Hourly soil temperature data from a dynamically downscaled projection of past and future microclimates covering North America from 1980-1999 and 2080-2099

Ecological forecasting requires information about the climatic conditions experienced by organisms. Despite impressive methodological and computational advances, ecological forecasting still suffers from poor resolutions of environmental data. Published data comprise relatively few layers of surface climate and suffer from coarse temporal resolution. Hence, models using these data might underestimate heterogeneity of microclimates and miss biological consequences of climatic extremes. Moreover, we currently lack predictions about vegetation cover in future environments, a key factor for estimating the spatial heterogeneity of microclimates and hence the capacity for behavioral thermoregulation. Here, we describe microclimates and vegetation for the past and the future at spatial and temporal resolutions of 36 km (approximately 0.3°) and 1 h, respectively. We used the Weather Research and Forecasting model to downscale published, bias-corrected predictions of a global-circulation model from a resolution of 0.9° latitude and 1.25° longitude (approximately 100 km in latitude and 130 km in longitude). Output from this model was used as input for a microclimate model, which generated predictions for 19 variables for 1980-1999 and 2080-2099 at various heights, depths, sun angle and shade intensities. The data was evaluated using several criteria, each of which shed light on a different aspect of value to researchers. The metadata describe the modeling protocol, microclimate calculations, computer programs, and the evaluation process. The 19 predicted variables include albedo, snow layers, microclimate temperatures and pressures, among others. For a list of all variables please see the 'Model variables table' below. The dataset is structured as follows: (1) Main package: 19 monthly summaries, one for each microclimate variable (listed above) are available in this packagePackage structure schema/infographicR-script to extract and save NetCDF filesLocations table with latitude and longitude points covered in this data (csv)19 sub-packages (externally hosted, linked below) are available for this project, one for each microclimate variable.(2) Sub-packages: Within each sub-package are 44 tar files representing: 2 scenarios (past; future) across 22 geographical regions (see CoverageMap_Levy.png for distribution of regions)..tar file name template is [region]_[variable code]_[scenario]; i.e. B3_ISNOW_future.(3) .tar file: Each .tar file contains projection data in NetCDF format binary files for one region, one variable and for either past or future climates (1980-1999 and 2080- 2099).(4) NetCDF files : Each NetCDF file is a time-series of data for a particular variable in one location (indexed by the longitudinal-latitudinal coordinates) for either past or future climates (1980-1999 and 2080-2099).Resolutions are of 36 km and 1 hour. This sub-package contains past and future predictions of soil temperature at different depths below the ground (at 3-cm intervals from 3 to 30cm, 18-cm intervals from 30 to 174 cm, and at 198 cm) in North America. In the microclimate model, soil temperatures (unit: K) were calculated at different shade densities (no shade, 25%, 50%, 75%, and full shade) by solving partial differential equations for heat transfer between the ground and soil. Calculations were driven by outputs from the Weather Research and Forecasting model simulation. For more details, see Levy et al. (2016). There are 18 other sub-packages containing predictions for other variables, please see the main data package (doi:10.5072/FK2FX78N9G) for details and access.生态预报需要了解生物所经历的气候条件。尽管方法学与计算技术取得了长足进步，生态预报仍面临环境数据分辨率不足的困境。已发表的气候数据多为表层气候的少量图层，且时间分辨率较为粗糙。因此，使用此类数据构建的模型可能会低估微气候（microclimate）的异质性，同时忽略气候极端事件所带来的生物学影响。此外，当前我们仍缺乏未来环境下植被覆盖的预测数据——而这正是估算微气候空间异质性、进而评估行为性体温调节能力的关键因素。 本文提供了过去与未来时段的微气候与植被数据，其空间分辨率为36 km（约0.3°），时间分辨率为1小时。我们采用天气研究与预报模型（Weather Research and Forecasting model），将分辨率为0.9°纬度、1.25°经度（纬度方向约100 km，经度方向约130 km）的已发表偏差校正全球环流模型（global-circulation model）预测结果进行降尺度处理。将该模型的输出作为微气候模型的输入，进而针对1980-1999年与2080-2099年两个时段，在不同高度、深度、太阳角度及遮阴强度下，生成19个变量的预测结果。本数据集通过多项标准进行评估，每项标准均可反映研究人员关注的不同价值维度。元数据涵盖了建模流程、微气候计算方法、计算机程序及评估过程。19个预测变量包括反照率、积雪层、微气候温度与气压等，完整变量列表请参见下文的“模型变量表”。 本数据集的组织结构如下： (1) 主数据包：本数据包包含19份月度汇总文件，分别对应前文列出的每一个微气候变量；同时附带数据包结构示意图/信息图、用于提取并保存网络通用数据格式（NetCDF）文件的R脚本、涵盖本数据集覆盖的经纬度点位的位置表（csv格式）。本项目另有19个子数据包（外部托管，链接见下文），分别对应每一个微气候变量。 (2) 子数据包：每个子数据包内包含44个tar包，分别对应22个地理区域的2种情景（过去、未来）（区域分布详见CoverageMap_Levy.png）。tar包的命名格式为：[区域]_[变量代码]_[情景]，例如B3_ISNOW_future。 (3) 单个tar包：每个tar包包含单区域、单变量、单情景（过去或未来，即1980-1999年与2080-2099年）的网络通用数据格式（NetCDF）二进制投影数据。 (4) 网络通用数据格式（NetCDF）文件：每个NetCDF文件为单个点位（以经纬度坐标索引）的特定变量的时间序列数据，覆盖过去或未来时段（1980-1999年与2080-2099年），分辨率为36 km与1小时。 本次展示的子数据包包含北美地区地下不同深度的土壤温度预测数据（过去与未来），其中3 cm至30 cm区间以3 cm为步长，30 cm至174 cm区间以18 cm为步长，另包含198 cm深度的温度数据。在微气候模型中，我们通过求解地面与土壤间的热传递偏微分方程，结合不同遮阴密度（无遮阴、25%遮阴、50%遮阴、75%遮阴及全遮阴）计算土壤温度（单位：开尔文），计算过程以天气研究与预报模型（Weather Research and Forecasting model）的模拟输出作为驱动数据。更多细节请参见Levy等人（2016）的研究。本数据集另有18个子数据包，涵盖其余变量的预测结果，详情与获取方式请参见主数据包（DOI:10.5072/FK2FX78N9G）。