Subsurface Salinity Research Articles

The Crucial Role of the Subpolar North Atlantic for Skillful Decadal Climate Predictions

AbstractWe investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies.

A Fresh Take: Seasonal Changes in Terrestrial Freshwater Inputs Impact Salt Marsh Hydrology and Vegetation Dynamics

Salt marshes exist at the terrestrial-marine interface, providing important ecosystem services such as nutrient cycling and carbon sequestration. Tidal inputs play a dominant role in salt marsh porewater mixing, and terrestrially derived freshwater inputs are increasingly recognized as important sources of water and solutes to intertidal wetlands. However, there remains a critical gap in understanding the role of freshwater inputs on salt marsh hydrology, and how this may impact marsh subsurface salinity and plant productivity. Here, we address this knowledge gap by examining the hydrologic behavior, porewater salinity, and pickleweed (Sarcocornia pacifica also known as Salicornia pacifica) plant productivity along a salt marsh transect in an estuary along the central coast of California. Through the installation of a suite of hydrometric sensors and routine porewater sampling and vegetation surveys, we sought to understand how seasonal changes in terrestrial freshwater inputs impact salt marsh ecohydrologic processes. We found that salt marsh porewater salinity, shallow subsurface saturation, and pickleweed productivity are closely coupled with elevated upland water level during the winter and spring, and more influenced by tidal inputs during the summer and fall. This seasonal response indicates a switch in salt marsh hydrologic connectivity with the terrestrial upland that impacts ecosystem functioning. Through elucidating the interannual impacts of drought on salt marsh hydrology, we found that the severity of drought and historical precipitation can impact contemporary hydrologic behavior and the duration and timing of the upland-marsh hydrologic connectivity. This implies that the sensitivity of salt marshes to climate change involves a complex interaction between sea level rise and freshwater inputs that vary at seasonal to interannual timescales.

Convformer: A Model for Reconstructing Ocean Subsurface Temperature and Salinity Fields Based on Multi-Source Remote Sensing Observations

Observational data on ocean subsurface temperature and salinity are patently insufficient because in situ observations are complex and costly, while satellite remote-sensed measurements are abundant but mainly focus on sea surface data. To make up for the ocean interior data shortage and entirely use the abundant satellite data, we developed a data-driven deep learning model named Convformer to reconstruct ocean subsurface temperature and salinity fields from satellite-observed sea surface data. Convformer is designed by deeply optimizing Vision Transformer and ConvLSTM, consisting of alternating residual connections between multiple temporal and spatial attention blocks. The input variables consist of sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Our results demonstrate that Convformer exhibits superior performance in estimating the temperature-salinity structure of the tropical Pacific Ocean. The all-depth average root mean square error (RMSE) of the reconstructed subsurface temperature (ST)/subsurface salinity (SS) is 0.353 °C/0.0695 PSU, with correlation coefficients (R²) of 0.98663/0.99971. In the critical thermocline, although the root mean square errors of ST and SS reach 0.85 °C and 0.121 PSU, respectively, they remain smaller compared to other models. Furthermore, we assessed Convformer’s performance from various perspectives. Notably, we also delved into the potential of Convformer to extract physical and dynamic information from a model mechanism perspective. Our study offers a practical approach to reconstructing the subsurface temperature and salinity fields from satellite-observed sea surface data.

The Effects of Upper-Ocean Sea Temperatures and Salinity on the Intensity Change of Tropical Cyclones over the Western North Pacific and the South China Sea: An Observational Study

With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River.

Dynamics of hydrogen storage in subsurface saline aquifers: A computational and experimental pore-scale displacement study

The catastrophic effects of climate change can be mitigated by transitioning to renewable energy sources such as wind, solar, and hydropower while utilizing hydrogen (H2) as an energy carrier. As renewable fuel sources are intermittent and location-specific, large-scale, long-term storage options for H2 must be explored with high necessity. Subsurface hydrogen storage in saline aquifers provides a vital scope to store H2 gas in available pore voids with minimal environmental risk. In this work, a highly heterogeneous porous micromodel was used to study the immiscible two-phase dynamics at high-temperature, high-pressure saline aquifer conditions using computational fluid dynamics (CFD) simulation based on volume of fluid (VOF) method. A set of microfluidic experiments were carried out under atmospheric conditions to validate the numerical model. The VOF model was observed to show good agreement with microfluidic experiments. An unstable displacement pattern with viscous fingering was observed during various flow conditions that captured high pressure (15 and 25 MPa) and temperature conditions (323 and 343 K). It was observed that fingering displacement of brine limits the storage spaces for hydrogen, which consequently follows the high permeable path. As a result, only 0.272 fraction of brine was invaded by the H2 at a temperature of 323 K and pressure of 15 MPa. A comparison between H2-brine and nitrogen (N2)-brine flow dynamics revealed a 46% less storage capacity of porous media for H2. Formations bearing high temperature (343 K) showed an 11.27% increase in H2 storage capacity, while pressure increase had negligible effect. At low capillary number (Ca), more snap-off effects resulted in higher trapping of H2 gas. This study aims to provide meaningful insights into the complexities of flow dynamics and displacement patterns that are crucial for optimizing hydrogen storage efficiency in saline aquifers and for potential ‘white’ hydrogen production.

Formation and Variability of Barrier Layer in the South Pacific

AbstractThe oceanic barrier layer (BL), dominated by salinity stratification, plays an important role in regulating heat exchange between the mixed layer and the interior ocean. However, the salinity contribution is ignored or underestimated when analyses relying only on temperature threshold methods. Here we introduce the Turner Angle method to quantify the salinity contribution and to obtain relatively accurate BL thickness (BLT), and further to investigate the spatial‐temporal distribution, thickness variation and formation mechanism of the BL in the South Pacific using the updated Argo data during 2005–2021. With the successful usage of this method, we eliminate 41% of the invalid BLs and therefore improve the assessment of the BLs in the South Pacific. Formation of the thick BLs in the South Pacific is attributed to the faster deepening rate of the isothermal layer than the mixed layer. The deepening rate of the isothermal layer is facilitated by subsurface subantarctic mode water while that of the mixed layer is constrained by the subsurface salinity maximum in the South Pacific. On the interannual scale, the BLT in the South Pacific manifests a quasi‐biennial variability, which is closely connected with the variability in the isothermal layer depth and is primarily caused by the anomalous winds driven by the Southern Annular Mode (SAM). The SAM‐related anomalous winds regulate the isothermal layer and further the BL by exerting influences air‐sea heat fluxes and the Ekman layer advection.

Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Evaluation of aquifer parameters through integrated approach of geophysical investigations, pumping test analysis and Dar-Zarrouk parameters in the central part of Bari Doab, Punjab, Pakistan.

In the central part of Bari Doab in Punjab Province of Pakistan, the factors such as sporadic rainfall pattern, decrement of water in rivers, subsurface salinity and excessive mining of groundwater have badly affected the hydrogeology and recharge system of aquifer. The present research work is an endeavour to evaluate the characteristics and potential of aquifer for its future sustainable availability within the study area of central part of Bari Doab. The geophysical studies, pumping tests data, borehole logs and Dar-Zarrouk parameters were used integrally to evaluate the aquifer hydraulic and hydrologic parameters in the study area. VES technique of geophysical investigations using Schlumberger electrodes configuration was carried out at sparsely distributed 435 locations. Litho-logs and VES results altogether decipher that the subsurface alluvial succession is primarily composed of intermixed layers of sand, gravel, clay, silt and some kankar inclusions. The VES data allied with pumping test analysis of test wells in the study area were used to evaluate the aquifer hydraulic properties. Comparatively low values of discharge rate, hydraulic conductivity and transmissivity were evaluated in two wells whilst relatively higher values of these parameters were evaluated in rest of six wells. The results of hydrologic parameters also confirm the results of hydraulic parameters in the wells. Finally, the Dar-Zarrouk parameters were used for the estimation of hydraulic parameters for whole study area and the aquifer zones of relatively high and low potential were delineated.

An observing system experiment framework for the tropical Indian Ocean salinity: A case study using a constellation of three satellites

In this study impact of assimilating Sea Surface Salinity (SSS) from multi-satellites (SMOS, Aquarius and SMAP) on numerical ocean model simulations in the north Indian Ocean has been analysed under the observing system experiment (OSE) framework. Daily data sets of Aquarius, SMAP and SMOS, which were available for a common period of April–May 2015, are used to constrain the ocean model using ensemble optimal interpolation technique. Apart from the control simulation in which satellite data were not assimilated, a total of seven assimilation experiments using different combinations of satellite SSS were conducted. The impact of assimilation experiments is analysed by comparing the model-simulated variables with in situ observations. Assimilating satellite SSS results in a reduction in Root Mean Square Error (RMSE) in SSS (∼ 54%) and also in subsurface salinity (∼ 21%) over the control run. The impact of assimilating SMAP observations is maximum on model simulations with the errors reducing by ∼ 54%. Subsurface salinity improvement is better with three satellites with ∼31% improvement in RMSE in the halocline region, which was ∼11% more than single satellite assimilation. Assimilation of SSS also resulted in improved simulations of the model surface, subsurface temperature and mixed layer depth. Model results show the ability of SSS observations to complement other ocean observation networks. One important observation from this study is that while the impact of assimilating SSS observations from a single satellite was on par with the impact of assimilating SSS observations from two or three satellites in correcting simulated surface salinity, assimilation from more than one satellite had a larger impact in the salinity of deeper layers of the ocean.

Estimation of subsurface salinity and analysis of Changjiang diluted water volume in the East China Sea

In the East China Sea (ECS), the sea surface salinity (SSS) changes as the Changjiang Diluted Water (CDW) propagates toward the Korean Peninsula via the ocean current and winds every summer annually. Although the vertical stratifications resulting from the CDW volume changes are important, it has not been analyzed yet. Therefore, in this study, we aimed to estimate the salinity at a depth of 10 m (S10m) using convolutional neural network (CNN) model based on multi-satellite measurements and analyze CDW volume variations. The main CDW mass in the ECS reaches approximately 10 m in depth; thus, the CNN model was developed using sea surface physical factors as input and in situ S10m obtained from the National Institute of Fisheries Science (NIFS) as ground truth data from 2015 to 2021. The CNN tests result showed a determination coefficient (R2) of 0.81, root mean square error (RMSE) of 0.63 psu, and relative RMSE (RRMSE) of 2.00%. Unlike the sea surface distribution, the spatial distribution of S10m showed that the CDW was predominantly present in the center of the ECS. From SHapley Additive exPlanations (SHAP) analysis, SSS exhibited a strong positive relationship with S10m, and the sea level anomaly showed a strong negative relationship. After calculating the volume of the CDW from the surface to a depth of 10 m, the maximum (3.01×1012 m3) and minimum volumes (1.31×1012 m3) were represented in 2016 and 2018, respectively. Finally, the warming effect induced by the CDW volume changes was analyzed in two different years: 2016 and 2018. Specifically, in 2016, the sea surface temperature increased by more than 4.79 °C in the Ieodo location, while in 2018, it increased by 2.19 °C. Thus, our findings can obtain information about the volume variation of the CDW and its effect on the ECS in summer.

Application of the Electromagnetic Method to the Spatial Distribution of Subsurface Saline and Fresh Water in the Coastal Mudflat Area of Jiangsu Province.

Interfacial zones straddling terrestrial and marine realms, colloquially known as mudflats, epitomize a dynamic nexus between these environments and are fundamental to the coastal ecosystem. The investigation of these regions is paramount for facilitating infrastructural developments including ports, wharfs, cross-sea bridges, and the strategic utilization of freshwater resources sequestered from mainland islands amid ongoing economic progress. Terrestrial realms conventionally employ electromagnetic techniques as efficacious modalities to delineate subterranean geological information, encompassing structural details and water-bearing strata. However, the peculiar topographic and geological nuances of mudflat regions pose substantial challenges for the efficacious application of electromagnetic methodologies. The present paper endeavors to address these challenges by suggesting innovative modifications to the existing instrumentation and evolving novel data acquisition techniques specifically tailored for electromagnetic exploration within mudflat environments. This paper delves into the electrical characteristics of water-bearing layers within mudflats, and ascertains details pertaining to the subterranean structure and the spatial distribution of fresh and saline water resources, through the holistic interpretation of a multitude of profiles.

Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model

Despite the increasing volume of oceanographic data, the available ocean salinity data remains patently insufficient which limits studying ocean dynamics, climate change and the calculation of salinity-related ocean elements. Considering that traditional salinity reconstruction methods often suffer from various factors such as additional constraints, priori physical assumptions and large of specific regression coefficients, a generative adversarial networks (GANs) based deep learning (DL) framework is proposed to directly construct a near real-time, high-resolution daily three-dimensional ocean subsurface salinity (3D OSS) dataset from a data-driven perspective in this study. Four models with different structural combinations are designed in the China’s marginal seas. Experimental results demonstrate that the models can successfully reconstruct the high-precision and high-resolution 3D OSS on a daily scale in 12 depth levels (from 2 m to 200 m). The asymmetric inception-3DGAN model with all enhanced structures has the highest accuracy, the average root-mean-squared error (RMSE) is 0.135psu, the average coefficient of determination (R2) is 0.5641 and the percent bias is 0.436%. Comparing with model without enhanced structures, the average RMSE is decreased by 22.41% when adding all the enhancement structures. Besides, temporal and spatial error analysis are also conducted to evaluate the models’ performance from different aspects. Finally, the model results are used to analyze common ocean elements, such as water mass properties, dynamic fields and geostrophic velocity fields, demonstrating that the 3D OSS dataset construction approach proposed in this study not only provides some new insights into ocean observations, but also has high application values.

Reconstructing Ocean Subsurface Temperature and Salinity from Sea Surface Information Based on Dual Path Convolutional Neural Networks

Satellite remote sensing can provide observation information of the sea surface, and using the sea surface information to reconstruct the subsurface temperature (ST) and subsurface salinity (SS) information has significant application values. This study proposes an intelligent algorithm based on Dual Path Convolutional Neural Networks (DP-CNNs) to reconstruct the ST and SS. The DP-CNN can integrate known information including sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) to reconstruct the ST and SS. The reconstruction model based on DP-CNN can solve the problem of detail information loss in traditional CNN (Convolutional Neural Network) models. This study performs experiments for the South China Sea under different seasons using reanalysis data. The experimental results show that the DP-CNN models have higher reconstruction accuracy than the CNN models, and this proves that DP-CNNs effectively mitigate the loss of detailed information in the CNN models. Compared with the ground truth data, the ST/SS reconstruction results of the DP-CNN model exhibited a high coefficient of determination (0.93/0.86) and a low root mean square error (around 0.31 °C/0.05 PSU). Therefore, the DP-CNN models can be used as an effective approach to reconstruct ST and SS using sea surface information.

Reconstructing three-dimensional salinity field of the South China Sea from satellite observations

High-resolution salinity information is of great significance for understanding the marine environment. We here propose a deep learning model denoted the “Attention U-net network” to reconstruct the daily salinity fields on a 1/4° grid in the interior of the South China Sea (SCS) from satellite observations of surface variables including sea surface salinity, sea surface temperature, sea level anomaly, and sea surface wind field. The vertical salinity profiles from the GLORYS2V4 reanalysis product provided by Copernicus Marine Environment Monitoring Service were used for training and evaluating the network. Results suggest that the Attention U-net model performs quite well in reconstructing the three-dimensional (3D) salinity field in the upper 1000 m of the SCS, with an average root mean square error (RMSE) of 0.051 psu and an overall correlation coefficient of 0.998. The topography mask of the SCS in the loss function can significantly improve the performance of the model. Compared with the results derived from the model using Huber loss function, there is a significant reduction of RMSE in all vertical layers. Using sea surface salinity as model inputs also helps to yield more accurate subsurface salinity, with an average RMSE near the sea surface being reduced by 16.4%. The good performance of the Attention U-net model is also validated byin situmooring measurements, and case studies show that the reconstructed high-resolution 3D salinity field can effectively capture the evolution of underwater signals of mesoscale eddies in the SCS. The resolution and accuracy of sea surface variables observed by satellites will continue to improve in the future, and with these improvements, more precise 3D salinity field reconstructions will be possible, which will bring new insights about the multi-scale dynamics research in the SCS.

Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model

Accurately estimating the ocean’s subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model’s accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean.

A global gridded ocean salinity dataset with 0.5° horizontal resolution since 1960 for the upper 2000 m

A gridded salinity dataset with high resolution is essential for investigating global ocean salinity variability and understanding its role in climate and the ocean ecosystem. In this study, a new version of the Institute of Atmospheric Physics gridded salinity dataset with a higher resolution (0.5° by 0.5°) is provided by using a revised ensemble optimal interpolation scheme with a dynamic ensemble. The performance of this dataset is evaluated using “subsample test” and the high-resolution satellite-based data. Compared with the previous 1° by 1° resolution IAP product, the new dataset is more capable of representing regional salinity changes with the meso-scale and small-scale signals (i.e., in the coastal and boundary currents regions), meanwhile, maintains the large-scale structure and variability. Therefore, the new dataset complements the previous data product. Besides, the new dataset is compared with in situ observations and several international salinity products for the salinity multiscale variabilities and patterns. The comparison shows the smaller magnitude of mean difference and Root-mean-square deviation (RMSD) in basin scale for the new dataset, some differences in strength and fine structure of the “fresh gets fresher, salty gets saltier” surface and subsurface salinity pattern amplification trends from 1980 to 2017, a broad similarity for the salinity changes associated with El Niño-Southern Oscillation (ENSO) and a consistent salinity dipole mode in the tropical Indian Ocean (S-IOD). These results support the future use of gridded salinity data.

Biomolecular condensates under extreme martian salt conditions.

Biomolecular condensates formed by liquid–liquid phase separation (LLPS) are considered one of the early compartmentalization strategies of cells, which still prevail today forming non-membraneous compartments in biological cells. Studies of the effect of high pressures, such as those encountered in the subsurface salt lakes of Mars or in the depths of the sub-seafloor on Earth, on biomolecular LLPS will contribute to questions of protocell formation under prebiotic conditions. We investigated the effects of extreme environmental conditions, focusing on highly aggressive Martian salts (perchlorate and sulfate) and high pressure, on the formation of biomolecular condensates of proteins. Our data show that the driving force for phase separation of proteins is not only sensitively dictated by their amino acid sequence but also strongly influenced by the type of salt and its concentration. At high salinity, as encountered in Martian soil and similar harsh environments on Earth, attractive short-range interactions, ion correlation effects, hydrophobic, and π-driven interactions can sustain LLPS for suitable polypeptide sequences. Our results also show that salts across the Hofmeister series have a differential effect on shifting the boundary of immiscibility that determines phase separation. In addition, we show that confinement mimicking cracks in sediments and subsurface saline water pools in the Antarctica or on Mars can dramatically stabilize liquid phase droplets, leading to an increase in the temperature and pressure stability of the droplet phase.

A Simple Bias Correction Scheme in Ocean Data Assimilation

The mode bias is present and time-dependent due to imperfect configurations. Data assimilation is the process by which observations are used to correct the model forecast, and is affected by the bias. How to reduce the bias is an important issue. This paper investigates the roles of a simple bias correction scheme in ocean data assimilation. In this scheme, the misfits between modeled and monthly temperature and salinity with interannual variability from the Met Office Hadley Centre subsurface temperature and salinity data set (EN4.2.2) are used for the innovations in assimilation via the Ensemble Optimal Interpolation method. Two assimilation experiments are implemented to evaluate the impacts of bias correction. The first experiment is a data assimilation system without bias correction. In the second experiment, the bias correction is applied in assimilation. For comparison, the nature run with no assimilation and no bias correction is also conducted. When the bias correction is not applied, the assimilation alone leads to a rising trend in the heat and salt content that is not found in the observations. It is a spurious temporal variability due to the effect of the bias on the data assimilation. Meanwhile, the assimilation experiment without bias correction also produces significant negative impacts on the subsurface salinity. The experiment with bias correction performs best with notable improvements over the results of the other two experiments.

Calcite-functionalized micromodels for pore-scale investigations of CO2 storage security

Carbon capture and subsequent storage (CCS) is identified as a necessity to achieve climate commitments. Permanent storage of carbon dioxide (CO2) in subsurface saline aquifers or depleted oil and gas reservoirs is feasible, but large-scale implementation of such storage has so far been slow. Although sandstone formations are currently most viable for CO2 sequestration, carbonates play an important role in widespread implementation of CCS; both due to the world-wide abundancy of saline aquifers in carbonate formations, and as candidates for CO2-EOR with combined storage. Acidification of formation brine during CO2 injection cause carbonate dissolution and development of reactive flow patterns. Using calcite-functionalization of micromodels we experimentally investigate fundamental pore-scale reactive transport dynamics relevant for carbonate CO2 storage security. Calcite-functionalized, two-dimensional and siliconbased, pore scale micromodels were used. Calcite precipitation was microbially induced from the bacteria Sporosarcina pasteurii and calcite grains were formed in-situ. This paper details an improved procedure for achieving controlled calcite precipitation in the pore space and characterizes the precipitation/mineralization process. The experimental setup featured a temperature-controlled micromodel holder attached to an automatic scanning stage. A high-resolution microscope enabled full-model (22x27 mm) image capture at resolution of 1.1 µm/pixel within 82 seconds. An in-house developed image-analysis python script was used to quantify porosity alterations due to calcite precipitation. The calcite-functionalized micromodels were found to replicate natural carbonate pore geometry and chemistry, and thus may be used to quantify calcite dissolution and reactive flow at the pore-scale.

Cytobacillus pseudoceanisediminis sp. nov., A Novel Facultative Methylotrophic Bacterium with High Heavy Metal Resistance Isolated from the Deep Underground Saline Spring

In this study, we present the characterization of the BNO1T bacterial strain isolated from the deep subsurface saline spring at the Baksan Neutrino Observatory INR RAS (Kabardino-Balkaria, Russia). The complete genome sequence of the strain BNO1T is 5,347,902bp, with a GC content 41 and 49%. The cell wall peptidoglycan contains meso-diaminopimelic acid. The major isoprenoid quinone is MK-7 and the polar lipids are diphosphatidylglycerol, phosphatidylglycerol, and phosphatidylethanolamine. The major fatty acids are anteiso-C15:0 (23.34%), iso-C15:0 (20.10%), C16:0 (11.96%), iso-C16:0 (10.88%), and anteiso-C17:0 (10.79%). The 16S rRNA gene sequence clearly demarcated the strain as belonging to Cytobacillus genera. Based on the phylogenetic analysis, ANI (average nucleotide identity) and dDDH (digital DNA-DNA hybridization) assessments we propose to assign the strain BNO1T and other related strains to new species and to name it Cytobacillus pseudoceanisediminis sp. nov. (The values of ANI and dDDH between BNO1T and Cytobacillus oceanisediminis CGMCC 1.10115T are 80.65% and 24.7%, respectively; values of ANI and dDDH between BNO1T and Cytobacillus firmus NCTC 10335T are 89% and 38%, respectively). Genomic analysis of strain BNO1T revealed pathways for C1 compounds oxidation and two pathways for C1 compounds assimilation: serine and ribulose monophosphate pathways. In addition, strain BNO1T contains a plasmid (342,541bp) coding multiple genes involved in heavy metal ion balance. Moreover, heavy metal toxicity testing confirmed the high potential of the strain BNO1T as a source of metal resistance genes and enzymes. The type strain is BNO1T (= BIM B-1921T = VKM B-3664T).