Fuentes Montserrat, Kittel Timothy G E, Nychka Doug
Department of Statistics, North Carolina State University, Box 8203, Raleigh, North Carolina 27695, USA.
Ecol Appl. 2006 Feb;16(1):99-116. doi: 10.1890/04-1157.
Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.
陆地生态系统动力学的全球和区域数值模型需要高空间分辨率和时间上完整的历史气候场作为输入变量。然而，由于气候观测数据的空间分布不均匀且记录不完整，这些气候场需要进行估算。此外，与这些估算相关的气候场不确定性很少得到评估。生态模型通常由这些气候场的地质统计模型均值估计（克里金法）驱动，而没有考虑这种不确定性，更不用说在生态模拟中评估这种误差的传播情况了。我们引入了一个贝叶斯统计框架来对气候观测数据进行建模，以创建空间均匀且时间完整的气候场，同时考虑时间和空间上的相关性、空间异质性、非正态性以及所有这些因素的不确定性。贝叶斯模型的一个关键优势在于它能为生成的气候场生成不确定性度量。为了演示这种方法，我们在美国西部气候异质区域的精细分辨率网格上重建了历史月降水量场（生态模型的一个驱动因素）。这项工作的主要目标是评估生态模型对与其气候驱动因素预测相关的不确定性的敏感性。为了评估它们对预测输入变量的数值敏感性，我们使用一组不同版本的重建气候场生成了一系列生态模型模拟。我们通过从气候场的后验预测分布中抽样来构建这样一个集合。我们证明了气候场的估计预测误差可能非常高。我们使用历史降水时间序列集合，通过生态数值模型Century模拟草地生物地球化学动态，评估了这种误差在生态模型实验中的重要性。我们展示了预测降水场的不确定性是如何传播到生态模型结果中的，并且这种传播有不同的模式。根据输出变量的不同，模型动态对输入不确定性的响应范围从与输入匹配的输出不确定性到减弱或有偏差的输出不确定性，以及在输入误差消失后仍持续存在的时间上的不确定性。
