Recent Variations of Snow Cover and Snowfall in North America and Their Relation to Precipitation and Temperature Variations

1993 ◽  
Vol 6 (7) ◽  
pp. 1327-1344 ◽  
Author(s):  
Thomas R. Karl ◽  
Pavel Ya Groisman ◽  
Richard W. Knight ◽  
Richard R. Heim
Keyword(s):  
North America ◽  
Snow Cover ◽  
Temperature Variations ◽  
Precipitation And Temperature

The bioclimatic envelope of the wolverine (Gulo gulo): do climatic constraints limit its geographic distribution?

2010 ◽  
Vol 88 (3) ◽  
pp. 233-246 ◽  
Author(s):  
J. P. Copeland ◽  
K. S. McKelvey ◽  
K. B. Aubry ◽  
A. Landa ◽  
J. Persson ◽  
...  
Keyword(s):  
North America ◽  
Snow Cover ◽  
Geographic Distribution ◽  
Climatic Factors ◽  
Spatial Relationship ◽  
Gulo Gulo ◽  
Average Maximum ◽  
Summer Temperatures ◽  
Bioclimatic Envelope ◽  
Temperature Maps

We propose a fundamental geographic distribution for the wolverine ( Gulo gulo (L., 1758)) based on the hypothesis that the occurrence of wolverines is constrained by their obligate association with persistent spring snow cover for successful reproductive denning and by an upper limit of thermoneutrality. To investigate this hypothesis, we developed a composite of MODIS classified satellite images representing persistent snow cover from 24 April to 15 May, which encompasses the end of the wolverine’s reproductive denning period. To investigate the wolverine’s spatial relationship with average maximum August temperatures, we used interpolated temperature maps. We then compared and correlated these climatic factors with spatially referenced data on wolverine den sites and telemetry locations from North America and Fennoscandia, and our contemporary understanding of the wolverine’s circumboreal range. All 562 reproductive dens from Fennoscandia and North America occurred at sites with persistent spring snow cover. Ninety-five percent of summer and 86% of winter telemetry locations were concordant with spring snow coverage. Average maximum August temperature was a less effective predictor of wolverine presence, although wolverines preferred summer temperatures lower than those available. Reductions in spring snow cover associated with climatic warming will likely reduce the extent of wolverine habitat, with an associated loss of connectivity.

scholarly journals EMDNA: an Ensemble Meteorological Dataset for North America

2021 ◽  
Vol 13 (7) ◽  
pp. 3337-3362
Author(s):  
Guoqiang Tang ◽  
Martyn P. Clark ◽  
Simon Michael Papalexiou ◽  
Andrew J. Newman ◽  
Andrew W. Wood ◽  
...  
Keyword(s):  
North America ◽  
Bayesian Model Averaging ◽  
Precipitation Amount ◽  
Daily Temperature ◽  
Probabilistic Methods ◽  
Optimal Interpolation ◽  
Small Subset ◽  
Mountainous Regions ◽  
The Impact ◽  
Precipitation And Temperature

Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, “equally plausible” ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

scholarly journals Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

2020 ◽  
Author(s):  
Xiongxin Xiao ◽  
Shunlin Liang ◽  
Tao He ◽  
Daiqiang Wu ◽  
Congyuan Pei ◽  
...  
Keyword(s):  
North America ◽  
Land Cover ◽  
Brightness Temperature ◽  
Snow Cover ◽  
Temperature Data ◽  
Passive Microwave ◽  
Retrieval Models ◽  
Snow Cover Area ◽  
Microwave Brightness Temperature ◽  
Fractional Snow Cover

Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, climate system, and ecosystem function. Although optical satellite remote sensing has proved to be an effective tool for monitoring global and regional variations of snow cover, it is still problematic to accurately capture the snow dynamics characteristics at a finer spatiotemporal resolution, because the observations from optical satellite sensors are seriously affected by clouds and solar illumination. Besides, traditional methods of mapping snow cover from passive microwave data only provide binary information with a 25-km spatial resolution. In this study, we first present an approach to predict fractional snow cover over North America under all-weather conditions, derived from the enhanced resolution passive microwave brightness temperature data (6.25 km). This estimation algorithm used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2010 and 2017 to create the reference fractional snow cover data as the "true" observations. Further, the influence of many factors, including land cover, topography, and location, were incorporated into the retrieval models. The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)). The results of the output evaluated by using independent data indicate that the root-mean-square error (RMSE) of the estimated fractional snow cover ranges from 16.7 % to 19.8 %. In addition, the estimated fractional snow cover is verified in the snow mapping aspect by using snow cover observation data from meteorological stations (more than 0.31 million records). The result shows that the binary snow cover obtained by the proposed retrieval algorithm is in a good agreement with the ground measurements (kappa: 0.67). The accuracy of our algorithm estimation in the snow cover identification shows significant improvement when benchmarked against the Grody’s snow cover mapping algorithm: overall accuracy is increased by 18 % (from 0.71 to 0.84), and omission error is reduced by 71 % (from 0.48 to 0.14). Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. According to our experiment results, we can conclude that it is feasible for estimating fractional snow cover from passive microwave brightness temperature data, and this strategy also has a great advantage in detecting snow cover area.

Influence of December snow cover over North America on January surface air temperature over the midlatitude Asia

2019 ◽  
Vol 40 (1) ◽  
pp. 572-584
Author(s):  
Jingyi Li ◽  
Fei Li ◽  
Shengping He ◽  
Huijun Wang ◽  
Yvan J. Orsolini
Keyword(s):  
North America ◽  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature

scholarly journals Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter

2018 ◽  
Vol 11 (5) ◽  
pp. 2983-2994 ◽  
Author(s):  
Matthew J. Cooper ◽  
Randall V. Martin ◽  
Alexei I. Lyapustin ◽  
Chris A. McLinden
Keyword(s):  
North America ◽  
Snow Cover ◽  
Trace Gas ◽  
Data Sets ◽  
Good Precision ◽  
Average Sensitivity ◽  
Depth Analysis ◽  
Surface Snow ◽  
Tropospheric No2 ◽  
Surface Reflectivity

Abstract. Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (> 50 %) of the TEMPO field of regard can be snow covered in January and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered.We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93 %, precision of 87 %, recall of 83 %). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90 % for Aqua and Terra), but underestimated the presence of snow (recall of 74 % for Aqua, 75 % for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51 %, recall of 43 % for Aqua; precision of 69 %, recall of 45 % for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83 %) but missed a significant number of snow-covered pixels (recall of 45 %). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91 %, precision of 79 %, recall of 82 %). We use the Fscore, which balances precision and recall, to determine overall product performance (F = 85 %, 82 (82) %, 81 %, 58 %, 46 (54) % for IMS, MAIAC Aqua (Terra), CMC, NISE, MODIS Aqua (Terra), respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.

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