aprp

A python script and some sample data are provided that demonstrate how to use the approximate partial radiative perturbation (APRP) technique to compute TOA SW radiation anomalies due to changes in individual components of the climate system.

Sample data are from a short (2-year) period of MPI-ESM-LR using the difference between sstClimAerosol and sstClim runs.

One should difference longer periods for more robust results -- these are just for demonstrative purposes

Reference

Taylor, K. E. et al. (2007), Estimating shortwave radiative forcing and response in climate models, J. Clim., 20(11), 2530-2543, doi:10.1175/JCLI4143.1.

Input

The code makes use of the following data (all provided):

Frequency	Name	Description	Unit	File Format
monthly mean	clt	total cloud fraction	%	nc
monthly mean	rsdt	downwelling SW flux at the TOA	W/m^2	nc
monthly mean	rsut	upwelling SW flux at the TOA	W/m^2	nc
monthly mean	rsutcs	upwelling SW flux at the TOA under clear skies	W/m^2	nc
monthly mean	rsuscs	upwelling SW flux at the surface under clear skies	W/m^2	nc
monthly mean	rsdscs	downwelling SW flux at the surface under clear skies	W/m^2	nc
monthly mean	rsds	downwelling SW flux at the surface	W/m^2	nc
monthly mean	rsus	upwelling SW flux at the surface	W/m^2	nc

Output

TOA SW flux anomalies attributable to changes in:

1. surface albedo (for all-, clear-, and overcast-sky conditions)
2. clouds (total change and contributions from changing cloud cover, scattering, and absorption)
3. non-cloud atmosphere (e.g., from changes in water vapor, aerosols, ozone)

Each output field is size (MO,LAT,LON)

Figure Generation

For the provided sample imput data, two figures are generated. These are provided in the images directory.