Widespread lake drainage can lead to large-scale drying in Arctic lake-rich areas, affecting hydrology, ecosystems and permafrost carbon dynamics. To date, the spatio-temporal distribution, driving factors, and post-drainage dynamics of lake drainage events across the Arctic remain unclear. Using satellite remote sensing and surface water products, we identify over 35,000 (~0.6% of all lakes) lake drainage events in the northern permafrost zone between 1984 and 2020, with approximately half being relatively understudied non-thermokarst lakes. Smaller, thermokarst, and discontinuous permafrost area lakes are more susceptible to drainage compared to their larger, non-thermokarst, and continuous permafrost area counterparts. Over time, discontinuous permafrost areas contribute more drained lakes annually than continuous permafrost areas. Following drainage, vegetation rapidly colonizes drained lake basins, with thermokarst drained lake basins showing significantly higher vegetation growth rates and greenness levels than their non-thermokarst counterparts. Under warming, drained lake basins are likely to become more prevalent and serve as greening hotspots, playing an important role in shaping Arctic ecosystems.

The Arctic is warming far faster than the global average, threatening the release of large amounts of carbon presently stored in frozen permafrost soils. Increasing Earth’s albedo by the injection of sulfate aerosols into the stratosphere has been proposed as a way of offsetting some of the adverse effects of climate change. We examine this hypothesis in respect of permafrost carbon-climate feedbacks using the PInc-PanTher process model driven by seven earth system models running the Geoengineering Model Intercomparison Project (GeoMIP) G4 stratospheric aerosol injection scheme to reduce radiative forcing under the Representative Concentration Pathway (RCP) 4.5 scenario. Permafrost carbon released as CO₂ is halved and as CH₄ by 40% under G4 compared with RCP4.5. Economic losses avoided solely by the roughly 14 Pg carbon kept in permafrost soils amount to about US$ 8.4 trillion by 2070 compared with RCP4.5, and indigenous habits and lifestyles would be better conserved.

Climate change is the challenge of the century, and achieving the goals of the Paris Agreement will require worldwide cooperation and mutual effort. Over 120 countries have made their net-zero commitments, and quantifying the social cost of carbon (SCC), i.e. the climate damage caused by an additional ton of CO₂ emissions, under a carbon-neutral pathway would provide a carbon price benchmark for policymakers. Here, we set in detail the emission trajectories of different jurisdictions under a carbon-neutral pathway based on the submitted nationally determined contributions. We then assess global and regional warming, climate change damages, and the SCC with the Policy Analysis of Greenhouse Effect integrated assessment model. We find a peak warming of about 2.1 °C relatives to pre-industrial levels in this century under our carbon-neutral emission pathway. And even if all countries meet their carbon-neutral commitments, this would not be sufficient to limit global warming to 1.5 °C relative to pre-industrial levels. We compare the SCC using fixed discount rates, dynamic discounting, and an equity weighting approach. Notably, the introduction of equity weights would increase the estimated SCC from 79 (11–186) to 291 (83–592) US$ per tCO₂. Climate change damages will be borne primarily by warmer and poorer countries, and this profound inequality would likely undermine efforts to eradicate extreme poverty. Statistics on current carbon taxes and carbon trading prices show that they are notably lower than global or even regional SCCs, suggesting that the current system does not adequately reflect the global externalities of CO₂ emissions. More studies are needed to assess the equity aspects of climate change impacts, to help refine mechanisms to align domestic interests with global interests, and to facilitate the implementation of national carbon-neutral commitments in place.

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.

Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. Recently, the ICESat-2 and GEDI spaceborne LiDAR missions have significantly advanced global canopy height mapping. However, due to inherent sensor limitations, their footprint-level estimates often show systematic bias. Tall forests tend to be underestimated, while short forests are often overestimated. To address this issue, we used coincident G-LiHT airborne LiDAR measurements to correct footprint-level canopy heights from both ICESat-2 and GEDI, aiming to improve the canopy height retrieval accuracy across Puerto Rico’s tropical forests. The bias-corrected LiDAR dataset was then combined with multi-source predictors derived from Sentinel-1/2 and the 3DEP DEM. Using these inputs, we trained a canopy height inversion model based on the AutoGluon stacking ensemble method. Accuracy assessments show that, compared to models trained on uncorrected single-source LiDAR data, the new model built on the bias-corrected ICESat-2–GEDI fusion outperformed in both overall accuracy and consistency across canopy height gradients. The final model achieved a correlation coefficient (R) of 0.80, with a root mean square error (RMSE) of 3.72 m and a relative RMSE of 0.22. The proposed approach offers a robust and transferable approach for high-resolution canopy structure mapping and provides valuable support for carbon accounting and tropical forest management.

Circum-Arctic permafrost stores large amounts of frozen carbon that must be maintained to avoid catastrophic climate change. Solar geoengineering has the potential to cool the Arctic surface by increasing planetary albedo but could also reduce tundra productivity. Here, we improve the data-constrained PInc-PanTher model of permafrost carbon storage by including estimates of plant productivity and rhizosphere priming on soil carbon. Six earth system models are used to drive the model, running G6solar (solar dimming) and G6sulfur (stratospheric sulfate aerosols) experiments, which reduce radiative forcing from SSP5-8.5 (no mitigation) to SSP2-4.5 (substantive mitigation) levels. By 2100, simulations indicate a loss of 9.2 ± 0.4 million km² (mean ± standard error) of permafrost area and 81 ± 8 Pg of soil carbon under the SSP5-8.5 scenario. In comparison, under SSP2-4.5, G6solar, and G6sulfur, permafrost area loss would be mitigated by approximately 39 %, 37 %, and 34 % and soil carbon loss by 42 %, 54 %, and 47 %, respectively, relative to SSP5-8.5. Uncertainties in permafrost soil C loss estimates arise mainly from changes in vegetation productivity. Increased carbon flux from vegetation to soil raises soil C storage, while the priming effects of root exudates lowers it, with a net mitigating effect on soil C loss. Despite model differences, the protective effects of G6solar and G6sulfur on permafrost area and soil C storage are consistent and significant for all ESMs. G6 experiments mitigate ∼ $\mathrm{1}/\mathrm{3}$ of permafrost area loss and halve carbon loss for SSP5-8.5, averting USD 0–70 trillion (mean of USD 20 trillion) in economic losses through reduced permafrost emissions.