DOI: 10.5285/c9ed4746-f104-4117-ba60-174c5275e2fe
title: Sea-air methane fluxes measured using an eddy-covariance technique on RRS James Clark Ross from January 2019 to March 2021

description: This dataset contains sea-air methane flux data from January 2019 to March 2021 measured using a Picarro G2311-f greenhouse gas analyser onboard RRS James Clark Ross, in the Southern Ocean, Arctic Ocean and Atlantic Ocean. The fluxes are 2 hour averaged and have been filtered based on wind direction to data corresponding to wind coming from behind the ship to remove sources of pollution from the ship stack. Limit of detection for the flux data are calculated for each cruise by multiplying the standard deviation of the random noise by three.

This work was supported by the Natural Environment Research Council and the ARIES Doctoral Training Partnership (grant no. NE/S007334/1). Royal Holloway, University of London was funded by NERC through grants NE/V000780/1 and NE/N016211/1. Anna E. Jones and Katrin Linse were part of the British Antarctic Survey Polar Science for Planet Earth Programme funded by the Natural Environment Research Council (NERC) [NC-Science]. The measurements from the Royal Research Ship James Clark Ross (JCR) were principally supported by the UK Natural Environment Research Council's ORCHESTRA project (Grant No. NE/N018095/1). The Picarro analyser was funded by the European Space Agency funding (ESA AMT4OceanSatFlux project, Grant No. 4000125730/18/NL/FF/gp). This work further contributes to the NERC MOYA project (Grant No. NE/N015932/1).
description: The eddy-covariance sea-air methane flux data was collected on the RRS James Clark Ross during cruises JR18004, JR18005, JR18006, JR18007, JR19001, JR19002, and during periods 15/11/2020 - 30/11/2020 and 02/12/2020 - 23/03/2021. The data processing procedure is described: The raw high-frequency wind and CH4 data are processed to calculate fluxes in 20 minute intervals. Related statistics are used for quality control. Linear detrending is employed to identify turbulent fluctuations (w ' and c '). Ship motion is corrected by applying a complementary filtering method using Euler angles which eliminates apparent winds caused by ship movements. The motion-corrected winds are decorrelated against ship motion and double-rotated to account for the wind streamline over the ship. The resulting vertical wind velocity (w) is used to calculate flux. The processed wind data is used to compute variables such as friction velocity and sensible heat flux. CH4 data is despiked, decorrelated against analyser cell pressure and temperature, and decorrelated against factors related to ship motion. A lag (of a few seconds) between CH4 and wind data is addressed using a puff system or the maximum covariance method. The inlet tube, particle filter, and dryer introduce attenuation in CH4 flux signals, which is compensated for in the calculations. Horizontal CH4 fluxes and other statistics are computed for quality control. Finally, the computed fluxes undergo filtering to exclude non-ideal ship manoeuvres or violations of the EC technique requirements. Additionally, the CH4 mixing ratio data was filtered based on wind direction to remove data corresponding to wind coming from behind the ship to remove sources of pollution from the ship stack. 20 minute intervals that pass the QA/QC (Quality Assurance/Quality Control) are further averaged to 2 hours to reduce noise.
description: Data was collected using a eddy-covariance system installed on the JCR. The system was located at the top of the foremast (approximately 20 m a.m.s.l). The system comprises of a Picarro G2311-f greenhouse gas analyser, a three-dimensional sonic anemometer (Metek Inc., Sonic-3 Scientific) and a motion sensor (initially Systron Donner Motionpak II, which was replaced by 169 a Life Performance-Research LPMS-RS232AL2 in April 2019). Python 3 is used to analyse the data and create the dataset.
description: QA/QC filters used are: 
Basic CH4 flux filtering: same filtering for wind direction and ship movement as for CO2 flux (Dong et al, 2021), then filter data based on these stats within 20min window: 
CH4(range) &lt; 0.02ppm
CH4(sdev) &lt; 0.0014ppm 
abs(CH4(slope)) &lt; 0.02ppm/hr 
Finally remove flux outside of 5 sdev and average to 2hr intervals (retaining 2hr periods with at least 4 good flux segments).

Missing flux data is due to quality control filtering/instrument not collecting data.