Package: climkern
ClimKern: a Python package for calculating radiative feedbacksCitationIf 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 fromhttps://pypi.org/project/climkern/1.0.0/.https://doi.org/10.5281/zenodo.10291284.OverviewThe radiative kernel technique outlined inSoden & Held (2006)andSoden 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.ClimKernstandardizes the assumptions used in producing radiative feedbacks using kernelssimplifies the calculations by giving users access to functions tailored for climate model outputprovides access to a repository of12 different radiative kernelsto quantify interkernel spreadInstallationClimKern 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 usingcondaormamba:conda create -n ck_env python=3.9 esmpy xarray xesmf cftime pooch tqdm importlib-resources plac netcdf4 -c conda-forgeormamba create -n ck_env python=3.9 esmpy xarray xesmf cftime pooch tqdm importlib-resources plac netcdf4 -c conda-forgeThen, activate the environment:conda activate ck_envormamba activate ck_envFinally, install ClimKern withpip:pip install -i https://test.pypi.org/simple/ climkernOnce installed, ClimKern requires kernels found onZenodo. 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 downloadBasic tutorialTemperature, water vapor, and surface albedo feedbacksThis 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:importclimkernasckctrl,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 changedTS_glob_avg=ck.spat_avg(pert.TS-ctrl.TS)# normalize temperature feedbacks by temperature change and take# the annual averageprint("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 feedbacksThe cloud feedbacks, calculated usingSoden 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 errorIRF['lat']=ctrl.latNext, we need the clear-sky versions of the temperature, water vapor, and surface albedo feedbacks.#_cs means clear-skyLR_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 soonClimKern has several other useful features:Four different methods for calculating water vapor feedbacks.The ability to calculate the "relative humidity" version of all feedbacks followingHeld & Shell (2012)andZelinka 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 attjanoski@ccny.cuny.edu.