Paper edits

.github/workflows/draft-pdf.yml

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+name: Draft PDF
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

paper/paper.bib

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+@inproceedings{de2008z3,
+  title={Z3: An efficient SMT solver},
+  author={De Moura, Leonardo and Bj{\o}rner, Nikolaj},
+  booktitle={International conference on Tools and Algorithms for the Construction and Analysis of Systems},
+  pages={337--340},
+  year={2008},
+  organization={Springer}
+}
+
+@article{danabasoglu2020community,
+  title={The community earth system model version 2 (CESM2)},
+  author={Danabasoglu, Gokhan and Lamarque, J-F and Bacmeister, J and Bailey, DA and DuVivier, AK and Edwards, Jim and Emmons, LK and Fasullo, John and Garcia, R and Gettelman, Andrew and others},
+  journal={Journal of Advances in Modeling Earth Systems},
+  volume={12},
+  number={2},
+  pages={e2019MS001916},
+  year={2020},
+  publisher={Wiley Online Library}
+}
+
+@misc{ipywidgets,
+  author = {Jupyter},
+  title = {ipywidgets: Interactive HTML Widgets for Jupyter Notebooks},
+  year = {2015},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/jupyter-widgets/ipywidgets}},
+}
+
+@article{wu2021coupled,
+  title={Coupled aqua and ridge planets in the community earth system model},
+  author={Wu, Xiaoning and Reed, Kevin A and Wolfe, Christopher LP and Marques, Gustavo M and Bachman, Scott D and Bryan, Frank O},
+  journal={Journal of Advances in Modeling Earth Systems},
+  volume={13},
+  number={4},
+  pages={e2020MS002418},
+  year={2021},
+  publisher={Wiley Online Library}
+}
+
+@article{polvani2017less,
+  title={When less is more: Opening the door to simpler climate models, Eos, 98},
+  author={Polvani, LM and Clement, AC and Medeiros, B and Benedict, JJ and Simpson, IR},
+  journal={Eos, Transactions American Geophysical Union},
+  volume={99},
+  number={3},
+  pages={15--16},
+  year={2017}
+}
+
+@article{maher2019model,
+  title={Model hierarchies for understanding atmospheric circulation},
+  author={Maher, Penelope and Gerber, Edwin P and Medeiros, Brian and Merlis, Timothy M and Sherwood, Steven and Sheshadri, Aditi and Sobel, Adam H and Vallis, Geoffrey K and Voigt, Aiko and Zurita-Gotor, Pablo},
+  journal={Reviews of Geophysics},
+  volume={57},
+  number={2},
+  pages={250--280},
+  year={2019},
+  publisher={Wiley Online Library}
+}
+
+@article{de2011satisfiability,
+  title={Satisfiability modulo theories: introduction and applications},
+  author={De Moura, Leonardo and Bj{\o}rner, Nikolaj},
+  journal={Communications of the ACM},
+  volume={54},
+  number={9},
+  pages={69--77},
+  year={2011},
+  publisher={ACM New York, NY, USA}
+}
+
+@book{biere2009handbook,
+  title={Handbook of satisfiability},
+  author={Biere, Armin and Heule, Marijn and van Maaren, Hans},
+  volume={185},
+  year={2009},
+  publisher={IOS press}
+}

paper/paper.md

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+---
+title: 'visualCaseGen: An SMT-based Experiment Configurator for Community Earth System Model'
+tags:
+  - Python
+  - cesm
+  - climate modeling
+  - constraint solver
+  - SMT
+authors:
+  - name: Alper Altuntas
+    orcid: 0000-0003-1708-9518
+    affiliation: "1"
+  - name: Manish Venumuddula
+    orcid: 0009-0009-5047-2018
+    affiliation: "1"
+  - name: Isla R. Simpson
+    orcid: 0000-0002-2915-1377
+    affiliation: "1"
+  - name: Scott D. Bachman
+    orcid: 0000-0002-6479-4300
+    affiliation: "1"
+  - name:  Samuel Levis
+    orcid: 0000-0003-4684-6995
+    affiliation: "1"
+  - name:  Brian Dobbins
+    orcid: 0000-0002-3156-0460
+    affiliation: "1"
+  - name: William J. Sacks
+    orcid: 0000-0003-2902-5263
+    affiliation: "1"
+  - name: Gokhan Danabasoglu
+    orcid: 0000-0003-4676-2732
+    affiliation: "1"
+
+affiliations:
+ - name: U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR), Boulder, CO
+   index: 1
+date: 29 June 2025
+bibliography: paper.bib
+
+---
+
+# Introduction
+
+visualCaseGen is a graphical user interface (GUI) designed to streamline the
+creation and configuration of Community Earth System Model (CESM) experiments.
+CESM is a highly flexible, state-of-the-art climate model that allows researchers
+to simulate the Earth system across a wide range of spatial and temporal scales
+and at varying levels of complexity, depending on scientific objectives [@danabasoglu2020community].
+However, configuring non-standard or idealized CESM experiments is often a technically
+demanding and time-consuming process, requiring detailed knowledge of component
+compatibility, grid definitions, parameterization schemes, and model hierarchies.
+visualCaseGen simplifies this process by guiding users through each stage of
+experiment setup and automating key configuration steps via an intuitive, 
+interactive interface.
+
+To ensure consistency and compatibility across various model settings, visualCaseGen
+incorporates a constraint solver based on satisfiability modulo theories (SMT)
+[@de2011satisfiability]. This solver systematically analyzes dependencies between
+configuration parameters, detects conflicts, and provides detailed explanations of
+incompatibilities, allowing users to make informed adjustments. The SMT-based
+approach enables dynamic, real-time validation of model settings, significantly
+reducing setup errors and ensuring that only scientifically viable configurations
+are selected.
+
+On the frontend, visualCaseGen is implemented as a Jupyter-based GUI, offering
+an intuitive, step-by-step interface for browsing standard CESM configurations,
+defining custom experiment setups, and modifying grid and component settings.
+Designed with a wizard-like interface, visualCaseGen walks users through each
+stage of the CESM configuration process, ensuring that all necessary settings
+are selected in a logical sequence while dynamically updating available options
+based on user choices. Additionally, the tool features utilities for creating
+and editing model input files, such as ocean grid, bathymetry, and land
+surface properties, further simplifying model customization and
+minimizing the need for manual file creation and modification.
+
+By automating and simplifying CESM configuration, visualCaseGen makes the model
+more accessible and custumizable, particularly for researchers exploring
+hierarchical modeling [@maher2019model], idealized experiments
+[@polvani2017less], or custom coupled simulations. As such, the tool allows
+users to focus on their scientific objectives rather than technical setup
+challenges, ultimately enabling a more efficient and streamlined experiment
+workflow.
+
+
+# Statement of need
+
+CESM is a highly flexible and comprehensive modeling framework that
+encompasses multiple components representing the atmosphere, ocean, land,
+ice, river, biogeochemistry, and other systems. While this
+flexibility supports a wide range of scientific experiments, it also introduces
+significant complexity in model configuration. Setting up custom CESM
+experiments requires navigating intricate component compatibility constraints,
+grid configurations, and parameterization choices, often demanding extensive
+expertise in the model’s internal structure. For non-standard experiments, users
+must manually modify CESM’s codebase and runtime parameter files, maintain
+numerical and scientific consistency, and troubleshoot compatibility issues.
+This process is time-intensive, error-prone, and can take weeks before yielding
+a functional setup.
+
+A recent study by @wu2021coupled exemplifies these challenges. Their work
+involved configuring an idealized CESM experiment to study atmosphere-ocean
+interactions using two simplified aquaplanet models: one without continents and
+another with a pole-to-pole strip continent. The goal was to investigate how
+these configurations influence Hadley circulation, equatorial upwelling, and
+precipitation patterns (\autoref{fig:wuEtAl}). However, setting up these
+experiments required extensive manual intervention, including modifying CESM
+codebase, creating custom input files, adjusting runtime parameters, consulting
+domain experts for component-specific configurations, and conducting numerous
+trial-and-error iterations. visualCaseGen was developed to address such
+usability barriers and streamline CESM experiment setup. As an interactive GUI,
+it eliminates the need for manual modifications and provides an
+intuitive, structured workflow for constructing model configurations. 
+
+![Sea surface temperature and precipitable water distribution from Aqua and
+Ridge planet simulations using CESM. Adapted from @wu2021coupled.
+\label{fig:wuEtAl}](wuEtAl.png){height="280pt"}
+
+# Constraint Solver
+
+One of the main challenges in configuring CESM experiments is ensuring that
+different model settings remain compatible. CESM’s configuration involves
+determining components, physics, grids, and parameterization choices, many of
+which have strict compatibility constraints. visualCaseGen addresses this
+challenge by integrating an SMT-based constraint solver, built using the Z3
+solver [@de2008z3]. Z3 was chosen for its robust Python API and its efficiency
+in managing complex logical relationships involving multiple parameter types
+such as integers, reals, booleans, and strings. As such, Z3 is well suited 
+to handling the intricate dependencies and constraints inherent in CESM
+configurations.
+
+In visualCaseGen, constraints are specified as key-value pairs, where the key
+represents a Z3 logical expression defining a condition, and the value is the
+error message displayed when the constraint is violated. These constraints
+enforce compatibility rules and prevent invalid model configurations. Below
+are three examples of visualCaseGen constraints with increasing complexity,
+demonstrating how the SMT solver can enforce simple value bounds, conditional
+dependencies, and more complex multi-component logical rules:
+
+```python
+
+LND_DOM_PFT >= 0.0:
+    "PFT/CFT must be set to a nonnegative number",
+
+Implies(OCN_GRID_EXTENT=="Regional", OCN_CYCLIC_X=="False"):
+    "Regional ocean domain cannot be reentrant"
+    "(due to an ESMF limitation.)",
+
+Implies(And(COMP_OCN=="mom", COMP_LND=="slnd", COMP_ICE=="sice"),
+        OCN_LENY<180.0):
+    "If LND and ICE are stub, custom MOM6 grid must exclude poles"
+    "to avoid singularities in open water",
+
+```
+
+## Why Use a Constraint Solver?
+
+Configuring CESM is inherently a constraint satisfaction problem (CSP), which
+can quickly become computationally complex as the number of configuration
+variables increases [@biere2009handbook]. Manually enforcing constraints would be impractical, making
+an SMT solver an ideal choice. The benefits of using a solver include:
+
+- **Detecting Hidden Conflicts:** Individual constraints may be satisfied
+  independently, yet their combination can lead to conflicts that are nontrivial
+  to detect manually.
+
+- **Preventing Dead-Ends:** Without a solver, users may unknowingly select
+  settings that lead to an unsatisfiable configuration, forcing them to restart
+  their setup. Thanks to the solver, visualCaseGen dynamically guides users
+  toward valid options.
+
+- **Enabling Constraint Analysis:** The solver can answer critical questions, such as:
+  - Are all constraints satisfiable?
+  - Are there unreachable options that need adjustment?
+  - Are any constraints redundant and can be optimized?
+
+- **Scalability and Efficiency:** As the number of variables and constraints grows
+  exponentially, manually checking compatibility becomes infeasible. The 
+  solver efficiently handles large-scale constraint resolution, ensuring rapid
+  feedback even for large number of variables.
+
+
+# The Stage Mechanism
+
+A key backend concept in visualCaseGen is the Stage Mechanism, which structures
+the CESM configuration process into consecutive steps (stages). Each stage
+includes a set of related configuration variables that can be adjusted together.
+Based on the user's selections, different stages are activated dynamically,
+guiding the user through a structured workflow.
+
+## Stage Pipeline
+
+All possible stage paths collectively form the stage pipeline as shown in
+\autoref{fig:pipeline}, which dictates the sequence in which configuration
+variables are presented to the user, and the *precedence of variables* where
+earlier stages have higher priority over later ones. A complexity arises when
+the same variable appears in multiple stages. This is allowed as long as it is
+not reachable along the same path within the stage pipeline. To prevent cyclic
+dependencies, the stage pipeline must therefore form a directed acyclic graph
+(DAG), enabling a consistent variable precedence hierarchy and eliminating the
+possibility of loops or contradictory variable settings.
+
+![The visualCaseGen stage pipeline, starting from the top node (1. Component Set) and ending at the bottom node (3. Launch). The user follows a path along this pipeline based on their modeling needs and selections. \label{fig:pipeline}](stage_pipeline.png)
+
+## Constraint Graph and its Traversal
+
+Using the stage pipeline and specified constraints, visualCaseGen constructs a
+constraint graph, as shown in \autoref{fig:cgraph}. In this graph:
+
+ - Nodes represent configuration variables. 
+ - Directed edges represent dependencies or constraints between variables.
+ - Edges are directed from higher-precedence variables to lower-precedence variables.
+
+During the configuration process, when a user makes a selection, the constraint
+graph is traversed to identify all variables that are affected by the selection.
+This traversal is done in a breadth-first manner, starting from the selected
+variable and following the edges in the direction of the constraints. The
+traversal stops at variables whose options' validities are not affected by the
+selection. As such the traversal is limited to the variables that are directly
+or indirectly affected by the user's selection, which in turn depends on the the
+user input, stage hierarchy, and the specified constraints. By dynamically
+re-evaluating constraints and adjusting available options, visualCaseGen
+provides real-time feedback, preventing invalid configurations and ensuring
+scientific consistency in CESM setups.
+
+![The visualCaseGen constraint graph. \label{fig:cgraph}](cgraph.png)
+
+# Frontend 
+
+The visualCaseGen frontend provides an intuitive and interactive interface for
+configuring CESM experiments. Built with Jupyter ipywidgets [@ipywidgets], it
+can operate on local
+machines, HPC clusters, and cloud environments. This portability and flexibility allows
+researchers to configure CESM experiments efficiently, whether prototyping
+lightweight simulations on personal computers or running sophisticated applications on remote supercomputing
+systems.
+
+\autoref{fig:Stage1_7} displays an example stage from the visualCaseGen GUI,
+where users can select the individual models to be coupled in their CESM
+experiment. As the user makes selections, the GUI dynamically updates available
+options by crossing out incompatible choices, ensuring that only valid
+configurations are presented. This interactive feedback mechanism guides users
+through the configuration process, helping them make informed decisions and
+avoiding incompatible selections.
+
+![The "Components" stage. \label{fig:Stage1_7}](Stage1_7.png){width="90%"}
+
+At any stage, users can click on crossed-out options to view a
+brief explanation of why a particular choice is incompatible with their current
+selections, as illustrated in \autoref{fig:Stage1_8}. This helps guide
+users through complex dependencies, and helping them make informed
+adjustments.
+
+![Interactive feedback in incompatible choices. \label{fig:Stage1_8}](Stage1_8.png){width="90%"}
+
+As another example of streamlining model customization, \autoref{fig:TopoEditor}
+shows the TopoEditor widget that comes with visualCaseGen. This tool allows users
+to interactively modify ocean bathymetry, enhancing customizability and
+ease of use.
+
+![TopoEditor widget \label{fig:TopoEditor}](TopoEditor.png){width="90%"}
+
+# Remarks
+
+visualCaseGen can significantly accelerate CESM experiment setup for a wide
+range of studies by automating many aspects of experiment configuration. Instead
+of manual intervention, modelers can use visualCaseGen’s interactive
+GUI to define model setups, mix and match component configurations,
+generate custom grids and parameters. The SMT-based constraint solver
+ensures that only valid model settings are selected, reducing the need for
+trial-and-error debugging. While complex custom cases may still require
+fine-tuning, visualCaseGen allows modelers to generate an initial working
+configuration in a matter of hours rather than weeks, greatly improving
+efficiency and ease-of-use.
+
+By automating tedious configuration tasks, visualCaseGen enables researchers to
+focus on scientific exploration rather than technical setup, making CESM more
+accessible for both idealized and complex climate modeling studies.
+
+# Acknowledgements
+
+This work was supported by the NSF Cyberinfrastructure for Sustained Scientific
+Innovation (CSSI) program under award number 2004575. Special thanks to the
+CESM Software Engineering Working Group for their support and feedback during
+the development of visualCaseGen.
+
+The NSF National Center for Atmospheric Research (NCAR) is a major facility
+sponsored by the NSF under Cooperative Agreement No. 1852977.
+
+# References
+