Search Results (298)

Tropical cyclones typically weaken rapidly during poleward propagation due to decreasing sea surface temperatures and increasing vertical wind shear. Super Typhoon Oscar (1995) deviated from this pattern by maintaining Category-5 intensity at an anomalously high latitude. This study investigates the oceanic mechanisms driving this resilience by integrating satellite SST data with atmospheric (ERA5) and oceanic (HYCOM) reanalysis products. Our analysis shows that the storm track intersected a persistent marine heatwave (MHW) characterized by a deep thermal anomaly extending to approximately 150 m. This elevated heat content formed a strong stratification barrier at the base of the mixed layer (~32 m) that prevented the typical entrainment of cold thermocline water. Instead, storm-induced turbulence mixed warm subsurface water upward to effectively mitigate the negative cold-wake feedback. This process sustained extreme upward enthalpy fluxes exceeding 210 W m⁻² and generated a regime of thermodynamic compensation that enabled the storm to maintain its structure despite an unfavorable atmospheric environment with moderate-to-strong vertical wind shear (15–20 m s⁻¹). These results indicate that the three-dimensional ocean structure acts as a more reliable predictor of typhoon intensity than SST alone in regions affected by MHWs. As MHWs deepen under climate warming, this cold-wake mitigation mechanism is likely to become a significant factor influencing future high-latitude cyclone hazards. Full article

Thermal anomalies serve as potential earthquake precursors and are crucial for understanding the mechanisms underlying seismogenic mechanisms and geodynamic perturbations. To address the limited understanding of the polarity evolution of thermal anomalies, we developed a dynamic spatiotemporal adaptive framework to quantify global thermal anomaly responses. Four parameters—the coefficient of determination (R²), spatiotemporal uncertainty (SU), temporal–spatial uncertainty ratio (TSUR), and spatiotemporal correlation coefficient (SCC)—were established to characterize the spatiotemporal patterns of thermal anomaly responses. Additionally, the Anomaly Emphasis Proximity (AEP) was introduced to identify statistically significant thermal anomaly events. The results indicate that the spatiotemporal evolution of thermal anomalies exhibits a transition from pre-earthquake mixed anomalies (both positive and negative) to post-earthquake unipolar anomalies (TIB decreased from 92% to 49%), accompanied by pronounced sea–land differentiation (SST increased from 0.3% to 98.7%). The AEP reveals significant thermal anomaly clustering highly consistent with earthquake activity (e.g., the 2008 Mw 8.0 Wenchuan earthquake in the Qinghai–Tibet Plateau), showing strong correlations in structurally active regions (e.g., SCA and SWS; FDR < 18.5%, STCW > 3.7%) but weaker ones in stable regions (e.g., CNA and ECA). Overall, this framework significantly enhances the robustness and reliability of seismic thermal anomaly detection. Full article

The marine environment in the South Pacific Island Countries (SPICs) is sensitive and vulnerable to climate change. While large-scale changes in this region are well-documented, national-scale analyses that address management needs remain limited. This study evaluated the performance of satellite-derived datasets—including sea surface temperature (SST), sea surface salinity (SSS), Secchi disk depth (SDD), chlorophyll-a (Chl-a), net primary production (NPP), and sea level anomaly (SLA)—against in situ observations, and analyzed their spatial and temporal variability across 12 national Exclusive Economic Zones (EEZs) during 1998–2023. Validation results presented that current satellite datasets could provide applicable information for EEZ-scale analyses. In the past decades, the SPICs experienced a general increase in SST and SLA, accompanied by marked within-EEZ heterogeneity in Chl-a and NPP variations, with Papua New Guinea exhibiting the largest within-EEZ inter-annual variability. In addition to monitoring, satellite data would help to constrain the uncertainty of CMIP6 results in the SPICs, subject to the accuracy of specific products. By 2100, Nauru might experience the most vulnerable EEZ, while the marine environment in the French Polynesian EEZ can keep relatively stable among all 12 EEZs. Meanwhile, CMIP6 projections in the Southeastern EEZs are more sensitive to satellite-based constraints, showing pronounced adjustments. Our results demonstrate the potential of combining validated satellite data with CMIP6 models to provide national-scale decision support for climate adaptation and marine resource management in the SPICs. Full article

We examine how the Arctic Oscillation (AO) shapes winter sea-surface-temperature (SST) variability in the East/Japan Sea, with a focus on sub-seasonal SST memory (how long anomalies persist) and air–sea coupling (where SST and atmospheric anomalies co-vary). Using daily OISST v2.1 and ERA5 reanalysis for 1993–2022, we first analyze winter persistence of SST and key atmospheric drivers and identify East Korea Bay and the Subpolar Front as hotspots of long-lived SST anomalies. A rank-reduced multivariate maximum covariance analysis then extracts the leading coupled mode between SST and a set of atmospheric fields under positive and negative AO phases; in both phases the coupled mode is front-anchored, but its amplitude and spatial focus differ. Finally, to quantify the mixed-layer memory, we construct Ornstein–Uhlenbeck-like time-integrated responses of the atmospheric principal components. The effective integration timescales, determined by maximizing zero-lag correlations with the SST mode, cluster at approximately 2–3 weeks for wind-stress curl and near-surface variables and 4–7 weeks for sea-level pressure and meridional wind, with longer timescales during negative AO. The time-integrated atmospheric responses exhibit SST-like persistence, confirming the mixed layer’s role as a stochastic integrator. These AO-conditioned memory windows define practical lead times over which integrated atmospheric indices can act as predictors of winter marine heatwaves and cold-surge-impacted SST anomalies. Full article

Heat extremes or heatwave events have significantly impacted socioeconomic activities and ecological systems, causing serious health issues and increased mortality rates in Pakistan over the past few decades. This study investigates the relationship between heat extremes in the northern Indian Ocean’s sea surface temperature (SST) and atmospheric temperature over Land (ATL) in Pakistan, and their connection to the Niño 3.4 Index, for monthly (March–August) and seasonal (spring and summer) basis from 1979 to 2015. Results show that SST has a higher frequency of heat extreme anomalies over different stretches of days than ATL. On a seasonal scale, heat extremes in ATL showed a significant correlation with SST, while the relationship was insignificant on a monthly basis. Both ATL and SST exhibited strong associations with the Niño 3.4 Index for land and ocean. These findings suggest that large-scale ocean-atmosphere interactions, particularly El Niño Southern Oscillation (ENSO), play a key role in modulating heat extremes in the region. The results of this study support SDGs by improving adaptive capacity and resilience on health, hunger, and climate by guiding policymakers in mitigating heat extremes. Integrating the findings of this study into national and provincial heat extreme plans may facilitate timely resource allocation and adaptation strategies in one of the world’s most climate-vulnerable regions. Full article

The South China Sea (SCS) sea surface temperature (SST) plays a crucial modulating effect on the climate of East Asia. While the interannual variability of South China Sea SST has been extensively examined, the decadal-scale linkages and underlying physical mechanisms between South China Sea SST and the three major ocean basins (the Atlantic, Pacific, and Indian Oceans) remain inadequately comprehended. To fill the gap, the study investigates the decadal variability of winter SST in the SCS during 1940–2023, utilizing long-term observational datasets and methods such as empirical orthogonal function decomposition, regression analysis, and teleconnections analysis. The first dominant mode of this decadal variability is characterized by basin-warming across the SCS, which is mainly driven by the Atlantic Multidecadal Oscillation (AMO, r = 0.62, p < 0.05). Specifically, the AMO imposes its remote influence on the SCS through three distinct pathways: the tropical Pacific pathway, the North Pacific pathway, and the tropical Indian Ocean pathway. These pathways collectively trigger an anomalous cyclone in the western North Pacific and SCS, and further induce basin-wide SST warming via a positive feedback that includes SST, sea level pressure, cloud cover, and longwave radiation. The second leading mode of SCS winter SST decadal variability displays a north–south dipole pattern, which is positively correlated with the Interdecadal Pacific Oscillation (IPO, r1 = 0.85, p1 < 0.05). Notably, this South China Sea SST dipole–IPO relationship weakened significantly after 1985 (r2 = 0.23, p2 < 0.05), related to the strengthening of the anomalous anticyclone over the SCS and the weakening of the anomalous cyclone over the tropical Indian Ocean. Furthermore, both the AMO and IPO influence the SST in the northern SCS by regulating wind field anomalies in the bifurcation region of the North Equatorial Current. This wind-driven modulation subsequently affects the intensity of Kuroshio intrusion into the SCS. These findings provide a novel mechanistic pathway for interpreting decadal-scale climate variability over East Asia, with implications for improving long-term climate prediction in the region. Full article

In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. Full article

Coastal upwelling off La Guajira, Colombia, is an atypical system where persistent easterly winds drive upwelling along a zonally oriented coastline. To characterize its seasonal cycle and variability, the ROMS AGRIF hydrodynamic model was implemented under climatological forcing. Three indicators were analyzed: the 25 °C isotherm, the 36.5 isohaline, and sea-level anomalies. The simulations showed that upwelling initiates in December, reaches maximum intensity during February–April, and weakens from September to November. At maturity, vertical velocities up to 8.5 m·day⁻¹ and the shoaling of Subtropical Underwater (T = 22–25 °C; S = 36.5–37.0) dominate the coastal domain, producing widespread surface cooling (23–24 °C) and salinity enhancement. During relaxation, weaker winds and the influence of the Caribbean Coastal Undercurrent displace the upwelled waters to below 80–100 m in depth, with surface temperatures above 27 °C. Model performance against MODIS Aqua SST was high (d > 0.99; RMSE < 1.7 °C), confirming its reliability to reproduce the observed thermal cycle. The multiparametric approach reveals that upwelling persistence depends on both seasonal trade wind forcing and regional circulation. This framework provides a more integrated description of the Guajira upwelling system than previous studies and supports applications in fisheries management, ecosystem monitoring, and maritime operations. Full article

El Niño fundamentally elevates the southern South China Sea (SSCS) winter sea surface temperature (SST), and this relationship exhibits significant interdecadal modulation by the Pacific Decadal Oscillation (PDO). Correlation analyses reveal a negative linkage between El Niño-SSCS SST relationship and PDO index (r = −0.5, p < 0.05). Mechanistically, negative PDO phase reconfigures the multi-anticyclone system: a weaker and northeastward-shifted Philippine Sea anticyclone (PSAC, 25° poleward), dissipating northern Indian Ocean anticyclone (NIOAC) and persistent southeastern Indian Ocean anticyclone (SEIOAC) through a reduction in Aleutian low and El Niño intensity. In the negative-minus-positive PDO phase composite, this drives anomalous southerlies/southwesterlies over the SSCS, establishing a zonal SST dipole (west-cooling/east-warming; −0.1 °C/+0.2 °C east/west of 108° E). Ekman dynamics (positive/negative wind stress curl west/east of 108° E), horizontal heat advection and latent heat flux (driven by southwesterly wind) dominate the SST dipole formation. From December to February, Aleutian low suppression and El Niño decay progressively modify the multi-anticyclone system configuration and replace southerly anomalies with northerlies, reducing regional warm SST in the N-P composite. The multi-anticyclone system thus mediates SSCS SST interannual variability, with critical implications for marine predictability under climate oscillations. Full article

This study presents a comprehensive assessment of long-term nutrient dynamics in the northern South China Sea (NSCS), a region that hosts the world’s largest marine ranching cluster and serves as a cornerstone of China’s “Blue Granary” initiative. By integrating multi-sensor satellite remote sensing data (Landsat and Sentinel-2, 2002–2024) with in situ observations, we developed robust retrieval algorithms for total nitrogen (TN) and total phosphorus (TP), achieving high accuracy (TN: R² = 0.82, RMSE = 0.09 mg/L; TP: R² = 0.94, RMSE = 0.0071 mg/L; n = 63). Results showed that TP concentrations increased significantly faster than TN, leading to a decline in the TN:TP ratio (NP) from 19.2 to 13.2 since 2013. This shift indicates a transition from phosphorus (P) limitation to nitrogen (N) limitation, driven by warming sea surface temperatures (SST) (about 1.16 °C increase) and increased anthropogenic phosphorus inputs (about 27.84% increase). The satellite-based framework offers a scalable, cost-effective solution for monitoring aquaculture water quality. When integrated with artificial intelligence (AI) technologies, these near-real-time nutrient anomaly data can support early warning of harmful algal blooms (HABs), offering key insights for ecosystem-based management and climate adaptation. Overall, our findings highlight the utility of remote sensing in advancing sustainable marine resource governance amid environmental change. Full article

Global warming has intensified the hydrological cycle, resulting in more frequent extreme precipitation events and altered spatiotemporal precipitation patterns in urban areas, thereby increasing the risk of urban flooding and threatening socio-economic and ecological security. This study investigates the characteristics of summer extreme precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022, utilizing the China Daily Precipitation Dataset and NCEP/NCAR reanalysis data. Nine extreme precipitation indices were examined through linear trend analysis, Mann–Kendall tests, wavelet transforms, and correlation methods to quantify trends, periodicity, and atmospheric drivers. The key findings include: (1) All indices exhibited increasing trends, with RX1Day and R95p exhibiting significant rises (p < 0.05). PRCPTOT, R20, and SDII also increased, indicating heightened precipitation intensity and frequency. (2) R50, RX1Day, and SDII demonstrated east-high-to-west-low spatial gradients, whereas PRCPTOT and R20 peaked in the eastern and western PLCG. More than over 88% of stations recorded rising trends in PRCPTOT and R95p. (3) Abrupt changes occurred during 1993–2009 for PRCPTOT, R50, and SDII. Wavelet analysis revealed dominant periodicities of 26–39 years, linked to atmospheric oscillations. (4) Strong subtropical highs, moisture convergence, and negative OLR anomalies were closely associated with extreme precipitation. Warmer SSTs in the eastern equatorial Pacific amplified precipitation in preceding seasons. This study provides a scientific basis for flood prevention and climate adaptation in the PLCG and highlighting the region’s vulnerability to monsoonal shifts under global warming. Full article

High-precision Sea Surface Temperature (SST) prediction is critical for understanding ocean–atmosphere interactions and climate anomaly monitoring. We propose GRU_EKAN, a novel hybrid model where Gated Recurrent Units (GRUs) capture temporal dependencies and the Enhanced Kolmogorov–Arnold Network (EKAN) models complex feature interactions between SST and multivariate ocean predictors. This study integrates GRU with EKAN, using B-spline-parameterized activation functions to model high-dimensional nonlinear relationships between multiple ocean variables (including sea water potential temperature at the sea floor, ocean mixed layer thickness defined by sigma theta, sea water salinity, current velocities, and sea surface height) and SST. L2 regularization addresses multicollinearity among predictors. Experiments were conducted at 25 South China Sea sites using 2011–2021 CMEMS data. The results show that GRU_EKAN achieves a superior mean R² of 0.85, outperforming LSTM_EKAN, GRU, and LSTM by 5%, 25%, and 23%, respectively. Its average RMSE (0.90 °C), MAE (0.76 °C), and MSE (0.80 °C²) represent reductions of 31.3%, 27.0%, and 53.2% compared to GRU. The model also exhibits exceptional stability and minimal Weighted Quality Evaluation Index (WQE) fluctuation. During the 2019–2020 temperature anomaly events, GRU_EKAN predictions aligned closest with observations and captured abrupt trend shifts earliest. This model provides a robust tool for high-precision SST forecasting in the South China Sea, supporting marine heatwave warnings. Full article

This study investigates anomalous precipitation patterns in the Taihu Basin, located in the Yangtze River Delta of eastern China, using high-resolution daily data from 1960 to 2019. Leveraging a deep learning autoencoder and self-organizing map, three spatially distinct types are identified—north type (72%), south type (19.7%), and center type (8.3%). The north type exhibits a pronounced upward trend (+0.11 days/year, p < 0.05), indicating intensifying extreme rainfall under climate warming, while the south type displays a bimodal temporal structure, peaking in early summer and autumn. Composite analyses reveal that these patterns are closely associated with the westward extension of the Western North Pacific Subtropical High (WNPSH), meridional shifts of the East Asian Westerly Jet (EAJ), low-level moisture convergence, and SST–OLR anomalies. For instance, north-type events often coincide with strong anticyclonic anomalies and enhanced moisture transport from the Northwest Pacific and South China Sea, forming favorable convergence zones over the basin. For flood management in the Taihu Basin, the identified spatial patterns, particularly the bimodal south type, have clear implications. Their strong link to specific circulation features enables certain flood-prone scenarios to be anticipated 1–2 seasons in advance, supporting proactive measures such as reservoir scheduling. Overall, this classification framework deepens the understanding of atmospheric patterns associated with flood risk and provides practical guidance for storm design and adaptive flood risk management under a changing climate. Full article

Using multiple reanalysis datasets, this study investigates the decadal variability in the relationship between Northeast Pacific Sea surface temperature (SST) and Arctic stratospheric ozone (ASO), with a focus on the role of atmospheric dynamics in mediating this connection. A significant decadal shift is identified around the year 2000, characterized by a weakening of the previously strong negative correlation between January–February SST anomalies and February–March ASO. Prior to 2000 (1980–2000), warm SST in the northeastern Pacific suppressed upward planetary wave propagation, resulting in decreased stratospheric wave activity and a weakened Brewer–Dobson circulation. The weakened BD circulation reduced poleward transport of tropical ozone and heat, yielding a colder, ozone-poor polar vortex. The strong relationship enabled skillful seasonal predictability of ASO using SST precursors in a linear regression model. However, post-2000 (2001–2022), the weakened planetary wave response to SST anomalies resulted in a breakdown of this relationship, yielding non-significant predictive skill. The findings highlight the non-stationary nature of ocean-stratosphere coupling and underscore the importance of accounting for such decadal shifts in climate models to improve projections of Arctic ozone recovery and its surface climate impacts. Full article

Change in sea level (SL) is an important indicator of global warming, since it reflects alterations in several components of the climate system at once. The main factors behind this phenomenon are the melting of glaciers and thermal expansion of ocean water, with the latter contributing about 40% to the overall rise in SL. Rising SL indirectly indicates an increase in ocean heat content and, consequently, its surface temperature. Previous studies have found that tropical sea surface temperature (SST) is critical to regulating the Earth’s climate and weather patterns in high and mid-latitudes. For this reason, SST and SL in the tropics can be considered as precursors of both global climate change and the emergence of climate anomalies in extratropical latitudes. Although SST has been used in this capacity in a number of studies, similar research regarding SL had not been conducted until recently. In this paper, we examine the links between SL in the tropical North Atlantic and North Pacific Oceans and surface air temperature (SAT) at mid- and high latitudes, with the aim of assessing the potential of SL as a predictor in forecasting SAT anomalies. To identify similarities between the variability of tropical SL and SST and that of SAT in high- and mid-latitude regions, as well as to estimate possible time lags, we applied factor analysis, clustering, cross-correlation and cross-spectral analyses. The results reveal a structural similarity in the internal variability of tropical SL and extratropical SAT, along with a significant lagged relationship between them, with a time lag of several years. Full article