CHARACTERIZATION OF OCEAN SUBMESOSCALE TURBULENCE REGIMES FROM SATELLITE OBSERVATIONS OF SEA SURFACE TEMPERATURES

Transcription

1 Submesoscale Processes: Mechanisms, Implications and new Frontiers CHARACTERIZATION OF OCEAN SUBMESOSCALE TURBULENCE REGIMES FROM SATELLITE OBSERVATIONS OF SEA SURFACE TEMPERATURES J. Isern-Fontanet, A. Turiel, E. Olmedo Institut de Ciències del Mar (CSIC), Barcelona, Catalonia University of Liège, Liège (Belgium)

2 OBSERVED REGIMES BT, not SST (Isern-Fontanet & Hascoët JGR 2014)

3 SST PATTERNS Under certain circumstances SST exhibit convoluted patterns along fronts that can crowd relatively large areas of the ocean Mixed Layer Instabilities (MLIs) have been identified as a possible mechanism (e.g. Boccaletti et al. JPO 2007) Ubiquitous: Argentinian shelf (Capet et al. GRL 2008), Mediterranean, South Atlantic, Seasonal variability (e.g. Capet et al. GRL 2008, Callies et al. Nature 2015) Relevant for climate models (Fox-Kemper et al. JPO 2008, OM 2011)

4 OBJECTIVES Objective 1 (not this talk): characterize the spatiotemporal variability of these patterns at global scale Objective 2 (this talk): find a metric able to quantify the presence of submesoscale instabilities in a satellite image (or model snapshot)

5 WHY (INFRARED) SST? High resolution instruments Typically O(1 km) Global coverage Limited by clouds Many dedicated instruments short revisit time O(1h) Long time-series are available AVHRR (80s - now) (A)ATSR/SLSTR (90s - now)

6 DATA 512 pixels 512 pixels 512 km 512 km P. Le Borgne (Pers. Comm) SST (not BT): AATSR (Envisat), Ionian basin (Mediterranean Sea): favorable cloud cover, MLI Focus on an initial set of images, satellite pass: 457 & 915 However, horrible masks!

7 CLASSIFICATION OF TURBULENT REGIMES Here, we have classified SST images into two regimes: Local: dominated by submesoscale instabilities O(1-10 km) Non-local: dominated by mesoscale vortices and filaments

8 SPECTRAL ANALYSIS Spectral slopes do not allow to identify different regimes Well-known result 80% cloud-free pixels Additional difficulties Bad cloud mask k -2

9 THE IMPORTANCE OF THE PHASE Leonardo da Vinci ( ) captured the nature of turbulent flows by drawing only its phase. Armi & Flamand (JGR 1985): spectra do not reflect the complexity of the surface patterns produced by oceanic flows that is generally contained into the phase of Fourier transforms.

10 SINGULARITY EXPONENTS Mathematically, the Da Vinci approach can be implemented through singularity analysis The behavior of SST,, around any point is described by a local power law The exponent is called the singularity exponent. Local degree of singularity or regularity around the point provides information about the location and intensity of fronts

11 APPLICATION TO SST We use the algorithm developed by Pont et al. (IJCM 2013) to compute for each pixel This method introduces a -1 shift in the value of Robust numerical implementation

12 THE SINGULARITY SPECTRUM The singularity spectrum D(h) describes the fractal dimension d F of a subset of points that have the same Computed from the PDF of singularity exponents It gives the volume occupied by fronts with intensity given by h Change of view to fractal dimension vs. front intensity

13 THE OBSERVED SINGULARITY SPECTRUM The observed singularity spectra are always asymmetric to respect h max. Fractal dimension Observations Parabolic model stronger gradients (smaller h) weaker gradients (larger h)

14 PHENOMENOLOGY (I) 2009/11/ /06/11 The presence of convoluted fronts generates wider singularity spectra We use the amplitude of D(h) to classify the observed regimes

15 PHENOMENOLOGY (II) 2009/11/ /08/ /06/11 The amplitude changes continuously Sensititive to weak fronts: asymmetric growth

16 SPECTRAL SLOPES VS. SINGULARITY SPECTRUM D(h) of images dominated by MLI tend to envelope those of images dominated by mesoscale features Some exceptions: wrong/ambiguous visual classification Spectral slopes does not distinguish between them 2008/08/ /11/ /06/11

17 IMPACT OF CLOUD COVERAGE AND NOISE Care must be taken when comparing from different sensors Noise tends to decrease More robust results are obtained reducing resolution (by 2) Large data gaps due to cloud cover generates unphysical D(h). More difficult to visually classify images 2009/11/ /08/ /06/11 Cloud coverage can be used as a quality index

18 INTERMITTENCY The amplitude of D(h) is a measure of the intermittency of the flow Observations point to an increase of intermittency with the development of instabilities The scaling exponents of the structure functions can be derived from D(h) No additional information is provided

19 SUMMARY AND CONCLUSIONS Singularity spectra provides a complementary view: fractal dimension vs. front intensity The presence of MLI continuously widens the singularity spectra, which implies an increase of the intermittency of the flow Preliminary results suggest that the amplitude of the singularity spectra can be used to quantify the presence of MLI Current work: Analyze a larger dataset (and confirm the results) Challenge: develop a local criterion (long-term)

20 Thank you