1. Thermosalinograph ( TSG) data from ships

Where does temperature vs. salinity drive density variations? De�ne 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 Cli�ord Hoang). Australian Integrated Marine Observing System, https://imos.aodn.org.au/.

Global variability in submesoscale density variabilityfrom historical thermosalinograph data

σρ, kg/m3Fig. 1: Number of 2-km-spacing TSG datapoints per 3°x3° box

Fig. 4: Density variability ratio for regions with strong variabilityTEMPERATURE

DOMINATES DENSITY

VARIABILITY

SALINITY DOMINATES

DENSITYVARIABILITY

Fig. 2: TSG data from an example ship transectTemperatureSalinity

Density

Standard deviation calculatedover each 100 km segment: σρ

(Averaged over 3°x3° boxes to produce Fig 3)

Fig. 3: Binned σρ (computed over 100-km TSG segments)

AUSTRALIA

Regions with strong currents and mean

SST gradients

Tropics & high-latitudes (river out-

�ow & ice melt)

Sources: LEGOS, GOSUD, SAMOS, pangaea.de, IMOS, NOAA75 ships, 103 transects, 106 total good datapoints (T and S)

2. Submesoscale density variability

Strongest in regions with: – strong currents or background gradients – river out�ow, ice melt Same spatial patterns if computed over segments 10–100 km long

4. Spectral slopesFig. 7: Slope of surface density wavenumber spectrum

(�t over 20-100 km wavelength)

slope

Summer ice-melt increases submesoscale

density variability

σρ, kg/m3

Fig. 5: South of Greenland: ice melt

Winter Summer

Fig. 6: Eastern tropical Atlantic: river out�ows from West Africa

April-May-June-July Aug-Sept-Oct-Nov

SSS, psu

GREENLAND

WEST AFRICA

Mean sea surface salinity

Submesoscale density variability

σρ, kg/m3

April-May-June-July Aug-Sept-Oct-Nov

Mean sea surface salinity

Submesoscale density variability

SSS, psu

Winter Summer

River out�ow increases submesoscale density

variability

Kyla DrushkaApplied 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 )

pow

er sp

ectr

al d

ensi

ty

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?