The Asian Monsoon in the Superparameterized CCSM and Its Relationship to Tropical Wave Activity

5134 JOURNAL OF CLIMATE VOLUME 24

CHARLOTTE A. DEMOTT Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

CRISTIANA STAN Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

DAVID A. RANDALL Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

JAMES L. KINTER III Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia

MARAT KHAIROUTDINOV School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York

(Manuscript received 15 November 2010, in ﬁnal form 7 March 2011)

ABSTRACT

Three general circulation models (GCMs) are used to analyze the impacts of air–sea coupling and superparameterized (SP) convection on the Asian summer monsoon: Community Climate System Model (CCSM) (coupled, conventional convection), SP Community Atmosphere Model (SP-CAM) (uncoupled, SP convection), and SP-CCSM (coupled, SP). In SP-CCSM, coupling improves the basic-state climate relative to SP- CAM and reduces excessive tropical variability in SP-CAM. Adding SP improves tropical variability, the simulation of easterly zonal shear over the Indian and western Pacific Oceans, and increases negative sea surface temperature (SST) biases in that region. SP-CCSM is the only model to reasonably simulate the eastward-, westward-, and northward-propagating components of the Asian monsoon. CCSM and SP-CCSM mimic the observed phasing of northward- propagating intraseasonal oscillation (NPISO), SST, precipitation, and surface stress anomalies, while SP-CAM is limited in this regard. SP-CCSM produces a variety of tropical waves with spectral characteristics similar to those in observations. Simulated equatorial Rossby (ER) and mixed Rossby–gravity (MRG) waves may lead to different simulations of the NPISO in each model. Each model exhibits some northward propagation for ER waves but only SP-CCSM produces northward-propagating MRG waves, as in observations. The combination of ER and MRG waves over the Indian Ocean influences the spatiotemporal structure of the NPISO and contributes to the differences seen in each model. The role of ocean coupling must be considered in terms of the time scale of the SST response compared to the time scale of tropical variability. High-frequency disturbances experience coupling via its changes to the basic state, while lower-frequency disturbances may respond directly to SST fluctuations.

Corresponding author address: Charlotte DeMott, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. E-mail: [email protected]

DOI: 10.1175/2011JCLI4202.1

Ó 2011 American Meteorological Society 1OCTOBER 2011 D E M O T T E T A L . 5135

1. Introduction of the mature phase of the BSISO. Lin et al. (2008) analyzed the BSISO in 14 ocean–atmosphere coupled GCMs The Asian summer monsoon is a complex system of participating in the Fourth Assessment Report (AR4) of wind and precipitation anomalies that spans from the the Intergovernmental Panel on Climate Change (IPCC) Indian Ocean to the western tropical Pacific Ocean. The and found that most models 1) concentrate too much summer monsoon season features enhanced rainfall precipitation near the equator and 2) underestimate expanding northward over the Indian subcontinent and subseasonal (2–128 days) and 12–24-day westward- Southeast Asia, beginning in late April to early May, propagating variance. They also noted that most models and retreating equatorward in September–October. In produce at least some form of northward and westward the western Pacific Ocean, onset and retreat occur earlier propagation but lack eastward-propagating intraseasonal and later, respectively. Subseasonal eastward-, northward-, variability. Sperber and Annamalai (2008) found that most and westward-propagating wind and precipitation anom- coupled models simulate eastward propagation over the alies produce ‘‘active’’ and ‘‘break’’ cycles with a period of Indian Ocean but fail to extend this mode into the western 30;50 days over western India, the Bay of Bengal, and Pacific Ocean, missing an important part of the MJO life Southeast Asia (Yasunari 1979; Krishnamurti and Sub- cycle. The missing extension into the western Pacific pre- rahmanyam 1982; Lawrence and Webster 2002). The slow vents the models from replicating the observed northwest– (;5ms21) eastward-propagating mode of the Asian southeast tilted rainband structure, which arises from the monsoon is generally interpreted (Murakami et al. 1984; eastward-moving BSISO and its associated northwest- Madden 1986; Nakazawa 1986) as the boreal summer propagating Rossby waves. manifestation of the 30–60-day intraseasonal oscillation MJO-linked NPISO events arise from equatorially (ISO) described by Madden and Julian (1971, 1972, 1994) trapped Rossby (ER) waves (Matsuno 1966; Wheeler and Zhang (2005). Unlike the boreal winter ISO, the and Kiladis 1999) excited by MJO convection (Wang boreal summer ISO (BSISO) also exhibits northward and Xie 1997; Annamalai and Slingo 2001; Lawrence and propagation at a rate of ;18 latitude per day, as the Webster 2002), which are modified by the Indian Ocean northwest–southeast tilted ISO convective envelope trans- boreal summer easterly zonal shear profile (Xie and lates eastward (Yasunari 1979; Lau and Chan 1986). Not Wang 1996; Lawrence and Webster 2002; Jiang et al. all northward-propagating monsoon events are linked 2004; Drbohlav and Wang 2007) and surface latent and to eastward propagation, but various studies (Wang and sensible heat fluxes (Kemball-Cook and Wang 2001; Rui 1990; Lawrence and Webster 2002; Sperber and Sperber and Annamalai 2008) so that a northward Annamalai 2008; Klingaman et al. 2008b) estimate the component is introduced to their nominal westward fraction to be around 50%–85%. Like the northern win- propagation. While the mechanism for northward ter ISO, the BSISO is also characterized by 12–24-day propagation is not yet fully understood, the combination westward-propagating equatorial Rossby waves (e.g., Lin of eastward-propagating MJO convection and associ- et al. 2008 and references therein), which are associated ated northwestward-propagating ER waves produces with the NPISO. For the remainder of our discussion, a northwest–southeast tilted rainband that translates to NPISO refers to the northward-propagating component of the east. For an observer at a fixed point, the eastward the BSISO and the Madden–Julian oscillation (MJO) re- passage of this tilted rainband appears as a northward- fers to the eastward-propagating component of the BSISO. moving convective anomaly. These ideas have been Multimodel analyses of the MJO in GCMs (Lin et al. described by Wang and Xie (1997) and Lawrence and 2006; Zhang et al. 2006) concluded that most models fail Webster (2002) and are illustrated schematically in Fig. 1. to produce significant spectral peaks at MJO spatial and The NPISO is accompanied by coherent variations in temporal scales and do not propagate intraseasonal SST and associated surface fluxes, with most studies convection east of the Maritime Continent. Furthermore, concluding that atmospheric anomalies (such as cloud- simulated equatorially trapped Kelvin and Rossby waves iness and surface winds) drive the surface anomalies associated with the MJO are often characterized by ex- (Hendon and Glick 1997; Krishnamurti et al. 1988; cessive equivalent depths, indicative of a lack of convec- Shinoda et al. 1998; Woolnough et al. 2000; Vecchi and tive coupling. Harrison 2002), although modeling studies by Bellon Simulation of the Asian monsoon system is a par- et al. (2008) and Marshall et al. (2008) suggest that ticularly challenging test for GCMs. Kang et al. (2002) surface fluxes may modify the period of the disturbance. and Waliser et al. (2003) found that a collection of Evidence for the role of surface fluxes on the NPISO atmosphere-only GCMs mostly failed to produce the behavior is found in the improvement of NPISO simu- eastward-propagating convection and the signature lation by GCMs driven with high-frequency specified northwest–southeast-tilted rainband that is characteristic SSTs (Klingaman et al. 2008a) or coupled to ocean 5136 JOURNAL OF CLIMATE VOLUME 24

FIG. 1. Schematic illustration of the relationship between the MJO and ER waves in the boreal summer Asian monsoon. (top) Spatial relationship of MJO (light blue) and ER (light red) convective anomalies, and Rossby gyre at ;850 hPa. Smaller convective ‘‘centroids’’ are indicated and used as MJO or ER convective ‘‘tags’’ in (bottom) the temporal evolution of eastward- propagating MJO convection (blue) and ER wave convective anomalies (red) that propagate to the northwest (dashed lines). Relative timing of ISO and Rossby convection is indicated by ‘‘t 5 0, 1, 2 ...’’ labels, where the time increment is ;1–3 days. At any given time, the northwestward- propagating Rossby waves and eastward-propagating MJO convection are aligned from the northwest to the southeast, leading to a northwest–southeast tilted rainband (green line). models (Fu et al. 2003, 2007; Fu and Wang 2004a; Wu Randall 2003), which is the atmosphere-only compo- and Kirtman 2005; Seo et al. 2007; Weng and Yu 2010). nent of SP-CCSM. Our goals are to determine which Nevertheless, many coupled models still struggle to pro- monsoon-related processes are improved in SP-CCSM duce the observed NPISO magnitude, phase relationship, and whether SP and/or coupling are responsible for the and local versus nonlocal control of SST, precipitation, improvement. and surface flux anomalies (Bollasina and Nigam 2009). Section 2 describes the models and provides some In this study, we analyze the simulated Asian mon- background information on their simulated MJOs. soon system in three GCMs using various combinations Section 3 compares the Indo-Pacific climate and annual of conventional cumulus parameterization, ‘‘superparame- cycle of precipitation in models and observations. Mon- terization,’’ and ocean–atmosphere coupling. Superparame- soon variability and its relation to surface fluxes and terization (SP) refers to the replacement of parameterized equatorially trapped waves are described in section 4. A convective tendencies with those simulated by a cloud- concluding discussion is given in section 5. resolving model (CRM) that is run within each GCM grid column (Grabowski 2001). We examine the BSISO 2. Model description and datasets in three simulations: 1) the ocean–atmosphere coupled Community Climate System Model (CCSM) version 3 The implementation of SP into CAM is described by (Collins et al. 2006b,a) with conventional cumulus param- Khairoutdinov and Randall (2001, 2003) and results eterization, 2) the superparameterized CCSM (SP-CCSM) are presented by Khairoutdinov and Randall (2003), (Stan et al. 2010), and the superparameterized Community Khairoutdinov et al. (2005, 2008), and DeMott et al. Atmosphere Model (SP-CAM) (Khairoutdinov and (2007, 2010). The use of SP improves the mean climate 1OCTOBER 2011 D E M O T T E T A L . 5137

FIG. 2. June–September mean precipitation and 850-hPa winds for (a) GPCP and NCEP reanalysis 1998–2008, (b) SP-CCSM years 4– 23, (c) SP-CAM 1986–2003, and (d) CCSM years 4–23. Longest wind vector in observations is 17 m s21, wind scaling consistent in all four panels. Precipitation contour interval is every 2 mm day21 starting at 2. Spatial correlation and rms errors for region plotted are given for each simulation. in some ways (diurnal cycle of rainfall, distribution of and the MJO. Tropical variability was improved without cirrus clouds) and degrades it in others (reduced marine significant degradation of the basic-state atmosphere. stratocumulus clouds, high precipitation bias in the In this study, all simulations are performed using T42 northwest Pacific Ocean during boreal summer). The resolution for the atmosphere with a semi-Lagrangian most notable improvement resulting from the addition dynamical core (Rasch et al. 2006). The simulation with of SP is an increase in tropical variability, including the SP-CAM uses 1986–2003 observed monthly-mean SSTs MJO. interpolated to daily mean values as its boundary condi- Benedict and Randall (2009) reported that, compared tions. SP-CCSM and CCSM utilize a low-resolution 38 to the standard CAM, SP-CAM better reproduces the version of the Parallel Ocean Program (POP) ocean observed space–time structures of the MJO and the model (Smith and Gent 2002) initialized at rest with vertical transfer of boundary layer heat and moisture to Levitus (1982) temperatures and salinities. Only the final the upper atmosphere but that the MJO is too strong in 20 years of the 23-yr coupled simulations are analyzed. SP-CAM. Thayer-Calder and Randall (2009) argued Several observational and reanalysis datasets are used that the excessive MJO activity in SP-CAM might be to evaluate the monsoon simulation in each model. All due to overmoistening of the atmosphere due to exces- data are obtained at daily resolution (or averaged to daily sive near-surface winds and latent heat fluxes. resolution if finer temporal sampling is available) and Meehl et al. (2006) found that, while the CCSM im- regridded to the GCM grid resolution. National Centers proves several aspects of the monsoon simulation com- for Environmental Prediction reanalysis (Kalnay et al. pared to the CAM, biases remain, some new ones are 1996) is the source of daily mean wind fields. Global- introduced, or some are overcorrected. A strong im- scale precipitation estimates are provided by the Global provement is seen in the precipitation annual cycle. Precipitation Climatology Project One-Degree Daily Stan et al. (2010) performed the first simulation with an (GPCP 1DD) dataset for 1998–2008 (Huffman et al. ocean–atmosphere coupled superparameterized GCM, 2001). Outgoing longwave radiation (OLR) from 1986 to adding SP to the CCSM. SP-CCSM exhibited improve- 2003 is obtained from NOAA (Liebmann and Smith ments over CCSM, including the mean precipitation 1996). Daily SST and surface wind stress estimates from pattern, equatorial SST cold tongue structure and asso- 1998 to 2008 were taken from the European Centre for ciated double intertropical convergence zone, periodicity Medium-Range Weather Forecasts Interim Re-Analysis of the El Ninõ–Southern Oscillation, the Asian monsoon, (ERA-Interim) dataset (Simmons et al. 2006). 5138 JOURNAL OF CLIMATE VOLUME 24

(JJAS) average precipitation and 850-hPa winds for observations and simulations are presented in Fig. 2. Pre- cipitation maxima are observed (Fig. 2a) near the west coast of India and along the eastern shore of the Bay of Bengal (BoB). Western Pacific precipitation maxima are located over the Philippines and just north of the equator in the ITCZ. Maximum mean 850-hPa winds (magnitude ;15 m s21) are observed at 108;158NovertheIndian Ocean. The low-level westerlies decrease east of the Indo-China Peninsula, shifting to easterlies near 1308E. In the CCSM (Fig. 2d), precipitation is affected by a double ITCZ bias, with anomalous zonally oriented precipitation bands in the Southern Hemisphere. Northern Hemisphere maxima are located too close to the equator and are too weak, while the western India maximum is largely absent. The CCSM 850-hPa winds are weaker than observed for the Indian Ocean and shift to easterly flow too far west, near 1108E. In SP-CAM (Fig. 2c), the precipitation pattern contrasts sharply with observations with a zonally oriented band of intense precipitation extending 658–1608E. The Indian Ocean winds are too FIG. 3. June–September SST bias (K) for (a) SP-CCSM and (b) strong, and the westerly flow extends all the way to 1558E. CCSM, contour interval 0.5 K with positive (negative) biases drawn In contrast, SP-CCSM (Fig. 2b) precipitation shows better with solid (dashed) lines. Observed climatology is from the HadISST agreement with observations, capturing the maxima over dataset 1982–2001; rms errors for the region plotted are indicated. western India, BoB, and the Philippines. The Southern Hemisphere secondary maximum is too weak, while 3. Basic state and mean annual cycle the Northern Hemisphere maxima remain somewhat We begin our assessment of monsoon simulations with greater than observed. The 850-hPa westerlies are slightly an evaluation of the Southeast Asian boreal summer cli- stronger than observed, but their zonal extent is well mate simulated with the three models. June–September simulated.

FIG. 4. June–September mean zonal shear, deﬁned as the difference in zonal winds at 200 and 850 hPa, for (a) NCEP reanalysis 1998–2008, (b) SP-CCSM years 4–23, (c) SP-CAM 1986–2003, and (d) CCSM years 4–23, contour interval 10 m s21 with westerly positive (easterly negative) shear indicated with solid (dashed) lines: zero contour is denoted with thick solid line. Model vs observations pattern correlation and rms differences are indicated. 1OCTOBER 2011 D E M O T T E T A L . 5139

FIG. 5. Mean annual cycle of precipitation vs latitude and the northern and southern limits of easterly shear for the Indian Ocean (658– 1008E) and western Paciﬁc Ocean (1008–1408E) for GPCP precipitation and NCEP winds (16a and 16e), SP-CCSM (16b and 16f), SP- CAM (16c and 16g), and CCSM (16d and 16h). Precipitation contour interval is 2 mm day21. Reference dates 1 May and 31 October are indicated with thin vertical lines.

CCSM and SP-CCSM SST biases are shown in Fig. 3. and easterly shear begin their northward advance Both models produce a small region of positive SST around 1 May (Fig. 5a). The monsoon precipitation biases near the African coast. Otherwise, SP-CCSM maximum remains at ;158N through mid-July, while the Indian Ocean SSTs are too cool with a maximum neg- oceanic convergence zone at ;58S persists throughout ative bias of ;2.5 K occurring in the northern Arabian the season (Bollasina and Nigam 2009). Each model Sea. In CCSM, Indian Ocean SST biases are smaller (Figs. 5b–d) captures some, but not all, elements of the than in SP-CCSM. The western Pacific Ocean is ;1.5 K annual cycle in the Indian Ocean. SP-CCSM and CCSM too cold in both models. The cold SST bias in SP-CCSM show a delayed onset of Indian Ocean monsoon rains may be responsible for some of the reduction in mean compared to observations. In SP-CCSM, the southern rainfall compared to SP-CAM. Indian Ocean precipitation band disappears in mid July Figure 4 shows the JJAS zonal shear between 200 and (near pentad 43). This coincides with the emergence of 850 hPa for observations and for each model. Observed the cold SST bias in that model, which may explain the easterly shear during JJAS (Fig. 4a) extends zonally lack of precipitation in this region. In SP-CAM (Fig. 5c), from central Africa eastward to near the date line and monsoon precipitation is too strong and too persistent. In meridionally from 88Sto;258N. SP-CAM (Fig. 4c) the western Pacific Ocean (Figs. 5e–h), SP-CAM and SP- simulates easterly shear that is too strong over the Indian CCSM both successfully capture the observed abrupt Ocean and extending too far east north of the equator, onset (Wang et al. 2009) of monsoon precipitation, as consistent with the zonally extensive band of westerlies in seen in the near-vertical orientation of the 4 mm day21 Fig. 2c. SP-CCSM easterly shear (Fig. 4b) is weaker than contour at approximately pentad 27. In contrast, the observed but has realistic areal coverage. CCSM (Fig. 4d) northward extension of precipitation in CCSM occurs easterly shear is weak and limited in areal coverage. For more gradually. In CCSM, the 850-hPa westerly winds the region plotted, SP-CCSM easterly shear exhibits the typically do not extend far enough east (Fig. 2d), which most optimal combination of high spatial correlation and may account for the delayed onset of easterly shear and low rms errors compared to observations. northward-propagating precipitation in this region. Figure 5 shows the mean annual cycle of precipitation as a function of latitude for the Indian and western Pa- 4. Monsoon variability cific Oceans. The latitudinal extent of the easterly shear a. Overview is indicated by the black curves highlighting the relationship between the northward excursions of precip- We begin our assessment of monsoon variability by itation and shear. In the Indian Ocean, observed rainfall examining the May–October longitudinal distribution of 5140 JOURNAL OF CLIMATE VOLUME 24

FIG. 6. Variance of May–October (a) MJO and (b) 12–24-day westward precipitation vs longitude for GPCP (1998–2008), SP-CCSM, SP-CAM, and SP-CCSM. Filtering is performed as in Lin et al. (2008): MJO (westward- propagating) precipitation is computed by averaging rainfall from 58 to 258N (108–208N) and filtering to retain eastward-propagating wavenumbers 1–6 (all westward wavenumbers) and periods 24–70 (12–24) days. Standard deviation as a percentage of May–October mean precipitation over the same latitude bands is shown in (c) and (d). variance for eastward- and westward-propagating pre- The zonal distribution of 12–24-day westward- cipitation (Fig. 6), and the latitudinal distribution of propagating precipitation is shown in Fig. 6b. CCSM northward-propagating precipitation (Fig. 7). The plots variance is again much weaker than observed. SP-CCSM are designed to be directly comparable to those shown in variance exceeds the observed variance at nearly all Lin et al. (2008), enabling comparison to other CGCMs. longitudes, with the largest biases in the Indian Ocean. Filtering is 24–70 days and positive zonal wavenumbers The three peaks in the SP-CCSM curve correspond to 1–6 for eastward propagation, 12–24 days and all nega- the seasonal precipitation maxima over the Bay of Bengal, tive zonal wavenumbers for westward propagation, and the Philippines, and the western Pacific Ocean. SP-CAM 24–70 days and positive meridional wavenumbers for westward-propagating precipitation variance maximizes northward propagation [see Fu and Wang (2004a,b) at nearly the same longitudes and is also excessive for details of meridional filtering]. The GPCP and sim- compared to observations. The standard deviation of ulated zonal distributions of the eastward-propagating precipitation as a percentage of May–October mean intraseasonal modes are shown in Fig. 6a. Observed precipitation (Figs. 6c and 6d) provides an estimate of variance for this mode maximizes in the eastern Indian how reasonable each model’s variance is for its basic- Ocean and minimizes just east of the date line. CCSM- state precipitation. Compared to observations, both east- simulated MJO activity is weak, while SP-CCSM produces ward and westward SP-CCSM variability are remarkably a realistic MJO with the magnitude and location of appropriate for that model’s basic-state precipitation. eastward-propagating precipitation in good agreement SP-CAM MJO variance is weak for its mean-state pre- with observations. When compared to Fig. 9 from Lin cipitation, but its excessive westward variance (Fig. 6b) is et al. (2008), SP-CCSM simulates a more realistic boreal appropriate for its mean state. summer MJO than any of the CGCMs analyzed in that Northward-propagating disturbances are extracted study. SP-CAM places too much variance in the western using the same method described by Lin et al. (2008); Pacific Ocean, consistent with the high precipitation that is, the time–latitude array of precipitation is de- anomaly in this region (Fig. 2c). composed into meridional wavenumber and frequency 1OCTOBER 2011 D E M O T T E T A L . 5141

FIG. 7. Variance of May–October NPISO precipitation in the (a) Indian Ocean (708–1008E) and (b) western Paciﬁc Ocean (1208–1608). Filtering and longitude range is as in Lin et al. (2008): NPISO precipitation is computed by averaging rainfall over the selected longitude range and ﬁltering 458S–458N rainfall to retain all northward-propagating wavenumbers. Standard deviation as a percentage of May–October mean precipitation over the same longitude bands is shown in (c) and (d).

Fourier coefficients, and the inverse transform is then with few interruptions in the precipitation front. CCSM applied to positive (northward) wavenumber co- NPISO events are weak, with northward propagation efficients. In the Indian Ocean (Fig. 7a), observed accomplished by discrete events that propagate poleward northward propagation is uniformly distributed from the forlessthan;108 before weakening. SP-CCSM NPISO equator to ;178N at which point it decreases mono- events are similar to those observed, with meridionally tonically to 358N. In CCSM, northward propagation is elongated precipitation bands. SP-CAM produces roughly weak and peaks at ;58S. Northern Hemisphere SP- equal numbers of both continuous and discrete NPISO CCSM northward propagation is too strong, similar to events. the westward propagation results. SP-CAM variability is In Fig. 9, the spatiotemporal behavior of ‘‘typical’’ again excessive with peak variance shifted north of the NPISO and eastward-propagating MJO events is il- SP-CCSM variance. Results for the western Pacific lustrated through the use of lag correlation analysis. The Ocean (Fig. 7b) are similar to those for the Indian figures, based on those of the Climate Variability and Ocean, except that SP-CCSM variance is not as exces- Predictability (CLIVAR) Research Program MJO sive and SP-CAM variance is even more excessive. The Working Group (CLIVAR Madden–Julian Oscillation scaled standard deviations (Figs. 7c and 7d) indicate that Working Group 2009), are constructed by correlating SP-CCSM northward variability is slightly low compared 20–100-day filtered winds or precipitation at each lag to the model’s mean-state precipitation, while SP-CAM and longitude or latitude with a base time series of 20– variability is excessive, even for its greater mean-state 100-day filtered precipitation averaged over the equa- precipitation. torial Indian Ocean (108S–58N, 708–1008E). Use of Individual Indian Ocean northward-propagating events equatorial 20–100-day precipitation as the base time in observations and models are shown in Fig. 8. In obser- series implicitly selects NPISO events that are linked to vations, NPISO events tend to originate near the equator the MJO. In observations, the MJO appears first in the and then propagate northward and, to some extent, western Indian Ocean at lag 218 (Fig. 9a), propagates southward. Northward propagation is nearly continuous, eastward across the Maritime Continent and into the 5142 JOURNAL OF CLIMATE VOLUME 24

western Pacific Ocean, and decays near the date line at lag 23. Low-level easterly (westerly) winds lead (lag) precipitation by one quarter cycle. The MJO in CCSM is confined to the Indian Ocean and fails to propagate across the Maritime Continent. SP-CCSM produces the most realistic MJO of the three models but with lower correlations east of the Maritime Continent and slightly faster phase speed compared to observations. SP-CAM (Fig. 9d) also propagates convection into the western Pa- cific Ocean, but it decays more rapidly than in SP-CCSM. The quadrature relationship between rainfall and low- level winds is also observed in the Indian Ocean NPISO (Fig. 9e), with easterly winds preceding NPISO events. Precipitation starts near the equator and then propagates northward and southward. Southward propagation is faster, weaker, and of shorter duration than northward propagation. SP-CCSM (Fig. 9f) produces lag correlations slightly lower than those in observations but with realistic northward and southward phase speeds. In contrast, CCSM (Fig. 9h) weakly simulates northward, but not southward, propagation. SP-CAM (Fig. 9g) simulates some of the southward-propagating mode and a realistic temporal duration for the northward mode but with lower correlations than observed, suggesting that a larger fraction of variability north of ;208N in SP- CAM results from disturbances not associated with the NPISO. The visual similarity of lag correlations between SP-CCSM and observations suggests that SP-CCSM captures many facets of the MJO/NPISO that both CCSM and SP-CAM fail to simulate. Spatial composites of BSISO OLR give further insight into the ability of the models to simulate the BSISO. Figure 10 (also based on the CLIVAR MJO diagnostics package) presents May–October composite OLR anomalies for observations and models based on the combined EOF method of Wheeler and Hendon (2004). In observations (Fig. 10a), convection begins in the In- dian Ocean (phases 1 and 2) and expands and/or propagates eastward in phases 3–5, corresponding to the eastward BSISO propagation across the Maritime Continent and the northward propagation of convection onto the Indian subcontinent, resulting in the northwest– southeast tilted rainband (seen in phases 3–7). Eastward translation continues over the South China Sea in phases 6 and 7, leading to the northward shift of precipitation over Southeast Asia, while the western Pacific Ocean is characterized by in situ decay in phases 6–8. SP-CAM and SP-CCSM (Figs. 10c and 10b, respectively) FIG. 8. Single year time–latitude diagrams of 20–100-day filtered precipitation averaged from 708 to 1008E for observations and capture many of these elements in phases 1–4. Both models of year indicated. Positive (negative) anomalies greater models propagate convection across the Maritime (less) than 1 mm day21 are dark (light) shaded. Contour interval is Continent. In phases 5–7, however, SP-CAM convec- 21 every 4 mm day starting at 2 with positive (negative) anomalies tion is concentrated north of the equator so that it be- indicated by solid (dashed) contours. comes zonally oriented rather than tilted. SP-CCSM, 1OCTOBER 2011 D E M O T T E T A L . 5143

on the other hand, reproduces the tilted rainband structure in phases 3–6. CCSM (Fig. 10d) does produce some BSISO activity, especially in the Indian Ocean, but it is weak and does not propagate into the western Pacific Ocean. The results presented in this subsection demonstrate that SP-CCSM simulates a more realistic Asian monsoon than either SP-CAM or CCSM. The addition of SP improves the monsoon in coupled simulations, and coupling improves the monsoon in SP simulations. We now turn our attention to the relative contributions of SP and coupling to the improved monsoon simulation. b. NPISO and surface fluxes Figure 11 illustrates the essential phase relationships (as depicted by the 10.2 correlation contour) of NPISO precipitation, SST, and surface stress anomalies in observations and the three simulations. Both land and ocean points are used for the precipitation correlation, but only ocean points are used for the surface temperature and surface stress contours. Surface wind stress is highly correlated with latent heat flux, but is more directly observable than the latter and thus provides a more direct comparison to simulations. As with Fig. 9, all variables were correlated with equatorial Indian Ocean precipitation anomalies. In observations (Fig. 11a), a coherent relationship is seen between precipitation, SST, and Northern Hemisphere surface stress. Warm SST anomalies lead northward-propagating convection by ;10 days. Maximum surface stress is associated with westerly winds (Fig. 9a) and maximizes near the equator and ;158N, lagging precipitation by approximately five days. Not surprisingly, the surface flux relationships in SP- CAM (Fig. 11c) bear only limited resemblance to the observations. The specified SSTs in this model are based on monthly-mean data interpolated to daily resolution. Northern Hemisphere surface stress correlations are weak, but exhibit positive correlations over a broad region ahead of southward-propagating precipitation in the Southern Hemisphere. Ocean coupling in SP-CCSM (Fig. 11b) improves the phasing and northward extent of

FIG. 9. (a)–(d) Longitude vs lag correlation and (e)–(h) latitude vs lag correlation of 20–100-day ﬁltered Indian Ocean precipitation (108S–58N, 758–1008E) with 20–100-day ﬁltered precipitation (shaded) and 850-hPa zonal wind (contour) at each longitude–lag or latitude–lag pair. Data are averaged from 108Sto108N for (a)– (d) and from 808 to 1008E for (e)–(h). Positive (negative) correlations are indicated by red (blue) shading or solid (dashed) contours. Contour interval is 0.1. 5144 JOURNAL OF CLIMATE VOLUME 24

NPISO precipitation, SST, and surface stress anomalies. As observed in the Northern Hemisphere, SP-CCSM warm SSTs lead precipitation by about 7 days, which leads positive surface stress anomalies by 7–10 days. Simulated correlations are greater in the Southern Hemisphere than in observations, with surface stress leading southward-propagating convection by a few days. The relationship of precipitation, SST, and surface stress anomalies in CCSM (Fig. 11d) is similar to that simulated by SP-CCSM (including the Southern Hemi- sphere extension of the correlation curves), indicating that ocean coupling, rather than SP, controls the temporal relationships among these variables. SP clearly improves the realism of the NPISO simulation (represented by the precipitation contour in these plots), but it appears to have little influence on the interplay of SST and surface stress anomalies as they relate to the NPISO. Because the phasing of the surface fluxes are similar in CCSM and SP- CCSM, improvements to the NPISO in SP-CCSM may be more directly related to internal atmospheric dynamics, rather than surface fluxes, on these time scales. c. NPISO and internal atmospheric dynamics Here we assess the relationship between the NPISO and equatorial waves. The westward-propagating n 5 1 equatorial Rossby wave is frequently studied in connec- tion with the NPISO. We also examine the westward- propagating mixed Rossby–gravity (MRG) wave because it is of similar spatial scale to the ER wave and has been linked to NPISO activity by Straub and Kiladis (2003), who found that 1) MRG group velocity accounted for a large fraction of MJO OLR variance and 2) individual MRG waves exhibited northwestward propagation, similar to ER waves. Takayabu and Nitta (1993), Dunkerton and Baldwin (1995), and Yang et al. (2007b,c) described the tendency of westward-propagating waves to transition from MRG to ER waves, or to exhibit hybrid MRG–ER structures over the western Pacific. Figure 12 shows the distribution of equatorially symmetric OLR signal-to-noise ratio in wavenumber– frequency space for observations and models. In observations (Fig. 12a) significant peaks correspond to the MJO (wavenumber 11andperiods;50 days), the Kelvin wave, the n 5 1 ER wave, and the higher-frequency n 5 1 inertio–gravity (IG) wave. The peak observed near wavenumber 14 and frequency 0.1 is an artifact of com- bining OLR observations from multiple geostationary satellites and is not physical (Wheeler and Kiladis 1999). FIG. 10. Composite life cycle of BSISO OLR anomalies con- The simulation of equatorial waves improves as one structed with the multivariate EOF method of Wheeler and Hendon moves from CCSM (Fig. 12d) to SP-CAM (Fig. 12c) to (2004) for (a) observations, (b) SP-CCSM, (c) SP-CAM, and (d) CCSM. Negative (positive) OLR anomalies are shown with dark SP-CCSM (Fig. 12b). In CCSM, the MJO peak is poorly (light) shading. Number of days contained in each sample is in- defined and appears at a lower frequency than in obser- dicated at lower right of each panel. vations. Typical of many coupled models (Lin et al. 2006), 1OCTOBER 2011 D E M O T T E T A L . 5145

FIG. 11. As in Figs. 9e–h but only the r = 0.2 contour for 20–100-day ﬁltered precipitation (solid), SST (dashed), and surface stress (dotted). Observed correlations are based on GPCP precipitation and ERA-Interim reanalysis SST and surface stress for 1988–2008.

Kelvin wave activity lies to the left of the h 5 50 m 14 is also present. The simulation of antisymmetric equivalent depth dispersion curve, scaling to a deeper waves again improves as one moves from CCSM (Fig. equivalent depth (h ’ 90 m) compared to observations. 13d) to SP-CAM (Fig. 13c) to SP-CCSM (Fig. 13b). In The n 5 1 ER wave is reasonably well simulated by CCSM, few signals rise above the noise threshold (the CCSM. SP-CAM (Fig. 12c) is an improvement over signal-to-noise ratio is less than 1). In SP-CAM, spectral CCSM in its ability to produce a spectral peak in the ISO peaks begin to emerge near the dispersion curves, but region, although the peak is not clearly separated from they are weak and disorganized. SP-CCSM simulates the Kelvin wave. The SP-CAM Kelvin wave convection MRG/EIG waves that stand out above the background. is aligned with the h 5 25 m dispersion curve, as in ob- Compared to the 14 coupled models analyzed by Lin servations. SP-CCSM (Fig. 12b) produces spectral peaks et al. 2006, the SP-CCSM is unique in its ability to sim- similar to those observed. The MJO peak is well separated ulate MRG waves with the same dispersion character- from the Kelvin wave, the Kelvin wave peak is compa- istics seen in observations. The clear presence of MRG rable to that seen in observations, and the n 5 1ERwave waves in SP-CCSM, but not in CCSM or SP-CAM, sug- peak is present. gests that coupling may modify the basic-state environ- The distribution of equatorially antisymmetric OLR ment so that SP-generated variability produces MRG spectral peaks is shown in Fig. 13. In observations (Fig. waves. The role of coupling in improving tropical vari- 13a) a spectral peak corresponds to the h 5 25 m ability is discussed in section 5. equivalent depth dispersion curve for the MRG and We examined the spatial distributions and magnitudes eastward inertio–gravity (EIG) waves. A weaker peak of ER and MRG wave power for observations and models, is also observed near wavenumber 25 and frequency following the extraction method described in Wheeler ;0.25 cycles per day, associated with tropical depressions and Kiladis (1999) but ﬁltering the full (symmetric 1 (Wheeler and Kiladis 1999; Straub and Kiladis 2003). antisymmetric) time series so as to capture the effects The artiﬁcial ‘‘data merging’’ peak near wavenumber of landmasses and seasonality. ER waves were extracted 5146 JOURNAL OF CLIMATE VOLUME 24

FIG. 12. Wavenumber vs frequency distribution of spectral-power-divided estimate background background spectra for equatorially symmetric OLR anomalies. Signal-to-noise ratios (SNRs) greater than (less than) one are shaded in yellows and reds (blues). Contour interval is 0.1. Shallow water dispersion relationships for equivalent depths of h 5 12, 25, and 50 m are shown for the n 5 1 equatorial Rossby, Kelvin, and n 5 1 inertio–gravity waves. from the full time series by applying the reverse Fourier two ridges of enhanced variability at 58N and 158N, ex- transform to coefﬁcients contained in a wavenumber– tending toward the Indian Ocean. ER variance patterns frequency region bounded by the 8-m and 90-m equiva- are similar in SP-CCSM and SP-CAM (Figs. 14b and lent depth curves, westward (negative) wavenumbers 14d) but with excessive magnitudes. CCSM ER variance 1–10, and periods 10–35 days. MRG waves were ex- (Fig. 14c) differs from observations and the two SP tracted using the same equivalent depth and wavenumber models, with maximum variance located near the date boundaries but for periods 3–8 days. line and no variance ridge in the Indian Ocean. The Observed and simulated May–October ER wave increase of Indian Ocean ER variance with latitude in OLR variances are plotted in Fig. 14. In the observations CCSM may indicate a midlatitude source of tropical ER (Fig. 14a), ER variance is greatest in the Northern wave activity in that model. Hemisphere, consistent with the northward shift of the Figure 15 shows observed and simulated May– ITCZ during this season. ER variance maximizes in the October MRG wave OLR variance. For all models, western Paciﬁc Ocean, just east of the Philippines, with the spatial distribution of simulated MRG variance 1OCTOBER 2011 D E M O T T E T A L . 5147

FIG. 13. As in Fig. 12 but for equatorially antisymmetric OLR anomalies. Shallow water dispersion relationships for equivalent depths of h 5 12, 25, and 50 m are shown for the mixed Rossby–gravity (MRG) and n 5 0 eastward inertio–gravity (EIG) waves. agrees well with observations although, as mentioned while SP-CCSM succeeds, we ask whether the northward earlier, only SP-CCSM produces statistically sig- propagation in SP-CCSM results from the same niﬁcant MRG spectral peaks. Observed Northern mechanisms as in nature, particularly with regard to Hemisphere MRG variance (Fig. 15a) is centered at ER and MRG wave activity. The distribution of ER ;108N with maximum variance spanning the Indian and MRG variance with latitude over the Indian Ocean and Paciﬁc Oceans. The longitudinal extent of MRG (608–1008E)forobservationsandmodelsisshownin variance in SP-CCSM (Fig. 15b) is similar to observations, Fig. 16. The excessive Northern Hemisphere ER vari- although the maximum is located near the Maritime ance in SP-CCSM and SP-CAM is apparent, although Continent rather than the date line. Both CCSM and SP- south of ;108Nthe biasesare smallerinSP-CCSM than CAM (Figs. 15d and 15c, respectively) appear to con- in SP-CAM. SP-CCSM MRG variance agrees well with centrate MRG variance over a smaller longitudinal range. observations, while SP-CAM and CCSM MRG vari- Because so many coupled models fail to simulate re- ances do not. In all cases, however, Northern Hemi- alistic NPISO events (Sperber and Annamalai 2008), sphere MRG wave power ranges from 25% to 80% of 5148 JOURNAL OF CLIMATE VOLUME 24

FIG. 14. Distribution of June–September ER OLR variance for (a) NOAA OLR 1998–2008, (b) SP-CCSM, (c) CCSM, and (d) SP-CAM: contour interval 30 (W m22)2; values greater than 90 (W m22)2 shaded.

ERpower,withanaverageratioof;50%, so MRG respectively), where MRG waves propagate due west wave activity is nonnegligible compared to ER wave or slightly southward. The observed MRG wave en- activity. velope (Fig. 17d) is nearly fixed at 108N, a feature also Next we address how simulated ER and MRG waves seen in SP-CAM and CCSM (Figs. 17l and 17p). In contribute to the Indian Ocean NPISO. Lag-correlation contrast, the MRG wave envelope in SP-CCSM ex- plots of the observed and simulated latitudinal and hibits some northward drift. ER and MRG waves also longitudinal propagation of ER waves at 58N and 158N propagate zonally (Fig. 18), as dictated by their wave- and MRG waves at 108N are shown in Figs. 17 and 18, number filtering limits. Zonal propagation of ER waves respectively. We also examine the propagation of the is especially well maintained in the SP models (i.e., the MRG wave envelope, described by the 9-day running two left-hand columns of Fig. 18). All three models also variance of MRG OLR anomalies. Unlike the results reflect the eastward MRG group velocity (right-hand shown in Fig. 9, there is no implied link to the MJO in column of Fig. 18), given by cg 5 dv/dk, where cg is Fig. 17 because each wave type defines its own base time group velocity, v is frequency, and k is zonal wave- series. Zonal propagation of ER and MRG waves is number. primarily determined by filtering constraints, whereas To understand how propagation characteristics of ER meridional propagation of ER and MRG waves is not. and MRG waves relate to the NPISO, composite wave Therefore, differences in lag–latitude correlations are trajectories were computed, with the aid of Figs. 17 and attributable to model differences. 18, by plotting the latitude–longitude pair of maximum In observations (Figs. 17a and 17b) and SP-CAM correlation for each wave and lag (Fig. 19). For each (Figs. 17i and 17j), ER waves propagate smoothly from wave type, lag 5 0 is the day at which OLR minimizes in the equator to 208N. In SP-CCSM and CCSM, ER the 708–808E longitude band. In observations (Fig. 19a), waves (Figs. 17e and 17f and 17m and 17n, respectively) both ER and individual MRG waves propagate to the exhibit less northward propagation. In observations, northwest. ER waves that propagate across the eastern high-frequency MRG waves appear in groups with in- Indian Ocean originate near the equator, presumably in dividual waves propagating northward (Fig. 17c). This association with eastward-moving MJO convection and behavior is well simulated in SP-CCSM (Fig. 17g), then propagate to the northwest. The northern ER wave but not in SP-CAM or CCSM (Figs. 17k and 17o, originates farther east than the southern ER wave and 1OCTOBER 2011 D E M O T T E T A L . 5149

22 2 FIG. 15. As in Fig. 15 but for MRG OLR variance, values greater than 30 (W m ) shaded. takes longer to reach western India than the southern ER waves is somewhat disrupted by westward- or ER wave, consistent with the idea of the MJO acting as southwestward-propagating MRG waves. In CCSM, the the ER source. MRG waves, whose convective signal unrealistic southward-propagating ER waves at low maximizes at ;108N, also propagate to the NW, re- latitudes and northward-propagating ER waves at higher inforcing the slow northward propagation over western latitudes may be sufficient to explain the poor simulation India associated with the northwestward-propagating of the NPISO over western India (Fig. 8d). However, ER wave train. westward- or southwestward-propagating MRG waves Each of the models (Figs. 19b–d) simulates ER and may also account for some of CCSM’s errors in the MRG wave trajectories that differ somewhat from those NPISO. seen in observations. In both SP-CAM and SP-CCSM, Several recent studies have focused on mechanisms ER waves originate near the equator, as in observations, responsible for the northward propagation of ER waves. and propagate to the northwest, especially in SP-CAM. Lawrence and Webster (2002) analyzed the NPISO in In CCSM, ER waves exhibit more westward than north- NOAA OLR and NCEP reanalysis winds. They concluded ward propagation with the southern ER wave propagat- that surface frictional convergence into the low pres- ing to the southwest. For MRG waves, only SP-CCSM sure center Rossby waves accounted for the northward simulates the observed northwestward propagation of movement of monsoon precipitation, with oceanic individual waves; SP-CAM and CCSM produce westward- surface fluxes playing a supporting role. Several mod- or southwestward-propagating MRG waves. eling studies (Jiang et al. 2004; Wu et al. 2006; Drbohlav The relationship of ER and MRG waves to the slow and Wang 2007; Seo et al. 2007; Boos and Kuang 2010) northward drift of NPISO convection over western In- support the hypothesis that internal atmospheric dia may be interpreted (with reference to Fig. 8) as dynamics—particularly the interactions of waves follows: In observations, northwestward-propagating with easterly shear—are primarily responsible for the MRG waves reinforce the northward propagation aris- northward-propagating monsoonal rainfall, with surface ing from ER waves. In SP-CCSM, the reduced north- fluxes playing a secondary role. Other modeling studies, ward propagation associated with ER waves is apparently however, suggest that high-frequency SST variability compensated for by MRG waves, both from individual and their associated surface fluxes play a crucial, rather waves and their northeastward group velocity (Fig. 17h). than secondary, role in the NPISO (Klingaman et al. In SP-CAM, realistic northwestward propagation of 2008a; Wang et al. 2009; Weng and Yu 2010). 5150 JOURNAL OF CLIMATE VOLUME 24

FIG. 16. Indian Ocean (608–1008E) ER (thin solid) and MRG (dashed) OLR variance and the MRG/ER ratio (thick sold) as a function of latitude for (a) observations, (b) SP-CCSM, (c) SP-CAM, and (d) CCSM.

5. Discussion and conclusions model that struggles to produce the MJO may also struggle to produce the Rossby waves associated with We have attempted to understand the improved the NPISO. A model that produces some semblance of simulation of the Asian monsoon in SP-CCSM in terms the MJO, but fails to propagate the MJO across the of how cloud representation and ocean coupling in- Maritime Continent into the western Pacific Ocean, may fluence three factors important to the monsoon: the at- struggle to propagate the NPISO as far north as it does mospheric mean state, atmospheric variability and in the real world because the Pacific source region of the internal dynamics, and ocean–atmosphere interactions. northernmost ER waves would not be simulated. In terms of the seasonal-mean climatology, SP-CCSM SP-CCSM simulates the eastward-, westward-, and produces a more realistic distribution of Northern northward-propagating components of the Asian mon- Hemisphere summer rainfall and easterly shear than soon, whereas they are either missing or very weak in CCSM, with Indian Ocean (western Pacific Ocean) SSTs CCSM. We ask How does the explicit representation of slightly cooler (warmer) than CCSM. Realistic simula- convection help simulate the observed modes of vari- tion of zonal shear is especially critical for the monsoon ability seen in the Asian monsoon? Thayer-Calder and because it has been hypothesized to provide a mechanism Randall (2009) and Benedict and D. A. Randall (2011) for the northward propagation of precipitating systems. demonstrated the ability of SP-CAM and SP-CAM However, successful simulation of this basic-state vari- coupled to a slab ocean model, respectively, to replicate able is not sufficient to produce a realistic monsoon. This the observed gradual deepening with height of low-level is seen in CCSM, which, despite its realistic Indian Ocean humidity leading up to MJO convective events. DeMott mean shear profile, fails to produce the NPISO. et al. (2007) described the differences in convective Subseasonal tropical variability is critical to the Asian behavior in SP-CAM and the conventionally parame- monsoon, which is frequently linked to the eastward- terized CAM, noting: 1) frequent and light rain rates in propagating 20–100-day MJO and the associated CAM versus a more realistic broad rain rate distribution northwest-propagating equatorial Rossby waves. A in SP-CAM and 2) SP-CAM’s gradual deepening of 1OCTOBER 2011 D E M O T T E T A L . 5151

FIG. 17. Latitude vs lag autocorrelation of eastern Indian Ocean (708–808E) ER and MRG OLR anomalies, and 9-day running variance of MRG OLR anomalies. Base time series are computed as (ﬁrst column) ER OLR 2.58–7.58N, (second column) ER OLR 12.58–17.58N, (third column) MRG OLR 7.58–12.58N, and (fourth column) MRG OLR variance 7.58–12.58N. Positive (negative) correlations are dark (light) shaded with solid (dashed) contours. Contour interval is 0.1, zero contour omitted. Averaging range is indicated with heavy horizontal line at lag 5 0. Latitude of Northern Hemisphere maximum positive correlation for each lag is indicated with small diamond. low-level moisture in advance of major precipitation by reevaporation is favorable for later deeper convection. events versus the CAM tendency to simultaneously Kuang and Bretherton (2006) and Waite and Khouider moisten the upper and lower troposphere coincident with (2010) have demonstrated the ability of two different maximum precipitation. In the presence of positive CAPE, CRMs to simulate this sequence of events. CAM with parameterized convection generates con- This conceptual model of SP convection is hypothe- vective plumes that are required to detrain above the sized to explain the role of SP in the simulation of con- level of minimum moist static energy (Zhang and vectively coupled MRG waves and other tropical modes McFarlane 1995), thereby eliminating ‘‘preconditioning not explicitly discussed here. Wheeler and Kiladis (1999) effects’’ of detrainment and moistening at lower levels discuss the reduction of the effective static stability by (Thayer-Calder and Randall 2009). On the other hand, convective heating as a mechanism favoring slower detrainment of SP convection occurs whenever convec- propagation. As discussed by Kiladis et al. (2009), with the tive elements reach their level of neutral buoyancy. exception of ERs, heating and moistening proﬁles in Entraining convection in a dry environment quickly convectively coupled equatorial waves (CCEWs) of all loses buoyancy, resulting in detrainment and reevapo- scales (easterly and westerly inertio–gravity waves, Kelvin ration of cloud liquid water at low levels. This moistening waves, MRG waves) are observed to evolve in a manner 5152 JOURNAL OF CLIMATE VOLUME 24

FIG. 18. As in Fig. 17 but for longitude vs lag autocorrelation. Maximum correlation markers are omitted for clarity. Eastern Indian Ocean base point is indicated with thin vertical line at 758E. consistent with the notion of shallow convection precon- but also from a combination of the slow propagation ditioning the tropical atmosphere for subsequent deeper speed of these waves and the presence of strong convection (Johnson et al. 1999; Straub and Kiladis easterly shear (e.g., Wang and Xie 1996; Sperber and 2003, Khouider and Majda 2008), a sequence that is Annamalai 2008). simulated by SP-CAM and SP-CCSM but not by CCSM. Tropospheric moistening in ERs differs from other Further study is needed to determine if SP improves the CCEWs. Rather than exhibiting a gradually deepening convection–wave coupling via proposed mechanisms such layer of moisture from the surface, ER moistening occurs as moisture–stratiform instability (Mapes 2000; Kuang throughout the entire depth of the troposphere. Deep 2008) or interactions with gross moist stability (Raymond tropospheric moistening begins about four days ahead of and Fuchs 2007; Frierson 2007). the minimum ER OLR (Fig. 18d of Kiladis et al. 2009). The role of SP in improving ER waves is more difﬁcult The unusual CCEW moistening sequence resembles that to identify. Over most of the tropics, ER waves exhibit simulated by SP-CAM in the monsoon region. This may a deep unimodal structure in their temperature and wind help to explain why ER variance is so high in SP-CAM proﬁles (Kiladis and Wheeler 1995), but in the con- compared to observations and the coupled models. It has vectively active warm pool region they acquire a highly been hypothesized (Luo and Stephens 2006; Benedict baroclinic structure (Wheeler et al. 2000; Yang et al. and Randall 2009) that the cyclic boundary condition on 2007a). Such changes may arise from convective coupling the CRM in SP-CAM can lead to a positive feedback 1OCTOBER 2011 D E M O T T E T A L . 5153

FIG. 19. ER (red) and MRG (green) wave composite trajectories based on the latitude–longitude pair of maximum correlation as a function of lag from Figs. 17 and 18 for ER waves at 58N and 158N and MRG waves at 108N. Each wave moves from east to west. Lags (days) are denoted with dots, with negative (positive) lags connected by dashed (solid) lines. ER lags (observations only) and MRG lags (all panels) are labeled. Note that equal lags among different waves are not coincident in time. between convection and column moisture in regions of experienced both directly as the disturbances evolve and warm SSTs. SP-CCSM may be less vulnerable to this indirectly as they interact with the basic-state atmosphere. problem because convectively driven surface winds and reduced surface shortwave fluxes cool the SST, poten- Acknowledgments. This work was supported by the tially reducing the vigor of subsequent convection (Wu National Science Foundation Science and Technology and Kirtman 2005). A more detailed study of moistening Center for Multiscale Modeling of Atmospheric Pro- associated with simulated ER waves, as well as their in- cesses, managed by Colorado State University under teraction with environmental shear and surface fluxes, is Cooperative Agreement ATM-0425247. We acknowledge needed to better understand their interactions with the the support of the Computational and Information simulated monsoon. Systems Laboratory at NCAR for providing computer The role of ocean–atmosphere feedbacks on the time for this work. C. S. and J. L. K. were supported monsoon simulation must be considered over a range of by the NSF (Grant AGS-0830068), NOAA (Grants temporal scales. On one hand, we have demonstrated NA09OAR4310058, NA09OAR4310137), and NASA that the atmosphere-only model (SP-CAM) produces (Grants NNX09AN50G). We thank Katherine Straub a different basic state from the coupled model (SP- for her suggested improvements to the manuscript, and CCSM). Seasonally, the atmosphere and ocean surface Nick Klingaman and Zhiming Kuang for their helpful temperature evolve together via seasonal solar flux comments during the review process. variations and ocean dynamics (Webster et al. 1998) so that the ocean directly affects the seasonal mean state of the atmosphere. On the other hand, for a high-frequency REFERENCES wave such as the MRG (period ;5 days), even intra- Annamalai, H., and J. M. Slingo, 2001: Active/break cycles: Di- seasonal SST fluctuations are ‘‘slow’’ and unlikely to agnosis of the intraseasonal variability of the Asian summer directly impact wave development. However, high- monsoon. Climate Dyn., 18, 85–102. frequency waves may respond to SST variations in- Bellon, G., A. H. Sobel, and J. Vialard, 2008: Ocean–atmosphere coupling in the monsoon intraseasonal oscillation: A simple directly via their interactions with the basic-state climate, model study. J. Climate, 21, 5254–5270. which is itself affected by coupling with the ocean. We Benedict, J. J., and D. A. Randall, 2009: Structure of the Madden– hypothesize that ocean coupling in SP-CCSM leads to a Julian oscillation in the superparameterized CAM. J. Atmos. basic-state climate that is favorable to MRG waves and Sci., 66, 3277–3296. their northward propagation but that the presence of those ——, and ——, 2011: Impacts of idealized air–sea coupling on Madden–Julian oscillation structure in the superparameterized waves is attributed to the enhanced convective variability CAM. J. Atmos. Sci., 68, 1990–2008. of SP. ER waves and the MJO represent an intermediate Bollasina, M., and S. Nigam, 2009: Indian Ocean SST, evaporation, time scale on which ocean–atmosphere feedbacks may be and precipitation during the South Asian summer monsoon 5154 JOURNAL OF CLIMATE VOLUME 24

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