Date of Award
Master of Science (MS)
College of Science and Mathematics
Earth and Environmental Studies
Thesis Sponsor/Dissertation Chair/Project Chair
Clement A. Alo
Robert W. Taylor
Joshua C. Galster
Global Climate Models (GCMs) are increasingly becoming useful tools for predicting future climatic changes. These GCMs typically employ large spatial scales while municipalities may experience varied impacts at the local level. By downscaling and bias-correcting GCM outputs, more accurate predictions concerning specific regions can be made. The Multivariate Adaptive Constructed Analogs (MACA) models provide daily precipitation and temperature information for point localities by modifying coarse resolution data from GCMs to a higher spatial resolution. In this study, trends in climate extremes over the Passaic River Basin (PRB) between 1981-2005 are estimated based on three MACA models (bcc-csm1-1m, CCSM4, and MRI-CGCM3). The historical trends obtained from the MACA models are validated using an observational dataset and further corrected for bias, and then projected trends for 2051-2075 relative to the 1981-2005 investigated. The models are united in their expectations of a decrease in very cold nights, ranging from -0.05% to -0.25%. Warm nights show slightly less agreement; while bcc-csm1-1m and MRI-CGCM3 see an increase ranging from 0.05% to 0.18%, CCSM4 sees a decrease of 0.075% for RCP 8.5. Consecutive dry days decrease by up to 3 days between CCSM4 and MRI-CGCM3, whereas bcc-csm1-1m only shows an increase in CDD for scenario RCP 8.5. Rainy days also increase per model from 1-3 days except for bcc-csm1-1m, which sees a decrease by 1 day. The 95th percentile of (or extreme) precipitation also sees almost universal increase ranging from 25% to 80% except for MRI-CGCM3, which projects a slight decrease of the extreme at only -5%. This analysis presents a unique opportunity to glimpse at the projected changes in the PRB with regards to the impacts of climate change.
Prasad, Archana, "Projected Trends in Climate Extremes in the Passaic River Basin Based on Bias-Corrected and Spatially Downscaled Global Climate Model Simulations" (2018). Theses, Dissertations and Culminating Projects. 185.