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The monsoon transitional zone (MTZ) is the interactional belt between humid and arid regions. This study examines the interannual variation of the MTZ rainy season withdrawal over China. A withdrawal index is firstly defined according to pentad mean precipitation data. The index shows pronounced interannual variations, with a significant dominant period around 2–4 years. When the withdrawal of the MTZ rainy season is later than normal, pronounced precipitation increase appears over the MTZ in August. Meanwhile, a significant anticyclonic anomaly appears over the tropical western North Pacific (WNP) and a marked atmospheric wave train is seen originating from the North Atlantic and flowing across Eurasia to East Asia. Both the anomalous anticyclone over the WNP and the negative geopotential height anomalies related to the Eurasian wave train around the MTZ contribute to the precipitation increase over the MTZ in August, and lead to the late withdrawal of the MTZ rainy season in China. It is showed that preceding winter El Niño-like events have a contribution to the generation of anticyclonic anomalies over the WNP. In addition, the northern tropical Atlantic (NTA) sea surface temperature (SST) warming, which is independent of the preceding winter El Niño, is found to play a crucial role in the formation of the WNP anticyclone and the Eurasian atmospheric wave train. The importance of the NTA SST anomalies on the MTZ rainy season withdrawal is also confirmed by a set of atmospheric general circulation model experiments.

This study uses both observations and numerical modeling experiments to investigate the lead-lag relationship and the associated physical mechanism of the western North Pacific anomalous anticyclone (WNPAC) with sea surface temperature (SST) anomalies over the northern tropical Atlantic (NTA). The results show that the WNPAC from late spring to the middle of autumn has a significant in-phase relationship with the NTA SST anomalies up to two seasons ahead. This relationship reaches a peak when the NTA SST leads by approximately 1–2 months and is nearly independent of the El Niño-Southern Oscillation variability. Diagnosis based on observations and numerical experiments using the Community Atmospheric Model version 5.3 reveals that the NTA warming favors intensified local convection activity during the spring–autumn seasons, causing enhanced low-level convergence and upper-level divergence (i.e., ascending motion) over the NTA and opposite flow anomalies over the central tropical Pacific. The enhanced subsidence over the central tropical Pacific, in turn, triggers an anomalous low-level anticyclone over the western North Pacific. Moreover, the intensity of the anomalous local convection activity that is associated with the NTA SST is closely related to the seasonal migration of the Atlantic intertropical convergence zone (ITCZ). As the Atlantic ITCZ migrates northward, the NTA SST-induced local convection activity extends northward from a narrow band near the equator during early spring to nearly the entire NTA region during the middle of summer, leading to the strongest remote effect of the NTA SST anomalies on the WNPAC during late summer and early autumn.

Seasonal prediction of tropical cyclone (TC) activity has been a hot research theme in the past decades. Usually, the tropical sea surface temperatures (SSTs) provide considerable predictability sources for the western North Pacific (WNP) TC activity. Here, we emphasized that the Chukchi-Beaufort (C-B) and Greenland (GL) sea ice variability is closely linked to the year-to-year variations of the early autumn WNP TC formation frequency (TCF). Observational and numerical evidence proved that the excessive C-B and GL sea ice sustains from August to the following early autumn and triggers the southeastward propagation of the Rossby wave trains originating from the Arctic across Western Eurasia (Okhotsk Sea) to the WNP. The resultant anomalous low pressure over WNP provides suitable environmental conditions for TC formation―the enhancement of the lower-level relative vorticity and water moisture, and the decrease of vertical wind shear. For the reduced sea ice, an opposite situation tends to emerge. The persistent combined sea ice signal makes it a physically meaningful precursor for TCF prediction. The cross-validated hindcast and independent forecast based on both the tropical SST and the Arctic sea ice precursors present that the TCF index is predicted with much higher correlation coefficients than those of the empirical models with only the tropical SST predictors. The results demonstrate that the Arctic sea ice truly promotes the seasonal prediction capability of the WNP TCF.

The monsoon is regarded as a key system influencing tropical cyclone (TC) activity over the Western North Pacific (WNP). However, the relationship between WNP TC frequency (TCF) and the monsoon across different timescales remains incompletely understood. This study explores the interannual-scale relationship between WNP TCF and the WNP summer monsoon over the period 1982–2020. We found that the interannual variation in basin-scale TCF is dominated by dynamic factors, particularly lower troposphere vorticity and middle troposphere ascending motion, which are driven by the WNP summer monsoon. Enhanced monsoonal precipitation over the WNP intensifies convective heating, which acts as a diabatic heat source and triggers a Rossby wave response to the west. This response generates anomalous lower troposphere cyclonic circulation and ascending motion in the main TC development region. In turn, the strengthened WNP summer monsoon circulation further amplifies precipitation, establishing positive feedback between atmospheric circulation and convection. This mechanism establishes dynamic conditions favorable for TC genesis, thereby dominating the basin-scale interannual variation in TCF.

The tropical North Atlantic (TNA) sea surface temperature (SST) has been identified as one of regulators on the boreal summer climate over the western North Pacific (WNP), in addition to SSTs in the tropical Pacific and Indian Oceans. The major physical process proposed is that the TNA warming induces a pair of cyclonic circulation anomaly over the eastern Pacific and negative precipitation anomalies over the eastern to central tropical Pacific, which in turn lead to an anticyclonic circulation anomaly over the western to central North Pacific. This study further demonstrates that the modulation of the TNA warming to the WNP summer climate anomaly tends to be intensified under background of the weakened Atlantic thermohaline circulation (THC) by using a water-hosing experiment. The results suggest that the weakened THC induces a decrease in thermocline depth over the TNA region, resulting in the enhanced sensitivity of SST variability to wind anomalies and thus intensification of the interannual variation of TNA SST. Under the weakened THC, the atmospheric responses to the TNA warming are westward shifted, enhancing the anticyclonic circulation and negative precipitation anomaly over the WNP. This study supports the recent finding that the negative phase of the Atlantic multidecadal oscillation after the late 1960s has been favourable for the strengthening of the connection between TNA SST variability and WNP summer climate and has important implications for seasonal prediction and future projection of the WNP summer climate.

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness.

Previous studies have revealed that warm (cold) sea surface temperature (SST) anomalies in the northern tropical Atlantic (NTA) can enhance (weaken) the anomalous low-level anticyclone over the western North Pacific (WNP) during boreal summer. This study assesses the ability of current atmospheric general circulation models (AGCMs) to simulate such an NTA–WNP connection by using Atmospheric Model Intercomparison Project experiments from 23 Coupled Model Intercomparison Project Phase 5 (CMIP5) and 35 CMIP6 climate models. It is shown that both the CMIP5 and CMIP6 multimodel ensemble (MME) averages and the majority of the individual AGCMs can reasonably reproduce the observed pattern of the NTA-related anomalous anticyclone over the WNP during boreal summer. Overall, the performance of the CMIP6 AGCMs in representing the NTA–WNP connection is similar to that of the CMIP5 AGCMs, except that the former tends to have a smaller spread than the latter among models. Additionally, both the CMIP5 and CMIP6 MME averages as well as the individual models can reasonably represent the mechanism responsible for the boreal summer NTA–WNP connection, which involves a zonally westward-extending overturning circulation over the Pacific–Atlantic Oceans. Furthermore, the intensity of the NTA-related WNP anomalous anticyclone is positively correlated with that of the WNP local climatological convection activity for both the CMIP5 and CMIP6 AGCMs, implying that better representation of the WNP climatological convection activity may be crucial for improving the skill of AGCMs to simulate the boreal summer NTA–WNP connection. However, model bias in the simulation of climatological convection activity over the WNP remains large for the current CMIP6 AGCMs, although the bias is reduced over most of the tropical and subtropical Pacific–Atlantic regions compared to that for the CMIP5 AGCMs during boreal summer.

This study shows that the Pacific‐Japan (PJ) teleconnection pattern has a significant influence on tropical cyclone (TC) activities over the western North Pacific (WNP) during the boreal summer (July, August, and September). During positive (negative) PJ phase, TCs form at a more northward (southward) location, recurve at a more northeastward (southwestward) location, and frequently pass over the northeast Asian (southeast Asian) region, including Korea and Japan (South China Sea and southern China). In particular, this difference in the TC track between the two phases is observed as a dipole‐like pattern between the regions of Southeast and Northeast Asia. The TC characteristics during the positive PJ phase are caused by the following two stronger atmospheric circulations over the WNP: an anticyclonic circulation centered to the east of Japan and a cyclonic circulation centered to the east of Taiwan. The southeasterly between these two circulations serves as steering flow that TCs move northward toward Korea and Japan from the northeast of the Philippines. Conversely, TCs during the negative PJ phase mainly move westward toward the South China Sea and southern China by the easterly from a stronger anticyclonic circulation centered to the east of Taiwan. As a result of this feature of TC track during the negative PJ phase, TC lifetime is shorter and TC intensity is weaker.

This study examines the impacts of sea surface temperature (SST) configuration on the monthly prediction of summer monsoon over the western North Pacific (WNP) by conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model and its atmospheric component model. The results show that the atmosphere-only model exhibits limited skill in predicting the WNP monsoon rainfall and circulation, and this skill can hardly be improved by simply increasing the frequency of prescribed SST observation. Compared to the atmosphere-only model, the coupled model shows much better performance in predicting the WNP monsoon rainfall and circulation, which can be further improved by adopting the observed SST with relatively higher frequency in the model initialization. This indicates that the high frequency of observed SST used is much more important in the coupled model than in the uncoupled model. In addition, the uncoupled model forced by the SST predicted by coupled model tends to produce better prediction of WNP monsoon rainfall and circulation than that forced by the observed SST. Both the coupled model and the atmosphere-only model forced by the coupled model predicted SST can well reproduce the surface latent heat flux and shortwave radiation flux over the WNP, leading to a reasonable SST-monsoon relationship and thus skillful predictions of WNP monsoon. Therefore, although the Tier-1 approach based on coupled model is increasingly popular, the Tier-2 approach based on atmosphere-only model is still feasible for the monthly prediction of WNP summer monsoon despite the lack of air-sea interaction. To obtain more skillful Tier-2 prediction, we recommend seeking for SST forcing that is unrealistic but consistent with the atmospheric model rather than SST forcing with very high accuracy.

Storm surges in the Western North Pacific cause significant economic damage and loss of life, highlighting the need for accurate storm surge predictions. This study evaluated four storm surge models: the Global Tide and Surge Model (GTSMv3.0), ERA20C neural network (ERA20C_nn), ERA20C multiple linear regression (ERA20C_ml), and 20th Century Reanalysis multiple linear regression (20CR_ml), using data from 160 tidal stations. The results show that the ERA20C_nn model outperformed others, with the highest correlation to tide-gauge observations. The GTSMv3.0 model follows closely, although slightly less accurate. The ERA20C_ml and 20CR_ml models were less effective, especially in predicting extreme surges. The ERA20C_nn model also provided more reliable estimates for 100-year return surge levels, outperforming other models. These findings suggest that neural network-based models, particularly ERA20C_nn, are better suited for assessing coastal flood risks in the region.