scholarly journals Hemispheric Snow Cover and Surface Albedo for Model Validation

1997 ◽  
Vol 25 ◽  
pp. 241-245 ◽  
Author(s):  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Surface Albedo ◽  
Climate Models ◽  
Empirical Studies ◽  
Early Summer ◽  
Late Winter ◽  
Accurate Information ◽  
The Individual

Accurate information concerning snow cover, and associated impacts of snow on regional surface albedo, needs to be available for empirical studies and for the validation of climate models. Here, a new integrated dataset for Northern Hemisphere lands is discussed, including files of visible and microwave satellite-derived snow estimates and in situ station data. These files will be used to examine snow extent, snow depth and surface albedo over five-day intervals, and have been generated using geographic-information system techniques. Visible and station observations extend from 1972 to present, and microwave estimates from 1979 to present, The 1×1° gridded files permit the strengths and weaknesses of the individual data sources to be identified and quantified. Also included is a hemispheric time series of snow extern derived from the visible satellite file. Of note are the two pronounced regimes of Northern Hemisphere extent during the past several decades. Between 1972 and 1985, 12 month running means of snow extent fluctuated around a mean of 25.9 × 10 km2. An abrupt transition occurred in 1986 and 1987, and since then mean annual extern has been 24.2 × 106km2. Recent decreases are found from late winter to early summer.

scholarly journals Hemispheric snow cover and surface albedo for model validation

1997 ◽  
Vol 25 ◽  
pp. 241-245 ◽  
Author(s):  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Surface Albedo ◽  
Climate Models ◽  
Empirical Studies ◽  
Early Summer ◽  
Late Winter ◽  
Accurate Information ◽  
The Individual

Accurate information concerning snow cover, and associated impacts of snow on regional surface albedo, needs to be available for empirical studies and for the validation of climate models. Here, a new integrated dataset for Northern Hemisphere lands is discussed, including files of visible and microwave satellite-derived snow estimates and in situ station data. These files will be used to examine snow extent, snow depth and surface albedo over five-day intervals, and have been generated using geographic-information system techniques. Visible and station observations extend from 1972 to present, and microwave estimates from 1979 to present, The 1×1° gridded files permit the strengths and weaknesses of the individual data sources to be identified and quantified. Also included is a hemispheric time series of snow extern derived from the visible satellite file. Of note are the two pronounced regimes of Northern Hemisphere extent during the past several decades. Between 1972 and 1985, 12 month running means of snow extent fluctuated around a mean of 25.9 × 106 km2. An abrupt transition occurred in 1986 and 1987, and since then mean annual extern has been 24.2 × 106 km2. Recent decreases are found from late winter to early summer.

Modeled Climate Responses to Realistic Extremes of Northern Hemisphere Spring and Summer Snow Anomalies

2020 ◽  
Vol 33 (22) ◽  
pp. 9905-9927
Author(s):  
Shizuo Liu ◽  
Qigang Wu ◽  
Lin Wang ◽  
Steven R. Schroeder ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Surface Air Temperature ◽  
Early Summer ◽  
The Arctic ◽  
Snow Cover Extent ◽  
Climate Responses

AbstractNorthern Hemisphere (NH) snow cover extent (SCE) has diminished in spring and early summer since the 1960s. Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) estimated about half as much NH SCE reduction as observed, and thus underestimated the associated climate responses. This study investigates atmospheric responses to realistic decreasing snow anomalies using multiple ensemble transient integrations of climate models forced by observed light and heavy NH snow cover years, specifically satellite-based observations of NH SCE and snow water equivalent from March to August in 1990 (light snow) and 1985 (heavy snow), as a proxy for the trend. The primary atmospheric responses to March–August NH snow reduction are decreased soil moisture, increased surface air temperature, general tropospheric warming in the extratropics and the Arctic, increased geopotential heights, and weakening of the midlatitude jet stream and eddy kinetic energy. The localized response is maintained by persistent increased diabatic heating due to reduced snow anomalies and resulting soil moisture drying, and the remote atmospheric response results partly from horizontal propagation of stationary Rossby wave energy and also from a transient eddy feedback mechanism. In summer, atmospheric responses are significant in both the Arctic and the tropics and are mostly induced by contemporaneous snow forcing, but also by the summer soil moisture dry anomaly associated with early snow melting.

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 Climatic influence on the composition of snow cover at Austre Okstindbreen, Norway, 1989 and 1990

1994 ◽  
Vol 19 ◽  
pp. 1-6 ◽  
Author(s):  
He Yuanqing ◽  
Wilfred H. Theakstone
Keyword(s):  
Snow Cover ◽  
North Atlantic ◽  
Early Summer ◽  
Late Winter ◽  
Crustal Material ◽  
Air Masses ◽  
The North ◽  
Air Temperatures ◽  
Winter Snow ◽  
Marine Source

Winter snow cover at Austre Okstindbreen is influenced strongly by patterns of atmospheric circulation, and by air temperatures during precipitation. Differences of circulation over the North Atlantic and Scandinavia during the winters of 1988–89 and 1989–90 were reflected in the ionic and isotopic composition of snow that accumulated at the glacier. Early summer ablation did not remove, or smooth out, all the initial stratigraphic differences. In the first half of the 1988–89 winter, most air masses took a relatively short route between a marine source and Okstindan; late winter snowfalls were from air masses which had taken a longer continental route. The snow that accumulated in the first half of the 1989–90 winter was associated with air masses which had followed longer continental routes, and so brought higher concentrations of impurities from forests, lakes and crustal material. The ablation season began earlier in 1990 than in 1989, and summer winds and rain supplied more impurities to the snowpack surface.

scholarly journals Assessing Snow Albedo Feedback in Simulated Climate Change

2006 ◽  
Vol 19 (11) ◽  
pp. 2617-2630 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Northern Hemisphere ◽  
Surface Albedo ◽  
Climate Models ◽  
Attenuation Effect ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
The Mean ◽  
Planetary Albedo

Abstract In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

scholarly journals Snow Thickness Estimation on First-Year Sea Ice from Late Winter Spaceborne Scatterometer Backscatter Variance

2019 ◽  
Vol 11 (4) ◽  
pp. 417 ◽  
Author(s):  
John Yackel ◽  
Torsten Geldsetzer ◽  
Mallik Mahmud ◽  
Vishnu Nandan ◽  
Stephen Howell ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Winter Season ◽  
Late Winter ◽  
First Year ◽  
Snow Layer ◽  
Independent Case ◽  
Brine Volume ◽  
Snow Thickness

Ku- and C-band spaceborne scatterometer sigma nought (σ°) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for estimating relative snow thickness on first-year sea ice based on the variance in σ° from daily time series ASCAT and QuikSCAT scatterometer measurements during the late winter season prior to melt onset. We first describe our theoretical basis for this approach, including assumptions and conditions under which the method is ideally suited and then present observational evidence from four independent case studies to support our hypothesis. Results suggest that the approach can provide a relative measure of snow thickness prior to σ° detected melt onset at both Ku- and C-band frequencies. We observe that, during the late winter season, a thinner snow cover displays a larger variance in daily σ° compared to a thicker snow cover on first-year sea ice. This is because for a given increase in air temperature, a thinner snow cover manifests a larger increase in basal snow layer brine volume owing to its higher thermal conductivity, a larger increase in the dielectric constant and a larger increase in σ° at both Ku- and C bands. The approach does not apply when snow thickness distributions on first-year sea ice being compared are statistically similar, indicating that similar late winter σ° variances likely indicate regions of similar snow thickness.

scholarly journals Special Issue on Remote Sensing of Snow and Its Applications

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 277 ◽  
Author(s):  
Ali Nadir Arslan ◽  
Zuhal Akyürek
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Land Surface ◽  
Climate Models ◽  
Weather Forecasting ◽  
Special Issue ◽  
Carbon Cycles ◽  
Wide Range ◽  
Remote Sensing Techniques

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

Integrating Model Evaluation and Observations into a Production-Release Pipeline

2020 ◽  
Author(s):  
Philipp S. Sommer ◽  
Ronny Petrik ◽  
Beate Geyer ◽  
Ulrike Kleeberg ◽  
Dietmar Sauer ◽  
...  
Keyword(s):  
Evaluation System ◽  
Large Scale ◽  
Climate Models ◽  
Model Simulation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Active Communication ◽  
The Individual ◽  
Scientific Questions

<p>The complexity of Earth System and Regional Climate Models represents a considerable challenge for developers. Tuning but also improving one aspect of a model can unexpectedly decrease the performance of others and introduces hidden errors. Reasons are in particular the multitude of output parameters and the shortage of reliable and complete observational datasets. One possibility to overcome these issues is a rigorous and continuous scientific evaluation of the model. This requires standardized model output and, most notably, standardized observational datasets. Additionally, in order to reduce the extra burden for the single scientist, this evaluation has to be as close as possible to the standard workflow of the researcher, and it needs to be flexible enough to adapt it to new scientific questions.</p><p>We present the Free Evaluation System Framework (Freva) implementation within the Helmholtz Coastal Data Center (HCDC) at the Institute of Coastal Research in the Helmholtz-Zentrum Geesthacht (HZG). Various plugins into the Freva software, namely the HZG-EvaSuite, use observational data to perform a standardized evaluation of the model simulation. We present a comprehensive data management infrastructure that copes with the heterogeneity of observations and simulations. This web framework comprises a FAIR and standardized database of both, large-scale and in-situ observations exported to a format suitable for data-model intercomparisons (particularly netCDF following the CF-conventions). Our pipeline links the raw data of the individual model simulations (i.e. the production of the results) to the finally published results (i.e. the released data). </p><p>Another benefit of the Freva-based evaluation is the enhanced exchange between the different compartments of the institute, particularly between the model developers and the data collectors, as Freva contains built-in functionalities to share and discuss results with colleagues. We will furthermore use the tool to strengthen the active communication with the data and software managers of the institute to generate or adapt the evaluation plugins.</p>

scholarly journals Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models with satellite-based SnowCCI data during 1982–2014

2021 ◽  
Author(s):  
Kerttu Kouki ◽  
Petri Räisänen ◽  
Kari Luojus ◽  
Anna Luomaranta ◽  
Aku Riihelä
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Dominant Factor ◽  
Large Variability ◽  
Cold Temperatures ◽  
Continental Scale ◽  
Snow Water

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Monitoring snow water equivalent (SWE) at continental scale is only possible from satellites, yet substantial uncertainties have been reported in NH SWE estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared the CMIP6 (Coupled Model Intercomparison Project Phase 6) and satellite-based NH SWE estimates north of 40° N for the period 1982–2014, and analyzed with a regression approach whether temperature (T) and precipitation (P) could explain the differences in SWE. We analyzed separately SWE in winter and SWE change rate in spring. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ data. The analysis shows that CMIP6 models tend to overestimate SWE, however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. This is in line with the expectation that even too cold temperatures cannot cause too high SWE without precipitation. T contributes to SWE biases mainly in regions, where T is close to 0 °C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P can not explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2012 ◽  
Vol 6 (4) ◽  
pp. 3317-3348 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Linear Correlation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Snow Cover Extent ◽  
Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

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