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cloud-radiative-kernels

Python, matlab, and ncl scripts are provided that demonstrate how to use cloud radiative kernels to compute cloud-induced radiation anomalies and cloud feedback. In addition, a python script is provided that demonstrates how to partition the cloud feedbacks in to components due to changes in cloud amount, altitude, optical depth, and a residual, for all clouds, non-low clouds, and low clouds.

References

Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels. J. Climate, 25, 3715-3735. doi:10.1175/JCLI-D-11-00248.1.

Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth. J. Climate, 25, 3736-3754. doi:10.1175/JCLI-D-11-00249.1.

Zelinka, M.D., S.A. Klein, K.E. Taylor, T. Andrews, M.J. Webb, J.M. Gregory, and P.M. Forster, 2013: Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5. J. Climate, 26, 5007-5027. doi:10.1175/JCLI-D-12-00555.1.

Zelinka, M. D., C. Zhou, and S. A. Klein, 2016: Insights from a Refined Decomposition of Cloud Feedbacks, Geophys. Res. Lett., 43, 9259-9269, doi:10.1002/2016GL069917.

Zhou, C., M. D. Zelinka, A. E. Dessler, P. Yang, 2013: An analysis of the short-term cloud feedback using MODIS data, J. Climate, 26, 4803–4815. doi:10.1175/JCLI-D-12-00547.1.

Input

The code makes use of the following data:

Frequency Name Description Unit File Format
monthly mean clisccp ISCCP simulator cloud fraction histograms % 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 tas surface air temperature K nc
monthly mean LWkernel LW cloud radiative kernel W/m^2/% nc
monthly mean SWkernel SW cloud radiative kernel W/m^2/% nc

Two sets of cloud radiative kernels available at https://github.com/mzelinka/cloud-radiative-kernels/tree/master/data

  1. cloud_kernels2.nc: The cloud radiative kernels that are appropriate for use with climate model output were developed using zonal mean temperature and humidity profiles averaged across control runs of six CFMIP1 climate models as input to the radiation code. Please refer to Zelinka et al. (2012a,b) for details.

  2. obs_cloud_kernels3.nc: The cloud radiative kernels that are appropriate for use with observations were developed using zonal mean temperature, humidity, and ozone profiles from ERA Interim over the period 2000-2010 as input to the radiation code. Please refer to Zhou et al. (2013) for details.

Output

SW and LW cloud feedbacks.

Each feedback is size (MO,TAU,CTP,LAT,LON)=(12,7,7,90,144)

For the provided sample imput data, the code should print the following output, which is the global annual mean LW and SW cloud feedbacks. The values are slightly different in the Matlab and Python versions, possibly due to differences in regridding and in area-weighted averaging.

Average Cloud Feedback Component Matlab Python
LW 0.816 0.833
SW 0.414 0.402

Figure Generation

Is a script to draw a figure in the paper included? Yes, see the images directory.