scholarly journals Eurasian snow depth in long-term climate reanalyses

2016 ◽  
Author(s):  
Martin Wegmann ◽  
Yvan Orsolini ◽  
Emanuel Dutra ◽  
Olga Bulygina ◽  
Alexander Sterin ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Global Climate ◽  
Good Representation ◽  
Climate Effects ◽  
Surface Energy Fluxes ◽  
Multi Scale ◽  
Eurasian Snow ◽  
Climate Evolution

Abstract. Snow cover variability has significant effects on local and global climate evolution. By changing surface energy fluxes and hydrological conditions, changes in snow cover can alter atmospheric circulation and lead to remote climate effects. To analyze such multi-scale climate effects, atmospheric reanalysis and derived products offer the opportunity to analyze snow variability in great detail far back in time. So far only little is know about their quality. Comparing four long-term reanalysis datasets with Russian in situ snow depth data, a good representation of daily to sub-decadal snow variability was found. However, the representation of pre-1950 inter-decadal snow variability is questionable, since datasets divert towards different base states. Limited availability of independent long-term snow data hinders investigating this bifurcation of snow states in great detail, but initial investigations reveal a non-stationary performance of snow evolution representation. This study demonstrates the ability of long-term reanalysis to reproduce snow variability accordingly.

scholarly journals Eurasian snow depth in long-term climate reanalyses

2017 ◽  
Vol 11 (2) ◽  
pp. 923-935 ◽  
Author(s):  
Martin Wegmann ◽  
Yvan Orsolini ◽  
Emanuel Dutra ◽  
Olga Bulygina ◽  
Alexander Sterin ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Decadal Variability ◽  
Global Climate ◽  
Good Representation ◽  
Climate Effects ◽  
Surface Energy Fluxes ◽  
Multi Scale ◽  
Climate Evolution

Abstract. Snow cover variability has significant effects on local and global climate evolution. By changing surface energy fluxes and hydrological conditions, changes in snow cover can alter atmospheric circulation and lead to remote climate effects. To document such multi-scale climate effects, atmospheric reanalysis and derived products offer the opportunity to analyze snow variability in great detail far back to the early 20th century. So far only little is know about their quality. Comparing snow depth in four long-term reanalysis datasets with Russian in situ snow depth data, we find a moderately high daily correlation (around 0.6–0.7), which is comparable to correlations for the recent era (1981–2010), and a good representation of sub-decadal variability. However, the representation of pre-1950 inter-decadal snow variability is questionable, since reanalysis products divert towards different base states. Limited availability of independent long-term snow data makes it difficult to assess the exact cause for this bifurcation in snow states, but initial investigations point towards representation of the atmosphere rather than differences in assimilated data or snow schemes. This study demonstrates the ability of long-term reanalysis to reproduce snow variability accordingly.

FEATURES OF SNOW COVER OF SEMI-DESERTS AND DRY STEPPES OF THE CASPIAN SEA ACCORDING TO SATELLITE DATA FOR THE PERIOD 2001...2020

2021 ◽  
Vol 101 (2) ◽  
pp. 80-87
Author(s):  
A.G Terekhov ◽  
◽  
N.I. Ivkina ◽  
N.N. Abayev ◽  
A.V. Galayeva ◽  
...  
Keyword(s):  
Snow Cover ◽  
Caspian Sea ◽  
Snow Depth ◽  
Eastern Coast ◽  
The Caspian Sea ◽  
Dry Steppe ◽  
Natural Tendency ◽  
Second Mode ◽  
Continental Climate

The Snow Depth FEWS NET daily product was used to analyze snowy regime of the upper part of the River Emba basin from January 1 to April 30 for the period of 2001...2020. The Emba River basin is situated in Kazakhstan at the Eastern coast of the Caspian Sea. The area is characterized by the arid and extreme continental climate with dry-steppe and semi-desert landscapes. The population is small and the anthropogenic impact on the snow cover is minimal there. These conditions give an opportunity to identify the natural tendency in long-term changes of snow covering in semidesert zone of Kazakhstan. This paper describes the characteristics of the formation and destruction of the snow cover in the last 20 years. It was indicated that snowy regime has a trigger structure including two states; low-snowy regime and others years. It was shown that the snowy conditions are triggered. There are two modes, the first, as a low-snowy regime (up to 50 % of the entire sample) and the second mode includes other years. Significant variations of snow depth in various years masked many years’ tendencies of snow cover characteristics. But low-snowy regime was observed four times during five last years that can relate with modern decreasing snow covering in semi-desert zone of Kazakhstan.

scholarly journals The Effect of Eurasian Snow Cover on Regional and Global Climate Variations

1989 ◽  
Vol 46 (5) ◽  
pp. 661-686 ◽  
Author(s):  
T. P. Barnett ◽  
L. Dümenil ◽  
U. Schlese ◽  
E. Roeckner ◽  
M. Latif
Keyword(s):  
Snow Cover ◽  
Global Climate ◽  
Climate Variations ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

scholarly journals Spatiotemporal variation of snow depth in the Northern Hemisphere from 1992 to 2016

2019 ◽  
Author(s):  
Xiongxin Xiao ◽  
Tingjun Zhang ◽  
Xinyue Zhong ◽  
Xiaodong Li ◽  
Yuxing Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Snow Water ◽  
Microwave Sensors ◽  
Global Surface

Abstract. Snow cover is an effective best indicator of climate change due to its effect on regional and global surface energy, water balance, hydrology, climate, and ecosystem function. We developed a long term Northern Hemisphere daily snow depth and snow water equivalent product (NHSnow) by the application of the support vector regression (SVR) snow depth retrieval algorithm to historical passive microwave sensors from 1992 to 2016. The accuracies of the snow depth product were evaluated against observed snow depth at meteorological stations along with the other two snow cover products (GlobSnow and ERA-Interim/Land) across the Northern Hemisphere. The evaluation results showed that NHSnow performs generally well with relatively high accuracy. Further analysis were performed across the Northern Hemisphere during 1992–2016, which used snow depth, total snow water equivalent (snow mass) and, snow cover days as indexes. Analysis showed the total snow water equivalent has a significant declining trends (~ 5794 km3 yr−1, 12.5 % reduction). Although spatial variation pattern of snow depth and snow cover days exhibited slight regional differences, it generally reveals a decreasing trend over most of the Northern Hemisphere. Our work provides evidence that rapid changes in snow depth and total snow water equivalent are occurring beginning at the turn of the 21st century with dramatic, surface-based warming.

scholarly journals The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

2019 ◽  
Vol 11 (16) ◽  
pp. 1879
Author(s):  
Jianwei Yang ◽  
Lingmei Jiang ◽  
Liyun Dai ◽  
Jinmei Pan ◽  
Shengli Wu ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Depth Estimation ◽  
Snow Cover Area ◽  
Dual Channel ◽  
Defense Meteorological Satellite Program ◽  
Snow Water ◽  
Microwave Imager

The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 104 km2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I- and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

scholarly journals Breeding Phenology of Red-Shouldered Hawks (Buteo lineatus) Is Related to Snow Cover and Air Temperature During the Pre-Laying Period

2021 ◽  
Vol 9 ◽  
Author(s):  
Cheryl R. Dykstra ◽  
Jeffrey L. Hays ◽  
Melinda M. Simon ◽  
Ann R. Wegman
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Air Temperature ◽  
Global Climate ◽  
Weather Conditions ◽  
Egg Laying ◽  
Laying Date ◽  
Hatching Date ◽  
Breeding Phenology

Global climate change has advanced the breeding phenology of many avian species. However, raptors’ breeding phenologies may not respond in the same way to the factors that influence passerine breeding dates. We studied reproduction of suburban and rural Red-shouldered Hawks (Buteo lineatus) in southern Ohio, United States, from 1997 to 2020. Mean hatching dates for 786 broods were 24 April [Julian day: 114.1 ± 0.3 d (SE)] for suburban birds and 25 April (Julian day: 114.5 ± 0.4) for rural birds. Egg-laying date averages approximately 33 days before hatching date, or about the third week of March. We used mixed models to test which factors influenced nestling hatching dates from 1997 to 2020. The best model included year, days of snow cover during the pre-laying period (February–March), and mean March temperature, with days of snow cover having the largest effect. Hatching date (in Julian days) was positively related to snow cover and negatively related to air temperature, i.e., young hatched earlier in years with fewer days of snow cover and in warmer years). Young also hatched slightly later as the study progressed. Overall, neither mean hatching date nor any of the weather variables showed a significant trend over the course of the study. Previously published reports indicate that many raptor species do not exhibit advancing hatching dates, and breeding phenologies often reflect local weather conditions. The complexity and diversity of raptor responses to climate change underscore the importance of long-term studies of raptors at multiple locations.

The Effect of Eurasian Snow Cover on Global Climate

Science ◽  
1988 ◽  
Vol 239 (4839) ◽  
pp. 504-507 ◽  
Author(s):  
T. P. BARNETT ◽  
L. DÜMENIL ◽  
U. SCHLESE ◽  
E. ROECKNER
Keyword(s):  
Snow Cover ◽  
Global Climate ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

scholarly journals Snowcover Survey over an Arctic Glacier Forefield: Contribution of Photogrammetry to Identify “Icing” Variability and Processes

2021 ◽  
Vol 13 (10) ◽  
pp. 1978
Author(s):  
Éric Bernard ◽  
Jean-Michel Friedt ◽  
Madeleine Griselin
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Measurement Techniques ◽  
Snow Accumulation ◽  
Surface Model ◽  
Bare Rock ◽  
Glacier Forefield ◽  
Arctic Glacier

The global climate shift currently underway has significant impacts on both the quality and quantity of snow precipitation. This directly influences the spatial variability of the snowpack as well as cumulative snow height. Contemporary glacier retreat reorganizes periglacial morphology: while the glacier area decreases, the moraine area increases. The latter is becoming a new water storage potential that is almost as important as the glacier itself, but with considerably more complex topography. Hence, this work fills one of the missing variables of the hydrological budget equation of an arctic glacier basin by providing an estimate of the snow water equivalent (SWE) of the moraine contribution. Such a result is achieved by investigating Structure from Motion (SfM) image processing that is applied to pictures collected from an Unmanned Aerial Vehicle (UAV) as a method for producing snow depth maps over the proglacial moraine area. Several UAV campaigns were carried out on a small glacial basin in Spitsbergen (Arctic): the measurements were made at the maximum snow accumulation season (late April), while the reference topography maps were acquired at the end of the hydrological year (late September) when the moraine is mostly free of snow. The snow depth is determined from Digital Surface Model (DSM) subtraction. Utilizing dedicated and natural ground control points for relative positioning of the DSMs, the relative DSM georeferencing with sub-meter accuracy removes the main source of uncertainty when assessing snow depth. For areas where snow is deposited on bare rock surfaces, the correlation between avalanche probe in-situ snow depth measurements and DSM differences is excellent. Differences in ice covered areas between the two measurement techniques are attributed to the different quantities measured: while the former only measures snow accumulation, the latter includes all of the ice accumulation during winter through which the probe cannot penetrate, in addition to the snow cover. When such inconsistencies are observed, icing thicknesses are the source of the discrepancy that is observed between avalanche probe snow cover depth measurements and differences of DSMs.

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