Collaborative Research: Process Dynamics in the Intermittent Snow Zone

间歇性的雪，在整个冬季出现和消失多次，为大气科学（通过改变地表温度和温度），水文学（通过融化对雨对雪洪水的贡献），土壤科学（通过绝缘地表）和生态学（通过绝缘和供水）领域提供了关键的反馈。 尽管间歇性降雪具有科学和社会重要性，但它仍然是一个建模挑战。 该研究项目将重点关注华盛顿瀑布和科罗拉多平原的间歇性降雪，将使用混合观测和基于物理的建模来提高对不同降雪过程如何结合联合收割机影响积雪演变的理解。 受控数值试验将检验（1）估算雪-气界面通量的多种方法，包括估算地面潜热通量、感热和潜热的湍流通量以及降水在雨和雪之间的分配;（2）模拟积雪内部过程的多种方法，包括热传导，短波辐射的穿透，液态水的垂直排放，以及与雪晶变质相关的积雪压实;（3）多种方法来模拟与土壤中的热传递相关的下边界通量。 模型模拟，一次隔离一个过程，将与详细的测量，无论是在点位置和分布在整个景观。 这项研究的目的是提供一个更好的了解间歇性积雪区的主导过程，更好地了解主要的建模不确定性，并朝着一个改进的，耦合的大气-水文模型前进的路径。 由于间歇性降雪几乎总是成熟融化，它会立即对能量输入做出反应，导致雪水当量（SWE）的变化，而不仅仅是内部积雪温度的变化。 这种融化的准备状态使间歇性雪成为雪模型性能的额外敏感指标。 因此，在这一领域进行的任何模型改进都将转化为更好的雪模型，包括季节性积雪区（整个冬天都有雪）。 虽然间歇性降雪只是冬季的一部分，但它对大气、地表和社会都有重要影响。 雪增加了地球表面的反射率，降低了温度，它也使土壤绝缘，保护地面免受潜在的破坏性霜冻。 在雨雪风暴期间，这些海拔较低的雪融化并导致洪水（一种危险），但在其他时候，该地区的融化雪有助于夏季供水（一种资源）。 城市和主要高速公路沿着的间歇性降雪阻碍了交通和城市运营。 间歇性降雪区已被明确确定为对气候变化最敏感的地区，预计目前有季节性降雪的许多地区将转向间歇性降雪。 由于所有这些原因，正确地模拟间歇性降雪非常重要。 该项目将改进用于水文和气候预测的下一代降雪模型。

Intermittent snow, which appears and disappears multiple times over the course of a winter, provides critical feedbacks to the fields of atmospheric science (by altering surface albedo and temperature), hydrology (through melt contributions to rain-on-snow floods), soil science (through insulating the land surface), and ecology (through insulation and water supply). Despite its scientific and societal importance, intermittent snow is a modeling challenge. Focusing on intermittent snow in both the Washington Cascades and the plains of Colorado, this research project will use a mix of observations and physically-based modeling to improve understanding of how different snow processes combine to influence snowpack evolution. Controlled numerical experiments will examine (1) multiple methods to estimate fluxes at the snow-atmosphere interface, including approaches used to estimate the surface albedo, the turbulent fluxes of sensible and latent heat, and the partitioning of precipitation between rain and snow; (2) multiple methods to simulate internal processes within the snowpack, including heat conduction, penetration of shortwave radiation, vertical drainage of liquid water, and compaction of the snowpack associated with metamorphism of the snow crystals; and (3) multiple methods to simulate fluxes at the lower boundary associated with heat transfer in the soil. Model simulations, isolating one process at a time, will be compared with detailed measurements, both at point locations and distributed across the landscape. This research aims to provide a better understanding of dominant processes in the intermittent snow zone, a better understanding of major modeling uncertainties, and a path forward towards an improved, coupled atmosphere-hydro model. Because intermittent snow is almost always ripe to melt, it responds immediately to energy inputs, resulting in a change in snow water equivalent (SWE) rather than just a change in internal snowpack temperature. This readiness-to-melt makes intermittent snow an extra sensitive indicator of snow model performance. Therefore, any model improvements vetted in this area will translate into better snow modeling everywhere, including the seasonal snow zone (where snow lasts all winter). Although intermittent snow is only present part of the winter, it has important impacts on the atmosphere, the land surface, and society. Snow increases the reflectivity of the Earth's surface and lowers the temperature, and it also insulates the soil, protecting the ground surface from potentially damaging frost. During rain-on-snow storms, this lower-elevation snow melts and contributes to flooding (a hazard), but at other times, melting snow from this zone contributes to summer water supplies (a resource). Intermittent snow in cities and along major highways hinders transportation and city operations. The intermittent snow zone has been clearly identified as the most sensitive region to climate change, and many areas that currently have seasonal snow are predicted to shift to an intermittent snow regime. For all of these reasons, it is important to model intermittent snow correctly. This project will improve the next generation of snow models used for hydrologic and climate prediction.