A comparison of stochastic and deterministic downscaling in eddy resolving ocean modelling: the Lakshadweep Sea case study

TL;DR

This study compares stochastic and deterministic downscaling models in ocean modeling, showing that the stochastic approach slightly outperforms the deterministic one in accuracy and is more computationally efficient, especially in simulating non-linear processes.

Contribution

It introduces and evaluates a stochastic-deterministic downscaling method for high-resolution ocean modeling, demonstrating its advantages over traditional primitive equation models.

Findings

SDD model performs slightly better than NEMO in temperature and salinity.

SDD is significantly more computationally efficient.

Both models effectively reproduce key ocean features.

Abstract

This study compares the skills of two numerical models having the same horizontal resolution but based on different principles in representing meso- and submesoscale features of ocean dynamics in the Lakshadweep Sea (North Indian Ocean). The first model, titled LD20-NEMO, is based on solving primitive equations using the NEMO (Nucleus for European Modelling of the Ocean) modelling engine. The second one, titled LD20-SDD, uses a newer Stochastic-Deterministic Downscaling method. Both models have 1/20o resolution and use the outputs from a Global Ocean Physics Analysis and Forecast model at 1/12o resolution available from Copernicus Marine Service (CMEMS). The LD20-NEMO uses only a 2D set of data from CMEMS as lateral boundary conditions. The LD20-SDD consumes the full 3D set of data from CMEMS and exploits the stochastic properties of these data to generate the downscaled field variables at higher resolution than the parent model. The skills of the three models, CMEMS, LD20-NEMO and LD20-SDD are assessed against remotely sensed and in-situ observations for the four-year period 2015-2018. All models show similar skills in reproducing temperature and salinity, however the SDD version performs slightly better than the NEMO version. This difference in resolution is particularly significant in simulation of vorticity and computation of the share of the sea occupied by highly non-linear processes. While the NEMO and SDD model show similar skill, the SDD model is more computationally efficient than the NEMO model by a large margin.