Search Results (285)

Analysis of global mean sea-level (GMSL) variations provides insights into their spatial and temporal characteristics. To analyze the sea-level cycle and its correlation with the El Niño–Southern Oscillation (ENSO, represented by the Oceanic Niño Index), this study proposes an enhanced analytical framework integrating Local Mean Decomposition with an improved wavelet thresholding technique and wavelet transform. The GMSL time series (January 1993 to July 2020) underwent multi-scale decomposition and noise reduction using Local Mean Decomposition combined with improved wavelet thresholding. Subsequently, the Morlet continuous wavelet transform was applied to analyze the signal characteristics of both GMSL and the Oceanic Niño Index. Finally, cross-wavelet transform and wavelet coherence analyses were employed to investigate their correlation and phase relationships. Key findings include the following: (1) Persistent intra-annual variability (8–16-month cycles) dominates the GMSL signal, superimposed by interannual fluctuations (4–8-month cycles) related to climatic and seasonal forcing. (2) Phase analysis reveals that GMSL generally leads the Oceanic Niño Index during El Niño events but lags during La Niña events. (3) Strong El Niño episodes (May 1997 to May 1998 and October 2014 to April 2016) resulted in substantial net GMSL increases (+7 mm and +6 mm) and significant peak anomalies (+8 mm and +10 mm). (4) Pronounced negative peak anomalies occur during La Niña events, though prolonged events are often masked by the long-term sea-level rise trend, whereas shorter events exhibit clearly discernible and rapid GMSL decline. The results demonstrate that the proposed framework effectively elucidates the multi-scale coupling between ENSO and sea-level variations, underscoring its value for refining the understanding and prediction of climate-driven sea-level changes. Full article

Tropical cyclones (TCs) over the Arabian Sea pose significant threats to coastal populations and result in substantial economic losses, yet their variability in response to major climate modes remains insufficiently understood. This study examines the relationship between the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Indo-Pacific Warm Pool (IPWP) with TC activity over the Arabian Sea from 1982 to 2021. Utilizing the India Meteorological Department (IMD)’s best-track data, reanalysis datasets, and composite analysis, we find that ENSO and IOD phases affect TC activity differently across seasons. The pre-monsoon season shows a limited association between TC activity and both ENSO and IOD, with minimal variation in frequency, intensity, and energy metrics. However, during the post-monsoon season, El Niño enhances TC intensity, resulting in a higher frequency of intense storms, leading to increased accumulated cyclone energy (ACE) and power dissipation index (PDI) in a statistically significant way. In contrast, La Niña favors the development of weaker TC systems and an increased frequency of depressions. While negative IOD (nIOD) phases tend to suppress TC formation, positive IOD (pIOD) phases are associated with increased TC activity, characterized by longer durations and higher ACE and PDI (statistically significant). Genesis sites shift with ENSO: El Niño favors genesis in the eastern Arabian Sea, causing westward or northeastward tracks, while La Niña shifts genesis toward the central-western basin, promoting northwestward movement. Composite analysis indicates that higher sea surface temperatures (SSTs), reduced vertical wind shear (VWS), increased mid-tropospheric humidity, and lower sea level pressure (SLP) during El Niño and pIOD phases create favorable conditions for TC intensification. In contrast, La Niña and nIOD phases are marked by drier mid-level atmospheres and less favorable SST patterns. The Indo-Pacific Warm Pool (IPWP), particularly its westernmost edge in the southeastern Arabian Sea, provides a favorable thermodynamic environment for genesis and exhibits a moderate positive correlation with TC activity. Nevertheless, its influence on interannual variability over the basin is less significant than that of dominant large-scale climate patterns like ENSO and IOD. These findings highlight the critical role of SST-related teleconnections (ENSO, IOD, and IPWP) in regulating Arabian Sea TC activity, offering valuable insights for seasonal forecasting and risk mitigation in vulnerable areas. Full article

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability on decadal timescales. This article synthesizes the extensive research on ENSO rectification, exploring a paradigm that resolves this causality problem by recasting ENSO as an active architect of its own mean state. Tracing the intellectual development of this theory, starting from fundamental concepts such as the “dynamical thermostat” and “heat pump” hypotheses, modern analysis has identified the core physical mechanism as nonlinear dynamical heating (NDH), which is rooted in nonlinear heat advection during asymmetric ENSO cycles. The convergence of evidence from forced ocean models and observational diagnostics confirms a rectified signal characterized by an off-equatorial spatial pattern, providing a primary mechanism for tropical Pacific decadal variability (TPDV). By establishing a coherent framework linking high-frequency asymmetry with low-frequency variations, this review lays the foundation for future research and emphasizes the critical role of the rectification effect in improving decadal climate prediction. Full article

In many regions, the El Niño Southern Oscillation (ENSO) cycle is a key factor in modulating climate processes that can influence seasonal variability in the production and dispersal of allergy-triggering pollen. However, the impacts on allergy health are not well known. We compare grass pollen seasons between the major modes of the ENSO cycle in Auckland, New Zealand’s largest city, within a region that is highly sensitive to quasi-predictable meteorological oscillations of the ENSO cycle. We find no clear difference in the timing of onset of the pollen seasons, but season length was shorter, by >30 days, and less severe during the La Niña phase than for the other phases. The difference in pollen season length may be explained by the greater summer rainfall typically experienced in Auckland and elsewhere in northern New Zealand during La Niña phases, which tend to suppress grass pollen abundance when excessive. As grass pollen is the principal source of allergenic pollen in New Zealand and in many other countries, these results have wider implications for allergy management. With ENSO forecasting offering the prospect of several month’s lead time, there is potential for improving community preparedness and resilience to inter-annual dynamics of the grass pollen season. This work points to the need to better understand the influence of short-term climate cycles on seasonal variability in pollen allergy, while we also emphasise that the strong geographical heterogeneity in ENSO cycle climate impacts necessitates a region-specific approach. This work also further underscores the need for standardised, local–regional pollen monitoring in NZ and the risk of relying upon static, nationwide pollen calendars for informing allergy treatment. 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

Wave climate variability, including seasonal cycles, long-term trends, and interannual anomalies of wave parameters, was investigated across five latitudinal sectors using ERA5 reanalysis data from 1980 to 2023. Pronounced seasonal cycles were observed in both Northern and Southern Hemisphere sectors, although the variability was more marked in the Northern Hemisphere. In contrast, the tropical region exhibited comparatively stable conditions throughout the year. Long-term trends revealed increases in both significant wave height and peak period across most sectors. The tropical region exhibited a trimodal regime driven by wind waves at low latitudes and remotely generated swells from both hemispheres. Teleconnections associated with the North Atlantic Oscillation (NAO) explained interannual variability in wind-wave direction in the tropics with an r² of 0.74 and wind-wave height variability in the Northern Hemisphere with an r² of 0.81. Additional indices, such as the Arctic Oscillation (AO), the Tropical North Atlantic (TNA) index, and the Northern Annular Mode (NAM), explained 30 to 60 percent of the directional variability. These results underscore the need to account for climate-driven variability in wave modeling frameworks to improve forecast accuracy and representation of directional trends. Full article

The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow-on mission (GRACE-FO, collectively referred to as GRACE) to investigate the spatiotemporal dynamics of hydrological mass changes in the Amazon Basin from 2002 to 2021. Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. This distribution likely reflects the interplay between precipitation and topography. Vertical displacement measurements from the Global Navigation Satellite System (GNSS) show strong correlations with GRACE-derived hydrological load deformation (mean Pearson correlation coefficient = 0.72) and reduce its root mean square (RMS) by 35%. Furthermore, the study demonstrates that existing hydrological models, which neglect groundwater dynamics, underestimate hydrological load deformation. Principal component analysis (PCA) of the Amazon GNSS network demonstrates that the first principal component (PC) of GNSS vertical displacement aligns with abrupt interannual TWS fluctuations identified by GRACE during 2010–2011, 2011–2012, 2013–2014, 2015–2016, and 2020–2021. These fluctuations coincide with extreme precipitation events associated with the El Niño–Southern Oscillation (ENSO), confirming that ENSO modulates basin-scale interannual hydrological variability primarily through precipitation anomalies. This study provides new insights for predicting extreme hydrological events under climate warming and offers a methodological framework applicable to other critical global hydrological regions. Full article

Stable oxygen isotopes in tree rings (δ¹⁸O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ¹⁸O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ¹⁸O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ¹⁸O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ¹⁸O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ¹⁸O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ¹⁸O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ¹⁸O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ¹⁸O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ¹⁸O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ¹⁸O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability. Full article

The Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate predictions. This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. The model successfully predicts monthly/annual average PDO index of up to 15 months/5 years in advance, achieving a correlation coefficient of 0.56/0.55. By utilizing the WOA to effectively optimize hyperparameters, the model enhances the PDO prediction skill compared to existing deep learning PDO prediction models, improving the correlation coefficient from 0.47 to 0.68 at a 6-month lead time. The combination of MMD and WOA further minimizes prediction errors and extends the forecasting effective time to 15 months by capturing essential modes. The BWM model can be employed for future PDO prediction and the predicted PDO will remain in its cool phase in the next year both using the PDO index from NECI and derived from near-time satellite data. This proposed model offers an effective way to advance the prediction skill of climate variability on multiple time scales by utilizing all kinds of data available including satellite data, and provides a large-scale background to monitor marine heatwaves. Full article

Water availability is a major constraint on socioeconomic development in northeastern Mexico, highlighting the need for effective water resource planning that accounts for the variability and extremes of precipitation. In this study, seasonal precipitation reconstructions were developed using tree-ring chronologies from spruce species (Picea spp.). A representative chronology for Picea mexicana Martínez was developed from two populations and spans the period 1786–2020, while a chronology for Picea martinezii T.F. Patterson was established from three populations covering 1746–2020. Both species exhibited significant positive correlations with January–May precipitation (r = 0.65 and 0.71, respectively; p < 0.01) and negative correlations with maximum temperature over the same period (r = −0.52 and −0.59, respectively). Two January–May precipitation reconstructions were produced for periods with adequate sample depth (EPS > 0.85): 1851–2020 for P. mexicana and 1821–2020 for P. martinezii. Both reconstructions revealed pronounced interannual variability, with recurrent droughts and persistently dry conditions, particularly evident in the P. mexicana series. Spatial correlation analyses indicated a historical link between reconstructed precipitation and the El Niño–Southern Oscillation (ENSO). These results highlight the value of spruce species for dendroclimatic reconstruction and their sensitivity to precipitation variability, especially as rising maximum temperatures may compromise their persistence in the Sierra Madre Oriental. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

The barotropic sea level difference (SLD) across the Korea/Tsushima Strait (KTS) is considered an index of the volume transport into the East/Japan Sea. This study investigates the interannual variability of the barotropic SLD (the KTS inflow) from 1985 to 2017 and its relationship to upper-ocean (<300 m) current variability in the western North Pacific. An increase in the KTS inflow is associated with a weakening of the Kuroshio current through the Tokara Strait and upper-ocean cooling in the North Pacific Subtropical Gyre, characteristic of a La Niña-like state. Diagnostic analysis reveals that the KTS inflow variability is linked to at least two statistically distinct and concurrent modes of oceanic variability. The first mode is tied to the El Niño–Southern Oscillation through large-scale changes in the Kuroshio system. The second mode, which is linearly uncorrelated with the first, is associated with regional eddy kinetic energy variability in the western North Pacific. The identification of these parallel pathways suggests a complex regulatory system for the KTS inflow. This study provides a new framework for understanding the multi-faceted connection between the KTS and upstream oceanic processes, with implications for the predictability of the ocean environmental conditions in the East/Japan Sea. Full article

El Niño–Southern Oscillation (ENSO), as the strongest source of interannual variability in the tropics, has far-reaching impacts on global climate through teleconnections. As a key factor modulating the vegetation changes, the impact of ENSO has been studied over the past two decades using satellite observations. The paper aims to review results from the past 10–20 years and put together into a consistent picture of ENSO global impacts on vegetation. While ENSO affects vegetation worldwide, its impact varies regionally. Different ENSO flavors, Central Pacific and Eastern Pacific events, can have distinct impacts in the same regions. The underlying mechanisms involve ENSO-driven changes in precipitation and temperature, modulated by the background climate states, with varying response from vegetations of different types. However, the interactions between vegetation and ENSO remain largely unexplored, highlighting a critical gap for future research. Full article

Efficient utilization of wind energy resources, including advances in weather and seasonal forecasting and climate projections, is imperative for the sustainable progress of wind power generation. Although temperature and precipitation data receive considerable attention in interannual variability and seasonal forecasting studies, there is a notable gap in exploring correlations between climate indices and wind speeds. This paper proposes the use of an ensemble of artificial neural networks to forecast wind speeds based on climate oscillation indices and assesses its performance. An initial examination indicates a correlation signal between the climate indices and wind speeds of ERA5 for the selected case study in eastern Canada. Forecasts are made for the season April–May–June (AMJ) and are based on most correlated climate indices of preceding seasons. A pointwise forecast is conducted with a 20-member ensemble, which is verified by leave-on-out cross-validation. The results obtained are analyzed in terms of root mean squared error, bias, and skill score, and they show competitive performance with state-of-the-art numerical wind predictions from SEAS5, outperforming them in several regions. A relatively simple model with a single unit in the hidden layer and a regularization rate of ${10}^{-2}$ provides promising results, especially in areas with a higher number of indices considered. This study adds to global efforts to enable more accurate forecasting by introducing a novel approach. Full article

Precipitation cyclicity plays a crucial role in regional water supply and climate predictions. In this study, we used observational data from 34 representative meteorological stations in the Xinjiang region, a major part of inland arid China, to characterize the interannual cyclicity of regional precipitation from 1951 to 2021 and analyze its contributing factors. The results indicated that the mean annual precipitation in Xinjiang (MAP_XJ) was dominated by a remarkably increasing trend over the past 70 years, which was superimposed by two bands of interannual cycles of approximately 3 years with explanatory variance of 56.57% (Band I) and 6–7 years with explanatory variance of 23.38% (Band II). This is generally consistent with previous studies on the cyclicity of precipitation in Xinjiang for both seasonal and annual precipitation. We analyzed the North Tropical Atlantic sea-surface temperature (NTA_SST), El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Indian Summer Monsoon (ISM) as potential forcing factors that show similar interannual cycles and may contribute to the identified precipitation variability. Two approaches, multivariate linear regression and the Random Forest model, were employed to ascertain the relative significance of each factor influencing Bands I and II, respectively. The multivariate linear regression analysis revealed that the AO index contributed the most to Band I, with a significance score of −0.656, whereas the ENSO index with a one-year lead (ENSO_−1yr) played a dominant role in Band II (significance score = 0.457). The Random Forest model also suggested that the AO index exhibited the highest significance score (0.859) for Band I, whereas the AO index with a one-year lead (AO_−1yr) had the highest significance score (0.876) for Band II. Overall, our findings highlight the necessity of employing different methods that consider both the linear and non-linear response of climate variability to driving factors crucial for future climate prediction. Full article