Supplementary figures for poster GC43E-0835
Assessing Alternate Topographic Boundary Condition Performance in a Perturbed Physics Ensemble
Dervla Meegan-Kumar1, Gregory Elsaesser2,3, Kaitlyn Loftus2,4, Clara Orbe2, and Jane W. Baldwin1,5
1Dept. of Earth System Science, University of California Irvine 2NASA Goddard Institute for Space Studies, 3Dept. of Applied Physics and Applied Mathematics, Columbia University 4Climate School, Columbia University, 5Lamont-Doherty Earth Observatory, Columbia University
Questions? Please email: dervlak@uci.edu
Related references:
Elsaesser, G. S., et al., (2025). Using machine learning to generate a GISS ModelE calibrated physics ensemble (CPE). Journal of Advances in Modeling Earth Systems, 17(4), e2024MS004713. doi.org/10.1029/2024MS004713
Meegan-Kumar, D., Elsaesser, G. S., Battisti, D. S., Colose, C. M., Wu, J., Sexton, J., & Baldwin, J. W. (2025). Optimizing Topographic Boundary Conditions for East Pacific Climate Simulation. Journal of Climate, 38(11), 2497-2524. doi.org/10.1175/JCLI-D-24-0316.1
Description of E3.1 parameters and ranges for the perturbed experiments conducted as part of this study. The topography input is not technically a parameter, but used as an index that mapped to specific boundary condition files. Parameters are unitless unless otherweise specified.
Scatter plots of global annual mean top-of-atmosphere (TOA) absorbed shortwave radiation (swabstoa_ave) as a function of the 52 perturbed parameters and resolved topography (TOPO). X-axis values for the topography "parameter" correspond to the n-sigma value of the envelope scheme, where "0.0" corresponds to the CTRL boundary condition.
Kernel density estimation plots of simulated top-of-atmosphere (TOA) net shortwave (NET SW TOA) and net longwave (NET LW TOA) radiation and shortwave and longwave cloud radiative effect (SW CRE and LW CRE, respectively) for E3.1 PPE members grouped by the imposed topographic forcing.
Higher pattern correlation values correspond to better agreement between 2-D spatial distribution of simulated and observed rainfall. Unlike the distributions of the calculated mean biases, the pattern correlation distributions that increasing orographic forcing only leads to continuous improvements in simulated precipitation in western North America, whereas the elevated topographic boundary conditions result in lower model skill in simulations of regional precipitation in the "Amazon" and "India" regions and globally.
Description of climatological mean-state observational metrics and model diagnostics used for E3.1 parameter calibration. For the ``range": scalar = global mean, 1D=global zonal means