AR4 climate model performance in simulating snow water equivalent over Catskill Mountain watersheds, New York, USA

摘要

In this study, we evaluate the ability of GCMs participating in the Intergovernmental Panel for Climate Change’s (IPCC)nFourth Assessment Report (AR4) to simulate variability in the snow water equivalent (SWE) in New York City Water Supplynwatersheds located northwest of NYC in the Catskill Mountains. SWE is estimated using an empirical temperature-basedndegree day model. Inputs to this model are either measured with historical meteorological (1961–2000) data or a GCM modelnoutput for the same historical period. The evaluation of the GCMs is based on a skill score developed using probabilityndistribution functions derived from the time series of simulated snowpack. From the skill scores (SS) calculated, the GCMsnare ranked based on their ability to simulate the snowpack. These results have implications for selecting a subset of GCMnsimulations for climate change impact assessments in New York City’s water supply.nResults show that the GFDL 2D0 (gf001) model has the highest SS (0D93) and CCSM (ncc09) model has the lowest SSn(0D26). On the basis of the SS, the GCM ensemble members are classified into three categories: high, medium and lownperformance. The probability density functions for the three performance classes show the largest between-model variabilitynfor models in low performance class. Differences between the means and medians of observation-based model simulation andnGCM-based simulation were greatest in the low-performance class. Copyright  2011 John Wiley & Sons, Ltd.