scholarly journals Spatial and temporal variations of snow cover in the forest area of middle and high latitude of Northern Hemisphere

2021 ◽  
Vol 932 (1) ◽  
pp. 012005
Author(s):  
Q Shao ◽  
C Huang ◽  
J F Huang
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
High Latitude ◽  
Land Surface ◽  
Temporal Variations ◽  
Cold Season ◽  
Forest Area ◽  
Significant Advance ◽  
Significant Delay

Abstract Snow cover is an important part of cryosphere and the most seasonally changing land surface cover, which is sensitive to climate change. Previous studies showed that climate warming has already altered the extent and phenology of snow cover, which influences the plant phenology of the forest ecosystem. This research investigates the spatial distribution and temporal trend of snow cover in the forest area of mid and high latitude in the Northern Hemisphere (50°N-90°N,180°W-180°E) based on a satellite-derived snow dataset. Results showed that the spatial distribution of snow cover exhibits a latitudinal gradientin the mid and high latitudes of the Northern Hemisphere. The snow cover onset week (SCOW) and snow cover end week (SCEW) shortened significantly at a rate of 0.23 weeks/10 yr. and 0.48 weeks/10 yr., respectively (P<0.05). Cold season (CS) and snow cover persistence week (SCPW) shortened at a rate of 0.25 weeks/10 yr. and 0.16 weeks/10 yr. 19.62% of the study area showed a trend of a significant advance in SCOW, and 1.36% showed a trend of significant delay (P<0.05). For SCEW, 44.91% of regions showed significant advance and 1.91% of regions showed significant delay (P<0.05). CS was a significantly shorted trend (P<0.05) in 16.95% of the study area and showed a significantly extended trend (P<0.05) in 3.76% of the area. SCPW and CS were similar but different, indicating that transient snowfall exists in parts of the study area.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals On the role of Eurasian autumn snow cover in dynamical seasonal predictions 

2021 ◽  
Author(s):  
Paolo Ruggieri ◽  
Marianna Benassi ◽  
Stefano Materia ◽  
Daniele Peano ◽  
Constantin Ardilouze ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
General Circulation ◽  
Dominant Role ◽  
General Circulation Models ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Eurasian Snow

&lt;p&gt;Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.&lt;/p&gt;

CMIP6 multi-model projection of summer season expansion over the Northern Hemisphere land areas

2021 ◽  
Author(s):  
Bo-Joung Park ◽  
Seung-Ki Min
Keyword(s):  
Northern Hemisphere ◽  
High Latitude ◽  
Coupled Model ◽  
Arctic Region ◽  
Summer Season ◽  
Significant Advance ◽  
Multiple Model ◽  
North Asia ◽  
Near Term

&lt;p&gt;Due to the ongoing robust global warming, summer season is expected to get warmer in future over the Northern Hemisphere (NH) land areas. This study examined how the summer season defined by local temperature-based thresholds would change during the 21st century under Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) using Coupled Model Intercomparison Project phase 6 (CMIP6) multiple model simulations. The projection results relative to the current climatology (1995-2014) indicate the significant advance of summer onset and delay of withdrawal over all NH land areas except high latitude locations, with longer than 10 days of summer expansion even in the weakest scenario (SSP1-2.6) in the near-term future (2021-2040). The advance and delay of summer season timing become stronger in the mid-term (2041-2060) and long-term (2081-2020) future periods, ranging from about 10 days to a month depending on SSP scenarios. Largest summer expansion is observed in the middle latitudes, including Europe in high latitude, while the weakest changes are seen over North Asia. Canadian Arctic region is characterized by an asymmetric change with a small advance of summer onset but a relatively large delay in summer ending. CMIP6 models exhibit large inter-model differences, which increase from near-term to long-term future periods. Western North Asia region display larger inter-model difference in summer onset projections while Europe has the largest inter-model spread of summer withdrawal changes. Physical mechanisms associated with these regional and timing-dependent changes in the future summer season lengthening will be further examined.&lt;/p&gt;

scholarly journals A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

2021 ◽  
Author(s):  
Xiaona Chen ◽  
Shunlin Liang ◽  
Lian He ◽  
Yaping Yang ◽  
Cong Yin
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Glass Product ◽  
Aggregation Process ◽  
Climate Data ◽  
Important Indicator ◽  
Continental Scale ◽  
Snow Cover Extent ◽  
Frequency Monitoring

Abstract. Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change due to its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among existing continental scale SCE products hampers its detailed studies. Using the latest Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free SCE covering the NH landmass for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. The development of GLASS SCE contains five steps. First, a decision tree algorithm with multiple threshold tests was applied to distinguish snow cover (NHSCE-D) with other land cover types from daily AVHRR-SR CDR. Second, gridcells with cloud cover and invalid observations were filled by two existing daily SCE products. The gap-filled gridcells were further merged with NHSCE-D to generate combined daily SCE over the NH (NHSCE-Dc). Third, an aggregation process was used to detect the maximum SCE and minimum gaps in each 8-day periods from NHSCE-Dc. Forth, the gaps after aggregation process were further filled by the climatology of snow cover probability to generate the gap-free GLASS SCE. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r = 0.61, p < 0.05) and MOD10C2 during 2001–2019 (r = 0.97, p < 0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data are available at https://doi.org/10.5281/zenodo.5775238 (Chen et al. 2021).

scholarly journals Seasonal snow cover and climate change in the Hadley Centre GCM

1997 ◽  
Vol 25 ◽  
pp. 362-366 ◽  
Author(s):  
Richard Essery
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Warming ◽  
Land Surface ◽  
Large Impact ◽  
Seasonal Snow ◽  
Sulphate Aerosols ◽  
Hadley Centre ◽  
Seasonal Snow Cover

Northern Hemisphere snow cover varies greatly through the year, and the presence of snow has a large impact on interactions between the land surface and the atmosphere. This paper outlines the representation of snow cover in the Hadley Centre GCM, and compares simulated snow cover with satellite and ground-based observations. Climate warming in a simulation with increased concentrations of CO2 and sulphate aerosols is found to lead to larger reductions in snow cover over North Ameriea and Europe than over Asia.

Improved Skill of Northern Hemisphere Winter Surface Temperature Predictions Based on Land–Atmosphere Fall Anomalies

2007 ◽  
Vol 20 (16) ◽  
pp. 4118-4132 ◽  
Author(s):  
Judah Cohen ◽  
Christopher Fletcher
Keyword(s):  
Snow Cover ◽  
Sea Level ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Northern Eurasia ◽  
Dynamical Models ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Skill Scores ◽  
And Sea Level

Abstract A statistical forecast model, referred to as the snow-cast (sCast) model, has been developed using observed October mean snow cover and sea level pressure anomalies to predict upcoming winter land surface temperatures for the extratropical Northern Hemisphere. In operational forecasts since 1999, snow cover has been used for seven winters, and sea level pressure anomalies for three winters. Presented are skill scores for these seven real-time forecasts and also for 33 winter hindcasts (1972/73–2004/05). The model demonstrates positive skill over much of the eastern United States and northern Eurasia—regions that have eluded skillful predictions among the existing major seasonal forecast centers. Comparison with three leading dynamical forecast systems shows that the statistical model produces superior skill for the same regions. Despite the increasing complexity of the dynamical models, they continue to derive their forecast skill predominantly from tropical atmosphere–ocean coupling, in particular from ENSO. Therefore, in the Northern Hemisphere extratropics, away from the influence of ENSO, the sCast model is expected to outperform the dynamical models into the foreseeable future.

scholarly journals Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

2013 ◽  
Vol 5 (10) ◽  
pp. 4819-4838 ◽  
Author(s):  
Guillermo Murray-Tortarolo ◽  
Alessandro Anav ◽  
Pierre Friedlingstein ◽  
Stephen Sitch ◽  
Shilong Piao ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
High Latitude ◽  
Land Surface ◽  
Land Surface Models ◽  
Surface Models

scholarly journals Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

2012 ◽  
Vol 13 (1) ◽  
pp. 204-222 ◽  
Author(s):  
Maheswor Shrestha ◽  
Lei Wang ◽  
Toshio Koike ◽  
Yongkang Xue ◽  
Yukiko Hirabayashi
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Hydrological Model ◽  
Lapse Rate ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent

Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

scholarly journals Representing Variability in Subgrid Snow Cover and Snow Depth in a Global Land Model: Offline Validation

2014 ◽  
Vol 27 (9) ◽  
pp. 3318-3330 ◽  
Author(s):  
T. Nitta ◽  
K. Yoshimura ◽  
K. Takata ◽  
R. O’ishi ◽  
T. Sueyoshi ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Surface Model ◽  
Snow Cover Fraction ◽  
Snow Covered Area ◽  
Covered Area ◽  
Global Land

Abstract Subgrid snow cover is one of the key parameters in global land models since snow cover has large impacts on the surface energy and moisture budgets, and hence the surface temperature. In this study, the Subgrid Snow Distribution (SSNOWD) snow cover parameterization was incorporated into the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. SSNOWD assumes that the subgrid snow water equivalent (SWE) distribution follows a lognormal distribution function, and its parameters are physically derived from geoclimatic information. Two 29-yr global offline simulations, with and without SSNOWD, were performed while forced with the Japanese 25-yr Reanalysis (JRA-25) dataset combined with an observed precipitation dataset. The simulated spatial patterns of mean monthly snow cover fraction were compared with satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The snow cover fraction was improved by the inclusion of SSNOWD, particularly for the accumulation season and/or regions with relatively small amounts of snowfall; snow cover fraction was typically underestimated in the simulation without SSNOWD. In the Northern Hemisphere, the daily snow-covered area was validated using Interactive Multisensor Snow and Ice Mapping System (IMS) snow analysis datasets. In the simulation with SSNOWD, snow-covered area largely agreed with the IMS snow analysis and the seasonal cycle in the Northern Hemisphere was improved. This was because SSNOWD formulates the snow cover fraction differently for the accumulation season and ablation season, and represents the hysteresis of the snow cover fraction between different seasons. The effects of including SSNOWD on hydrological properties and snow mass were also examined.

Snow depth trends from CMIP6 models conflict with observational evidence

2021 ◽  
pp. 1-40
Keyword(s):  
Northern Hemisphere ◽  
Air Temperature ◽  
High Latitude ◽  
Snow Depth ◽  
Cold Season ◽  
Observational Evidence ◽  
High Quality ◽  
Observational Dataset ◽  
Precipitation Increase

Abstract In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014. Simulated snow depths have low accuracy (RMSE = 17–36 cm) and are biased high, exceeding the observed baseline (1976–2005) on average (18 ± 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (NRMSDs of 0.77 ± 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.

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