SST Dynamics at Different Scales: Evaluating the Oceanographic Model Resolution Skill to Represent SST Processes in the Southern Ocean

Abstract

In this study we demonstrate the many strengths of scale analysis: we use it to evaluate the Nucleus for European Modelling of the Ocean model skill in representing sea surface temperature (SST) in the Southern Ocean by comparing three model resolutions: 1/12°, 1/4°, and 1°. We show that while 4–5 times resolution scale is sufficient for each model resolution to reproduce the magnitude of satellite Earth Observation (EO) SST spatial variability to within ±10%, the representation of ∼100‐km SST variability patterns is substantially (e.g., ∼50% at 750 km) improved by increasing model resolution from 1° to 1/12°. We also analyzed the dominant scales of the SST model input drivers (short‐wave radiation, air‐sea heat fluxes, wind stress components, wind stress curl, and bathymetry) variability with the purpose of determining the optimal SST model input driver resolution. The SST magnitude of variability is shown to scale with two power law regimes separated by a scaling break at ∼200‐km scale. The analysis of the spatial and temporal scales of dominant SST driver impact helps to interpret this scaling break as a separation between two different dynamical regimes: the (relatively) fast SST dynamics below ∼200 km governed by eddies, fronts, Ekman upwelling, and air‐sea heat exchange, while above ∼200 km the SST variability is dominated by long‐term (seasonal and supraseasonal) modes and the SST geography.

Low complexity model to study scale dependence of phytoplankton dynamics in the tropical Pacific

“…specific interval for the values of the power law exponent has been found for tracers passively advected by a turbulent flow [59,64,65]. In the recent work of [66] it has been shown that the scaling described by Eq.12 is frequently a piece-wise power law with the scaling transition between different power laws corresponding to a transition between different dynamical regimes (see also [60,62]). The power law exponents correspond to the scaling slope of ∆ ρ (we use ∆ ρ notation), which can be analysed by normalizing the ∆ ρ value as…”

Low complexity model to study scale dependence of phytoplankton dynamics in the tropical Pacific

“…In the recent work of [73] it has been shown that the scaling described by Eq.12 is frequently a piece-wise power law with the scaling transition between different power laws corresponding to a transition between different dynamical regimes (see also [66,68]). The power law exponents correspond to the scaling slope of ∆ ρ (we usẽ ∆ ρ notation), which can be analysed by normalizing the ∆ ρ value as∆…”

“…where L is some maximum spatial, or temporal scale of interest [73].∆ ρ can be then used as a simple "probe" to test the impact of dynamical drivers (e.g. eddy and mean advection, turbulent diffusion, biological productivity) on the variable of interest (e.g.…”

“…The increased chlorophyll concentrations then usually imply a higher chlorophyll variability. However, the different scales of eddy impact on the chlorophyll distributions can be estimated from the chlorophyll scaling slopes∆ ρ (Eq.13 and see also [73]), rather than directly from the magnitude of chlorophyll spatial variability (∆ ρ, Eq.12). Eddies should lower variability (steepen the scaling slope) above the characteristic eddy scale (they mix, therefore smooth) and increase variability (flatten the scaling slope) at the range of scales with eddies (due to characteristic eddy patchiness).…”

“…In this short section we will not distinguish between the specific processes driving chlorophyll, but we will demonstrate (Fig. 10) a methodology (developed in [73]) on how to find a relationship between the spatial and the temporal scales for the magnitude of chlorophyll variability. In essence, the relationship is defined by computing the magnitude of temporal variability for a sequence of low pass filtered CMEMS chlorophyll spatial distributions at a range of spatial scales (125 km, 500 km, 2000 km).…”