1. Introduction
The Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972) is a planetary-scale wavelike disturbance that dominates intraseasonal (30–90-day) variability in the tropics. The MJO is characterized by a dipole of enhanced and suppressed convection, which propagates eastward at a phase speed of approximately 5 m s−1 over the Indo-Pacific warm pool (e.g., Weickmann et al. 1985; Knutson et al. 1986). The MJO is a vital part of the global weather and climate system. Within the tropics, the MJO is known to influence the onsets and breaks of the Asian and Australian monsoons (Risbey et al. 2009; Wheeler et al. 2009; Marshall and Hendon 2015) and the initiation of El Niño–Southern Oscillation (ENSO) events (Hendon et al. 1999; Slingo et al. 1999; Marshall et al. 2009) and to alter the preferred location of tropical cyclone genesis (Maloney and Hartmann 2000a,b; Klotzbach 2014). The MJO can trigger Rossby wave trains that propagate into the midlatitudes and influence midlatitude weather patterns (e.g., Lin et al. 2006; L’Heureux and Higgins 2008; Flatau and Kim 2013; Stan et al. 2017) and temperature and precipitation anomalies (Higgins et al. 2000; Bond and Vecchi 2003; Zhang 2013; Stan et al. 2017).
The MJO shows a marked seasonality in its strength and propagation patterns (e.g., Kang et al. 1989). The MJO precipitation variance peaks in its amplitude in the Southern Hemisphere during boreal winter (Hendon et al. 1999; Zhang and Dong 2004; Adames et al. 2016). The boreal winter MJO tends to propagate south of the equator, through the oceanic passage between Indonesia and Australia (Kim et al. 2017; Zhang and Ling 2017). In boreal summer, the MJO variability shows a secondary peak, weaker than its boreal winter counterpart, and tends to be zonally elongated to the north of the equator (Zhang and Dong 2004; Adames et al. 2016). The boreal summer MJO tends to propagate toward the northeast, rather than directly eastward, leading to intraseasonal modulation of the Asian summer monsoon (Kang et al. 1989; Zhang 2013; Adames et al. 2016; Zhang and Ling 2017).
Despite the well-established seasonality of the MJO in observations, the mechanisms that lead to these seasonal patterns, particularly the maximum amplitude in the Southern Hemisphere during boreal winter, are not well understood. Previous studies have suggested that the seasonal cycle of MJO variance is tied to that of the mean state (Zhang and Dong 2004; Adames et al. 2016; Jiang et al. 2018; Singh and Kinter 2020). The maximum MJO variance in the Southern Hemisphere during boreal winter coincides with warm SST, mean lower-tropospheric westerlies, and enhanced moisture and moisture convergence, all of which favor enhanced convection (Zhang and Dong 2004). While previous studies agree that the mean state plays an important role in the boreal winter MJO amplitude, the following question has not been fully answered: what sets the magnitude of MJO variance in the Southern Hemisphere during boreal winter?
The moisture mode theory is one of the leading theories of the MJO (Fuchs and Raymond 2005, 2007; Raymond and Fuchs 2009; Sobel and Maloney 2012, 2013; Adames and Kim 2016) and can be employed to investigate the mechanisms responsible for the MJO’s seasonality (Adames et al. 2016; Jiang et al. 2018). The moisture mode theory relies on two characteristics of the tropical atmosphere: 1) the weak horizontal temperature gradient (Charney 1963; Sobel et al. 2001) and 2) the tight coupling of moisture and precipitation (Bretherton et al. 2004; Holloway and Neelin 2009; Rushley et al. 2018). Under this theory, changes in anomalous precipitation can be explained by those of the moisture anomalies (Benedict and Randall 2007; Maloney et al. 2010; Andersen and Kuang 2012; Kim et al. 2014; Wolding and Maloney 2015; Adames and Wallace 2015; Adames and Kim 2016; Adames et al. 2017a,b; Rushley et al. 2019). The behavior of the MJO’s precipitation can therefore be understood by examining the processes that affect moisture anomalies.
Previous studies that employed the moisture mode framework have been successful at explaining the difference in MJO variability under different mean states. For example, many studies have examined changes in the MJO amplitude and phase speed in response to a warming climate (e.g., Arnold et al. 2013, 2015; Chang et al. 2015; Adames et al. 2017a,b; Bui and Maloney 2018, 2019; Rushley et al. 2019). Rushley et al. (2019) and Adames et al. (2017a,b) showed that an increase in the MJO’s amplitude in a warming climate was largely due to the increase in the background total tropical precipitation, which increased due to increased moisture. Bui and Maloney (2018) linked the increase in the MJO’s amplitude with warming to the strengthening of the MJO-scale vertical moisture advection. Additionally, global climate models (GCMs) that represent the mean state of moisture and the meridional moisture gradient correctly tend to simulate the MJO more accurately, while models that have mean-state dry biases and weaker meridional moisture gradients tend to have relatively poorly simulated MJOs (Kim et al. 2009, 2014; Gonzalez and Jiang 2017; Ahn et al. 2020). The moisture mode framework has also been used to explain the differences in the seasonal patterns of the MJO (Adames et al. 2016; Jiang et al. 2018; Wei and Ren 2019; Wei et al. 2022).
One of the most important factors that determine the mean states of the atmosphere and ocean is the seasonal cycle in insolation. The amount of insolation at any given location on any given day is dependent on the eccentricity, precession, and obliquity of Earth’s orbit (Hartmann 1994; Battisti et al. 2014). Throughout Earth’s history, changes in Earth’s orbit have had a large influence on Earth’s climate (Joussaume et al. 1999; Clement et al. 2000; Held and Soden 2006; Battisti et al. 2014; Lough et al. 2014; Emile-Geay et al. 2016; An and Bong 2018). The eccentricity determines the annual cycle of insolation based on variations in the absolute distance of Earth from the sun; the precession measures the seasonal time of the year when Earth is closest to the sun and therefore, the timing of maximum insolation (Hartmann 1994).
Considering the known effects of the orbital parameters on Earth’s climate, changes to the orbital parameters could affect the aspects of the mean state that are important for the MJO, which may drive changes in the behavior of the MJO. Lough et al. (2014) suggested, based on the coral proxy data collected in the northwestern part of Australia, that intraseasonal precipitation variability around Australia was greater during the mid-Holocene (∼6000 years before present) than in the current climate; they theorized that this may indicate an increase in the MJO’s amplitude. The mid-Holocene is a climate state with a different precession that has been well studied, during which the perihelion occurred during boreal summer, whereas the present-day perihelion occurs during boreal winter. This led to an increase in the insolation during boreal summer, a stronger seasonal cycle in the Northern Hemisphere, and a reduced seasonal cycle in the Southern Hemisphere during the mid-Holocene, driving significant differences in the mean state during boreal winter (Joussaume et al. 1999; Clement et al. 2000; Emile-Geay et al. 2016; An and Bong 2018). Additionally, previous studies have shown that the seasonal asymmetry and variability of Earth’s climate is enhanced as the eccentricity of Earth’s orbit increases; the effect of the perihelion is greater at higher eccentricity as the perihelion at higher eccentricities is closer to the sun (Williams and Pollard 2002; Dressing et al. 2010; Huybers and Aharonson 2010).
In this study, we modulate Earth’s orbit to alter the seasonal cycle of the mean state to examine the changes to the boreal winter MJO’s amplitude. By doing so, we aim to understand how different boreal winter mean states driven by different insolation patterns affect the boreal winter maximum of the MJO’s amplitude. We employ the moisture mode framework and process-oriented diagnostics to shed light on the physical mechanisms that control the seasonality of the MJO’s amplitude. It will be shown that changing the precession and eccentricity of Earth’s orbit leads to large changes in the boreal winter MJO’s amplitude and that two processes—the moisture–precipitation relationship and surface latent heat flux feedback—are responsible for those changes.
The model simulations and the methods used in this study are described in section 2. Section 3 describes the changes to the mean state and the MJO in response to changes in the orbital forcing. A detailed discussion of the mechanisms behind the changes to precipitation due to orbit is presented in section 4. Section 5 examines the intraseasonal moisture budget and how changes due to orbit affect the MJO scale precipitation. Last, the summary and conclusions are in section 6.
2. Model and simulation design
a. Model
We use the Superparameterized Community Atmosphere Model (SPCAM; Randall et al. 2013; Khairoutdinov et al. 2005), in which the conventional parameterizations of moist physics, convection, turbulence, and boundary layer processes in the Community Atmosphere Model, version 5 (CAM5; Khairoutdinov and Randall 2001), have been replaced by a two-dimensional cloud-resolving model (CRM) in each grid cell. The CRMs are based on the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003), aligned in an east–west direction, with a total of 32 grid points and horizontal grid spacing of 4 km. It has been shown that SPCAM simulates a decent MJO (e.g., Randall et al. 2003; Pritchard and Bretherton 2014; Grabowski 2004; Benedict and Randall 2009; Andersen and Kuang 2012; Stan et al. 2010; Thayer-Calder and Randall 2009; Zhu et al. 2009). In our study, the SPCAM model is run on a 2.5° × 1.89° horizontal grid with 26 vertical levels on a slab ocean with prescribed mixed layer depth and ocean heat flux convergence. The air–sea interactions are active in the slab ocean model. Each simulation is run for a total of 50 years, and the last 30 years are examined. We mainly focus our analysis within the tropics between 30°N and 30°S.
b. Simulations
When altering the orbital parameters, we focus on the effects of eccentricity and precession. Our control simulation has modern-day orbital forcing and will be referred to as Corb. In the second simulation, the precession is altered to match that during the mid-Holocene (Morb). The main difference between Corb and Morb in the orbital forcing is the longitude of perihelion, which is the angle between the perihelion and the vernal equinox (Λ); this term in the SPCAM is defined as Λ − 180°. In Corb, the perihelion occurs during Northern Hemisphere winter, while in Morb, the perihelion occurs during Northern Hemisphere summer. This shift in the perihelion in Morb leads to an increase in insolation during Northern Hemisphere summer and a decrease in insolation during Northern Hemisphere winter (Fig. 1b), amplifying the seasonal cycle of insolation in the Northern Hemisphere, while reducing it in the Southern Hemisphere. Last, we ran an additional simulation with the intent to amplify the effects of changing the perihelion by changing the eccentricity (Williams and Pollard 2002; Dressing et al. 2010; Huybers and Aharonson 2010); this simulation, referred to as Eorb, alters the orbital parameters to mimic those 216 kyr before present (Battisti et al. 2014) and features a much higher eccentricity than the two other simulations, but a similar seasonal precession to Morb. The pattern of insolation changes (Figs. 1b,c) is similar in Morb and Eorb, with the Eorb simulation showing an amplified magnitude in insolation change (Fig. 1c). The longitude of perihelion in Eorb and Morb occurs in the same season; however, in Eorb, it occurs earlier in the calendar year by about one month. The orbital parameters used in these simulations are summarized in Table 1. To isolate the effects of the orbital forcing on the MJO, the carbon dioxide (CO2) concentration is held constant at 367 ppm and the same land–sea mask is used in all simulations. As a consequence, the mid-Holocene simulation is not representative of the real mid-Holocene climate, where CO2 concentrations are estimated at 280 ppm (Joussaume et al. 1999).
Zonal-mean insolation (W m−2) at each latitude and month for the (a) Corb simulations (contours and shading) and the differences in insolation (shading) overlaid onto the Corb insolation (contours) (b) for between Morb and Corb, and (c) between Eorb and Corb.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Orbital configuration for the SPCAM simulations. Eccentricity, obliquity, and the longitude of the perihelion (Λ − 180°) for the control simulation (Corb), the mid-Holocene orbit simulation (Morb), and the high-eccentricity simulation (Eorb).
It is worthwhile to mention that we focus on the mean state in a climate state already in equilibrium within each simulation. The orbital parameters are fixed in each simulation and the simulations reach equilibrium within the first 20 years, such that a 30-yr simulation is sufficient to characterize changes to the MJO and the mean state. Also, by running the SPCAM simulations on a slab ocean, ocean circulations are not resolved, and hence, are not considered in this study. While the ocean circulation is important for the MJO, we do not consider changes in the ocean circulation in this study. Rather, we focus on the higher-frequency ocean–atmosphere feedbacks, which are represented in the slab-ocean model.
3. Mean-state changes associated with orbital forcing
In this section, we document the changes in the mean state in response to the insolation changes imposed in Morb and Eorb. In Fig. 2, we focus on the mean-state changes in boreal winter months [December–February (DJF)] during which, as will be shown later, a large change in MJO variance appears in both Morb and Eorb (Fig. 4). In Morb, there is a cooling in the Northern Hemisphere due to the decreased insolation during DJF and weak warming in the Southern Hemisphere (Fig. 2a). The temperature changes strengthen the interhemispheric temperature gradient to a greater extent in Eorb; while the warming in the Southern Hemisphere is comparable, the cooling in the Northern Hemisphere is much greater (Fig. 2b). In both simulations, the mean precipitation (Figs. 2c,d) and mean precipitable water (Figs. 2e,f) increase in the Southern Hemisphere, with a large drying in the northern Indian Ocean and western Pacific in Eorb. An intriguing feature that appears in the Eorb simulation is that a large increase in the mean precipitation and moisture appears in the southern Indian Ocean, where the surface temperature is colder than Corb. The potential relationship between the decrease in temperature and the increase in moisture and precipitation is discussed in detail in section 5.
Difference in the mean DJF spatial structure for (left) Morb minus Corb and (right) Eorb minus Corb simulation for (a),(b) surface temperature (K), (c),(d) precipitation (mm day−1), (e),(f) precipitable water (mm), and (g),(h) zonal winds at 850 hPa (m s−1). Shading is the difference between the simulations and contours are the Corb simulation values. For zonal winds, solid contours indicate westerlies and dashed lines indicate easterlies (the zero contour is shown in gray); likewise, positive shading indicates stronger westerly winds, while negative indicates more easterly winds.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
It has been suggested that the background westerlies provide a favorable condition for the MJO’s growth via the wind-induced latent heat flux feedback (i.e., wind–evaporation feedback; Maloney 2002; Maloney and Sobel 2004; Sobel et al. 2008, 2010; Sobel and Maloney 2012; 2013; Kang et al. 2022). The DJF mean climatological lower-tropospheric zonal wind in the Corb simulation is dominated by easterlies in the Indo-Pacific warm pool, except for a narrow, zonally elongated region between ∼15°S and the equator, where the highest MJO precipitation variance exists during DJF in observations, (Zhang and Dong 2004) and will likewise be shown in the following section (see Fig. 5, contours). In Morb and Eorb simulations, these westerlies strengthen and expand southward (Figs. 2g,h).
In both Morb and Eorb, the mean precipitation increases to the south of the peak precipitation in Corb, and decreases to the north (Figs. 2c,d), suggesting a southward shift of the ascending branch of the Hadley circulation. To test this, we calculate the streamfunction at 700 hPa,
Zonal-mean precipitation (dashed lines) and streamfunction at 700 hPa (solid lines) for Corb (blue lines), Morb (pink lines), and Eorb (green lines). The zonal-mean precipitation is scaled on the left y axis from −2.5 to 18 (mm day−1). The zonal-mean streamfunction at 700 hPa is scaled on the right y axis from −1 × 1011 to 6 × 1011 kg s−1.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
4. Response of the MJO to orbital forcing
Figure 4 shows the seasonal cycle of MJO amplitude over the Indo-Pacific warm pool (60°E–180°). We obtain MJO amplitude as the variance of MJO-filtered (wavenumbers 1–6 and 30–90-day period) precipitation anomalies, which is obtained via the wavenumber–frequency filtering (Hayashi 1971; Wheeler and Kiladis 1999). The Corb simulation reasonably reproduces the observed seasonal cycle of MJO variance (see Fig. 4 in Zhang and Dong 2004); the MJO variance in the Corb simulation has a primary peak during boreal winter in the latitude band between 20° and 5°S, although the amplitude of the peak variance is somewhat stronger than observed (not shown). In response to the orbital forcing imposed in the Morb and Eorb simulations, the MJO variance increases around the peak season, although the signal appears in slightly different months (Fig. 4). In Morb, the increase is notable from December to March, whereas the largest increase in the MJO variance in Eorb occurs one month earlier (November to February). The discrepancy in the peak of MJO variance change is consistent with the difference in the insolation changes between Eorb and Morb: in the Southern Hemisphere, the peak increase of insolation occurs earlier by about 1 month in Eorb (Fig. 1). It is worth noting that there is a small decrease in the MJO precipitation variance during boreal summer (JJA); however, the magnitude is small (Fig. 4) and understanding changes to the seasonal cycle and boreal summer MJO are outside the scope of this study.
Difference in the climatological seasonal cycle of warm pool (60°E–180°) MJO precipitation variance (mm2 day−2; shading) (a) between Morb and Corb and (b) between Eorb and Corb. The contours in both panels indicate the MJO precipitation variance in the Corb simulation.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Geographically, the MJO variance in the Corb simulation (contours in Fig. 5) peaks in the Indo-Pacific warm pool, between 20° and 5°S. The pattern of MJO variance in Corb (contours in Fig. 5) is consistent with the observed MJO variance in boreal winter (e.g., Hendon et al. 1999; Wheeler and Kiladis 1999; not shown). In Morb and Eorb, the largest increase in MJO variance occurs where MJO variance peaks in Corb (Fig. 5), suggesting that the insolation changes amplify the MJO variance in Morb and Eorb without changing its spatial distribution much, with the exception of a slight southward shift in Eorb. Similar patterns are found in the variance to the intraseasonal column moisture and MJO-filtered moisture anomalies (see the online supplemental material). Quantitatively, over the Indo-Pacific warm pool (25°S–5°N, 60°–240°E), MJO variance increases by about 18% and 30% in Morb and Eorb, respectively (Fig. 6). The changes in the MJO variance due to orbital changes are much larger than the changes in the variance of all total (unfiltered) and intraseasonal (20–100 days bandpass filtered) precipitation anomalies (Fig. 6), suggesting the existence of a process that uniquely affects the MJO. Additionally, we see a larger increase in the west power (westward wavenumbers 1–6, periods of 30–90 days) in Morb than in Eorb (Fig. 6). This higher west power in Morb results in a lower east–west ratio (EWR), which is defined as the ratio of MJO (i.e., east power) to west power (Sperber and Kim 2012), due to the fact that the west power is increasing more than the MJO power. Oppositely, in Eorb, the percent change of MJO power is much larger than the west power, which results in a greater EWR. The large changes in the EWR due to orbital changes supports the existence of a process that uniquely affects the MJO in these simulations, as the MJO’s westward-propagating counterpart does not increase at the same rate as the MJO.
DJF spatial structure of MJO precipitation variance changes (mm2 day−2; shading) (a) between Morb and Corb and (b) between Eorb and Corb. The contours in both plots indicate the DJF spatial structure for MJO precipitation variance in the Corb simulation.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Percent changes for the total precipitation, intraseasonal precipitation, MJO precipitation variance, west power, and the EWR. Pink bars (left bars) show the percent changes from Corb to Morb and green bars (right bars) show the percent changes from Corb to Eorb.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
In all simulations, the propagation characteristics of the MJO show a clear eastward-propagating signal in precipitation and column-integrated specific humidity (Fig. 7) with a realistic zonal structure and phase speed. The propagation of the moisture anomalies (contours in Fig. 7) is consistent with the propagation of the precipitation in the Indian Ocean (shading in Fig. 7), indicating a tight coupling between moisture and precipitation (e.g., Bretherton et al. 2004; Holloway and Neelin 2009; Rushley et al. 2018). We compare the MJO propagation in the SPCAM simulations with precipitation from the Global Precipitation Climatology Project (GPCP; Huffman et al. 2001), which is obtained on a 2.5° × 2.5° grid over the period 1997–2012, and moisture is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) on a 2.5° × 2.5° grid over the period 1997–2012 (Dee et al. 2011). While the diagnosed MJO phase speed and zonal wavenumber differ between simulations (see supplemental material), the differences are not statistically significant, with the values for Morb and Eorb being within the range of MJO phase speed values calculated in Corb (Figs. 3 and 4 in the supplemental material).
Autolag-regressions of meridionally averaged intraseasonal filtered precipitation (mm day−1; shaded) and lag-regressions of meridionally averaged intraseasonal filtered column moisture (mm; contours) over 20°S–5°N regressed onto a time series of intraseasonal filtered precipitation centered in the southern Indian Ocean (20°–5°S, 90°–110°E) for (a) observations, (b) Corb, (c) Morb, and (d) Eorb. Contour intervals are 0.3 mm, solid contours indicate positive moisture anomalies, dashed contours indicate negative moisture anomalies, and the 0-mm contour is omitted. Observed precipitation is obtained from GPCP and moisture is obtained from ERA-Interim.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Figure 8 shows the intraseasonal wind, moisture, and precipitation anomalies regressed onto the time series of intraseasonal filtered precipitation averaged over the southern Indian Ocean (20°–5°S, 90°–110°E). The moisture and precipitation anomalies are tightly coupled in all simulations, while the wind anomalies resemble the Matsuno–Gill response to a heating source that is asymmetric to the equator (Matsuno 1966; Gill 1980). All simulations show a Kelvin wave–like feature to the east of positive precipitation anomalies that extends in the central Pacific and a Rossby wave–like feature in the southern Indian Ocean. This structure strongly resembles the swallow-tail structure of the boreal winter MJO (Adames et al. 2016).
Regression maps of intraseasonal precipitation (mm day−1; contours) and intraseasonal column moisture (mm; shading), and intraseasonal 850-hPa winds (m s−1; reference vector above color bar) regressed onto a time series of intraseasonal precipitation in the Indian Ocean (20°–5°S, 90°–110°E) for (a) Corb, (b) Morb, and (c) Eorb.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Figure 9a shows that the column processes contribute mostly to the growth of moisture anomalies, while the horizontal moisture advection contributes mostly to the decay. The contributions to moisture anomalies from the vertical moisture advection and precipitation (Fig. 9b) are large and of similar magnitude. Based on the decrease in the contribution of the moistening due to the column terms (Fig. 9a), we can infer that the contribution from the increase in precipitation exceeds the increase in vertical moisture advection (Fig. 9b). The decaying effect of the horizontal advection is of similar magnitude among all simulations. The growing effect of the column processes weakens in Morb and Eorb, despite the increase in MJO amplitude. The term that shows a trend that is consistent with the increase in the magnitude of the MJO is surface evaporation (Fig. 9a). Its decaying effect in Corb becomes weaker in Morb, and it even serves as a growing mechanism in Eorb, suggesting that feedback from evaporation may play an important role in amplifying the MJO in Morb and Eorb. This is consistent with the stronger mean westerlies over the southern Indo-Pacific warm pool in Eorb, which creates a more favorable environment for enhanced latent heat flux feedback (e.g., Sobel and Maloney 2012).
(a) Lag day-0 projection of the horizontal moisture advection, column moisture processes, and evaporation onto the intraseasonal moisture. (b) Lag day-0 projection of the vertical moisture advection and precipitation. The contributions in the Corb simulation are shown in blue (left bars), the Morb simulation are shown in pink (middle bars), and the Eorb simulation are shown in green (right bars). Projections are taken over the southern Indian Ocean (20°S–5°N, 60°–110°E).
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
5. Understanding the changes in the MJO’s amplitude
In the previous section, we found that the insolation changes in Morb and Eorb exert substantial impacts on the amplitude of the MJO, whereas it only weakly affects the MJO’s zonal wavenumber and phase speed (Figs. 1 and 2 in the supplemental material). In this section, we attempt to explain the robust increase in MJO variance in the Morb and Eorb simulations.
Figure 10 shows the regressions of intraseasonal evaporation (shading) and intraseasonal moisture (contours) onto a time series of intraseasonal filtered precipitation averaged over the southern Indian Ocean (20°–5°S, 90°–110°E). Between 20° and 10°S in the Indian Ocean, the latent heat flux anomalies change sign from negative in Corb and Morb to positive in Eorb. In this region, there are strong intraseasonal westerlies in all simulations (Fig. 8). The differences in the mean zonal wind (Fig. 2) are most pronounced in this region, with stronger mean westerlies in the Eorb simulation than in Corb and Morb, which may explain the change in the sign in Eorb. Our results add to a growing volume of literature that supports the importance of a background westerly wind component to the MJO (Maloney 2002; Maloney and Sobel 2004; Sobel et al. 2008, 2010; Sobel and Maloney 2012, 2013; Kang et al. 2022).
Regression maps of intraseasonal evaporation (kg m−2 s−1; shading) and intraseasonal moisture (mm; contours) regressed onto a time series of intraseasonal precipitation anomalies in the Indian Ocean (20°–5°S, 90°–110°E) for (a) Corb, (b) Morb, and (c) Eorb.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Nonlinear fit between precipitation (mm day−1) and CRH over the tropical band (30°S–30°N) for Corb (blue), Morb (pink), and Eorb (green); star symbols identify the reference CRH, which corresponds to the mean tropical precipitation between 30°N and 30°S. The inset shows the zoomed-in region between CRH of 74%–78% and precipitation of 2.5–5.5 mm day−1.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
Figure 12a shows that Eq. (7) holds reasonably well for all simulations when calculated over the tropical band (solid symbols in Fig. 12a) or the Indo-Pacific warm pool (hollow symbols in Fig. 12a). Equation (7) appears to work better for the intraseasonal anomalies, while slightly underestimating the total precipitation anomalies and overestimating the MJO-filtered precipitation anomalies, which may be due to the fact that we assume a constant τc for all spatial and temporal scales. In Fig. 12b, the actual changes in the standard deviation of precipitation (y axis) are compared with the predicted values from the sum of the left-hand side of Eq. (8) (x axis) for the changes in Morb and Eorb relative to Corb. The changes in the magnitude of precipitation anomalies are well correlated with the predicted changes, suggesting that the increase in MJO and other scale precipitation anomalies can be primarily explained by those in moisture variability and the moisture sensitivity of convection.
(a) Predicted standard deviation of precipitation anomalies from the standard deviation of anomalous column moisture and the convective moisture adjustment time scale {ln[std(〈q〉′/τc)]} and the actual standard deviation of precipitation anomalies {ln[std(P′)]} for Corb (blue symbols), Morb (pink symbols), and Eorb (green symbols). (b) The combined percent changes of the standard deviation of anomalous moisture and the convective moisture adjustment time scale vs the standard deviation of precipitation for the difference between the Morb and the Corb simulation (pink) and the difference between the Eorb and Corb simulations (green). Symbol shapes indicate the scale: unfiltered fields (circles), intraseasonal filtered fields (squares), and MJO-filtered fields (triangles). Open symbols are calculated over the Indo-Pacific warm pool (25°S–5°N, 60°–240°E) and filled symbols are calculated over the tropical band (30°S–30°N).
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
We next examine the changes in the MJO precipitation variance relative to the contributions to the MJO-scale Δ|P′| from the MJO-scale Δ|〈q′〉| and Δτc (Fig. 13). The changes in MJO precipitation variability are found to be mainly due to those in the convective moisture adjustment time scale, rather than the MJO-scale moisture anomalies. In Eorb, there is a small change in the MJO-scale moisture anomalies, while the decrease in the convective moisture adjustment time scale largely dominates the changes in the variability of the MJO-scale precipitation anomalies. Similarly, in Morb, there is a larger increase in the magnitude of MJO-scale moisture anomalies, with a comparable contribution to the increase in precipitation variability from the decrease in τc. The decrease in τc suggests that moisture is more efficiently removed from the column by precipitation, especially in Eorb. Therefore, despite similar (or even smaller) moisture anomalies, convection can produce more precipitation because moisture is more rapidly converted to precipitation (i.e., a decrease in τc). This result suggests that the changes in τc are an important driver of the changes in the MJO amplitude.
Percent changes in the standard deviation of MJO-scale precipitation over the tropical band (30°S–30°N) between Morb and Corb (pink bars) and Eorb and Corb (green bars). The percent changes are broken into the contribution from the convective moisture adjustment time scale and the standard deviation of MJO-scale moisture anomalies. The predicted change in the MJO-scale precipitation standard deviation shows the sum of the changes due to the convective moisture adjustment time scale and standard deviation of moisture anomalies [Eq. (8)].
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
To examine how and why τc changes, the changes in τc are calculated as percent changes relative to Corb and then compared with the predicted changes using Eq. (9) (Fig. 14). The predicted τc changes compare well with the actual τc changes, suggesting that Eq. (9) is a good approximation and can be used to examine which term contributes most to the changes in τc. In both simulations, the decrease in τc is largely due to the increase in the mean precipitation, while the decrease in
Changes to the convective moisture adjustment time scale (τc) between Morb and Corb (pink bars) and between Eorb and Corb (green bars). The actual changes in the convective moisture adjustment time scale are broken into the changes to the saturation specific humidity (〈qs〉), the slope of the nonlinear fit (ad), and the mean precipitation (
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0725.1
6. Summary and conclusions
This study examined the effect of the mean state on the MJO by perturbing the orbital parameters in a global climate model, the SPCAM. The model simulates a realistic mean state and MJO in the control simulation (Corb). Motivated by the studies of the climate during mid-Holocene (Otto-Bliesner et al. 2017; Joussaume et al. 1999) and with high-eccentricity orbits (Battisti et al. 2014), we perturbed the precession and eccentricity of Earth’s orbit. In the Morb simulation, the precession was changed to match the orbital configuration of the mid-Holocene, and in the Eorb simulation, the eccentricity was increased to match the conditions of 216 kyr before present. The effect of precession was recognized in both Morb and Eorb by a shift in the seasonal cycle of insolation. In Eorb, the eccentricity changes amplify the effect of the precession changes.
This shift in the insolation increases the heating imbalance between the two hemispheres, leading to changes in atmospheric circulation as the upper-tropospheric poleward branch of the Hadley cell becomes stronger in order to transport more heat from the warmer hemisphere to the cooler hemisphere to maintain energy balance (Frierson and Hwang 2012; McFarlane and Frierson 2017; Kang et al. 2018). This stronger Hadley circulation is seen in Morb and Eorb and is accompanied by stronger upward motion within the ITCZ and increased mean precipitation as well as a southward shift in zonal-mean precipitation.
The changes in insolation led to notable changes in the mean state in Morb and Eorb. During DJF, strong cooling appeared in the Northern Hemisphere, while relatively weak warming occurred in the Southern Hemisphere. The mean precipitation increased in Morb and Eorb south of the equator, where lower-tropospheric zonal winds become increasingly westerly in Morb and Eorb. A strong southward shift in mean precipitation was observed in Eorb, which is consistent with the theories of the latitudinal position of the Hadley circulation being linked to the strength of the hemispheric heating imbalance (Kang et al. 2008; Frierson and Hwang 2012; McFarlane and Frierson 2017; Kang et al. 2018). There was an increase in the precipitation variance at all scales, with the increase in MJO variance being greater than that in total or intraseasonal precipitation variance. No robust changes in the MJO’s phase speed and zonal wavenumber were found.
It would be of interest to examine the relative contributions of the obliquity, precession, and eccentricity to the MJO changes. Based on this study and previous research (Williams and Pollard 2002; Dressing et al. 2010; Huybers and Aharonson 2010; Battisti et al. 2014), we speculate that the eccentricity is the largest driver of MJO changes, due to the large changes to the mean state associated with it. Quantifying the contributions from each orbital parameter on the MJO would require additional simulations in which each orbital parameter was changed individually, rather than all together as was done in this study. A detailed analysis of the individual contributions from the orbital parameters is an important and interesting question, but is beyond the scope of this study.
Under the moisture mode framework, the changes in the maintenance mechanisms of the MJO during DJF were examined using the column-integrated intraseasonal moisture budget. Although small in magnitude when compared with the other terms, surface evaporation exhibited a trend that is consistent with the amplification of MJO variability: it has a decaying effect in Corb, a weaker decaying effect in Morb, and a growing effect in Eorb.
To understand the increase in MJO variance in Morb and Eorb, a framework based on the nonlinear moisture–precipitation relationship was developed. The framework allowed us to examine the relative contributions of the convective moisture adjustment time scale and moisture anomalies to the precipitation variance changes. It was shown that the changes in total, intraseasonal, and MJO precipitation variance can largely be explained by those in the moisture variance and convective moisture adjustment time scale. The decrease in the convective moisture adjustment time scale was found to be the main cause of the increase in MJO precipitation variance. A faster convective moisture adjustment time scale indicates a higher moisture sensitivity of convection, with which anomalous moisture in the column would be more efficiently converted to precipitation. The decrease in the convective moisture adjustment time scale was shown to be driven primarily by the increase in the mean precipitation. This is even more apparent in the Eorb simulation, where the reduction in temperature leads to a lower mean saturation specific humidity, which then requires less moisture to reach saturation, allowing convection to trigger with weaker moisture anomalies.
The current study also showed that the sign of the wind-induced latent heat flux feedback can change depending on the mean state. The increase in the mean westerly background winds in Morb and Eorb increases evaporation in Morb and Eorb and suggests a significant role for the boreal winter mean westerlies in the observed boreal winter MJO amplitude (Zhang and Dong 2004). It will be of interest to examine whether and to what extent the role of latent heat flux feedback in the MJO’s maintenance changes in a warming climate. Additionally, we presented a novel way of examining the changes in precipitation variability. Our results highlighted the importance of the convective moisture adjustment time scale in precipitation variability changes. It is warranted to apply the framework to multimodel simulations to see if the convective moisture adjustment time scale can explain the intermodel differences in MJO variance and its change under the global warming scenarios.
Acknowledgments.
The authors thank Dargan Frierson, David Battisti, and Shuyi Chen for their discussions and suggestions on the first author’s Ph.D. thesis, which is the basis of this paper. This work was funded by NASA Modeling, Analysis, and Prediction program (80NSSC17K0227), NOAA Climate Program Office’s Climate Variability and Predictability (NA18OAR4310300), U.S. DOE Regional and Global Model Analysis program (DE-SC0016223), and KMA R&D program (KMI2021-01210). Daehyun Kim was also funded by the Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (NRF-2021H1D3A2A01039352). Soon-Il An was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korea government (MSIT) (NRF-2018R1A5A1024958). Daehyun Kang was supported by a Sejong Science Fellowship funded by the Ministry of Science and ICT through NRF (NRF-2021R1C1C2004621). The authors additionally thank the University of Washington Program on Climate Change Interdisciplinary Fellowship for funding the exploratory part of this project. This research was partly performed while Stephanie S. Rushley held an NRC Research Associateship award at the Naval Research Laboratory.
Data availability statement.
The GPCP precipitation data are available at https://rda.ucar.edu/datasets/ds728.3/. Model simulations will be made available upon request.
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