Level 0 data from the model intercomparison of Mixed-Phase Cloud Thinning included in "The Geoengineering Model Intercomparison Project (GeoMIP) contribution to CMIP7"

Data and code for MCT-MIP Level 0 figures: cloud radiative effect and water path responses to INP perturbations.

This dataset contains the processed climatology files and analysis scripts used to produce two figures in "The Geoengineering Model Intercomparison Project (GeoMIP) contribution to CMIP7" by Visioni et al.

— Spatial maps of the change in net cloud radiative effect (ΔCRE) over the Arctic for November–February (NDJF), comparing four global climate models at equivalent INP perturbation levels (~100 cm⁻³): ECHAM-HAM, E3SM, CESM, and ICON-HAM (LD06 scheme).

— Arctic (60–90°N) NDJF mean-state cloud properties — net CRE, liquid water path (LWP), ice water path (IWP), and total water path (TWP) — as a function of prescribed Ice Nucleating Particle (INP) concentration, for six model configurations: CESM, E3SM, ECHAM-HAM, ECHAM-HAM (MLO), ICON-HAM (LD06), ICON-HAM (Niemand), and an observational reference (TWP from ESA Cloud CCI v3).

# Contents

`data/pp/` — Seasonal climatology files (`*_clim.nc`) for five model configurations (CESM, E3SM, ECHAM-HAM, ICON-HAM LD06, ICON-HAM Niemand). Each file contains a 12-month climatology of CRE, LWP, or IWP on the model's native grid, for one control and multiple INP sensitivity runs.

`data/echam_mlo/` — 25-year time-mean output files (`CloudLiq.nc`, `CloudIce.nc`, `CloudNetto_abs.nc`) for ECHAM-HAM run in mixed-layer ocean (MLO) mode: one control run and one INP-perturbed run (INP seeding rate ~1% h⁻¹).

`data/obs/` — 12-month climatology of total cloud water path from the ESA Cloud CCI AVHRR v3.0 product, covering 35 years (1982–2016). Variable `cwp_ext` is the cloud-fraction-weighted liquid+ice water path.

`scripts/` — Python scripts to reproduce both figures from the archived data:- `plot_dcre.py` — overlay plots of cloud properties vs INP concentration- `plot_dcre_maps.py` — spatial map panels of ΔCRE- `reproduce.sh` — shell script to regenerate all figures (`bash scripts/reproduce.sh`)

**Requirements:** Python 3, xarray, numpy, matplotlib, cartopy, pandas. 

# Model grids

| Model | Grid | Resolution ||-------|------|------------|| CESM | Regular lat–lon | 192 × 288 (≈0.9°×1.25°) || E3SM | Regular lat–lon | 180 × 360 (1°×1°) || ECHAM-HAM | Regular lat–lon | 96 × 192 (T63, ≈1.9°) || ECHAM-HAM (MLO) | Regular lat–lon | 96 × 192 (T63, ≈1.9°) || ICON-HAM LD06 | Unstructured (R2B4) | 20 480 cells (≈160 km) || ICON-HAM Niemand | Unstructured (R2B4) | 20 480 cells (≈160 km) || OBS (CCI v3) | Remapped to Regular lat–lon | 90 × 180 (2°×2°) |

**Observational reference:** ESA Cloud CCI AVHRR-PM v3.0, processed at DWD. 1982–2016 (35 years). doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003