Optical observation of particles and responses to particle composition in the GEOTRACES GP16 section

Accepted Manuscript Optical observation of particles and responses to particle composition in the GEOTRACES GP16 section

Daniel C. Ohnemus, Phoebe J. Lam, Benjamin S. Twining PII: DOI: Reference:

S0304-4203(17)30204-9 doi: 10.1016/j.marchem.2017.09.004 MARCHE 3498

To appear in:

Marine Chemistry

Received date: Revised date: Accepted date:

16 June 2017 13 September 2017 14 September 2017

Please cite this article as: Daniel C. Ohnemus, Phoebe J. Lam, Benjamin S. Twining , Optical observation of particles and responses to particle composition in the GEOTRACES GP16 section. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Marche(2017), doi: 10.1016/ j.marchem.2017.09.004

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ACCEPTED MANUSCRIPT Optical Observation of Particles and Responses to Particle Composition in the GEOTRACES GP16 Section Daniel C. Ohnemus1 *, Phoebe J. Lam2 , Benjamin S. Twining1 1 2

Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA Dept. of Ocean Sciences, University of California Santa Cruz, Santa Cruz, CA, USA

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*Corresponding author. Email: [email protected]

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Abstract

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Transmissometers record high-frequency, high-sensitivity measurements of beam attenuation due to particles (beam cP), enabling determination of POC and SPM concentrations (via regression). Modern instruments have unique sensitivities to particle loads of various

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composition, but can exhibit unresolved thermal effects: even well-calibrated and monitored instruments exhibit asymmetry in upcast and downcast traces. On the US GEOTRACES EPZT

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transect, three independently deployed 650 nm, 25-cm pathlength (CStar) transmissometers and a broad-angle turbidity meter (Seapoint) were deployed at up to 36 stations. We report a

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procedure for improving agreement in upcast and downcast transmissometry via simple thermal

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modeling of instrument temperature using CTD temperature traces. Observed particle features from thermally corrected beam cP and turbidity include surface-derived biogenic phases, hydrothermal plumes, benthic nepheloid layers, and low-oxygen particle maxima. Optical

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responses to particles from a wide range of oceanographic regimes are examined against measured particle composition. In contrast to the transmissometers, the turbidity meter expresses

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a noted sensitivity to Fe(OH)3 . The derived turbidity/cP optical ratio thus shows promise in

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discriminating regions of elevated Fe(OH)3 content at high vertical spatial resolution. Introduction

In natural marine waters, suspended particulate matter (SPM) concentrations range over several orders of magnitude, from < 10 to >10,000 µg L–1 (Brewer et al., 1976; Gardner et al., 1985), and the composition of particle mixtures is highly variable in time and space. Dominant particulate phases include biogenic particles of multiple origins, sizes, and biomineral contents (particulate organic carbon/matter [POC or POM], biogenic opal, particulate inorganic carbon [PIC or CaCO 3 ]); as well as minerals of lithogenic and authigenic (Mn- and Fe-oxyhydroxides)

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ACCEPTED MANUSCRIPT origins and size distributions. Commercially available transmissometers are highly sensitive instruments designed to measure one of the inherent optical properties of (sea)water: beam attenuation (c), which is presumed after factory calibration in particle-free water to be due to particle scattering (beam cP) and is reported in units of m–1 . Together with turbidity sensors that measure particle backscatter, transmissometers provide the opportunity for much higher resolution measurements of particle distribution than is possible by discrete sampling, but these

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measurements must be made carefully.

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Boss et al. (2015), in their review of optical techniques available to the GEOTRACES

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program, and standardized procedures for GEOTRACES deployments (Cutter et al., 2010) recommend regular air-dark calibrations to monitor transmissometer sensitivity drift, optical

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window rinsing, and covering of optical windows while not in use, in addition to regular factory calibration and maintenance. Even well-calibrated and otherwise identical transmissometers

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deployed simultaneously are known to exhibit small differences in response to natural particle concentrations, suggesting instrument-specific effects and unique phase-sensitivities that can be

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challenging to investigate. Many seagoing instruments are nevertheless deployed very passively by teams that are focused on discrete water sampling, leading to less-than-ideal monitoring and

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maintenance of optical instruments.

In full ocean depth casts, upcast-vs-downcast offsets of 0.01 m–1 are not uncommon in

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transmissometer data. While such variations are small relative to the instrument’s total operating range, they represent a significant amount of local signal at many sites and depths, corresponding

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to several µg of SPM or 0.1 µM of POC (Bishop, 1999; Cetinić et al., 2012). Temperature affects the intensity of LED light sources used in many optical instruments, with colder temperatures

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associated with stronger LED output. In commonly used CStar transmissometers, the light beam is split and continuously monitored by a reference detector to compensate for thermal effects via firmware loaded with thermal correction parameters determined from a factory-controlled temperature bath run. Nevertheless, in the rapidly changing ambient environments of many surface-to-deep CTD casts, profiling instruments are often not in full thermal equilibrium with the environment, a state referred to as “thermal shock”. Disagreement between up- and downcast traces can also beg the question of which trace is “more correct” regarding target parameters, i.e. SPM or POC concentrations that are typically regressed against beam cP. Most sampling systems, including the GEOTRACES rosettes, sample

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ACCEPTED MANUSCRIPT on the upcast to minimize contamination risks, while optical data are often more consistent on the downcast due to the uninterrupted, generally constant wire-speed of descent. Thus, on long or deep CTD casts or casts on systems with significant delays between up- and downcast due to sampling (e.g. McLane pumps), the downcast trace can be temporally offset from sample collection by several hours. Particle fields of the mid to deep ocean may be stable over this interval, but those of coastal environments, active bottom nepheloid layers, diurnally influenced

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surface systems, and stratified systems with strong biogeochemical gradients (e.g. oxygen

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deficient zones), can be much more dynamic.

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We had a unique opportunity to address these challenges—optical dataset corrections and differential optical responses to particle composition—following the GEOTRACES EPZT

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transect from Peru to Tahiti in Nov-Dec 2013. At up to 36 stations across the basin, three CStar 660 nm, 25-cm pathlength transmissometers were regularly deployed: two on profiling CTDs

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(Seabird 9plus CTDs on the GEOTRACES carousel [GTC] and Ocean Data Facility carousel [ODF]; each recording 12-bit instrument output) and one on the McLane (MCL) pump casts (a Seabird 19plus SeaCAT CTD; recording full 14-bit instrument output). A Seapoint Turbidity

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meter (880 nm in high-gain mode; hereafter “turbidity” or “turb”) was also deployed on the GTC

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system, providing a different particle scattering assessment (broad-angle particle scattering) than measured by the transmissometers, which also respond to particle absorption. We sought to

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correct these datasets as best possible using available calibrations and thermal data, examine the transect’s major particle features, and characterize optical instrument responses to high-

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Methods

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resolution particulate composition measured in GEOTRACES samples.

Air/dark voltage monitoring and calibration tuning of transmissometers On-carousel recording of the open air (maximum open-path transmittance voltage) and windows-covered (dark, closed-path) transmissometer voltages can be used in conjunction with instrument factory calibration and clear-water voltages to monitor transmissometer sensitivity drift over time. Air/dark monitoring is also used to ensure that any step-function shifts to instrument response (e.g. due to CTD cabling changes, dirty or scratched windows) are promptly noticed and corrected. Air/dark calibrations for the GTC and ODF transmissometers (deployed at 35 and 36 stations, respectively) were recorded at cruise start, cruise end, and three times mid-

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ACCEPTED MANUSCRIPT cruise, along with rinsing with freshwater after each cast and optical caps to protect optical windows while not in use. Air/dark calibration and freshwater rinsing of the MCL transmissometer was also conducted at cruise start and end, and more frequently mid-cruise: before 14 of 19 stations where it was deployed; optical caps were also used to protect windows while the instruments were not in use. Transmissometer sensitivity drift in air was non-linear over time for all instruments,

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potentially due in part to atmospheric thermal gradients experienced during the 6300-km transect

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from cooler conditions associated with coastal upwelling to warmer conditions in the gyre. In the

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case of the GTC rosette, unplanned at-sea cabling changes and hydrowire re-termination may also have affected instrument voltage readouts, leading to unexpected shifts in dark voltage

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which would otherwise be expected to remain constant over time. Shape-preserving piecewise polynomials (PCHIP) were fit through the air and dark calibration voltages versus deployment

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number in MATLAB to allow deployment-by-deployment determination of applicable air/dark parameters. Use of the factory-provided clear-water calibration voltages (100% transmission

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reference voltage) typically lead to >100% transmission in deep water and thus negative cP values at many offshore stations. To determine appropriate clear-water transmission voltages and

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expected beam transmission percentages, we examined the lowest-SPM samples of the transect, those the 2000 – 3000 m depth range at stations west of 100˚W (excluding ridge axis stations 18

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and 20 with elevated hydrothermal particle loads), which had a median ± 1 s.d. of 2.6 ± 0.51 µg SPM (of which ≈ 30% was POM and ≈ 47% was CaCO 3 ). Conversion using typical published

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cP/SPM relationships (0.22 m2 g–1 (Hill et al., 2011)) suggest that beam cP values of ~ 0.00057 m–1 ,or beam transmission > 99.99%, should be measured at these transect-lowest SPM

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concentrations. Clear-water factory calibration voltages for each system were then tuned to approach this range for these samples. We note that the Hill et al. SPM relationships are predominantly derived in high-lithogenic coastal systems and thus likely represent a lowerbound of beam transmission/SPM sensitivities.

Major Particle Composition Major particle composition—POM, CaCO 3 , biogenic opal, Fe(OH)3 , MnO 2 , and lithogenic content—was determined on size-fractionated McLane pump samples as described in detail by Lam et al. (2017; this issue). McLane samples were collected at full-depth “regular”

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ACCEPTED MANUSCRIPT and “super” GEOTRACES stations at a subset of depths (typically 24 depths per station) from higher-resolution GTC and ODF casts (36 depths per station). The MCL system was not deployed at “demi” stations, which only sampled the upper 1000 m. Particle digestions were conducted on large and small size-fraction samples (LSF and SSF, respectively) collected in-line by the pumps over 4 hours using filter cutoffs of > 51 µm and 0.8 – 51µm, respectively. The sum

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of the abundances is referred to as total (TOT = LSF + SSF) in the text and figures.

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Thermal corrections

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Disagreement in upcast and downcast transmissometer traces is most notable in the thermocline, where instrument internal temperature is most likely to differ from ambient water

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temperature due to delayed cooling (downcast) or warming (upcast) of the instrument. Though this “thermal shock” hysteresis effect has been noted in previous transmissometer studies (e.g.

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Bishop, 1986; Gardner et al., 1985) and is ideally corrected by internal instrument hardware and software controls, we are unaware of detailed published methods for making thermal corrections

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to improve down/upcast agreement, so we present those details here. To calculate thermal corrections, it is important to begin with full time-domain

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(unbinned) instrument data, which was only readily available for the GTC and MCL systems. This process is distinct from the thermal mass correction typically applied to CTD data during

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Seabird CTD data processing, which compensates for heat transfer with the conductivity cell casing. We modeled transmissometer temperature lag as a temperature- and time-dependent

cooling:

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process using a lumped capacitance (single internal temperature) solution for Newton’s law of dT/dt = -r ∆T(t)

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where r is an unknown, but constant, quality of the system in units of time–1 (the cooling coefficient) and ∆T at time t is the difference between instrument and ambient temperature. The cooling coefficient, r, can be measured in the lab and should be similar for instruments of similar manufacture due to their similar materials, surface area, and heat capacity. The cooling coefficient can also be determined empirically for each instrument using cast data, however. To do so, we first calculated potential instrument temperatures for a range of cooling constants ranging from slow to fast thermal equilibration with the ambient T trace. Specifically, in MATLAB, an ordinary differential equation (ODE) implementation of the cooling equation was

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ACCEPTED MANUSCRIPT solved using a range of cooling constants applied to the cruise’s T cast traces, each of which had been fit with tight cubic interpolating splines to allow differentiation by the ODE-solver. The boundary condition at each cast start was set as the mean pre-cast, on-deck (air) CTD temperature. Beam cP offsets between downcast and upcast at any given depth (∆cP) were predicted based on previous reports (e.g. Bishop, 1986) to be a linear function of instrument temperature

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difference, which can be expressed relative to downcast versus upcast (∆Tinstr,downup ) or relative to

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ambient T in a stable water column (∆Tinstr,ambient ). We found that the relationships between ∆cP

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and ∆Tinstr,downup were indeed linear and consistent in slope across deployments for a fairly narrow range of cooling constants. We used robust least-squares fits between 1-m depth-binned

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cP offsets (∆cP) and instrument temperature offsets (∆Tinstr,downup ) for all casts deeper than 1000 m, to determine the best-fit cooling constants of 0.0048 s–1 (GTC) and 0.0080 s–1 (MCL). These

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values are similar to laboratory-determined water bath experimental values of ~ 0.0057 s–1 using similar CStar instruments (Lerner et al., 2013). Use of robust linear fits helps diminish the

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influence of off-trend outliers (Holland and Welsch, 1987) which are most common in nearsurface depths where natural particle variability and/or water-mass drift create true differences in

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the particle field between downcast and upcast.

With instrument T calculated using the best-fit cooling constants, we then used the

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median slope, McP,T , of (depth-binned) ∆cP vs. ∆Tinstr,ambient across stations and depths deeper than 1000 m to correct all instrument data to ambient T:

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Results

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cP corr = cP uncorr – McP,T *(Tinstr – Tamb)

Correction of transmissometer responses to temperature Unbinned instrument output from the whole GP16 transect (map: Fig. S1) was available for the GTC and MCL transmissometers and histograms of these datasets are compared to the available bin-averaged ODF data in Fig. 1. The MCL system, which uses a Seabird 19plus SeaCAT CTD, recorded all 14-bits of analog transmissometer voltage output at 4 Hz, resulting in a finer distribution of values than recorded by the Seabird 9plus GTC system recording a 12-bit data stream at 24 Hz. In the ODF dataset, signal averaging, perhaps during analog signal processing by the CTD or during depth binning, results in clustering of transmissometry signal

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ACCEPTED MANUSCRIPT around the discrete 12-bit voltages. A similar, weaker version of this effect is visible in the 14-bit MCL data, but the MCL instrument exhibits an overall smoother response due to the MCL system’s finer voltage resolution. Due to the extreme spikiness of the ODF dataset and lack of readily-available unbinned source data, we focused on thermally correcting and examining the GTC and MCL datasets. Linear regression of upcast-downcast cP offsets against temperature offsets calculated (data not shown). Demonstrative temperature traces and modeled thermal offsets for a full

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from a wide range of thermal constants showed the best fits using thermal constants ≈ 0.005 sec–

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MCL cast are shown in Fig. 2. The modeled instrument T profiles demonstrate that wire-stops for pump deployment and recovery (typically a few minutes each) are long enough for some

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thermal equilibration of the MCL transmissometer with ambient temperature. Comparing the upcast and downcast transmissometry data from all MCL and GTC

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deployments in the 100 – 1000 m depth range (Fig. 3), we find similar distributions and ranges of thermal offsets despite the differences in cast styles, with median offsets of ~ 1 deg in both systems. While most instrument-ambient T offsets are within 0.5 – 4 deg C, offsets exceeding 10

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deg C—equivalent to a beam cP offset of ≈0.015 m–1 —do occur, being observed in 15 of 65 non-

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shelf GTC deployments. By comparison, the transect mean beam cP value in the 100 – 1000 m depth range is 0.023 m–1 , highlighting the importance of correcting thermal offsets especially in

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this critical portion of the water column.

Final correction of transmissometer responses—to cP that would be measured if the

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instruments were in constant equilibrium with ambient T—was conducted after regressing the cP offsets between bin-averaged upcast and downcast traces against modeled instrument T offsets (Fig. 4). Median slopes of the ∆cP/∆T relationship (McP,T ) used for correcting each system were

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consistent within ~25% across deployments and also across instruments: 0.00119 m–1 deg C–1 for the GTC system and 0.00134 m–1 deg C–1 for the MCL system. Application of thermal corrections greatly improves upcast/downcast agreement, especially in the 100 – 1000 m depth range (Fig. 3). Particle abundance is effectively constant over the timescale of up-vs-down-casts for most of the water column, and most upcast-downcast differences in cP (∆cP) fall along a line predicted by the instrument thermal offset. The exception to this is near the ocean surface where there can be real particle variability or water-mass drift between down- and up-casts (brown, offtrend points, Fig. 4).

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Optically observed particle features Optical responses for transmissometers and the turbidity meter ranged over several orders of magnitude, so section views of the optical data are best visualized on logarithmic scales (Fig. 5; upper panels). Particle maxima are observed near the surface in association with autotrophic production, while local maxima are observed in the upper oxygen deficient zone (ODZ) east of

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100˚W, and along the local oxygen minimum (≈ 250 – 450 m) west of 100˚W. Deep (> 1000 m)

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particle loads are higher in the eastern basin especially beneath the ODZ, in the west near the

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Marquesas plateau around 140˚W, and near the ocean floor. Deep optical particle minima are observed west of the ridge axis in association with the westward-transported hydrothermal

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plume. Some features appear more strongly in either beam cP or turbidity (e.g. the local oxygen minimum feature better observed in turbidity; the deep maximum in cP near the Marquesas scaling choices used in their visualization.

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plateau), but we caution that the apparent intensity of many features is also dependent on the

To improve visualization of both major and minor particle features across optical

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platforms, we calculated derivatives of beam cP and turbidity versus depth (dcP/dz (m-2 ) and

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dTurb/dz (NTU m–1 )). These plots contrast regions of particle increase with depth (positive slope) vs. regions of particle decrease with depth (negative slope) (Fig. 5; lower panels). In

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taking depth derivatives of transmissometry data we used the 14-bt MCL transmissometer, which recorded a finer sensor response resolution but had lower spatial coverage, as it was deployed

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only at full-depth stations. Optical derivative plots emphasize that particle enrichments in the upper water column are often located near the base of the surface mixed layer, especially

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offshore once the transect entered more oligotrophic waters between 100˚-125˚W (Fig. 5, “A”). Local maxima are also clearly seen along the oxic-suboxic upper boundary of the oxygen deficient zone (ODZ) (Fig. 5, “B”) and associated with depths of local oxygen minima west of 100˚W (Fig. 5, “C”), features we have characterized in this transect previously (Ohnemus et al., 2016) and which have also observed in the Arabian Sea ODZ (Morrison et al., 1999). The lowoxygen-associated particle enrichment observed between 250 – 450 m depth west of 100˚W (“C”) is especially notable in the turbidity derivative. More subtle, but persistent, particle increases with depth are also observed in the lower oxycline—i.e. beneath the suboxic ODZ where oxygen begins to increase again with depth—and in association with lithogenic particle

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ACCEPTED MANUSCRIPT inputs along the S. American boundary (Fig. 5, “D”). The East Pacific Rise (EPR) hydrothermal input is visible as a strong particle increase at the ridge axis itself (Station 18; 112.75˚W) and at several stations to the immediate west (Fig. 5, “E”). Near-bottom benthic nepheloid layer (BNL) maxima were a nearly universally observed phenomenon, with most stations exhibiting increases in beam cP and turbidity that begin 600 – 750 m above bottom (Fig. 5, “F”). Near-bottom features were more discernable in the full 14-bit resolution MCL transmissometer data and high-

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gain turbidity data than in the 12-bit GTC transmissometer data (Fig. 6).

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Preservation of full time-resolution optical data (i.e. before depth binning) also enables

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calculation of sensor “spikiness” which is posited to be correlated with numerically rare large particles (e.g. zooplankton, large aggregates) that transiently enter the responsive volume of the

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sensors (Bishop and Wood, 2009; Briggs et al., 2011; Cetinić et al., 2012; Gardner et al., 2000). To derive this metric, we calculated and subtracted seven-data point moving-median responses

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for all casts and optical datasets and examined the frequency of spikes above each sensor’s median (baseline) response. To exclude background sensor noise from the spike metric, several

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empirically-determined “low” and “high” cutoff levels above the median (excluding 94.5% and 97.5% of all data, respectively) were examined, though spike spatial distributions determined

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using both cutoffs (and other, stronger cutoffs > 97.5%) were similar. We present the frequency of “high”-cutoff spikes (those greater than 97.5% of median-filtered data) from the downcast

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turbidity dataset (Fig. 7), which we use because this was the most sensitive optical instrument on downcast GTC casts during which wire-out speed was consistently 40 m min–1 . Spikes are first

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calculated in the full time-domain, then bin-averaged and presented as the frequency of all optical data collected in each 1 m depth bin.

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Large particles determined via the spike-metric are consistently observed in the upper 50100 m in association with the surface mixed layer. Beneath the surface mixed layer, clusters of large particle spikes were observed in the upper oxycline offshore and in the upper ODZ, especially east of 90˚W where low-light fluorescence is also consistently observed at the oxicsuboxic boundary (Ohnemus et al., 2016). Spikes in the upper mesopelagic zone east of 110°W and west of 130 °W coincide with a higher fraction of particulate organic matter in the > 51 m size-fraction (Fig. 7, upper panel), consistent with previous interpretations of large, organic-rich aggregates being the primary cause of optical spikes beneath the surface mixed layer (Briggs et al., 2011). In deep water, large particle spikes were generally constrained to within 50 – 100 m

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ACCEPTED MANUSCRIPT above bottom of the transect’s generally weak, but consistent, benthic nepheloid layers. Spikes in the intervening depth ranges (between ≈ 1000 m depth and > 400 m above bottom) were not consistent features and are not shown.

Discussion Thermal Corrections

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A major driver of our desire to thermally correct the transmissometry in this basin was to

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improve the usability of transmissometer data from MCL pump deployments on which major

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particle composition samples were collected. Unlike the rosette-based (GTC and ODF) systems which transit uninterrupted on the downcast and stop only briefly during upcast for sampling,

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MCL casts have multiple, several minute- long stops in both directions for pump attachment and removal. These stops allow partial thermal equilibration of the transmissometer with ambient T

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and thus constantly change the levels of thermal shock, which is the internally-uncompensated disparity between instrument temperature and ambient T. Additionally, four-hour pumping periods separate the MCL downcast and upcast traces, and the MCL system operates at slower

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wire speeds than the CTD-rosettes (30 m min–1 vs. 40 m min–1 , respectively), presenting the

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possibility that MCL transmissometer thermal effects could differ from those on the fastermoving rosettes.

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Our thermal correction procedure can be applied to all cast styles, however, by modelling instrument temperature lag using Newtonian cooling applied to the cast temperature trace (Fig.

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2). As long as raw (unbinned, time- or sample-domain) data are retained, this procedure can be conducted after the cast. In this equatorial transect with its strong thermoclines and warm surface

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temperatures approaching 30˚C at some stations, transmissometer temperature differences between down and upcast of 0 – 14 deg C were observed (Fig. 3, left). Calculated thermal offsets consistently explained most of the observed beam cP differences between upcast and downcast traces (Fig. 4, left), allowing optical data to be corrected to what would be reported if the instruments were in constant thermal equilibrium with the water column. Left uncorrected, profiling beam cP offsets could result in oceanographically significant misestimation of SPM or POC concentrations and inaccurate profile shapes, especially in the thermocline where typical mean, offshore cP values were 0.02 to 0.04 m–1 . Thermally corrected cP data show significantly better agreement between traces, especially in the strong T, POC, and SPM gradients of the

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ACCEPTED MANUSCRIPT upper 1000 m (Fig. 4, right). In the plotted profiles, most remaining (off-trend) differences in beam transmission between upcast and downcast are observed in the surface mixed layer or just below at the deep chlorophyll maximum. These optical differences are likely attributable to true surface biogenic particle changes and/or water-mass drift during the > 4-hour MCL casts. The direction of the observed offsets—colder instruments on the upcast presenting lower cP values—is consistent with the known phenomenon of colder instrument temperatures

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producing stronger LED beam output. In the CStar transmissometers used here, an internal beam

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splitter and reference channel monitors the LED output and compensates the output voltage. Our

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observed thermal effects appear to be due to thermal shock, however, in which a profiling instrument does not maintain complete thermal equilibrium with the environment, preventing

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internal controls from fully compensating. Factory-supplied thermal corrections, which are continuously conducted by the firmware during operation, had also likely drifted since the

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instruments’ prior factory calibrations and may not have fully compensated as designed. Even so, these effects are likely to apply to many seagoing instruments and datasets. The linearity and

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consistency of our thermal corrections makes it appear likely that they are in addition to internally compensated responses and thus represent an improvement in upcast-downcast

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agreement that can be conducted by other users. Simple thermal modeling could also be fairly easily incorporated into transmissometer data processing by CTD software packages. Our data

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and MATLAB code are available via the National Science Foundation’s Biological and Chemical Oceanography Data Management Office data archive [BCO-DMO dataset number

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pending].

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Major optically determined particle features The transect’s major particle features can be visualized using high-sensitivity optical data at a resolution much greater than discrete sampling allows. Near-surface particle maxima observed in derivatives of optical responses at the base of the euphotic zone in the most oligotrophic section of the transect between 100°W-125°W reflect deep chlorophyll maxima. In the east, local particle maxima were often observed in the oxic-suboxic boundary of the upper ODZ, a feature recently characterized by our group for its unique trace metal stoichiometries (Ohnemus et al., 2016). This feature has received attention in earlier examinations of “intermediate nepheloid layers” associated with the Peruvian and other marine ODZs (Spinrad et

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ACCEPTED MANUSCRIPT al., 1989; Whitmire et al., 2009) and is attributed to a rapid vertical succession of autotrophic and chemotrophic organisms and scavenged phases tightly constrained by the narrow redox gradients of the upper ODZ (Ulloa et al., 2012). Biogenic particles found in the ODZ are present against a background of surface-derived cells and aggregates, bio-authigenic Fe-minerals (Heller et al., 2017), as well as laterally transported mixed-composition sediments from continental shelf sources onto which the ODZ impinges.

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In the subsurface depths outside the ODZ, derivatives of optical data highlight a

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persistent maximum in particles between ≈ 250 – 450 m depth associated with the local oxygen

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minimum, even though stations west of 100˚W do not achieve the suboxic conditions that give rise to the complex oxic-suboxic boundary ecosystems of the ODZ. Given the expansiveness and

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persistence of this offshore oxycline feature, these particles are likely to have originated locally rather than from lateral transport. The feature broadly follows the depth of pBa maximum, but is

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generally deeper than the pMn maximum, which is located in upper oxygen gradient just below the euphotic zone (not shown). Its association with the pBa maximum and presence at the base of

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the upper oxycline suggests it represents a stable, sub-euphotic/upper mesopelagic zone associated with the breakup and degradation of surface-derived particles (Bishop, 1988; Dehairs

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et al., 2008). Indeed, this feature coincides with an increase in the concentration of several particulate phases in the small size fraction (SSF; 0.8-51 µm) at the same depths, and exists just

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below a distinct minimum in particle concentrations at 100 – 300 m depth. Lam et al. (2017, this issue) explain the feature as evidence of a common particle dynamic in which net aggregation

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processes package SSF into the faster-sinking large size fraction (LSF; > 51 µm) around 100 – 300 m, while net disaggregation processes fragment LSF into SSF particles in the 300 – 500 m

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depth zone. Notably, the feature was most obvious in the distributions of CaCO 3 and biogenic silica, which would be expected to contribute more to backscatter than to beam attenuation, perhaps explaining why the signal is stronger in the turbidity data than in the transmissometer data. It also sometimes presents with a weak local P maximum, thus bearing similarity to the biogenic and Fe-scavenged P enrichments previously reported in the upper ODZ, though without the suboxic conditions (Ohnemus et al., 2016). This zone thus likely coincides with a complex vertical shift in particle number, character, and composition that includes roles for prokaryotic organisms, zooplankton, and inorganic scavenging, thereby influencing the fates of numerous settling phases.

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ACCEPTED MANUSCRIPT Several recent publications have investigated hydrothermally sourced dissolved and particulate elements and isotopes at the EPR ridge axis and in the hydrothermal plume to the west that were measured on this cruise (Fitzsimmons et al., 2017; Resing et al., 2015). Optical instruments show the strong hydrothermal particle enrichments at the ridge axis itself, while more subtle optical derivatives track the plume in the 2000-3000 m depth range for several stations to the west. Optical ratios, described further below, appear to follow the plume over even

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longer distances.

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Benthic nepheloid layers

Weak but consistent benthic nepheloid layers were seen in transmissometer and turbidity

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data across the basin, generally beginning 600-750 m above bottom. Though we broadly distinguish continental shelf BNLs (stations 2-5, < 1500 m depth) from deep open-ocean BNLs

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(stations 1, 6-36; west of 80˚W), these zones express a wide range of chemical, isotopic, and major particulate compositions that largely resist simplistic characterization. Our goal here,

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however, is to display the sensitivity that the various optical instruments and their CTD configurations have in discerning these layers. Demonstrative profiles from stations 9 and 25

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highlight the differences between full 14-bit MCL transmissometer data, the high-gain turbidity meter, and the 12-bit GTC transmissometer data (Fig. 6). Full 14-bit MCL data notably improve

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discernment of BNL start depth, vertical gradients, and internal structure. The station 25 trace includes an inversion in particle abundance at c.a. 25 – 50 m above bottom that was commonly

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observed in many deep and shelf BNLs of this transect and has also been observed in the Atlantic (McCave, 1983).

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Large particles, which can be visualized as transient “spikes” in unbinned, highfrequency optical measurements, appear mostly constrained within 75 – 100 m above bottom in the benthic nepheloid layer (Fig. 7), which may be useful in studies examining element or isotope cycling in this zone. Large particles also appeared more frequently and at greater depths above bottom east of the EPR where total water column particle loads were also generally higher. The comparative drop in spike abundance and overall BNL intensity just to the west of the EPR suggests a role for near-axis hydrothermal particles in suppressing large particle generation or benthic resuspension beneath the most intense eastern portion of the plume. Lee et al. (2017, this issue) report a “leak” of fine/SSF-hydrothermal particles to several BNLs east of

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ACCEPTED MANUSCRIPT the EPR (stations 13-17; 99-109˚W), suggesting that fine hydrothermal particles do, however, remain susceptible to mobilization and longer-range transport by bottom currents.

Simple relationships between particle composition and optical responses This GEOTRACES transect spanned a wide range of particle regimes that were sampled at high spatial resolution for size-fractionated major particle composition (POM, CaCO 3 ,

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biogenic opal, Fe(OH)3 , MnO2 , and lithogenic content). We thus have a unique opportunity to

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examine controls on optical instrument sensitivity using the 324 size-fractionated McLane

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samples for which major composition was determined. To simplify examination of the optical responses, we divided the basin into seven general particle “regions”: (1) the fully oxic surface

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(O2 sat. ≥ 95%); (2) the subsurface upper oxycline where oxygen generally decreases with depth (O2 sat. ≤ 95% but ≥ 5%; depth < 500m); (3) the OMZ/ODZ in the east (O 2 sat. < 5% east of

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100˚W); (4) the deep hydrothermal plume (station-specific increases in Mn and/or Fe content of McLane particle samples); (5) shelf-associated and (6) deep benthic nepheloid layers (station-

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specific near-bottom increases in optical responses); and (7) all other deep data collected ≥ 500 m. Mean fractional composition of suspended particulate matter (SPM) from each region is

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shown for large and small size-fractions (LSF and SSF, respectively) and their total sum (TOT) in Fig. 8.

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Responses of the transmissometers and turbidity meter to SPM and POC abundances can be visualized as scatterplots of transmissometer beam attenuation (cP) and turbidity versus SPM

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and POC (Fig. 9; single linear regression [SLR] parameters and statistics in Table 2), and as sections of the optical response ratios to SPM (Fig. 10). Beam cP responded more linearly to and

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had a higher variance explained by total POM abundances than to overall SPM (Bishop and Wood, 2009; Cetinić et al., 2012; Son et al., 2009) (Table 2; SLR). Slopes of cP vs. both SPM and POC, which are considered hereafter as optical sensitivities, were lower in the relatively opal-rich ODZ (Fig. 9, blue points) and in high-SPM (lithogenic- and opal-rich) shelf BNLs (Fig. 9, brown points) compared to the POM-rich surface (Fig. 9, green points). Lower cP sensitivity to total SPM (i.e. lower cP/SPM ratios) is also apparent in many off-axis hydrothermal plume samples and is especially notable in section (Fig. 10), though we caution that, due to the very low SPM of deep samples, many points are at the low-end of the instruments’ responses and are thus more sensitive to subtle, unconstrained calibration effects. Subsurface particles associated with

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ACCEPTED MANUSCRIPT the upper oxycline elicited a range in sensitivities that appear station-dependent, likely reflecting more complex variations in optical sensitivity to local particle composition in the upper mesopelagic. Turbidity exhibited a more linear response to and had higher variance (r2 ) explained by total SPM abundances than cP (Fig. 9; and Table 2 SLR). The low-cost turbidity instrument also required significantly less upkeep than the transmissometers, which is an especially valuable trait

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on long transects. Though the section of turbidity sensitivity (Turb/SPM) has many similarities to

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the section of transmissometer sensitivity (cP/SPM) (Fig. 10)—sensitivity maxima in the

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subsurface upper oxycline, sensitivity minima in the eastern basin and in eastern shelf BNLs— the ratio of the two measurements, the Turb/cP ratio (Fig. 11a), demonstrates a keen ability to

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discriminate between many particle environments, with maxima apparent in many BNLs and other key regions of interest (ODZ, near-surface, EPR plume). The ratio thus appears to highlight

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differential responsiveness to particle composition between the two optical instruments. A strength of this parameter is that, while assessments of optical ratios to SPM and composition

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high resolution for an entire transect.

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can only be conducted where discrete samples are collected, optical ratios can be calculated at

Alternative visual representations and hints of mechanisms

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Major particle compositional data can be visualized in other ways to examine instrument, region-, and composition-specific trends. Sorted response plots, in which samples are first

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sorted by apparent sensitivity to SPM, and then the corresponding particle compositions are shown beneath each sample (Fig. 12a), qualitatively demonstrate the high sensitivity of

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transmissometers to high-POM samples with low-opal loads (right side, figure 12a), and progressively diminished sensitivity (lower cP/SPM) as biogenic opal, lithogenic, and authigenic mineral fractional composition increases (towards the left). Such an effect may be due to a combination of diminished absorption and scattering ability of non-POM minerals in these particular oceanographic environments, as well as to scavenging or sequestration of POM by various mineral surfaces. Surface-sequestration of POM by hard minerals could make less POM surface area available for beam attenuation but not prevent POM from being measured during analytical digestion. Though POM sequestration is by no means a new hypothesis (Armstrong et al., 2002; Keil et al., 1994; Mayer, 1994; Ransom et al., 1998), it may provide a conceptual

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ACCEPTED MANUSCRIPT analog to phase-specific scavenging known to be important for a many elements and isotopes (Hayes et al., 2015). Sorted turbidity/SPM responses highlight the turbidity sensor’s elevated sensitivity at high Fe-fractional abundances in many plume, deep BNL, and ODZ samples, and a general increase in sensitivity as fractional CaCO 3 content increases (see also, Fig. 12b). Sorting the turbidity/cP ratio further demonstrates this metric’s previously described ability to discriminate

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high Fe-samples over the individual sensors alone, with Fe- (and Mn-) enriched BNLs and ODZ

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samples observed having high turbidity/cP ratios towards the right (Fig. 12c). Many lithogenic-

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rich, but POM-poor BNLs (e.g. Stns 7, 9, 15, 32, 36) sort towards the low-end of this response

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(Table 1, Fig. 11).

Quantitative modeling of optical responses: the Turbidity/cP ratio as a broad optical particulate

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Fe-sensor

To quantitatively examine the relationships between optical responses and particle

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composition, we conducted modeling of optical parameters against the measured abundances of particulate phases using both single and multiple linear regressions (SLR; MLR). The seven

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general regions sampled expressed a wide variety of particulate character, and regression analysis can potentially provide statistical estimates of optical sensitivity to each phase or within

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each region. There are many ways to conduct these analyses, including consideration of sizefractionation (SSF and LSF vs. TOT), allowing models to categorically compensate for sample

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region (e.g. surface, subsurface, ODZ, plume, etc.), and allowing/disallowing interaction effects between particulate phases (which should not be expected to be fully independent of one

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another). Consideration of size-fractionation of phases and their poorly understood interaction terms can quickly swamp the models and complicate their interpretation (not shown). We focus instead on results from multiple linear regressions from two types of transect-wide models: those that consider TOT phase abundances 1) without consideration of categorical sample regions and 2) with consideration of categorical sample regions (Table 2). Regressions were performed in the software package JMP, and statistical significance of each coefficient (whether significantly different from zero at the p < 0.05 level) were determined using a two-sided Student’s t-test. Only coefficients where p < 0.05 are shown.

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ACCEPTED MANUSCRIPT Multiple linear regression analyses that consider all measured phases show consistent cP sensitivity to POM as well as significant cP sensitivity to CaCO 3 and lithogenic phases (Table 2). Allowing regression models to categorically consider sample region, which allows for different means in optical responses across regions and can help compensate for unquantified variability in particle character (e.g. size) that affect optical sensitivity, improves the fits and slightly increases the coefficients of non-POM phases relative to POM. Opal exhibits a negative

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coefficient in the cP models, suggesting an interactive relationship between POM and opal that

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diminishes optical responsiveness to POM (e.g. lower optical sensitivity due to larger particle

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sizes in high-opal environments). Turbidity shows a significant positive response to all the same phases as cP (POM, CaCO 3 , and lithogenic material at broadly similar ratios to POM), but unlike

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cP, is also consistently sensitive to Fe(OH)3 in all MLR models. The response of turbidity to opal also differs from cP: with no regional consideration, there is no significant response, and with

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regional consideration, there is a significant and positive response. Increases in the turbidity/cP ratio thus appear to be broadly indicative of elevated Fe(OH)3 content, and may also respond to

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opal content. Although the ODZ region did not have high fractional Fe(OH)3 (Fig. 8), the upper ODZ was characterized by high absolute Fe(OH)3 concentrations (Fig. 11b; Ohnemus et al.

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2016; Heller et al. 2017; Lam et al., this issue). The elevated turbidity/cP ratio in the upper ODZ and hydrothermal plume are thus consistent with the elevated Fe(OH)3 concentrations in these

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regions. In the far west of the transect, GTC bottle particle samples show somewhat elevated particulate Fe there (Fig. 11b), which remains even after correction for lithogenic material (not

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shown).

In general, only 32% of the variance in Turb/cP across the entire transect can be

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explained by particle composition using a multiple linear regression (Table 2), suggesting there are other factors that we have not considered that may influence Turb/cP, and/or that linear regressions are not well suited for directly modeling this relationship. Indeed, when regional consideration is allowed, models prefer to fit separate means to the Turb/cP ratio in each region and find no significant coefficients to the absolute phase abundances (not shown). Similarly, simple linear models of sensitivity ratios (e.g. cP/SPM) versus either phase abundances (not shown) or fractional phase composition (Fig. S2) may also not be appropriate or sufficient to explain their variance. The underlying causes for the ranges in optical responses can likely be attributed to factors our sampling does not quantify, including particle size, volume, density,

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ACCEPTED MANUSCRIPT spatially variable surface effects within phases, and interactions among phases that alter optical character. Nevertheless, the potential usefulness of simple optical ratios in discriminating broad changes in particle composition is promising and should be more rigorously examined using a combination of these instruments and others, such as the LISST (Laser In-Situ Scattering and Transmissometer), which is increasing in use.

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Conclusions

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High-resolution optical data and optical derivatives exhibit the particle distributions of

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the Eastern Pacific Zonal transect. Key features include surface mixed layer maxima and local maxima at the upper and lower boundaries of the eastern ODZ; within the upper mesopelagic

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oxygen minimum west of the ODZ; at sites of hydrothermal and lithogenic inputs; and in deep, persistent BNLs of most stations, typically beginning > 600 m above bottom.

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Simple thermal modeling of profiling transmissometer instrument temperature can be used to correct upcast and downcast beam attenuation traces that diverge due to thermal shock

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that is uncompensated by instrument internals. In this low-latitude transect, downcast-upcast transmissometer thermal offsets ranging to > 14 deg C [GTC median ± s.d.: 1.1 ± 3.2 deg C] in

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the thermocline were observed. The resultant beam cP offsets can represent a significant portion of the transmissometer signal especially in the upper mesopelagic, a zone of complex particle

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dynamics. Preservation of unbinned transmissometer data—ideally at full 14-bit resolution for widely-used CStar transmissometers—and frequent at-sea monitoring of instrument air/dark

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calibrations can further improve the data quality of these instruments. A broad-angle Seapoint turbidity meter provided stable, linear responses to total SPM and required significantly less

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upkeep and at-sea monitoring than transmissometers. The turbidity meter and transmissometers exhibited different responses to particulate phases: all optical instruments exhibited significant coefficients to POM, CaCO 3 , and lithogenic particles while turbidity alone expressed a significant sensitivity to Fe(OH)3 . Higher values of the turbidity/cP ratio thus appear grossly responsive to elevated Fe(OH)3 content, though the fundamental driver of this sensitivity in mixed particle environments is undoubtedly complex. In future sectional studies, this optical ratio may prove helpful in discriminating zones of differential Fe(OH)3 composition at higher resolution than is possible using discrete sampling.

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ACCEPTED MANUSCRIPT Figure and Table Captions Fig. 1. Transmissometer voltage data over similar dynamic ranges from the whole cruise for the GTC, ODF, and MCL systems. Top row: 12-bit data recording with 1.2 mV response spacing from the GTC (left; 74 deployments, unbinned), and ODF (right: 115 deployments, depth-binaveraged) systems. Bottom row: unbinned, full 14-bit data with 0.3 mV response spacing from the MCL system (46 deployments; unbinned). Signal processing by the ODF system, for which

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unbinned data were not available, tends to cluster data around 12-bit response values resulting in

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a spiky distribution. This effect is likely due to rounding during analog-to-digital signal

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conversion and/or depth-bin averaging. Some related “spikiness” is observed in the MCL data

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[inset], but the associated voltage jumps are finer, leading to a smoother overall response.

Fig. 2. Measured ambient T (AmbTemp) and modeled instrument T (InstrTemp) for a typical

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McLane pump deployment. Upcast (u) traces are shown in cool colors; downcast (d) in warm colors. Black traces at left: modeled instrument minus ambient T offsets (∆T), consistent with

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previous data from instruments that recorded internal T (Gardner et al., 1985; Bishop, 1986). Inset: zoom of depths where hydrowire stops for McLane pump attachment/removal allow partial

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thermal equilibration of the transmissometer with ambient T (arrow indicates a pronounced

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example). Noisier upcast T data are due to intermittent upcast issues with the MCL CTD pump.

Fig. 3. Left: distributions of down-vs-upcast MCL (top) and GTC (bottom) instrument T offsets

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in the 100–1000 m depth range after 1-m depth-binning of all deployment data. Right: distributions of beam cP offsets in the same depth range before (red) and after (blue) thermal

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corrections have been applied.

Fig. 4. Left: typical relationships between 1-m binned modeled instrument T offset (x-axes) and beam cP offset (y-axes) for two MCL deployments. Off-trend points: near-surface depths (brown colors) where variability in the particle field or water-mass drift drives true beam cP differences between upcast and downcast, which are separated by > 4 hr. Dotted lines and annotations: robust least-squares fits and slopes, McP,T , consistent within ≈ 25% across deep deployments. Transect-wide median slopes for each instrument were used to correct original time-domain

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ACCEPTED MANUSCRIPT beam cP data. Right: Demonstrative cP profiles from the GTC system before and after thermal correction. Inset: zoom of upper 150 m.

Fig. 5. Left upper panels: section of transmissometer beam cP (GTCd) plotted on log scale; right upper panels: section of turbidity (GTCd) on log-scale. Oxygen saturation contours are provided in upper 800 m for context; individual profiles are indicated by gray vertical lines. Color scaling

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changes below 800 m to better show gradients in optical responses. Bottom panels: Derivatives

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versus depth of beam cP MCLd (left; full-depth stations only) and Turbidity (right; GTCd, all

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stations). Red: particle concentrations increase with increasing depth; blue: particle concentrations decrease with increasing depth. A) surface-associated particle maxima at the base

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of the surface mixed layer often observed between 50-100 m depth; B) local particle maxima at the upper and lower ODZ boundaries (solid 5% contour east of 100˚W); C) a particle

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disaggregation-associated maximum in the 250-450 m depth range that follows non-ODZ oxygen minimum west of 100˚W; D) deep lithogenic particle maxima associated with the South American shelf; E) the near-vent EPR hydrothermal plume; and F) increases in particle

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abundance beginning ≈ 600-750 m above bottom in BNLs at nearly all stations.

Fig. 6. Representative responses of the GTC turbidity meter (black), and thermally-corrected

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GTC and MCL transmissometers (green and red, respectively) near the ocean bottom. Profiles are normalized to near-bottom values to allow simplified visual comparison. Actual values at

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clear-water minima ≈ 600 m above bottom at station 9 and 25, respectively, were 0.0678 and

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0.0539 NTU (turbidity), and 0.0130 and 0.0027 m–1 (transmissometer: MCLd). Fig. 7. Fractional abundance of >51 µm POM (fLSF_POM; top panel) and sections of turbidity sensor spike frequency in the upper 800 m (middle panel; O 2 saturation contours shown for context) and within 400 m of the ocean bottom (lower panel; note reduced color axis range). Large particle maxima are observed A) throughout the upper 100 m with poor correspondence with overall fLSF_POM, B) along O 2 gradients in the 250 – 450 m depth range in association with higher fLSF_POM, especially C) in the eastern basin and ODZ and D) near the Marquesas plateau. Near bottom, large particle maxima appear generally constrained E) within 50-75 m but

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ACCEPTED MANUSCRIPT occasionally > 100 m above bottom in deep benthic nepheloid layers. Benthic optical spikes were more common east of the EPR and beneath the ODZ.

Fig. 8. Mean fractional composition, by weight, of MCL-collected size-fractionated particles in key particle regimes of the transect, demonstrating the range of compositions sampled by optical instruments. From left to right: large and small size-fractions (LSF, >51 µm; SSF ≈ 1 - 51 µm)

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and total particles (TOT = LSF+SSF). Regions defined as follows. Surface: 0-100 m O 2 sat ≥

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95%; Subsurface: > 100 m and < 500 m, O 2 sat. 95 to 5%; ODZ: O2 sat. < 5% (E. basin only);

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BNLs: within 1000 m of bottom, station-specific increase in optical responses: east of 80˚W BNL-Shelf, west of 80˚W BNL-Deep; Plume: station-specific maxima in particulate Fe and/or

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Mn between 2000-3000 m depth. Deep: all other data ≥ 500 m.

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Fig. 9. Responses (y-axes) of the GTC (left) and MCL (center) transmissometers and Seapoint turbidity meter (right) to total (>0.8 µm) size-fractionated SPM concentrations (top row) and

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POC concentrations (bottom row). Data-point styles correspond to the particle regimes defined in Fig. 8. Regression coefficients and statistics for the single linear regression (SLR) trendlines

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are also provided in Table 2, SLR.

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Fig. 10. Sections of optical instrument sensitivity to SPM, defined as optical response divided by SPMT OT concentrations measured at discrete sampling depths [markers]. Left: MCLd

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transmissometer beam cP/SPMT OT . Right: Seapoint turbidity meter (GTCd) NTU/SPMT OT . In upper panels, 0 – 800 m, O2 saturation contours are provided to show upper water column

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oxycline structure and the approximate extent of the eastern ODZ (O2 sat < 5 %, solid contour). Fig. 11. A) the Turbidity/cP ratio (GTC) uses the differential particle responses of two optical instruments to visualize broad changes in particle composition, especially Fe(OH)3 abundances. Contours of O 2 saturation [%] are provided in the upper 800 m to show oxycline structure and the approximate extent of the ODZ (< 5%, solid contour). Color scaling changes below 800 m to properly show gradients associated with the HT plume. Labels: higher Turb./cP ratios are seen in regions of elevated lithogenic and authigenic phase abundances: in the upper ODZ (Fe(OH)3 ); near the Marquesas plateau, S. Am boundary, and deep trench station (lithogenics); in the HT

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ACCEPTED MANUSCRIPT plume, and in many BNLs (Fe(OH)3 , MnO 2 , and lithogenic material). B) Particulate Fe from the GTC system bottle particle dataset (Ohnemus et al. 2016; Fitzsimmons et al. 2017).

Fig. 12. Upper panels: samples sorted by increasing (A, B) SPM-normalized instrument sensitivity and (C) Turbidity/cP ratio. A small amount of jitter is added in the y-axis of upper panels to visually separate data symbols. Lower panels: corresponding fractional major particle

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composition of the sorted samples, after Hayes et al. (2015).

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Table 1 (goes with Fig. 11): Mean Turbidity/cP ratios ± 1 s.d. in sampled BNLs. The highest Turbidity/cP ratios are observed at (bold) trench station 1 and shelf stations (2-5) and (italics) at

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at stations immediately west of the ridge axis beneath the most intense portion of the plume (stations 20, 21, and 23). At the ridge-axis itself (station 18), any BNL was indistinguishable

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from the intense plume.

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Table 2. Best-fit coefficients from single and multiple linear regression (SLR; MLR, respectively) modeling of instrument responses to the total particulate phase abundances (rows).

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SLR relationships are shown in Fig. 9. Units of intercepts are the provided units multiplied by mg. In MLR models, only coefficients significantly different from zero at the p < 0.05 level by t-

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test are shown; in SLR models, all slopes and intercepts are significant. The MLR model with regional consideration includes a categorical variable for region that allows for different means

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of optical responses in each region. For MLR models, “Sens. Rel. to POM” is the ratio of each

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phase coefficient to that of POM, expressed as a percentage.

Supplemental Figure Caption Figure S1: GP16 cruise map, including major station numbers and inset of the eastern S. American shelf portion of the transect.

Fig. S2: Scatterplots of optical sensitivity versus fractional phase composition exhibit broad trends that reinforce those visible in linear model results, sections, and in sorted responses. Fractional composition cannot be presumed to directly drive linear relationships with optical parameters, so we refrain from fitting trendlines to these data.

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ACCEPTED MANUSCRIPT A) For the transmissometers, suppression of beam cP/SPM sensitivity is seen in samples with the highest fraction opal, lithogenic, MnO 2 , and Fe(OH)3 contents (to the far right of each plot; Fig. S2a). Broadly elevated sensitivity is observed in samples with higher fractional POM content, especially to POM in the subsurface. In addition to its demonstrated CaCO 3 sensitivity and the fractional enrichment of CaCO 3 as POM is degraded with depth (Table 2 MLR and Fig. 8), a weak subsurface enhancement of transmissometer sensitivity to POM may also reflect the

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smaller mean size of POM away from the surface. Enhanced (per unit mass) light scattering by

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sub-euphotic microbes that dominate in the oxycline where surface-derived organic matter is

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degraded may be indicated.

B) The turbidity meter expresses an overall higher SPM sensitivity at higher fractional

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CaCO 3 content, the increased sensitivity at plume-enriched Fe(OH)3 loadings discussed in the main text (Table 2 MLR), and a low sensitivity at the high opal loadings of the ODZ.

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C) Any apparent optical-sensitivity relationships to MnO 2 , specifically for turbidity (S2c, middle panel; x-axis now on log-scale) are unlikely causative considering the extremely low

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fractional abundance of this phase in marine particles (<< 1%) except potentially near the ridge axis and in associated, near-axis, Mn-rich BNLs. Instead, these weak optical-MnO2 trends likely

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reflect a weak positive relationship between total MnO 2 and fractional CaCO 3 content (Fig. S2c)., which as a major phase, is more clearly linked to turbidity sensitivity. The MnO 2 /CaCO 3

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relationship may reflect ingrowth of scavenged MnO 2 in deep settling particles as POM is

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degraded and fractional CaCO 3 abundance increases.

Acknowledgements

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We thank the captain and crew of the R/V Thomas G. Thompson, cruise TT303, and chief scientists James Moffett and Chris German. Analytical support at the University of Maine was provided by Mike Handley. Funding was provided by the US and International GEOTRACES programs, via National Science Foundation grants OCE-1232814 to B.S.T. and OCE-518110 to PJL. References Cited Armstrong, R. A., Lee, C., Hedges, J. I., Honjo, S. and Wakeham, S. G.: A new, mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast minerals, Deep. Res. Part II Top. Stud. Oceanogr., 49(1–3), 219–236, doi:10.1016/S0967-

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ACCEPTED MANUSCRIPT 0645(01)00101-1, 2002. Bishop, J. K. B.: The correction and suspended particulate matter calibration of Sea Tech transmissometer data, Deep Sea Res. Part A, Oceanogr. Res. Pap., 33(1), 121–134, doi:10.1016/0198-0149(86)90111-1, 1986. Bishop, J. K. B.: The barite-opal-organic carbon association in oceanic particulate matter, Nature, 332, 24, 1988.

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Bishop, J. K. B. and Wood, T. J.: Year-round observations of carbon biomass and flux variability in the Southern Ocean, Global Biogeochem. Cycles, 23(2), doi:10.1029/2008GB003206, 2009. Boss, E., Guidi, L., Richardson, M. J., Stemmann, L., Gardner, W., Bishop, J. K. B., Anderson, R. F. and Sherrell, R. M.: Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES, Prog. Oceanogr., 133, 43–54, doi:10.1016/j.pocean.2014.09.007, 2015.

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Brewer, P. G., Spencer, D. W., Biscaye, P. E., Hanley, A., Sachs, P. L., Smith, C. L., Kadar, S. and Fredericks, J.: The distribution of particulate matter in the Atlantic Ocean, Earth Planet. Sci. Lett., 32(2), 393–402, 1976.

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Briggs, N., Perry, M. J., Cetinić, I., Lee, C., D’Asaro, E., Gray, A. M. and Rehm, E.: Highresolution observations of aggregate flux during a sub-polar North Atlantic spring bloom, Deep Sea Res. Part I Oceanogr. Res. Pap., 58(10), 1031–1039, doi:10.1016/j.dsr.2011.07.007, 2011.

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Cetinić, I., Perry, M. J., Briggs, N. T., Kallin, E., D’Asaro, E. A., Lee, C. M., Cetini, I., Perry, M. J., Briggs, N. T., Kallin, E., Asaro, E. A. D. and Lee, C. M.: Particulate organic carbon and inherent optical properties during 2008 North Atlantic Bloom Experiment, J. Geophys. Res., 117(C6), doi:10.1029/2011JC007771, 2012.

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Cutter, G. A., Andersson, P. S., Codispoti, L. A., Croot, P. L., Francois, R., Lohan, M. C., Obata, H. and Rutgers Van Der Loeff, M. M.: Sampling and Sample-handling Protocols for GEOTRACES Cruises, , 1–238 [online] Available from: http://www.geotraces.org/libraries/documents/Intercalibration/Cookbook.pdf, 2010. Dehairs, F., Jacquet, S., Savoye, N., Van Mooy, B. A. S., Buesseler, K. O., Bishop, J. K. B., Lamborg, C. H., Elskens, M., Baeyens, W., Boyd, P. W., Casciotti, K. L. and Monnin, C.: Barium in twilight zone suspended matter as a potential proxy for particulate organic carbon remineralization: Results for the North Pacific, Deep Sea Res. Part II Top. Stud. Oceanogr., 55(14–15), 1673–1683, doi:10.1016/j.dsr2.2008.04.020, 2008. Fitzsimmons, J. N., John, S. G., Marsay, C. M., Hoffman, C. L., Nicholas, S. L., Toner, B. M., German, C. R. and Sherrell, R. M.: Iron persistence in a distal hydrothermal plume supported by dissolved–particulate exchange, Nat. Geosci., 10(3), 195–201, doi:10.1038/ngeo2900, 2017.

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Gardner, W. D., Biscaye, P. E., Zaneveld, J. R. V. and Richardson, M. J.: Calibratio n and comparison of the LDGO nephelometer and the OSU transmissometer on the Nova Scotian rise, Mar. Geol., 66(1–4), 323–344, doi:10.1016/0025-3227(85)90037-4, 1985. Gardner, W. D., Richardson, M. J. and Smith, W. O.: Seasonal patterns of water column particulate organic carbon and fluxes in the Ross Sea, Antarctica, Deep Sea Res. Part II Top. Stud. Oceanogr., 47(15–16), 3423–3449, doi:10.1016/S0967-0645(00)00074-6, 2000.

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Hayes, C. T., Anderson, R. F., Fleisher, M. Q., Vivancos, S. M., Lam, P. J., Ohnemus, D. C., Huang, K. F., Robinson, L. F., Lu, Y., Cheng, H., Edwards, R. L. and Moran, S. B.: Intensity of Th and Pa scavenging partitioned by particle chemistry in the North Atlantic Ocean, Mar. Chem., 170, 49–60, doi:10.1016/j.marchem.2015.01.006, 2015.

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Heller, M. I., Lam, P. J., Moffett, J. W., Till, C. P., Lee, J.-M., Toner, B. M. and Marcus, M. A.: Accumulation of Fe oxyhydroxides in the Peruvian oxygen deficient zone implies non-oxygen dependent Fe oxidation, Geochim. Cosmochim. Acta, Accepted;, 2017.

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Hill, P. S., Boss, E., Newgard, J. P., Law, B. A. and Milligan, T. G.: Observations of the sensitivity of beam attenuation to particle size in a coastal bottom boundary layer, J. Geophys. Res., 116(C2), C02023, doi:10.1029/2010JC006539, 2011.

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Holland, P. W. and Welsch, R. E.: Robust regression using iteratively reweighted least-squares, Commun. Stat. - Theory Methods, 6(9), 813–827, 1987.

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Keil, R. G., Tsamakis, E., Fuh, C. B., Giddings, J. C. and Hedges, J. I.: Mineralogical and textural controls on the organic composition of coastal marine sediments: Hydrodynamic separation using SPLITT-fractionation, Geochim. Cosmochim. Acta, 58(2), 879–893, doi:10.1016/0016-7037(94)90512-6, 1994.

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Lam, P. J., Lee, J.-M., Heller, M. I., Mehic, S., Xiang, Y. and Bates, N. R.: Size-fractionated distributions of suspended particle concentration and major phase composition from the U.S. GEOTRACES Eastern Pacific Zonal Transect (GP16), Mar. Chem., In review, 2017.

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Lee, J.-M., Heller, M. I. and Lam, P. J.: Size distribution of particulate trace elements in the U.S. GEOTRACES Eastern Pacific Zonal Transect (GP16), Deep Sea Res. Part II Top. Stud. Oceanogr., In review, 2017. Lerner, P., Bishop, J. K. B., Strabhar, W. D., Bernard, A. and Moore, C.: Transmissometer Measurement of Particle Beam Attenuation Coefficient, in ASLO Aquatic Sciences Meeting, p. SS53-380., 2013. Mayer, L. M.: Surface area control of organic carbon accumulation in continental shelf sediments, Geochim. Cosmochim. Acta, 58(4), 1271–1284, doi:10.1016/0016-7037(94)90381-6, 1994. McCave, I. N.: Particulate size spectra, behavior, and origin of nepheloid layers over the Nova

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MLR with regional consideration MCLd GTCd cP cP [m– [m– 1mg–1] 1mg–1] *1000 *1000 POM_TOT 1.71 1.31 CaCO3_TOT 2.23 2.20

MLR without regional consideration Turbidit y [NTU mg–1] *1000 5.63 10.62

GTCd cP [m– 1mg–1] *1000 POM_TOT 2.65 CaCO3_TOT 2.63

MCLd cP [m– 1mg–1] *1000 2.32 2.90

Turbidit y [NTU mg–1] *1000 7.49 12.07

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FeOH3_TOT Litho_TOT MnO2_TOT Opal_TOT Intercept Model r2 Sens. Rel. to POM CaCO3_TOT FeOH3_TOT Litho_TOT MnO2_TOT Opal_TOT

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MLR with regional consideration GTCd cP MCLd cP [m–1mg– [m–1mg– 1] 1] *1000 *1000 POM_TOT 1.71 1.31 CaCO3_TOT 2.23 2.20 FeOH3_TOT Litho_TOT 6.44 2.79 MnO2_TOT Opal_TOT -1.04 Intercept 6.18 12.50 Model r2 0.944 0.948

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Turbidity [NTU mg– 1] *1000 5.63 10.62 27.78 15.25 2.07 48.41 0.950

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Fig. 1: Transmissometer voltage data over similar dynamic ranges from the whole cruise for the GTC, ODF, and MCL systems. Top row: 12-bit data recording with 1.2 mV response spacing from the GTC system (left; 74 deployments, unbinned), and ODF system (right: 115 deployments, depth-bin-averaged). Bottom row: unbinned, full 14-bit data with 0.3 mV response spacing from the MCL system (46 deployments; unbinned). Signal processing by the ODF system, for which unbinned data were not available, tends to cluster data around 12-bit response values resulting in a spiky distribution. This effect is likely due to rounding during analog-todigital signal conversion and/or depth-bin averaging. Some related “spikiness” is observed in the MCL data [inset], but the associated voltage jumps are finer, leading to a smoother overall response. Fig. 2: Measured ambient T (AmbTemp) and modeled instrument T (InstrTemp) for a typical McLane pump deployment. Upcast (u) traces are shown in cool colors; downcast (d) in warm colors. Black traces at left: model-derived instrument minus ambient T offsets (∆T), consistent with previous data from instruments that recorded internal T (Gardner et al., 1985; Bishop, 1986). Inset: zoom of depths where hydrowire stops for McLane pump attachment and removal allow partial thermal equilibration of the transmissometer with ambient T (arrow indicates a pronounced example). Noisier upcast T data are due to intermittent upcast issues with the MCL CTD pump. Fig. 3: Left: distributions of down-vs-upcast MCL (top) and GTC (bottom) instrument T offsets in the 100–1000 m depth range after 1-m depth-binning of all offshore deployments. Right: distributions of beam cP offsets in the same depth range before (red) and after (blue) thermal corrections have been applied. Fig. 4: Left: typical relationships between 1m-binned modeled instrument T offsets (x-axes) and beam cP offsets (y-axes) for two MCL deployments. Off-trend points: near-surface depths (brown colors) where variability in the particle field drives true beam cP differences between

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downcast and upcast, which are separated by > 4 hr. Dotted lines and annotations: robust leastsquares slopes, McP,T , consistent within ≈ 25% across deep deployments. Transect-wide median slopes for each instrument were used to correct full time-domain beam cP data. Right: Demonstrative cP profiles from the GTC system before and after thermal correction. Inset: zoom of upper 150 m. Fig. 5: Left upper panels: section of transmissometer beam c P (GTCd) plotted on log scale; right upper panels: section of turbidity on log-scale. Oxygen saturation contours are provided in upper 800m for context, and profile locations and depth are marked in full sections. Color scaling changes in full sections to better show gradients in deep optical responses. Bottom panels: Derivatives versus depth of (left) beam cP MCLd and (right) turbidity GTCd. Red: particle concentrations increase with increasing depth; blue: particle concentrations decrease with increasing depth. A) surface-associated particle maxima at the base of the surface mixed layer often observed between 50-100 m depth; B) local particle maxima at the upper and lower ODZ boundaries (solid 5% contour east of 100˚W); C) a disaggregation-associated particle maximum in the 250-450 m depth range that follows the non-ODZ oxygen minimum west of 100˚W; D) deep lithogenic particle maxima associated with the South American shelf; E) the near-axis EPR hydrothermal plume; and F) increases in particle abundance beginning ≈ 600-750 m above bottom in BNLs at nearly all stations. Fig. 6: Representative responses of the GTC turbidity meter (black), and thermally-corrected GTC and MCL transmissometers (green and red, respectively) near the ocean bottom. Profiles are normalized to near-bottom values to simplify their visual comparison. Actual values at clearwater minima ≈ 600 m above bottom at stations 9 and 25, respectively, were 0.0678 and 0.0539 NTU (turbidity), and 0.0130 and 0.0027 m–1 (transmissometer: MCLd). Fig. 7: Fractional abundance of > 51 µm POM (top panel) and sections of turbidity sensor spike frequency in the upper 800 m (middle panel; oxygen saturation contours shown for context) and within 400 m of the ocean bottom (lower panel; note reduced color axis range). Optical spikes indicative of large particles were frequently observed A) throughout the upper 100 m, in poor correspondance with > 51 µm fractional POM. Optical spikes and large POM fractional abudances more broadly corresponded in the subsurface: B) along O 2 gradients of the 200 – 450 m depth range, C) in the eastern basin and ODZ, and D) near the Marquesas plateau. Near bottom, large particle optical spikes were generally constrained E) within 50-75 m but occasionally > 100 m above bottom in benthic nepheloid layers. Benthic optical spikes were more common east of the EPR and beneath the ODZ. Fig. 8: Mean fractional composition, by weight, of MCL-collected size-fractionated particles in key particle regimes of the transect, demonstrating the wide range of compositions sampled by optical instruments. From left to right: large and small size-fractions (LSF, >51 µm; SSF ≈ 1 - 51 µm) and total particles (TOT = LSF+SSF). Regions defined as follows. Surface: 0-100 m O 2 sat ≥ 95%; Subsurface: > 100 m and < 500 m, O 2 sat. 95 to 5%; ODZ: O2 sat. < 5% (E. basin only); BNLs: within 1000 m of bottom, station-specific increase in optical responses: east of 80˚W BNL-Shelf, west of 80˚W BNL-Deep; Plume: station-specific maxima in particulate Fe and/or Mn between 2000-3000 m depth. Deep: all other datapoints ≥ 500 m. Fig. 9: Responses (y-axes) of the GTC (left) and MCL (center) transmissometers and Seapoint turbidity meter (right) to total (>0.8 µm) size-fractionated SPM concentrations (top row) and POC concentrations (bottom row). Datapoint styles correspond to the particle regimes defined in Fig. 8. Regression coefficients and statistics for single linear regression (SLR) trendlines are also provided in Table 2, SLR.

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Fig. 10: Sections of optical instrument sensitivity to SPM, defined as optical response divided by SPMT OT concentration measured at discrete sampling depths [data markers]. Left: MCLd transmissometer beam cP/SPMT OT . Right: GTCd Seapoint turbidity meter NTU/SPMT OT . In upper panels, 0 – 800 m, oxygen saturation contours are provided to show upper water column oxycline structure and the approximate extent of the eastern ODZ (O 2 sat < 5 %, solid contour). Fig. 11 (and next). A) The Turbidity/cP ratio (GTCd) uses the differential particle responses of two optical instruments to visualize broad changes in particle composition, especially Fe(OH) 3 abundances. Contours of oxygen saturation [%] are provided in upper 800 m to show upper water column oxycline structure and approximate extent of the eastern ODZ (< 5%, solid contour). Color scale changes below 800 m to properly show gradients associated with the hydrothermal (HT) plume. Labels: higher Turb./cP ratios are observed in regions of elevated lithogenic and authigenic phase abundances, especially Fe(OH)3 : in the upper ODZ (Fe(OH)3 ); near the Marquesas plateau, S. Am boundary, and deep trench station (lithogenics); within and beneath the HT plume and in many BNLs (Fe(OH)3 , MnO2 , and lithogenics). B) Particulate Fe > 0.45 µm from the GTC bottle particle dataset (Ohnemus et al. 2016; Fitzsimmons et al. 2017). Fig. 12 (and next). Upper panels: samples sorted by increasing A & B) SPM-normalized instrument sensitivity or C) Turb/cP ratio. A small amount of jitter is added in the y-axis of upper panels to separate data symbols. Lower panels: corresponding fractional major particle composition of the sorted samples, after Hayes et al. (2015).

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Particle maxima in benthic nepheloid layers, surface, hydrothermal, low-oxygen zones

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An optical ratio highlights different particle zones and acts as a crude Fe sensor

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