DOI: 10.5281/zenodo.7539066
title: Radiative feedbacks in CAM6 PPE

description: Radiative feedbacks in a CAM6 perturbed parameter ensemble (PPE) are calculated using radiative kernels. Feedbacks are calculated as the difference between a control 'PD' simulation and a simulation with an imposed uniform 4K SST warming 'SST4K.' The PPE are three-year long simulations of CAM6 which vary in their atmospheric parameter values. Simulations 0 is the default. Simulations 001 through 262 have varied atmospheric parameter values. The parameters are varied concurrently and randomly using latin hypercube sampling. The parameter values are in 'parameter_262_w_control.nc'. Note that the simulation number 175 was removed, so simulation number and index are not the same.

Three kinds of kernels are used. All three kernels give similar cloud-radiative feedbacks. The 'Zelinka' kernels include only cloud-radiative feedbacks but are further partitioned by cloud top pressure and optical depth. Both the 'Zelinka' and 'Pendergrass' kernels use CAM5, which has the same radiative transfer model as the PPE. The 'Huang' kernels use ERA-Interim reanalsysis data. Further details on each kernel is provided in the publications listed below.

There is an additional dataset with feedbacks from the Zelinka kernels for some of the PPE ensemble members here: https://zenodo.org/records/16416130

Please contact Margaret Duffy with any questions or comments at mduffy at berkeley dot edu.

The data were created for the following paper:

Duffy, M. L., B. Medeiros., A. Gettelman, and T. Eidhammer, 2024: Perturbing Parameters to Understand Cloud Contributions to Climate Change. J. Climate, 37, 213-227, doi: 10.1175/JCLI-D-23-0250.1.

Zelinka kernels:

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.

Pendergrass kernels:

Pendergrass, A.G., Andrew Conley and Francis Vitt: Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5. Earth System Science Data. doi:10.5194/essd-2017-108.

Huang kernels:

Huang, Y., Xia, Y., and Tan, X. (2017), On the pattern of CO2 radiative forcing and poleward energy transport, J. Geophys. Res. Atmos., 122, 10,578– 10,593, doi:10.1002/2017JD027221.