1. Introduction
The Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972)—the most prominent intraseasonal mode of variability over the tropics—is characterized by planetary-scale circulation–convection coupled patterns and propagates eastward from the Indian Ocean to the western Pacific where warm sea surface temperature (SST) prevails (Zhang 2005). As it propagates eastward, the MJO interacts with local weather and climate systems and excites Rossby wave trains, causing far-reaching impacts on monsoons, tropical cyclones, floods, droughts, heat waves, and tornadoes worldwide (Zhang 2013; Hsu et al. 2016, 2017; Stan et al. 2017; Tippett 2018; Li et al. 2020; Liu et al. 2022). The MJO has been seen as the key predictability source for subseasonal-to-seasonal prediction (Schubert et al. 2002; Waliser et al. 2003; Vitart et al. 2017), yet accurate simulation and prediction in the current climate models are lacking, due to our incomplete understanding of MJO dynamics (Jiang et al. 2015; Klingaman et al. 2015; Kim et al. 2019; Ling et al. 2019; Le et al. 2021).
Modulated by background states at different time scales, MJO variability displays seasonal (Salby et al. 1994; Zhang and Dong 2004; Lu and Hsu 2017), interannual (Madden and Julian 1994; Hendon et al. 1999, 2007; Slingo et al. 1999; Chen et al. 2016; Hsu and Xiao 2017), interdecadal (Jones and Carvalho 2006; Dasgupta et al. 2020; Fu et al. 2020), and long-term variations mostly associated with global warming (Liu et al. 2013; Arnold et al. 2015; Adames et al. 2017; Wolding et al. 2017; Bui and Maloney 2018, 2019; Roxy et al. 2019). Among the diverse mechanisms proposed for the changes in MJO variability, the anomalous SST patterns and accompanying large-scale thermodynamic (moisture) and dynamic (circulation) anomalies have been commonly identified as the dominant contributors to the MJO intensity change via inducing anomalous moist static energy (MSE)/moisture processes. For instance, the seasonal migration in latitude of MJO signal is in phase with the annual cycle of SST center (Salby et al. 1994; Zhang and Dong 2004). During the warm events of El Niño–Southern Oscillation (ENSO), active MJO signals tend to propagate farther east as the warm-pool SST extends eastward (Hendon et al. 1999, 2007). The diversity of ENSO patterns, such as the central or eastern Pacific warming events, leads to distinct features of MJO initiation and development due to the disparate changes in background flow and moisture processes over the Indian Ocean and Pacific Ocean (Chen et al. 2016; Hsu and Xiao 2017). Similarly, the enhanced variability of MJO precipitation under global warming is found to be closely connected with the abundant moisture induced by SST warming (Bui and Maloney 2018; Roxy et al. 2019), whereas the long-term changes in MJO circulation variability are related to the background static stability (Maloney et al. 2019; Fu et al. 2020).
Owing to the limitation of observational data length, the interdecadal variability of MJO and its modulation by the internal modes associated with interdecadal SST changes have received far less attention. Using the NCEP-I reanalysis data during 1958–2004, Jones and Carvalho (2006) found that the frequency of summertime MJO events varied at the interdecadal time scale with a period of ∼18.5 years. Benefitting from the century-long (twentieth century) reanalysis datasets, which reasonably capture the basic characteristics of the MJO (Cui et al. 2020; Fu et al. 2020), Wang et al. (2021) documented that the intensity of boreal-winter MJO has a significant spectral peak at 12–20 years. Fu et al. (2020) identified the active decades with intensified MJO precipitation and circulation variability during 1970–99, in contrast to the weak MJO variability during the earlier decades of 1920–49. The background atmospheric static stability and anomalous moisture processes were found to control the amplitude of the MJO at the decadal time scale (Fu et al. 2020; Wang et al. 2021). Dasgupta et al. (2020) highlighted the modulation of MJO phase and periodicity by the Pacific decadal oscillation (PDO) during the boreal winter. Xiu et al. (2019) found that the MJO tends to propagate with a faster (slower) phase speed over the tropical Indian (Pacific) Ocean during 2000–12 than the earlier period of 1985–97. They attributed the interdecadal variation of MJO propagation to the PDO-induced changes in atmospheric moisture and MSE over the Maritime Continent.
These studies reviewed above focused primarily on the impacts of local effects, such as the effects of Indo-Pacific warm-pool SST and ENSO-/PDO-related anomalous thermodynamic and dynamic conditions on the MJO variation over the Indian Ocean and Pacific. Other studies (Kerr 2000; McGregor et al. 2014; Kucharski et al. 2015; Sun et al. 2017, 2020; Yang et al. 2020a) proposed the interbasin mechanisms to explain the decadal climate variability. Up to the present, only limited studies paid attention to the interbasin connection between the Atlantic Ocean and MJO. Based on climatological composites, Cassou (2008) and Jiang et al. (2017) found that the MJO over the warm pool regions may modulate the North Atlantic Oscillation through exciting a Rossby wave train. However, few findings about the influence of Atlantic variability on the MJO were documented. Yan et al. (2018) proposed that at the interannual time scale, the Atlantic SST anomalies show a strong correlation with the persistent anomalies of summer MJO, which in turn affect wintertime Pacific ENSO events. The decadal SST changes in the North Atlantic, namely, the Atlantic multidecadal oscillation (AMO; Kerr 2000), have been found to exert remote influences on the tropical Pacific through either an atmospheric bridge (Dong and Sutton 2007; Liu and Alexander 2007; Kucharski et al. 2011) or the global oceanic wave adjustment (Timmermann et al. 2005; Ding et al. 2012; Frauen and Dommenget 2012). Accordingly, the interbasin effects of the AMO on ENSO (Dong et al. 2006; Kang et al. 2014), Asian monsoons (Lu et al. 2006; Feng and Hu 2008; Monerie et al. 2019), and tropical cyclones over the western Pacific (Zhang et al. 2018; Sun et al. 2020) were recognized. To date, whether the AMO plays a role in the interdecadal variability of MJO has not been discussed. The mechanism behind the possible AMO–MJO linkage also needs a detailed study. This study aims to address the following questions: Is the interdecadal variability of MJO coupled with North Atlantic SST variation associated with the AMO? If the interbasin connection between AMO and MJO exists, how is this connection established?
The datasets, models, and methods used in the study are described in section 2. The potential connection between MJO and North Atlantic SST at the interdecadal time scale is examined in section 3 using several reanalysis datasets, followed by detailed diagnoses for understanding the mechanisms responsible for the interbasin linkage. In section 4, we conduct a series of numerical experiments to confirm the impact of North Atlantic SST on MJO variability over the tropical Pacific. We also assess the AMO–MJO linkage using larger samples from the preindustrial simulations of phase 6 of the Coupled Model Intercomparison Project (CMIP6) to support the observational findings presented in this study. Finally, a summary and discussion are provided in section 5.
2. Data and methods
a. Reanalysis and CMIP6 datasets
Two sets of century-long (covering the period of 1900–2010) reanalysis data are adopted to examine the decadal-to-multidecadal variability of the MJO. They are the Twentieth Century Reanalysis version v2c (20CR; Compo et al. 2011) from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) first atmospheric reanalysis of the twentieth century (ERA-20C; Poli et al. 2016). Although the data quality in the earlier period before the satellite era is less reliable, a few studies showed that the two century-long reanalysis datasets contain the basic features of the MJO and its long-term variations (Cui et al. 2020; Fu et al. 2020). For verifying the interdecadal changes in MJO and large-scale background states associated with AMO, two additional reanalysis datasets of 1979–2015 are also used: ECMWF Reanalysis v5 (ERA5; Hersbach et al. 2020) and the NCEP–U.S. Department of Energy Reanalysis II (NCEP-R2; Kanamitsu et al. 2002). These datasets provide zonal and meridional wind components (u and υ), air temperature (T), vertical pressure velocity (ω), specific humidity (q), and geopotential height (z) fields from 1000 to 100 hPa. Considering the internal data consistency, variables related to convective activity, such as outgoing longwave radiation (OLR) and precipitation, are also collected from these reanalysis datasets. Note that the MJO convective variation represented by reanalysis OLR and precipitation is fairly consistent with that of observation-based data (such as the NOAA OLR and GPCP precipitation) (Bosilovich et al. 2008; Lee and Biasutti 2014). The monthly SST is from the NOAA ERSST version 5 dataset (Huang et al. 2017) from 1854 to 2015. All the datasets are interpolated into 2.5° × 2.5° resolution.
Preindustrial control (PI-control) simulations are useful for understanding the climate internal variability without the effects of anthropogenic forcing (Eyring et al. 2016). In this study, the observed connection between the Pacific MJO and North Atlantic SST variation at the multidecadal time scale is further examined by using the PI-control simulations from 11 CMIP6 models (Table 1), which provide long-term outputs (more than 300 years) and show reasonable MJO simulation skills. The pattern correlations of winter-mean state and MJO-related precipitation variability between these models and observations are above ∼0.75. They also capture the dominant eastward-propagating component of intraseasonal signals shown in the wavenumber–frequency spectrum, defined as the east to west (E/W) ratio (Waliser et al. 2009). The E/W ratios from these 11 models are 1.5–3.6 for precipitation and 2.1–7.8 for 850-hPa zonal wind (figures not shown).
Information on the preindustrial (PI) control simulations of CMIP6 models used in this study.
b. Diagnostic methods
MJO-related signals are extracted by applying the Lanczos bandpass (20–90 days) filter (Duchon 1979) to certain fields. Since the column MSE and associated low-level moisture processes play crucial roles in MJO dynamics, the budget terms of MSE and moisture tendencies are diagnosed for quantitatively identifying the physical processes responsible for the interdecadal MJO variability.
Because the eastward-propagating MJO is most vigorous during the boreal winter, we focus on the interdecadal variability of MJO in the extended winter season (November–April). The AMO index is defined as SST anomalies in the North Atlantic (0°–60°N, 70°W–0°) (Sutton and Hodson 2005; Knight et al. 2006), while the PDO index is defined by the leading pattern of SST anomalies in the North Pacific basin (20°–60°N, 110°E–110°W) (Mantua et al. 1997). To emphasize the effects of climate internal variability at the decadal time scale on MJO changes, the linear trend in each variable (which is closely related to anthropogenic forcing) at each grid point has been removed before analysis. Note that the results remain the same when we removed the trends derived from each global-mean variable. Moreover, the major conclusions still hold even without removing the linear trend embedded in each variable.
c. Model and numerical experiments
The Nanjing University of Information Science and Technology (NUIST) coupled Earth System Model (NESM) v1 (Cao et al. 2015), which participates in the CMIP6, is used to conduct a series of sensitivity experiments. The atmospheric component of the NESM is version 5.3 of the Max Planck Institute’s ECHAM atmospheric model (Roeckner et al. 2003), a global spectral model with a horizontal resolution of 2.8° × 2.8° and 31 vertical levels. The ocean component is version 3.4 of the Nucleus for European Modelling of the Ocean (NEMO) with a horizontal resolution of 2° × 2° and 31 vertical levels; the sea ice component is version 4.1 of the Los Alamos sea ice model (CICE) (Hunke et al. 2010). These components are coupled by the OASIS3-MCT parallel coupler (Larson et al. 2005) once a day; in total 36 variables exchange among them. The NESM performs well in simulating basic climate states and variability in SST (such as ENSO) and tropical convective systems (like the MJO and monsoons) (Cao et al. 2015).
To confirm the Pacific MJO activity responses to the AMO-related SST forcing (ΔSST), the SST evolution in the North Atlantic (0°–60°N, 70°W–0°) is prescribed by nudging SST toward the field of climatological-mean monthly SST derived from the control experiment superposed on ΔSST. In the basins beyond the North Atlantic domain, air–sea interactions are allowed. The ΔSST is obtained by regressing the ERSST data onto the AMO index and amplifying its magnitude (3 times greater) to ensure that the NESM can produce a relatively steady and strong response to SST variation in a short-term (30 years) integration (Sutton and Hodson 2007; Sun et al. 2017; Li and Wang 2018). Note that applying the amplifiers (1–4 times) to the AMO-related SST forcing would not change the atmospheric response patterns in the Pacific basin but would strengthen the amplitude of these responses (not shown). The description of numerical experiments is listed in Table 2. Briefly, a 50-yr free coupled run was performed over the global domain in the control experiment (EXP_CTRL) for assessing model capability in simulating the MJO and background conditions. Two sensitivity experiments with imposed positive and negative ΔSST in the North Atlantic associated with different AMO phases (EXP_AMO+ and EXP_AMO−) were carried out for 30-yr integration each, to understand atmospheric responses to the combined effect of North Atlantic SST anomalies and air–sea interactions outside of the North Atlantic.
Experiments using the NESM model.
3. Modulation of MJO variability by AMO at multidecadal time scale
Singular value decomposition (SVD) analysis (Bretherton et al. 1992) is applied to the century-long records of MJO-related variances in 25°S–25°N and global SST during winters of 1900–2010. The SVD method aims to isolate patterns in the MJO and SST fields that maximize the linear relation between them. Here, we use multivariate fields (variances of 20–90-day precipitation and 850-hPa zonal wind in each winter) to define MJO activity, similar to the commonly used Real-Time Multivariate MJO (RMM) and boreal summer intraseasonal oscillation (BSISO) indices (Wheeler and Hendon 2004; Lee et al. 2013). Because the variables for SVD analysis have different units, these variables were normalized by corresponding standard deviations at each grid point. To underscore the natural variability at the decadal-to-interdecadal time scale, the normalized yearly wintertime MJO variance and SST are both detrended and undergo a 10-yr low-pass filtering before SVD analysis.
The first mode of the SVD (SVD1), explaining 41% of the total covariance, shows a weakened MJO variability in the southern Indian Ocean and central-eastern Pacific, while the enhanced MJO precipitation and circulation activities appear over the South China Sea and Philippine Sea regions (Fig. 1a). The changes in MJO variability are coupled with significant warm SST anomalies in the North Atlantic and central North/South Pacific but the cold SST anomaly in the mid-to-high latitudes of the southern oceans, resembling the positive phase of the AMO (Fig. 1b). The time series of SVD1 confirm the effect of AMO-related SST anomaly on the interdecadal variability of the MJO; their correlation coefficient is 0.96 (Fig. 1c). Along with the temporal evolutions of MJO amplitude (blue curve) and SST signals (red curve), we plot the decadal-to-interdecadal (detrended and 10-yr low-pass filtered) components of the AMO index (solid black curve) and PDO index (dashed black curve) for comparison. It is clear that the SVD1 features the linkage between the AMO and MJO with a significant correlation coefficient (0.83; Fig. 1c).
The first mode of singular value decomposition (SVD1) analysis between the multidecadal components of (a) MJO-related precipitation (shading) and 850-hPa zonal wind (contours from −0.6 to 0.6 with an interval of 0.2; positive and negative values are in red and blue, respectively) variances derived from 20CR and (b) global SST (shading) during winters from 1900 to 2009. (c) Time series of the first principal components (PC1) related to MJO variability (blue) and SST (red), as well as the decadal-to-interdecadal (10-yr low-pass-filtered) components of AMO (solid black) and PDO (dashed black). The correlation coefficients between multidecadal variations of MJO and AMO/PDO are listed in the bottom left corner (with an asterisk next to the coefficient for the significant relationship at the 90% confidence level). Stippling in (a) and (b) marks the region with a statistically significant change at the 90% confidence level considering the effective degree of freedom. (d)–(f) As in (a)–(c), but for the corresponding SVD2 and PC2 time series.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
Apart from the remote modulation of the North Atlantic SST, the decadal-to-interdecadal changes in local SST in the Indo-Pacific warm pool also exert an impact on the MJO activity there, as shown in SVD2 (Figs. 1d,e). This mode accounts for 21% of the total covariance. The correlation coefficient between the MJO and SST time series for this mode (blue and red curves in Fig. 1f) is 0.93. The PDO index is significantly correlated with the time series of SVD2 (Fig. 1f), suggesting the connection between the PDO and MJO reported in previous studies (Xiu et al. 2019; Dasgupta et al. 2020).
The interbasin connection between the AMO and interdecadal MJO variability revealed as the dominant coupled mode in the twentieth century has not previously been documented. To show the robustness of this finding, we analyzed the changes in MJO variability and winter-mean background circulation over the Indian and Pacific Oceans accompanied by AMO-related SST anomalies using various reanalysis datasets (Fig. 2). The positive and negative phases of the AMO are defined by the decadal-to-interdecadal component of the AMO index (solid black curves in Figs. 1c,f). Accordingly, two periods of 1904–25 and 1963–97 are identified as the negative phase of the AMO (−AMO); two periods of 1926–62 and 1998–2009 are selected for representing the positive AMO phase (+AMO). Figure 2 shows the decadal differences in MJO-related precipitation and background circulation during the negative AMO phase relative to those during the positive AMO phase. Significant changes in MJO intensity and background flow response to SST anomaly associated with different AMO phases (Fig. 2a) exist in the central tropical South Pacific (20°S–0°, 170°E–150°W) from all datasets of the 20CR (Fig. 2b), ERA-20C (Fig. 2c), ERA5 (Fig. 2d), and NCEP2 (Fig. 2e). The cold (warm) SST anomalies in the North Atlantic favor enhanced (decreased) MJO variability over the central Pacific (Fig. 2), consistent with the results of SVD analysis (Figs. 1a–c). The background winter-mean westerly anomalies over the tropical Pacific during the negative phase of the AMO suggest the weakening of the trade wind and Walker circulation (Ding et al. 2012; McGregor et al. 2014), which may lead to changes in MSE/moisture processes associated with MJO development, as will be diagnosed next.
Interdecadal differences in winter-mean (a) SST (units: K) and (b)–(e) the standard deviations of MJO-related precipitation (shading; mm day−1) and winter-mean 850-hPa wind field (vectors; m s−1) between negative and positive phases of AMO (−AMO minus +AMO) derived from different reanalysis datasets. Composites of (b) NOAA-20CR and (c) ERA-20C are based on data during 1900–2010 but (d) ERA5 and (e) NCEP2 are using the data of 1979–2015. Stippling marks the region with statistically significant change in MJO precipitation intensity at the 90% confidence level.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
Although the anomalous SST pattern and associated MJO changes over the tropical Pacific look similar to the conditions observed during the El Niño event (Hendon et al. 1999, 2007; Kessler 2001), we would like to emphasize that the current results (Fig. 2) are analyzed at the decadal-to-interdecadal time scale associated with different AMO phases. Even after removing the ENSO-related components of SST, MJO variability, and seasonal circulations (by subtracting their regressed fields onto the Niño-3.4 index) from the original data, the composite results show consistent patterns as in Fig. 2, but with slightly weakened amplitude (not shown). Therefore, the interbasin connection between Pacific MJO and North Atlantic SST at the decadal time scale is a robust feature, regardless of the interannual ENSO influences.
To understand the mechanisms responsible for the changes in Pacific MJO during the negative AMO phase, we compare the evolutions of precipitation, moisture, and MSE perturbations associated with the MJO over the central Pacific (Figs. 3 and 4). The Hovmöller diagrams of MJO-related precipitation along the near-equatorial latitudinal band during the negative and positive phases of AMO and their difference are displayed in Fig. 3. Although the phase speeds of MJO propagation are similar for different AMO phases, the spatial extents of enhanced MJO activity reveal a distinct feature associated with the AMO variability. The MJO convections tend to propagate farther eastward into the central tropical Pacific during the negative phase of the AMO (Fig. 3a), while they are confined over the western Pacific during the positive phase of the AMO (Fig. 3b). Similar to the ENSO effect on MJO (Hendon et al. 1999, 2007; Kessler 2001), the AMO mainly influences the east–west displacement of MJO activity. The decadal difference (−AMO minus +AMO) in MJO amplitude highlights the enhanced MJO variability over the central tropical Pacific (Fig. 3c), consistent with the results of SVD analysis (Fig. 1) and standard deviation distributions of MJO precipitation (Fig. 2). Thus, the question that needs to be asked is why the western Pacific (150°–170°E) MJO convective variations tend (do not tend) to propagate eastward over the central Pacific when the AMO is in a negative (positive) phase.
Longitude–time (units: days) evolutions of rainfall anomalies (shading; units: mm day−1) along the equator (10°S–0° averaged) during (a) negative and (b) positive AMO phases, respectively, and (c) their difference. The evolution patterns are derived by lag regression of intraseasonal (20–90-day filtered) rainfall anomalies against itself averaged over the base point (10°S–0°, 130°–150°E). Only the signals exceeding the 90% significance test are shown in (a) and (b) and stippled in (c). Dashed lines denote the eastward propagation phase speed of 5 m s−1.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
Composites of column-integrated (1000–100 hPa) MJO-related MSE (shading; units: 106 J m−2) and its tendency (contours; units: W m−2) based on all active MJO events (amplitude exceeding 1σ) occurring in each longitude over the western equatorial Pacific (10°S–0°, 150°–170°E) during (a) negative and (b) positive phases of the AMO, respectively, and (c) their differences (−AMO minus +AMO). Only the regions with significant changes at the 90% confidence level are shown. (d)–(f) As in (a)–(c), but for 1000–700-hPa-integrated intraseasonal moisture (shading; units: 10−1 kg m−2) and its tendency (contours; units: 10−7 kg m−2 s−1).
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
Following the method of Bui and Maloney (2020), we composited all active MJO events (amplitude exceeding one standard deviation) that occurred in each longitude over the western equatorial Pacific (10°S–0°, 150°–170°E). We defined the MJO amplitude thresholds for the negative and positive AMO phases, respectively. In fact, the results do not change if a unified criterion based on climatological amplitude is applied (not shown). The convective center is defined as the reference longitude 0°. While the MJO-related MSE and moisture perturbations maximize at the convective center (Figs. 4a,b,d,e), their tendencies display distinct features during the negative AMO phase relative to the positive AMO phase (Figs. 4c,f). The positive tendency anomalies of MSE and moisture appear evidently to the east of the convective center for the negative AMO cases (Figs. 4a,d), favoring the eastward propagation of MJO convection (Bui and Maloney 2020). For the positive phase of the AMO (Figs. 4b,e), the tendencies of both MSE and moisture are small negative values to the east of the convective center (over the central Pacific). Therefore, the MJO convection tends to continue moving over the central Pacific from the western Pacific during the negative AMO phase, but it is confined over the western Pacific during the positive AMO phase (Fig. 3).
To understand what contributes to the eastward displacement of enhanced MJO activity during the negative AMO phase, we show the differences in individual budget terms of column MSE and low-level moisture equations [Eqs. (1) and (2)] over the region with the most significant enhanced MSE/moisture tendency to the east of the MJO convective center (longitude 20°–50° to the east, 20°S–0°) during individual AMO phases (Fig. 5). Note that only a small residual appears between the sum of the terms on the right-hand side of Eq. (1) and the MSE tendency, suggesting that the budget analysis here is reliable. The enhanced MSE tendency favoring the eastward displacement of MJO activity comes mainly from horizontal MSE advection [−(V ⋅ ∇m)′], which reveals a larger value during the negative AMO phase than during the positive AMO phase (Fig. 5a). The critical role of horizontal MSE advection in MJO propagation was reported in several studies (e.g., Maloney 2009; Andersen and Kuang 2012; Sobel et al. 2014). The secondary contributor to the stronger MSE growth is vertical MSE advection,
(a) As in Fig. 4, but for column-integrated (1000–100 hPa) 20–90-day MSE tendency and individual budget terms (units: W m−2) averaged over the region to the east of the MJO center (longitude 20°–50° to the east, 20°S–0°) during the negative AMO periods (blue bars) and positive AMO periods (red bars), and their difference (−AMO minus +AMO; gray bars). (b) As in (a), but for column-integrated (1000–700 hPa) 20–90-day moisture tendency and individual budget terms (units: 10−6 kg m−2 s−1).
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
Since the moisture processes are highly related to variation in MSE (Maloney 2009; Kiranmayi and Maloney 2011), particularly at the lower (1000–700 hPa) troposphere, we also perform low-level moisture diagnosis to verify whether horizontal advection is a key process (Fig. 5b). Similar to the result of Fig. 5a, the enhanced horizontal moisture advection [−(V ⋅ ∇q)′] to the east of precipitation perturbations also dominates the larger moisture tendency for the negative phase of AMO. Such a configuration will transport abundant moisture for promoting the eastward displacement of precipitation perturbations during the negative AMO phase. The vertical moisture advection displays a negative effect on the eastward displacement of the MJO mainly because of the anomalous descending motion there (not shown). This anomalous downward motion can induce adiabatic warming and increased evaporation, contributing to the apparent moisture source term (Fig. 5b).
To unveil the detailed processes of 20–90-day MSE/moisture advection induced by interactions between different time-scale components, the individual terms on the right-hand side of Eqs. (4) and (5) are diagnosed. In Figs. 6a and 6b, the advection processes associated with the interaction between LFBS and MJO, either the advection of MSE/moisture perturbations by the LFBS flow [
As in Fig. 5, but for the individual terms contributing to the horizontal (a) MSE and (b) moisture advection through interactions between different time scale components [i.e., nine terms on the rhs of Eqs. (4) and (5)].
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
Figure 7 shows the interdecadal changes (−AMO minus +AMO) in the fields related to the moisture advection processes mentioned above. The negative AMO phase is accompanied by the background westerly anomaly over the tropical Pacific associated with the weakening of the trade wind (Ding et al. 2012; McGregor et al. 2014). The westerly anomaly may convey the increased MJO moisture perturbations toward an eastern area (i.e., the central Pacific) (Fig. 7a). Meanwhile, the easterly perturbations related to the Kelvin wave response of enhanced MJO convection appear to the east of the convective center (20°–50° east of 0°) (Fig. 7b). During the negative AMO phase, the background (>90 days) moisture components tend to increase over the central-eastern tropical Pacific (red shading in Fig. 7b). The abundant moisture interacting with MJO-associated easterly perturbation would result in an increased influx of moisture content to the east of the convective center (Fig. 7b), prompting the eastward displacement of MJO precipitation perturbations during the negative phase of the AMO (Figs. 3 and 4).
Interdecadal differences in (a) MJO moisture perturbations (shading; 10−4 kg kg−1) and LFBS (>90 days) flow at 850 hPa (vectors; m s−1) between negative and positive phases of AMO (−AMO minus +AMO). (b) As in (a), but for LFBS moisture (shading; 10−4 kg kg−1) and MJO-related flow perturbations at 850 hPa (vectors; m s−1).
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
In summary, the interdecadal changes in LFBS thermodynamic (
4. Model simulations to verify the AMO effect
To verify whether the background changes in the Pacific that control the amplitude of the MJO indeed result from the remote forcing of the AMO, we performed a set of numerical experiments using the NESM (Table 2). These experiments include a global free coupled run (EXP_CTRL) and two sensitivity experiments with SST in the North Atlantic basin being nudged toward the climatological-mean monthly SST superposed on AMO-related forcing (EXP_AMO− and EXP_AMO+).
The NESM captures reasonably the equatorial MJO-related variability during the boreal winter (Cao et al. 2015; Yang et al. 2020b). The eastward-to-westward intraseasonal power ratio (E/W ratio) is about 2.3 in EXP_CTRL (Fig. 8c), close to the observed value of 2.34 (Fig. 8a). The geographical distribution of MJO variability in the NESM (Fig. 8d) is also consistent with that of the observation (Fig. 8b), with the maximum standard deviation of 20–90-day precipitation over the tropical (0°–20°S) southern Indian and western Pacific Oceans. Even though the NESM tends to overestimate the amplitude of MJO variability and eastward-propagating components (Cao et al. 2015), it would not affect the conclusions because we consider the differences in MJO between sensitivity runs and control run.
(a) Wavenumber–frequency spectra of observed (GPCP) precipitation (shading; mm2 day−2) during winters of 1997–2012. (b) Standard deviation of MJO-related precipitation (shading; mm day−1) during winters of 1997–2012 using the GPCP. (c),(d) As in (a) and (b), but for the results of EXP_CTRL. E/W in (a) and (c) indicates the ratio between eastward and westward spectral powers over the MJO-scale (20–90-day) band.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
The simulated variations of 850-hPa circulation and SST outside of the North Atlantic in EXP_AMO− and EXP_AMO+ represent the responses of atmosphere/ocean to the regional forcing over the North Atlantic by including air–sea coupling processes. Figure 9a shows the differences between EXP_AMO− and EXP_AMO+, in which the responses of dynamic and thermodynamic conditions over the Pacific resemble the observations (Figs. 2 and 7). Forced by the SST cooling in the North Atlantic, significantly enhanced westerly anomaly appears in the western and central tropical Pacific. The warm SST anomaly in the central-eastern near-equatorial Pacific, a response to the AMO (Kucharski et al. 2011; McGregor et al. 2014; Yang et al. 2020a), also favors the increased moisture there. Under these conditions, the significant strengthening of MJO precipitation appears over the central tropical Pacific (Fig. 9b), as observed (Fig. 2). Hovmöller diagrams of MJO-related precipitation also suggest a farther eastward propagation of the MJO into the central tropical Pacific in EXP_AMO− (not shown). The results of the modeling experiments support that the changes in Pacific MJO variability can be induced by the remote forcing of North Atlantic SST.
(a) Changes in the winter-mean SST (shading; K) and 850-hPa wind (vectors; m s−1) in EXP_AMO− relative to EXP_AMO+ (EXP_AMO− minus EXP_AMO+). (b) As in (a), but for the standard deviation of MJO-related (20–90-day) precipitation. Stippling marks the region with a statistically significant change at the 90% confidence level.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
The atmospheric responses to the Atlantic SST/heating have been shown and discussed by several studies (Dong et al. 2006; Kucharski et al. 2011; Hong et al. 2013; McGregor et al. 2014; Yu et al. 2015; Yang et al. 2020a), which are readily applied to explain the appearance of background westerly and moisture anomalies that produce the increased horizontal moisture advection and enhanced MJO over the central tropical Pacific during the AMO negative phase. According to the previous studies (McGregor et al. 2014), the interbasin linkage can be established through the changes in the Walker circulation, as performed by the differences in velocity potential at 200 hPa between the two sensitivity experiments (Fig. 10a). The cold Atlantic SST may induce a descending motion over the tropical Atlantic Ocean, but an ascending motion along with upper-level divergence over the central tropical Pacific Ocean (Fig. 10a). This pattern is conducive to the weakening of the Walker circulation and thus the appearance of westerly anomalies over the western and central tropical Pacific (Fig. 9a).
(a) Changes in winter-mean velocity potential at 200 hPa (shading; units: 106 m2 s−1) in EXP_AMO− relative to EXP_AMO+ (EXP_AMO− minus EXP_AMO+). Contour shows the 200-hPa velocity potential in EXP_AMO+. (b),(c) As in Figs. 7a and 7b, respectively, but for the differences in these fields between EXP_AMO− and EXP_AMO+ (EXP_AMO− minus EXP_AMO+).
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
The changes in background circulation (
One may wonder whether the local air–sea interaction over the Pacific is essential to bridge the AMO–MJO connection. Parallel simulations using the AGCM (ECHAM4) with the same forcing (Table 2) were carried out (not shown). The consistent results of background westerly (easterly) anomaly over the western tropical Pacific and larger (smaller) MJO amplitude over the central tropical Pacific are shown, when only the negative (positive) North Atlantic SST was prescribed. The modeling results support the statistical findings (section 3) that the Pacific SST anomaly accompanied by the occurrence of AMO variability is not the major/essential contributor triggering the decadal changes in zonal displacement of the MJO. However, the ENSO-like pattern may modulate locally the amplitude of seasonal-mean and intraseasonal anomalies over the Pacific.
The importance of AMO-related anomalous states over the Pacific on MJO variability is also validated based on larger samples from the long-term (>300 years) outputs from 11 CMIP6 simulations. The PI-control experiments without the effect of anthropogenic forcing are suitable to examine the internal variability like the decadal-to-interdecadal changes in North Atlantic SST and their connection with MJO variability. Note that the issue about why some of the CMIP6 simulations have capability in reproducing the AMO variability and the interbasin connection is not the main subject of this study. Here, we aim to address whether the Pacific mean state changes associated with interdecadal SST anomalies over the North Atlantic indeed play a role in regulating the zonal displacement of MJO variability.
Most of the PI-control simulations display the interdecadal signals over the North Atlantic SST, although their spectral peak and power have more or fewer biases compared to the observation (not shown). As for the observed AMO index (Fig. 11a), the modeled AMO index is constructed by the detrended and 10-yr low-pass-filtered SST anomalies in the North Atlantic (0°–60°N, 70°W–0°) (Figs. 11b–l). The negative and positive phases of the AMO are then selected when the interdecadal SST anomalies persist longer than 20 years and with the minimum and maximum anomalous values exceed −0.2 and 0.2 K (similar to the observed amplitude), respectively. As a result, there are 11 positive AMO events and 11 negative AMO events identified from the PI-control simulations for the composite (Fig. 11). Note that most of the selected AMO events are from the EC-Earth3 family, suggesting the good capability of this model in simulating AMO variability. Further research is required to understand the sources of simulation biases in the CMIP6 models.
The AMO index (units: K) derived from (a) observation and (b)–(l) PI-control simulations of 11 CMIP6 models using the detrended and 10-yr low-pass-filtered SST anomalies in the North Atlantic (0°–60°N, 70°W–0°). The negative (blue shading) and positive (red shading) phases of the AMO are defined when the interdecadal SST anomalies persist longer than 20 years and the minimum and maximum anomalous values exceed −0.2 and 0.2 K, respectively.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
The differences in winter-mean flow and MJO activity between negative and positive AMO phases in the PI-control simulations (Fig. 12) are consistent with the observation (Figs. 2b–e). Along with the westerly anomaly, the significantly enhanced MJO convection variability appears over the central Pacific during the negative AMO phase (Fig. 12), providing further evidence for the linkage between the North Atlantic SST and Pacific MJO activity at the interdecadal time scale.
As in Figs. 2b–e, but for the composite results of MJO-related OLR (shading; W m−2) and 850-hPa wind field (vectors; m s−1) from the PI-control simulations.
Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0819.1
5. Summary and discussion
As the dominant intraseasonal mode over the tropics, the MJO changes will affect not only the tropical weather and climate systems but also the mid-to-high-latitude events through teleconnection, serving as the key source of subseasonal predictability (Zhang 2005; Waliser et al. 2003; Stan et al. 2017; Vitart et al. 2017). However, there is little understanding of decadal-to-interdecadal changes in MJO activity, largely due to the lack of data. By using the new century-long reanalysis datasets, numerical experiments, and long-integration (>300 years) outputs from the CMIP6, we found the interbasin linkage between the North Atlantic SST associated with the AMO and the zonal displacement of tropical Pacific MJO variability. This provides some innovative information as most of the existing studies focused on local SST effects, such as the PDO, on decadal MJO changes over the Indo-Pacific warm pool (Xiu et al. 2019; Dasgupta et al. 2020).
Observational analysis showed that during the negative phase of the AMO, the enhanced MJO perturbations over the western Pacific tend to propagate farther eastward and thus more vigorous MJO variability is identified over the central tropical Pacific (20°S–0°, 170°E–150°W) relative to the positive phase of the AMO. Based on the MSE/moisture budget diagnoses, we discussed why the MSE/moisture tendency is increased to the east of the western Pacific MJO center during the negative AMO phase, favoring the eastward-propagating MJO toward the central Pacific. The diagnostic results show that the major contributor is the horizontal MSE/moisture advection. Both the advection of LFBS moisture over the central-eastern Pacific by the MJO easterly perturbation [
Modeling results support our observational findings. When the North Atlantic SST is prescribed by the patterns mimicking different AMO phases in the NESM, the amplitude of central tropical Pacific MJO precipitation changes consistently with the observation. The negative SST anomaly in the North Atlantic (like the negative AMO phase) induces the enhanced background westerly and positive moisture fields over the tropical Pacific through modulating the Walker circulation (Kucharski et al. 2011; McGregor et al. 2014). The anomalous winter-mean states over the tropical Pacific then produce the strengthened horizontal moisture/MSE advection, which transports greater moisture/MSE toward the central Pacific and favors the growth of MJO precipitation there. The long-term PI-control experiments, in which the anthropogenic effect is excluded, also show the enhanced MJO precipitation amplitude at the interdecadal time scale when the background westerly and increased moisture are present in the tropical Pacific along with the occurrence of negative AMO phase.
The zonal shift of enhanced MJO precipitation variability found in this study can further exert impacts on the frequency and geographical distributions of extreme events outside the tropics worldwide via the MJO teleconnection (Carvalho et al. 2004; Lin et al. 2010). For example, Hsu et al. (2021) documented that the increased (decreased) occurrence of MJO convection over the western Pacific (Indian Ocean), which was observed in the recent decades (Roxy et al. 2019), contributes to the East Antarctic cooling. The accumulation of anomalous MJO heating-induced Rossby wave train over a few decades has also been reported as an important contributor to the Arctic amplification (Lee et al. 2011; Yoo et al. 2012). Previous studies focused mainly on the teleconnection patterns induced by MJO heating over the Indian Ocean and western Pacific, where the MJO is the most vigorous, but little is known about how the MJO teleconnection varies when MJO convective center shifts toward the central Pacific. Future studies are needed to investigate the decadal-to-interdecadal changes in MJO convection patterns and the associated weather/climate extreme events in the extratropics, which may have important implications for improving the decadal prediction system.
Acknowledgments.
We appreciate the valuable comments and suggestions from anonymous reviewers, which significantly improved the manuscript. This study is supported by the National Natural Science Foundation of China (42088101).
Data availability statement.
The reanalysis data from 20CR v2c, NCEP2, and ERSST v5 are openly available from NOAA https://psl.noaa.gov/data/gridded/tables/sst.html. ERA-20C and ERA5 data can be acquired from ECMWF website at https://apps.ecmwf.int/datasets/ and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5, respectively. The PI-control simulation outputs used in this study can be obtained from the CMIP6 archives at https://esgf-node.llnl.gov/search/cmip6/. The outputs of NESM sensitivity experiments are available at https://doi.org/10.7910/DVN/2HEDZX.
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