A statistical interpolation code for ocean analysis and forecasting

2021 ◽  
Author(s):  
T. M. Chin ◽  
E. P. Chassignet ◽  
M. Iskandarani ◽  
N. Groves
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
Markov Random Fields ◽  
Information Matrix ◽  
Ocean Model ◽  
Global Ocean ◽  
Error Statistics ◽  
Data Format ◽  
Covariance Models

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse - the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov Random Fields are provided and its relationship to finite difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in-situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4°ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multi-core architectures and uses Climate and Forecasts compliant Network Common Data Format for Input/Output. The package is freely available with an open source license from www.tendral.com/tsis/

Evaluating Effects of Observational Data Assimilation in General Ocean Circulation Model by Ensemble Kalman Filtering: Numerical Experiments with Synthetic Observations

2021 ◽  
pp. 50-66
Author(s):  
V. N. Stepanov ◽  
◽  
Yu. D. Resnyanskii ◽  
B. S. Strukov ◽  
A. A. Zelen’ko ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Observational Data ◽  
Ocean Circulation ◽  
Numerical Experiments ◽  
Turbulent Viscosity ◽  
Bottom Topography ◽  
Synthetic Data ◽  
Circulation Model ◽  
Ocean Model ◽  
Global Ocean

The quality of simulation of model fields is analyzed depending on the assimilation of various types of data using the PDAF software product assimilating synthetic data into the NEMO global ocean model. Several numerical experiments are performed to simulate the ocean–sea ice system. Initially, free model was run with different values of the coefficients of horizontal turbulent viscosity and diffusion, but with the same atmospheric forcing. The model output obtained with higher values of these coefficients was used to determine the first guess fields in subsequent experiments with data assimilation, while the model results with lower values of the coefficients were assumed to be true states, and a part of these results was used as synthetic observations. The results are analyzed that are assimilation of various types of observational data using the Kalman filter included through the PDAF to the NEMO model with real bottom topography. It is shown that a degree of improving model fields in the process of data assimilation is highly dependent on the structure of data at the input of the assimilation procedure.

scholarly journals Multi-grid algorithm for passive tracer transport in the NEMO ocean circulation model: a case study with the NEMO OGCM (version 3.6)

2020 ◽  
Vol 13 (11) ◽  
pp. 5465-5483
Author(s):  
Clément Bricaud ◽  
Julien Le Sommer ◽  
Gurvan Madec ◽  
Christophe Calone ◽  
Julie Deshayes ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Ocean Circulation ◽  
Circulation Model ◽  
Ocean Model ◽  
Ocean Dynamics ◽  
Global Ocean ◽  
Passive Tracer ◽  
Tracer Transport ◽  
Ocean Circulation Model ◽  
Grid Algorithm

Abstract. Ocean biogeochemical models are key tools for both scientific and operational applications. Nevertheless the cost of these models is often expensive because of the large number of biogeochemical tracers. This has motivated the development of multi-grid approaches where ocean dynamics and tracer transport are computed on grids of different spatial resolution. However, existing multi-grid approaches to tracer transport in ocean modelling do not allow the computation of ocean dynamics and tracer transport simultaneously. This paper describes a new multi-grid approach developed for accelerating the computation of passive tracer transport in the Nucleus for European Modelling of the Ocean (NEMO) ocean circulation model. In practice, passive tracer transport is computed at runtime on a grid with coarser spatial resolution than the hydrodynamics, which reduces the CPU cost of computing the evolution of tracers. We describe the multi-grid algorithm, its practical implementation in the NEMO ocean model, and discuss its performance on the basis of a series of sensitivity experiments with global ocean model configurations. Our experiments confirm that the spatial resolution of hydrodynamical fields can be coarsened by a factor of 3 in both horizontal directions without significantly affecting the resolved passive tracer fields. Overall, the proposed algorithm yields a reduction by a factor of 7 of the overhead associated with running a full biogeochemical model like PISCES (with 24 passive tracers). Propositions for further reducing this cost without affecting the resolved solution are discussed.

Internal salt content: a useful framework for understanding the oceanic branch of the water cycle

2021 ◽  
Author(s):  
Christopher Bladwell ◽  
Ryan M. Holmes ◽  
Jan D. Zika
Keyword(s):  
Fresh Water ◽  
Ocean Circulation ◽  
Water Cycle ◽  
Salt Content ◽  
Ocean Model ◽  
Global Ocean ◽  
Freshwater Forcing ◽  
Freshwater Fluxes ◽  
Ocean Salinity ◽  
Isopycnal Mixing

AbstractThe global water cycle is dominated by an atmospheric branch which transfers fresh water away from subtropical regions and an oceanic branch which returns that fresh water from subpolar and tropical regions. Salt content is commonly used to understand the oceanic branch because surface freshwater fluxes leave an imprint on ocean salinity. However, freshwater fluxes do not actually change the amount of salt in the ocean and – in the mean – no salt is transported meridionally by ocean circulation. To study the processes which determine ocean salinity we introduce a new variable: “internal salt” and its counterpart “internal fresh water”. Precise budgets for internal salt in salinity coordinates relate meridional and diahaline transport to surface freshwater forcing, ocean circulation and mixing, and reveal the pathway of fresh water in the ocean. We apply this framework to a 1° global ocean model. We find that in order for fresh water to be exported from the ocean’s tropical and subpolar regions to the subtropics, salt must be mixed across the salinity surfaces that bound those regions. In the tropics, this mixing is achieved by parameterized vertical mixing, along-isopycnal mixing, and numerical mixing associated with truncation errors in the model’s advection scheme, while along-isopycnal mixing dominates at high latitudes. We analyze the internal freshwater budgets of the Indo-Pacific and Atlantic Ocean basins and identify the transport pathways between them which redistribute fresh water added through precipitation, balancing asymmetries in freshwater forcing between the basins.

scholarly journals Data assimilation of SMOS observations into the Mercator Ocean operational system: focus on the Nino 2015 event

2018 ◽  
Author(s):  
Benoît Tranchant ◽  
Elisabeth Remy ◽  
Eric Greiner ◽  
Olivier Legalloudec
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
Positive Impact ◽  
Atmospheric Precipitation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Data Sets ◽  
The Impact ◽  
Over The Top

Abstract. Monitoring Sea Surface Salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the acceleration of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, most often closer to 5 meters deep than the surface, were available to estimate the SSS. The recent satellite missions of ESA's SMOS, NASA's Aquarius, and now SMAP have made possible for the first time to measure SSS from space. The SSS drivers can be quite different than the temperature ones. The model SSS can suffer from significant errors coming not only from the ocean dynamical model but also the atmospheric precipitation and evaporation as well as ice melting and river runoff. Satellite SSS can bring a valuable additional constraint to control the model salinity. In the framework of the SMOS Nino 2015 ESA project (https://www.godae-oceanview.org/projects/smos-nino15/), the impact of satellite SSS data assimilation is assessed with the Met Office and Mercator Ocean global ocean analysis and forecasting systems with a focus on the Tropical Pacific region. This article presents the analysis of an Observing System Experiment (OSE) conducted with the 1/4° resolution Mercator Ocean analysis and forecasting system. SSS data assimilation constrains the model SSS to be closer to the observations in a coherent way with the other data sets already routinely assimilated in an operational context. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 meters. Comparisons to independent data sets show a small but positive impact. The sea surface height (SSH) has also been impacted by implying a reinforcement of TIWs during the El-Niño 2015/16 event. Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance.

scholarly journals An Intrinsic Mode of Interannual Variability in the Indian Ocean

2017 ◽  
Vol 47 (3) ◽  
pp. 701-719 ◽  
Author(s):  
Christopher L. Wolfe ◽  
Paola Cessi ◽  
Bruce D. Cornuelle
Keyword(s):  
Indian Ocean ◽  
Interannual Variability ◽  
Ocean Circulation ◽  
Baroclinic Instability ◽  
Ocean Model ◽  
South Atlantic ◽  
Global Ocean ◽  
Leeuwin Current ◽  
The Indian Ocean ◽  
Atmospheric State

AbstractAn intrinsic mode of self-sustained, interannual variability is identified in a coarse-resolution ocean model forced by an annually repeating atmospheric state. The variability has maximum loading in the Indian Ocean, with a significant projection into the South Atlantic Ocean. It is argued that this intrinsic mode is caused by baroclinic instability of the model’s Leeuwin Current, which radiates out to the tropical Indian and South Atlantic Oceans as long Rossby waves at a period of 4 yr. This previously undescribed mode has a remarkably narrowband time series. However, the variability is not synchronized with the annual cycle; the phase of the oscillation varies chaotically on decadal time scales. The presence of this internal mode reduces the predictability of the ocean circulation by obscuring the response to forcing or initial condition perturbations. The signature of this mode can be seen in higher-resolution global ocean models driven by high-frequency atmospheric forcing, but altimeter and assimilation analyses do not show obvious signatures of such a mode, perhaps because of insufficient duration.

scholarly journals Impact of Parameterized Internal Wave Drag on the Semidiurnal Energy Balance in a Global Ocean Circulation Model

2016 ◽  
Vol 46 (5) ◽  
pp. 1399-1419 ◽  
Author(s):  
Maarten C. Buijsman ◽  
Joseph K. Ansong ◽  
Brian K. Arbic ◽  
James G. Richman ◽  
Jay F. Shriver ◽  
...  
Keyword(s):  
Internal Wave ◽  
Ocean Circulation ◽  
Deep Water ◽  
Mode Conversion ◽  
Circulation Model ◽  
Wave Drag ◽  
Ocean Model ◽  
Internal Tides ◽  
Global Ocean ◽  
Numerical Dissipation

AbstractThe effects of a parameterized linear internal wave drag on the semidiurnal barotropic and baroclinic energetics of a realistically forced, three-dimensional global ocean model are analyzed. Although the main purpose of the parameterization is to improve the surface tides, it also influences the internal tides. The relatively coarse resolution of the model of ~8 km only permits the generation and propagation of the first three vertical modes. Hence, this wave drag parameterization represents the energy conversion to and the subsequent breaking of the unresolved high modes. The total tidal energy input and the spatial distribution of the barotropic energy loss agree with the Ocean Topography Experiment (TOPEX)/Poseidon (TPXO) tidal inversion model. The wave drag overestimates the high-mode conversion at ocean ridges as measured against regional high-resolution models. The wave drag also damps the low-mode internal tides as they propagate away from their generation sites. Hence, it can be considered a scattering parameterization, causing more than 50% of the deep-water dissipation of the internal tides. In the near field, most of the baroclinic dissipation is attributed to viscous and numerical dissipation. The far-field decay of the simulated internal tides is in agreement with satellite altimetry and falls within the broad range of Argo-inferred dissipation rates. In the simulation, about 12% of the semidiurnal internal tide energy generated in deep water reaches the continental margins.

scholarly journals How essential are Argo observations to constrain a global ocean data assimilation system?

2015 ◽  
Vol 12 (3) ◽  
pp. 1145-1186 ◽  
Author(s):  
V. Turpin ◽  
E. Remy ◽  
P. Y. Le Traon
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Global Ocean ◽  
Altimeter Data ◽  
Western Boundary ◽  
Argo Data ◽  
Ocean Data Assimilation ◽  
In Situ Observations ◽  
The Impact

Abstract. Observing System Experiments (OSEs) are carried out over a one-year period to quantify the impact of Argo observations on the Mercator-Ocean 1/4° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of Argo data sets are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation-model forecast error is also significant from the surface down to a depth of 2000 m. Differences between independent observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. All Argo observations thus matter, even with a 1/4° model resolution. The main conclusion is that the performance of global data assimilation systems is heavily dependent on the availability of Argo data.

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