Thermosteric dominance of sea level rise in the North Indian Ocean: sub- basin budget analysis (2003-2021)

Thermosteric dominance of sea level rise in the North Indian Ocean: sub- basin budget analysis (2003-2021) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Thermosteric dominance of sea level rise in the North Indian Ocean: sub- basin budget analysis (2003-2021) Ullas M Pillai, Franck Eitel Kemgang Ghomsi, Ajith Joseph Kochuparampil, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8870568/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This study investigates sea level trends in the North Indian Ocean (NIO), quantifying the relative contributions of thermosteric, halosteric, and ocean mass components using satellite altimetry, reanalysis, and GRACE gravimetry data over the 2003–2021 period. Over the NIO, sea level increased at a rate of 4.55 ± 0.61 mm/yr, a trend primarily driven by thermosteric expansion associated with ocean warming. Separation of NIO into six sub-basins reveal marked spatial heterogeneity in sea level trends: (1) Western Arabian Sea (4.10 ± 0.72 mm/yr); (2) Eastern Arabian Sea (4.62 ± 0.44 mm/yr); (3) Western Bay of Bengal (4.95 ± 0.83 mm/yr); (4) Eastern Bay of Bengal (5.15 ± 0.64 mm/yr); (5) Western Equatorial Indian Ocean (4.49 ± 0.47 mm/yr); and (6) Eastern Equatorial Indian Ocean (4.84 ± 0.54 mm/yr). Even though thermosteric changes dominate the basin-wide mean, the dominant drivers vary regionally. Halosteric effects exhibit a negative trend over the entire Arabian Sea, and is linked to inflows of saline water from the Red Sea and Persian Gulf, in contrast to a positive trend in the Bay of Bengal, influenced by substantial freshwater runoff from major rivers. In the Eastern Equatorial Indian Ocean, the mass component is predominant, likely influenced by crustal adjustments after the December 2004 Sumatra-Andaman earthquake. Interannual sea level variability closely follows steric changes, which are modulated by climate modes such as ENSO and the Indian Ocean Dipole, resulting in region-specific and often opposing phase relationships across the basin. Our results confirm that while the recent global and African sea level rise is predominantly mass-driven, the NIO remains distinctively steric-dominated, with a larger contribution from thermosteric changes. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Ocean sciences North Indian Ocean Sea level rise Steric Sea level GRACE gravimetry Indian Ocean Dipole Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction In recent decades, sea level research has gained critical importance. Globally, nearly 40% of the population resides within 100 km of coastlines, with about 10% inhabiting vulnerable low-lying areas, including Small Island States that face increasing risks from coastal flooding, erosion, saltwater intrusion, and ecosystem degradation resulting from the rising sea level 1 . Current sea level rise (SLR), along with its observed acceleration as measured by satellite altimetry 2 , 3 , is largely driven by human-caused global warming. The thermal expansion from ocean warming and mass addition from land ice melt are considered to be the primary components of sea level rise which are directly linked to anthropogenic climate change. The Global Mean Sea Level (GMSL) has been designated by the World Meteorological Organization (WMO) as one of the seven essential climate change indicators, following recommendations from the Global Climate Observing System 4 . The consequences of rising seas extend beyond threats to coastal urban centres, potentially transforming entire shorelines, and leading to permanent inundation 5 . These physical changes have fundamentally altered coastal ecosystems and tidal dynamics, creating cascading environmental and societal impacts 6 – 8 . Changes in GMSL arise from two primary physical processes. The first is steric change, resulting from variations in ocean density due to temperature and salinity changes. The second is barystatic (or ocean mass) change, driven by the addition of water by ice loss from glaciers and ice sheets of Greenland and Antarctica, as well as changes in terrestrial water storage, such as groundwater depletion or reservoir retention. According to Thompson et al. 9 , the updated global mean sea level trend from satellite altimetry for 1993–2022 is 3.4 ± 0.4 mm/yr, while the global mean ocean mass trend for 2005–2022 is 2.1 ± 0.4 mm/yr. While ocean mass addition remains the dominant contributor to the recent global sea-level rise, followed by steric contributions 7 , 10 – 12 , these processes are fundamentally driven by climatic factors, including modifications to atmospheric and oceanic conditions 13 . Recent observations show that ice sheet melt is accelerating, particularly in Greenland 14 . While these factors drive the long-term trend, research has established that interannual GMSL fluctuations are predominantly influenced by natural climate variability. The El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) are the primary drivers of these short-term anomalies 15 – 17 . At a regional scale, satellite altimetry data demonstrate that SLR often varies significantly from the global mean (e.g., Raj et al. 18 ). Regional sea-level variations at decadal scales reflect global mean rise plus spatial anomalies from steric effects, ocean dynamics, atmospheric forcing, and solid Earth responses to mass redistribution 7 , 19 . Precise monitoring of these regional variations and their drivers is critical for detection-attribution studies, especially when it comes to identifying when anthropogenic signals appear in different areas 20 . Locally, the most relevant metric for assessing societal impacts is the change in sea level relative to the land surface. Coastal sea-level trends often differ from open-ocean trends due to the influence of small-scale processes, such as winds, currents, and vertical land motion (isostatic adjustments), that modify the broader regional and global signals 21 . Palanisamy et al. 22 distinguished "climate-related" sea level change (global mean plus regional pattern) from "total relative" sea level (including vertical land motion), showing that subsidence can locally amplify Indian Ocean coastal sea level change well beyond the climatic signal. The consequences of SLR are evident worldwide in coastal regions, with increasingly persistent effects 7 , 23 . The launch of satellite altimetry missions in 1992, including TOPEX/Poseidon and its successors, revolutionized sea-level monitoring by enabling precise, global measurements to within a millimeter. This capability was further enhanced by the Gravity Recovery and Climate Experiment (GRACE, 2002–2017) and GRACE Follow-On mission (2018-present), which quantified mass redistribution through gravity anomaly measurements 24 . Together, these satellite systems have dramatically improved the separation and estimation of individual sea-level components (steric and barystatic) with unprecedented accuracy. Earlier studies in the North Indian Ocean (NIO) has already exploited such multi-sensor combinations, for example, Ghosh et al. 25 combined altimetry, GRACE, and Argo in the Bay of Bengal and showed that the sum of steric and mass components was broadly consistent with altimetric sea level within uncertainties, while highlighting regional discrepancies linked to sparse Argo coverage and GRACE noise. More recently, Ghomsi et al. 7 applied a similar framework to Africa's Large Marine Ecosystems (LMEs) and found that mass contributions now account for over 80% of total sea level rise in those regions, a stark contrast with the steric-dominated signal reported for the tropical Indian Ocean. NIO exhibits complex oceanographic dynamics due to its unique geographical constraints and seasonal forcing due to the monsoon system. As a semi-enclosed ocean bounded by land on the northern side, it experiences complete seasonal current reversals driven by monsoon wind patterns 26 – 29 . The ocean features two distinct sub-seas: the Arabian Sea in the west, which is characterized by high-salinity water outflows from the Persian Gulf and the Red Sea, and the Bay of Bengal (BoB) in the east, which is dominated by strong freshwater input from major river systems. The sea level in the North Indian Ocean is dominated by thermosteric changes, which have accelerated in recent decades 30 , 31 . Swapna et al. 30 identified a mechanistic link between multidecadal weakening of the Indian summer monsoon circulation and thermosteric sea level rise in the NIO, demonstrating that reduced southward heat transport via the Cross-Equatorial Cell (CEC) leads to enhanced heat retention and thermosteric expansion, particularly in the Arabian Sea. Furthermore, Srinivasu et al. 32 documented a distinct decadal reversal in NIO sea level trends around 2003, with sea level falling during 1993–2003 and rising sharply during 2004–2013, attributing this reversal to changes in surface turbulent heat flux and meridional heat transport driven by decadal wind variability. Salim et al. 33 showed that, over the broader tropical Indian Ocean during 1993–2007, steric processes accounted for about 35% of long-term sea level change, with a particularly strong steric control (~ 72%) in the south tropical Indian Ocean but weaker and even negative steric contributions in parts of the north, underlining strong meridional contrasts. Interannual sea-level variability in the Indian Ocean is primarily driven by major climate modes, particularly the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) 34 . Nidheesh et al. 35 demonstrated that decadal sea level variability in the Indo-Pacific is driven by wind stress variations that are relatively independent between the Pacific and Indian Ocean basins, with thermosteric changes almost entirely dominating decadal fluctuations, while cautioning that long-term trend estimates from forced ocean models are sensitive to wind product uncertainties. Palanisamy et al. 22 further demonstrated, using a 60-year reconstruction (1950–2009), that steric changes and IOD-related variability dominate the climatic component of Indian Ocean Sea level and that regional patterns such as the east-west trend increase below ~ 15°S and evolve on multidecadal time scales. Rising sea levels due to climate change pose significant threats to a coastal nation like India, where approximately 420 million of its 1.4 billion inhabitants live in coastal regions 36 . India's eastern coastline faces increased vulnerability to sea-level rise, with 35% of the country's population residing within 100 km of the shore, and the risk is compounded by intensifying cyclonic activity 1 . Parekh et al. 34 reported that the BoB exhibits substantially higher sea level rise rates (up to 8.8 mm/yr at Charchanga, Bangladesh) compared to the Arabian Sea, with steric contributions dominating mass-driven changes by a factor of two, highlighting the regional heterogeneity that our study aims to characterize at sub-basin scales. This study assesses the individual contributions of steric (thermosteric and halosteric effects) and ocean mass components to sea-level variations across distinct sub-basins of the North Indian Ocean (see Fig. 1 ) during the GRACE period 2003–2021. This analysis also addresses critical gaps in our understanding of regional sea level trends and variability by providing a spatial and temporal analysis of these contributions. By extending earlier basin-scale work over the tropical Indian Ocean 33 and longer-term reconstructions 22 into the GRACE/altimetry era with higher-resolution ARMOR3D fields, our study focuses specifically on sub-basin differences within the NIO and quantifies how the relative importance of thermosteric, halosteric, and mass terms has evolved in the most recent decades. Our study period (2003–2021) corresponds to the period of accelerated NIO sea level rise identified by Srinivasu et al. 32 and extends the multidecadal analysis of Swapna et al. 30 , allowing us to assess whether the thermosteric dominance and monsoon-driven mechanisms they identified continue to characterize the most recent decades. We also address sea level contributions from glacial isostatic adjustment (GIA), which is a long-term process whereby the Earth's crust rises or subsides as it responds to the residual effects of ice sheet loading from the past 37 including the contribution from atmospheric forcing. Furthermore, this study evaluates how climate modes, such as ENSO and Indian Ocean Dipole (IOD), influence interannual sea-level variability in each sub-basin during the altimetry period from 1993–2021. This allows us to distinguish natural variabilities from long-term anthropogenic signals. The paper is structured as follows: Section 2 describes the data and methods; Section 3 presents the results and discussion; and Section 4 provides the summary and conclusions. 2. Data and Methods 2.1. Altimetry sea level data Daily gridded altimetric sea level anomaly (SLA) data with a spatial resolution of 1/8° x 1/8° over the NIO from 1993 to 2021 ( https://doi.org/10.48670/moi-00148 , accessed June 2024) were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS). This dataset was processed using the DUACS multimission altimeter processing system, which involves merging observations from several altimeter missions to ensure data reliability. Additionally, all standard corrections, including sea state bias, instrumental drift, and dynamic atmospheric correction, were applied to the dataset 38 . For each grid point, the monthly mean of SLA was calculated from the daily values. Our approach is consistent with previous Indian Ocean studies using multi-mission merged altimetry 25 , 33 and with recent applications to African LMEs 7 , ensuring robustness of regional sea level trends. 2.2. Estimation of steric sea level components Monthly average temperature and salinity profiles were obtained from the ARMOR3D dataset, distributed by the Copernicus Marine Environment Monitoring Service ( https://doi.org/10.48670/moi-00052 ). This dataset is available from 1993 with a spatial resolution of 0.25° × 0.25° and 50 vertical levels. It is a combination of satellite and in-situ observations that provide reliable steric sea level estimates 39 , 40 . Steric sea level anomaly (SSLA) was computed by vertically integrating density anomalies and then decomposing them into thermosteric (TSLA) and halosteric (HSLA) components using established methods 10 , 41 , 42 as shown in Eq. ( 1 ) $$\:SSLA=TSLA+HSLA=\:\frac{-1}{{\rho\:}_{0}}{\int\:}_{-H}^{0}\varDelta\:\rho\:dz$$ $$\:-{\int\:}_{H}^{0}\left(\alpha\:\varDelta\:T-\beta\:\varDelta\:S\right)dz$$ 1 Here, \(\:{\rho\:}_{0}\) ​ is the reference density (1025 kg/m³), and \(\:z\) represents depth. \(\:\varDelta\:\rho\:\) , \(\:\varDelta\:T\) , and \(\:\varDelta\:S\) denote anomalies in density, temperature, and salinity, respectively, relative to their climatological means at each depth level. The coefficients \(\:\alpha\:\) (thermal expansion) and \(\:\beta\:\) (haline contraction) were derived from monthly temperature and salinity fields using the Thermodynamic Equation of Sea Water − 2010 (TEOS-10) 43 and the Gibbs Sea Water (GSW) Oceanographic Toolbox 44 . The reference depth \(\:H\) is defined as 700 m because interpolation uncertainties increase below this depth due to sparse observational data 45 . Additionally, steric contributions from depths exceeding 700 m in ARMOR3D are expected to be negligible 46 . This upper-ocean focus is consistent with earlier steric analyses in the Indian Ocean, which either limited integration to 700 m 22,33 and reflects the dominant role of the upper ocean in regional steric variability. 2.3. Dynamic atmospheric correction (DAC), glacial isostatic adjustment (GIA), and ocean mass component The contribution of atmospheric forcing to the sea level variability can be assessed using the Dynamic Atmospheric Correction dataset. This dataset, provided by Aviso ( https://tds.aviso.altimetry.fr/thredds/catalog/dataset-auxiliary-dynamic-atmospheric-correction/catalog.html ), is updated every six hours and has a spatial resolution of 0.25°×0.25°, starting from 1992. Contributions from the ocean mass component were analyzed using gridded data from the Center for Space Research (CSR) RL06 GRACE/GRACE-FO mascon solutions, and are obtained from http://www2.csr.utexas.edu/grace . This data is available starting from April 2002 with a spatial resolution of 0.25°×0.25°, although the native grid resolution of GRACE data is 300 km 47 . Corrections for glacial isostatic adjustment (GIA) are applied using the ICE6G-D model 48 . Following the approach of Ghosh et al. 25 over the BoB, Palanisamy et al. 22 over the wider Indian Ocean, and Ghomsi et al. 7 over African coastal waters, GIA corrections are essential to ensure consistency between GRACE-based mass trends, steric components and altimetric sea level, especially in regions where in situ measurements of vertical land motion are sparse. To understand the role of climatic influence on sea level, we considered the variability of sea level during ENSO and IOD years and used the Oceanic Niño Index (ONI) and the Dipole Mode Index (DMI). The ONI was obtained from the Physical Sciences Laboratory of NOAA ( https://psl.noaa.gov/gcos_wgsp/timeseries/ ), and the DMI was obtained from the Jet Propulsion Laboratory of NASA ( https://sealevel.jpl.nasa.gov/overlay-iod/ ). Additionally, the Western Tropical Indian Ocean (WTIO) and Southeastern Tropical Indian Ocean (SETIO) sea surface temperature (SST) indices were obtained from https://stateoftheocean.osmc.noaa.gov/sur/ind/setio.php . The DMI is the difference between the WTIO and SETIO SST indices. We applied a non-parametric Mann-Kendall trend analysis, which is robust in detecting monotonic trends in the presence of noise and non-normality in order to statistically assess the spatial trends for significance at a 95% confidence level for the various sea level contributions, following the methodologies used in previous studies 6 , 10 . Subsequently, regionally averaged linear trends for individual sea level components were computed, and the spatial correlations between sea level variability and different climate modes were evaluated. For the comparison of trends, all the datasets are arranged according to the time period of the GRACE dataset, which has missing time steps of totalling approximately 14% of the data, as a result of the inter-mission gap and battery issues. Thus, the altimetry, steric, and residual components of sea level also have these gaps, ensuring that any differences between the estimates are not due to gaps in the GRACE dataset. 3. Results and Discussion 3.1. Spatial trends of various contributions to sea level during the GRACE period This section discusses the major contributing factors and their respective roles in shaping the total sea level trend. Figure 2 illustrates the spatial trends of various components contributing to total sea level variability over the North Indian Ocean during the period 2003–2021. Altimetric SLA (Fig. 2 a) shows strong positive trends, exceeding 6 mm/yr in several regions, especially in the north and south of the BoB. These high coastal trends are broadly consistent with earlier altimetric estimates in the BoB 25 , but our comparatively longer time span and higher-resolution inputs reveal stronger accelerations, especially along the eastern boundary. Coastal areas of India exhibit consistent rising sea level trends, ranging from approximately 2.01 mm/yr off the northern BoB coast near 21°N, 87°E to 6.81 mm/yr off the southwestern coast near 18°N, 72°E. Non-significant or weakly increasing trends (below 2 mm/yr) are observed primarily in the western Arabian Sea, including the Somali coast and along the Omani margin as isolated patches, and also over the western BoB near 14°N, 82°E. Over the Arabian Gulf, the altimetry sea level exceeds 4 mm/yr, in agreement with the results of Al-Subhi & Abdulla 49 . In the equatorial Indian Ocean sub-basins, the eastern sub-basin shows higher averaged trends (~ 4.84 mm/yr), with peaks nearing 5.5 mm/yr along the Indonesian coastline. The steric components, comprising Fig. 2 b (total steric), Fig. 2 c (thermosteric), and Fig. 2 d (halosteric) sea level, are computed for the upper 700 m of the water column. The total steric component shares spatial similarity with the thermosteric pattern, reinforcing that ocean temperature is the dominant driver of steric sea level variability. This thermosteric dominance is consistent with the findings of Nidheesh et al. 35 , who demonstrated that decadal sea level variations in the Indo-Pacific are almost entirely thermosteric, with halosteric contributions significant only in localized regions such as the western Indonesian seas. Figures 2 c and 2 d depict the contributions of thermal expansion and salinity changes, respectively. The thermosteric trends are more prominent in the Arabian Sea compared to the Bay of Bengal. The enhanced thermosteric signal in the Arabian Sea aligns with the mechanism proposed by Swapna et al. 30 , who showed that weakening monsoon circulation reduces coastal upwelling off Somalia and Arabia, leading to increased heat retention and thermosteric sea level rise in this region. Notably, the western Arabian Sea, particularly around the Gulf of Oman and Gulf of Aden, shows a dipole pattern in its steric components, with strong positive thermosteric trends and strong negative halosteric trends. These features are influenced by the warm, saline outflows from the Persian Gulf and the Red Sea, respectively, which alter local temperature and salinity profiles. Halosteric changes (Fig. 2 d) exhibit significant trends in both the Arabian Sea and the BoB, although their magnitude is generally lower than thermosteric contributions. Nevertheless, in the northeastern BoB, eastern equatorial Indian Ocean, and off India’s western coast (east of 70°E, north of 15°N), halosteric trends reach up to ~ 1 mm/yr, indicating localized salinity-driven SLR. This pattern of strong thermosteric control with secondary halosteric modulation is consistent with the CEOF-based decomposition of SSH and steric height found by Salim et al. 33 , who reported close coherence between the leading steric and SSH modes over the tropical Indian Ocean. It also contrasts markedly with African LMEs, where Ghomsi et al. 7 found that the halosteric contribution is typically negative or negligible while mass contributions dominate the sea level budget. Importantly, the halosteric patterns we observe, negative in the Arabian Sea and positive in the BoB, are consistent with the contrasting salinity regimes described by Jyoti et al. 50 for the South Indian Ocean, where halosteric contributions account for approximately 40% of the accelerated sea level rise during 2000 to 2015, primarily driven by Indonesian Throughflow transport of fresher Pacific waters. The GRACE satellite mission offers complementary insights by quantifying the mass-driven component of sea level (Fig. 2 e). The Arabian Sea, in particular, exhibits non-significant trend values, with an area-averaged trend of 0.33 mm/yr. In contrast, the eastern NIO displays a complex mix of strong positive and negative trends. These spatial anomalies have been linked to crustal adjustments following the 2004 Sumatra-Andaman earthquake, which induced significant geophysical changes in the regional geoid and ocean mass distribution 51 – 53 . Nonetheless, GRACE-derived estimates must be interpreted with caution. The mission’s coarse spatial resolution increases the risk of signal leakage, especially from land into adjacent coastal ocean regions, potentially distorting the actual magnitude and spatial structure of mass changes 54 . Numerous studies have reported challenges in estimating ocean mass using GRACE data, particularly for specific ocean basins like Indian Ocean 55 , where the signal leakage and measurement uncertainties are significant. Also, the non-steric sea level variability induced by the earthquake, while detectable through satellite gravimetry as geoid height variations, remains indistinguishable in satellite altimetry because of the overwhelming influence of steric sea level variations 56 . Ghosh et al. 25 similarly reported poor regional agreement between GRACE-based mass trends and the difference between altimetry and Argo-based steric sea level in the BoB, underlining that GRACE-derived basin-scale mass budgets over the north Indian Ocean currently remain more uncertain than global estimates. Despite these limitations, GRACE data remain valuable for identifying large-scale ocean mass variability, particularly when integrated with steric and altimetric observations to assess the full spectrum of sea level change drivers. The DAC is applied to account for the contributions from the atmosphere. The DAC (Fig. 2 f) shows negligible trends across the NIO, with values nearly 0.01 mm/yr and lacking statistical significance. This confirms that atmospheric pressure-related effects, as accounted for SLR by the DAC, have minimal influence on long-term sea level trends in the region and do not show any spatial variability. The GIA dataset from Peltier et al. 48 is presented as an integrated linear trend for the full period (Fig. 2 g); as in Palanisamy et al. 22 and Ghomsi et al. 7 . This prevents a formal statistical assessment of trend significance but provides a necessary correction for long-term solid-Earth adjustment. Figure 2 h shows the residual SLA, estimated by subtracting the steric, ocean mass component, and GIA from the altimetry SLA, capturing contributions of unmodeled signals which may originate from various sources, including deep-ocean contributions below 700 m depth, drifts or biases in the models used for steric and barystatic components, and effects of small-scale ocean dynamic processes such as wind-driven currents. The residual sea level accounts for about 28% of the observed sea level trend across the NIO. Exceptions include the northern Arabian Sea and the Gulf of Aden, where steric contributions are more pronounced. However, the residual SLA likely reflects a combination of unresolved processes such as vertical land motion, and potentially uncorrected tidal effects, which are not isolated in this study. Similar residual discrepancies between the sum of steric and mass components and altimetric sea level have been reported previously in the BoB 25 and at the scale of the tropical Indian Ocean 33 , and have been attributed to sparse in situ coverage below ~ 700–900 m, GRACE noise, and land-ocean signal leakage. In light of these uncertainties, the thermosteric component (Fig. 2 c) emerges as the most consistent and physically robust driver of sea level trends in the NIO, confirming the conclusions of Swapna et al. 57 and Jyoti et al. 58 . The trend values from Fig. 2 , estimated for the six sub-basins of the NIO, given in Table 1 , highlight notable differences in sea level change dynamics across the Arabian Sea, BoB, and Equatorial Indian Ocean (EIO). Each region exhibits distinct contributions from steric, mass, and residual components. Based on satellite altimetry, the BoB records the highest basin-wide, area-averaged sea level trend of 5.23 ± 0.77 mm/yr, followed by the EIO (4.99 ± 0.51 mm/yr) and the Arabian Sea (4.64 ± 0.61 mm/yr). These basin-scale trends are computed directly from area-averaged sea level and are not derived from averaging the western and eastern sub-basin trends. The total steric contribution in the Arabian Sea is relatively lower than in the other two basins. However, when the steric components are considered separately, the thermosteric trend in the Arabian Sea is the highest among all regions, at 2.51 ± 0.65 mm/yr, indicating a strong influence of ocean warming on the sea level rise. This pronounced thermosteric signal in the Arabian Sea supports the hypothesis of Swapna et al. 30 that weakened summer monsoon circulation has reduced southward heat export via the Cross-Equatorial Cell, resulting in enhanced heat storage and thermosteric expansion in the NIO, particularly in regions of suppressed upwelling. In contrast, the halosteric component in the Arabian Sea shows a significant negative trend of approximately − 0.89 mm/yr, which reduces the net steric trend to 1.62 ± 0.58 mm/yr. This salinity-driven contraction offsets much of the thermosteric expansion, thereby moderating the overall steric SLR in the region. Table 1 Area-averaged trends (Mann-Kendall) in sea level and its components across the North Indian Ocean (NIO) from 2003 to 2021 (units in mm/yr). The Total SLA consists of the entire altimetry trend from 2003 to 2021, whereas for the remaining components, the sea level trend corresponding to the time period of GRACE dataset (including the missing periods) from 2003 to 2021 is considered. Region Sub-basin Total Altimetry SLA (mm/yr) Altimetry SLA (mm/yr) Steric (mm/yr) Thermosteric (mm/yr) Halosteric (mm/yr) Residual (mm/yr) GRACE (mm/yr) Arabian Sea WAS 4.10 ± 0.72 4.38 ± 0.74 1.50 ± 0.69 2.53 ± 0.78 -1.03 ± 0.25 2.59 ± 0.37 0.22 ± 0.15 EAS 4.62 ± 0.44 4.99 ± 0.44 1.80 ± 0.41 2.48 ± 0.46 -0.68 ± 0.22 2.72 ± 0.29 0.48 ± 0.18 Bay of Bengal WBoB 4.95 ± 0.83 5.28 ± 0.85 1.76 ± 0.82 1.34 ± 0.72 0.41 ± 0.17 3.67 ± 0.34 -0.01 ± 0.20 EBoB 5.15 ± 0.64 5.19 ± 0.68 1.71 ± 0.63 1.26 ± 0.54 0.45 ± 0.14 -0.93 ± 0.45 3.12 ± 0.47 Equatorial Indian Ocean WEIO 4.49 ± 0.47 5.0 ± 0.48 2.12 ± 0.49 2.17 ± 0.48 -0.03 ± 0.12 1.62 ± 0.22 1.26 ± 0.14 EEIO 4.84 ± 0.54 4.95 ± 0.57 1.83 ± 0.56 1.68 ± 0.56 0.13 ± 0.13 -1.75 ± 0.49 4.85 ± 0.43 NIO NIO 4.55 ± 0.61 4.90 ± 0.63 1.86 ± 0.62 2.01 ± 0.62 -0.15 ± 0.16 1.40 ± 0.33 1.51 ± 0.24 Across the Arabian Sea sub-basins and the western BoB, the residual sea level, which represents the remainder of the SLR derived from altimetric signals that is not accounted for by steric, ocean mass components and GIA effects, contributes substantially to the total trend. Our estimation of residual trends is subject to increased uncertainty in the Eastern Arabian Sea (EAS) and Eastern Bay of Bengal (EBoB). This limitation arises from the spatial constraint imposed by the 700 m depth-integration used to define the steric component, which effectively masks the contribution of steric variability over the continental slope and shelf which are shallower than 700 m depth. Part of these residuals may also reflect vertical land motion, as shown for certain Indian Ocean coasts and islands by Palanisamy et al. 22 and for African deltaic regions by Ghomsi et al. 7 , although our study does not explicitly separate this contribution. Even after accounting for the GRACE-derived mass component, the Arabian Sea and the BoB retain strong basin-scale residual trends of 2.67 mm/yr and 1.91 mm/yr, respectively (Fig. 2 h). A study by Unnikrishnan & Antony 59 mentioned the increase in mean sea level at the head of the Bay, possibly due to subsidence of the Ganga-Brahmaputra delta, which further results in large extreme sea levels. In comparison, regionally averaged residual trend of the EIO is nearly 0.31 mm/yr, mainly due to uncertainties related to the GRACE data over the Eastern Equatorial Indian Ocean. These results reveal that the dominant drivers of sea level rise vary across the NIO. Although the thermosteric component is one of the major influencers in most of these basins, especially in the Arabian Sea, the ocean mass component measured from GRACE gravimetry also contributes to the total sea level trend, as observed in the BoB and the EIO. Apart from these, the residual trends observed in the sub-basins also reveal the influence of some non-steric sea level components that need to be accounted for in future studies. This underscores the regional complexity of SLR in the Indian Ocean. When expressed as a fraction of the total NIO trend, our results imply that steric processes explain roughly 40–45% of recent sea level rise, which is larger than the basin-wide 35% reported by Salim et al. (2012) for the 1993–2007 period and substantially larger than the ~ 20% steric contribution found for African LMEs by Ghomsi et al. 7 . This contrast highlights that the NIO is unusually steric-dominated compared to other regional seas, where mass-driven contributions from ice melt and terrestrial water storage changes have become increasingly prominent. Our finding that thermosteric contributions have increased relative to earlier periods is consistent with the acceleration of NIO thermosteric sea level rise from 0.68 mm/yr (1958–2015) to 2.3 mm/yr (1993–2015) documented by Swapna et al. 30 , suggesting that the monsoon-driven heat retention mechanism they identified has continued or intensified during our study period (2003–2021). 3.2. Spatial trends of temperature and salinity The spatial distribution of temperature trends (Fig. 3 a) closely resembles the pattern of thermosteric SLR and broadly corresponds to total sea level trends observed in satellite altimetry, particularly across the Arabian Sea. This dominant ocean warming signal is critical because it is the primary driver of steric SLR. Significant positive temperature trends prevail throughout most of the NIO, with only minor, statistically insignificant cooling patches present in the western Arabian Sea, western BoB, and along the southern boundary of the basin. This extensive warming is consistent with previous findings that link regional ocean temperature increases to enhanced thermosteric SLR 58 . The spatial pattern of warming, with maxima in the northwestern Arabian Sea off the coasts of Somalia and Arabia, is consistent with the mechanism proposed by Swapna et al. 30 , who attributed enhanced warming in these regions to reduced upwelling associated with weakening summer monsoon circulation and spin-down of the Cross-Equatorial Cell. At the same time, salinity variations can also affect regional sea level changes 60 . Salinity trends (Fig. 3 b) reveal distinct regional variability that also influences sea level changes by affecting water density. The Arabian Sea shows a significant salinity increase, mainly along its northern boundary and the region extending from the Gulf of Oman to the Somali coast. Conversely, a pronounced negative salinity trend occurs along the west coast of India, driven by greater freshwater influx from river discharge and seasonal precipitation 61 . These contrasting salinity patterns play an important role in modulating steric SLR, partially counterbalancing the thermal expansion in the Arabian Sea, where elevated salinity increases water density and limits steric expansion despite warming. In contrast, the BoB experiences a widespread and statistically significant salinity decrease. This freshening is mainly attributed to increased riverine input and monsoonal runoff. The resulting reduction in water density enhances steric SLR, further contributing to the region’s observed sea level trends. These observations confirm the significant role of salinity changes, alongside temperature, in influencing BoB sea level variability through steric variations, as previously observed by Akhter et al. 62 and Nidheesh et al. 35 . They are also consistent with the negative halosteric trends and decreasing net freshwater content inferred by Ghosh et al. 25 in the northern BoB when using Argo-based profiles, highlighting the sensitivity of halosteric signals to the choice of observational product and integration depth. A similar contrast between basin-scale salinification and localized freshening has been documented in African LMEs, where the Mediterranean exhibits strong negative halosteric trends due to high evaporation while the Gulf of Guinea shows positive halosteric contributions near major river mouths 7 . 3.3. Temporal variability of sea level components in the NIO The de-seasonalized monthly time series of major sea level components demonstrate that steric sea level variability generally tracks the altimetry-derived sea level pattern across all NIO sub-basins (Fig. 4 ). However, in the Arabian Sea and Western Bay of Bengal (WBoB) sub-basins, the contribution of residual sea level is significant. Across the entire NIO (Fig. 4 a), the residual sea level contributes approximately 1.42 mm/yr to the total sea level rise, while the steric component alone accounts for about 1.94 mm/yr. These small variations in trend values compared to Table 1 are due to the use of a linear regression method here, in contrast to the Mann-Kendall method used previously for spatial trends. This partitioning is broadly compatible with earlier basin-scale estimates of steric versus total trends from Salim et al. 33 , but our longer record and focus on sub-basin scales reveal larger contrasts between west and east, especially in the equatorial Indian Ocean. Similarly, the Western Equatorial Indian Ocean (WEIO, Fig. 4 f), and Eastern Equatorial Indian Ocean (EEIO, Fig. 4 g) show significant trends in steric sea level compared to their residual trends. However, the EEIO also exhibits higher trends in the ocean mass component (4.90 mm/yr) possibly arising from uncertainties associated with the crustal response to the Sumatra-Andaman earthquake. In the WBoB, the residual component is more prominent (~ 3.98 mm/yr) whereas the EBoB is dominated by the ocean mass component (~ 3.16 mm/yr). Since steric sea level change includes both thermosteric and halosteric components, Fig. 5 depicts these individual contributions alongside satellite altimetry sea level anomalies and GRACE-observed mass variations. Among the NIO sub-basins, the Arabian Sea, comprising the Western Arabian Sea (WAS) and EAS, shows the strongest combined influence from thermosteric and halosteric components on sea level variability. Thermosteric effects emerge as the leading driver of sea level trends here, with notable influence also observed in the Equatorial Indian Ocean (both WEIO and EEIO). A distinct negative trend in halosteric sea level is present in the Arabian Sea sub-basins, partially offsetting the strong positive thermosteric trends. High salinity outflows from the Persian Gulf and Red Sea have caused elevated salinity in the Arabian Sea. This elevated salinity enhances saline contraction and strengthens the negative halosteric sea level changes 63 , 64 , enhances saline contraction and strengthens the negative halosteric sea level changes. In contrast, the Bay of Bengal (WBoB and EBoB) exhibits positive halosteric trends, driven by increased freshwater input from river discharges. This freshening effect plays a major role in halosteric sea level variability in the BoB region. A similar contrast between Arabian Sea salinification and BoB freshening has also been noted in steric analyses of the upper Indian Ocean 33 , reinforcing the robustness of this pattern across different datasets and methodologies. 3.4. Inter-annual variability of sea level and the role of climate modes Steric variations play a leading role in shaping the temporal evolution of sea level across the North Indian Ocean, with thermosteric effects contributing most significantly to the observed variability (Fig. 5 a). While other components of sea level, such as mass and residual contributions, exhibit different patterns than the total sea level from satellite altimetry, steric and thermosteric signals remain closely correlated with the altimetric observations, particularly at interannual timescales. This strong coherence between SSH and steric signals in the NIO is consistent with Salim et al. 33 , who found that the first two complex EOF modes of steric height explained more than 70% of SSH variability over the tropical Indian Ocean. To examine this relationship, further, Fig. 6 compares detrended monthly anomalies of total sea level from altimetry with both steric and thermosteric sea level across the NIO sub-basins. A strong positive correlation, significant at the 99% confidence level, is observed between altimetric sea level and both steric components in all sub-basins. In most regions, steric sea level shows slightly higher correlations with altimetry than the thermosteric component alone. However, in the Arabian Sea, and especially in the WAS, thermosteric sea level anomalies exhibit a stronger correlation with altimetric sea level than the full steric signal, emphasizing the dominant role of temperature in driving sea level variability in this basin. This thermosteric dominance in the Arabian Sea at interannual timescales is consistent with the findings of Srinivasu et al. 32 , who showed that the upper 700 m thermosteric sea level explains approximately 94% of observed NIO sea level rise during their Period II (2004–2013), with particularly strong thermosteric control in the Arabian Sea. The Arabian Sea is also characterized by pronounced salinity variability (Fig. 3 b), which appears to modulate the steric sea level signal, particularly at interannual timescales. This influence underscores the complex interplay between temperature and salinity in determining regional sea level variability. However, the influence of the ocean mass component on the interannual variability of total sea level is observed to be statistically insignificant in the Arabian Sea sub-basins. To further assess the impact of large-scale climate modes on sea level variability, vertical dashed lines representing years of major climate events were added to Fig. 6 . These include the 1997-98 El Niño, the 2007-08 La Niña, and the 2019-20 positive Indian Ocean Dipole (pIOD) event (see Table 2 ). These periods exhibit clear sea level anomalies, especially in the BoB and the Equatorial Indian Ocean, indicating that strong ENSO and IOD events significantly influence regional sea level patterns. These variations could be primarily linked to large-scale planetary waves like Kelvin waves moving eastward and Rossby waves moving westward, which dominate the first mode of sea level anomaly patterns 65 . Equatorial wind patterns play a key role by generating these Kelvin waves that then influence sea levels across the ocean basin 66 . The Indian Ocean Dipole (IOD) primarily affects regional climate, while ENSO influences global weather patterns, including impacts on the Indian Ocean through atmospheric circulation changes 67 . Both ENSO and IOD significantly drive sea level fluctuations in the Eastern Indian Ocean 68 . Table 2 Classification of IOD and ENSO years based on datasets from NOAA PSL (ONI) and NASA JPL (DMI) Events Years pIOD 1994, 1997, 2006, 2019 (all strong events) El Niño 1994-95 1997-98 (strong) 2002-03 2006-07 2009-10 (strong) 2015-16 (strong) nIOD 1998, 2010, 2016 (all strong events) La Niña 1998-99, 2007-08, 2010-11 (all strong events) To better understand the spatial footprint of these climate drivers, Fig. 7 presents spatial correlation maps between sea level anomalies and key climate indices, including the Oceanic Niño Index (ONI), Dipole Mode Index (DMI), and SST anomalies in the Western Tropical Indian Ocean (WTIO) and Southeastern Tropical Indian Ocean (SETIO). DMI, which is calculated as the difference between WTIO and SETIO SSTs 69 , offers insight into the spatial structure of IOD-related sea level responses. The correlation patterns for ONI and DMI reveal distinct, partially overlapping influences across the NIO. ONI exhibits strong positive correlations over the southwestern part of the basin, with negative correlations dominating the eastern sector, extending westward to nearly 75°E along the equator. DMI mirrors this spatial pattern but with a more extensive influence across the Arabian Sea, suggesting a stronger and more regionally concentrated impact than ONI. In the BoB, both indices display a dipole-like pattern, with positive correlations in the southwest and negative correlations elsewhere in the basin. These dipole-like correlation patterns are consistent with the findings of Nidheesh et al. 35 , who demonstrated that at interannual timescales, IOD events drive sea level variations in the Eastern Equatorial Indian Ocean and BoB through equatorial and coastal waveguide dynamics, with Kelvin waves propagating from the central Indian Ocean to the eastern boundary and subsequently into the BoB as coastal Kelvin waves. The Equatorial Indian Ocean similarly shows a dipole structure, with positive correlations in the western part (WEIO) and negative correlations in the eastern part (EEIO). Correlations with the WTIO and SETIO SST indices provide further clarity. The WTIO shows a spatial correlation structure that closely resembles that of the DMI, indicating its dominant role in regulating sea level variability across the region. This resemblance highlights WTIO’s influence in mediating the DMI-sea level relationship, particularly over the Arabian Sea and BoB. Such dipole-like patterns, and their non-stationarity over multidecadal time scales, are in line with the evolving spatial trend structures inferred from reconstructed sea level fields in the Indian Ocean by Palanisamy et al. 22 . Ghomsi et al. 7 similarly found that remote climate drivers, including ENSO and the IOD, modulate sea level variability across the Somali Coastal Current and Red Sea LMEs, with regression coefficients of up to 24 mm for the DMI, underscoring the basin-wide reach of these teleconnections. Figure 8 illustrates the first principal component (PC1) of the leading empirical orthogonal function (EOF) mode of altimetry-derived sea level anomalies (SLA), alongside the Dipole Mode Index (DMI) and Oceanic Niño Index (ONI), to examine regional interannual sea level variability across the NIO. As PC1 captures the dominant mode of SLA variability, the extent to which large-scale climate modes influence sea level dynamics within specific sub-basins can be assessed by comparing it with major climate indices. A notable feature in the PC1 patterns is a dipole structure between the western and eastern sub-basins of both the Bay of Bengal (WBoB and EBoB) and the Equatorial Indian Ocean (WEIO and EEIO), with opposing phase variability in sea level between these sectors. This phase variability is not evident in the Arabian Sea sub-basins, suggesting that sea level dynamics in these regions are governed by different processes or influenced less directly by large-scale climate modes. Comparative analysis reveals that DMI exhibits a stronger correlation with PC1 across most sub-basins than ONI, particularly in the BoB and Equatorial Indian Ocean. Both indices, however, show relatively weak to moderate correlations with PC1 in the WAS and EAS, reinforcing the notion that sea level variability in these areas is less sensitive to ENSO or IOD forcing. The time series of PC1 from each sub-basin displays temporal variations that are consistent with the spatial correlation structures identified in Fig. 7 , providing further evidence of the climate-sea level connection. Western sub-basins of both the BoB and Equatorial Indian Ocean show PC1 variability that closely tracks phases of the DMI and, to a lesser extent, the ONI, while eastern sub-basins show opposite-phase behavior. This anti-phase response suggests a coherent basin-wide adjustment of sea level anomalies to the shifting patterns of climate forcings 70 . Palanisamy et al. 22 similarly highlighted, that Indian Ocean Sea level exhibits east-west dipole structures strongly correlated with IOD events, which our analysis confirms for the more recent 1993–2021 period at sub-basin scales. During strong positive IOD (pIOD) events or combined pIOD-El Niño years, western sub-basins generally exhibit increased sea level, whereas eastern sub-basins tend to show negative anomalies. Conversely, strong negative IOD (nIOD) events or combined nIOD, La Niña years produce the opposite effect: elevated sea level in the eastern sub-basins and reductions in the western sub-basins. This spatial signature matches previously reported patterns of sea level change associated with ENSO and IOD phases 17 , 71 . During pIOD events, strong easterly winds replace the usual westerlies over the equatorial Indian Ocean, causing sea levels to drop in the eastern basin. These easterlies generate upwelling Kelvin waves that travel eastward, reflect off the coast, and propagate back as Rossby waves, further lowering sea levels, or propagate as coastal trapped Kelvin waves that move poleward 72 . The easterly wind anomalies also generate an equatorial-downwelling Rossby wave, which reflects off the western boundary as a downwelling Kelvin wave. This wave propagation leads to elevated sea levels in the WEIO 73 – 75 . Nidheesh et al. 35 demonstrated that this wave-mediated teleconnection results in high correlations (r > 0.8) between EEIO and BoB Sea level at both interannual and decadal timescales, while the southwestern Indian Ocean region is more weakly correlated with eastern basin variability at decadal timescales due to the independent influence of southern Indian Ocean wind stress curl. At such times, the usual downwelling Kelvin waves, which typically elevate sea levels in the eastern BoB, are significantly weakened or absent. Instead, only upwelling-favourable Kelvin waves dominate, generating Rossby waves along the eastern BoB rim that further reduce sea levels. In contrast, normal years feature stronger downwelling Kelvin waves, leading to higher sea levels in this region 72 . Meanwhile, the southwestern BoB shows higher sea levels during pIOD/El Niño events due to wind-driven Ekman pumping, where easterly anomalies create an anticyclonic (clockwise) circulation that increases the sea level 68 . Thus, IOD/ENSO phases produce contrasting sea level responses in the BoB. Anomalous sea level rise during El Niño years in the eastern Pacific and concurrent positive SLA anomalies in the western equatorial Indian Ocean have been well documented, supporting the observed correlations in the current study. However, not all strong El Niño years lead to significant SLA changes in the NIO. For instance, the 2015–2016 El Niño, despite its intensity, did not result in notable SLA variations across the region. In contrast, the strong negative IOD event in 2016 produced pronounced sea level increases in the eastern sub-basins and significant decreases in the western counterparts, as shown in Fig. 8 . These patterns highlight the asymmetric and regionally specific responses of NIO sea level to different modes of climate variability. The results are in agreement with previous studies that emphasize the modulation of interannual sea level variability in the NIO by IOD and ENSO-related processes 31 , 34 . 4. Summary and conclusions This study quantifies the relative contributions of different sea level components to total sea level trends in the North Indian Ocean during 2003–2021 when the GRACE gravimetry measurements of the mass component were available. Six sub-basins were analysed, covering the Arabian Sea, BoB, and Equatorial Indian Ocean. The North Indian Ocean (NIO) represents a region with complex dynamics for the sea level budget. Unlike the global mean sea level, which is primarily mass-driven, trends in the NIO are predominantly governed by thermosteric sea level rise, consistent with the accelerated regional warming observed over recent decades. This thermosteric dominance confirms and extends the findings of Swapna et al. 30 , who demonstrated that weakening Indian summer monsoon circulation drives increased heat retention in the NIO through reduced southward heat transport via the Cross-Equatorial Cell, and of Srinivasu et al. 32 , who documented the onset of accelerated NIO sea level rise after 2003. Spatial trend analysis reveals a significant positive sea level trend across the NIO (4.55 ± 0.61 mm/yr), with the thermosteric component dominating the regional average. This rate exceeds the 1993–2023 global mean of ~ 3.4 mm/yr and is comparable to the accelerated rates (~ 4.3 mm/yr since 2010) reported for African coastal waters by Ghomsi et al. 7 , indicating that the north Indian Ocean is experiencing similarly intensified sea level rise. Our NIO-averaged trend of 4.55 mm/yr for 2003–2021 represents a continuation of the acceleration documented by Swapna et al. 30 , who reported thermosteric SLR increasing from 0.68 mm/yr (1958–2015) to 2.3 mm/yr (1993–2015). Sub-basin differences emerge, however. The Eastern Equatorial Indian Ocean (EEIO) exhibits a mass-dominated trend (4.85 ± 0.43 mm/yr), more than three times the thermosteric contribution. Meanwhile, the Western Bay of Bengal (WBoB) shows substantial residual trends (~ 3 .66 mm/yr) that are largely independent of mass change. Halosteric influences differ sharply across basins: the Arabian Sea exhibits strong negative trends (-0.89 mm/yr), while the Bay of Bengal (BoB) sub-basins show positive halosteric contributions (~ 0.42 mm/yr), linked to freshwater input. These contrasting halosteric patterns complement the findings of Jyoti et al. 50 , who demonstrated that halosteric effects contributed 40% to South Indian Ocean Sea level rise during 2000–2015 primarily through Indonesian Throughflow freshwater transport, highlighting the distinct salinity forcing mechanisms operating in the North versus South Indian Ocean. A similar halosteric suppression of sea level rise has been documented in the Mediterranean by Ghomsi et al. 7 , where increased salinity offsets thermosteric expansion, highlighting the importance of salinity-driven density changes in semi-enclosed and marginal seas. Regional differences in ocean mass trends appear to be shaped by distinct processes. For example, the 2004 Sumatra-Andaman earthquake likely contributed to the sharp trend variations in the EEIO, while in the BoB, river discharge and sediment transport may play a more dominant role. Parekh et al. 34 noted that the highest tide gauge trends in the NIO occur in the northern BoB (8.8 mm/yr at Charchanga), where land subsidence in the Ganges-Brahmaputra delta may amplify apparent sea level rise, consistent with the elevated residual trends we observe in the WBoB. Basin-level decomposition reveals contrasting drivers: thermosteric changes dominate sea level trends in the Arabian Sea, while the EIO shows a west-east gradient, transitioning from thermosteric-dominated in the west to ocean mass component dominated in the east. In the BoB, though the steric contributions are nearly 1.7 mm/yr for both sub-basins, residual signals and ocean mass components respectively form the principal contributions to long-term trends in WBoB and Eastern Bay of Bengal (EBoB). However, uncertainties in the steric sea level measurements could be introduced by the absence of trend values near the northern and eastern Bay of Bengal coasts, in addition to limitations associated with the ocean mass trends from the GRACE dataset in the Andaman-Sumatra region of Eastern Indian Ocean. Taken together with previous Indian Ocean studies 22 , 25 , 33 and the pan-African synthesis of Ghomsi et al. 7 , our results indicate that recent decades have seen both an acceleration of NIO sea level rise and an increasing fractional contribution from thermosteric processes, while mass and residual terms remain highly heterogeneous and locally important. Interannual variability in NIO sea level is primarily governed by thermosteric fluctuations, which show strong spatial and temporal agreement with altimetry-derived anomalies. This consistency highlights the influence of regional warming and temperature variability on total sea level change. Climate modes, particularly ENSO and the Indian Ocean Dipole (IOD), display strong correlations with sea level, but their effects vary spatially. Western sub-basins of the BoB and Equatorial Indian Ocean exhibit in-phase relationships with these climate indices, whereas eastern sub-basins show anti-phase responses, resulting from changes in wind anomalies and dynamics related to the long-period waves associated with the climatic events. The Arabian Sea appears less affected by these large-scale climate modes. Srinivasu et al. 32 similarly found that decadal sea level variability in the NIO is primarily controlled by surface wind stress changes over the Indian Ocean, with limited influence from the Pacific through the Indonesian Throughflow, consistent with our finding that Arabian Sea sea level is less responsive to ENSO/IOD forcing. Analysis of the Western Tropical Indian Ocean (WTIO) and Southeastern Tropical Indian Ocean (SETIO) SST indices further shows that WTIO captures the Dipole Mode Index (DMI) pattern and is a strong predictor of regional sea level variability. This reinforces earlier conclusions that IOD-related variability is a primary driver of Indian Ocean regional sea level changes on interannual time scales 22 , 33 , and aligns with the finding of Ghomsi et al. 7 that Atlantic and Indian Ocean climate modes (including ENSO and IOD) modulate regional sea level anomalies through SST-driven thermal expansion and circulation changes. In summary, the total sea level trend in the NIO was 4.55 mm/yr, with thermosteric sea level driven by ocean warming emerges as the dominant contributor, also influencing interannual variability. However, regional contributions vary significantly, with mass changes, halosteric effects, and residual processes also playing important roles in specific sub-basins. In contrast to African LMEs, where mass contributions now dominate 7 , the NIO remains a thermosteric-dominated system, although the EEIO and parts of the BoB exhibit substantial mass and residual signals that warrant further investigation. Future research should prioritize deeper investigation into the residual component, which may reflect unmodelled processes such as sediment loading, vertical land motion, or regional hydrology, and remains a key source of uncertainty in regional sea level budgets. Combining our sub-basin-scale budget with independent constraints on vertical land motion (as in Palanisamy et al. 22 ) and deeper-water steric estimates (beyond 700–900 m; cf. Ghosh et al. 25 ), and placing NIO trends in the context of broader Indian Ocean and African coastal changes 7 , would be logical next steps toward closing the NIO sea level budget and distinguishing climatic from anthropogenic land-based contributions. Declarations Competing interests The authors declare no competing interests. Funding Financial support for this study was provided to Ullas M. P. through the Nansen Fellowship awarded by the Nansen Scientific Society in support of his Ph.D. research. Roshin P. R. received support from the c3-eKerala project funded by the Research Council of Norway. Ghomsi F. E. K. was supported by Canada’s C150 Research Program (Grant No. 50296) and Schmidt Sciences, LLC. Author Contribution U.M.P.: conceptualization; writing-original draft; writing-review & editing. F.E.K.G., R.P.R., A.J.K.: writing-original draft; writing-review & editing. O.M.J. have made the initial conception, revisions and improvements to the manuscript. Acknowledgement Ullas M. P and Ola M. J acknowledges the support from the Nansen Scientific Society. Ullas M. P thank the Faculty of Ocean Science and Technology, Kerala University of Fisheries and Ocean Studies, for the academic support to undertake this study. Ajith J. 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Comment on “An assessment of the ICE‐6G_C (VM5a) glacial isostatic adjustment model” by Purcell et al. J. Geophys. Res. Solid Earth 123 , 2019-2028 (2018). Al-Subhi, A. M. & Abdulla, C. P. Sea-level variability in the Arabian Gulf in comparison with global oceans. Remote Sens. 13 , 4524 (2021). Jyoti, J., Swapna, P., Krishnan, R. & Naidu, C. V. Pacific modulation of accelerated south Indian Ocean sea level rise during the early 21st Century. Clim. Dyn. 53 , 4413-4432 (2019). Han, S.-C., Shum, C.-K., Bevis, M., Ji, C. & Kuo, C.-Y. Crustal dilatation observed by GRACE after the 2004 Sumatra-Andaman earthquake. Science (80-. ). 313 , 658-662 (2006). Johnson, G. C. & Chambers, D. P. Ocean bottom pressure seasonal cycles and decadal trends from GRACE Release‐05: Ocean circulation implications. J. Geophys. Res. Ocean. 118 , 4228-4240 (2013). Wu, Q., Zhang, X., Church, J. A. & Hu, J. Variability and change of sea level and its components in the I ndo‐P acific region during the altimetry era. 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Changes in Extreme Sea-Level in the North Indian Ocean. in Extreme Natural Events: Sustainable Solutions for Developing Countries 281-303 (Springer, 2022). Durack, P. J., Wijffels, S. E. & Gleckler, P. J. Long-term sea-level change revisited: the role of salinity. Environ. Res. Lett. 9 , 114017 (2014). Behara, A., Vinayachandran, P. N. & Shankar, D. Influence of rainfall over eastern Arabian Sea on its salinity. J. Geophys. Res. Ocean. 124 , 5003-5020 (2019). Akhter, S. et al. Seasonal and long-term sea-level variations and their forcing factors in the northern Bay of Bengal: A statistical analysis of temperature, salinity, wind stress curl, and regional climate index data. Dyn. Atmos. Ocean. 95 , 101239 (2021). Johns, W. E. et al. Observations of seasonal exchange through the Straits of Hormuz and the inferred heat and freshwater budgets of the Persian Gulf. J. Geophys. Res. Ocean. 108 , (2003). L’Hégaret, P., Marez, C. de, Morvan, M., Meunier, T. & Carton, X. Spreading and vertical structure of the Persian Gulf and Red Sea outflows in the northwestern Indian Ocean. J. Geophys. Res. Ocean. 126 , e2019JC015983 (2021). Deepa, J. S., Gnanaseelan, C., Kakatkar, R., Parekh, A. & Chowdary, J. S. The interannual sea level variability in the Indian Ocean as simulated by an ocean general circulation model. Int. J. Climatol. 38 , 1132-1144 (2018). Yamagata, T., Mizuno, K. & Masumoto, Y. Seasonal variations in the equatorial Indian Ocean and their impact on the Lombok throughflow. J. Geophys. Res. Ocean. 101 , 12465-12473 (1996). Schott, F. A., Xie, S. & McCreary Jr, J. P. Indian Ocean circulation and climate variability. Rev. Geophys. 47 , (2009). Aparna, S. G., Mccreary, J. P., Shankar, D. & Vinayachandran, P. N. Signatures of Indian Ocean Dipole and El Niño - Southern Oscillation events in sea level variations in the Bay of Bengal. 117 , 1-13 (2012). Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the tropical Indian ocean. Nature 401 , 360-363 (1999). Pillai, U. M., Kochuparampil, A. J., Raj, R. P. & Johannessen, O. M. Influence of Climatic Events on Sea Level Variability over the Bay of Bengal : Insights from EOF Representation. Def. Sci. J. 75 , 698-703 (2025). Chambers, D. P., Tapley, B. D. & Stewart, R. H. Anomalous warming in the Indian Ocean coincident with El Niño. J. Geophys. Res. Ocean. 104 , 3035-3047 (1999). Rao, R. R. et al. Interannual variability of Kelvin wave propagation in the wave guides of the equatorial Indian Ocean, the coastal Bay of Bengal and the southeastern Arabian Sea during 1993-2006. Deep. Res. Part I Oceanogr. Res. Pap. 57 , 1-13 (2010). Cai, W. et al. Opposite response of strong and moderate positive Indian Ocean Dipole to global warming. Nat. Clim. Chang. 11 , 27-32 (2021). Wang, G. et al. The Indian Ocean Dipole in a warming world. Nat. Rev. Earth Environ. 5 , 588-604 (2024). Zhang, Y. & Du, Y. Oceanic Rossby waves induced two types of ocean-atmosphere response and opposite Indian Ocean Dipole phases. J. Clim. 35 , 3927-3945 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8870568","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":620154037,"identity":"e0dbb3e7-7b5b-4cf2-b4b9-fde08ad57d48","order_by":0,"name":"Ullas M Pillai","email":"","orcid":"","institution":"Nansen Environmental Research Centre India","correspondingAuthor":false,"prefix":"","firstName":"Ullas","middleName":"M","lastName":"Pillai","suffix":""},{"id":620154038,"identity":"ca6c2a03-d7b0-4668-957f-b4f24b4aeb0f","order_by":1,"name":"Franck Eitel Kemgang Ghomsi","email":"","orcid":"","institution":"Centre for Earth Observation Science, University of Manitoba","correspondingAuthor":false,"prefix":"","firstName":"Franck","middleName":"Eitel Kemgang","lastName":"Ghomsi","suffix":""},{"id":620154039,"identity":"3ad24850-9795-4afb-8975-cf0dfe3522e4","order_by":2,"name":"Ajith Joseph Kochuparampil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFCCgw3MDAxsBvxgjgEDDzFaGptBWiQbiNfCwNgMUmtwgFhn8Tcebn9cwMBnbHy7+diHHwUMMuaEtEgcADpsBgObmdmdY8kze4AOs2wgpAekhYeBzcbsRo4xM8gvBF0oD9NiPCP/M3FaDKBazAwkcpiJ02II1DIbqMVY4kaaMWOPgQRhLXI3jj/4zMNwzLB/RvJjhh9/bOwJh7YEUAXjv2NwLiH1QMDfACJriFA5CkbBKBgFIxYAAPtFO9w7xmEbAAAAAElFTkSuQmCC","orcid":"","institution":"Nansen Environmental Research Centre India","correspondingAuthor":true,"prefix":"","firstName":"Ajith","middleName":"Joseph","lastName":"Kochuparampil","suffix":""},{"id":620154040,"identity":"cd620697-2995-4ce4-84d7-4de11de30d92","order_by":3,"name":"Roshin P. Raj","email":"","orcid":"","institution":"Nansen Environmental and Remote Sensing Center and Bjerknes Center for Climate Research","correspondingAuthor":false,"prefix":"","firstName":"Roshin","middleName":"P.","lastName":"Raj","suffix":""},{"id":620154041,"identity":"5f787422-e587-4bf0-8af8-64bd9f8b1352","order_by":4,"name":"Ola M. Johannessen","email":"","orcid":"","institution":"Nansen Scientific Society","correspondingAuthor":false,"prefix":"","firstName":"Ola","middleName":"M.","lastName":"Johannessen","suffix":""}],"badges":[],"createdAt":"2026-02-13 10:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8870568/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8870568/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107083784,"identity":"fe846359-c907-4a82-84bf-50beb7c4a7ef","added_by":"auto","created_at":"2026-04-16 14:43:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313524,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area showing the North Indian Ocean (NIO; 45°E-100°E; 10°S-30°N) and its sub-basins considered in this study: \u003cstrong\u003e(a)\u003c/strong\u003e Western Arabian Sea (WAS; 50°E-65°E, 8°N-25°N), \u003cstrong\u003e(b)\u003c/strong\u003e Eastern Arabian Sea (EAS; 65°E-78°E, 8°N-25°N), \u003cstrong\u003e(c)\u003c/strong\u003e Western Bay of Bengal (WBoB; 80°E-90°E, 8°N-22°N), \u003cstrong\u003e(d)\u003c/strong\u003e Eastern Bay of Bengal (EBoB; 90°E-100°E, 8°N-22°N), \u003cstrong\u003e(e)\u003c/strong\u003e Western Equatorial Indian Ocean (WEIO; 48°E-75°E, 5°S-5°N), and \u003cstrong\u003e(f)\u003c/strong\u003eEastern Equatorial Indian Ocean (EEIO; 78°E-100°E, 5°S-5°N). The coloured circles along the coastal boundaries shows the population density within 20 kms from the coast that are less than 10 m above sea level.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/af1fa8d87ff2b52bb29f0a6c.png"},{"id":107083780,"identity":"988d4d13-6347-4484-a509-6337ca7d73fc","added_by":"auto","created_at":"2026-04-16 14:43:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":625266,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial trends of SLA (mm/yr) from 2003 to 2021 for different components of sea level: \u003cstrong\u003e(a)\u003c/strong\u003e Satellite Altimetry, \u003cstrong\u003e(b)\u003c/strong\u003e Steric, \u003cstrong\u003e(c)\u003c/strong\u003e Thermosteric, \u003cstrong\u003e(d)\u003c/strong\u003e Halosteric, \u003cstrong\u003e(e)\u003c/strong\u003eGRACE Ocean mass, \u003cstrong\u003e(f)\u003c/strong\u003e DAC, \u003cstrong\u003e(g)\u003c/strong\u003e GIA, and \u003cstrong\u003e(h)\u003c/strong\u003e Residual. Regions where trends are not statistically significant at the 95% confidence level are stippled, except for GIA, for which only the integrated linear trend is available.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/af12e91f6671656184a907ac.png"},{"id":107083778,"identity":"8fb5c9f0-b132-4293-829a-cc277429d0ee","added_by":"auto","created_at":"2026-04-16 14:43:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":445227,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial trends of depth-averaged (0-700 m) \u003cstrong\u003e(a)\u003c/strong\u003e temperature and \u003cstrong\u003e(b)\u003c/strong\u003e salinity over the period 2003-2021 in the North Indian Ocean. Areas with trends that are not statistically significant at the 95% confidence level are hatched.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/686e5304d5408d7c00b1e7fb.png"},{"id":107481688,"identity":"f15af0fd-d0d8-4541-8e78-9d78d9aa6673","added_by":"auto","created_at":"2026-04-22 02:19:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":349929,"visible":true,"origin":"","legend":"\u003cp\u003eDe-seasonalized monthly time series and linear trends (mm/yr) of mean altimetry SLA, steric sea level (0-700 m depth-integrated from ARMOR3D), GRACE ocean mass component, and residual sea level over \u003cstrong\u003e(a)\u003c/strong\u003e NIO, \u003cstrong\u003e(b)\u003c/strong\u003e WAS, \u003cstrong\u003e(c)\u003c/strong\u003e EAS, \u003cstrong\u003e(d)\u003c/strong\u003e WBoB, \u003cstrong\u003e(e)\u003c/strong\u003eEBoB, \u003cstrong\u003e(f)\u003c/strong\u003e WEIO, and \u003cstrong\u003e(g)\u003c/strong\u003e EEIO for the period 2003-2021.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/c81342c4d862ef9cac278406.png"},{"id":107481543,"identity":"45a104c9-3ee9-4ae6-8c99-c9e5c5f59ef1","added_by":"auto","created_at":"2026-04-22 02:18:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":352859,"visible":true,"origin":"","legend":"\u003cp\u003eDe-seasonalized monthly time series and linear trends (mm/yr) of mean altimetry-derived sea level anomaly (SLA), steric, thermosteric, and halosteric sea level components (integrated over 0-700 m from ARMOR3D), as well as GRACE-derived ocean mass, for the period 2003-2021. Results are shown for \u003cstrong\u003e(a)\u003c/strong\u003e the entire NIO, \u003cstrong\u003e(b)\u003c/strong\u003e WAS, \u003cstrong\u003e(c)\u003c/strong\u003eEAS, \u003cstrong\u003e(d)\u003c/strong\u003e WBoB, \u003cstrong\u003e(e)\u003c/strong\u003e EBoB, \u003cstrong\u003e(f)\u003c/strong\u003e WEIO, and \u003cstrong\u003e(g)\u003c/strong\u003e EEIO.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/fe3193f8cb9c92ac9a869277.png"},{"id":107083781,"identity":"92a8b40a-57bb-4b7d-b4a7-e1c61206c45b","added_by":"auto","created_at":"2026-04-16 14:43:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":471495,"visible":true,"origin":"","legend":"\u003cp\u003eDetrended monthly sea level anomalies of steric, thermosteric, GRACE-derived ocean mass, and altimetry-derived sea level during the period 1993-2021 for the following North Indian Ocean sub-basins:\u003cstrong\u003e(a)\u003c/strong\u003e WAS, \u003cstrong\u003e(b)\u003c/strong\u003e EAS, \u003cstrong\u003e(c)\u003c/strong\u003e WBoB, \u003cstrong\u003e(d)\u003c/strong\u003eEBoB, \u003cstrong\u003e(e)\u003c/strong\u003e WEIO, and \u003cstrong\u003e(f)\u003c/strong\u003e EEIO. Vertical lines indicate the years of occurrence of various climate modes, as detailed in Table 2.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/4b419567b119a62629eb3844.png"},{"id":107083783,"identity":"23ac5dba-4597-4e0e-8ce6-f1e7277ea764","added_by":"auto","created_at":"2026-04-16 14:43:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":603186,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial correlation maps of monthly mean SLA with \u003cstrong\u003e(a)\u003c/strong\u003e ONI, \u003cstrong\u003e(b)\u003c/strong\u003e DMI, \u003cstrong\u003e(c)\u003c/strong\u003e WTIO SST index, \u003cstrong\u003e(d)\u003c/strong\u003eSETIO SST index for the period 1993-2021.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/63151050b01cc7f816428157.png"},{"id":107481690,"identity":"db4fc6c7-15a5-4064-a245-ac185fa3b54c","added_by":"auto","created_at":"2026-04-22 02:19:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":403559,"visible":true,"origin":"","legend":"\u003cp\u003eFirst Principal Component (PC1) of altimetry SLA and its comparison with \u003cstrong\u003e(a)\u003c/strong\u003e DMI and \u003cstrong\u003e(b)\u003c/strong\u003e ONI across various sub-basins of the NIO during 1993-2021.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/3308815c1f9bd7a7d030ca75.png"},{"id":107484343,"identity":"4bcba316-ab72-48e1-a6f8-bda1798d8c36","added_by":"auto","created_at":"2026-04-22 02:31:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4024015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8870568/v1/6ed4b98e-1f8f-4a3e-843d-e062cbf67cc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Thermosteric dominance of sea level rise in the North Indian Ocean: sub- basin budget analysis (2003-2021)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent decades, sea level research has gained critical importance. Globally, nearly 40% of the population resides within 100 km of coastlines, with about 10% inhabiting vulnerable low-lying areas, including Small Island States that face increasing risks from coastal flooding, erosion, saltwater intrusion, and ecosystem degradation resulting from the rising sea level\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Current sea level rise (SLR), along with its observed acceleration as measured by satellite altimetry\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, is largely driven by human-caused global warming. The thermal expansion from ocean warming and mass addition from land ice melt are considered to be the primary components of sea level rise which are directly linked to anthropogenic climate change. The Global Mean Sea Level (GMSL) has been designated by the World Meteorological Organization (WMO) as one of the seven essential climate change indicators, following recommendations from the Global Climate Observing System\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The consequences of rising seas extend beyond threats to coastal urban centres, potentially transforming entire shorelines, and leading to permanent inundation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These physical changes have fundamentally altered coastal ecosystems and tidal dynamics, creating cascading environmental and societal impacts\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChanges in GMSL arise from two primary physical processes. The first is steric change, resulting from variations in ocean density due to temperature and salinity changes. The second is barystatic (or ocean mass) change, driven by the addition of water by ice loss from glaciers and ice sheets of Greenland and Antarctica, as well as changes in terrestrial water storage, such as groundwater depletion or reservoir retention. According to Thompson et al.\u003csup\u003e9\u003c/sup\u003e, the updated global mean sea level trend from satellite altimetry for 1993\u0026ndash;2022 is 3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 mm/yr, while the global mean ocean mass trend for 2005\u0026ndash;2022 is 2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 mm/yr. While ocean mass addition remains the dominant contributor to the recent global sea-level rise, followed by steric contributions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, these processes are fundamentally driven by climatic factors, including modifications to atmospheric and oceanic conditions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Recent observations show that ice sheet melt is accelerating, particularly in Greenland\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. While these factors drive the long-term trend, research has established that interannual GMSL fluctuations are predominantly influenced by natural climate variability. The El Ni\u0026ntilde;o-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) are the primary drivers of these short-term anomalies\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt a regional scale, satellite altimetry data demonstrate that SLR often varies significantly from the global mean (e.g., Raj et al.\u003csup\u003e18\u003c/sup\u003e). Regional sea-level variations at decadal scales reflect global mean rise plus spatial anomalies from steric effects, ocean dynamics, atmospheric forcing, and solid Earth responses to mass redistribution\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Precise monitoring of these regional variations and their drivers is critical for detection-attribution studies, especially when it comes to identifying when anthropogenic signals appear in different areas\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Locally, the most relevant metric for assessing societal impacts is the change in sea level relative to the land surface. Coastal sea-level trends often differ from open-ocean trends due to the influence of small-scale processes, such as winds, currents, and vertical land motion (isostatic adjustments), that modify the broader regional and global signals\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e distinguished \"climate-related\" sea level change (global mean plus regional pattern) from \"total relative\" sea level (including vertical land motion), showing that subsidence can locally amplify Indian Ocean coastal sea level change well beyond the climatic signal. The consequences of SLR are evident worldwide in coastal regions, with increasingly persistent effects\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe launch of satellite altimetry missions in 1992, including TOPEX/Poseidon and its successors, revolutionized sea-level monitoring by enabling precise, global measurements to within a millimeter. This capability was further enhanced by the Gravity Recovery and Climate Experiment (GRACE, 2002\u0026ndash;2017) and GRACE Follow-On mission (2018-present), which quantified mass redistribution through gravity anomaly measurements\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Together, these satellite systems have dramatically improved the separation and estimation of individual sea-level components (steric and barystatic) with unprecedented accuracy. Earlier studies in the North Indian Ocean (NIO) has already exploited such multi-sensor combinations, for example, Ghosh et al.\u003csup\u003e25\u003c/sup\u003e combined altimetry, GRACE, and Argo in the Bay of Bengal and showed that the sum of steric and mass components was broadly consistent with altimetric sea level within uncertainties, while highlighting regional discrepancies linked to sparse Argo coverage and GRACE noise. More recently, Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e applied a similar framework to Africa's Large Marine Ecosystems (LMEs) and found that mass contributions now account for over 80% of total sea level rise in those regions, a stark contrast with the steric-dominated signal reported for the tropical Indian Ocean.\u003c/p\u003e \u003cp\u003eNIO exhibits complex oceanographic dynamics due to its unique geographical constraints and seasonal forcing due to the monsoon system. As a semi-enclosed ocean bounded by land on the northern side, it experiences complete seasonal current reversals driven by monsoon wind patterns\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The ocean features two distinct sub-seas: the Arabian Sea in the west, which is characterized by high-salinity water outflows from the Persian Gulf and the Red Sea, and the Bay of Bengal (BoB) in the east, which is dominated by strong freshwater input from major river systems. The sea level in the North Indian Ocean is dominated by thermosteric changes, which have accelerated in recent decades\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Swapna et al.\u003csup\u003e30\u003c/sup\u003e identified a mechanistic link between multidecadal weakening of the Indian summer monsoon circulation and thermosteric sea level rise in the NIO, demonstrating that reduced southward heat transport via the Cross-Equatorial Cell (CEC) leads to enhanced heat retention and thermosteric expansion, particularly in the Arabian Sea. Furthermore, Srinivasu et al.\u003csup\u003e32\u003c/sup\u003e documented a distinct decadal reversal in NIO sea level trends around 2003, with sea level falling during 1993\u0026ndash;2003 and rising sharply during 2004\u0026ndash;2013, attributing this reversal to changes in surface turbulent heat flux and meridional heat transport driven by decadal wind variability. Salim et al.\u003csup\u003e33\u003c/sup\u003e showed that, over the broader tropical Indian Ocean during 1993\u0026ndash;2007, steric processes accounted for about 35% of long-term sea level change, with a particularly strong steric control (~\u0026thinsp;72%) in the south tropical Indian Ocean but weaker and even negative steric contributions in parts of the north, underlining strong meridional contrasts. Interannual sea-level variability in the Indian Ocean is primarily driven by major climate modes, particularly the El Ni\u0026ntilde;o-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Nidheesh et al.\u003csup\u003e35\u003c/sup\u003e demonstrated that decadal sea level variability in the Indo-Pacific is driven by wind stress variations that are relatively independent between the Pacific and Indian Ocean basins, with thermosteric changes almost entirely dominating decadal fluctuations, while cautioning that long-term trend estimates from forced ocean models are sensitive to wind product uncertainties. Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e further demonstrated, using a 60-year reconstruction (1950\u0026ndash;2009), that steric changes and IOD-related variability dominate the climatic component of Indian Ocean Sea level and that regional patterns such as the east-west trend increase below ~\u0026thinsp;15\u0026deg;S and evolve on multidecadal time scales. Rising sea levels due to climate change pose significant threats to a coastal nation like India, where approximately 420\u0026nbsp;million of its 1.4\u0026nbsp;billion inhabitants live in coastal regions\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. India's eastern coastline faces increased vulnerability to sea-level rise, with 35% of the country's population residing within 100 km of the shore, and the risk is compounded by intensifying cyclonic activity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Parekh et al.\u003csup\u003e34\u003c/sup\u003e reported that the BoB exhibits substantially higher sea level rise rates (up to 8.8 mm/yr at Charchanga, Bangladesh) compared to the Arabian Sea, with steric contributions dominating mass-driven changes by a factor of two, highlighting the regional heterogeneity that our study aims to characterize at sub-basin scales.\u003c/p\u003e \u003cp\u003eThis study assesses the individual contributions of steric (thermosteric and halosteric effects) and ocean mass components to sea-level variations across distinct sub-basins of the North Indian Ocean (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) during the GRACE period 2003\u0026ndash;2021. This analysis also addresses critical gaps in our understanding of regional sea level trends and variability by providing a spatial and temporal analysis of these contributions. By extending earlier basin-scale work over the tropical Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and longer-term reconstructions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e into the GRACE/altimetry era with higher-resolution ARMOR3D fields, our study focuses specifically on sub-basin differences within the NIO and quantifies how the relative importance of thermosteric, halosteric, and mass terms has evolved in the most recent decades. Our study period (2003\u0026ndash;2021) corresponds to the period of accelerated NIO sea level rise identified by Srinivasu et al.\u003csup\u003e32\u003c/sup\u003e and extends the multidecadal analysis of Swapna et al.\u003csup\u003e30\u003c/sup\u003e, allowing us to assess whether the thermosteric dominance and monsoon-driven mechanisms they identified continue to characterize the most recent decades. We also address sea level contributions from glacial isostatic adjustment (GIA), which is a long-term process whereby the Earth's crust rises or subsides as it responds to the residual effects of ice sheet loading from the past\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e including the contribution from atmospheric forcing. Furthermore, this study evaluates how climate modes, such as ENSO and Indian Ocean Dipole (IOD), influence interannual sea-level variability in each sub-basin during the altimetry period from 1993\u0026ndash;2021. This allows us to distinguish natural variabilities from long-term anthropogenic signals. The paper is structured as follows: Section 2 describes the data and methods; Section 3 presents the results and discussion; and Section 4 provides the summary and conclusions.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Altimetry sea level data\u003c/h2\u003e \u003cp\u003eDaily gridded altimetric sea level anomaly (SLA) data with a spatial resolution of 1/8\u0026deg; x 1/8\u0026deg; over the NIO from 1993 to 2021 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48670/moi-00148\u003c/span\u003e\u003cspan address=\"10.48670/moi-00148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed June 2024) were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS). This dataset was processed using the DUACS multimission altimeter processing system, which involves merging observations from several altimeter missions to ensure data reliability. Additionally, all standard corrections, including sea state bias, instrumental drift, and dynamic atmospheric correction, were applied to the dataset\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. For each grid point, the monthly mean of SLA was calculated from the daily values. Our approach is consistent with previous Indian Ocean studies using multi-mission merged altimetry\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and with recent applications to African LMEs\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, ensuring robustness of regional sea level trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Estimation of steric sea level components\u003c/h2\u003e \u003cp\u003eMonthly average temperature and salinity profiles were obtained from the ARMOR3D dataset, distributed by the Copernicus Marine Environment Monitoring Service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48670/moi-00052\u003c/span\u003e\u003cspan address=\"10.48670/moi-00052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This dataset is available from 1993 with a spatial resolution of 0.25\u0026deg; \u0026times; 0.25\u0026deg; and 50 vertical levels. It is a combination of satellite and in-situ observations that provide reliable steric sea level estimates\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSteric sea level anomaly (SSLA) was computed by vertically integrating density anomalies and then decomposing them into thermosteric (TSLA) and halosteric (HSLA) components using established methods\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e as shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SSLA=TSLA+HSLA=\\:\\frac{-1}{{\\rho\\:}_{0}}{\\int\\:}_{-H}^{0}\\varDelta\\:\\rho\\:dz$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:-{\\int\\:}_{H}^{0}\\left(\\alpha\\:\\varDelta\\:T-\\beta\\:\\varDelta\\:S\\right)dz$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e​\u003c/b\u003e is the reference density (1025 kg/m\u0026sup3;), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z\\)\u003c/span\u003e\u003c/span\u003e represents depth. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:T\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:S\\)\u003c/span\u003e\u003c/span\u003e denote anomalies in density, temperature, and salinity, respectively, relative to their climatological means at each depth level. The coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e (thermal expansion) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e (haline contraction) were derived from monthly temperature and salinity fields using the Thermodynamic Equation of Sea Water\u0026thinsp;\u0026minus;\u0026thinsp;2010 (TEOS-10)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and the Gibbs Sea Water (GSW) Oceanographic Toolbox\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The reference depth \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:H\\)\u003c/span\u003e\u003c/span\u003e is defined as 700 m because interpolation uncertainties increase below this depth due to sparse observational data\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Additionally, steric contributions from depths exceeding 700 m in ARMOR3D are expected to be negligible\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This upper-ocean focus is consistent with earlier steric analyses in the Indian Ocean, which either limited integration to 700 m\u003csup\u003e22,33\u003c/sup\u003e and reflects the dominant role of the upper ocean in regional steric variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Dynamic atmospheric correction (DAC), glacial isostatic adjustment (GIA), and ocean mass component\u003c/h2\u003e \u003cp\u003eThe contribution of atmospheric forcing to the sea level variability can be assessed using the Dynamic Atmospheric Correction dataset. This dataset, provided by Aviso (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tds.aviso.altimetry.fr/thredds/catalog/dataset-auxiliary-dynamic-atmospheric-correction/catalog.html\u003c/span\u003e\u003cspan address=\"https://tds.aviso.altimetry.fr/thredds/catalog/dataset-auxiliary-dynamic-atmospheric-correction/catalog.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), is updated every six hours and has a spatial resolution of 0.25\u0026deg;\u0026times;0.25\u0026deg;, starting from 1992. Contributions from the ocean mass component were analyzed using gridded data from the Center for Space Research (CSR) RL06 GRACE/GRACE-FO mascon solutions, and are obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www2.csr.utexas.edu/grace\u003c/span\u003e\u003cspan address=\"http://www2.csr.utexas.edu/grace\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This data is available starting from April 2002 with a spatial resolution of 0.25\u0026deg;\u0026times;0.25\u0026deg;, although the native grid resolution of GRACE data is 300 km \u003csup\u003e47\u003c/sup\u003e. Corrections for glacial isostatic adjustment (GIA) are applied using the ICE6G-D model\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Following the approach of Ghosh et al.\u003csup\u003e25\u003c/sup\u003e over the BoB, Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e over the wider Indian Ocean, and Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e over African coastal waters, GIA corrections are essential to ensure consistency between GRACE-based mass trends, steric components and altimetric sea level, especially in regions where in situ measurements of vertical land motion are sparse.\u003c/p\u003e \u003cp\u003eTo understand the role of climatic influence on sea level, we considered the variability of sea level during ENSO and IOD years and used the Oceanic Ni\u0026ntilde;o Index (ONI) and the Dipole Mode Index (DMI). The ONI was obtained from the Physical Sciences Laboratory of NOAA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psl.noaa.gov/gcos_wgsp/timeseries/\u003c/span\u003e\u003cspan address=\"https://psl.noaa.gov/gcos_wgsp/timeseries/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the DMI was obtained from the Jet Propulsion Laboratory of NASA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sealevel.jpl.nasa.gov/overlay-iod/\u003c/span\u003e\u003cspan address=\"https://sealevel.jpl.nasa.gov/overlay-iod/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, the Western Tropical Indian Ocean (WTIO) and Southeastern Tropical Indian Ocean (SETIO) sea surface temperature (SST) indices were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stateoftheocean.osmc.noaa.gov/sur/ind/setio.php\u003c/span\u003e\u003cspan address=\"https://stateoftheocean.osmc.noaa.gov/sur/ind/setio.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The DMI is the difference between the WTIO and SETIO SST indices.\u003c/p\u003e \u003cp\u003eWe applied a non-parametric Mann-Kendall trend analysis, which is robust in detecting monotonic trends in the presence of noise and non-normality in order to statistically assess the spatial trends for significance at a 95% confidence level for the various sea level contributions, following the methodologies used in previous studies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Subsequently, regionally averaged linear trends for individual sea level components were computed, and the spatial correlations between sea level variability and different climate modes were evaluated. For the comparison of trends, all the datasets are arranged according to the time period of the GRACE dataset, which has missing time steps of totalling approximately 14% of the data, as a result of the inter-mission gap and battery issues. Thus, the altimetry, steric, and residual components of sea level also have these gaps, ensuring that any differences between the estimates are not due to gaps in the GRACE dataset.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Spatial trends of various contributions to sea level during the GRACE period\u003c/h2\u003e \u003cp\u003eThis section discusses the major contributing factors and their respective roles in shaping the total sea level trend. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the spatial trends of various components contributing to total sea level variability over the North Indian Ocean during the period 2003\u0026ndash;2021. Altimetric SLA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) shows strong positive trends, exceeding 6 mm/yr in several regions, especially in the north and south of the BoB. These high coastal trends are broadly consistent with earlier altimetric estimates in the BoB\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, but our comparatively longer time span and higher-resolution inputs reveal stronger accelerations, especially along the eastern boundary. Coastal areas of India exhibit consistent rising sea level trends, ranging from approximately 2.01 mm/yr off the northern BoB coast near 21\u0026deg;N, 87\u0026deg;E to 6.81 mm/yr off the southwestern coast near 18\u0026deg;N, 72\u0026deg;E. Non-significant or weakly increasing trends (below 2 mm/yr) are observed primarily in the western Arabian Sea, including the Somali coast and along the Omani margin as isolated patches, and also over the western BoB near 14\u0026deg;N, 82\u0026deg;E. Over the Arabian Gulf, the altimetry sea level exceeds 4 mm/yr, in agreement with the results of Al-Subhi \u0026amp; Abdulla\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In the equatorial Indian Ocean sub-basins, the eastern sub-basin shows higher averaged trends (~\u0026thinsp;4.84 mm/yr), with peaks nearing 5.5 mm/yr along the Indonesian coastline.\u003c/p\u003e \u003cp\u003eThe steric components, comprising Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb (total steric), Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec (thermosteric), and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed (halosteric) sea level, are computed for the upper 700 m of the water column. The total steric component shares spatial similarity with the thermosteric pattern, reinforcing that ocean temperature is the dominant driver of steric sea level variability. This thermosteric dominance is consistent with the findings of Nidheesh et al.\u003csup\u003e35\u003c/sup\u003e, who demonstrated that decadal sea level variations in the Indo-Pacific are almost entirely thermosteric, with halosteric contributions significant only in localized regions such as the western Indonesian seas. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed depict the contributions of thermal expansion and salinity changes, respectively. The thermosteric trends are more prominent in the Arabian Sea compared to the Bay of Bengal. The enhanced thermosteric signal in the Arabian Sea aligns with the mechanism proposed by Swapna et al.\u003csup\u003e30\u003c/sup\u003e, who showed that weakening monsoon circulation reduces coastal upwelling off Somalia and Arabia, leading to increased heat retention and thermosteric sea level rise in this region. Notably, the western Arabian Sea, particularly around the Gulf of Oman and Gulf of Aden, shows a dipole pattern in its steric components, with strong positive thermosteric trends and strong negative halosteric trends. These features are influenced by the warm, saline outflows from the Persian Gulf and the Red Sea, respectively, which alter local temperature and salinity profiles. Halosteric changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) exhibit significant trends in both the Arabian Sea and the BoB, although their magnitude is generally lower than thermosteric contributions. Nevertheless, in the northeastern BoB, eastern equatorial Indian Ocean, and off India\u0026rsquo;s western coast (east of 70\u0026deg;E, north of 15\u0026deg;N), halosteric trends reach up to ~\u0026thinsp;1 mm/yr, indicating localized salinity-driven SLR. This pattern of strong thermosteric control with secondary halosteric modulation is consistent with the CEOF-based decomposition of SSH and steric height found by Salim et al.\u003csup\u003e33\u003c/sup\u003e, who reported close coherence between the leading steric and SSH modes over the tropical Indian Ocean. It also contrasts markedly with African LMEs, where Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e found that the halosteric contribution is typically negative or negligible while mass contributions dominate the sea level budget. Importantly, the halosteric patterns we observe, negative in the Arabian Sea and positive in the BoB, are consistent with the contrasting salinity regimes described by Jyoti et al.\u003csup\u003e50\u003c/sup\u003e for the South Indian Ocean, where halosteric contributions account for approximately 40% of the accelerated sea level rise during 2000 to 2015, primarily driven by Indonesian Throughflow transport of fresher Pacific waters.\u003c/p\u003e \u003cp\u003eThe GRACE satellite mission offers complementary insights by quantifying the mass-driven component of sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The Arabian Sea, in particular, exhibits non-significant trend values, with an area-averaged trend of 0.33 mm/yr. In contrast, the eastern NIO displays a complex mix of strong positive and negative trends. These spatial anomalies have been linked to crustal adjustments following the 2004 Sumatra-Andaman earthquake, which induced significant geophysical changes in the regional geoid and ocean mass distribution\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Nonetheless, GRACE-derived estimates must be interpreted with caution. The mission\u0026rsquo;s coarse spatial resolution increases the risk of signal leakage, especially from land into adjacent coastal ocean regions, potentially distorting the actual magnitude and spatial structure of mass changes\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Numerous studies have reported challenges in estimating ocean mass using GRACE data, particularly for specific ocean basins like Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, where the signal leakage and measurement uncertainties are significant. Also, the non-steric sea level variability induced by the earthquake, while detectable through satellite gravimetry as geoid height variations, remains indistinguishable in satellite altimetry because of the overwhelming influence of steric sea level variations\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Ghosh et al.\u003csup\u003e25\u003c/sup\u003e similarly reported poor regional agreement between GRACE-based mass trends and the difference between altimetry and Argo-based steric sea level in the BoB, underlining that GRACE-derived basin-scale mass budgets over the north Indian Ocean currently remain more uncertain than global estimates. Despite these limitations, GRACE data remain valuable for identifying large-scale ocean mass variability, particularly when integrated with steric and altimetric observations to assess the full spectrum of sea level change drivers.\u003c/p\u003e \u003cp\u003eThe DAC is applied to account for the contributions from the atmosphere. The DAC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef) shows negligible trends across the NIO, with values nearly 0.01 mm/yr and lacking statistical significance. This confirms that atmospheric pressure-related effects, as accounted for SLR by the DAC, have minimal influence on long-term sea level trends in the region and do not show any spatial variability. The GIA dataset from Peltier et al.\u003csup\u003e48\u003c/sup\u003e is presented as an integrated linear trend for the full period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg); as in Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e and Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e. This prevents a formal statistical assessment of trend significance but provides a necessary correction for long-term solid-Earth adjustment.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh shows the residual SLA, estimated by subtracting the steric, ocean mass component, and GIA from the altimetry SLA, capturing contributions of unmodeled signals which may originate from various sources, including deep-ocean contributions below 700 m depth, drifts or biases in the models used for steric and barystatic components, and effects of small-scale ocean dynamic processes such as wind-driven currents. The residual sea level accounts for about 28% of the observed sea level trend across the NIO. Exceptions include the northern Arabian Sea and the Gulf of Aden, where steric contributions are more pronounced. However, the residual SLA likely reflects a combination of unresolved processes such as vertical land motion, and potentially uncorrected tidal effects, which are not isolated in this study. Similar residual discrepancies between the sum of steric and mass components and altimetric sea level have been reported previously in the BoB\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and at the scale of the tropical Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and have been attributed to sparse in situ coverage below ~\u0026thinsp;700\u0026ndash;900 m, GRACE noise, and land-ocean signal leakage. In light of these uncertainties, the thermosteric component (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) emerges as the most consistent and physically robust driver of sea level trends in the NIO, confirming the conclusions of Swapna et al.\u003csup\u003e57\u003c/sup\u003e and Jyoti et al.\u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe trend values from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, estimated for the six sub-basins of the NIO, given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, highlight notable differences in sea level change dynamics across the Arabian Sea, BoB, and Equatorial Indian Ocean (EIO). Each region exhibits distinct contributions from steric, mass, and residual components. Based on satellite altimetry, the BoB records the highest basin-wide, area-averaged sea level trend of 5.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 mm/yr, followed by the EIO (4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51 mm/yr) and the Arabian Sea (4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 mm/yr). These basin-scale trends are computed directly from area-averaged sea level and are not derived from averaging the western and eastern sub-basin trends. The total steric contribution in the Arabian Sea is relatively lower than in the other two basins. However, when the steric components are considered separately, the thermosteric trend in the Arabian Sea is the highest among all regions, at 2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65 mm/yr, indicating a strong influence of ocean warming on the sea level rise. This pronounced thermosteric signal in the Arabian Sea supports the hypothesis of Swapna et al.\u003csup\u003e30\u003c/sup\u003e that weakened summer monsoon circulation has reduced southward heat export via the Cross-Equatorial Cell, resulting in enhanced heat storage and thermosteric expansion in the NIO, particularly in regions of suppressed upwelling. In contrast, the halosteric component in the Arabian Sea shows a significant negative trend of approximately\u0026thinsp;\u0026minus;\u0026thinsp;0.89 mm/yr, which reduces the net steric trend to 1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 mm/yr. This salinity-driven contraction offsets much of the thermosteric expansion, thereby moderating the overall steric SLR in the region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea-averaged trends (Mann-Kendall) in sea level and its components across the North Indian Ocean (NIO) from 2003 to 2021 (units in mm/yr). The Total SLA consists of the entire altimetry trend from 2003 to 2021, whereas for the remaining components, the sea level trend corresponding to the time period of GRACE dataset (including the missing periods) from 2003 to 2021 is considered.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-basin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Altimetry SLA (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAltimetry SLA (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSteric (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThermosteric (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHalosteric (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eResidual (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGRACE (mm/yr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArabian Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" 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\u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBay of Bengal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBoB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEBoB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEquatorial Indian Ocean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWEIO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEEIO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAcross the Arabian Sea sub-basins and the western BoB, the residual sea level, which represents the remainder of the SLR derived from altimetric signals that is not accounted for by steric, ocean mass components and GIA effects, contributes substantially to the total trend. Our estimation of residual trends is subject to increased uncertainty in the Eastern Arabian Sea (EAS) and Eastern Bay of Bengal (EBoB). This limitation arises from the spatial constraint imposed by the 700 m depth-integration used to define the steric component, which effectively masks the contribution of steric variability over the continental slope and shelf which are shallower than 700 m depth. Part of these residuals may also reflect vertical land motion, as shown for certain Indian Ocean coasts and islands by Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e and for African deltaic regions by Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e, although our study does not explicitly separate this contribution.\u003c/p\u003e \u003cp\u003eEven after accounting for the GRACE-derived mass component, the Arabian Sea and the BoB retain strong basin-scale residual trends of 2.67 mm/yr and 1.91 mm/yr, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). A study by Unnikrishnan \u0026amp; Antony\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e mentioned the increase in mean sea level at the head of the Bay, possibly due to subsidence of the Ganga-Brahmaputra delta, which further results in large extreme sea levels. In comparison, regionally averaged residual trend of the EIO is nearly 0.31 mm/yr, mainly due to uncertainties related to the GRACE data over the Eastern Equatorial Indian Ocean. These results reveal that the dominant drivers of sea level rise vary across the NIO. Although the thermosteric component is one of the major influencers in most of these basins, especially in the Arabian Sea, the ocean mass component measured from GRACE gravimetry also contributes to the total sea level trend, as observed in the BoB and the EIO. Apart from these, the residual trends observed in the sub-basins also reveal the influence of some non-steric sea level components that need to be accounted for in future studies. This underscores the regional complexity of SLR in the Indian Ocean. When expressed as a fraction of the total NIO trend, our results imply that steric processes explain roughly 40\u0026ndash;45% of recent sea level rise, which is larger than the basin-wide 35% reported by Salim et al. (2012) for the 1993\u0026ndash;2007 period and substantially larger than the ~\u0026thinsp;20% steric contribution found for African LMEs by Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e. This contrast highlights that the NIO is unusually steric-dominated compared to other regional seas, where mass-driven contributions from ice melt and terrestrial water storage changes have become increasingly prominent. Our finding that thermosteric contributions have increased relative to earlier periods is consistent with the acceleration of NIO thermosteric sea level rise from 0.68 mm/yr (1958\u0026ndash;2015) to 2.3 mm/yr (1993\u0026ndash;2015) documented by Swapna et al.\u003csup\u003e30\u003c/sup\u003e, suggesting that the monsoon-driven heat retention mechanism they identified has continued or intensified during our study period (2003\u0026ndash;2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Spatial trends of temperature and salinity\u003c/h2\u003e \u003cp\u003eThe spatial distribution of temperature trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) closely resembles the pattern of thermosteric SLR and broadly corresponds to total sea level trends observed in satellite altimetry, particularly across the Arabian Sea. This dominant ocean warming signal is critical because it is the primary driver of steric SLR. Significant positive temperature trends prevail throughout most of the NIO, with only minor, statistically insignificant cooling patches present in the western Arabian Sea, western BoB, and along the southern boundary of the basin. This extensive warming is consistent with previous findings that link regional ocean temperature increases to enhanced thermosteric SLR\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. The spatial pattern of warming, with maxima in the northwestern Arabian Sea off the coasts of Somalia and Arabia, is consistent with the mechanism proposed by Swapna et al.\u003csup\u003e30\u003c/sup\u003e, who attributed enhanced warming in these regions to reduced upwelling associated with weakening summer monsoon circulation and spin-down of the Cross-Equatorial Cell.\u003c/p\u003e \u003cp\u003eAt the same time, salinity variations can also affect regional sea level changes\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Salinity trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) reveal distinct regional variability that also influences sea level changes by affecting water density. The Arabian Sea shows a significant salinity increase, mainly along its northern boundary and the region extending from the Gulf of Oman to the Somali coast. Conversely, a pronounced negative salinity trend occurs along the west coast of India, driven by greater freshwater influx from river discharge and seasonal precipitation\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. These contrasting salinity patterns play an important role in modulating steric SLR, partially counterbalancing the thermal expansion in the Arabian Sea, where elevated salinity increases water density and limits steric expansion despite warming. In contrast, the BoB experiences a widespread and statistically significant salinity decrease. This freshening is mainly attributed to increased riverine input and monsoonal runoff. The resulting reduction in water density enhances steric SLR, further contributing to the region\u0026rsquo;s observed sea level trends. These observations confirm the significant role of salinity changes, alongside temperature, in influencing BoB sea level variability through steric variations, as previously observed by Akhter et al.\u003csup\u003e62\u003c/sup\u003e and Nidheesh et al.\u003csup\u003e35\u003c/sup\u003e. They are also consistent with the negative halosteric trends and decreasing net freshwater content inferred by Ghosh et al.\u003csup\u003e25\u003c/sup\u003e in the northern BoB when using Argo-based profiles, highlighting the sensitivity of halosteric signals to the choice of observational product and integration depth. A similar contrast between basin-scale salinification and localized freshening has been documented in African LMEs, where the Mediterranean exhibits strong negative halosteric trends due to high evaporation while the Gulf of Guinea shows positive halosteric contributions near major river mouths\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Temporal variability of sea level components in the NIO\u003c/h2\u003e \u003cp\u003eThe de-seasonalized monthly time series of major sea level components demonstrate that steric sea level variability generally tracks the altimetry-derived sea level pattern across all NIO sub-basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, in the Arabian Sea and Western Bay of Bengal (WBoB) sub-basins, the contribution of residual sea level is significant. Across the entire NIO (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), the residual sea level contributes approximately 1.42 mm/yr to the total sea level rise, while the steric component alone accounts for about 1.94 mm/yr. These small variations in trend values compared to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are due to the use of a linear regression method here, in contrast to the Mann-Kendall method used previously for spatial trends. This partitioning is broadly compatible with earlier basin-scale estimates of steric versus total trends from Salim et al.\u003csup\u003e33\u003c/sup\u003e, but our longer record and focus on sub-basin scales reveal larger contrasts between west and east, especially in the equatorial Indian Ocean. Similarly, the Western Equatorial Indian Ocean (WEIO, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), and Eastern Equatorial Indian Ocean (EEIO, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg) show significant trends in steric sea level compared to their residual trends. However, the EEIO also exhibits higher trends in the ocean mass component (4.90 mm/yr) possibly arising from uncertainties associated with the crustal response to the Sumatra-Andaman earthquake. In the WBoB, the residual component is more prominent (~\u0026thinsp;3.98 mm/yr) whereas the EBoB is dominated by the ocean mass component (~\u0026thinsp;3.16 mm/yr).\u003c/p\u003e \u003cp\u003eSince steric sea level change includes both thermosteric and halosteric components, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts these individual contributions alongside satellite altimetry sea level anomalies and GRACE-observed mass variations. Among the NIO sub-basins, the Arabian Sea, comprising the Western Arabian Sea (WAS) and EAS, shows the strongest combined influence from thermosteric and halosteric components on sea level variability. Thermosteric effects emerge as the leading driver of sea level trends here, with notable influence also observed in the Equatorial Indian Ocean (both WEIO and EEIO). A distinct negative trend in halosteric sea level is present in the Arabian Sea sub-basins, partially offsetting the strong positive thermosteric trends. High salinity outflows from the Persian Gulf and Red Sea have caused elevated salinity in the Arabian Sea. This elevated salinity enhances saline contraction and strengthens the negative halosteric sea level changes\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, enhances saline contraction and strengthens the negative halosteric sea level changes. In contrast, the Bay of Bengal (WBoB and EBoB) exhibits positive halosteric trends, driven by increased freshwater input from river discharges. This freshening effect plays a major role in halosteric sea level variability in the BoB region. A similar contrast between Arabian Sea salinification and BoB freshening has also been noted in steric analyses of the upper Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, reinforcing the robustness of this pattern across different datasets and methodologies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Inter-annual variability of sea level and the role of climate modes\u003c/h2\u003e \u003cp\u003eSteric variations play a leading role in shaping the temporal evolution of sea level across the North Indian Ocean, with thermosteric effects contributing most significantly to the observed variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). While other components of sea level, such as mass and residual contributions, exhibit different patterns than the total sea level from satellite altimetry, steric and thermosteric signals remain closely correlated with the altimetric observations, particularly at interannual timescales. This strong coherence between SSH and steric signals in the NIO is consistent with Salim et al.\u003csup\u003e33\u003c/sup\u003e, who found that the first two complex EOF modes of steric height explained more than 70% of SSH variability over the tropical Indian Ocean.\u003c/p\u003e \u003cp\u003eTo examine this relationship, further, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e compares detrended monthly anomalies of total sea level from altimetry with both steric and thermosteric sea level across the NIO sub-basins. A strong positive correlation, significant at the 99% confidence level, is observed between altimetric sea level and both steric components in all sub-basins. In most regions, steric sea level shows slightly higher correlations with altimetry than the thermosteric component alone. However, in the Arabian Sea, and especially in the WAS, thermosteric sea level anomalies exhibit a stronger correlation with altimetric sea level than the full steric signal, emphasizing the dominant role of temperature in driving sea level variability in this basin. This thermosteric dominance in the Arabian Sea at interannual timescales is consistent with the findings of Srinivasu et al.\u003csup\u003e32\u003c/sup\u003e, who showed that the upper 700 m thermosteric sea level explains approximately 94% of observed NIO sea level rise during their Period II (2004\u0026ndash;2013), with particularly strong thermosteric control in the Arabian Sea. The Arabian Sea is also characterized by pronounced salinity variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), which appears to modulate the steric sea level signal, particularly at interannual timescales. This influence underscores the complex interplay between temperature and salinity in determining regional sea level variability. However, the influence of the ocean mass component on the interannual variability of total sea level is observed to be statistically insignificant in the Arabian Sea sub-basins.\u003c/p\u003e \u003cp\u003eTo further assess the impact of large-scale climate modes on sea level variability, vertical dashed lines representing years of major climate events were added to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. These include the 1997-98 El Ni\u0026ntilde;o, the 2007-08 La Ni\u0026ntilde;a, and the 2019-20 positive Indian Ocean Dipole (pIOD) event (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These periods exhibit clear sea level anomalies, especially in the BoB and the Equatorial Indian Ocean, indicating that strong ENSO and IOD events significantly influence regional sea level patterns. These variations could be primarily linked to large-scale planetary waves like Kelvin waves moving eastward and Rossby waves moving westward, which dominate the first mode of sea level anomaly patterns\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Equatorial wind patterns play a key role by generating these Kelvin waves that then influence sea levels across the ocean basin\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The Indian Ocean Dipole (IOD) primarily affects regional climate, while ENSO influences global weather patterns, including impacts on the Indian Ocean through atmospheric circulation changes\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Both ENSO and IOD significantly drive sea level fluctuations in the Eastern Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of IOD and ENSO years based on datasets from NOAA PSL (ONI) and NASA JPL (DMI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epIOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1994, 1997, 2006, 2019 (all strong events)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEl Ni\u0026ntilde;o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1994-95\u003c/p\u003e \u003cp\u003e1997-98 (strong)\u003c/p\u003e \u003cp\u003e2002-03\u003c/p\u003e \u003cp\u003e2006-07\u003c/p\u003e \u003cp\u003e2009-10 (strong)\u003c/p\u003e \u003cp\u003e2015-16 (strong)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enIOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1998, 2010, 2016 (all strong events)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLa Ni\u0026ntilde;a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1998-99, 2007-08, 2010-11 (all strong events)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo better understand the spatial footprint of these climate drivers, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents spatial correlation maps between sea level anomalies and key climate indices, including the Oceanic Ni\u0026ntilde;o Index (ONI), Dipole Mode Index (DMI), and SST anomalies in the Western Tropical Indian Ocean (WTIO) and Southeastern Tropical Indian Ocean (SETIO). DMI, which is calculated as the difference between WTIO and SETIO SSTs\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, offers insight into the spatial structure of IOD-related sea level responses. The correlation patterns for ONI and DMI reveal distinct, partially overlapping influences across the NIO. ONI exhibits strong positive correlations over the southwestern part of the basin, with negative correlations dominating the eastern sector, extending westward to nearly 75\u0026deg;E along the equator. DMI mirrors this spatial pattern but with a more extensive influence across the Arabian Sea, suggesting a stronger and more regionally concentrated impact than ONI. In the BoB, both indices display a dipole-like pattern, with positive correlations in the southwest and negative correlations elsewhere in the basin. These dipole-like correlation patterns are consistent with the findings of Nidheesh et al.\u003csup\u003e35\u003c/sup\u003e, who demonstrated that at interannual timescales, IOD events drive sea level variations in the Eastern Equatorial Indian Ocean and BoB through equatorial and coastal waveguide dynamics, with Kelvin waves propagating from the central Indian Ocean to the eastern boundary and subsequently into the BoB as coastal Kelvin waves.\u003c/p\u003e \u003cp\u003eThe Equatorial Indian Ocean similarly shows a dipole structure, with positive correlations in the western part (WEIO) and negative correlations in the eastern part (EEIO). Correlations with the WTIO and SETIO SST indices provide further clarity. The WTIO shows a spatial correlation structure that closely resembles that of the DMI, indicating its dominant role in regulating sea level variability across the region. This resemblance highlights WTIO\u0026rsquo;s influence in mediating the DMI-sea level relationship, particularly over the Arabian Sea and BoB. Such dipole-like patterns, and their non-stationarity over multidecadal time scales, are in line with the evolving spatial trend structures inferred from reconstructed sea level fields in the Indian Ocean by Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e. Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e similarly found that remote climate drivers, including ENSO and the IOD, modulate sea level variability across the Somali Coastal Current and Red Sea LMEs, with regression coefficients of up to 24 mm for the DMI, underscoring the basin-wide reach of these teleconnections.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the first principal component (PC1) of the leading empirical orthogonal function (EOF) mode of altimetry-derived sea level anomalies (SLA), alongside the Dipole Mode Index (DMI) and Oceanic Ni\u0026ntilde;o Index (ONI), to examine regional interannual sea level variability across the NIO. As PC1 captures the dominant mode of SLA variability, the extent to which large-scale climate modes influence sea level dynamics within specific sub-basins can be assessed by comparing it with major climate indices. A notable feature in the PC1 patterns is a dipole structure between the western and eastern sub-basins of both the Bay of Bengal (WBoB and EBoB) and the Equatorial Indian Ocean (WEIO and EEIO), with opposing phase variability in sea level between these sectors. This phase variability is not evident in the Arabian Sea sub-basins, suggesting that sea level dynamics in these regions are governed by different processes or influenced less directly by large-scale climate modes. Comparative analysis reveals that DMI exhibits a stronger correlation with PC1 across most sub-basins than ONI, particularly in the BoB and Equatorial Indian Ocean. Both indices, however, show relatively weak to moderate correlations with PC1 in the WAS and EAS, reinforcing the notion that sea level variability in these areas is less sensitive to ENSO or IOD forcing.\u003c/p\u003e \u003cp\u003eThe time series of PC1 from each sub-basin displays temporal variations that are consistent with the spatial correlation structures identified in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, providing further evidence of the climate-sea level connection. Western sub-basins of both the BoB and Equatorial Indian Ocean show PC1 variability that closely tracks phases of the DMI and, to a lesser extent, the ONI, while eastern sub-basins show opposite-phase behavior. This anti-phase response suggests a coherent basin-wide adjustment of sea level anomalies to the shifting patterns of climate forcings\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e similarly highlighted, that Indian Ocean Sea level exhibits east-west dipole structures strongly correlated with IOD events, which our analysis confirms for the more recent 1993\u0026ndash;2021 period at sub-basin scales.\u003c/p\u003e \u003cp\u003eDuring strong positive IOD (pIOD) events or combined pIOD-El Ni\u0026ntilde;o years, western sub-basins generally exhibit increased sea level, whereas eastern sub-basins tend to show negative anomalies. Conversely, strong negative IOD (nIOD) events or combined nIOD, La Ni\u0026ntilde;a years produce the opposite effect: elevated sea level in the eastern sub-basins and reductions in the western sub-basins. This spatial signature matches previously reported patterns of sea level change associated with ENSO and IOD phases\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. During pIOD events, strong easterly winds replace the usual westerlies over the equatorial Indian Ocean, causing sea levels to drop in the eastern basin. These easterlies generate upwelling Kelvin waves that travel eastward, reflect off the coast, and propagate back as Rossby waves, further lowering sea levels, or propagate as coastal trapped Kelvin waves that move poleward\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. The easterly wind anomalies also generate an equatorial-downwelling Rossby wave, which reflects off the western boundary as a downwelling Kelvin wave. This wave propagation leads to elevated sea levels in the WEIO\u003csup\u003e\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Nidheesh et al.\u003csup\u003e35\u003c/sup\u003e demonstrated that this wave-mediated teleconnection results in high correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8) between EEIO and BoB Sea level at both interannual and decadal timescales, while the southwestern Indian Ocean region is more weakly correlated with eastern basin variability at decadal timescales due to the independent influence of southern Indian Ocean wind stress curl. At such times, the usual downwelling Kelvin waves, which typically elevate sea levels in the eastern BoB, are significantly weakened or absent. Instead, only upwelling-favourable Kelvin waves dominate, generating Rossby waves along the eastern BoB rim that further reduce sea levels. In contrast, normal years feature stronger downwelling Kelvin waves, leading to higher sea levels in this region\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the southwestern BoB shows higher sea levels during pIOD/El Ni\u0026ntilde;o events due to wind-driven Ekman pumping, where easterly anomalies create an anticyclonic (clockwise) circulation that increases the sea level\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Thus, IOD/ENSO phases produce contrasting sea level responses in the BoB.\u003c/p\u003e \u003cp\u003eAnomalous sea level rise during El Ni\u0026ntilde;o years in the eastern Pacific and concurrent positive SLA anomalies in the western equatorial Indian Ocean have been well documented, supporting the observed correlations in the current study. However, not all strong El Ni\u0026ntilde;o years lead to significant SLA changes in the NIO. For instance, the 2015\u0026ndash;2016 El Ni\u0026ntilde;o, despite its intensity, did not result in notable SLA variations across the region. In contrast, the strong negative IOD event in 2016 produced pronounced sea level increases in the eastern sub-basins and significant decreases in the western counterparts, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. These patterns highlight the asymmetric and regionally specific responses of NIO sea level to different modes of climate variability. The results are in agreement with previous studies that emphasize the modulation of interannual sea level variability in the NIO by IOD and ENSO-related processes\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Summary and conclusions","content":"\u003cp\u003eThis study quantifies the relative contributions of different sea level components to total sea level trends in the North Indian Ocean during 2003\u0026ndash;2021 when the GRACE gravimetry measurements of the mass component were available. Six sub-basins were analysed, covering the Arabian Sea, BoB, and Equatorial Indian Ocean. The North Indian Ocean (NIO) represents a region with complex dynamics for the sea level budget. Unlike the global mean sea level, which is primarily mass-driven, trends in the NIO are predominantly governed by thermosteric sea level rise, consistent with the accelerated regional warming observed over recent decades. This thermosteric dominance confirms and extends the findings of Swapna et al.\u003csup\u003e30\u003c/sup\u003e, who demonstrated that weakening Indian summer monsoon circulation drives increased heat retention in the NIO through reduced southward heat transport via the Cross-Equatorial Cell, and of Srinivasu et al.\u003csup\u003e32\u003c/sup\u003e, who documented the onset of accelerated NIO sea level rise after 2003. Spatial trend analysis reveals a significant positive sea level trend across the NIO (4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 mm/yr), with the thermosteric component dominating the regional average. This rate exceeds the 1993\u0026ndash;2023 global mean of ~\u0026thinsp;3.4 mm/yr and is comparable to the accelerated rates (~\u0026thinsp;4.3 mm/yr since 2010) reported for African coastal waters by Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e, indicating that the north Indian Ocean is experiencing similarly intensified sea level rise. Our NIO-averaged trend of 4.55 mm/yr for 2003\u0026ndash;2021 represents a continuation of the acceleration documented by Swapna et al.\u003csup\u003e30\u003c/sup\u003e, who reported thermosteric SLR increasing from 0.68 mm/yr (1958\u0026ndash;2015) to 2.3 mm/yr (1993\u0026ndash;2015). Sub-basin differences emerge, however. The Eastern Equatorial Indian Ocean (EEIO) exhibits a mass-dominated trend (4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 mm/yr), more than three times the thermosteric contribution. Meanwhile, the Western Bay of Bengal (WBoB) shows substantial residual trends (~\u0026thinsp;3 .66 mm/yr) that are largely independent of mass change. Halosteric influences differ sharply across basins: the Arabian Sea exhibits strong negative trends (-0.89 mm/yr), while the Bay of Bengal (BoB) sub-basins show positive halosteric contributions (~\u0026thinsp;0.42 mm/yr), linked to freshwater input. These contrasting halosteric patterns complement the findings of Jyoti et al.\u003csup\u003e50\u003c/sup\u003e, who demonstrated that halosteric effects contributed 40% to South Indian Ocean Sea level rise during 2000\u0026ndash;2015 primarily through Indonesian Throughflow freshwater transport, highlighting the distinct salinity forcing mechanisms operating in the North versus South Indian Ocean. A similar halosteric suppression of sea level rise has been documented in the Mediterranean by Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e, where increased salinity offsets thermosteric expansion, highlighting the importance of salinity-driven density changes in semi-enclosed and marginal seas.\u003c/p\u003e \u003cp\u003eRegional differences in ocean mass trends appear to be shaped by distinct processes. For example, the 2004 Sumatra-Andaman earthquake likely contributed to the sharp trend variations in the EEIO, while in the BoB, river discharge and sediment transport may play a more dominant role. Parekh et al.\u003csup\u003e34\u003c/sup\u003e noted that the highest tide gauge trends in the NIO occur in the northern BoB (8.8 mm/yr at Charchanga), where land subsidence in the Ganges-Brahmaputra delta may amplify apparent sea level rise, consistent with the elevated residual trends we observe in the WBoB. Basin-level decomposition reveals contrasting drivers: thermosteric changes dominate sea level trends in the Arabian Sea, while the EIO shows a west-east gradient, transitioning from thermosteric-dominated in the west to ocean mass component dominated in the east. In the BoB, though the steric contributions are nearly 1.7 mm/yr for both sub-basins, residual signals and ocean mass components respectively form the principal contributions to long-term trends in WBoB and Eastern Bay of Bengal (EBoB). However, uncertainties in the steric sea level measurements could be introduced by the absence of trend values near the northern and eastern Bay of Bengal coasts, in addition to limitations associated with the ocean mass trends from the GRACE dataset in the Andaman-Sumatra region of Eastern Indian Ocean. Taken together with previous Indian Ocean studies\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and the pan-African synthesis of Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e, our results indicate that recent decades have seen both an acceleration of NIO sea level rise and an increasing fractional contribution from thermosteric processes, while mass and residual terms remain highly heterogeneous and locally important.\u003c/p\u003e \u003cp\u003eInterannual variability in NIO sea level is primarily governed by thermosteric fluctuations, which show strong spatial and temporal agreement with altimetry-derived anomalies. This consistency highlights the influence of regional warming and temperature variability on total sea level change. Climate modes, particularly ENSO and the Indian Ocean Dipole (IOD), display strong correlations with sea level, but their effects vary spatially. Western sub-basins of the BoB and Equatorial Indian Ocean exhibit in-phase relationships with these climate indices, whereas eastern sub-basins show anti-phase responses, resulting from changes in wind anomalies and dynamics related to the long-period waves associated with the climatic events. The Arabian Sea appears less affected by these large-scale climate modes. Srinivasu et al.\u003csup\u003e32\u003c/sup\u003e similarly found that decadal sea level variability in the NIO is primarily controlled by surface wind stress changes over the Indian Ocean, with limited influence from the Pacific through the Indonesian Throughflow, consistent with our finding that Arabian Sea sea level is less responsive to ENSO/IOD forcing. Analysis of the Western Tropical Indian Ocean (WTIO) and Southeastern Tropical Indian Ocean (SETIO) SST indices further shows that WTIO captures the Dipole Mode Index (DMI) pattern and is a strong predictor of regional sea level variability. This reinforces earlier conclusions that IOD-related variability is a primary driver of Indian Ocean regional sea level changes on interannual time scales\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and aligns with the finding of Ghomsi et al.\u003csup\u003e7\u003c/sup\u003e that Atlantic and Indian Ocean climate modes (including ENSO and IOD) modulate regional sea level anomalies through SST-driven thermal expansion and circulation changes.\u003c/p\u003e \u003cp\u003eIn summary, the total sea level trend in the NIO was 4.55 mm/yr, with thermosteric sea level driven by ocean warming emerges as the dominant contributor, also influencing interannual variability. However, regional contributions vary significantly, with mass changes, halosteric effects, and residual processes also playing important roles in specific sub-basins. In contrast to African LMEs, where mass contributions now dominate\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, the NIO remains a thermosteric-dominated system, although the EEIO and parts of the BoB exhibit substantial mass and residual signals that warrant further investigation. Future research should prioritize deeper investigation into the residual component, which may reflect unmodelled processes such as sediment loading, vertical land motion, or regional hydrology, and remains a key source of uncertainty in regional sea level budgets. Combining our sub-basin-scale budget with independent constraints on vertical land motion (as in Palanisamy et al.\u003csup\u003e22\u003c/sup\u003e) and deeper-water steric estimates (beyond 700\u0026ndash;900 m; cf. Ghosh et al.\u003csup\u003e25\u003c/sup\u003e), and placing NIO trends in the context of broader Indian Ocean and African coastal changes\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, would be logical next steps toward closing the NIO sea level budget and distinguishing climatic from anthropogenic land-based contributions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFinancial support for this study was provided to Ullas M. P. through the Nansen Fellowship awarded by the Nansen Scientific Society in support of his Ph.D. research. Roshin P. R. received support from the c3-eKerala project funded by the Research Council of Norway. Ghomsi F. E. K. was supported by Canada\u0026rsquo;s C150 Research Program (Grant No. 50296) and Schmidt Sciences, LLC.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eU.M.P.: conceptualization; writing-original draft; writing-review \u0026amp; editing. F.E.K.G., R.P.R., A.J.K.: writing-original draft; writing-review \u0026amp; editing. O.M.J. have made the initial conception, revisions and improvements to the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eUllas M. P and Ola M. J acknowledges the support from the Nansen Scientific Society. Ullas M. P thank the Faculty of Ocean Science and Technology, Kerala University of Fisheries and Ocean Studies, for the academic support to undertake this study. Ajith J. K gratefully acknowledges the support from the NERSC-NERCI Board of Directors for the facilities.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the results of this study is available in the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDas, A. \u0026amp; Swain, P. K. Navigating the sea level rise: Exploring the interplay of climate change, sea level rise, and coastal communities in india. \u003cem\u003eEnviron. Monit. Assess.\u003c/em\u003e \u003cstrong\u003e196\u003c/strong\u003e, 1010 (2024).\u003c/li\u003e\n \u003cli\u003eCazenave, A., Palanisamy, H. \u0026amp; Ablain, M. Contemporary sea level changes from satellite altimetry: What have we learned? What are the new challenges? \u003cem\u003eAdv. Sp. Res.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 1639-1653 (2018).\u003c/li\u003e\n \u003cli\u003eNerem, R. 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Over the NIO, sea level increased at a rate of 4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 mm/yr, a trend primarily driven by thermosteric expansion associated with ocean warming. Separation of NIO into six sub-basins reveal marked spatial heterogeneity in sea level trends: (1) Western Arabian Sea (4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 mm/yr); (2) Eastern Arabian Sea (4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 mm/yr); (3) Western Bay of Bengal (4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 mm/yr); (4) Eastern Bay of Bengal (5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 mm/yr); (5) Western Equatorial Indian Ocean (4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 mm/yr); and (6) Eastern Equatorial Indian Ocean (4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 mm/yr). Even though thermosteric changes dominate the basin-wide mean, the dominant drivers vary regionally. Halosteric effects exhibit a negative trend over the entire Arabian Sea, and is linked to inflows of saline water from the Red Sea and Persian Gulf, in contrast to a positive trend in the Bay of Bengal, influenced by substantial freshwater runoff from major rivers. In the Eastern Equatorial Indian Ocean, the mass component is predominant, likely influenced by crustal adjustments after the December 2004 Sumatra-Andaman earthquake. Interannual sea level variability closely follows steric changes, which are modulated by climate modes such as ENSO and the Indian Ocean Dipole, resulting in region-specific and often opposing phase relationships across the basin. Our results confirm that while the recent global and African sea level rise is predominantly mass-driven, the NIO remains distinctively steric-dominated, with a larger contribution from thermosteric changes.\u003c/p\u003e","manuscriptTitle":"Thermosteric dominance of sea level rise in the North Indian Ocean: sub- basin budget analysis (2003-2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 14:43:24","doi":"10.21203/rs.3.rs-8870568/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-24T01:00:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297815496383995248096699697309495882662","date":"2026-04-09T08:32:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120177932111243544662055265982246692443","date":"2026-04-09T01:44:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T14:26:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T02:46:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-17T02:45:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-13T10:17:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cbb3d22-a1ad-4adb-b96b-dbf19a4c4968","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65993654,"name":"Earth and environmental sciences/Climate sciences"},{"id":65993655,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-04-16T14:43:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 14:43:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8870568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8870568","identity":"rs-8870568","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}