Can We Use 1D Models to Predict 3D Model Response to Forcing in an Idealized Framework?

This repository contains the data and scripts required to reproduce the results of the manuscript "Can We Use 1D Models to Predict 3D Model Response to Forcing in an Idealized Framework?" submitted to the Journal of Advances in Modeling Earth Systems (JAMES).

Brief description of project

This project aims to examine the comparability of results obtained using a 1D (or single-column model, SCM) versus 3D model under radiative-convective equilibrium (RCE) conditions. Simulations are carried out with the Weather Research and Forecasting (WRF) model (version 4.0.2). A 20 x 20 Multi-column model (MCM) setup is used as a stepping stone for the 3D model. The comparability of the SCM and MCM results is assessed based on their mean states, linear responses to small tendency perturbations (following the linear response function framework of Kuang, 2010), and adjustment responses to a doubled-CO₂ forcing. 

Five widely used convection schemes are tested:

• Kain-Fritsch
• New-Tiedtke
• New-simplified Arakawa-Schubert
• Betts-Miller-Janjic
• Zhang-McFarlane

Repository structure

• /data directory: contains the model output data, in csv format
  • /data/anomalies directory: contains the T and q responses to dT and dq tendency perturbations 
  • /data/double_co2: contains the doubled-CO₂ forcing adjustment responses 
  • /data/matrices: contains the X and dX matrices of the five convection schemes in SCM, which are required to construct their M^-1 matrices
  • /data/mean_states: contains the T and RH mean state of the schemes 
  • /data/precip: contains the convective and large-scale rain data, as well as the data for rainfall PDFs
  • /data/pressures: contains the pressure levels of the five convection schemes
  • /data/pw: contains the precipitable water data required to plot the org_pw metric time series
  • /data/subsidence: contains the vertical wind velocity (W) values required to compute the subsidence fraction metric
  • /data/dataframes: contains the dataframe files for statistical analyses (linear mixed effects [lme] models)
• /scripts directory: contains the scripts required to plot all figures in the manuscript
  • plot_convective_rain.py: script to plot Figures 1 and 2 (daily accumulated convective rain)
  • plot_org_time_series.py: script to plot Figure 3 (time series of the org_pw metric)
  • plot_mean_state_mcm.py: script to plot Figure 4 (T and RH mean state for the MCM setup and for all experimental configurations)
  • plot_scm_mcm_mean_state_difference.py: script to plot Figure 5 (T and RH difference between the SCM and MCM for the organized and disorganized cases)
  • plot_rain_pdf_scm_mcm_daily.py: script to plot Figure 6 (PDFs of daily convective rain of SCM vs MCM)
  • plot_lrf_response_scm_mcm_org_disorg.py: script to plot Figures 7 and 8 (SCM vs. MCM T and q responses to small tendency perturbations)
  • plot_scatter_plot_org_vs_various.py: script to plot Figure 9 (scatter plots of org_pw vs. RH and org_pw vs. SCM-MCM-deviation)
  • plot_scatter_plot_scm_mcm_relative_difference.py: script to plot Figure 10 (scatter plots of SCM-pair-difference vs. MCM-pair-difference for the organized and disorganized cases)
  • plot_double_co2_response_scm_mcm.py: script to plot Figure 11 (comparison of SCM vs. MCM adjustments to doubled-CO₂ forcing)
  • plot_double_co2_response_sim_pred.py: script to plot Figure 12 (comparison of simulated vs. predicted adjustment responses to doubled-CO₂ forcing)
  • plot_M_inv_matrices.py: script to plot Figure A1 (M^-1 matrices of the five convection schemes in SCM setup)
  • /scripts/applications directory: contains the utilities script that include helper functions for the plotting scripts