Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time have received less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, USA. We first compare the fit of, and support for, models employing observed temperatures, GHCP temperatures, and GHCP temperatures with an elevation adjustment, finding (1) greater support for, and better fit using, elevation‐adjusted vs. raw temperature models and (2) overall similar fits of elevation‐adjusted models employing temperatures from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation‐adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2°C, finding good agreement among GHCPs though with between‐GHCP differences and variation primarily at middle elevations (~1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models, particularly in mid‐elevation areas where the position of treeline may be changing, suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.

中文翻译：

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在当前和预测温度下评估生态模型中的多个历史气候产品


在对大尺度生态现象进行建模并预测对预计气候变化的生态响应时，网格化历史气候产品（GHCP）的使用频率越来越高。与此同时，人们越来越认识到在使用全球环流模型（GCM）和排放情景的气候预测时需要考虑不确定性。尽管 GHCP 的使用量不断增加，并且记录的差异不断增加，但不确定性表征主要集中在 GCM 和排放情景选择上，而使用单个 GHCP 进行空间和时间预测的后果却很少受到关注。在这里，我们利用观测数据和七个 GHCP 的 7 月平均温度数据来模拟美国阿拉斯加中部的植物冠层覆盖和树木断面积。我们首先比较了采用观测温度、GHCP 温度和高程调整后的 GHCP 温度的模型的拟合度和支持度，发现 (1) 与原始温度模型相比，高程调整模型对使用高程调整模型有更大的支持和更好的拟合度，并且（ 2）使用观测温度或 GHCP 的海拔调整模型的总体相似拟合。接下来，我们将重点放在基底面积上，比较当前条件下使用 GHCP 数据的海拔调整模型所生成的预测以及当前温度加 2°C 的变暖情景，发现 GHCP 之间具有良好的一致性，尽管 GHCP 之间存在差异，并且主要在中等海拔地区存在变化（~1,000 米）。这些差异在变暖的情况下被放大。最后，利用 GHCP 模型的预测变化和差异的汇总指数，我们确定了在当前和变暖条件下最有可能表现出预测不确定性的区域的特征。 尽管（1）相对于模型中的观测结果，GHCP 数据总体表现良好，并且（2）模型预测之间存在正相关性，但模型之间的预测存在差异，特别是在林线位置可能发生变化的中海拔地区，建议研究人员应该进行练习如果选择在模型中使用单个 GHCP，请务必小心。我们建议使用多个 GHCP 来提供超出标准估计预测区间的额外不确定性信息，特别是在保护规划中采用模型预测时。