scholarly journals A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover

2014 ◽  
Vol 8 (6) ◽  
pp. 2063-2074 ◽  
Author(s):  
K. Gisnås ◽  
S. Westermann ◽  
T. V. Schuler ◽  
T. Litherland ◽  
K. Isaksen ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Ground Surface ◽  
Grid Cell ◽  
Large Degree ◽  
Small Scale ◽  
Statistical Representation ◽  
Surface Temperatures ◽  
Drifting Snow ◽  
The Mean ◽  
Strong Winds

Abstract. In permafrost environments exposed to strong winds, drifting snow can create a small-scale pattern of strongly variable snow heights, which has profound implications for the thermal regime of the ground. Arrays of 26 to more than 100 temperature loggers were installed to record the distribution of ground surface temperatures within three study areas across a climatic gradient from continuous to sporadic permafrost in Norway. A variability of the mean annual ground surface temperature of up to 6°C was documented within areas of 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow height. Permafrost models, employing averages of snow height for grid cells of, e.g., 1 km2, are not capable of representing such sub-grid variability. We propose a statistical representation of the sub-grid variability of ground surface temperatures and demonstrate that a simple equilibrium permafrost model can reproduce the temperature distribution within a grid cell based on the distribution of snow heights.

scholarly journals A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover

2014 ◽  
Vol 8 (1) ◽  
pp. 509-536 ◽  
Author(s):  
K. Gisnås ◽  
S. Westermann ◽  
T. V. Schuler ◽  
T. Litherland ◽  
K. Isaksen ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Ground Surface ◽  
Grid Cell ◽  
Large Degree ◽  
Small Scale ◽  
Statistical Representation ◽  
Surface Temperatures ◽  
Drifting Snow ◽  
The Mean ◽  
Strong Winds

Abstract. In permafrost environments exposed to strong winds, drifting snow can create a small-scale pattern of strongly variable snow heights which has profound implications for the thermal regime of the ground. Arrays of 26 to more than 100 temperature loggers were installed to record the distribution of ground surface temperatures within three study areas across a climatic gradient from continuous to sporadic permafrost in Norway. A variability of the mean annual ground surface temperature of up to 6 °C was documented within areas of 0.5 km2. The observed variation can to a large degree be explained by variation in snow height. Permafrost models employing averages of snow height for grid cells of e.g. 1 km2 are not capable of representing such sub-grid variability. We propose a statistical representation of the sub-grid variability of ground surface temperatures and demonstrate that a simple equilibrium permafrost model can reproduce the temperature distribution within a grid-cell based on the distribution of snow heights.

scholarly journals Small-scale variation of snow in a regional permafrost model

2016 ◽  
Vol 10 (3) ◽  
pp. 1201-1215 ◽  
Author(s):  
Kjersti Gisnås ◽  
Sebastian Westermann ◽  
Thomas Vikhamar Schuler ◽  
Kjetil Melvold ◽  
Bernd Etzelmüller
Keyword(s):  
Snow Cover ◽  
Ground Surface ◽  
Grid Cell ◽  
Ground Temperature ◽  
Fine Tuning ◽  
Small Scale ◽  
Near Surface ◽  
Ground Temperatures ◽  
Surface Temperatures ◽  
Scale Variation

Abstract. The strong winds prevalent in high altitude and arctic environments heavily redistribute the snow cover, causing a small-scale pattern of highly variable snow depths. This has profound implications for the ground thermal regime, resulting in highly variable near-surface ground temperatures on the metre scale. Due to asymmetric snow distributions combined with the nonlinear insulating effect of snow, the spatial average ground temperature in a 1 km2 area cannot be determined based on the average snow cover for that area. Land surface or permafrost models employing a coarsely classified average snow depth will therefore not yield a realistic representation of ground temperatures. In this study we employ statistically derived snow distributions within 1 km2 grid cells as input to a regional permafrost model in order to represent sub-grid variability of ground temperatures. This improves the representation of both the average and the total range of ground temperatures. The model reproduces observed sub-grid ground temperature variations of up to 6 °C, and 98 % of borehole observations match the modelled temperature range. The mean modelled temperature of the grid cell reproduces the observations with an accuracy of 1.5 °C or better. The observed sub-grid variations in ground surface temperatures from two field sites are very well reproduced, with estimated fractions of sub-zero mean annual ground surface temperatures within ±10 %. We also find that snow distributions within areas of 1 km2 in Norwegian mountain environments are closer to a gamma than to a lognormal theoretical distribution. The modelled permafrost distribution seems to be more sensitive to the choice of distribution function than to the fine-tuning of the coefficient of variation. When incorporating the small-scale variation of snow, the modelled total permafrost area of mainland Norway is nearly twice as large compared to the area obtained with grid-cell average snow depths without a sub-grid approach.

scholarly journals A case study of snowstorm gusts and blowing/drifting snow

1993 ◽  
Vol 18 ◽  
pp. 142-148 ◽  
Author(s):  
Masayuki Maki ◽  
Sento Nakai ◽  
Tsuruhei Yagi ◽  
Hideomi Nakamura
Keyword(s):  
Doppler Radar ◽  
Gravity Current ◽  
Ground Surface ◽  
Leading Edge ◽  
Radiosonde Data ◽  
The Sea Of Japan ◽  
Cold Air ◽  
Drifting Snow ◽  
Cold Air Outbreak ◽  
Strong Winds

The mechanisms of strong winds associated with snow clouds, and the relationship between strong winds and blowing/drifting snow, were investigated. A snowstorm occurred with a typical L-mode snow band which was generated and organized longitudinally during a continental cold-air outbreak over the Sea of Japan. Doppler radar observations revealed that the snow band consisted of small echo cells arranged along the direction of the snow band. When one of the echo cells passed, blowing/drifting snow was generated and intensified by a snow cloud-induced gust, and the horizontal visibility near the ground surface was significantly decreased. Doppler radar and radiosonde data showed that the gust was due to the cold air outflow (CAO) from the snow clouds. The leading edge of the CAO was about 9 km ahead of the center of the snow cloud and the depth of the CAO was about 600 m near the forward flank of the snow cloud. The CAO was caused by a downdraft at the center of the snow cloud, which was initiated at a height of about 1.3 km and with a velocity in excess of 1 ms−1. The observed CAO speed was explained by the theory of the gravity current.

scholarly journals Mixed layer sub-mesoscale parameterization – Part 1: Derivation and assessment

Ocean Science ◽  
2010 ◽  
Vol 6 (3) ◽  
pp. 679-693 ◽  
Author(s):  
V. M. Canuto ◽  
M. S. Dubovikov
Keyword(s):  
High Resolution ◽  
Mixed Layer ◽  
Wind Direction ◽  
Small Scale ◽  
Mean Flow ◽  
Mean Velocity ◽  
Geostrophic Velocity ◽  
Global Circulation Models ◽  
The Mean ◽  
Strong Winds

Abstract. Several studies have shown that sub-mesoscales (SM ~1 km horizontal scale) play an important role in mixed layer dynamics. In particular, high resolution simulations have shown that in the case of strong down-front wind, the re-stratification induced by the SM is of the same order of the de-stratification induced by small scale turbulence, as well as of that induced by the Ekman velocity. These studies have further concluded that it has become necessary to include SM in ocean global circulation models (OGCMs), especially those used in climate studies. The goal of our work is to derive and assess an analytic parameterization of the vertical tracer flux under baroclinic instabilities and wind of arbitrary directions and strength. To achieve this goal, we have divided the problem into two parts: first, in this work we derive and assess a parameterization of the SM vertical flux of an arbitrary tracer for ocean codes that resolve mesoscales, M, but not sub-mesoscales, SM. In Part 2, presented elsewhere, we have used the results of this work to derive a parameterization of SM fluxes for ocean codes that do not resolve either M or SM. To carry out the first part of our work, we solve the SM dynamic equations including the non-linear terms for which we employ a closure developed and assessed in previous work. We present a detailed analysis for down-front and up-front winds with the following results: (a) down-front wind (blowing in the direction of the surface geostrophic velocity) is the most favorable condition for generating vigorous SM eddies; the de-stratifying effect of the mean flow and re-stratifying effect of SM almost cancel each other out, (b) in the up-front wind case (blowing in the direction opposite to the surface geostrophic velocity), strong winds prevents the SM generation while weak winds hinder the process but the eddies amplify the re-stratifying effect of the mean velocity, (c) wind orthogonal to the geostrophic velocity. In this case, which was not considered in numerical simulations, we show that when the wind direction coincides with that of the horizontal buoyancy gradient, SM eddies are generated and their re-stratifying effect partly cancels the de-stratifying effect of the mean velocity. The case when wind direction is opposite to that of the horizontal buoyancy gradient, is analogous to the case of up-front winds. In conclusion, the new multifaceted implications on the mixed layer stratification caused by the interplay of both strength and directions of the wind in relation to the buoyancy gradient disclosed by high resolution simulations have been reproduced by the present model. The present results can be used in OGCMs that resolve M but not SM.

scholarly journals A case study of snowstorm gusts and blowing/drifting snow

1993 ◽  
Vol 18 ◽  
pp. 142-148 ◽  
Author(s):  
Masayuki Maki ◽  
Sento Nakai ◽  
Tsuruhei Yagi ◽  
Hideomi Nakamura
Keyword(s):  
Doppler Radar ◽  
Gravity Current ◽  
Ground Surface ◽  
Leading Edge ◽  
Radiosonde Data ◽  
The Sea Of Japan ◽  
Cold Air ◽  
Drifting Snow ◽  
Cold Air Outbreak ◽  
Strong Winds

The mechanisms of strong winds associated with snow clouds, and the relationship between strong winds and blowing/drifting snow, were investigated. A snowstorm occurred with a typical L-mode snow band which was generated and organized longitudinally during a continental cold-air outbreak over the Sea of Japan. Doppler radar observations revealed that the snow band consisted of small echo cells arranged along the direction of the snow band. When one of the echo cells passed, blowing/drifting snow was generated and intensified by a snow cloud-induced gust, and the horizontal visibility near the ground surface was significantly decreased. Doppler radar and radiosonde data showed that the gust was due to the cold air outflow (CAO) from the snow clouds. The leading edge of the CAO was about 9 km ahead of the center of the snow cloud and the depth of the CAO was about 600 m near the forward flank of the snow cloud. The CAO was caused by a downdraft at the center of the snow cloud, which was initiated at a height of about 1.3 km and with a velocity in excess of 1 ms−1. The observed CAO speed was explained by the theory of the gravity current.

scholarly journals Warming permafrost and active layer variability at Cime Bianche, Western Alps

2014 ◽  
Vol 8 (4) ◽  
pp. 4033-4074
Author(s):  
P. Pogliotti ◽  
M. Guglielmin ◽  
E. Cremonese ◽  
U. Morra di Cella ◽  
G. Filippa ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Layer Thickness ◽  
Active Layer ◽  
Ground Surface ◽  
Small Scale ◽  
Active Layer Thickness ◽  
Spatial And Temporal Variability ◽  
Monitoring Site ◽  
Surface Temperatures

Abstract. The objective of this paper is to provide a first synthesis on the state and recent evolution of permafrost at the monitoring site of Cime Bianche (3100 m a.s.l.). The analysis is based on seven years of ground temperatures observations in two boreholes and seven surface points. The analysis aims to quantify the spatial and temporal variability of ground surface temperatures in relation to snow cover, the small scale spatial variability of the active layer thickness and the warming trends on deep permafrost temperatures. Results show that the heterogeneity of snow cover thickness, both in space and time, is the main factor controlling ground surface temperatures and leads to a mean range of spatial variability (2.5±0.15°C) which far exceeds the mean range of observed inter-annual variability (1.6±0.12°C). The active layer thickness measured in two boreholes 30 m apart, shows a mean difference of 2.03±0.15 m with the active layer of one borehole consistently lower. As revealed by temperature analysis and geophysical soundings, such a difference is mainly driven by the ice/water content in the sub-surface and not by the snow cover regimes. The analysis of deep temperature time series reveals that permafrost is warming. The detected linear trends are statistically significant starting from depth below 8 m, span the range 0.1–0.01°C year−1 and decrease exponentially with depth. Our findings are discussed in the context of the existing literature.

Quantification of small-scale heterogeneity in aquatic aminopeptidase activity

2020 ◽  
Vol 84 ◽  
pp. 127-140
Author(s):  
BM Gaas ◽  
JW Ammerman
Keyword(s):  
Chlorophyll Fluorescence ◽  
Leucine Aminopeptidase ◽  
Hudson River ◽  
Aminopeptidase Activity ◽  
Small Scale ◽  
Analysis Instrument ◽  
Sequential Samples ◽  
Degree Of Confidence ◽  
The Mean ◽  
Hydrolysis Of

Leucine aminopeptidase (LAP) is one of the enzymes involved in the hydrolysis of peptides, and is sometimes used to indicate potential nitrogen limitation in microbes. Small-scale variability has the potential to confound interpretation of underlying patterns in LAP activity in time or space. An automated flow-injection analysis instrument was used to address the small-scale variability of LAP activity within contiguous regions of the Hudson River plume (New Jersey, USA). LAP activity had a coefficient of variation (CV) of ca. 0.5 with occasional values above 1.0. The mean CVs for other biological parameters—chlorophyll fluorescence and nitrate concentration—were similar, and were much lower for salinity. LAP activity changed by an average of 35 nmol l-1 h-1 at different salinities, and variations in LAP activity were higher crossing region boundaries than within a region. Differences in LAP activity were ±100 nmol l-1 h-1 between sequential samples spaced <10 m apart. Variogram analysis indicated an inherent spatial variability of 52 nmol l-1 h-1 throughout the study area. Large changes in LAP activity were often associated with small changes in salinity and chlorophyll fluorescence, and were sensitive to the sampling frequency. This study concludes that LAP measurements in a sample could realistically be expected to range from zero to twice the average, and changes between areas or times should be at least 2-fold to have some degree of confidence that apparent patterns (or lack thereof) in activity are real.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

scholarly journals Quantification of Measurement and Model Effects in Monopile Foundation Scour Protection Experiments

2021 ◽  
Vol 9 (6) ◽  
pp. 585
Author(s):  
Minghao Wu ◽  
Leen De Vos ◽  
Carlos Emilio Arboleda Chavez ◽  
Vasiliki Stratigaki ◽  
Maximilian Streicher ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Flow Field ◽  
Three Dimensional ◽  
Small Scale ◽  
Maximum Wave Height ◽  
Chaotic Flow ◽  
The Mean ◽  
Scour Protection ◽  
Damage Number ◽  
Measurement Effects

The present work introduces an analysis of the measurement and model effects that exist in monopile scour protection experiments with repeated small scale tests. The damage erosion is calculated using the three dimensional global damage number S3D and subarea damage number S3D,i. Results show that the standard deviation of the global damage number σ(S3D)=0.257 and is approximately 20% of the mean S3D, and the standard deviation of the subarea damage number σ(S3D,i)=0.42 which can be up to 33% of the mean S3D. The irreproducible maximum wave height, chaotic flow field and non-repeatable armour layer construction are regarded as the main reasons for the occurrence of strong model effects. The measurement effects are limited to σ(S3D)=0.039 and σ(S3D,i)=0.083, which are minor compared to the model effects.

scholarly journals Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR

2018 ◽  
Author(s):  
Nicholas J. Roberts ◽  
Bernhard T. Rabus ◽  
John J. Clague ◽  
Reginald L. Hermanns ◽  
Marco-Antonio Guzmán ◽  
...  
Keyword(s):  
Ground Deformation ◽  
Ground Surface ◽  
Slope Instability ◽  
Small Scale ◽  
Spatial And Temporal Patterns ◽  
La Paz ◽  
Fine Grained ◽  
Measured Displacement ◽  
Optical Satellite Images ◽  
Displacement Patterns

Abstract. We characterize and compare creep preceding and following the 2011 Pampahasi landslide (∼ 40 Mm3 ± 50 %) in the city of La Paz, Bolivia, using spaceborne RADAR interferometry (InSAR) that combines displacement records from both distributed and point scatterers. The failure remobilised deposits of an ancient landslide in weakly cemented, predominantly fine-grained sediments and affected ∼ 1.5 km2 of suburban development. During the 30 months preceding failure, about half of the toe area was creeping at 3–8 cm/a and localized parts of the scarp area showed displacements of up to 14 cm/a. Changes in deformation in the 10 months following the landslide are contrary to the common assumption that stress released during a discrete failure increases stability. During that period, most of the landslide toe and areas near the headscarp accelerated, respectively, to 4–14 and 14 cm/a. The extent of deformation increased to cover most, or probably all, of the 2011 landslide as well as adjacent parts of the slope and plateau above. The InSAR-measured displacement patterns – supplemented by field observations and by optical satellite images – indicate that kinematically complex, steady-state creep along pre-existing sliding surfaces temporarily accelerated in response to heavy rainfall, after which the slope quickly achieved a slightly faster and expanded steadily creeping state. This case study demonstrates that high-quality ground-surface motion fields derived using spaceborne InSAR can help to characterize creep mechanisms, quantify spatial and temporal patterns of slope activity, and identify isolated small-scale instabilities. Characterizing slope instability before, during, and after the 2011 Pampahasi landslide is particularly important for understanding landslide hazard in La Paz, half of which is underlain by similar, large paleolandslides.

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