Collaborative Research: Reducing uncertainty of climatic trends in the Sierra Nevada: an ensemble-based reanalysis via the merger of disparate measurements

内华达山脉系统在北美西部的水文循环、能量循环、生态系统和水资源供应中发挥着不可或缺的作用。在过去的50年里，从现场网络观测到的雪水当量(SWE)有提前春季融雪径流和减少春季雪水当量的趋势。这些趋势将导致冬季洪水的可能性增加，可用水减少。因此，迫切需要对观察到的变化进行详细的、基于过程的理解。此外，至关重要的是，必须对这些趋势进行空间上的连续描述，跨越内华达山脉目前的地形梯度。并不是所有可用的信息都被用于趋势的诊断，即。星载遥感测量。这意味着，趋势只评估了特定站点所在的地方(而不是整个内华达山脉)。此外，在评估趋势时，没有考虑到对观测不确定性的正式估计。对物理过程和对气候变化的复杂敏感性跨地形梯度的详细了解尚未实现。因此，我们建议利用所有可用的数据集进行重新分析，包括现场测量和遥感测量。我们的动机是四个主要数据流中每一个的独特和互补的特点：原位积雪测量、径流、从可见光和近红外数据得出的积雪覆盖面积(SCA)，以及被动微波测量。我们将使用积雪物理过程的集合模型模拟来提供积雪变量的先验估计。测量模型将用于将积雪状态与所有四个主要数据流相关联。数据同化分析步骤计算积雪变量的后验估计，该估计考虑了所有四个数据流以及气象数据和雪物理过程的科学知识。对每个积雪变量的时空不确定性的估计可以直接从集合中计算出来。将使用这些积雪状态的后验估计来评估趋势。在我们的两年项目中，我们将对内华达山脉南部的King河和Kaweah河流域进行上述重新分析。这些流域中的每一个在空间上都与南塞拉利昂临界区天文台(https://snri.ucmerced.edu/CZO).)的研究区域重叠国王河流域的普罗维登斯CZO和Kaweah河流域的Wolverton CZO代表了正在进行的研究导致对跨海拔梯度的积雪有了更深层次的了解，这可能有助于对已观察到的长期趋势的洞察。我们将把这一为期两年的项目的重点放在制定可靠的方法上，以便在后续工作中应用于整个内华达山脉。我们将重点发展形成再分析同化计划核心的空间变异性和不确定性的统计和物理模式。特别是，我们将专注于开发将雪水观测网点测量等同于基于网格的雪特性估计的模型，以及将水文气象输入的不确定性模型参数化。我们将把这项工作的模型、方法和结果整合到加州大学洛杉矶分校现有的本科生和研究生课程中，并整合到伯德极地研究中心正在进行的推广工作中。此外，这里开发的高分辨率模拟框架可以为未来的气候变化研究提供一个独特的试验台，在这种情况下，区域或全球气候模型的大气模型输出可以被用来迫使离线模型在现有文献中增加关于塞拉积雪和春季径流预计如何变化的内容。

AbstractThe Sierra Nevada system plays an integral role in the hydrologic cycle, energy cycle, ecological systems, and in water resources supply in Western North America. Trends toward earlier spring snowmelt runoff and decreasing springtime observations of snow water equivalent (SWE) from in situ networks have been observed in the past fifty years. These trends will result in both increased likelihood of winter floods and decreased water availability. It is therefore urgent to develop detailed, process-based understanding of the observed changes. Moreover, it is vital to develop a spatially-continuous characterization of these trends, spanning the physiographic gradients present in the Sierra Nevada. Not all available information has been used in the diagnosis of the trends, viz. spaceborne remote sensing measurements. This means that trends have only been evaluated where particular stations are located (not over the entire Sierra Nevada). Moreover, formal estimates of observation uncertainty have not been taken into account in the assessment of the trends. Detailed understanding of the physical processes and complex sensitivity to climatic change across physiographic gradients has not been achieved. We thus propose a reanalysis utilizing all available datasets, including both in situ and remote sensing measurements. Our motivation is the unique and complementary characteristics of each of four primary datastreams: in situ snow measurements, streamflow, snow-covered area (SCA) derived from visible and near-infrared data, and passive microwave measurements. We will use ensemble model simulations of snowpack physical processes to provide a priori estimates of snowpack variables. Measurement models will be used to relate snowpack states to all four primary datastreams. The data assimilation analysis step calculates an a posteriori estimate of snowpack variables that takes into account all four datastreams, as well as meteorological data and scientific knowledge of snow physics processes. Estimates of the spatiotemporal uncertainty in each snowpack variable can be calculated directly from the ensemble. Trends will be assessed using these posterior estimates of snowpack states.During our two-year project, we will perform the reanalysis described above for the King River and Kaweah River basins in the Southern Sierra Nevada. Each of these river basins overlaps spatially with study areas from the Southern Sierra Critical Zone Observatory (https://snri.ucmerced.edu/CZO). The Providence CZO in the King River basin and the Wolverton CZO in the Kaweah River basin represent locations where ongoing research is leading to a deeper process-level understanding of the snow cover across elevation gradients, which may provide insight into the long-term trends that have been observed. We will focus this two-year project on developing solid methodology that could be applied to the entire Sierra Nevada in follow-on work. We will focus on development of the statistical and physical models of spatial variability and uncertainty that form the core of the reanalysis assimilation scheme. In particular, we will focus on development of models for relating observing network point measurements of snow water equivalent to grid-based estimates of snow properties, and on parameterization of the uncertainty models for the hydrometeorological inputs to the snow physics models.We will integrate the models, methods, and results from the work into existing undergraduate and graduate courses at UCLA, and into ongoing outreach work of the Byrd Polar Research Center. Additionally, the high-resolution modeling framework developed here could provide a unique testbed for future climate change studies, where atmospheric model output from regional or global climate models could be used as forcing to the offline model to add to the existing literature on how the Sierra snowpack and spring streamflow is expected to change.