Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

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.

Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among‐population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among‐population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single “universal response function” (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

Use of downscaled global climate model projections is expanding rapidly as climate change vulnerability assessments and adaptation planning become mainstream in many sectors. Many climate change impact analyses use climate model projections downscaled at very high spatial resolution (~1 km) but very low temporal resolution (20‐ to 30‐year normals). These applications have model selection priorities that are distinct from analyses at high temporal resolution. Here, we select a 13‐model ensemble and an 8‐model subset designed for robust change‐factor downscaling of monthly climate normals, and describe their attributes in North America. All models are selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archives. The 13‐model ensemble is representative of the distribution of equilibrium climate sensitivity, grid resolution, and transient regional climate changes in the CMIP6 generation. The 8‐model subset is consistent with the IPCC's recent assessment of the very likely range of Earth's equilibrium climate sensitivity. Our results emphasize several principles for selection and use of downscaled climate ensembles: (a) the ensemble must be observationally constrained to be meaningful; (b) analysis of multiple models is essential as the ensemble mean alone can be misleading; (c) small (<8‐member) ensembles should be region‐specific and used with caution; (d) higher grid resolution is not necessarily better; and (e) multiple simulations of each model/scenario combination are necessary to represent precipitation uncertainty. Although we have focused our documentation on North America, our model selection uses primarily global criteria and is applicable to downscaling climate normals in other continents. Downscaled projections for the selected models are available in ClimateNA (http://climatena.ca/). An accompanying web application (https://bcgov-env.shinyapps.io/cmip6-NA/) provides tools for further model selection and visualization of the ensemble.

Cyclocarya paliurus has been widely used as an ingredient in functional foods in China. However, the antioxidant properties of phenolic compounds and the effect of the plant origin remain unclear. The present study evaluated the geographical variation of this plant in term of its phenolic composition and antioxidant activities based on leaf materials collected from five regions. high-performance liquid chromatography (HPLC) analysis showed that there are three major components, quercetin-3-O-glucuronide, kaempferol-3-O-glucuronide, and kaempferol-3-O-rhamnoside, and their contents varied significantly among sampling locations. The investigated phenolic compounds showed substantial antioxidant activities, both in vitro and in vivo, with the highest capacity observed from Wufeng and Jinzhongshan. Correlation analysis revealed that quercetin and kaempferol glycosides might be responsible for the antioxidant activities. Our results indicate the importance of geographic origin, with sunny hours and temperature as the main drivers affecting the accumulation of C. paliurus phenolics and their antioxidant properties.