Department of Biological Sciences, University of South Carolina, 715 Sumter Street, Columbia, South Carolina 29208, USA.
Ecol Appl. 2011 Oct;21(7):2678-90. doi: 10.1890/10-2049.1.
Gridded weather data were evaluated as sources of forcing variables for biophysical models of intertidal animal body temperature with model results obtained using local weather station data serving as the baseline of comparison. The objective of the study was to determine which gridded data are sufficient to capture observed patterns of thermal stress. Three coastal sites in western North America were included in this analysis: Boiler Bay, Oregon; Bodega Bay, California; and Pacific Grove, California. The gridded data with the highest spatial resolution, the 32-km North American Regional Reanalysis (NARR) and the 38-km Climate Forecasting System Reanalysis (CFSR), predicted daily maximum intertidal animal temperature most similarly to the local weather Station data. Time step size was important for variables that change rapidly throughout the day, such as solar radiation. There were site-based differences in the ability of the model to predict daily maximum intertidal animal temperature, with the gridded data predictions being the closest to local weather station predictions in Boiler Bay, Oregon. In a review of gridded data used as part of ecological studies, there was broad use of the data across subject areas and ecosystems so the recent improvements in the spatial (from 2 degrees to 32 km) and temporal scales (from 6 hours to 1 hour) of gridded data will further add to the applicability within the ecological community particularly for mechanistic studies.
网格化天气数据被评估为潮间带动物体温生物物理模型的强迫变量源，使用当地气象站数据作为比较基准来获得模型结果。本研究的目的是确定哪些网格化数据足以捕捉到观察到的热应激模式。本分析包括北美洲西部的三个沿海地点：俄勒冈州的 Boiler Bay；加利福尼亚州的 Bodega Bay；以及加利福尼亚州的 Pacific Grove。具有最高空间分辨率的网格化数据，即 32-km 北美区域再分析（NARR）和 38-km 气候预测系统再分析（CFSR），最类似于当地气象站数据预测每日最大潮间带动物温度。对于在一天中变化迅速的变量（如太阳辐射），时间步长大小很重要。模型预测每日最大潮间带动物温度的能力存在基于地点的差异，在俄勒冈州 Boiler Bay，网格化数据预测与当地气象站预测最为接近。在对作为生态研究一部分使用的网格化数据进行审查时，发现数据在各个学科领域和生态系统中得到了广泛应用，因此，网格化数据在空间（从 2 度到 32 公里）和时间尺度（从 6 小时到 1 小时）上的最近改进将进一步增加其在生态社区中的适用性，特别是对于机制研究。
