ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. OBSERVING WINTER CARBONATE CHEMISTRY DYNAMICS THROUGHOUT THE MID-ATLANTIC BIGHT SHELF USING NOVEL GLIDER TECHNOLOGY MARISSA GUZIK, GRACE SABA (FACULTY ADVISOR), ELIZABETH WRIGHT-FAIRBANKS PHD ✵ ABSTRACT Increased atmospheric carbon dioxide (CO2) has led to global climate change and ocean acidification (OA) via the absorption of atmospheric CO2 by the ocean. Coastal shelves are also affected by various processes that influence the acidity of seawater, causing acidity to vary over time and space. These variations in ocean acidity can nega- tively impact marine species, especially calcifying organisms such as surfclams and sea scallops. In the Mid-Atlantic Bight (MAB), a subsection of the U.S. Northeast Shelf (NES), this variation in acidity gener- ates ecological and economic concerns as the MAB is home to some of the nation’s most productive and profitable estuaries and fisheries. In this study, Rut- gers University (southern MAB) and Stony Brook University (northern MAB, Hudson Canyon) de- ployed two gliders equipped with sensors measur- ing depth, temperature, salinity, pH, dissolved oxy- gen, and chlorophyll to monitor winter 2021 car- bonate chemistry conditions on the shelf as well as in slope waters of the MAB. For both deployments, measured pH and calculated aragonite saturation state (Ωarag) showed opposing patterns, with high pH and low Ωarag in shelf/nearshore and low pH and high Ωarag in slope waters. These trends were at- tributed to different driving factors whereas pH was more influenced by biological processes (i.e. photo- synthesis) and Ωarag was influenced mostly by ther- modynamics and chemical factors (i.e. temperature, total alkalinity). The results of this study underscore the importance of monitoring coastal acidity to un- derstand potential impacts on important species. KEY TERMS: ocean acidification, carbonate chemistry, glider, aragonite saturation state, pH, total alkalin- ity, Mid-Atlantic Bight, vertical mixing, Gulf Stream, Labrador Current COMMON TERMS AND ABBREVIATIONS MID-ATLANTIC BIGHT (MAB): ocean region spanning just offshore from Cape Hatteras, NC to Martha's Vineyard, MA. COLD POOL: mass of dense, cold bottom-water trapped due to stratification between surface and deep water during the late spring through summer months. ARAGONITE SATURATION STATE (ΩARAG): calcium car- bonate saturation state with respect to aragonite, measure for the potential of carbonate ions to form or dissolve; Ω < 1: dissolve, Ω > 1: form. TOTAL ALKALINITY (TA): measure of the water’s capacity to neutralize acids (H+ ions), measures the concen- tration of all alkaline ions (including carbonate and bicarbonate). OCEAN ACIDIFICATION (OA): the increasing acidity levels of the ocean due to increased atmospheric CO2 ab- sorption from anthropogenic CO2 emissions. PARTIAL PRESSURE CO2 (PCO2): the pressure from car- bon dioxide gas molecules; increasing pCO2 in- creases the solubility of carbon dioxide. SHELF: region of shallower ocean water over the con- tinental shelf (refer to FIGURE 1). IN-SLOPE: deeper water just around shelf-break (the edge of shelf). ON SHELF: water on the continental shelf. NEARSHORE: water sampled closest to the shoreline, shallower water. U.S. NORTHEAST SHELF (NES): a nearshore system with an extended shallow shelf. DISSOLVED INORGANIC CARBON (DIC): total sum of inor- ganic carbon (CO2, HCO3-, CO3-2) in seawater. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV WATER-MIXING: the mixing of surface and deeper wa- ter from wind-driven forces (waves). GLIDER: autonomous underwater robots that sample continuously as they travel up and down through the water column along a programmed route (de- ployment refers to the initial release of a glider and recovery refers to the retrieval of the glider from the ocean). COASTAL DOWNWELLING: surface water that is forced downward as wind moves it toward the coastal boundary which then becomes deep water that trav- els away from the coast. COASTAL UPWELLING: deep water that is dragged up along the coast to replace surface water that was moved offshore by the wind. STRATIFICATION: the separation of water column layers due to differing water densities and properties, typ- ically observed in seasons with less ocean-mixing. THERMOCLINE: a thin transition layer of water separat- ing the surface and bottom water layers; due to the strong temperature differences between these lay- ers, a rapid change in temperature is measured as one moves through the thermocline. EDDIES: counter-current flow of water generating small whirlpools and circular movement of water. BUFFERING CAPACITY: the ability of seawater to main- tain a more neutral pH after being introduced to more acidic solutions, due to the concentration of salts and ions (such as CO3-2) in seawater absorbing free-floating protons (H+). 1 INTRODUCTION Anthropogenic activity, including the in- creased burning of fossil fuels and deforestation, has increased atmospheric carbon dioxide (CO2) and altered global climate and ocean conditions (IPCC, 2019). The increased rate of atmospheric CO2 absorption in the oceans, which results in com- plex chemical reactions that decrease seawater pH, has generated global concern over the acidification of ocean waters (Wanninkhof et al., 2015). Ocean acidification (OA) alters the balance of the car- bonate system, including decreasing calcium car- bonate saturation state in addition to the aforemen- tioned decrease in pH. This presents a challenge for marine species by increasing physiological stress, as they must increase energy expenditure on main- taining body structures or processes that depend on calcium carbonate stability. This specifically af- fects calcifying organisms such as sea scallops, which use carbonate to form their protective car- bonate shells (Kroeker et al., 2013; Saba et al., 2019a). Seawater pH is projected to continue to globally decline over the next century, decreasing from the global average of 8.1 by 0.1-0.4 pH units (IPCC, 2019). This is expected to cause significant impacts on the health of ecosystems and play a role in the future distribution and range of species based on a species’ tolerance to more acidic conditions. COASTAL PROCESSES INFLUENCING NEARSHORE CAR- BONATE CHEMISTRY While ocean acidification is increasing glob- ally at a relatively consistent rate, carbonate chemis- try conditions of the seawater vary greatly across dif- ferent spatial and temporal scales in nearshore coastal systems (Goldsmith et al. 2019). Nearshore waters experience these shifts due to various fresh- water and terrestrial inputs, as well as mixing pro- cesses, which can alter the chemistry of the water (Wanninkhof et al., 2015; Goldsmith et al., 2019). For example, freshwater is naturally more acidic due to lower salinity and total alkalinity (TA) (Kwiatkowski & Orr, 2018). Additionally, photosynthesis and res- piration can biologically alter carbonate chemistry, which increases and decreases pH, respectively. Respiration will dominate and increase partial pres- sure of CO2 (pCO2) at deeper depths (Cai et al., 2011). Increased organic carbon and nutrients from rivers can lead to eutrophication of coastal systems and exacerbate this biological control on acidifica- tion (Anderson et al., 2002; Cai et al., 2011; Xu et al., 2020). Upwelling of deep, CO2 and nutrient rich wa- ter can support algal blooms and has previously been shown to result in increased acidity in the sur- face water (Anderson et al., 2002; DeGrandpre et al., 2002) seen along the U.S. west coast and sea- sonally off the coast of New Jersey. Water mass mix- ing, or a lack thereof, plays a significant role in con- trolling carbonate chemistry in coastal regions (Cai et al.,2020; Wright-Fairbanks et al., 2020). During ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV periods of strong stratification, a lack of mixing gen- erates pCO2 rich bottom water and lower pCO2 sur- face water, creating vertical variation in carbonate chemistry. In contrast, well-mixed water columns have a relatively uniform pCO2, meaning variation of carbonate chemistry is more minimal throughout the water column. In the northeast U.S., strong strat- ification begins in the spring and persists through summer when higher temperatures strengthen ther- mocline and prevent mixing, while high mixing oc- curs throughout the fall during seasonal overturn and persists into winter (DeGrandpre et al., 2002; Wright-Fairbanks et al., 2020). INFLUENCE OF CURRENTS AND SEASONALITY ON CAR- BONATE CHEMISTRY The two major currents that influence the chemistry of the U.S. Northeast Shelf (NES) and slope water are the Labrador Current and Gulf Stream (FIGURE 1). The Labrador Current is a cold, fresher current that transports water southward from the Nova Scotia region to the Mid-Atlantic Bight (MAB). This colder, less alkaline water allows for more CO2 absorption, leading to lower calcium carbonate saturation state with respect to arago- nite (Ωarag). The Gulf Stream is a warm, salty cur- rent that transports water northward from the Gulf of Mexico along the coastline, diverging offshore at the edge of the MAB near Cape Hatteras, NC. Compared to the Labrador Current, it is character- ized by a high Ωarag and TA. Overall, these two opposing currents influence the carbonate chemis- try of the Northeast Shelf through mixing of waters with different CO2 buffering capacities (Wannink- hof et al., 2015; Xu et al., 2020). Along with the in- fluence of these currents, the aforementioned mix- ing processes and coastal influences drive spatial and temporal variations in NES seawater carbonate chemistry. High summer temperatures generate a strengthened thermocline leading to a highly strati- fied water column. This warmer, less dense surface water lays on top of a bottom layer of colder fresher water — most likely originating from the Labrador Current — which becomes confined un- derneath by the density difference. This trapped FIGURE 1: (A) Map of the NES, white box highlighting the MAB. (B) Map focused in on the MAB area, illustrating concept of nearshore, on-shelf, and in-slope water. A B ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV cold, fresher water mass creates a unique shelf fea- ture specific to the MAB referred to as the “Cold Pool,” and its presence or absence (based on the degree of mixing) can impact the carbonate chem- istry throughout the water column. With the for- mation of the Cold Pool during the summer months, a pocket of high pCO2 is contained at the shelf floor, creating a stark difference in carbonate chemistry between the Cold Pool water and the lower pCO2 water of the rest of the water column (Wright-Fairbanks et al., 2020). ECONOMIC AND ECOLOGICAL SIGNIFICANCE It is important to note that these coastal pro- cesses and their seasonal cycles are not well studied on a regional scale, and therefore the effects of sea- sonal variation regarding OA and the impacts on ecosystem health along the NES are not well under- stood either (Xu et al., 2020). However, studies on OA and understanding coastal variations are im- portant, especially for coastal fisheries. The NES supports many of the nation’s most productive and valued commercial fisheries (National Marine Fish- eries, 2021). Atlantic sea scallops (Placopecten mag- ellanicus) and Atlantic surfclams (Spisula solidis- sima) are both calcifying organisms that rely on car- bonate molecules to develop their shells; these im- portant Atlantic fisheries generate $569.9 million and $30.7 million, respectively (National Marine Fisheries, 2021). Shellfish like scallops and surfclams that inhabit low carbonate environments (Hart et al., 2004; Wanninkhof et al., 2015; Pousse et al., 2020) can be at increased risk of mortality from predation with acidification, causing shell deformities and weaker shells (reviewed in Saba et al., 2019a). GLIDERS Due to the dynamic nature of coastal sys- tems and their importance in economically produc- tive fisheries, it is critical to monitor and understand OA and carbonate chemistry. This is especially ur- gent as climate change is predicted to generate a global trend of increased acidity. High-resolution data collected over short-term, or seasonal, time pe- riods can help to construct a better understanding of the effects of the different coastal processes on acidity trends along the Northeast Shelf (Xu et al., 2020; Xu et al., 2017; Wanninkhof et al., 2015; Wright-Fairbanks et al., 2020). Gliders are autono- mous underwater vehicles that can be equipped with various sensors, including the recently devel- oped pH sensor, enabling continuous pH measure- ments to be taken throughout their deployments. Gliders travel off shelf and back to shore, continu- ously taking measurements while climbing up and down through the water column, providing high- resolution observations of the shelf. Glider science sensors sample at a rate of 0.5 Hz, resulting in inter- val measurements of every 20-30cm vertically pro- ducing these high-resolution datasets (Saba et al., 2019b; Wright-Fairbanks et al., 2020). This ability to provide high-resolution data and cross-sectional perspectives of the water column makes gliders ideal for monitoring OA and carbonate saturation along the Northeast Shelf (Saba et al., 2019b). Rut- gers University continues to use pH gliders to con- duct seasonal surveys of the shelf to investigate car- bonate chemistry dynamics as well as to map poten- tial hot spots and time periods of acidification in im- portant fisheries’ habitats. The purpose of my re- search project is to examine shelf-wide winter car- bonate chemistry dynamics using data collected from two pH glider deployments conducted in the northern and southern MAB. 2 MATERIALS AND METHODS Data collection and analysis methods fol- lowed those used in seasonal surveys described in Wright-Fairbanks et al. (2020). The winter 2021 sur- veys were conducted as a collaborative effort be- tween Rutgers University (southern MAB deploy- ment) and Stony Brook University (northern MAB deployment). Teledyne-Webb Slocum G2 gliders were deployed in each MAB site during similar timeframes. Both gliders were deployed on Febru- ary 26, 2021. The Rutgers University glider was re- covered on March 21, 2021, and the Stony Brook University glider was recovered on March 23, 2021, with missions lasting about 24 and 25 days respec- tively (FIGURE 2A). The Rutgers University glider was deployed and recovered out of Tuckerton, NJ (FIG- URE 2A – ROUTE R), and the Stony Brook University B ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV glider was deployed out of Shinnecock Inlet, NY and recovered out of Manasquan, NJ (FIGURE 2A – ROUTE S). Each glider was equipped with a recently devel- oped deep-ISFET pH sensor (Saba et al., 2019b) as well as CTD (conductivity, temperature, and pres- sure/depth), an Aanderaa optode measuring dis- solved oxygen (DO), and an optics puck measuring chlorophyll fluorescence and spectral backscatter. Further details on the specific procedures for prep- aration, deployment, and recovery of gliders is out- lined in Wright-Fairbanks et al. (2020). During de- ployment and recovery, discrete water samples were collected and sent to a lab at the University of New Hampshire for chemical analysis for the pur- pose of ground-truthing glider pH and carbonate parameter data (Wright-Fairbanks et al., 2020). This validation sample analysis is still ongoing. Data collected by the gliders were con- verted using Slocum Power Tools (Kerfoot, 2014) into a format for data analysis using MATLAB soft- ware (version R2021a). Analysis techniques de- scribed in Wright-Fairbanks et al. (2020) were ap- plied to the data collected from these winter 2021 deployments and included sensor response time shift calculations and application to the full dataset, data quality assurance and quality control (QA/QC), estimating TA-salinity regression relationships to calculate TA, calculation of the full suite of car- bonate chemistry parameters (pH, TA:DIC, Ωarag) (using CO2SYS v3.0 in MATLAB), binning parameter data by depth and distance/time, plotting parame- ters, and running statistical analyses/tests on the full quality-controlled dataset. Methods for this step- wise analysis are described in detail below. Sensor time lag can occur because temper- ature measurements are taken external to the CTD conductivity cell while conductivity measurements, used for calculating other variables including salin- ity and TA, are taken within the cell. This separation can cause a “thermal lag” where temperature meas- urements misalign and generate offset measure- ments (Saba et al., 2019b). To account and correct this thermal lag, different potential time shifts from 0 to 60 seconds (at 1 second intervals) were run on paired upcast/downcast data, and average time shift for the entire deployment was applied to da- taset (Wright-Fairbanks et al., 2020). Once time shift was applied, QA/QC tests (gap test, syntax test, lo- cation test, gross range test, and spike test) were run on the glider dataset to assess the data for any “bad” data points (outlying, incredibly deviated points) created from sensor failure/malfunction and re- move if present (further detailed in IOOS, 2019). Be- cause discrete sample analysis is ongoing, we were unable to construct a deployment-specific linear re- gression model of TA as a function of salinity. In- stead, a previous winter deployment (2019) TA-sa- linity relationship was used to derive regression co- efficients for calculating TA values, meaning slight variations in TA values between true calculations (if values were calculated using 2021 discrete sam- ples) and calculated are possible. The previous win- ter deployment used to calculate TA values lasted a duration of 19 days, from February the 1st to the 19th, 2019, and was deployed offshore Sandy Hook, NJ maintaining a course in the MAB in between the glider tracks of the winter 2021 deployments (Wright-Fairbanks et al., 2020). This refined data set was then used to calculate carbonate chemistry pa- rameters using CO2SYS for MATLAB (v3.0) (Lewis & Wallace, 1998; Sharp et al., 2020; van Heuven et al., 2011; Wright-Fairbanks et al., 2020). Following this, the data were then binned by time (1-hour bins) to generate a series of profiles of the measured or cal- culated parameters over depth (m) and time (hours). These parameters included: temperature (°C), salin- ity (PSU), dissolved oxygen (mg/L), chlorophyll (µg/L), pH, TA (µmol/kg), and Ωarag. To further under- stand and visualize the data, in addition to the depth profiles, 3-dimensional cross-section models of the shelf were constructed. Finally, statistical analyses were conducted to determine significance (p<0.05) of data spread and the relationships between pa- rameters via correlation coefficients (Wright-Fair- banks et al., 2020). Correlation coefficients were cal- culated using MATLAB package “Statistics and Ma- chine Learning Toolbox,” version 2021a, and tables for both datasets for carbonate parameters were generated using Microsoft Excel (2008) and the conditional formatting function. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 3 RESULTS GLIDER DATA Both glider missions exhibited similar trends in both chemical and physical parameters. Temperatures above 12 °C and salinities above 35 PSU were observed offshore/in-slope waters, while lower temperatures (below 8 °C) and salinities (be- low 33 PSU) were observed nearshore (FIGURE 3 A, B; FIGURE 4 A, B). Higher dissolved oxygen (above 9.5 mg/L) and chlorophyll (above 2 µg/L) concentra- tions were observed nearshore and along shelf, while lower concentrations for both (below 7.5 mg/L; below 1 µg/L) were observed at the shelf break and in-slope water (FIGURE 3 C, D; FIGURE 4 C, D). Looking at carbonate chemistry parameters, pH was lower (7.95 – 8) in deeper in-slope water, while pH was higher (8.1-8.15) on shelf (FIGURE 3 E; FIGURE 4 E). Higher TA (2300-2350 µmol/kg) and Ωarag (2.4 – 2.6) were observed at the shelf break and in deeper in- slope water and lower TA (below 2250 µmol/kg) and Ωarag (below 2) were observed nearshore and on the shelf (FIGURE 3 F, G; FIGURE 4 F, G). Prevalent mixing of the water column and uniform surface and bot- tom waters on the shelf were also observed in both glider datasets. Unique oceanographic features were evi- dent in the Rutgers glider dataset (FIGURE 3). Past the shelf break, significant water column mixing was re- flected in all profiles (3/6 – 3/9), in the upper 200 m where the glider sampled. Additionally, when the Rutgers glider was returning to the shelf (3/11 – 3/12) on the southernmost transect, a pocket of ele- vated chlorophyll (3 µg/L) was noted to coincide with elevated pH (8.15; FIGURE 3E) and Ωarag (2.4; FIGURE 3G). FIGURE 2: (A) Maps illustrating glider mission tracks for the winter 2021 survey along NES. (Route R) Glider tracks for MAB deploy- ment (white) (star = recovery site, green dot = deployment site). (Route S) Glider tracks for Hudson Canyon deployment (magenta) (star = recovery site, green dot = deployment site). (B) At right, photography of Rutgers glider (RU 30) being deployed (Photo credit: Elizabeth Wright-Fairbanks). ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV whd. Additionally, when FIGURE 3: RUTGERS GLIDER PROFILES- Depth profiles for tem- perature (A), salinity (B), oxygen (C), chlorophyll (D), pH (E), TA (F), aragonite saturation state (G). Depth is meas- ured on the y-axis and time (m/d; from start of mission to end) is tracked on the x-axis, while each parameter’s val- ues are plotted across a color gradient in respect to depth and time. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV FIGURE 4: STONY BROOK GLIDER PROFILES- Depth profiles for temperature (A), salinity (B), oxygen (C), chlorophyll (D), pH (E), TA (F), aragonite saturation state (G). Depth is meas- ured on the y-axis and time (m/d; from start of mission to end) is tracked on the x-axis, while each parameter’s values are plotted across a color gradient in respect to depth and time. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV EXAMINING THE RELATIONSHIP BETWEEN PH AND ΩARAG Similar patterns in the relationship between pH and Ωarag relative to temperature and salinity were observed with both gliders (FIGURE 5). For pH, higher values occurred at higher temperature and salinity, while lower pH values were associated with lower temperature and salinity values (FIGURE 5 A, B). The opposite pattern was observed with Ωarag, where low values occurred at low temperature and salinity, while high values were observed at high temperature and salinity (FIGURE 5 C, D). To understand the drivers behind the pH and Ωarag, the correlation coefficient tables (Tables 1, 2) were used. For both datasets, pH had the strongest positive correlation with oxygen concen- trations (0.73; 0.78) and, unlike Ωarag, had a stronger positive correlation with chlorophyll concentration (0.59) as well. While Ωarag had the strongest positive correlation with salinity and TA (0.94; 0.83), both pH and Ωarag had a stronger correlation to temperature. The pH had a negative relationship with tempera- ture (- 0.56; - 0.70) while Ωarag had a stronger positive FIGURE 5: Temperature-Salinity plots for pH (top) and aragonite saturation state (bottom) for both Rutgers (A, C) and Stony Brook (B, D) gliders. Temperature is measured on the y-axis and salinity is measured on the x-axis, lines representing isopycnals (points of specific water densi- ties) overlayed to give visuals of ocean water layering. Parameter of interest values plotted across a color gradient in respect to temperature and salinity. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV relationship with temperature (0.91; 0.78). Finally, TA had the greatest positive correlations to temper- ature (0.98) and Ωarag (0.94; 0.83) as well as strong negative correlations with oxygen (- 0.88; - 0.94) and latitude (-0.72; -0.86). These trends in the correla- tion coefficients and drivers for the carbonate pa- rameters align with patterns of high/low values of related parameters, such as salinity and chlorophyll, observed by the gliders in nearshore/on shelf and in-slope waters. 4 DISCUSSION In this study, two sets of glider data pro- vided by Rutgers University (southern Mid Atlantic Bight) and Stony Brook University (northern Mid At- lantic Bight/Hudson Canyon) illustrated carbonate chemistry patterns in the MAB. Over the course of the winter 2021 mission, pH and Ωarag varied across time and space from the influences of biological, chemical, and physical processes. This glider data can then be related to commercial fishery manage- ment zones, particularly shellfish fisheries, to under- stand the potential responses of marine animals to acidification. IMPACT OF CURRENTS ON PARAMETER PROFILES Depth profiles of in-slope waters of both de- ployments suggest direct influence from the warmer, saltier Gulf Stream that produced higher temperature and salinity conditions, similar to those observed during a previous winter glider deploy- ment in the MAB (Wright-Fairbanks et al., 2020). These warmer, more saline conditions acted as driv- ers for high TA and Ωarag values in deep in-slope wa- ters for both datasets and surface in-slope waters for Rutgers deployment. The spatial variation in Ωarag values, with higher values seen in Rutgers (southern) compared to Stony Brook (northern) deployment, is consistent with the “south-to-north decline” of Ωarag described in Cai et al. (2020) from the weakening influence of highly saturated and buffered Gulf Stream water. The Gulf Stream carries warmer saltier water from the tropics, which in effect has a lower DIC/TA ratio, thus increasing the buffering capacity of the water maintaining higher Ωarag (Cai et al., 2020; Wright-Fairbanks et al., 2020). This northward current traveling alongside the shelf can be pushed up onto and mix with shelf waters via warm-core ed- dies (Fratantoni et al., 2001; Zhang et al., 2015; TABLES 1 AND 2: Correlation coefficients for both Rutgers (left) and Stony Brook (right) glider data for the different carbonate parameters (pH, TA, Ωarag). All coefficients were significant (p << 0.05). Co- efficient values greater than |0.5| (in bold) indicate stronger correlation. Orange shaded cells = posi- tive correlation, blue shaded cells = negative correlation; increased color saturation indicates in- creased correlation. *TA calculated as a function of glider salinity values, therefore coefficient of 1. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV Wright-Fairbanks et al., 2020) and consequently lead to high TA and Ωarag, which was especially no- table in the more southern Rutgers dataset. Conversely, nearshore shelf waters were generally colder and fresher, influenced both by coastal freshwater input and potentially the south- ward flowing Labrador Current (Xu et al., 2017; Wright-Fairbanks et al., 2020). These fresher and shallow depth conditions of nearshore and shelf wa- ter were also characterized by higher chlorophyll and oxygen concentrations, the latter of which was likely a function of both higher productivity (i.e., ox- ygen production through photosynthesis) and cold- water temperatures (i.e., increased gas solubility). Furthermore, these nearshore and shelf conditions translated to high pH and low TA and Ωarag values, which is consistent with observations from the pre- vious winter survey Wright-Fairbanks et al. (2020). The high observed pH values can be potentially at- tributed to the influence of increased biological up- take of CO2 via increased photosynthesis in shal- lower shelf and nearshore water (Cai et al., 2020; Wright-Fairbanks et al., 2020). PH AND ΩARAG DRIVERS The significant strong correlations of pH with chlorophyll and oxygen indicate that pH was more directly driven by biological influences, such as photosynthesis-respiration rates. Conversely, Ωarag was more so driven by physical and chemical influences, such as currents and water temperature, with significant strong correlations to TA, salinity, and temperature. This supports the conclusions of Cai et al. (2020), who found that Ωarag is temperature- driven in the coastal western Atlantic while pH is FIGURE 6: Satellite data for surface chlorophyll concentrations in the southern MAB (3/9 – 3/15) (NASA God- dard Space), red box indicating area Rutgers glider traveling through during same timeframe. Rutgers glider chlorophyll data (top left) included for comparison, red box indicating elevated chlorophyll concen- tration of interest. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV more susceptible to short-timescale drivers like phy- toplankton blooms. The drivers of pH and Ωarag are further un- derstood by looking at the unique, event-based fea- tures that occurred during the Rutgers glider mis- sion (FIGURE 3). On the southern-most transect, as the glider returned on shelf (3/11 – 3/12), a pocket of both high pH and elevated chlorophyll was ob- served (FIGURE 3 D, F). Satellite data from the same timeframe (FIGURE 6) confirmed the elevated glider chlorophyll measurements, supporting the fact that biological interactions — specifically photosynthesis rates — have a large impact on pH values. Addition- ally, the direct relationships between Ωarag and TA, salinity, and temperature were evident on the south- ern-most transect (3/11 – 3/12; FIGURE 3), likely due to a storm-driven “sloshing event.” During early March strong and variable wind events, such as Nor’easters and South/West winds, were observed within the glider data and promoted water column mixing and shifting currents (FIGURE 7). Earlier Nor’easters (3/1, 3/5) produced strong northeast winds that tradition- ally promote downwelling of shelf waters due to the buildup of water along the coast. Strong South and West winds beginning on 3/9 likely caused currents to shift in the opposite direction and potentially pro- moted a switch to intrusion of high Ωarag in-slope wa- ter onto the shelf. These events further support that Ωarag was more impacted by physical influences; in this case, Ωarag was driven by wind-driven mixing. It is important to acknowledge that this is an observa- tional study for an area of research where limited ex- perimental studies have been conducted, limiting the ability to predict which variables drive pH and Ωarag changes the most. The scarcity of experimental studies leaves opportunities for researchers to ex- pand on our understanding of the drivers of pH and FIGURE 7: 3D glider transects for Ωarag, Rutgers glider tracks (south) and Stony Brook (north), with shelf floor bathym- etry and northeast coastline (drawn around long. -74.5) illustrated. Black arrows = Nor’easter (northeast winds, winds promoting downwelling on shelf water); magenta arrows = South and West winds (winds triggering shift to upwelling of offshore/in slope water). White box area of interest highlighting potential sloshing event from shifting currents. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV Ωarag through controlled experiments in which signif- icantly correlated parameters (oxygen and chloro- phyll concentrations, temperature, etc.) are manip- ulated individually. STRONG WINTER MIXING EVENT In any given depth profile nearshore or on the shelf, the water column was fully mixed, as is typ- ical for MAB winter conditions (Castelao et al., 2010). Stratification of in-slope waters occurred dur- ing the early part of the deployment (early March) but disintegrated over time, likely due to increased storm-related winds. This event was most pro- nounced in the southern (Rutgers) dataset where strong mixing of the water column was observed (3/6 – 3/9) within deeper in-slope water, revealing full water column homogenization for the maximum depth sampled (200m) (FIGURE 3). This was the first time this degree of slope water mixing event has been observed in a Rutgers winter survey. This mix- ing event highlights the prevalence of interannual variability of the Gulf Stream-influenced shelf break jet in the MAB, which has been described by Linder (1996) and Linder and Gawarkiewicz (1998). Typi- cally, the shelf break jet is more stratified for in-slope waters due to differing salinities between denser, saltier Gulf Stream water and fresher overlaying sur- face water in the winter, which was seen in the Stony Brook deployment (FIGURE 4) (Linder & Gawarkie- wicz, 1998). However, in the Rutgers deployment, the described stratification of water masses of the winter shelf break jet was not present during 3/6 – 3/9, and instead a uniform, unstratified shelf break jet from full column (200 m) mixing was observed (FIGURE 3). SIGNIFICANCE To stress the significance and importance of monitoring carbonate chemistry in coastal systems, it is important to relate this data to further biological and economic implications. Atlantic surfclams and Atlantic sea scallops are both calcifying organisms that reside in and are major fisheries of the MAB (National Marine Fisheries, 2021). Atlantic surfclams reside nearshore in waters of 10-50m in depth (Pousse et al., 2020), while Atlantic sea scallops in- habit mid- to outer-shelf waters 27-80m deep (Hart et al., 2004). Referring back to the Ωarag plots (FIGURE 7, FIGURE 3G, FIGURE 4G), the observed glider data in these essential habitat regions highlight that in win- ter surfclams are exposed to relatively lower Ωarag while sea scallops inhabit waters with higher Ωarag due to greater influence from the Gulf Stream. The difference in habitat range between these species, along with the natural variation in Ωarag across the shelf, is important for understanding which species may be more impacted with ongoing ocean acidifi- cation and episodic coastal acidification events. Fur- thermore, recent laboratory OA experiments reveal that surfclams have a higher tolerance to more acidic waters (0.57 Ωarag, Pousse et al., 2020; 1.09 Ωarag, Meseck et al., 2021) yet may suffer increased energy expenditure and metabolic losses from the acidic stress (Pousse et al., 2020). This signifies the importance of understanding the carbonate sys- tems when designing future laboratory OA pertur- bation experiments for different species based on the naturally occurring variation already present within these environments. 5 CONCLUSIONS The MAB and other coastal zones have vari- ous biological, chemical, and physical processes that impact acidity and carbonate chemistry. Acidi- fication in coastal zones is further complicated by the influence of these processes varying temporally and spatially. In the winter 2021 survey described here, slope waters of the MAB were strongly influ- enced by the warm saline Gulf Stream transporting water with a high buffering capacity (high TA) north- ward. Nearshore and shelf waters were more influ- enced by terrestrial freshwater inputs and the cold, fresher Labrador Current, leading to higher chloro- phyll and oxygen concentrations. Opposing pat- terns in pH and Ωarag were reflected in both glider datasets, indicating pH and Ωarag were influenced by different drivers. The results described above indi- cate that pH was more biologically driven while Ωarag was more thermodynamically and chemically driven. Short-term biological and physical events in- ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV fluenced spatial differences in carbonate parame- ters over the course of deployment. Understanding and monitoring these short-term seasonal variations in pH and Ωarag is significant for developing experi- mental designs for understanding and predicting biological responses to acidification based on natu- rally occurring fluctuations in acidity as well as pro- jected future acidification. Moving forward, the win- ter deployments analyzed here will contribute to a broader project that will deploy pH gliders in the same region seasonally for the next two years. The integration of my results with those from future mis- sions will allow for a better understanding of the var- iability and drivers of carbonate chemistry over both space and time in this dynamic, economically im- portant region∎ 6 ACKNOWLEDGEMENTS I would like to give special thanks to Assis- tant Professor Grace Saba and Dr. Elizabeth Wright- Fairbanks for their careful guidance and support given throughout the research process. I would also like to thank the members of the Rutgers Center for Ocean Observing Leadership, including the glider technicians (David Aragon, Nicole Waite, Chip Hal- deman), Laura Nazzaro, Hugh Roarty, and Theodore Thompson; and Charlie Flagg of Stony Brook Uni- versity for their work with glider deployment and re- covery making this research possible, as well as Daphne Monroe for reviewing my work. 7 REFERENCES [1] Anderson, D. M., Gilbert, P. M., Burkholder, J. M. (2002). Harmful Algal Blooms and Eutrophication: Nutrient Sources, Composition, and Consequences. Estuaries, 25, 704–726. Accessed from HTTPS://WWW.WHOI.EDU/CMS/FILES/ANDER- SON_ETAL_2002_ESTUARIES_29903.PDF [2] Cai, W.-J., Hu, X., Huang, W.-J., Murrell, M. C., Lehrter, J. C., Lohrenz, S. E., Chou, W.-C., Zhai, W., Hollibaugh, J. T., Wang, Y., Zhao, P., Guo, X., Gunder- sen, K., Dai, M., & Gong, G.-C. (2011). Acidification of subsurface coastal waters enhanced by eutrophica- tion. Nature Geoscience, 4(11), 766-770. doi:10.1038/ngeo1297 [3] Cai, WJ., Xu, YY., Feely, R.A. et al. (2020). Controls on surface water carbonate chemistry along North American ocean margins. Nat Commun 11, 2691. HTTPS://DOI.ORG/10.1038/S41467-020-16530-Z [4] Castelao, R., Glenn, S., and Schofield, O. (2010). Tem- perature, salinity, and density variability in the central Middle Atlantic Bight, J. Geophys. Res., 115, C10005, doi:10.1029/2009JC006082. [5] DeGrandpre, M.D., Olbu, G.J., Beatty, C.M., Hammar, T.R.. (2002). Air–sea CO2 fluxes on the US Middle At- lantic Bight. Deep Sea Research Part II: Topical Stud- ies in Oceanography, 49(20), 4355-4367. doi: HTTPS://DOI.ORG/10.1016/S0967-0645(02)00122-4 [6] Fratantoni, P. S., Pickart, R. S., Torres, D. J., & Scotti, A. (2001). Mean Structure and Dynamics of the Shelfbreak Jet in the Middle Atlantic Bight during Fall and Winter, Journal of Physical Oceanography, 31(8), 2135-2156. doi: HTTPS://DOI.ORG/10.1175/1520- 0485(2001)031%3C2135:MSADOT%3E2.0.CO;2 [7] Goldsmith, K. A., Lau, S., Poach, M. E., Sakowicz, G. P., Trice, T. M., Ono, C. R., Nye, J., Shadwick, E. H., StLau- rent, K. A., & Saba, G. K. (2019). Scientific Considera- tions for Acidification Monitoring in the U.S. Mid-At- lantic Region. Estuarine, Coastal and Shelf Science 225: 106189, HTTPS://DOI.ORG/10.1016/J.ECSS.2019.04.023. [8] Hart, D. R., Chute, A. S., Northeast Fisheries Science Center (U.S.). (2004). Essential fish habitat source document. Sea scallop, Placopecten magellanicus, life history and habitat characteristics, NOAA tech- nical memorandum NMFS-NE, 189, HTTPS://REPOSI- TORY.LIBRARY.NOAA.GOV/VIEW/NOAA/4031. [9] IOOS. (2019). Manual for Real-Time Quality Control of pH Data Observations: A Guide to Quality Control and Quality Assurance for pH Observations. 13. doi: 10.25923/111k-br08 [10] IPCC. (2019): Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczan- ska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, N. Weyer (eds.)]. In press. HTTPS://WWW.IPCC.CH/SITE/ASSETS/UP- LOADS/SITES/3/2019/11/03_SROCC_SPM_FINAL.PDF [11] Kerfoot, John. (2014). SPT: Slocum Power Tools. GitHub Repository, HTTPS://GITHUB.COM/KERFOOT/SPT.GIT [12] Kroeker, K. J., Kordas, R. L., Crim, R., Hendriks, I. E., Ramajo, L., Singh, G. S., Duarte, C. M., & Gattuso, J. P. (2013). Impacts of ocean acidification on marine or- ganisms: quantifying sensitivities and interaction with warming. Glob Chang Biol, 19(6), 1884-1896. doi:10.1111/gcb.12179 [13] Kwiatkowski, L., & Orr, J. C. (2018). Diverging sea- sonal extremes for ocean acidification during the https://www.whoi.edu/cms/files/Anderson_etal_2002_Estuaries_29903.pdf https://www.whoi.edu/cms/files/Anderson_etal_2002_Estuaries_29903.pdf https://doi.org/10.1038/s41467-020-16530-z https://doi.org/10.1016/S0967-0645(02)00122-4 https://doi.org/10.1175/1520-0485(2001)031%3C2135:MSADOT%3E2.0.CO;2 https://doi.org/10.1175/1520-0485(2001)031%3C2135:MSADOT%3E2.0.CO;2 https://doi.org/10.1016/j.ecss.2019.04.023 https://repository.library.noaa.gov/view/noaa/4031 https://repository.library.noaa.gov/view/noaa/4031 https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/03_SROCC_SPM_FINAL.pdf https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/03_SROCC_SPM_FINAL.pdf https://github.com/kerfoot/spt.git ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV twenty-first century. Nature Climate Change, 8(2), 141-145. doi:10.1038/s41558- 017-0054-0 [14] Lewis, E. and Wallace, D. W. R. (1998). Program De- veloped for CO2 System Calculations, ORNL/CDIAC- 105, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., Oak Ridge, Tenn., 38 pp., HTTPS://SALISH- SEA.PNNL.GOV/MEDIA/ORNL-CDIAC-105.PDF [15] Linder, Christopher. (1996). A climatology of the Mid- dle Atlantic Bight shelfbreak front. doi:10.1575/1912/5671 [16] Linder, Christopher & Gawarkiewicz, Glen. (1998). A Climatology of the Middle Atlantic Bight Shelf break Front. Journal of Geophysical Research. 103. 97. doi: 10.1029/98JC01438. [17] Meseck, S., Mercaldo-Allen, R., Clark, P., Kuropat, C., Redman, D., Veilleux, D., Milke, L. (2021). Effects of ocean acidification on larval Atlantic surfclam (Spisula solidissima) from Long Island Sound in Connecticut. Fishery Bulletin, NOAA, 119(1). doi:10.7755/FB.119.1.8 [18] NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Mod- erate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data; 2018 Reprocessing. NASA OB.DAAC, Greenbelt, MD, USA. doi: data/10.5067/AQUA/MODIS/L3B/CHL/2018. [19] National Marine Fisheries Service (2021). Fisheries of the United States, 2019. U.S. Department of Com- merce, NOAA. Current Fishery Statistics No. 2019 Available at: HTTPS://WWW.FISHERIES.NOAA.GOV/NATIONAL/SUSTAINABLE- FISHERIES/FISHERIES-UNITED-STATES [20] Pousse, E., Poach, M., Redman, D., Sennefelder, G., White, L., Lindsay, J., Munroe, D., Hart, D., Hennen, D., Dixon, M., Li, Y., Wikfors, G., Meseck, S.. (2020). Energetic response of Atlantic surfclam Spisula solidissima to ocean acidification. Marine Pollution Bulletin, 161(B). doi: HTTPS://DOI.ORG/10.1016/J.MARPOL- BUL.2020.111740 [21] Saba, G.K., Goldsmith, K.A., Cooley, S.R., Grosse, D., Meseck, S.L., Miller, W., Phelan, B., Poach, M., Rheault, R., St. Laurent, K., Testa, J., Weis, J.S., Zim- merman, R. (2019a). Recommended Priorities for Research on Ecological Impacts of Coastal and Ocean Acidification in the U.S. Mid-Atlantic. Estua- rine, Coastal and Shelf Science 225: 106188, HTTPS://DOI.ORG/10.1016/J.ECSS.2019.04.022. [22] Saba G. K., Wright-Fairbanks, E., Chen, B., Cai, W-J, Barnard, A.H., Jones, C.P., Branham, C.W., Wang K. and Miles T. (2019b). The Development and Valida- tion of a Profiling Glider Deep ISFET-Based pH Sen- sor for High Resolution Observations of Coastal and Ocean Acidification. Front. Mar. Sci. 6:664. doi: 10.3389/fmars.2019.00664 [23] Sharp, J.D., Pierrot, D., Humphreys, M.P., Epitalon, J.- M., Orr, J.C., Lewis, E.R., Wallace, D.W.R. (2020). CO2SYSv3 for MATLAB (Version v3.1.1). Zenodo. HTTP://DOI.ORG/10.5281/ZENODO.3950562 [24] Van Heuven, S., D. Pierrot, J.W.B. Rae, E. Lewis, and D.W.R. Wallace. (2011). MATLAB Program Devel- oped for CO2 System Calculations. ORNL/CDIAC- 105b. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee. HTTPS://DOI.ORG/10.3334/CDIAC/OTG.CO2SYS_MATLAB_V1.1 [25] Wanninkhof, R., Barbero, L., Byrne, R., Cai, W.-J., Huang, W.-J., Zhang, J.-Z., Baringer, M., Langdon, C. (2015). Ocean acidification along the Gulf Coast and East Coast of the USA. Continental Shelf Research. 98, 54-71. doi: HTTP://DX.DOI.ORG/10.1016/J.CSR.2015.02.008 [26] Wright‐Fairbanks, E. K., Miles, T. N., Cai, W.‐J., Chen, B., Saba, G. K. (2020). Autonomous observation of seasonal carbonate chemistry dynamics in the Mid‐ Atlantic Bight. Journal of Geophysical Re- search: Oceans, 125, e2020JC016505. HTTPS://DOI.ORG/10.1029/2020JC016505 [27] Xu, Y.-Y., Cai, W.-J., Gao, Y., Wanninkhof, R., Salis- bury, J., Chen, B., Reimer, J. J., Gonski, S. and Hussain, N. (2017). Short-term variability of aragonite saturation state in the central Mid-Atlantic Bight, J. Geophys. Res. Oceans, 122, 4274–4290, doi:10.1002/2017JC012901. [28] Xu, Y.‐Y., Cai, W.‐J., Wanninkhof, R., Salisbury, J., Reimer, J., & Chen, B. (2020). Long‐Term Changes of Carbonate Chemistry Variables Along the North American East Coast. Journal of Geophysical Re- search: Oceans, 125, e2019JC015982. HTTPS://DOI.ORG/10.1029/2019JC015982 [29] Zhang, W. G., and G. G. Gawarkiewicz (2015). Dynam- ics of the direct intrusion of Gulf Stream ring water onto the Mid-Atlantic Bight shelf, Geophys. Res. Lett., 42, 7687–7695, doi:10.1002/2015GL065530 https://salish-sea.pnnl.gov/media/ORNL-CDIAC-105.pdf https://salish-sea.pnnl.gov/media/ORNL-CDIAC-105.pdf https://www.fisheries.noaa.gov/national/sustainable-fisheries/fisheries-united-states https://www.fisheries.noaa.gov/national/sustainable-fisheries/fisheries-united-states https://doi.org/10.1016/j.marpolbul.2020.111740 https://doi.org/10.1016/j.marpolbul.2020.111740 https://doi.org/10.1016/j.ecss.2019.04.022 http://doi.org/10.5281/zenodo.3950562 https://doi.org/10.3334/CDIAC/otg.CO2SYS_MATLAB_v1.1 http://dx.doi.org/10.1016/j.csr.2015.02.008 https://doi.org/10.1029/2020JC016505 https://doi.org/10.1029/2019JC015982 ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV Marissa is a recent graduate (class of 2021) of the School of Environmental and Biological Sciences with a Bachelor of Science in Marine Biology and a minor in Ecology, Evolution, and Natural Resources. Her interest in the marine sciences is focused on conservation and environmental research, as climate change threatens to alter our planet she hopes to be a part of research guid- ing us through it. During her time at the Rutgers, Marissa worked with gliders under the Rutgers Center for Ocean Observing Leadership (RUCOOL) assist- ing in the preparation and deployment of gliders as well as the data analysis of the glider data her sophomore summer into junior year. Her senior year, Marissa worked under the Saba Laboratory Ocean Acidification focus and guided by mentors Grace Saba and Elizabeth Wright-Fairbanks she devel- oped her honors thesis, using gliders for monitoring winter ocean chemistry. Currently, she is working as a Fisheries and Data Analyst for the marine re- search non-profit group, Beyond Our Shores Foundation, gaining experience before potentially pursuing a graduate degree. Marissa can be reached at: marissaguzik@gmail.com. mailto:marissaguzik@gmail.com