Democratizing Urban Climate Modeling
Integration and execution of Community Land Model Urban (CLMU) in a containerized environment.
J. Yu, Y. Sun, S. Lindley, C. Jay, D.O. Topping, K.W. Oleson, Z. Zheng
The Challenge
Barriers to Urban Climate Science
The Community Land Model Urban (CLMU) is a powerful process-based numerical model for simulating interactions between the atmosphere and urban surfaces. However, traditional usage presents significant hurdles:
- Complex Installation: Requires specific UNIX-style OS, Fortran compilers, and strict library dependencies.
- Intricate Configuration: Users must master machine environment settings, often leading to tedious debugging.
- Data Heavy: Generating model inputs requires navigating complex urban surface and atmospheric forcing data.
Critical Impact
Rapid urbanization has intensified the need to understand micro-climates.
Rising urban temperatures pose severe risks to resident health. This toolkit enables precise simulation of Human Thermal Stress and Urban Heat Island (UHI) effects to guide adaptation strategies.
The Solution: A Dual-Toolkit Approach
01. CLMU-App
Containerized Execution
An OS-independent Docker container that packages code and dependencies into a standardized unit.
- Run Anywhere: Compatible with Windows, macOS (Apple Silicon), and Linux.
- Standardized: Uses a stable version of CLM 5.0 with identical physical processes.
- Zero Compilation: Just pull the image and run.
> docker run -hostname clmu-app ...
02. Pyclmuapp
Python Interface
A Python package to streamline execution and automate data creation (Surface & Forcing).
- Automated Workflow: Manages configuration, case creation, and execution.
- Data Generation: Creates surface data and atmospheric forcing data (ERA5) for any location.
- Scenario Testing: Easily modifies parameters (e.g., roof albedo) for adaptation studies.
usp = usp_clmu()
usp.run(case_name="London_Test", ...)
Presentation
Project Overview Slides
Detailed breakdown of the integration, execution flow, and validation results.
Case Study
Cool Roof Adaptation
Simulating the implementation of cool roofs (increasing albedo by 0.2) demonstrates a significant reduction in urban air temperatures, particularly during peak daytime hours. This highlights the toolkit's capability to evaluate climate adaptation strategies effectively.
Energy Balance
Successfully models UHI effects. Outputs include sensible, latent, and anthropogenic heat fluxes validation against King's College London data.
Water Balance
Simulates urban runoff and evaporation, aiding in flood risk assessment and water stewardship planning.
Cloud Support
Now supported by AWS. Run local-scale (1KM) simulations directly in the browser without local installation.