A COMPREHENSIVE, HIGH-RESOLUTION DATABASE OF HISTORICAL AND PROJECTED CLIMATE SURFACES FOR WESTERN NORTH AMERICA
A COMPREHENSIVE, HIGH-RESOLUTION DATABASE OF HISTORICAL AND PROJECTED CLIMATE SURFACES FOR WESTERN NORTH AMERICA
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DOI：
10.1175/bams-d-12-00145.1
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发表时间：
2013-09-01
影响因子：
8
通讯作者：
Murdock, Trevor Q.
中科院分区：
文献类型：
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作者：
Hamann, Andreas;Wang, Tongli;Murdock, Trevor Q.
我们提供了一套完整的北美西部内插气候数据，包括上个世纪（1901-2009）的月度数据，大气-海洋环流模式（WCRP CMIP 3多模式数据集的A2，A1 B和B1情景）的未来预测，以及上个世纪的十年平均值和多个气候正常值。对于这些时间段中的每一个，我们都提供了大量的基本和衍生的生物相关气候变量，例如生长和寒冷度日，生长季节长度描述符，无霜日，极端最低温度等。我们提供了一种独立的软件解决方案，该解决方案将历史数据和未来预测作为中等分辨率的异常（偏差）从高分辨率的1961-90基线正常数据集中添加或减去。该程序通过双线性插值和使用偏导数函数的高程调整的组合进一步缩小基线数据。来自3,353个气象站的观测数据被用来评估我们的降尺度算法的气候估计。我们发现，该算法大大提高了月气候变量，特别是温度变量的预测精度。他们消除了高达65%的观测到的月度温度的无法解释的变化，并将气候估计的标准误差降低了40%。
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%.