, CDRY_ADS, and NDRY_ADS (Fig. 7, right). Nonetheless, precipitation, when substantial, can be a strong sink of atmospheric moisture (Figs. 7a,b,d, left), and convergence represents a significant moisture source or sink among all experiments (Fig. 7, right).

Fig. 7.
Fig. 7.

Moisture budget (mm h−1) associated with precipitation (; dashed–dotted), convergence [; dashed], and ET (; dotted), as well as the precipitable water tendency (mm h−1) diagnosed from the WRF Model (; solid). They are averaged over the EADS and from the innermost grid (Δh = 1.2 km), with the thick gray (thin black) for the WRFCTL (STRICT) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY.

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

In the WLLJ_ADS and SLLJ_ADS, the maximum CAPE over the EADS decreases appreciably, with peak values dropping to about 500 J kg−1 from about 1200 J kg−1 in the corresponding WRFCTL simulations (Figs. 8a,b). This is consistent with the reduced low-level atmospheric water vapor content shown in Figs. 9a–e because of the elimination of surface moisture supply and the decrease of moisture convergence (Figs. 7a,b). Accordingly, the lifting condensation level (LCL), which is proportional to the dewpoint depression and a rough measure of potential cloud base, increases by as much as approximately 400 m (Figs. 8a,b). The same situation applies to the NDRY_ADS, except with smaller magnitude. For the CDRY (Fig. 8d) and EDRY (Fig. 8e), increase of LCL is still observed, although the modifications to CAPE are hardly detected since it is already very low in the control simulations.

Fig. 8.
Fig. 8.

Surface latent heat flux removed from the atmosphere (W m−2; dotted thin black) in the STRICT experiments, as well as the maximum CAPE (J kg−1; solid) and LCL (m; dashed) from the WRFCTL (thick gray) and STRICT (thin black) simulations for (a) WLLJ, (b) SLLJ, (c) NDRY, (d) CDRY, and (e) EDRY. They are averaged over the EADS and from the innermost domain (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

Fig. 9.
Fig. 9.

Daytime-averaged vertical profiles of (a)–(e) water vapor mixing ratio (g kg−1; double dotted), (f)–(j) equivalent potential temperature (K; solid) and saturation equivalent potential temperature (K; dashed), (k)–(o) latent heating (K h−1; dashed–dotted), and (p)–(t) latent and turbulent heating (K h−1; dotted) from the WRFCTL (thick gray) and STRICT (thin black) simulations for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. They are averaged over the EADS and from the innermost domain (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

An approach to quantify directly the contribution of surface ET to CAPE is difficult to accomplish, because CAPE is released as the atmosphere evolves. However, a first-order understanding can be obtained under radiative–convective equilibrium (RCE) conditions, in which energy removed by an external forcing (e.g., radiation to space or adiabatic cooling due to forced ascent) is balanced by fluxes of sensible and latent heat from Earth’s surface that have been mixed through the troposphere via convective motion. Assuming dissipation of kinetic energy is the only irreversible entropy source and using the Raymond (1995) subcloud-layer moist enthalpy budget as the closure, Emanuel and Bister (1996) showed that
e9
where () represents the average moist static energy of downdraft air (the subcloud layer), () is the mass-integrated radiative heating of the atmosphere (the cloud layer), denotes the mean temperature at which entropy is produced by irreversible processes (i.e., the temperature at which mechanical energy is dissipated; Klein 1997), is the mean temperature at which radiation is emitted by the atmosphere, and is the surface temperature. When the subcloud layer is relatively shallow (e.g., Fig. 5, left), is close to and related to the net absorption of radiation at the land surface as
e10
where () is the contribution from the surface sensible (latent) heating and is the Bowen ratio. In Eq. (10), the ground heat flux is omitted, because during the daytime it is one to two orders of magnitude smaller than the surface sensible and latent heat fluxes. Hence, the contribution to CAPE owing to surface latent heat flux can be approximated by
e11
which decreases as increases and drops fast when is small. Specifically for the WLLJ, SLLJ, and NDRY simulations in this study, when is close to unity, the maximum is around 60% (Figs. 8a–c), roughly in agreement with Eq. (11).

b. Convection and the environment

Since the convective velocity scale
e12
convection is expected to weaken if surface latent heat flux is prevented from entering the atmosphere, especially when it accounts for a large fraction of the surface energy budget. Indeed, the daytime maximum vertical velocity is strongly damped in the WLLJ_ADS (cf. Figs. 10a,f) and SLLJ_ADS (cf. Figs. 10b,g), as well as in the NDRY_ADS, although to a lesser extent (cf. Figs. 10c,h). For CDRY, it appears that the formation of clouds (cf. Figs. 5d,i) and therefore precipitation (Figs. S4d and S4i) is not associated with conditional instability. This is because, despite the upward-decreasing saturation equivalent potential temperature in the lower troposphere, the equivalent potential temperature of an air parcel lifted from below about 700 hPa can never reach along its path (Fig. 9i), consistent with the low CAPE values in CDRY (Fig. 8d). On the other hand, typical of situations during the intrusion of Southern Hemisphere midlatitude systems, forced ascent over the EADS can be enhanced in the CDRY. The atmosphere in CDRY_CTL, however, is potentially stable ( increases with height) and merely modified slightly in CDRY_ADS (Fig. 9i). These properties imply that the precipitation produced in the CDRY experiment is primarily from elevated convection, and the role of the land surface becomes secondary. In EDRY, the atmosphere is conditionally stable throughout the entire troposphere (level of free convection does not exist) and potentially near neutral below about 450 hPa (Fig. 9j). The corresponding latent heating profile in Fig. 9o indicates that either moist convection is absent or all the water condenses inside the cloud, if it exists, and eventually reevaporates. Consequently, although precipitable water vapor is reduced to some extent in the EDRY_ADS (solid black in Fig. 7e) and thus the elevated LCL (Fig. 8e), no changes in convective activity can be detected (cf. Figs. 10e,f), as surface moisture support accounts for a large portion of moisture budget (Fig. 7e).
Fig. 10.
Fig. 10.

Daytime maximum vertical velocity (m s−1) along the cross section denoted by the dashed line in Fig. 3a for (a)–(e) the WRFCTL simulations and (f)–(j) the STRICT experiments, with the black lines representing terrain height (m). They are from the innermost grid (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

The effects of convective clouds on the atmospheric environment can be quantified using and shown in Fig. 11. In the subcloud layer, however, the steep vertical gradients of and essentially result from boundary layer turbulent motions, instead of convection. This is because at low levels the major contributors to and are the and terms associated with boundary layer turbulent mixing4 rather than (cf. Figs. 9k–o and 9p–s) and , which is very small (not shown) [see Eqs. (4) and (5)]. In other words, although the penetrative updrafts and downdrafts can interact with the boundary layer and modify its properties, turbulent mixing dominates vertical eddy flux transport below the cloud base. Consequently, the enhanced boundary layer moisture sink in the STRICT experiments is associated with the elimination of surface moisture supplies (Fig. 11f–j). As the difference between and represents the source or sink of moist static energy (MSE) as (e.g., Yanai et al. 1973; Emanuel 1994)
e13
Figs. 11k–o suggest that, during the daytime, especially for the WLLJ and SLLJ, the boundary layer serves as a source of MSE, which is significantly reduced when the surface ET is removed. This explains the lower in the STRICT experiments (Figs. 9f–j) and thus the damped conditional instability, because of the relationship between these two variables (; Holton 2004). The extent of this reduction appears to be somewhat proportional to the surface latent heat taken away from the system (Fig. 8), as illustrated by the fact that CDRY exhibits the slightest variations (Fig. 11i).
Fig. 11.
Fig. 11.

Daytime-averaged (a)–(e) (solid), (f)–(j) (dashed), and (k)–(o) (dashed–dotted) (K h−1) from the WRFCTL (thick gray) and STRICT (thin black) experiments for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. The spatial average is conducted over the EADS within the innermost nest (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

In the free atmosphere, the profiles of and are dominated by convection (cf. Figs. 9k–o and 9p–s). When deep convection is present, such as in WLLJ, SLLJ, and CDRY, as indicated by the relevant latent heating profiles (Figs. 9k,l,n), convective clouds affect nearly the entire depth of the troposphere. This is further confirmed by the fact that peaks at an altitude above the peak of (Yanai et al. 1973) but not in NDRY (Figs. 11c,h) and EDRY (Figs. 11e,j). Overall, deep convection tends to heat and dehumidify the surrounding environment (Figs. 11a,b,d,f,g,i), mainly through compensating subsidence (Yanai et al. 1973). Accompanied with damped convection, the free atmosphere in WLLJ_ADS and SLLJ_ADS is colder and drier than in the corresponding WRFCTL runs. Clearly, convection acts as a net sink of MSE at low levels (above the boundary layer) but as a source in the middle and upper troposphere (Fig. 11k–o) and tends to neutralize the atmosphere.

The vertical eddy fluxes also vary with the strength of convection. For instance, the upward transport of is much weaker in the WLLJ_ADS and SLLJ_ADS than in the WLLJ_CTL and SLLJ_CTL (Figs. 12a,b). In the lower troposphere, including the boundary layer, the downward can be associated with evaporation of raindrops and enhanced in the STRICT experiments. Above that level, however, the profiles of from the WRFCTL and STRICT experiments are nearly indistinguishable. In part, this can be due to the offset between reduced drying and damped convection in the WLLJ_ADS and SLLJ_ADS throughout the middle to upper troposphere. Alternatively, as is more sensitive to microphysical processes (Emanuel 1994), the close agreement of profiles in these two cases could be associated with adjustments of entrainment and detrainment processes. Figure 12i shows that, despite its deep convective nature, the vertical eddy flux of latent heat in CDRY is trivial, supporting our inference that the precipitation is not closely related to surface thermodynamic processes but externally forced by large-scale ascent. For EDRY, the monotonically decreased vertical eddy flux for total heat is indicative of convective inhibition, similar to the suppressed regime described by Thompson et al. (1979, their Fig. 20).

Fig. 12.
Fig. 12.

Daytime-averaged vertical eddy flux for (a)–(e) sensible (solid), (f)–(j) latent (dashed), and (k)–(o) total (dashed–dotted) heat (W m−2) from the WRFCTL (thick gray) and STRICT (thin black) experiments for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. The spatial average is conducted over the EADS within the innermost domain (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

c. Atmospheric adjustment at the mountain range scale

Characterized by damped convection, the atmosphere is generally colder in the STRICT experiments (Figs. 13a–e), except within the boundary layer of the SLLJ_ADS (Fig. 13b) and EDRY_ADS (Fig. 13e). This implies that in these two experiments, even though evaporative cooling is enhanced at low levels (not shown) as the atmosphere is relatively dry (Figs. 9a–e), other processes (e.g., advection) can dominate. Nevertheless, the differences do not exceed 0.3 K among all simulations. In ARW3.4.1 (Skamarock et al. 2008), the moist Brunt–Väisälä frequency is defined as
e14
where stands for total water mixing ratio; represents saturation water vapor mixing ratio; is cloud water mixing ratio; is model height; and with () as the gas constant for dry air (water vapor), as temperature, and as the ratio of the molecular weight of water to the mean molecular weight of dry air. In the lower troposphere and when the atmosphere is saturated, the effect of eliminating surface ET on is not straightforward, since both Figs. 9f–j) and (not shown) tend to become less negative. Under unsaturated conditions, however, the Brunt–Väisälä frequency in the subcloud layer may be approximated as
e15
since . Thus, can be less negative or more positive in the STRICT experiments, because tends to be less negative (Figs. 9a–e) while the variation of is trivial, as indicated by the marginally modified temperature profiles shown in Figs. 13a–e. Indeed, as documented in Figs. 13f–j, the atmosphere is more statically stable in the lower troposphere in the STRICT simulations. This renders a relatively small Froude number (, where is the basic flow speed and denotes mountain height) for that layer and therefore weaker upslope flows (Lin 2007) (not shown). The impact on mass convergence over the EADS is trivial (Figs. 13k–o), but the influence on the low-level moisture convergence is appreciable, for the WLLJ and SLLJ in particular (Figs. 13p,q), because of the moisture-rich atmosphere. This is in agreement with the moisture budget analysis documented in Fig. 7 (right).
Fig. 13.
Fig. 13.

(a)–(e) The vertical profiles of the residual of daytime-averaged temperature (K; dotted gray) between the WRFCTL and STRICT simulations (STRICT − WRFCTL) for (left)–(right) WLLJ, SLLJ, NDRY, CDRY, and EDRY. (f)–(j) As in (a)–(e), but for moist Brunt–Väisälä frequency (s−2; solid gray). Also shown are the daytime-averaged vertical profiles of (k)–(o) mass convergence (hPa h−1; solid) and (p)–(t) moisture convergence (mm h−1; dashed) from the WRFCTL (thick gray) and STRICT (thin black) simulations. They are averaged over the EADS and from the innermost grid (Δh = 1.2 km).

Citation: Journal of the Atmospheric Sciences 72, 1; 10.1175/JAS-D-14-0048.1

6. Conclusions and discussion

Instead of perturbing soil moisture, the role of surface evapotranspiration (ET) along the eastern flanks of the tropical Andes (EADS) was investigated using an assembled land surface. Essentially, it is composed of 13 “connectors” from the land surface to the atmosphere and proved to be necessary and sufficient according to a group of 1-h simulations described in section 2. This framework, as well as a comprehensive examination of the Noah and RMM5 schemes, provides a clear representation of land surface sensible heat effects in the WRF Model. In this regard, quasi-idealized experiments were conducted, where at every time step the surface sensible heat effects are exactly the same as in the reference runs while surface moisture and latent heat fluxes are prevented from entering the atmosphere if they represent a source.

Covering a wide spectrum of weather conditions in South America and through simulations down to 1.2-km grid spacing, our results suggest that surface ET along the tropical EADS has a significant influence on moist convection through its impact on conditional instability (e.g., in the WLLJ, SLLJ, and NDRY), because it acts as an important source of moist entropy to the air parcels within the boundary layer, as illustrated by the detailed energy balance analysis. Specifically, up to about 60% CAPE can be explained by surface ET along the EADS. Given that convective motion is strongly attenuated after removing surface ET, there is a dramatic decrease in cloudiness, with daily precipitation reduced by up to about 50% in the WLLJ_ADS and SLLJ_ADS experiments. In contrast, when convection is not thermally driven (CDRY) or under conditionally stable conditions (EDRY), the effect of surface ET becomes secondary.

The importance of surface on atmospheric energetics is also supported by another supplementary experiment, where was repartitioned into and upward longwave radiation (where is the Stefan–Boltzmann constant) instead of being removed. This model integration was conducted for the WLLJ case at 18-km grid spacing and implemented by specifying potential evaporation as zero at every time step [see Eqs. (2.27)–(2.31) of Sun (2014)], thus preserving the total surface energy supply to the atmosphere nearly the same as in WLLJ_CTL but in a different form. In this experiment, the simulated precipitation reduction over the tropical EADS is much smaller than in the WLLJ_ADS (not shown).

At the mountain-range scale, removal of surface ET weakens upslope flows by increasing static stability in the lower troposphere. As illustrated by Eq. (15), this is related to the fact that the vertical gradient of water vapor mixing ratio in the lower troposphere tends to be less negative. Although the impact on mass convergence is trivial, the influence on moisture transport is obvious, because the atmospheric water vapor content primarily concentrates at low levels. In the STRICT experiments, the decreased moisture convergence operates in concert with the elimination of surface ET.

Further seasonal simulations of South America at 18-km grid increment show that, during most of the time in the austral summer, the troposphere over the EADS is conditionally unstable, which is also largely the case during the transition from the dry season to the wet season (X. Sun and A. P. Barros 2014, unpublished manuscript). This implies that the surface ET associated with montane forests plays an active role in retaining cloudiness and consequently in rainfall harvesting at high elevations (Barros 2013). Immersed in low clouds for additional water and nutrients through canopy interception (Beiderwieden et al. 2007), Andean cloud forests are more vulnerable to this process, since, without surface ET, the LCL can rise by as much as approximately 400 m.

Acknowledgments

We thank three anonymous reviewers for their valuable suggestions and comments. The first author benefited from the discussions on Noah and HRLDAS with Drs. Fei Chen and Michael Barlage at the National Center for Atmospheric Research (NCAR). We appreciate the Computational and Information Systems Laboratory (CISL) at NCAR for providing computational resources. This research was supported by the National Science Foundation (NSF) under Grant EAR-0711430 and the National Aeronautics and Space Administration (NASA) Grant NNX1010H66G with the second author.

APPENDIX A

Hydrostatic Surface Pressure Tendency

For an air column of unit area
eq1
where () is evaporation (condensation) per unit mass (kg kg−1) and , , , , and are the column-integrated mass of air, dry air, water vapor, evaporation, and condensate, respectively. As is conserved, but not ,
ea1
ea2
where represents the velocity vector. Under hydrostatic balance,
ea3
Substitute Eqs. (A1) and (A2) into Eq. (A3) and, given that the vertical velocity vanishes at the top of the atmosphere and the land surface, as well as the fact that ,
ea4
which is equivalent to Eq. (2) when applied to a limited region.

APPENDIX B

Apparent Heat Source and Apparent Moisture Sink in σ Coordinates

According to Kasahara (1974),
eb1
eb2
where A can be any scalar functions and the subscripts p and σ indicate a particular vertical coordinate to be held constant for partial differentiations. Thus,
eb3
eb4
eb5
eb6
Divided by cp to be converted into temperature tendency, using the relationship , and after substituting Eqs. (B3)(B6) into Eqs. (4) and (5), Q1 and Q2 in σ coordinates are
eb7
eb8
in which [where is the vertical velocity (m s−1)].

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1

Equation (1) is model and parameterization dependent.

2

For weather-scale simulations, is generally set as time invariant.

3

The evaluation of WRFCTL simulations against observations is presented as supplemental material (available online).

4

These two terms can be obtained from the YSU boundary layer scheme and equivalently parameterized as and .

Supplementary Materials

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