Growth and climate sensitivity of native and non-native urban trees under varying soil conditions in Santiago, Chile

Greater climate sensitivity of older forests than younger forests in the Extratropical Northern Hemisphere

A BIM-based integrated framework for building sustainability assessment in India: Framework development, implementation, and climate sensitivity analysis

ABSTRACT The sustainable management of Pinus nigra plantations under changing climatic conditions requires a comprehensive understanding of how tree growth dynamics and climate–growth relationships are influenced by site orientation and tree social status. This study investigates growth parameters in two P. nigra plantations situated on contrasting slopes in central Albania: northwest (NW) and southeast (SE). Trees were classified into dominant (Dom) and suppressed (Supp) social classes. Radial growth responses to temperature, precipitation, and drought measured via the Standardized Precipitation Evapotranspiration Index (SPEI), were assessed using Pearson correlation analyses. The results revealed Dom trees exhibiting greater growth than Supp individuals, and overall higher growth observed at the SE site. Trees at the SE site were more responsive to summer temperature and precipitation than those at the NW site. Radial growth responses to SPEI were most pronounced in latewood, with the strongest effects observed in Dom trees at NW and Supp trees at SE. The highest correlations were found with the 1-month SPEI in July at NW, and the 4-month SPEI in August at SE. These findings offer valuable insights for the development of adaptive forest management strategies under increasingly warm and arid conditions.

Abstract Decision-analytic frameworks under climate uncertainty include Cost-Benefit Analysis (CBA), which maximizes welfare by trading mitigation costs against quantified damages; Cost-Effectiveness Analysis (CEA), used here in its probabilistic form, which minimizes the cost of meeting a predefined temperature target via a chance constraint that accounts for uncertainty when damages cannot be reliably estimated; and Cost-Risk Analysis (CRA), which reinterprets adherence to the temperature target within an unconstrained utility-maximization framework by penalizing the probability of target exceedance via a risk function. This study operationalizes Cost-Benefit-Risk Analysis (CBRA), a novel framework that extends CRA by retaining its risk function while adding an explicit, partial damage function, thereby internalizing quantified impacts and leaving residual, unquantified impacts to be represented by the reduced risk term. In our application, the partial global damage function is derived from a forward-looking, regionally and sectorally disaggregated Computable General Equilibrium (CGE) model. This allows us to assess how much of the precautionary risk embedded in climate targets is captured by explicit economic losses. We implement CBRA in the integrated assessment model MIND, coupling a modified version of the FaIR climate model that accounts for climate sensitivity uncertainty. Our findings reveal that explicit damages from agriculture, labor productivity, and human health explain 58% of the risk captured by a 2 $$^{\circ}$$ C target under a 65% safety level. We demonstrate that when MIND is updated with FaIR, CRA and CEA deliver near-equivalent outcomes (differences of 1.3% in peak emissions, 0.40% in peak temperature, and 0.36% in cumulative emissions), confirming the theoretical equivalence suggested in previous studies. These results suggest that as damage estimates improve, a greater share of precautionary risk is accounted for within cost-benefit models, reducing the need for rigid precautionary targets and narrowing the gap between CBA and CEA. However, uncertainty in climate sensitivity remains a dominant factor, highlighting the need for a more precise understanding of the climate system response to guide policy.

Corrigendum to “Interannual climatic sensitivity of surface energy flux densities and evapotranspiration in a disturbed and rewetted ombrotrophic bog” [Agricultural and Forest Meteorology 367 (2025) 110501

Shifting climatic sensitivities of drought-related yield gaps signal potential increases in irrigation reliance in the Yellow River Basin

Assessing the climate sensitivity of wind power resources: Multi scenario-based analysis via bias-corrected CMIP6 scenarios

Wastewater-based epidemiology (WBE) has emerged as a powerful population-level surveillance approach for monitoring infectious diseases by detecting biomarkers shed into communal wastewater systems. Its relevance is particularly pronounced in low-income communities, where clinical reporting is often fragmented, delayed, or inaccessible, and where enteric diseases remain a major public health burden. Recent advances in artificial intelligence (AI) offer new opportunities to enhance WBE by enabling scalable data integration, pattern recognition, and predictive modeling across complex environmental and socio-demographic contexts. From a broad public health perspective, integrating WBE with AI-driven analytics can transform passive wastewater measurements into proactive early-warning systems capable of informing targeted interventions, optimizing resource allocation, and strengthening outbreak preparedness. This study narrows the focus to enteric disease transmission dynamics in low-income settings, where infrastructural variability, informal sanitation networks, and climate sensitivity complicate traditional surveillance. We propose an AI-augmented WBE framework that combines microbial load data, temporal wastewater signals, environmental covariates, and community-level indicators to model transmission pathways and forecast outbreak risks. By leveraging machine learning and time-series modeling, the framework aims to improve prediction accuracy, reduce detection latency, and support equitable public health decision-making. The integration of WBE and AI thus represents a scalable, cost-effective strategy for strengthening enteric disease surveillance and resilience in resource-constrained communities.

This study assesses the vulnerability of coastal tourism to climate change in Sam Son using the Analytic Hierarchy Process (AHP) and vulnerability index. The framework incorporates three components: exposure, sensitivity, and adaptive capacity, with each component being quantified through weighted indicators derived from expert evaluations and standardized data. The results indicate that key tourism zones, particularly the Truong Le mountain area and Quang Cu ward, face high vulnerability due to their geographical location and low adaptive capacity, while inland areas exhibit greater resilience. These findings highlight significant spatial disparities in climate risk and underscore the importance of tailored adaptation strategies, including investment in infrastructure, disaster preparedness training, and climate-resilient tourism planning. This integrated approach not only provides a replicable model for assessing the climate sensitivity of tourism systems but also contributes to the development of sustainab

Abstract. Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (Ni) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and Ni, and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and Ni over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.

Introduction Climate change significantly influences tree radial growth, particularly in high-elevation forests. As a typical plateau lake in the Hengduan Mountains, Lugu Lake lacks sufficient dendrochronological research, hindering the understanding of regional conifers’ responses to climate change. Methods Using dendrochronological methods, we constructed residual chronologies from tree-ring width data of Larix potaninii Batalin. (Chinese larch), Picea likiangensis Franch. (Lijiang spruce) and Pinus yunnanensis Franch. (Yunnan pine) collected around Lugu Lake. We used Response Function Analysis (RFA) and Redundancy Analysis (RDA) to quantify growth–climate relationships. We further identified the key climatic drivers of radial growth for the three conifers. Results and discussion The radial growth of L. potaninii , P. likiangensis , and P. yunnanensis around Lugu Lake was jointly influenced by temperature and precipitation. Specifically, the mean minimum temperature ( T min ) of previous September, current January precipitation, the mean temperature ( T mean ) of current May, and the mean maximum temperature ( T max ) of current September were common factors influencing the radial growth of three conifers. L. potaninii was more influenced by temperature in the early growing season (April–May) and moisture conditions in the post growing season (September–October). Elevated growing-season temperatures were detrimental to the growth of P. likiangensis . P. yunnanensis was more affected by spring drought stress and summer precipitation. Under projected warming with slightly reduced precipitation, the observed climate sensitivities suggest that growth of L. potaninii and P. likiangensis may respond differently, whereas the response of P. yunnanensis is likely more complex. RFA and RDA demonstrated consistency and could effectively complement each other in dendroclimatological studies. This study provides new tree-ring evidence from northwestern Yunnan and insights into potential future growth responses in the region under climate change.

Aims: This study quantifies climatic drivers of Kharif-season rice yield variability in Manipur. Study design: The present study was conducted using seasonally aggregated rainfall and temperature indicators with regression-based attribution and scenario modelling. Place and Duration of Study: This study focused on the state of Manipur in Northeast India, where rice constitutes the dominant staple crop and agricultural production is largely dependent on monsoon rainfall. The present study was conducted during 2014–2021 using seasonal climate data obtained from the Indian Meteorological Department (IMD) Data Service Portal. Methodology: Climate data were aggregated over the Kharif growing season (June–October) to represent hydroclimatic conditions relevant to key rice growth stages. The analysis estimated bivariate relationships between rice yield and rainfall, and between rice yield and temperature, as well as conditional (partial) effects that controlled for covariance between the two climate variables. A structural multivariate model was subsequently used to generate mean yield projections under counterfactual climate perturbations relative to a recent baseline period. Results: Seasonal rainfall during the Kharif period exhibited pronounced inter-annual variability, ranging from 589.6 to 1252.1 mm, whereas seasonal mean temperature varied within a comparatively narrow range (25.2–26.1 °C). Rice yield fluctuated substantially between 1.74 and 2.68 t ha⁻¹. Correlation analysis revealed a strong negative association between rainfall and yield (r = −0.78) and a weaker negative association between temperature and yield (r = −0.54). Regression results indicate that a 100 mm increase in Kharif-season rainfall is associated with an average yield reduction of approximately 0.10 t ha⁻¹ over 2014–2021, while temperature effects are not statistically significant once rainfall is accounted for. Across bivariate, conditional, and multivariate specifications, rainfall consistently emerged as the dominant climatic driver, with higher seasonal rainfall significantly reducing yield (β ≈ −0.001, p < 0.05), whereas temperature effects remained statistically weak. The joint climate model explained 70% of inter-annual yield variation (R² = 0.700). Scenario projections anchored to recent climate conditions yielded a baseline estimate of 2.44 t ha⁻¹ and indicated progressively larger yield declines under wetter and warmer conditions. Conclusion: Overall, short-term climate risk in Manipur’s rainfed rice systems is governed primarily by rainfall variability, particularly excess monsoon precipitation, highlighting the need for adaptation strategies focused on flood management, drainage, climate-resilient varieties, and early-warning systems.

Abstract Satellite observations indicate a substantial increase in Earth's top‐of‐atmosphere (the top of the atmosphere (TOA)) radiative imbalance since 2010. We estimate trends in effective radiative forcing (ERF) by separating TOA flux changes into forcing and response components, using feedback parameters derived from observed and simulated interannual variability and the CO 2 ‐forced response. From 2010 to 2024, ERF trends are ∼1.0 W m −2 per decade for both net and shortwave fluxes, exceeding those for 2001–2024 and substantially larger than projections from state‐of‐the‐art models. This discrepancy persists across a wide range of climate sensitivities and forcing scenarios and shows limited sensitivity to feedback assumptions. The largest contribution arises from the shortwave component, with spatial patterns indicating particularly strong forcing increases over northern midlatitude oceans. These results suggest that the gap between observations and models is widening, although the contribution of internal variability cannot be entirely excluded.

Background: Hybrid Renewable Energy Systems (HRES) are considered an attractive option for rural electrification in off-grid areas, where solar, wind and biomass/micro-hydro resources can be integrated with storage technologies to improve the reliability and lessen dependence on diesel. With the rapidly evolving optimisation techniques, energy management schemes and the methodologies of developing smart microgrids, it requires a fresh review of these topics. Objectives: This review investigates acceptable technical, economic, environmental and policy pathways of HRES; discusses successes and barriers encountered in the past; suggests a better operation regimen to meet next-generation rural microgrids. Method: A systematic literature search was performed using predefined keywords in hybrid systems, rural electrification, energy storage and optimisation. The study selection process was conducted according to PRISMA-compliant protocols and included studies published from 2020 to 2025. The selection criteria were multi-source HRES with quantitative performance information. Exclusion criteria removed single-source systems and studies lacking technical indicators. Comparative analysis considered reliability metrics, levelized cost of energy, renewable penetration, emission reduction, and control strategies. Findings: Recent studies show a 30–40%increase in reliability, 10–25% less expensive and 40–60% emission reduction relative to diesel ones. Primary bottlenecks are due to high up-front costs of capital, battery degradation and high dependence on the financing environment, as well as climate sensitivity. Innovative concepts such as AI-based control, hybrid battery–hydrogen storage, and IoT-enabled monitoring facilitate the optimisation of overall system performance. Significance: This review presents more insight into self-adaptive HRES with Digital Twin technology, which fills in the gap of previous reviews by focusing on life-cycle optimal operation, climate resilient HRES, predictive maintenance and community-centric operation and offers novel understandings to scalable rural electrification. Keywords: Hybrid Renewable Energy Systems, Off-Grid Electrification, Digital Twin, Optimisation, Rural Energy Access

ABSTRACT Understanding species-specific vegetation responses to climate variability is crucial for assessing mangrove resilience under climate change. This study presents a 12-year (2013–2024) multi-sensor satellite analysis of three dominant mangrove species (Rhizophora mucronata, R. apiculata, and Avicennia marina) in southern Thailand using a combined Google Earth Engine (GEE) and Python-based framework. Monthly Enhanced Vegetation Index (EVI) time series were derived from Landsat and Sentinel-2 imagery and analysed using Fourier-based time-series decomposition to characterize long-term trends, seasonal variability, and phenological timing in a tropical monsoon mangrove system. Environmental drivers – including rainfall, temperature, solar radiation, and oceanographic variables – were extracted from open-access climate datasets. Species-level analyses revealed contrasting vegetation trajectories and phenological strategies, with R. mucronata exhibiting the strongest and most consistent seasonal amplitude, R. apiculata showing pronounced monsoon-aligned seasonality, and A. marina displaying weak and irregular seasonal expression alongside sustained vegetation decline. Lagged correlation analysis (0–6 months) identified delayed and species-specific responses to climatic and oceanographic drivers, with R. apiculata demonstrating the highest climate sensitivity and shortest response lags, while A. marina showed limited climate coupling despite marked decline in the greenness of A. marina stands, suggesting a stronger influence of local non-climatic stressors on A. marina. A species-level vulnerability assessment based on long-term vegetation trends and climate sensitivity revealed distinct risk pathways, with R. apiculata and A. marina exhibiting elevated vulnerability through climate sensitivity and local degradation, respectively, and R. mucronata showing comparatively higher resilience. This study highlights the value of species-resolved phenological analysis and lag-aware remote sensing frameworks for tropical evergreen ecosystems. The integrated GEE – Python workflow provides a scalable and transferable approach for long-term mangrove monitoring and early warning of climate-induced ecosystem change in tropical monsoon coastal systems.

ABSTRACT A cointegrated vector equilibrium correction model of key climate variables including sea surface temperature, ocean heat content, Arctic sea‐ice extent and sea‐level change is built, driven by radiative forcing in which a stochastic trend arises due to anthropogenic emissions of greenhouse gases. A valid and congruent statistical model requires saturation estimation to model breaks in trends, while also conditioning on natural radiative forcings and El Niño–Southern Oscillation. The model is stable over 150 years, reflecting the slow adjustment of the deep oceans to increased greenhouse gas concentrations, and predicts an equilibrium climate sensitivity of 2.6°C. Projections out to 2100 highlight the many uncertainties over the coming decades.

Global warming severely threatens parasitic plants worldwide. However, little is known about how a parasite with multiple hosts responds to climate change in its distribution. Sapria himalayana is an endangered endo-holoparasite, obligately parasitizing Tetrastigma species. We employed MaxEnt to predict suitable habitats for S. himalayana and its five hosts, and determined key environmental factors. Then, we calculated niche overlaps for the five parasite-host pairs. Currently, it covers a suitable area of 1.35 × 104 km2, accounting for 0.14% of China's total territory. Temperature-related variables were identified as the key factors shaping potential distribution for this parasite and three hosts (i.e., T. planicaule, T. obovatum, and T. cruciatum), while precipitation-related ones were identified for the other hosts (i.e., T. obtectum and T. serrulatum). Collectively, the five pairs presented low niche overlaps under current and future scenarios. While S. himalayana will increase by 37.78% in future suitable habitat, the two host categories show contrasting trends in potential habitat shifts. Divergent climatic sensitivities across host species, along with parasite-host suitability mismatches, could shape the survival and distribution of S. himalayana. Consequently, this research offers valuable insights for the conservation of S. himalayana in China, highlighting the necessity of safeguarding its distinct hosts under global warming.

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth choice and the impact of dimensionality in nonparametric regression. An empirical example is provided involving climate data regression to assess Earth’s climate sensitivity to CO $_2$ , where nonstationarity is a prominent feature of both the regressors and covariates in the model. To our knowledge, this application is the first nonparametric empirical analysis to assess potential nonlinear impacts of CO $_2$ on Earth’s climate while allowing for nonstationarity in both the regressors and covariates.

Abstract The probability density function of drops is difficult to model. Current approaches make assumptions that are often problematic, as they allow negative values for the mean of the distribution. While the statistical goodness of fit of those models might be reasonable for precipitation radar estimation, the situation is unsatisfactory if a fully consistent physical modeling of precipitation across scales is desired. This is the case of weather and climate models. This paper discusses a model that satisfies mathematical and physical consistency. The model can be seamlessly integrated into the parameterizations of the microphysics of precipitation and is tested on an extensive disdrometer data set. Comparison with existing models shows that the new method has substantial practical and theoretical advantages. The research has implications in elucidating the role of clouds in the climate sensitivity of climate models.