Regional oceanic and biogeochemical modeling strategy :forced or coupled model ?

<p><span> Regional high resolution biogeochemical modeling studies generaly use an oceanic model forced by prescribed atmospheric conditions. The computational cost of such approach is far lower than using an high resolution ocean-atmosphere coupled model. However, forced oceanic models cannot represent adequately the atmospheric reponse to the oceanic mesoscale (~10-100km) structures and the impact on the oceanic dynamics.</span></p><p><span>To assess the bias introduce by the use of a forced model, we compare here a regional high resolution (1/12º) ocean-atmosphere coupled model with oceanic simulations forced by the outputs of the coupled simulation. Several classical forcing strategies are compared : bulk formulae, prescribed stress, prescribed heat fluxes with or without Sea Surface Temperature (SST) restoring term, .... We study the Chile Eastern Boundary Upwelling System, and the oceanic model includes a biogeochemical component,</span></p><p><span>The coupled model oceanic mesoscale impacts the atmosphere through surface current and SST anomalies. Surface currents mainly affect the wind stress while SST impacts both the wind stress and the heat fluxes. In the forced simulations, mesoscale structures generated by the model internal variability does not correspond to those of the coupled simulation. According to the forcing strategy, the atmospheric conditions are not modified by the forced model mesoscale, or the modifications are not realistic. The regional dynamics (coastal upwelling, mesoscale activity, …) is affected, with impact on the biogeochemical activity.</span></p><p> </p><p> </p><p><em>This work was supported by the FONDECYT project 3180472 (Chile), with computational support of the NLHPC from the Universidad de Chile, the HPC from the Pontificia Universidad Catolica de Valparaiso and the Irene HPC from the GENCI at the CEA (France).</em></p>

Roles of vertical distributions of atmospheric transient eddy dynamical forcing and diabatic heating in midlatitude unstable air-sea interaction

Atmospheric transient eddy dynamical forcing (TEDF)-driven midlatitude unstable air-sea interaction has recently been recognized as a crucial positive feedback for the maintenance of the extratropical decadal variabilities. Our previous theoretical work by Chen et al. (2020) characterizes such an interaction with building an analytical midlatitude barotropic atmospheric model coupled to a simplified upper oceanic model. This study firstly extends the analytical model to a two-layer quasi-geostrophic baroclinic atmospheric model coupled to a simplified upper oceanic model and then identifies the roles of vertical distributions of atmospheric TEDF and diabatic heating in midlatitude unstable air-sea interaction. It is found that the midlatitude air-sea coupling through atmospheric TEDF and diabatic heating with more realistic vertical profile destabilizes the oceanic Rossby wave mode over the entire range of zonal wavelengths, and the most unstable mode exhibits an equivalent barotropic structure with geopotential lows (highs) over cold (warm) water. The spatial configuration structure and period of the most unstable coupled mode are more consistent with the observation than those from the previous model. Although either TEDF or diabatic heating alone can lead to unstable air-sea interaction, the former is dominant to the instability. TEDF in both higher and lower layers can cause unstable coupled mode individually, while the lower-layer forcing stimulates instability more effectively. Surface diabatic heating always destabilizes the coupled mode, while the mid-level heating always decays the coupled mode. Moreover, the influences of oceanic adjustment processes, air-sea coupling strength and background zonal wind on the unstable coupled mode are also discussed. The results of this study further prove the TEDF-driven positive feedback mechanism in midlatitude air-sea interaction proposed by recent observational and numerical experiment studies.

Iterative Schwarz Method to Couple Ocean and Atmosphere in a Global Climate Model

<p>For historical and practical reasons, present-day coupling algorithms implemented in ocean-atmosphere models are primarily driven by the necessity to conserve energy and water at the air-sea interface. However the asynchronous coupling algorithms currently used in ocean-atmosphere do not allow for a correct phasing between the ocean and the atmosphere.</p><p>In an asynchronous coupling algorithm, the total simulation time is split into smaller time intervals (a.k.a. coupling periods) over which averaged-in-time<br>boundary data are exchanged. For a particular coupling period, the average atmospheric fluxes are computed in the atmospheric model using the oceanic surface properties computed and averaged by the oceanic model over the previous coupling period. Therefore, for a given coupling period, the fluxes used by the oceanic model are not coherent with the oceanic surface properties considered by the atmospheric model. The mathematical consistency of the solution at the interface is not guaranteed.</p><p>The use of an iterative coupling algorithm, such as Schwarz methods, is a way to correct this inconsistency and to properly reproduce the diurnal cycle when the coupling period is less than one day. In Lemarié et al. (2014), preliminary numerical experiments using the Schwarz coupling method for the simulation of a tropical cyclone with a regional coupled model were carried out. In ensemble simulations, the Schwarz iterative coupling method leads to a significantly reduced spread in the ensemble results (in terms of cyclone trajectory and intensity), thus suggesting that a source of error is removed with respect to the asynchronous coupling case.</p><p>In the present work, the Schwarz iterative method is implemented in IPSLCM6, a state-of-the-art global ocean-atmosphere coupled model used to study past, present and future climates. We analyse the convergence speed and the quality of the convergence. A partial iterative method is also tested: in a first phase, only the atmosphere physics and the vertical diffusion terms are computed, until the convergence. This provide a first guess for the full model which is then iterated until convergence of the whole system. The impact on the diurnal cycle will also be presented.</p>

The Current Feedback to the Atmosphere: Implications for the Ocean Dynamics, Air-Sea Interactions, and Climate.

<div> <div> <div> <p>In the past few years, it has been demonstrated that the regional Ocean-Atmosphere interactions can strongly modulate the variability and the mean physical and biogeochemical state of the ocean. In this presentation, the focus will be on the influence of the surface current on the atmosphere (i.e., current feedback). Based on satellite observations and using a set of regional ocean and atmosphere coupled simulations carried out over different regions encompassing a realistic Tropical Channel, and Eastern and Western boundary current systems, we will illustrate to which extent those interactions can control the exchange of energy between the Ocean and the Atmosphere, the mean, mesoscale, and submesoscale circulations, and the Western Boundary Currents Dynamics. Implications for climate, thermal air-sea interactions and how to force an oceanic model is furthermore discussed.</p> </div> </div> </div>

Atmospherically Forced Regional Ocean Simulations of the South China Sea: Scale Dependency of the Signal-to-Noise Ratio

When subjecting ocean models to atmospheric forcing, the models exhibits two types of variability—a response to the external forcing (hereafter referred to as signal) and inherently generated (internal, intrinsic, unprovoked, chaotic) variations (hereafter referred to as noise). Based on an ensemble of simulations with an identical atmospherically forced oceanic model that differ only in the initial conditions at different times, the signal-to-noise ratio of the atmospherically forced oceanic model is determined. In the large scales, the variability of the model output is mainly induced by the external forcing and the proportion of the internal variability is small, so the signal-to-noise ratio is large. For smaller scales, the influence of the external forcing weakens and the influence of the internal variability strengthens, so the signal-to-noise ratio becomes less and less. Thus, the external forcing is dominant for large scales, while most of the variability is internally generated for small scales.

Chlorophyll distribution by oceanic model and satellite data in the Bay of Bengal and Andaman Sea

An oceanic model and satellite data are used to evaluate the seasonal distribution of chlorophyll-a (Chl-a) in the Bay of Bengal (BoB) and Andaman Sea.Satellite data show high Chl-a concentrations because high Chl-a concentrations reduce CO2 and increase O2 at the sea surface, indicating fish abundance in the ocean. Sample collection alone cannot provide an accurate overview of Chl-a concentration over an entire region.The satellite data concerning Chl-a concentration, phytoplankton absorption coefficient, and Sea Surface Temperature (SST) are from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) project and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The oceanic model is created to give the surface circulation as a result. The research finds that the simulation is in agreement with SST, Chl-a concentration, and phytoplankton absorption coefficients obtained from satellites. The conclusion is that the oceanic model can be used to implicitly explain the seasonal distribution of Chl-a in the Bay of Bengal and Andaman sea.