Atmospheric and Oceanic Sciences

The Earth’s energy imbalance (EEI) that develops at the top of the atmosphere is accommodated by gains or losses of energy in Earth’s heat reservoirs, leading to temperature and sea level change. The global ocean has stored about 90% of the EEI from anthropogenic forcing, but the attribution of past changes of EEI remains largely unknown, thus obscuring our understanding of past climate change. Here, we reconstruct changes in mean ocean temperature over the past 150,000 years that, with a reconstruction of ice sheet–volume changes, allow us to isolate the contributions of the dominant ocean and ice sheet heat reservoirs to the global energy inventory as well as to derive their associated contributions to EEI. We attribute orbital-scale EEI variability to joint precessional and CO 2 forcing that caused changes in rates of ice sheet energy storage. In contrast, millennial-scale EEI variability can be attributed to radiative responses to decreases in the Atlantic meridional overturning circulation and increasing CO 2 during Heinrich stadials that caused changes in rates of ocean energy storage.

Abstract Microplastics are widely detected in rivers, lakes, and oceans, yet global-scale assessments remain constrained by uneven monitoring coverage, inconsistent reporting units, and limited integration of particle attributes with spatial and temporal analyses. Here, we compiled 6464 georeferenced water-sample concentration records from 2014 to 2024 by integrating Web of Science-indexed studies with compatible records from NOAA NCEI and Adventure Scientists; 1469 sampling sites also contained particle-attribute information. We characterized spatial heterogeneity, regional contrasts, particle composition, nonlinear dynamics, and remote-sensing-assisted hotspot mapping. Sampling records clustered in North America, Europe, East Asia, South Asia, and the midlatitude Northern Hemisphere. River records showed the highest central concentrations, followed by lakes, whereas ocean records had lower medians but distinct local hotspots. Fibers dominated river and lake MPs, while marine records contained higher proportions of fragments and particles. Spearman’s tests showed no significant monotonic trends in annual median concentrations, but long short-term memory reproduced river and ocean dynamics well (R2 = 0.971 and 0.962) and indicated divergent Sen-slope directions, with rivers decreasing and lakes, oceans, and pooled global records increasing. Remote-sensing-assisted mapping identified nearshore, estuarine, bay, and hydrodynamically confined zones as microplastic accumulation areas. This framework supports monitoring prioritization, targeted sampling, ecological risk assessment, and coordinated freshwater–ocean governance.

Abstract Rapid progress in the field of machine-learning for weather prediction has led to the emergence of algorithms whose forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to improve the quality of forecasting services provided by operational centers, particularly given the speed at which machine-learning based models generate predictions. Despite the clear promise of these systems, questions remain about the ability of the current generation of machine-learning models to generate physically consistent predictions of the full suite of required forecast fields under all conditions. Answering these questions will require careful comparisons between the well-understood physically based models, current state-of-the-art machine-learning models, and the hybrid models that combine elements of these two archetypes. The Weather Prediction Model Intercomparison Project (WP-MIP) is a World Meteorological Organization-supported initiative whose initial goal is to create a centralized database of physically based, machine-learning, and hybrid model forecasts to enable a distributed assessment and evaluation effort. The first instance of WP-MIP focuses on global deterministic predictions using both center-specific and common initializations to facilitate sensitivity studies. Forecasts contributed by institutions across six continents will be used to develop AI-ready verification techniques that highlight the strengths and weaknesses of each class of prediction system, with the goal of establishing best-practice guidance to model developers and national weather centers. The broad engagement of the operational and forecast-evaluation communities in WP-MIP will ensure that the project’s results are highly relevant to the development and deployment of next-generation weather prediction systems.

Abstract The recent triple-dip La Niña event from 2020 to 2023 in the equatorial Pacific, which occurred without a preceding strong El Niño, challenges the traditional understanding of multi-year La Niña dynamics governed solely by the recharge oscillator theory. This study investigates the mechanisms behind the initiation of the 2020–2023 La Niña and evaluates the predictive capabilities of a fully coupled climate model. Utilizing a multi-year climate prediction system based on the Community Earth System Model version 2 with ocean data assimilation techniques, we assess the contributions of various climate factors, including inter-basin forcings such as the Indian Ocean Dipole (IOD) and tropical Atlantic warming (TAW). Our findings reveal that, while the model underestimates the role of the IOD, the combination of positive IOD and TAW substantially influenced first-dip La Niña initiation through the eastward propagation of velocity potential anomalies. Partial ocean data assimilation experiments corroborate these results and further suggest that accurately predicting the phases of the Pacific Meridional Mode (PMM) is essential for forecasting the initiation process. Our hindcast experiment indicates that the La Niña event typically decays within one year rather than persisting, suggesting that distinct drivers, such as PMM, South Pacific, or aerosol forcing, may have influenced the second and third phases of cooling. These insights highlight the critical role of inter-basin interactions (IOD, TAW, and PMM forcings) in extending ENSO predictability and improving long-term climate forecasting capabilities.

Accurate assessment of salt marsh dynamics is crucial to reverse their widespread decline. Previous modelling studies investigated the resilience to mean sea level rise, ignoring the tidal dynamics observed in many estuaries. Building on the principle that salt marsh occurrence is determined by soil elevation relative to tidal datums, this study aims to map the current extent of salt marshes and project their long-term evolution by combining high-resolution LiDAR data with spatially varying tidal datums. The Ria de Aveiro, a lagoon undergoing tidal amplification, was used as a case study. A simplified, rule-based model of long-term marsh evolution was developed, in which vertical accretion is derived endogenously as a function of tidal datums. It was validated in hindcast mode by reconstructing the 1987 marsh extent, and applied to project marsh dynamics to 2100. A Monte Carlo analysis quantified the projection uncertainty and disentangled the influence of accretion from that of the datum variability. The soil elevation–datum relationship proved effective for mapping, yielding an overall accuracy of 90.3%. The model reproduced the 1987 extent and captured a 26% decline in marsh area between 1987 and 2024 (from 34.8 to 25.7 km 2 ). This decline is projected to continue through 2100, reaching a median area of 17.5 km 2 , partly due to anthropogenic barriers that impede landward migration. The projected area is controlled by accretion, with datum variability becoming decisive in the inner regions. Future research should apply this model to other estuarine systems, especially those with salt marshes threatened by tidal amplification.

Abstract. Compound heatwave-extreme precipitation (CHWEP) events, the rapid succession of heatwaves and extreme precipitation, pose growing compound and cascading risks. However, global-scale comparisons of their spatiotemporal evolution against single extremes remain limited. This study systematically examines the changes in CHWEP and corresponding single extremes from 1980 to 2100 using climate observations and projections under SSP (Shared Socioeconomic Pathway) 2–4.5 and SSP5-8.5 scenarios. We find that CHWEP exhibit higher frequency, stronger precipitation, and longer heatwave duration in mid-to-high latitudes, while tropical CHWEP feature more intense heatwaves than single heatwave events. These spatial contrasts persist in future projections. Under both scenarios, CHWEP and single extreme metrics intensify globally by 2056–2100, with post-heatwave precipitation exceeding that of single precipitation extremes, particularly under SSP5-8.5, highlighting sensitivity to greenhouse forcing. Critically, the co-occurrence is non-random, indicating an emerging physical linkage. In the tropics, the likelihood of extreme rainfall following heatwaves increases markedly. Our findings demonstrate that CHWEPs are evolving into a distinct, intensifying hazard class, necessitating their integration into climate resilience, early warning, and adaptation frameworks.

Abstract. Wildfires in the Amazon, increasingly influenced by climate variability and anthropogenic activities, pose severe environmental and health challenges. While drought events amplify fire activity and emissions, the cascading effects of droughts and deforestation on air quality and health remain underexplored. This study addresses this gap by combining satellite observations of fire activities with the Global Fire Emissions Database (GFEDv4s) and the chemical transport model, GEOS-Chem High Performance (GCHP) to quantify the impacts of droughts and deforestation on fire emissions, air quality, and health risks from 2010 to 2015. “Fire-on” and “fire-off” simulation reveal that biomass burning dominates dry-season (July–November) air quality, contributing 50 % to regional CO and PM2.5 and 33 % for ozone in non-drought years. These contributions increase to 60–80 % for CO and PM2.5 and 50 % for ozone during drought years. Significant correlations between pollutant levels and drought intensity reflect a climate-driven amplification of fire impacts. Using the Global Exposure Mortality Model (GEMM) and exposure-response relations, we estimate that fire-induced PM2.5 and ozone increase premature mortality by 6.0 % and 18.6 % in non-drought years, which rise to 8.9 % and 24.4 % during drought years. These findings underscore the critical roles of droughts in exacerbating fire emissions and health risks, even under stable deforestation rates. This study highlights the urgent need for integrated wildfire management and climate adaptation strategies to protect public health and achieve sustainability goals.

Abstract Anthropogenic aerosol emissions are projected to decline rapidly in the near future as air quality regulations strengthen. Crucially, however, the response of large-scale atmospheric circulation to these reductions remains a major source of uncertainty. Here, we use multi-model ensembles from Regional Aerosol Model Intercomparison Project (RAMIP) to provide a diagnostic assessment of tropical circulation changes in response to global and regional aerosol reductions during 2015-2050. We find that global aerosol reductions lead to a poleward expansion of the Northern Hemisphere tropical width by 0.10° ± 0.08°, a weakening of the Northern Hemisphere Hadley Circulation by 1.77 ± 1.09 109 kg s-1 and a strengthening of the Southern Hemisphere Hadley Circulation by 2.53 ± 1.30 109 kg s-1. The Intertropical Convergence Zone (ITCZ) also shows a northward shift by 0.19° ± 0.10°. These meridional circulation changes consistently emerge across the regional experiments, with aerosol reductions over East Asia and North America+Europe contributing the most. Although the response of the zonal Pacific Walker circulation to global aerosol reductions is not statistically significant, models show a strengthening tendency that is significant under Africa+Middle East aerosol reductions. Notably, the ITCZ response to greenhouse gas forcing is weak and uncertain, whereas aerosol reductions produce a stronger and more robust response, even in the regional experiments. Our results suggest that both global and regional aerosol reduction significantly contribute to large-scale tropical circulation changes.

In the context of South Korea’s recurring drought challenges, this study explores season-ahead predictability of hydrological drought, focusing on Bayesian autoregressive modeling incorporating stochastic volatility. To better understand uncertainty, our approach integrates Bayesian inference and stochastic volatility, with comparisons of conventional autoregressive (AR), autoregressive with exogenous variables (ARX), and autoregressive exogenous with stochastic volatility (ARXSV) models. Our study explores the integration of climate-based information with statistical modeling, presenting a comprehensive framework for water resource management. We aim to predict June–July–August (JJA) streamflow and to assess the influence of large-scale climate predictors, such as the sea surface temperature and sea level pressure associated with the North Atlantic Oscillation. The methodology encompasses exploratory data analyses, the use of hydrological drought indices, and considerations related to dam operation. The research provides tailored season-ahead streamflow forecasts specifically for the critical monsoon season JJA, where hydrometeorological variability is paramount. By incorporating climate information within a Bayesian inference framework and stochastic volatility, we show that the proposed approach outperforms conventional autoregressive models, providing decision-makers with an efficient tool to address climate risk and support dam operation planning, ultimately contributing to improved water-resources management.

Monitoring forest degradation using medium-resolution optical sensors often results in an underestimation of the actual ecological impacts, limiting conservation strategies in threatened regions. We evaluated the forest disturbance dynamics (2004–2025) in the Permanent Production Forests of Tahuamanu, Madre de Dios, Peru. We processed multitemporal Landsat images in Google Earth Engine to map change trajectories by applying Spectral Mixture Analysis to derive the Normalized Difference Fraction Index (NDFI) integrated with a stratified area estimator. This approach yielded overall accuracies of ≥93%. Our findings show that structural degradation is replacing deforestation as the main driver of forest alteration. By 2025, the footprint of this disturbance, which silently affects the understory, had quadrupled the extent of deforestation, a trend evidenced by the stratified adjustment that revealed over 10,000 hectares of structural damage previously hidden under the label of intact forest, demonstrating the typical omission bias of passive sensors. Relying on raw maps means underestimating biomass lost to understory degradation. To address this systematic bias, it is necessary to operationalize the NDFI model along with a rigorous stratified estimation. With this combined approach, a scalable, cost-effective, and statistically robust framework is offered to monitor the subtle degradation that traditional mapping systems overlook. To our knowledge, this is the first long-term, area-corrected assessment of forest disturbance within Peru’s oldest formal timber concessions, and it shows that degradation persists and accelerates even under a regulated management model.