发表机构
Department of Geosciences, University of Oslo; Instituto Pirenaico de Ecología, Spanish Research Council (IPE-CSIC); WSL Institute for Snow and Avalanche Research SLF; Mountain Futures GmbH; Potsdam Institute for Climate Impact Research (PIK); Institute of Geophysics, Polish Academy of Sciences（奥斯陆大学地质科学系; 西班牙研究委员会皮耶林生态研究所; 瑞士联邦雪崩与雪研究所; 山未来有限公司; 波茨坦气候影响研究所; 波兰科学院地球物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出气候知情冰冻圈再分析方法，采用分层贝叶斯部分汇集，联合推断状态与参数，优于独立和静态校准，提升51%评分并大幅降低计算成本。
AI 中文摘要
冰冻圈调节全球水、能量和碳循环，影响社会和生态系统。通过将观测数据同化到模型中，我们可以推断状态变量的过去轨迹，生成冰冻圈再分析。现有的冰冻圈再分析通常将每个水文年独立处理，不跨年汇集信息。在观测稀疏且有噪声的情况下，这种“气候假设”再分析表现不佳，会退回到假设但未校准的背景气候态。相反，采用静态参数的贝叶斯校准完全汇集跨水文年的信息，推断气候参数却不捕捉年际变率。在此，我们提出一种新的使用部分汇集的分层贝叶斯方法，称之为“气候知情”冰冻圈再分析。它通过嵌套混合粒子平滑工作流联合推断状态轨迹、年度参数和局部气候超参数。我们通过三个实验测试该方法，将原位雪水当量、基于卫星的积雪覆盖面积分数和冰川尺度质量平衡数据同化到八个研究区（从瑞士阿尔卑斯山到高北极）的温度指数模型中。在标定期所有实验以及验证期除一个实验外的所有实验中，采用部分汇集的分层再分析优于逐年独立的冰冻圈再分析（无汇集）和静态参数校准（完全汇集）。总体而言，相对于先验，其连续等级概率评分提高了51%，显著超过年度再分析（38%）和静态校准（19%）。与粒子马尔可夫链蒙特卡洛基准相比，我们的工作流通过回收和期望最大化将计算成本降低了几个数量级，且技能相当或更优。
英文摘要
The cryosphere regulates global cycles of water, energy, and carbon, affecting societies and ecosystems. By assimilating observations into models, we can infer the past trajectory of state variables, generating a cryospheric reanalysis. Existing cryospheric reanalyses typically treat each water year independently, with no pooling of information across years. With sparse and noisy observations, such 'climate-assumed' reanalyses perform poorly, reverting to an assumed yet uncalibrated background climatology. Conversely, Bayesian calibration with static parameters completely pools information across water years, inferring climatological parameters without capturing inter-annual variability. Here, we propose a new hierarchical Bayesian approach using partial pooling, which we coin 'climate-informed' cryospheric reanalysis. It jointly infers state trajectories, annual parameters, and local climatological hyperparameters using a nested hybrid particle smoothing workflow. We test the approach through three experiments by assimilating in situ snow water equivalent, satellite-based fractional snow-covered area, and glacier-wide mass balance data into a temperature index model at eight study areas, from the Swiss Alps to the High Arctic. Hierarchical reanalysis with partial pooling outperforms annually independent cryospheric reanalysis (no pooling) and static parameter calibration (complete pooling) for all experiments during calibration and for all but one during validation. Overall, it yields a 51% improvement in continuous ranked probability score relative to the prior, markedly exceeding that of annual reanalysis (38%) and static calibration (19%). Compared to a particle Markov chain Monte Carlo benchmark, our workflow reduces computational cost by several orders of magnitude through recycling and expectation maximization, with comparable or improved skill.
Comments80 pages, 11 figures. A supplement with 19 figures and a line-numbered copy of the manuscript for review are provided as ancillary files
