Edda

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Bitcoin Runes: How Do the New Tokens on the Bitcoin Network Differ From BRC-20?

Ever wondered how the magic of seamless software development and deployment happens? Say hello to GitOps – your new best friend in the world of coding wonders! Are you ready to enter into the world of GitOps? Let’s dive in! What is GitOps? GitOps is a term that describes a set of practices that use Git as the source of truth for software development and deployment. GitOps leverages the power of version control to manage the entire lifecycle of applications and infrastructure. is a vital component of DevOps, enabling developers to track, collaborate, and review code changes consistently and reliably. Version control also facilitates continuous integration and delivery, as it allows for automated testing and deployment of code changes. Version control GitOps addresses some of the challenges that traditional workflows face, such as manual intervention, configuration drift, human error, etc. Using Git, enables the desired state of the system to always be defined, documented, and versioned in Git. Core Principles of GitOps GitOps also follows some key principles that make it a compelling approach for modern development and deployment. Some of these principles and benefits are: GitOps uses declarative configuration files to describe the desired state of the system. Declarative being that it is supported by a set of facts not instructions. This makes the system easier to understand, maintain, and reproduce. Declarative: It uses automation tools to balance the current state of the system with the desired state defined in Git. This eliminates human intervention which reduces human error while enabling faster and more reliable deployments. Automated: It ensures that the system can be reproduced in any environment, as long as the same configuration files are used. This helps us produce the same result across different stages and platforms. Reproducible: GitOps allows for easy reversal and recovery, as any change made to the system can be traced back to a specific commit in Git. Reversible: GitOps provides a complete history and audit trail of all changes made to the system, as every change is recorded and tracked in Git. This enables transparency, accountability, and compliance. Auditable: Understanding GitOps Concepts 1. Version Control with Git is a distributed version control system that enables developers to manage code changes collaboratively and efficiently. Git allows developers to create branches, which are separate copies of the code base that can be modified independently without affecting the main branch. Your codebase on git is like a tree, it can have so many branches if a branch is cut off, it doesn't affect the entire tree. Git Branching strategies are important in GitOps workflows, as they define how developers work on different features, fixes, or enhancements, and how they merge them back to the main branch. 2. Infrastructure as Code (IaC) Infrastructure as Code (IaC) is a practice that involves using code or configuration files to define, provision, and manage infrastructure resources such as servers, networks, storage, etc. IaC enables developers to treat infrastructure as software, applying the same principles of version control, automation, reproducibility, etc. IaC plays a crucial role in GitOps, as it allows developers to store the configuration files that describe the infrastructure state in Git, along with the application code. This ensures that the infrastructure is always aligned with the application requirements, and can be deployed or updated automatically. Some various tools and technologies enable IaC, such as Aptible which helps in automating infrastructure management. Declarative Configuration Management Declarative configuration management is a technique that involves using configuration files to specify the desired state of a system or an application, rather than using commands or scripts to instruct how to achieve it. Declarative configuration management simplifies the process of defining and maintaining complex systems or applications by abstracting away the implementation details. Declarative configuration management is widely used in GitOps workflows, as it enables developers to store the configuration files that describe the application state in Git, along with the infrastructure state. This ensures that the application is always deployed or updated according to the configuration files, and any discrepancy between the actual and desired state is detected and corrected automatically. Declarative configuration files are usually written in YAML or JSON formats, which are human-readable and machine-parsable. YAML and files can be used to define various aspects of an application, such as its components, dependencies, resources, environment variables, etc. JSON GitOps in Action: Implementation and Tools GitOps Workflow Lifecycle A typical GitOps workflow consists of four main phases: code development, continuous integration, continuous deployment, and release automation. These phases are illustrated in the following diagram: Code Development Phase: This phase involves developing and testing the application code and the configuration files that define the infrastructure and application state. Developers use Git to create branches, make commits, and perform code reviews. Once the code and configuration files are ready, they are merged into the main branch. Continuous Integration Phase: This phase involves building and testing the application code and the configuration files using automation tools. The tools check for syntax errors, code quality, security vulnerabilities, etc., and run unit tests, integration tests, etc. If the tests pass, the code and configuration files are pushed to a remote repository in Git. Continuous Deployment Phase: This phase involves deploying and updating the infrastructure and application state using automation tools. The tools monitor the remote repository in Git for any changes and synchronize the actual state of the system with the desired state defined in the code and configuration files. If there are any discrepancies, the tools apply the changes automatically or notify the developers for manual intervention. Release Automation Phase: This phase involves releasing and delivering the application to the end-users using automation tools. The tools manage the release process, such as creating release notes, tagging versions, rolling out updates, etc. The tools also enable features such as canary deployments, blue-green deployments, etc., to ensure smooth and safe releases. GitOps Tools and Platforms Some various tools and platforms enable GitOps workflows, such as ArgoCD, Flux, Jenkins X, WeaveWorks, etc. These tools and platforms differ in scope, features, and capabilities, but they all share the common goal of automating GitOps workflows. Some of the features and capabilities that these tools and platforms offer are: Git Integration: The tools and platforms integrate with Git repositories to monitor and synchronize changes to the code and configuration files. They also support various Git operations, such as branching, merging, tagging, etc. IaC Support: The tools and platforms support various IaC tools and technologies, such as Aptible. They also support various cloud providers and platforms, such as AWS, Azure, GCP, Kubernetes, etc. Declarative Configuration Management: The tools and platforms support declarative configuration management using YAML or JSON files. They also support various configuration formats and standards, such as Kubernetes manifests, Helm charts, Customize overlays, etc. Continuous Integration and Testing: The tools and platforms provide continuous integration and testing capabilities using various automation tools and frameworks. They also provide feedback loops and quality gates to ensure code quality and reliability. Continuous Deployment and Synchronization: The tools and platforms provide continuous deployment and synchronization capabilities using various automation tools and mechanisms. They also provide reconciliation loops and health checks to ensure system consistency and stability. Release Automation and Delivery: The tools and platforms provide release automation and delivery capabilities using various automation tools and strategies. They also provide observability and traceability features to ensure system performance and availability. Advantages of GitOps Adoption Enhanced Collaboration and Transparency One of the main advantages of GitOps adoption is that it enhances collaboration and transparency among developers and other stakeholders involved in software development and deployment. By using Git as the single source of truth for both code and configuration files. Git also facilitates collaboration by enabling developers to work on different branches without affecting each other’s work. Developers can also perform code reviews using pull requests or merge requests before merging their changes to the main branch. This ensures that the code quality is maintained throughout the development process. GitOps also provides transparency by recording every change made to the system in Git history. Developers can easily track who made what change when using commit messages or tags. This enables accountability and traceability for every change made to the system. Improved Traceability and Auditing Another advantage of GitOps adoption is that it improves traceability and auditing for software development and deployment. GitOps also provides auditing by creating a complete audit trail of all changes made to the system in Git. Developers can easily verify who authorized, approved, or deployed any change using pull requests or merge requests. This enables compliance and governance for software development and deployment. Scalability and Resource Efficiency It enhances scalability and resource efficiency for software development and deployment. By using automation tools to synchronize the system state with the code and configuration files in Git, GitOps ensures that the system can scale up or down according to the demand and requirements. GitOps also enables resource optimization by using automation tools to deploy or update the system efficiently and consistently. GitOps reduces the need for manual intervention and human error, which can lead to wasted resources or inconsistent results. GitOps also leverages the cloud-native capabilities of platforms such as Kubernetes, which can dynamically allocate and manage resources for applications and infrastructure. Challenges and Best Practices in GitOps Common Challenges in GitOps Implementation GitOps is not without its challenges, as it requires a shift in mindset, culture, and processes for software development and deployment. Some of the common challenges that developers may face when implementing GitOps are: Synchronization Issues: One of the main challenges in GitOps is ensuring that the system state is always synchronized with the code and configuration files in Git. This requires a reliable and robust automation tool that can detect and correct any discrepancies between the actual and desired state. It also requires clear communication and coordination among developers and other stakeholders to avoid conflicts or inconsistencies. Secrets Management: Another challenge in GitOps is managing secrets and sensitive information, such as passwords, keys, tokens, etc., that are required for accessing or deploying the system. Storing secrets in plain text in Git is not secure, as it exposes them to unauthorized access or leakage. Therefore, developers need to use encryption or other methods to protect secrets and use tools that can securely inject secrets into the system at runtime. Best Practices for GitOps Success To overcome the challenges and achieve success with GitOps, developers need to follow some best practices that can help them implement GitOps effectively and efficiently. Some of these best practices are: One of the best practices for GitOps is to securely manage access control and permissions for both code and configuration files in Git. Developers need to use authentication and authorization mechanisms to ensure that only authorized users can access or modify the code and configuration files. Developers also need to use encryption and signing methods to ensure that the code and configuration files are not tampered with or corrupted. Securely Managing Access Control and Permissions: I Another best practice for GitOps is to implement effective monitoring and alerting systems for both code and configuration files in Git. Developers need to use tools that can monitor the status and health of the code and configuration files, as well as the system state. Developers also need to use tools that can alert them of any changes, errors, or issues that occur in the code and configuration files, or the system state. mplementing Effective Monitoring and Alerting: GitOps is not just a theoretical concept, but a practical approach that has been adopted by many organizations across different industries. Some of these organizations are: is one of the world’s leading streaming platforms, serving over 200 million subscribers across 190 countries. Netflix uses GitOps to manage its complex and dynamic infrastructure and application state on AWS. Netflix uses tools such as Spinnaker, Terraform, and Chaos Monkey to automate its deployment and synchronization processes, as well as its resilience testing. Netflix also uses tools such as Atlas, Edda, and Vizceral to monitor and visualize its system state. Netflix: Netflix : Weaveworks is one of the pioneers and advocates of GitOps, providing a platform and tools that enable developers to implement GitOps workflows on Kubernetes. Weaveworks uses GitOps internally to manage its own infrastructure and application state on Kubernetes. Weaveworks uses tools such as Flux, Helm, Prometheus, and Grafana to automate its deployment and synchronization processes, as well as its monitoring and alerting systems. Weaveworks Intuit is one of the world’s leading financial software companies, providing products and services such as QuickBooks, TurboTax, and Mint. Intuit uses GitOps to manage its infrastructure and application state on Kubernetes. Intuit uses tools such as ArgoCD, Kustomize, and Istio to automate its deployment and synchronization processes, as well as its service mesh. Intuit also uses tools such as Wavefront, PagerDuty, and Slack to monitor and alert its system state. Intuit: GitOps is not a final destination, but a journey that continues to evolve and improve over time. As the software development and deployment landscape changes and advances, so does GitOps. Conclusion GitOps is a powerful and promising approach that can transform your software development and deployment processes. By using Git as the source of truth for both code and configuration files, you can achieve faster, smarter, and safer software development and deployment. It is more than just a technical approach, but a cultural and organizational one. It is a way of thinking and doing software development and deployment. However, remember that GitOps is not a one-size-fits-all solution, but a flexible and adaptable one. You need to continuously learn and adapt to the changing requirements and challenges of software development and deployment. You also need to experiment and innovate with new tools and technologies that can enhance your GitOps workflows. We believe that GitOps is the future of software development and deployment. We invite you to join us in this journey and embrace the transformative power of GitOps. Reference What is a GitOps workflow? | GitLab Guide To GitOps (weave.works) How to Deploy to Kubernetes using Argo CD and GitOps | DigitalOcean IBM Developer GitOps Workflow - Introduction to GitOps - Flux Based Course (cloudacademy.com) What is GitOps? (redhat.com) Guide To GitOps (weave.works)

A Deep Dive Into the GitOps World

Some General Remarks on Psychoanalysis

Physical Geography

This morning, after watching President-Elect Donald Trump's announcement regarding his plan to "shatter" the censorship regime, I experienced a profound epiphany. The discussion surrounding free speech and the manipulation of information sparked thoughts about the potential for change in the current political landscape, particularly concerning figures like Edward Snowden. For years, Snowden has been a polarizing figure—celebrated by many as a whistleblower and condemned by others as a traitor. His revelations about government surveillance practices opened a critical dialogue about privacy, security, and the role of government in the lives of citizens. Yet, despite the importance of his contributions to public discourse, he has remained in exile, away from his homeland. In Trump's speech, he emphasized the need to dismantle what he described as a "censorship cartel" created by various entities, including government agencies and corporate media. This call to action resonated with me, especially when considering Snowden's situation. If there is indeed a movement towards restoring free speech and challenging the status quo, could this pave the way for Snowden's return to the United States? Since 2013, Snowden has made a life for himself in Russia. His story has become emblematic of the complex relationship between government secrecy and the public's right to know. The speech I watched, with its bold promises about preventing federal agencies from "colluding" to control information and its calls for investigations into what it termed "online censorship," made me wonder about the possibility of change. The words echoed through the transcript – departments like Homeland Security, Health and Human Services, the FBI, and Department of Justice all mentioned by name. Each reference carried the weight of potential transformation in how our government handles information and those who choose to reveal it. President Trump's emphasis on "reclaiming" free speech struck a particular chord, though the reality of whistleblower protection remains far more complex than any single speech or policy proposal can address. Just as Julian Assange recently returned to Australia, there may be hope for Snowden as well. The political climate appears to be shifting, with more citizens becoming aware of the importance of transparency and the dangers of unchecked power. If the Trump administration truly prioritizes the restoration of free speech, it might also reconsider the stance towards those who have fought for that very principle. Snowden's return would not only symbolize a victory for whistleblowers everywhere but also serve as a reminder that accountability and justice can prevail. It is essential for our democracy to embrace those who have risked everything to expose the truth. As we look towards the future, the light at the end of the tunnel seems brighter. With ongoing conversations about censorship and the rights of individuals, perhaps we are on the brink of a significant shift. It is time to advocate for a system where whistleblowers are protected and celebrated rather than vilified. In conclusion, my hope for Edward Snowden's return is intertwined with the broader movement towards reclaiming free speech and accountability. As we navigate these complex issues, let us remain vigilant and support those who dare to stand up for the truth, for they are the ones who illuminate the path toward a more just society. This piece represents my own personal observations and thoughts on complex issues. Readers should pursue their own research and consult official sources for ground-truthing. Follow me here on Hackernoon for more insights and updates. This morning, after watching President-Elect Donald Trump's announcement regarding his plan to "shatter" the censorship regime, I experienced a profound epiphany. The discussion surrounding free speech and the manipulation of information sparked thoughts about the potential for change in the current political landscape, particularly concerning figures like Edward Snowden. For years, Snowden has been a polarizing figure—celebrated by many as a whistleblower and condemned by others as a traitor. His revelations about government surveillance practices opened a critical dialogue about privacy, security, and the role of government in the lives of citizens. Yet, despite the importance of his contributions to public discourse, he has remained in exile, away from his homeland. In Trump's speech, he emphasized the need to dismantle what he described as a "censorship cartel" created by various entities, including government agencies and corporate media. This call to action resonated with me, especially when considering Snowden's situation. If there is indeed a movement towards restoring free speech and challenging the status quo, could this pave the way for Snowden's return to the United States? Since 2013, Snowden has made a life for himself in Russia. His story has become emblematic of the complex relationship between government secrecy and the public's right to know. The speech I watched, with its bold promises about preventing federal agencies from "colluding" to control information and its calls for investigations into what it termed "online censorship," made me wonder about the possibility of change. The words echoed through the transcript – departments like Homeland Security, Health and Human Services, the FBI, and Department of Justice all mentioned by name. Each reference carried the weight of potential transformation in how our government handles information and those who choose to reveal it. President Trump's emphasis on "reclaiming" free speech struck a particular chord, though the reality of whistleblower protection remains far more complex than any single speech or policy proposal can address. Just as Julian Assange recently returned to Australia, there may be hope for Snowden as well. The political climate appears to be shifting, with more citizens becoming aware of the importance of transparency and the dangers of unchecked power. If the Trump administration truly prioritizes the restoration of free speech, it might also reconsider the stance towards those who have fought for that very principle. Snowden's return would not only symbolize a victory for whistleblowers everywhere but also serve as a reminder that accountability and justice can prevail. It is essential for our democracy to embrace those who have risked everything to expose the truth. As we look towards the future, the light at the end of the tunnel seems brighter. With ongoing conversations about censorship and the rights of individuals, perhaps we are on the brink of a significant shift. It is time to advocate for a system where whistleblowers are protected and celebrated rather than vilified. In conclusion, my hope for Edward Snowden's return is intertwined with the broader movement towards reclaiming free speech and accountability. As we navigate these complex issues, let us remain vigilant and support those who dare to stand up for the truth, for they are the ones who illuminate the path toward a more just society. This piece represents my own personal observations and thoughts on complex issues. Readers should pursue their own research and consult official sources for ground-truthing. Follow me here on Hackernoon for more insights and updates. This piece represents my own personal observations and thoughts on complex issues. Readers should pursue their own research and consult official sources for ground-truthing. Follow me here on Hackernoon for more insights and updates. Hackernoon

Finding Hope: The Possibility of Edward Snowden's Return

আশার খোঁজ: এডওয়ার্ড স্নোডেনের প্রত্যাবর্তনের সম্ভাবনা

Hoffnung finden: Die Möglichkeit einer Rückkehr von Edward Snowden

Encontrando esperanza: la posibilidad del regreso de Edward Snowden

Trouver l'espoir : la possibilité du retour d'Edward Snowden

आशा की तलाश: एडवर्ड स्नोडेन की वापसी की संभावना

希望を見つける：エドワード・スノーデンの復帰の可能性

희망을 찾다: 에드워드 스노든의 귀환 가능성

Encontrando esperança: a possibilidade do retorno de Edward Snowden

В поисках надежды: возможность возвращения Эдварда Сноудена

Umut Bulmak: Edward Snowden'ın Geri Dönme İhtimali

Tìm kiếm hy vọng: Khả năng Edward Snowden trở về

寻找希望：爱德华·斯诺登回归的可能性

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References References Alford, M. H., 2001: Internal swell generation: The spatial distribution of energy flux from the wind to mixed layer near-inertial motions. Journal of Physical Oceanography, 31 (8), 2359–2368. Alford, M. H., 2020: Revisiting near-inertial wind work: Slab models, relative stress, and mixed layer deepening. Journal of Physical Oceanography, 50 (11), 3141–3156. Alford, M. H., J. A. MacKinnon, H. L. Simmons, and J. D. Nash, 2016: Near-inertial internal gravity waves in the ocean. Annual review of marine science, 8, 95–123. Arbic, B. K., and G. R. Flierl, 2004: Baroclinically unstable geostrophic turbulence in the limits of strong and weak bottom ekman friction: Application to midocean eddies. Journal of Physical Oceanography, 34 (10), 2257–2273. Asselin, O., and W. R. Young, 2020: Penetration of wind-generated near-inertial waves into a turbulent ocean. Journal of Physical Oceanography, 50 (6), 1699–1716. Balmforth, N., S. G. Llewellyn Smith, and W. Young, 1998: Enhanced dispersion of near-inertial waves in an idealized geostrophic flow. Journal of marine research, 56 (1), 1–40. Balmforth, N. J., and W. R. Young, 1999: Radiative damping of near-inertial oscillations in the mixed layer. Journal of marine research, 57 (4), 561–584. Buckingham, C. E., and Coauthors, 2016: Seasonality of submesoscale flows in the ocean surface boundary layer. Geophysical Research Letters, 43 (5), 2118–2126. Burns, K. J., G. M. Vasil, J. S. Oishi, D. Lecoanet, and B. P. Brown, 2020: Dedalus: A flexible framework for numerical simulations with spectral methods. Phys. Rev. Res., 2, 023 068. Callies, J., R. Barkan, and A. N. Garabato, 2020: Time scales of submesoscale flow inferred from a mooring array. Journal of Physical Oceanography, 50 (4), 1065–1086. Damerell, G. M., K. J. Heywood, D. Calvert, A. L. Grant, M. J. Bell, and S. E. Belcher, 2020: A comparison of five surface mixed layer models with a year of observations in the north atlantic. Progress in Oceanography, 187, 102 316. Damerell, G. M., K. J. Heywood, A. F. Thompson, U. Binetti, and J. Kaiser, 2016: The vertical structure of upper ocean variability at the porcupine abyssal plain during 2012–2013. Journal of Geophysical Research: Oceans, 121 (5), 3075–3089. D’Asaro, E. A., 1985: The energy flux from the wind to near-inertial motions in the surface mixed layer. Journal of Physical Oceanography, 15 (8), 1043–1059. D’Asaro, E. A., 1995: Upper-ocean inertial currents forced by a strong storm. Part III: Interaction of inertial currents and mesoscale eddies. Journal of physical oceanography, 25 (11), 2953–2958. D’Asaro, E. A., C. C. Eriksen, M. D. Levine, P. Niiler, P. Van Meurs, and Coauthors, 1995: Upperocean inertial currents forced by a strong storm. Part I: Data and comparisons with linear theory. Journal of Physical Oceanography, 25 (11), 2909–2936. Dewar, W. K., and G. R. Flierl, 1987: Some effects of the wind on rings. Journal of Physical Oceanography, 17 (10), 1653–1667. Essink, S., E. Kunze, R.-C. Lien, R. Inoue, and S.-i. Ito, 2022: Near-inertial wave interactions and turbulence production in a kuroshio anticyclonic eddy. Journal of Physical Oceanography, 52 (11), 2687–2704. Ferrari, R., and C. Wunsch, 2009: Ocean circulation kinetic energy: Reservoirs, sources, and sinks. Annual Review of Fluid Mechanics, 41, 253–282. Flexas, M. M., A. F. Thompson, H. S. Torres, P. Klein, J. T. Farrar, H. Zhang, and D. Menemenlis, 2019: Global estimates of the energy transfer from the wind to the ocean, with emphasis on near-inertial oscillations. Journal of Geophysical Research: Oceans, 124 (8), 5723–5746. Gill, A. E., 1984: On the behavior of internal waves in the wakes of storms. Journal of Physical Oceanography, 14 (7), 1129–1151. Gnanadesikan, A., M.-A. Pradal, and R. Abernathey, 2015: Isopycnal mixing by mesoscale eddies significantly impacts oceanic anthropogenic carbon uptake. Geophysical Research Letters, 42 (11), 4249–4255. Guan, S., W. Zhao, J. Huthnance, J. Tian, and J. Wang, 2014: Observed upper ocean response to typhoon megi (2010) in the northern south china sea. Journal of Geophysical Research: Oceans, 119 (5), 3134–3157. Hersbach, H., and Coauthors, 2018: Era5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds), 10 (10.24381). Jayne, S. R., and J. Marotzke, 2002: The oceanic eddy heat transport. Journal of Physical Oceanography, 32 (12), 3328–3345. Jochum, M., B. P. Briegleb, G. Danabasoglu, W. G. Large, N. J. Norton, S. R. Jayne, M. H. Alford, and F. O. Bryan, 2013: The impact of oceanic near-inertial waves on climate. Journal of Climate, 26 (9), 2833–2844. Kato, H., and O. Phillips, 1969: On the penetration of a turbulent layer into stratified fluid. Journal of Fluid Mechanics, 37 (4), 643–655. Kunze, E., 1985: Near-inertial wave propagation in geostrophic shear. Journal of Physical Oceanography, 15 (5), 544–565. Large, W., and S. Pond, 1981: Open ocean momentum flux measurements in moderate to strong winds. Journal of physical oceanography, 11 (3), 324–336. Lee, D.-K., and P. P. Niiler, 1998: The inertial chimney: The near-inertial energy drainage from the ocean surface to the deep layer. 103 (C4), 7579–7591. Moehlis, J., and S. G. L. Smith, 2001: Radiation of mixed layer near-inertial oscillations into the ocean interior. Journal of physical oceanography, 31 (6), 1550–1560. M¨uller, P., J. McWilliams, and J. Molemaker, 2011: Routes to dissipation in the ocean: The 2D/3D turbulence conundrum. Marine Turbulence: Theories, Observations, and Models, Cambridge University Press. Nikurashin, M., G. K. Vallis, and A. Adcroft, 2013: Routes to energy dissipation for geostrophic flows in the Southern Ocean. Nature Geoscience, 6 (1), 48–51. Plueddemann, A., and J. Farrar, 2006: Observations and models of the energy flux from the wind to mixed-layer inertial currents. Deep Sea Research Part II: Topical Studies in Oceanography, 53 (1-2), 5–30. Pollard, R. T., and R. Millard, 1970: Comparison between observed and simulated wind-generated inertial oscillations. Deep Sea Research and Oceanographic Abstracts, 17, 813–821. Pollard, R. T., P. B. Rhines, and R. O. Thompson, 1973: The deepening of the wind-mixed layer. Geophysical Fluid Dynamics, 4 (4), 381–404. Rama, J., C. J. Shakespeare, and A. M. Hogg, 2022: The wavelength dependence of the propagation of near-inertial internal waves. Journal of Physical Oceanography, 52 (10), 2493–2514. Renault, L., M. J. Molemaker, J. C. McWilliams, A. F. Shchepetkin, F. Lemarie, D. Chelton, ´ S. Illig, and A. Hall, 2016: Modulation of wind work by oceanic current interaction with the atmosphere. Journal of Physical Oceanography, 46 (6), 1685–1704. Rocha, C. B., G. L. Wagner, and W. R. Young, 2018: Stimulated generation: extraction of energy from balanced flow by near-inertial waves. Journal of Fluid Mechanics, 847, 417–451. Taburet, G., A. Sanchez-Roman, M. Ballarotta, M.-I. Pujol, J.-F. Legeais, F. Fournier, Y. Faugere, and G. Dibarboure, 2019: Duacs dt2018: 25 years of reprocessed sea level altimetry products. Ocean Science, 15 (5), 1207–1224. Thomas, L. N., L. Rainville, O. Asselin, W. R. Young, J. Girton, C. B. Whalen, L. Centurioni, and V. Hormann, 2020: Direct observations of near-inertial wave 𝜁-refraction in a dipole vortex. Geophysical Research Letters, 47 (21), e2020GL090 375. Thompson, A. F., A. Lazar, C. Buckingham, A. C. N. Garabato, G. M. Damerell, and K. J. Heywood, 2016: Open-ocean submesoscale motions: A full seasonal cycle of mixed layer instabilities from gliders. Journal of Physical Oceanography, 46 (4), 1285–1307. von Storch, J.-S., and V. L¨uschow, 2023: Wind power input to ocean near-inertial waves diagnosed from a 5-km global coupled atmosphere-ocean general circulation model. Journal of Geophysical Research: Oceans, 128 (2), e2022JC019 111. Wagner, G., and W. Young, 2016: A three-component model for the coupled evolution of near-inertial waves, quasi-geostrophic flow and the near-inertial second harmonic. Journal of Fluid Mechanics, 802, 806–837. Wunsch, C., and R. Ferrari, 2004: Vertical mixing, energy, and the general circulation of the oceans. Annu. Rev. Fluid Mech., 36, 281–314. Xie, J.-H., and J. Vanneste, 2015: A generalised-lagrangian-mean model of the interactions between near-inertial waves and mean flow. Journal of Fluid Mechanics, 774, 143–169. Young, W., and M. Ben Jelloul, 1997: Propagation of near-inertial oscillations through a geostrophic flow. JMR, 55, 735–766. Yu, X., A. C. Naveira Garabato, C. Vic, J. Gula, A. C. Savage, J. Wang, A. F. Waterhouse, and J. A. MacKinnon, 2022: Observed equatorward propagation and chimney effect of near-inertial waves in the midlatitude ocean. Geophysical Research Letters, 49 (13), e2022GL098 522. Zhai, X., H. L. Johnson, and D. P. Marshall, 2010: Significant sink of ocean-eddy energy near western boundaries. Nature Geoscience, 3 (9), 608–612. This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References References Alford, M. H., 2001: Internal swell generation: The spatial distribution of energy flux from the wind to mixed layer near-inertial motions. Journal of Physical Oceanography, 31 (8), 2359–2368. Alford, M. H., 2020: Revisiting near-inertial wind work: Slab models, relative stress, and mixed layer deepening. Journal of Physical Oceanography, 50 (11), 3141–3156. Alford, M. H., J. A. MacKinnon, H. L. Simmons, and J. D. Nash, 2016: Near-inertial internal gravity waves in the ocean. Annual review of marine science, 8, 95–123. Arbic, B. K., and G. R. Flierl, 2004: Baroclinically unstable geostrophic turbulence in the limits of strong and weak bottom ekman friction: Application to midocean eddies. Journal of Physical Oceanography, 34 (10), 2257–2273. Asselin, O., and W. R. Young, 2020: Penetration of wind-generated near-inertial waves into a turbulent ocean. Journal of Physical Oceanography, 50 (6), 1699–1716. Balmforth, N., S. G. Llewellyn Smith, and W. Young, 1998: Enhanced dispersion of near-inertial waves in an idealized geostrophic flow. Journal of marine research, 56 (1), 1–40. Balmforth, N. J., and W. R. Young, 1999: Radiative damping of near-inertial oscillations in the mixed layer. Journal of marine research, 57 (4), 561–584. Buckingham, C. E., and Coauthors, 2016: Seasonality of submesoscale flows in the ocean surface boundary layer. Geophysical Research Letters, 43 (5), 2118–2126. Burns, K. J., G. M. Vasil, J. S. Oishi, D. Lecoanet, and B. P. Brown, 2020: Dedalus: A flexible framework for numerical simulations with spectral methods. Phys. Rev. Res., 2, 023 068. Callies, J., R. Barkan, and A. N. Garabato, 2020: Time scales of submesoscale flow inferred from a mooring array. Journal of Physical Oceanography, 50 (4), 1065–1086. Damerell, G. M., K. J. Heywood, D. Calvert, A. L. Grant, M. J. Bell, and S. E. Belcher, 2020: A comparison of five surface mixed layer models with a year of observations in the north atlantic. Progress in Oceanography, 187, 102 316. Damerell, G. M., K. J. Heywood, A. F. Thompson, U. Binetti, and J. Kaiser, 2016: The vertical structure of upper ocean variability at the porcupine abyssal plain during 2012–2013. Journal of Geophysical Research: Oceans, 121 (5), 3075–3089. D’Asaro, E. A., 1985: The energy flux from the wind to near-inertial motions in the surface mixed layer. Journal of Physical Oceanography, 15 (8), 1043–1059. D’Asaro, E. A., 1995: Upper-ocean inertial currents forced by a strong storm. Part III: Interaction of inertial currents and mesoscale eddies. Journal of physical oceanography, 25 (11), 2953–2958. D’Asaro, E. A., C. C. Eriksen, M. D. Levine, P. Niiler, P. Van Meurs, and Coauthors, 1995: Upperocean inertial currents forced by a strong storm. Part I: Data and comparisons with linear theory. Journal of Physical Oceanography, 25 (11), 2909–2936. Dewar, W. K., and G. R. Flierl, 1987: Some effects of the wind on rings. Journal of Physical Oceanography, 17 (10), 1653–1667. Essink, S., E. Kunze, R.-C. Lien, R. Inoue, and S.-i. Ito, 2022: Near-inertial wave interactions and turbulence production in a kuroshio anticyclonic eddy. Journal of Physical Oceanography, 52 (11), 2687–2704. Ferrari, R., and C. Wunsch, 2009: Ocean circulation kinetic energy: Reservoirs, sources, and sinks. Annual Review of Fluid Mechanics, 41, 253–282. Flexas, M. M., A. F. Thompson, H. S. Torres, P. Klein, J. T. Farrar, H. Zhang, and D. Menemenlis, 2019: Global estimates of the energy transfer from the wind to the ocean, with emphasis on near-inertial oscillations. Journal of Geophysical Research: Oceans, 124 (8), 5723–5746. Gill, A. E., 1984: On the behavior of internal waves in the wakes of storms. Journal of Physical Oceanography, 14 (7), 1129–1151. Gnanadesikan, A., M.-A. Pradal, and R. Abernathey, 2015: Isopycnal mixing by mesoscale eddies significantly impacts oceanic anthropogenic carbon uptake. Geophysical Research Letters, 42 (11), 4249–4255. Guan, S., W. Zhao, J. Huthnance, J. Tian, and J. Wang, 2014: Observed upper ocean response to typhoon megi (2010) in the northern south china sea. Journal of Geophysical Research: Oceans, 119 (5), 3134–3157. Hersbach, H., and Coauthors, 2018: Era5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds), 10 (10.24381). Jayne, S. R., and J. Marotzke, 2002: The oceanic eddy heat transport. Journal of Physical Oceanography, 32 (12), 3328–3345. Jochum, M., B. P. Briegleb, G. Danabasoglu, W. G. Large, N. J. Norton, S. R. Jayne, M. H. Alford, and F. O. Bryan, 2013: The impact of oceanic near-inertial waves on climate. Journal of Climate, 26 (9), 2833–2844. Kato, H., and O. Phillips, 1969: On the penetration of a turbulent layer into stratified fluid. Journal of Fluid Mechanics, 37 (4), 643–655. Kunze, E., 1985: Near-inertial wave propagation in geostrophic shear. Journal of Physical Oceanography, 15 (5), 544–565. Large, W., and S. Pond, 1981: Open ocean momentum flux measurements in moderate to strong winds. Journal of physical oceanography, 11 (3), 324–336. Lee, D.-K., and P. P. Niiler, 1998: The inertial chimney: The near-inertial energy drainage from the ocean surface to the deep layer. 103 (C4), 7579–7591. Moehlis, J., and S. G. L. Smith, 2001: Radiation of mixed layer near-inertial oscillations into the ocean interior. Journal of physical oceanography, 31 (6), 1550–1560. M¨uller, P., J. McWilliams, and J. Molemaker, 2011: Routes to dissipation in the ocean: The 2D/3D turbulence conundrum. Marine Turbulence: Theories, Observations, and Models, Cambridge University Press. Nikurashin, M., G. K. Vallis, and A. Adcroft, 2013: Routes to energy dissipation for geostrophic flows in the Southern Ocean. Nature Geoscience, 6 (1), 48–51. Plueddemann, A., and J. Farrar, 2006: Observations and models of the energy flux from the wind to mixed-layer inertial currents. Deep Sea Research Part II: Topical Studies in Oceanography, 53 (1-2), 5–30. Pollard, R. T., and R. Millard, 1970: Comparison between observed and simulated wind-generated inertial oscillations. Deep Sea Research and Oceanographic Abstracts, 17, 813–821. Pollard, R. T., P. B. Rhines, and R. O. Thompson, 1973: The deepening of the wind-mixed layer. Geophysical Fluid Dynamics, 4 (4), 381–404. Rama, J., C. J. Shakespeare, and A. M. Hogg, 2022: The wavelength dependence of the propagation of near-inertial internal waves. Journal of Physical Oceanography, 52 (10), 2493–2514. Renault, L., M. J. Molemaker, J. C. McWilliams, A. F. Shchepetkin, F. Lemarie, D. Chelton, ´ S. Illig, and A. Hall, 2016: Modulation of wind work by oceanic current interaction with the atmosphere. Journal of Physical Oceanography, 46 (6), 1685–1704. Rocha, C. B., G. L. Wagner, and W. R. Young, 2018: Stimulated generation: extraction of energy from balanced flow by near-inertial waves. Journal of Fluid Mechanics, 847, 417–451. Taburet, G., A. Sanchez-Roman, M. Ballarotta, M.-I. Pujol, J.-F. Legeais, F. Fournier, Y. Faugere, and G. Dibarboure, 2019: Duacs dt2018: 25 years of reprocessed sea level altimetry products. Ocean Science, 15 (5), 1207–1224. Thomas, L. N., L. Rainville, O. Asselin, W. R. Young, J. Girton, C. B. Whalen, L. Centurioni, and V. Hormann, 2020: Direct observations of near-inertial wave 𝜁-refraction in a dipole vortex. Geophysical Research Letters, 47 (21), e2020GL090 375. Thompson, A. F., A. Lazar, C. Buckingham, A. C. N. Garabato, G. M. Damerell, and K. J. Heywood, 2016: Open-ocean submesoscale motions: A full seasonal cycle of mixed layer instabilities from gliders. Journal of Physical Oceanography, 46 (4), 1285–1307. von Storch, J.-S., and V. L¨uschow, 2023: Wind power input to ocean near-inertial waves diagnosed from a 5-km global coupled atmosphere-ocean general circulation model. Journal of Geophysical Research: Oceans, 128 (2), e2022JC019 111. Wagner, G., and W. Young, 2016: A three-component model for the coupled evolution of near-inertial waves, quasi-geostrophic flow and the near-inertial second harmonic. Journal of Fluid Mechanics, 802, 806–837. Wunsch, C., and R. Ferrari, 2004: Vertical mixing, energy, and the general circulation of the oceans. Annu. Rev. Fluid Mech., 36, 281–314. Xie, J.-H., and J. Vanneste, 2015: A generalised-lagrangian-mean model of the interactions between near-inertial waves and mean flow. Journal of Fluid Mechanics, 774, 143–169. Young, W., and M. Ben Jelloul, 1997: Propagation of near-inertial oscillations through a geostrophic flow. JMR, 55, 735–766. Yu, X., A. C. Naveira Garabato, C. Vic, J. Gula, A. C. Savage, J. Wang, A. F. Waterhouse, and J. A. MacKinnon, 2022: Observed equatorward propagation and chimney effect of near-inertial waves in the midlatitude ocean. Geophysical Research Letters, 49 (13), e2022GL098 522. Zhai, X., H. L. Johnson, and D. P. Marshall, 2010: Significant sink of ocean-eddy energy near western boundaries. Nature Geoscience, 3 (9), 608–612. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Want to Learn More About the Interactions Between Near-Inertial Waves and Mesoscale Eddies?

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References APPENDIX A YBJ kinetic energy Budget The YBJ kinetic energy budget may be formed by multiplying (2) by −𝑀∗ /2 and adding the complex conjugate. Using integration by parts, the resulting kinetic energy budget can be written as where We are free to choose C as a boundary condition on the new field 𝐴, i.e., we can choose 𝐴 at 𝑧 = −𝐻 as the solution of (A4) for some specified C. A sensible choice for the transformation is thus setting C = 0 and Under this transformation, the kinetic energy budget becomes (4), with the horizontal and vertical fluxes given by The divergence of the flux terms vanishes when the budget is integrated over the entire domain, and the only terms that can alter the domain-integrated kinetic energy are the wind work and dissipation: This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References APPENDIX A YBJ kinetic energy Budget The YBJ kinetic energy budget may be formed by multiplying (2) by −𝑀∗ /2 and adding the complex conjugate. Using integration by parts, the resulting kinetic energy budget can be written as where We are free to choose C as a boundary condition on the new field 𝐴, i.e., we can choose 𝐴 at 𝑧 = −𝐻 as the solution of (A4) for some specified C. A sensible choice for the transformation is thus setting C = 0 and Under this transformation, the kinetic energy budget becomes (4), with the horizontal and vertical fluxes given by The divergence of the flux terms vanishes when the budget is integrated over the entire domain, and the only terms that can alter the domain-integrated kinetic energy are the wind work and dissipation: This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

What Was the YBJ Kinectic Energy Budget In Our Near-Inertial Waves and Mesoscale Eddies Study?

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References 6. Conclusion The evolution of the NIW field at the OSMOSIS site in the Northeast Atlantic Ocean is strongly modulated by mesoscale eddies. The observed evolution can be captured by the YBJ model, which includes NIW refraction, advection, and dispersion. If these processes are omitted as in the PM model, the observations cannot be reproduced as well, even if the parameterized damping rate is tuned. The YBJ model provides a powerful interpretive framework for understanding observations of NIWs in the upper ocean. It allows us to attribute the observed evolution to specific physical processes. Lateral differences in the NIW amplitude across the mooring array are caused by 𝜁-refraction, which causes NIW kinetic energy to be fluxed into anticyclonic regions. While correlations between NIW amplitudes and mesoscale vorticity can be diagnosed from the observations alone (e.g., Yu et al. 2022), the YBJ framework produces a quantitative prediction for the NIW amplitudes that matches observations and provides a physical interpretation. The YBJ model also allows us to calculate changes in the NIW potential energy, which are expected to arise from transfers of balanced mesoscale energy (Xie and Vanneste 2015; Rocha et al. 2018). For the strongest NIW event observed during the OSMOSIS campaign, the NIW potential energy gain is at least two orders of magnitude smaller than the global average energy input into mesoscale kinetic energy. Stimulated generation therefore does not appear to have a major impact on the mesoscale eddy field in this part of the ocean. Acknowledgments. The authors thank two anonymous reviewers whose comments helped improve the presentation of this work. This material is based upon work supported by the National Science Foundation under Grant No. OCE-1924354 and by the National Aeronautics and Space Administration under Grant No. 80NSSC22K1445 issued through the Science Mission Directorate (Future Investigators in NASA Earth and Space Science and Technology). Data availability statement. The code to run the 3D YBJ model is available at https: //github.com/scott-conn/3DYBJ. All of the OSMOSIS data used as part of this study is available from the British Oceanographic Data Centre. The OSMOSIS mooring data is available at https://www.bodc.ac.uk/data/bodc_database/nodb/data_ collection/6093/. The OSMOSIS glider data is available at https://doi.org/10.5285/ 6cf0b33e-a192-549f-e053-6c86abc01204. The SSH data is available from the E.U.’s Copernicus Marine Service at https://doi.org/10.48670/moi-00148. The ERA5 reanalysis data is available from the Copernicus Climate Change Service (C3S) Climate Data Store at https: //doi.org/0.24381/cds.adbb2d47. This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References 6. Conclusion The evolution of the NIW field at the OSMOSIS site in the Northeast Atlantic Ocean is strongly modulated by mesoscale eddies. The observed evolution can be captured by the YBJ model, which includes NIW refraction, advection, and dispersion. If these processes are omitted as in the PM model, the observations cannot be reproduced as well, even if the parameterized damping rate is tuned. The YBJ model provides a powerful interpretive framework for understanding observations of NIWs in the upper ocean. It allows us to attribute the observed evolution to specific physical processes. Lateral differences in the NIW amplitude across the mooring array are caused by 𝜁-refraction, which causes NIW kinetic energy to be fluxed into anticyclonic regions. While correlations between NIW amplitudes and mesoscale vorticity can be diagnosed from the observations alone (e.g., Yu et al. 2022), the YBJ framework produces a quantitative prediction for the NIW amplitudes that matches observations and provides a physical interpretation. The YBJ model also allows us to calculate changes in the NIW potential energy, which are expected to arise from transfers of balanced mesoscale energy (Xie and Vanneste 2015; Rocha et al. 2018). For the strongest NIW event observed during the OSMOSIS campaign, the NIW potential energy gain is at least two orders of magnitude smaller than the global average energy input into mesoscale kinetic energy. Stimulated generation therefore does not appear to have a major impact on the mesoscale eddy field in this part of the ocean. Acknowledgments . The authors thank two anonymous reviewers whose comments helped improve the presentation of this work. This material is based upon work supported by the National Science Foundation under Grant No. OCE-1924354 and by the National Aeronautics and Space Administration under Grant No. 80NSSC22K1445 issued through the Science Mission Directorate (Future Investigators in NASA Earth and Space Science and Technology). Acknowledgments Data availability statement. The code to run the 3D YBJ model is available at https: //github.com/scott-conn/3DYBJ. All of the OSMOSIS data used as part of this study is available from the British Oceanographic Data Centre. The OSMOSIS mooring data is available at https://www.bodc.ac.uk/data/bodc_database/nodb/data_ collection/6093/. The OSMOSIS glider data is available at https://doi.org/10.5285/ 6cf0b33e-a192-549f-e053-6c86abc01204. The SSH data is available from the E.U.’s Copernicus Marine Service at https://doi.org/10.48670/moi-00148. The ERA5 reanalysis data is available from the Copernicus Climate Change Service (C3S) Climate Data Store at https: //doi.org/0.24381/cds.adbb2d47. Data availability statement. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

The End of the Ocean Trip: Concluding Our Near-Inertial Waves and Mesoscale Eddies Study

Authors:
(1) Scott Conn, California Institute of Technology, Pasadena, California;
(2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California;
(3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Observations Models Results Discussion Conclusion APPENDIX A APPENDIX B APPENDIX C References 5. Discussion The YBJ model does well in reproducing the mooring observations of NIWs. There are still differences between the model and the observations, however, as well as a couple of events where the YBJ model does less well. Given the observational inputs to the model, it would be surprising if this were not the case. One major limitation is the use of altimetry for the mesoscale streamfunction. As mentioned previously, the altimetry data are a heavily smoothed version of the real field. We suggested above that some of the discrepancies between the YBJ model and the observations were due to this smoothing. We also interpolated the mesoscale vorticity onto a smaller simulation grid. The resolution of the altimetry product is 0.25◦×0.25◦ , which is larger than the size of the mooring array. Differences across the mooring array come from the interpolation between neighboring altimetry grid cells, which will have introduced interpolation errors. Furthermore, we assumed that the mesoscale eddy field was barotropic. This is a reasonable assumption if the vertical scale of the waves is much smaller than the vertical scale of the eddies. There are certainly errors in the YBJ evolution, however, that arise from neglecting the baroclinicity of mesoscale eddies. These effects could be investigated in the future by running similar simulations using an in-situ data set that resolves the vertical structure of one or more eddies. In event 1, the vorticity has large variations across the mooring array (Fig. 2a). This is reflected in the NIW observations as large differences in the amplitude across the mooring array. However, this need not be the case for all events. Event 3, by contrast, shows weak variations in the vorticity (as diagnosed from altimetry) across the mooring array. The result is that the YBJ simulations also show weak variations in the NIW field across the mooring array region. The altimetry vorticity field is a heavily smoothed representation of the real vorticity field of the ocean; smaller scale vorticity features are invisible to measurements from altimetry. If there is little variation in the larger-scale vorticity across the mooring array, then these smaller-scale vorticity features may play a more important role in governing the variations in 𝜕𝑧𝑀. While we do not have a spatial map of smaller-scale vorticity features, we can estimate their magnitude at the mooring array by calculating the vorticity by applying Stokes’ theorem to the area bounded by the outer moorings (Buckingham et al. 2016). The velocity that we use in Stokes’ theorem is low-pass filtered to estimate the balanced signal. This vorticity agrees in general with the vorticity calculated from SSH but shows more high-frequency variability (Fig. 9). Event 3 shows the biggest disagreement between the two estimates of all the events. The mooring estimate shows cyclonic vorticity, whereas the altimetry estimate shows anticyclonic vorticity. This likely explains why we see the amplitude decay much quicker in observations compared to simulations because there would have been a horizontal energy flux out of the region while the simulations have a flux into the region, which acts to maintain the amplitude. The other events show better agreement between the two vorticity estimates, although there are times where the deviation is larger. Events 1 and 2 specifically show two anticyclonic periods that are not captured by altimetry. These may explain some of the mismatch between simulations and observations. For example, the anticyclonic excursion in event 1 probably explains why the YBJ simulation results decay more quickly than observations. For event 2, the excursion occurs primarily near the start of the event, where the NIW amplitude is weak, so its effect is tempered. Many of these anomalies are short in duration, which limits the error in using the altimetric vorticity. The vorticity error for event 4a is minimal (Figure 9). We suspect the mismatch between the YBJ model and the observations in this event is due to the forcing. The reanalysis product used is not the exact wind-forcing felt at the OSMOSIS site. Both YBJ and PM show an initial peak in the NIW amplitude that is not seen in the observations, indicating that the wind-stress may be wrong at this point. The forcing event seen in observations then has to destroy these waves before forcing new ones, which causes the lower amplitude of the NIW peak in the YBJ simulation compared to observations. Notwithstanding the caveats above, it is significant that the YBJ model can reproduce much of the NIW evolution with only the mesoscale vorticity as derived from altimetry. One may expect the larger-magnitude submesoscale vorticity to be at least as important for the NIW evolution. But the dispersion term in the YBJ equation depends on the Laplacian of 𝑀 and hence in spectral space scales as 𝜅 2 . This means that the refractive generation of small-scale structure in the wave field will be opposed by increasingly strong dispersion. Our results therefore suggest that, at the location of the mooring array, dispersion indeed outpaces refraction at submesoscales and mesoscale refraction is more important for the NIW evolution (cf., Yu et al. 2022). Using a time-averaged stratification profile likely also contributes to differences between the observations and simulations, despite our attempts to keep simulation times as short as possible to avoid this effect. The assumption that the stratification does not vary in time is inherent in the YBJ scaling assumptions. Related to this is the question of what depth to force the model over. We used a fixed forcing depth, although this certainly varied over the simulation periods. This problem could be side-stepped by representing the wind forcing as a surface stress and adding a parameterized turbulent vertical momentum flux to the YBJ equation to transfer momentum from the wind downwards. The YBJ equation contains no term to represent the breaking of NIWs. The scaling 𝐻 ∗ = 𝑢 ∗ / √︁ 𝑁∗ 𝑓 , where 𝑢 ∗ = √︁ 𝜏/𝜌0 is the friction velocity and 𝑁 ∗ is the stratification at the base of the mixed layer, indicates the depth to which wind-driven turbulence could deepen the mixed layer (Pollard et al. 1973). If the mixed layer depth is shallower than 𝐻 ∗ , the effect of NIW dissipation would be important as there is potential for mixed layer deepening by breaking of NIWs (Plueddemann and Farrar 2006). During the OSMOSIS study, Yu et al. (2022) showed that the mixed layer was generally deeper than 𝐻 ∗ . This is especially true outside of summer and during the events we consider here. This is due to strong surface buoyancy forcing deepening the mixed layer by convection to a depth beyond what the winds alone could do. While one could include a critical Richardson number criterion to add NIW breaking to the YBJ equation, we believe that it is justifiable to ignore dissipation of NIWs in the surface layer in this study. In event 1, the wind work predicted by YBJ agreed well with that calculated by observations. The PM model greatly overestimated the wind-work. The YBJ and PM models also disagree on the wind work for all the other events (not shown). If mesoscale interactions change the alignment of the NIWs with the winds, then this results in a very different wind work. The difference in wind work between the YBJ and PM models depends on the degree to which the mesoscale changes the alignment of the waves with the wind and the power of the winds at different frequencies. The differences in wind work can be large for individual events but the five events we simulated are not enough to estimate what the average difference might be when averaged over many events. Nonetheless, this may provide motivation to revisit previous estimates of the NIW wind work in order to determine whether the mesoscale may modulate wind work into the NIW band globally. This paper is available on arxiv under CC 4.0 license. Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Authors: Authors: (1) Scott Conn, California Institute of Technology, Pasadena, California; (2) Joseph Fitzgerald, California Institute of Technology, Pasadena, California; (3) Jorn Callies, California Institute of Technology, Pasadena, California. Table of Links Abstract and Intro Abstract and Intro Observations Observations Models Models Results Results Discussion Discussion Conclusion Conclusion APPENDIX A APPENDIX A APPENDIX B APPENDIX B APPENDIX C APPENDIX C References References 5. Discussion The YBJ model does well in reproducing the mooring observations of NIWs. There are still differences between the model and the observations, however, as well as a couple of events where the YBJ model does less well. Given the observational inputs to the model, it would be surprising if this were not the case. One major limitation is the use of altimetry for the mesoscale streamfunction. As mentioned previously, the altimetry data are a heavily smoothed version of the real field. We suggested above that some of the discrepancies between the YBJ model and the observations were due to this smoothing. We also interpolated the mesoscale vorticity onto a smaller simulation grid. The resolution of the altimetry product is 0.25◦×0.25◦ , which is larger than the size of the mooring array. Differences across the mooring array come from the interpolation between neighboring altimetry grid cells, which will have introduced interpolation errors. Furthermore, we assumed that the mesoscale eddy field was barotropic. This is a reasonable assumption if the vertical scale of the waves is much smaller than the vertical scale of the eddies. There are certainly errors in the YBJ evolution, however, that arise from neglecting the baroclinicity of mesoscale eddies. These effects could be investigated in the future by running similar simulations using an in-situ data set that resolves the vertical structure of one or more eddies. In event 1, the vorticity has large variations across the mooring array (Fig. 2a). This is reflected in the NIW observations as large differences in the amplitude across the mooring array. However, this need not be the case for all events. Event 3, by contrast, shows weak variations in the vorticity (as diagnosed from altimetry) across the mooring array. The result is that the YBJ simulations also show weak variations in the NIW field across the mooring array region. The altimetry vorticity field is a heavily smoothed representation of the real vorticity field of the ocean; smaller scale vorticity features are invisible to measurements from altimetry. If there is little variation in the larger-scale vorticity across the mooring array, then these smaller-scale vorticity features may play a more important role in governing the variations in 𝜕𝑧𝑀. While we do not have a spatial map of smaller-scale vorticity features, we can estimate their magnitude at the mooring array by calculating the vorticity by applying Stokes’ theorem to the area bounded by the outer moorings (Buckingham et al. 2016). The velocity that we use in Stokes’ theorem is low-pass filtered to estimate the balanced signal. This vorticity agrees in general with the vorticity calculated from SSH but shows more high-frequency variability (Fig. 9). Event 3 shows the biggest disagreement between the two estimates of all the events. The mooring estimate shows cyclonic vorticity, whereas the altimetry estimate shows anticyclonic vorticity. This likely explains why we see the amplitude decay much quicker in observations compared to simulations because there would have been a horizontal energy flux out of the region while the simulations have a flux into the region, which acts to maintain the amplitude. The other events show better agreement between the two vorticity estimates, although there are times where the deviation is larger. Events 1 and 2 specifically show two anticyclonic periods that are not captured by altimetry. These may explain some of the mismatch between simulations and observations. For example, the anticyclonic excursion in event 1 probably explains why the YBJ simulation results decay more quickly than observations. For event 2, the excursion occurs primarily near the start of the event, where the NIW amplitude is weak, so its effect is tempered. Many of these anomalies are short in duration, which limits the error in using the altimetric vorticity. The vorticity error for event 4a is minimal (Figure 9). We suspect the mismatch between the YBJ model and the observations in this event is due to the forcing. The reanalysis product used is not the exact wind-forcing felt at the OSMOSIS site. Both YBJ and PM show an initial peak in the NIW amplitude that is not seen in the observations, indicating that the wind-stress may be wrong at this point. The forcing event seen in observations then has to destroy these waves before forcing new ones, which causes the lower amplitude of the NIW peak in the YBJ simulation compared to observations. Notwithstanding the caveats above, it is significant that the YBJ model can reproduce much of the NIW evolution with only the mesoscale vorticity as derived from altimetry. One may expect the larger-magnitude submesoscale vorticity to be at least as important for the NIW evolution. But the dispersion term in the YBJ equation depends on the Laplacian of 𝑀 and hence in spectral space scales as 𝜅 2 . This means that the refractive generation of small-scale structure in the wave field will be opposed by increasingly strong dispersion. Our results therefore suggest that, at the location of the mooring array, dispersion indeed outpaces refraction at submesoscales and mesoscale refraction is more important for the NIW evolution (cf., Yu et al. 2022). Using a time-averaged stratification profile likely also contributes to differences between the observations and simulations, despite our attempts to keep simulation times as short as possible to avoid this effect. The assumption that the stratification does not vary in time is inherent in the YBJ scaling assumptions. Related to this is the question of what depth to force the model over. We used a fixed forcing depth, although this certainly varied over the simulation periods. This problem could be side-stepped by representing the wind forcing as a surface stress and adding a parameterized turbulent vertical momentum flux to the YBJ equation to transfer momentum from the wind downwards. The YBJ equation contains no term to represent the breaking of NIWs. The scaling 𝐻 ∗ = 𝑢 ∗ / √︁ 𝑁∗ 𝑓 , where 𝑢 ∗ = √︁ 𝜏/𝜌0 is the friction velocity and 𝑁 ∗ is the stratification at the base of the mixed layer, indicates the depth to which wind-driven turbulence could deepen the mixed layer (Pollard et al. 1973). If the mixed layer depth is shallower than 𝐻 ∗ , the effect of NIW dissipation would be important as there is potential for mixed layer deepening by breaking of NIWs (Plueddemann and Farrar 2006). During the OSMOSIS study, Yu et al. (2022) showed that the mixed layer was generally deeper than 𝐻 ∗ . This is especially true outside of summer and during the events we consider here. This is due to strong surface buoyancy forcing deepening the mixed layer by convection to a depth beyond what the winds alone could do. While one could include a critical Richardson number criterion to add NIW breaking to the YBJ equation, we believe that it is justifiable to ignore dissipation of NIWs in the surface layer in this study. In event 1, the wind work predicted by YBJ agreed well with that calculated by observations. The PM model greatly overestimated the wind-work. The YBJ and PM models also disagree on the wind work for all the other events (not shown). If mesoscale interactions change the alignment of the NIWs with the winds, then this results in a very different wind work. The difference in wind work between the YBJ and PM models depends on the degree to which the mesoscale changes the alignment of the waves with the wind and the power of the winds at different frequencies. The differences in wind work can be large for individual events but the five events we simulated are not enough to estimate what the average difference might be when averaged over many events. Nonetheless, this may provide motivation to revisit previous estimates of the NIW wind work in order to determine whether the mesoscale may modulate wind work into the NIW band globally. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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