Hourly canopy gap 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 the gap between canopy in North America. The canopy gap (unit: %) is the fraction of area where solar radiation can reach the ground. Predictions were extracted from Weather Research and Forecasting model simulation, run at a resolution of 36km and 1 hour. 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.生态预测需要获取生物所经历的气候条件相关信息。尽管方法学与计算技术已取得长足进步，生态预测仍受限于环境数据分辨率不足的问题。已发表的地表气候数据通常仅包含少量图层，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）的空间异质性，同时忽略气候极端事件带来的生物学效应。此外，当前我们仍缺乏未来环境下的植被覆盖预测——而植被覆盖是估算微气候空间异质性、进而评估生物行为性体温调节能力的关键因子。 本数据集提供过去与未来时段的微气候与植被数据，空间分辨率为36 km（约0.3°），时间分辨率为1小时。本研究采用天气研究与预报模式（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式（global-circulation model）预测结果进行降尺度处理。将该模式的输出作为微气候模式的输入数据，最终生成1980-1999年与2080-2099年、涵盖不同高度、深度、太阳高度角与遮蔽强度下的19个变量的预测结果。 本数据集通过多项评估标准进行了验证，每项标准均从不同维度阐明了该数据集对研究者的应用价值。元数据详细说明了建模流程、微气候计算方法、所用计算机程序以及评估过程。本次预测的19个变量涵盖反照率、积雪层数、微气候气温与气压等；完整变量列表请参见下文的「模型变量表」。 本数据集的结构如下： (1) 主数据包：本数据包包含19份月度汇总数据，分别对应上述每一种微气候变量；同时提供数据包结构示意图/信息图、用于提取并保存NetCDF文件的R脚本，以及涵盖本数据集所有经纬度点位的位置表（CSV格式）。本项目共包含19个子数据包（外部托管，链接见下文），每个子数据包对应一种微气候变量。 (2) 子数据包：每个子数据包内包含44个tar归档文件，对应2种气候情景（过去、未来）以及22个地理区域（区域分布详见CoverageMap_Levy.png）。tar文件名模板为`[区域]_[变量代码]_[情景]`，例如`B3_ISNOW_future`。 (3) tar归档文件：每个tar归档文件包含单个区域、单个变量、对应过去（1980-1999年）或未来（2080-2099年）气候情景的NetCDF格式二进制投影数据。 (4) NetCDF文件：每个NetCDF文件对应单个点位（以经纬度坐标索引）、单个变量的时间序列数据，涵盖过去（1980-1999年）或未来（2080-2099年）气候情景，空间分辨率为36 km，时间分辨率为1小时。 本数据包包含北美地区林冠间隙的过去与未来预测结果。林冠间隙（单位：%）指太阳辐射可到达地面的区域占比。该预测结果提取自分辨率为36 km、1小时的天气研究与预报模式模拟结果。更多细节请参见Levy等人（2016）的研究。本项目另有18个子数据包，涵盖其余变量的预测结果，完整详情与获取方式请参见主数据包（DOI:10.5072/FK2FX78N9G）。