Atmospheric and Oceanic Sciences

Abstract The Palaeocene–Eocene Thermal Maximum (PETM) occurred ~56 million years ago and was characterized by large-scale carbon release and transient warming. Erosion and subsequent burial of terrestrial organic carbon in marine sediments could have sequestered organic carbon during the PETM—stabilizing global carbon and climate systems—yet direct evidence for this process is lacking. Here we present source-specific biomarker records from five globally distributed shallow marine sites and show that vascular plant and soil organic carbon contributed ~40–95% of total organic carbon in coastal sediments during the PETM. This is higher than modern marine sediments (~12–20% of total organic carbon) and indicates greater terrestrial organic carbon delivery to marine environments during warmer climates. We find that terrestrial organic carbon burial fluxes can increase ~10-to-50-fold during the PETM due to enhanced physical erosion and higher coastal sedimentation rates, implying that marine burial of terrestrial organic carbon sequestered excess carbon released during the PETM. Palaeoclimate model simulations do not account for enhanced delivery of terrestrial organic carbon into the marine realm and are thus missing an important carbon sink. Terrestrial organic carbon burial could act as a negative feedback during other hyperthermals and may aid the long-term (>10,000-year) recovery of the Earth system.

Abstract Dynamical Earth System Models are extremely expensive, requiring ∼10 6 core‐hours for century‐scale simulations. This limits climate projections to only a few Shared Socioeconomic Pathways (SSPs) and leaves numerous policy trajectories unexplored. Here, we develop an external forcing boundary‐constrained generative emulator trained on CMIP6 model outputs, enabling rapid projection of temperature, precipitation and sea‐surface height under arbitrary CO 2 forcing pathways. The model learns the continuous manifold of future climate states by conditioning on the physical boundary defined by two extreme scenarios (SSP1‐1.9 and SSP5‐8.5). It can reconstruct climate responses under pathways held out during training (e.g., SSP2‐4.5). We further apply it to four newly constructed CO 2 pathways (higher‐emission, lower‐emission, overshoot, and step‐change) to demonstrate its generalization capability across distinct forcings. This allows us to capture regional shifts in extreme events and threshold timings. The emulator substantially reduces computational demand and provides a high‐throughput, user‐driven platform for evaluating flexible mitigation strategies.

Glacial terminations stand out for their high rates of sea-level rise, particularly during meltwater pulses. Termination IV (T-IV; ~340,000 years before present) is a prominent example, with sea level rising at up to ~5 m per century. Due to sparse absolute age constraints on marine records, the causes for the high rates of sea-level rise at T-IV remain elusive. In this work, we provide a speleothem chronology from northern Italy, which we transpose to North Atlantic marine records. We infer that the high T-IV sea-level rise rate likely relates to a feedback whereby protracted meltwater release caused enhanced ocean heat storage, followed by heat release upon circulation recovery, driving additional ice-sheet collapse. This analysis highlights the critical role of oceanic feedbacks in driving exceptional rates of sea-level rise during terminations. Precisely dated cave records show that a prolonged slowdown in Atlantic ocean circulation drove exceptional ocean heat buildup and rapid ice-sheet melting during Termination IV (~340,000 years ago), when sea-level rise rates exceeded those of today.

Abstract. Investigating how mesoscale convective systems (MCSs) over Europe will change in a warming climate relies on robust projections from climate models. To confidently use these projections, researchers must first understand model capabilities and uncertainties through strict, process-based evaluation against past observations. However, a lack of suitable reference datasets currently hinders this evaluation. Existing observational trackers often use variables unavailable in European climate model ensemble output or group mesoscale convection together with co-occurring synoptic-scale fronts. Because climate models represent synoptic and mesoscale dynamics through fundamentally different physical pathways, accurately evaluating a model's convective performance requires a benchmark that strictly isolates mesoscale processes. To address this, we introduce the EMMA-Tracker (Evolution-based MCS Model Assessment), an algorithm purpose-built to evaluate models using only standard output variables. We apply this tracker to IMERG precipitation and ERA5-derived atmospheric instability to generate a 27-year (1998–2024) warm-season climatology – the longest reference dataset of European MCSs to date. Following the initial tracking, EMMA applies post-processing filters to retain only distinct MCSs, systems that maintain a clear shape and steady movement, remaining structurally distinct from any co-occurring synoptic triggers. Our results show that these distinct MCSs are the dominant driver of extreme hourly precipitation. Their contribution to hourly warm season precipitation systematically increases with hourly precipitation intensity, accounting for over 60 % of heavy precipitation (P99.9) across most of continental Europe and 80 % over parts of the Mediterranean. The EMMA-Tracker provides both an observational reference for climatological studies and a targeted, process-oriented benchmark for evaluating regional and convection-permitting climate models.

Abstract Forecasting tropical cyclones (TCs) remains one of the most enduring challenges in numerical weather prediction, particularly in terms of storm intensity and track at medium to extended lead times. Recent advances in artificial intelligence (AI)-based global models have demonstrated strong skill in capturing large-scale atmospheric dynamics; however, their coarse spatial resolution and limited variable suites constrain their ability to produce storm-resolving forecasts critical for high-impact events. Here, we introduce a regional NWP system for TC forecasting driven entirely by AI-generated lateral boundary conditions, in which the European Centre for Medium-Range Weather Forecasts’ AI/Integrated Forecast System (AIFS) is run locally to provide all initial boundary conditions for a convection-permitting regional model. This framework operates using a single atmospheric analysis and incorporates dynamic vortex initialization and spectral nudging to enhance storm realism and environmental consistency. Applied to Northwest Pacific TCs, the system demonstrated substantial improvement in intensity forecasts relative to those of the AIFS alone, whilst also delivering more accurate track guidance compared with that of conventional regional systems driven by physics-based global models. These gains highlight the system’s ability to simultaneously deliver accurate forecasts of both track and intensity—a capability traditionally considered difficult to achieve within a single framework. The proposed approach establishes a scalable, efficient, and operationally viable paradigm for tropical cyclone prediction based entirely on AI-driven regional modeling.

This study systematically investigates the spatiotemporal evolution of sea level rise under climate change, employing a tri-scale quantitative framework (global, China's coastal waters, and Shanghai municipality) to elucidate its underlying drivers and regional disparities. By synthesizing IPCC AR6 datasets and NASA sea level projection models, we integrate the Theil-Sen Median Method with Mann-Kendall Test to analyze trajectory patterns from 2030 to 2100. Spatial clustering effects are further identified through hotspot analysis (Getis-Ord Gi*) implemented in ArcGIS Pro. The findings reveal a statistically significant upward trend in global sea levels, primarily attributed to thermal expansion and cryospheric melt (glaciers and polar ice sheets), with localized subsidence observed in certain high-latitude regions. China's coastal waters exhibit accelerated sea level rise, particularly in the South China Sea and East China Sea, where rates surpass the global mean-a phenomenon driven by coupled effects of monsoon circulation, Kuroshio Current dynamics, and freshwater discharge from major rivers (e.g., Yangtze and Yellow Rivers). At the urban scale, Shanghai's coastal zone demonstrates exacerbated relative sea level rise due to superimposed land subsidence and localized hydrodynamic processes, manifesting distinct spatiotemporal clustering patterns. By integrating global-scale thermodynamic baselines, regional oceanic drivers, and local land subsidence patterns, this study provides a quantitative foundation for place-based adaptation strategies in delta cities. The findings enable evidence-based risk assessment and inform anticipatory governance measures-such as targeted infrastructure reinforcement and land-use planning adjustments-to address the compound sea level risks identified at each scale.

Abstract. The future evolution of the Greenland ice sheet (GrIS) depends on the rate and intensity of climate change and can transition to a mostly ice-free state under strong enough global warming. By applying different rates of temperature change in a state-of-the-art ice-sheet model coupled to a regional energy-moisture balance atmospheric model, oscillations in the total ice-sheet volume are found under warming magnitudes between 1.0 and 1.3 K above present-day temperatures. These oscillations are due to two ice streams located in the northern GrIS that each alternate between fast streaming and stagnation, manifesting in build-up/surge variability. These ice streams interact due to their spatial proximity, resulting in irregular periodicity. The ice streams are situated in a region where the collapse of the GrIS to an ice-free state initiates, impacting the time it takes before this transition occurs. For a fixed warming magnitude and an ensemble of warming rates and initial conditions, the timing of the collapse can differ by tens or hundreds of thousands of years. This delay is proposed to be due to a chaotic transient, suggesting that ice-stream oscillations are a potential source of internal chaotic variability in ice sheets and can complicate prospects of anticipating a collapse.

The Arctic Ocean is increasingly stressed by anthropogenic pollution and rapid environmental change. River discharge plays a crucial role in this transition by delivering freshwater, nutrients, carbon, and contaminants to the ocean. Yet how river-borne materials will spread through the Arctic under future climate warming remains unclear. Here we show that climate warming accelerates and expands the dispersal of Arctic river waters and drifting materials through multi-scale changes in ocean circulation linked to sea-ice decline and reduced upper-ocean density. Stronger ocean eddy activity and altered wind-driven circulation transform the Beaufort Gyre from a predominantly regional reservoir into a pan-Arctic convergence zone that efficiently accumulates river-derived materials from Siberia. Meanwhile, intensified boundary currents and Transpolar Drift accelerate the export of Siberian discharge toward the North Atlantic. Together, these circulation changes greatly increase cross-basin connectivity, with broad implications for marine ecosystems in the northern high-latitude oceans and for Arctic coastal communities.

The complexity of water fluxes and storage changes, and of the water balance closure they imply across different scales, is further enhanced by climate change. Shifting precipitation patterns along with the rising temperatures, and changed frequencies and intensities of heatwaves, floods, and droughts imply altered hydro-climatic conditions in ways that heighten water security uncertainty, expose vulnerabilities in water systems, and complicate water-related risk management. Assessments of water security, based on water availability from surface and sub-surface water bodies, are also subject to large uncertainties due to lacking observational data for critical water fluxes, such as runoff (Collins et al., 2024). In addition, spatial heterogeneity across scales (Zarei & Destouni, 2024b) and fragmentation of knowledge, research, and management of the terrestrial water and its various components, aspects, and roles in the Earth System (Destouni, 2025) amplify water security and resilience challenges. The changes in water conditions and uncertainties about them make it difficult, for example, for planners and policymakers, to decide on and develop reliable and sustainable long-term strategies for managing water resources and water-related risks (e.g., floods, droughts, water pollution) under ongoing and future climatic change.Reliable hydrological data forms a fundamental structure of our ability to track, interpret, and anticipate climate-driven changes in water systems. Such data come from an increasingly diverse mix of data streams, including ground observations, satellite missions, reanalysis products, and land-surface and climate model outputs that provide various data for water conditions across the atmosphere, land, and oceans. Despite the advances in data availability, large parts of the world still suffer from incomplete, inconsistent, or short-term observational records, limiting our capacity to capture hydroclimatic variability with confidence. A persistent complication is that different datasets might provide inconsistent representations of key hydrological variables, raising questions about which products are most credible for regional assessments, and why the discrepancies occur, how large they are, and what they imply for hydrological interpretation (Bring et al., 2015;Ghajarnia et al., 2021;Zarei & Destouni, 2024a). Choices of which water data to use also depend on different disciplinary traditions, methodological constraints, and particular study interests (Yang et al., 2019;Zhang et al., 2016). Over the past decade, recognition of divergent dataset implications has intensified calls for comparative evaluation frameworks that systematically test the consistency of available datasets (Destouni et al., 2025). Such efforts are crucial not only for scientific progress but also for informing water-related practices, where robust and transparent water system assessments are essential for preparing societies to navigate toward secure and resilient water availability under hydro-climatic uncertainty.South America has an outsized contribution to global hydrological dynamics. Much of the foundational understanding of South American hydrology emerged from early basin-scale field campaigns and regional assessments undertaken prior to satellite-and reanalysis-era datasets. For example, early analyses of evapotranspiration and precipitation recycling in the Amazon (Salati & Vose, 1984) provided some first systematic insights into the continent's hydrological dynamics (Salati & Vose, 1984;Sioli, 1975). These works, along with global syntheses (Lʹvovich, 1979), established baselines for understanding rainfall regimes, runoff generation, and moisture recycling in major South American basins. However, significant research, knowledge, and data gaps still remain for South America (Zarei & Destouni, 2024b), where densely populated regions depend heavily on freshwater, and from where the impacts of essential rainforest changes extend far beyond the regional water cycling (Diniz et al., 2018;Gatti et al., 2021). Growing water demands for agriculture, hydropower, and urban use together with accelerating deforestation may undermine the resilience of regional water supplies (Ferrari et al., 2021), while the Amazon and Andean forests also drive large-scale evapotranspiration and "flying river" moisture transport, which sustains rainfall in distant agricultural regions like the Cerrado, La Plata Basin, and central Brazil (Fearnside, 2018;Ferrari et al., 2021).Despite South America's essential role for global hydrological dynamics, with the Amazon alone contributing approximately 18% of the global freshwater discharge to the ocean, the largest of any river system worldwide (Collins et al., 2024), the continent still lacks harmonized comparative datasets for its catchment-wise linked hydrological fluxes and storage changes, and the hydro-climatic links across its multiple hydrological catchments and climate zones. Existing data sources are fragmented, with each providing limited understanding of the overall water system functioning and how different observational and model-based products represent it in and across the diverse climate and environmental settings of South America. South America experiences the combined influences of continental-scale atmospheric circulation systems, particularly the South American Monsoon System and associated moisture transport pathways (Garreaud et al., 2009), together with the steep climatic and hydrological gradients imposed by the Andes (Vuille, 2013), and the regulating roles of various human activities within its major river basins. In addition, the Cerrado, a vast tropical savanna region in Central Brazil, functions as a hydrological "water tower" for much of the continent, supplying the headwaters of major basins such as the Amazon, São Francisco, Tocantins-Araguaia, and La Plata (Althoff et al., 2021;Nobre et al., 2016). Climate change is amplifying sensitivities by altering precipitation distributions, which may increase the frequency and magnitude of floods and droughts, and reshape seasonal and long-term patterns of water availability and security. Such transformations also affect ecological stability, agricultural productivity, hydropower generation, water-supply reliability, and sanitation infrastructure -key sectors for climate-resilient development. Recent largesample hydrology initiatives, such as CAMELS-CL for Chile (Alvarez-Garreton et al., 2018) and CAMELS-BR for Brazil (Chagas et al., 2020), have made substantial contributions by providing detailed daily time series and comprehensive catchment attributes. Yet, these datasets remain geographically limited and do not offer a continent-scale, multi-variable synthesis covering diverse hydro-climatic interaction regimes across South America. Existing global resources -such as hydrographic databases (Lehner et al., 2008) and streamflow archives provide valuable structural information but do not integrate the full set of hydro-climatic variables required to evaluate catchmentscale water balance closure or to assess climate sensitivities in a coordinated way.Considering, for example, the main water flux of runoff (R), in situ streamflow (discharge) gauges provide broad opportunity for hydrological monitoring, indispensable for understanding variations and changes in surface water availability. Specifically, observation-based average R is derived from the discharge measurements, providing an integrated signal of the upstream hydrological processes across the whole contributing catchment. However, the global stream-gauging network has been declining for decades, resulting in spatial and temporal gaps in the coverage of discharge observations. Gauges are often clustered in politically stable, economically developed, and hydrologically accessible regions, leading to strong biases in observational records relative to the wide spectra of climate, environmental, and geopolitical conditions. Data sharing restrictions across national boundaries and reductions in long-term reporting have further reduced the availability of consistent discharge time series for scientific and operational use (Famiglietti et al., 2015;Hannah et al., 2011;Ruhi et al., 2018).Where direct discharge observations are sparse, global and regional climate models and landsurface models (LSMs) offer alternative simulated estimates of gridded runoff, river discharges and, in some cases, water storage dynamics (David et al., 2011). However, the reliability of these simulated estimates is highly sensitive to both the accuracy of input runoffa variable often not directly observed and to the quality and resolution of underlying hydrography (David et al., 2019). Although numerous advances have been made to correct biases, improve routing algorithms, and enhance the consistency of modelled discharge at regional to global scales (Beck et al., 2015;Pan & Wood, 2013), these correction methods are often computationally demanding or dependent on reference datasets that themselves carry uncertainties. Moreover, achieving full water balance closure from remote sensing products, particularly for water storage changes, remains challenging due to error accumulation and retrieval limitations (Oliveira et al., 2014). These constraints are problematic for the horizontal water fluxes of runoff (R). In comparison, the catchment-wise linked main vertical water fluxesprecipitation (P) and evapotranspiration (ET)have markedly different data availability profiles. Precipitation is relatively well-characterized owing to relatively dense networks of meteorological stations and widely used global precipitation products that allow spatial interpolation to derive catchment-average P. Evapotranspiration, however, remains difficult to observe directly over whole catchments. Eddy-covariance flux towers provide high-quality point measurements of latent heat and water vapor exchange, but tower networks are spatially sparse and unevenly distributed, particularly across tropical regions such as much of South America, limiting their representativeness for regional ET estimates (Pastorello et al., 2020;Villarreal & Vargas, 2021). As a result, model-based ET products, informed by satellite observations (Ruhoff et al., 2022), are commonly used to estimate ET across entire catchments, despite substantial methodological variability and uncertainty among products.As a contribution to overcoming some important data limitations and directly comparing, assessing and using various hydro-climatic datasets for South America, we here introduce a South America Hydro-Climatic Data (SAHCD) synthesis -a harmonized, openly accessible multi-dataset synthesis for the region derived from a corresponding global data compilation (GHCD; Zarei & Destouni, 2024a). The regional SAHCD provides continuous, quality-checked monthly and annual time series for 95 large, independent, non-overlapping hydrological catchments distributed across the continent, including major basins such as the Amazon and La Plata (Figure 1A). Collectively, these catchments cover 10,628,392 km², i.e., nearly two-thirds of the South American land area. For these catchments and the land area they cover, the SAHCD includes four comparative datasets (Figure 1B). While conceptually aligned with the global GHCD synthesis, the SAHCD is specifically structured to support South America-focused analyses by tailoring all quantification steps, cross-dataset checks, and derived calculations to the region's heterogeneous hydro-climatic settings (Figure 2).The SAHCD has been developed with several interconnected objectives that directly support efforts to strengthen climate-resilient water management across South America. First, it enables a consistent and transparent comparison of observational, satellite-informed, and model-or reanalysisbased estimates of freshwater fluxes and storage change dynamics -information that is fundamental for diagnosing how climate change may be reshaping hydrological behavior from local catchment to continental scales. Second, the data synthesis allows systematic assessment of catchment-wise water balance closure, a fundamental diagnostic for evaluating dataset realism for each catchment and the reliable the stability, and resilience of water under the by four comparative the SAHCD provides a for where different data sources or in their of key hydro-climatic robust patterns major providing a data the SAHCD on impacts on and resilience of water availability, security, and risks across South America. evaluating hydrological to and land-surface and uncertainties in water balance components, and assessing patterns of hydro-climatic across diverse catchments. For water resources management and the SAHCD a for assessing and and informing consistent cross-dataset the SAHCD structural uncertainties and data that are crucial for model and across the South American water SAHCD provides a harmonized, multi-variable and multi-dataset hydro-climatic synthesis with diagnostic based on catchment-wise water balance across South America that are for While the underlying methodological that of the Hydro-Climatic Data (Zarei & Destouni, SAHCD is not a regional but a regional where all dataset and diagnostic are specifically for the South American catchments. enables consistent of hydro-climatic variables for the wide of across continent, including its tropical basins (e.g., regions, and Andean systems. As a result, the methodological are to for the the resulting and hydro-climatic are and provide for further South America. SAHCD provides not only but hydroclimatic synthesis and for the South American the SAHCD provides a for a wide of scientific and questions crucial for South America's hydro-climatic For do observational and model-based datasets in hydrological to and precipitation variability across diverse such representations which dataset provides the most consistent and signal for and spatial and temporal patterns in droughts, floods, and hydro-climatic and how do these patterns to large-scale climate and local do changes into sectors such as agriculture, hydropower, urban water and functioning central to regional resilience In addition, the SAHCD a for and climate, and land-surface models for and across the continent's heterogeneous from tropical forests to and Andean quantification of dataset uncertainties and structural different dataset the SAHCD support reliable assessments and where or methodological is most synthesis not only hydro-climatic analyses at continental but also enables local variability and global climate such as long-term and in moisture transport also provides a critical for and to enhance water security and water management across South America. The catchments and datasets in the South America Hydro-Climatic Data the of the 95 catchments with catchment boundaries derived from et al., et and the by the using and with the dataset structure established in the GHCD synthesis (Zarei & Destouni, the SAHCD is in four datasets (Figure that allow systematic comparison of hydro-climatic conditions and closure specifically across the South American catchments (Figure 1A). The first data and directly observation-based data is from & precipitation (P) from the et al., and runoff from the et al., et al., these and R long-term evapotranspiration is the ET average storage change over A the observational for and and them with modelled estimates of ET from the et al., et al., 2011). also provides moisture which is The runoff records used in and also the catchments contributing to the that all observation-based and datasets in SAHCD to an set of hydrological The provides a model-based of all variables and the Data System & et al., The multiple (e.g., and that in their data and use of data The and products are in that data is the model data the in the conceptually only the an of hydro-climatic from the reanalysis et al., or provide data for storage change In the is and provided for each dataset based on the associated water fluxes and their balance as change is derived from the catchment-wise water balance and as a diagnostic variable to assess the consistency of hydro-climatic fluxes across the comparative datasets. all four the ability to evaluate closure over each catchment for all datasets and approximately for average consistent of how the different data sources South American freshwater fluxes and their catchment-wise links and balance under precipitation and Such comparison is essential for assessing dataset consistency and for understanding how regional hydroclimatic are across observational, and reanalysis data products et al., et al., et al., of be for from satellite or observations with field evapotranspiration be by the as ET et al., 2019). However, achieving in remains In regions such as the Amazon and the where dense tropical or the use of satellite (e.g., is limited by spatial signal and et al., et al., assess the consistency in how the comparative datasets in the SAHCD represent based on their modelled ET and observation-based or modelled and R we also the moisture change by the data provided from each dataset which not and it with the by the water flux data provided by the dataset is of the storage change these variables variability and (Destouni & 2014). The and comparison provides a diagnostic and how each dataset the dynamics of terrestrial water for regions where from is or Recent in the Amazon have that data used in to moisture and storage dynamics, the of such in densely and hydrologically et al., and provides the discharge data for catchment-average R in both the and and also the water of evapotranspiration in ET For all hydro-climatic variables, gridded products and the corresponding data for each as the catchment contributing to each discharge consistency with the runoff derived directly discharge by the contributing catchment from the observations, we R using an of all each allows each dataset of its spatial resolution be in a harmonized catchment data at the temporal resolution available across the including harmonized monthly time series for each hydro-climatic variable with data for catchment in each as not monthly data for ET or providing only annual for these variables annual and annual and long-term also derived for all variables in all datasets to support analyses and of both variability and long-term hydro-climatic For the model-based runoff in as the of surface runoff and runoff while runoff to the runoff variable provides a of the used to the SAHCD (Zarei & Destouni, As in the the of SAHCD the and in regional and global hydro-climatic including the GHCD (Zarei & Destouni, the for the (Zarei & Destouni, and the for (Zarei & Destouni, The of dataset consistency across variables, harmonized spatial and temporal across all data The most and detailed of these has been provided for the GHCD and we to that for a of the and (Zarei & Destouni, of the and for the South America Hydro-Climatic Data (SAHCD) for data consistency hydro-climatic variables, and within the These are used to to catchments, in where data availability are not SAHCD synthesis provides monthly and annual time series for all hydro-climatic variables in each together with associated long-term average over the reference the of using to climatic The data synthesis however, all and long-term are over the reference to consistency with established catchments with at monthly runoff observations temporal coverage for hydro-climatic The SAHCD is to allow as data available and to of reference a in temporal resolution the which not monthly for ET or the hydrological constraints to its water balance ET and are only for annual or not estimates of ET and derived in are direct measurements, and be to be reliable in with significant storage such are widely used in large-scale water balance where direct measurements of ET and are (Althoff & Destouni, et al., 2019). have that long-term average storage changes to be relatively over whole catchments et al., and continental to global (Zarei & Destouni, the use of these for annual the SAHCD synthesis, the observation-based estimates ET and in be further for any study catchment and at scales by direct comparison with the corresponding ET and of the datasets variables in the SAHCD represent catchment-average the climatic variables and and the associated hydrological flux and storage change variables and the dataset also includes several derived These catchment-average change from across the full the long-term where is and the hydrological and these derived variables provide diagnostic of hydro-climatic conditions and their variations and changes across the 95 South American of the SAHCD the catchment used to and the global hydro-climatic datasets into catchment-average time These from the et al., et al., and have been to the in the A is provided in the and includes each SAHCD of catchment and catchment area In catchments are associated with the in which the reporting is of the upstream contributing catchment area is or national the catchment-average monthly and annual time series for all hydro-climatic variables of and each of the four datasets As the dataset provides only annual ET and and not the constraints of the underlying water balance dataset a consistent structure and an annual time a monthly time series where monthly are not as for ET and in a with long-term for and for all 95 catchments, along with derived hydro-climatic and essential catchment and and a and providing comprehensive on data variable and For the also the associated in the and are to the with the to correct interpretation of variables and their corresponding temporal and spatial progress in hydro-climatic South America along with parts of the world still with reliable data availability in with large hydrological Such limitations have for decades, scientific to regions with observational The SAHCD directly such by a harmonized, and hydro-climatic synthesis for 95 major South American hydrological basins. four comparative datasets with consistent spatial and temporal the SAHCD critical and gaps by analyses of how freshwater fluxes and storage dynamics are catchment-wise in and over time along with the climate across the The comparative structure of the SAHCD both of and of for the water fluxes and storage changes provided and by the different datasets. The dataset comparison is valuable it where and observations on robust hydro-climatic and where uncertainties are the consistent patterns of catchment-wise linked water fluxes and storage changes across the datasets key hydro-climatic across South America, while divergent dataset key underlying of the dataset for the The of may from sparse observations and interpolation to model and in representations and for the hydroclimatic and Such insights to and and understanding of how climate change with changes in water availability and hydrological and water security and resilience across South America's heterogeneous hydro-climatic conditions the Amazon and to the Andean headwaters and the of the La Plata SAHCD is to support a broad of and water and management evaluating long-term hydrological including and surface water storage assessing and including how these to hydro-climatic variability and representations of hydrological across observational, and datasets to reliable or informing of and seasonal by key hydro-climatic and change and and assessing impacts on water security at and for agricultural hydropower, ecological and integrated water resources management. These are for multiple such as and climate dataset and evaluating model risk on and regional planners and hydro-climatic information to for to climatic changes and that while the four datasets in the SAHCD are harmonized, they are not input data and methodological some each dataset also observational and For example, discrepancies the observation-based and the datasets from their in evapotranspiration while and may land-surface and hydrological routing and despite atmospheric such is essential for the and their and across datasets. As with any synthesis, the SAHCD limitations from its Precipitation datasets such as remain in regions, particularly in the Amazon and remote Andean The streamflow records, to quality are unevenly distributed and by products (e.g., and provide spatially coverage but on and data frameworks developed for regions with observations, and biases to conditions in South America. These limitations are and that evaluate dataset realism Despite the constraints, of the four datasets in the SAHCD provides a and important for robust of South America's hydro-climatic and where ground monitoring, remote may be the SAHCD a foundational for climate-resilient water management by research, monitoring, and that is in transparent and robust hydro-climatic information across datasets for of the most hydrologically critical data for study and in with and of spatial using The data in study and used for data and non-overlapping catchments are provided with by (Zarei & Destouni, at in the for and further The data are provided as a the in the main along with the together with an The dataset is distributed under the The catchments and datasets in the South America Hydro-Climatic Data the of the 95 catchments with catchment boundaries derived from et al., et and the by the using The four comparative datasets in the SAHCD catchment data

Abstract Tropospheric ozone influences Earth's radiative energy budget and has increased in recent decades. With initial‐condition ensembles from a single chemistry‐climate model, we show that global surface anthropogenic NO x emissions explain about 90% of the simulated 1995–2014 tropospheric ozone increase (1.8 DU) and that this increase exceeds those arising internally from natural climate variability. South and East Asian NO x emissions account for about 40% of the global NO x ‐driven ozone increase but contribute about 80% of the associated net positive stratospheric‐adjusted radiative forcing (SARF; tropospheric ozone plus methane), reflecting a weaker methane response to Asian NO x than to tropical surface NO x emissions. Considering both CO + NMVOC and NO x emissions from Asia produces a larger net SARF than from global emissions (+0.032 vs. +0.023 W m −2 ). Tropospheric ozone changes over 1995–2014 from global CO + NMVOC emissions or methane concentrations alone are too small to be detected relative to internal climate variability.