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Tongli Wang, Andreas Hamann, David L. Spittlehouse, and Trevor Q. Murdock


This study addresses the need to provide comprehensive historical climate data and climate change projections at a scale suitable for, and readily accessible to, researchers and resource managers. This database for western North America (WNA) includes over 20 000 surfaces of monthly, seasonal, and annual climate variables from 1901 to 2009; several climate normal periods; and multimodel climate projections for the 2020s, 2050s, and 2080s. A software package, ClimateWNA, allows users to access the database and query point locations, obtain time series, or generate custom climate surfaces at any resolution. The software uses partial derivative functions of temperature change along elevation gradients to improve medium-resolution baseline climate estimates and calculates biologically relevant climate variables such as growing degree-days, number of frost-free days, extreme temperatures, and dryness indices. Historical and projected future climates are obtained by using monthly temperature and precipitation anomalies to adjust the interpolated baseline data for the location of interest. All algorithms used in the software package are described and evaluated against observations from weather stations across WNA. The downscaling algorithms substantially improve the accuracy of temperature variables over the medium-resolution baseline climate surfaces. Climate variables that are usually calculated from daily data are estimated from monthly climate variables with high statistical accuracy.

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Andreas Hamann, Tongli Wang, David L. Spittlehouse, and Trevor Q. Murdock

We present a comprehensive set of interpolated climate data for western North America, including monthly data for the last century (1901–2009), future projections from atmosphere–ocean general circulation models (A2, A1B, and B1 scenarios of the WCRP CMIP3 multimodel dataset), as well as decadal averages and multiple climate normals for the last century. For each of these time periods, we provide a large set of basic and derived biologically relevant climate variables, such as growing and chilling degree days, growing season length descriptors, frost-free days, extreme minimum temperatures, etc. To balance file size versus accuracy for these approximately 20,000 climate surfaces, we provide a stand-alone software solution that adds or subtracts historical data and future projections as medium-resolution anomalies (deviations) from the high resolution 1961–90 baseline normal dataset. The program further downscales the baseline data through a combination of bilinear interpolation and elevation adjustment using partial derivative functions. Observations from 3,353 weather stations were used to evaluate the climate estimates of our downscaling algorithms. We found that the algorithms substantially improved prediction accuracy of the monthly climate variables, especially for temperature variables. They eliminated up to 65% of the unexplained variance in observed monthly temperatures and reduced standard errors of climate estimates by up to 40%.

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