Climate Central

Ocean alkalinity enhancement (OAE) is an approach for the deliberate removal of CO2 from the atmosphere. This emerging technology relies on human intervention to increase the alkalinity of seawater which, in turn, induces a net flux of atmospheric CO2 into the ocean. OAE is considered comparatively scalable and promises to deliver durable carbon removal but, to the best of our knowledge, a detailed feasibility study has not been undertaken. Key aspects to consider in such an analysis are: 1) how much alkalinity can be added at coastal outfall sites before breaching regulatory and geochemical constraints on seawater pH and carbonate saturation state, and 2) whether delivery of alkalinity can be achieved with a sufficiently low carbon footprint. We present results addressing both questions using the coast of Nova Scotia in eastern Canada as a test case. The first operational deployment of OAE started in Halifax Harbour, Nova Scotia, in October 2023. By October 2025, the climate-tech company Planetary Technologies had retired carbon credits for the removal of 1,800 tons of CO2. We ask what it would take to scale up from an annual net carbon removal of 1000 t/y of CO2 to 1 Gt/y, which is commonly considered the threshold for a carbon removal technology to be scalable. Specifically, we analyze the maximum CDR capacity of individual outfalls since this will meaningfully influence deployment strategies for OAE. Our results are based on a suite of simulations using a coupled circulation-biogeochemical model for Halifax Harbour that has been used to support OAE field work and verification of OAE credits and a prospective Life Cycle Analysis conducted for this site.

Abstract Accurate wind speed forecasting is essential for daily activities and socio‐economic production. This study employs an explainable machine learning (ML) model to correct wind speed forecasts from a numerical weather prediction (NWP) model over weather stations across lead times of 1–24 hr in Zhejiang Province, China. Eleven meteorological variables derived from the NWP model are used as predictors. The results demonstrate that ML model substantially improves the original NWP forecasts, reducing the root mean square error of approximately 20%–40% at most stations and about 10% at certain coastal stations. The corrected forecasts also consistently achieve higher Kling‐Gupta Efficiency scores and Forecast Accuracy across all lead times. To enhance model interpretability, we apply Split Count and SHapley Additive exPlanation analyses. The results indicate that the friction velocity (UST) is the most influential predictor and reveal a threshold beyond which the ML model's correction performance improves markedly. The ML model performance slightly declines in coastal regions, likely due to the inconsistent representation of UST between the land and water surface in the NWP model. Monthly analysis further shows superior model performance in November and December, when UST values are higher. This study underscores the value of explainable ML models in improving NWP wind forecasts and provides insights for correcting meteorological variables in NWP models.

Abstract We announce updates to the aims and scope of Earth's Future that introduce three Thematic Areas: Climate Impacts, Communities and Resilience, and Sustainable Resource Systems. These additions provide focal points for community building and editorial leadership within the journal's broad, transdisciplinary mission. We describe the motivation for these changes, outline each thematic area, and reaffirm that Earth's Future remains open to submissions across the full scope of Anthropocene science.

Abstract Tornado outbreaks, involving multiple destructive tornadoes in close succession, result from interactions between large-scale atmospheric patterns and mesoscale dynamics. With a warming climate altering atmospheric behavior, the frequency and distribution of outbreak-favorable patterns may change, influencing future risks. Building on our recently introduced methodology for identifying tornado outbreak-favorable atmospheric patterns in a global climate model, we apply the method, for the first time, to future projections (2065–2100) across four Shared Socioeconomic Pathways using the MPI-ESM1-2-HR model. Focusing on May tornado outbreaks in the United States, we find that outbreak-supportive patterns are projected to increase in frequency and expand northward and eastward, beyond their historical geographic focus in the Great Plains and southeastern U.S. This application demonstrates how a dual-variable, threshold-based method can be applied to global climate model projections to investigate event-specific extremes, offering a pathway for assessing future risks of severe convective hazards under a warming climate.

Abstract Observations of atmospheric carbon dioxide (CO 2 ) are expected to play a critical role in monitoring, reporting, and verification designed to track progress toward the reduction goals of greenhouse gas emissions. In this study, we construct a comprehensive regional carbon budget for seven National Climate Assessment regions of the contiguous United States (CONUS) over 2015–2020. We demonstrate that lateral carbon movement across regions, along with their resulting endpoint CO 2 emissions, represent a major contributor to the interpretation of regional net surface‐atmosphere fluxes of CO 2 . When accounting for lateral carbon fluxes, “top‐down” flux estimates from v10 Orbiting Carbon Observatory 2 (OCO‐2) Modeling Intercomparison Project (MIP) agree, within uncertainty, with inventory‐based carbon stock change estimates in six out of seven National Climate Assessment regions ‐ except for the Southwest. The total net carbon exchange from the top down is 925 ± 339 Tg carbon per year (Tg C/year), consistent with the 974 Tg C/year estimate from the inventory. Notably, lateral carbon flows in the form of harvested wood products and riverine carbon burial are responsible for an estimated 16% of the total carbon stock changes occurring across CONUS. Averaged over the seven regions, accounting for lateral transport and their endpoint carbon emissions improves the consistency between inventory and top‐down estimates by 31%. Our study presents a roadmap for reconciling the inventories and top‐down atmospheric inversions at sub‐national scale and highlights the importance of accurate representation of lateral carbon flows for using top‐down estimates with national inventories.

Effectively addressing the climate crisis at scale in a timely manner will require novel engagement strategies that move beyond laboratory findings and policy dictates. In this practice insight, we present the Moving Farm Tour, a movement-based, farm- and community-centered exploration of the intersection of art and culture with agriculture and climate change. Through this model, we highlight the use of dance and creative engagement as tangible mechanisms for learning about, sharing, understanding and creating new perspectives. Additionally, we demonstrate the value of not only bringing science (and scientists) out of the lab, but of establishing a visceral, physical connection with place and community. Our collaborative efforts have resulted in a scalable, replicable model that demonstrates how live, interactive experiences are useful for cross-sector learning, broadening perspectives, fostering community building, and inspiring novel approaches to collaboration that can lead to better outcomes for researchers, industries, communities, and the planet.

Abstract. The boreal forest has experienced the fastest warming of any forested biome in recent decades. While vegetation–climate models predict a northward migration of boreal tree cover, the long-term studies required to test the hypothesis have been confined to regional analyses, general indices of vegetation productivity, and data calibrated to other ecoregions. Here we report a comprehensive test of the magnitude, direction, and significance of changes in the distribution of the boreal forest based on the longest and highest-resolution time-series of calibrated satellite maps of tree cover to date. From 1985 to 2020, boreal tree cover expanded by 0.844 million km2, a 12 % relative increase since 1985, and shifted northward by 0.29° mean and 0.43° median latitude. Gains were concentrated between 64–68° N and exceeded losses at southern margins, despite stable disturbance rates across most latitudes. Forest age distributions reveal that young stands (up to 36 years) now comprise 15.4 % of forest area and hold 1.1–5.9 Pg of aboveground biomass carbon, with the potential to sequester an additional 2.3–3.8 Pg C if allowed to mature. These findings confirm the northward advance of the boreal forest and implicate the future importance of the region's greening to the global carbon budget.

The atmospheric methane (CH4) growth rate surged after 2019, peaking at 16.2 parts per billion per year (ppb year-1) in 2020 before declining to 8.6 ppb year-1 in 2023. Using multiple atmospheric inversions constrained by observation- and model-based prescribed hydroxyl radical (OH) fields and CH4 atmospheric data, we show that a drop of OH radicals in 2020-2021, followed by recovery in 2022-2023, accounted for 83% of year-on-year variations in the CH4 growth rate, the rest being explained by wetland and inland water emissions, which increased between 2019 and 2020-2022 [+8.6 ± 2.6 teragrams of CH4 per year (TgCH4 year-1)] and then decreased between 2022 and 2023 (-9.9 ± 3.3 TgCH4 year-1). Most emission changes from 2019 to 2023 occurred in northern tropical wetlands in Africa and Asia, whereas South American wetlands emissions declined and Arctic emissions increased after 2019.

Abstract The polar ice sheets, their surrounding land fringes and oceans (68 × 10 6 km 2 ; 13% of Earth's surface) are hot spots for carbon cycle perturbation under future climate change due to glacier retreat, rising meltwater fluxes, reduced sea ice, thawing permafrost, warming land‐surfaces and increased precipitation. Here we assess carbon stored and exchanged with the atmosphere (as carbon dioxide and methane) across an expansive bipolar ice‐to‐ocean domain. We show that the polar regions harbor large reserves of carbon stored in sediments, rocks and the ocean, which differ in their reactivity and turnover times: 5,300–22,200 PgC of organic carbon and 5,600–8,600 PgC of inorganic carbon. These carbon reservoirs include potential reserves of marine and subglacial methane hydrate (80–570 PgC), which could become destabilized under future warming scenarios. Oceans (270–360 PgC) and ice sheets (14–96 PgC Greenland, 5,000–21,000 PgC Antarctica) dominate organic carbon stores, with smaller (but regionally important) stocks found in ice sheet land fringes (13–58 PgC). Estimates of natural CO 2 and CH 4 fluxes from these polar regions to the atmosphere present high uncertainty but highlight oceanic CO 2 sinks in Greenland (−110 to −49 TgC‐CO 2 a −1 ) and in the ICE and SPSS biomes of the Southern Ocean (−480 to 55 TgC‐CO 2 a −1 ), with potential CH 4 sources associated with the Greenland Ice Sheet. Such high uncertainty in polar carbon reservoirs and fluxes is important to resolve if future feedbacks between the polar regions, Earth's carbon cycle and climate are to be conclusively determined.