More Like this

1. Improved Acoustic Tracking of RAFOS-Enabled Profiling Floats through the New Software Package artoa4argo

   https://doi.org/10.1175/JTECH-D-23-0020.1  

   Hancock, Cathrine; Boebel, Olaf (January 2024, Journal of Atmospheric and Oceanic Technology)

   Abstract In sea ice–covered polar oceans, profiling Argo floats are often unable to surface for 9 months or longer, rendering acoustic RAFOS tracking the only method to obtain unambiguous under-ice positions. Tracking RAFOS-enabled floats has historically relied on the ARTOA3 software, which had originally been tailored toward nonprofiling floats in regions featuring the sound fixing and ranging (SOFAR) channel with acoustic ranges of approximately 1000 km. However, in sea ice–covered regions, RAFOS tracking is challenged due to (i) reduced acoustic ranges of RAFOS signals, and (ii) enhanced uncertainties in float and sound source clock offsets. A new software, built on methodologies of previous ARTOA versions, called artoa4argo, has been created to overcome these issues by exploiting additional float satellite fixes, resolving ambiguous float positions when tracking with only two sources and systematically resolving float and sound source clock offsets. To gauge the performance of artoa4argo, 21 RAFOS-enabled profiling floats deployed in the Weddell Sea during 2008–12 were tracked. These have previously been tracked in independent studies with a Kalman smoother and a multiconstraint method. The artoa4argo improves tracking by automating and streamlining methods. Although artoa4argo does not necessarily produce positions for every time step, which the Kalman smoother and multiconstraint methods do, whenever a track location is available, it outperforms both methods. Significance StatementArgo is an international program that collects oceanic data using floats that drift with ocean currents and sample the water column from 2000-m depth to the surface every 7–10 days. Upon surfacing, the float acquires a satellite position and transmits its data via satellite. In polar regions, with extensive seasonal sea ice coverage, floats are unable to surface for many months. Thus, any under-ice samples collected are missing positions, hampering their use in scientific endeavors. Since monitoring of polar regions is imperative to better understand and predict the effects of climate change, hydroacoustic tracking is employed there. Here a new acoustic tracking software, artoa4argo, is introduced, which improves tracking of these floats. 

   more » « less
2. Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix

   https://doi.org/10.1175/JTECH-D-22-0070.1  

   Chamberlain, Paul; Talley, Lynne D.; Mazloff, Matthew; van Sebille, Erik; Gille, Sarah T.; Tucker, Tyler; Scanderbeg, Megan; Robbins, Pelle (September 2023, Journal of Atmospheric and Oceanic Technology)

   Abstract The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE). 

   more » « less
3. Oxygen data from Eastern Tropical North Pacific cruises and floats 2021-2022

   https://doi.org/10.5061/dryad.8kprr4xwk  

   D'Asaro, Eric; Altabet, Mark; McNeil, Craig; Garcia-Robledo, Emilio (January 2024, Dryad)

   Using standard calibration schemes commercial oxygen optode sensors typically yield oxygen concentrations in the range of 2-4 umol/kg under anoxic conditions. They are thus unable to detect the roughly 0.1 umol/kg levels of oceanic functional anoxia. Here, a modified Stern-Volmer equation is used to characterize and calibrate 26 optodes deployed on 16 autonomous floats in the Eastern Tropical Pacific (ETNP) oxygen deficient zone (ODZ) using a combination of manufacturers', laboratory, and in-situ data. Laboratory calibrations lasting several months and conducted over 2 years show that optodes kept under anoxic conditions drift at rates of order 0.2 umol/kg/yr, with much higher drifts in the first month. The initial transient is plausibly due to the degassing of plastic components of the optodes and might be reduced by replacing these with metal. Oxygen concentrations measured by these calibrated optodes in the nearly anoxic ODZ core of the ETNP deviated from both the laboratory calibrations and ship-based STOX measurements by similar amounts. Thus with current sensors, an in-situ anoxic oxygen calibration only once or twice a year is needed to maintain an accuracy close to 0.2 umol/kg. An algorithm to find the anoxic cores of the ETNP ODZ is developed and used to remove the drift in the float optodes to this accuracy. This is an order-of-magnitude improvement in the low oxygen performance of the optodes and could be implemented on the existing database of Argo oxygen floats to map the geography of functional anoxia. This dataset contains the raw float data, the float data calibrated using the manufacturers’ schemes and our new scheme. The calibration points and our final calibration constants, as well as the STOX data used to validate our new calibrations, are included. Data was collected on 10 custom-built profiling 'ODZ' floats equipped with oxygen optodes and gas tension devices and on 6 standard Argo floats with oxygen sensors. Argo data was processed by Argo and recalibrated at APL/UW. ODZ float data was processed at APL/UW as described in the associated manuscript. # Oxygen data from Eastern Tropical North Pacific cruises and floats 2021-2022 [https://doi.org/10.5061/dryad.8kprr4xwk](https://doi.org/10.5061/dryad.8kprr4xwk) ## Description of the data and file structure ## **ODZ Level2.zip** contains scientific data for the ODZ floats converted from raw data using nominal calibrations. Level_2 in NASAspeak. A README, Diagnostic plots, and a Matlab conversion program are included.  The script ***MRVFloatDecode_2023.m*** reads the raw files for the ODZ floats and puts them in a single Matlab file **xo110-.mat** where the first is the float number and the second is the boot number. It makes lots of plots, which I also include. Matlab substructures and variables are: ***CTD*** – Structure containing Seabird 41CT data * P, T, S – pressure [dbar], temperature [deg C], practica salinity as computed by Seabird [psu] * time, mtime – time in Matlab datetime and datenum formats * SA, CT, Sig0 – Absolute salinity [g/kg], conservative temperature [deg C], potential density [kg/m^3] * CC, W, Drag–estimated oil volume [cc], vertical velocity [m/s], Drag force (for ballasting) [N] ***GPS*** – position * time, mtime - time as Matlab datetime, Matlab datnum * lat,lon- location degrees latitude, degrees longitude * nsat, hdop – number of satellites, horizontal dilution of precision ***GTD*** – Gas Tension Sensor * time, mtime - time as Matlab datetime, Matlab datnum, * P, T, S, Sig0 – Pressure [dbar], temperature [deg C], practical salinity [psu], potential density [kg/m^3] * GT – gas tension [mbar] * Tgtd – temperature of GTD [ deg C]  * Ref- time [matlab datenum], temperature [deg C], pressure [mbar] for reference sensor * Other variables are calibration constants and check values. ***SBE5M1,SBE5M2*** - status of pumps. 1 is for optode(1) and GTD. 2 is for reference optod  ***oldGTD*** - One float had an old-style GTD for reference.  ***optode*** - SBE63 optodes (1) is water optode, (2) is reference optode * time, time -time as Matlab datum and date time * SN – optode serial number * red_amp, blue_amp- amplitudes of red and blue LEDs [counts] * red-phase, blue-phase- phases [microvolts] of fluorescence phase. * O2phase- their difference [microvolts] used to compute oxygen * T – optode temperature [deg C] * O2uM – optode’s computed oxygen concentration converted to uMol/kg. * Tctd, S, P, Sig0 – CTD interpolated to optode time - temperature [deg C], practical salinity [psu], pressure [dbar], potential density [kg/m^3] ***ADC, AirPump, AirValve, OilPump,*** ***OilValve*** - structures diagnosing the buoyancy system operations. Scientfically uninteresting. ## ***STOX Oxygen Profiles.zip*** Contains high precision oxygen profiles taken on the two Sally Ride cruises using STOX oxygen sensors. The data is provided as .txt and .mat formats along with miscellaneous data from the CTD. Oxygen measurements from the floats were referenced to STOX oxygen profiles taken from the ship on the two cruises because these provide much more stable and high precision measurements. STOX sensors are described in detail in Revsbech, N. P.; Larsen, L. H.; Gundersen, J.; Dalsgaard, T.; Ulloa, O. and Thamdrup, B. ( 2009) Determination of ultra‐low oxygen concentrations in oxygen minimum zones by the STOX sensor. Limnology and Oceanography: Methods, 7, pp.371-381. DOI:10.4319/lom.2009.7.371. And from their manufacturer [https://unisense.com/products/stox-microsensor/](https://unisense.com/products/stox-microsensor/) STOX data was collected on two cruises of the research vessel, Sally Ride, SR 2114 and SR2011. Data from each CTD cast with a STOX profile is in a separate folder in this archive. In each, the raw data is in a ****.txt*** file and the converted Matlab data is in a ****.mat*** file. MATLAB scripts to read the ****.mat*** file are included in each folder. Data names and units are: Ship Cruise Station Cast Year Month Day Hour Minute * Depth [m] * Latitude [deg]  * Longitude [deg] * Density [sigma-theta,kg/m^3]  * Temperature [ºC]  * Salinity * Beam Attenuation [1/m] * Fluorescence [mg Chla/m3]  * PAR [umol/m2/s] * Oxygen_SBE [µmol/kg]) * Oxygen_STOX [µmol/kg] * STOX_SD [µmol/kg] * STOX_n [µmol/kg] * NO3-Suna [uM] ## **Optode Calibration.zip**  Contains all of the calibration data used to calibrate the optodes including the anoxic laboratory points, the manufacturers' calibration points, and the coefficients of the calibration model for each optode.  **Seabird 63 Optodes** Anoxic calibration data and model fit are in ***AnoxicCalibration/SBE63/2020/*** and ***/2021/***. The 2020 data was used in the final calibration. * Files are *******Tau0model.mat*** where **** is the optode serial number * Variable ***meta*** explains each variable, repeated here. Calibration model is '1./Taup.*exp(-(Etau+Etau2.*(K-283.15).^2)/R/K )*(1+Drift *(days since start) )' Variables are * Taup: 'Phase [uS]' * Etau: 'Energy is Etau+Etau2*(T-10C) [J/mol] * Drift: 'Drift coefficient in the model [1/days] * Ttau: 'Time scale of drift [days] * Drift_uSday: 'Model Drift uS/day' * Dcal: 'Robust Drift. The drift line is Dcal(2)+ Dcal(1)*(Yearday of 2021) in uMol. Drift is Dcal(1) [uMol/day] * Drms: 'RMS drift fit error [uS] * Derr: 'Uncertainty in Dcal; Drift uncertainty is Derr(1) [uMol/day]'  Calibration points from the anoxic tank are in structure ***RawS.*** Variable ***meta*** explains each variable, repeated here. * K: 'Temperature [Kelvin]' * O2phase: 'O2 phase tau [uS]' * R: 'Gas constant [J/K/mol] * dyd: 'Time since start of record [days]' * TIME: 'Time [matlab datetime] * Omodel: 'Tau computed from model with drift [uS] * OmodelND: 'Tau computed from model with drift removed [uS] **Full Calibration/** contains the oxic calibration points and calibration coefficients Calibration points from Seabird supplied with optode are in **SBE63/*FactoryCalibration/ ****_dd_mmm_yyyy.mat ***where **** is the optode serial number. The calibration date follows. Variables are * Caltime - Calibration time [matlab datum] * ID - Serial number * O2in_mll - Oxygen in tank from winklers [ml/L] * O2out_mll - Oxygen computed from Seabird calibration [ml/L] * S - Salinity [psu] * T - Temperature [deg C] * resid_mll - model residual [ml/L] * tau_us - optode phase lag [microseconds] The oxic part of the optode model calibration coefficients are in ***SBE63/Calfiles/*** Calibration model, coefficients, and check values are in ***Calfiles/_oxic_model.mat*** where **** is the optode SN Data is in structure ***Kfile*** ***Kfile.meta*** explains the variables, repeated here. Model is pO2=eta/K(T) * (1 + a(T)*eta^2.3)^q(T) ; eta= tau0(T)/tau-1.  Note that tau0(T) is computed from *******_Tau0model.mat*** coefficients above. Variables are * Check: 'Test values of T, Tau, and pO2 from SBE cal' * Lk: 'K(T)=polyval(Lk, T) - Matlab call to compute K from Lk polynomial coefficients and T [deg C] * La: 'a(T)=polyval(La,T)' * Lq: 'q(T)=polyval(Lq,T)' **Aanderaa 4330 Optodes** **Anoxic calibration** data and model fit is in ***AnoxicCalibration/AA/*** \**                  **File names and formats are the same as for SBE63 optodes **Full Calibration/AA** **/Factory Calibrations** contains the calibration information supplied with the optodes Files are *******_dd-mmm-yyyy.mat*** with the same format as for the SBE63 The relevant variables are: * Caltime - Calibration time [matlab datenum] * ID - Serial number of optode * O2in_uMol - Calibration bath oxygen [uMol/L] * S - Salinity [psu] * T - Temperature from optode [deg C] * tau_deg - optode output phase [degrees] * meta - Misc information **/Calfiles/********_M0_oxic_model.mat** contain oxic part of the optode model calibration coefficients The format is the same as for SBE63, but there is an extra variable * eta_off: Add this to eta to account for drift since calibration [uS] ## **Calibrated Oxygen.zip** contains both uncalibrated and calibrated optode data for both the ODZ and Argo floats. A README file and Matlab processing programs are included. /***SBE63/xo110**-***.mat*** contain the calibrated data for **ODZ float xo110** Format and data is identical to that in the ***optode*** structure in ***ODZ_Level2_Mat,*** but with 2 extra variables * pO2 – partial pressure of oxygen [mbar] in uncalibrated data * Cal – a structure containing calibrated data -- FINAL DATA IS HERE * pO2: partial pressure of oxygen [mbar] in calibrated data  * Tau0m: Calibration model of anoxic phase [microsecond]. Includes offset. * Tau: Measured phase [microsecond] * Tauoff: offset in Tau from in situ calibration [uS] * eta: (Tau0m+Tauoff)/Tau-1 * O2uM: oxygen concentration [micromoles/kg] * O2umol: same Note that optode(1) is the water oxygen. Optode(2) is a reference optode, which is not of scientific interest.  **/SBE63/Reprocess_SBE63.m** is a MATLAB script showing how to combine calibration data and float data to make calibrated data for SBE63 optodes **/AA/Mat/*FloatID*/*FloatID_profilenum*.mat** contains Argo float data from float FloatID, profile number profilenum. Variables are Data from Argos float archive * mtime, time - time in datetime and datenum formats * lat, lon - GPS position latitude degrees and longitude degrees * P, T, S - CTD pressure [dbar], temperature [deg C], salinity [psu] * Optode - Optode serial number * O2T - Optode temperature [deg C] * O2phase - Optode phase [degrees] * O2umol - Optode oxygen [micromole/kg] Added variables * Kfile - Structure as in Optode Calibration files. Kfile.meta also has metadata * Cal - Structure containing calibrated optode data on the same timebase * Tau - measured phase [degrees] * Tau0m - Model anoxic phase [degrees] * Tauoff - Offset from laboratory calibration [degrees]. Includes offset & drift. * Drift - Drift [degrees/year] * mtime0 - base time for drift [matlab datenum format] * eta - Tau0m/Tau-1 * pO2 - Calibrated Oxygen partial pressure [mbar] * O2umol - Calibrated Oxygen concentration [micromole/kg] * meta - similar list to this one. * SN - same as Optode * Float - FloatID **/AA/Mat/*FloatID*/*FloatID_profilenum*.xls** contains the calibrated data in Excel format ***/AA/Reprocess_3_AA.m*** is a MATLAB script showing how to combine calibration data and float data to make calibrated data for AA optodes ## ***ODZ Raw\.zip*** contains the raw data from 9 custom-built ODZ floats. Level_1 in NASAspeak. They can be read by ***MRVFloatDecode_2023.m*** included in ***ODZ Level 2 files*** ## Code/Software Processing and reading scripts in Matlab (24.1.0.2628055 (R2024a) Update 4) are provided. 

   more » « less
4. Net Community Production in the Argentine Basin Estimated From Nitrate Drawdown Using Biogeochemical Argo Floats

   https://doi.org/10.1029/2023JC019858  

   Alkire, Matthew B.; Riser, Stephen (August 2023, Journal of Geophysical Research: Oceans)

   Abstract Net community production (NCP) was estimated from nitrate profiles measured via biogeochemical Argo floats drifting in the Argentine Basin. Two criteria were tested for defining hydrographic fronts used to separate the study area into five zones: potential density anomaly at 450 m and potential temperature at 100 m. The latter definition was preferred as it minimized overlapping among zones. Float profiles within each zone were used to construct monthly median profiles of nitrate. Monthly nitrate inventories were calculated for each zone by integrating the median profiles between the surface and a depth of 100 or 200 m. Three methods were utilized to estimate NCP from the nitrate drawdown. The resulting mean NCP estimates indicated a decline in NCP from 3 to 4 mol C m⁻² yr⁻¹south of ∼40°S to ≤1 mol C m⁻² yr⁻¹north of ∼40°S. The monthly median profiles suggested 20%–100% of drawdown occurred by the end of December; however, chlorophyll fluorescence indicated phytoplankton activity persisted through austral summer. We speculate that primary production during these summer months was supported by regenerated nitrogen sources (not nitrate), despite replete concentrations, likely due to the relative scarcity of bioavailable iron known to persist in the region. While a northward advective flux of nitrate was strongly suggested by meridional nitrate gradients over the upper 0–300 m, vertical mixing was apparently necessary to stimulate new production, indicating both processes are important for NCP in the Argentine Basin. This work highlights the potential for floats in studying biogeochemical cycles in hydrographically complex regions. 

   more » « less
5. Deep Argo Observations of Antarctic Bottom Water in the Deep Fracture Zones of the Southwest Indian Ridge

   https://doi.org/10.1029/2024JC021165  

   Menezes, Viviane_V; Robbins, Pelle; Furey, Heather; Mazloff, Matthew (July 2024, Journal of Geophysical Research: Oceans)

   Abstract The Madagascar Basin is the primary pathway for Antarctic Bottom Water to ventilate the entire western Indian Ocean as part of the Global Overturning Circulation. The only way for this water mass to reach this basin is by crossing the Southwest Indian Ridge through its deep fracture zones. However, due to the scarcity of observations, the Antarctic Bottom Water presence has only been well‐established in the Atlantis II fracture zone. In May 2023, the Deep Madagascar Basin Experiment deployed three Deep SOLO Argo floats in the exit of the fracture zones that were more likely to transport Antarctic Bottom Water: Atlantis II, Novara, and Melville. These floats have been collecting temperature and salinity profiles every 3–5 days with high vertical resolution in the deep ocean. In the present paper, we use the first 7 months of float data to characterize the Antarctic Bottom Water in the deep fracture zone area, revisiting a half‐century puzzle about the Melville contribution. We also collected shipboard‐based profiles to calibrate float salinity and show it is within the Deep Argo program target accuracy. We find Antarctic Bottom Water in both Melville and Novara fracture zones, not only in Atlantis II. This is the first time the Novara contribution has been revealed. The floats also uncover their distinct properties, which may result from the different mixing histories. 

   more » « less