Measurements of surface-atmosphere carbon dioxide (CO2) and methane (CH4) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO2 flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO2 fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH4 fluxes. For aquatic ecosystems, we split CH4 fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO2 and CH4 concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH4 fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO2 dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH4 flux site-months (3098) were more frequent than CO2 flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO2 flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH4 data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our understanding of climate feedbacks. The data can be accessed at ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2448 (Virkkala et al., 2026).

Extensive offshore renewable energy installations have raised concerns about their environmental impacts. These concerns highlight the need for high fidelity modeling of conditions within wind-farm regions, where wave-structure interactions through reflection, diffraction, and dissipation reshape local and regional wave dynamics, thereby influencing energy conversion efficiency and altering surrounding hydrodynamic conditions. However, accurately representing these wave-structure interactions remains a major challenge for spectral wave models, which often oversimplify turbines as energy sinks and thus introduce nonphysical dissipation. This study develops a new parameterization to represent distinct regimes of wave-structure interactions according to the ratio of wavelength to structural size. When wave and structure scales are comparable, wave scattering dominates and is represented as an energy-conserving source term based on diffraction theory, allowing for directional redistribution of wave energy. Drag-induced dissipation dominates for cases where the wavelength greatly exceeds the structural scale and is parameterized by a dissipative source term. Both regimes are formulated within a unified framework and implemented in the wave spectral model, WAVEWATCH III. Numerical simulations demonstrate that the proposed parameterization improves the physical realism of wave-structure interactions. The modeled wave field exhibits a strong dependence on wave-structure scale ratio and a distinct spatial pattern in significant wave height, with amplification upstream of the farm and attenuation downstream. These findings offer a physics-based solution, supporting future offshore renewable energy development and improving the understanding of its impacts on the marine environment.

Freshwater systems are important sources of atmospheric methane (CH 4 ). However, estimated emissions are associated with high uncertainties due to limited knowledge about the temporal variability in emissions and their associated controls, such as air–water gas transfer velocity. Here, we determined the gas transfer velocity of CH 4 based on a novel measurement setup that combines simultaneous eddy covariance flux measurements with continuously monitored CH 4 water‐ and air‐side concentrations. Measurements were conducted during a 10‐d campaign in a freshwater lake in mid‐Sweden. The gas transfer velocity fell within the range of existing wind‐speed‐based parameterizations derived for carbon dioxide in other lakes. For wind speeds below 4 m s −1 , the gas transfer velocity for CH 4 followed parameterizations predicting faster gas exchange, while for wind speeds above 5 m s −1 , it aligned with those predicting relatively lower gas exchange. This pattern can be explained by ebullition. Extending the wind speed range for such combined eddy covariance measurements with continuously monitored CH 4 water‐ and air‐side concentrations would improve model reliability.

Understanding uncertainties in meteorological extremes induced by Atmospheric river (AR) structural uncertainties can help to develop effective strategies to mitigate AR induced hazards and adapt to changing climate conditions. As a first step, this study examines the statistical relationship between AR structural uncertainty and the characterisation of associated meteorological extremes over the Euro-Atlantic region, using long-term historical data from ECMWF Reanalysis v5 (ERA5) during 1940 to 2022. Leveraging the Bayesian AR detection (BARD), a form of statistical machine learning model in the Toolkit for Extreme Climate Analysis (TECA), we examine the impact of structural uncertainties in AR dimensions on daily precipitation (wet), wind speeds (windy), and temperature (warm/cold) anomalies and extremes over Europe, the UK and Scandinavia. A large spread in the aggregated detected AR probabilities (ARP) spatially and temporally led to differences in ARs’ attributes, such as frequency, integrated water vapour transport (IVT; intensity), and their impact on weather parameters, anomalies and extremes at selected probability thresholds across space and time. The magnitude of AR impacts and associated meteorological phenomena over land varies based on the chosen deciles (dividing ARP into ten equal parts with a 0.1 increase) of ARPs, along with the default threshold from the model ( $$ARP \ge 0.67$$ ). AR intensities and landfalling area are increasing over the study period, irrespective of the selected ARP. The effects of AR structural uncertainties are more prominent over inland Europe and Scandinavia than over coastal Europe and the UK. The physical and meteorological phenomena underlying these results require further exploration to understand the impact of landfalling ARs on land.

Atmospheric rivers (AR) are essential to the global water cycle, significantly impacting regional precipitation patterns and extreme weather events. Existing AR scales, while valuable, often lack regional specificity, particularly in capturing the nuances of ARs in subpolar regions. This study addresses these limitations by developing an improved AR scale and impact metrics tailored to the Euro-Atlantic region. Using high-resolution reanalysis data and the Bayesian AR Detector, we analyse AR characteristics and highlight contrasts between Pacific and Atlantic ARs. Recognising the limitations of the current AR scale developed by the Centre for Western Weather and Water Extremes, we introduce the new Uppsala University AR scale (NS), specifically designed for the Euro-Atlantic region. The NS incorporates a lower integrated water vapour threshold, equal weights for all categories, and a 72-hour persistence, reflecting regional AR behaviour more accurately. The study proposes alternative impact metrics - AR Severity Index (ARSI), ARSI-kinetic, and Risk Index - to comprehensively assess AR impacts. While ARSI focuses on physical strength, the Risk Index combines intensity with contextual factors for a broader view of overall risk. These refined metrics and the region-specific AR scale enhance understanding of AR impacts and hazards in the Euro-Atlantic region, contributing to improved forecasting, disaster preparedness, and water resource management.

Offshore wind energy, as a clean and renewable resource, offers numerous advantages over onshore wind energy due to higher wind speeds and greater turbine capacity. However, the inadequate representation of wave- atmosphere interaction within the marine-atmospheric boundary layer may constrain wind resource assessments and, consequently, the design and layout of offshore wind turbines. By using the third-generation spectral wave model WAVEWATCH III, high-resolution numerical experiments which are capable of resolving the wind turbine foundation have been conducted to explore the blocking impact of wind turbine foundation on downstream wind stress. The findings provided significant insights into the factors influencing this effect, including foundation diameter, sea state, water depth, and wave directional spreading. Specifically, higher model resolution enables a more detailed characterization of wind stress distribution behind wind turbines. Increasing the foundation diameter resulted in a reduction of downstream wind stress. Notably, changes in wind stress exhibit a strong dependence on the sea state. The use of double periodic boundary conditions for the simulation domain enables an approximate representation of the entire wind farm by using a single turbine. Model results indicate that after waves pass through 20 turbines, domain-averaged wind stress decreases by approximately 3%, a figure that increases to 6% after passing through 50 turbines. The findings suggest the need to explore to what extent the changes in wind stress can alter wind profiles and how to parameterize the blocking impact of turbine foundations in wave models. This could have significant implications for wind farm site selection and layout design.

Swell waves, characterized by the long wavelength components generated by distant weather systems or storms, exert a significant influence on various air-sea interaction processes, thereby impacting weather and climate systems. Over recent decades, substantial progress has been achieved in comprehending the dynamics of swell waves and their implications for air-sea interactions. This paper presents a comprehensive review of advancements and key findings concerning surface swell waves and their interactions with the atmosphere. It encompasses a range of topics, including wave growth theory, the effects of swell waves on air-sea momentum, heat, and mass fluxes, as well as their influence on atmospheric turbulence and mixed layer processes. The most important characteristics of the swell impact (where it differs from wind sea conditions) are the wave-induced upward component of the surface stress leading to alteration of total surface stress, generation of a low-level wind maxima or changed wind profile and change of scale and behaviour of turbulence properties (turbulence kinetic energy and integral length scale). Furthermore, the paper explores the modelling of swell dissipation, the integration of swell influences in weather and climate models, and the broader climatic implications of surface swell waves. Despite notable advances in understanding swell processes, persistent knowledge gaps remain, underscoring the need for further research efforts, which are outlined in the paper.

While orographic and regional climate effects play a crucial role in how Atmospheric Rivers (ARs) affect the weather, it is essential to reduce uncertainty in detecting ARs to understand their impact on weather patterns better. This study delves into the relationship between AR detection uncertainty and the characterisation of meteorological extremes over the Euro-Atlantic region using long-term historical data from ERA5 during 1940-2022. Leveraging the Bayesian AR detection (BARD), a form of statistical machine learning model in the Toolkit for Extreme Climate Analysis (TECA), we examine AR detections and variability and their consequential impact on precipitation, wind speeds and temperature patterns over Europe, the UK and Scandinavia. There is widespread uncertainty and disagreement among AR detections from 1024 sets of AR priories (detectors) from the model at a given instance, which led to a large spread in the aggregated detected AR probabilities (ARP) spatially and temporally. The study reveals significant differences in ARs’ attributes, such as frequency, intensity, and their impact on weather phenomena at selected probability thresholds across space and time. The magnitude of meteorological phenomena associated with ARs over land varies based on the chosen deciles of ARPs. Although AR frequencies and intensities are increasing in many parts of Europe and the UK, the effects of AR detection uncertainties are more prominent over inland Europe and Scandinavia than over coastal Europe and the UK. Using the entire probability spectrum is an alternative to reduce uncertainty but may result in computational constraints and imprecise discernment of extreme events. Understanding meteorological extremes induced by AR uncertainties can help us develop effective strategies to mitigate hazards and adapt to changing climate conditions.

Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment; the U.S. Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis, and the possibility to observe these variables in real-time via measurements. The wind speed at 10 m a.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the Symmetric Extremal Dependence Index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

Over the past decade, the number of methods for Atmospheric River (AR) detection has increased, highlighting the growing understanding that uncertainty in detection may affect scientific knowledge. This study evaluates and validates the regional scale implementation of Bayesian AR Detector (BARD), a statistical machine learning model developed to reduce the uncertainty in AR tracking (ART), using three different horizontal and vertical domains of background integrated water-vapour transport (IVT) field and focusing on the pan-Atlantic region during 1940-2022 using ERA5 data. The consistency in seasonal AR Probability (ARP) and IVT differences across 3 model runs indicates that all configurations capture the general seasonal cycle of ARs, with enhanced activity and moisture transport in the midlatitudes during winter. However, discrepancies in selected IVT backgrounds and domains led to anomalies’ magnitude and spatial distribution, particularly in AR detection probability and AR IVT over Western and Northern Europe. These discrepancies among model runs are large over the ocean where ARs take shape and are consistent in climate modes such as a strong positive El Niño-Southern Oscillation (ENSO+) of 2015-2016. This reflects the robust and inherent differences in how each configuration maps AR dimensions and their associated transport processes. Further, these biases in AR mapping across model runs led to higher differences in AR-induced precipitation, wind speed, and temperature in Northern Europe and Scandinavia. This comparison underscores the importance of evaluating model configurations to assess uncertainties in AR representation under varying IVT background fields across regional domains and climate conditions.

After this paper was published, one of the readers of our paper pointed out that CA-Est and FI-Van did not have any T water data.Therefore, for each panel in figure 4, the mutual information score for T water and CA-Est will now be replaced with a white color.The same will be carried out for T water and FI-Van.A brief sentence has also been added at the end of the figure 4 caption to indicate lack of T water data for CA-Est and FI-Van.This correction does not affect our results.We apologize for any inconvenience these errors may have caused.

Abstract. The change of wind direction with height (the directional shear) affects both the power production from a wind turbine, wake effects and aerodynamic loading. In this study, a climatology of the relative occurrence of strong directional shear over Scandinavia is created using 43 years of hourly ERA5 data covering the height range of a modern wind turbine and at wind speeds of operation. It is shown that strong directional shear (≥15° over the rotor) is occurring 20–30 % of the time over land and 10–25 % of the time over the extended Baltic Sea. The height of the atmospheric boundary-layer and the wind speed at hub height are identified as the most important predictors for strong directional shear, with low boundary-layer heights and weak winds being the main causes. Associated with this, a strong land–sea seasonality is observed. Furthermore, ERA5 is validated against lidar soundings from two coastal sites, both indicating a major underestimation in the distribution of the directional shear in ERA5. Especially in strongly stratified boundary-layers ERA5 struggles, with 25 % of the data having errors exceeding 24° and 28° for Östergarnsholm and Utö respectively.

Low-level jets (LLJs) are examples of non-logarithmic wind speed profiles affecting wind turbine power production, wake recovery, and structural/aerodynamic loading. However, there is no consensus regarding which definition should be applied for jet identification. In this study we argue that a shear definition is more relevant to wind energy than a falloff definition. The shear definition is demonstrated and validated through the development of a European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) LLJ climatology for six sites. Identification of LLJs and their morphology, frequency, and intensity is critically dependent on the (i) vertical window of data from which LLJs are extracted and (ii) the definition employed.

Strong winds mean a lot of power production by wind turbines! In the search for the windiest locations with the potential to produce lots of power, the coastal zone has popped up as one of the most appealing places. However, there are some peculiar wind patterns in coastal regions that need to be well-understood to accurately predict how much power can be produced. In this article, we will explain the mechanisms behind two of these wind patterns: the sea breeze and low-level jets.

One of the most prominent mesoscale phenomenon in the coastal zone is the sea breeze/land breeze circulation. The pattern and its implications for the weather in coastal areas is well described and with mesoscale resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of land-sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes.

Recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and floods. On the other hand, the role of ocean variability is at a focal point in improving the forecasting at different time scales. Here we study the effect of Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) on Indian summer monsoon rainfall during 1993-2019. While SSTA and MSLA have been increasing in the southwestern Indian Ocean (SWIO), these parameters' large-scale variability and pre-monsoon winds could impact the inter-annual Indian monsoon rainfall variability over homogeneous regions. Similarly, antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability from an oceanic perspective. Though both SSTA and MSLA over SWIO have been influenced by El Niño-southern oscillation (ENSO), the impact of SWIO variability was low on rainfall variability over several homogeneous regions. However, rainfall over northeast (NE) and North India (NI) has been moulded by ENSO, thus changing the annual rainfall magnitude. Nevertheless, the impact of ENSO on monsoon rainfall through SWIO variability during the antecedent months is moderate. Thus, the ENSO influence on the atmosphere could be dominating the ocean part in modulating the inter-annual variability of the summer monsoon. Analysis shows that the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period.

Accounting for temporal changes in carbon dioxide (CO _2 ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO _2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO _2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO _2 . Annual CO _2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO _2 flux variability were delineated through mutual information analysis. Sample analysis of CO _2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO _2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Diel to interannual variation in carbon dioxide emissions from lakes and reservoirsAuthorsMalgorzataGolubiDNikaanKoupaei-AbyazaniiDTimoVesalaIvanMammarellaiDAnneOjalaGilBohreriDGesa AWeyhenmeyeriDPeter D.BlankeniDWernerEugsteriDFranziskaKoebschJiquanCheniDKevin P.CzajkowskiiDChandrashekharDeshmukhFrédéricGuérinJouniHeiskanenElynHumphreysiDAndersJonssoniDJanKarlssoniDGeorge W.KlingiDXuhuiLeeiDHepingLiuAnnaleaLohilaiDErik JohannesLundiniDTimothy HectorMorinEvaPodgrajsekMariaProvenzaleAnnaRutgersoniDTorstenSachsiDErikSahléeDominiqueSerçaiDChangliangShaoiDChristopherSpenceIan B.StrachaniDWeiXiaoiDAnkur RashmikantDesaiiDSee all authors Malgorzata GolubiDDundalk Institute of TechnologyiDhttps://orcid.org/0000-0001-9361-0331view email addressThe email was not providedcopy email addressNikaan Koupaei-AbyazaniiDUniversity of Wisconsin-MadisoniDhttps://orcid.org/0000-0001-6982-230Xview email addressThe email was not providedcopy email addressTimo VesalaUniversity of Helsinki, Institute for Atmospheric and Earth System Researchview email addressThe email was not providedcopy email addressIvan MammarellaiDUniversity of HelsinkiiDhttps://orcid.org/0000-0002-8516-3356view email addressThe email was not providedcopy email addressAnne OjalaNatural Resources Instituteview email addressThe email was not providedcopy email addressGil BohreriDOhio State UniversityiDhttps://orcid.org/0000-0002-9209-9540view email addressThe email was not providedcopy email addressGesa A WeyhenmeyeriDEcology and Genetics/LimnologyiDhttps://orcid.org/0000-0002-4013-2281view email addressThe email was not providedcopy email addressPeter D. BlankeniDUniversity of Colorado BoulderiDhttps://orcid.org/0000-0002-7405-2220view email addressThe email was not providedcopy email addressWerner EugsteriDETH ZurichiDhttps://orcid.org/0000-0001-6067-0741view email addressThe email was not providedcopy email addressFranziska KoebschGFZ German Research Centre for Geosciencesview email addressThe email was not providedcopy email addressJiquan CheniDMichigan State UniversityiDhttps://orcid.org/0000-0003-0761-9458view email addressThe email was not providedcopy email addressKevin P. CzajkowskiiDUniversity of ToledoiDhttps://orcid.org/0000-0002-0472-4204view email addressThe email was not providedcopy email addressChandrashekhar DeshmukhAPRIL Asiaview email addressThe email was not providedcopy email addressFrédéric GuérinIRD - Marseille, France.view email addressThe email was not providedcopy email addressJouni HeiskanenUniversity of Helsinkiview email addressThe email was not providedcopy email addressElyn HumphreysiDCarleton UniversityiDhttps://orcid.org/0000-0002-5397-2802view email addressThe email was not providedcopy email addressAnders JonssoniDDepartment of Ecology and Environmental ScienceiDhttps://orcid.org/0000-0002-0807-0201view email addressThe email was not providedcopy email addressJan KarlssoniDUmea UniversityiDhttps://orcid.org/0000-0001-5730-0694view email addressThe email was not providedcopy email addressGeorge W. KlingiDUniversity of Michigan-Ann ArboriDhttps://orcid.org/0000-0002-6349-8227view email addressThe email was not providedcopy email addressXuhui LeeiDYale University, School of Forestry and Environmental StudiesiDhttps://orcid.org/0000-0003-1350-4446view email addressThe email was not providedcopy email addressHeping LiuWashington State Universityview email addressThe email was not providedcopy email addressAnnalea LohilaiDFinnish Meteorological InstituteiDhttps://orcid.org/0000-0003-3541-672Xview email addressThe email was not providedcopy email addressErik Johannes LundiniDSwedish Polar Research SecretariatiDhttps://orcid.org/0000-0002-3785-8305view email addressThe email was not providedcopy email addressTimothy Hector MorinState University of New York College of Environmental Science and Forestryview email addressThe email was not providedcopy email addressEva PodgrajsekOX2view email addressThe email was not providedcopy email addressMaria ProvenzaleUniversity of Helsinkiview email addressThe email was not providedcopy email addressAnna RutgersoniDUppsala UniversityiDhttps://orcid.org/0000-0001-7656-1881view email addressThe email was not providedcopy email addressTorsten SachsiDHelmholtz Centre Potsdam - German Research Centre for Geosciences (GFZ)iDhttps://orcid.org/0000-0002-9959-4771view email addressThe email was not providedcopy email addressErik SahléeEarth Sciencesview email addressThe email was not providedcopy email addressDominique SerçaiDLaboratoire d'Aérologie, Université de Toulouse, CNRS, UPS, FranceiDhttps://orcid.org/0000-0001-8688-1440view email addressThe email was not providedcopy email addressChangliang ShaoiDInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural SciencesiDhttps://orcid.org/0000-0002-4968-8577view email addressThe email was not providedcopy email addressChristopher SpenceEnvironment and Climate Change Canadaview email addressThe email was not providedcopy email addressIan B. StrachaniDMcGill UniversityiDhttps://orcid.org/0000-0001-6457-5530view email addressThe email was not providedcopy email addressWei XiaoiDNanjing University of Information Science and TechnologyiDhttps://orcid.org/0000-0002-9199-2177view email addressThe email was not providedcopy email addressAnkur Rashmikant DesaiiDCorresponding Author• Submitting AuthorUniversity of Wisconsin-MadisoniDhttps://orcid.org/0000-0002-5226-6041view email addressThe email was not providedcopy email address

Abstract Recent rapid changes in the global climate and warming temperatures increase the demand for local and regional weather forecasting and analysis to improve the accuracy of seasonal forecasting of extreme events such as droughts and floods. On the other hand, the role of ocean variability is at a focal point in improving the forecasting at different time scales. Here we study the effect of Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) on Indian summer monsoon rainfall during 1993-2019. While SSTA and MSLA have been increasing in the southwestern Indian Ocean (SWIO), these parameters' large-scale variability and pre-monsoon winds could impact the inter-annual Indian monsoon rainfall variability over homogeneous regions. Similarly, antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability from an oceanic perspective. Though both SSTA and MSLA over SWIO have been influenced by El Niño-southern oscillation (ENSO), the impact of SWIO variability was low on rainfall variability over several homogeneous regions. However, rainfall over northeast (NE) and North India (NI) has been moulded by ENSO, thus changing the annual rainfall magnitude. Nevertheless, the impact of ENSO on monsoon rainfall through SWIO variability during the antecedent months is moderate. Thus, the ENSO influence on the atmosphere could be dominating the ocean part in modulating the inter-annual variability of the summer monsoon. Analysis shows that the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period.