Modified snow algorithms in the Canadian land surface scheme: Model runs and sensitivity analysis at three boreal forest stands

Bartlett, Paul; Mackay, Murray; Verseghy, Diana

doi:10.3137/ao.440301

Cited by 122 publications

(99 citation statements)

References 52 publications

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“…Later versions of CLASS (Bartlett et al, 2006) use a different algorithm in which both ρ fr and ρ max are functions of temperature and snow depth respectively, but the simpler description of snow density evolution in Eq. (4) has the advantage that the parameters can be estimated from the transect data.…”

Section: Fresh Snow Densitymentioning

confidence: 99%

Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

et al. 2014

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Abstract. An increasing number of studies have demonstrated significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled Earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both system processes and its initial state. This study focuses on snow-related variables and makes extensive use of a historical data set of field snow measurements acquired across the extent of the former Soviet Union to evaluate a range of simulated snow metrics produced by several land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific failings in simulating snowpack properties such as magnitude, inter-annual variability, timings of snow water equivalent and evolution of snow density. We develop novel and model-independent methodologies that use the field snow measurements to extract the values of fresh snow density and snowpack sublimation, and exploit them to assess model outputs. By directly forcing the surface heat exchange formulation of a land surface model with field data on snow depth and snow density, we evaluate how inaccuracies in simulating snow metrics affect soil temperature, thaw depth and soil carbon decomposition. We also show how field data can be assimilated into models using optimization techniques in order to identify model defects and improve model performance.

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Section: Fresh Snow Densitymentioning

confidence: 99%

Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

et al. 2014

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“…Some examples of models using linear dual threshold methods are: Community Atmospheric Model (CAM) Version 3.0 with TS −5 °C and TR 0 °C, The University of British Columbia (UBC) Watershed Model [31] with TS 0.6 °C and TR 3.6 °C. Curvelinear examples are: WATCLASS 2.7 [34], and CLASS 3.1 [4] with TS 0.45 °C and TR 5.97 °C based on the Auer [59] polynomial.…”

Section: Dual Air Temperature Threshold Schemesmentioning

confidence: 99%

“…Truthful identification of the precipitation phase (rain/snow) is of course crucial for the functioning of meteorological models that forecast the precipitation phase itself [2] but also for accurate correction of gauge measured winter precipitation [3] and for land surface models (LSM) predicting snow accumulation and melt [4], glacier and polar ice water balance models [5], models for lake and sea ice growth [6], and climate change models [7]. It is also important for models predicting avalanche hazards [8], sublimation of snow in forests [9], urban snowmelt quality [10], winter road safety [11], infiltration into frozen soils [12], survival of mammals and plants under snow cover [13], flooding from rain on snow events [14] etc.…”

Section: Introductionmentioning

confidence: 99%

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

et al. 2015

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An accurate precipitation phase determination-i.e., solid versus liquid-is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer OPEN ACCESSHydrology 2015, 2 267 temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.

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“…Bartlett et al (2006) use an exponential relationship between newly fallen snow density (ρ s0 ) and air temperature (T a ) developed by Hedstrom and Pomeroy (1998) from the data of Schmidt and Gluns (1991) and the US Army Corps of Engineers (1956). This approach was combined with the definition of a threshold temperature below which precipitation turns from pure rain to snow, as proposed by Gustafsson et al (2004).…”

Section: Accumulation Modulementioning

confidence: 99%

Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

Martelloni

Segoni

Lagomarsino

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. We propose a simple snow accumulation/melting model (SAMM) to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds.SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation algorithm (simplex flexible) using calibration measures of snowpack thickness.From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing about the additional benefit of a relatively easy implementation.After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS) and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

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Modified snow algorithms in the Canadian land surface scheme: Model runs and sensitivity analysis at three boreal forest stands

Abstract: ABSTRACT

Cited by 122 publications

References 52 publications

Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

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