Atmospheric and Oceanic Sciences

Abstract This overview of the voluminous wildfire literature addresses the questions: “How seriously is the current spate of wildfires impacting the boreal forests and the forests of the western United States? Is the current rate of burning of these forests unprecedented? Has it been systematically increasing over the past few decades and, if so, is it in response to human-induced global warming?” The article contains diagrams and tables based on a suite of fire-related datasets: satellite measurements of burned area, fire-related carbon emissions, and aerosol optical depth, dating back to around the year 2000, and ground-based records of area burned by fires in the western US. The graphics and tables reveal the remarkable extent of the forested area burned. The average area burned per year has increased by roughly a factor of three since the year 2000. The area burned by severe fires with areas >100 km 2 has been increasing more rapidly than the area burned by smaller fires. Wildfire smoke has also increased appreciably over much of North America. Forested burned area and fire-related carbon emissions are highly sensitive to measures of aridity on time scales ranging from days to decades. Aridity has increased, mainly in response to the warming trend, which is comparable in magnitude to that in climate model simulations of the response to rising greenhouse gas concentrations. It can thus be concluded with a high degree of confidence that the rapid increase in forested areas burned by wildfires is mainly due to human-induced global warming.

Abstract New particle formation is an important source of Arctic atmospheric particles and cloud condensation nuclei, yet their precursor sources and molecular-level mechanisms remain poorly understood. Here we report comprehensive ship-based observations from 19 May to 26 June 2022 from southeastern to western Greenland and into the Davis Strait’s marginal ice zone to investigate sources and processes controlling atmospheric particles and cloud condensation nuclei. Our observations provide field evidence of frequent nucleation events driven by the multicomponent iodine oxoacid and sulfuric acid mechanism recently identified in laboratory studies. Newly formed particles grew rapidly beyond 20 nm on 8 out of 13 nucleation days, mainly driven by oxygenated organic molecules from aldehyde and monoterpene oxidation. We also report a previously unobserved class of iodine-containing oxygenated organic molecules that contributed to particle growth and enhanced cloud condensation nuclei formation. We show that marginal sea ice zone produces precursors that drive rapid new particle formation and enhance cloud condensation nuclei concentrations by up to 50-fold. Our findings demonstrate that Arctic iodine, sulfur and organic precursors can enhance cloud condensation nuclei abundance through new particle formation, highlighting a potential but unquantified pathway for influencing cloud cover, radiative balance and the hydrological cycle.

Abstract Tropical Cyclones (TCs) damage assets and threaten populations globally, multiple times a year. Forecasting products from meteorological agencies across the world can help anticipate their likely path, intensity and broad regional impact. They are critical in informing safety warnings and potential evacuation measures at the county scale. These products are not designed to represent experience on the ground at a scale characteristic of individual neighborhoods (i.e. the local scale, ~10 3 m). This limits their usability for granular decision making. Local-scale simulations are achievable using full physics numerical weather prediction models, but the associated computational requirements typically allow for only a handful of deterministic simulations to be performed in real-time. This restricts their use in applications requiring probabilistic information. Recent developments from AI based weather forecasting models provide vastly more efficient TC forecasting solutions that can run simulation ensembles to provide probabilistic information in real-time. Yet these are fundamentally limited by the resolution of the data they train on, which currently fails to represent the local scale. We here introduce LiveCyc, a machine learning approach that can augment any TC track and intensity forecast with a probabilistic local-scale wind forecast. After an overview of the algorithms forming LiveCyc, we introduce an extensive dataset of historical back tests. Using this dataset, we show the value of LiveCyc in informing local-scale decision making in the days before a TC makes landfall. In particular, we demonstrate how objective cost-saving optimization can calibrate and automate the triggering of protective actions ahead of a TC event.

Abstract The strength of the Atlantic Meridional Overturning Circulation (AMOC), in terms of streamfunction, is considered to depend partly on salt supply from the Indian Ocean through Agulhas Leakage (AL). In turn, AL is regulated by the position of the Southern Ocean subtropical front, which creates the pathway of the leaking around the African continent. Here, to evaluate the links between the subtropical front, AL and AMOC, we present latitudinal shifts of the subtropical front, and thereby potential AL changes, using dinoflagellate cysts and biomarkers in sediment cores from the Agulhas Plateau covering the late Pliocene (3,600–2,580 thousand years ago (ka)). We subsequently compare our frontal migration and AL reconstruction with ocean surface and bottom records along the AMOC pathway, and climate model simulations, to evaluate the influence of the AL on the AMOC. We find that the northward migration of the subtropical front gradually reduced the volume and salt supply of AL from 3,600 ka to 3,300 ka, whereas the AMOC intensified. We conclude that the causal link between the AL and AMOC was disconnected during the late Pliocene, challenging the presumed role of the AL in modulating the AMOC.

City-scale, source-resolved methane (CH4) inventories are needed in China because prefecture-level cities differ in energy systems, agricultural activities, and waste management. These differences lead to divergent dominant sources and mitigation needs. Yet city-level CH4 inventories across the energy, agriculture, and waste sectors remain scarce. This scarcity limits policy design, targeting, and evaluation. Here we develop a harmonized, annually consistent, multisector, source-resolved CH4 inventory for 339 prefecture-level cities in China during 2018-2024. We further use Logarithmic Mean Divisia Index (LMDI) driver attribution and scenario analysis to examine recent emission drivers and explore possible mitigation pathways. Over this period, national anthropogenic CH4 emissions increased by 2.61%, with regional heterogeneity shaped by differences in energy dependence, livestock management, and waste treatment. LMDI decomposition identifies economic activity as the dominant positive driver. Emission-intensity effects alternated between mitigating and reinforcing impacts, while structural effects were minor and population effects varied across space and time. Scenario simulations suggest that integrated multisector strategies could reduce national CH4 from 61.69 Mt in 2024-51.26 Mt in 2030 and 22.26 Mt in 2060, corresponding to a 63.9% reduction. These reductions are primarily driven by energy-sector measures, complemented by improvements in agriculture and waste management. This data set and attribution inform city-specific mitigation planning and source targeting, support monitoring, reporting, and verification, and provide a baseline for benchmarking progress toward national methane targets.

Abstract Flood risk is correlated in space and time, challenging insurance systems that rely on diversification across assets. Financial instruments governing flood coverage are typically structured as 1–5‐year contracts, exposing portfolios to climate‐driven risk at interannual‐to‐decadal scales. Yet existing tools address climate risk either through seasonal forecasts extending only months or multidecadal projections misaligned with fiscal horizons, leaving a critical gap in actionable flood risk simulation. We introduce a multisite flood simulation framework combining attention‐based analog retrieval with stochastic generation of multivariate flood frequency, intensity, and duration sequences. Applied to over 100 sites in the Mississippi River Basin, the model produces spatiotemporally coherent flood portfolios conditioned on interannual climate variability. Explainable AI attribution paired with wavelet analysis links simulated clustering to large‐scale climate drivers, yielding physically interpretable flood clusters for portfolio‐scale loss simulation. The framework provides plausible, out‐of‐sample flood risk catalogs for interannual‐to‐decadal insurance risk assessment and financial planning.

Mudslides are sudden and highly destructive; their source areas typically undergo slow, millimeter-scale creep over a period of months or even years before destabilization. If these precursor signals can be detected, valuable time can be gained for disaster prevention and mitigation. However, in the weathered crust and residual deposits of potential debris flow source areas, the long-term coupled action of freeze–thaw cycles and rainfall causes continuous reorganization of internal particle contact force chains, generating weak, metastable creep signals. The high-order nonlinearity and spatial heterogeneity of the interference phase gradient in low-coherence regions lead to pixel-spanning jumps in the unwrapped phase that are blurred by integer multiples of π. The high rate of phase jumps between adjacent pixels severely hampers the early detection of weak deformation. To address this, we propose a method for the early detection of weak deformation in potential debris flow source areas based on phase-unwrapping convolutional neural networks and long-time-series InSAR technology. First, we use long-time-series InSAR technology to construct a spatiotemporal map of interferogram sequences and establish feature propagation paths between high- and low-coherence interferogram pairs using the coherence coefficient as an edge weight. Second, we design a phase-unwrapping graph convolutional network that aggregates phase gradient information from neighboring nodes through two graph convolutional layers to correct the unwrapping results of low-coherence interferogram pairs and suppress cross-pixel jumps caused by π-integer-multiple blurring. Finally, by combining a dual-criterion classification approach based on temporal attention scores and deformation acceleration, the method captures the complete evolutionary process from stable creep to accelerated deformation. Experimental results show that the maximum phase jump rate of this method is approximately 0.02, effectively resolving the phase jump issue caused by high-order nonlinear gradients; in some areas of the study region, where deformation ranges from −1 mm to −9 mm, the inversion error is consistently controlled within ±1 mm. A total of five potential debris flow source areas were identified, classified by creep stage as follows: one in the accelerated deformation stage, two in the stable creep stage, and two in the early creep stage. No significant surface failure occurred in any of these source areas. This method provides reliable technical support and a decision-making basis for refined early warning, disaster prevention, and mitigation of debris flow hazards and holds significant engineering application value.

Abstract The objective of this paper is twofold. First, it documents the second version of the global atmospheric model ARP-GEM and its calibration at kilometer-scale resolution. The model is currently able to run simulations at a resolution of up to 1.3 km. Second, this paper focuses on multi-year global atmospheric simulations at a 2.6 km resolution with and without parameterized convection and associated calibration. Simulations without deep convection tend to be similar to those with infinite, or at least large, entrainment values. Consistently, entrainment and detrainment can be used as primary drivers for the gradual reduction of convection as resolution increases. The results indicate that, in this hydrostatic model, parameterized convection still plays a significant role in the correct representation of the mean state at the kilometer scale. Additionally, they suggest some added value of high resolution in representing climate variability. However, a compromise between the adequate representation of the mean state and variability is necessary, as both are differently favored by the degree of parameterized convection.

Linking abiotic forcing to coherent biotic responses over Phanerozoic time remains difficult to quantify because both environmental drivers and biological dynamics are heterogeneous, irregularly sampled, and affected by proxy and geochronological uncertainty. Here we introduce a regime-based description of the climate-carbon system in which Phanerozoic evolution is organized into a small number of recurring, long-lived mega-climate states separated by relatively sharp transitions. Using recurrence diagnostics on paired carbon and oxygen stable isotope records, together with complementary early-warning indicators, we identify five persistent regimes and their boundaries. We interpret the resulting structure with a conceptual climate-carbon cycle model that admits multiple coexisting equilibria, providing a mechanistic basis for state changes that switch between stable attractors under changing environmental forcings and perturbations. We connect these results to a biosphere vulnerability metric that incorporates biotic turnover with sample-standardized global diversity to show that vulnerability is systematically elevated near major regime transitions. These results suggest that transitions between persistent climate-carbon attractors provide a dynamical context for why some perturbation intervals coincide with heightened biospheric stress, beyond what is expected from more gradual background environmental changes alone.

Abstract Forest disturbances are rising globally due to climate change and land-use pressures, weakening the terrestrial carbon sink. However, declines in aboveground biomass from tree cover loss remain poorly quantified, limiting our understanding of the role of disturbances in global carbon dynamics. Here we present a spatially explicit estimate of aboveground biomass losses from natural disturbances and harvest across Europe’s 216 million hectares of forests, using satellite remote sensing. From 1985 to 2023, gross aboveground biomass losses totalled 6.5 ± 0.8 Pg. Of these, 18% were caused by high-severity natural disturbances, whereas stand-replacing harvests accounted for 82%. From 2018 onward, aboveground biomass losses increased by 46% reaching annual values unprecedented in the preceding four decades. This acceleration coincided with high natural disturbance activity in biomass-rich temperate forests. After 2018, the sensitivity of aboveground biomass loss to disturbance area increased substantially, suggesting that even small increases in natural disturbances can result in large losses. As climate-driven natural disturbances intensify, we thus expect sustained aboveground biomass losses across Europe, despite policies to enhance the forest carbon sink until 2030.