The Western INDian Ocean Simulation (WINDS) is a regional configuration of the Coastal and Regional Ocean Community Model (CROCO) for the southwestern Indian Ocean. WINDS has a horizontal resolution of
The western Indian Ocean is a relatively data-sparse region. Surface current data are required to simulate the dispersion of buoyant particles such as marine debris or coral larvae
The entire WINDS domain, with contours representing the bathymetry used in WINDS. Circles represent the 12 rivers included in WINDS, scaled by the total annual discharge
We ran WINDS using version 1.1 of the Coastal and Regional Ocean Community Model
We built the model grid using
CROCO uses a terrain-following (
We use GEBCO 2019
WINDS is forced at the surface through a bulk formulation based on ERA-5
Surface forcing is parameterised using a bulk formulation based on the ERA-5 global atmosphere reanalysis Surface air temperature ( Sea-surface temperature ( Sea-level pressure ( 10 m wind speed ( Surface wind stress ( Specific humidity ( Relative humidity ( Precipitation rate ( Shortwave radiation flux ( Longwave radiation flux ( Downwelling longwave radiation flux (
Unit conversions are required for most of these quantities to put them into the form used by CROCO. Since ERA-5 is computed on a different (coarser) grid to WINDS, there is a land–sea mask mismatch between ERA-5 and WINDS. To avoid terrestrial values erroneously being applied to ocean cells in WINDS, we masked out land values from ERA-5 using the ERA-5 land–sea mask and carried out a nearest neighbour interpolation over the small number of coastal WINDS cells that are counted as land cells in ERA-5.
WINDS is forced at the lateral boundaries with the
WINDS is forced at the lateral boundaries with 10 tidal constituents (barotropic tidal currents and surface height) from the TPXO9-atlas
We have simplistically included 12 major rivers in WINDS: the Zambezi, Rufiji, Tsiribihina, Mangoky, Ikopa, Betsiboka, Tana, Mahavavy Nord, Sambirano, Manambolo, Mananjary, and Ruvu rivers. We assume that water in the river-mouth area has a constant temperature of 25
We have made three sets of output available from WINDS Output frequency of 30 min
Zonal surface velocity ( Meridional surface velocity ( Output frequency of 1 d
Sea-surface temperature ( Sea-surface salinity ( Free-surface height ( Depth-averaged zonal velocity ( Depth-averaged meridional velocity ( Kinematic wind stress ( Surface zonal momentum stress ( Surface meridional momentum stress ( Surface freshwater flux, E-P ( Surface net heat flux ( Net shortwave radiation at surface ( Net longwave radiation at surface ( Latent heat flux at surface ( Sensible heat flux at surface ( Output frequency of 5 d
Zonal velocity ( Meridional velocity ( Temperature ( Salinity (
We did not output the vertical velocity. This can in principle be reconstructed at a 5 d frequency using the ocean depth, free-surface height, and zonal and meridional velocities.
The following validation relates to WINDS surface properties only, as relevant for marine dispersal, since this was the primary use case WINDS-M and WINDS-C were run for. WINDS may, of course, be used for other purposes as well, but for these applications the model is provided
We extracted the five largest tidal constituents (
Agreement between WINDS and TPXO9 is generally good, with tidal amplitude mismatch on the order of a few centimetres for almost all sites (well within the error associated with the TPXO9-atlas itself). A few regions associated with greater WINDS-TPXO9 disagreement include (1) the Sofala Bank (Mozambique) and (2) the mainland-facing sides of Mafia and Zanzibar islands (Tanzania). Both are shelf regions with extensive shallow water and, in the case of Tanzania, complex effects from nearby islands. The roughness length scale used in the bottom friction parameterisation in WINDS is constant, and the true ocean depth at these locations is occasionally shallower than the minimum depth used in WINDS, so it is possible that a combination of these two factors could explain the poorer tidal performance of WINDS in some shelf seas.
We have also carried out a comparison of WINDS tidal predictions with selected in situ tidal gauges spanning the longitudinal and latitudinal range of WINDS, at Mombasa (Kenya), Aldabra (Outer Islands, Seychelles), Mahé (Inner Islands, Seychelles), Diego Garcia (Chagos Archipelago), and Mauritius and Rodrigues (Mauritius) (Table
Observational sources:
Monthly climatological surface currents (1993–2020) from WINDS (left), Copernicus GlobCurrent Surface (centre), and Global Drifter Program-derived near-surface currents (right) for January to April.
Monthly climatological surface currents (1993–2020) from WINDS (left), Copernicus GlobCurrent Surface (centre), and Global Drifter Program-derived near-surface currents (right) for May to August.
Monthly climatological surface currents (1993–2020) from WINDS (left), Copernicus GlobCurrent Surface (centre), and Global Drifter Program-derived near-surface currents (right) for September to December.
Figures
To assess the ability of WINDS to reproduce surface current variability associated with eddies, Fig.
Eddy kinetic energy (EKE) from WINDS (top) and Copernicus GlobCurrent (bottom). EKE was computed by passing daily mean surface velocity through a high-pass filter with a cutoff period of 30 d, thereby removing high-frequency variability associated with tides, and low-frequency variability associated with time mean currents and the seasonal cycle. Circles represent the EKE at 10/12 m depth from the RAMA array. EKE is also plotted as a monthly climatology in Figs. S1–S3 and MKE (annual mean and monthly climatological) in Figs. S4–S7.
Variability of sea-surface height from 1993–2020 from WINDS
Monthly mean surface currents averaged across 10 key regions (see Fig. S8 for geographical reference) for WINDS (black, with grey shading representing the monthly range), CMEMS GLORYS12V1 (red), and GlobCurrent (blue).
The monthly mean surface current speed in WINDS associated with major surface currents in the southwestern Indian Ocean is shown is Fig.
Colour: fraction of virtual drifters advected with half-hourly WINDS-M surface currents that pass through each 0.5
Coloured by instantaneous speed, 25 virtual drifter trajectories released from analogous coral reef cells at the southern tip of Zanzibar (Tanzania) on 1 July 2019 in WINDS-M (left) and GLORYS12V1 (right). Land cells for both models are shaded in dark grey. Red cells are coral reefs
As WINDS was designed for the primary purpose of simulating marine dispersal (for instance, for coral larvae), it is important to test whether WINDS can reproduce observed pathways of surface drift in the ocean. Although Global Drifter Program (GDP) deployments are low in the southwestern Indian Ocean
Difference between monthly climatological SST simulated by WINDS and satellite and in situ-derived sea-surface temperature (SST) estimates from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Blues indicate that WINDS simulates cooler temperatures and reds indicate that WINDS is warmer.
Long-distance dispersal patterns predicted by WINDS are similar to those predicted by the CMEMS GLORYS12V1
We have validated WINDS SST and SSS predictions by comparing monthly climatological SST and SSS from WINDS-M to monthly climatological SST from OSTIA
WINDS, and specifically the realistic WINDS-M experiment, reproduces surface circulation well in the southwestern Indian Ocean. Although surface current variability may be overestimated by WINDS in certain regions, such as within 5
The full dataset (WINDS-C and WINDS-M), as summarised in Sect.
We have also provided the CROCO configuration files that were used to run WINDS and the model grid and forcing files used by WINDS-C (the forcing files used by WINDS-M were too large to store permanently, but are described in Sect.
CROCO V1.1 is available to download at
Supplementary video 1 (
The supplement related to this article is available online at:
NSVV: conceptualisation, methodology, software, validation, writing (original draft), visualisation, and funding acquisition. HLJ: conceptualisation, methodology, resources, supervision, and funding acquisition.
The contact author has declared that neither of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work used the ARCHER2 UK National Supercomputing
Service (
This research has been supported by the Natural Environment Research Council (grant no. NE/S007474/1).
This paper was edited by Riccardo Farneti and reviewed by two anonymous referees.