code_CESM_LCZ

Introduction

This repository is supplementary to the manuscript "Enhancing Global-Scale Urban Land Cover Representation Using Local Climate Zones in the Community Earth System Model".

The objectives of this project are:

• Modify CESM source code to incorporate built LCZ representation in a modular way;
• Validate model performance with the new LCZ scheme using Urban-PLUMBER data;
• Examine model sensitivity to LCZ urban parameters.

Scripts and data

1_code_modification

The standard source code comes from CTSM, with the release tag: ctsm5.2.005. See modified code lines labeled with !YS.

• Add a new command use_lcz to the namelist for case build:

  • ‎bld/namelist_files/namelist_definition_ctsm.xml

• Apply use_lcz to determine land cover classification:

  • src/main/landunit_varcon.F90
  • src/main/initGridCellsMod.F90
  • src/main/subgridMod.F90

• Define LCZ classes:

  • src/main/LandunitType.F90

• Modify the PIO process for a time-varying urban variable T_BUILDING_MAX:

  • src/cpl/share_esmf/UrbanTimeVarType.F90
  • src/cpl/mct/UrbanTimeVarType.F90

• Modify subgrid-level:

  • src/dyn_subgrid/dynInitColumnsMod.F90

• Apply use_lcz when controlling model set-up:

  • src/main/clm_varctl.F90

  • src/main/controlMod.F90

2_simulation_output_analysis

The scripts listed below are used for processing simulation output and visualization

Num.	Subject	Simulation	Output data process	Visualization
2.1	Flux variations at the UK-KingsCollege site	CNTL, WRF_LCZ, LI_LCZ	Use Export.ipynb to get export_uk_kingscollege_df.csv	Figure.ipynb
2.2	Taylor diagram over all flux sites	CNTL, WRF_LCZ, LI_LCZ	Use Export.ipynb to get results4taylor.csv	Figure.ipynb
2.3	Overall model performance	CNTL, WRF_LCZ, LI_LCZ, CESM_LCZ	Use Export_ahf.ipynb and Export_flux.ipynb to get ahf.csv and flux.csv, respectively	Figure.ipynb
2.4	Model sensitivity to parameters	BASE, SENS	Use Export.ipynb to get result.csv	Figure.ipynb
2.5	Variations in anthropogenic heat flux	BASE, SENS	Use Export.ipynb to get ahf.csv and qh.csv	Figure.ipynb

3_illustration

The figures listed below are used to illustrate details of implementing built LCZ in CLMU.

Subject	Visualization
CLM5 representation hierarchy with default and LCZ classes	Figure
A modular way of incorporating LCZ alongside the default scheme	Figure
Future directions	Figure

4_supplimentary_information

The scripts listed below are used to show supplementary information such as input data and output variations over sites.

Num.	Subject	Simulation	Output data process	Visualization
4.1	Flux tower locations	NA	NA	Figure.ipynb
4.2	Sensible heat flux over sites	CNTL, WRF_LCZ, LI_LCZ, CESM_LCZ	Use *csv from output	Figure.ipynb
4.3	Momentum flux sensitivity to roughness length	BASE, SENS1, SENS2, SENS3, SENS4	Use Export.ipynb to get result.csv	Figure.ipynb
4.4	Flux variables over sites	CNTL, WRF_LCZ, LI_LCZ, CESM_LCZ	Use Export.ipynb to get *csv stored in output	Figure.ipynb

5_generate_LCZ_inputs

The scripts listed below are used to generate LCZ-based land surface inputs for simulations. Note: For LCZ simulations, we set nlevurb = 5.

Num.	Simulation	Input data process
5.1	WRF_LCZ	WRF_LCZ.ipynb
5.2	LI_LCZ	LI_LCZ.ipynb
5.3	CESM_LCZ	CESM_LCZ.ipynb
5.4	BASE	BASE.ipynb
5.5	SENS	SENS.ipynb

6_sourcemods_for_UrbanPLUMBER

The scripts listed below modify source code to use several parameters provided by Urban-PLUMBER. Lines between !KO are modified by K. W. O. while !YS by Y. S..

• Modifiy the nlevurb:
  • clm_varpar.F90
• Add a new parameter wall_to_plan_area_ratio:
  • LandunitType.F90
  • UrbanParamsType.F90
• Determine air conditioning adoption:
  • UrbanTimeVarType.F90
• Other:
  • SurfaceAlbedoMod.F90
  • WaterStateType.F90

Acknowledgments

• We dedicate this work to the memory of Dr. Jason Ching, whose groundbreaking contributions and inspiring vision laid the foundation for this research. His legacy continues to guide and inspire us.
• This work used the ARCHER2 UK National Supercomputing Service and JASMIN, the UK’s collaborative data analysis environment. This work was supported by the U.K. Natural Environment Research Council.
• The authors would like to acknowledge the assistance of Research IT and the use of the HPC Pool and Computational Shared Facility at The University of Manchester. The support of Dr. Douglas Lowe and Christopher Grave from Research IT at The University of Manchester is gratefully acknowledged.
• We thank Prof. David M. Schultz for his comments on an earlier version of the manuscript.
• Additionally, we appreciate the assistance of Dr. Congyuan Li at the National University of Defense Technology in China.
• Z. Z. appreciates the support provided by the academic start-up funds from the Department of Earth and Environmental Sciences at The University of Manchester.
• Y. S. is supported by the PhD studentship of Z. Z.'s academic start-up funds.
• Contributions from K. W. O. are based upon work supported by the NSF National Center for Atmospheric Research, which is a major facility sponsored by the U.S. National Science Foundation under Cooperative Agreement No. 1852977.
• Contributions from M. D. are supported by the European Union’s HORIZON Research and Innovation Actions under grant agreement No. 101137851, project CARMINE (Climate-Resilient Development Pathways in Metropolitan Regions of Europe).
• L. Z. acknowledges the support of the U.S. National Science Foundation (CAREER award grant no. 2145362).
• The authors declare no conflict of interest.