Earths Future最新文献

Bivalve mollusks are vital to coastal economies and food security, yet the impact of ocean warming and acidification on aquaculture remains unclear due to a lack of ground truth data on future production. Most experimental studies rely on short-term, single-factor experiments in stable and food-unlimited environments, making it difficult to provide practical guidance to growers and decision-makers. To address this knowledge gap, we developed a land-based automated system to expose bivalves to future climate scenarios under field-realistic conditions using unfiltered, ambient seawater, assessing survival, growth, reproduction, and next-generation development. Here we present the first results of exposing Pacific oysters and Mediterranean mussels, the two most cultivated species in the Mediterranean area, to present conditions and projected scenarios for the years 2050, 2075, and 2100. For the first time, our results reveal that future warming and acidification conditions have a dramatic impact on the production yield of oysters and mussels. Oysters exposed to conditions projected for 2100 exhibited a 7% reduction in survival and a 40% reduction in growth rate, along with lower reproductive maturity, which in turn negatively affected the early development of their offspring. Mussels are already experiencing summer temperatures above their upper thermal limits, with around 40% mortality observed under current conditions and near-total mortality under those projected for 2050. These patterns reflect sporadic mass-mortality events reported elsewhere in the Mediterranean and indicate that mussel farming in the region could be severely compromised by mid-century. Our results urgently call for the development of adaptation strategies in the Mediterranean.

The desert climate of the Arabian Peninsula (AP), marked by sparse rainfall, extreme temperatures, and frequent dust events, significantly impacts its 80-million population, environment, and economy. Rising temperatures and dust incursions exacerbate these harsh conditions, yet the AP's climate is underrepresented in global climate research. Understanding its variability is crucial for improving predictions on subseasonal-to-seasonal timescales and for developing reliable climate change projections. Existing climate models fail to capture the region's unique environment, topography, and land-use changes, leading to poor representation of key processes like local convection, aridity, and moisture transport. To address these gaps, Saudi Arabia established the Climate Change Center (CCC) in 2022, part of the Saudi Vision 2030 initiative. The CCC aims to study climate variability and project future changes using advanced Earth system models developed in collaboration with international partners. This study presents the CCC's roadmap, focusing on its relevance for global climate research and policymaking, including the Saudi and Middle East Green Initiatives. We also discuss regional uncertainties in the IPCC's climate projections for the AP and highlight the development of high-resolution regional models that account for local atmospheric, land, and oceanic processes. The CCC is developing subseasonal-to-seasonal forecasting systems and drought monitoring tools, alongside user-friendly dashboards to offer stakeholders customized climate data. These tools, set for launch in 2025, will aid informed decision-making in addressing extreme weather events and climate-related challenges in Saudi Arabia.

The summer of 2023 was the most significant wildfire and smoke season on record in Canada. Data from five different satellite instruments going back to 2001 show that Canada and most provinces and territories experienced peak visible-wavelength aerosol optical depth and ultraviolet aerosol index values in 2023. Longer-term, 2023 had the highest number of “smoke” or “haze” reports in weather records by a factor of two compared with the previous record in 1981, and by a factor of seven compared with the 1953–2022 average. These reports show an east-to-west shift in Canada's summer air pollution patterns. Smoke and haze in eastern Canada have decreased since the 1980s because of pollution control measures domestically and in the US. On the other hand, wildfire smoke has increased in the Northwest Territories, British Columbia, Alberta, and Saskatchewan since the 2010s, and is now the main air quality concern in western Canada. Interpreting the analysis here for Canada alongside previous work over the US, there was a shift over North America in summer air quality concerns from the east to the west. Climate model projections suggest more wildfire-driven smoke in the future throughout North America, particularly in the west. In contrast to air pollution from smokestacks and tailpipes that can be addressed at the source through government regulation, a future with more wildfire smoke will require downwind mitigation and will be the responsibility of public health officials.

Earth's snow cover strongly influences the climate system and represents an important resource for agricultural, industrial, and domestic water use. The last decade of snow-focused research has improved our understanding of snow across scales. These efforts have culminated in new snow measurement instruments and methods, operational models for tracking snowpack evolution and forecasting snowmelt, multi-year and international snow and remote sensing field campaigns, and satellite mission proposals to measure snowpack water resources from space, with two submitted to NASA's Earth Explorer AO and the Environment and Climate Change Canada Terrestrial Snow Mass Mission moving closer to a launch opportunity. Yet, shortcomings in each snowpack observation system still exist, including uncertainty in product performance, mission proposal advancement, and synergies across methods. The snow community aims to navigate next actionable steps toward improved and global-scale snow monitoring for climate and human purposes. Building from recent advances in snow research and operations and carrying momentum from the conclusion of the NASA SnowEx field campaigns, NASA's Terrestrial Hydrology Program (THP) sponsored a Community Snow Meeting in August 2024 in Boulder, Colorado, USA, with 200 total in-person and virtual attendees. Meeting objectives were to outline existing and ongoing snowpack monitoring techniques and identify knowledge gaps and recommended next steps for the snow community. We broadly summarize the state of numerous snow science sub-disciplines and share the insights and takeaways from the Community Snow Meeting, focused largely but not exclusively on NASA opportunities, and intended to support ongoing and future pathways toward the next decade of snow research and development.

Irrigation plays a crucial role in the earth system, yet our understanding of irrigation water withdrawal (IWW) remains limited due to the scarcity of spatially explicit data. While process-based models and remote sensing can bridge this data gap, their estimates often fail to capture real IWW and are associated with large uncertainties. Here, we present a knowledge-informed, explainable machine learning framework that combines random forest (RF) with Shapley additive explanations to generate spatially explicit IWW estimates across China. Our framework incorporates irrigation domain knowledge, state-of-the-art irrigated cropland maps, and various socioeconomic, hydroclimatic, and auxiliary factors. RF shows reasonable performance in spatial and temporal cross-validation, achieving a coefficient of determination exceeding 0.85 and a root mean square error below 0.45 km³/year when evaluated against held-out prefecture-level data. The predictions of IWW depth are primarily driven by geographic and knowledge-based predictors, most of which exhibit nonlinear and non-monotonic impacts on model outputs. By integrating the RF model with a temporal downscaling approach, we develop a new gridded IWW product for China (named CIWW1km), which provides monthly IWW depth and volume at 1 km resolution from 2000 to 2020. CIWW1km aligns closely with prefecture-level IWW reports and explains over 85% of the variance in independent IWW observations (i.e., data excluded from training) across over 150 basins and counties. It highlights a rapid increase in IWW in China's arid zone, driven by irrigated area expansions. CIWW1km outperforms existing products and is well-suited for hydrological and climate studies, and water-food nexus analyses.

Despite contributing minimally to global greenhouse gas emissions, Africa is warming faster than the global average and is projected to experience disproportionately severe heatwave-related impacts on ecosystems, human health, and economic activity. Using Coupled Model Intercomparison Project Phase 6 (CMIP6) models, we assess projected changes in compound heatwaves (CHWs, defined as co-occurring daytime and nighttime heatwaves) across Africa at three global warming levels (GWLs; 1.5°C, 2°C, and 3°C) and under three Shared Socioeconomic Pathways (SSP245, SSP370, and SSP585). Our findings reveal a robust intensification of CHWs with increasing GWLs across all scenarios. Even though the projected change is consistent across all three scenarios, under SSP370 the population exposure is higher due to a larger increase in population. Similarly, under SSP585, the Gross Domestic Product (GDP) exposure is higher because of rapid GDP growth fueled by fossil energy. Western, central, and eastern Africa are the most affected, with exposures increasing by tens to thousands of times. Additionally, rare CHWs historically occurring once every 50 or 100 years are projected to become more frequent, potentially recurring every 5–6 years, even under modest 1.5°C warming. We find a strong, statistically significant positive correlation (r > 0.85, p < 0.01) between near-surface temperature and changes in CHW metrics, indicating continued warming will further exacerbate CHWs. Surface energy budget analysis reveals that this projected warming is driven primarily by enhanced net downwelling surface radiation, modulated by enhanced downwelling longwave radiation under clear-sky conditions. Strengthening mid-to upper-tropospheric anticyclonic systems further contribute to warming.

Land-use change is a primary driver of ecosystem degradation in terrestrial biodiversity hotspots, undermining global sustainability commitments. However, tracking progress under the Land Degradation Neutrality (LDN) policy, which aims to balance degradation with restoration, requires a systematic assessment of cumulative trends, a critical underexplored gap. Here, we present the first comprehensive annual assessment of land-use changes in these hotspots (1992–2022) guided by the LDN framework. We found that 9.4% of hotspots experienced land-use change, with rates declining by 0.06 Mha/year (1992–2015) before accelerating to 0.4 Mha/year (2016–2022). While human-driven impacts (e.g., deforestation, agriculture shifts) initially dominated, causing net natural ecosystem losses, post-2015 land-use improvement efforts (e.g., revegetation) increased. Yet, net land-use degradation outweighed improvement, resulting in a land-use debt of 29.1 Mha (0.9% of global hotspots). Driven by tropical deforestation, dryland degradation, agricultural shifts and urbanization, this debt correlated with impaired ecosystem structure and function. For instance, agricultural dynamics strongly correlated with reduced greenness in high-debt continents like Asia and the Americas (r = −0.92 and r = −0.80, respectively). Despite continental-scale greenness stabilization post-2015 (stable NDVI > 92%), these regions showed reversed carbon uptake, with net primary productivity declining to −0.0032 and −0.002 Kg C/m² yr⁻¹, respectively. This vegetation greenness paradox, where stable structural greenness masks functional degradation, reveals that current improvements remain insufficient to offset historical degradation, and that structural stabilization may not guarantee functional integrity. Thus, reversing land-use debt necessitates a locally tailored policy framework that prioritizes functional recovery alongside structural greening to ensure LDN restoration practices deliver ecosystem integrity in these hotspots.

Under global brightening and warming, how intense solar radiation amplifies crop stress via photoinhibition remains poorly understood. Using data from 905 counties in China (1992–2018), we reveal that a 1% increase in dry-heat-intensive radiation (DHR) frequency reduces summer and spring maize yields by 0.36 ± 0.12% and 1.26 ± 0.48%, surpassing declines from other extreme events. High-frequency DHR events depend on strong temperature-VPD (>0.6) and temperature-solar radiation coupling (>0.5). Due to projected increases in DHR frequency, 2021–2050 maize production is expected to change by −4,151 ± 1,472t to −80 ± 28t for summer maize and −1,938 ± 681t to 1,787 ± 620t for spring maize under SSP1-2.6, with similar trends under SSP5-8.5. Although increased precipitation and radiation partially offset losses, the warming effect largely negates these benefits, resulting in production declines of 1.55% for summer maize and 9.35% for spring maize. This study highlights the overlooked role of photoinhibition in yield loss and underscores the urgency of mitigating DHR risks to safeguard agricultural production under climate change.

Marine cloud brightening (MCB) via sea-salt aerosol (SSA) injections is one commonly researched method to cool the Earth either regionally or globally, and potentially reduce impacts of global warming. There is evidence from both high-resolution climate modeling and natural analogs that the introduction of aerosols in the Arctic atmosphere leads to cloud brightening. This study is the first comparison of Arctic MCB using multiple Earth System Models (ESMs). All three models suggest that SSA injection induces cloud and sky brightening that can substantially cool the Arctic. However, uncertainties in aerosol-cloud interactions mean that the SSA mass required for cooling varies greatly between models, a feature which was also found for injections at lower latitudes. We evaluate a possible Arctic MCB scenario in which SSA injection is scaled up over time to maintain near present-day annual-mean Arctic surface air temperature under a moderate greenhouse gas emissions scenario. The MCB cooling of the Arctic successfully maintains Arctic sea ice and, in contrast to our expectation that cooling one hemisphere leads to the large tropical rainfall shifts, we do not see robust precipitation changes outside of the Arctic. The Atlantic Meridional Overturning Circulation (AMOC) is also shown to be maintained but we caution that not all processes driving the AMOC are represented in these ESMs. Finally, we emphasize that we idealize aspects of the SSA injection in these simulations and we do not consider the technical or governance feasibility of deploying Arctic MCB, nor the impacts on coastal communities, ecosystems, and atmospheric chemistry.

This study explores the drivers of urban water use and their spatial-temporal patterns in 142 small and mid-sized cities across the Contiguous United States (CONUS) by analyzing the data directly collected from these cities and using advanced machine learning techniques. We identify five distinguished clusters across CONUS, each showing unique trends of the impact of drivers on water use. We find that socioeconomic factors significantly influence water use in eastern and southwestern cities, while climatic variables such as precipitation and temperature range dominate in central and northwestern regions. Temporal analysis reveals the impacts of major socioeconomic and climatic disruptions on urban water use in the period 2011–2021, including the COVID lockdown, the rapid growth of data centers, and the drought of 2012. In addition, our analysis suggests that economic growth in small and mid-sized US cities continues to be accompanied by rising water use, contrasting with the opposite trend observed in large cities in prior studies. This implies that as smaller cities develop, their water use may increase above current levels until incomes reach a higher threshold, highlighting the need to improve water use efficiency. This study also presents useful insights for developing effective water demand management strategies in response to climatic variability and socioeconomic growth in small and mid-sized cities.