Earths Future最新文献

Land-based mitigation strategies, such as afforestation and avoided deforestation, are critical to achieving the Paris Agreement's goal of limiting global warming to 1.5°C or 2°C. However, the biophysical impacts of anthropogenic land use and land cover change (LULCC), particularly deforestation and afforestation, on extreme weather events in West Africa remain poorly understood at the regional scale. In this study, we present the first high-resolution LULCC experiments (at 3 km resolution, covering 2012–2022) using the advanced fully coupled atmosphere-hydrology WRF-Hydro model system to assess the potential impacts of idealized land use and land management scenarios on extreme events in the West African savannah region. By analyzing 18 extreme weather indices, we show that deforestation significantly affects temperature extremes (up to 0.45 ± 0.04°C), with effects on regional rainfall extremes being approximately twice as pronounced as those on mean rainfall conditions, along with a significant increase in the number of dry days. Conversely, afforestation generally leads to increases in both mean and extreme precipitation, along with fewer dry days and shorter drought durations. Notably, afforestation produces contrasting responses in temperature extremes depending on vegetation type: converting grassland to mixed or evergreen forest reduces extreme heat via increased transpiration, while conversion to savanna or woody savanna may intensify heat extremes due to albedo-induced warming effects.

The UK high-plus-plus (H++) scenario for high-end sea level rise is used in sensitivity testing for significant infrastructure (e.g., nuclear facilities) and forms part of the Environment Agency planning guidance in England. However, the existing H++ scenario, developed as part of the UK Climate Projections in 2009 (UKCP09), does not reflect the latest science knowledge on ice sheet instability processes and has limitations, as revealed in consultations with users of this information. Here, we outline a new, co-produced H++ framework to inform decision-making that involves: (a) screening decisions against an updated H++ storyline that reflects major scientific advances since UKCP09; (b) evaluating adaptation options and damage costs against a wider library of alternative, plausible storylines; and, (c) a decision-exploring initiative to facilitate long-term strategic thinking. Our H++ screening storyline is based on the Intergovernmental Panel on Climate Change Sixth Assessment Report low-likelihood high-impact sea level rise assessment. In response to user needs, all storylines within the H++ framework provide time-continuous, geographically-specific sea level rise projections for the UK to 2300 and information on sea level rise rates. For all UK capital city locations, our screening storyline projects high-end sea level rise greater than: 1 m by 2100; 4 m by 2150; 9 m by 2200; and, 15 m by 2300. At all locations, maximum rates reach over 100 mm/yr. Our H++ framework can be adapted for different climate impact drivers, sectors or regions, and respond to emerging evidence and user feedback, supporting robust adaptation planning and decision-making under deep uncertainty.

This study reviews results from literature to investigate top-down versus bottom-up emission estimates from oil and natural gas (O&NG) operations. Ten years ago, a landmark study by Brandt et al. (2014, https://doi.org/10.1126/science.1247045) found generally higher top-down emissions determinations than industry estimates. Here, we revisit this topic by examining 10 years of peer-reviewed literature that has been published since. A total of 73 results for top-down/bottom-up ratios from 57 articles were included. Most of the published literature focuses on methane emissions, with only 11 articles reporting other O&NG emissions. In the newer literature, 49 (86%) of the studies reported that inventory (bottom-up) emissions underestimated results from determinations based on ambient observations (top-down). This finding is similar to the Brandt et al. (2014) study, which found that 82% of literature reported higher top-down emissions than inventory-based estimates, suggesting little improvement in inventory data accuracy over the past decade despite this discrepancy having been well documented earlier. However, fewer extreme ratio values (top-down/bottom-up) >10 were reported after 2014. A meta-analysis building on carefully selected literature that has been published since 2014 resulted in a mean ratio value of 2.50 ± 0.62, implying that measured emissions were on average 250% of inventory values. For North American countries, the mean ratio values ranged from 2.59 to 3.75, exceeding the global average. The lowest ratio values were observed when United Nations Framework Convention on Climate Change (UNFCCC) reports were used as inventory comparisons to top-down measurements (mean = 1.45); all other inventory types resulted in mean ratios >2.

Changes in rainfall and temperature regimes increasingly threaten global crop productivity, particularly in water-limited regions. Climate-smart agriculture aims to improve yields while minimizing its climate impact, such as from soil greenhouse gas (GHG) emissions driven by microbial activity. From an irrigation perspective, this underscores the need to assess irrigation practices beyond the traditional objectives of maximizing yield and water use efficiency by also considering their climate impact from soil GHG emissions. To address this gap, we frame climate-smart irrigation as a multi-objective optimization problem and derive a dual-index framework for evaluating irrigation practices across productivity, water consumption, and climate impact dimensions. The Marginal Irrigation Water Productivity (MIWP) index quantifies additional yield per unit of irrigation water, while the Marginal Irrigation Climate Impact (MICI) index measures the associated changes in soil GHG emissions. We apply this dual-index framework to wheat and rice field irrigation studies with varying soil GHG compositions, showing its ability to assess irrigation across different crop systems. Crop model simulations further demonstrate how different irrigation practices are mapped within the MIWP-MICI space, where Pareto-optimal solutions highlight trade-offs between productivity and climate impact goals. Our approach provides a consistent, quantitative basis for comparing irrigation across multiple dimensions of climate-smart irrigation.

Urban green infrastructure (UGI) is critical for mitigating fine particulate matter (PM_2.5) pollution, a major obstacle to sustainable urban development. However, the morphological spatial patterns of UGI and their impact on PM_2.5 remain largely unexplored, as most related studies have focused solely on case studies. This study employed morphological spatial pattern analysis to document the national scale spatial distribution of seven UGI morphology space patterns (MSPs) across 288 Chinese cities. It verified the disparities of each MSP under varying geographic conditions and scalar categories. Using advanced interpretable machine learning methods that account for aggregated contribution of location features, the study confirmed the positive role of UGI proportion in mitigating PM_2.5 levels. Significantly, the findings revealed that smaller non-core UGI areas, such as perforation and islet, exert a more pronounced positive impact on reducing PM_2.5. Furthermore, the study explored the potential PM_2.5 risks facing Chinese cities due to temporal changes of UGI. The study results not only fill the gap in UGI research, but also contributes a feasible urban planning method and provide a basis for reducing PM_2.5 to promote sustainable urban development.

Climate change poses a serious risk for the freshwater ecosystem of Western Patagonia and threatens glacial and non-glacial water resources. Here, we model the historical glacio-hydrology of 2,236 catchments across the Western Patagonia, and project climate change impacts through the 21st century. To this end, we develop a novel modeling framework that combines Long Short-Term Memory (LSTM) neural networks with ice-dynamical glacier modeling using the Open Global Glacier Model (OGGM). We evaluate the ability of this hybrid framework to predict streamflow in ungauged basins (PUB) and regions (PUR) through 10-fold cross-validation and compare the results with those obtained with a LSTM model without a glacier component, and two process-based coupled glacio-hydrological models. The hybrid modeling approach outperforms all other approaches in 38% and 44% of the catchments considering PUB and PUR evaluations, respectively. Using our new hybrid approach, we estimate an average regional freshwater flux of 19,815 m³ s⁻¹ for the period 2000–2019, with glacier melt contributing 29% during the summer season. Under a high-emission scenario (Shared socioeconomic pathways 5-8.5), the northern region (>46°S) is projected to experience the largest reductions in runoff, with dry season runoff decreasing by almost 50% by the end of the century. In contrast, runoff increases are projected for glacierized basins in the southern regions, with average relative changes of 10%–25% and a marked seasonality shift. The results highlight the potential of hybrid modeling in glacio-hydrology and provide important information for climate change adaptation in Western Patagonia.

Coastal flooding from tropical cyclone (TC)-induced storm surges is among the most devastating natural hazards in the US. Accurately quantifying storm surge hazards is crucial for risk mitigation and climate adaptation. In this study, we conduct climatology-hydrodynamic modeling to estimate TC surge hazards along the US northeast coastline under future climate scenarios. In this methodology, we generate synthetic TCs for the northeastern US to drive a hydrodynamic model (ADCIRC) to simulate storm surges. Observing their significant effect on storm surge, for the first time, we bias-correct landfall angles of synthetic TCs, in addition to bias-correcting their frequency and intensity. Our findings show that under the combined effects of sea level rise (SLR) and TC climatology change, historical 100-year extreme water levels (EWLs) along the US northeast coastline would occur annually at the end of the century in both SSP2-4.5 and SSP5-8.5 emissions scenarios. 500-year EWLs are also projected to occur every 1–60 (1–20) years under SSP2-4.5 (SSP5-8.5). SLR is the dominant factor in the dramatic changes in the EWLs. However, while in higher latitudes ($��>�40.5�°�$) TC climatology change modestly affect EWLs ( contribution for 100-year and for 500-year EWL changes), in lower latitudes the impact is more significant (up to 40% contribution to 100-year and 55% for 500-year EWL changes). Extending previous methods, the physics-based probabilistic framework presented here can be applied to project future coastal flood hazards under the effects of SLR and storm climatology change for any TC-prone region.

Accuracy and uncertainty of the probability estimates associated with extreme events of long return periods in a typical event attribution context are not well understood, making it difficult to interpret the meaning of event attribution results. This study evaluates the effectiveness of approaches used in event attribution studies for estimating the return periods of hot extremes based on large samples from large ensembles of climate model simulations. We found that, even with large sample sizes, annual maxima of temperature extremes do not fit well to a generalized extreme value (GEV) distribution in the far right tail, leading to an overestimation of return period. However, the maxima of multi-year blocks generally fit well to a GEV distribution, improving the accuracy of exceedance probability estimates for very rare events. For events with shorter return periods, such as those spanning tens of years or less, fitting annual maxima to a GEV distribution can generally still provide robust estimates. Additionally, estimates based on data samples of similar lengths to observational records are less reliable due to limited sample sizes. These findings highlight the need for caution when interpreting event attribution results for events with long return periods.

With the rapid expansion of photovoltaic (PV) technology, the deployment of PV facilities in ecologically fragile areas has raised concerns about its environmental impacts. Although previous studies have used remote sensing data to assess the impact of PV facilities on greenness, there is still a lack of in-depth exploration into the spatial heterogeneity of vegetation changes within these facilities and their driving factors. This study focuses on the Yellow River Basin, a region severely affected by soil erosion, using PV data sets from 2010 to 2021 and 30-m resolution Normalized Difference Vegetation Index to conduct a pixel-level analysis of the environmental impacts of PV facilities. The results reveal that, while 60% of PV-occupied areas exhibited increased greenness post-deployment, only 14% of facilities showed overall greening, and 72% exhibited less greening than their respective control areas. These findings indicate that PV deployment does not result in net greening at the landscape scale. Higher background greenness was associated with better vegetation recovery within PV facilities. Human activities also significantly influenced greenness change within PV facility. The intensity of land disturbance during PV construction may hinder vegetation recovery, but human management post PV construction appears to mitigate previous land uses, leading to vegetation improvement within PV installations. These findings underscore the importance of scientifically planning PV facilities in fragile regions, suggesting that appropriate site selection and management can facilitate sustainable renewable energy development and promote ecosystem restoration.

Vegetation greening, primarily driven by climate change and land management, has been widely reported globally, with China experiencing one of the most pronounced instances of greening in recent decades. Current studies have mainly focused on the temperature effects of changes in land cover types; however, the impacts of vegetation self-greening, characterized by natural physiological processes such as aging and canopy structure development, on land surface temperature (LST) remain unclear. Here, we employed a space-for-time approach to quantify the biophysical effects of vegetation greening on LST (i.e., ΔLST) across China from 2000 to 2018 using multi-year satellite observations. We found that vegetation greening caused significant daytime cooling (−0.36 K), surpassing that of nighttime (−0.07 K) over the study period. Spatially, pronounced cooling dominated lower latitudes, contrasting with slight warming in mid-to-high latitudes, with this warming phenomenon being more evident at nighttime. The latitudinal transition of LST from cooling to warming occurred at around 47°N for daytime, nighttime, and daily mean values. This latitudinal pattern was season-dependent, shifting to its northernmost extent in summer and southward to around 40°N in winter. Data-driven methods revealed that albedo decreased by −0.0035 ± 0.009 in total, contributing to warming in Northeastern China, while evapotranspiration (ET) increased by 37.3 ± 33.9 mm across China, driving cooling in other regions. Our findings highlight the importance of vegetation greening in shaping local climate and advance understanding of vegetation feedback on climate, providing valuable insights for developing targeted land management strategies.