Subnivean Arctic and sub-Arctic net ecosystem exchange (NEE)

2013 ◽  
Vol 37 (4) ◽  
pp. 484-515 ◽  
Author(s):  
Kristina A. Luus ◽  
John C. Lin ◽  
Richard E.J. Kelly ◽  
Claude R. Duguay
Keyword(s):  
Remote Sensing ◽  
Soil Respiration ◽  
Snow Cover ◽  
Net Ecosystem Exchange ◽  
Snow Accumulation ◽  
The Arctic ◽  
Freeze Thaw ◽  
Snow Properties ◽  
Snow Season ◽  
Fractional Snow Cover

In the Arctic and sub-Arctic, up to half of annual net ecosystem exchange (NEE) occurs during the snow season. Subnivean soil respiration can persist at a greater rate when the overlying snowpack has a lower thermal conductivity, and the rate of photosynthetic uptake at the start and end of the snow season can be diminished by fractional snow cover. Although recent studies have indicated that uncertainty in model estimates of NEE can be reduced by representing the influence of a modeled snowpack on soil respiration, models of NEE have not represented the influence of snowpack dynamics on processes such as subnivean photosynthesis or CO2 diffusivity, and have not used remote sensing observations to characterize snow season processes. We therefore: (1) review snow season processes and their effects on NEE; (2) assess the suitability of cryospheric remote sensing approaches for models of NEE; and (3) suggest strategies for representing snow season processes in models of NEE. Strategies include: using observations of fractional snow cover in spring and fall to restrict estimates of photosynthetic uptake; combining observations of snow accumulation and soil freeze/thaw with observations of air temperature to generate more realistic estimates of soil temperature and soil respiration; and using observations of depth to estimate the influence of snow accumulation and tree wells on soil respiration. Including remote sensing observations of snow properties in models of NEE could reduce uncertainty in snow season estimates of NEE, resulting in a better understanding of the northern carbon cycle and how it is responding to climate-driven changes in the interconnected biospheric, atmospheric and cryospheric systems.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

scholarly journals The Changing Cryosphere: Pan-Arctic Snow Trends (1979–2009)

2011 ◽  
Vol 24 (21) ◽  
pp. 5691-5712 ◽  
Author(s):  
Glen E. Liston ◽  
Christopher A. Hiemstra
Keyword(s):  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Reanalysis Data ◽  
The Arctic ◽  
Regional Variability ◽  
Time Step ◽  
Snow Season ◽  
Regional Trends ◽  
Dominant Feature

Abstract Arctic snow presence, absence, properties, and water amount are key components of Earth’s changing climate system that incur far-reaching physical and biological ramifications. Recent dataset and modeling developments permit relatively high-resolution (10-km horizontal grid; 3-h time step) pan-Arctic snow estimates for 1979–2009. Using MicroMet and SnowModel in conjunction with land cover, topography, and 30 years of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, a distributed snow-related dataset was created including air temperature, snow precipitation, snow-season timing and length, maximum snow water equivalent (SWE) depth, average snow density, snow sublimation, and rain-on-snow events. Regional variability is a dominant feature of the modeled snow-property trends. Both positive and negative regional trends are distributed throughout the pan-Arctic domain, featuring, for example, spatially distinct areas of increasing and decreasing SWE or snow season length. In spite of strong regional variability, the data clearly show a general snow decrease throughout the Arctic: maximum winter SWE has decreased, snow-cover onset is later, the snow-free date in spring is earlier, and snow-cover duration has decreased. The domain-averaged air temperature trend when snow was on the ground was 0.17°C decade−1 with minimum and maximum regional trends of −0.55° and 0.78°C decade−1, respectively. The trends for total number of snow days in a year averaged −2.49 days decade−1 with minimum and maximum regional trends of −17.21 and 7.19 days decade−1, respectively. The average trend for peak SWE in a snow season was −0.17 cm decade−1 with minimum and maximum regional trends of −2.50 and 5.70 cm decade−1, respectively.

scholarly journals Impact of Surface Albedo Assimilation on Snow Estimation

2020 ◽  
Vol 12 (4) ◽  
pp. 645 ◽  
Author(s):  
Sujay Kumar ◽  
David Mocko ◽  
Carrie Vuyovich ◽  
Christa Peters-Lidard
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Snow Accumulation ◽  
High Plains ◽  
Surface Model ◽  
Fractional Snow Cover ◽  
The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

scholarly journals Seasonal cycle of the snow coverage in the former Soviet Union and its relation with atmospheric circulation

2004 ◽  
Vol 38 ◽  
pp. 106-114 ◽  
Author(s):  
Kunio Rikiishi ◽  
Junko Sakakibara
Keyword(s):  
Soviet Union ◽  
Arctic Ocean ◽  
Snow Cover ◽  
Snow Depth ◽  
Former Soviet Union ◽  
Snow Accumulation ◽  
River Valley ◽  
The Arctic ◽  
Lena River ◽  
The Arctic Ocean

AbstractHistorical snow-depth observations in the former Soviet Union (FSU) during the period September 1960–August 1984 have been analyzed in order to understand the seasonal cycle of snow coverage in the FSU. Snow cover first appears in September in northeastern regions, and spreads over the entire territory before early January. Snowmelt begins in mid-January in the southern regions and then snow cover retreats rapidly northward until it disappears completely before late June. Northward of 60°N, the land surface is snow-covered for more than half the year. The longest snow-cover duration is observed on the central Siberian plateau (about 9.5 months) and along the Arctic coastal regions (about 8.5 months). One of the most conspicuous features of the snow coverage in the FSU is that the length of the snow-accumulation period differs considerably from region to region (2–7 months), while the length of the snowmelt period is rather short and uniform over almost the entire territory (1–2 months). Although the maximum snow depths are 20–50 cm in most regions of the FSU, they exceed 80 cm in the mountainous regions in central Siberia, Kamchatka peninsula, and along theYenisei river valley. Values for the maximum snow depth are very small along the Lena river valley in spite of the air temperature being extremely low in winter. By calculating correlation coefficients between the snowfall intensities and the sea-level pressures or 500 hPa heights, it is shown that deep snow along the Yenisei river valley is caused by frequent migration of synoptic disturbances from the Arctic Ocean. Snowfalls along the Lena river valley are also caused by traveling disturbances from the Arctic Ocean. Snow accumulation is suppressed after the Arctic Ocean has been frozen.

scholarly journals Quantifying and Diagnosing Sources of Uncertainty in Midcentury Changes in North American Snowpack from NARCCAP

2019 ◽  
Vol 20 (11) ◽  
pp. 2229-2252 ◽  
Author(s):  
Rachel R. McCrary ◽  
Linda O. Mearns
Keyword(s):  
Climate Change ◽  
North America ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
The Arctic ◽  
Climate Change Signal ◽  
Future Projections ◽  
Climate Change Uncertainty ◽  
Snow Season ◽  
Snow Cover Extent

Abstract The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.

scholarly journals Estimation of Snow Reserves in Watercourses in the Arctic Region

2018 ◽  
Vol 64 (1) ◽  
pp. 5-15
Author(s):  
I. I. Vasilevich ◽  
A. A. Chernov
Keyword(s):  
Snow Cover ◽  
Arctic Region ◽  
Snow Accumulation ◽  
The Arctic ◽  
Water Yield ◽  
Reliable Estimate ◽  
Good Repeatability ◽  
Ground Penetrating ◽  
River Valleys ◽  
Seasonal Snow Cover

For a reliable estimate of snow reserves in theArctic Archipelagoproper allowance must be made for snow accumulation in the areas of relief lowering such as riverbeds, ravines and canyons. As applied to calculating the water yield from the catchment area, unaccounted reserves in the channels may be even larger than recorded. For thickness of the snow cover on similar objects measuring was used Picor-Led ground-penetrating radar. Test measurements of the thickness of the snow cover performed both by radar and manually showed good repeatability of measurements. Data on snow reserves in the catchments of the Mushketov and Amba rivers were obtained using the radar method for northern part of Bolshevik Island in spring 2017.Measurements results has revealed significant differences in the amount of snow reserves between the plateau sections and the river valleys. The thickness of seasonal snow cover in the riverbeds and canyons varied widely and reached10.5 metersdepth. The average values of snow thickness cover on the plateau and in the riverbeds of the Mushketov and Amba rivers were 0.37, 1.80 and1.86 mrespectively during the period of maximum snow accumulation. Our estimates showed that snow deposits in riverbeds have specific snow reserves 6.5–7.5 times higher than specific snow reserves on the plateau. In addition,

scholarly journals Ground surface temperatures indicate the presence of permafrost in North Africa (Djebel Toubkal, High Atlas, Morocco)

2016 ◽  
Author(s):  
Gonçalo Vieira ◽  
Carla Mora ◽  
Ali Faleh
Keyword(s):  
Snow Cover ◽  
North Africa ◽  
Thermal Regime ◽  
Ground Surface ◽  
Cold Season ◽  
Snow Accumulation ◽  
Landsat 8 ◽  
High Atlas ◽  
Freeze Thaw ◽  
Air Temperatures

Abstract. Relict and present-day periglacial activity have been reported in the literature for the upper reaches of the High Atlas mountains, the highest range in North Africa (Djebel Toubkal – 4,167 m a.s.l.). Lobate features in the Irhzer Ikbi South at 3,800 m a.s.l. have been previously interpreted as an active rock glacier, but no measurements of ground or air temperatures are known to exist for the area. In order to assess on the possible presence of permafrost, analyse data from June 2015 to June 2016 from two air temperature sites at 2,370 and 3,200 m a.s.l., and from four ground surface temperature (GST) sites at 3,200, 3,815, 3,980 and 4,160 m a.s.l. allowing to characterize conditions along an altitudinal gradient along the Oued Ihghyghaye valley to the summit of the Djebel Toubkal. GST were collected at 1-hour intervals and the presence of snow cover at the monitoring sites was validated using Landsat-8 and Sentinel-2 imagery. Two field visits allowed for logger installation and collection and for assessing the geomorphological features in the area. The results show that snow plays a major role on the thermal regime of the shallow ground, inducing important spatial variability. The lowest site at 3,210 m showed a regime characterized by frequent freeze-thaw cycles during the cold season but with a small number of days of snow. When snow sets, the ground remains isothermal at 0 °C and the thermal regime indicates the absence of permafrost. The highest sites at 3,980 and 4,160 m a.s.l. showed very frequent freeze-thaw cycles and a small influence of the snow cover on GST, reflecting the lack of snow accumulation due to the their wind-exposed settings in a ridge and in the summit plateau. The site located at 3,815 m in the Irhzer Ikbi South valley showed a stable thermal regime from December to March with GST varying from −4.5 to −6 °C, under a continuous snow cover. The site's location in a concave setting favours snow accumulation and lower incoming solar radiation due to the effect of a southwards ridge, favouring the maintenance of a thick snow pack. The stable and low GST are interpreted as a strong indicator of the probable presence of permafrost at this site, an interpretation which is supported by the presence of lobate and arcuate forms in the talus deposits. These results are still a first approach and observations through geophysics and boreholes are foreseen. This is the first time that probable permafrost is reported from temperature observations in the mountains of North Africa.

scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Liquid Water ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Cloud Properties ◽  
Snow Properties ◽  
Surface Snow ◽  
Effective Grain Size

Abstract. In the Arctic, the passive solar remote sensing of cloud properties over highly reflecting ground is challenging due to the low contrast between the clouds and underlying surfaces (sea ice and snow). Uncertainties in retrieved cloud optical thickness τ and cloud droplet effective radius reff,C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the commonly unknown snow effective grain size reff,S. Therefore, in a first step this snow grain size effect is quantified systematically for a conventional bi-spectral retrieval of τ and reff,C for liquid water clouds. The largest impact of reff,S of up to 83 % on τ and 62 % on reff,C was found in case of small reff,S and optically thin clouds. In the second part of the paper a retrieval method is presented that simultaneously retrieves all three parameters (τ, reff,C, reff,S) in order to account for changes of the snow grain size in the cloud retrieval algorithm. Spectral cloud reflectivities at the three wavelength λ1 = 1040 nm (sensitive to reff,S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff,C) were normalized to reflectivity ratios and combined in a tri-spectral retrieval algorithm. Measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART-Albedometer) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct sea ice edge were analyzed. The retrieved values of τ, reff,C, and reff,S consistently represented the cloud properties across this transition from snow-covered sea ice to the open water and were comparable to estimates based on satellite data. Analysis showed, that the uncertainties of the tri-spectral retrieval increase for high τ, and low reff,S, but nevertheless allows a simultaneous retrieval of cloud and surface snow properties providing snow effective grain size estimates in cloud-covered areas.

scholarly journals New observations indicate the possible presence of permafrost in North Africa (Djebel Toubkal, High Atlas, Morocco)

2017 ◽  
Vol 11 (4) ◽  
pp. 1691-1705 ◽  
Author(s):  
Gonçalo Vieira ◽  
Carla Mora ◽  
Ali Faleh
Keyword(s):  
Snow Cover ◽  
North Africa ◽  
Thermal Regime ◽  
Ground Surface ◽  
Cold Season ◽  
Snow Accumulation ◽  
Landsat 8 ◽  
High Atlas ◽  
Freeze Thaw ◽  
Air Temperatures

Abstract. Relict and present-day periglacial features have been reported in the literature for the upper reaches of the High Atlas mountains, which is the highest range in North Africa (Djebel Toubkal – 4167 m a.s.l.). A lobate feature in the Irhzer Ikhibi south at 3800 m a.s.l. has been previously interpreted as an active rock glacier, but no measurements of ground or air temperatures are known to exist for the area. In order to assess the possible presence of permafrost, we analyse data from June 2015 to June 2016 from two air temperature measurement sites at 2370 and 3210 m a.s.l. and from four ground surface temperature (GST) sites at 3220, 3815, 3980 and 4160 m a.s.l. to characterize conditions along an altitudinal gradient along the Oued Ihghyghaye valley to the summit of the Djebel Toubkal. GSTs were collected at 1 h intervals, and the presence of snow cover at the monitoring sites was validated using Landsat 8 and Sentinel-2 imagery. Two field visits allowed for logger installation and collection and for assessing the geomorphological features in the area. The results show that snow plays a major role on the thermal regime of the shallow ground, inducing important spatial variability. The lowest site at 3220 m had a thermal regime characterized by frequent freeze–thaw cycles during the cold season but with few days of snow. When snow settled, the ground surface remained isothermal at 0 °C , indicating the absence of permafrost. The highest sites at 3980 and 4160 m a.s.l. showed very frequent freeze–thaw cycles and a small influence of the snow cover on GST, reflecting the lack of snow accumulation due to the wind-exposed settings on a ridge and on the summit plateau. The site located at 3815 m in the Irhzer Ikhibi south valley had a cold, stable thermal regime with GST varying from −4.5 to −6 °C from December to March, under a continuous snow cover. The site's location in a concave setting favours wind-driven snow accumulation and lower incoming solar radiation due to the shading effect of a ridge, inducing the conservation of a thick snow pack. The stable and low GSTs are interpreted as a strong indicator of the probable presence of permafrost at this site, which is an interpretation supported by the presence of lobate and arcuate features in the talus deposits. We present first results and further observations using geophysics, and borehole measurements are foreseen. This is the first time that probable permafrost has been reported from temperature observations in the mountains of North Africa.

scholarly journals Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements

2021 ◽  
Vol 8 ◽  
Author(s):  
Marcel Nicolaus ◽  
Mario Hoppmann ◽  
Stefanie Arndt ◽  
Stefan Hendricks ◽  
Christian Katlein ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Weddell Sea ◽  
The Arctic ◽  
Validation Data ◽  
Antarctic Sea Ice

Snow depth on sea ice is an essential state variable of the polar climate system and yet one of the least known and most difficult to characterize parameters of the Arctic and Antarctic sea ice systems. Here, we present a new type of autonomous platform to measure snow depth, air temperature, and barometric pressure on drifting Arctic and Antarctic sea ice. “Snow Buoys” are designed to withstand the harshest environmental conditions and to deliver high and consistent data quality with minimal impact on the surface. Our current dataset consists of 79 time series (47 Arctic, 32 Antarctic) since 2013, many of which cover entire seasonal cycles and with individual observation periods of up to 3 years. In addition to a detailed introduction of the platform itself, we describe the processing of the publicly available (near real time) data and discuss limitations. First scientific results reveal characteristic regional differences in the annual cycle of snow depth: in the Weddell Sea, annual net snow accumulation ranged from 0.2 to 0.9 m (mean 0.34 m) with some regions accumulating snow in all months. On Arctic sea ice, the seasonal cycle was more pronounced, showing accumulation from synoptic events mostly between August and April and maxima in autumn. Strongest ablation was observed in June and July, and consistently the entire snow cover melted during summer. Arctic air temperature measurements revealed several above-freezing temperature events in winter that likely impacted snow stratigraphy and thus preconditioned the subsequent spring snow cover. The ongoing Snow Buoy program will be the basis of many future studies and is expected to significantly advance our understanding of snow on sea ice, also providing invaluable in situ validation data for numerical simulations and remote sensing techniques.

Sign in / Sign up

Export Citation Format

Share Document