Validation of a semi-Lagrangian, canonical regression model of climates in the southwestern United States

EXTRACT (SEE PDF FOR FULL ABSTRACT): A local climate model (LCM) has been developed to simulate the modern and 18 ka climate of the southwestern United States. ... LCM solutions indicate summers were about 1°C cooler and winters 11°C cooler at 18 ka. Annual PREC increased 68% at 18 ka, with large increases in spring and fall PREC and diminished summer monsoonal PREC. ... Validation of simulations of 18 ka climate indicate general agreement with proxy estimates of climate for that time. However, the LCM estimates of summer temperatures are about 5 to 10°C higher than estimates from proxy reconstructions.

Geographic patterns in the lag of temperature response to insolation for El Niño vs. La Niña conditions [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): The most important control on the annual cycle of temperature is insolation, with secondary influences from terms related to moisture, yet direct statistical analysis of the moisture-driven modulations (such as El Niño) of the response of temperature to insolation are not available. We have examined one aspect of the relationship between insolation and the instrumental record of maximum daily temperature - the lag between the two - at 252 stations in the western United States.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Geographic variation of Chevrier's field mouse (Apodemus chevrieri) (Milne-Edwards, 1868) (Muridae : Murinae) from southwestern China based on cranial morphometric variables

Geographic variation of Chevrier's field mouse (Apodemus chevrieri) (Milne-Edwards, 1868) (Muridae: Murinae) from southwestern China based on cranial morphometric variables. Zoological Studies 47(4): 393-401. A sample of 134 specimens of Apodemus chevrieri was investigated in the present study. Individuals were divided into male and female groups, and these were respectively subjected to multivariate analysis. Results indicated that 3 geographic populations of A. chevrieri inhabit southwestern China: a Sichuan population in western Sichuan Province; a northwestern Yunnan population ranging from northwestern Yunnan Province eastward to southern Sichuan Province; and a central Yunnan population in central Yunnan Province. In addition, a coefficient of difference analysis was performed among these 3 geographic populations. The results suggested that these 3 geographical populations of A. chevrieri belonged to 2 subspecies. Furthermore, we discuss the relationships of the subspecific differentiation of A. chevneri with changes in latitude in southwestern China.