Horizontal sampling of the ocean has been sparse for decades because of technical limitations. This can contribute to an incomplete depiction and misleading understanding of the hydrography. This is a particular concern for complex submesoscale and smaller scale flow structures that influence stratification and vertical transport of properties.
We use high resolution observations from a Triaxus towed undulating vehicle and develop a statistical subsampling pipeline in order to present the first multi-scale investigation of subsurface and interior horizontal density variability in a global context. Hydrographic transects were performed between 2018 and 2022 with vertical ranges extending from near-surface values down to depths varying between 50 and 350m in the oceanographically distinct regimes of the Arctic marginal ice zone, of a coastal upwelling area, of the equatorial Atlantic, and of the Antarctic Circumpolar Current. The investigation of lateral density gradient fields follows a baseline spanning four orders of magnitude, from 2m to 25km. Our main objectives are to determine the scaling properties of density fronts and to identify oceanic regimes that are susceptible to an underestimation of their thermohaline variability.
We find that the amplitude of horizontal density gradients increases non-linearly as the horizontal resolution is increased, closely following a proposed power law over all observed scales. This relation is applicable throughout all study regions allowing for a potential prediction of the gradient distribution for scales not resolved by measurements. Submesoscale density gradients are of higher amplitude along the base of shallow mixed layers, and in the presence of subsurface currents, frontal systems, and eddies. The latter two create strong lateral anisotropies in the density field, masking other contributions to the multi-scale spread of gradients. Furthermore, the gradient fields are primarily driven by salinity variability at high northern latitudes and by temperature variability in regions closer to the equator; in the Southern Ocean temperature and salinity largely compensate. The decay rate of the estimated gradients with increasing horizontal distance is related to fractal properties and a scale-dependent compensation of the density field.
This highlights that there is a certain arbitrariness regarding the strengths of density gradients in the present literature. We recommend that the employed horizontal resolution always be quoted alongside values of the horizontal density gradient.
How to cite: Duong, B. L. and von Appen, W.-J.: Scale Dependence of Subsurface Horizontal Density Gradients as Observed In-Situ Across Four Orders of Magnitude, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1518, https://doi.org/10.5194/egusphere-egu25-1518, 2025.
The Atlantic Meridional Overturning Circulation (AMOC) is a key component of the climate system, exhibiting strong variability across daily to millennial timescales and significantly influencing global climate. Sensitive to external conditions such as freshwater input, greenhouse gas concentrations, and aerosol forcing, important variations of the AMOC can be triggered by anthropogenic emissions. This study presents a comprehensive analysis of sources of AMOC variance in state-of-the-art climate ensemble models. By decomposing the effects of scenario, model, ensemble, and time variability, along with their interactions, through an Analysis of Variance (ANOVA), we identify three distinct regimes of AMOC variability from 1850 to 2100. The first regime, spanning most of the historical period, is characterized by a relatively stable AMOC dominated by internal variability. The second regime, initiated by AMOC decline at the end of the 20th century and lasting until mid-21st century, is governed by a transient increase of time variability. Notably, the direct effect of forcing differences remains muted all along this regime, despite the start of emission-scenarios in 2015. The third regime, beginning around 2050, is marked by the emergence and rapid dominance of inter-scenario variability. Throughout the simulations, model variability remains the primary source of uncertainty, influenced by aerosol forcing response, AMOC decline magnitude, and the physical variability. A key finding of this work is the evidence that internal variability decreases simultaneously with AMOC intensity and seems proportional to emission-scenario intensity.
How to cite: Coquereau, A., Sévellec, F., Huck, T., Hirschi, J., and Jamet, Q.: Past, Present, and Future Variability of Atlantic Meridional Overturning Circulation in CMIP6 Ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10891, https://doi.org/10.5194/egusphere-egu25-10891, 2025.
The Indonesian Throughflow (ITF) carries an annual average of about 15 Sv of water from the Pacific through the Indonesian Seas Into the Indian Ocean, and its year-to-year variation ranges from 1 to 4 Sv. A 10-member ensemble of 41-year integrations of a semi-global eddy-resolving oceanic general circulation model is examined to explore the intrinsic (chaotic) variability of the ITF transport and associated flow. It is found that the annual-mean ITF transport is different by about 1 Sv between the ensemble members at several years. The characteristic vertical and horizontal structures of the ensemble anomaly (deviation from the ensemble average) are described. These structures and the basin-scale spread of the anomaly suggest that the intrinsic variability of the ITF is a genuine increase or decrease of the classical ITF rather than variability due to local eddies or nonlinear currents within the Indonesian Seas. The lagged correlation of the intrinsic component of the ITF transport with sea-surface height and barotropic streamfunction suggests that the intrinsic variability may come from zonal jets in the western subtropical North Pacific.
How to cite: Furue, R., Nonaka, M., and Sasaki, H.: Intrinsic interannual variability of the Indonesian Throughflow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14639, https://doi.org/10.5194/egusphere-egu25-14639, 2025.
This study investigates the low-frequency variability of the Mediterranean Sea using an ensemble of 30 eddy-permitting (1/12°) NEMO-based regional ocean simulations. The ensemble members were slightly perturbed in their initial conditions and forced by the same atmospheric variability during 34 years, allowing us to separate the intrinsic and atmospherically-forced components of the ocean variability.
At interannual timescales, our analysis of sea surface height (SSH) reveals distinct patterns of intrinsic variability across the basin. While the variability of certain circulation features, such as the North Ionian Gyre (NIG), is mostly paced by the atmosphere, low-frequency fluctuations of other features —like in the Algerian Basin— are largely intrinsic and random. The variance decomposition reveals that intrinsic processes control most of the total SSH variability over one-fifth of the basin, highlighting their pivotal role in shaping the interannual fluctuations in the basin.
Inspired by previous studies of the Atlantic Meridional Overturning Circulation (Gregorio et al., 2015; Leroux et al., 2018), we investigate the forced and intrinsic components of the Mediterranean Zonal Overturning Circulation (ZOC) interannual variability, focusing on the eastward flow of Atlantic waters and westward flow of intermediate waters in density coordinates. While the transport in the western basin shows moderate variability, our results reveal an increase in total variability in the Levantine Basin, driven by both forced and intrinsic components. EOF analyses of ZOC fluctuations suggest distinct variability modes east and west of Sicily, which remain to be further investigated.
This work highlights the substantial contribution of intrinsic variability in various features of the Mediterranean's fluctuations, up to decadal timescales. A better understanding of the relative contributions of externally-driven and internally-generated oceanic fluctuations is crucial for accurately interpreting simulated and observed signals, making reliable predictions, and exploring possible impacts on marine ecosystems.
How to cite: Héron, D., Brankart, J.-M., and Brasseur, P.: Forced and Intrinsic Low-Frequency Variability in the Mediterranean Sea from a Multi-Decadal Ensemble Simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20796, https://doi.org/10.5194/egusphere-egu25-20796, 2025.
In some places and over some time horizons, coastal sea-level is highly predictable. For example, there are locations where the seasonal cycle dominates and the monthly-mean sea- level is predictable for many years ahead. However, in other places, we know that there are frequent storm surges, that AMOC changes are linked to coastal sea-level, that mass anomalies propagate around the continental shelf slope boundary and can affect remote changes in coastal sea level, but also that there is a manifestation of internal or intrinsic, nonlinear processes which have a chaotic signature. From place to place, globally, there is a need to optimally predict coastal sea-level for societal planning and adaptation, to mitigate the effects of climate change and sea-level rise. However, on the regional and local scales, there are still many gaps, both in terms of observation and modelling of coastal sea-level on timescales relevant to people’s lives and wellbeing.
Using an ensemble modelling approach, one can use the ensemble mean and ensemble variance to estimate a potentially predictable,”forced” component of the system and a potentially unpredictable, ”unforced” component. In terms of sea-level, the unforced, chaotic intrinsic variability (CIV) component can, in some locations, dominate the forced component, even out to decadal timescales. This is known to be a major source of uncertainty in sea-level trends, relevant to IPCC projections, but analogously so for other temporal components on seasonal to decadal timescales too.
This study:
- a) verifies where and over which timescales of variability the OCCIPUT, eORCA025, 50- member initial condition ensemble simulation captures coastal sea-level from the GESLA3 tide gauge dataset.
- b) generates maps of the potential predictability of coastal sea-level.
- c) explores predictive suitability of statistical models versus GCMs.
- d) suggests relevant processes behind potential predictability characteristics.
How to cite: Wilson, C. and Williams, S. D. P.: Where, why, and over which timescales is coastal sea-level potentially predictable?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19468, https://doi.org/10.5194/egusphere-egu25-19468, 2025.
The Southwest Indian Ocean (SWIO) is characterized by diverse dynamic regimes, with intense energy fluxes and intricate atmospheric interactions (Phillips et al., 2021, OS). The Mascarene area, to the east of Madagascar, is influenced by the South Equatorial Current and the Indian subtropical gyre, the Mozambique Channel presents numerous mesoscale eddies, which play an important role in the biogeochemical dynamics, and the Equatorial zone is affected by the inversion of seasonal Monsoon circulation. Modeling such complex systems requires the consideration of multiple sources of uncertainty. In the context of global warming and climate projections, it is essential to simulate these uncertainties in order to obtain a more accurate representation and understanding of the SWIO ocean dynamics. The objective of this project is to identify and analyze the dominant sources of uncertainty affecting surface circulation in the SWIO. To address this issue, a probabilistic approach is integrated into the CROCO model (Coastal and Regional Ocean Community), following three key steps. Firstly, a realistic regional configuration of the CROCO model is developed for the SWIO region, which is forced and validated by CMEMS and ECMWF operational and satellite products. Then, a stochastic perturbation generator (referred to as STOGEN and originally developed in the NEMO model, Brankart et al., 2015, GMD) is implemented into CROCO, associated with an ensemble generator. Finally, several ensemble simulations are performed using stochastic processes with varying correlation structures in space and time within the defined regional setting. This allows to test the cumulative effect of different sources of uncertainty associated with surface ocean circulation by analyzing the ensemble statistics and variability based on surface variables such as sea surface height, temperature, salinity or velocity fields. We starts with the simulation of an ensemble by perturbing the wind stress. Then, three additional ensemble simulations will be generated by perturbing the vertical mixing, the initial conditions to analyze the intrinsic ocean variability and the open boundary conditions. The integration of stochastic parameterization within CROCO allow to simulate and partially quantify some of the non-deterministic effects of unresolved processes and scales. It enables an objective statistical comparison between model and observations associated with uncertainty description for data assimilation systems (Popov et al., 2024, OS).
How to cite: Weiss, L., Brankart, J.-M., Jamet, Q., and Brasseur, P.: A stochastic framework for modeling surface ocean variability in the Southwest Indian Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15591, https://doi.org/10.5194/egusphere-egu25-15591, 2025.
The non-linear nature of the ocean dynamics motivates the use of ensemble ocean simulations to discriminate the intrinsic and extrinsic sources of oceanic variability. Separating the mean and eddy flows from ensemble statistics also gives access to their local and instantaneous interactions in the non-stationary and inhomogeneous ocean. We here take advantage of this idea to quantify the local/instantaneous roles of laminar and eddy fluxes in the seasonal cycle of the North Atlantic subtropical mode water (STMW) that is formed through ocean-atmosphere interaction and controls large-scale oceanic ventilation.
We employ an ensemble of 48 North Atlantic 1/12-degree ocean simulations, where all members are driven by the same atmospheric forcing after slight initial perturbations. We achieve a space/time-dependent mean-eddy flow separation by obtaining a residual-mean flow that represents the common oceanic response of all ensemble members to the atmosphere, and a set of residual eddies that reflect the ensemble dispersion. We characterise the STMW as a low Ertel potential vorticity (PV) pool and find that its PV budget is mostly controlled by the ensemble mean PV flux: the formation and erosion of the STMW is predominantly driven by the residual-mean flow. The contribution of eddy PV transport is secondary; this can be attributed to the low intrinsic variability within the PV pool, as captured by the residual eddies.
Overall, our results show that ensemble ocean simulations are powerful to investigate inhomogeneous, non-stationary, nonlinear multiscale ocean dynamics, providing deeper insights into the life cycle of large-scale climate-relevant features like STMW.
How to cite: Sun, L., Dewar, W., Deremble, B., Wienders, N., and Poje, A.: Ensemble Ocean Simulations in the North Atlantic: Exploring the Intrinsic Variability in Subtropical Mode Water Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12552, https://doi.org/10.5194/egusphere-egu25-12552, 2025.
When studying large tuburlent regions of the ocean, interactions between the mean flow and eddies plays a
central role in shaping large-scale circulation patterns by redistributing heat, momentum, and energy across the
ocean. Accurately representing these interactions in General Circulation Models (GCMs) remains a challenge,
particularly due to the subgrid-scale modelling issues and the limitations of traditional parameterization methods.
In this study we highlights the limitations of the diagnostics that can be performed with a too small in size en-
semble of simulations for capturing the Reynolds stress tensor as well as accurately diagnosing the work of such
tensor. In the context of studying energy exchange between the mean flow and eddies, the work of the Reynolds
stress is associated with the mean-to-eddy energy conversion rate MEC (Jamet et al. 2022).
To address the above issues, we explore the capabilities of the Location Uncertainty (LU) framework (Mémin
2014) to provide a better representation of eddy-mean flow energy transfer. By introducing stochastic variability
directly into the governing equations of fluid motion, LU provides an approach to model the unresolved turbulent
effects. A rederivation of the equation for the energy transfers is then possible through a stochastic version of
the Reynolds Transport Theorem (Bauer et al. 2020) and leads to an alternative representation of the interactions
between mean flow and eddies.
Based on 48-member ensemble simulation of the North Atlantic under realistic forcing, we provide a robust
comparison between deterministic and stochastic estimates of the work of Reynolds stress (MEC). By comparing
deterministic and stochastic estimates of MEC, we show that LU can effectively address the issues of stastistical
convergence by inflating intrinsic variability leading to a more robust representation of these non-linear terms. In
addition, statistical moments are shown to be more stable than from the deterministic formulation of the eddy-mean
flow interactions. Key results of this study include a detailed formulation of kinetic energy evolution equations
under the LU framework, which reveals significant improvements compared to the deterministic formulation of
the work of the Reynolds stress in terms of statistical moments. The noise definition relies, in this study, on the
snapshot proper orthogonal decomposition (POD) in the ensemble dimension, offering a time varying orthogonal
eigenfunctions basis. These diagnostics provide usefull tools to observe moving patterns and stability regions,
leading to physical interpretation of the eddy-mean flow interactions in the Gulf Stream.
How to cite: Nex, M., Jamet, Q., Mémin, E., and Sévellec, F.: A Stochastic description of eddy-mean flow interactions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17449, https://doi.org/10.5194/egusphere-egu25-17449, 2025.
The ocean is forced by the fluxes of momentum, heat, and fresh water at the sea surface. When driving an ocean model using stationary fluxes for a sufficiently long time, we expect the model to produce an equilibrated ocean characterized by stationary fluctuations. These fluctuations are not all synchronized with the surface fluxes. For an ensemble obtained by forcing the ocean model with the same fluxes (starting from slightly different initial states), fluctuations (at a time) that are synchronized with the surface fluxes can be identified as the mean across the ensemble (at that time), and those not synchronized with the surface fluxes as the deviations from the mean across the ensemble. In case that the model has a sufficiently fine resolution, we expect that the latter — also known as intrinsic ocean variability — is substantial. The intrinsic ocean variability has to get its energy from somewhere. The only possible energy source is the surface fluxes, which originate from atmospheric motions supported (essentially) by the Sun. In this sense, intrinsic ocean variability can be considered as a feature of an ocean that is in equilibrium with a huge reservoir.
The principle that governs equilibrium fluctuations — no matter how the equilibrium is reached — is a form of fluctuation-dissipation relation. The relation ensures that in an equilibrium with a reservoir, anything that generates fluctuations must also dissipate fluctuations, and anything that dissipates fluctuations must also generate fluctuations. This principle makes a dynamical system in equilibrium with a reservoir be inherently random, even when the forcing resulting from the reservoir, such as the surface fluxes, is purely deterministic. We evaluate this principle using solutions from the Lorenz's 1963 model and solutions obtained from the ICON ocean model with a horizontal resolution of 5 km.
How to cite: von Storch, J.-S.: Principle of equilibrium fluctuations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7149, https://doi.org/10.5194/egusphere-egu25-7149, 2025.
In the ocean, fine scales (1-100 km) are short-lived structures (days to weeks) that drive ocean physics, chemistry, ecology, and can influence climate. Among them, fronts are ubiquitous fine-scale physical features that separate different water masses and create gradients of biogeochemical contents. Fronts are often associated with vertical mixing of the water column, allowing the availability of nutrients that support phytoplankton dynamics. However, how such structures affect phytoplankton distribution is not well understood, especially in oligotrophic regions. We hypothesize that the phytoplankton community observed in the frontal zone is a mixture of communities observed in the adjacent water masses, plus another community.
Here, we are interested in the community composition based on nine phytoplankton functional types (PFTs) observed by flow cytometry in a front, in the western oligotrophic Mediterranean Sea. During the PROTEVSSWOT MED cruise (doi:10.17183/protevsmed_swot_2018_leg2), south of the Balearic Islands, high-resolution measurements allowed us to collect samples in the front and the two adjacent water masses along a strong salinity gradient.
Our objective is to model the frontal phytoplankton community as a finite mixture of the adjacent water mass communities A and B, and a new community C. In this model, we specified that the communities in the adjacent water masses and the new community can arise from a discrete mixture of multivariate normal distributions. First, we estimated the parameters and number of components in the finite mixture for the adjacent water mass communities A and B using an Expectation Maximization algorithm. From a larger dataset, we estimated the parameters of a set of likely communities for C. Then, we developed a hierarchical Bayesian model to estimate the weight of each component of the discrete mixture. Finally, the hierarchical Bayesian model was run a second time using only the most significant components for community C.
One component was sufficient to model community A (North to the front), while communities B (South to the front) and C were modeled with two components. The new community C explained a significant part of the frontal community. With very few observations in the frontal zone (n=11), our Bayesian approach highlighted the spatial distribution of the phytoplankton community around the front. Our result suggests that local environmental conditions in the front allow the emergence of a new community. This work is a first step in understanding frontal zones in an oligotrophic region, representative of the global ocean. Our modeling approach will be further applied in a larger dataset (BIOSWOT-MED cruise, doi:10.17600/18002392). In these further analyses, environmental data will be included to disentangle the physical-biological processes that shape phytoplankton distribution.
This work was funded by the Institut des Mathématiques pour la Planète Terre which supports collaborations between mathematicians and life and earth sciences.
How to cite: Garcia, T., Oms, L., Milhaud, X., Doglioli, A., Messié, M., Lacour, C., Vandekerkhove, P., Gregori, G., and Pommeret, D.: Modelling the phytoplankton community in a front: a Mediterranean Sea case study., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9954, https://doi.org/10.5194/egusphere-egu25-9954, 2025.
A multi-centennial ocean simulation focusing on the Mediterranean Sea intrinsic dynamics is performed using an eddy-permitting nonlinear, shallow-water multilayer numerical model forced by steady transports of Atlantic Water and Levantine Intermediate Water. These transports are prescribed along two western and eastern open boundaries located along meridional sections crossing the strait of Gibraltar and the Levantine basin, respectively. Low-frequency oscillations in the inflowing Atlantic Water density are imposed, which mimic the effect of long-term North Atlantic variability on the water masses entering the Mediterranean basin. In this contribution we compare the simulated annual mean surface displacements with corresponding absolute dynamic topography altimetric observations. This research is supported by the Italian INVMED-P.R.I.N. project.
How to cite: Rubino, A., Gnesotto, M., Zanchettin, D., and Pierini, S.: A multi-centennial ocean simulation reveals aspects of the Mediterranean Sea intrinsic dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16511, https://doi.org/10.5194/egusphere-egu25-16511, 2025.
We analyze the tidal forcing effects on the internal variability in two marginal seas, the Bohai and Yellow Seas, and interpretate such effects from stochastic climate model and physical process (for instance, baroclinic instability) aspects. Ensemble simulations of the numerical module (Finite-volume Coastal Ocean Model) with and without tidal forcings are used to analyze the tidal forcing effects on the internal variability. EOF analysis is used to separate the variability into different spatial scales. The results show that the internal variability is significantly decreased especially in large (100 Km) and medium (60 km) scales, less so in small scales (23 km), when the tidal forcing is turned off. This result is well explained by Hasselmann's theory. Ocean memory, represented by the temporal autocorrelation function, is a critical element in this theory. Ocean memory is enhanced when the tidal forcing is excluded in all spatial scales, more obvious in large and medium scales; correspondingly, the internal variability increased significantly in the large and medium scales, compared with small scales in no-tide simulation. Physically, it can be explained as when the tidal forcing is turned off, once an anomaly appears in the system, it can survive for a longer time and easier to grow into large-scale variability. From the physical process aspect, we demonstrated that internal variability level and baroclinic instability variation co-vary consistently when comparing summer and winter seasons, and with and without tides. Our interpretation is that a stronger baroclinic instability causes more potential energy to be transformed into kinetic energy, allowing the unforced disturbances to grow.
How to cite: Lin, L. and von Storch, H.: The variability caused by external forcing and internal forcing in the marginal sea, Bohai and Yellow Sea , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4907, https://doi.org/10.5194/egusphere-egu25-4907, 2025.
The ocean circulation system in the Western Pacific consists of western boundary currents (WBCs, Kuroshio Current, Mindanao Current, Ryuku Current) and connected with North Equatorial Current (NEC). The system is one of the most complicated current systems, vitally regulating the exchange of mass, energy, and heat transport between the open ocean and the adjacent marginal seas. Previous studies in Western Pacific circulations most focused on the variability of the circulations in specific sections without addressing the intrinsic connectivity and dynamics of currents in the system. Using the high-resolution, validated three-dimensional and time-dependent China Sea Multi-scale Ocean Modeling System (CMOMS, https://odmp.hkust.edu.hk/cmoms/), we quantitatively characterize the variability of the western boundary currents and related circulations in Western Pacific, and investigate their underlying physical processes. Based on physically sensible definitions of the jet stream and currents in the system, we identified characteristic width, depth, and along-/cross-stream transports and their unique spatiotemporal variability in the 3D current system. The momentum and vorticity analyses show the couplings between extrinsic inflow and intrinsic dynamic response of the Kuroshio Current in connections among currents in the system and between the marginal seas and open oceans. Synchronized structures in downstream variations of core velocity, cross-stream transport, eddy kinetic energy and path variability is pronounced along the Kuroshio Current. We found that the spatial patterns of Kuroshio are fundamentally modulated by mean flow-topography interactions, where shelf slope and shelf-current separation distance regulate the horizontal scales of the western boundary current, and thereby modify strain and shear characteristics and subsequent along-stream variability. The effect of topography on the synchronized spatial patterns is studied by energy budgets along the Kuroshio Current. Upstream influx and local flow-topography interaction acts as an external and internal forcing process to modulate the barotropic and baroclinic instability in Kuroshio variability, respectively. Associated time-averaged eddy fluxes are fundamentally reshape the mean current. By resolving three-dimensional, spatiotemporal variability of western current system, the study provides a new understanding to the dynamic connections in the Western Pacific Current system.
How to cite: Han, J.-H. and Gan, J.: Three-dimensional characteristic and variability of the current system in the western Pacific, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6633, https://doi.org/10.5194/egusphere-egu25-6633, 2025.
Understanding the role of ocean-atmosphere interactions is crucial in determining the drivers of ocean variability. Indeed, a part of this variability is not driven by the atmosphere but spontaneously and randomly generated by the ocean through non-linear processes. This internal variability is associated with multiple spatial and temporal scales, and may complicate the detection and attribution of climate change signals. Hence, quantifying the relative importance of atmospherically-forced and chaotic intrinsic variability is necessary to understand the mechanisms of climate change in the ocean-atmosphere system. However, both atmospherically-forced and intrinsic variability cannot be estimated with a single model experiment alone : an ensemble simulation approach is required. The ensemble mean approximates the component of the oceanic variability that is due to the influence of the atmosphere, while the spread represents the range of the estimated intrinsic variability. This work investigates the possibility of describing and predicting the random part of the ocean's variability from observations using an ensemble of ocean simulations in the North Atlantic ocean. An analog-based method is developed, and applied to Sea Surface Height data, with the aim of obtaining a less-computationally expensive method of estimating the time-varying probability function (PDF) that is normally obtained through ensemble simulation. The ensemble is supplied by the multi-decadal (1960-2015) global ocean/sea-ice eddy-permitting (1/4° resolution) large (50-member) ensemble simulation (OCCIPUT Experiment). The ensemble of SSH data as a whole provides the target PDF that we seek to estimate in a regions representative of the diversity of flows in the North Atlantic (e.g. at the centre of the North Atlantic gyre and in the Gulfstream current). The individual members are used to form the catalog of simulations in order to find analogs situations on which the estimate of the target PDF is based at time t. First results are promising and show that we are able to estimate the ensemble mean, but the variance is still a subject of active work due to the complexity of the shape of the PDF. The method greatly reduces the time and resources of computation by producing mean and variance of time-varying PDF for the entire time series in generally a few tens of minutes.
keywords : Internal variability, detection and attribution, model uncertainty, ocean-atmosphere interaction, predictability
How to cite: Presse, B., Close, S., Tandeo, P., and Maze, G.: Estimation of the time-varying probability density function from ensemble simulations and observations using Analogs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6284, https://doi.org/10.5194/egusphere-egu25-6284, 2025.
Ensemble ocean model experiments can be useful to understand the extent to which internal variability has exerted an influence on a given observation, or indeed modelled event. This is important in contexts such as that of ongoing climate change, for example, where this probabilistic information can be useful for the purposes of detection and attribution. However, ensemble simulation has certain disadvantages, including its very high computational and energetic cost, the technical skill required to implement such modelling strategies, and the inherent dependence of the results on model physics. The aim of this study is to address these drawbacks by directly estimating the ensemble mean using statistical methods applied to individual model simulations, or observations. The effects of internal variability should be strongly reduced in these artificial ensemble mean estimates, enabling better insight into the direct effects of atmospheric forcing on the chosen ocean variables.
In previous work, we showed that the ensemble mean sea surface height can be estimated with good accuracy by filtering an individual member of the ensemble. Here, we extend this result to sea surface temperature (SST), which requires a more complicated spatiotemporal filter to estimate the ensemble mean, but again shows good agreement with the true ensemble mean SST at very low computational cost. However, examination of the full 3D temperature fields show a more complicated spectral coherence signature, suggesting that application of the filtering method to these 3D fields would be more challenging. In a second step, a neural network is thus trained to reproduce 3D ocean temperature fields using SST and sea surface height as inputs. By combining the filtered fields with the neural network, first estimates are made of the ensemble mean 3D temperature field, based on observations. Comparisons with the true ensemble mean 3D fields are encouraging, and suggest that the method may be useful as a cheap alternative to numerical simulation to better identify the atmospheric influence on ocean variability.
How to cite: Close, S. and Penduff, T.: Estimates of artificial ensemble mean ocean properties from individual simulations and observations to better isolate the atmospheric influence on ocean variability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15192, https://doi.org/10.5194/egusphere-egu25-15192, 2025.
