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OS1.5

Theoretical and model studies show that the ocean is a chaotic system interacting with the atmosphere: uncertainties in ocean model initial states may grow and strongly affect the simulated variability up to multidecadal and basin scales, with or without coupling to the atmosphere. In addition, ocean simulations require both the use of subgrid-scale parameterizations that mimick crudely unresolved processes, and the calibration of the parameters associated with these parameterizations, while respecting numerical stability constraints. Oceanographers are increasingly adopting ensemble simulation strategies and probabilistic analysis methods, and developing stochastic parameterizations for modeling and understanding the ocean variability in this context of multiple uncertainties.

Presentations are solicited about the conception and analysis of ocean ensemble simulations, the characterization of ocean model uncertainties, and the development of stochastic parameterizations for ocean models. The session will also cover the dynamics and structure of the ocean chaotic variability, its relationship with the atmospheric variability, and the use of dynamical system or information theories for the investigation of the oceanic variability. We welcome as well studies about the propagation of the ocean chaotic variability towards other components of the climate system, about its consequences regarding ocean predictability, operational forecasts, detection and attribution of climate signals, climate simulations and projections.

Public information:
OS1.5 : CHAOTIC VARIABILITY AND MODELLING UNCERTAINTIES IN THE OCEAN: TOWARDS PROBABILISTIC OCEANOGRAPHY
WEDNESDAY : 16:15 - 18:00 : TENTATIVE SCHEDULE FOR THE CHAT (Public on EGU website)
12 minutes for hightlighted talk (Sinha et al)
7 minutes for all other talks

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16:15 - 16:18 SESSION INTRODUCTION
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16:20 - 17:00 FORCED AND CHAOTIC OCEAN VARIABILITY
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01. D2581 | EGU2020-7226 | HIGHLIGHT —> 12 min
Quantifying uncertainty in decadal ocean heat uptake due to intrinsic ocean variability.
Bablu Sinha, Alex Megann, Thierry Penduff, Jean-Marc Molines, and Sybren Drijfhout

02. D2582 | EGU2020-5689 —> 7 min
Forced and chaotic variability of interannual variability of regional sea level and its causes scale over 1993-2015.
Alice Carret, William Llovel, Thierry Penduff, Jean-Marc Molines, and Benoît Meyssignac

03. D2592 | EGU2020-2737 —> 7 min
Forced and chaotic variability of basin-scale heat budgets in the global ocean: focus on the South Atlantic crossroads. 
Thierry Penduff, Fei-Er Yan, Imane Benabicha, Jean-Marc Molines, and Bernard Barnier

04. D2583 | EGU2020-19875 —> 7 min
Year-to-year meridional shifts of the Great Whirl driven by oceanic internal instabilities 
Kwatra Sadhvi, Iyyappan Suresh, Izumo Takeshi, Jerome Vialard, Matthieu Lengaigne, Thierry Penduff, and Jean Marc Molines.

05. D2584 | EGU2020-20309 —> 7 min
Deconstructing the subtropical AMOC variability.
Quentin Jamet, William Dewar, Nicolas Wienders, Bruno Deremble, Sally Close, and Thierry Penduff

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17:00 - 17:25 OCEAN PROCESSES AND PARAMETERIZATIONS
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06. D2586 | EGU2020-21330 —> 7 min
Eddy-Mean flow oscillations in the Southern Ocean. 
Sebastiano Roncoroni and David Ferreira

07. D2585 | EGU2020-22418 —> 7 min
On wind-driven energetics of subtropical gyres.
William K. Dewar, Quentin Jamet, Bruno Deremble, and Nicolas Wienders

08. D2587 | EGU2020-11312 —> 7 min
Stochastic Advection for eddy parameterisation in Primitive Equation Models.
Stuart Patching

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17:25 - 17:50 OCEAN MODELLING UNCERTAINTIES
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09. D2589 | EGU2020-11127 —> 7 min
Ensemble quantification of short-term predictability of the ocean fine-scale dynamics: a western mediterranean test case at kilometric-scale resolution. 
Stéphanie Leroux, Jean-Michel Brankart, Aurélie Albert, Pierre Brasseur, Laurent Brodeau, Julien Le Sommer, Jean-Marc Molines, and Thierry Penduff

10. D2590 | EGU2020-6489 —> 7 min
Predictability of estuarine model using Information Theory: ROMS Ocean State Ocean Model 
Aakash Sane, Baylor Fox-Kemper, David Ullman, Christopher Kincaid, and Lewis Rothstein

11. D2591 | EGU2020-6000 —> 7 min
Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea 
Siva Reddy Sanikommu, Habib Toye, Peng Zhan, Sabique Langodan, George Krokos, Omar Knio, and Ibrahim Hoteit

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17:50 - 18:00 OPEN DISCUSSION - CLOSING THE SESSION
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Co-organized by NP5
Convener: Thierry Penduff | Co-conveners: William K. Dewar, Guillaume Sérazin, Laure Zanna
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| Attendance Wed, 06 May, 16:15–18:00 (CEST)

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Session materials Download all presentations (53MB)

Chat time: Wednesday, 6 May 2020, 16:15–18:00

D2581 |
EGU2020-7226
| Highlight
Bablu Sinha, Alex Megann, Thierry Penduff, Jean-Marc Molines, and Sybren Drijfhout

Remarkably, global surface warming since 1850 has not proceeded monotonically, but has consisted of a series of decadal timescale slowdowns (hiatus periods) followed by surges. Knowledge of a mechanism to explain these fluctuations would greatly aid development and testing of near term climate forecasts. Here we evaluate the influence of ocean intrinsic variability on global ocean heat uptake and hence the rate of global surface warming, using a 50-member ensemble of eddy-permitting ocean general circulation model simulations (OCCIPUT ensemble) forced with identical surface atmospheric condition for the period 1960-2015. Air-sea heat flux, integrated zonally and accumulated with latitude provides a useful measure of ocean heat uptake. We plot the ensemble mean difference of this quantity between 2000-2009 (hiatus) and 1990-1999 (surge). OCCIPUT suggests that the 2000s saw increased ocean heat uptake of ~0.32 W m-2compared to the 1990s and that the increased uptake was shared between the tropics and the high latitudes. OCCIPUT shows that intrinsic ocean variability modifies the mean ocean heat uptake change by up to 0.05 W m-2or ±15%. Moreover composite analysis of the ensemble members with the most extreme individual decadal heat uptake changes pinpoints the southern and northern high latitudes as the regions where intrinsic variability plays a large role: tropical heat uptake change is largely fixed by the surface forcing. The western boundary currents and the Antarctic Circumpolar Current (i.e. eddy rich regions) are responsible for the range of simulated ocean heat uptake, with the North Pacific exhibiting a particularly strong signal. The origin of this North Pacific signal is traced to decadal timescale latitudinal excursions of the Kuroshio western boundary current.

How to cite: Sinha, B., Megann, A., Penduff, T., Molines, J.-M., and Drijfhout, S.: Quantifying uncertainty in decadal ocean heat uptake due to intrinsic ocean variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7226, https://doi.org/10.5194/egusphere-egu2020-7226, 2020

D2582 |
EGU2020-5689
Alice Carret, William Llovel, Thierry Penduff, Jean-Marc Molines, and Benoît Meyssignac

Since the early 1990s, satellite altimetry has become the main observing system for continuously measuring the sea level variations with a near global coverage. Satellite altimetry has revealed a global mean sea level rise of 3.3 mm/yr since 1993 with large regional sea level variability that differs from the mean estimate. These measurements highlight complex structures especially for the western boundary currents or the Antarctic Circumpolar Current. A recent study shows that the chaotic ocean variability may mask atmospherically-forced regional sea level trends over 38% of the global ocean area from 1993 to 2015. The chaotic variability is large for the western boundary currents and in the Southern Ocean. The present study aims to complement this previous work in focusing on the interannual variability of regional sea level. A global ¼° ocean/sea-ice 50-member ensemble simulation is considered to disentangle the imprints of the atmospheric forcing and the chaotic ocean variability on the interannual variability of regional sea level over 1993-2015. We investigate the forced (i.e., ensemble mean) versus the chaotic variability (i.e., ensemble standard deviation) for the interannual variability of regional sea level and its causes (i.e., steric sea level and manometric sea level contribution). We complement our investigations by partitioning the steric component into thermosteric sea level (i.e., temperature change only) and halosteric sea level (i.e., salinity change only). One of the goals of the study is to highlight the hot spots region of large chaotic variability for regional sea level and its different components.

How to cite: Carret, A., Llovel, W., Penduff, T., Molines, J.-M., and Meyssignac, B.: Forced and chaotic variability of interannual variability of regional sea level and its causes scale over 1993-2015, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5689, https://doi.org/10.5194/egusphere-egu2020-5689, 2020

D2583 |
EGU2020-19875
Kwatra Sadhvi, Iyyappan Suresh, Izumo Takeshi, Jerome Vialard, Matthieu Lengaigne, Thierry Penduff, and Jean Marc Molines

The Great Whirl (GW) is a quasi-permanent anticyclonic eddy that forms off the horn of Africa in the western Arabian Sea. It generally appears in June, peaks in July-August, and dissipates in September. While the annual cycle of the GW has been described by past literature, its year-to-year variability has not yet been thoroughly explored. Satellite sea-level observations reveal that the leading mode of interannual variability (half of the interannual summer variance in the GW region) is associated with a typically ~100-km GW northward or southward shift. This meridional shift is associated with coherent sea surface temperature (SST) and surface chlorophyll signals, with warmer SST and reduced marine primary productivity in regions with positive sea level anomalies (and vice versa). Eddy-resolving (~10-km resolution) simulations with an ocean general circulation model capture those observed patterns reasonably well, even in the absence of interannual variations in the surface forcing. Interannual surface forcing variations enhance the GW interannual variability, but do not constrain its phase. Our results hence indicate that year-to-year variations in the Somalia upwelling SST and productivity associated with the GW are thus not a deterministic response to surface forcing, but largely arise from oceanic internal instabilities.

How to cite: Sadhvi, K., Suresh, I., Takeshi, I., Vialard, J., Lengaigne, M., Penduff, T., and Molines, J. M.: Year-to-year meridional shifts of the Great Whirl driven by oceanic internal instabilities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19875, https://doi.org/10.5194/egusphere-egu2020-19875, 2020

D2584 |
EGU2020-20309
Quentin Jamet, William Dewar, Nicolas Wienders, Bruno Deremble, Sally Close, and Thierry Penduff

Mechanisms driving the North Atlantic Meridional Overturning Circulation (AMOC) variability at low-frequency are of central interest for accurate climate predictions. However, the origin of this variability remains under debate, complicating for instance the interpretation of the observed time series provided by the RAPID-MOCHA-WBTS program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing, the signal of remote origin and the ocean intrinsic dynamics for the subtropical low-frequency AMOC variability. We analyse for this a set of four ensembles of a regional (20oS - 55oN), eddy-resolving (1/12o) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals.

The analysis of the four ensemble mean AMOCs reveals predominance of local, atmospherically forced signal at interannual time scales (2-10 years), while signals imposed by the boundaries imprint at decadal (10-30 years) time scales. Due to this marked time scale separation, we show that most of the subtropical AMOC forced variability can be understood as a linear superposition of these two signals. Analyzing the ensemble spread of the four ensembles, we then show that the subtropical AMOC is also characterized by an intrinsic variability, which organizes as a basin scale mode peaking at interannual time scales. This basin scale mode is found to be weakly sensitive to the surrounding forced signals, suggesting no causal relationship between the two. Its spatio-temporal pattern shares however similarities with the atmospherically forced signal, which is likely to make the attribution from a single eddy-resolving simulation, or from observations, more difficult.

How to cite: Jamet, Q., Dewar, W., Wienders, N., Deremble, B., Close, S., and Penduff, T.: Deconstructing the subtropical AMOC variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20309, https://doi.org/10.5194/egusphere-egu2020-20309, 2020

D2585 |
EGU2020-22418
William K. Dewar, Quentin Jamet, Bruno Deremble, and Nicolas Wienders

The flow of energy in the wind-driven circulation is examined in a 
combined theoretical and numerical study. Based on a multiple scales 
analysis of the ocean interior, we find the mesoscale field is strongly 
affected by the ventilated thermocline, but no feed back from the eddies 
to the large scale is found.  We then analyze the western boundary 
region arguing that the associated currents divide between coastal jets, 
which conserve mean energy, and open ocean, separated jet extensions
where the mesoscale is energized by the mean field.   It is the 
separated jet zone where the primary loss of general circulation energy 
to the mesoscale occurs.  Connections to the `Thickness Weighted 
Average' form of the primitive equations are made which support the 
differing roles of the eddies in these regions.  These ideas are then 
tested by an analysis of a regional primitive equation 1/12-degree 
numerical model of the North Atlantic. The predictions of the theory are 
generally supported by the numerical results.  The one exception is that 
topographic irregularities in the coastal jet spawn eddies, although 
they contribute modestly to the energy budget.  We therefore conclude 
the primary sink of wind input into the mean circulation is in the 
separated jet, and not the interior.  The analysis also shows
wind forcing is much smaller than the interior energy fluxes. Thus, the 
general circulation is characterized as recirculating energy in the 
manner of a Fofonoff gyre.

How to cite: Dewar, W. K., Jamet, Q., Deremble, B., and Wienders, N.: On wind-driven energetics of subtropical gyres, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22418, https://doi.org/10.5194/egusphere-egu2020-22418, 2020

D2586 |
EGU2020-21330
Sebastiano Roncoroni and David Ferreira

Geostrophic eddies have a leading order effect on the dynamics of the Southern Ocean (SO), and numerous studies have shown that they are also key to the response of both the zonal transport and the meridional overturning circulation to wind stress changes. The role played by eddies in setting the intrinsic variability of the SO, however, is less well-understood. Here, inspired by recent work on the atmospheric jet, we investigate whether the eddy-mean flow interaction in the Antarctic Circumpolar Current can be described by a prey-predator nonlinear model.

 

To this end, we analyse data from a high-resolution eddy-resolving configuration of the MIT general circulation model: an idealised “channel” model with mechanical and thermodynamical forcing at the surface, and plausible zonal and meridional circulations.

 

Here, we show that a mechanism of eddy-mean flow interaction driving the intrinsic variability of the SO-like model is well described by a stochastic non-linear oscillator with damping. This model is a generalisation of the Ambaum-Novak oscillator, which has been successfully employed to describe the atmospheric storm track variability.

 

We find that, on length scales similar to that of individual zonal jets, the eddy-mean flow interaction is characterised by a high-frequency oscillatory mode, and that the characteristic time scale of the oscillation is comparable with classical estimates of the baroclinic life-cycle. A Gaussian smoothing of the phase space diagram also reveals the damped oscillatory character of the oscillation: this is in contrast with the atmospheric case, where damping is negligible and orbits are confined to energy surfaces.

 

This result may help inform the interpretation of the SO intrinsic and forced variability (such as, for example, the response to wind stress changes), and pave the way to further studies featuring more realistic model configurations.

How to cite: Roncoroni, S. and Ferreira, D.: Eddy-Mean flow oscillations in the Southern Ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21330, https://doi.org/10.5194/egusphere-egu2020-21330, 2020

D2587 |
EGU2020-11312
Stuart Patching

A major challenge of modern ocean modelling is how to represent in ocean models small-scale features with length scales smaller than the grid spacing. It is known that small scale eddies are important for maintaining Western boundary currents such as the Gulf Stream and Kuroshio; it is therefore of great importance that these are well represented in any global ocean model. The small scales are often parameterised by viscosity closures or GM parameterisations. The Stochastic Advection by Lie Transport (SALT) method is a propsed alternative which is defined so as to preserve important physical properties of the flow solution. Stochasticity is introduced into the fluid dynamical variational principle so that the resulting Euler-Poincaré equations give a stochastic version of the fluid equations which maintain a Kelvin circulation theorem and conservation of potential vorticity. The stochastic terms are then tuned using empirical orthogonal functions obtained from fine-grid model runs in order to capture the small-scale effects. This method has been shown to be effective for quasigeostrophic models and the 2D Euler equations. Here we present an application to the Finite volumE Sea-ice Ocean Model (FESOM2.0), a primitive equation model; we show preliminary results from this implementation.

How to cite: Patching, S.: Stochastic Advection for eddy parameterisation in Primitive Equation Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11312, https://doi.org/10.5194/egusphere-egu2020-11312, 2020

D2588 |
EGU2020-12290
Dmitri Kondrashov

Oceanic turbulent flows consist of complex motions (fronts, eddies and waves) that co-exist on many different spatio-temporal scales and nonlinearly interacting with each other. In this study data-adaptive harmonic decomposition (DAHD) has been applied to high-dimensional datasets of complex turbulent flows simulated by ocean models of different complexity. DAHD allows a low-rank description of multiscale and chaotic dynamics by a small subset of data-adaptive patterns oscillating harmonically at given temporal frequency. The shape and scaling laws of temporal energy spectrum of the extracted patterns reveal global fingerprint of underlying dynamics, providing new opportunities to characterize and compare oceanic datasets and models. 


1. Ryzhov, E.A., D. Kondrashov, N. Agarwal, and P.S. Berloff, 2019: 
On data-driven augmentation of low-resolution ocean model dynamics, 
Ocean Modelling, 142, doi:10.1016/j.ocemod.2019.101464. 

2. Kondrashov, D., M. D. Chekroun and P. Berloff, 2018: 
Multiscale Stuart-Landau Emulators: Application to Wind-Driven Ocean Gyres,
Fluids, 3(1), 21, doi:10.3390/fluids3010021.

How to cite: Kondrashov, D.: Data-adaptive harmonic analysis of high-dimensional oceanic turbulent flows, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12290, https://doi.org/10.5194/egusphere-egu2020-12290, 2020

D2589 |
EGU2020-11127
Stéphanie Leroux, Jean-Michel Brankart, Aurélie Albert, Pierre Brasseur, Laurent Brodeau, Julien Le Sommer, Jean-Marc Molines, and Thierry Penduff

“Predictability” in operational forecasting systems can be viewed as the ability to meet the forecast accuracy that is required for a given application. In the literature, the most usual approach is to assume that predictability is mainly limited by model instability (i.e. the chaotic behaviour of the system), which means assuming that initial and model errors are small. But, in operational systems, initial and model errors cannot usually be assumed small, because of the complexity of the system and because observations and model resources are limited. In this study, we propose  a practical approach to take into account such model and initial condition errors, in the aim to evaluate the predictability of the fine-scale dynamics in a CMEMS-like operational system, based on ensemble experiments with the ocean numerical model NEMO.

    To do so, we set up a regional model configuration MEDWEST60 with NEMO v3.6,  212 vertical levels and a kilometric-scale horizontal resolution (1/60º). Such a resolution allows to simulate the fine-scale dynamics up to an effective resolution of  ~10 km. The domain covers the Western Mediterranean sea from Gibraltar to Corsica-Sardinia. The configuration includes tides and is forced at the western and eastern boundaries with hourly outputs from a reference simulation on a larger domain, also including tides, and based on the exact same horizontal and vertical grid.

    The practical approach we follow consists first in performing a set of several  short (~1month) ensemble forecast experiments to study the growth of forecast errors for different levels of  model error and initial condition error. In practice, we need to implement a tunable source of model error in MEDWEST60, that might represent e.g. numerical errors, forcing errors, missing or uncertain physics via stochastic parameterization (in this presentation, we will focus on a first set of ensemble experiments where stochastic perturbations are added on the model vertical grid). It is then used to generate different levels of error on the initial conditions. 

    In a second step, by inverting the dependence between forecast error on the one hand and initial and model error on the other hand, we aim to diagnose the level of initial and model accuracy needed for a given targeted accuracy of the forecasting system. 

Practical questions addressed by such experiments relate to the relative importance of model accuracy vs initial condition accuracy for the  forecast of the finest scales in a CMEMS system. From this we can infer information about (a) predictability - for instance, the time along which a forecast remains meaningful for the fine scales. And information about (b) controllability by the observations, for instance, the minimal time to consider between two passes of a future satellite to be able to follow a given observed fine-scale structure - front, eddy, etc

How to cite: Leroux, S., Brankart, J.-M., Albert, A., Brasseur, P., Brodeau, L., Le Sommer, J., Molines, J.-M., and Penduff, T.: Ensemble quantification of short-term predictability of the ocean fine-scale dynamics: a western mediterranean test case at kilometric-scale resolution., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11127, https://doi.org/10.5194/egusphere-egu2020-11127, 2020

D2590 |
EGU2020-6489
Aakash Sane, Baylor Fox-Kemper, David Ullman, Christopher Kincaid, and Lewis Rothstein

With a focus on modelling physical aspects of estuaries covering Rhode Island, USA, the Ocean State Ocean Model (OSOM) has been implemented using the Regional Ocean Modeling System. The estuary includes Narragansett Bay, Mt. Hope Bay, and nearby regions including the shelf circulation from Long Island to Nantucket. Our goal is to find predictability and estuarine time scales in order to build a forecasting system 

 

Perturbed ensemble simulations with altered initial condition parameters (temperature, salinity) are combined with concepts from Information Theory to quantify the predictability of the OSOM forecast system. Predictability provides a theoretical estimate of the potential forecasting capabilities of the model in the form of prediction time scales and enhances readily estimable timescales such as the freshwater/ saline water flushing timescale. The predictability of the OSOM model is around 10-40 days, varying by perturbation parameters and season. Internal variability is low when compared to forced variability for the current resolution of OSOM suggesting modest chaos at this resolution.

 

Freshwater flushing time scale and total exchange flow was calculated for the OSOM model. The freshwater flushing time scale was found to be ~20 days and varies with the choice of the estuary boundary. The predictability time scales and flushing time scales reveal important dynamics of the tracers involved and elucidate their role in driving the estuary.  

How to cite: Sane, A., Fox-Kemper, B., Ullman, D., Kincaid, C., and Rothstein, L.: Predictability of estuarine model using Information Theory: ROMS Ocean State Ocean Model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6489, https://doi.org/10.5194/egusphere-egu2020-6489, 2020

D2591 |
EGU2020-6000
siva reddy sanikommu, Habib Toye, Peng Zhan, Sabique Langodan, George Krokos, Omar Knio, and Ibrahim Hoteit

The Ensemble Adjustment Kalman Filter of the Data Assimilation Research Testbed is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height and in-situocean temperature and salinity profiles into an eddy-resolving 4km-Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. We investigate the impact of three different assimilation strategies (1) Iexp– inflates filter error covariance by 10%, (2) IAexp– adds ensemble of atmospheric forcing to Iexp, and (3) IAPexp– adds perturbed model physics toIAexp. The assimilation experiments are run for one year, starting from the same initial ensemble on 1stJanuary, 2011 and the data are assimilated every three days.

Results demonstrate that the Iexp mainly improved the model outputs with respect to assimilation-free MITgcm run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data-sparse subsurface layers. The IAexp yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscales features resulting in an improved forecasts for eddies, both in terms of intensity and location. Perturbing model physics in IAPexp slightly improved the forecast statistics. It further increased smoothness in the ocean forecasts and improved the placement of basin-scale eddies, but caused loss of some high-resolution features. Increasing hydrographic coverage helps recovering the losses and yields more improvements in IAPexp compared to IAexp. Switching off inflation in IAexp and IAPexp leads to further improvements, especially in the subsurface layers.

How to cite: sanikommu, S. R., Toye, H., Zhan, P., Langodan, S., Krokos, G., Knio, O., and Hoteit, I.: Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6000, https://doi.org/10.5194/egusphere-egu2020-6000, 2020

D2592 |
EGU2020-2737
Thierry Penduff, Fei-Er Yan, Imane Benabicha, Jean-Marc Molines, and Bernard Barnier

The OCCIPUT eddy-permitting (1/4°) global ocean/sea-ice 50-member ensemble simulation is analyzed over the period 1980-2015 to identify how the atmosphere and the intrinsic/chaotic ocean variability modulate the basin-scale Ocean Heat Content (OHC) at various timescales. In all regions of the simulated world ocean, the atmospherically-forced interannual OHC variability is driven by both air-sea heat fluxes (Qnet) and advective heat transport convergences (Conv), while the intrinsic component is driven by Conv, and damped by Qnet. 

We focus on the Atlantic sector of the Southern Ocean (SA), where the oceanic “chaos” explains 36 to 90% of the interannual and decadal heat transport variability across the limits of the basin, and 22% of this huge basin’s OHC variability at interannual and decadal timescales.

The model also simulates the Antarctic Circumpolar Wave (ACW) that was observed in the 80-90’s, with large impacts on OHC and heat transports in the Southern Ocean. This forced signal appears south of Australia, propagates eastward around Antarctica and northward into the Tropical Atlantic and the Tropical Indian Ocean. 

These results highlight the substantial contribution of large-scale low-frequency chaotic heat advection in eddy-active regions, and its major impact on decadal OHC variations over key basins. They suggest that climate simulations using eddying ocean models include an oceanic and random source of large-scale low-frequency variability whose atmospheric impacts remain to be assessed.

How to cite: Penduff, T., Yan, F.-E., Benabicha, I., Molines, J.-M., and Barnier, B.: Forced and chaotic variability of basin-scale heat budgets in the global ocean: focus on the South Atlantic crossroads., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2737, https://doi.org/10.5194/egusphere-egu2020-2737, 2020