Characteristics and Trends of North American Snowfall from

a Comprehensive Gridded Data Set

Daria Kluver

MS Thesis Presentation

Department of Geography

University of Delaware

April 10, 2007

Outline

• Introduction• Brief review of Previous

literature• Aims of this study• Data Verification • Snowfall Climatology• Trend Analysis• Teleconnection Analysis• Conclusions

http://www.wunderground.com/blog/smadsen8486/archive.html?tstamp=200601

http://www.cfcl.com/~vlb/weblog/images/WinterTimeRoad.jpeg

Introduction• The factors controlling each snowfall event are numerous, and sometimes

last for only a few hours (Leathers et al. (1993)).

• In contrast, snow cover studies, concerned with the presence of the snow cover over at least several days, incorporate a low temperature persistence factor (Harrington et al., 1987).

• Because of these differences, snowfall may be more representative of the short-term meteorological events that produce it.

• There have recently been several snow cover, and snow water equivalent (SWE), studies yet few have assessed the trends, climatological aspects, and climate change indication capabilities of actual snowfall (IPCC, 2001, Leathers et al., 1993).

North American Snowfall• Half-century snowfall trends show decreases in the Pacific North West and

increases in the Ohio River Valley (Scott and Kaiser, 2003,2004)

• Great Lakes/Upper Mid-West and High Plains experienced increases in snowfall from 1945-1984 (Leathers et al., 1993).

– Number and intensity of Alberta Clippers

• Studies on Lake-effect snowfall show increases (Burnett et al, 2003; Ellis and Leathers, 1996; and Leather and Ellis, 1996; Scott and Kaiser, 2003, 2004)

• In southern Canadian regions increases in frequency of snowfall events corresponds to increases in winter snow cover (Brown and Goodison, 1996). However, reduced spring snowfall events is associated with decreased snow cover duration.

• Snowfall’s human impacts and cost (Changnon, 1979; Schmidlin, 1993).

Teleconnections and snowfall

• Arctic Oscillation- leading empirical orthogonal function of wintertime monthly mean Northern Hemisphere sea level pressure.– Positive phase corresponds to low

pressure over the polar region and high pressure at the midlatitudes. Oceanic storms in the Pacific are pushed to the North, so the western U.S is dryer, Alaska is wetter. East of the Rocky Mountains cold weather outbreaks are not as severe.

www.cpc.noaa.gov

• North Atlantic Oscillation (NAO)- fluctuation in sea level pressure between two centers of action (Azores high and Icelandic low). It is the leading wintertime mode of variability in the Atlantic basin.– Positive years have stronger than normal

subtropical high pressure center and deeper than normal Icelandic low. This larger pressure difference leads to stronger storms crossing the Atlantic Ocean at a more northerly track. Associated with warm and wet winters in both Europe and the eastern United States.

http://www.ldeo.columbia.edu/NAO/

• El Nino Southern Oscillation (ENSO)- phenomenon in the equatorial Pacific that affects precipitation, pressure and wind patterns in the tropics. Its effect on the position of the mid-latitude jet stream influences U.S. storm tracks. – Warm phase strengthens the upper

level ridge off western North America, producing warmer temperatures. While the more frequent/stronger Pacific storms in the Pacific Northwest during a cold phase produces cool and wet conditions. http://www.cpc.noaa.gov/products/analysis_monitoring/ensocycle/nawinter.html

• Pacific Decadal Oscillation (PDO)- Pacific Ocean phenomenon, defined as a leading model of multi-decadal variability in SSTs in the extratropical North Pacific.– Warm (positive) phase is characterized by cool SSTs

in the central North Pacific, a more intense Aleutian low, and warm SSTs along the West Coast of North America. There is correspondingly wet periods in the coastal Gulf of Alaska.

http://jisao.washington.edu/pdo/

• Pacific North American Index (PNA)- fluctuation of the mid-tropospheric mean flow resulting in an intensification or damping of the typical PNA pattern. – Positive years, the flow is more meridional

(ridge over the Rocky Mountains, trough in eastern North America), and during negative years, the flow is more zonal. This changes temperature characteristics, and frequency of precipitation across North America.

www.cpc.noaa.gov

• Few snowfall studies

• No studies cover all of North America

• Few studies looking at teleconnection patterns

• Existing studies have spatial or temporal limitations.

Aims of study

• Determine the quality of a new gridded data set• Construct a climatology of North American

snowfall• Calculate trends in various snowfall

characteristics• Identify correlations between snowfall and

teleconnection patterns

•1 by 1 interpolated snowfall data (Dyer and Mote, 2006) from U.S. National Weather Service (NWS) cooperative stations and the Canadian daily surface observations

•The interpolation was completed using the Spheremap spatial interpolation program, (Willmott et. al, 1984; Shepard, 1968)

•quality controlled using criteria from Robinson (1989)

•The period of record is 1900-2000 with a daily resolution

•Grid values for each day include maximum snowfall, minimum snowfall, median snowfall, mean snowfall, standard deviation.

• In this study, mean snowfall values were chosen to approximate the daily value at each grid point.

Data

Data Verification Results

Number of reporting stations per season

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1880 1900 1920 1940 1960 1980 2000 2020

Season

mea

n n

um

ber

of

stat

ion

s

mean number of stations

Snow season is July 1 to June 30.

From a total of 2891 grid cells.

The first ~50 years of the record had a large increase in the number of reporting stations contributing to the interpolation

1900 1909 1919

1929 1939 1949

1959

The first season with reporting stations

Last season with reporting stations

Period of Record with Station Data. Calculated as last season – first season from 1900 to 1999.

We wanted to find a period of record with the best spatial coverage possible

Grids with 100 seasons period of record. Grids with >= 90 seasons period of record.

Grids with >=75 seasons period of record.

Grids with >=50 seasons period of record.

Grids with >=25 seasons period of record.

This becomes an issue when trends are calculated.

Example:

total snowfall per season for -114/36

y = -8.1912x + 16271

R 2 = 0.5469

-200

0

200

400

600

800

1000

1200

1400

1880 1900 1920 1940 1960 1980 2000 2020

season

tota

l s

no

wfa

ll (

mm

)

-5

0

5

10

15

20

nu

mb

er

of

sta

tio

ns

total snowfall

number of stations

Linear (total snowfall)

Black out grids with 10 or more seasonal differences that are greater than or equal to 10% of the number of stations over the period 1949 through 1999. Gray grid cells indicate no station data in the cell for the period of record.

Solution: determine a criteria for grid cells to be blacked out.

Summary of Data verification results

• Potential data problems– Number of stations vary greatly with time– Only 50% of grid cells have a reporting station within

them at a given time.– Spatially, before the 1940s few regions have continuous

coverage of grid cells containing stations within them.– Grid cell timeseries illustrates how changes in station

density can effect trend analysis.• Reasons for problems

– Data has historically be available where there are people to record it

– Number of stations depend on amount of government funding available

– Earlier data that has been recorded on paper is still in the process of being digitized.

Data verification results cont.

• The reliability of this data set is maximized by selecting a time period with the most consistent data distribution possible– Reduces the amount of variability and bias due to

uneven spatial coverage.– 1949 to 1999 most consistent data distribution

• Some grid cells are deemed unreliable and left out of the trend analysis

Snowfall Climatology

Mean seasonal snowfall over the time period 1949 to 1999

Remember: Snow season is July 1 to June 30.

Seasonal coefficient of variation over the time period 1949 to 1999

Seasonal maximum snowfall over the time period 1949 to 1999

Seasonal minimum snowfall over the time period 1949 to 1999

Pacific Northwest mean snowfall over the time period 1949 to 1999

This shows that the 1° by 1° resolution can capture smaller features

Northeast mean snowfall over the time period 1949 to 1999

Summary of Climatology

• The annual cycle of North American snowfall is well documented by this data set.– Summer-Few grid cells with any mean snowfall, with the

exception of Northern Canada– Autumn-consistent snowfall moves south from Canada,

first is the Rocky Mountains, then western U.S., northeastern U.S. and Great Lakes. Canada and Alaska grid cells also have minimum values above zero.

– Winter-high mean values in southern Alaska, inter-mountain west, eastern Canada and Great Lakes region.

– Spring-mean snowfall values decrease. Ephemeral snow line moves north.

• Data set’s fine scale resolution identifies smaller scale features in the regional maps.

Snowfall Trends 1949-1999

• Least squares linear regressions are calculated between each variable and time.– Slope of the linear regression identifies temporal

changes in the dependent variable

• Correlation coefficients are calculated but not shown. Of interest to this study are physically significant changes in snowfall over time and highlighting statistically significant grid cells with physically insignificant trends would be a distraction.

Total seasonal snowfall, slope of the linear regression for 1949 to 1999.

Number of seasonal snowfall events, slope of the linear regression for 1949 to 1999

Date of first seasonal snowfall, slope of the linear regression for 1949 to 1999

Date of last seasonal snowfall, slope of the linear regression for 1949 to 1999.

Length of snowfall season, slope of the linear regression for 1949 to 1999.

March

March May

September October November

December February January

Monthly Trends:

April

Summary of Trend Analysis

• Decreases in of up to -30 mm of snowfall per half-century in the Pacific Northwest

• Increases in total seasonal snowfall are seen in several areas across the continent– Alaska, the Great Plains, the Great Lakes, Northeast United

States, and reliable grid cells in northern Canada of as much as 30 mm over the period

• a shorter snow season in Southern California and parts of the Rocky mountains, reduced at both ends of the annual cycle.

• Monthly trend maps identify the largest changes in monthly snowfall as occurring in the winter and spring months.

Teleconnection patterns

Data

Teleconnection Pattern

Data source Record length

Arctic Oscillation (AO) Climate Prediction Center’s Monitoring and Data Index page

1950 -2004

North Atlantic Oscillation (NAO)

Climate Prediction Center’s Monitoring and Data Index page

1821-2000

Pacific North American (PNA) index

Climate Prediction Center’s Monitoring and Data Index page

1950-2004

Pacific Decadal Oscillation (PDO)

University of Washington website 1900-2000

Southern Oscillation Index (SOI)

University of East Anglia Climate Research Unit’s Data site

1866-2003.

• In order to identify basic relationships between snowfall and teleconnection patterns, simple linear regressions as well as Pearson correlation coefficients are calculated.

• These calculations are done monthly for each of the 5 teleconnection patterns and snowfall.

• Only maps with strong, visible signals are shown.

Arctic Oscillation and monthly snowfall

North Atlantic Oscillation and monthly snowfall

Pacific Decadal Oscillation and monthly snowfall

Pacific North American index and monthly snowfall

Southern Oscillation Index and monthly snowfall

• For each month a multilinear stepwise regression is calculated between monthly snowfall (dependent variable) and the AO, NAO, PDO, PNA, and SOI (independent variables).

• Time period used for this analysis is 1950 to 1999 due to availability of teleconnection data.

• A multiple linear regression model – is built with stepwise selection of independent variables. – This is done using the variance-covariance matrix of the

monthly snowfall and monthly teleconnection data. – This method of analysis is useful in this situation because

it allows the observational data (snowfall) to be characterized by several different variables (teleconnection data).

January February

March April

September October November

December

Multilinear Stepwise Regressions

Summary of Teleconnections• AO-

– general negative correlation between the eastern half of the United States and the AO are consistent with the previous literature.

– During a positive AO phase, the reduction of cold air outbreaks east of the Rocky Mountains leads to higher temperatures. This could cause the precipitation to fall more frequently as rain rather than snow, decreasing the snowfall amounts in these regions during a positive AO.

• NAO-– Correlations most likely related to changes of the mid-tropospheric flow,

such as an eastward displacement of the eastern trough (Bradbury et al.,2002) during its negative phase

– which could explain the negative correlations with the northern Great Plains in September, and the Great Lakes/ Eastern United States in October.

• PDO-– striking areas of strong correlations that are fairly persistent throughout the

snow season. – The negative correlations in the Pacific Northwest are consistent with

previous findings of decreases in winter precipitation in the Pacific Northwest and of a strengthened Aleutian low during positive (warm) phases (Gedalof and Mantua, 2002).

• PNA-– Similar to the PDO. – Negative correlations in the Pacific Northwest correspond to

the more meridional characteristics of the Pacific North American pattern during the positive phase of the PNA index (the ridge over the western part of the continent is accentuated, resulting in changes in storm tracks and higher temperatures).

– The positive correlations in the eastern United States are associated with the deepening of the eastern trough, which results in colder temperatures and changes in storm tracks (Leathers et al., 1991; Serreze et al., 1998; Bradbury et al., 2002; Notaro et al., 2006).

• SOI-– Largest correlations in the central United States, along with a

small area of positive correlations in the West. • Multilinear stepwise regressions

– teleconnection patterns can account for up to 70 % of the variance in snowfall, principally in the Pacific Northwest.

Conclusions

• The Dyer and Mote (2006) gridded snowfall product for North America is a unique and useful data set for climatological studies. However, a thorough understanding of the limitations of the data is necessary before using the gridded product.

• The Rocky Mountains, coastal Alaska, Great Lakes snowbelts and southeastern Canada have the largest snowfall accumulations on the continent. Smallest accumulations are found along the southern tier of the United States.

• Temporal trends in snowfall identified in this study agree with previous work. Large decreases in snowfall are seen in the Pacific Northwest, and increases in snowfall are seen in several areas across the continent

• Atmospheric teleconnections account for a substantial amount of variation in snowfall, especially the AO, PDO, and PNA.

• Future research is warranted in many areas. – In order to improve predictions of North American

snowfall, the relationship between teleconnection patterns and snowfall requires more exploration.

– More detailed studies of regional teleconnection/snowfall relationships could enhance our understanding of the physical processes involved in the statistical relationships.

– Finally, continued extension of the trend analysis into the past can aid in the attribution of climate change.