Submerged vegetated ecosystems like seagrass meadows and kelp forests capture and turnover substantial amounts of carbon and are increasingly being considered for global carbon budgets and offset schemes. Yet these ecosystems are vulnerable to climate-driven extreme events such as marine heatwaves (MHWs), leading to uncertainties in the reliability of carbon abatement benefits from restoration and protection. We quantified blue carbon loss (CO 2 equivalent release) and subsequent recovery of carbon stocks associated with reported MHWs over the past two decades. These events damaged 228,432 ha of kelp forest and seagrass meadows and resulted in a lost carbon abatement of 13 million tonnes of CO 2 (±2 SE) due to release of carbon stocks and diminished carbon sequestration capacity after the MHW. Recovery was often slow or nonexistent, with areal recovery rates of 7% y -1 (±4 SE) for kelp and 11% y -1 (±5 SE) for seagrass, leaving 91% of the impacted area unrecovered. Together the lost carbon abatement and area from MHWs was 1-2 orders of magnitude higher than current abatement from restoration. Our findings indicate that increasing MHW frequency could exacerbate emissions through the release of stored blue carbon and compromise the effectiveness of these nature carbon sinks.

Marine heatwaves (MHWs), prolonged extreme thermal events, are reshaping ocean ecosystems, yet their influence on global productivity patterns remains poorly understood. Here, we use a global regression framework to disentangle linear thermal effect from nonlinear feedback and demonstrate that MHWs restructure the dominant drivers of ocean net primary production (NPP). MHWs induce a regime shift from sea surface temperature (SST)-independent to SST-dependent controls on NPP anomaly, reflecting an enhanced thermal effect in response to extreme warming. MHW suppressed the NPP anomaly across nutrient-poor low latitudes but increased it in nutrient-rich higher latitudes. The contrasting responses arise from differences in nutrient baselines, with low-nutrient regions exhibiting greater sensitivity to extreme warming. Together, these results reveal an emerging poleward redistribution of ocean productivity and highlight the need to incorporate MHWs into projections of marine ecosystem resilience and climate-biosphere feedbacks.

Abstract Marine heatwaves (MHWs)—discrete and prolonged warm ocean temperature extremes—can pose serious threats to marine ecosystems. While the previous work of authors has highlighted the diversity of MHWs, future changes in six representative MHW types, under different climate change scenarios, are unknown. Here, we analyze changes in MHW diversity projected for the late 21st century using multi‐model ensembles from CMIP6 under the SSP2‐4.5 and SSP5‐8.5 scenarios. Our results show that under the normal‐emission scenario, annual occurrence days of bimodal MHWs keep increasing, while those of the other five types rise early but level off around 2050. Under the high‐emission scenario, annual occurrence days of three long, intense types keep rising, whereas the other three decline after mid‐century. We further find that higher‐intensity MHWs are increasingly associated with bimodal events. Spatially, MHW distributions shift toward bimodal dominance, with nearly all ocean regions projected to be governed by bimodal types by mid‐century under the both scenarios. Improved understanding of these projected shifts is critical for informing marine conservation planning and climate adaptation strategies in ocean ecosystems.

The Mesoamerican midsummer drought (MSD) is a distinctive precipitation feature characterized by a mid-season rainfall reduction within the boreal summer wet season. Despite its socioeconomic significance, most previous studies have emphasized its canonical bimodal structure, leaving the diversity of MSD expressions less explored. Using ERA5 reanalysis (1979–2019), we objectively identify MSD events for each grid cells over the domain and classify them into four distinct clusters via K-means analysis. These clusters reveal diverse temporal structures, intensities, and spatial preferences, spanning southern Mexico, Central America, the Caribbean basin, and adjacent oceans. Composite analyses of sea surface temperature (SST), cloud fraction, winds, and moisture flux convergence (MFC) indicate that low-level circulations—particularly the Caribbean and Chocó low-level jets, along with eastern Pacific winds—play a central role in shaping MSD diversity. In contrast, eastern Pacific SST anomalies exhibit only weak and inconsistent associations, suggesting a secondary role of SST–cloud feedbacks. Decomposition of MFC further highlights the combined zonal and meridional moisture transport as the primary driver of bimodality, with meridional fluxes being especially important for Caribbean MSD events. An evaluation of 33 CMIP6 models shows that while most capture the overall MSD frequency, they underperform in reproducing asymmetric precipitation structures, particularly those over the Caribbean. These results emphasize the need to incorporate MSD diversity into model evaluation frameworks to improve regional precipitation projections. Our findings provide a new perspective on the mechanisms underlying MSD variability and establish a foundation for more reliable seasonal prediction and climate change assessments in Mesoamerica.

In 2023–2024, widespread marine heatwaves associated with record ocean temperatures impacted ocean processes, marine species, ecosystems and coastal communities, with economic consequences. Despite warnings, interventions were limited. Proactive strategies are needed for inevitable future events.

Mechanistic understanding of marine heatwaves (MHWs) requires a suitable definition for their detection, an approach to characterise their evolution, and an effective method to understand their causality. Much of our recent knowledge regarding MHWs has been achieved using a point-wise statistical definition that quantitatively defines MHWs as measurable warm ocean temperature extremes relative to a given threshold. While this commonly used definition is easy to use, with MHWs readily detectable and with near-global coverage from satellite sea surface temperature data, it does not quantify the spatial scale of events, their evolution in space and time, nor the association of that evolution with the key drivers. To overcome some of these limitations, more recent studies have investigated the evolution of MHWs as objects evolving in space and time to help broaden our understanding of MHWs. Our new approach represents an important step toward mechanistically characterising the space and time evolution of MHWs – it not only builds upon and extends object-based kinematic studies of MHWs but additionally connects these spatiotemporally evolving MHWs with their key drivers. Finally, we examine the potential predictability of these MHWs based on a linear inverse modelling approach.

Marine heatwaves (MHWs)-discrete and prolonged warm ocean temperature extremes-can cause substantial ecological and socioeconomic impacts, and have been widely investigated based on satellite sea surface temperature observations. However, most studies emphasize lifespan-averaged metrics or separately as onset and decay phases, without taking account of their complete temporal evolution. Here, we present a comprehensive analysis of MHW diversity based on temporal evolutions, classifying global events into six types with distinct patterns in evolution, spatial distribution, and temporal variability. Notably, bimodal MHWs, which are strongly linked to El Ni & ntilde;o events, show the largest area growth rate when compared to other types, such as the canonical type, which features a single peak and balanced growth and decay phases. Furthermore, subsurface analysis reveals a strong correlation between subsurface structure and surface evolution. Finally, using a classification approach, we quantify MHW type predictability. We find that two types demonstrate high early classification skill at the initial time-step, indicating strong early stage predictability.

Under the dual constraints of ensuring economic growth and achieving the "dual carbon" goals, the impact of e-commerce development on urban carbon emissions has become a new frontier in this era. This study utilizes urban panel data from 2008 to 2021 to systematically investigate the specific effects and mechanisms of e-commerce on carbon emissions from both theoretical and empirical perspectives. The research reveals that pilot policies such as the construction of national e-commerce demonstration cities significantly reduce carbon emissions, a conclusion that holds true even after a series of robustness tests. Further exploration indicates that promoting green innovation and enhancing energy efficiency are vital mechanisms through which pilot policies of national e-commerce demonstration city construction drive carbon emission reductions. Regions in the eastern and southern parts of the country, along the Yangtze River Economic Belt, non-resource-based cities, and peripheral cities are better positioned to leverage the digital economy dividend to advance carbon emissions reduction. This study contributes to a deeper understanding of carbon reduction and the social impacts of e-commerce development.

The annual cycle of precipitation over most parts of Central America and southern Mexico is climatologically characterized by a robust bimodal distribution, normally termed as the midsummer drought (MSD), influencing a large range of agricultural economic and public insurances. Compared to studies focusing on mechanisms underpinning the MSD, less research has been undertaken related to its climatological signatures. This is due to a lack of generally accepted methods through which to detect and quantify the bimodal precipitation accurately. The present study focuses on characterizing the MSD climatological signatures over Central America and Mexico using daily precipitation observations between 1979 and 2017, aiming to provide a comprehensive analysis of MSD in fine scale over this region. This was completed using a new method of detection. The signatures were analyzed from three aspects, namely (1) climatological mean states and variability; (2) connections with large scale modes of climate variability (El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO)); and (3) the potential afforded by statistical modelling. The development of MSDs across the region is attributed to changes of surface wind–pressure composites, characterized by anomalously negative (positive) surface pressure and onshore (offshore) winds during the peak (trough) of precipitation. ENSO’s modulation of MSDs is also shown by modifying the surface wind–pressure patterns through MSD periods, inducing the intensified North Atlantic Subtropical High and associated easterlies from the Caribbean region, which induce relatively weak precipitation at corresponding time points and subsequently intensify the MSD magnitude and extend the MSD period. Building on previous research which showed MSDs tend to start/end in MJO phases 1 and 8, a fourth–order polynomial was used here to statistically model the precipitation time series during the rainy season. We show that the strength of the bimodal precipitation can be well modelled by the coefficient of the polynomial terms, and the intra-seasonal variability is largely covered by the MJO indices. Using two complete MJO cycles and the polynomial, the bimodal precipitation during the rainy season over Central America and Mexico is synoptically explained, largely contributing to our understanding of the MJO’s modulation on the MSD.

A systematic analysis of historical and modeled marine heatwaves (MHWs) off eastern Tasmania has been performed based on satellite observations and a high–resolution regional ocean model simulation, over the period from 1994–2016. Our analysis suggests that the distribution of large and intense mesoscale warm core eddies off northeast Tasmania contribute to the development of MHWs further south associated with changes in the circulation and transports. Importantly, we find that eddy distributions in the Tasman Sea can act as predictors of MHWs off eastern Tasmania. We used self-organizing maps to distinguish sea surface height anomalies (SSHA) and MHWs into different, but connected, patterns. We found the statistical model performs best (precision ~ 0.75) in the southern domain off eastern Tasmania. Oceanic mean states and heat budget analysis for true positive and false negative marine heatwave events revealed that the model generally captures ocean advection dominated MHWs. Using SSHA as predictor variable, we find that our statistical model can forecast MHWs off southeast Tasmania up to 7 days in advance above random chance. This study provides improved understanding of the role of circulation anomalies associated with oceanic mesoscale eddies on MHWs off eastern Tasmania and highlights that individual MHWs in this region are potentially predictable up to 7 days in advance using mesoscale eddy-tracking methods.

China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

Bimodal precipitation is a globally observed and regionally significant event that has a significant influence on the agriculture, public health, and insurance needs of associated regions. Many studies have focused on the mechanisms behind the generation and development of this event; however, little research into its characteristics exists due to a lack of a widely accepted method for accurate detection and quantification. Using a function collection containing various methods, different methods can be compared in terms of their performance in the detection and quantification of bimodal precipitation signals, allowing the proposal of appropriate criteria for method choice in various study types. Five methods (Mosiño and García, 1966; Curtis, 2002; Angeles et al., 2010; Karnauskas et al., 2013; Zhao et al., 2020) are adapted to the Climate Prediction Centre data during 1979–2017 in the domain of southern Mexico and Central America, and their performances are evaluated and compared. While outputs from the five methods reach general consistence for strong bimodal features over the Pacific side of Central America and Yucatán Peninsula, some biases are identified, specifically shown by the fact that methods using monthly climatological data demonstrates bimodal precipitation over the Caribbean side of Central America, while those using daily annual data indicate the existence of bimodal precipitation over the Pacific side of southern Mexico. By comparing two typical algorithms, we determined that this bias was induced by the limitation of temporal resolution in monthly climatological data and the nature of algorithms applying daily annual data. As part of a case study, a cluster algorithm was applied to outputs from an algorithm using daily annual precipitation, and a classification algorithm was used to test clustering performance. The resultant general high accuracy shows that annual bimodal signals offer good adaption to cluster and other potential machine learning algorithms.

Annual precipitation over Central America and large areas of Mexico is typically characterised by its bimodal distribution, with a precipitation minimum in July to August that occurs between two separate maxima from May to July and August to October. Several theories have been proposed to explain this phenomenon, which is often termed the mid-summer drought (MSD), but most fail to address the different characteristics associated with individual MSD events. Here, a regression-based approach is used to detect and quantify the annual and climatological MSD signature over Central America and Mexico. This approach has been evaluated and shown to be robust for various datasets with different spatial resolutions. It was found that in the southeast of the Mexico/Central America region, MSDs start earlier and end later than elsewhere, and are thus longer in duration. However, the coast of the Gulf of Mexico, Cuba, and large areas of Central America, exhibit climatologically stronger MSDs. Changes in precipitation, brought about by the interaction between reversals of the onshore/offshore winds and orographic forcing associated with the steep mountainous terrain, have also been shown to be significant factors in the timing of MSD occurrences, offering support for a combined theory of large-scale dynamics and regional forcing. Using self-organising maps (SOMs) as an analysis tool, it was found that MSD events over the domain display strong spatial variability. The MSDs over the domain also generate distinct signatures and may be forced by particular mechanisms. We found that El Niño-Southern Oscillation (ENSO) could be a potential classifier for the SOM identified atmospheric states, based on the correspondence of MSD occurrences with ENSO phases.

In this study, a new technique to determine distinct cloud regimes and their variation in space and time is proposed, evaluated, and applied to two satellite products over the Maritime Continent (MC). Compared to previous methods, the method presented here allows different types of cloud to co-exist in the same grid at the same time, giving rise to physically explainable and spatially continuous patterns in cloud regimes. Similar results generated by ISCCP – H and Himawari 8 data suggests that the method is robust. The 4 cloud regimes determined using this method are associated with shallow, mid-level, deep convective and high level clouds respectively. The analysis shows that he MJO–induced variation in total cloud fraction is dominated by day-time high–level clouds, while the diurnal MJO variability is mostly demonstrated by low–level cumulus. Spatial and temporal rainfall variability over the MC during austral summer is dominated by high–level clouds, while most local signatures and land–sea differences are attributed to deep convective clouds. Using an artificial neural network, the cloud patterns over the MC can be classified into nine categories, largely dominated by the MJO-phase. Active MJO activity is shown by systematic propagation around the cloud categories, with one category associated with the inactive MJO phase. The inhomogenous propagation of the MJO can partially be revealed in the generated patterns, which can be physically explained by the enhanced/suppressed convection over the Indian Ocean. This work has implications for understanding the MJO-scale variation in precipitation and diabatic heating associated with different cloud regimes, and its representation in mesoscale and climate scale modelling systems.

1 School of Earth Sciences, The University of Melbourne, Melbourne, Victoria, Australia 2 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia 3 Australian Research Council Centre of Excellence for Climate System Science, Hobart, Tasmania, Australia 4 College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China 5 CSIRO Oceans & Atmosphere, Indian Ocean Marine Research Centre, Crawley 6009, Western Australia, Australia DOI: 10.21105/joss.01124

The Central American mid‐summer drought (MSD) is the decline of precipitation during the middle of the wet season (July and August) over Central America and southern Mexico. It affects agriculture and favours the initiation of bushfires in Costa Rica's national parks, particularly during El Niño years. The MSD is a seasonal phenomenon that varies in intensity and timing inter‐annually. The Madden–Julian oscillation (MJO) has been shown to influence Costa Rican rainfall on intra‐seasonal time scales, and therefore may be important to the MSD. In this study we use rainfall data from seven stations in Costa Rica to analyse the MJO's influence on the timing of the onset and end of the MSD. We find that the MSD is more likely to start and end in MJO Phases 1 and 8, respectively. Our findings indicate enhanced MSD predictability on intra‐seasonal time scales, which could be beneficial to agricultural planning in Costa Rica.