Ocean-atmosphere Interactions Research Articles

Benefits of the coupling in the downscaling the South American climate

We evaluate the benefits of the use of a regional coupled model over its stand-alone atmospheric component when forced by reanalysis data in the simulation of the South American climate. We find that the coupling allows for a better simulation of important features of the atmospheric circulation and surface temperature. The simulated 2 meters air temperature is improved over most of the continent, the sea level pressure over the South Pacific Anticyclone area is better represented in the coupled simulation and the location of the ITCZ is improved during the austral winter. The precipitation, especially over the Andes, benefits less from the coupling, although a more realistic humidity transport leads to a reduction of the precipitation biases over extensive regions. The austral summer precipitation bias is reduced in areas such as eastern Colombia, northern Bolivia, eastern Brazil and central Argentina. For austral winter, the coupled model has a better performance in a large part of the Amazon region, in areas such as east of Peru, west Brazil, north Bolivia and south Argentina. Moreover, the regionally coupled model not only improves the simulation of important features of the observed atmospheric fields but also demonstrates good skills in reproducing the Humboldt upwelling system. Therefore, our study highlights the advantages of regional coupled models for the simulation of the South American climate, as the ocean-atmosphere interaction is of utmost importance for the circulation mechanisms that determine the climate of the region.

Oceanic Pollution and Its Influence on Global Climate Variability in India

Purpose: The aim of the study was to analyze oceanic pollution and its influence on global climate variability.
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: Oceanic pollution, including plastics, oil spills, heavy metals, and chemical contaminants, significantly impacts global climate variability. These pollutants harm marine ecosystems, disrupt food webs, and contribute to climate change. Plastic debris poses a severe threat to marine life, while oil spills devastate coastal environments. Heavy metals and chemical contaminants bio accumulate in marine organisms, posing risks to human health and altering biogeochemical processes essential for climate regulation.
 Unique Contribution to Theory, Practice and Policy: Ocean-atmosphere interaction theory, pollutants as climate forcers theory & feedback loop theory of oceanic pollution may be used to anchor future studies on oceanic pollution and its influence on global climate variability. Implement practical measures to reduce oceanic pollution, including stricter regulations on waste disposal, improved waste management infrastructure, and the promotion of sustainable consumption practices. Foster collaboration among nations to address oceanic pollution on a global scale.

Impact of the Gulf Stream front on atmospheric rivers and Rossby wave train in the North Atlantic

AbstractThe Gulf Stream (GS) ocean front exhibits intense ocean–atmosphere interaction in winter, which has a significant impact on the genesis and development of extratropical cyclones in the North Atlantic. The atmospheric rivers (ARs), closely related with the cyclones, transport substantial moisture from the North Atlantic towards the Western European coast. While the influence of the GS front on extratropical cyclones has been extensively studied, its effect on ARs remains unclear. In this study, two sets of ensemble experiments are conducted using a high-resolution global Community Atmosphere Model forced with or without the GS sea surface temperature front. Our findings reveal that the inclusion of the GS front leads to approximately 25% enhancement of water vapor transport and precipitation associated with ARs in the GS region, attributed to changes in both AR frequency and intensity. Furthermore, this leads to a more pronounced downstream response in Western Europe, characterized by up to 60% (40%) precipitation increases (reductions) around Spain (Norway) for the most extreme events (exceeding 90 mm/day). The influence of the GS front on ARs is mediated by both thermodynamic and dynamic factors. The thermodynamic aspect involves an overall increase of water vapor in both the GS region and Western Europe, promoting AR genesis. The dynamic aspect encompasses changes in storm tracks and Rossby wave train, contributing to downstream AR shift. Importantly, we find the co-occurrence of ARs and the GS front is crucial for inducing deep ascending motion and heating above the GS front, which perturbs the deep troposphere and triggers upper-level Rossby wave response. These findings provide a further understanding of the complex interaction between the oceanic front in the western boundary current regions and extratropical weather systems and the associated dynamics behind them.

Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning

ABSTRACT Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.

On The Interannual Variability of Indonesian Monsoon Rainfall (IMR): A Literature Review of The Role of its External Forcing

The IMR variability is notorious for its hydrometeorological disasters. This paper examines recent studies on IMR and the main factors controlling its variability. The focus of this study is to investigate the impact of the atmosphere-ocean interaction that acts as the external forcing of IMR in the tropical Indian and Pacific Oceans. Specifically, the study will examine the influence of two climate phenomena, namely the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) and their interdecadal changes associated Pacific Decadal Oscillation (PDO), on the IMR. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Furthermore, data sets (such as rainfall, wind field, and SST) spanning 1990-2020 were used to verify the key findings. In general, this study concludes that the majority of the authors coincided with the following conclusion: ENSO and IOD events impact IMR by changing its amplitude, duration, intensity, and frequency of mean and extreme rainfall. Additionally, it has been shown that their impacts on IMR are most substantial during the dry seasons, specifically in June, July, and August (JJA), and not as strong as during the wet seasons, specifically in December, January, and February (DJF). Spatially, the effects of ENSO and IOD on IMR variability are clearly found more eastward and westward of the region, respectively. The expansions towards the east and west directions were facilitated by the displacement of the ascending and descending of Walker circulation patterns in the Indonesian region, respectively. Given the interannual fluctuations in IMR, caused mainly by ocean-atmosphere interactions, the knowledge gap of atmospheric factors like the Quasi-Biennial Oscillation (QBO) must be investigated in the future, as suggested by previous research and our preliminary study.

Ocean atmospheric interaction on the cyclogenesis and rapid intensification of tropical cyclone Ockhi

Ocean atmospheric interaction on the cyclogenesis and rapid intensification of tropical cyclone Ockhi

MONITORING OF GLACIERS ON HORSESHOE ISLAND, ANTARCTICA BASED ON A DEEP LEARNING APPROACH FROM HIGH-RESOLUTION ORTHOPHOTOS (TAE-6 & TAE-7)

Abstract. Global climate change is a phenomenon that seriously affects the balance of a wide variety of ecosystems and is the intense focus of climate scientists and environmental researchers. In this context, periodic monitoring of glacier areas in terms of a better understanding of atmosphere-ocean interactions; thus, predicting the effects of climate change and planning against future threats by evaluating environmental impacts is an important research area. Especially the polar regions, where the melting of glaciers and the rise of sea levels are visibly observed, are important for climate scientists in providing crucial observations to understand and predict global climate change. In this study, within the scope of the international bilateral cooperation project carried out in cooperation with Istanbul Technical University (ITU) and the Bulgarian Academy of Sciences (BAS) (Project No: 121N033), the spatial changes in snow/glacier areas obtained from UAV Photogrammetry products generated during the 6th and 7th Antarctic National Science Expeditions. Snow/glacier areas were segmented with the K-Net deep learning approach which has been previously tested for accuracy and provides glacier mapping with accuracy metrics over 99%, on the high spatial resolution orthophotos produced during the two periods. The snow/glacier areas difference between the two periods were calculated and compared and water bodies which are critical areas, were specifically examined. The result of this comparison shows that the glacier area decreased by approximately 11% in just 1 year. However, to better understand these changes in snow/glacier areas, the region needs to be observed closely for longer time periods. It is thought that future studies will contribute to efforts to manage global environmental impacts and cope with climate change by focusing on monitoring and better understanding changes in these critical regions.

Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region

Abstract. In this article the development of a high-resolution Earth System Model (ESM) for the Baltic Sea region is described. In contrast to conventional coupling approaches, the presented model features an additional (technical) component, the flux calculator, which calculates fluxes between the model components on a common exchange grid. This approach naturally ensures conservation of exchanged quantities, a locally consistent treatment of the fluxes, and facilitates interchanging model components in a straightforward manner. The main purpose of this model is to downscale global reanalysis or climate model data to the Baltic Sea region as typically, global model grids are too coarse to resolve the region of interest sufficiently. The regional ESM consists of the Modular Ocean Model 5 (MOM5) for the ocean and the COSMO model in CLimate Mode (CCLM, version 5.0_clm3) for the atmosphere. The bi-directional ocean–atmosphere coupling allows for a realistic air–sea feedback that outperforms the traditional approach of using uncoupled standalone models, as typically pursued with the EURO-CORDEX protocol. In order to address marine environmental problems (e.g., eutrophication and oxygen depletion), the ocean model is internally coupled with the marine biogeochemistry model, ERGOM, set up for the Baltic Sea's hydrographic conditions. The regional ESM can be used for various scientific questions such as climate sensitivity experiments, reconstruction of ocean dynamics, study of past climates, and natural variability, as well as investigation of ocean–atmosphere interactions. Therefore, it can serve for a better understanding of natural processes via attribution experiments that relate observed changes to mechanistic causes.

Multi-scale spatial features and temporal attention mechanisms: advancing the accuracy of ENSO prediction

The exponential progression in oceanic observational technology has fostered the accumulation of substantial time series data pivotal for predictions in ocean meteorology. Foremost among the phenomena observed is El Niño-Southern Oscillation (ENSO), a critical determinant in the interplay of global ocean atmosphere interactions, with its severe manifestations inducing extreme meteorological conditions. Therefore, precisely predicting ENSO events carries immense gravitas. Historically, predictions hinged primarily on dynamic models and statistical approaches; however, the intricate and multifaceted spatiotemporal dynamics of ENSO events have often impeded the accuracy of these traditional methodologies. A notable lacuna in contemporary research is the insufficient exploration of long-term dependencies within oceanic data and the suboptimal integration of spatial information derived from spatiotemporal data. To address these limitations, this study introduces a forward-thinking ENSO prediction framework synergizing multiscale spatial features with temporal attention mechanisms. This innovation facilitates a more profound exploration of temporal and spatial domains, enhancing the retention of extensive-period data while optimizing the use of spatial information. Preliminary analyses executed on the global ocean data assimilation system dataset attest to the superior efficacy of the proposed method, underscoring a substantial improvement over established methods including SA-convolutional long short-term memory, particularly in facilitating long-term predictions.The source code and datasets are provided. The code is available at https://github.com/tse1998/ENSO-prediction.

Modulations of ocean-atmosphere interactions on squid abundance over Southwest Atlantic

Anthropogenic shifts in seas are reshaping fishing trends, with significant implications for aquatic food sources throughout this century. Examining a 21-year abundance dataset of Argentine shortfin squids Illex argentinus paired with a regional oceanic analysis, we noted strong correlations between squid annual abundance and sea surface temperature (SST) in January and February and eddy kinetic energy (EKE) from March to May in the Southwest Atlantic. A deeper analysis revealed combined ocean-atmosphere interactions, pinpointed as the primary mode in a rotated empirical orthogonal function analysis of SST. This pattern produced colder SST and amplified EKE in the surrounding seas, factors crucial for the unique life stages of squids. Future projections from the CMIP6 archive indicated that this ocean-atmosphere pattern, referred to as the Atlantic symmetric pattern, would persist in its cold SST phase, promoting increased squid abundance. However, rising SSTs due to global warming might counteract the abundance gains. Our findings uncover a previously unrecognized link between squids and specific environmental conditions governed by broader ocean-atmosphere interactions in the Southwest Atlantic. Integrating these insights with seasonal and decadal projections can offer invaluable information to stakeholders in squid fisheries and marine conservation under a changing climate.

Unraveling sub-seasonal precipitation variability in the Middle East via Indian Ocean sea surface temperature

This study examines sub-seasonal precipitation anomalies, challenging to predict yet vital for society and the environment. Focusing on October, we investigate correlations between the Indian Ocean Dipole Mode Index (DMI), West Tropical Indian Ocean Index (WTIO), and Middle Eastern precipitation. We find robust correlations (~ 0.7), up to a two-month lag, demonstrating strong links between these climate indices and rainfall patterns, potentially suggesting sub-seasonal precipitation predictability. Over the past four decades, DMI and WTIO have shown a significant upward trend of ~ 0.4 °C, intensifying their impact on precipitation dynamics. This trend signifies evolving Indian Ocean climate patterns with potential regional consequences and is projected to continue in the twenty-first century. Significant correlations also emerge between DMI, WTIO, and maximum daily precipitation, highlighting their role in extreme rainfall events. Finally, our study attributes most of October’s precipitation variability to Indian Ocean sea surface temperature variations. These temperature anomalies influence the Indian Ocean’s Walker circulation, affecting water vapor flux to the Middle East and shaping regional precipitation. Our findings underscore the importance of these indices in understanding and predicting Middle East climate variability, revealing intricate ocean–atmosphere interactions.

Relationship between El Niño-Southern Oscillation and Atmospheric Aerosols in the Legal Amazon

The El Niño-Southern Oscillation (ENSO) stands out as the most significant tropical phenomenon in terms of climatic magnitude resulting from ocean–atmosphere interaction. Due to its atmospheric teleconnection mechanism, ENSO influences various environmental variables across distinct atmospheric scales, potentially impacting the spatiotemporal distribution of atmospheric aerosols. Within this context, this study aims to evaluate the relationship between ENSO and atmospheric aerosols across the entire Legal Amazon during the period from 2006 to 2011. Over this five-year span, four ENSO events were identified. Concurrently, an analysis of the spatiotemporal variability of aerosol optical depth (AOD) and Black Carbon radiation extinction (EAOD-BC) was conducted alongside these ENSO events, utilizing data derived from the Aerosol Robotic Network (AERONET), MERRA-2 model, and ERSSTV5. Employing the Windowed Cross-Correlation (WCC) approach, statistically significant phase lags of up to 4 to 6 months between ENSO indicators and atmospheric aerosols were observed. There was an approximate 100% increase in AOD immediately after El Niño periods, particularly during intervals encompassing the La Niña phase. The analysis of specific humidity anomaly (QA) revealed that, contrary to expectations, positive values were observed throughout most of the El Niño period. This result suggests that while there is a suppression of precipitation events during El Niño due to the subsidence of drier air masses in the Amazon, the region still exhibits positive specific humidity (Q) conditions. The interaction between aerosols and humidity is intricate. However, Q can exert influence over the microphysical and optical properties of aerosols, in addition to affecting their chemical composition and aerosol load. This influence primarily occurs through water absorption, leading to substantial alterations in radiation scattering characteristics, and thus affecting the extinction of solar radiation.

Simulation of MJO with improved deep convection scheme in different resolutions of BCC-CSM2 models

AbstractThis study investigates the impacts of modifying the deep convection scheme on the ability to simulate the Madden–Julian Oscillation (MJO) in the Beijing Climate Center Climate System Model version 2 with a medium resolution (BCC-CSM2-T159) and a high resolution (BCC-CSM2-T382). On the basis of the original deep convection scheme, a modified scheme is suggested, which involves the transport processes of deep convective cloud water. The liquid cloud water that is detrained is transferred horizontally to its neighboring grids, and a portion of the cloud water that is horizontally transported is allowed to be transported downward into the lower troposphere. Both BCC-CSM2-T159 and BCC-CSM2-T382 with the modified deep convection scheme perform better than that used the original deep convection scheme in reproducing the major features of the MJO, such as its spectrum, period, intensity, eastward propagation and life cycle. Further analysis shows that those pronounced improvements in the MJO features in both BCC-CSM2-T159 and BCC-CSM2-T382 with the modified scheme are caused by transport processes of deep convective cloud water. The modified deep convection scheme enhances moisture and energy exchange from the lower troposphere to the upper troposphere around convective cloud, and promotes the convergence of moisture in the lower troposphere to the east of the MJO convection center, and then induces eastward propagation of the MJO. The comparisons between the coupled experiments and their corresponding experiments following Atmospheric Model Intercomparison Project (AMIP) simulations indicated that atmosphere–ocean interactions are also important to improve MJO simulations in the models.

Processes Contributing to Bering Sea Temperature Variability in the Late Twentieth and Early Twenty-First Century

Abstract Over recent decades, the Bering Sea has experienced oceanic and atmospheric climate extremes, including record warm ocean temperature anomalies and marine heatwaves (MHWs), and increasingly variable air–sea heat fluxes. In this work, we assess the relative roles of surface forcing and ocean dynamical processes on mixed layer temperature (MLT) tendency by computing a closed mixed layer heat budget using the NASA/JPL Estimating the Circulation and Climate of the Ocean (ECCO) Ocean State and Sea Ice Estimate. We show that surface forcing drives the majority of the MLT tendency in the spring and fall and remains dominant to a lesser degree in winter and summer. Surface forcing anomalies are the dominant driver of monthly mixed layer temperature tendency anomalies (MLTa), driving an average of 72% of the MLTa over the ECCO record length (1992–2017). The surface turbulent heat flux (latent plus sensible) accounts for most of the surface heat flux anomalies in January–April and September–December, and the net radiative flux (net longwave plus net shortwave) dominates the surface heat flux anomalies in May–August. Our results suggest that atmospheric variability plays a significant role in Bering Sea ocean temperature anomalies through most of the year. Furthermore, they indicate a recent increase in ocean warming surface forcing anomalies, beginning in 2010. Significance Statement In recent years, the Bering Sea has experienced extremes in ocean temperature, which have had adverse impacts on ocean ecology and marine fisheries and have contributed to increasingly variable sea ice extent. Our results identify anomalous heating by air–sea heat flux anomalies as the process responsible for most of the observed ocean temperature anomalies over the period 1992–2017. We additionally show that there has been an increase in atmosphere-driven ocean warming since 2010. Our work highlights the importance of investigating how ocean–atmosphere interactions might change under future climate change and how this will impact the Bering Sea.

The Significance of the Melt-Pond Scheme in a CMIP6 Global Climate Model

Abstract The impact of melt ponds on sea ice albedo has been observed and documented. In general circulation models, ponds are now accounted for through indirect diagnostic treatments (“implicit” schemes) or prognostic melt-pond parameterizations (“explicit” schemes). However, there has been a lack of studies showing the impacts of these schemes on simulated Arctic climate. We focus here on rectifying this using the general circulation model HadGEM3, one of the few models with a detailed explicit pond scheme. We identify the impact of melt ponds on the sea ice and climate, and associated ice–ocean–atmosphere interactions. We run a set of constant forcing simulations for three different periods and show, for the first time, that using mechanistically different pond schemes can lead to very significantly different sea ice and climate states. Under near-future conditions, an implicit scheme never yields an ice-free summer Arctic, while an explicit scheme yields an ice-free Arctic in 35% of years and raises autumn Arctic air temperatures by 5° to 8°C. We find that impacts on climate and sea ice depend on the ice state: under near-future and last-interglacial conditions, the thin sea ice is very sensitive to pond formation and parameterization, whereas during the preindustrial period the thicker sea ice is less sensitive to the pond scheme choice. Both of these two commonly used parameterizations of sea ice albedo yield similar results under preindustrial conditions but in warmer climates lead to very different Arctic sea ice and ocean and atmospheric temperatures. Thus, changes to physical parameterizations in the sea ice model can have large impacts on simulated sea ice, ocean, and atmosphere. Significance Statement This study investigates the impacts of melt ponds on Arctic sea ice under different climate conditions, using the HadGEM3-GC3.1-LL general circulation model (GCM). Additionally, we study the impact of changing the type of pond scheme used. We find that changing the pond scheme causes large differences to how a GCM simulates Arctic sea ice, the ocean, and the atmosphere, for both near-future and warmer paleoclimate conditions. These large differences have not been found previously, because this is one of the first GCM studies of this type. Our results demonstrate the importance of melt ponds, and their wider impacts on ocean and atmosphere. Furthermore, they suggest that better evaluation of the representation of sea ice processes is vital for the robust projection of future climate change.

Ocean-atmosphere interactions in climate formation, variability and change

Ocean-atmosphere interactions in climate formation, variability and change

Improved tropical cyclone wind speed estimation for microwave altimeter using machine learning

Satellite altimeters can provide excellent global wind speed at 10 m above the sea surface (U10), however, the U10 becomes inaccurate and difficult to measure in tropical cyclone conditions. The violent wind, rough waves and torrential rain manifest an exceptionally complex ocean-atmospheric environment for wind estimation. Although the backscatter signal is measured equally well in normal condition, the interpretation is not straightforward in tropical cyclones that requires complex associations with ocean-atmospheric geophysical variables. Typical U10 regression model developed in normal atmospheric conditions would inevitably reduce the estimation quality and encounter high modelling uncertainties from high dimensional input data that provide ill-posed solutions in extreme U10 estimation. However, other secondary parameters simultaneously measured by the altimeter have properties that convey additional atmospheric information to enhance U10 estimation accuracy in storm condition. Therefore, the present study proposes machine learning (ML) approaches based on artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR) to integrate the multi-dimensional parameters and provide accurate U10 estimates for different parameter combination. Results suggest that the GPR method, considering atmospheric and sea surface related parameters, can provide the highest accuracy of U10 up to 35 ms−1 with quality perseverance against rain contamination. This study highlights the quality improvement of altimeter U10 estimation and the assessment method of ML in dealing with complex ocean-atmospheric interactions.

Investigating Atmospheric Responses to and Mechanisms Governing North Atlantic Sea Surface Temperatures over 10-Year Periods

Abstract North Atlantic sea surface temperature (SST) variability plays a critical role in modulating the climate system. However, characterizing patterns of North Atlantic SST variability and diagnosing the associated mechanisms is challenging because they involve coupled atmosphere–ocean interactions with complex spatiotemporal relationships. Here we address these challenges by applying a time-evolving self-organizing map approach to a long preindustrial coupled control simulation and identify a variety of 10-yr spatiotemporal evolutions of winter SST anomalies, including but not limited to those associated with the North Atlantic Oscillation–Atlantic multidecadal variability (NAO–AMV)-like interactions. To assess mechanisms and atmospheric responses associated with various SST spatiotemporal evolutions, composites of atmospheric and oceanic variables associated with these evolutions are investigated. Results show that transient-eddy activities and atmospheric circulation responses exist in almost all the evolutions that are closely correlated to the details of the SST pattern. In terms of the mechanisms responsible for generating various SST evolutions, composites of ocean heat budget terms demonstrate that contributions to upper-ocean temperature tendency from resolved ocean advection and surface heat fluxes rarely oppose each other over 10-yr periods in the subpolar North Atlantic. We further explore the potential for predictability for some of these 10-yr SST evolutions that start with similar states but end with different states. However, we find that these are associated with abrupt changes in atmospheric variability and are unlikely to be predictable. In summary, this study broadly investigates the atmospheric responses to and the mechanisms governing the North Atlantic SST evolutions over 10-yr periods. Significance Statement Climate variability in the North Atlantic Ocean has wide-ranging impacts on global and regional climate. However, the processes involved include interactions between the ocean and atmosphere that vary across both space and time, making it challenging to characterize and predict. Using a novel machine learning approach, this study identifies various time evolutions of North Atlantic sea surface temperature patterns over 10-yr periods. This includes evolutions with similar start states but different trajectories, which have important implications for predictability. Furthermore, we investigate the mechanisms responsible for these evolutions and how different sea surface temperature patterns affect atmospheric circulation through small-scale atmospheric disturbances. These new insights into the complex ocean–atmosphere interactions over time are critical for improving decadal prediction skill.

Two-phase hydrological changes during the mid-to late Holocene transition in Southwest China

The Indian summer monsoon (ISM) rainfall feeds a vast majority of the densely populated Asian continent. A deep understanding of the ISM rainfall variations and the underlying driving mechanisms during the Holocene is essential to predict its future behaviors and formulate improved mitigating strategies under the context of global warming. It has been widely recognized that the ISM intensity declined from the early to late Holocene, with a mid-to late Holocene transition. However, the nature of hydrological changes during this critical transition still remains vague. In this study, hydrological changes in Southwest China during the mid-to late Holocene transition are reconstructed based on the molecular and compound-specific carbon and hydrogen isotopic compositions of leaf waxes (δ13C and δD of n-alkanes) from the Yanjiang peat deposit in western Yunnan. Results reveal two-phase changes of hydroclimate during the mid-to late Holocene transition in Southwest China. During the first phase (5.5–4.2 cal kyr BP), Southwest China experienced an abrupt drying as indicated by the pronounced increases in both δ13C and δD values of the C29n-alkane (δ13CC29 and δDC29) by 2.2% and 26‰, respectively. The second phase (4.2–2.6 cal kyr BP) witnessed shortening in the duration of the ISM season without further aridification, as demonstrated by an increase up to 35‰ in the δDC29 values and moderate oscillations in the δ13CC29 values. Such a two-phase hydrological transition is likely to have been triggered by the changes in the summer solar insolation and amplified by the ocean-atmosphere interactions. The first phase is primarily modulated by the positive Indian Ocean Dipole mean state, while the second phase is mainly linked with a more El Niño-like mean state over the Pacific Ocean.

Tropical Cyclonic Energy Variability in North Indian Ocean: Insights from ENSO

Tropical cyclones (TC) are one of the deadliest natural meteorological hazards with destructive winds and heavy rains, resulting losses often reach billions of dollars, imposing a substantial and long-lasting burden on both local and national economies. The El-Niño Southern Oscillation (ENSO), a tropical ocean–atmosphere interaction, is known to significantly impact cyclonic systems over global ocean basins. This study investigates the variability of TC activity in the presence of ENSO over the North Indian Ocean (NIO), comprising the Arabian Sea (ARB) and the Bay of Bengal (BOB) basins during the pre- and post-monsoon season, using accumulated cyclone energy (ACE) over the last 29 years. Our analysis reveals a significant rise in tropical cyclone energy intensity over the past two decades, with eight of the ten most active years occurring since the 2000s. Total ACE over the NIO is found to be higher in La-Niña. Higher ACE observed over ARB is strongly associated with a combination of elevated sea surface height (SSH) anomaly and low vertical wind shear during the El-Niño episodes, with higher sea surface temperatures (SST) during the post-monsoon season. Whereas in the BOB, El Niño not only reduces ACE, but also decreases basin-wide variability, and more pronounced effects during the post-monsoon season, coinciding with warmer SST and higher SSH along the coast during La-Niña.