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experiments. c. Analysis method and observations Anomalies are calculated as departures from seasonal climatology, which is defined as annual mean plus the first four harmonics of long-term average. To focus on the intraseasonal variability, most analyses are based on intraseasonal anomalies obtained by applying 20–100-day bandpass filtering to the raw daily mean anomalies. When evaluating the zonal propagation features of the simulated MJO, lead–lag correlations or regressions are calculated for the 10°S
experiments. c. Analysis method and observations Anomalies are calculated as departures from seasonal climatology, which is defined as annual mean plus the first four harmonics of long-term average. To focus on the intraseasonal variability, most analyses are based on intraseasonal anomalies obtained by applying 20–100-day bandpass filtering to the raw daily mean anomalies. When evaluating the zonal propagation features of the simulated MJO, lead–lag correlations or regressions are calculated for the 10°S
structure of the wind field in the subtropics was crucial in elucidating the negative impact of the MJO on the cold surges in the South China Sea. This information that is revealed in the two-dimensional SVD analysis was lost when the one-dimensional RMM scheme is used to represent the MJO. Since the MJO influences EA wintertime rainfall through a process of meridional interaction, we will use the SVD method to resolve the MJO phase structure. The purpose of this paper is to find out if a two
structure of the wind field in the subtropics was crucial in elucidating the negative impact of the MJO on the cold surges in the South China Sea. This information that is revealed in the two-dimensional SVD analysis was lost when the one-dimensional RMM scheme is used to represent the MJO. Since the MJO influences EA wintertime rainfall through a process of meridional interaction, we will use the SVD method to resolve the MJO phase structure. The purpose of this paper is to find out if a two
IO. Fig . 1. The 20–100-day bandpass-filtered anomalies of OLR (W m −2 ; shaded) and column-integrated moisture tendency (kg m −2 ; contour) regressed onto the reference OLR time series from the IO base point (5°S–5°N, 75°–85°E). The column-integrated moisture tendency anomalies are weighted by 1 / [ τ c ¯ ] . c. MJO moisture budget weighted by the convective moisture adjustment timescale To understand MJO propagation under the moisture mode framework, a moisture budget analysis is conducted. We
IO. Fig . 1. The 20–100-day bandpass-filtered anomalies of OLR (W m −2 ; shaded) and column-integrated moisture tendency (kg m −2 ; contour) regressed onto the reference OLR time series from the IO base point (5°S–5°N, 75°–85°E). The column-integrated moisture tendency anomalies are weighted by 1 / [ τ c ¯ ] . c. MJO moisture budget weighted by the convective moisture adjustment timescale To understand MJO propagation under the moisture mode framework, a moisture budget analysis is conducted. We
. We also use 26 El Niño and 25 La Niña events identified based on a threshold that the 3-month averaged SSTA in Niño-3.4 region (120°–170°W and 5°S–5°N) is equal to or exceeds ±0.5°C for at least five consecutive months ( Lee et al. 2014 , 2018 ) ( supplementary Table 4 ). c. Spatiotemporal EOF analysis A longitude–time map of tropical south Indian SSTAs is derived for each of the 21 JS Niña and 19 JS Niño events ( supplementary Figs. 1 and 2 ). The time and longitude axes span from January
. We also use 26 El Niño and 25 La Niña events identified based on a threshold that the 3-month averaged SSTA in Niño-3.4 region (120°–170°W and 5°S–5°N) is equal to or exceeds ±0.5°C for at least five consecutive months ( Lee et al. 2014 , 2018 ) ( supplementary Table 4 ). c. Spatiotemporal EOF analysis A longitude–time map of tropical south Indian SSTAs is derived for each of the 21 JS Niña and 19 JS Niño events ( supplementary Figs. 1 and 2 ). The time and longitude axes span from January
solver and parameters. c. Rain and column-integrated moisture We derive the linear relationship between precipitation and first moisture mode q 1 using the DYNAMO northern sounding dataset. First, empirical orthogonal analysis is performed on 6-hourly moisture anomalies. The leading empirical orthogonal function (EOF; Fig. A1a ) explains more than 66% of total variances. Second, the column-integrated first EOF of moisture anomalies is regressed to surface precipitation. Regression
solver and parameters. c. Rain and column-integrated moisture We derive the linear relationship between precipitation and first moisture mode q 1 using the DYNAMO northern sounding dataset. First, empirical orthogonal analysis is performed on 6-hourly moisture anomalies. The leading empirical orthogonal function (EOF; Fig. A1a ) explains more than 66% of total variances. Second, the column-integrated first EOF of moisture anomalies is regressed to surface precipitation. Regression
summary is given in section 5 . 2. Data, methodology, and model description a. Data Primary observational datasets used in the present analysis include 1) interpolated outgoing longwave radiation (OLR) from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites ( Liebmann and Smith 1996 ) and 2) atmospheric three-dimensional fields including zonal and meridional wind ( u and υ ), temperature ( T ), pressure vertical velocity ( ω ), geopotential height ( ϕ ), and specific
summary is given in section 5 . 2. Data, methodology, and model description a. Data Primary observational datasets used in the present analysis include 1) interpolated outgoing longwave radiation (OLR) from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites ( Liebmann and Smith 1996 ) and 2) atmospheric three-dimensional fields including zonal and meridional wind ( u and υ ), temperature ( T ), pressure vertical velocity ( ω ), geopotential height ( ϕ ), and specific
freedom of 12 is indicated in blue. The significant lagged correlation between the southeastern Indian Ocean salinity anomaly and the PDO index makes it possible to assess its predictability. Here we propose a statistical prediction model based on a regression analysis: (5) S SEIO ′ ( τ ) = γ 0 + γ 1 PDO ( τ − 10 ) , where S SEIO ′ ( τ ) is the mean salinity anomaly in the southeastern Indian Ocean at month τ , and γ 0 and γ 1 are coefficient estimates for a multilinear regression of the
freedom of 12 is indicated in blue. The significant lagged correlation between the southeastern Indian Ocean salinity anomaly and the PDO index makes it possible to assess its predictability. Here we propose a statistical prediction model based on a regression analysis: (5) S SEIO ′ ( τ ) = γ 0 + γ 1 PDO ( τ − 10 ) , where S SEIO ′ ( τ ) is the mean salinity anomaly in the southeastern Indian Ocean at month τ , and γ 0 and γ 1 are coefficient estimates for a multilinear regression of the
2008 ). The early models used in these MJO predictability studies, however, were generally poor in simulating the MJO (e.g., Zhang et al. 2006 ). During the recent years when extensive hindcast datasets (e.g., the S2S hindcast dataset) became available, the MJO predictability was reevaluated (e.g., Rashid et al. 2011 ; Kim et al. 2014 ; Neena et al. 2014 ; Liu et al. 2017 ). For example, Neena et al. (2014) conducted a comprehensive analysis about the MJO predictability based on hindcasts by
2008 ). The early models used in these MJO predictability studies, however, were generally poor in simulating the MJO (e.g., Zhang et al. 2006 ). During the recent years when extensive hindcast datasets (e.g., the S2S hindcast dataset) became available, the MJO predictability was reevaluated (e.g., Rashid et al. 2011 ; Kim et al. 2014 ; Neena et al. 2014 ; Liu et al. 2017 ). For example, Neena et al. (2014) conducted a comprehensive analysis about the MJO predictability based on hindcasts by
, complex coastlines, and steep topography ( Birch et al. 2015 ). This region is surrounded by islands and continents with complex topography, which cultivates prominent diurnal variability of convection. Periodically and zonally propagating modes of tropical convection at different temporal and spatial scales can be found active over the SCS–MC. These are regarded as convectively coupled tropical waves based on the theoretical study of Matsuno (1966) and the analysis of Wheeler and Kiladis (1999
, complex coastlines, and steep topography ( Birch et al. 2015 ). This region is surrounded by islands and continents with complex topography, which cultivates prominent diurnal variability of convection. Periodically and zonally propagating modes of tropical convection at different temporal and spatial scales can be found active over the SCS–MC. These are regarded as convectively coupled tropical waves based on the theoretical study of Matsuno (1966) and the analysis of Wheeler and Kiladis (1999
patterns of 25–90-day filtered OLR during boreal winter (DJF). Percentages in parentheses show the contribution of each EOF mode to total variance. OLR values are multiplied by one standard deviation of the corresponding PCs to obtain a typical value and unit (W m −2 ) for ISOs. c. Successive and primary events Since some conventional MJO analysis techniques (e.g., lag regression) tend to produce a repeating MJO cycle, it is difficult to separate the individual attribution from current and previous
patterns of 25–90-day filtered OLR during boreal winter (DJF). Percentages in parentheses show the contribution of each EOF mode to total variance. OLR values are multiplied by one standard deviation of the corresponding PCs to obtain a typical value and unit (W m −2 ) for ISOs. c. Successive and primary events Since some conventional MJO analysis techniques (e.g., lag regression) tend to produce a repeating MJO cycle, it is difficult to separate the individual attribution from current and previous
