Data from Estimating Uncertainty in Daily Weather Interpolations: a Bayesian Framework for Developing Climate Surfaces

<b>Paper Abstract: </b>Conservation of biodiversity demands comprehension of evolutionary and ecological patterns and processes that occur over vast spatial and temporal scales. A central goal of ecology is to understand the climatic factors that control ecological processes and this has become even more important in the face of climate change. Especially at global scales, there can be enormous uncertainty in underlying environmental data used to explain ecological processes, but that uncertainty is rarely quantified or incorporated into ecological models. In this study a climate-aided Bayesian kriging approach is used to interpolate 20 years of daily meteorological observations (maximum and minimum temperature and precipitation) to a 1 arc-minute grid for the Cape Floristic Region of South Africa. Independent validation data revealed overall predictive performance of the interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum temperature, minimum temperature, and precipitation, respectively. A suite of ecologically-relevant climate metrics that include the uncertainty introduced by the interpolation were then generated. By providing the high resolution climate metric surfaces and uncertainties, this work facilitates richer and more robust predictive modeling in ecology and bio- geography. These data can be incorporated into ecological models to propagate the uncertainties through to the final predictions.<b>Data Description:</b>NetCDF file with the mean and standard deviation of the daily temperature and precipitation posterior distributions. Metadata is included within the file.<b>Code:</b>Available at https://github.com/adammwilson/BayesianClimate<br><br>论文摘要：生物多样性保护有赖于阐明发生于广袤时空尺度上的演化与生态模式及过程。生态学的核心目标之一是厘清调控生态过程的气候因子，而在气候变化背景下，该目标的重要性愈发凸显。尤其在全球尺度下，用于解释生态过程的基础环境数据往往存在显著不确定性，但此类不确定性极少被量化并纳入生态模型。本研究采用气候辅助贝叶斯克里金（Bayesian kriging）方法，将南非开普植物区（Cape Floristic Region）20年的每日气象观测数据（最高气温、最低气温与降水量）插值至1弧分分辨率的网格。独立验证数据表明，该插值方法的整体预测性能为：最高气温的决定系数（R²）为0.90，最低气温为0.85，降水量为0.59。随后本研究生成了一系列与生态学相关的气候指标，并将插值过程引入的不确定性纳入其中。通过提供高分辨率的气候指标曲面与不确定性数据，本研究可为生态学与生物地理学领域构建更丰富、更稳健的预测模型提供支撑。此类数据可被嵌入生态模型，使不确定性传递至最终预测结果。<br><br>数据说明：包含每日气温与降水后验分布的均值与标准差的NetCDF文件，文件内附带元数据（Metadata）。<br><br>代码：可于https://github.com/adammwilson/BayesianClimate 获取。