Researchers often use gridded datasets to develop statistical relationships between climate and natural resources at locations that are distant from weather stations. These gridded datasets may provide inaccurate estimates of temperature at sites with elevations that differ significantly from the elevation assigned to the corresponding grid cell. We assess the accuracy of three gridded climate datasets commonly used in ecological studies (800‐m resolution PRISM, 4‐km GRIDMET, and 1‐km Daymet) compared with a network of 153 temperature dataloggers arranged along elevation transects in Yellowstone National Park (Wyoming, Montana, Idaho). Measured lapse rates for monthly average daytime high temperatures were generally steeper (more cooling per unit of elevation increase) than lapse rates in gridded datasets and steeper than those in similar mountainous regions. Measured lapse rates for monthly average nighttime lows were similar to gridded data lapse rates. Our measured lapse rates are most useful for the adjustment of daytime highs from weather stations or gridded data during warmer months. Temperatures during cooler months sometimes are strongly affected by other factors. Ecologically relevant metrics calculated from temperatures adjusted with constant lapse rates can increase multiplicatively and by varying amounts from year to year. For example, high‐elevation estimates of climatic water deficit (CWD) calculated from gridded data that were not adjusted with our measured lapse rates were 2.5–4 times greater (depending on the year) than calculations from temperatures that were adjusted. Our results emphasize the importance of correcting grid‐based climate estimates for elevation in complex terrain when accurate, site‐specific data are required. When the elevation assigned to grid cells differs significantly from the elevation of points within the cell, the lapse rate obtained for data extracted from grids can be shallower than the true rate. We illustrate the implications of these findings with case studies in Yellowstone.

中文翻译：

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在生态学研究中正确应用流失率：比较黄石中的温度观测值和网格数据

研究人员经常使用网格化的数据集来开发远离气象站的气候与自然资源之间的统计关系。这些网格化的数据集可能无法提供海拔高度与分配给相应网格单元的海拔高度明显不同的站点的温度估计值。我们比较了沿黄石国家公园（怀俄明州）沿海拔样线排列的153个温度数据记录器网络，比较了生态研究中常用的三个栅格化气候数据集的准确性（800 m分辨率PRISM，4 km GRIDMET和1 km Daymet）。 ，爱达荷州蒙大拿州）。通常，每月平均白天高温的测量失误率比栅格数据集中的失误率更陡峭（每升高一个单位升高的降温幅度更大），并且比相似山区的陡峭度更大。每月平均夜间低点的测得失误率与网格数据失误率相似。我们测得的失误率对于在温暖月份调整气象站的日间高点或网格数据最有用。凉爽月份的温度有时会受到其他因素的强烈影响。从以恒定流失率调整的温度计算得出的与生态相关的指标可以逐年增加，并且每年变化的量也不同。例如，根据未经我们测得的失误率调整的网格数据计算得出的高海拔气候缺水（CWD）值，比根据调整后的温度计算得出的高2.5至4倍（取决于年份）。我们的结果强调了在需要准确的特定地点数据时，校正基于网格的气候估计值对于复杂地形中海拔的重要性。当分配给网格单元的高程与单元内点的高程显着不同时，从网格提取的数据获得的流逝速率可能会比真实速率低。我们通过黄石公园的案例研究说明了这些发现的含义。我们的结果强调了在需要准确的特定地点数据时，校正基于网格的气候估计值对于复杂地形中海拔的重要性。当分配给网格单元的高程与单元内点的高程显着不同时，从网格提取的数据获得的流逝速率可能会比真实速率低。我们通过黄石公园的案例研究说明了这些发现的含义。我们的结果强调了在需要准确的特定地点数据时，校正基于网格的气候估计值对于复杂地形中海拔的重要性。当分配给网格单元的高程与单元内点的高程显着不同时，从网格提取的数据获得的流逝速率可能会比真实速率低。我们通过黄石公园的案例研究说明了这些发现的含义。