Hourly fraction of vegetation 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 simulations of fraction of vegetation in North America. The fraction of vegetation (unit: %) measures the fraction of ground covered by green vegetation. It corresponds to the spatial extent of the vegetation, which is important for land surface heat fluxes calculation. The green vegetation cover also influences the ability of organisms to seek shade, hide from predators and find food. 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.生态预报需要获取生物体所经历的气候条件相关信息。尽管方法学与计算技术取得了显著进展，但生态预报仍面临环境数据分辨率不足的困境。已公开的气候数据仅包含较少的地表气候图层，且时间分辨率较为粗糙。因此，基于此类数据构建的模型可能会低估微气候（microclimate）异质性，且无法捕捉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植被覆盖的相关预测——而植被覆盖是评估微气候空间异质性、进而评估生物体行为性体温调节能力的关键因子。 本数据集提供了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。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）的已公开、经偏差校正的全球环流模型（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年的不同高度、深度、太阳高度角及遮蔽强度场景，生成19个变量的预测结果。 本数据集通过多项标准进行了评估，每项标准均可从不同维度体现数据对研究者的价值。元数据涵盖了建模流程、微气候计算方法、计算机程序与评估过程。本次预测的19个变量包括反照率、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，分别对应上述全部微气候变量；此外还包含数据包结构示意图/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、覆盖本数据集所有经纬度点位的位置表（逗号分隔值（CSV）格式）；本项目另有19个子数据包（外部托管，链接见下文），分别对应一个微气候变量。 2. 子数据包：每个子数据包内包含44个tar压缩包，分别对应2种情景（过去、未来）与22个地理区域（区域分布详见CoverageMap_Levy.png）。tar压缩包的命名格式为`[区域]_[变量代码]_[情景]`，例如`B3_ISNOW_future`。 3. tar压缩包：每个tar压缩包包含单个区域、单个变量、对应过去或未来气候时段（1980-1999年与2080-2099年）的NetCDF格式二进制投影数据。 4. NetCDF文件：每个NetCDF文件为单个点位（以经纬度坐标索引）、单个变量的时间序列数据，对应过去或未来气候时段（1980-1999年与2080-2099年），空间分辨率为36 km，时间分辨率为1小时。 本子数据包包含北美地区植被占比的过去与未来模拟结果。植被占比（单位：%）指绿色植被覆盖的地表比例，对应植被的空间覆盖范围，对地表热通量计算至关重要。绿色植被覆盖同时会影响生物体寻找遮蔽、躲避天敌与觅食的能力。本预测结果源自分辨率为36 km、1小时的天气研究与预报模型模拟。更多细节请参见Levy等人（2016）的研究。另有18个子数据包包含其他变量的预测结果，详情与获取方式请参见主数据包（DOI:10.5072/FK2FX78N9G）。