Collaborative Research: Process Dynamics in the Intermittent Snow Zone

间歇性降雪在整个冬季多次出现和消失，为大气科学(通过改变地表反照率和温度)、水文学(通过融化对雨雪洪水的贡献)、土壤科学(通过隔离地表)和生态学(通过绝缘和供水)等领域提供了关键的反馈。尽管间歇性降雪在科学和社会上具有重要意义，但它是一个建模挑战。这项研究项目聚焦于华盛顿瀑布和科罗拉多州平原的间歇性降雪，将结合观测和基于物理的建模，以提高对不同降雪过程如何结合在一起影响积雪演变的理解。受控数值试验将检验(1)估计雪-大气界面通量的多种方法，包括用于估计地表反照率、感热和潜热的湍流通量以及降水在雨雪之间的分配的方法；(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、液态水的垂直排放和与雪晶变质有关的积雪的压实；以及(3)与土壤中的热传输有关的下界面通量的多种模拟方法。模型模拟，一次隔离一个过程，将与详细的测量结果进行比较，既有点上的，也有分布在整个地区的。这项研究旨在更好地了解间歇性雪区的主导过程，更好地理解主要的模拟不确定性，并为改进的大气-水耦合模式提供一条前进的道路。由于间歇性积雪几乎总是融化的成熟时间，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。这种随时可以融化的状态使间歇性降雪成为雪模型性能的一个额外敏感的指标。因此，在这一地区审查的任何模型改进都将转化为各地更好的雪模型，包括季节性雪区(那里的雪持续整个冬天)。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，它还隔离了土壤，保护地面免受潜在的破坏性霜冻的影响。在雨雪风暴期间，这种海拔较低的积雪融化并导致洪水(一种危险)，但在其他时候，该地区融化的积雪有助于夏季供水(一种资源)。城市和主要公路沿线的间歇性降雪阻碍了交通和城市运营。间歇性降雪地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变为间歇性降雪状态。由于所有这些原因，正确地模拟间歇性降雪是很重要的。该项目将改进用于水文和气候预测的下一代积雪模型。

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.