Earth and Environmental Sciences

• G lignin and suberin promoted lPOM while H lignin and N promoted MAOM. • More C was recovered in lPOM relative to MAOM, especially in the subsoil. • Litter composition effects were similar in the surface and subsoil. Subsoils have been identified as a potential target for building soil C stocks, given the older age and slower cycling of organic matter at depth. Plant roots deliver C deep into the soil profile, but there is limited experimental evidence as to which root traits are most effective at increasing subsoil C. To that end, we conducted a 13 C natural abundance study to trace C from roots of differing quality into organic matter fractions in a shallow and deep soil layer of an Alfisol in Lexington, KY. The roots of four species were packed into nylon-wrapped tubes with soil and incubated in the field at depths of 0–10 cm and 40–50 cm for one year. Roots with high guaiacyl lignin (G-lignin) and suberin had more of their C retained in light particulate organic matter (lPOM), while those with high p -hydroxyphenyl lignin and N compounds had more C transferred into mineral-associated organic matter (MAOM). The effects of litter type and chemical composition on lPOM and MAOM formation persisted at both depths. More litter-derived C was recovered in lPOM than MAOM at both depths, but this effect was more pronounced in the subsoil than topsoil (30.2% as lPOM and 8.6% as MAOM in the subsoil; 21.3% as lPOM and 10.4% as MAOM in the topsoil). Overall, our results suggest that lPOM is an important reservoir for recent inputs of root litter in the subsoil, and this fraction can be enhanced by adding roots with high G-lignin and suberin.

Abstract Humans shape landscapes through the management of organisms and ecosystems, either degrading or sustaining biodiversity. Despite their essential role in sustaining biodiversity, stewardship practices have been overlooked by conventional conservation research and action. To improve understanding of biophysical stewardship practices, we propose a classificatory framework and illustrate it by applying it to cultural keystone species (CKS), which are rich in examples due to their high value to people. We identified 19 types of biophysical stewardship practices with impacts across ecological levels. Through a review of CKS literature, we identified 343 reports of biophysical stewardship practices directed toward almost 1000 CKS and 1652 reports of nature’s contributions to people associated with these species. Integrating stewardship practices into biodiversity conservation frameworks would facilitate consideration of both biodiversity and its stewards. Strengthening Indigenous, local, and other place-based stewardship practices within scientific and policy settings could contribute to more effective and inclusive conservation.

This study proposes a method for evaluating accessibility to neighborhood parks within pedestrian sheds in environments where pedestrian network data are limited and aims to analyze the effects of residential development period and housing type on park accessibility. The study area is Daegu, South Korea. In residentially dense areas, residential activity blocks were delineated using roads with four or more lanes in consideration of pedestrian safety. This approach was intended to establish residential activity areas that account for pedestrian discontinuities. Residential activity areas are classified into five categories of park accessibility, based on whether a neighborhood park lies within walking distance, the number of parks available, and their proportional relationship to the total block area. In addition, periods of residential development are defined according to the year of building approval, and their associations with park accessibility are analyzed in relation to housing type. The analysis identified 464 residential activity blocks within the study area, of which 253 contained parks within pedestrian sheds. The actual distribution of parks within the blocks differed from the results of the conventional buffer-based accessibility analysis conducted for parks within pedestrian sheds. For example, although some blocks included parks within the statutory maximum walking distance of 1 km under the conventional buffer criterion, residents were in practice required to cross roads with four or more lanes to access the parks, indicating that the parks were not effectively located within the residential activity area. In terms of the relationship with the period of residential development, areas densely occupied by residential buildings established before 1980 exhibited relatively low park accessibility, whereas those established since 1990 demonstrated relatively favorable park accessibility. These findings suggest that spatial disparities in park accessibility are structurally shaped by the timing of urban development and patterns of residential formation, rather than by population density alone. This study presents an approach to evaluating accessibility that is applicable even in the absence of pedestrian network data and provides policy implications by identifying priority areas for neighborhood park provision to improve park equity in older residential areas.

Background/Objectives: Although urban greening interventions are increasingly implemented to improve livability, environmental quality, and adaptation capacity in cities, their evaluation still predominantly relies on physical outputs rather than validated, resident-centered outcomes. This study examined whether the five-item attachment dimension of the Urban Green Attachment Scale (UGAS) can reliably indicate the social integration of newly introduced greenery in an SDG 11-oriented evaluation context. The present adaptation of the UGAS captures the perceived importance of the planting, its contribution to well-being, anticipated loss, willingness to protect it, and aesthetic appreciation. Methods: Data were collected through two independent face-to-face surveys conducted among residents of the same housing estate shortly after a greening intervention in May 2025 (n = 150) and September 2025 (n = 191). The first sample was used for exploratory factor analysis (EFA) and the second for confirmatory factor analysis (CFA). Reliability was assessed using Cronbach’s α and McDonald’s ω; inter-item associations were estimated using Kendall’s tau-b; and construct validity was examined through known-groups comparisons with theoretically relevant appraisals and stewardship-related indicators. Results: The adapted UGAS demonstrated high internal consistency, low floor and ceiling effects, and moderate to strong inter-item associations. Exploratory factor analysis supported a unidimensional solution with high loadings and 65.7% explained variance, and confirmatory factor analysis corroborated this structure after minor, theory-guided localized refinements. Higher UGAS scores were consistently observed among residents who reported stronger calming and home-related effects, perceived healthier local conditions, expressed willingness to help care for the plants, and demonstrated a readiness to cooperate in improving the area. Conclusions: The results support the five-item UGAS attachment score as a compact, psychometrically adequate measure of residents’ attachment to newly planted urban greenery. Rather than replacing official SDG indicators, the UGAS can complement them at the project level by determining if urban greening becomes socially meaningful and accepted and if it supports stewardship. In this sense, UGAS offers municipalities a practical tool for linking physical greening outputs with resident-centered outcomes relevant to inclusive public spaces, participatory urban development, and the long-term social durability of urban greening interventions.

Marine spatial planning (MSP) is gaining momentum globally, driven by the need to sustainably use marine resources and protect biodiversity amid growing environmental pressures and competing ocean uses. Yet, the potential of MSP to catalyze transformative change in ocean governance within the small Pacific Islands, particularly among the large Indigenous coastal communities, remains the subject of considerable debate. This study uses surveys and focus group discussions to investigate how the MSP process in Fiji aligns with the perspectives of Indigenous Fijian (iTaukei) coastal communities. Despite global recognition of the importance of Indigenous and local knowledge in MSP and ocean governance, our findings reveal limited community awareness, low levels of trust, and minimal participation. Intra-community dynamics within Indigenous Fijian coastal communities, including disparities in leadership influence and representation, further risk reproducing exclusion under the guise of Indigenous participation. Our study concludes that MSP and Indigenous peoples can mutually influence each other in ways that are potentially transformative. Indigenous worldviews, knowledge, and practices can inform more inclusive, culturally grounded MSP through processes of knowledge co-creation, while MSP offers avenues for communities to strengthen agency, co-management capacity, and engagement with broader governance systems. We recommend establishing localized communities of practice, supported by trusted intermediaries, as a context-appropriate mechanism to bridge epistemological divides, foster co-creation, strengthen social capital, and advance socially just and resilient ocean governance. • Indigenous iTaukei communities show limited awareness and participation in Fiji’s MSP. • Top-down governance weakens trust, participation, and MSP legitimacy. • Customary leadership and co-creation enhance MSP transparency and policy relevance. • MSP engagement with Indigenous governance fosters mutual learning and transformation. • Women and youth participation strengthens equity, resilience, and cultural grounding.

Abstract Prior work suggests that the 2020–2022 three-year long La Niña event was triggered by the combination of a strong positive Indian Ocean Dipole (IOD) event that began in 2019 and a strong Atlantic Niño; however, it is unclear what caused the positive IOD event. This analysis suggests that cross-timescale interference between the IOD and Madden Julian Oscillation (MJO) contributed to the generation of this positive IOD event, with interactions occurring in two stages. First, during late April 2019, strong winds associated with MJO phases 2 and 3, and the related formation of two twin tropical cyclones, produced downwelling Kelvin waves in the Indian Ocean that propagated to the western coast of Java and Sumatra and reflected to the west as downwelling Rossby waves, subsequently deepening the thermocline in the eastern basin, and triggering a weak positive IOD state. Within this weakly positive IOD state, a second MJO phase 2 and 3 event created additional westward propagating downwelling Rossby waves that depressed the thermocline in the western basin, allowing for the progression of anomalous positive sea-surface temperature anomalies towards the western part of the basin that triggered the strong positive IOD event. Based on these findings, and results from a linear wave model analysis, this study concludes that through cross-timescale interference, the MJO preconditioned the positive IOD state during the first MJO event and contributed to the triggering of the strong positive IOD event during the second MJO occurrence. These combined mechanisms acted as a catalyst and contributed to the formation of the positive IOD event in 2019 that preceded the three-year long La Niña event.

Abstract Lake Victoria (LV), the world’s largest tropical lake, significantly influences East Africa’s regional climate through diurnally varying lake-land breeze circulations. In particular, lake-breeze fronts (LBFs) often trigger cloudiness, deep moist convection and heavy precipitation, leading to weather-related hazards. To systematically study LBFs over LV, we developed an Observation-based Lake Breeze Detection Algorithm (OLBDA), which objectively identifies LBF passages using 15-minute data from 45 automatic weather stations across Uganda’s extended coastal region over six years (2017–2022). Focusing on daytime (0900–1900 LT) during dry months (December–February and June–August), the algorithm applies thresholds based on the lowest (highest) 30% of temperature drops (dew point and wind speed increases), enabling detection of subtle meteorological changes associated with LBF passage. Validation against visible satellite imagery shows strong performance with high accuracy (ACC = 0.76) and detection rate (POD >0.7), and low false alarms (POFA = 0.2). Application of the OLBDA revealed that LBFs predominantly occur from early afternoon to evening, peaking at 1300 LT near the coast and shifting inland with distance. LBFs were observed on 48% of days, with December–February and January having the highest seasonal and monthly frequencies, respectively. LBF propagation speed varied in relation to the background wind in different hinterland sectors of LV, with fastest movement westward at 8 ms −1 and slowest northward at 5.8 ms −1 . LBFs penetrate up to ~130 km inland. The OLBDA offers a robust framework for analyzing LBFs, providing valuable insights for disaster preparedness and climate mitigation in the Lake Victoria Basin.

Abstract Rainfed crops account for approximately 40% of India’s food production and support 60% of its livestock. Although linked to oceanic monsoon rainfall, their productivity also depends on terrestrially-sourced rainfall, particularly in the non-monsoon season. The degree to which rainfed crops rely on moisture sourced from evaporation in upwind irrigated areas remains largely unknown. Using a combination of models and observations, we show that evaporation from upwind irrigated crops contributes 7% (mean) ±5% (spread) of the rainfall over rainfed areas annually, rising to 15%±10% during the pre-monsoon months (averaged over the years 2000–2020). In the absence of this input, water stress experienced by rainfed crops can increase by 5–10% during the crucial mid to late crop growth phases, potentially affecting yields. Our results reveal an unrecognized atmospheric link between irrigated and rainfed agriculture that is overlooked in current agricultural policies. Planning and managing these systems holistically can help strengthen regional food and water security under future climates.

This study presents a framework for nowcasting and forecasting urban air pollution using meteorological and wind profile data. It is designed to support urban air-quality management and to monitor the durability of the built environment within smart city digital twins. The proposed framework employs the Series-cOre Fused Time Series forecaster (SOFTS) as the primary predictive model. Its performance is evaluated using data from Lamezia Terme, Italy, under both fully informed and missing/reconstructed data scenarios. For benchmarking purposes, two widely used machine-learning approaches — extreme gradient boosting (XGBoost) and long short-term memory (LSTM) — are also considered. Numerical results demonstrate the accuracy and robustness of the framework. In real-time nowcasting scenarios at Lamezia, the SOFTS model achieved coefficients of determination (R2) exceeding 0.91 and weighted mean absolute percentage errors (WMAPE) below 21% for all target pollutants, outperforming the XGBoost benchmark. For short-term forecasting up to 6 h, the model maintained strong predictive skill, particularly for particulate matter, with R2 values above 0.70 at the sixth hour. Results from the independent Cabauw site in the Netherlands further support the generalizability of the framework, with R2>0.97 for nowcasting and R2>0.62 at the 6-hour forecasting horizon. Model interpretability is also examined, and the results are discussed in relation to the existing literature. Overall, the framework advances short-term urban air-pollution prediction for smart city digital twin applications.

Arctic sea ice concentration (SIC) serves as a key geophysical parameter for monitoring sea ice variability and constraining numerical weather and climate models. Although deep learning approaches that integrate active and passive microwave data have recently gained attention, most simply concatenate features from different sensors, ignoring the physical dependencies and spatial–temporal co-structures among radiative, scattering, and environmental driving features, which limits the physical consistency and spatial generalization ability of the retrieval. To address these challenges, this paper proposes a framework for SIC estimation via physical information-guided multi-source data fusion and spatial continuity preservation (PIMS-Net). PIMS-Net designs a physically decoupled three-branch encoder structure to independently extract layered features of passive microwave brightness temperatures, active microwave backscattering signatures, and meteorological reanalysis data. Subsequently, a physical-driven residual correction module (PRCM) is introduced at the multi-scale feature level to explicitly guide the physical correction of passive microwave features with active microwave and meteorological features in latent feature space. Further, a spatial continuity modeling module (SCMM) is embedded in the encoding stage to capture the spatially continuous changes in the sea ice field, enhancing the structural consistency and detail resolution ability of the model in the ice edge and thin ice areas. Experiments on the AI4Arctic Challenge dataset indicate that PIMS-Net achieves better results than the compared baseline models, with a spatial resolution of 80 m, with a mean square error of 0.0138 and a coefficient of determination of 93.5%. • A continuity-aware deep framework is proposed for Arctic SIC retrieval. • PRCM enables physics-guided fusion of multi-source observations. • SCMM captures spatial continuity and regional transitions in SIC fields. • The method improves SIC accuracy and physical consistency over baselines.

Abstract. Understanding the evolving patterns of intense rainfall in the Mediterranean under climate change is an urgent challenge. Focusing on southern Italy, a representative sub-region of the Mediterranean basin, we examine in detail the role of sea-atmosphere-orography interactions, particularly the impact of increasing sea surface temperatures (SSTs), in enhancing heavy precipitation despite overall drying. Twenty consecutive precipitation events, identified during a particularly intense rainy season (September–December 2019), are reproduced at convection-permitting resolution (2 km) using the Weather Research and Forecasting (WRF) model forced by ERA5 reanalysis boundary conditions. Two additional scenarios are then tested: one with past SST levels approximating those of 1980, and another with SST increases consistent with end-of-century Shared Socioeconomic Pathways (SSPs), such as SSP 3-7.0 and SSP 5-8.5. The WRF simulations accurately reproduce cyclone tracks and precipitation patterns, indicating that, with all other factors held constant, increased SSTs could boost the frequency of heavy rainfall over land by intensifying otherwise weaker events. However, for the most intense events analyzed, spatially averaged accumulated precipitation over land remains largely unchanged because the heaviest precipitation occurs over the sea. The study highlights the value of high-resolution, convection-permitting simulations for capturing complex processes in orographically challenging regions and helps shed light on the seemingly contradictory coexistence of increasing daily precipitation extremes and declining annual precipitation totals in Southern Europe.

Abstract. An accurate representation of biomass burning aerosol emissions is essential in Earth System Models to capture aerosol properties and reduce uncertainties in their interactions with radiation and climate. Sources of wildfire smoke include both widespread prevalence of numerous small fires and more extreme episodic events, such as the unprecedented Californian wildfires of September 2020. Our global modelling study evaluates how well aerosol emissions from extreme wildfires are captured in the UK Earth System Model (UKESM), alongside those from other fires. Running with daily emissions from Global Fire Emission Database v.4.1s (GFED4.1s) enables a realistic simulation of the thick smoke plumes from the Californian fires and large boreal fires more generally, with little overall mean bias error (−0.08) in aerosol optical depths (AODs) between UKESM and collocated VIIRS observations (Western US, September 2020). Modelled AODs were biased low across other regions in 2020 (e.g. savannah, mean bias error = −0.48) dominated by fires with lower fuel consumption, unless emissions were scaled up by a factor of 2 (mean bias error = −0.15). We therefore develop a means of selectively scaling up aerosol emissions from GFED4.1s pixels with lower area-averaged daily dry matter consumption (DM) and not scaling those with higher daily DM, associated with extremely large or intense fires. Applying daily rather than monthly-mean emissions was also found crucial in capturing the spatial and temporal variability of AOD and instantaneous radiative forcing (IRF) during extreme events. These approaches ensure both means and extremes in biomass burning smoke are well represented.

Land use and land cover changes (LULCCs) influence air quality via modifications in local meteorology and natural emissions, yet their future impacts and pathway contributions remain inadequately quantified. Here, we employed an online coupled meteorology–chemistry model to assess the effects of LULCCs by the mid-21st century on O3 and PM2.5 in China, and to disentangle the roles of meteorological influences versus biogenic volatile organic compound (BVOC) emission changes. Our results show that, with anthropogenic emissions and meteorological fields fixed at current conditions, LULCCs under SSP1-2.6 and Afforestation scenarios (characterized by forest expansion) induce a summer cooling of 0.04 °C and 0.09 °C in China but raise summertime daily maximum 8-h O3 by 1.8 µg/m3 and 4.7 µg/m3 due to the dominance of BVOC-driven enhancement. Meanwhile, afforestation triggers north–south and seasonal variations in PM2.5 changes: winter decreases in the north but increases in the south, with the pattern reversing in summer, resulting in a net national increase. Conversely, deforestation under SSP5-8.5 would cause warming but reduce BVOC emissions, slightly lowering summer O3 (−0.8 µg/m3) and winter PM2.5 (−0.1 µg/m3) across China. These findings underscore the potential importance of incorporating land-use strategies to support future integrated climate and air quality governance.

Abstract. Some global atmospheric chemistry modeling applications assume that intra-month variability in anthropogenic emissions averages out at monthly timescales. To systematically quantify the impacts of resolving daily and hourly emissions, we use a global model with a refined ∼ 14 km resolution over the contiguous United States (CONUS; MUSICAv0) and a regional CONUS inventory for July 2018. Switching from daily to hourly nitric oxide (NO) emissions (typically higher during the day and lower at night) yields contrasting spatial responses in nitrogen oxides (NOx≡ NO+ nitrogen dioxide (NO2)) and ozone (O3) concentrations in the western versus eastern CONUS and in urban versus rural areas. Neglecting hourly variations in CONUS NO emissions leads to grid-cell level discrepancies in monthly mean surface O3 concentrations of −22 % to +11 % (−7 to +5 ppb) and surface NO2 of −49 % to +86 % (−1 to +8 ppb), with tropospheric NO2 columns showing similar spatial patterns (−12 % to +56 %). While comparable in magnitude to a uniform 30 % NO emission reduction (grid-cell level surface O3 differences of −12 % to +9 %, −7 to +3 ppb), the spatial response patterns differ with location-specific timing of emissions and meteorology. For example, Los Angeles shows higher morning NOx concentrations and stronger NOx-saturated O3 suppression relative to New York City. A simple scaling analysis suggests that neglecting hourly emissions variability can bias NOx emissions inferred from monthly mean tropospheric NO2 columns, with absolute relative differences ranging from ∼ 1 % to ∼ 56 % within individual model grid cells.

Abstract Arctic sea ice radiative forcing remains incompletely quantified because of persistent observational gaps in ice surface temperature (IST) during polar night, when temperature-driven radiative effects are most active in the surface energy balance. The mid-2000s Arctic transition further raises the question of whether same-season coupling between atmospheric circulation and the radiative response has persisted or weakened. We address these issues by reconstructing gap-free IST fields for 1988–2023 using Automated Machine Learning to gap-fill MODIS observations, validated against Ice Mass Balance buoys (R = 0.86; RMSE = 4.33 K; bias = −2.35 K). This enables year-round assessment of sea ice radiative forcing (SIRF). Temperature-driven and albedo-driven SIRF exhibit full-period trends of 0.29 ± 0.02 and 0.13 ± 0.01 W m⁻² yr⁻¹ respectively; in seasonally consistent windows winter temperature-SIRF (0.40–0.55 W m⁻² yr⁻¹) is comparable to summer albedo-SIRF, indicating that temperature-driven forcing is not negligible in the Arctic radiative balance. Change-point analysis identifies a statistically significant transition around 2004–2005 (Pettitt test, p < 0.05 under an AR(1) Monte Carlo reference), marking a shift to predominantly positive SIRF anomalies. Empirical Orthogonal Function (EOF) analysis documents a reorganisation of the dominant DJF sea level pressure mode across this transition, with the leading mode's variance fraction increasing from 52.5% to 62.8%. Multiple regression of SIRF anomalies onto the leading PCs shows a spatial contraction of the statistically significant atmosphere–SIRF coupling: pre-2005 coupling (β* ≈ ±0.2 to ±0.6) is retained over the Barents–Kara–Laptev–East Greenland sector but largely disappears over the Pacific-sector and Central Arctic after 2005. This sector retains positive SIRF anomalies despite the post-2005 shift toward anticyclonic SLP — a regional co-occurrence we term feedback entrenchment.

Abstract The stratospheric temperature has continued to decline over the past half‐century, in contrast to the steady increase in the tropospheric temperature. However, the future changes in stratospheric temperature under anthropogenic influences, as well as the specific role of stratospheric interactive chemistry in their attribution and projection, remain uncertain. Here, we show that current climate models generally underestimate (overestimate) the observed temperature trends in the lower (upper) stratosphere. These temperature biases may lead to an overestimation of upper tropospheric warming, particularly in models that lack interactive chemistry. By applying observational constraints, we demonstrate that interactive chemistry models can reduce the uncertainty in future lower stratospheric temperature projections by approximately 15% compared to non‐interactive chemistry models. In conclusion, these results highlight the dominant role of anthropogenic forcing in shaping stratospheric temperatures and underscore the importance of incorporating interactive chemistry in global climate models to improve the reliability of projections.

Abstract Mesoscale eddies play a key role in redistributing oceanic heat and salt, yet their global three‐dimensional (3D) transport structures remain poorly understood. Here, we estimate global meridional eddy heat and salt transports in the upper 1,800 m from satellite altimeter and Argo observations, revealing distinct vertical structures. Eddy heat transport reverses with depth, being poleward in the upper ocean but equatorward below, resulting in strong vertical cancellation and a weak net contribution to total meridional heat transport. In contrast, eddy salt transport exhibits pronounced latitude dependence, weakly equatorward at low latitudes but strongly poleward at mid‐latitudes, where it contributes nearly half of the total meridional salt transport. These contrasting behaviors primarily arise from differences in background temperature and salinity gradients, consistent with down‐gradient eddy fluxes. Our results provide novel observational insights into the global 3D structures of eddy heat and salt transports, offering a benchmark for evaluating ocean and climate models.