Identification of large scale climate patterns affecting snow variability in the eastern United States

Morin, Jennifer; Block, Paul; Rajagopalan, Balaji; Clark, Martyn P.

doi:10.1002/joc.1534

Cited by 16 publications

(21 citation statements)

References 22 publications

(31 reference statements)

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“…PNA was therefore able to influence SWEmax over northern Québec during this period ( Figure 9) through a combination of an early start to the snow season and more winter precipitation (positive PNA) or a later start to the snow season and less winter precipitation (negative PNA). This finding is in agreement with previous studies which documented significant relationships between PNA and snowfall (Serreze et al, 1998;Morin et al, 2008) and SWE (Sobolowski and Frei, 2007) over eastern NA. The PNA, like the EP/NP, exhibits a strong positive correlation to PDO (r D 0Ð73 for DJFM from 1950) and the fall temperature correlation patterns of PDO, EP/NP and PNA are all very similar ( Figure B2).…”

Section: Pacific-north American Patternsupporting

confidence: 95%

“…html). There is extensive evidence linking NAO to snow cover and snowfall variability over eastern NA (Gutzler and Rosen, 1992;Brown and Goodison, 1996;Sobolowski and Frei, 2007;Morin et al, 2008). In terms of the climate of the Québec region, the positive mode of NAO is associated with northwesterly flow over the region with cold, dry winters (low SWE), while the negative mode of the NAO is associated with weaker winds, warmer air temperatures and more frequent influence of marine air masses from North Atlantic (high SWE).…”

Section: North Atlantic Oscillationmentioning

confidence: 97%

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Analysis of snow cover variability and change in Québec, 1948–2005

Brown

2010

Hydrological Processes

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Abstract:The spatial and temporal characteristics of annual maximum snow water equivalent (SWEmax) and fall and spring snow cover duration (SCD) were analysed over Québec and adjacent area for snow seasons 1948/1949-2004/2005 using reconstructed daily snow depth and SWE. Snow cover variability in Québec was found to be significantly correlated with most of the major atmospheric circulation patterns affecting the climate of eastern North America but the influence was characterized by strong multidecadal-scale variability. The strongest and most consistent relationship was observed between the Pacific Decadal Oscillation (PDO) and fall SCD variability over western Québec. El Niño-Southern Oscillation (ENSO) was found to have a limited impact on Québec snow cover. Evidence was found for a shift in circulation over the study region around 1980 associated with an abrupt increase in sea level pressure (SLP) and decreases in winter precipitation, snow depth and SWE over much of southern Québec, as well as changes in the atmospheric patterns with significant links to snow cover variability. Trend analysis of the reconstructed snow cover over 1948-2005 provided evidence of a clear north-south gradient in SWEmax and spring SCD with significant local decreases over southern Québec and significant local increases over north-central Québec. The increase in SWEmax over northern Québec is consistent with proxy data (lake levels, tree growth forms, permafrost temperatures), with hemispheric-wide trends of increasing precipitation over higher latitudes, and with projections of global climate models (GCMs).

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Section: Pacific-north American Patternsupporting

confidence: 95%

Section: North Atlantic Oscillationmentioning

confidence: 97%

Analysis of snow cover variability and change in Québec, 1948–2005

Brown

2010

Hydrological Processes

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“…PCA is a data reduction method that uses eigenvectors of the covariance matrix to define the patterns of simultaneous variation within the dataset. This method is considered a standard multivariate analysis technique in climate science and has also been used specifically in snow studies (Leathers et al, 1993;Cayan, 1996;Hughes and Robinson, 1996;Serreze et al, 1998;Frei and Robinson, 1999;von Storch and Zwiers, 1999;Derksen et al, 2000;McCabe and Dettinger, 2002;Bradbury et al, 2003;Jin et al, 2006;Wilks, 2006;Sobolowski and Frei, 2007;Morin et al, 2008;.…”

Section: Methodsmentioning

confidence: 99%

“…Several earlier studies have examined the spatial variability in United States snowfall, but are either not current or not spatially extensive enough for a robust snowfall regionalization (Harrington et al, 1987;Leathers et al, 1993;Groisman and Easterling, 1994;Hughes and Robinson, 1996;Hartley and Keables, 1998;Serreze et al, 1998;Smith and O'Brien, 2001;Bradbury et al, 2003;Patten et al, 2003;Morin et al, 2008;Ghatak et al, 2010). Some of these studies have described snowfall regions based on harmonic analysis (Harrington et al, 1987) or on principal component loading patterns (Leathers et al, 1993;Hughes and Robinson, 1996).…”

Section: Introductionmentioning

confidence: 97%

Regionalization of snowfall frequency and trends over the contiguous United States

Kluver

Leathers

2015

Intl Journal of Climatology

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This study examines the regional variations in the frequency of snowfall across the conterminous United States from 1930 to 2007. Principal components analysis and cluster analysis are used to group stations together based on the main modes of variation in snowfall frequency. Results indicate the existence of seven unique snowfall regions, which correspond to predominant storm tracks across the United States. These are the southeast, the south central Plains and southwest, the Ohio River Valley and mid-Atlantic, the Pacific Northwest, and three sub-regions in the Upper-Midwest. Quantile regression reveals that the distribution functions of each region's snowfall frequency are different and in some regions, changing over time. The northern part of the Upper-Midwest is experiencing increasing trends in all percentiles of snowfall frequencies, the Pacific northwest is experiencing declines in greater than median snowfall frequencies, and the southeast is seeing a decline in extreme frequency years. Correlation analysis between large-scale teleconnection patterns and regionally averaged snowfall frequencies corroborate previous findings and indicate specific forcing mechanisms for snowfall frequency in each region.

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“…These include numerical weather prediction model output fields, regional circulation pattern variables and global modes of variability that affect precipitation expectation [58][59][60], and any ground-based radar or weather stations available in near real time. Improvements in the availability and quality of past ground-based precipitation datasets for model calibration (such as extending APHRODITE past 2007) would also be expected to improve precipitation retrievals.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Precipitation Estimation with a Satellite Product

Krakauer

Pradhanang

Panthi

et al. 2015

Climate

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Satellite-based precipitation products have been shown to represent precipitation well over Nepal at monthly resolution, compared to ground-based stations. Here, we extend our analysis to the daily and subdaily timescales, which are relevant for mapping the hazards caused by storms as well as drought. We compared the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT product with individual stations and with the gridded APHRODITE product to evaluate its ability to retrieve different precipitation intensities. We find that 3B42RT, which is freely available in near real time, has reasonable correspondence with ground-based precipitation products on a daily timescale; rank correlation coefficients approach 0.6, almost as high as the retrospectively calibrated TMPA 3B42 product. We also find that higher-quality ground and satellite precipitation observations improve the correspondence between the two on the daily timescale, suggesting opportunities for improvement in satellite-based monitoring technology. Correlation of 3B42RT and 3B42 with station observations is lower on subdaily timescales, although the mean diurnal cycle of precipitation is roughly correct. We develop a probabilistic precipitation monitoring methodology that uses previous observations (climatology) as well as 3B42RT as input to generate daily precipitation accumulation probability distributions at each 0.25• × 0.25• grid cell in Nepal and surrounding areas. We quantify the information gain associated with using 3B42RT in the probabilistic model instead of relying only on

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Identification of large scale climate patterns affecting snow variability in the eastern United States

Cited by 16 publications

References 22 publications

Analysis of snow cover variability and change in Québec, 1948–2005

Analysis of snow cover variability and change in Québec, 1948–2005

Regionalization of snowfall frequency and trends over the contiguous United States

Probabilistic Precipitation Estimation with a Satellite Product

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