Evaluation of an operational ocean model configuration at 1/12° spatial resolution for the Indonesian seas (NEMO2.3/INDO12) – Part 2: Biogeochemistry

Abstract

Abstract. In the framework of the INDESO (Infrastructure Development of Space Oceanography) project, an operational ocean forecasting system was developed to monitor the state of the Indonesian seas in terms of circulation, biogeochemistry and fisheries. This forecasting system combines a suite of numerical models connecting physical and biogeochemical variables to population dynamics of large marine predators (tunas). The physical–biogeochemical coupled component (the INDO12BIO configuration) covers a large region extending from the western Pacific Ocean to the eastern Indian Ocean at 1/12° horizontal resolution. The NEMO-OPA (Nucleus for European Model of the Ocean) physical ocean model and the PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies) biogeochemical model are running simultaneously ("online" coupling), at the same resolution. The operational global ocean forecasting system (1/4°) operated by Mercator Océan provides the physical forcing, while climatological open boundary conditions are prescribed for the biogeochemistry. This paper describes the skill assessment of the INDO12BIO configuration. Model skill is assessed by evaluating a reference hindcast simulation covering the last 8 years (2007–2014). Model results are compared to satellite, climatological and in situ observations. Diagnostics are performed on nutrients, oxygen, chlorophyll a, net primary production and mesozooplankton. The model reproduces large-scale distributions of nutrients, oxygen, chlorophyll a, net primary production and mesozooplankton biomasses. Modelled vertical distributions of nutrients and oxygen are comparable to in situ data sets although gradients are slightly smoothed. The model simulates realistic biogeochemical characteristics of North Pacific tropical waters entering in the archipelago. Hydrodynamic transformation of water masses across the Indonesian archipelago allows for conserving nitrate and oxygen vertical distribution close to observations, in the Banda Sea and at the exit of the archipelago. While the model overestimates the mean surface chlorophyll a, the seasonal cycle is in phase with satellite estimations, with higher chlorophyll a concentrations in the southern part of the archipelago during the SE monsoon and in the northern part during the NW monsoon. The time series of chlorophyll a anomalies suggests that meteorological and ocean physical processes that drive the interannual variability of biogeochemical properties in the Indonesian region are reproduced by the model.

Tidal Variability of Chl-a in the Indonesian Seas

“…Experiments to improve the estimates were conducted using spatially-coupled harmonic analysis (not shown); however, results were found to be overly sensitive to details of the spatial basis functions used. Ocean models with the capability of simulating the tidal-biological interactions are now being implemented (e.g., Gutknecht et al 2016 andCapuano et al 2022), and it may be fruitful to use the surface Chl-a fields from these models as spatial basis functions within a spatially-coupled harmonic analysis, as was recently done for sea surface-height data (Egbert and Erofeeva, 2021). While imperfect, such an approach may provide the capability to utilize the gappy data more effectively to map tidally-phase-locked components of the Chl-a fields.…”

The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis

“…As coupled oceanatmosphere-ice simulations progressed, the capability to produce meaningful seasonal forecasts was demonstrated. It then became important to have a comprehensive global physical ocean state, including sea ice, to initialize seasonal forecasts (MacLachlan et al, 2015), to provide boundary conditions for regional models having higher resolution and smaller-scale physical processes (Tranchant et al, 2016), and to force biogeochemical models (Gutknecht et al, 2016).…”

Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation

“…QUICKEST (Leonard, 1979), but using the limiter of Zalesak (1979). These options have been tested by Gutknecht et al (2016) and are now used in the IBI-MFC operational system (http://marine.copernicus.eu/).…”