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ClimateWNA-High-Resolution Spatial Climate Data for Western North America

机译:ClimateWNA-北美西部高分辨率空间气候数据

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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.
机译:这项研究满足了以研究人员和资源管理人员适合并易于获得的规模提供全面的历史气候数据和气候变化预测的需求。该北美西部(WNA)数据库包含1901年至2009年的2万多个月度,季节和年度气候变量。几个气候正常时期;以及2020、2050和2080年代的多模式气候预测。 ClimateWNA软件包使用户可以访问数据库和查询点位置,获取时间序列或以任何分辨率生成自定义的气候面。该软件使用沿海拔梯度的温度变化的偏导函数来改善中等分辨率的基线气候估计值,并计算生物学上相关的气候变量,例如生长天数,无霜天数,极端温度和干燥指数。通过使用月度温度和降水异常来调整内插基线数据以获取感兴趣位置,可以获得历史和未来的气候预测。描述了软件包中使用的所有算法,并根据来自WNA的气象站的观测进行了评估。缩减算法大大提高了中分辨率基线气候表面上温度变量的准确性。通常根据每日数据计算出的气候变量是根据每月的气候变量估算出来的,统计精度很高。

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