Output data: Varying sources of uncertainty in risk-relevant hazard projections across the United States

Abstract Physical climate risk assessment requires understanding how different sources of uncertainty affect hazard projections. However, the relative importance of these uncertainties can differ across end-uses. Here, we combine three state-of-the-art downscaled climate model ensembles to characterize how different uncertainties affect projections of several temperature- and precipitation-based risk metrics across the contiguous United States. We focus on long-term trends of aggregate indices as well as the intensity of rare events with 10- to 100-year return periods. By including new downscaled initial condition ensembles, we characterize the role and relative importance of internal variability at local scales. Our results demonstrate systematic differences in patterns of uncertainty between average and extreme indices, across recurrence intervals, and between temperature- and precipitation-derived metrics. We show that temperature metrics are more sensitive to the choice of emissions scenario and Earth system model, while internal variability can be dominant for precipitation-based metrics. Additionally, we find that the statistical uncertainty from extreme value distribution fitting can often exceed climate-related factors, particularly at recurrence intervals of 50 years or longer. These results highlight the challenge of providing general guidance for climate impacts assessment and the need to consider a wide variety of potential uncertainties when quantifying climate risk. Journal reference Currently undergoing peer review: https://doi.org/10.22541/essoar.15003332/v1 Data description - `ensemble_summary` contains the ensemble means and upper/lower quantiles for each downscaling method and SSP combination; the trend metrics are grouped into a single netcdf file while the return level outputs are separated by variable. - `trends` contains the individual trend estimates for each member of the meta-ensemble; there are separate files for each variable and for each sub-ensemble (i.e., downscaling method). - `GEV` contains the GEV outputs, including the GEV parameter estimates and calculated associated return levels; there separate files for the GEV parameters and return levels, and for each variable and sub-ensemble (i.e., downscaling method). - `uncertainty_results` contains our main uncertainty decomposition results; the trend metrics are grouped into a single netcdf file while the return level outputs are separated by variable. Notes: - NetCDF files are compressed via zlib so may take longer than expected to open, especially the trend and GEV estimates that contain outputs for all individual ensemble members - To ensure maximal compatibility, strings coordinates (e.g. for SSPs or ESMs) are encoded as fixed-length byte strings (NetCDF4 char arrays); python users can use `ds[coord].astype(str)` to convert after loading. - All trend and GEV calculations were performed on the native grids of the downscaled outputs, then regridded to the LOCA2 grid using a nearest neighbors algorithm. All uncertainty results are valid over CONUS only. Contact Additional details can be found in the preprint (https://doi.org/10.22541/essoar.15003332/v1) or corresponding GitHub repository (https://github.com/david0811/conus_comparison_lafferty-etal-2026). Email: [e-mail]摘要 物理气候风险评估需要厘清各类不确定性来源对灾害预估的影响机制。然而，这些不确定性的相对重要性会因应用场景而异。本研究整合三套当前顶尖的降尺度气候模型集合，系统分析不同不确定性来源对美国本土（Contiguous United States, CONUS）多类基于气温与降水的风险指标预估结果的影响。研究聚焦于综合指标的长期趋势，以及重现期为10至100年的极端事件强度。通过纳入新增的降尺度初始条件集合，本研究厘清了局地尺度内部变率的作用及其相对重要性。研究结果表明，平均指标与极端指标之间、不同重现期之间，以及气温衍生与降水衍生指标之间，不确定性分布均存在系统性差异。结果显示，气温指标对排放情景与地球系统模型（Earth System Model, ESM）的选择更为敏感，而降水类指标的不确定性则主要由内部变率主导。此外，本研究发现极值分布拟合带来的统计不确定性往往超过气候相关因素的影响，尤其在重现期为50年及以上的场景中。上述研究结果凸显了为气候影响评估提供通用指导所面临的挑战，同时也表明在量化气候风险时，需要考虑各类潜在的不确定性来源。 期刊引用 目前处于同行评审阶段：https://doi.org/10.22541/essoar.15003332/v1 数据说明 - `ensemble_summary`（集合概要）包含各降尺度方法与共享社会经济路径（Shared Socioeconomic Pathways, SSP）组合的集合均值及上下分位数；趋势指标被整合至单个NetCDF（网络通用数据格式）文件中，而重现水平输出则按变量分别存储。 - `trends`（趋势项）包含元集合各成员的单独趋势估计结果；每个变量与每个子集合（即降尺度方法）均对应独立的数据文件。 - `GEV`（广义极值分布，Generalized Extreme Value）包含广义极值分布的输出结果，包括参数估计值与计算得到的对应重现水平；广义极值分布参数、重现水平，以及每个变量与子集合（即降尺度方法）均对应独立的数据文件。 - `uncertainty_results`（不确定性结果）包含本研究核心的不确定性分解结果；趋势指标被整合至单个NetCDF文件中，而重现水平输出则按变量分别存储。 备注 - NetCDF文件通过zlib压缩，因此打开所需时间可能长于预期，尤其是包含所有集合成员输出结果的趋势项与广义极值分布估计文件。 - 为确保最大兼容性，字符串型坐标（例如共享社会经济路径或地球系统模型的坐标）被编码为定长字节字符串（NetCDF4字符数组）；Python用户可在加载数据后通过`ds[coord].astype(str)`完成格式转换。 - 所有趋势项与广义极值分布的计算均在降尺度输出的原始网格上完成，随后通过最近邻算法重采样至LOCA2网格（局部构建类比模型2，Localized Constructed Analogs 2）。所有不确定性结果仅适用于美国本土。 联系方式 更多详细信息可查阅预印本（https://doi.org/10.22541/essoar.15003332/v1）或对应的GitHub仓库（https://github.com/david0811/conus_comparison_lafferty-etal-2026）。 电子邮箱：[e-mail]