1. Introduction
The seasonal cycle of rainfall in populous southeastern (SE) Brazil is marked by a sharp minimum in precipitation during February within an otherwise rainy summer season, evident in long-term gauge climatology from several stations near 20°S (Fig. 1). However, we are unaware of any scientific literature documenting or explaining this climatological time series structure, which appears to be an overlooked calendar-locked form of variability along the edge of the broad climatological rain belt known as the South Atlantic convergence zone [SACZ or ZCAS in Portuguese; reviewed in Carvalho et al. (2004)].
Rainfall climatologies (mm day−1) at various coastal stations in the southeastern Brazil region (black circles in map at left). Daily data (black line) and monthly averaged time series (red line) are shown. February climatological rainfall minimum highlighted by cyan-filled circle. Data are from the Global Historical Climatology Network–Daily dataset (Menne et al. 2012). Only stations with at least 25 yr of valid data were used to make climatologies.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
This February rainfall minimum appears to be a southern analog of the Northern Hemisphere subtropical midsummer drought (MSD; Magaña et al. 1999) signal in the intra-American seas region in July–August. While there are regional complexities and local mechanisms for various aspects of the MSD [reviewed in Gamble and Curtis (2008)], our prior work suggested that this MSD signal might have a robust global basis (since global models reproduce it), evidently connected with the powerful Asian summer monsoon (Mapes et al. 2005; Kelly and Mapes 2011). Modeling experiments in Kelly and Mapes (2013) clarified the apparent chain of causality; increased Asian monsoon rainfall and latent heating, evoked by reducing (darkening) soil albedo boundary conditions in South Asia, was shown to increase the stationary wave momentum flux [u*υ*] on the northwestern edge of the upper-level Tibetan high. As a consequence, zonal-mean barotropic easterlies were enhanced, and a westward displacement of the North Atlantic subtropical anticyclone and its weather zones reduced rainfall in the Central American region.
The austral summer monsoon in the Asia–Australia sector [hereafter called the Australian monsoon for brevity; see McBride (1987) for a review] is less dramatic than its boreal counterpart. At upper levels its stationary waves are far less dramatic than the majestic closed Tibetan high. Might the same mechanisms nonetheless be at work?
Past work has found linkages between summer climate in SE Brazil and the Australian region on seasonal to interannual time scales, but these may both be driven by Pacific SST anomalies associated with ENSO (e.g., Hill et al. 2011; Nogués-Paegle and Mo 1997). Recently, Coelho et al. (2015) linked the devastating drought in SE Brazil during summer 2014/15 to anomalous heating near northern–northeastern Australia. Might the Australian monsoon linkage to SE Brazil be evident as well on non-ENSO-driven and subseasonal time scales, for instance the February secondary minimum in monthly climatology (Fig. 1)?
These questions are addressed below through both observational correlations and GCM modeling experiments. Section 2 details the data, model, and methods used. Section 3 shows the backdrop of February climatology, including rainfall in the SACZ (section 3a), and the annual cycle of zonal momentum (section 3b) whose poleward excursion of tropical easterlies peaks in February. Section 4 explores the hypothesis that the Australian monsoon is an ultimate forcing mechanism for the SE Brazil rainfall signature. First, observational correlations are examined among year-to-year differences in the Australian monsoon, mean zonal wind, and SE Brazil (section 4a). Then, to clarify causality, section 4b shows model responses to prescribed heating near Australia in an atmosphere general circulation model (AGCM). Conclusions and discussion are in section 5.
2. Data, model, and methods
a. Observational datasets and filtering
For gridded precipitation data, this study used the CPC Merged Analysis of Precipitation (CMAP) at pentad resolution (Xie and Arkin 1997). Details of the rain gauge data in Fig. 1 are given in its caption. Outgoing longwave radiation (OLR) data are from NOAA’s interpolated product at daily temporal resolution (Liebmann and Smith 1996). Sea level pressure (SLP) and surface wind data used in section 3a are from ERA-Interim (Dee et al. 2011) at daily temporal resolution. All data listed above are for years 1979–2013 and are obtained at 2.5° horizontal resolution. The four-dimensional wind analysis data for the atmospheric momentum budget calculations in section 3b are also from ERA-Interim for years 1979–2013 (available at http://apps.ecmwf.int/), obtained on 6-hourly grids of 1° horizontal spacing and 20 equally spaced pressure levels from 1 to 1000 hPa.
We define high-frequency climatological anomalies (HFCAs) as deviations of a climatological time series from a smooth curve truncated to two Fourier harmonics (annual and semiannual periods). HFCAs highlight climatological features in subseasonal frequency bands. Since there is essentially no direct astronomical (orbital) forcing at these frequencies, HFCAs necessarily indicate the importance of nonlinear mechanisms within the subseasonal-to-seasonal climate system. Such nonlinearities (and their associated linear linkages) are of intrinsic scientific interest, even if the magnitude or societal impact of the HFCAs is small. Section 3a uses HFCAs of precipitation, OLR, SLP, and near-surface winds.
To define Australian monsoon intensity, we utilized two versions of the Australian monsoon index (AUSMI; Wang et al. 2004; Kajikawa et al. 2010) to help bound definitional uncertainties. Indices are calculated using daily averages of ERA-Interim zonal wind at 850 hPa (u850) at 1° horizontal resolution (available at http://apps.ecmwf.int/). Both versions are based on tropical low-level zonal wind anomalies (u850) near northern Australia, with subtle differences in the region averaged, and have been shown to be correlated to rainfall anomalies on many scales. All data in section 4a were first filtered using a high-pass digital filter to remove variance with periods greater than 170 days. This high-pass filter is related to the HFCA “climatological” Fourier harmonic analysis in section 3a, but it applies to individual years. This high-pass filtering removes the significant and well-known influence of ENSO on the Australian monsoon (Tanaka 1981; Allan 1983) and on mean zonal wind (L’Heureux and Thompson 2006). In other words, we examined year-to-year sample differences among aspects of subseasonal anomalies, not interannual variability.
To illustrate the utility of high-pass filtering, Fig. 2 shows yearly scatterplots (with each data point representing a year) among standardized 〈[u]〉 at 15°–25°S, the Niño-3.4 index (Bamston et al. 1997), and AUSMI (Wang et al. 2004). Figure 2 (top) shows unfiltered monthly mean February anomalies from 1979 to 2013. A very significant negative correlation prevails (r = −0.63) between 〈[u]〉 and AUSMI (Fig. 2, top left). However, both of these indices are strongly correlated with the Niño-3.4 SST anomalies (top-center and top-right panels of Fig. 2). Figure 2 (bottom) shows standardized scatterplots of the same indices but now for February monthly means of high-pass-filtered anomalies computed from daily series. The significant negative correlation between 〈[u]〉 and the AUSMI (Fig. 2, bottom left) remains (r = −0.58; 34% of variance explained), but there is now no significant common dependence on Niño-3.4.
Scatterplots of standardized 〈[u]〉 anomalies at 15°–25°S against Niño-3.4 index and AUSMI (Wang et al. 2004) for individual years from 1979 to 2013. (top) The total February values for each index; (bottom) the high-pass-filtered February values for each index (as used in Figs. 8 and 9). Australian monsoon and 〈[u]〉 indices are constructed from ERA-Interim (Dee et al. 2011). Total Niño-3.4 index in (top) are constructed from NOAA ERSST version 4 monthly data (Huang et al. 2015). Filtered Niño-3.4 index in (bottom) are constructed using NOAA high-resolution daily SST data (Reynolds et al. 2007).
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
b. Momentum budget
c. Model experiments
Section 4b shows results from AGCM experiments using the Community Atmosphere Model, version 5 (CAM5; Conley et al. 2012). CAM5 was configured with a finite-volume dynamical core on a 1.9° × 2.5° horizontal grid with 30 vertical levels. Its deep convection scheme is a 2012 version of the Zhang–McFarlane (ZM) scheme (Zhang and McFarlane 1995). Sea surface temperature (SST) was prescribed to be a repeating monthly mean climatology derived from the merged Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) and NOAA Optimum Interpolation Sea Surface Temperature (OISST) datasets (Hurrell et al. 2008). Based on examination of many graphics, we consider this model configuration’s flow and precipitation solution (e.g., see Fig. 10) sufficiently realistic to serve as the control run for our experiments, as discussed in section 4b.
The relatively coarse horizontal resolution of CAM5 (1.9° × 2.5°) is hopefully adequate for our purposes, since we are seeking a hypothesized signal consisting of a basin-scale displacement of the subtropical anticyclone and its weather patterns, driven by changes in the mean zonal flow. Although the precipitation signal per se is produced by debatable parameterizations, spatial shifts of large-scale patterns may still be a trustworthy aspect of the results.
Two experiments were performed: AUSheat and AUSheat_x2. In these experiments, an artificial heat source was prescribed in the model’s temperature equation. While this approach slightly violates global energy conservation, tiny increases in global temperature are not at issue here, only the pattern of responses. The prescribed heating has geographical and vertical structure as indicated in Figs. 3a,b. Horizontal structure is Gaussian, centered at 10°S, 140°E, with a half-width of 5° latitude and 20° longitude. The vertical structure is a half sine wave that peaked at 550 hPa and vanished at 1000 and 150 hPa (Fig. 3b), resembling a “middle–heavy” idealized deep convective heating profile (Schumacher et al. 2007; Zhang and Hagos 2009). Temporal structure is triangular, vanishing on 1 January and 31 March. The peak value of this four-dimensional heating tendency structure is 1 and 2 K day−1 in AUSheat and AUSheat_x2, respectively.
(a) Horizontal location and (b) vertical distribution of artificial diabatic heating term added to the temperature tendency equation for February in CAM5. The artificial heating tendency has a Gaussian distribution centered at 10°S, 140°E in the horizontal and a half sine wave centered at 550 hPa in the vertical. CMAP climatology (1979–2013) in (c) February and (d) for annual cycle in AUS.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
To motivate our choices of heating structure, Fig. 3 also shows observed February rainfall from CMAP climatology (Fig. 3c) and the seasonal cycle of rainfall in the Australian region (AUS; box in Fig. 3c). Monsoon rainfall in AUS has a fairly sharp peak in January–February (Fig. 3d), inspiring the triangular timing, while the Gaussian horizontal structure encompasses the main rainfall centers of the Australian monsoon. The standard deviation of February AUS rainfall totals in this study’s CMAP data is 4.7 mm day−1, which corresponds to a vertical-averaged heating rate of approximately 1.3 K day−1. The magnitudes of heating rates in AUSheat and AUSheat_x2 are thus roughly one-half and one standard deviation of observed interannual variability in AUS. These are sufficiently large to provide a clear signal-to-noise ratio. The factor of 2 between the experiments helps to assess the linearity as well as statistical significance of the responses. We shall see that the response appears linear, which means that magnitudes can simply be rescaled at will.
For both the control run and the two experiments, the model was integrated for 30 continuous years. The first two years were discarded, so model results shown are a 28-yr mean response (control minus experiment) for February only.
3. February drying and mean easterlies
In this section, observational spatial patterns and relationships associated with the February minimum in SE Brazil from Fig. 1 are investigated in order to motivate our causal hypothesis to be tested in section 4.
a. A spatial view: The subtropical high and its western edge
Rainfall in SE Brazil falls in a time-mean rain belt that protrudes southeastward from the tropical South American monsoon over the Amazon basin. Here, we call this general mean feature the SACZ, not to be confused with a particular synoptic weather pattern sometimes called the same name. In the fork between these rain belts, where the South Atlantic subtropical anticyclone (SASA) and its flanking easterly trade winds extend farthest west, lies semiarid northeastern Brazil (gray contours in Fig. 4a). Populous SE Brazil, along the coastal bulge in the black square of Fig. 4a, thus lies in a convective margin between a deep, convecting monsoon regime over the Amazon basin and the dry subsiding regime of the SASA. Such convective margins are highly sensitive to low-level zonal wind changes (Lintner and Neelin 2007, 2008) with anomalous easterlies yielding negative rainfall anomalies in SE Brazil (Jones and Carvalho 2002). It may strike some readers as counterintuitive to speak of dry air from the ocean impinging on a wet continental region, but this is the picture: in a column-integrated sense, the oceanic subtropics are desert air masses (e.g., Liu et al. 1992; Trenberth and Guillemot 1995)
Maps of HFCAs for February in the South Atlantic subtropics for (a) CMAP rainfall, (c) NOAA OLR, and (e) ERA-Interim SLP and 850 hPa wind vectors, where anomalies are defined as the total climatological time series minus the sum of the annual and semiannual Fourier harmonics. Also shown are the broad summer [January–March (JFM)] mean values overlaid in gray contours, with tick marks pointing towards local minimum. (b),(d),(f) The total time series of each variable from (a),(c),(e) near SE Brazil (black box in the left panels) is shown.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
Shaded maps in Figs. 4a, 4c, and 4e show February mean HFCAs of precipitation, OLR, and SLP with 850-hPa wind, respectively. Contours of the broad summer (January–March) mean field are overlaid for reference. Figures 4b,d,f show the total time series averaged over the black boxes in Figs. 4a,c,e. The distinct February drying in the station data of Fig. 1 is seen to be part of a larger dry tongue (brown) extending over the western Atlantic, lying along the eastern flank of the mean SACZ (Fig. 4a). The February rainfall minimum in SE Brazil has an amplitude of about 30% of the November–March wet season mean rain rate (Fig. 4b) averaged over SE Brazil (black boxes in Figs. 4a,c,e). Collocated with low rainfall is high OLR (Fig. 4c), implying reduced cloudiness. In OLR, the curve’s February excursion is almost as large as the entire seasonal amplitude (Fig. 4d). The February rainfall decrease is partially compensated in space by opposite-signed HFCAs centered near 30°S, 310°E, but those features are weaker and involve smaller excursions of the time series whose statistical significance appears far less robust (not shown). Changes in the near-surface wind field during February (Fig. 4e) indicate a westward extension of the SASA, which is centered near 30°S, 355°E in the eastern South Atlantic but extends westward into SE Brazil in February. Positive SLP anomalies are seen in the western half of the basin, suggesting anomalous subsidence, while easterly wind anomalies impinge on the coast at 15°–25°S where the negative rainfall and positive OLR HFCAs are. In the eastern half of the basin, SLP anomalies are of the opposite sign, indicating a westward displacement of the SASA from its mean position (gray contours in Fig. 4e) rather than a basin-scale enhancement of its size or strength.
To illustrate the year-to-year repeatability of the February dry HFCAs, Fig. 5 shows the individual-year data on the high-frequency component of CMAP rainfall computed over SE Brazil (boxes in Fig. 4). The vertical alignment of negative HFCAs (blue colors) in February between redder areas on both sides is a visual indicator of statistical significance; this signal also strongly passes more formal significance tests (not shown). The red–blue dipole along the coast in Fig. 4c suggests that the reversal of HFCA anomalies between February and March reflects the alternation of the SACZ between two favored positions (near 20° and 30°S along the coastline, as indicated by the positive and negative lobes in Figs. 4a,c). A similar standing pattern of variability has also been identified in free (noncalendar aligned) variability on time scales from submonthly to interannual (Nogués-Paegle and Mo 1997; Liebmann et al. 1999; Robertson and Mechoso 2000).
Individual-year contribution to HFCAs (i.e., true rainfall on individual years minus a two-harmonic fit to climatology) from CMAP (1979–2013) for the SE Brazil box shown in Fig. 4. Dashed lines indicate the time period of averaging for the climatology shown in Fig. 4.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
To interpret SACZ location variability, spatial patterns must be invoked. The mean structure and location of the SACZ is set by the planetary flow and stationary waves, along with the influences of regional geography (see, e.g., Figueroa et al. 1995). Its locational variability may then be affected by the flow patterns that shape it and in particular by fluctuation of the SASA to its east. Tying rainfall to a large-scale flow feature (the anticyclone) empirically in this manner has proven fruitful in interpreting a broad range of phenomena driving SE Brazil rainfall variations from synoptic fluctuations to intraseasonal Rossby wave trains to low-frequency surface forcing [reviewed in Carvalho et al. (2004)].
b. Zonal wind and its global barotropic component
If the dry February rainfall HFCA in SE Brazil is indeed another instance of a displacement of the SACZ’s underlying spatial flow pattern (in particular the SASA), the next question is what causes a February shift of the SASA? The HFCA pattern for SLP (Fig. 4e) shows a zonal dipole structure, indicating a westward displacement of the SASA from its summer mean position in February, not a strengthening. We hypothesize that this zonal shift may be driven by a change in the zonal-mean wind background state 〈[u]〉 from westerlies to easterlies in the subtropics in midsummer (Fig. 6). The physical mechanism has been illustrated theoretically in Chen et al. (2001): a zonal advective tendency of eddy potential vorticity displaces the time-mean location of the SASA by perturbing the balance of tendencies composing that forced–damped steady eddy.
Annual cycle of the climatological zonal mean (average across all longitudes) of 150-hPa HEMFC averaged across 15°–25°S (m s−1 day−1). HEMFC is computed from ERA-Interim 6-hourly fields (1979–2013) for the total (black line) and stationary (blue line) eddy fluxes. Filled blue and red circles indicate the monthly mean of stationary and total HEMFC, respectively. The transient HEMFC is equal to the total minus stationary components by construction. The barotropic zonal-mean zonal momentum 〈[u]〉 is also shown (m s−1).
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
The mechanistic reasoning hypothesized here is the same as that invoked by Kelly and Mapes (2011, 2013) to interpret a Northern Hemisphere counterpart (an anticyclone displacement and midsummer rain minimum in the North Atlantic subtropics in July). In those studies, the subtropical summer 〈[u]〉 minimum was found to be driven prominently by an Asian monsoon-driven stationary eddy momentum flux [u*υ*] near the northwest corner of the upper-level Tibetan high. The resulting 〈[u]〉 easterlies can cause a westward displacement of the subtropical anticyclone, reducing rainfall on the western edge of the North Atlantic subtropical high. This narrative was supported by observed year-to-year correlations among its elements in Kelly and Mapes (2011), while its robustness and the direction of causality were shown in Kelly and Mapes (2013); perturbing only soil albedo in a South Asian geographical box, in a sufficiently realistic GCM, was sufficient to drive the whole chain of mechanisms above, leading to robust reductions in western Atlantic rainfall like the observed.
Pursuing the analogy in the Southern Hemisphere, we seek upper-level stationary eddy momentum flux driving 〈[u]〉 easterlies in midsummer. While it is a vertical integral of HEMC that contributes to the 〈[u]〉 budget [Eq. (1)], eddy momentum flux is predominantly in upper levels, so that 150-hPa level diagnostics sufficiently tell the story. A localized zonal force is rapidly spread longitudinally by the pressure gradient force, a term that vanishes from the budget of the zonal average [u]. Similarly, a zonal torque at upper levels is rapidly spread vertically by an induced meridional circulation (e.g., Holton 2004, section 10.2), a term that essentially vanishes in the vertical average budget for 〈[u]〉. As a result, the causal story investigated here can be appreciated from displays of [u*υ*] and HEMFC at 150 hPa and 〈[u]〉.
Figure 6 shows the annual cycle of total and stationary HEMFC at 150 hPa in the 15°–25°S latitude belt, along with 〈[u]〉. The stationary term shows a January–February minimum, indicating a negative torque on 〈[u]〉. The transient term (total minus stationary) is also negative and is large in winter but small in summer. While the transient contribution is larger than the stationary term, it cannot be viewed as a “forcing” but rather is an intrinsic aspect of the coupled eddy–mean flow system. More to the point, the climatological time signature of stationary HEMFC, with its prominent minimum in midsummer, suggests it as a key mechanism for explaining the change to mean easterlies observed in February (Fig. 6).
Figure 7 shows the longitudinal distribution of stationary eddies that compose this zonal-mean minimum in January plus February. Streamfunction has a zonal wavenumber-1 pattern (Fig. 7a), with a large anticyclonic eddy (negative streamfunction) centered off western Australia (20°S, ~110°E) and extending across the Maritime Continent westward past the southern tip of Africa. Cyclonic eddy streamfunction (red) centered near 240°E, covers most of the Pacific and South America. Stationary eddy momentum flux (the product u*υ*) and the corresponding integral of HEMFC computed across 15°–25°S is shown in Figs. 7b and 7c, respectively. There are five minima in Fig. 7c, corresponding to synoptic features that compose the zonally asymmetric circulation in Fig. 7b: three continental anticyclones (or monsoon eddy highs) over South Africa (~355°–60°E), western Australia (~60°–170°E), and South America (~280°–320°E). These are presumably driven by elevated diabatic heating and deep-layer ascent in the monsoons. In addition, two oceanic cyclones or tropical upper-tropospheric troughs (TUTTs; Sadler 1975) reside above the near-surface anticyclones in the Pacific (~170°–280°E) and Atlantic (~320°–355°E) basins, maintained by subsidence and shallow cooling in those longitudes (Miyasaka and Nakamura 2005, 2010).
(a) The January plus February mean 150-hPa zonal eddy streamfunction (106 m2 s−1; color shading) overlaid with vectors of stationary zonal eddy component of the wind (u* and υ*). (b) The stationary eddy momentum flux u*υ* overlaid with vectors of stationary zonal eddy component of the wind. (c) The stationary HEMFC (m s−1 day−1) across latitude band 15°–25°S. Numbers in (c) indicate the total value calculated across each of the five circulation features, indicating the respective contribution to the zonal-mean quantity. Data in all panels are computed from ERA-Interim climatology (1979–2013).
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
All these circulation features are tilted, such that they produce negative (blue) and positive (red) HEMFC lobes in Fig. 7c on the western and eastern edges, indicating easterly and westerly torques of the zonal wind, respectively (Fig. 7c). The single largest easterly torque is on the western edge of the Pacific cyclone (TUTT) near 220°E, where southeasterlies lie south of 20°S and southwesterlies lie north of 20°S, yielding net divergence of zonal momentum around 20°S. However, the Pacific cyclone is a quite symmetric feature (Fig. 7a) and convergence of zonal momentum on the eastern edge compensates for that divergence, yielding only weakly negative acceleration when averaged across the breadth of the feature (numerical value listed in Fig. 7c). The largest, uncompensated contribution to negative, zonal-mean HEMFC thus comes from the western edge of the tilted anticyclone around northern Australia (~110°E), with the easterly torque in that sector composing about two-thirds of the zonal-mean value of −1 m s−1 day−1 (Figs. 6 and 7c). This finding suggests that Australian monsoon heating may be a primary governor of negative, zonal-mean HEMFC and hence easterly 〈[u]〉 at 15°–25°S in February.
To summarize, we hypothesize based on the evidence presented above and in prior works that monsoon heating in the Australian sector drives stationary eddy momentum fluxes, which drives observed midsummer easterlies in 〈[u]〉, which in turn shift the Atlantic sector SASA westward and thereby shift the SACZ rain pattern, explaining the February HFCA in the observations of Fig. 1. The next section will test this long-chain hypothesis, using year-to-year differences and correlations and a global model.
4. Australian monsoon heating as an ultimate driver
a. Interannual correlation analysis
To test the hypothesis summarized above, year-by-year correlations are shown in this section among the Australian monsoon 〈[u]〉 and rainfall in SE Brazil. Figures 8 and 9 show regression maps of high-pass-filtered (<170 day) February anomalies on individual years from 1979–2013. Figure 8 (left) shows precipitation anomalies regressed on 〈[u]〉 and AUSMI anomalies. Scatterplots of the individual correlation in SE Brazil (box in Fig. 4) at right are shown to facilitate significance estimation. On the maps, hatching indicates regions of significance at the 95% confidence level using a Student’s t test, where correlation values higher than r = 0.29 are significant with n = 35 degrees of freedom (dof). For these HFCAs, it is reasonable to assume 1 dof yr−1.
(left) Maps show the high-pass-filtered February anomalies of precipitation (from CMAP) for individual years (1979–2013) regressed on three predictors: (a) high-pass filtered 〈[u]〉 anomalies at 15°–25°S, (b) high-pass-filtered Australian monsoon index anomalies (Wang et al. 2004), and (c) high-pass-filtered Australian monsoon index anomalies (Kajikawa et al. 2010). (right) Scatterplots of precipitation vs the respective predictor for the SE Brazil box defined in Fig. 4. Statistical significance at the 95% confidence level is indicated by hatching in the left panels.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
As in Fig. 8, but for ERA-Interim sea level pressure anomalies as the predictand.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
A broad region of negative anomalies (blue) over the Australian and Maritime Continent region are seen when rainfall is regressed on 〈[u]〉 (Fig. 8a) and positive anomalies (red) are seen when regressed on AUSMI (Figs. 8b,c). This implies an anticorrelation between 〈[u]〉 and the AUSMI, which is also directly confirmed in Fig. 2. The relationship between negative 〈[u]〉 anomalies and positive monsoon rainfall anomalies seen here in interannual samples is consistent with our causal hypothesis that the monsoon is a principal driver of 〈[u]〉 variations in summer due to negative HEMFC on the western edge of Australia (Fig. 7). There are also negative anomalies when precipitation is regressed on the AUSMI (Figs. 8b,c) around 20°S on the eastward flank of the South Pacific convergence zone (SPCZ) and SACZ near 210° and 315°E, respectively. Again, regression on 〈[u]〉 gives a similar pattern to regression on AUSMI but of the opposite sign, with positive precipitation anomalies along the eastward flanks of the SPCZ and SACZ when 〈[u]〉 is large and positive (Fig. 8a).
Summarizing, the February rainfall minimum in SE Brazil is larger during years when Australian monsoon rainfall and/or mean subtropical easterlies are larger (Fig. 8, right). Figure 9 is of similar form but for SLP regressed on 〈[u]〉 and the AUSMI. A similar pattern of anomalies is seen to Fig. 8, as evident of the close relationship between SLP and rainfall. Large and positive February AUSMI anomalies, and/or large and negative 〈[u]〉 values, correlate to positive SLP anomalies in SE Brazil (Fig. 9). These patterns of year-to-year February rainfall (Fig. 8) and SLP (Fig. 9) anomalies in SE Brazil broadly resemble the climatological anomalies (Fig. 4), indicating that the February rainfall depression (Fig. 1) is a robust and repeatable feature of subseasonal climate variability.
b. Model response to a prescribed heat source
The correlations above indicate that the quantities examined are physically linked. However, correlations are ambiguous about causality pathways, including the possibility that another unexamined factor may govern all the signals examined. For this reason, active experiments in a credible model are needed to further test the hypothesis at the end of section 3.
Our causal hypothesis could be divided into two main parts: 1) the downstream impact of 〈[u]〉 on western Atlantic rainfall patterns and 2) the upstream hypothesis that monsoon heating-driven stationary eddy HEMFC is the cause of relevant 〈[u]〉 variations. Each of these encompasses a set of detailed submechanisms, which will not be decomposed here for brevity. However, experiments on part 1 alone may be seen in Fig. 7.4 of Kelly (2012), showing rainfall pattern displacements in response to zonally uniform torques, as used in Shaw and Boos (2012) and Boos and Shaw (2013). These experiments suggest that CAM’s mechanisms of precipitation production (cumulus parameterizations, etc.), while debatable in detail, are sufficient for study of horizontal displacements of its fairly realistic large-scale patterns of flow and rainfall.
The climatology of CAM5 (out control run) is shown in Fig. 10. The map of February mean rainfall shows reasonable values over northern Australia in February (Fig. 10a) but with some western bias compared to observations (Fig. 3c). The annual cycle of rainfall in AUS (Fig. 10b) has a peak in February of about 10 mm day−1, similar to observations (Fig. 3d). Moreover, the distinct February rainfall minimum in SE Brazil is also present in the model (Fig. 10b). The ability of the coarsely configured CAM5 to capture the February drying in SE Brazil suggests the origin of the drying may not hinge on very subtle physical processes such as local geographical effects.
(a) February rainfall climatology map and (b) annual cycle in the indicated box regions in (a) for control CAM5 run.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
To test the entire hypothesized chain of causality from the Australian monsoon through mean zonal flow to rainfall in SE Brazil, here we show the response to imposed heatings (AUSheat and AUSheat_x2), as described in section 2 and illustrated in Fig. 3. Figure 11 shows the multiyear mean response (experiment minus control, February only) of 150-hPa eddy momentum flux and geopotential height. The heating centered at 10°S, 140°E (pink star in Fig. 11) yields enhanced anticyclonic circulation (positive geopotential anomaly) off the west coast of Australia, driving positive momentum flux or eddy northeasterlies (red colors) near 15°S, 100°E and negative momentum flux or eddy northwesterlies (blue colors) near 35°S, 100°E (Figs. 11a,b). The divergence of these fluxes produces easterly (westward) acceleration in the latitude band 20°–25°S (Fig. 11c), in agreement with eddy momentum flux divergence patterns in reanalysis (Figs. 7b,c). The model response near longitudes 80°–120°E (Fig. 11c) approximately doubles with a doubling of the forcing, indicating linearity of this part of the response. Interestingly, there is a secondary forcing proportional response near longitudes 200°–240°E (Fig. 11c) due to an enhancement of cyclonic circulation (negative geopotential anomaly), which also coincides with the South Pacific cyclone or TUTT in observations (Figs. 7b,c). This anomalous TUTT southeast of the forcing includes eddy southeasterlies (blue) near 30°–35°S and eddy southwesterlies (red) near 15°–20°S (Figs. 12a,b), also yielding net divergence (easterly acceleration) on its western edge. In the Northern Hemisphere, the marine TUTTs have similarly been shown to be a remote, linear response to monsoon heating (Hoskins and Rodwell 1995).
The mean February response (experiment minus control) of 150-hPa stationary eddy momentum fluxes u*υ* (color shading) and eddy geopotential height (black contours, contour interval of ±20 m; solid and dashed contours indicate a positive and negative response, respectively) from (a) AUSheat and (b) AUSheat_x2 experiments. (c) The respective HEMFC integrated across the 20°–25°S latitude belt for each experiment and the control simulation. Solid (dashed) contours in (a),(b) indicate positive (negative) height anomalies. The pink star in (a),(b) indicates location of forcing (i.e., heating) centered at 10°S, 140°E.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
(a),(b) Planetary zonal-mean (average across all longitudes) stationary HEMFC and (c),(d) mean meridional streamfunction response (109 kg s−1) in February for (left) AUSheat and (right) AUSheat_x2 experiments.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
The response of upper-level momentum fluxes to heating thus appears twofold: 1) Upper-level divergence drives an anticyclone to the southwest, with divergence of zonal momentum on its western edge (~100°E). 2) In addition, upper-level convergence of weaker magnitude strengthens the upper-level cyclone (TUTT) in the central-eastern Pacific, also yielding divergence of zonal momentum on its western edge (~220°E). The phase tilt of the anticyclone and cyclone pair, and the resulting poleward flux of easterly momentum in Fig. 11, resemble the steady Rossby wave response predicted by linear solutions (e.g., Fig. 9 of Matsuno 1966) and also seen in related, nonlinear simulations on the sphere (Norton 2006; Showman and Polvani 2010).
Since the net effect of stationary eddies on 〈[u]〉 is via their zonal mean, Fig. 12 shows the zonal average of the stationary HEMFC response to the heating experiments. Also shown is the response of the secondary mean meridional circulation (MMC) as seen by zonally averaged (across all longitudes) streamfunction contours. There is net divergence of zonal momentum (blue contours), or easterly acceleration, on the order of 1–2 m s−1 day−1 in the subtropics centered near 150–200 hPa around 20°–25°S (Figs. 12a,b). In the tropical forcing region itself (10°S), there is net convergence of zonal momentum (red contours), or westerly acceleration aloft, but also near the surface, reflecting the strong, northwesterly, cross-equatorial inflow into northern Australia originating near East Asia (not shown). The heating and heating-induced eddy momentum fluxes drive a secondary MMC with upward vertical motion (zero contour) near the forcing latitude of 10°S (Figs. 12c,d), which acts to redistribute zonal momentum downward from the upper levels where the direct HEMFC occurs.
Forced changes to [u] and 〈[u]〉 can be seen in Fig. 13. A robust forcing proportional equivalent barotropic response of the mean zonal wind is seen in the subtropics centered around 20°–25°S. Peak changes are in the upper troposphere but with a deep easterly response extending all the way down to the surface in the subtropics. Since angular momentum is conserved, there is also a westerly response, primarily in the deep tropics in the forcing region, and also poleward near 35°–40°S. This westerly response is also consistent with the geopotential and wind pattern in Fig. 11, with positive u*υ* equatorward of 20°S and negative u*υ* poleward of 30°S on the western flank of the anomalous monsoon anticyclone (80°–120°E) and marine TUTT (200°–240°E).
(a),(c) Model response (experiment minus control) in February of the planetary zonal-mean (average across all longitudes) wind on pressure levels and (b),(d) the vertically averaged zonal-mean zonal wind for (top) AUSheat and (bottom) AUSheat_x2 experiments.
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
Could this wave–mean flow interaction be interpreted in terms of classical Rossby wave source and propagation frameworks and the associated concept of pseudomomentum? Chapter 12.1 of Vallis (2006) offers a general discussion. In the literature of zonal-mean, eddy-driven jets, “stirring” of transient Rossby waves by low-level baroclinic instability causes upward and equatorward Rossby wave propagation (Edmon et al. 1980), whose pseudomomentum flux leads to eastward acceleration in the midlatitude stirred regions and westward acceleration in the subtropics where the waves break and dissipate. The net effect is a tightening of vorticity gradients and an enhancement of the mean meridional shear of 〈[u]〉 across latitudes 20°–40°S. In our monsoonally driven problem, involving longitudinally resolved but stationary rather than transient waves, the main Rossby wave source (Sardeshmukh and Hoskins 1988) in the model is off the southwest corner of Australia near 40°S, 105°E, where the divergent outflow from the heating interacts with the background northwesterlies on the western edge of the upper-level anticyclone (Fig. 11). The present result is analogous to that in Kelly and Mapes (2013, their section 3). It is unclear whether linear Rossby wave propagation theory, for a spectrally and spatially complex wave source and wavy background state, can offer more satisfying explanations of the results above than our straightforward spatial description of modeled [u*υ*] fields.
The remote impact of the heating experiments on the climate in the subtropics is summarized in Figs. 14 and 15, which show the February SLP and 850-hPa wind (Fig. 14) and precipitation (Fig. 15) responses. Both the Pacific and Atlantic subtropical anticyclones are preferentially strengthened on their northwest edges, with easterly anomalies of about 1 m s−1 impinging on SE Brazil at 20°S in AUSheat (Fig. 14a). The model SLP increase of 0.5–1.0 hPa in the western Atlantic between AUSheat and AUSheat_x2 (Fig. 14) is similar to the observed February anomaly (February minus January–March mean) seen in SE Brazil climatology (Fig. 4f) of about 0.5 hPa. The pattern of the SLP model response in the South Atlantic is also consistent with observed February anomalies, as seen both in the climatology (Fig. 4) and interannual variations (Fig. 9), which show positive anomalies on the northwest corner of the SASA near SE Brazil and negative anomalies on its southeast corner near South Africa. The pattern in Fig. 14 suggests a westward displacement of the entire subtropical high flow structure, rather than simply a basin-scale strengthening. This pattern is consistent with the enhancement of the meridional shear profile of 〈[u]〉 (easterlies equatorward of 30°S and westerlies poleward of 30°S; see Fig. 13), a result interpretable in light of Chen et al. (2001), where a sheared zonal-mean flow shapes the horizontal phase tilt of the subtropical anticyclones.
February sea level pressure (color shading) and 850-hPa wind (vectors) response (experiment minus control) from (a) AUSheat and (b) AUSheat_x2 experiments. Black contours show the total sea level pressure in the control run to indicate the mean position of the subtropical anticyclones for reference (contour interval of 2 hPa with maximum closed contour value of 1021 hPa).
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
As in Fig. 14, but for the February precipitation response (model minus control).
Citation: Journal of Climate 29, 20; 10.1175/JCLI-D-15-0838.1
Is the westward displacement of the SASA best viewed as a result of forcing-modified [u] advecting climatological zonal vorticity gradients? Or alternatively, might the subtropical streamfunction changes be viewed as the downstream end of a stationary Rossby wave train on the climatological mean flow, refracted from its source region near western Australia, in a pattern that might resemble a Pacific–South American (PSA)-type mode (Mo and Paegle 2001)? Are these viewpoints truly distinct? Formal analysis using Rossby wave tracing and vorticity budget diagnostics might be illuminating in future work but is beyond the present scope.
The forced precipitation response is shown in Fig. 15. Rising motion driven by our deep prescribed heat source enhances convective precipitation locally (unsurprisingly) and also over a broad region of northern Australia and the Maritime Continent west of the date line in a pattern that closely resembles the positive rainfall anomalies seen in interannual regressions (Fig. 8). In the South Atlantic, there is a distinct drying in the western basin with anomalies oriented in the northwest–southeast belt. The magnitude of this dry belt increases from 1 to 2 mm day−1 with doubled forcing, while the pattern of the response remains unchanged, indicating linearity of the entire chain of effects. Negative values centered near 330°E in the eastern Atlantic extend to the northwest into SE Brazil near 20°–30°S, a pattern that corresponds to the February drying seen on the east flank of SACZ in observed climatology (Fig. 4) and interannual variations (Fig. 8). The magnitude of the drying is about 1 mm day−1 in SE Brazil, comparable to observations (Fig. 4b). Interestingly, over the Amazon basin (10°S, ~300°E), there is also drying under the influence of easterly anomalies, despite lower SLP values (Fig. 14). The model shows a broader scale of drying over the Amazon compared to observations (Fig. 4a).
The model precipitation response on the east flank of the SPCZ near 220°–240°E also resembles observations (Fig. 8). The similarity of drying on the western margin of the subtropical anticyclone over both the Atlantic and Pacific basins (Figs. 8 and 15) supports the hypothesis that the effects flow from changes in the mean zonal flow 〈[u]〉, whose effect is felt across all longitudes by definition.
5. Summary and discussion
A dry February signal with a magnitude of about 30% of the seasonal mean has been identified in Brazilian rainfall data along the eastern flank of the SACZ. The local rainfall reduction in SE Brazil coincides with a westward displacement of the SASA in February, as seen in observed climatology (Fig. 4e), interannual variation of February anomalies (Fig. 9), and model experiments (Fig. 14) and is negatively associated with Australian monsoon precipitation (Fig. 8) and forced enhancements thereof (Fig. 15).
We hypothesized that the westward displacement of the SASA in February is caused by zonal-mean easterly flow, whose principle cause is a stationary eddy momentum torque due to stationary heating of the Australian monsoon. The poleward expansion of tropical, zonal-mean easterlies to around 20°S in February (Fig. 6), driven by momentum fluxes on the western edge of the upper-level monsoon high (Figs. 7 and 11), appears to be a key intermediate process connecting Australian monsoon heating to drying along the western edge of the near-surface anticyclones in the Southern Hemisphere subtropics. This hypothesis draws on an analogy to the better-studied boreal midsummer drought (Magaña et al. 1999) in the North Atlantic. As in Kelly and Mapes (2011, 2013), we have tested these hypotheses using both correlation analysis and model experiments with specified forcing at the upstream (monsoon) end of the hypothesized causal chain.
Australian monsoon heating near the Maritime Continent appears to cause easterly 〈[u]〉 in the Southern Hemisphere, albeit less prominently than its South Asian summer counterpart. In this sense, the notion of the Asian and Australian monsoons as a unified system (e.g., Webster et al. 1998) may be appropriate despite the hemispheric asymmetries.
The role of monsoon heating on the formation and maintenance of the mean summertime oceanic subtropical anticyclones has been previously documented (Rodwell and Hoskins 1996; Rodwell and Hoskins 2001; Chen et al. 2001). Here, we suggest that monsoon eddy–mean flow interactions are also at play and govern subseasonal HFCA changes in the structure of these stationary waves, with 〈[u]〉 at subtropical latitudes causing a westward displacement of the anticyclones in those latitudes in midsummer. Climatological mean 〈[u]〉 at these latitudes decreases and even turns negative in midsummer (Fig. 6). This decrease can have several physical effects. One is the direct effect of 〈[u]〉 in zonal advective tendencies in various budget equations, such as potential vorticity, shearing the shape of the subtropical anticyclones in ways relevant to this problem (Chen et al. 2001). Alternatively, changes in the meridional structure of 〈[u]〉 may dictate changes in the refractive index of stationary Rossby waves (Karoly 1983; Hoskins and Ambrizzi 1993), whose propagation has been recently shown as important to the origin of the subtropical convergence zones (van der Wiel et al. 2015).
While the role of eddy momentum flux in the climatological flow is far from a new subject [reviewed in Kraucunas and Hartmann (2005) and Dima et al. (2005)], it remains an active research area, and the feature studied here is a fine-scale structure in a less-examined aspect of the [u] budget. Much research focuses on the jets and their eddy interactions in the vigorous macroturbulence of the midlatitudes (e.g., Lorenz 2014a,b), but this paper suggests that the subtle dynamics of relatively small summertime [u] changes in the more quiescent subtropics, where the background [u] is smaller, can also be consequential. Reconciling the dynamics presented here with Rossby wave propagation and pseudomomentum theory might add valuable insights and generality, and metrics other than total [u] and its jets might prove a useful focal point for deepening the study of eddy–mean flow interactions and their consequences for climate and weather.
Acknowledgments
The authors gratefully acknowledge financial support from NSF Grant 0731520 and ONR Grant N000141310704. Computing resources were provided by the University of Miami’s Center for Computational Science (CCS) as well as NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation.
REFERENCES
Ait-Chaalal, F., and T. Schneider, 2015: Why eddy momentum fluxes are concentrated in the upper troposphere. J. Atmos. Sci., 72, 1585–1604, doi:10.1175/JAS-D-14-0243.1.
Allan, R. J., 1983: Monsoon and teleconnection variability over Australasia during the Southern Hemisphere summers of 1973–77. Mon. Wea. Rev., 111, 113–142, doi:10.1175/1520-0493(1983)111<0113:MATVOA>2.0.CO;2.
Bamston, A. G., M. Chelliah, and S. B. Goldenberg, 1997: Documentation of a highly ENSO-related SST region in the equatorial Pacific: Research note. Atmos.–Ocean, 35, 367–383, doi:10.1080/07055900.1997.9649597.
Boos, W. R., and T. A. Shaw, 2013: The effect of moist convection on the tropospheric response to tropical and subtropical zonally asymmetric torques. J. Atmos. Sci., 70, 4089–4111, doi:10.1175/JAS-D-13-041.1.
Carvalho, L. M. V., C. Jones, and B. Liebmann, 2004: The South Atlantic convergence zone: Intensity, form, persistence, and relationships with intraseasonal to interannual activity and extreme rainfall. J. Climate, 17, 88–108, doi:10.1175/1520-0442(2004)017<0088:TSACZI>2.0.CO;2.
Chen, P., M. P. Hoerling, and R. M. Dole, 2001: The origin of the subtropical anticyclones. J. Atmos. Sci., 58, 1827–1835, doi:10.1175/1520-0469(2001)058<1827:TOOTSA>2.0.CO;2.
Coelho, C. A. S., and Coauthors, 2015: The 2014 southeast Brazil austral summer drought: Regional scale mechanisms and teleconnections. Climate Dyn., 46, 3737–3752, doi:10.1007/s00382-015-2800-1.
Conley, A. J., and Coauthors, 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 289 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf.]
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Dima, I. M., J. M. Wallace, and I. Kraucunas, 2005: Tropical zonal momentum balance in the NCEP reanalyses. J. Atmos. Sci., 62, 2499–2513, doi:10.1175/JAS3486.1.
Edmon, H. J., B. J. Hoskins, and M. E. McIntyre, 1980: Eliassen-Palm cross sections for the troposphere. J. Atmos. Sci., 37, 2600–2616, doi:10.1175/1520-0469(1980)037<2600:EPCSFT>2.0.CO;2.
Figueroa, S. N., P. Satyamurty, and P. L. Da Silva Dias, 1995: Simulations of the summer circulation over the South American region with an eta coordinate model. J. Atmos. Sci., 52, 1573–1584, doi:10.1175/1520-0469(1995)052<1573:SOTSCO>2.0.CO;2.
Gamble, D. W., and S. Curtis, 2008: Caribbean precipitation: Review, model, and prospect. Prog. Phys. Geogr., 32, 265–276, doi:10.1177/0309133308096027.
Hill, K. J., A. S. Taschetto, and M. H. England, 2011: Sensitivity of South American summer rainfall to tropical Pacific Ocean SST anomalies. Geophys. Res. Lett., 38, L01701, doi:10.1029/2010GL045571.
Holton, J. R., 2004: An Introduction to Dynamic Meteorology. 4th ed. Academic Press, 532 pp.
Hoskins, B. J., and T. Ambrizzi, 1993: Rossby wave propagation on a realistic longitudinally varying flow. J. Atmos. Sci., 50, 1661–1671, doi:10.1175/1520-0469(1993)050<1661:RWPOAR>2.0.CO;2.
Hoskins, B. J., and M. J. Rodwell, 1995: A model of the Asian summer monsoon. Part I: The global scale. J. Atmos. Sci., 52, 1329–1340, doi:10.1175/1520-0469(1995)052<1329:AMOTAS>2.0.CO;2.
Huang, B., and Coauthors, 2015: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4). Part I: Upgrades and intercomparison. J. Climate, 28, 911–930, doi:10.1175/JCLI-D-14-00006.1.
Hurrell, J. W., J. J. Hack, D. Shea, J. M. Caron, and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary data set for the Community Atmosphere Model. J. Climate, 21, 5145–5153, doi:10.1175/2008JCLI2292.1.
Jones, C., and L. M. V. Carvalho, 2002: Active and break phases in the South American monsoon system. J. Climate, 15, 905–914, doi:10.1175/1520-0442(2002)015<0905:AABPIT>2.0.CO;2.
Kajikawa, Y., B. Wang, and J. Yang, 2010: A multi-time scale Australian monsoon index. Int. J. Climatol., 30, 1114–1120, doi:10.1002/joc.1955.
Karoly, D. J., 1983: Rossby wave propagation in a barotropic atmosphere. Dyn. Atmos. Oceans, 7, 111–125, doi:10.1016/0377-0265(83)90013-1.
Kelly, P., 2012: Planetary dynamics of the western Atlantic mid-summer drought and its relationship to the Asian monsoon. Ph.D. dissertation, University of Miami, 143 pp.
Kelly, P., and B. Mapes, 2011: Zonal mean wind, the Indian monsoon, and July drying in the western Atlantic subtropics. J. Geophys. Res., 116, D00Q07, doi:10.1029/2010JD015405.
Kelly, P., and B. Mapes, 2013: Asian monsoon forcing of subtropical easterlies in the Community Atmosphere Model: Summer climate implications for the western Atlantic. J. Climate, 26, 2741–2755, doi:10.1175/JCLI-D-12-00339.1.
Kraucunas, I., and D. L. Hartmann, 2005: Equatorial superrotation and the factors controlling the zonal-mean zonal winds in the tropical upper troposphere. J. Atmos. Sci., 62, 371–389, doi:10.1175/JAS-3365.1.
L’Heureux, M. L., and D. W. J. Thompson, 2006: Observed relationships between the El Niño–Southern Oscillation and the extratropical zonal-mean circulation. J. Climate, 19, 276–287, doi:10.1175/JCLI3617.1.
Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277.
Liebmann, B., G. N. Kiladis, J. A. Marengo, T. Ambrizzi, and J. D. Glick, 1999: Submonthly convective variability over South America and the South Atlantic convergence zone. J. Climate, 12, 1877–1891, doi:10.1175/1520-0442(1999)012<1877:SCVOSA>2.0.CO;2.
Lintner, B. R., and J. D. Neelin, 2007: A prototype for convective margin shifts. Geophys. Res. Lett., 34, L05812, doi:10.1029/2006GL027305.
Lintner, B. R., and J. D. Neelin, 2008: Eastern margin variability of the South Pacific convergence zone. Geophys. Res. Lett., 35, L16701, doi:10.1029/2008GL034298.
Liu, W. T., W. Tang, and F. J. Wentz, 1992: Precipitable water and surface humidity over global oceans from special sensor microwave imager and European Centre for Medium-Range Weather Forecasts. J. Geophys. Res., 97, 2251–2264, doi:10.1029/91JC02615.
Lorenz, D. J., 2014a: Understanding midlatitude jet variability and change using Rossby wave chromatography: Poleward-shifted jets in response to external forcing. J. Atmos. Sci., 71, 2370–2389, doi:10.1175/JAS-D-13-0200.1.
Lorenz, D. J., 2014b: Understanding midlatitude jet variability and change using Rossby wave chromatography: Wave–mean flow interaction. J. Atmos. Sci., 71, 3684–3705, doi:10.1175/JAS-D-13-0201.1.
Lorenz, D. J., and D. L. Hartmann, 2001: Eddy–zonal flow feedback in the Southern Hemisphere. J. Atmos. Sci., 58, 3312–3327, doi:10.1175/1520-0469(2001)058<3312:EZFFIT>2.0.CO;2.
Magaña, V., J. A. Amador, and S. Medina, 1999: The midsummer drought over Mexico and Central America. J. Climate, 12, 1577–1588, doi:10.1175/1520-0442(1999)012<1577:TMDOMA>2.0.CO;2.
Mapes, B. E., P. Liu, and N. Buenning, 2005: Indian monsoon onset and America’s midsummer drought: Out-of-equilibrium responses to smooth seasonal forcing. J. Climate, 18, 1109–1115, doi:10.1175/JCLI-3310.1.
Matsuno, T., 1966: Quasi-geostrophic motions in the equatorial area. J. Meteor. Soc. Japan, 44, 25–43.
McBride, J. L., 1987: The Australian monsoon. Monsoon Meteorology, C. P. Change and T. N. Krishnamurti, Eds., Oxford University Press, 203–213.
Menne, M. J., I. Durre, B. G. Gleason, T. G. Houston, and R. S. Vose, 2012: An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Oceanic Technol., 29, 897–910, doi:10.1175/JTECH-D-11-00103.1.
Miyasaka, T., and H. Nakamura, 2005: Structure and formation mechanisms of the Northern Hemisphere summertime subtropical highs. J. Climate, 18, 5046–5065, doi:10.1175/JCLI3599.1.
Miyasaka, T., and H. Nakamura, 2010: Structure and mechanisms of the Southern Hemisphere summertime subtropical anticyclones. J. Climate, 23, 2115–2130, doi:10.1175/2009JCLI3008.1.
Mo, K. C., and J. Paegle, 2001: The Pacific–South American modes and their downstream impact. Int. J. Climatol., 21, 1211–1229, doi:10.1002/joc.685.
Nogués-Paegle, J., and K. C. Mo, 1997: Alternating wet and dry conditions over South America during summer. Mon. Wea. Rev., 125, 279–291, doi:10.1175/1520-0493(1997)125<0279:AWADCO>2.0.CO;2.
Norton, W. A., 2006: Tropical wave driving of the annual cycle in tropical tropopause temperatures. Part II: Model results. J. Atmos. Sci., 63, 1420–1431, doi:10.1175/JAS3698.1.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, doi:10.1175/2007JCLI1824.1.
Robertson, A. W., and C. R. Mechoso, 2000: Interannual and interdecadal variability of the South Atlantic convergence zone. Mon. Wea. Rev., 128, 2947–2957, doi:10.1175/1520-0493(2000)128<2947:IAIVOT>2.0.CO;2.
Rodwell, M. J., and B. J. Hoskins, 1996: Monsoons and the dynamics of deserts. Quart. J. Roy. Meteor. Soc., 122, 1385–1404, doi:10.1002/qj.49712253408.
Rodwell, M. J., and B. J. Hoskins, 2001: Subtropical anticyclones and summer monsoons. J. Climate, 14, 3192–3211, doi:10.1175/1520-0442(2001)014<3192:SAASM>2.0.CO;2.
Sadler, J. C., 1975: The upper tropospheric circulation over the global tropics. Department of Meteorology, University of Hawaii, UHMET-75-05, 35 pp. [Available from Department of Meteorology, University of Hawaii, 2525 Correa Rd., Honolulu, HI 96822.]
Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci., 45, 1228–1251, doi:10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.
Schumacher, C., M. H. Zhang, and P. E. Ciesielski, 2007: Heating structures of the TRMM field campaigns. J. Atmos. Sci., 64, 2593–2610, doi:10.1175/JAS3938.1.
Shaw, T. A., and W. R. Boos, 2012: The tropospheric response to tropical and subtropical zonally asymmetric torques: Analytical and idealized numerical model results. J. Atmos. Sci., 69, 214–235, doi:10.1175/JAS-D-11-0139.1.
Showman, A. P., and L. M. Polvani, 2010: The Matsuno–Gill model and equatorial superrotation. Geophys. Res. Lett., 37, L18811, doi:10.1029/2010GL044343.
Tanaka, M., 1981: Interannual fluctuations of the tropical monsoon circulation over the greater WMONEX area. J. Meteor. Soc. Japan, 59, 825–831.
Trenberth, K. E., and C. J. Guillemot, 1995: Evaluation of the global atmospheric moisture budget as seen from analyses. J. Climate, 8, 2255–2272, doi:10.1175/1520-0442(1995)008<2255:EOTGAM>2.0.CO;2.
Vallis, G. K., 2006: Atmospheric and Oceanic Fluid Dynamics. Cambridge University Press, 745 pp.
van der Wiel, K., A. J. Matthews, D. P. Stevens, and M. M. Joshi, 2015: A dynamical framework for the origin of the diagonal South Pacific and South Atlantic convergence zones. Quart. J. Roy. Meteor. Soc., 141, 1997–2010, doi:10.1002/qj.2508.
Wang, B., I. S. Kang, and J. Y. Lee, 2004: Ensemble simulations of Asian–Australian monsoon variability by 11 AGCMs. J. Climate, 17, 803–818, doi:10.1175/1520-0442(2004)017<0803:ESOAMV>2.0.CO;2.
Webster, P. J., V. O. Magaña, T. N. Palmer, J. Shukla, R. A. Tomas, M. Yanai, and T. Yasunari, 1998: Monsoons: Processes, predictability, and the prospects for prediction. J. Geophys. Res., 103, 14 451–14 510, doi:10.1029/97JC02719.
Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17 year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.
Zhang, C., and S. M. Hagos, 2009: Bi-modal structure and variability of large-scale diabatic heating in the tropics. J. Atmos. Sci., 66, 3621–3640, doi:10.1175/2009JAS3089.1.
Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.–Ocean, 33, 407–446, doi:10.1080/07055900.1995.9649539.