On the simulation of regional scale sublimation over boreal and agricultural landscapes in a climate model

Mackay, Murray; Bartlett, Paul; Chan, Ed; Derksen, Chris; Guo, Song; Leighton, H.G.

doi:10.3137/ao.440306

Cited by 20 publications

(12 citation statements)

References 47 publications

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“…In addition, SWE data are not accurate after the initial snowmelt [ Walker and Goodison , 1993]. SWE algorithms usually underestimate the actual snow amount [e.g., Dong et al , 2005; MacKay et al , 2006]. The underestimation is consistent with our field observations in this region.…”

Section: Methodssupporting

confidence: 81%

“…Four distinct time periods are labeled based on melt timing: premelt (no melt), transition period (mild melt), full melt, and no snow. When snowpack starts to melt, moisture will make the SWE data unreliable [ MacKay et al , 2006]. We use the maximum SWE at snowmelt onset date ±5 days as the maximum SWE for the whole year (shown as vertical arrow).…”

Section: Datamentioning

confidence: 99%

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Modeling of high‐latitude spring freshet from AMSR‐E passive microwave observations

Yan

Ramage

McKenney

2009

Water Resources Research

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[1] Snowmelt runoff in high latitudes has significant impacts on global climatic and hydrologic systems. Snowmelt timing and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) are inputs to the new flux-based SWEHydro model to simulate the spring streamflow without meteorological data for high-latitude, snow-dominated drainages. The model was developed for the Ross River (7250 km 2 ) and tested on the Pelly River (49,000 km 2 ), nested tributaries to the Yukon River. The model uses four parameters: snowmelt rate during and after the melt transition (as defined by passive microwave observations), and flow timing during and after the melt transition. A normalized mismatch function was used to calculate the error compared with observed discharge. Curves were ranked by lowest error in freshet timing, peak timing, and magnitude. Melt timing is a good predictor of freshet timing across years and basins. The system is most sensitive to the flow timing after the transition.

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Section: Methodssupporting

confidence: 81%

Section: Datamentioning

confidence: 99%

Modeling of high‐latitude spring freshet from AMSR‐E passive microwave observations

Yan

Ramage

McKenney

2009

Water Resources Research

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“…Our site‐level evaluation of albedo algorithms in CLASS limits the influence of errors in vegetation properties. While initially developed for use in Environment Canada's GCM, CLASS is also used in the Canadian Earth System Model (Arora and Boer, ) and regional climate models (MacKay et al , ), and at the basin scale with coupled land surface and sub‐surface hydrological models (Pietroniro et al , ). As such, the ability for canopy parameterizations to apply across scales is desirable.…”

Section: Discussionmentioning

confidence: 99%

Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme

Bartlett

Verseghy

2015

Hydrol. Process.

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Abstract:The Canadian Land Surface Scheme (CLASS) was modified to correct an underestimation of the winter albedo in evergreen needleleaf forests. Default values for the visible and near-infrared albedo of a canopy with intercepted snow, α VIS,cs and α NIR,cs , respectively, were too small, and the fraction of the canopy covered with snow, f snow , increased too slowly with interception, producing a damped albedo response. A new model for f snow is based on z I* , the effective depth of newly intercepted snow required to increase the canopy albedo to its maximum, which corresponds in the model with f snow = 1. Snow unloading rates were extracted from visual assessments of photographs and modelled based on relationships with meteorological variables, replacing the time-based method employed in CLASS. These parameterizations were tested in CLASS version 3.6 at boreal black spruce and jack pine forests in Saskatchewan, Canada, a subalpine Norway spruce and silver fir forest at Alptal, Switzerland, and a boreal maritime forest at Hitsujigaoka, Japan. Model configurations were assessed based on the index of agreement, d, relating simulated and observed daily albedo. The new model employs α VIS,cs = 0.27, α NIR,cs = 0.38 and z I* = 3 cm. The best single-variable snow unloading algorithm, determined by the average cross-site d, was based on wind speed. Two model configurations employing ensemble averages of the unloading rate as a function of total incoming radiation and wind speed, and air temperature and wind speed, respectively, produced larger minimum cross-site d values but a smaller average. The default configuration of CLASS 3.6 produced a cross-site average d from October to April of 0.58. The best model employing a single parameter (wind speed at the canopy top) for modelling the unloading rate produced an average d of 0.86, while the two-parameter ensemble-average unloading models produced a minimum d of 0.81 and an average d of 0.84.

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“…Snow also modulates the hydrologic cycle (Dyer, 2008;Graybeal and Leathers, 2006;Leathers et al, 1998;Todhunter, 2001); influences ecosystem functioning (Jones et al, 2001); and is a significant resource for many mid latitude populations and for populations whose water is derived from mountainous and northerly cold regions (Barnett et al, 2005;Barry et al, 2007). Snow observations are critical for the validation of climate models Frei et al, 2003MacKay et al, 2006;Roesch et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

A review of global satellite-derived snow products

Frei

Tedesco

Lee

et al. 2012

Advances in Space Research

271

184

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Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation.Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow.

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On the simulation of regional scale sublimation over boreal and agricultural landscapes in a climate model

Cited by 20 publications

References 47 publications

Modeling of high‐latitude spring freshet from AMSR‐E passive microwave observations

Modeling of high‐latitude spring freshet from AMSR‐E passive microwave observations

Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme

A review of global satellite-derived snow products

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