1. Introduction
The western North Pacific (WNP) is the most active basin for tropical cyclones (TCs) on an annually averaged basis (Schreck et al. 2014). These TCs often result in both significant loss of life and financial damage for countries in East Asia (Zhang et al. 2009; Schreck et al. 2014). Over the past several decades, there has been a remarkable improvement in forecasting TC tracks, but the forecasts for TC intensity, especially rapid intensification (RI), remain a challenge (Rappaport et al. 2009; DeMaria et al. 2014; Elsberry et al. 2007). One of the most fundamental reasons for this relative lack of improvement is an incomplete understanding of the physical mechanisms responsible for RI (Kaplan and DeMaria 2003; Hendricks and Peng et al. 2010; Kaplan et al. 2015; Ma and Fei 2022). Further investigation of RI at various time scales is essential for improving the forecast skill of TC intensity.
TC genesis and development is highly sensitive to the large-scale environment. Weak vertical wind shear, high midlevel relative humidity, high low-level vorticity, and warm sea surface temperatures (SSTs) have been recognized to be favorable factors for RI from both observational and numerical modeling studies (Kaplan and DeMaria 2003; Wang and Zhou 2008; Gu et al. 2015; Guo and Tan 2018). Additionally, high levels of ocean heat content (OHC) have been noted to be a crucial factor for RI to occur (Emanuel 1999; Mainelli et al. 2008; Goni et al. 2009; C. Wang et al. 2017; Wang et al. 2015; Wang and Liu 2016; Fudeyasu et al. 2018; H. Zhao et al. 2018; Gao et al. 2020). Prior research has suggested that WNP TCs undergoing RI (RITCs) generally form at lower latitudes and often have prevailing westward tracks. Furthermore, these TCs usually develop in the monsoon trough, have a longer residence time over warm oceans with a deep mixed layer, and have lower inertial stability and enhanced latent heat release (Wang and Zhou 2008; Shu et al. 2012).
On interannual time scales, RITCs undergo significant fluctuations associated with changes in tropical Pacific climate (Wang et al. 2015; Wang and Zhou. 2008; H. Zhao et al. 2018). El Niño–Southern Oscillation (ENSO) is one important factor affecting interannual changes of RITCs over the WNP basin (Wang and Zhou 2008), with a significant correlation between RITCs over the southeastern portion of the WNP basin and the Niño-3.4 index (SSTs averaged over the region bounded by 5°S–5°N, 170°–120°W). Guo and Tan (2018) noted that the mean occurrence of TC rapid intensification tended to migrate westward over the WNP basin during short-duration El Niño events compared with long-duration El Niño events. These changes were associated with changes in the large-scale environment and especially easterly advection of zonal OHC. Gao et al. (2018) have suggested that the positive phase of the Pacific meridional mode (PMM) promotes TC genesis and intensification due to a low-level cyclonic vorticity anomaly induced by warm SSTAs over the subtropical eastern Pacific via a Gill-type Rossby response (Gill 1980). Gao et al. (2020) found that changes in relative humidity and OHC associated with the tropical Indian Ocean (TIO) appear to be two important factors modulating WNP RITCs.
Several studies have recently highlighted the importance of interdecadal changes in interannual TC teleconnections (Zhao et al. 2019a,b; Hu et al. 2018; Zhao and Wang 2016, 2019) associated with the tropical Pacific climate shift and shifting ENSO conditions (Lee and McPhaden 2010; Yu et al.2012). For example, H. Zhao et al. (2018) noted an increasing proportion of RITCs over the WNP since 1998 and emphasized the importance of large-scale thermodynamic factors in response to the Pacific decadal oscillation (PDO) phase change from warm to cool and shifting conditions from predominately eastern Pacific ENSO events to central Pacific ENSO events. In summary, most existing studies have focused on the role of changes in the large-scale environment in response to changes in tropical and subtropical ocean thermodynamic forcing. These ocean changes have been associated with both the tropical Pacific climate shift and shifting ENSO conditions that have contributed to changes in RITCs and other TC metrics over the WNP basin.
The Tibetan Plateau (TP), known as the “third pole” of the world, is the highest plateau on Earth with an average altitude of more than 4000 m. Several studies have focused on the thermal forcing from the TP since its high altitude can significantly affect the global large-scale circulation (Ding 1992; Yanai and Li 1992; Wang et al. 2008; G. Wu et al. 2012; Lu et al. 2018) and thus potentially impact WNP TC activity. Snow cover over the TP, through albedo and hydrologic effects, can modulate diabatic heating and thus affect local and/or remote climate (Yasunari et al. 1991; Wu and Qian 2003; Souma and Wang 2010; Turner and Slingo 2011; Liu et al. 2014). Observational and numerical studies have found that Tibetan Plateau snow cover (TPSC) can have significant impacts on the Asian monsoon and is therefore regarded as a potential factor for short-term prediction of weather and climate (Blanford 1884; Turner and Slingo 2011; Zhang and Tao 2001; Yu and Hu 2008; Wang et al. 2018). Si and Ding (2013) suggested that decreased snow depth would increase tropospheric temperature over the TP, reducing the land–sea contrast and thus shifting the location of the East Asian summer monsoon and its associated precipitation. On interdecadal and interannual time scales, changes in TPSC can affect the large-scale Pacific circulation (Zhang and Tao 2001; Zhang et al. 2004; Liu et al. 2020), the relationship between ENSO and the East Asian summer monsoon (Z. Wu et al. 2012), and North American climate via wave train dynamics (Lin and Wu 2011; Wang et al. 2020; Qian et al. 2019). Several studies have noted an out-of-phase relationship between TPSC and total WNP TC frequency as well as landfalling TC frequency in China (Xie 2005; Xie and Yan 2007; Zhan et al. 2016; Yan et al. 2015), highlighting the role of TPSC anomalies in the forcing of WNP TC activity. Zhan et al. (2016) found an abrupt enhancement in the relationship between winter TPSC and TC genesis frequency after the late 1990s. In summary, current studies have focused on the impact of TP thermal forcing on WNP TC frequency and landfalling TCs in China through alterations of the large-scale environment. However, there have been no studies to date on the impact of boreal winter–spring TPSC on RITCs and of decadal changes in the relationship between TPSC and RITCs over the WNP basin. This study focuses on these issues and provides hypothesized physical mechanisms for the observed relationship.
The rest of this study is arranged as follows. The methodology and datasets used in this study are described in section 2. Section 3 examines changes in the interannual relationship between TPSC and RITCs over the WNP basin. Section 4 presents a possible physical mechanism for the observed decadal change in the relationship between TPSC and RITCs over the WNP basin. A brief summary and discussion is presented in section 5.
2. Data and methods
a. Data
TC data from 1979–2014 are obtained from Joint Typhoon Warning Center (JTWC) best track dataset (Chu et al. 2002), which includes latitude, longitude, and maximum sustained wind speed at 6-h intervals. TCs in this study are defined as those with maximum sustained wind speeds of at least 34 kt (∼17 m s−1), and only the peak season for WNP TCs (July–November) is considered, since ∼78% of all WNP TCs formed during July–November from 1979 to 2014.
Tibetan Plateau snow depth (TPSD) is derived from the Global Land Data Assimilation System (GLDAS) with a resolution of 1° × 1° for the period 1979–2014 covering the region bounded by 60°S–90°N, 0°–360°E (Rodell et al. 2004). GLDAS is developed by the Goddard Space Flight Center together with the National Centers for Environmental Prediction. GLDAS provides land surface information for a better representation of climate characteristics including soil moisture and snow depth. Previous studies have explored the impact of several GLDAS land variables on climate (Yang and Wang 2019; Y.-F. Wang et al. 2017; Zhang et al. 2019). The GLDAS analysis ends in 2014, which is why our study ends with that year. We also use the TPSD from the ERA-Interim (Dee et al. 2011), ERA5 (Hersbach et al. 2020), and JRA-55 (Kobayashi et al. 2015) datasets to confirm the results using the GLDAS dataset. We find that the GLDAS dataset likely has the best representation of TPSD. This study shows results using the TPSD data from GLDAS unless specifically noted otherwise.
Monthly atmospheric datasets (e.g., wind, relative humidity, vertical velocity, air temperature, and geopotential height) are obtained from the NCEP–Department of Energy (NCEP–DOE) AMIP-II Reanalysis, with a 2.5° × 2.5° horizontal resolution and 17 vertical pressure levels extending from 1000 to 10 hPa (Kanamitsu et al. 2002). In the discussion that follows, wind shear refers to the vector wind difference between 200 and 850 hPa.
Monthly SSTs are obtained from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstruction SST version 5 (ERSSTv5) with a horizontal resolution of 2° × 2° (Huang et al. 2017). Monthly subsurface temperatures are derived from the Simple Ocean Data Assimilation 3 (SODA3) with a horizontal resolution of 0.5° × 0.5° (Carton et al. 2018).
b. Definition of RITC
Previous studies have often identified an RI process when the TC maximum wind speed increased by at least 30 kt (1 kt ≈ 0.51 m s−1) or the minimum sea level pressure decreased by 42 hPa or more in a 24-h period (Kaplan and DeMaria 2003; Holliday and Thompson 1979; Brand 1973; Ventham and Wang 2007; Huang et al. 2020). To prevent ambiguity in determining the onset and duration of the RI process, following Wang and Zhou (2008), the criteria for RI that must be satisfied are as follows: 1) an increase of at least 5 kt in TC intensity in the first 6 h; 2) an increase of at least 10 kt in TC intensity in the first 12 h; and 3) an increase of at least 30 kt in TC intensity in the first 24 h. An RITC is identified if a TC undergoes RI at least once during its lifetime. This definition of an RITC was adopted in previous studies examining changes of RITCs in response to tropical ocean forcing (H. Zhao et al. 2018; Gao et al. 2020). Given this definition of RITC, ∼82% of all WNP RITCs formed during July–November from 1979 to 2014. The TC development region in this study is a region in the off-equatorial WNP (7°–20°N, 120°–170°E). This region is also the main region for RITCs, with ∼78% of RITCs occurring here (H. Zhao et al. 2018).
c. Index of snow depth over the Tibetan Plateau
We define the TPSD index to be normalized snow depth averaged over the eastern TP (28°–40°N, 90°–105°E) during the boreal winter and early boreal spring [January–March (JFM)]. This index is used to examine the impact of TPSC on western North Pacific RITCs during the following TC season. The TPSD shows vigorous interannual variability, as seen from the time series of the JFM TPSD index during 1979–2014 (Fig. 1b). Furthermore, there is an obvious decadal change in the TPSD index, with significantly reduced TPSD since 2000 and a significant decreasing trend during 1979–2014. These observed features have also been noted in previous studies (Si and Ding 2013; You et al. 2020). We also regressed 2-m air temperature obtained from the ERA-Interim dataset on TPSD and found that the salient negative temperature anomaly mainly exists over the eastern TP (figure not shown). This finding confirms the reasonable representation of our index for the TP region.
(a) Time series of TC counts (green line) and RITC counts (black line) during July–November of 1979–2014. (b) Time series of normalized January–March snow depth over the eastern Tibetan Plateau (TPSD) from GLDAS along with a third-order polynomial fit. (c) 11-yr sliding correlations between January–March TPSD from the GLDAS, JRA-55, ERA5, and ERA-Interim datasets and the following peak season (July–November) RITC frequency. The dashed–dotted line indicates the 90% confidence level for statistical significance.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
(a) Time series of TC counts (green line) and RITC counts (black line) during July–November of 1979–2014. (b) Time series of normalized January–March snow depth over the eastern Tibetan Plateau (TPSD) from GLDAS along with a third-order polynomial fit. (c) 11-yr sliding correlations between January–March TPSD from the GLDAS, JRA-55, ERA5, and ERA-Interim datasets and the following peak season (July–November) RITC frequency. The dashed–dotted line indicates the 90% confidence level for statistical significance.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
(a) Time series of TC counts (green line) and RITC counts (black line) during July–November of 1979–2014. (b) Time series of normalized January–March snow depth over the eastern Tibetan Plateau (TPSD) from GLDAS along with a third-order polynomial fit. (c) 11-yr sliding correlations between January–March TPSD from the GLDAS, JRA-55, ERA5, and ERA-Interim datasets and the following peak season (July–November) RITC frequency. The dashed–dotted line indicates the 90% confidence level for statistical significance.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
d. ENSO index and PMM index
e. Statistical significance
Correlation coefficients and partial correlation coefficients are used to assess agreement between variables, and their statistical significance is tested by a two-tailed Student’s t test. Unless stated otherwise, correlations are deemed to be significant when the confidence level is higher than 90%, corresponding to P values less than or equal to 0.10.
3. On the relationship between RITCs and TPSD
a. Change in the interannual correlation and its seasonal variation
Figure 1a shows the time series of TC counts and RITC counts in the peak WNP TC season and highlights its significant interannual variability. On interdecadal time scales, TC and RITC frequency display different features, with a significant decrease in TC frequency (21.3 vs 17.8) and an insignificant change in RITC frequency (8.3 vs 8.8) from 1979–99 to 2000–14. These changes result in a significant increase in the proportion of RITC frequency, consistent with the results found in previous studies (H. Zhao et al. 2018; Gao et al. 2020). To examine decadal changes in the interannual relationship between RITC frequency over the WNP during the peak TC season (July–November) and TPSD, an 11-yr sliding correlation from 1979 to 2014 is calculated as shown in Fig. 1c. We find a significant negative correlation between TPSD and RITC frequency since 2000. When the full period is divided into two subperiods, 1979–99 and 2000–14, there is a significant relationship (r = −0.65; p < 0.01) between TPSD and RITC frequency during 2000–14. This correlation is insignificant (r = 0.04) during 1979–99 (Table 1). These results suggest that the relationship between January–March TPSD and late-summer and early-autumn RITCs over the WNP basin has strengthened since the beginning of the twenty-first century. We further examine the relationship between TPSD and RITCs for different seasons by calculating lead–lag correlations of TPSD with RITCs (Table 2). The negative relationship between January–March TPSD and RITC frequency gradually strengthens over the course of the peak TC season from July to November.
Correlation coefficients between January–March TPSD and the following peak season (July–November) PMM with the following peak season TC frequency and the following peak season RITC frequency during 1979–99, 2000–14, and the full period 1979–2014. The coefficients in boldface are significant at the 90% confidence level.
Correlation coefficients of January–March TPSD and the following peak season (July–November) RITC frequency for different seasons during the full period from 1979–2014 and the two subperiods of 1979–99 and 2000–14. The coefficients in boldface are significant at the 90% confidence level.
We next examine the sensitivity of these observed TPSD–RITC results to uncertainty in TPSD data. To address this issue, we have computed the relationship between RITCs over the WNP basin with the TPSD from the ERA-Interim, ERA5, and JRA-55 datasets. There is an almost identical interdecadal change in the relationship between RITCs and TPSD using either the ERA-Interim or JRA-55 datasets to that obtained using the GLDAS dataset (Fig. 1c). Although the correlation between TPSD from the ERA5 dataset and RITCs is weaker than from the other three datasets, the TPSD from the ERA5 also shows a tendency for weaker interdecadal changes in the relationship since 2000. The relatively small amplitude of the correlation coefficient between RITCs over the WNP basin and ERA5 TPSD relative to other datasets may be due to ERA5 not assimilating Interactive Multisensor Snow and Ice Mapping System snow cover data at high altitudes. The result of this lack of assimilation may be that ERA5 TPSD represents interannual variability relatively poorly compared to TPSD in other datasets (Orsolini et al. 2019). The consistency between the TPSD–RITC relationships found using multiple reanalysis products raises our confidence in the robustness of the observed interdecadal changes in the January–March TPSD and RITC over the WNP basin relationship during the following peak season.
b. Modulation of large-scale environmental factors by TPSD
In this section we focus on how TPSD modulates RITCs over the WNP basin since 2000 by correlating atmospheric and oceanic environmental factors during the TC peak season in June–November with January–March TPSD. In 2000–14, when TPSD is higher, there is a significant low-level anomalous anticyclonic circulation pattern over the WNP (Fig. 2a). There is also anomalous descending motion across most of the tropical western North Pacific and anomalous ascending motion predominating to the northwest of this region (Fig. 3b). To the southeast of the low-level anticyclonic circulation anomaly over the WNP basin, low-level northeasterly anomalies enhance the background trade winds and result in a significant increase in vertical wind shear over the eastern WNP (Fig. 3c). By contrast, there are negative anomalies (albeit insignificant) over the western WNP.
Correlation between January–March TPSD and the following peak season 850-hPa winds and vorticity during (a) 2000–14 and (b) 1979–99. Black vectors and white dots indicate correlation coefficients that are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Correlation between January–March TPSD and the following peak season 850-hPa winds and vorticity during (a) 2000–14 and (b) 1979–99. Black vectors and white dots indicate correlation coefficients that are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Correlation between January–March TPSD and the following peak season 850-hPa winds and vorticity during (a) 2000–14 and (b) 1979–99. Black vectors and white dots indicate correlation coefficients that are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
As in Fig. 2, but with large-scale environmental factors including (a),(e) 600-hPa relative humidity, (b),(f) 500-hPa omega, (c),(g) vertical wind shear, and (d),(h) TCHP. Black dots denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
As in Fig. 2, but with large-scale environmental factors including (a),(e) 600-hPa relative humidity, (b),(f) 500-hPa omega, (c),(g) vertical wind shear, and (d),(h) TCHP. Black dots denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
As in Fig. 2, but with large-scale environmental factors including (a),(e) 600-hPa relative humidity, (b),(f) 500-hPa omega, (c),(g) vertical wind shear, and (d),(h) TCHP. Black dots denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
As shown in Fig. 3a, TPSD has a weak negative correlation with midlevel relative humidity over the eastern WNP and a positive correlation closer to the coast. The anomalously negative relative humidity may be a result of cold and dry air advected by northeasterly flow along the eastern flank of the anticyclonic circulation in response to increased TPSD. Warm and moist air advected by southwesterlies may be responsible for the increase in midlevel moisture near the coast. Both the anomalous increase in relative humidity and increase in ascending motion over the northwestern part of the WNP are favorable for TC activity.
As shown in Fig. 3d, TCHP and TPSD positively correlate over most of the WNP basin, with significant negative correlations confined to around the date line. This correlation pattern between TCHP and TPSD is inconsistent with the TPSD–RITC relationship, implying that TCHP as modulated by TPSD is likely not a primary factor modulating RITCs during 2000–14. There are significant negative correlations between TPSD and relative humidity (Fig. 3a) and positive correlations between TPSD and vertical wind shear over the eastern WNP (Fig. 3c). When TPSD increases, anomalously dry midlevels, anomalously strong vertical wind shear, and an anomalous anticyclonic circulation (Fig. 2a) act in concert to suppress RITCs over the WNP basin.
We now examine the different responses of environmental factors in various seasons to TPSD by showing correlation maps between TPSD and each environmental factor at three different lag months of TPSD over the 2000–14 period (Fig. 4). The correlation patterns of relative humidity and vertical wind shear at a 6-month lag [July–September (JAS)] with the TPSD show similar patterns as for the full period examined (July–November) (Fig. 3). There are no significant correlations of TPSD and vorticity during JAS over the WNP (Fig. 4j), consistent with the lack of a significant correlation between TPSD and JAS RITC frequency (Table 2), implying a limited impact of vorticity on RITCs over the WNP basin during JAS. However, consistent with the significant correlation between TPSD and ASO RITC frequency (Table 2), the regions with significant correlations between TPSD and low-level anticyclonic vorticity, descending motion, lower midlevel relative humidity, and positive vertical wind shear in ASO and SON extend westward and cover most of the TC development region (middle and right column of Fig. 4). The relative humidity and vertical motion over the northwest part of the WNP as well as vertical wind shear over the western WNP have an opposite correlation to that seen over the main region for RITCs, likely causing the insignificant correlation between TPSD and TC genesis frequency during 2000–14 (Table 1). By contrast, there is a weaker relationship between TPSD and the environmental factors examined here during 1979–99 relative to 2000–14. Areas with significant correlations are smaller, likely resulting in an insignificant correlation between TPSD and RITC frequency during 1979–99 (Fig. 2b, right column of Fig. 3). In summary, changes in the relationship between TPSD and RITC frequency over the WNP basin can largely be explained by changes in the relationship between TPSD and the large-scale atmospheric circulation, with changes in the low-level circulation and associated vertical motion appearing to be the two most important factors.
Correlations between January–March TPSD and (a) JAS 600-hPa relative humidity, (b) ASO 600-hPa relative humidity, and (c) SON 600-hPa relative humidity for 2000–14. (d)–(f) As in (a)–(c), but for 500-hPa omega. (g)–(i) As in (a)–(c), but for vertical wind shear. (j)–(l) As in (a)–(c) but for 850-hPa vorticity. (m)–(o) As in (a)–(c), but for TCHP. Black dots denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Correlations between January–March TPSD and (a) JAS 600-hPa relative humidity, (b) ASO 600-hPa relative humidity, and (c) SON 600-hPa relative humidity for 2000–14. (d)–(f) As in (a)–(c), but for 500-hPa omega. (g)–(i) As in (a)–(c), but for vertical wind shear. (j)–(l) As in (a)–(c) but for 850-hPa vorticity. (m)–(o) As in (a)–(c), but for TCHP. Black dots denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Correlations between January–March TPSD and (a) JAS 600-hPa relative humidity, (b) ASO 600-hPa relative humidity, and (c) SON 600-hPa relative humidity for 2000–14. (d)–(f) As in (a)–(c), but for 500-hPa omega. (g)–(i) As in (a)–(c), but for vertical wind shear. (j)–(l) As in (a)–(c) but for 850-hPa vorticity. (m)–(o) As in (a)–(c), but for TCHP. Black dots denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
4. Possible physical mechanism: Linkage with the PMM
a. Role of the PMM in the TPSD–RITC relationship over the WNP
Previous studies have proposed two mechanisms for the lag effect of TPSD on global climate. First, snowpack over the TP during the prior winter or spring can modulate local soil moisture due to snowmelt in the following months, but this effect is short-lived (Qian et al. 2003; You et al. 2020). Second, TPSD may influence SST or co-occur with tropical climate modes that prolong its impacts due to the long-term memory of ocean (Z. Wu et al. 2012; Wang et al. 2018, 2020). We find a gradual increase in the relationship between TPSD and environmental factors from boreal late summer to autumn. SSTAs may play an important role in dictating the long lag effect of TPSD.
As shown in Fig. 5a, during 2000–14, there are significant negative SST correlations in the eastern subtropical Pacific extending to the equator related to increased TPSD, consistent with a negative PMM-like phase (Chiang and Vimont 2004). By contrast, there are no significant SSTAs related to TPSD during 1979–99 (Fig. 5b). There is a significant correlation between PMM and TPSD from 2000–14 (r = −0.75, p < 0.01), while there is an insignificant correlation between the PMM and TPSD during 1979–99 (r = −0.20, p = 0.65). Given the significant role of the PMM on WNP TC intensity found by Gao et al. (2018), we hypothesize that TPSD-induced large-scale circulation anomalies sustain the development of the PMM and thus impact RITCs over the WNP basin through an anomalous anticyclone via a Gill-type Rossby wave. We find a significant correlation between the PMM and RITC frequency during 2000–14 (r = 0.64, p < 0.01) but no significant correlation between the PMM and RITC frequency during 1979–99 (r = 0.08, p = 0.72) (Table 1).
Correlation between January–March TPSD and the following July–November SSTA over the North Pacific for the two subperiods of (a) 2000–14 and (b) 1979–99. The values with black dots are significant at the 90% confidence level. Correlation coefficients between January–March TPSD and the following July–November Pacific meridional mode (PMM) index and ENSO Modoki index (EMI) are also shown. Values with an asterisk (*) are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Correlation between January–March TPSD and the following July–November SSTA over the North Pacific for the two subperiods of (a) 2000–14 and (b) 1979–99. The values with black dots are significant at the 90% confidence level. Correlation coefficients between January–March TPSD and the following July–November Pacific meridional mode (PMM) index and ENSO Modoki index (EMI) are also shown. Values with an asterisk (*) are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Correlation between January–March TPSD and the following July–November SSTA over the North Pacific for the two subperiods of (a) 2000–14 and (b) 1979–99. The values with black dots are significant at the 90% confidence level. Correlation coefficients between January–March TPSD and the following July–November Pacific meridional mode (PMM) index and ENSO Modoki index (EMI) are also shown. Values with an asterisk (*) are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
To further elucidate the important role of the PMM in the strengthening of the relationship between TPSD and RITC frequency over the WNP since 2000, we compute partial correlation maps of the upper-tropospheric and lower-tropospheric circulation with the TPSD and the PMM, while removing the linear effects of the PMM and TPSD, respectively (Fig. 6). After removing the linear effect of the PMM, the response of the low-level anticyclonic circulation anomaly (Fig. 6b), and the upper-level cyclonic anomaly (Fig. 6a) to increased TPSD diminished greatly. By contrast, significant correlations between wind anomalies and the PMM index remain when removing the linear effect of TPSD, with the low-level cyclonic anomaly (Fig. 6d) diminishing somewhat but a pronounced upper-level anticyclonic anomaly (Fig. 6c) remaining over the WNP basin during the positive PMM phase. The lagged influences in changes in TPSD become stronger as the TC peak season approaches, when it induces an upper-level circulation favoring the PMM-like pattern. The lower-tropospheric circulation pattern Gill-like response is the key to the modulation of RITCs over the WNP basin during 2000–14. We find a close connection between TPSD and SSTA over the subtropical eastern Pacific during the later subperiod.
Partial correlations between January–March TPSD and July–November (a) 200- and (b) 850-hPa winds through linear removal of the July–November PMM from the January–March TPSD during 2000–14. (c),(d) As in (a),(b), but for partial correlations between July–November PMM and July–November 200- and 850-hPa winds through linear removal of the January–March TPSD from the July–November PMM during 2000–14. Red vectors are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlations between January–March TPSD and July–November (a) 200- and (b) 850-hPa winds through linear removal of the July–November PMM from the January–March TPSD during 2000–14. (c),(d) As in (a),(b), but for partial correlations between July–November PMM and July–November 200- and 850-hPa winds through linear removal of the January–March TPSD from the July–November PMM during 2000–14. Red vectors are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlations between January–March TPSD and July–November (a) 200- and (b) 850-hPa winds through linear removal of the July–November PMM from the January–March TPSD during 2000–14. (c),(d) As in (a),(b), but for partial correlations between July–November PMM and July–November 200- and 850-hPa winds through linear removal of the January–March TPSD from the July–November PMM during 2000–14. Red vectors are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
b. Plausible influence of TPSD on the PMM during the later period
We find that the PMM appears to be an important factor connecting January–March TPSD and July–November RITCs over the WNP basin. As suggested by previous studies (Chiang and Vimont 2004; Xie and Philander 1994; Linkin and Nigam 2008), the PMM is forced by extratropical atmospheric variability via wind–evaporation–SST (WES) feedback and advection. To clearly show the impact of the PMM, we computed partial correlations between all fields and TPSD after removing the linear effect of ENSO.
During 2000–14, when TPSD is anomalously high, strengthening trade winds (Fig. 7b) may favor the triggering of a negative PMM southwest of Baja California over the subtropical Pacific due to both enhanced evaporation and increased advection of cold water from high latitudes. These cold SSTAs further strengthen the trade winds by increasing the meridional surface temperature gradient. This positive feedback for the development of the PMM is also indicated by Sanchez et al. (2019). During the late summer and autumn, an anticyclonic anomaly is seen over the WNP basin in response to the development of the PMM-like pattern (Fig. 7d). By contrast, there is no significant anomalous circulation over the WNP basin during late summer and autumn from 1979 to 1999 (Fig. 7h). Weakening trade winds in the eastern Pacific associated with positive SST anomalies are found in JFM, but these SST anomalies appear to have a limited role in modulating the PMM in the following seasons (Figs. 7f,g,h).
Partial correlation map between January–March TPSD and individual season SSTAs (shading) as well as 850-hPa wind (vectors) with Niño-3.4 linearly removed for (a),(e) JFM, (b),(f) MAM, (c),(g) JAS, and (d),(h) SON during (a)–(d) 2000–14 and (e)–(h) 1979–99. White dots and black vectors denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlation map between January–March TPSD and individual season SSTAs (shading) as well as 850-hPa wind (vectors) with Niño-3.4 linearly removed for (a),(e) JFM, (b),(f) MAM, (c),(g) JAS, and (d),(h) SON during (a)–(d) 2000–14 and (e)–(h) 1979–99. White dots and black vectors denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlation map between January–March TPSD and individual season SSTAs (shading) as well as 850-hPa wind (vectors) with Niño-3.4 linearly removed for (a),(e) JFM, (b),(f) MAM, (c),(g) JAS, and (d),(h) SON during (a)–(d) 2000–14 and (e)–(h) 1979–99. White dots and black vectors denote correlations significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Figure 8 shows the response of the upper tropospheric (300 hPa) circulation to TPSD anomalies over the subtropical Pacific. When TPSD increases during 2000–14, there are significant negative correlations with geopotential height over the TP and positive correlations north of the TP with geopotential height at 300 hPa during both JFM and FMA, associated with cyclonic and anticyclonic anomalies over the TP and north of the TP, respectively. Over the subtropical eastern Pacific, we find an anomalous meridional dipole in geopotential height (Fig. 8a). This geopotential height dipole corresponds to an anomalous meridional gyre over the subtropical eastern Pacific that strengthens in FMA (Fig. 8b) and decays in MAM (Fig. 8c), consistent with the low-level wind anomalies shown in Figs. 7a and 7b. Previous studies have suggested similar impacts of the effect of snow cover on the large-scale upper-tropospheric circulation into the boreal summer (Ose 1996; Zhao et al. 2007; G. Wu et al. 2012; Liu et al. 2020). Although similar patterns are found during 1979–99, geopotential height anomalies in the eastern Pacific are characterized by a tripole pattern and a southward shift of positive geopotential height anomalies. Anomalous southwesterly winds along the southern flank of the negative geopotential height anomalies during 1979–99 are replaced by anomalous northeasterlies during 2000–14. This strengthened response to the north–south geopotential height dipole and associated upper-level gyres resemble the western Pacific teleconnection (WP; Wallace and Gutzler 1981; Barnston and Livezey 1987) but with an eastward shift. The WP pattern is regarded as the barotropic pattern signature of the North Pacific Oscillation (NPO) (Wallace and Gutzler 1981; Linkin and Nigam 2008). As suggested in previous studies (Vimont et al. 2001; Chiang and Vimont 2004), the NPO/WP pattern can enhance Pacific trade winds and thus connect with the PMM and CP ENSO and its associated relative barotropic structure (Figs. 7 and 8).
Partial correlation map between (a) January–March (JFM) TPSD and JFM geopotential height (shading) as well as wind (vectors; units: m s−1) at 300 hPa with Niño-3.4 linearly removed during JFM of 2000–14. (b) As in (a), but for JFM TPSD and February–April 300-hPa geopotential height and wind vectors during 2000–14. (c) As in (a), but for JFM TPSD and March–May 300-hPa geopotential height and wind vectors during 2000–14. (d) As in (a), but for JFM TPSD and JFM 300-hPa geopotential height and wind vectors during 1979–99. (e) As in (a), but for JFM TPSD and February–April 300-hPa geopotential height and wind vectors during 1979–99. (f) As in (a), but for JFM TPSD and March–May 300-hPa geopotential height and wind vectors during 1979–99.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlation map between (a) January–March (JFM) TPSD and JFM geopotential height (shading) as well as wind (vectors; units: m s−1) at 300 hPa with Niño-3.4 linearly removed during JFM of 2000–14. (b) As in (a), but for JFM TPSD and February–April 300-hPa geopotential height and wind vectors during 2000–14. (c) As in (a), but for JFM TPSD and March–May 300-hPa geopotential height and wind vectors during 2000–14. (d) As in (a), but for JFM TPSD and JFM 300-hPa geopotential height and wind vectors during 1979–99. (e) As in (a), but for JFM TPSD and February–April 300-hPa geopotential height and wind vectors during 1979–99. (f) As in (a), but for JFM TPSD and March–May 300-hPa geopotential height and wind vectors during 1979–99.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlation map between (a) January–March (JFM) TPSD and JFM geopotential height (shading) as well as wind (vectors; units: m s−1) at 300 hPa with Niño-3.4 linearly removed during JFM of 2000–14. (b) As in (a), but for JFM TPSD and February–April 300-hPa geopotential height and wind vectors during 2000–14. (c) As in (a), but for JFM TPSD and March–May 300-hPa geopotential height and wind vectors during 2000–14. (d) As in (a), but for JFM TPSD and JFM 300-hPa geopotential height and wind vectors during 1979–99. (e) As in (a), but for JFM TPSD and February–April 300-hPa geopotential height and wind vectors during 1979–99. (f) As in (a), but for JFM TPSD and March–May 300-hPa geopotential height and wind vectors during 1979–99.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
As noted above, changes in TPSD can cause anomalous meridional dipole gyres over the subtropical eastern Pacific during the boreal winter and spring (Figs. 8b,c). Similar Pacific circulation patterns also appear at lower (Fig. 7), and middle tropospheric and even stratospheric (figure not shown) levels, indicating these gyres are barotropic in structure, with an anomalous low-level cyclonic circulation centered at ∼50°N and an anomalous anticyclonic circulation centered at ∼30°N accompanied by anomalous low-level northeasterly flow on its southeastern boundary during 2000–14 (Fig. 7b). The strengthened low-level northeasterlies accelerate the background trade wind flow, thus cooling SSTs through the WES feedback as well as advection (Xie and Philander 1994), favoring the development of a negative PMM. This anomalous low-level northeasterly flow also brings both cold and dry air from higher latitudes (Figs. 8b,c), thus reducing precipitation and associated latent heating. As indicated in previous studies (Ham et al. 2013; Gao et al. 2018), this decreased heating induces an anticyclonic anomaly to the east of the SSTA, which enhances northeasterly flow along its eastern edge, leading to further anomalous SST cooling and latent heat decreases. The anticyclonic anomaly induced by the negative PMM propagates westward via air–sea thermal coupling. This anticyclonic anomaly becomes centered over the WNP during SON (Figs. 7c,d). This air–sea feedback process relays the influence of the negative PMM to the WNP, thus impacting WNP RITCs.
Why would TPSD be linked to the meridional dipole circulation over the eastern Pacific? Liu et al. (2020) investigated the global atmospheric response to winter–spring TP snow anomalies based on coupled model simulations. Increased TP snow can induce a negative WP-like circulation throughout the troposphere and stratosphere [Fig. 6 in Liu et al. (2020)]. Similar results are found in our observational analysis. Here, following Liu et al. (2020), we find two possible ways to elucidate how anomalous TPSD can generate an anomalous circulation over the eastern Pacific. Due to the high albedo of snow and the effect of spring melt (Yasunari et al. 1991), increased TPSD cools the TP atmosphere from the surface throughout most of the troposphere with anomalous warming in the upper troposphere and stratosphere (Fig. S1 in the online supplemental material). This anomalously cold air near the surface through the midlevels of the atmosphere combined with anomalously warm air at upper levels over the TP is then advected to the midlatitude Pacific by the prevailing subtropical westerly jet. Climatologically, the subtropical westerly jet at upper levels is located near 30°N (Fig. 9). Thermodynamically, this westerly jet is attributed to the north–south temperature gradient, while dynamically, the strong westerly component of the jet is partly attributed to the northward divergent wind originating from the ascending branch of the Hadley circulation that then rotates toward the right due to the Coriolis effect. The westerly jet extends to the northeastern Pacific, with a gradually weakening wind speed.
Partial correlation of January–March TPSD and (a) February–April averaged temperature from 300 to 150 hPa, (b) zonal wind at 200 hPa, (c) averaged temperature from 1000 to 400 hPa, (d) 500-hPa zonal wind anomalies, and (e) 500-hPa vertical velocity during 2000–14 with Niño-3.4 linearly removed. Black dots denote correlations significant at a 90% confidence level. Green contours in (b) and (d) denote the climatological zonal wind speed with a maximum speed of 50 m s−1 (interval: 5 m s−1).
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlation of January–March TPSD and (a) February–April averaged temperature from 300 to 150 hPa, (b) zonal wind at 200 hPa, (c) averaged temperature from 1000 to 400 hPa, (d) 500-hPa zonal wind anomalies, and (e) 500-hPa vertical velocity during 2000–14 with Niño-3.4 linearly removed. Black dots denote correlations significant at a 90% confidence level. Green contours in (b) and (d) denote the climatological zonal wind speed with a maximum speed of 50 m s−1 (interval: 5 m s−1).
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Partial correlation of January–March TPSD and (a) February–April averaged temperature from 300 to 150 hPa, (b) zonal wind at 200 hPa, (c) averaged temperature from 1000 to 400 hPa, (d) 500-hPa zonal wind anomalies, and (e) 500-hPa vertical velocity during 2000–14 with Niño-3.4 linearly removed. Black dots denote correlations significant at a 90% confidence level. Green contours in (b) and (d) denote the climatological zonal wind speed with a maximum speed of 50 m s−1 (interval: 5 m s−1).
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Convergence (∂u/∂x < 0) occurs over the eastern Pacific, thus causing warming at upper levels and cooling at middle levels (Fig. 9). As shown in Figs. 9a and 9c, upper-level warm anomalies are advected by the prevailing westerly jet over the North Pacific. In the middle troposphere, air temperature anomalies show an opposite response, consistent with the results of Liu et al. (2020).
At upper levels, warm temperature anomalies in the North Pacific (Fig. 9a) weaken the north–south temperature gradient to the south of the jet axis (the positive anomalies of ∂T/∂y; Fig. S2c). These changes in the temperature gradient result in weakened westerly anomalies south of the jet axis, thus promoting a divergent wind shifting northward, thereby strengthening zonal wind anomalies north of the jet axis. That indicates a northward shift of the prevailing westerly jet (Fig. 9b). The northward shift of the prevailing jet accompanied by weakening (strengthening) zonal wind south (north) of the jet favor descending (ascending) motion in the eastern North Pacific. The descending motion promotes the formation of an anticyclonic circulation with northeasterly anomalies on its southern flank (Fig. 9e). The variability of the jet and its associated vertical motion has been examined in previous studies (Jiang and Zhou 2021; Zhang et al. 2021).
In the middle troposphere, cold temperature anomalies in the North Pacific strengthen the north–south temperature gradient (e.g., the negative anomalies of ∂T/∂y; Fig. S2a), enhancing the zonal wind in the midlatitude Pacific. The enhanced zonal wind anomalies are overlaid on the jet axis, resulting in anticyclonic anomalies to its south and cyclonic anomalies to its north (Y. Zhao et al. 2018). In summary, the thermodynamic effect at mid- and upper levels favors an anomalous anticyclone in the North Pacific, further favoring a negative PMM-like mode.
We also use a linear baroclinic model (LBM) developed by Watanabe and Kimoto (2000) to verify this proposed physical process. This model has 20 sigma levels with a horizontal resolution of T42 and a basic state defined as the mean state during 1949–99 as provided by Watanabe and Kimoto (2000). To mimic the vertical distribution of the diabatic heating in response to increased snow depth, we refer to the correlation pattern of temperature with TPSD (Fig. S1). Consequently, anomalously cool air at lower levels and midlevels and anomalously warm air at upper levels are set over the TP as in the vertical profile shown in Fig. 10a. The atmospheric response of a 25-day integration of this idealized model is shown in Figs. 10b and 10c. The simulated pattern over the eastern Pacific is similar to the observations, although the center of the negative temperature anomalies shifts westward. At upper levels, anomalously warm air is carried by the prevailing westerly jet in the North Pacific, resulting in a weakening and strengthening of the zonal wind to the south and north of the jet axis, respectively. The negative zonal winds mainly occur to the south of the maximum positive temperature anomalies, indicating that thermal wind is the primary driver. At middle levels, cold temperature anomalies over the North Pacific and the dipole gyres are well simulated as shown in Fig. 10c. Figure 10d shows the response of 850-hPa wind anomalies in response to heating over the Tibetan Plateau. Over the North Pacific, there are strengthened zonal wind anomalies with a cyclone to the north and a weaker anticyclone to the south, accompanying by northeasterlies that promote formation of the negative phase of the PMM over the eastern subtropical Pacific. Although the anticyclone is weaker, these results provide evidence in support of the physical processes by which changes in the TPSD are linked to the anomalous circulation over the subtropical eastern Pacific.
Results of thermal forcing over the eastern TP in the linear baroclinic model (LBM). (a) Vertical profile of the specific heat source (K day−1) around the maximum heating center located at 34°N, 92.5°E. The y axis is displayed using vertical sigma coordinates. (b) 200-hPa temperature (shading; units: K) and zonal wind speed (contours; units: m s−1) at day 25 of the LBM model integration. (c) 500-hPa air temperature (shading; units: K) and winds (vectors; units: m s−1) at day 25 of the LBM model integration. (d) 850-hPa winds (vectors; units: m s−1) at day 25 of the LBM model integration. The black curve in (b) and (c) and black shading in (d) highlight the portion of the TP region with an altitude greater than 3000 m.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Results of thermal forcing over the eastern TP in the linear baroclinic model (LBM). (a) Vertical profile of the specific heat source (K day−1) around the maximum heating center located at 34°N, 92.5°E. The y axis is displayed using vertical sigma coordinates. (b) 200-hPa temperature (shading; units: K) and zonal wind speed (contours; units: m s−1) at day 25 of the LBM model integration. (c) 500-hPa air temperature (shading; units: K) and winds (vectors; units: m s−1) at day 25 of the LBM model integration. (d) 850-hPa winds (vectors; units: m s−1) at day 25 of the LBM model integration. The black curve in (b) and (c) and black shading in (d) highlight the portion of the TP region with an altitude greater than 3000 m.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Results of thermal forcing over the eastern TP in the linear baroclinic model (LBM). (a) Vertical profile of the specific heat source (K day−1) around the maximum heating center located at 34°N, 92.5°E. The y axis is displayed using vertical sigma coordinates. (b) 200-hPa temperature (shading; units: K) and zonal wind speed (contours; units: m s−1) at day 25 of the LBM model integration. (c) 500-hPa air temperature (shading; units: K) and winds (vectors; units: m s−1) at day 25 of the LBM model integration. (d) 850-hPa winds (vectors; units: m s−1) at day 25 of the LBM model integration. The black curve in (b) and (c) and black shading in (d) highlight the portion of the TP region with an altitude greater than 3000 m.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
c. Why has the relationship between the TPSD and PMM strengthened since 2000?
As shown in Figs. 7 and 8, there is a significant meridional dipole gyre with an associated barotropic structure over the eastern North Pacific at lags of 1–2 months for both subperiods, but there is a southward shift in the gyre during the 1979–99 subperiod (Figs. 8e,f). Due to this southward shift in the dipole circulation, years with high TPSD were characterized by an anomalous low-level anticyclone during the boreal spring with southwesterlies on its northwestern flank in the subtropical eastern North Pacific during 1979–99 (Fig. 7f), leading to weakly positive SSTAs there and consequently no SSTAs consistent with PMM development. The TPSD-driven barotropic dipole circulation is responsible for the prevailing westerly jet anomalies. There is an increase in zonal wind to the north of the climatological jet and decreased zonal wind to the south of the jet in 2000–14 relative to 1979–99 (Fig. 11b), suggesting a northward shift of the jet during the latter period. In summary, the northward shift of the jet during 2000–14 compared to during 1979–99 contributes to the displacement of the dipole circulation at the exit region of the westerly jet. As a result, the corresponding anomalous zonal wind is displaced southward, leading to a southward shift of ascending motion and an associated cyclonic circulation, with anomalous southwesterlies prevailing over the subtropical eastern Pacific (Figs. S2 and S3).
(a) Difference in SST anomalies (shading; units: °C) and 850-hPa wind (vectors; units: m s−1) between 2000–14 and 1979–99 (i.e., 2000–14 minus 1979–99) for January–March. (b) Pressure–latitude cross section of the difference of January–March zonal wind (shading; units: m s−1) averaged over 180°–120°W between 2000–14 and 1979–99. The climatological mean zonal wind during 1979–2014 is shown in contours with an interval of 5 m s−1. Dashed contours and thick black contours in (b) indicate eastward flow and zero zonal wind, respectively. White dots and black vectors denote differences that are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
(a) Difference in SST anomalies (shading; units: °C) and 850-hPa wind (vectors; units: m s−1) between 2000–14 and 1979–99 (i.e., 2000–14 minus 1979–99) for January–March. (b) Pressure–latitude cross section of the difference of January–March zonal wind (shading; units: m s−1) averaged over 180°–120°W between 2000–14 and 1979–99. The climatological mean zonal wind during 1979–2014 is shown in contours with an interval of 5 m s−1. Dashed contours and thick black contours in (b) indicate eastward flow and zero zonal wind, respectively. White dots and black vectors denote differences that are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
(a) Difference in SST anomalies (shading; units: °C) and 850-hPa wind (vectors; units: m s−1) between 2000–14 and 1979–99 (i.e., 2000–14 minus 1979–99) for January–March. (b) Pressure–latitude cross section of the difference of January–March zonal wind (shading; units: m s−1) averaged over 180°–120°W between 2000–14 and 1979–99. The climatological mean zonal wind during 1979–2014 is shown in contours with an interval of 5 m s−1. Dashed contours and thick black contours in (b) indicate eastward flow and zero zonal wind, respectively. White dots and black vectors denote differences that are significant at the 90% confidence level.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Previous studies have shown that the location of the westerly jet can be modulated by multidecadal SST variability (Zhang and Delworth 2007; Sung et al. 2014). Since the late 1990s when the PDO phase change occurred, there has been an enhanced SST gradient north of the oceanic front. This SST gradient has led to a northward displacement of the storm track via anchoring of the oceanic baroclinic region (Kwon et al. 2010), further facilitating a northward shift of the westerly jet via the momentum transport from transient vorticity (Hartmann 2007; Sung et al. 2014). Figure 11a displays the difference in SST between 2000–14 and 1979–99. Positive SSTAs are located over the western North Pacific and North Atlantic and negative SSTAs are located over the eastern subtropical North Pacific, in agreement with the switch of the PDO from a predominately positive to a predominately negative phase. Other studies have suggested that the phase switch of the Atlantic multidecadal oscillation (AMO) from negative to positive may also play a role in the poleward shift of the westerly jet, thus strengthening the subtropical high over the eastern Pacific (Yu et al. 2015; Zhang and Delworth 2007), mainly due to changes in oceanic heat transport reducing eddy heat transport and eddy vorticity fluxes. The interdecadal change in northeasterly winds that overlap with the PMM region shown in Fig. 11 partly agree with the results of Yu et al. (2015). The intensified coupling between the atmosphere and ocean over the subtropical Pacific is hypothesized to increase the interannual SST variability associated with the PMM, thus promoting the close link between TPSD and the PMM during 2000–14. This atmosphere–ocean coupling in the subtropical eastern Pacific is weaker during 1979–99 (Figs. 5b and 7e–h), likely due to changes in the interdecadal SSTA pattern associated with both the PDO and AMO.
5. Summary and discussion
This study finds an enhanced correlation between TPSD and RITC frequency during the peak TC season (July–November) over the WNP during 2000–14. Since 2000, unfavorable conditions for RITCs including an anomalous low-level anticyclone anomaly, anomalously low relative humidity, anomalous descending motion, and anomalously strong vertical wind shear over the main development region of the WNP basin have occurred when TPSD is high during January–March. Meanwhile, TCHP modulations associated with TPSD appear to play a limited role in contributing to the enhanced relationship between January–March TPSD and July–November WNP RITCs. By contrast, during 1979–99, large-scale environmental factors modulated by the TPSD are relatively weak, thus leading to the weak association between TPSD and RITCs over the WNP basin during this time period.
The PMM appears to play an important role in contributing to the significant relationship between TPSD and RITCs after 2000, due to the significant negative correlation between TPSD and PMM from 2000 to 2014. The physical mechanisms linking TPSD, PMM, and RITCs are summarized in a schematic diagram (Fig. 12) for both subperiods. Figure 12, corresponding to 2000–14, shows that increased TPSD cools the surface to the middle troposphere and warms the upper troposphere over the TP by increasing surface albedo over the TP and increasing local soil moisture. These temperature anomalies are then advected to the midlatitude North Pacific by the prevailing westerly jet. Based on thermal wind, cooling at midlevels over the midlatitude North Pacific intensifies the exit region portion of the subtropical westerly jet. Warming at upper levels leads to the jet shifting northward, accompanied by weaker zonal winds south of the jet axis.
Schematic diagram of the potential relationship between January–March TPSD with July–November RITC over the WNP during 2000–14. The red and blue vectors represent warm and cold advection at upper levels and midlevels, respectively. The dotted arrow denotes weaker winds. The label “AC” represents an anticyclonic circulation at 850 hPa. The red curve represents the portion of the TP above 3000 m.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Schematic diagram of the potential relationship between January–March TPSD with July–November RITC over the WNP during 2000–14. The red and blue vectors represent warm and cold advection at upper levels and midlevels, respectively. The dotted arrow denotes weaker winds. The label “AC” represents an anticyclonic circulation at 850 hPa. The red curve represents the portion of the TP above 3000 m.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
Schematic diagram of the potential relationship between January–March TPSD with July–November RITC over the WNP during 2000–14. The red and blue vectors represent warm and cold advection at upper levels and midlevels, respectively. The dotted arrow denotes weaker winds. The label “AC” represents an anticyclonic circulation at 850 hPa. The red curve represents the portion of the TP above 3000 m.
Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0758.1
A dipole circulation on each side of the jet also occurs with a cyclonic anomaly on its northern side and an anticyclonic anomaly on its southern side. This dipole circulation resembles a WP/NPO pattern, with an obvious eastward shift relative to the canonical position of the WP/NPO. Subsequently, the subtropical eastern Pacific is dominated by low-level northeasterlies along the southeastern side of the anticyclonic circulation anomaly. Northeasterlies enhance the background trade winds, which trigger a negative PMM phase through a wind–evaporation–SST feedback and cold advection. During the boreal summer and autumn, cold SST anomalies linked to a negative PMM spreading into the tropical central and western Pacific can induce an anticyclonic circulation over the WNP via a Gill-type Rossby wave response. The anomalous anticyclone and associated large-scale factors act to suppress RITCs over the WNP.
By contrast, during 1979–99, the dipole circulation response is shifted southward compared to 2000–14. This southward shift is due to the southward shift of the climatological jet. The extratropical eastern Pacific is dominated by low-level southwesterly winds and weak positive SST anomalies. These weak SST anomalies during 1979–99 result in an insignificant PMM response to TPSD anomalies. The changes in this relationship may be due to interdecadal changes in the SST base state of the Pacific and Atlantic Oceans in the 1990s.
Correlation maps of large-scale environmental factors with TPSD (Fig. 3) highlight a possible relationship between TPSD and TC genesis location over the WNP during the more recent subperiod (2000–14). Positive relative humidity and anomalous ascending motion over the northwest quadrant of the WNP basin and negative vertical wind shear anomalies over the southwest quadrant of the WNP basin (Fig. 3) favor TC genesis in these regions. However, these anomalies are of the opposite sign and consequently suppress TC genesis in other portions of the WNP basin. These regional differences in correlations between large-scale environmental factors and TPSD result in a weak correlation between TPSD and basinwide WNP TC frequency (r = 0.08) (Table 1). The significant negative correlation between TPSD and vertical velocity (e.g., increased vertical motion) and negative vertical wind shear near the Philippines (Figs. 3f,g) promote TC genesis during the 1979–99 subperiod. As a result, the correlation between TC genesis and TPSD is significantly positive during this time period (r = 0.30).
This study presents statistical evidence for the observed decadal changes in the relationship between TPSD and RITCs over the WNP basin, while also providing a possible physical mechanism. A linear baroclinic model has also been used to verify the teleconnection between TPSD and RITCs over the WNP basin. The TPSD shows a significant interannual relationship with RITCs over the WNP during the peak TC season (July–November) mainly via modulation of the prevailing westerly jet and the strength of the PMM during 2000–14, whereas these significant correlations disappear during 1979–99. Decadal-to-multidecadal variability of SST anomalies (e.g., PDO and AMO) appears to play a potentially dominant role in these interdecadal changes of interannual relationships. More recently, several studies have suggested that the simultaneous and strong impact of the PMM on TC genesis over WNP is mainly due to tropical central Pacific SST forcing (Zhang et al. 2020; Wu et al. 2020). How the PMM and central Pacific ENSO effect interannual changes in RITCs over the WNP basin deserve further exploration. Moreover, interdecadal changes in these interannual relationships and the associated underlying physical mechanisms deserve more investigation through observational analyses and numerical simulations.
Acknowledgments.
This research was jointly supported by the National Natural Science Foundation of China (Grants 41730961, 41922033, and 42192551), the project of the “Six Talent Peaks Project in Jiangsu Province” (2019-JY-100), and the open grants of the State Key Laboratory of Severe Weather (2021LASW-B08). P. Klotzbach would like to acknowledge a grant from the G. Unger Vetlesen Foundation. The numerical calculations in this study have been done on the supercomputing system at the Supercomputing Center of the Nanjing University of Information Science and Technology.
Data availability statement.
The TC best track data from JTWC are available at https://www.metoc.navy.mil/jtwc/jtwc.html?western-pacific. The NCEP–DOE AMIP-II Reanalysis data are available at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. The SST data from the NOAA ERSST V5 are available at https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/netcdf/. The SODA version 3 monthly data can be obtained from http://dsrs.atmos.umd.edu/DATA/soda3.12.2/REGRIDED/ocean/. The PMM index can be downloaded at https://www.aos.wisc.edu/∼dvimont/MModes/Data.html. All monthly snow depth data in the study are obtained from GLDAS at https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS, JRA-55 at https://rda.ucar.edu/datasets/ds628.1/, ERA-Interim at https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/, and ERA5 at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form.
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