climkern 1.0.1

No project description provided

ClimKern: a Python package for calculating radiative feedbacks

Citation

If you use this package or any part of this code, please cite it! Until we have a paper prepared, please cite this package at software.

Janoski, T. P., & Mitevski, I. (2023, December 7). ClimKern (Version 1.0.0). Retrieved from https://pypi.org/project/climkern/1.0.0/. https://doi.org/10.5281/zenodo.10291284.

Overview

The radiative kernel technique outlined in Soden & Held (2006) and Soden et al. (2008) is commonly used to calculate climate feedbacks. The "kernels" refer to datasets containing the radiative sensitivities of TOA (or surface) radiation to changes in fields such as temperature, specific humidity, and surface albedo; they are typically computed using offline radiative transfer calculations.

ClimKern

• standardizes the assumptions used in producing radiative feedbacks using kernels
• simplifies the calculations by giving users access to functions tailored for climate model output
• provides access to a repository of 12 different radiative kernels to quantify interkernel spread

Installation

ClimKern is built on the architecture of Xarray and requires several other packages for compatibility with climate model output. The easiest method to install is to create a new conda environment with prerequisite packages using conda or mamba:
conda create -n ck_env python=3.9 esmpy xarray xesmf cftime pooch tqdm importlib-resources plac netcdf4 -c conda-forge
or
mamba create -n ck_env python=3.9 esmpy xarray xesmf cftime pooch tqdm importlib-resources plac netcdf4 -c conda-forge

Then, activate the environment:
conda activate ck_env
or
mamba activate ck_env

Finally, install ClimKern with pip:
pip install -i https://test.pypi.org/simple/ climkern

Once installed, ClimKern requires kernels found on Zenodo. These kernels (and tutorial data) are stored separately because of PyPI size limitations. You can download the kernels easily using the download script included in the package.
python -m climkern download

Basic tutorial

Temperature, water vapor, and surface albedo feedbacks

This brief tutorial will cover the basics of using ClimKern before more complete documentation can be written (I'm only one person, here!). We start by importing ClimKern and accessing our tutorial data:

import climkern as ck

ctrl,pert = ck.tutorial_data('ctrl'),ck.tutorial_data('pert')

These datasets have all the necessary variables for computing feedbacks. Let's start with temperature feedbacks.

LR,Planck = ck.calc_T_feedbacks(ctrl.T,ctrl.TS,ctrl.PS,
                                pert.T,pert.TS,pert.PS,pert.TROP_P,
                                kern="GFDL")

To produce succinct output, let's use ClimKern's spatial average function. Additionally, we will normalize the feedbacks by global average surface temperature change to convert from Wm^-2, the output of ClimKern functions, to the more commonly used units of Wm^-2K^-1.

# compute global average surface temperature change
dTS_glob_avg = ck.spat_avg(pert.TS - ctrl.TS)

# normalize temperature feedbacks by temperature change and take
# the annual average
print("The global average lapse rate feedback is {val:.2f} W/m^2/K.".format(
    val=(ck.spat_avg(LR)/dTS_glob_avg).mean()))
print("The global average Planck feedback is {val:.2f} W/m^2/K.".format(
    val=(ck.spat_avg(Planck)/dTS_glob_avg).mean()))

Expected result with the GFDL kernel:

The global average lapse rate feedback is -0.41 W/m^2/K.

The global average Planck feedback is -3.12 W/m^2/K.

The water vapor and surface albedo feedbacks are calculated similarly:

q_lw,q_sw = ck.calc_q_feedbacks(ctrl.Q,ctrl.T,ctrl.PS,
                                pert.Q,pert.PS,pert.TROP_P,
                                kern="GFDL",method="zelinka")
alb = ck.calc_alb_feedback(ctrl.FSUS,ctrl.FSDS,
                           pert.FSUS,pert.FSDS,
                           kern="GFDL")

print("The global average water vapor feedback is {val:.2f} W/m^2/K.".format(
    val=(ck.spat_avg(q_lw+q_sw)/dTS_glob_avg).mean()))
print("The global average surface albedo feedback is {val:.2f} W/m^2/K."
      .format(
    val=(ck.spat_avg(alb)/dTS_glob_avg).mean()))

Expected result:

The global average water vapor feedback is 1.44 W/m^2/K.

The global average surface albedo feedback is 0.38 W/m^2/K.

Cloud feedbacks

The cloud feedbacks, calculated using Soden et al. (2008) adjustment method, require all-sky and clear-sky versions of other feedbacks and the instantaneous radiative forcing.

First, we need the longwave and shortwave cloud radiative effects, which ClimKern can calculate.

dCRE_LW = ck.calc_dCRE_LW(ctrl.FLNT,pert.FLNT,ctrl.FLNTC,pert.FLNTC)
dCRE_SW = ck.calc_dCRE_SW(ctrl.FSNT,pert.FSNT,ctrl.FSNTC,pert.FSNTC)

Let's also read in the tutorial IRF.

IRF = ck.tutorial_data('IRF')
# overwrite IRF latitude because of rounding error
IRF['lat'] = ctrl.lat

Next, we need the clear-sky versions of the temperature, water vapor, and surface albedo feedbacks.

#_cs means clear-sky
LR_cs,Planck_cs = ck.calc_T_feedbacks(ctrl.T,ctrl.TS,ctrl.PS,
                                pert.T,pert.TS,pert.PS,pert.TROP_P,
                                kern="GFDL",sky="clear-sky")
q_lw_cs,q_sw_cs = ck.calc_q_feedbacks(ctrl.Q,ctrl.T,ctrl.PS,
                                pert.Q,pert.PS,pert.TROP_P,
                                kern="GFDL",method="zelinka",sky="clear-sky")
alb_cs = ck.calc_alb_feedback(ctrl.FSUS,ctrl.FSDS,
                           pert.FSUS,pert.FSDS,
                           kern="GFDL",sky="clear-sky")

At last, we can calculate the longwave and shortwave cloud feedbacks.

cld_lw = ck.calc_cloud_LW(LR + Planck,LR_cs+Planck_cs,q_lw,q_lw_cs,dCRE_LW,
                          IRF.IRF_lwas,IRF.IRF_lwcs)
cld_sw = ck.calc_cloud_SW(alb,alb_cs,q_sw,q_sw_cs,dCRE_SW,IRF.IRF_swas,
                          IRF.IRF_swcs)

print("The global average SW cloud feedback is {val:.2f} W/m^2/K.".format(
    val=(ck.spat_avg(cld_sw)/dTS_glob_avg).mean()))
print("The global average LW cloud feedback is {val:.2f} W/m^2/K.".format(
    val=(ck.spat_avg(cld_lw)/dTS_glob_avg).mean()))

Expected result:

The global average SW cloud feedback is 0.48 W/m^2/K.

The global average LW cloud feedback is 0.02 W/m^2/K.

Other features & coming soon

ClimKern has several other useful features:

• Four different methods for calculating water vapor feedbacks.
• The ability to calculate the "relative humidity" version of all feedbacks following Held & Shell (2012) and Zelinka et al. (2020).
• Functions to calculate stratospheric temperature and water vapor feedbacks.

We are continuously updating the package and are working on improved documentation. For now, we recommend exploring the other functions by referencing the package with a "?" in a Jupyter notebook.

Want to help? Get involved!

Our hope is that this package will be useful to the climate science community and we appreciate any contributions our fellow scientists and programmers can offer. For more information and to get involved, email Ty at [e-mail].