Scale-aware neural calibration for wide swath altimetry observations

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which provide one-dimensional-only along-track satellite observations of the SSH. The Surface Water and Ocean Topography (SWOT) mission deploys a new sensor that acquires for the first time wide-swath two-dimensional observations of the SSH. This provides new means to observe the ocean at previously unresolved spatial scales. A critical challenge for the exploiting of SWOT data is the separation of the SSH from other signals present in the observations. In this paper, we propose a novel learning-based approach for this SWOT calibration problem. It benefits from calibrated nadir altimetry products and a scale-space decomposition adapted to SWOT swath geometry and the structure of the different processes in play. In a supervised setting, our method reaches the state-of-the-art residual error of ~1.4cm while proposing a correction on the entire spectral from 10km to 1000kComment: 8 pages, 7 figures, Preprin

Training neural mapping schemes for satellite altimetry with simulation data

Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL) schemes have emerged as appealing solutions to address space-time interpolation problems. The scarcity of real altimetry dataset, in terms of space-time coverage of the sea surface, however impedes the training of state-of-the-art neural schemes on real-world case-studies. Here, we leverage both simulations of ocean dynamics and satellite altimeters to train simulation-based neural mapping schemes for the sea surface height and demonstrate their performance for real altimetry datasets. We analyze further how the ocean simulation dataset used during the training phase impacts this performance. This experimental analysis covers both the resolution from eddy-present configurations to eddy-rich ones, forced simulations vs. reanalyses using data assimilation and tide-free vs. tide-resolving simulations. Our benchmarking framework focuses on a Gulf Stream region for a realistic 5-altimeter constellation using NEMO ocean simulations and 4DVarNet mapping schemes. All simulation-based 4DVarNets outperform the operational observation-driven and reanalysis products, namely DUACS and GLORYS. The more realistic the ocean simulation dataset used during the training phase, the better the mapping. The best 4DVarNet mapping was trained from an eddy-rich and tide-free simulation datasets. It improves the resolved longitudinal scale from 151 kilometers for DUACS and 241 kilometers for GLORYS to 98 kilometers and reduces the root mean squared error (RMSE) by 23% and 61%. These results open research avenues for new synergies between ocean modelling and ocean observation using learning-based approaches

A posteriori learning for quasi-geostrophic turbulence parametrization

The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models. In practice, training data are generated from higher resolution numerical simulations transformed in order to mimic coarse resolution simulations. By essence, these strategies optimize subgrid parametrizations to meet so-called $\textit{a priori}$ criteria. But the actual purpose of a subgrid parametrization is to obtain good performance in terms of $\textit{a posteriori}$ metrics which imply computing entire model trajectories. In this paper, we focus on the representation of energy backscatter in two dimensional quasi-geostrophic turbulence and compare parametrizations obtained with different learning strategies at fixed computational complexity. We show that strategies based on $\textit{a priori}$ criteria yield parametrizations that tend to be unstable in direct simulations and describe how subgrid parametrizations can alternatively be trained end-to-end in order to meet $\textit{a posteriori}$ criteria. We illustrate that end-to-end learning strategies yield parametrizations that outperform known empirical and data-driven schemes in terms of performance, stability and ability to apply to different flow configurations. These results support the relevance of differentiable programming paradigms for climate models in the future.Comment: 36 pages, 14 figures, submitted to Journal of Advances in Modeling Earth Systems (JAMES

Can we map the interannual variability of the whole upper Southern Ocean with the current database of hydrographic observations?

International audienceWith the advent of Argo floats, it now seems feasible to study the interannual variations of upper ocean hydrographic properties of the historically undersampled Southern Ocean. To do so, scattered hydrographic profiles often first need to be mapped. To investigate biases and errors associated both with the limited space-time distribution of the profiles and with the mapping methods, we colocate the mixed-layer depth (MLD) output from a state-of-the-art 1/12° DRAKKAR simulation onto the latitude, longitude, and date of actual in situ profiles from 2005 to 2014. We compare the results obtained after remapping using a nearest neighbor (NN) interpolation and an objective analysis (OA) with different spatiotemporal grid resolutions and decorrelation scales. NN is improved with a coarser resolution. OA performs best with low decorrelation scales, avoiding too strong a smoothing, but returns values over larger areas with large decorrelation scales and low temporal resolution, as more points are available. For all resolutions OA represents better the annual extreme values than NN. Both methods underestimate the seasonal cycle in MLD. MLD biases are lower than 10 m on average but can exceed 250 m locally in winter. We argue that current Argo data should not be mapped to infer decadal trends in MLD, as all methods are unable to reproduce existing trends without creating unrealistic extra ones. We also show that regions of the subtropical Atlantic, Indian, and Pacific Oceans, and the whole ice-covered Southern Ocean, still cannot be mapped even by the best method because of the lack of observational data

Stochastic variability of oceanic flows above topography anomalies

International audienceWe describe a stochastic variability mechanism which is genuinely internal to the ocean, i.e. not due to fluctuations in atmospheric forcing. % The key ingredient is the existence of closed contours of bottom topography surrounded by a stirring region of enhanced eddy activity. This configuration leads to the formation of a robust but highly variable vortex above the topography anomaly. The vortex dynamics integrates the white noise forcing of oceanic eddies into a red noise signal for the large scale volume transport of the vortex. The strong interannual fluctuations of the transport of the Zapiola anticyclone ($\sim 100 \ Sv$) in the Argentine basin are argued to be partly due to such eddy-driven stochastic variability, on the basis of a $310$ years long simulation of a comprehensive global ocean model run driven by a repeated-year forcing

RNAseq Profiling of Leukocyte Populations in Zebrafish Larvae Reveals a cxcl11 Chemokine Gene as a Marker of Macrophage Polarization During Mycobacterial Infection

Macrophages are phagocytic cells from the innate immune system, which forms the first line of host defense against invading pathogens. These highly dynamic immune cells can adopt specific functional phenotypes, with the pro-inflammatory M1 and anti-inflammatory M2 polarization states as the two extremes. Recently, the process of macrophage polarization during inflammation has been visualized by real time imaging in larvae of the zebrafish. This model organism has also become widely used to study macrophage responses to microbial pathogens. To support the increasing use of zebrafish in macrophage biology, we set out to determine the complete transcriptome of zebrafish larval macrophages. We studied the specificity of the macrophage signature compared with other larval immune cells and the macrophage-specific expression changes upon infection. We made use of the well-established mpeg1, mpx, and lck fluorescent reporter lines to sort and sequence the transcriptome of larval macrophages, neutrophils, and lymphoid progenitor cells, respectively. Our results provide a complete dataset of genes expressed in these different immune cell types and highlight their similarities and differences. Major differences between the macrophage and neutrophil signatures were found within the families of proteinases. Furthermore, expression of genes involved in antigen presentation and processing was specifically detected in macrophages, while lymphoid progenitors showed expression of genes involved in macrophage activation. Comparison with datasets of in vitro polarized human macrophages revealed that zebrafish macrophages express a strongly homologous gene set, comprising both M1 and M2 markers. Furthermore, transcriptome analysis of low numbers of macrophages infected by the intracellular pathogen Mycobacterium marinum revealed that infected macrophages change their transcriptomic response by downregulation of M2-associated genes and overexpression of specific M1-associated genes. Among the infection-induced genes, a homolog of the human CXCL11 chemokine gene, cxcl11aa, stood out as the most strongly overexpressed M1 marker. Upregulation of cxcl11aa in Mycobacterium-infected macrophages was found to require the function of Myd88, a critical adaptor molecule in the Toll-like and interleukin 1 receptor pathways that are central to pathogen recognition and activation of the innate immune response. Altogether, our data provide a valuable data mining resource to support infection and inflammation research in the zebrafish model

The contribution of surface and submesoscale processes to turbulence in the open ocean surface boundary layer

The ocean surface boundary layer is a critical interface across which momentum, heat, and trace gases are exchanged between the oceans and atmosphere. Surface processes (winds, waves, and buoyancy forcing) are known to contribute significantly to fluxes within this layer. Recently, studies have suggested that submesoscale processes, which occur at small scales (0.1–10 km, hours to days) and therefore are not yet represented in most ocean models, may play critical roles in these turbulent exchanges. While observational support for such phenomena has been demonstrated in the vicinity of strong current systems and littoral regions, relatively few observations exist in the open‐ocean environment to warrant representation in Earth system models. We use novel observations and simulations to quantify the contributions of surface and submesoscale processes to turbulent kinetic energy (TKE) dissipation in the open‐ocean surface boundary layer. Our observations are derived from moorings in the North Atlantic, December 2012 to April 2013, and are complemented by atmospheric reanalysis. We develop a conceptual framework for dissipation rates due to surface and submesoscale processes. Using this framework and comparing with observed dissipation rates, we find that surface processes dominate TKE dissipation. A parameterization for symmetric instability is consistent with this result. We next employ simulations from an ocean front‐resolving model to reestablish that dissipation due to surface processes exceeds that of submesoscale processes by 1–2 orders of magnitude. Together, these results suggest submesoscale processes do not dramatically modify vertical TKE budgets, though such dynamics may be climatically important owing to their ability to remove energy from the ocean

Merger Histories in Warm Dark Matter Structure Formation Scenario

Observations on galactic scales seem to be in contradiction with recent high resolution N-body simulations. This so-called cold dark matter (CDM) crisis has been addressed in several ways, ranging from a change in fundamental physics by introducing self-interacting cold dark matter particles to a tuning of complex astrophysical processes such as global and/or local feedback. All these efforts attempt to soften density profiles and reduce the abundance of satellites in simulated galaxy halos. In this paper, we explore a somewhat different approach which consists of filtering the dark matter power spectrum on small scales, thereby altering the formation history of low mass objects. The physical motivation for damping these fluctuations lies in the possibility that the dark matter particles have a different nature i.e. are warm (WDM) rather than cold. We show that this leads to some interesting new results in terms of the merger history and large-scale distribution of low mass halos, as compared to the standard CDM scenario. However, WDM does not appear to be the ultimate solution, in the sense that it is not able to fully solve the CDM crisis, even though one of the main drawbacks, namely the abundance of satellites, can be remedied. Indeed, the cuspiness of the halo profiles still persists, at all redshifts, and for all halos and sub-halos that we investigated. Despite the persistence of the cuspiness problem of DM halos, WDM seems to be still worth taking seriously, as it alleviates the problems of overabundant sub-structures in galactic halos and possibly the lack of angular momentum of simulated disk galaxies. WDM also lessens the need to invoke strong feedback to solve these problems, and may provide a natural explanation of the clustering properties and ages of dwarfs.Comment: 11 pages, 17 figures, MNRAS submitted, high-res figures can be found at http://www-thphys.physics.ox.ac.uk/users/AlexanderKnebe/publications.html, replaced with accepted version (warmon masses corrected!