Search Results (164)

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

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). Winter (0.27 °C decade⁻¹) and spring (0.20 °C decade⁻¹) exhibited the strongest warming, followed by autumn (0.18 °C decade⁻¹) and summer (0.10 °C decade⁻¹). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article

This study investigated the influence of long-term climatic phenomena on the hydroclimatic dynamics of the Grey River Basin in Chilean Patagonia. By analyzing hydroclimatological datasets from the last four decades (1980 to 2020), including precipitation, temperature, wind speed, potential evapotranspiration, and streamflow, we identified key trends and correlations with three large-scale climate indices: the Antarctic Oscillation (AAO), El Niño—Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO). Statistical methods such as the Mann–Kendall test, Sen’s slope, PCA, and wavelet coherence were applied. The results indicate a significant upward trend in streamflow, with Sen’s slope of 0.710 m³/s/year (p-value = 0.020), particularly since 2002, while other variables showed limited or no significant trends. AAO exhibited the strongest correlations with streamflow and wind speed, while ENSO 3.4 was the most influential ENSO index, especially during the two extreme El Niño events in 1982, 1997, and 2014. PDO showed weaker relationships overall. Wavelet analysis revealed coherent periodicities at 1- and 2-year frequencies between AAO and flow (wavelet coherence = 0.44), and at 2- to 4-year intervals between ENSO and precipitation (wavelet coherence = 0.63). These findings highlight the sensitivity of the Grey River basin to climatic variability and reinforce the need for integrated water resource management in the face of ongoing climate change. Full article

Climate change has significantly affected marine fisheries. In recent years, marine heatwaves (MHWs) have intensified concurrently with increasing sea surface temperature (SST), particularly along the coast of Japan in the Northwest Pacific. Although the relationships between MHWs and large-scale climate patterns are well established, the long-term effects of MHWs on fisheries remain uncertain. Considering thermal adaptability, we analyzed the catches of warm- and cold-water species from commercially important fisheries in adjacent sea regions around Japan, correlating them with regional SSTs and MHW indices. Our results show that regional SSTs exhibited a persistent increasing trend, with major shifts occurring around 1988/89 and 1998/99. Pronounced interannual–decadal variabilities were observed in the leading principal components (PCs) of different species groups, with step changes concentrated in 1989~1992, 1999~2003, and 2009~2012. Notably, there was a significant negative response of cold groups to warming SSTs. Among warm-water species, only the Japanese sardine (Sardinops melanostictus) catch exhibited a strong correlation with climate change. Gradient forest analysis and threshold generalized additive models (TGAMs) further revealed the nonlinear, threshold-driven responses of the fish groups to environmental variability, which occurred after step changes in both the environmental factors and catches. Matching analysis between the annual change rates of catches and MHW indices confirmed the detrimental effects of strong MHWs on marine fisheries. Full article

The Arabian Sea has undergone significant warming since the mid-20th century, highlighting the importance of assessing how decadal climate patterns influence chlorophyll-a (Chl-a) and broader marine ecosystem dynamics. This study investigates the variability of Chl-a, sea surface temperature (SST), and sea level anomaly (SLA) over the past three decades, and their relationships with the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The mean Chl-a concentration was 1.10 mg/m³, with peak levels exceeding 2 mg/m³ between 2009 and 2013, and the lowest value (0.6 mg/m³) was recorded in 2014. Elevated Chl-a levels were consistently observed in February and March across both coastal and offshore regions. Empirical orthogonal function (EOF) analysis revealed distinct spatial patterns in Chl-a and SST, indicating dynamic regional variability. The SST increased by 0.709 °C over the past four decades, accompanied by a steady rise in the SLA of approximately 1 cm. The monthly mean Chl-a exhibited a strong inverse relationship with both the SST and SLA and a positive correlation with SST gradients (R² > 0.5). A positive correlation (R² > 0.5) was found between the PDO and Chl-a, whereas the PDO was negatively correlated with the SST and SLA. In contrast, the AMO was negatively correlated with Chl-a but positively associated with warming and SLA rise. These findings underline the contrasting roles of the PDO and AMO in modulating productivity and ocean dynamics in the Arabian Sea. This study emphasizes the need for continued monitoring to improve predictions of ecosystem responses under future climate change scenarios. 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

Arctic sea ice variations exhibit relatively strong statistical associations with precipitation variability over northeastern and southern China. Using Arctic Ocean reanalysis data from the EU Copernicus Project, this study examines the time-lagged statistical relationships between winter Arctic sea ice conditions and subsequent spring precipitation variability over China through wavelet analysis and Granger causality tests. Singular value decomposition (SVD) identifies the Barents, Kara, East Siberian, and Chukchi Seas as key regions exhibiting strong associations with spring precipitation anomalies. Increased winter sea ice in the East Siberian and Chukchi Seas generates positive geopotential height anomalies over the Arctic and negative anomalies over Northeast Asia, adjusting upper-level jet streams and influencing precipitation patterns in Northeast China. Conversely, increased sea ice in the Barents–Kara Seas leads to persistent negative geopotential height anomalies simultaneously occurring over both the Arctic and South China regions, enhancing southern jet stream activity and intensifying warm-moist airflow at the 850 hPa level, thus favoring precipitation in southern China. Compared to considering only climate factors such as the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Arctic Oscillation (AO), the inclusion of Arctic sea ice significantly enhances the influence of multiple climate factors on precipitation variability in China. Full article

Trachurus murphyi have been studied for population genetic structures for decades, identifying only one large population across the South Pacific Ocean. Although all of these studies have extensively examined the spatial genetic pattern, there remains a gap in understanding the potential role of temporality. Our study aims to elucidate spatial and temporal genetic patterns in T. murphyi populations in the South Pacific Ocean, examining genetic composition across seasons, including feeding and spawning seasons, where the latter was not previously investigated. Using 10 microsatellite loci, our study confirms an overall consistent and stable population genetic pattern in T. murphyi across its geographic distribution observed over multiple years and seasons. The only exception was found for New Zealand in the spring–summer season. Furthermore, we identify potential genetic markers for monitoring variability in the species. Full article

This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in Rio Grande do Sul (RS) during 45-day subseasonal periods from 2006 to 2022. Precipitation data from 670 rain gauges were categorized into three clusters: cluster 1, located in western RS, displayed the lowest precipitation variation; cluster 2, in eastern RS, exhibited the greatest variability; and cluster 3, situated in northern RS. ENSO demonstrated the strongest positive correlation with precipitation during spring in clusters 1 and 3 (0.65–0.79), while PDO also correlated positively, especially in summer and spring. AOC exhibited negative correlations, most pronounced in spring. Significant inter-index correlations were identified, including a high positive correlation between SASH and AOC (0.7) and a high negative correlation between NINO34 and SOI (−0.73). Within clusters, NINO34 and PDO showed low positive correlations with precipitation (0.24–0.32), while SOI demonstrated low negative correlations (−0.21 to −0.30). Seasonal analysis revealed that NINO34 influenced summer and spring precipitation, correlating with above-average rainfall during El Niño events. SASH and PDO also showed positive correlations with summer and spring rainfall, with PDO’s positive phase associated with a 25% increase in precipitation. These findings provide valuable insights into the complex interactions between global climatic indices and regional precipitation patterns, enhancing the understanding of subseasonal climate variability in RS and supporting the development of more accurate climate prediction models for the region. Full article

This study reconstructed the annual lake surface area (LSA) and absolute lake water storage (LWS) changes of Lake Sarez, the world’s largest high-altitude landslide-dammed lake, from 1992 to 2023 using multi-source remote sensing data. All available Landsat images were used to extract the LSA using an improved multi-index threshold method, which incorporates a slope mask and threshold adjustment to enhance the boundary delineation accuracy (Kappa coefficient = 0.94). By combining the LSA with high-resolution DEM and the GLOBathy bathymetry dataset, the absolute LWS was reconstructed, fluctuating between 12.3 × 10⁹ and 12.8 × 10⁹ m³. A water balance analysis revealed that inflow runoff (IRO) was the primary driver of LWS changes, contributing 54.57%. The cross-wavelet transform and wavelet coherence analyses showed that the precipitation (PRE) and snow water equivalent (SWE) were key climatic factors that directly influenced the variability of IRO, impacting the interannual water availability in the lake, with PRE having a more sustained impact. Temperature indirectly regulated IRO by affecting SWE and potential evapotranspiration. Furthermore, IRO exhibited different resonance periods and time lags with various atmospheric circulation factors, with the Pacific Decadal Oscillation and North Atlantic Oscillation having the most significant influence on its interannual variations. These findings provide crucial insights into the hydrological behavior of Lake Sarez under climate change and offer a novel approach for studying water storage dynamics in high-altitude landslide-dammed lakes, thereby supporting regional water resource management and ecological conservation. Full article

To study the long-term hydroclimate variability in the Satluj Basin, streamflow data was reconstructed using tree-ring width datasets from multiple taxa available from the Kullu Valley, western (Indian) Himalaya. Five ring-width tree-ring chronologies of three conifer tree taxa (Abies pindrow, Cedrus deodara, and Pinus roxburghii) significantly correlate with the streamflow during the southwest monsoon season. Based on this correlation, a 228-year (1787–2014 CE) June–August streamflow was reconstructed using average tree-ring chronology. The reconstruction accounts for 34.5% of the total variance of the gauge records from 1964 to 2011 CE. The annual reconstruction showed above-average high-flow periods during the periods 1808–1811, 1823–1827, 1833–1837, 1860–1863, 1876–1881, and 1986–1992 CE and below-average low-flow periods during the periods 1792–1798, 1817–1820, 1828–1832, 1853–1856, 1867–1870, 1944–1947, and 1959–1962 CE. Furthermore, a period of prominent prolonged below-average discharge in the low-frequency streamflow record is indicated during the periods 1788–1807, 1999–2011, 1966–1977, 1939–1949, and 1854–1864. The low-flow (dry periods) observed in the present streamflow reconstruction are coherent with other hydroclimatic reconstructions carried out from the local (Himachal Pradesh and Kashmir Himalaya) to the regional (Hindukush mountain range in Pakistan) level. The reconstruction shows occurrences of short (2.0–2.8 and 4.8–8.3 years) to medium (12.5 years) periodicities, which signify their teleconnections with large-scale climate variations such as the El Niño–Southern Oscillation and the Pacific Decadal Oscillation. Full article

Sea fog significantly impacts marine activities, ecosystems, and radiation balance. We analyzed the decadal variation characteristics of sea fog frequency (SFF) over the northwestern Pacific and investigated the roles of the Pacific decadal oscillation (PDO) and sea surface temperature (SST) warming in driving these changes. The results show that SFF experienced a significant and sudden decadal increase around 1978 (up by 12.9%) and a prominent decadal decrease around 1999 (down by 7.8%). The sudden increase in SFF around 1978 was closely related to the PDO. A positive PDO phase induced unusual anticyclonic circulation and southerly winds over the northwestern Pacific, enhancing low-level atmospheric stability and moisture supply, thus facilitating sea fog formation. Nevertheless, the decrease in SFF around 1999 was related to SST warming in the north Pacific. The rise in sea temperatures weakened the SST front south of the foggy region, reducing the cooling and condensation of warm air necessary for sea fog formation. This study enhances the understanding of the decadal variability mechanism of SFF over the northwestern Pacific regulated by large-scale circulation systems and provides a reference for future sea fog forecasting work. Full article

This study examines the trends and interannual variability of extreme precipitation in Antarctica, using six decades (1963–2023) of daily precipitation data from Russia’s Novolazarevskaya Station in East Antarctica. The results reveal declining trends in both the annual number of extreme precipitation days and the total amount of extreme precipitation, as well as a decreasing ratio of extreme to total annual precipitation. These trends are linked to changes in northward water vapor flux and enhanced downward atmospheric motion. The synoptic pattern driving extreme precipitation events is characterized by a dipole of negative and positive height anomalies to the west and east of the station, respectively, which directs southward water vapor flux into the region. Interannual variability in extreme precipitation days shows a significant correlation with the Niño 3.4 index during the austral winter semester (May–October). This relationship, weak before 1992, strengthened significantly afterward due to shifting wave patterns induced by tropical Pacific sea surface temperature anomalies. These findings shed light on how large-scale atmospheric circulation and tropical-extratropical teleconnections shape Antarctic precipitation patterns, with potential implications for ice sheet stability and regional climate variability. Full article

Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of the ERA5 and CHIRPS datasets, and the spatio-temporal evolution of extreme weather indices and their potential relationship/response to climate variability modes in the Pacific, Indian, and Atlantic oceans, namely, the El Niño−Southern Oscillation, Indian Ocean Dipole, and Tropical Atlantic Variability (ENSO, IOD, and TAV). The CHIRPS dataset showed strong positive correlations with CPC in spatial patterns and similarity in simulating interannual variability and in almost all seasons. Based on daily CHIRPS and CPC data, nine extreme indices were evaluated focusing on regional trends and change detection, and the maximum lag correlation method was applied to investigate fluctuations caused by climate variability modes. The results revealed a significant decrease in total precipitation (PRCPTOT) in north−central SSA, accompanied by a reduction in Consecutive Wet Days (CWDs) and maximum 5-day precipitation indices (RX5DAYS). At the same time, there was an increase in Consecutive Dry Days (CDDs) and maximum rainfall in 1 day (RX1DAY). With regard to temperatures, absolute minimums and maximums (TNn and TXn) showed a tendency to increase in the center−north and decrease in the south of the SSA, while daily maximums and minimums (TXx and TNx) showed the opposite pattern. The IOD, TAV, and ENSO modes of climate variability influence temperature and precipitation variations in the SSA, with distinct regional responses and lags between the basins. Full article

The nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high in coastal cities, where the flat terrain and impervious cover present an additional challenge. In this paper, we estimate the time-varying probability distributions for hourly and daily extreme precipitation using the Generalized Additive Model for Location Scale and Shape (GAMLSS), employing different climate indices, such as Atlantic Multi-Decadal Oscillation (AMO), the El Niño 3.4 SST Index (ENSO), Pacific Decadal Oscillation (PDO), the Western Hemisphere Warm Pool (WHWP) and other covariates. Applications to selected coastal cities in the USA are considered. Overall, the AMO, PDO and WHWP are the dominant factors influencing the extreme rainfall. The nonstationary model outperforms the stationary model in 92% of cases during the fitting period. However, in terms of its predictive performance over the next 5 years, the ST model achieves a higher log-likelihood in 86% of cases. The implications for the time-varying design rainfall in coastal areas are considered, whether this corresponds to a structural design or the duration of a contract for a financial instrument for risk securitization. The opportunity to use these time-varying probabilistic models for adaptive flood risk management in a coastal city context is discussed. Full article