Uncertainties in Modeling Spatially-Resolved Climate Change Health Impacts

描述（由申请人提供）：为了描述人口对气候变化的脆弱性，非常需要更好地了解和量化与健康影响预测相关的幅度，分布以及不确定性。我们建议通过分析健康影响（即，在2000年、2030年和2050年的几十年里，在两种温室气体排放情景下，以4公里分辨率测量空气污染和热浪暴露造成的死亡率。我们提出的研究将利用目前由疾病控制和预防中心（CDC）资助的一个项目（“评估美国东部累积的气候相关健康风险”）中产生的空气污染和天气预测数据。IPCC第五次评估报告（AR 5）情景用于生成这一独特的数据集，模拟基于增强的社区气候系统模型（CCSM 4），加上社区多尺度空气质量模型（CMAQ）和天气研究和预报模型（WRF）。该数据集的高分辨率使我们能够研究健康影响预测的空间异质性，这对于确定目标位置以促进备灾工作和有效的适应战略尤为重要。除了确定与气候变化相关的健康影响增加的脆弱地理位置外，我们还将研究气候变化健康影响建模的每个分析步骤所引入的不确定性的大小。特别是，我们将确定四个组成部分的相对重要性-温室气体排放情景，气象和空气质量建模，气候响应特征，未来人口分布预测和年龄结构。我们将结合联合收割机敏感性分析和蒙特卡罗模拟来检验假设，即气候变化健康影响建模的每个分析步骤所引入的不确定性的大小存在显着变化。不确定性分析将有助于政府机构制定强有力的和适当的应对气候变化造成的健康影响，并为未来的研究分配资源。我们建议的工作的最终产品将是1）根据美国东海岸沿着的三种压力源（PM2.5、臭氧和热浪）的综合健康影响进行的特定县死亡率预测，包括到2050年的十年的时间演变，以及脆弱地点的图形表示，2）这些估计的不确定性的空间明确表示，分配到错误源。我们的工作将有助于在非常精细的空间尺度上提高卫生成果预测的准确性，促进弱势群体有针对性的准备工作。

DESCRIPTION (provided by applicant): To characterize population vulnerability to climate change, there is a tremendous need to better understand and quantify the magnitude, distribution, as well as the uncertainties associated with the health impacts projections. We propose to study the spatial variations in population vulnerability related to climate change in the east coast of U.S. through analyzing the health impacts (i.e., mortality) from air pollution and heat waves exposures at 4km resolution during the decades of 2000, 2030 and 2050 under two greenhouse gas emission scenarios. Our proposed study will take advantage of the air pollution and weather projections data that are currently being generated in a project ('Assessing the Cumulative Climate-Related Health Risks in the Eastern U.S') funded by the Centers for Disease Control and Prevention (CDC). IPCC 5th assessment reports (AR5) scenarios are used to generate this unique dataset and the simulations are based on the enhanced Community Climate System Model (CCSM4), coupled with the Community Multi-scale Air Quality model (CMAQ) and the Weather Research and Forecasting model (WRF). The fine resolution in this dataset allows us to study the spatial heterogeneity in the health impacts projections, particularly important for identifying target locations to facilitate preparedness efforts and effective adaptation strategies. In addition to identifying vulnerable geographical locations with increased health impacts related to climate change, we will also study the magnitude of uncertainties introduced by each analytical step of climate change health impacts modeling. In particular, we will determine the relative importance of four components - greenhouse gas emission scenarios, meteorological and air quality modeling, exposure-response characterization, and future population distribution projections and the age structure. We will combine sensitivity analysis and Monte Carlo simulations to test the hypothesis that there are significant variations in the magnitude of uncertainties introduced by each analytical step of climate change health impacts modeling. The uncertainty analysis will help government agencies develop robust and appropriate responses to health impacts caused by climate change, and allocate resources for future research. The end products of our proposed work will be 1) county-specific mortality projections based on combined health impacts from three stressors, PM2.5, ozone and heat waves, along the east coast of U.S. including their temporal evolution up to the decade of 2050, and a graphical representation of the vulnerable locations, 2) a spatially explicit representation of the uncertainty of these estimates, apportioned to error sources. Our work will allow for improved precision of health outcome projections on a very fine spatial scale, facilitating targeted preparedness in the vulnerable populations.