Search Results (1,533)

Forest productivity varies interannually in association with climate variability, but the strength and direction of these associations differ across hydroclimatic settings. Here, we examined how growing-season temperature and climatic water-balance anomalies covaried with forest net primary productivity (NPP) across persistent forests in Northeast China. Using harmonized 1 km data spanning 1985–2024, we applied pixel-wise correlations and a two-predictor association model to detrended and standardized time series, alongside original-unit sensitivity analyses. Climatic-water-balance associations exhibited a distinct hydroclimatic ordering: median water-balance–NPP correlations shifted from $-0.082$ in humid forests to $0.234$ in semi-arid forests, with positive associations becoming progressively more prevalent toward drier forest margins. Temperature associations likewise shifted toward less positive or more negative values along the same gradient, although this pattern showed lower stability across analytical definitions. The water-balance ordering remained robust across alternative preprocessing choices and screening rules. Across all hydroclimatic zones, pixel-wise models showed modest explanatory power, with zonal median $R^2$ ranging from $0.054$ to $0.090$. Overall, the direction and relative prominence of synchronous temperature and climatic-water-balance associations with NPP showed a consistent zonal ordering along the regional hydroclimatic gradient, indicating that spatially aggregated averages can obscure contrasting local climate–productivity patterns while the observed relationships remain descriptive rather than causal. Full article

Stratosphere–troposphere exchange (STE) governs the transport of air mass and chemical constituents, including ozone, across the tropopause and therefore plays an important role in coupling the stratosphere and troposphere. While the annual-mean STE has been well characterized, its interannual variability and the processes that control it remain uncertain. Recent studies based on ERA5 and MERRA2 reanalysis datasets show that large-scale climate modes such as the El Niño–Southern Oscillation (ENSO), the Quasi-Biennial Oscillation (QBO), and variability in the Brewer–Dobson circulation (BDC) explain only a small fraction of STE variability. Moreover, these two reanalyses can explain only about 33% of each other’s variance in global ozone STE monthly anomalies. This raises the question of whether the inferred drivers of STE variability are robust or instead reflect limitations in the underlying reanalysis datasets. Herein we investigate the climatology and interannual variability of the STE of air mass and ozone using six different models from Phase 2 of the Chemistry–Climate Model Initiative (CCMI). Models provide dynamically consistent representations of stratospheric transport and chemistry, offering an independent framework for evaluating the dynamical controls on STE variability. We apply a lagged multiple linear regression framework to monthly anomalies to quantify the contributions of ENSO, the QBO, and BDC variability. To isolate BDC variability independent of ENSO and QBO, we regress out ENSO and QBO signals from the BDC index. The relative importance of these drivers varies by region and between air mass and ozone exchange. Across the models on a global basis, ENSO explains the largest fraction of variance in ozone STE, with a mean contribution of 40% (range: 24–65%). The BDC contributes a smaller but non-negligible share, with a mean of 13% (range: 7–21%), while the QBO accounts for a mean contribution of 8% (range: ~0–29%). Nevertheless, a substantial fraction of variability in global ozone STE remains unexplained, ranging from 19 to 62%, with a mean of 39%. Applying the same methodology to the ERA5 and MERRA-2 reanalyses yields larger unexplained variance of 71% and 81%, respectively, in global ozone STE. Despite the large unexplained residuals in the reanalyses and the wide range across CCMI models, the relative response of ozone STEs exhibits consistent signs: positive for the BDC, negative for the QBO, and negative for ENSO in the tropics but positive over the extratropics, based on both reanalyses and most CCMI models. Full article

Coral reefs throughout the Caribbean are increasingly exposed to climate-driven thermal variability, yet long-term coral growth records remain scarce for Haiti. This study examined long-term growth variability in four massive coral colonies (Siderastrea siderea and Diploria labyrinthiformis) collected from three Haitian reef systems: Léogâne, Caracol, and Belle-Anse. Annual growth bands were identified from X-radiographs, and linear extension and skeletal density were quantified using sclerochronological and densitometric analyses, while calcification was derived from their product. Relationships between annual growth parameters and site-specific sea surface temperature (SST) anomalies were assessed using Pearson correlations, while annual mean Niño 3.4 anomalies were used to characterize El Niño–Southern Oscillation (ENSO) variability. Composite analyses were also used descriptively to examine growth responses during selected El Niño and La Niña conditions. The four colonies exhibited substantial interannual variability in skeletal growth. Mean linear extension ranged from 0.47 ± 0.16 to 0.64 ± 0.14 cm yr⁻¹, skeletal density from 0.82 ± 0.11 to 1.47 ± 0.24 g cm⁻³, and calcification from 0.53 ± 0.21 to 0.81 g cm⁻² yr⁻¹. Pearson correlations revealed colony-specific SST–growth relationships, with the strongest positive associations observed for skeletal density in B82 (r = 0.318) and calcification in Diploria labyrinthiformis SE1 (r = 0.302), whereas other relationships were weaker or differed in direction. ENSO composites likewise showed heterogeneous responses among colonies and climatic phases. Overall, these results highlight pronounced colony-specific growth variability and heterogeneous associations with SST and ENSO, providing an important long-term sclerochronological baseline for Haitian reefs. Full article

Climate change can alter runoff in semi-arid basins even when annual precipitation changes little. We combined historical meteorological observations, daily WEP-L simulations, and bias-adjusted CMIP6 projections for the Fen River Basin. Historical precipitation and runoff covered 1956–2025, and temperature covered 1956–2018. Future simulations covered 2026–2050 under SSP2-4.5 and SSP5-8.5. Land surface conditions were fixed at 2020, and withdrawals, irrigation, and reservoir operation were excluded. Historical precipitation showed no significant trend, whereas temperature increased by 0.307 °C decade⁻¹. Simulated annual runoff declined at five reporting locations, with trends of −12.1 to −187.2 × 10⁶ m³ decade⁻¹. Future temperatures averaged 10.57 and 11.00 °C under the two scenarios, with trends of 0.392 and 0.652 °C decade⁻¹, respectively. Across seven reporting locations, SSP5-8.5 ensemble-mean runoff was numerically 3.4–6.8% lower. Paired differences at the six locations with complete ten-model coverage were not significant and varied in sign among models. These tests are interpreted descriptively because some GCMs belong to related families. Hejin results are supplementary because model coverage is incomplete. Annual precipitation and runoff anomalies were positively correlated. Downstream inter-model runoff IQRs were comparable to ensemble means. These projections provide a climate response baseline with substantial model uncertainty, rather than forecasts of regulated river flow. Full article

Subtropical bamboo forests are important carbon sinks, yet long-term regional assessments of their net ecosystem production (NEP) and associated factor contributions remain scarce. Here, using approximately 646 km² of mapped Moso bamboo (Phyllostachys pubescens) in Anji County, China, we generated a monthly NEP reconstruction on a 500 m output grid for 2003–2022 by integrating eddy covariance observations, meteorological data, and multi-source remote sensing products within an automated machine learning framework. Bamboo NEP showed weak overall spatial heterogeneity but followed a clear unimodal elevational pattern, with the highest carbon sequestration at mid-elevations (342–550 m). Regional carbon uptake peaked in summer and reached a minimum in winter, consistent with bamboo phenology. Annual mean NEP was 0.43 Tg C yr⁻¹ and increased significantly over the study period (3.35 Gg C yr⁻², p < 0.001), with 95.8% of bamboo pixels exhibiting upward trends. Negative modeled anomalies coincided with drought years, whereas the biennial growth cycle of Moso bamboo had little effect on annual NEP at the regional scale. SHapley Additive exPlanations (SHAP) shows that long-term NEP enhancement was dominated by canopy vegetation indices and exhibited a descriptive transition around 2013, when the leading control changed from solar radiation to vegetation conditions. Structural equation modeling (SEM) further showed statistical pathways in which meteorological variables were associated with modeled NEP partly through vegetation indicators. These findings reveal a sustained strengthening of the regional bamboo carbon sink and identify shifting controls on its long-term variability, providing a scientific basis for carbon sink management in subtropical bamboo forests under climate change. Full article

As the global aquaculture industry moves towards high density and integrated efficiency, the precision and immediacy of water quality management have become key to increasing productivity and reducing risks. Traditional aquaculture relies on manual experience or simple threshold controls, which often suffer from response delays and energy waste. This study proposes an IoT environmental prediction model based on edge computing designed specifically to address complex and variable outdoor aquaculture environments. The system integrates multimodal sensor data such as water level, temperature, and turbidity, and employs a 1D-CNN-LSTM (One-Dimensional Convolutional Neural Network–Long Short-Term Memory) model deployed on ESP32 edge computing nodes to achieve low-latency environmental change prediction. Based on five core control rules (turbidity control and bidirectional regulation of water level and temperature), this study simulates 360 days of operational data in a real-world environment, covering seasonal climate changes and extreme weather events (such as typhoons). Experimental results show that, compared with traditional hysteresis control, the predictive control strategy proposed in this study can provide early warnings of environmental anomalies 15 to 60 min in advance, effectively increasing the proportion of time in which water quality parameters are maintained within safe thresholds to 99.8%. This paper details the system architecture, prediction model design, and empirical benefits of long-term simulation data analysis, providing a solution with both academic depth and practical value for smart aquaculture. Full article

Regional climate variability arises from the interaction between long-term anthropogenic warming and large-scale atmospheric circulation processes. In particular, the coupling between the Stratospheric Polar Vortex (SPV), jet stream dynamics, and surface temperature variability remains a critical component of climate variability across the Euro-Atlantic sector. In this study, ERA5 reanalysis data covering the period of 1979–2024 were used to investigate long-term temperature trends, atmospheric circulation changes, and stratosphere–troposphere interactions. Random Forest and Extreme Gradient Boosting (XGBoost) models were applied to identify the dominant drivers of surface temperature anomalies, while risk-ratio analysis was employed to quantify the occurrence of anomalous thermal conditions under different circulation regimes. The results reveal substantial spatial heterogeneity in warming rates across the study domain. Central Europe, represented by Poland, emerged as a pronounced warming hotspot, exhibiting a trend of +0.59 °C decade⁻¹, substantially exceeding warming rates observed over the North Atlantic sector. This enhanced warming was accompanied by increasing jet stream waviness, indicating greater dynamical instability within the Euro-Atlantic circulation system. Diagnostic analyses further demonstrated that stratospheric circulation anomalies propagate downward into the troposphere, providing a measurable source of predictability for surface weather regimes and temperature extremes. These findings suggest that Arctic Amplification is contributing to a reorganization of Euro-Atlantic atmospheric circulation, increasing regional climate variability despite continued background warming. The results highlight the importance of jointly considering thermodynamic warming and atmospheric dynamical processes when assessing future climate risks and extreme weather occurrence in Central Europe. Full article

As cities increasingly face extreme heat, urban planning plays a crucial role in mitigating the urban heat island effect through sustainable cooling strategies. This study investigates the combined potential of vegetation and high-albedo surfaces to reduce urban heat while providing additional ecological benefits in a public square in Florence (Italy). Currently dominated by asphalt, the square represents a summer thermal hot spot, with surface temperatures reaching 50 °C. Using QGIS, ENVI-met, and i-Tree Eco, existing conditions were compared with a redesign scenario including 87 new deciduous broadleaf trees (resulting in a total of 90 trees in the design scenario), 37% permeable green surfaces, 12% semi-permeable parking areas, and 51% high-albedo pavements. Simulations show summer average reductions of about 0.7 °C in air temperature and 6 °C in surface temperature, with maximum reductions of 1.3 °C and ~13 °C on average across the square (up to 20 °C locally under tree canopies and over high-albedo surfaces), reducing “strong” thermal stress areas by 50–60%. Additionally, the new trees provide ecological benefits, including pollutant removal, oxygen production, carbon sequestration, and stormwater interception. This study highlights the importance of predictive microclimatic assessment to optimize urban design and prevent unintended consequences. Full article

Glacier collapse hazard events on the Tibetan Plateau have attracted increasing attention, and previous studies have documented substantial pre-collapse changes in the Aru glaciers, including dynamic acceleration, crevasse development, and glacier thickening. However, the relative effectiveness and temporal behavior of different remote sensing indicators for identifying collapse-related precursory signals have not been systematically compared within a common framework. Here, multi-source remote sensing datasets spanning 1990–2016 were integrated to compare glacier geometry, surface elevation change, glacier surface velocity (GSV), surface albedo, glacier surface temperature (GST), and Sentinel-1 SAR backscatter before the 2016 collapses of Aru 53 and Aru 50. Anomalies were quantified using standardized Z-scores, with |Z| > 1.96 and |Z| > 2.57 representing anomalous and strongly anomalous conditions, respectively. Both glaciers experienced upstream surface lowering and downstream thickening, followed by pronounced GSV acceleration toward collapse, consistent with previously reported dynamic and mass-redistribution changes. In contrast, surface albedo and GST exhibited long-term variations broadly consistent with regional climatic conditions but lacked distinctive collapse-related anomalies. Sentinel-1 observations revealed a contrasting response between the two glaciers: Aru 53 showed a progressive increase in σ⁰, reaching 0.70 dB at the glacier-wide scale and 1.15 dB within the collapse zone, whereas Aru 50 exhibited only limited changes. The spatial concentration of enhanced σ⁰ in Aru 53 coincided with areas of rapid crevasse development, suggesting that SAR backscatter provides complementary information on localized surface structural degradation. Overall, the comparison demonstrates distinct diagnostic sensitivities among remote sensing indicators: elevation redistribution and GSV acceleration characterize mass transport and dynamic instability, whereas SAR backscatter can provide additional information on localized surface structural degradation. These results support the use of indicator-specific anomaly responses rather than a single universal precursor criterion for assessing glacier instability. Full article

Dengue transmission has expanded across the Americas, but the spatiotemporal distribution of mortality recorded in death certificates and its ecological relationship with climate remain insufficiently characterized. Objective: To evaluate dengue-associated mortality recorded in Peruvian death certificates during 2017–2024, characterize geographic emergence across fixed provinces, estimate associations with local climate anomalies, and triangulate findings with epidemiological surveillance. Methods: We conducted a retrospective ecological spatiotemporal study combining the National Death Information System, national dengue surveillance, official population projections, gridded climate data, and a fixed 196-province framework. The primary outcome was an A90 or A91 code in any certificate position; province-month was the principal unit. Poisson models included a population offset, provincial fixed effects, calendar month, linear time, a 2020–2021 indicator, and temperature and precipitation anomalies at lags 0–3 months. Results: The updated snapshot contained 987 certificates with A90/A91 during 2017–2024; 968 were territorially eligible. Thirty-six provinces were classified as emergent. Surveillance contributed 772,387 notified cases, and department-year deaths and cases were correlated (Spearman ρ = 0.772). In the corrected primary model, the cumulative ecological mortality rate ratio comparing the 90th with the 50th percentile was 1.75 (95% CI: 1.41–2.17) for mean-temperature anomalies and 2.69 (95% CI: 1.97–3.67) for precipitation anomalies. Estimates remained positive under position-A, code-specific, syndromic, and temporal sensitivity definitions. Conclusions: Recorded dengue-associated mortality was concentrated during recent outbreaks and appeared in provinces without baseline-period mortality. Local climate anomalies preceded higher province-month mortality rates, but these ecological associations are compatible with indirect transmission and health-system pathways and do not establish direct individual-level effects. Full article

Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates the Cyclone Global Navigation Satellite System (CYGNSS) Level 3 soil moisture product in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023 using reference-free collocation diagnostics. Extended Triple Collocation (ETC) was applied to CYGNSS, the Soil Moisture Active Passive (SMAP) Enhanced Level 3 product, and European Centre for Medium-Range Weather Forecasts fifth-generation land reanalysis (ERA5-Land) 31-day centered anomalies to estimate model-derived correlation with a latent soil moisture anomaly signal, estimated error standard deviation, and signal-to-noise ratio (SNR). A covariance-pathway Quadruple Collocation (QC) analysis then introduced the European Space Agency Climate Change Initiative active microwave soil moisture product (ESA CCI ACTIVE) as a fourth, structurally distinct product to test whether the CYGNSS–SMAP pair exhibited significant direct error correlation. The regional ETC configuration gave CYGNSS an estimated latent correlation of $r=0.425$, an estimated error standard deviation of $0.036{\mathrm{m}}^3{\mathrm{m}}^{-3}$, and an SNR of $-6.56$ dB. In the common quadruplet sample, the SMAP-inclusive CYGNSS estimate was $r=0.423$, whereas the SMAP-independent configuration gave $r=0.386$, indicating a modest configuration-dependent inflation of $\Delta r=0.0368$. However, the QC cross-error correlation was not statistically significant ($r_{\epsilon }=0.0007$, 95% confidence interval (CI) [$-0.0270$, $0.0283$]). Performance was weakest under dry soils $(r=0.331)$, where drought detection is most important. Harmattan diagnostics showed that dry-season ETC failure was associated with reduced anomaly variance and selective CYGNSS decoupling from SMAP and ERA5-Land rather than numerical ill-conditioning alone. Skill was higher over cropland $(r=0.447)$, shrubland or grassland $(r=0.455)$, and moderate precipitation conditions $(r=0.630)$, but lower over tree cover $(r=0.342)$. These findings indicate that uncorrected CYGNSS Level 3 soil moisture should not be used as a standalone year-round drought-monitoring product in Guinea savanna agriculture. Its strongest value is as part of environment-aware, bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality. Full article

This study investigates the spatiotemporal variability, spatial consistency, and non-stationarity of meteorological and hydrological droughts in three major water management basins (WMBs) of northern and central Kazakhstan: Nura–Sarysu, Esil, and Tobyl–Torgai. Long-term instrumental observations from meteorological stations and hydrological gauges were used to calculate the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Streamflow Drought Index (SDI). Structural changes were detected using the Pettitt, Buishand, and CUSUM tests, while spatial consistency was quantitatively assessed as the proportion of active meteorological stations simultaneously experiencing drought conditions. The results showed that major drought episodes represented spatially coherent regional events rather than isolated local anomalies; however, their spatial extent varied substantially among the basins. The highest synchronization of meteorological droughts was observed in the Esil WMB, whereas the Tobyl–Torgai WMB exhibited greater spatial heterogeneity. Statistically significant structural changes were identified in both meteorological and hydrological drought series; however, their timing and frequency differed among indices and basins, indicating pronounced temporal non-stationarity. A higher proportion of structural changes was identified for SPI-3 than for SPEI-3. However, this difference is interpreted as reflecting differences in the statistical behavior of the precipitation index and the climatic water balance index rather than direct evidence of the relative contributions of precipitation and atmospheric evaporative demand. SDI-12 exhibited more persistent and temporally smoothed hydrological drought dynamics compared with the short-term meteorological indices, while the timing of hydrological changes differed among river systems. The results demonstrate that drought development in Northern and Central Kazakhstan is characterized by spatial heterogeneity and temporal non-stationarity, while the joint interpretation of SPI, SPEI, and SDI provides a more comprehensive basis for basin-scale drought monitoring and adaptive water-resource management. Full article

Coffee productivity in Latin America is exposed to climatic variability, input-use pressures, and pronounced cross-country heterogeneity. This study evaluates dynamic associations between coffee yield and climatic, productive, and economic conditions in twelve major producing countries from 1992 to 2023 using an ARDL(1,1) framework with Pooled Mean Group (PMG) and Mean Group (MG) estimators. Both models yield negative and statistically significant adjustment coefficients (−0.866 and −1.087, respectively; p < 0.001). Because coffee yield, the constructed NPK-use/coffee-area proxy, and the temperature anomaly are stationary in levels, these parameters are interpreted as dynamic mean reversion within the ECM parameterization rather than as standalone evidence of cointegration; the Westerlund statistics are therefore treated as supplementary diagnostics. A Swamy-type test rejects common long-run slopes (chi-square = 101.62, df = 55, p < 0.001), while none of the five long-run covariates is significant at the 5% level in either central model. A targeted denominator-free sensitivity check replacing the area-normalized proxy with national NPK agricultural use in tonnes of product also finds no statistically significant fertilizer association. The results support country-sensitive adaptation and productivity strategies and caution against causal farm-level interpretations of national macro-panel associations. Full article

This study examines the impact of a financial market anomaly (weather) as a physical climate risk on the stock returns and changes in trading volumes of 13 major banks listed on the Warsaw Stock Exchange (WSE) from 2020 to 2025, advancing our understanding of sustainable finance aspects. Employing GARCH-family models, the analysis contrasts these environmental factors with fundamental firm-specific and market-wide variables. The empirical findings demonstrate that fundamental variables, specifically the STOXX Europe 600 index and the EUR/PLN exchange rate, exhibit overwhelming dominance over the investigated bank stock returns and changes in trading volumes, subordinating the impact of meteorological factors. Nevertheless, the results reveal that deviations from average historical values for weather variables can affect stock returns and changes in trading volumes rather than nominal readings. Furthermore, extreme macro-financial volatility during the compounding 2020–2025 crises likely masked these high-frequency perturbations, supporting the efficient market hypothesis. These insights underscore the necessity of integrating localized physical climate risks into broader sustainability stress-testing and macroprudential frameworks in Central and Eastern Europe. Full article

The Andean–Amazonian transition zone in northeastern Peru is one of the most climatically diverse and complex regions in South America, yet knowledge of its ecoregions under climate change remains limited. This study assessed projected changes in mean annual temperature, accumulated annual precipitation, and incident solar radiation in the Amazonas department using an ensemble of five global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP5-8.5 scenarios for the periods 2031–2060 and 2071–2100. Historical data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) were used to analyze the spatial distribution of climate, climate anomalies, variability among ecoregions, and inter-model variability. The results show a generalized temperature increase across the department, with average anomalies between 1.20 and 3.96 °C, while precipitation increased between 3.93 and 16.59%, but with greater spatial heterogeneity and inter-model variability. Solar radiation showed moderate changes, with increases of up to 2.07 W m⁻² toward the end of the century. Ecoregions maintained their climatic differences, but the largest relative increase in precipitation was projected for montane grasslands and shrublands. Temperature showed the greatest agreement in the direction of projected change among models, while precipitation and solar radiation showed greater inter-model variability. These results provide spatially explicit climate information that can contribute to land-use planning, ecosystem conservation, and the design of regional climate change adaptation strategies in the Andean–Amazonian transition zone. These findings may also provide useful climate information for landscape restoration planning and for assessing future ecosystem responses to changing climatic conditions. Full article