Rain Reevaporation, Boundary Layer–Convection Interactions, and Pacific Rainfall Patterns in an AGCM

Julio T. Bacmeister Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland

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Max J. Suarez Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland

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Franklin R. Robertson NASA MSFC, Huntsville, Alabama

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Abstract

Sensitivity experiments with an atmospheric general circulation model (AGCM) show that parameterized rain reevaporation has a large impact on simulated precipitation patterns in the tropical Pacific, especially on the configuration of the model’s intertropical convergence zone (ITCZ). Weak reevaporation leads to the formation of a “double ITCZ” during the northern warm season. The double ITCZ is accompanied by strong correlation between precipitation and high-frequency vertical motion in the planetary boundary layer (PBL). Strong reevaporation leads to a better overall agreement of simulated precipitation with observations. The model’s double ITCZ bias is reduced. At the same time, correlation between high-frequency (periods < 15 days) vertical motion in the PBL and precipitation is reduced. Experiments with modified physics indicate that evaporative cooling by rain near the PBL top weakens the coupling between precipitation-related heating and vertical motion in high-frequency motions. The strength of high-frequency vertical motions in the PBL was also reduced directly through the introduction of a diffusive cumulus momentum transport (DCMT) parameterization. The DCMT had a visible impact on simulated precipitation in the Tropics but did not reduce the model’s double ITCZ bias in all cases.

Further analyses of mass and water vapor budgets, as well as vertical motion statistics, in the ITCZ complex, show that time-mean moisture convergence in the southern ITCZ is largely dominated by high-frequency modes, while in the northern ITCZ time-mean moisture convergence contains large contributions from slower modes. This may explain why the simulated southern ITCZ is more susceptible to parameterization changes that alter high-frequency coupling between moist heating and PBL convergence.

Corresponding author address: Dr. Julio T. Bacmeister, NASA GSFC, Code 900.2, Greenbelt, MD 20771. Email: julio.bacmeister@gsfc.nasa.gov

Abstract

Sensitivity experiments with an atmospheric general circulation model (AGCM) show that parameterized rain reevaporation has a large impact on simulated precipitation patterns in the tropical Pacific, especially on the configuration of the model’s intertropical convergence zone (ITCZ). Weak reevaporation leads to the formation of a “double ITCZ” during the northern warm season. The double ITCZ is accompanied by strong correlation between precipitation and high-frequency vertical motion in the planetary boundary layer (PBL). Strong reevaporation leads to a better overall agreement of simulated precipitation with observations. The model’s double ITCZ bias is reduced. At the same time, correlation between high-frequency (periods < 15 days) vertical motion in the PBL and precipitation is reduced. Experiments with modified physics indicate that evaporative cooling by rain near the PBL top weakens the coupling between precipitation-related heating and vertical motion in high-frequency motions. The strength of high-frequency vertical motions in the PBL was also reduced directly through the introduction of a diffusive cumulus momentum transport (DCMT) parameterization. The DCMT had a visible impact on simulated precipitation in the Tropics but did not reduce the model’s double ITCZ bias in all cases.

Further analyses of mass and water vapor budgets, as well as vertical motion statistics, in the ITCZ complex, show that time-mean moisture convergence in the southern ITCZ is largely dominated by high-frequency modes, while in the northern ITCZ time-mean moisture convergence contains large contributions from slower modes. This may explain why the simulated southern ITCZ is more susceptible to parameterization changes that alter high-frequency coupling between moist heating and PBL convergence.

Corresponding author address: Dr. Julio T. Bacmeister, NASA GSFC, Code 900.2, Greenbelt, MD 20771. Email: julio.bacmeister@gsfc.nasa.gov

1. Introduction

Accurate simulations of tropical precipitation remain a challenge for atmospheric climate models (AGCMs). Basic dynamical issues such as the relationship between low-level convergence and precipitation remain unresolved. Recent studies suggest that intertropical convergence zones (ITCZs) identified using precipitation or outgoing longwave radiation (OLR) may not always correspond with convergence zones identified using satellite surface wind measurements (e.g., Liu and Xie 2002). Earlier Hastenrath and Lamb (1977), using ship-based wind observations, also concluded that surface convergence may exist in the absence of precipitation. Nevertheless, determining the strength of surface wind convergence in nature remains a challenge. Perhaps as a result of this observational gap, little attention has been paid to examining convergence–precipitation coupling in AGCM simulations, even though all the necessary quantities are easily accessible.

A common problem in AGCM precipitation simulations, which may be related to PBL–precipitation coupling, is the so-called double ITCZ bias (e.g., Meehl and Arblaster 1998). Many AGCMs form a spurious second ITCZ in the Southern Hemisphere (8°–10°S) under conditions in which observed precipitation is concentrated in a single ITCZ centered around 10°N. While nature does show hints of a southern ITCZ over the Pacific, particularly during March through May (Zhang 2001), this feature in AGCMs is usually too strong and persistent, lasting through the northern warm season June–September. The occurrence of double ITCZs in AGCMs leads to large rms errors in simulated precipitation since it represents a spurious rearrangement of the most intense precipitation on earth. Connections between double ITCZs and other AGCM simulation biases have not been conclusively established. However, it is clearly of concern to climate modelers if AGCMs are producing large errors in the horizontal distribution of atmospheric latent heating. Finally, the wide distribution and similar structure of this bias in a variety of AGCMs suggests the existence of a shared misunderstanding in current implementations of convection parameterizations.

In this study we will examine the connection between PBL convergence and precipitation and the double ITCZ bias in the NASA Seasonal-to-Interannual Prediction Project version 2 (NSIPP-2) AGCM. A principal motivation for performing this work is a robust sensitivity in the NSIPP AGCM’s tropical precipitation to the strength of rain reevaporation. With stronger rain reevaporation the model tends toward a realistic single ITCZ configuration. With weak reevaporation the model produces a strong double ITCZ. This sensitivity has existed in earlier versions of the NSIPP AGCM despite substantially different formulations of reevaporation. Although this sensitivity has been useful in empirical “tuning” of the NSIPP AGCM to improve precipitation simulations, the physical origin of the sensitivity has not been explained. Anecdotal evidence from other modeling groups suggests that this sensitivity may exist in some form in other AGCMs (I. M. Held 2005, personal communication) and also that other sensitivities may exist to parameters such as cumulus friction (GFDL Global Atmospheric Model Development Team 2004).

The goals of this study are to shed light on mechanisms controlling the formation of double ITCZs in the NSIPP AGCM and to suggest relevant, parameterization-independent diagnostics that can be applied to other AGCM simulations. The paper is organized as follows. Section 2 provides a description of the AGCM used in this study. Section 3 outlines the AGCM experiments performed. Section 4 presents the basic sensitivity of the model simulations to reevaporation. Seasonal mean fields are shown, as well as some analysis of vertical profiles, reevaporation tendencies, and high frequency transients. Section 5 describes three experiments with modified physics including changes to the vertical profile of reevaporation cooling and the addition of a simple diffusive cumulus momentum transport (DCMT) parameterization. Section 6 analyzes the mass and water vapor budgets in the simulations. This analysis addresses the questions of how reevaporation suppresses precipitation in the southern ITCZ and why the suppression operates preferentially on the southern ITCZ.

2. Model description

We use a development version of the NSIPP-2 AGCM (NSIPP-2.0) for this study. NSIPP-2.0 was developed from the NSIPP-1 AGCM, which was documented in Bacmeister et al. (2000) and Bacmeister and Suarez (2002). Simulated seasonal means and responses to interannual SST variation in NSIPP-1 were both in good agreement with meteorological analyses (e.g., Schubert et al. 2001, 2002). The significant modifications to NSIPP-2.0 and NSIPP-1 involve the cloud, boundary layer, and convection schemes. These include introduction of a prognostic cloud scheme in place of the Slingo (1987)-type diagnostic scheme used in NSIPP-1, as well as a simple moist boundary layer entrainment scheme, which is called in addition to the existing first-order dry turbulence parameterization of Louis et al. (1982). These modifications were aimed at improving the models simulation of subtropical marine stratus decks and, while they also impact simulated precipitation in the Tropics, they do not affect the general nature of the ITCZ sensitivities examined in this study. Cloud fields from NSIPP-2 are examined by Zhang et al. (2005).

The dynamical core of NSIPP-2.0 is the same as in NSIPP-1 and is described in Suarez and Takacs (1995). Radiative effects in NSIPP-2.0 are parameterized using the approach of Chou and Suarez (1994). Land surface effects are parameterized according to Koster and Suarez (1996), and orographic wave drag is treated according to Zhou et al. (1996).

a. Convection

Convection in the NSIPP AGCM is parameterized according to the relaxed Arakawa–Schubert (RAS) scheme of Moorthi and Suarez (1992). The implementation of RAS in NSIPP-2.0 is modified to include a convective condensate calculation with autoconversion to rain. RAS works by invoking a series of linearly entraining plumes (or “cloud types”) that detrain at selected levels in the vertical. Consistency is achieved by calculating the entrainment rate necessary to ensure zero buoyancy at the selected level. RAS is flexible as far as the number and distribution of plumes or cloud types tested. Our implementation invokes 30 cloud types per gridbox per physics time step. These are drawn at random from a uniform distribution in σ. We also emphasize that our implementation does not include an explicit downdraft parameterization.

b. Prognostic cloud condensate scheme

The NSIPP-2 prognostic condensate scheme considers only a single phase of condensate but tracks two separate species of condensate: a large-scale species qc,LS originating from gridbox condensation and an “anvil” species qc, originating from detraining convection. The rationale for this separation is that both the subgrid statistics and the microphysical properties of rapidly processed anvil condensate may be distinct from those of condensate produced by slower, large-scale dynamics (e.g., Lawson 2003). The key distinctions in our current scheme are slower autoconversion and higher number densities for qc,AN. These higher assumed number densities for qc,AN enter into the optical thickness calculation used by the model’s radiation scheme. We impose an arbitrary e-folding time of 3 h for conversion of qc,AN to qc,LS. A third species, convective condensate qc,, is calculated internally within each RAS cloud type but does not interact with the model’s radiation calculation. When ice–liquid partitioning of total condensate is needed, by the radiation scheme, for example, it is accomplished diagnostically using a linear ramp in temperature that decreases from 1 (all ice) below 263 K to 0 (all liquid) above 273 K.

c. Convective autoconversion and reevaporation

Our basic approach in parameterizing convective microphysical processes is based on a Lagrangian parcel picture. We estimate an updraft speed for each plume in RAS (Bacmeister 2005) that is combined with the model’s vertical grid spacing to give a time interval for autoconversion in a given model layer. Autoconversion rates are determined from a nonlinear temperature-dependent expression (Sundqvist 1988). The approach used is similar to that in Sud and Walker (1999), although we employ a cruder calculation for the convective updraft speed. Profiles of convective precipitating condensate qp, are accumulated over all RAS plumes and then passed to a scheme that accumulates the condensate and also calculates reevaporation, accretion, and surface precipitation fluxes. In addition to precipitating condensate produced by convection, our scheme considers autoconversion of qc,AN and qc,LS. These autoconversions are calculated separately using the Sundqvist (1988) formulation to give two additional precipitating species qp,AN and qp,LS.

Reevaporation is treated separately for each of the three streams of precipitation (“showers”) qp,CN, qp,AN, and qp,LS. This calculation also proceeds according to a Lagrangian viewpoint. First, an estimate of the local subgrid-scale precipitation rate is made using the grid mean precipitation flux and estimates of fractional shower area. A representative particle size for this precipitation is estimated from a Marshall and Palmer (1948) distribution. This particle size gives an evaporation rate, fall speed, ventilation factor, and residence time within a given model layer. These quantities are used to calculate a net loss of precipitating condensate due to evaporation during a time step. In NSIPP-2 both liquid and frozen precipitation are treated in the same way. We also allow a fraction of the convective rain shower to be “shielded” from reevaporation. This is meant to represent rain falling through a saturated environment such as a convective tower or saturated downdraft.

In the experiments discussed here bulk reevaporation of convective precipitation qp,CN was modified by changing shear-dependent parameters that control the shielded fraction, as well as the relationship between diagnosed updraft areal fraction and convective shower area. Roughly speaking more reevaporation is allowed in high shear environments in all experiments, but the strength of this shear dependence is changed to give higher or lower total reevaporation. Experience with previous versions of the NSIPP model suggests that the details of the rain reevaporation scheme are unimportant in producing the sensitivities discussed here. For clarity we will simply refer to three settings of reevaporation parameters—weak, moderate, and strong. More details on the formulation of the reevaporation calculation can be found in (Bacmeister 2005).

3. Description of experiments

We analyze results from six experiments (Table 1). The first three of these, denoted B1, B2, and B3, were performed with the same “baseline” model physics, differing only in the choices made for the rain reevaporation parameters. These experiments were initialized on 1 June 1981 from restarts derived from an existing AMIP simulation and forced with observed SSTs (Reynolds 1988). Experiments B1 and B3 ran through December 1987. Experiment B2 was an AMIP-style run conducted for the National Science Foundation (NSF) Climate Process Team (CPT) on Low-Latitude Cloud Feedbacks and ran through December 1999. For most of the analysis here we will focus on results from 1984 and 1985.

In addition to the three baseline experiments we conducted three experiments with modified physics. In the first of these, H1, the cooling produced by rain reevaporation was redistributed in the vertical; that is, at each time step, total mass-weighted reevaporation cooling below 850 hPa was found and then uniformly applied between 850 and 300 hPa. The moistening from reevaporation was not modified. Thus, moist energy conservation is violated locally but preserved in a column-integrated sense. The motivation for this experiment was to reduce the direct impact of reevaporation on boundary layer circulations, while retaining as much of the original moistening profile as possible.

The remaining two experiments M1 and M2 employed a simple diffusive cumulus momentum transport scheme devised for the Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model version 2 (AM2; GFDL Global Atmospheric Model Development Team 2004). The scheme simply enhances Km proportional to the total local cumulus mass flux diagnosed by RAS. The DCMT scheme has been used in the GFDL model with positive effects on both the simulated precipitation and on the simulated spectrum of ENSO variability in coupled mode. Here we apply DCMT in two experiments: M1, with weak reevaporation settings as in B1, and M2, with moderate reevaporation as in B2. Experiments H1, M1, and M2 were initialized on 1 December 1983 and run through 31 December 1985.

All experiments were conducted at a horizontal resolution of 2° × 2.5° with 40 unequally spaced σ layers. Extensive suites of diagnostic tendency outputs on σ surfaces were saved as daily averages along with standard outputs. These additional diagnostics included most of the significant water substance conversion terms such as moistening by reevaporating rain, which we denote here by R.

4. Basic model sensitivity to reevaporation

a. Mean seasonal precipitation

Seasonal mean precipitation fields and biases for June–August (JJA, hereafter 3-month periods are denoted by the first letter of each respective month) 1984–85 in experiments B1, B2, and B3 are shown in Figs. 1 and 2 along with observational estimates of precipitation rates from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997). The results illustrate the important climatological control exerted by the reevaporation strength in the NSIPP AGCM. Experiment B1 with weak reevaporation (Figs. 1a, 2a) tends strongly toward a “double ITCZ” configuration with precipitation rates in excess of 8 mm day−1 extending in a narrow, zonally aligned band along 10°S well into the central Pacific. As reevaporation is strengthened in B2 (Figs. 1b, 2b) and B3 (Figs. 1c, 2c) the double ITCZ in all three tropical ocean basins becomes less pronounced, although the change in the Pacific basin is most noticeable owing to its size. In connection with the weakening of the double ITCZ, a dry bias along the equator in the western Pacific in B1 is also reduced with increased reevaporation. Overall, the simulations in experiments B2 and B3 appear to be in better agreement with the CMAP climatology. Pattern correlations for the seven-season (1981–87) JJA mean are significantly lower for B1 than for B2 and B3 (Table 1). Wet biases over summertime tropical continents also appear to decrease as rain reevaporation is strengthened. Over sub-Saharan Africa as well as over the northern Amazon Basin wet biases of over 4 mm day−1 exist in experiment B1, while in B3 these regions are nearly bias free.

Unfortunately, not all precipitation biases are reduced by increasing reevaporation strength in the model. A noticeable deterioration in the simulated precipitation occurs over much of the northern tropical Pacific between Hawaii and Southeast Asia (5° to 20°N, 120°E to 150°W) as reevaporation increases. Stronger reevaporation leads to increasing wet biases in this region, culminating in the >8 mm day−1 biases evident in the “Philippine Hotspot” (15°N, 130°E) in experiment B3 (Figs. 1c, 2c). A JJA dry bias in the Indian Ocean also becomes more pronounced with increasing reevaporation. This strong wet bias is associated with excessively strong low-level monsoon westerlies over Indochina, the Philippines, and surrounding ocean. As will be shown in section 6, much of the water vapor flowing into this region does so in a strong convergent flow located above the 850-hPa surface. By contrast, in the ITCZs mass and water vapor convergence are largely restricted to the PBL.

We have focused on northern summer because the double ITCZ bias, in models which possess it, is most pronounced during the northern warm season, roughly April–November. During December–February (not shown) some double ITCZ bias remains in our weak reevaporation simulation. However, overall the DJF precipitation simulations in all three experiments are in better agreement with the CMAP climatology.

b. Fractional reevaporation

Figure 3 shows maps of the fraction
i1520-0469-63-12-3383-e1
that is, the ratio of the vertical mass integral of the reevaporation tendency R to the surface precipitation flux. This quantity provides a measure of reevaporation “strength” that does not depend on the details of the rain reevaporation parameterization used. Figure 3 shows that in experiment B1 values of f are below 1 almost everywhere except over arid continental regions. As expected, in expirements B2 and B3 the reevaporation fraction increases dramatically. In both experiments values of f are over 1 across most of the Tropics and subtropics. Only stratocumulus regions show values of f below 1 in these simulations. In experiment B3 most of the Indian Ocean is characterized by f close to or exceeding 2. That is, over twice as much rain evaporates during its fall through the atmosphere than makes it to the surface. Much of the southern Pacific ITCZ also possesses f > 2.

Table 2 lists domain averages of P0, ∫R, and other quantities in the box-shaped domains shown in Fig. 4. The domain-averaged precipitation in the central southern Pacific ITCZ (domain “SITCZ”) for the baseline experiments varies from just over 6.1 mm day−1 in B1 to just under 4 mm day−1 in B3. By comparison the surface evaporation E0 (column 8) varies little from experiment to experiment, hovering between 5.2 and 5.4 mm day−1 in SITCZ. The difference E0P0 (last column) is the implied transport water vapor in or out of each domain, with negative numbers implying a net horizontal transport into the domain. Domain SITCZ is a net water vapor sink in experiment B1, but becomes a net source with stronger rain reevaporation in B2 and B3. For the strongest reevaporation tried (B3), both ITCZ domains become net sources of water vapor.

Results for two warm season continental domains are also shown: an arid one containing the southwestern United States (“WUSA”) and a moist one containing the West African ITCZ region (“WAFR”). Despite large differences in the amounts of precipitation and in the fractions of reevaporated rain, these continental domains exhibit interesting similarities in their sensitivity to reevaporation. Both rain and surface evaporation decrease markedly with increasing rain reevaporation. This is in contrast to the situation over ocean (SITCZ and NITCZ) where surface evaporation is largely unaffected by rain reevaporation. This may reflect an additional feedback between land surface processes and precipitation. Note the large values of f (column 6, Table 2) in WUSA for all experiments. These may be related to the lack of an explicit downdraft parameterization in the model.

Observational estimates of domain-averaged precipitation from CMAP are also shown in Table 2. Comparisons of these with the simulation results tend to confirm that experiment B2 possesses the “best” precipitation, as implied by the pattern correlations and normalized variances in Table 1. Comparison of observations and simulations in WUSA show that our model has a pronounced dry bias in this region, probably related to excessive reevaporation of rain. Unfortunately, global observational estimates of ∫R are not currently available.

c. Vertical profile of rain reevaporation

Figure 5 shows seasonal mean profiles of reevaporation tendency R, horizontally averaged within box NITCZ. This box straddles the northern ITCZ during JJA. Reevaporation in experiments B2 and B3 is generally strong (1 to 2 g kg−1 day−1) throughout the lowest 500 hPa of the atmosphere. A minimum in R occurs in the upper portion of the model PBL where relative humidities are high, but both within the PBL and immediately above the PBL top reevaporation is high. It is worth noting that the reevaporation profile in experiment B1 is dominated by evaporation of large-scale precipitation and anvil precipitation (qp,AN and qp,LS), which are unaffected by the shear-dependent, reevaporation parameters varied in this study.

Reevaporation of condensate is not a process for which we have direct observational data to validate models. However, efforts are underway to infer some of the gross features of reevaporation from Tropical Rainfall Measuring Mission (TRMM) radar precipitation rate profiles. Initial examination suggests that significant disagreements may exist between simulated reevaporation profiles and TRMM inferences (F. R. Robertson 2005, personal communication).

d. Water vapor distribution

Figure 6 shows mean water vapor profiles for experiments B1–B3 in boxes SITCZ and NITCZ along with estimates from National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR; Kalnay et al. 1996) and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Simmons and Gibson 2000) reanalyses. In NITCZ (Fig. 6, top) the two reanalyses disagree by nearly 3 g kg−1 over much of the layer below 300 hPa. The three model profiles generally lie between the two reanalysis profiles. The profile for experiment B3 is up to 2 g kg−1 wetter than that for B1 with the largest differences centered around 700 hPa. The profile for B2 lies between those for B1 and B3. In Box SITCZ (Fig. 6, bottom) the situation is somewhat different. The q profiles from our three model experiments are quite similar to the corresponding profiles in NITCZ with B3 the wettest, B1 the driest, and B2 in between. The NCEP reanalysis profile in SITCZ is also similar to that in NITCZ. However, the ERA-40 reanalysis profile is significantly drier and here agrees closely with the NCEP profile. Generally speaking it is clear that increasing reevaporation in our model leads to midtropospheric moistening. However, the differences in q-profiles for different reevaporation strengths appear to be comparable to uncertainties in reanalysis q-profiles. Comparison with SSMI total precipitable water (TPW) measurements (not shown) exhibit a small but worsening global mean wet bias as reevaporation increases from B1 to B3, although the spatial distribution of simulated TPW improves.

e. Relation of low-level convergence and rainfall

The strength of the high-frequency coupling between low-level flow convergence and precipitation in nature is still not well known (e.g., Gu and Zhang 2002). However, there are indications that the connection between low-level convergence and precipitation at time scales of several days and shorter may not be as strong as commonly assumed. We examine this coupling in our simulations by looking at the correlation of daily mean vertical motion at 850-hPa ω850 with daily rainfall P in our simulations. Straightforward time series correlations are calculated at each model grid point using results from the 183-day period 1 May–31 October 1985. A 15-day, high-pass Lanczos filter (Duchon 1979) was first applied to each field to remove low-frequency variability. Figure 7 shows maps of the correlation r(ω̃850, ), where ω̃850 and denote the time-filtered fields. There is a pronounced difference in the strength of this correlation as reevaporation changes. For weak reevaporation as in B1 (Fig. 7a) the correlations are over 0.8 over much of the tropical Pacific. By contrast, with strong reevaporation, as in B3 (Fig. 7c), correlations are generally between 0.4 and 0.6 and fall below 0.4 over large portions of the warm pool and the southern ITCZ region (0°–10°S, 180°–150°W). A strong reduction in r(ω̃850, ) is notable along the equator in the western Pacific as reevaporation increases.

The rms amplitude of ω̃850 is shown in Fig. 8. Maps of show that the pattern of dynamical variability is not straightforwardly related to the correlation patterns in Fig. 7. For example, over the northern warm pool and western tropical Pacific (0–20°N, 120°E–180°) is similar in B1, B2, and B3 with values around 50–70 hPa day−1. However, r(ω̃850, ) in the same region varies from 0.6 to >0.8 in B1 to <0.4 in B3. Interestingly, this is the region in which larger reevaporation appears to lead to a strengthening wet bias. It appears safe to conclude that this bias, and precipitation generally, in this region is not controlled principally by boundary layer–precipitation interactions. By contrast, over both ITCZ regions the dynamical variability weakens systematically as reevaporation increases. West of 150°W B1 shows values of of 60 to 80 hPa day−1, while B3 shows values of 30 to 50 hPa day−1. The decrease in dynamical variability over the southern ITCZ between experiments B1 and B3 is especially dramatic.

Figure 9 shows Hovmoeller diagrams of P0 and ω̃850 along 8°S for experiments B1 and B3. The association of high precipitation rates with westward moving high frequency disturbances in both experiments is clear. There is a general connection between areas of high precipitation (dark blue) in the left panels with areas of strong upward motion at 850 (blue–purple) in the right panels. As expected, the variability is stronger in experiment B1 (top panels), although the difference for variability in ω850 is not as pronounced as for precipitation.

The simulated ITCZ disturbances in Fig. 9 appear to amplify as they move west. Figure 10a shows probability density functions (PDFs) of ω850 (unfiltered) in a box immediately west of the South American coast (10°–6°S, 110°–97.5°W) for May–October 1985. This box is situated over cool water and can be thought of as the “entrance” region for disturbances propagating westward along the southern ITCZ. This figure shows that the amplitude distribution of ω850, before strong interactions with moist heating have occurred, is remarkably similar in experiments B1 (black curve) and B3 (red curve). However, farther west (10°–6°S, 162.5°–175°E) the PDFs diverge markedly (Fig. 10b). In B1 the PDF of ω850 becomes highly skewed with an extensive tail region at large negative values of ω850, that is, upward motion, and a concentrated peak at small positive values. This means that even at the model grid scale there is a distinctly “convective” character to vertical motion, with rare but intense updrafts embedded in extensive but weak subsidence. For B3 the PDF in the western box is also somewhat more skewed than in the entrance region, but the overall shape of the PDF is much less distorted.

Taken together the results in this section imply the existence of a similar background dynamical variability in experiments B1 and B3. This “seed” variability is then amplified to different degrees by interactions with precipitation, with stronger feedback occurring when rain reevaporation is weak. The possible nature of this interaction will be examined by direct experimentation with altered model physics (section 5) and by analysis of water vapor budgets in the ITCZ (section 6).

We have not attempted a detailed comparison of our simulated xt spectra of rainfall or vertical motion with observations (e.g., Wheeler and Kiladis 1999; Gu and Zhang 2001). However, a cursory look at our model’s background spectra of precipitation along the ITCZ suggests at least a qualitative resemblance with the background OLR spectra in Gu and Zhang (2001).

5. Experiments with altered physics

a. Vertically redistributed reevaporation cooling

From the results shown in section 4 we speculate that the formation of ITCZs in our simulations is at least partly driven by strong coupling between boundary layer convergence and precipitation-related moist heating. The disappearance of double ITCZs with increased reevaporation suggests that this coupling is interrupted by evaporative cooling in or near the PBL. To test this hypothesis, we performed an experiment, H1 (Table 1), in which we arbitrarily removed cooling driven by rain reevaporation below 850 hPa. This experiment used “strong” reevaporation settings as in B3. To conserve at least column-integrated moist static energy, we calculated a mass-weighted integral of the reevaporation cooling between 850 hPa and the surface. This mass-weighted cooling was then redistributed uniformly between 300 and 850 hPa. The corresponding moistening profile was not altered. The motivation for this procedure was to remove the low-level cooling from the strong reevaporation case while minimizing modifications to the moistening profile.

Figure 11a shows seasonal mean JJA 1984–85 precipitation from H1. A strong double ITCZ reappears in this experiment even though reevaporation parameters are as in experiment B3 (Figs. 1c, 2c). In fact, Fig. 11b shows that the fraction of reevaporated rain is generally higher than in B3. Domain averages of precipitation and reevaporation-related quantities for H1 are shown in Table 2. Figure 11c shows r(ω̃850, ) for H1. Comparison with the same quantity for experiments B1, B2, and B3 (Fig. 7) reveals that that r(ω̃850, ) is even higher in H1 than it was in B1. Thus, this experiment suggests that the low-level cooling associated with rain reevaporation does, in fact, disrupt positive feedback between low-level convergence and precipitation. Artificially removing the low-level cooling from an experiment with strong reevaporation restores the feedback and leads to the reappearance of strong double ITCZ bias in the model.

b. Diffusive cumulus momentum transport

As described in the introduction, simulations of tropical precipitation in other AGCMs exhibit sensitivities to other processes. The GFDL AGCM has shown a strong sensitivity to the presence of a DCMT parameterization (GFDL Global Atmospheric Model Development Team 2004). The GFDL DCMT scheme is formulated as a momentum diffusivity proportional to the total cumulus mass flux passing through a given level. Its effects are largest at low levels where all clouds, both weakly entraining deep clouds as well as strongly entraining shallow clouds, are present. When DCMT is present in the GFDL model, the simulated tropical precipitation is improved; in particular, the model’s tendency to form double ITCZs is reduced.

We performed two experiments with DCMT (Table 1). One, M1, used reevaporation parameters as in B1. In the baseline model these parameters led to a pronounced double ITCZ bias (Figs. 1a, 2a). The second experiment with DCMT, M2, used reevaporation parameters as in B2. In the baseline model these parameters produced a reasonable simulation of precipitation overall, with a weak double ITCZ bias (Figs. 1b, 2b). The 1984–85 JJA mean precipitation for experiments M1 and M2 is shown in Figs. 12a and 12c. The small double ITCZ bias in experiment B2 is further reduced by the added DCMT in M2, although the wet bias in the northwestern tropical Pacific (0°–20°N, 120°E–180°) appears to have been exacerbated, much as in experiment B3. On the other hand, the strong double ITCZ bias in the Pacific in B1 is not qualitatively reduced by the added DCMT in M1. There are a number of subtle differences between precipitation fields in M1 and B1. The northern ITCZ in M1 is somewhat weaker and more diffuse looking than in B1, particularly in the eastern Pacific between 150° and 120°W. Nevertheless, a strong, well-developed spurious ITCZ persists in the Pacific around 10°S from 150°E to around 130°W. Interestingly, in the Atlantic, there is a more distinct reduction in the double ITCZ bias in experiment M1.

The correlation r(ω̃850, ) (Figs. 12b and 12d) shows slight increases with added DCMT primarily along the eastern margins of the Pacific ITCZs and also in the dry band along the equator. In regions of strong precipitation r(ω̃850, ) appears relatively insensitive to DCMT. Thus, changes in precipitation patterns between B1 and M1 or B2 and M2 are not brought about by changes in the coupling strength of the PBL–precipitation coupling.

Figures 10c and 10d show PDFs of ω850 in M1 and M2. In the eastern Pacific (Fig. 10c) the PDFs for M1 and M2 are similar to each other and to those of B1 and B3 (Fig. 10a). This suggests that the addition of DCMT to the model does not reduce the “background” level of dynamical variability in the model. However, in the core of the southern ITCZ region (Fig. 10d) it is clear that the addition of DCMT reduces the magnitude of ω fluctuations compared with the baseline experiments (Fig. 10b). This is evident in the reduced spread of the PDF for M1 (solid light blue) compared with that for B1. The PDF for M1 shows clear evidence of negative skew, which suggests that the dissipative effects of DCMT do not eliminate feedbacks between moist heating and PBL convergence. However, the negative tail in M1 is less pronounced, with more frequent weaker updrafts, while the peak associated with ubiquitous weak subsidence is also less pronounced than in B1.

6. Convective and large-scale transports in the ITCZs

Our results suggest the existence of a feedback between high-frequency, low-level convergence and precipitation, which contributes to the maintenance of the spurious ITCZ. The feedback is weakened by low-level cooling from rain reevaporation, and as a consequence our model’s double ITCZ bias is reduced when parameterized reevaporation is made stronger. The effects of DCMT are more ambiguous, yet it is clear that directly changing the character of dynamical variability in the ITCZs also has an important impact on the resulting simulation of tropical precipitation. In this section, we examine convective transports, as well as mass and water budgets in the ITCZ complex, to understand the relationship between convection, low-level convergence, and precipitation. This analysis also suggests how the southern ITCZ and the northern ITCZ differ in their response to high-frequency low-level convergence.

a. Convective fluxes

Figure 13 shows longitudinal profiles of total, cloud-base, convective mass flux ϕCB from RAS along 8°S and 8°N averaged over May–October 1985. The striking aspect of this figure is the similarity in the magnitude of the convective mass fluxes, everywhere except in the Pacific warm pool region (100° to 140°E along 8°N), where fluxes in B3 are substantially higher. Along the southern ITCZ (Fig. 13b), the total cloud-base convective mass flux in both experiments shows little sensitivity to reevaporation. In fact, somewhat higher mass fluxes exist in experiment B3 (dashed curve), which does not have a pronounced double ITCZ in precipitation. Thus, it is clear that reevaporation in our model does not suppress precipitation in the southern ITCZ by suppressing convection overall. Vertical profiles of convective mass flux (not shown) show some differences between experiments, but these are relatively small compared to the overall fluxes.

The water vapor flux entering convective updrafts is given by ϕCBqCB, where qCB is the specific humidity in the subcloud layer (here an average of the lowest two model layers). We calculate this flux from daily values of ϕCB and qCB so that transient correlations between convection and PBL humidity with time scales longer than one day are included. The mean of this quantity over May–October 1985 is plotted in Figs. 13c and 13d, along with the corresponding average of total precipitation. Here again, the differences between ϕCBqCB are opposite to those in precipitation for the southern ITCZ. Consistent with its generally higher mean values of ϕCB, experiment B3 has higher ϕCBqCB than experiment B1. The ratio of precipitation to ϕCBqCB is another measure of precipitation efficiency, like f in (1). This quantity is shown in Figs. 13e,f. As expected this efficiency is lower overall in experiment B3 with high reevaporation although, interestingly, over parts of the northern ITCZ efficiencies in B3 are higher. Nevertheless, over the southern ITCZ, efficiency in experiment B3 is systematically less than that in B1. The largest proportional decreases in precipitation efficiency caused by increased reevaporation occur around 120°W in the northern ITCZ and between 180° and 150°W in the southern ITCZ.

The mean, May–October 1985, surface evaporation E along 8°S and 8°N for B1 and B3 is plotted in Figs. 13c and 13d as well. As suggested by the budgets in Table 2, evaporation is a relatively featureless quantity, both in physical space and parameter space. With the exception of a notable enhancement in evaporation over the western warm pool region in experiment B3 (Fig. 13c, 90°–130°E), ITCZ evaporation values hover between 5 and 7.5 mm day−1. It should be noted that, even in the warm pool region of B3, the increased evaporation is only about 1/3 as large as the increase in precipitation going from B1 to B3, and less than 1/10 as large as the increases in ϕCBqCB. The lack of structure in the evaporation suggests that, at least locally, evaporation feedbacks do not play a major role in maintaining the ITCZ in our model.

b. Horizontal transport of water vapor

Figure 13 shows that the southern ITCZ region in our model contains strong convective cloud-base fluxes of water vapor that cannot be supplied by local surface evaporation. Figure 14 shows a number of vertically integrated, mass-weighted, horizontal water vapor transport terms from experiments B1, B3, and H1. The first row (Figs. 14a,f,k) shows maps of the time mean of
i1520-0469-63-12-3383-e2
the column integral of the total, mass-weighted, horizontal water vapor flux convergence for the period JJA 1995. Daily mean values of all quantities were used for the sum in (2). The quantity T01 should equal the local value of PE + ∂t(TPW), where TPW is the total precipitable water in the column. We examined daily time series of T01 at individual grid points and found that water vapor budgets closed within truncation errors due to different finite difference formulations in (2) and in the model.

Since the model evaporation field lacks structure compared with precipitation (e.g., Figs. 13c,d) and time-mean values of ∂t(TPW) are small, the patterns of T01 in Figs. 14a,f,k closely reflect the precipitation patterns in each experiment with positive values of T01 corresponding approximately to P larger than 5 to 7.5 mm day−1. The second and third rows of Fig. 14 separate the total water vapor flux convergence roughly into a low-level, or PBL, component T0.851, where the subscript and superscript indicate new limits for the summation performed in (2), and a free-tropospheric component T0.20.85. In both ITCZs T0.851 (Figs. 14b,g,l) tends to exceed the total water vapor convergence T01. Even in B3 (Fig. 14g), positive T0.851 in the southern ITCZ extends well east of 150°W. Overall, the pattern and magnitude of T0.851 appear to show more similarity across experiments than is the case for T01, although T0.851 is clearly reduced in B3 over the southern ITCZ. In the northern ITCZ, T0.851 is quite similar in all three experiments shown, with peak values in the eastern Pacific (100°W) of over 16 mm day−1.

The free-tropospheric water vapor flux convergence T0.20.85 (Figs. 14c,h,m) shows that the eastern portions of all simulated ITCZs export water vapor above the PBL. This loss of water vapor is most pronounced in experiment B3 (Fig. 14h) and weakest in H1 (Fig. 14m). However, in all experiments, negative T0.20.85 is strong enough to overcome low-level moisture convergence in the eastern extreme of the southern ITCZ, thereby limiting the eastward extension of southern ITCZ in precipitation. In B3, net convergence of water vapor T01 > 0 is confined west of the date line. The eastern extremes of the simulated northern ITCZs also exhibit large free-tropospheric divergence of water vapor, with values well past −8 mm day−1 in B3, for example. However, low-level convergence here is so strong that these regions retain strong net water vapor convergence and therefore remain zones of intense precipitation as well. Note also the large area of strong, free-tropospheric water vapor convergence over the Pacific warm pool region in B3. This suggests that a significant part of the large wet bias seen in Fig. 1 over the same region is fueled by midtropospheric transport of water vapor.

The remaining panels in Fig. 14 show a breakdown of T0.20.85 into a convergent flow component (Figs. 14d,i,n),
i1520-0469-63-12-3383-e3
and an advective component (Figs. 14e,j,o),
i1520-0469-63-12-3383-e4
where T0.20.85 = C0.20.85 + A0.20.85. The convergent-flow component C0.20.85 dominates in all cases within the ITCZs, although A0.20.85 approaches values of about one-third of those attained by C0.20.85. In B3 the advective loss of water vapor above the central and eastern ITCZs appears especially well organized. Nevertheless, it appears that over the ITCZs the dominant mechanism responsible for removal of water vapor in the overlying free troposphere is transport by locally divergent flow.

The major effects of reevaporation and its associated cooling appear to be 1) to reduce the strength of the time-mean PBL water vapor flux convergence in the southern ITCZ and 2) to increase water vapor removal by transport in the free troposphere above both ITCZs. Both effects contribute to eliminating the double ITCZ in experiment B3, while the lack of an appreciable reevaporation impact on T0.851 in the northern ITCZ may explain in part why it is resilient to increased reevaporation.

c. Time behavior of boundary layer convergence

Analysis of the T0.851 calculations in all experiments shows that it is dominated by the convergent flow component C0.851, and that furthermore both time and height variations of q within the PBL are of secondary importance in C0.851. So, we can write
i1520-0469-63-12-3383-e5
and over oceans the vertical sum of the mass divergence in (5) can approximated by ω850 so that we have finally
i1520-0469-63-12-3383-e6
Thus, we can understand the basic features of water vapor transport below 850 hPa in our model by examining ω850. Our earlier analysis of ω850 (cf. Figs. 8 –10) showed that transients in ω850 within the ITCZs were generally weaker with stronger reevaporation. This is reflected in the JJA-mean longitude profiles of shown in Figs. 15a–d. Reductions in transient activity are especially noticeable in the southern ITCZ between 150°E and 120°W where experiments B1 (Fig. 15a) and M1 (Fig. 15c) show well over 50 hPa day−1, while the corresponding experiments with stronger reevaporation have around 30 hPa day−1.

Of greater interest is the relationship between ω̃850 and the straightforward time means of ω850, shown by the thick dashed and solid curves in Figs. 15a–d. In the southern ITCZ (dashed curves) all experiments show a remarkable correlation between −t and . Mean upward motion along the southern ITCZ only occurs where strong high-frequency variability in ω850 is also found, for example, 150°E–180°. In the northern ITCZs there is also a degree of correlation between and −t especially on the western side of the Pacific. However, in the eastern portion of the northern Pacific ITCZ, is relatively constant at around 50 hPa day−1 while the magnitude of −t decreases strongly toward the west, from around 90 hPa day−1 near 90°W to 20 hPa day−1 near the date line.

The nearly linear relationship between and −t in the southern ITCZ suggests that the time-mean vertical motion there is determined by high-frequency transient events with intense upward motion. This is consistent with the highly skewed, asymmetric structure of the ω850 PDFs in Figs. 10a–d. These PDFs consist of a core peak centered somewhat above zero, representing weak ubiquitous subsidence, with an extended, decaying tail representing updrafts that become increasingly rare as they become more intense. On the other hand, the PDFs for B1 and B3 in the eastern portion of the northern ITCZ (Fig. 10e) are more suggestive of a bimodal distribution with a secondary broad peak at −100 to −300 hPa day−1, representing what may be active periods of convection, superimposed on skewed PDFs like those found in the southern ITCZ. We have not analyzed the time behavior of our model ω850 field in detail, but the relative lack of correlation between (15-day filtered ω) and −t in the northern ITCZ, suggests that these modes have time scales longer than 15 days. Such periods occurring on time scales of several weeks, and possibly related to the MJO, have been identified in observations over the eastern Pacific ITCZ by Maloney and Hartmann (2001) and Raymond et al. (2004).

7. Summary and discussion

We examined the effect of parameterized rain reevaporation on tropical precipitation P0 in a series of AGCM experiments. We found that stronger rain reevaporation led to reductions in the double ITCZ bias in our model’s simulated precipitation. The effect of rain reevaporation on seasonal mean precipitation appears to be at least partially due to low-level evaporative cooling, which prevents feedbacks between convective heating and PBL convergence in high frequency (T < 15 days) modes. This is evident in weakened correlations between filtered time series of vertical motion at 850 hPa ω850 and precipitation in Pacific ITCZ region as reevaporation increases (Fig. 7). An experiment in which strong reevaporation was used, but where evaporative cooling was eliminated below 850 hPa, yielded an intense double ITCZ, and high ω850P0 correlations, despite large column-integrated reevaporation moistening (Fig. 11). Probability density functions (PDFs) of ω850 showed an interesting shift from sharply peaked symmetric distributions in the far eastern section of the southern ITCZ to highly skewed, asymmetric distributions in the core region of the southern ITCZ (Fig. 10). The tails of these PDFs represent rare, but intense, updrafts associated with bursts of convection. The degree of asymmetry in the PDFs increases with decreasing reevaporation, that is, for stronger double ITCZ bias. We interpret the asymmetry in the PDFs of ω850 as another manifestation of the feedback between low-level convergence and precipitation that occurs in high-frequency westward propagating disturbances traveling along the ITCZ.

An obvious question is why eliminating or weakening the feedbacks between low-level convergence and precipitation has an apparently much weaker effect on the northern ITCZ. We addressed this question by examining convective transports and mass and water vapor budgets in the ITCZs. Surface evaporation was found to be a relatively “featureless” field in the ITCZ complex with little variation geographically or between experiments. Therefore, the strong variations in precipitation found in our experiments must be balanced by water vapor transport. As expected, water vapor convergence below 850 hPa is the dominant term over most of the ITCZ system (Fig. 14), so precipitation largely follows this field. Nevertheless, we note that significant water vapor divergence between 850 and 200 hPa is found over the eastern portions of all ITCZs (Figs. 14c,h,m). This divergence is strongest in experiment B3 and contributes to significant reductions in the extent and magnitude of the total vertically integrated water vapor convergence in both ITCZs. The effect is more noticeable in the southern ITCZ due to relative weakness of low-level water vapor convergence there.

To understand why low-level convergence is generally weaker in the southern ITCZ we compared the variance of high-frequency ω̃850 fluctuations with the time-mean profiles of ω850 in both ITCZs (Fig. 15). In the southern ITCZ of all experiments there is a remarkable, nearly linear, relationship between t and . This suggests that in the southern ITCZ the time-mean convergence is largely determined by the integrated effect of high-frequency convergence bursts. This is consistent with the skewed, highly asymmetric PDFs in Figs. 10b and 10d, in which both the variance and the mean are determined by the updraft tail region (as they would be, e.g., in an exponential PDF). On the other hand, in the northern ITCZ t and appear to be less tightly related. This implies the existence of a significant component of ω850 variability with time scales longer than 15 days. PDFs of ω850 from the northern ITCZ (Fig. 10e) exhibit hints of a bimodal structure with a broad secondary mode peaking at relatively strong values of upward motion ≈−200 hPa day−1. We suggest that reevaporative cooling has less effect on low frequency, perhaps remotely forced, modes in ω850. This may explain why reevaporation is less effective in reducing the strength of the northern ITCZ where a significant portion of the total water vapor convergence appears to be driven by such modes.

Experiments with a simple diffusive cumulus momentum transport scheme (DCMT) were encouraging in that they suggested that the Pacific ITCZs will respond directly to a reduction in dynamical variability in the Tropics. However, the effects of DCMT in our model were ambiguous. While variability in ω850 along the ITCZs was somewhat reduced, the time-mean ω850 in the two experiments with DCMT remained comparable to its values in the corresponding experiments without DCMT. The depth of the convergence layer also appeared to increase substantially with DCMT (Fig. 15e). The addition of DCMT did little to eliminate the double ITCZ in the case of weak reevaporation (Fig. 12a), although with moderate reevaporation (Fig. 12c) added DCMT largely eliminated the weak bias present in the corresponding experiment without DCMT (Fig. 1b). We conclude that our implementation of DCMT is not vigorous enough to compete with the strong convergence–precipitation feedbacks present in the model with weak reevaporation. However, in the case of stronger reevaporation, with reduced convergence–precipitation feedbacks, additional DCMT can effectively counteract the growth of disturbances moving along the southern ITCZ.

Recently, Wu et al. (2003) have shown improvement in the Community Climate Model version 3’s (CCM3’s) simulated seasonal evolution of the ITCZ, when a cumulus momentum transport (CMT) scheme (Zhang and Cho 1991) was introduced. The relationship of those results to the present study are not yet clear. However, it is of interest that some form of CMT is found to alleviate tropical precipitation biases to some degree, in at least three different AGCMs.

Gu and Zhang (2001) categorize theories of ITCZ formation into two broad categories: 1) SST forced and 2) internally forced by atmospheric dynamics. Category 2 is further divided into zonally symmetric and zonally asymmetric theories. In our model, the connection of high-frequency PBL convergence and precipitation in the southern ITCZ is suggestive of the wave-driven dynamical mechanisms proposed by Holton et al. (1971), Chang (1973), and Lindzen (1974), and later examined in aquaplanet GCM simulations by Hess et al. (1993).

The situation in our simulated northern ITCZ is not so clear. Here, PBL convergence appears to have an important mode of variability at time scales longer than 15 days. The origins of this slower variability are not clear from our analysis. However, based on its long time scale and its resilience to strong local cooling due to reevaporation, we speculate that this mode in NH ITCZ low-level convergence is part of a larger, slowly varying circulation. This circulation must also be relatively insensitive to the mean changes in the convective heating profiles resulting from changes in reevaporation. A shallow circulation forced by SST gradients (Lindzen and Nigam 1987) appears to satisfy these requirements, although we have presented no evidence here of such forcing.

We hope these results will encourage modelers to examine a number of, relatively simple, parameterization-independent diagnostics of precipitation processes in their models. Examples of such diagnostics examined here include the ratio of reevaporated rain to rain reaching the surface, profiles of domain-averaged reevaporation moistening, correlations of high-frequency time series of vertical motion and precipitation, and PDFs of PBL convergence. This list is certainly insufficient, but we believe more detailed examination of atmospheric water budgets and high-frequency precipitation and dynamical variability in climate models is called for despite the relatively poor observational basis available for validation. These analyses may reveal new dynamically significant similarities between models that suffer similar precipitation biases.

Acknowledgments

The authors thank A. Sobel and an anonymous reviewer for their comments, which led to substantial improvements in this analysis. The work described herein was funded through NASA’s Global Meeting and Assimilation Office.

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Fig. 1.
Fig. 1.

JJA averages of precipitation for 1984–85 (a) from expt B1 with weak reevaporation, contours are drawn for 1.0, 2.0, 4.0, 8.0, and 16.0 mm day−1; (b) for expt B2 (moderate reevaporation); (c) for expt B3 (strong reevaporation); and (d) for Xie–Arkin precipitation data.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 2.
Fig. 2.

JJA average precipitation biases with respect to Xie–Arkin (CMAP) climatology for 1984–85: (a) expt B1 with weak reevaporation, contours drawn for −16, −8, −4, −2, 2, 4, 8, and 16 mm day−1; (b) for expt B2 (moderate reevaporation); and (c) for expt B3 (strong reevaporation).

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 3.
Fig. 3.

Ratio of reevaporated precipitation to precipitation reaching the surface, f defined in (1). Seasonal means for JJA 1984–85 are shown for (a) B1, weak reevaporation; (b) B2, moderate reevaporation; and (c) B3, strong reevaporation.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 4.
Fig. 4.

Boxes used for regional precipitation and evaporation calculations presented in Table 2. Four regions are defined: northern and southern Pacific ITCZ (NITCZ and SITCZ), tropical West Africa (WAFR), and the southwestern United States (WUSA). Dashed contours show mean 4 mm day−1 contour for JJA 1984–85 precipitation in expt B1.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 5.
Fig. 5.

Mean profile of reevaporation moistening R in g kg−1 day−1 for JJA 1984–85 in box NITCZ. Solid line shows result for expt B3 (strong R), dashed line for B2 (moderate R), dotted line for B1 (weak R), and open diamonds for H1 (strong R with modified cooling profile).

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 6.
Fig. 6.

(top) Specific humidity q in g kg−1 as a function of pressure in boxes (left) NITCZ and (right) SITCZ. Profiles are averages for JJA 1984–85. Solid lines show result for expt B3, dashed lines for B2, and dotted lines for B1. Filled triangles show the ERA-40 q profiles, and filled diamonds show q for the NCEP–NCAR reanalysis. (bottom) Average TPW in mm for same period, as function of latitude and longitude in (left) expts B1 and (right) B3.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 7.
Fig. 7.

Correlation of daily 15-day, high-pass filtered vertical motion ω̃ and precipitation for 1 April–30 September 1984 and 1985: for (a) expt B1 (weak reevaporation), (b) expt B2, and (c) expt B3.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 8.
Fig. 8.

Rms value of 15-day high-pass filtered vertical motion field for 1 April–30 September 1984 and 1985 for (a) expt B1 (weak reevaporation), (b) expt B2, and (c) expt B3. Units are hPa day−1.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 9.
Fig. 9.

Hovmoeller diagrams of (right) vertical motion and (left) precipitation along 10°S for JAS 1985 in (top) expt B1 and (bottom) expt B3. The 15-day, high-pass filtered vertical motion ω̃ and unfiltered daily precipitation are shown.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 10.
Fig. 10.

(a)–(d) PDFs of unfiltered, daily ω850 for JJA 1985 for two regions along the southern ITCZ. (a) and (b) show results for expts B1 (solid black curve) and B3 (dashed red). (c) and (d) show results for perturbed physics expts H1 (solid black), M1 (solid light blue), and M2 (dashed dark blue). (a) and (c) show PDFs for a region in the eastern Pacific between 12° and 6°S bounded by 110° and 97.5°W. This region immediately off the coast of South America is meant to represent atmospheric variability that is as yet relatively unaffected by interactions between moist heating and dynamics. Sharp peaks at small positive values indicate ubiquitous weak subsidence. (b) and (d) show PDFs for a region in the central Pacific between 12° and 6°S bounded by 162.5° and 175°E. (e) PDFs for B1 and B3 for a box in the eastern portion of the northern ITCZ (6°–12°N, 110°–97.5°W) for comparison.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 11.
Fig. 11.

(a) Seasonal mean JJA 1984–85 precipitation for expt H1, scale as in Fig. 1; (b) f, fraction of reevaporated rain to surface rain, scale as in Fig. 3; and (c) correlation of ω̃ and time series for 1 April–30 Sep 1984 and 1985, with scale as in Fig. 7.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 12.
Fig. 12.

(a) JJA 1984–85 precipitation for expt M1. (b) Correlation of daily ω̃ and time series for 1 Apr–30 September 1984 and 1985 in expt M1. (c) As in (a) but for expt M2. (d) As in (b) but for expt M2. Scales for (a) and (c) are as in Fig. 1. Scales for (b) and (d) are as in Fig. 7.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 13.
Fig. 13.

Convective mass and moisture fluxes and precipitation efficiency for JJA 1985 in B1–B3, along (left) northern (8°–14°N) and (right) southern (12°–6°S) ITCZs. Key for lines in each row is shown in right panels. (a) and (b) show cloud-base mass flux summed over all cloud types invoked by RAS. (c) and (d) show diagnosed cloud-base convective moisture fluxes (thick solid and dashed lines). Thin solid and dashed lines in (c) and (d) show precipitation. Symbols in (c) and (d) show surface evaporation—large circles correspond to expt B3 and small diamonds to B1. (e) and (f) show the ratio of precipitation to convective moisture flux at cloud base.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 14.
Fig. 14.

Mean, vertically integrated, horizontal transport tendencies for water vapor, defined in (2)(4) of the text, as functions of longitude and latitude. Results are shown for JJA 1985; in (a)–(e) expt B1, (f)–(j) expt B3, and (k)–(o) expt H1. All quantities are displayed in units of mm day−1. (a), (f), and (k) show total, column-integrated water vapor flux convergence T01; (b), (g), and (l) T0.851, water vapor flux convergence in the layer bounded by σ = 1 and σ = 0.85; (c), (h), and (m) T0.20.85, water vapor flux convergence in the layer bounded by σ = 0.85 and σ = 0.2; (d), (i), and (n) C0.20.85, water vapor transport by large-scale convergent flow between σ = 0.85 and σ = 0.2; and (e), (j), and (o) A0.20.85, advective water vapor transport between σ = 0.85 and σ = 0.2.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Fig. 15.
Fig. 15.

(a)–(d) Mean vertical motion (heavy lines) and rms amplitude of 15-day high-pass filtered vertical motion (thin lines) as functions of longitude along 8°N and 8°S for JJA 1985 in (a) expt B1, (b) expt B3, (c) expt M1, and (d) expt M2. Thick dashed lines in each panel show mean vertical motion along southern ITCZ (8°S), and thin dashed lines show along 8°S. Notice distinct anticorrelation between these two quantities along 8°S. Thick and thin solid lines show and along northern ITCZ (8°N). Notice increasingly strong ascent (negative ) toward eastern portion of northern ITCZ, which is not accompanied by increasing . (e) Time-mean vertical profiles of convergence ∂pω (day−1) at a location in the core of the southern ITCZ.

Citation: Journal of the Atmospheric Sciences 63, 12; 10.1175/JAS3791.1

Table 1.

Summary of experiments performed. First column gives short-hand designation. Second column indicates strength of reevaporation. Third column indicates additional modifications to physical parameterizations. Fourth column gives pattern correlation of simulated precipitation with the CMAP climatology for a seven-season JJA 1981–87 mean. Fifth column is the rms error with respect to CMAP (mm day−1) for the same period.

Table 1.
Table 2.

Domain-averaged precipitation, reevaporation, and related quantities in the four boxes illustrated in Fig. 4 for JJA 1984–85. First column gives experiment designation from Table 1. Second column indicates box for averages. Third column gives average precipitation P0 in mm day−1. Fourth column gives the mass-weighted vertical integral of moistening due to reevaporation of falling precipitation, denoted by ∫R. This quantity is in units of mm day−1 and represents the additional rain that would reach the surface if not removed by reevaporation. Fifth column is the sum P0 + ∫R, i.e., the total precipitating condensate generated by autoconversion within a column. Columns 6 and 7 are ratios of ∫R to P0 and ∫R + P0, respectively. Column 8 gives the average surface evaporation E0 in units of mm day−1. Ninth column is the difference E0P0. Positive numbers in this column indicate that the box in question exports water horizontally to the rest of the atmosphere, while negative numbers mean water vapor must be imported to supply an excess of precipitation. The last four rows, labeled “CMAP,” give the observed precipitation in each box.

Table 2.
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  • Fig. 1.

    JJA averages of precipitation for 1984–85 (a) from expt B1 with weak reevaporation, contours are drawn for 1.0, 2.0, 4.0, 8.0, and 16.0 mm day−1; (b) for expt B2 (moderate reevaporation); (c) for expt B3 (strong reevaporation); and (d) for Xie–Arkin precipitation data.

  • Fig. 2.

    JJA average precipitation biases with respect to Xie–Arkin (CMAP) climatology for 1984–85: (a) expt B1 with weak reevaporation, contours drawn for −16, −8, −4, −2, 2, 4, 8, and 16 mm day−1; (b) for expt B2 (moderate reevaporation); and (c) for expt B3 (strong reevaporation).

  • Fig. 3.

    Ratio of reevaporated precipitation to precipitation reaching the surface, f defined in (1). Seasonal means for JJA 1984–85 are shown for (a) B1, weak reevaporation; (b) B2, moderate reevaporation; and (c) B3, strong reevaporation.

  • Fig. 4.

    Boxes used for regional precipitation and evaporation calculations presented in Table 2. Four regions are defined: northern and southern Pacific ITCZ (NITCZ and SITCZ), tropical West Africa (WAFR), and the southwestern United States (WUSA). Dashed contours show mean 4 mm day−1 contour for JJA 1984–85 precipitation in expt B1.

  • Fig. 5.

    Mean profile of reevaporation moistening R in g kg−1 day−1 for JJA 1984–85 in box NITCZ. Solid line shows result for expt B3 (strong R), dashed line for B2 (moderate R), dotted line for B1 (weak R), and open diamonds for H1 (strong R with modified cooling profile).

  • Fig. 6.

    (top) Specific humidity q in g kg−1 as a function of pressure in boxes (left) NITCZ and (right) SITCZ. Profiles are averages for JJA 1984–85. Solid lines show result for expt B3, dashed lines for B2, and dotted lines for B1. Filled triangles show the ERA-40 q profiles, and filled diamonds show q for the NCEP–NCAR reanalysis. (bottom) Average TPW in mm for same period, as function of latitude and longitude in (left) expts B1 and (right) B3.

  • Fig. 7.

    Correlation of daily 15-day, high-pass filtered vertical motion ω̃ and precipitation for 1 April–30 September 1984 and 1985: for (a) expt B1 (weak reevaporation), (b) expt B2, and (c) expt B3.

  • Fig. 8.

    Rms value of 15-day high-pass filtered vertical motion field for 1 April–30 September 1984 and 1985 for (a) expt B1 (weak reevaporation), (b) expt B2, and (c) expt B3. Units are hPa day−1.

  • Fig. 9.

    Hovmoeller diagrams of (right) vertical motion and (left) precipitation along 10°S for JAS 1985 in (top) expt B1 and (bottom) expt B3. The 15-day, high-pass filtered vertical motion ω̃ and unfiltered daily precipitation are shown.

  • Fig. 10.

    (a)–(d) PDFs of unfiltered, daily ω850 for JJA 1985 for two regions along the southern ITCZ. (a) and (b) show results for expts B1 (solid black curve) and B3 (dashed red). (c) and (d) show results for perturbed physics expts H1 (solid black), M1 (solid light blue), and M2 (dashed dark blue). (a) and (c) show PDFs for a region in the eastern Pacific between 12° and 6°S bounded by 110° and 97.5°W. This region immediately off the coast of South America is meant to represent atmospheric variability that is as yet relatively unaffected by interactions between moist heating and dynamics. Sharp peaks at small positive values indicate ubiquitous weak subsidence. (b) and (d) show PDFs for a region in the central Pacific between 12° and 6°S bounded by 162.5° and 175°E. (e) PDFs for B1 and B3 for a box in the eastern portion of the northern ITCZ (6°–12°N, 110°–97.5°W) for comparison.

  • Fig. 11.

    (a) Seasonal mean JJA 1984–85 precipitation for expt H1, scale as in Fig. 1; (b) f, fraction of reevaporated rain to surface rain, scale as in Fig. 3; and (c) correlation of ω̃ and time series for 1 April–30 Sep 1984 and 1985, with scale as in Fig. 7.

  • Fig. 12.

    (a) JJA 1984–85 precipitation for expt M1. (b) Correlation of daily ω̃ and time series for 1 Apr–30 September 1984 and 1985 in expt M1. (c) As in (a) but for expt M2. (d) As in (b) but for expt M2. Scales for (a) and (c) are as in Fig. 1. Scales for (b) and (d) are as in Fig. 7.

  • Fig. 13.

    Convective mass and moisture fluxes and precipitation efficiency for JJA 1985 in B1–B3, along (left) northern (8°–14°N) and (right) southern (12°–6°S) ITCZs. Key for lines in each row is shown in right panels. (a) and (b) show cloud-base mass flux summed over all cloud types invoked by RAS. (c) and (d) show diagnosed cloud-base convective moisture fluxes (thick solid and dashed lines). Thin solid and dashed lines in (c) and (d) show precipitation. Symbols in (c) and (d) show surface evaporation—large circles correspond to expt B3 and small diamonds to B1. (e) and (f) show the ratio of precipitation to convective moisture flux at cloud base.

  • Fig. 14.

    Mean, vertically integrated, horizontal transport tendencies for water vapor, defined in (2)(4) of the text, as functions of longitude and latitude. Results are shown for JJA 1985; in (a)–(e) expt B1, (f)–(j) expt B3, and (k)–(o) expt H1. All quantities are displayed in units of mm day−1. (a), (f), and (k) show total, column-integrated water vapor flux convergence T01; (b), (g), and (l) T0.851, water vapor flux convergence in the layer bounded by σ = 1 and σ = 0.85; (c), (h), and (m) T0.20.85, water vapor flux convergence in the layer bounded by σ = 0.85 and σ = 0.2; (d), (i), and (n) C0.20.85, water vapor transport by large-scale convergent flow between σ = 0.85 and σ = 0.2; and (e), (j), and (o) A0.20.85, advective water vapor transport between σ = 0.85 and σ = 0.2.

  • Fig. 15.

    (a)–(d) Mean vertical motion (heavy lines) and rms amplitude of 15-day high-pass filtered vertical motion (thin lines) as functions of longitude along 8°N and 8°S for JJA 1985 in (a) expt B1, (b) expt B3, (c) expt M1, and (d) expt M2. Thick dashed lines in each panel show mean vertical motion along southern ITCZ (8°S), and thin dashed lines show along 8°S. Notice distinct anticorrelation between these two quantities along 8°S. Thick and thin solid lines show and along northern ITCZ (8°N). Notice increasingly strong ascent (negative ) toward eastern portion of northern ITCZ, which is not accompanied by increasing . (e) Time-mean vertical profiles of convergence ∂pω (day−1) at a location in the core of the southern ITCZ.

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