Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation

Abstract

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model’s historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally-resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase.

Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3

“…For example, examination of cross-metric relationships between mean-state and variability biases can shed additional light on the propagation of errors (e.g., Kang et al, 2020;Lee et al, 2021b). There continues to be interest in ranking models for specific applications (e.g., Ashfaq et al, 2022;Goldenson et al, 2023;Longmate et al, 2023;Papalexiou et al, 2020) or to "move beyond one model one vote" in multi-model analysis to reduce uncertainties in the spread of multi-model projections (e.g., Knutti, 2010;Knutti et al, 2017;Herger et al, 2018;Hausfather et al, 2022;Merrifield et al, 2023).…”