Search Results (920)

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

Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article

The geographical response of western North Pacific subtropical high (SH) to environmental conditions such as the El Niño-Southern Oscillation (ENSO) and global warming has been one of the main concerns with respect to extreme events induced by tropical convections. By considering observed outgoing longwave radiation (OLR) as the strength of subtropical high, this study attempts to further understand the geographical response of SH strength to ENSO and global warming. Here, “SH strength” is defined as the inhibition of regional convections under SH environment. A meridional seesaw pattern among SH strength anomalies is found at 130°–175° E. In addition, the La Niña environment with weaker convections at lower latitudes is characterized by farther westward expansion of SH but with a weaker strength. Conversely, the El Niño environment with stronger convections at lower latitudes leads to shrunken SH but with a greater strength. The influence of the seesaw mechanism appears to be modulated by global warming. The western North Pacific subtropical high strengthens overall under warming in both the La Niña and El Niño environments. This suggests that the weakening effect by drier tropics is largely offset by anomalous highs induced by a warming atmosphere. It is most remarkable that the highest SH strengths appear in a warmer El Niño environment. The finding implies that every new El Niño environment may experience the driest atmosphere ever in the subtropics under global warming. The value of this study lies in the fact that OLR effectively illustrates how the ENSO variation and global warming bring the zonally undulating strength of boreal-summer SH. Full article

Since the 1980s, earlier European springs have led to the earlier arrival of migrant passerines. We predict that arrival is related to a suite of climate indices operating during the annual cycle (breeding, autumn migration, wintering, spring migration) in Europe and Africa over the year preceding arrival. The climate variables include the Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), and North Atlantic Oscillation (NAO). Furthermore, because migrants arrive sequentially from different wintering areas across Africa, we predict that relationships with climate variables operating in different parts of Africa will change within the season. We tested this using daily ringing data at Bukowo, a spring stopover site on the Baltic coast. We calculated an Annual Anomaly (AA) of spring passage (26 March–15 May, 1982–2024) for four long-distance migrants (Blackcap, Lesser Whitethroat, Willow Warbler, Chiffchaff). We decomposed the anomaly in two ways: into three independent main periods and nine overlapping periods. We used multiple regression to explore the relationships of the arrival of these species at Bukowo. We found sequential effects of climate indices. Bukowo is thus at a crossroads of populations arriving from different wintering regions. The drivers of phenological shifts in passage of wide-ranging species are related to climate indices encountered during breeding, wintering, and migration. Full article

Groundwater is a critical yet understudied resource in Peru, where surface water has traditionally dominated national assessments. This study provides the first country-scale analysis of groundwater storage (GWS) variability in Peru from 2003 to 2023 using satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. We used the GRACE Data Assimilation-Data Mass Modeling (GRACE-DA-DM GLV3.0) dataset at 0.25° resolution to estimate annual GWS trends and evaluated the influence of El Niño–Southern Oscillation (ENSO) events and anthropogenic extraction, supported by in situ well data from six major aquifers. Results show a sustained GWS decline of 30–40% in coastal and Andean regions, especially in Lima, Ica, Arequipa, and Tacna, while the Amazon basin remained stable. Strong correlation (r = 0.95) between GRACE data and well records validate the findings. Annual precipitation analysis from 2003 to 2023, disaggregated by climatic zone, revealed nearly stable trends. Coastal El Niño events (2017 and 2023) triggered episodic recharge in the northern and central coastal regions, yet these were insufficient to reverse the sustained groundwater depletion. This research provides significant contributions to understanding the spatiotemporal dynamics of groundwater in Peru through the use of satellite gravimetry data with unprecedented spatial resolution. The findings reveal a sustained decline in GWS across key regions and underscore the urgent need to implement integrated water management strategies—such as artificial recharge, optimized irrigation, and satellite-based early warning systems—aimed at preserving the sustainability of the country’s groundwater resources. Full article

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the effects of oceanic mesoscale activity during the periods of maximum and minimum Antarctic sea ice extent (September 2019 and February 2020), numerical experiments were conducted using a coupled regional model and an online two-dimensional spatial filter to remove high-frequency sea surface temperature (SST) oscillations. The largest SST anomalies were observed in the Brazil–Malvinas Confluence and along oceanic fronts in September, with maximum SST anomalies reaching 4.23 °C and −3.71 °C. In February, the anomalies were 2.18 °C and −3.06 °C. The influence of oceanic mesoscale activity was evident in surface atmospheric variables, with larger anomalies also observed in September. This influence led to changes in the vertical structure of the atmosphere, affecting the development of the marine atmospheric boundary layer (MABL) and influencing the free atmosphere above the MABL. Modulations in precipitation patterns were observed, not only in oceanic regions, but also in adjacent continental areas. This research provides a novel perspective on ocean–atmosphere thermodynamic coupling, highlighting the mesoscale role and importance of its representation in the study region. 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

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

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. 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

The mean annual water balance is essential for evaluating water availability in a catchment and planning water resources. Climate change alters this balance by affecting precipitation, evapotranspiration, and overall water availability. This study analyses the impact of climate change on the mean annual water balance in the Catamayo catchment, a key water source for irrigation and hydropower in southern Ecuador and northern Peru. A Budyko-based approach was employed due to its conceptual simplicity and proven robustness for estimating long-term water balances under changing climatic conditions. Using outputs from 23 Global Circulation Models (GCMs) under CMIP6’s SSP2-4.5 and SSP8.5 scenarios, the results indicate increasing aridity, particularly in the lower and middle parts of the catchment, which correspond to arid and semi-arid zones. Water availability may decrease by 26.3 ± 12.3% to 33.3 ± 17% until 2080 due to negligible changes (statistically speaking) in average precipitation but rising evapotranspiration. However, historical precipitation analysis (1961–2020) reveals an increasing trend over this historical period which can be attributed to natural climatic variability associated to the El Nino-Southern Oscillation (ENSO), possibly enhanced by anthropogenic climate change. A novel hybrid method combining the statistics of historical precipitation deficits with GCM mean projections provides estimates of future precipitation deficits. These findings suggest potential reductions in crop yields and hydropower capacity, which (although not quantitatively assessed in this study) are inferred based on the projected decline in water availability. Such impacts could lead to higher energy costs, increased reliance on fossil fuels, and intensified competition for water. Mitigation measures, including water-saving strategies, energy diversification, and integrated water resource management, are recommended to address these challenges. 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

Complex networks of stochastic processes are crucial for modeling the dynamics of interacting systems, particularly those involving high-order interactions (HOIs) among three or more components. Traditional measures—such as mutual information (MI), interaction information (II), the redundancy-synergy index (RSI), and O-information (OI)—are typically limited to static analyses not accounting for temporal correlations and become computationally unfeasible in large networks due to the exponential growth of the number of interactions to be analyzed. To address these challenges, first a framework is introduced to extend these information-theoretic measures to dynamic processes. This includes the II rate (IIR), RSI rate (RSIR), and the OI rate gradient ($\Delta \mathrm{OIR}$), enabling the dynamic analysis of HOIs. Moreover, a stepwise strategy identifying groups of nodes (multiplets) that maximize either redundant or synergistic HOIs is devised, offering deeper insights into complex interdependencies. The framework is validated through simulations of networks composed of cascade, common drive, and common target mechanisms, modelled using vector autoregressive (VAR) processes. The feasibility of the proposed approach is demonstrated through its application in climatology, specifically by analyzing the relationships between climate variables that govern El Niño and the Southern Oscillation (ENSO) using historical climate data. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds and aerosols on it and constructs monthly forecasting models to analyze the influence of climate indices on irradiance forecasts. The irradiance over mainland China shows a spatial distribution of being higher in the west and lower in the east. The influence of clouds on irradiance decreases from south to north, and the influence of aerosols is prominent in the east. The average explained variance of clouds on irradiance is 86.72%, which is much higher than that of aerosols on irradiance, 15.62%. Singular Value Decomposition (SVD) analysis shows a high correlation between the respective time series of irradiance and cloud influence, with the two fields having similar spatial patterns of opposite signs. The variation in solar irradiance can be attributed mainly to the influence of clouds. Empirical Orthogonal Function (EOF) analysis indicates that the variation in the first mode of irradiance is consistent in most parts of China, and its time coefficient is selected for monthly forecasting. Both the traditional multiple linear regression method and the Long Short-Term Memory (LSTM) network are used to construct monthly forecast models, with the preceding time coefficient of the first EOF mode and different climate indices as input. Compared with the multiple linear regression method, LSTM has a better forecasting skill. When the input length increases, the forecasting skill decreases. The inclusion of climate indices, such as the Indian Ocean Basin (IOB), El Nino–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), can enhance the forecasting skill. Among these three indices, IOB has the most significant improvement effect. The research provides a basis for accurate forecasting of solar irradiance over China on monthly time scale. Full article