Improving Earth System Model Selection Methodologies for Projecting Hydroclimatic Change: Case Study in the Pacific Northwest

• Published in: Journal of geophysical research. Atmospheres 2024-04, Vol.129 (7), p.n/a
• Main Authors: Lybarger, Nicholas D., Smith, Abigail, Newman, Andrew J., Gutmann, Ethan D., Wood, Andrew W., Frans, Christopher D., Warner, Michael D., Arnold, Jeffrey R.
• Format: Article
• Language: English
• Subjects: Assessments; Climate change; Climate models; CMIP6; Culling; Environmental impact; ESM; Future climates; Hydroclimate; Impact assessment; Intercomparison; model evaluation; Performance evaluation; Precipitation; Precipitation trends; Precipitation-temperature relationships; Regional development; Statistical analysis; Statistical tests; Trends; Vulnerability
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1029/2023JD039774

The rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments. Plain Language Summary This study provides an updated and rigorously tested method for evaluating the performance of climate models for applications relevant to water managers and other stakeholders. Using traditional metrics of climate model performance, both regional and global, as well as newly developed metrics based on processes important for the simulation of precipitation, we have created a generalizable, systematic, and succinct method for reducing the number of models to be considered for climate change impact applications. By reducing the number of relevant models to around 20% of the total models and not having a significant impact on future temperature and precipitation trend projections in doing so, we strongly reduce the computational effort needed to gain a realistic simulation