Search Results (171)

In March 2022, the FAO Food Price Index peaked at 159.7 as climate shocks collided with geopolitical disruption, pushing global hunger past 735 million and exposing how deeply climate variability penetrates the economics of agri-food systems. Yet the imprint of ocean–atmosphere oscillations on global food prices—the central economic signal of the agri-food system—has not, to our knowledge, been mapped systematically in the frequency domain. This study delivers, to our knowledge, one of the first multi-oscillation cross-spectral analyses of the climate–food price nexus, matching 7 climate indices across the Pacific, Atlantic, and Indian Ocean basins with 5 disaggregated FAO Food Price Index sub-components over 432 monthly observations (1990–2025), verified through 6 robustness checks, including surrogate data testing. Four findings carry direct policy relevance. ENSO indicators lead global food prices by three to four months with a 100% surrogate test pass rate—one of the cleanest actionable climate–price signals documented to date. The Indian Ocean Dipole leads prices by roughly one to two years (cross-correlation peak at sixteen months, though the peak is broad and not sharply localised within that window), extending the early warning horizon well beyond the ENSO signal. The apparent Atlantic Multidecadal Oscillation–price correlation (r ≈ +0.60) is revealed to be a common-trend artefact. Vegetable oils are the most consistently climate-exposed commodity chain across the seven oscillations; sugar and meat, often assumed less climate-sensitive, in fact show strong coherence with specific oscillations (sugar with the Indian Ocean Dipole and meat with the Southern Oscillation Index), indicating that commodity-level exposure is oscillation-specific rather than uniform and reflects each commodity’s position in the production-to-consumption chain—short-cycle, thinly buffered commodities transmit weather shocks to price quickly, while feed-market intermediation delays and smooths the pass-through for livestock. These results provide the empirical foundation for integrating real-time monitoring of climate oscillations into food system governance—a low-cost policy innovation that aligns economic stability objectives with climate adaptation goals, strengthens the resilience of agri-food value chains, and supports progress towards Sustainable Development Goal 2 (Zero Hunger). Full article

This study presents an integrated statistical framework for identifying representative large-scale climate teleconnection indices associated with total burned area and for supporting the selection of climate predictors in wildfire-related statistical models. A large set of seasonally resolved climate indices, including the North Atlantic Oscillation (NAO—two versions), the Arctic Oscillation (AO), the Atlantic Multidecadal Oscillation (AMO), the Mediterranean Oscillation (MO—two versions), the East Atlantic–West Russia pattern (EAWR), the Tropical North Atlantic (TNA), and the Atlantic Meridional Mode (AMM), was examined. Because many of these indices describe related atmospheric and oceanic processes, dimensionality reduction and predictor selection were required to limit multicollinearity. Principal component analysis (PCA) was first used to identify groups of interrelated climate indices, followed by partial correlation analysis to distinguish redundant predictors from those retaining independent information with respect to total burned area. Finally, LASSO regression was applied to evaluate the relative explanatory contribution of candidate indices and to perform automatic variable selection. The PCA solution identified ten rotated components explaining 82.31% of the total variance. The results indicate that several seasonal NAO and MO indices contain highly overlapping information, whereas selected indices, particularly MOI2 spring and MOI2 summer, retain comparatively stronger independent associations with total burned area. The integrated PCA–partial correlation–LASSO framework provides a systematic approach for reducing redundant climate predictors and identifying large-scale climate signals that may be informative for understanding variability in total burned area and for supporting statistical analyses of wildfire–climate relationships. Full article

The Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) are two major modes of interannual climate variability that interact across the Indo-Pacific region. Although the autumn IOD is known to influence ENSO with a lag of about one year, the decadal stability of this relationship remains poorly understood. Here we investigate the decadal variability of the lagged IOD–ENSO relationship using observations and 30 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Observational analyses reveal pronounced non-stationarity in the lagged IOD-ENSO linkage, characterized by a stronger (weaker) relationship during the negative (positive) phase of the Atlantic Multidecadal Oscillation (AMO). CMIP6 models exhibit a wide spread in their ability to reproduce this behavior, with only a subset capturing the observed decadal modulation. The inter-model differences are consistent with variations in the simulated amplitude of AMO variability. Models with more realistic AMO variability tend to better reproduce the observed decadal variability of the lagged IOD-ENSO linkage. These results suggest that AMO variability may be one contributing factor to the modulation of the lagged IOD–ENSO relationship, with potential implications for ENSO prediction. Full article

Assessing long-term hydroclimatic variability in central Mexico is essential to understand regional water availability and groundwater recharge for urban centers such as Querétaro. This study developed a multi-century winter–spring precipitation reconstruction for the Sierra Gorda Biosphere Reserve (SGBR) using ring width chronologies of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco. Standard dendrochronological techniques were applied to develop a 284-year master chronology (1731–2015). Following the accepted Subsample Signal Strength criterion (SSS ≥ 0.85) for chronology reliability, the reconstruction was restricted to the 1744–2015 period, yielding a statistically robust 271-year December–April precipitation record. A bootstrapped ordinary least-squares regression model relating tree-ring indices to instrumental December–April precipitation was calibrated and validated using split-sample cross-validation, explaining 46% of the instrumental precipitation variance (R² = 0.46) and yielding positive verification statistics (RE = 0.38–0.58; CE = 0.37–0.57). Spatial field correlations against gridded climate data (CRU TS4.08) confirmed a broad regional hydroclimatic signal centered over the Sierra Madre Oriental. Continuous wavelet transform (CWT), spectral analysis, superposed epoch analysis (SEA), and wavelet coherence (WTC) revealed significant interannual (2–8 years) and decadal (10–20 years) variability associated with large-scale ocean–atmosphere climate modes, including the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Tropical North Atlantic (TNA) index. The pronounced sensitivity of these conifer forests to pre-monsoonal moisture deficits highlights their vulnerability to projected warming and increasing spring evapotranspiration stress. Although the reconstruction is limited to pre-monsoonal (December–April) precipitation, it provides a robust centuries-long baseline for contextualizing regional hydroclimatic variability and supports water-resource management, groundwater conservation, and climate-adaptation strategies in central Mexico. Full article

This study presents a meta-analysis of relatively high-resolution paleohydrological proxies derived from geological archives in Sardinia and in the Italian Peninsula–Sicily over the last 2000 years, with particular emphasis on the Medieval Warm Period (MWP) and the Little Ice Age (LIA). The investigated climate proxies, ranging from annual-decadal to centennial resolution, include terrestrial and marine sediment cores, glaciers, pollen spectra, speleothems, lake-level fluctuations, as well as sedimentary and geomorphological inventories. Such datasets were analyzed through holistic and stratigraphic approaches along West–East and North–South transects across the central Mediterranean. Limited temporal resolution and incomplete stratigraphic continuity of several paleoclimatic records from the investigated regions thwart full reconstructions of paleohydrological trends. Nevertheless, the presented meta-analysis has enabled: (1) the recognition of reliable paleoclimatic correlations between the two regions, which exhibit long-lasting anti-phase hydroclimatic trends (wetter conditions in Sardinia and drier conditions in central Italy during the MWP, with the opposite pattern during the LIA); and (2) the identification of the North Atlantic Oscillation (NAO) as the primary driver of these paleohydrological variations. The significance of this anti-phase pattern is discussed in the context of the North–South and West–East climatic dipoles identified in the Mediterranean region during the middle to late Holocene. Furthermore, we assessed the potential of the investigated paleohydrological network to: (1) compare reconstructed hydrological patterns with mean temperature and precipitation records derived from empirical and model-based climate reconstructions in southern Europe and the Mediterranean; and (2) identify gaps in data coverage that currently limit our understanding of high-resolution spatiotemporal hydrological variability and dynamics.The hydroclimatic pattern in Sardinia and in the Italian Peninsula–Sicily has exhibited marked spatio-temporal divergences, with major hydroclimatic transitions coincident with well-known solar minima over the last millennium, thus suggesting a possible cause-and-effect relationship. The interpretations presented in this study provide a framework for understanding how changes in the paleoclimatic variability of water resources may have influenced different regions of Italy since the Middle Ages, potentially affecting societal transitions as well as historical and socioeconomic dynamics. Comparison of the multidecadal-to-centennial reconstructions of paleohydrological patterns is presented for both areas, pending the development of new, higher-resolution, and more precisely dated proxies from the Italian records. Their importance is emphasized in order to improve reconstructions of past climate variability and to enhance assessments of future climate trajectories. Full article

Nova Scotia is positioned to become the first Canadian province to develop offshore wind energy. Recently, Nova Scotia announced four Wind Energy Areas (WEAs) selected for bidding following extensive review of ecological and land-use considerations. In selecting these areas, the effect of climate change and extreme winds was neglected. This study looks to assess the impact of climate change, extreme winds, and tropical cyclones on turbine siting across the Scotian Shelf with a focus on the four WEAs. Analysis of historical wind climate using ERA5 reanalysis data and return period methods reveals that extreme winds intensify with distance from shore, with the highest values concentrated near Sable Island and outer shelf regions. Fifty-year return wind speeds across the WEAs range from approximately 40.7 to 45.4 m/s, resulting in IEC Class II designation for Sable Island Bank and Class III for the remaining sites. Projections derived from CMIP6 climate models indicate that future mean wind speed changes are modest across all emission scenarios, always within 4% of the historical baseline. Critically, these projected changes do not alter the IEC turbine class designations for any WEA, suggesting that classifications based on historical data remain valid under the range of climate futures considered. Three recommendations are made to strengthen future assessments: expanding the buoy observation network on the Scotian Shelf; investigating the influence of climate indicators such as sea surface temperatures on extreme winds and tropical cyclone activity; and conducting targeted measurement campaigns within the WEAs to support site-specific analysis and developer confidence. Full article

Under the continuous impact of global warming, the water cycle has undergone significant changes, causing a series of problems such as water shortage, frequent climate disasters and ecological environment deterioration. Therefore, understanding the evolution of regional historical and future drought and wet conditions is crucial for adapting and mitigating disasters. This paper discusses the evolution of drought and pluvial events in the Henan section of the Yellow River from 1970 to 2014, projects the future evolution of drought and wet conditions, and assesses the performance of various climate models from Coupled Model Intercomparison Project Phase 6 in simulating precipitation and temperature. Subsequently, future drought and wet conditions in the Henan section were projected for the 2015–2100 period across four SSP-RCP scenarios using Standardized Precipitation and Evapotranspiration Index (SPEI) and run theory. The results indicate that the Henan section of the Yellow River exhibited a significant drying trend during the historical period, with a rate of 0.15 per decade. Looking ahead, a wetting tendency is projected under the SSP1-2.6 scenario, with an increasing rate of 0.02 per decade, whereas the other three scenarios consistently show drying trends, with rates of −0.11, −0.15, and −0.23 per decade, respectively. Across all scenarios, drought and wetness variations exhibit pronounced periodicity, particularly at timescales of approximately 20–30 years, suggesting the persistence of multi-decadal hydroclimatic oscillations. Furthermore, drought and wetness events are projected to become more persistent and severe during the mid-to-late 21st century. Compared with the historical baseline, increasing radiative forcing is associated with an expansion in drought-affected areas, accompanied by reduced event frequency but longer duration and greater severity. In terms of risk, the SSP3-7.0 scenario presents the highest overall drought and wetness risk with the widest spatial extent, whereas the SSP2-4.5 scenario shows relatively lower risk levels and a more balanced spatial distribution. Full article

The Gulf of Mexico and the Caribbean Sea are key regions of the western Atlantic, where sea-state conditions are critical for coastal safety and offshore operations. This study analyzes wave climate trends (1981–2022) using WAVEWATCH III simulations validated against buoy observations. The Mann–Kendall test and Theil–Sen estimator were employed to quantify trends in significant wave height (Hs), energy period (Te), and wave power (P), while correlation analysis was performed to explore teleconnections with the Oceanic Niño Index (ONI), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The results reveal basin-wide increases in mean Hs and P, characterized by pronounced spatial and seasonal heterogeneity. The most robust positive trends occur during winter and spring; in summer and fall, the weaker or negative tendencies, particularly in Te, suggest an intensification of seasonal contrasts rather than uniform change. Teleconnection analysis demonstrates that, among the climate indices considered in this study, ENSO is the primary driver of interannual wave variability in the Caribbean, particularly modulating wave power through remotely generated swell. While the NAO exerts regionally dependent control associated with storm-track modulation, the AMO plays a secondary role, affecting swell-dominated sectors. In contrast, the Gulf of Mexico shows limited sensitivity to large-scale climate modes, with wave variability largely governed by local wind–sea processes. These findings highlight the contrasting wave dynamics between these two basins, providing critical insights for coastal hazard assessments, maritime traffic along major shipping routes, oil spill management, and regional wave energy planning. Full article

Drought is a highly destructive natural disaster that inflicts severe economic losses. Its formation mechanisms are complex, yet existing studies have often focused on single driving factors, leaving the synergistic effects of multiple factors insufficiently explored. Based on multi-source data from Xinjiang spanning 1981–2020, this study systematically examined the combined impacts of atmospheric circulation, underlying surface conditions, and human activities on drought, using the multi-temporal-scale Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI), along with partial correlation analysis, spatial autocorrelation, and principal component analysis. The results show that Xinjiang experienced a pronounced drying trend over the past 40 years, with the seasonal SPEI and SSI both exhibiting significant declines. Drought intensity was higher in northern Xinjiang than in the south. Correlations between drought indices and circulation indices, such as Atlantic Multidecadal Oscillation (AMO), were relatively weak, indicating a limited regulatory influence of large-scale circulation on regional drought under the dual constraints of topography and an inland setting. Among underlying surface factors, slope significantly influenced drought spatial patterns. Mountainous areas and basin interiors showed positive spatial correlations, characterized respectively by high–high clustering (high slope and high drought index) and low–low clustering (low slope and low drought index). In contrast, basin margins exhibited low–high clustering (low slope surrounded by high drought index), reflecting negative spatial correlation. Aspect showed no significant effect. Vegetation cover displayed clear seasonal coupling with drought, with strong negative correlations in spring due to intensified water stress. Human activities also played a prominent role. Since the mid-1990s, the expansion of built-up land and increased agricultural water use have shifted drought–land use relationships toward low–high clustering (low drought index surrounded by high land-use intensity) in southern Xinjiang oases, and toward low–low clustering (low drought index and low land-use intensity) in eastern Xinjiang. Meanwhile, ecological restoration projects promoted a transition from low–high to high–high clustering (high drought index and high land-use intensity) in some areas, alleviating local drying trends. Principal component analysis further revealed a shift in the dominant driver: land-use change was the primary factor before 2005, whereas vegetation cover became the key driver thereafter. By clarifying the mechanisms underlying multi-factor interactions in drought in Xinjiang, this study provides scientific support for integrated water resource management, ecological conservation, and climate adaptation strategies in arid regions. 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 Mar Menor is the second largest coastal lagoon in the Mediterranean Sea, with a surface area of about 136 km². It is restricted from the open sea by a sandy barrier system (La Manga) interrupted by three tidal inlets. As a result of high evaporation, it is hypersaline (42–47 ppt) in parts. This study examines the factors leading to the rise in sea surface temperature in the Mar Menor through an analysis of long-term sea surface temperature using HadSST1.1 data together with shorter-term Moderate-Resolution Imaging Radiometer and Optimum Interpolation Sea Surface Temperature data. A thermal box model has been constructed for the lagoon in an attempt to balance major heat sources and sinks. Additionally, a thermal probe was deployed in 0.3 m of water to evaluate the benthic flux of heat of the shelly fine sand that covers the lagoon seabed. The results show that the vertical thermal gradient in the seabed inverts between the day and night. Prior to circa 1977, there was no clear trend in SST, and variations were strongly associated with the Atlantic Mutidecadal Oscillation and the North Atlantic Oscillation. Post circa 1980, the maximum summertime sea surface temperature showed a steady increase of 0.34 °C/decade. The cross-correlation of SST in the Mar Menor with external drivers showed that it is dominated by the sea surface temperature of the Western Mediterranean, followed by local air temperature, with a minor contribution from the Indian Ocean Dipole. No other significant correlations were evident, suggesting that local temperature was dominated by local drivers. In addition, a Spearman rank order evaluation and principal component analysis showed that the general trends of the Mar Menor SST were also influenced by the Atlantic Multidecadal Oscillation, CO₂, and GDP. 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

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

Under concurrent global warming and multi-scale climate anomalies, regional precipitation has become more uneven and less stable, and extreme events occur more frequently, amplifying water scarcity and ecological risk. Focusing on mainland China, we analyze nearly 70 years of monthly station precipitation records together with eight climate drivers—the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Multivariate ENSO Index (MEI), Arctic Oscillation (AO), surface air pressure (AP), wind speed (WS), relative humidity (RH), and surface solar radiation (SR)—and precipitation outputs from eight CMIP6 models. Using wavelet analysis and partial redundancy analysis, we systematically evaluate the qualitative relationships between climate drivers and precipitation and quantify the contribution of each driver. The results show that seasonal precipitation decreases stepwise from the southeast toward the northwest, and that stability is markedly lower in the northern arid and semi-arid regions than in the humid south, with widespread declines near the boundary between the second and third topographic steps of China. During the cold season, and in the northern arid and semi-arid zones and along the margins of the Tibetan Plateau, precipitation varies mainly with interdecadal swings of North Atlantic sea surface temperature and with the strength of polar and midlatitude circulation, and it is further amplified by variability in near-surface winds; the combined contribution reaches about 32% across the Northeast Plain, the Junggar Basin, and areas north of the Loess Plateau. During the warm season, and in the eastern and southern monsoon regions, precipitation is modulated primarily by tropical Pacific sea surface temperature and convection anomalies and by related changes in the position and strength of the subtropical high, moisture transport pathways, and relative humidity; the combined contribution is about 22% south of the Yangtze River and in adjacent areas. Our findings reveal the spatiotemporal variability of precipitation in China and its responses to multiple climate drivers and their relative contributions, providing a quantitative basis for water allocation and disaster risk management under climate change. Full article

Large-scale climatic oscillations (COs) modulate extreme climate events (ECEs) globally and can trigger the Indian summer monsoons and associated ECEs. In this study, we introduced a Time-dependent Intrinsic Correlation (TDIC) analysis to quantify teleconnections between five major COs—the El Niño–Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO)—and multiple extreme climate indices (ECIs) over the southern Indian Peninsula. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to decompose COs and ECIs into intrinsic mode functions across varying timescales, enabling a dynamic TDIC assessment. The results revealed statistically significant correlations between COs and ECIs, with the strongest influences in low-frequency modes (>10 years). Distinct COs predominantly modulate specific ECIs (e.g., ENSO with monsoon rainfall extremes; AMO and PDO with temperature extremes). These findings advance the understanding of Indian climate system dynamics and support the development of improved ECE forecasting models. Full article