Approximating snow surface temperature from standard temperature and humidity data: New possibilities for snow model and remote sensing evaluation

Raleigh, M. S.; Landry, Christopher C.; Hayashi, Masaki; Quinton, William L.; Lundquist, Jessica D.

doi:10.1002/2013wr013958

Cited by 39 publications

(31 citation statements)

References 75 publications

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“…Date of snowmelt was identified in these time series of soil temperatures as the day where surface soil temperature changes from a near constant measurement of <1°C to diurnally fluctuating with measurements above this temperature (Lundquist and Lott , Raleigh et al. ). In the 6 yr of data collection, 9.5% of our sensors failed, in which cases date of snowmelt was interpolated using a linear regression model applied to all sensors (as plot‐level differences in snowmelt date were extremely consistent from year to year – model R 2 were >90%; Kroiss and HilleRisLambers ).…”

Section: Methodsmentioning

confidence: 99%

Climate drives phenological reassembly of a mountain wildflower meadow community

Theobald

Breckheimer

HilleRisLambers

2017

Ecology

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Spatial community reassembly driven by changes in species abundances or habitat occupancy is a well-documented response to anthropogenic global change, but communities can also reassemble temporally if the environment drives differential shifts in the timing of life events across community members. Much like spatial community reassembly, temporal reassembly could be particularly important when critical species interactions are temporally concentrated (e.g., plant-pollinator dynamics during flowering). Previous studies have documented species-specific shifts in phenology driven by climate change, implying that temporal reassembly, a process we term "phenological reassembly," is likely. However, few studies have documented changes in the temporal co-occurrence of community members driven by environmental change, likely because few datasets of entire communities exist. We addressed this gap by quantifying the relationship between flowering phenology and climate for 48 co-occurring subalpine wildflower species at Mount Rainier (Washington, USA) in a large network of plots distributed across Mt. Rainier's steep environmental gradients; large spatio-temporal variability in climate over the 6 yr of our study (including the earliest and latest snowmelt year on record) provided robust estimates of climate-phenology relationships for individual species. We used these relationships to examine changes to community co-flowering composition driven by 'climate change analog' conditions experienced at our sites in 2015. We found that both the timing and duration of flowering of focal species was strongly sensitive to multiple climatic factors (snowmelt, temperature, and soil moisture). Some consistent responses emerged, including earlier snowmelt and warmer growing seasons driving flowering phenology earlier for all focal species. However, variation among species in their phenological sensitivities to these climate drivers was large enough that phenological reassembly occurred in the climate change analog conditions of 2015. An unexpected driver of phenological reassembly was fine-scale variation in the direction and magnitude of climatic change, causing phenological reassembly to be most apparent early and late in the season and in topographic locations where snow duration was shortest (i.e., at low elevations and on ridges in the landscape). Because phenological reassembly may have implications for many types of ecological interactions, failing to monitor community-level repercussions of species-specific phenological shifts could underestimate climate change impacts.

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Section: Methodsmentioning

confidence: 99%

Climate drives phenological reassembly of a mountain wildflower meadow community

Theobald

Breckheimer

HilleRisLambers

2017

Ecology

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“…T snow is also the primary control of longwave radiation emitted from the snowpack. However, T snow is difficult to directly measure and is therefore estimated as a function of the dew-point (frost-point) temperature, T dew , as demonstrated by Raleigh et al (2013). Using T dew to estimate daily averages of T snow reduces bias and is a reasonable first-order approximation at standard height measurements (Raleigh et al, 2013).…”

Section: Snow Surface Energy Balancementioning

confidence: 99%

“…13) determines the stability of the atmosphere, and where values are greater than 0.2, this decoupling occurs. Although there is no consensus on what threshold this critical value should be, we use a threshold of 0.2 (Raleigh et al, 2013). Over the course of the study the Ri B value within each cover type at the Low and Mid elevation sites and the High Forest site exceeds the critical value for the majority of days.…”

Section: Energy Balancementioning

confidence: 99%

Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

Roth

Nolin

2017

Hydrol. Earth Syst. Sci.

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Abstract. Forest cover modifies snow accumulation and ablation rates via canopy interception and changes in subcanopy energy balance processes. However, the ways in which snowpacks are affected by forest canopy processes vary depending on climatic, topographic and forest characteristics. Here we present results from a 4-year study of snow-forest interactions in the Oregon Cascades. We continuously monitored snow and meteorological variables at paired forested and open sites at three elevations representing the Low, Mid, and High seasonal snow zones in the study region. On a monthly to bi-weekly basis, we surveyed snow depth and snow water equivalent across 900 m transects connecting the forested and open pairs of sites. Our results show that relative to nearby open areas, the dense, relatively warm forests at Low and Mid sites impede snow accumulation via canopy snow interception and increase sub-canopy snowpack energy inputs via longwave radiation. Compared with the Forest sites, snowpacks are deeper and last longer in the Open site at the Low and Mid sites (4-26 and 11-33 days, respectively). However, we see the opposite relationship at the relatively colder High sites, with the Forest site maintaining snow longer into the spring by 15-29 days relative to the nearby Open site. Canopy interception efficiency (C IE ) values at the Low and Mid Forest sites averaged 79 and 76 % of the total event snowfall, whereas C IE was 31 % at the lower density High Forest site. At all elevations, longwave radiation in forested environments appears to be the primary energy component due to the maritime climate and forest presence, accounting for 93, 92, and 47 % of total energy inputs to the snowpack at the Low, Mid, and High Forest sites, respectively. Higher wind speeds in the High Open site significantly increase turbulent energy exchanges and snow sublimation. Lower wind speeds in the High Forest site create preferential snowfall deposition. These results show the importance of understanding the effects of forest cover on subcanopy snowpack evolution and highlight the need for improved forest cover model representation to accurately predict water resources in maritime forests.

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“…However, such methods are most likely sensitive towards a range of factors, such as land cover and exposition (which strongly influence the true surface temperature), so they should be carefully developed and validated for a range of sites. Based on in situ measurements, Raleigh et al (2013) suggest that snow-covered ground dew point temperatures are a better approximation for surface temperatures compared to air temperatures at standard height. However, observations on Samoylov Island display only a small offset between snow surface and air temperatures, with the difference increasing from near zero in early winter to about 1 • C in late winter (Table 3, Langer et al, 2011b).…”

Section: Surface Temperaturementioning

confidence: 99%

Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

et al. 2017

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Abstract. Permafrost is a sensitive element of the cryosphere, but operational monitoring of the ground thermal conditions on large spatial scales is still lacking. Here, we demonstrate a remote-sensing-based scheme that is capable of estimating the transient evolution of ground temperatures and active layer thickness by means of the ground thermal model CryoGrid 2. The scheme is applied to an area of approximately 16 000 km 2 in the Lena River delta (LRD) in NE Siberia for a period of 14 years. The forcing data sets at 1 km spatial and weekly temporal resolution are synthesized from satellite products and fields of meteorological variables from the ERA-Interim reanalysis. To assign spatially distributed ground thermal properties, a stratigraphic classification based on geomorphological observations and mapping is constructed, which accounts for the large-scale patterns of sediment types, ground ice and surface properties in the Lena River delta.A comparison of the model forcing to in situ measurements on Samoylov Island in the southern part of the study area yields an acceptable agreement for the purpose of ground thermal modeling, for surface temperature, snow depth, and timing of the onset and termination of the winter snow cover. The model results are compared to observations of ground temperatures and thaw depths at nine sites in the Lena River delta, suggesting that thaw depths are in most cases reproduced to within 0.1 m or less and multi-year averages of ground temperatures within 1-2 • C. Comparison of monthly average temperatures at depths of 2-3 m in five boreholes yielded an RMSE of 1

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Approximating snow surface temperature from standard temperature and humidity data: New possibilities for snow model and remote sensing evaluation

Cited by 39 publications

References 75 publications

Climate drives phenological reassembly of a mountain wildflower meadow community

Climate drives phenological reassembly of a mountain wildflower meadow community

Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

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