HypOnFjordFish & GFI student cruise (GEOF232 & GEOF337) 2021 February/March

 

Data for calibration

To calibrate the oxygen data we collected water samples for winkler titration at different depths and compared these with upcast oxygen values from the CTD (recorded as the niskin bottle was fired).

Outlier removal procedure

Datapoints flagged as bad were removed prior to data analysis (bad data included sampling errors, values below the threshold of 10 umol/kg, negative winkler results and duplicate winkler samples with a difference larger than 3 umol/kg.).

We also excluded datapoints shallower than 45 m.

After removal of flagged data and data above 45 m, we removed additional outliers using an iterative outlier removal pocedure with 2.5 x RMSE as the outlier-threshold. If the point’s residual value (value of the difference between the regression line and the winkler value) was outside this limit, it was considered an outlier. After removal of outliers, a new regression line and 2.5 x RMSE threshold was calculated for the new dataset and the whole procedure was repeated until no more datapoints were considered outliers or until the regression had RMSE < 2.

Calibration method

We plotted CTD niskin bottle oxygen against winkler samples and calculated the linear regression line. Calibrated values were obtained by inserting raw CTD umol/kg as x in the linear regression equation (calibration equation).

Oxygen sample overview

Oxygen samples were collected by Martine R. Solås (HypOnFjordFish cruise) or students (GFI cruise). Winkler titrations were done by Kristin Jackson-Misje (HypOnFjordFish cruise) or by students (GFI cruise).

We collected at total of 182 winkler samples. Of these, 10 was/were flagged as bad during the initial collection or because uncalibrated values were below the threshold of 10 umol/kg.

20 samples were part of a duplicate or triplicate sampling (i.e. a total of 10 niskin bottle(s) had been sampled more than once). 2 these duplicates/triplicates (i.e. 4 individual winkler samples) were furthermore flagged as bad because the between-samples difference was more than 3 umol/kg. The remaining duplicates/triplicates were averaged before analysis so that we only had one winkler value per niskin bottle per station.

After averaging of duplicates/triplicates and removal of flagged data, we were left with a total of 160 unique winkler samples.

40 of these samples were collected shallower than 45 m and consequently removed before further analysis.

The plots below give an overview of the offset between CTD oxygen and winkler and whether there are clear systematic errors when the offset is plotted against dissolved oxygen concentration, niksin bottle, station number or depth/pressure.

Before we created our calibration curve, we removed 8 datapoint(s) (7 % of remaining points) through 4 round(s) of iterative outlier removal using 2.5 x RMSE as the outlier threshold.

This lead to a total of 112 datapoints for the final linear regression (calibration).

 

Calibration

To calibrate the raw CTD data we inserted raw umol/kg as x in the linear regression equation:

y = 5.08 + 1.0035 x

 

We can check for strong systematic errors in the calibration by plotting the calibration-winkler-offset across dissolved oxygen concentrations, depth, stations and niskin bottle.

Plots with a blue frame are plots where the y axis limits have been set to -10 to 10 for better inspection (and comparison with other cruises) of the trend visualized with the linear regression line in blue. Only data labelled “bad” have been excluded from this trend line (meaning that data above 45 m and those points that were removed in the iterative outlier removal process are included).

 

Profiles of raw and corrected data

Finally we plot the CTD stations with collected winkler data to compare the corrected data with raw data and winkler results. Points represent the niskin bottle values (uncalibrated and calibrated) and corresponding winkler data while the graphs represent the downcast CTD (uncalibrated and calibrated). Downcast values shown here have been smoothed through a rolling mean filter with window size 3.

Conversion of raw ml/l to corrected ml/l

We also created a calibration equation for the ml/l unit directly by converting corrected umol/kg to ml/l and creating a regression between corrected ml/l and raw ml/l:

y = 0.1169 + 1.003464 x