Spatio-temporal variability and seasonal dynamics of snow cover regime in Estonia

2019 ◽  
Vol 139 (1-2) ◽  
pp. 759-771
Author(s):  
Birgit Viru ◽  
Jaak Jaagus
2021 ◽  
Vol 56 (2) ◽  
pp. 220-233
Author(s):  
María Eugenia Fernández ◽  
Jorge Osvaldo Gentili ◽  
Ana Casado ◽  
Alicia María Campo

The objective of this work is to analyze the spatio-temporal distribution of Global Horizontal Irradiation (GHI) on a regional scale and its relationship with frequent synoptic situations in the south of the Pampeana region (Argentina). It was verified that the latitudinal pattern of distribution of the GHI is modified in the region by cloud cover, which is in turn determined by the seasonal dynamics of action centers and the passage of fronts in summer and winter. The South America Monsoon System (SAMS) defines differential situations of cloudiness and rainfall in the region, which affect GHI. GHI increased successively between the decades 1981–2010, a factor associated with the variability of rainfall that characterizes the region.


2005 ◽  
Vol 36 (1) ◽  
pp. 21-36 ◽  
Author(s):  
Achim A. Beylich

The intensity and spatio-temporal variability of chemical denudation was analyzed in the Latnjavagge drainage basin (9 km2; 950–1440 m a.s.l.; 68°20′N, 18°30′E), an arctic–oceanic periglacial environment in northernmost Swedish Lapland. Data on daily runoff and solute concentrations at different test sites within the selected representative drainage basin were collected during the entire arctic summer seasons of 2000, 2001, 2002 and 2003. The mean annual chemical denudation net rate for the Latnjavagge drainage basin is 5.4 t/km2 yr. Most of the annual runoff occurs when the ground is still frozen. The rate in Latnjavagge is much lower than chemical denudation rates reported for Kärkevagge (Swedish Lapland) situated close to Latnjavagge, but at a similar level to a number of other subarctic, arctic and alpine environments. Chemical denudation shows a spatio-temporal variability within the drainage basin, which is mainly caused by a spatio-temporal variability of snow cover and ground frost and a spatial variability of regolith thicknesses within Latnjavagge.


2018 ◽  
Vol 58 (4) ◽  
pp. 473-485
Author(s):  
A. Y. Komarov ◽  
Y. G. Seliverstov ◽  
P. B. Grebennikov ◽  
S. A. Sokratov

Te paper presents the results of studies aimed at investigation of the spatial and temporal variability of snow coverstructure on the basis of strength values and its variations obtained by means of the high-resolution penetrometer SnowMicroPen. Te possibilities of fast and independent from the observer identifcation of layers (including identifcation of weakened, potentially avalanche-dangerous layers) were estimated under the climatic conditions of Moscow and the Khibiny mountains. Horizontal areas with homogeneous underlying surface and vegetation were selected for the stratigraphic studies that made it possible to avoid a possible influence of slope relief and exposure from the obtained data on the spatial and temporal variability of the snow depth structure. Te analysis of the information obtained in winter seasons 2014/15 and 2016/17 allowed constructing detailed schemes of the snow cover evolution at the Moscow site as well as assessing the inter-annual and intra-seasonal variability of its structure. Afer the SnowMicroPen data were recorded in the course of the feld works carried out in winter 2015/16 on the Khibiny educational and scientifc base of the Lomonosov Moscow State University (city of Kirovsk), the 10-meter trench on the same profle was described in details, and direct data on the snow cover structure were obtained. Te strength values resulted from the above studies characterize the layers composed of crystals of various shapes and sizes, and they are considered as the frst step to methodology of operational defnition of the spatially-inhomogeneous stratigraphy and stability of snowpack without snowpit observations. Te data analysis showed high spatial and temporal variability of the structure and properties of snow cover even at a homogeneous area, usually described by a single snowpit.


2019 ◽  
Vol 23 (8) ◽  
pp. 3189-3217 ◽  
Author(s):  
Todd A. N. Redpath ◽  
Pascal Sirguey ◽  
Nicolas J. Cullen

Abstract. A 16-year series of daily snow-covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional-scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover and, combined with climatic data, provides insight into controls on variability. Seasonal snow cover metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Modes of spatial variability were characterised, whilst also preserving temporal signals by applying raster principal component analysis (rPCA) to maps of annual SCD anomaly. Sensitivity of SCD to temperature and precipitation variability was assessed in a semi-distributed way for mountain ranges across the catchment. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 d yr−1. A persistent mid-winter reduction in SCA, prior to a second peak in August, is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed, but substantial spatial and temporal variability was present. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. This analysis of SCD anomalies revealed strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large-scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.


2017 ◽  
Vol 31 (23) ◽  
pp. 4229-4237 ◽  
Author(s):  
Zachary J. Suriano ◽  
Daniel J. Leathers

Author(s):  
A.D. Kryuchkov ◽  
O.V. Istomina

The article examines the features of snow cover occurrence in the Perm region for the 30 year period. Data about the onset, destruction, duration of steady snow cover and snow depth are given. Statistical parameters for single stations and the entire region are calculated. Spatio-temporal variability of basic snow cover characteristics is analyzed. It is shown that the dates of onset and destruction of steady snow cover have shifted to later terms during 1988-2018 period. It is determined that the increase in the number of days with a stable snow cover was observed after a long reduction in recent years. The features of the spatial structure of snow cover distribution in the region are revealed. It is established that the snow depth decreased until the end of the 00-ies of the XXI century, in recent years there has been a tendency to increase in values.


2019 ◽  
Author(s):  
Todd A. N. Redpath ◽  
Pascal Sirguey ◽  
Nicolas J. Cullen

Abstract. A 16-year series of daily snow covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover, and combined with climatic data, provides insight into controls on variability. Metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Raster principal components analysis (rPCA) was applied to maps of annual SCD anomaly to characterise modes of spatial variability whilst preserving temporal signals. Semi-distributed analysis between SCD and temperature and precipitation anomalies allowed sensitivity of SCD to climatic forcings to be assessed spatially. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 days per year. A reduction in SCA through mid-winter, prior to a second peak in August and persistent throughout the time series is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. rPCA and semi-distributed analysis of SCD anomalies reveal strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.


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