Data from: Evaluation of downscaled, gridded climate data for the conterminous United States

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

天气与气候调控诸多生态过程，故而兼具空间连续性与精细分辨率的气象数据，成为生态研究与预测的迫切需求。目前已涌现大量降尺度（downscaled）气象数据集，但针对这些数据集开展系统性评估的研究却寥寥无几。本研究针对这一不足，围绕四大核心问题展开探讨：（1）经降尺度处理的网格化（gridded）气候数据集，其气温与降水估算精度如何？（2）不同数据集的精度是否存在显著区域差异？（3）相较于极端气象值，数据集的均值估算精度表现如何？（4）数据集的精度是否随空间分辨率发生变化？我们选取了8套近年在美国全境广泛应用的网格化逐日气象降尺度数据集进行对比评估。研究结果显示，不同数据集之间，以及降尺度数据集与地面气象站实测数据之间均存在显著差异：气温要素的估算精度优于降水要素，气候均值的估算精度优于极端气象值。与地面气象站实测数据契合度最高的数据集，随生态区域的不同而有所变化。令人意外的是，数据集的精度并未随空间分辨率发生明显变化。尽管数据集间与地面气象站实测数据间存在固有差异本属常理，但本研究结果凸显出：即便针对网格单元内邻近气象站的局域对比场景，不同插值方法对降尺度气象数据的影响也十分显著。从更广泛的视角来看，本研究结果提示：在为特定生态应用场景遴选降尺度气象数据集时，需审慎考量各数据集的优势变量、适用区域及空间分辨率等核心要素。