Climate Resilience in Mechanistic-Empirical Flexible Pavement Analysis: Investigation of Impacts for Subsurface Pavement Temperature from Average Ensemble and Individual Global Climate Model Selection Routines

Abstract

Assessment of climate change impacts in pavement design and analysis is not a widespread practice among practitioners, and there is a lack of consensus on approaches for preparing the outputs of future climate projections into mechanistic-empirical analysis inputs. The research question was: is there a difference in projected pavement temperatures using an average ensemble and individual model selection approach for climate models? This was assessed by computing performance grade binder classifications and subsurface temperature profiles, the latter of which has not been well studied. The asynchronous regional regression model and delta method were used to create impact-relevant hourly projections of air temperature for a future target year, 45 years after a base temporal year. The enhanced integrated climatic model was used to produce subsurface temperatures for a site in Minnesota within the long-term pavement performance InfoPave database seasonal monitoring program. Coupled Model Intercomparison Project Phase 5 (CMIP5) projections from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections archive were processed using the United States Department of Transportation CMIP5 tool. Variable preparation was completed for an average ensemble and individual model selection approach with 20 representative concentration pathway 8.5 models. A bias toward warmer minimum and maximum temperatures for the average ensemble approach compared with the individual model approach was discovered and attributed to the sophisticated averaging approach in the asynchronous regional regression model. The resultant impacts on subsurface temperature variation and performance grade binder classifications underscore the need for further investigation for improving guidance for climate model selection.