Search Results (279)

Precipitation variability in the VMD is a critical determinant of agricultural productivity, freshwater availability, and flood and drought dynamics in one of Southeast Asia’s most climate-vulnerable regions. Teleconnections between PPTA and three dominant climate modes (Niño 3.4, DMI and PDO) were quantified at ten meteorological stations from 1981 to 2025 using Pearson lag-correlation and WTC. ENSO is identified as the primary interannual driver, exhibiting a peak negative correlation at a lag of two months (r = −0.304, p < 0.001; 9.2% variance explained). The IOD exerts a secondary, delayed influence, peaking at lags of 11 to 12 months (r = 0.186, p < 0.001; 3.5% variance). The PDO functions as a persistent decadal modulator: positive phases suppress annual precipitation by 4.6%, while negative phases enhance it by 14.5% relative to the long-term mean (6.4% variance). WTC analysis reveals non-stationary coherence at 2–5 year (ENSO) and 8–16 year (PDO) periodicities. Compound El Niño and positive PDO events result in the most severe precipitation deficits, with non-linear responses during strong ENSO phases. These results establish a multi-index teleconnection framework that supports seasonal drought early warning and climate-adaptive water resource management in the VMD. Full article

Tunisia is characterized by high precipitation variability, which results in frequent extreme floods and droughts. This study aims to develop long-term forecasting models for total and daily maximum annual precipitation by incorporating information related to climate variability. These models use low-frequency climate oscillation indices as predictors. A linear functional model for scalar response is developed for this purpose. The model based on functional data analysis is also compared to a linear regression model. The station under study is located in north-eastern Tunisia. The association between precipitation and four climate indices is evaluated: the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO) climate indices. The results show that both linear and functional regression provide good and comparable results, likely due to the limited length of the data series. NAO, PDO and MO are the best indices to forecast total annual precipitation with an RMSE between 3.564% and 4.151% of the average precipitation, while MO seems to be the best index to forecast daily maximum annual precipitation achieving slightly higher RMSE between 11.174% and 11.916% of the average maximum precipitation. These results suggest that total precipitation at the study station is controlled by large-scale climatic processes operating over the Atlantic, Pacific, and Mediterranean regions, whereas the few most extreme precipitation events are primarily driven by regional climatic phenomena occurring at the Mediterranean scale. The results may have practical applications to improve disaster response preparedness and water resource management. Full article

Based on monthly meteorological observations from 57 stations in Jiangsu Province during 1961–2022, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated to characterize regional dry–wet variability. Empirical Orthogonal Function (EOF) analysis was applied to extract the dominant spatially coherent dry–wet modes, and cross-wavelet analysis was further employed to examine, in the time–frequency domain, the mode-specific responses to multiscale climate drivers, including the El Niño–Southern Oscillation (ENSO), Sunspot Number (SSN), Arctic Oscillation (AO), and Pacific Decadal Oscillation (PDO). The results show that dry–wet variability in Jiangsu Province is primarily organized by a regionally coherent mode (EOF1, explaining 56.3% of the total variance) and a north–south dipole mode (EOF2, explaining 17.8%), with the zero-value line of EOF2 closely aligned with the Huaihe River–Subei Irrigation Canal climatic transition zone. The temporal coefficient of EOF1 (PC1) exhibits a significant regime shift around 2013, followed by a pronounced wetting trend across the entire region. This change may reflect recent hydroclimatic adjustments in the study area, although the present study does not attempt a formal attribution of the respective thermal and precipitation contributions. In contrast, the temporal coefficient of EOF2 (PC2) undergoes an abrupt change around 1980, indicating a transition of the spatial dry–wet pattern from “southern drought–northern flood” to “southern flood–northern drought,” broadly consistent with an interdecadal climatic transition. Cross-wavelet analysis further reveals that PC1 is closely associated with ENSO at interannual timescales, with a lag of approximately 4–6 months, while its long-term variability shows time–frequency coherence with SSN. PC2 also exhibits time–frequency coherence with SSN at longer timescales, with an apparent phase transition around the 1980s; however, this low-frequency signal should be interpreted cautiously because the underlying physical mechanism remains uncertain. Overall, this study shows that dry–wet variability in Jiangsu Province is organized by two leading spatial modes with distinct temporal evolution and scale-dependent climate linkages. These findings provide new evidence for understanding hydroclimatic variability in monsoon transition zones and offer a basis for spatially differentiated drought–flood risk assessment. Full article

Interannual variability of sea level anomalies (SLA) in the South China Sea (SCS) is significantly influenced by large-scale climate modes; however, their temporal evolution and interdecadal modulation mechanisms remain insufficiently understood. Based on observational records and ERA5 reanalysis data spanning 1980–2022, this study employs a Bayesian Dynamic Linear Model (DLM) to quantify the time-varying impacts of El Niño-Southern Oscillation (ENSO) on interannual SLA variability across different subregions of the SCS and further investigates the modulation effect of the Pacific Decadal Oscillation (PDO) background state. The results indicate that ENSO is a key climatic driver of interannual SLA variability in the SCS; nevertheless, its influence exhibits pronounced non-stationarity, with dynamic regression coefficients showing clear phase-dependent fluctuations throughout the study period. The northern and eastern subregions display stronger responses to ENSO forcing, whereas the southern and western subregions exhibit relatively weaker signals. The negative phase of the PDO enhances the ENSO-SLA relationship, while the positive phase weakens it, with sign reversals occurring in certain subregions. Correlation analyses further suggest that ENSO influences SLA primarily through wind stress anomalies induced by sea level pressure (SLP) gradients, which regulate Ekman transport, whereas the PDO exerts an indirect effect mainly by modifying the large-scale background circulation structure. Full article

Understanding temporal variation in population productivity is critical for effective assessment and management of pelagic fish stocks under a changing climate. In this study, we applied a stochastic surplus production model in continuous time (SPiCT) with time-varying parameters to evaluate the productivity dynamics of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean and to examine the influence of environmental variability on productivity. Multiple time-varying parameterization scenarios were explored to characterize uncertainties in productivity estimates and associated biological reference points. Generalized additive models were subsequently used to quantify the relationships between environmental variables and time-varying productivity. Results indicate that productivity estimates exhibit consistent temporal patterns across alternative modeling scenarios, while their magnitude and associated uncertainty are sensitive to model structure. Among the environmental factors examined, the Pacific Decadal Oscillation (PDO) and mixed layer thickness (MLT) showed consistent and statistically significant associations with maximum net productivity. Higher PDO values and greater MLT were both positively associated with population productivity. Overall, the results highlight the importance of environmental variability in shaping time-varying productivity of yellowfin tuna and demonstrate the feasibility of incorporating key environmental indicators into a state-space model. This approach provides a complementary framework for interpreting stock dynamics and supports the development of ecosystem-based fisheries management strategies in the western and central Pacific. Full article

The mechanisms by which the regional hydroclimate responds to global climate forcing are complex, particularly in geographically heterogeneous countries like China. Focusing on Hunan Province, this study employs the Standardized Precipitation Index (SPI) derived from long-term precipitation records at 87 meteorological stations to delineate climatic sub-regions with coherent dry–wet variability. Using rotated empirical orthogonal function analysis, we systematically characterize the spatiotemporal patterns of SPI components and quantify their teleconnections with global ocean–atmosphere circulation modes. The analysis of multi-timescale SPI reveals four distinct sub-regions and a pronounced northwest–southeast dipole in long-term trends. Despite an overall reduction in annual drought, the northwestern sub-region experienced intensification. Seasonally, a pattern of spring/autumn drying versus summer/winter wetting emerged. Wavelet analysis identified dominant interannual (2–7 years) and interdecadal (13–71 months) oscillations. These periodicities are significantly teleconnected to large-scale circulation indices (e.g., Southern Oscillation and Pacific Decadal Oscillation), with influences peaking at 16–64-month and 2–5-year scales. Importantly, the primary circulating driver differs by sub-region, revealing a complex teleconnection landscape. The findings delineate region-specific atmospheric pathways, offering insights to bolster drought preparedness and optimize water allocation, thereby enhancing climate resilience in vulnerable monsoon transition zones. Full article

The relationship between the El Niño–Southern Oscillation (ENSO) and the East Asian winter monsoon (EAWM) shows pronounced decadal variability, and the modulation of the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) remain highly controversial. In this study, reanalysis data for 1951–2020 are used to re-examine the decadal modulation of the ENSO–EAWM relationship. A running-correlation decomposition is applied to identify the key source of nonstationarity, and a multiple regression framework is further used to quantify the respective contributions of the AMO, PDO, and their nonlinear interactions with ENSO. Results indicate that the decadal variations in the ENSO–EAWM relationship are mainly controlled by changes in their covariance rather than by variations in ENSO or monsoon amplitude. The AMO and PDO are found to modulate the relationship through distinct regional pathways: the AMO primarily affects the EAWM over central and South China, whereas the PDO exerts a strong influence over South China. These regionally dependent modulations help reconcile previous conflicting results and provide a more unified interpretation of the decadal variability of ENSO impacts over East Asia. Full article

The characteristics and causes of the long-term trends of diurnal variation of sea surface temperature (DSST) in the South China Sea (SCS) are investigated in this study based on the global hourly sea surface temperature data generated by the mixed layer model (MLSST) from the National Marine Environmental Forecasting Center (NMEFC) of China. Validation of the MLSST dataset demonstrates excellent agreement with in-situ buoy observations in the SCS with a correlation coefficient of 0.951, confirming its reliability in the SCS. Based on this dataset, the long-term trend of DSST in the SCS exhibits significant seasonal variations with the strongest magnitude in spring and the weakest in winter. Specifically, a significant decreasing trend of −0.0014 °C yr⁻¹ during 1982–2009 transitioned to a pronounced increasing trend of 0.0057 °C yr⁻¹ from 2010–2019. Both climatic factors and local atmospheric variables jointly modulate the DSST in the SCS. On the long-term timescale, the Pacific Decadal Oscillation (PDO) served as the dominant factor driving DSST changes in most areas of the SCS. After 2010, the PDO shifted to a persistent positive phase, providing a crucial climatic background for the basin-wide DSST increase. While the El Niño–Southern Oscillation (ENSO) showed enhanced correlation with DSST post-2010, the Indian Ocean Dipole (IOD) had negligible influence overall. In addition, the SCS summer monsoon played an important regulatory role in shaping the long-term trend of summer DSST by altering air–sea heat exchange processes. Among local atmospheric variables, sea surface wind speed was significantly negatively correlated with DSST, and net heat flux was significantly positively correlated with DSST, with their effects showing regional differentiation. The regulatory role of wind speed dominated in the western SCS, whereas the net heat flux exerted a more prominent impact in parts of the eastern SCS. This work clarifies the spatiotemporal patterns and multi-driver framework governing DSST variability in the SCS, providing a basis for understanding regional ocean–atmosphere interactions. Full article

Regional flood frequency analysis (RFFA) is a cornerstone for estimating design floods at ungauged or data-scarce sites by pooling information within hydrologically homogeneous regions. This study proposes and evaluates a hybrid RFFA framework that integrates the Index-Flood (IF) technique with a bivariate logistic extreme-value model whose marginal distributions are formulated under both stationary and non-stationary assumptions. Non-stationarity is incorporated through a covariate-dependent location parameter, using time and large-scale climate indices—the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI)—as explanatory variables. The proposed approach is applied to two contrasting hydrological regions in Mexico—RH10 (Sinaloa) and RH23 (Chiapas Coast)—to assess its performance under differing climatic and hydrological regimes. Model adequacy and stability are evaluated using likelihood-based goodness-of-fit criteria (log-likelihood and Akaike Information Criterion) and a leave-one-out (jackknife) cross-validation scheme embedded within the IF regionalization workflow. Results indicate that non-stationary bivariate formulations dominate model selection at most stations and yield stable regional growth curves, providing robust and engineering-relevant performance under cross-validation. Overall, the proposed framework offers a conservative and operational pathway for regional flood quantile estimation that bridges local data scarcity and regional hydrological characterization in environments influenced by climate variability and long-term change. Full article

This systematic review synthesizes current scientific knowledge on the drivers of climate variability and change across the South Pacific, with a particular focus on mechanisms influencing tropical cyclone behavior and regional hydroclimatic extremes. The review begins by contextualizing the unique vulnerabilities of Pacific Island nations, which arise from geographic isolation, socio-economic constraints, and extensive coastal exposures. It examines the foundational role of the South Pacific Convergence Zone in organizing regional convection and precipitation and explores the multi-scale climate oscillations that modulate environmental conditions across interannual, decadal, and intraseasonal timescales. The compounding effects of anthropogenic climate change—including rising temperatures, sea-level increase, shifting rainfall regimes, and changing storm characteristics—are critically assessed. Special attention is given to the complex interplay between natural variability and human-induced trends in altering tropical cyclone genesis, tracks, and intensity. The review identifies persistent knowledge gaps, such as data inhomogeneity, limited long-term records, and uncertainties in downscaled projections, and concludes with prioritized research directions aimed at enhancing predictive capacity and supporting climate-resilient adaptation across this highly vulnerable region. Full article

The frequency and intensity of tropical cyclones (TCs) in the Arabian Sea have increased in recent decades, heightening concerns regarding regional vulnerability and forecasting difficulties. This study examines the impact of the Madden–Julian Oscillation (MJO) on TCs activity—formation, frequency, and severity—over the Arabian Sea from 1982 to 2021. This study analyzes variations in convection, vertical wind shear (VWS), sea level pressure (SLP), and relative humidity (RH) across different MJO phases utilizing the best-track data from the India Meteorological Department (IMD), the Real-Time Multivariate MJO (RMM) index, and reanalysis datasets from the National Oceanic and Atmospheric Administration (NOAA) and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR). Results show that more than 80% of TCs form during the convectively active phases of the MJO (P1–P4). These phases have the most noticeable negative outgoing longwave radiation (OLR) anomalies, as well as higher mid-level moisture and low-pressure anomalies, which are good for cyclogenesis. On the other hand, suppressed phases (P6–P8) have positive outgoing longwave radiation, dry air in the middle troposphere, and high-pressure anomalies, which make it harder for TCs to form. While VWS is predominantly favorable during both active and inactive phases, thermodynamic and convective factors principally regulate the modulation of TC activity. The simultaneous presence of active MJO phases with positive Indian Ocean Dipole (pIOD) and neutral or El Niño conditions markedly increases TC frequency, highlighting a combined influence link between interannual–El Niño–Southern Oscillation (ENSO) and IOD– and intraseasonal (MJO) variability. Additionally, the association between MJO and the Indo-Pacific Warm Pool (IPWP) reveals that TC activity peaks during convectively active MJO phases under the second twenty years of this study, emphasizing the influence of large-scale oceanic warming on TC variability. These findings underscore the critical function of the MJO in regulating TC activity variability in the Arabian Sea and stress its significance for enhancing intraseasonal forecasting and disaster preparedness in the area. Full article

This study presents a 31-year (1993–2023) wave hindcast using a high-resolution two-domain nested numerical wave model implemented with Simulating Waves Nearshore (SWAN). The spatiotemporal variability and long-term trends of two wave parameters (significant wave height H_s and spectral peak period T_peak) are systematically analyzed for the Yangtze River Delta (YRD) and its adjacent waters. Validation against in situ buoy measurements confirms that the SWAN model effectively reproduces the regional wave conditions. Results indicate that mean wave conditions are primarily modulated by the Asian monsoon, whereas extreme wave events are predominantly influenced by typhoons. This leads to pronounced differences in spatial patterns and seasonal variability between mean and maximum H_s values. In addition, the regional interannual variations of H_s and T_peak exhibit different degrees of correlation with the Niño 3.4 index, the Pacific Decadal Oscillation (PDO) index and the Western Pacific Subtropical High Ridge Position (WPSH) Index. Overall, both H_s and T_peak exhibit positive trends over the study period, and both positive trends shift remarkably between seasons. The positive trends in mean wave conditions are mild during spring and summer but more pronounced in autumn and winter. Statistically significant increases in seasonal mean H_s are identified in parts of the East China Sea (0.35 cm a⁻¹ in autumn) and the southern Yellow Sea (0.27 cm a⁻¹ in winter). Notably, not all trends are positive: the 90th percentiles of both H_s and T_peak during summer exhibit widespread declining trends, although they are not statistically significant. Full article

Climate change can impact various facets of a region’s fire regime, such as the frequency and timing of fire ignitions. This study examines the temporal trends of monthly fire counts in the Northwest and Northeast Regions of Ontario, Canada, between 1960 and 2023. Fires ignited by human activities or lightning are analyzed separately. The significance of historical trends is investigated using the Cochrane–Orcutt method, which identifies decreasing trends in the number of human-caused fires for several months, including May through July. A complementary trend analysis of total area burned is also conducted. The forecasting of future months’ fire counts is explored using a Negative Binomial Autoregressive (NB-AR) model suitable for count time series data with overdispersion. In the NB-AR model, the use of climate teleconnections at a range of temporal lags as predictors is investigated, and their predictive skill is quantified through cross-validation estimates of Mean Absolute Error on a testing dataset. Considered teleconnections include the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO). The study finds the use of teleconnection predictors promising, with a notable benefit for forecasting human-caused fire counts but mixed results for forecasting lightning-caused fire counts. Full article

This study provides a systematic analysis of the spatiotemporal distribution and trends in the frequency of significant wave height (SWH) exceeding level 5 (SWH > 2.5 m) and level 7 (SWH > 6 m) in the Northwest Pacific (NWP) for 1993–2024, which are defined as f₅ and f₇, respectively, as well as their correlations with major climate indexes. Our results indicate that (1) the high-value zones for the annual mean f₅ and f₇ are both located in the south waters of the Aleutian Islands, with maximum values of 58.0% and 6.4%, respectively. Winter’s contribution is greatest (maximum values of 96.9% and 16.8% per year), while summer’s is the smallest. (2) f₅ exhibits a significant decline trend across the entire NWP basin (of −0.15 to −0.30%/yr), with the steepest decline occurring in autumn (−0.69%/yr) and the shallowest in summer. f₇ exhibits a significant linear decrease in the open ocean east of Japan (−0.08%/yr) while showing a significant linear increase in the waters east of the Kamchatka Peninsula (0.08%/yr). Both variations peak in winter (maximum values of −0.27% and 0.30% per year) and are smallest in summer. (3) Seasonal and regional variations in climate index–f₅ and f₇ relationships reflect large-scale atmospheric modulation of waves. For example, the Oceanic Niño Index shows a predominantly negative correlation with f₅ in winter (maximum correlation coefficient r_m = −0.70) around the Luzon Strait, shifting to a significant positive correlation in summer (r_m = 0.70) across the extensive region east of Taiwan Island and the Philippines. The Pacific Decadal Oscillation index shows a significant positive correlation with f₇ in summer and autumn (r_m = 0.69) east of Taiwan Island and a strong negative correlation in winter (r_m = −0.77) to the east of Kamchatka Peninsula. Full article

CLARA-A3 currently provides the longest temporal coverage among available albedo products, with improvements in both retrieval algorithms and product coverage compared to earlier versions. This study first evaluates the performance of the CLARA-A3-SAL product over Greenland Ice Sheet (GrIS) and subsequently applies it to investigate spatiotemporal trends in summer albedo from 1979 to 2024. Validation against 32 in situ observation sites indicates negligible bias in the interior regions, with RMSE values ranging from 0.01 to 0.07. Although larger errors exist in the coastal ablation zone due to unresolved sub-grid surface heterogeneity, the product successfully captures observed spatiotemporal variability and long-term trends, demonstrating that CLARA-A3-SAL provides a generally reliable representation of surface albedo. Since 1979, the summer surface albedo averaged over the entire ice sheet has decreased at a rate of −0.24% decade⁻¹. Albedo in the dry snow area has remained relatively stable and showed no significant correlation with most climate variables, except for the North Atlantic Oscillation (NAO) and the Greenland Blocking Index (GBI). Conversely, the marginal zone has undergone substantial darkening (−0.66% decade⁻¹), which is strongly correlated with temperature, snowfall and melt, with meltwater showing the highest correlation (r = −0.90, p < 0.01). This suggests that meltwater-driven grain growth and exposure of bare ice are the primary drivers of albedo reduction over the non-dry snow zone. Large-scale atmospheric circulation also plays a key role: the GBI exhibits the strongest association with albedo (r = −0.63, p < 0.05), underscoring the importance of persistent blocking in amplifying surface warming and darkening. Furthermore, decadal-scale variability associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) modulates both the magnitude and spatial pattern of albedo changes across GrIS, with AMO+ generally linked to reduced albedo and PDO+ tending to enhance it. Full article