Hourly visible radiation flux 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 downward flux of shortwave radiation in North America. The shortwave radiation includes the visible light and ultraviolet radiation that are emitted by the sun and able to 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.

生态预报需要获取生物体所经历的气候条件。尽管方法学与计算技术取得了长足进展，生态预报仍面临环境数据分辨率不足的困境。现有公开数据的地表气候图层相对匮乏，且时间分辨率较为粗糙。因此，依托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（behavioral thermoregulation）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, WRF），对分辨率为0.9°纬度、1.25°经度（纬度方向约100 km，经度方向约130 km）的已公开全球环流模型（global-circulation model）偏差校正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（microclimate model）的输入，即可针对1980–1999年与2080–2099年两个时段，在不同高度、深度、太阳高度角与遮阴强度下，生成19个变量的预测结果。

本数据集采用多项标准进行评估，每项标准均可从不同维度展现数据对研究者的应用价值。元数据（metadata）则详细说明了建模流程、微气候计算方法、计算机程序与评估过程。19个预测变量涵盖反照率（albedo）、积雪层数、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，对应前文所列的每一项微气候变量；同时提供数据包结构示意图/信息图表、用于提取并保存NetCDF文件的R脚本，以及涵盖本数据集覆盖的所有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），分别对应每一项微气候变量。
(2) 子数据包：每个子数据包内包含44个tar文件，分别对应22个地理区域（区域分布详见CoverageMap_Levy.png）的2种情景（过去、未来）。tar文件的命名格式为`[region]_[variable code]_[scenario]`，例如`B3_ISNOW_future`。
(3) .tar文件：每个.tar文件包含对应单个区域、单个变量、单种气候情景（1980–1999年或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）对应单个变量的时间序列数据，涵盖过去或未来气候情景（1980–1999年与2080–2099年），分辨率为36 km与1小时。

该子数据包包含北美地区短波辐射（shortwave radiation）向下通量的过去与未来预测结果。短波辐射包括太阳发射的可见光与紫外线辐射（ultraviolet radiation），可抵达地面。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结果，详细信息请参见Levy等人（2016）的研究。另有18个子数据包涵盖其他变量的预测结果，完整信息与获取方式请参见主数据包（doi:10.5072/FK2FX78N9G）。