Clear‐sky stable boundary layers with low winds over snow‐covered surfaces. Part 2: Process sensitivity

Abstract

This study evaluates the relative impact of snow-surface coupling, long-wave radiation, and turbulent mixing on the development of the stable boundary layer over snow. Observations at three sites are compared to WRF single-column model (SCM) simulations. All three sites have snow-covered surfaces but are otherwise contrasting: Cabauw (Netherlands, grass), Sodankylä (Finland, needle-leaf forest) and Halley (Antarctica, ice shelf). All cases are characterized by stable, clear-sky, and calm conditions. Part 1 of this study determined the optimal SCM forcing strategy. In this study, the process intensities from that reference are perturbed to study their relative significance and to assess which process could be responsible for the most optimal agreement between model and observation.The analysis reveals a large variability in the modelled atmospheric state and surface parameters. Overall, the modelled gradients of temperature and moisture are underestimated but decreasing the process intensities improves this. The impact is strongest with reduced mixing, though this then causes the model to overestimate the near-surface wind speed.To study the surface energy balance terms, we use so-called 'process diagrams'. The achieved variation between the sensitivity runs indicates the model sensitivity to each process. The overall sensitivity is similar for the three sites but the relative offsets in the position of the sensitivity runs with respect to the observations differ, hampering general recommendations for model improvement. Furthermore, sometimes a meaningful interpretation of observations is troublesome, which hampers the comparison with model results. Radiation is relatively more important at Cabauw and Sodankylä, whilst coupling plays a more important role at Halley.The sensitivity analysis is performed with two boundary-layer schemes (MYJ, YSU). YSU generates larger, more accurate gradients of atmospheric temperature and humidity, while wind speeds are predicted better with MYJ. The behaviour of an increase in 2 m temperature with decreased mixing is most obvious with YSU.

Antarctic boundary layer parametrization in a general circulation model: 1‐D simulations facing summer observations at Dome C

“…In fact, using parametrizations with stability functions that sharply decrease with increasing stability led to stronger katabatic winds and enhanced cooling Beyond Antarctica, the sensitivity of climate modelling to stable ABL parametrization was shown to be critical in other regions of the world. This is for instance the case, over the western parts of the North Atlantic and North Pacific oceans where persistent stable ABL form in summer [King et al, 2007], but also in the Arctic [Viterbo et al, 1999;Sterk et al, 2013;Holtslag et al, 2013;Sterk et al, 2015Sterk et al, , 2016]. An improved representation of the stable ABL in climate and weather forecast models calls not only for a better understanding of the physical mixing processes near the surface particularly in very stable conditions [e.g., Mahrt, 2014;Holtslag et al, 2013] but also for a better understanding of the subgrid parametrization itself [Mahrt, 1987;Sandu et al, 2013].…”

Heat Flux in the Strong-Wind Nocturnal Boundary Layer

“…Increased spatial variation with increasing stability was quantified in the numerical simulations of Stoll and Porté-Agel (2009). Sterk et al (2016) assessed the potential importance of advection by comparing numerical model results with observations over snow surfaces. Bonin et al (2015) observed development of stratification in the overlying residual layer with higher wind speeds, consistent with the larger-scale horizontal temperature gradient and the inferred increase of warm-air advection with height.…”

Surface‐Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

“…This configuration allows detailed analysis of model performance over a specific domain while constraining computational and storage overhead to easily manageable levels. SCMs have been applied in a number of research applications, from evaluation and testing of cloud parameterization (Cripe & Randall, 2001;Couvreux et al, 2014;Lenderink et al, 2004;Rochetin et al, 2014;Stirling & Stratton, 2012;Sušelj et al, 2012), cumulus parameterization (Aït-Mesbah et al, 2015;Jin et al, 2010;McNider et al, 2012;Sterk et al, 2016;Svensson et al, 2011), and evaluation of surface processes and/or surface-atmosphere interaction (Guichard et al, 2004;Gustafsson et al, 2003;Hu et al, 1999;Stap et al, 2014;Williams et al, 2016).…”