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1. Thermosalinograph ( TSG) data from ships Where does temperature vs. salinity drive density variations? Define the ”Density variability ratio”, 3. Regional–seasonal patterns References & Acknowledgements Callies & Ferrari (2013). Interpreting energy and tracer spectra of upper-ocean turbulence in the submesoscale range (1–200 km). J. Phys. Oceanogr., 43(11), 2456–2474. Callies, J., Ferrari, R., Klymak, J. M., & Gula, J. (2015). Seasonality in submesoscale turbulence. Nature communications. Rudnick and Ferrari (1999). Compensation of horizontal temperature and salinity gradients in the ocean mixed layer. Science, 283(5401), 526–529. Support from NASA is gratefully acknowledged (NNX14AQ54GW). Data sources (thank you!): GOSUD project, http://www.gosud.org/. LEGOS Sea Surface Salinity Observation Service, http://sss.sedoo.fr/. PANGAEA Data Publisher for Earth and Environmental Science, https://pangaea.de/. SAMOS (Shipboard Automated Meterological and Oceanographic System), http://samos.coaps.fsu.edu/html/. Sophie Clayton. NOAA Ship of Oppor- tunity Program, http://www.aoml.noaa.gov/phod/tsg/soop/index.php (QC by Clifford Hoang). Australian Integrated Marine Observing System, https://imos.aodn.org.au/. Global variability in submesoscale density variability from historical thermosalinograph data σ ρ , kg/m 3 Fig. 1: Number of 2-km-spacing TSG datapoints per 3°x3° box Fig. 4: Density variability ratio for regions with strong variability TEMPERATURE DOMINATES DENSITY VARIABILITY SALINITY DOMINATES DENSITY VARIABILITY Fig. 2: TSG data from an example ship transect Temperature Salinity Density Standard deviation calculated over each 100 km segment: σ ρ (Averaged over 3°x3° boxes to produce Fig 3) Fig. 3: Binned σ ρ (computed over 100-km TSG segments) AUSTRALIA Regions with strong currents and mean SST gradients Tropics & high- latitudes (river out- flow & ice melt) Sources: LEGOS, GOSUD, SAMOS, pangaea.de, IMOS, NOAA 75 ships, 10 3 transects, 10 6 total good datapoints (T and S) 2. Submesoscale density variability Strongest in regions with: – strong currents or background gradients – river outflow, ice melt Same spatial patterns if computed over segments 10–100 km long 4. Spectral slopes Fig. 7: Slope of surface density wavenumber spectrum (fit over 20-100 km wavelength) slope Summer ice-melt increases submesoscale density variability σ ρ , kg/m 3 Fig. 5: South of Greenland: ice melt Winter Summer Fig. 6: Eastern tropical Atlantic: river outflows from West Africa April-May-June-July Aug-Sept-Oct-Nov SSS, psu GREENLAND WEST AFRICA Mean sea surface salinity Submesoscale density variability σ ρ , kg/m 3 April-May-June-July Aug-Sept-Oct-Nov Mean sea surface salinity Submesoscale density variability SSS, psu Winter Summer River outflow increases submesoscale density variability Kyla Drushka Applied Physics Laboratory, University of Washington. Liège Colloquium on Submesoscale Processes – 23 -27 May 2016 – Liège, Belgium –5/3 Expect a slope of –5/3 from surface QG theory (Callies & Ferrari, 2013) Globally, we observe: – shallower in the subtropics – steeper in the tropics Fig 5 Fig 6 Fig 2 Expect a seasonal cycle in submesoscale energy (Callies et al., 2015: k –3 in summer, k –2 in winter ) power spectral density inverse wavenumber, km -1 5/3 South of Greenland (i.e., Fig. 5), we observe a shallower slope in summer than in winter – due to energetic small-scale surface variability?
