Simulation of the effect of doubled atmospheric CO2 on the climate of northern and central California

A Local Climate Model (LCM) is described that can provide a high-resolution (10 km) simulation of climate resulting from a doubling of atmospheric CO₂ concentrations. A canonicalregression function is used to compute the monthly temperature (mean of daily-maximum-temperature) and precipitation for any point, given a set of predictor variables. Predictor variables represent the influence of terrain, sea-surface temperature (SST), windfields, CO₂ concentration, and solar radiation on climate. The canonical-regression function is calibrated and validated using empirical windfield, SST, and climate data from stations in the western U.S. To illustrate an application of the LCM, the climate of northern and central California is simulated for a doubled CO₂ (600 ppmv) and a control scenario (300 ppmv CO₂). Windfields and SSTs used to compute predictor variables are taken from general circulation model simulations for these two scenarios. LCM solutions indicate that doubling CO₂ will result in a 3 C° increase in January temperature, a 2 C° increase in July temperature, a 16 mm (37%) increase in January precipitation, and a 3 mm (46%) increase in July precipitation.