Data used in "Storms regulate Southern Ocean summer warming"

The data included in this repository was used to generate the figures in the submitted manuscript "Storms regulate Southern Ocean summer warming" by du Plessis and co-authors.

Abstract: "Sea surface temperature (SST) in the Southern Ocean (SO) is the fingerprint of ocean heat uptake and critical for air-sea interactions. However, SO SST is biased warm in climate models, reflecting our limited understanding of the mechanisms that set its magnitude and variability. An important factor driving SST variability is synoptic-scale weather systems, such as storms, yet their impacts are difficult to directly observe. Using in-situ observations from underwater and surface robotic vehicles in the subpolar SO, we show evidence that storms regulate the summer evolution of SST through altering the mixed layer effective heat capacity and entraining colder water from below. Through these mechanisms, we determine that interannual variations in SO SST reflect changes in storm intensity and prevalence, which, in turn, are driven by the Southern Annular Mode. Our results demonstrate a causal link between storm forcing and lower frequency SST variability, which has implications for addressing SST biases in climate models."

Observations

The observations in this study were made as a part of the SOSCEx-STORM experiment, which fits into the larger observational programme the Southern Ocean Seasonal Cycle Experiment (Swart et al. 2012). SOSCEx-STORM undertook a twinned deployment of a Wave Glider and a profiling Slocum glider which were piloted in conjunction with each other. The platforms were deployed and retrieved from the R/V Agulhas II at 54°S, 0°E, south of the Polar Front, and sampled together between 20 December 2018 and 8 March 2019. 

Slocum glider data
The glider was equipped with a continuously pumped Seabird Slocum Glider CTD, which was processed with the GEOMAR MATLAB toolbox and vertically gridded to 1 m depth intervals. 

Relevant data name: slocum_grid_processed.nc

Slocum glider Microstructure data:
The Webb Teledyne G2 Slocum glider was equipped with a Rockland Scientific Microstructure Profiler (MicroRider). The MicroRider was equipped with two piezo-electric accelerometers and two air-foil shear probes oriented orthogonally. Microstructure data was only collected during the glider climbs to prolong battery life and obtain dissipation estimates as close to the surface as possible. See Nicholson et al. (2022) for details of the MicroRider processing. The mixing layer depth (XLD) was estimated as in Brainnerd and Gregg et al. (1995).

Relevant data name: slocum_eps_ds_processed_era5_23Sep2022.nc
Relevant data name: mixing_layer_xld.csv

Slocum glider SST data: Initial data processing removed temperature data from the upper 2 m during the glider climb phase, and so to obtain an SST value from the Slocum glider temperature profiles, we calculated the median value between 0.5 m and 10 m depth for each dive.  

Relevant data name: slocum_sst_mean_10m_20231110..nc

Wave Glider data
The Liquid Robotics SV3 Wave Glider was fitted with an Airmar WX-200 Ultrasonic Weather Station mounted on a mast at 0.7 m above sea level, providing wind speed measurements at a rate of 1 Hz, averaged into 1-hour bins. The wind measurements were corrected to a height of 10 m above sea level. Note that the Airmar WX-200 weather station of the Wave Glider was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data provided by ECMWF available at https://doi.org/10.24381/cds.bd0915c6. 

Relevant data name: WG_era5_1h_processed_28Aug2022.nc

Storm tracking dataset
To track storm trajectories, we used storm tracks contained in monthly files for the Southern Ocean identified and used in the JGR-Oceans publication:

Lodise, J., Merrifield, S. T., Collins, C., Rogowski, P., Behrens, & J., Terrill,E, (In Review). Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights from Satellite Radar Altimeter. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2022JC018925

Data can be accessed at https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker 

All Southern Ocean storm locations can be found at: ec_centers_1981_2020.nc

EN4 mixed layer depths
We use the EN4 database of quality controlled temperature and salinity profiles from 2004 to 2022 to produce our MLD for the interannual analysis (Good et al. 2013). We use the profiles that contain the Cheng et al. (2014) XBT corrections and Gouretski and Cheng (2020) MBT corrections. We limit the data intake to 2004 as this marks the beginning of the Argo period. All under-ice profiles are removed. We calculate the MLD for each individual profile using the density threshold of de Boyer Montegut et al. (2004) where the density value first exceeds the 10 m reference value by 0.03 kg m-3. We then determine the median MLD value for each month within 2 x 2 degree grid cells, then obtain a mean value for each DJF season per 2 x 2 degree grid cell. 

Relevant data name: en4_monthly_mixed_layer_depth_median.nc

Southern Ocean Fronts
Position of the Subantarctic Front and Polar Front are from: