mid-tropospheric circulation and surface melt on thegreenland ice sheet. part i: atmospheric...

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 18, 111±129 (1998)

MID-TROPOSPHERIC CIRCULATION AND SURFACE MELTON THE GREENLAND ICE SHEET. PART I:

ATMOSPHERIC TELECONNECTIONS

THOMAS L. MOTE*

Climatology Research Laboratory, Department of Geography, University of Georgia, Athens, GA 30602-2502Email: [email protected]

Received 2 December 1996Revised 25 June 1997

Accepted 26 June 1997

ABSTRACT

Daily values of the spatial extent of melting on the Greenland ice sheetÐmeasured from satellite passive microwavesensorsÐare compared with several mid-tropospheric teleconnection indices during May 1979 to June 1989. Theteleconnection indices are derived by performing a rotated principal components analysis on a 100-point subset of the 700 hPaheights from the National Meteorological Center (NMC, now National Center for Environmental Prediction) octagonal grid.Loading patterns from the principal components are mapped and compared with teleconnection patterns identi®ed in theclimatological literature. Several teleconnections are apparent in the loading patterns, the most signi®cant being the NorthAtlantic Oscillation (NAO). The component scores are used as predictor variables in a multiple regression analysis of surfacemelt extent for the entire ice sheet and for eight topographically de®ned regions of the ice sheet. The results from theregression analysis show that the ®rst ®ve principal components account for more than half of a trend in the microwave-derived melt extent between 1979 and 1989. The NAO is shown as the teleconnection most highly related to surface meltextent on the Greenland ice sheet. # 1998 Royal Meterological Society.

KEY WORDS: principal components analysis; multiple regression analysis; teleconnections; 700 hPa geopotential heights; Greenland; ice sheet;surface ice melt.

INTRODUCTION

Snow and ice cover play a large role in the global climate system, in part due to their radiative properties. The

high albedo of snow and ice, particularly fresh snow, results in a large reduction of net radiation compared with

bare soil or ice-free water. As the necessary energy becomes available to initiate melting, the snowpack albedo is

reduced and the result is a greater net radiative ¯ux that promotes further melting. This positive `ice-albedo

feedback' mechanism for snow cover and other cryospheric surfaces may amplify any global warming in the high

latitudes by reducing the surface re¯ectance. Accordingly, the cryosphere will play an instrumental role in any

global climate change and it should provide an ideal environment for identifying climate change.

The suspected sensitivity of the cryosphere to climate change has led a number of scientists to measure

variations in surface area of snow cover and sea-ice. For example, Robinson and Dewey (1990) used snow-cover

extent derived from visible satellite data to search for climate change. Gloersen and Campbell (1991) examined

sea-ice extent using satellite microwave data for the same purpose. The polar ice sheets of Greenland and

Antarctica do not have appreciable changes in spatial extent on a decadal basis. However, changes in the spatial

CCC 0899-8418/98/020111-19 $17.50

# 1998 Royal Meteorological Society

*Correspondence to: T. L. Mote, Climatology Research Laboratory, Department of Geography, University of Georgia, Athens, GA 30602-2502, USA.

extent of the summer melt can be measured with satellite sensors at microwave frequencies (Mote et al., 1993;

Abdalati and Steffen, 1995; Mote and Anderson, 1996).

The long-term effect of a warmer climate over the Greenland ice sheet is likely to be increases in melt and

snow accumulation. Empirical evidence (Braithwaite and Olesen, 1989) and modelling results (Bindschadler,

1985) both suggest an increase in mean global sea-level of approximately 0�6mm per year for each 1�C increase

in surface air temperature over the Greenland ice sheet. The sea-level rise is projected because the increased melt

would more than offset increased accumulation. Both of these calculations assume a spatially uniform change in

mean surface air temperature over the ice sheet. However, climate change likely will not result in a uniform

temperature change but instead should be manifested in the frequency, intensity and duration of particular types

of atmospheric ¯ow or circulation. If changes in these circulation patterns can explain observed variations in

melt, then similar circulation patterns identi®ed in output from climate models may be used to infer future

variations in melt. Therefore, determination of the relationship between melt and mean atmospheric ¯ow patterns

can lead to a better understanding of the climatic conditions that result in melt and provide a means of assessing

the impact of climate change on the ice sheet.

The present study uses a time series developed by Mote and Anderson (1995) of the daily areal extent of

surface melt on the Greenland ice sheet from 37GHz passive microwave data. The research presented here is

designed to determine the relationship between large-scale, low-frequency atmospheric variation and spatial

extent of melt on the Greenland ice sheet. The strength of the relationship between low-frequency variations in

700hPa geopotential heights and the melt extent is examined. In Part I of this paper, interannual variations in the

strength of particular circulation patterns are examined to understand the interannual variations in mean melt

extent. Once the link between the circulation and surface environment is established, idealized circulation

patterns, called `synoptic types', are identi®ed and related to the melt in order to investigate the physical

mechanisms that govern this relationship. That work is presented in Part II of this paper (Mote, 1998).

The results of this research will enable the scienti®c community to better understand the spatial and temporal

nature of snowpack melt occurrence on the Greenland ice sheet, the role of speci®c atmospheric circulation

patterns in promoting or impeding melt, and how the circulation governs the interannual variations in melt.

BACKGROUND

Interest in relationships between distant `centres of action' in surface climate ®elds, termed teleconnections, dates

back more than 50 years (Walker and Bliss, 1932). In the past two decades, published work in sea-level pressure

and mid-tropospheric teleconnections has become increasingly common (e.g. Horel, 1981; Wallace and Gutzler,

1981; Mo and Livezey, 1986; Livezey and Mo, 1987; Barnston et al., 1991). This body of research was produced

primarily to explain better the behaviour of quasi-stationary, low-frequency atmospheric circulation, particularly

as governed by boundary-layer conditions over the tropical Paci®c Ocean.

The mid-tropospheric teleconnection studies usually extract the teleconnections with a principal components

analysis (PCA) of 700 or 500hPa geopotential height ®elds (e.g. Wallace and Gutzler, 1981; Barnston and

Livezey, 1987). The PCA extracts main modes of variation in the height ®eld, which the investigator usually

attributes to some dynamic forcing mechanism. For the most part, the investigators have been interested in

diagnosing the climatic dynamics, and searching for the cause(s) of the identi®ed modes of variation, rather than

using the teleconnection patterns to explain variations in surface climate.

More recently, the speci®cation of individual teleconnections has been used to describe surface temperature

variations (e.g. Leathers et al., 1991), and the combined strength of various modes of circulation has been used to

explain long-term trends in surface temperature ®elds (Palecki and Leathers, 1993). A modi®ed version of the

approach by Palecki and Leathers (1993) is used in this paper.

METHODS

Principal components analysis (PCA) is used to extract characteristic modes of variation from the daily 700hPa

geopotential height data set, and the resulting component scores are used as predictor variables in multiple

112 T. L. MOTE

# 1998 Royal Meteorological Society Int. J. Climatol. 18: 111±129 (1998)

regression analysis of the daily surface melt extent time series. With this method, one can determine: (i) the total

amount of variance in the melt extent time series accounted for by particular atmospheric teleconnections; (ii) the

teleconnections that are most ef®cient at explaining the variance in the melt extent; and (iii) the degree to which

the teleconnections can explain the trends observed in the melt extent time series by Mote and Anderson (1995).

Both the daily 700hPa height data and the microwave-derived melt extent time series were ®rst ®ltered to

remove high-frequency temporal variations from the data sets. This ®ltering was done for two reasons. First, the

potential errors in the microwave-derived melt extent time series are believed to be greatest in the daily data

(Mote and Anderson, 1995). Secondly, the National Meteorological Center (NMC) gridded data are known to

contain spurious high-frequency (less than 2-day) variability due to the data processing (Blackmon, 1976).

Filtering the atmospheric data for low-frequency variations removes this spurious component.

The National Meteorological Center (now National Center for Environmental Prediction (NCEP)) 700hPa

geopotential height initialized ®elds were used in this study. The data ®elds are based on the NMC ®nal analysis,

and involve a four-dimension assimilation process from data received up to 10h after a rawinsonde launch. The

®nal analyses are on a 1977-point NMC octagonal grid, on which the points are equally spaced on a polar

stereographic map projection. These data are available twice a day from August 1963 to July 1989 on CD-ROM

from the National Center for Atmospheric Research (NCAR, 1990). Coincident melt extent and 700hPa

geopotential height data were available for October 1978 to June 1989. Upper air data ®elds from the NCEP/

NCAR reanalysis project became available only after this project was completed.

Main modes of variation in the 700hPa height ®eld are extracted and used to explain intra-annual and

interannual variations in the surface melt area on the Greenland ice sheet. A 100-point subset of the NMC grid

centred on Greenland and including all of the Arctic Basin and the North Atlantic was used to isolate the

variations in the height ®elds in high latitudes, a region most likely to have an in¯uence on the surface climate of

the Greenland ice sheet (Figure 1). The 100-point subset includes grid-points from every other row and column in

the NMC octagonal grid. The selection of a subset of grid-points from every other row and column serves as a

spatial ®lter on the NMC data set. The spatial autocorrelation present in geopotential height ®elds allows one to

characterize the circulation patterns with relatively low spatial resolution in the height data. A restricted spatial

domain was selected in an attempt to extract teleconnection patterns apparent in other months that may still be

present in the summer, but are `weakened' to the point that they do not appear when performing a rotated

principal components analysis (RPCA) for the Northern Hemisphere (north of 20�N). The principle components

(PC) from this restricted domain are compared with teleconnection patterns that are extracted for the Northern

Hemisphere by (i) comparing maps of PC loadings extracted in this study to those published in the climatological

literature, and (ii) correlating monthly score time series from the teleconnections extracted by the Climate

Prediction Center (CPC, 1997) for the Northern Hemisphere to score time series extracted in this study. The low-

pass ®ltered, daily score time series in this study are averaged for each month and then correlated to the monthly

score time series from CPC. Some caution must be used in this comparison because of the different spatial

domains and time averaging/®ltering processes. Moreover, the CPC analysis does not extract all of the possible

patterns for each month. The correlations are simply used as guidance when assessing which hemispheric

teleconnections the PCs most closely resemble.

Daily geopotential height data for 1200 UTC were selected instead of the 0000 UTC data or daily average

values, because 1200 UTC is centred on the daily window for which the melt extent observations are determined.

The additional data from the 0000 UTC observations are not needed because the low-pass temporal ®lter removes

the high-frequency variations from the time series of height data that otherwise would be removed by averaging

the 0000 and 1200 UTC observations.

In order to isolate only the low-frequency modes of temporal variations in the height ®elds, the subset of

700hPa height data was subjected to a digital ®lter. Blackmon (1976) provides the coef®cients for low-pass

(greater than 10-day variations preserved), band-pass (2�5±6 days) and high-pass (less than 2 days) digital ®lters

in the time domain. The low-pass coef®cients from the 31-point, ®nite impulse response ®lter (Blackmon, 1976)

were used to remove high-frequency variations in the time series of geopotential heights for each grid-point. The

700hPa heights for a period of 31 days, centred on the day of interest, were each multiplied by the coef®cient for

that day and summed to provide the low-pass ®ltered value for a given grid-point on the day of interest. The last

15 days of June 1989 were omitted from the analysis because no height data are available for July 1989.

GREENLAND ICE SHEET AND ATMOSPHERIC TELECONNECTIONS 113


The NMC data has 77 missing 1200 UTC observations during May to August between May 1979 and June

1989. The days immediately before and after a missing observation were also considered to have missing values

because of the relatively large weight placed on those days in the ®lter. If an observation was missing between 2

and 15 days before or after the day of interest, the missing value was ®lled through interpolation of the

immediately adjacent observations. As a result, 156 of the 1291 total possible days were omitted from the

analysis.

Normalization and standardization of daily melt extent values

The daily melt extents as calculated by Mote and Anderson (1995) were normalized and standardized for

comparison with the height data and to perform statistical signi®cance tests. The ice sheet was divided into eight

regions de®ned by topographic barriers, which are nearly identical to regions de®ned by Ohmura and Reeh

(1991) (Figure 2). The regions were de®ned because topographic barriers created by ridges in the ice sheet often

inhibit ¯ow across Greenland and create quite different climates in various portions of the ice sheet, as shown by

Ohmura and Reeh (1991). Moreover, Mote and Anderson (1995) demonstrated that many of these regions exhibit

unique variations in spatial extent of melt.

The daily melt-extent values are potentially bounded at both extremes, at the low extreme by no melt and at the

high extreme by the maximum area of the ice sheet (or topographic region). The maximum area of the ice sheet,

and the maximum area of most regions, do not prove true upper bounds. The melt extent on a given day never

covers the entire ice sheet or most of the topographic regions. However, in Regions 1 and 2 in the south-west,

Figure 1. NMC grid-points used in the principal components analysis

114 T. L. MOTE


melt can cover the entire region. For these two topographic regions, the total surface area of the region is a true

upper bound to the melt extent.

The melt extents are approximately log-normally distributed, so a logarithmic transform was performed

on the melt extent values. Values of zero melt extent were avoided by adding the lowest possible non-zero

melt extent (625km2, the size of one grid-cell in the melt extent grid) to each of the daily values for each region.

The resulting distributions are approximately normally distributed, although slightly skewed toward lower

values.

A mean annual cycle of the normalized, daily melt extent values was produced by averaging a 5-day-centred

window of the log-transformed daily melt-extent values across the 11 years of data (see ®gure 7, Mote and

Anderson, 1995). Because the melt extent values are available only every other day from May 1979 to June 1987,

compared with every day for the remainder of the time period, each missing day's value was interpolated using

data from the preceding and succeeding days. Interpolation of the missing data avoids a mean annual cycle that is

Figure 2. Topographic regions of the Greenland ice sheet, based on those de®ned by Ohmura and Reeh (1991)



biased toward 1988 and 1989. An annual cycle of standard deviations for the daily melt extents was produced

using the same 5-day-centred window across 11 years of data. The transformed daily values were then

standardized to Z-scores for each day. In order to isolate variations in the melt extent longer than 10 days, the

Z-score time series was smoothed with a 10-day-centred moving average.

Principal components analysis

The atmospheric data were subjected to an S-mode PCA, which is used to examine the structure of variations

in a univariate climatic data ®eld from multiple locations over time. Principal components analysis reduces the

number of variables under investigation and allows one to detect and identify groups of interrelated variables

(Shaw and Wheeler, 1985). The S-mode has been used in decomposition and regionalization of univariate

climatic data ®elds. The P-mode PCA, which examines the structure of variations in multivariate climatic data

over time, has been used to regionalize multivariate climatic data (Yarnal, 1984). The Q-mode PCA, which

examines the structure of variations in different climatic indices for numerous locations, also has been used for

climate regionalization (Balling, 1984). In essence, the research presented here is a form of climate

regionalization. An S-mode PCA was used to identify spatially coherent patterns of variance in the 700hPa height

®eld that are evident over time.

The procedure began with a 10061291 data matrix: 100 grid-points for each of the 1135 days (May±August

for May 1979 to June 1989 minus 156 missing days). A 1006100 correlation matrix was calculated. The PCA

extracted modes of variation that occurred in the height ®eld. These modes of variation, called the principal

components, are linear combinations of the original variables. The principle components eliminate the linear

correlations of the original variables, resulting in a reduced, orthogonal data set. The PCA ®rst extracts the

component that explains the greatest portion of variance in the original data, then eliminates that variance from

the next calculation. The component that explains the greatest amount of remaining variance subject to the

condition of orthogonality to preceding components is extracted next. Each successive component is extracted in

the same fashion.

As suggested by Barnston and Livezey (1987), a varimax rotation of the components was performed to aid

interpretation of the spatial pattern of the loadings, and to avoid the appearance of spurious Buell patterns in the

loadings. Richman (1986) used Monte Carlo simulations to demonstrate that orthogonally rotated principal

components are less exposed to sampling error than unrotated components, resulting in increased statistical

stability for the rotated components. The use of unrotated components tends to result in a series of patterns that

are non-robust (Barnston and Livezey, 1987). An orthogonal rotation of the PCA results in patterns that are more

stable, more physically interpretable and more comparable to teleconnection patterns derived from one-point

correlation analysis (Horel, 1981, 1984).

The principal components, which represent a reduced form of the original geopotential height data, have

component scores for each day included in the original data matrix. Component scores represent the strength of a

particular PC (i.e. teleconnection) on a given day. By examining the time series of component scores in relation

to the standardized melt-extent time series, one can determine the role that individual teleconnection patterns play

in the observed variations in melt extent.

Regression analysis of component scores

The component scores are used as independent variables and the standardized melt extent as the dependent

variable in a stepwise multiple regression analysis. Multiple regression analysis describes the predictive ability of

several independent variables for a single dependent variable based on a linear model. There is no a priori reason

to assume a linear relationship between the component scores and standardized melt extent, but it is presumed

that any relationship takes a linear form in order to utilize the powerful statistical tools provided by the general

linear model.

Although only one PCA is used to identify the circulation characteristics for all four summer months, the

regression analysis is conducted individually for each month of May through to August. The strength of

individual modes of variation is explicitly accounted for in the regression analysis through the component scores.

116 T. L. MOTE


Therefore, the strengths of individual modes of variation in atmospheric circulation (i.e. individual PCs) are

allowed to change through the summer. Air mass characteristics and the annual cycle of solar radiation are not

accounted for by the circulation characteristics (i.e. the component scores). By allowing the regression equations

to change for each month of the summer, one may be able to implicitly account for changes in the air mass

characteristics and radiation. The multiple regression analysis was performed for each of the eight topographic

regions, as well as the ice sheet as a whole, to determine if a particular component has more in¯uence on the melt

extent for different regions of the ice sheet.

The partial regression coef®cients for each component are analysed to determine which PCs are most important

in predicting melt extent for individual months and regions. The residuals of the regression equations are used to

determine if particular 700hPa atmospheric teleconnections can explain the interannual trends identi®ed in the

monthly and seasonally averaged melt extents. The residuals of the regression equation describe the variations in

melt extent once the effects of the teleconnections are removed. The trends in the residuals are compared with the

trends in the standardized melt extents for individual regions and the entire ice sheet to determine how well the

teleconnections explain the trends in melt extent.

DISCUSSION

Modes of atmospheric circulation

Nine principal components were extracted based on a scree plot. The nine PCs explain a total of 66�6 per cent

of the variance in the ®ltered 700hPa height ®eld (Table I).

The loading pattern for the ®rst PC (PC1; 13�6 per cent of variance) shows a centre over Baf®n Bay and

southern Greenland (Figure 3) with opposite-signed centres over central North America, the central Atlantic and

Scandinavia. This pattern closely resembles a commonly identi®ed pattern of variation in mid-tropospheric

heights. The dipole pattern of variation of the North Atlantic versus the central Atlantic and western Europe is

generally classi®ed as a mode of atmospheric circulation known as the North Atlantic Oscillation (NAO). The

NAO is the only mid-tropospheric teleconnection to show up in all months of the year, including the summer,

which typically has weak patterns for other commonly found teleconnections (Barnston and Livezey, 1987). The

tendency for warmer (colder) than normal winters in Jakobshavn, Greenland, to be associated with colder

(warmer) than normal winters in Copenhagen, Denmark, was observed as early as the eighteenth century (van

Loon and Rogers, 1978; Rogers and van Loon, 1979). A similar relationship, in which sea-level pressure

anomalies over Iceland and the Azores tend to be opposite signed, was found by Walker and Bliss (1932). More

recently, analogous patterns found in mid-tropospheric heights over the north and central Atlantic Ocean also

have been referred to as the NAO (Wallace and Gutzler, 1981).

The 359-point grid used by Barnston and Livezey (1987), hereafter referred to as BL, and the data grids used in

most other teleconnection studies cover the Northern Hemisphere north of 20�N. Because the teleconnection

Table I. Explained variance (in per cent) for the nine principal componentsextracted from the ®ltered 700hPa height data set

Component Explained variance Cumulative explained variance

1 13�6 13�62 9�1 22�73 7�6 30�34 7�3 37�55 6�6 44�26 6�3 50�57 5�7 56�28 5�3 61�69 5�0 66�6



patterns migrate poleward during the summer (as shown in BL), the variance in the lower latitudes tends to mask

the teleconnection patterns during that season. Therefore, the principal components derived from 100-point

subset used in this study should better describe the teleconnection patterns during the summer. Unfortunately, the

restricted spatial coverage of the 100-point grid also eliminates some of the `centres of action' that are evident in

other teleconnection studies.

The summer NAO as described by BL is a dipole teleconnection with a north centre in Baf®n Bay and a south

centre elongated along the 45�N parallel in the central Atlantic. They found weaker centres, with the same sign as

the Atlantic node, in central North America and Scandinavia. Both of these centres appear in PC1. The NAO

teleconnection has been found in all of the major studies, such as Horel (1981) and Wallace and Gutzler (1981),

hereafter referred to as WG. The NAO teleconnection index produced by CPC (1997) was highly correlated

(r�ÿ0�83, signi®cant at the 99�9 per cent con®dence interval) with the score time series for PC1.

PC2 (Figure 4; 9�1 per cent of variance) resembles a truncated West Paci®c (WP) pattern. The BL West Paci®c

pattern has one node near the Kamchatka Peninsula, with an opposite signed nodes over the western USA and

over eastern Canada. The WP pattern is a primary mode of low-frequency variability over the North Paci®c, and

was found by WG and BL in nearly all months. A north±south dipole, with one node over the Kamchatka

Peninsula and an opposite-signed node over south-east Asia and the western Paci®c is found in spring. Therefore,

strong positive or negative phases of this pattern re¯ect pronounced zonal and meridional variations in the

location and intensity of the entrance region of the Paci®c (or east Asian) jet stream (CPC, 1997). The WP pattern

becomes increasingly wave-like in the summer, and a third, opposite-signed node appears over Alaska and the

Beaufort (CPC, 1997). A weak opposite-signed node may also be present over eastern Canada. PC2 also shows a

Figure 3. Component loadings for PC1

118 T. L. MOTE


strong node near the Kamchatka Peninsula and a weak node in eastern Canada, but it is dif®cult to be certain that

this is the WP pattern because the 100-point grid used here does not include much of the Paci®c. The score time

series from PC2 was signi®cantly correlated with the WP (r�ÿ0�43, signi®cant at the 99 per cent con®dence

interval), the North Paci®c pattern (r� 0�41, signi®cant at the 90 per cent con®dence interval) and the East

Paci®c pattern (r� 0�45, signi®cant at the 99 per cent con®dence interval). This pattern will simply be referred to

as the Paci®c pattern.

The loading pattern for PC3 (Figure 5; 7�6 per cent of variance) strongly resembles the Scandinavian pattern

(SCA) identi®ed by the CPC (1997), called the Eurasian-1 pattern by BL. PC3 has a band of high loadings

throughout the central Arctic and a centre of opposite-signed loadings over the North Sea. The SCA consists of a

primary circulation centre that spans Scandinavia and large portions of the Arctic Ocean north of Siberia. The

Scandinavia pattern has been identi®ed in all months except June and July, and is often associated with blocking

anticyclones over Scandinavia and western Russia in its positive mode (CPC, 1997). The score time series for

PC3 has signi®cant correlations with both the East Atlantic Jet pattern (EA Jet) (r� 0�43, signi®cant at the 95 per

cent con®dence interval) and the Scandinavian pattern (r� 0�39, signi®cant at the 90 per cent con®dence

interval).

The Eurasian-2 pattern of BL and WG, with a strong centre over Finland and opposite-signed loadings

over south-west Europe and near Lake Baykal in central Asia, referred to as the East Atlantic±West

Russia pattern (EAWR) by CPC, is best represented by the loading patterns of PC4 (Figure 6; 7�3 per cent of

variance). The score time series from PC4 has signi®cant correlations with both the EAWR (r�ÿ0�60,




signi®cant at the 95 per cent con®dence interval), and the SCA patterns (r� 0�41, signi®cant at the 90 per cent

con®dence interval).

The loading patterns for PC5 (Figure 7; 6�3 per cent of variance) appear similar to the East Atlantic pattern

(EA) of BL and WG. This pattern includes a vigorous node in the central Atlantic, near 50�N, with a like-signed

centre over the Kara and Laptev Seas and an opposite-signed pattern over the Black Sea and north into eastern

Europe. The Black Sea node is truncated in PC5, but the two like-signed nodes are clearly represented. However,

BL note that the EA pattern, which appears like a southward displaced NAO, does not appear during the summer.

Principal component ®ve also has the same wavetrain appearance across Europe and western Asia as the EAWR

pattern. The score time series from PC5 is correlated with the EAWR pattern (r�ÿ0�59, signi®cant at the 90 per

cent con®dence interval).

PC6 (Figure 8; 5�7 per cent of variance) and PC8 (Figure 9; 5�3 per cent of variance) have similar features.

Both have like-signed nodes over North America and western Europe, with an opposite-signed node over the

central Atlantic Ocean. The North American node in PC6 is located over New England, whereas in PC8 it is

further west, over Hudson Bay. The European node is much stronger in PC6 and centred at approximately 0�

longitude, whereas it is weak and centred at approximately 20�E in PC8. The Atlantic node is stronger in PC8 and

is centred approximately 20� west of its location in PC6. These patterns do not clearly appear individually in the

teleconnection literature, but they are similar to a combination of the Tropical Northern Hemisphere pattern and

Eurasian-1 pattern of BL (a combination they also identi®ed) or the West Atlantic pattern of WG.

PC7 (Figure 10; 5�7 per cent of variance) and PC9 (Figure 11; 5�0 per cent of variance) do not appear at all

related to common teleconnection patterns described in the literature. Principal component seven has a single


120 T. L. MOTE


strong node centred over British Columbia. Principal component nine has a vigorous node centred over the

Greenland Sea, with a weak like-signed node over the western USA and a weak opposite-signed node over the

Canadian Archipelago.

Most of these modes of variation in the Northern Hemisphere have been carefully examined in the past decade.

Although the descriptions or dynamic causes of the teleconnection patterns are not related directly to the research

described here, these descriptions do provide a common language for the community engaged in this research and

provide a starting point for describing the modes of circulation associated with melt-extent variations on the ice

sheet.

Relationship of atmospheric circulation to melt

The ®rst ®ve PCs identi®ed above were entered into 36 different stepwise multiple regression analyses with the

standardized melt extent as the predicted variable. The ®rst ®ve PCs were selected because they were more

clearly associated with hemispheric teleconnection patterns that have been identi®ed in the climatological

literature. The regression equations were generated for each combination of the four summer months and the

eight topographically de®ned regions of the ice sheet. Regression equations were also generated for the four

months and the total melt extent.

The regression analysis was conducted for three reasons. First, the explained variance (multiple coef®cient of

determination) of the regression equation can indicate the degree to which the circulation characteristics are

responsible for variations in surface melt of a given region in a given month. Secondly, the PCs selected by the




stepwise process can indicate the circulation characteristics that are responsible for the explained variation in

snowpack melt extent. Finally, the residuals of the regression equations can be examined to determine if the

circulation can account for positive trend in the melt-extent time series (Mote and Anderson, 1995).

In general, the explained variance is greatest early in the season, and least late in the season (Table II). Tables

III±VI present the standardized partial regression coef®cients (beta values) for the months of May through to

August, respectively. These values indicate the importance of each teleconnection pattern in explaining the melt

extent for individual regions during a given month.

For the entire ice sheet, 42 per cent of the variance can be explained by a combination of PC1 and PC5 in May

(Tables II and III). July has more explained variance than June, 43 to 27 per cent, but from the same components

(Tables IV and V). Melt variations in the southern regions of the ice sheet are better explained by the circulation

early in the season. For example, 51 per cent of the variance of the Region 1 melt extent can be explained by the

circulation characteristics in May (Table III), but this value falls to 26 per cent in June and 20 per cent in July

(Tables IV and V). In contrast, in the north-east (Region 6), only 16 per cent of the variance is explained in May

(Table III), but the explained variance increases to 24 per cent by August (Tables IV±VI). For all regions except

the east (Regions 6 and 7), the NAO (PC1) is always included in the regression equations (Tables III±VI). In

August, PC1 is included in the regression equations for the east Region 7, but the melt extent is inversely related

to the strength of PC1 (Table VI).

Melt variations in the southern regions of the ice sheet, which have enough solar insolation in May for melt to

begin, should be dictated by the circulation characteristics. Although enough solar insolation is available to make


122 T. L. MOTE


melt possible, the melt is marginal, and additional sensible heat provided or denied by changes in the circulation

should prescribe the surface melt conditions. As the season progresses, solar insolation provides enough energy

for melt to occur on most days in the south, and the circulation should begin to play a smaller role. The

increasingly heterogeneous ice sheet, which may include exposed ice and meltwater lakes, also makes the

microwave-derived melt extent less reliable late in the season in regions with heavy melt (Mote and Anderson,

1995). The south-west regions, Regions 1 and 2, may be subjected to periods in which the entire region is

experiencing melt. This upper bound in melt extent may bias the comparison with circulation statistics because

this effect was not accounted for in the normalization process. At more northerly locations, almost no melt occurs

in May. Variations in circulation may be present that would produce extensive melt later in the season, but the

solar insolation is not available to produce melt in May. Therefore, the circulation statistics appear to predict melt

poorly in the northerly regions, such as Region 6, early in the season (Table II).

Melt variations in Regions 3 and 8 are best explained by the atmospheric circulation, whereas Regions 6 and 7

are not well explained. Regions 3 and 8 are similar in that they are large regions, with large areas in the

percolation zone, where the greatest variation in melt extent occurs. The eastern regions, Regions 6 and 7, are

near coastal mountains where local topographic variations are likely have a large in¯uence on the microclimate.

Additionally, these two regions do not receive much melt, which provides fewer extensive melt events to anchor

the regression equations.

The NAO (PC1) has the greatest in¯uence on snowpack melt extent in all regions and months, except for the

east regions. The frequencies at which the other PCs were included in the regression equations were examined to




determine which PCs are most ef®cient at explaining melt variations. PC4 (the EAWR-SCA pattern) appears in

more than 75 per cent of the regression models, primarily in the western and northern regions. PC3, which

represents the EA Jet and SCA patterns, is the least well represented in the regression models, particularly in

June. However, it is the PC that is most likely to appear in the models for the eastern regions. As one would

expect, the PCs with nodes near Greenland (most notably the NAO) are more highly associated with melt extent

than the PCs with more distant nodes.

The regression models demonstrate that about a quarter to two-®fths of the variance in the melt extentÐ

depending on the region and monthÐcan be attributed to characteristics of atmospheric circulation. They do not

make clear, however, if the positive trends in monthly and seasonally averaged melt extents during 1979±1991

can be attributed to changes in atmospheric circulation. The residuals from the regression equations were

compared with the standardized melt extents to determine if the atmospheric circulation is responsible for

interannual trends in melt extent.

The melt extent time series given by Mote and Anderson (1995) was standardized; the standardized melt

extents show a 4�0 per cent annual increase for the period 1979±1989 (compared with a 3�8 per cent increase

when 1990 and 1991 are included). The residuals from the regression equation show only a 1�8 per cent increase

in melt extent over the same period (Figure 12). This suggests that approximately 55 per cent of the trend found

in the melt extent time series for the entire ice sheet between 1979 and 1989 can be attributed to changes in the

strength of preferred modes of low-frequency atmospheric circulation. The NAO, in particular, accounts for

slightly more than half of the variance explained in the interannual trend. The remainder of the trend may be due


124 T. L. MOTE


to changes in air-mass characteristics. A positive trend in surface temperature of the air masses advected over the

ice sheet could result in a positive trend in melt extent, even after removing the effects of circulation.

CONCLUSIONS

A principal components analysis of a 100-point subset of the NMC 700hPa height grid, ®ltered to eliminate

variability with a frequency of less than 10 days, was used to extract nine preferred modes of variation in mid-

tropospheric geopotential heights. The PCs described here are very similar to atmospheric teleconnections

derived with a similar method as described in BL and WG. The NAO accounts for the greatest amount of

variance, followed by PCs that resemble the West Paci®c, the East Atlantic Jet, Scandinavian (Eurasian-1) and

East Atlantic±West Russia (Eurasian-2) patterns.

The NAO, which has a `centre of action' over West Greenland during the summer, is the most highly

correlated teleconnection pattern to the melt-extent time series. Approximately half of the variance in the surface

melt extent for the ice sheet as a whole was explained by using the component scores for these teleconnection

patterns as predictor variables in a multiple regression model. In the southern regions, the circulation

characteristics are more ef®cient at explaining the surface melt variations early in the season, and less so later in

the season. In the northern regions, the opposite is true. The atmospheric circulation was also found to account for

approximately 55 per cent of the interannual trend in melt extent for the entire ice sheet during 1979±1989. In

fact, after removing the variance accounted for by the circulation, the statistically signi®cant trend in melt extent

was no longer signi®cant. This suggests that changes in atmospheric circulation should be considered by

scientists attempting to model the impact of climate change on the mass balance of the ice sheet.




As useful as the circulation statistics are in understanding variations in surface melt, the loading patterns

represent the variance structure of the height ®eld. As such, the individual PCs are often dif®cult to interpret in

terms of the physical mechanisms that govern the relationship between the atmospheric circulation and the

surface melt. Part II of this paper (Motte, 1998) uses a synoptic climatological approach to produce idealized

synoptic types that are more easily interpreted as to their relationship to variations in surface melt extent.


Table II. Explained variance (in per cent) from the regression equationsfor each region and month, where the predictor variables are circulationteleconnections (component scores) and the predicted variables are the

melt extents

Region May June July August

1 51 26 20 192 38 27 33 213 40 27 42 464 31 35 24 405 29 16 20 186 16 19 18 247 34 10 14 158 34 20 46 8All regions 42 27 43 25

126 T. L. MOTE


Table IV. Standardized partial regression coef®cients (beta values) for the ®rst ®ve principal components during June. Thevalues are from nine regression models derived for each of the eight regions and for the entire ice sheet

June beta values

PC Similar patternRegion 1

(SW)Region 2

(SW)Region 3

(W)Region 4

(NW)Region 5

(NE)Region 6

(E)Region 7

(E)Region 8

(SE) All

1 NAO 0�43 0�42 0�38 0�49 0�41 Ð Ð 0�33 0�462 WP Ð Ð ÿ0�13 0�32 Ð ÿ0�26 ÿ0�20 ÿ0�24 Ð3 SCA±EA Jet Ð Ð Ð Ð Ð ÿ0�27 ÿ0�22 Ð Ð4 EAWR±SCA 0�23 0�30 0�29 0�26 Ð ÿ0�20 Ð 0�11 0�275 EA±EAWR 0�15 0�11 0�20 Ð Ð Ð Ð Ð Ð

Table V. Standardized partial regression coef®cients (beta values) for the ®rst ®ve principal components during July. Thevalues are from nine regression models derived for each of the eight regions and for the entire ice sheet

July beta values


(SW)Region 2

(SW)Region 3

(W)Region 4

(NW)Region 5

(NE)Region 6

(E)Region 7

(E)Region 8

(SE) All

1 NAO 0�34 0�43 0�60 0�38 0�33 0�19 0�19 0�48 0�542 WP Ð 0�18 Ð Ð ÿ0�19 ÿ0�13 Ð Ð Ð3 SCA±EA Jet ÿ0�17 ÿ0�18 Ð Ð Ð Ð ÿ0�18 ÿ0�20 ÿ0�104 EAWR±SCA Ð Ð 0�31 0�36 0�33 0�25 0�26 0�23 0�405 EA±EAWR 0�25 0�32 Ð Ð 0�13 ÿ0�21 Ð 0�43 0�15

Table VI. Standardized partial regression coef®cients (beta values) for the ®rst ®ve principal components during August. Thevalues are from nine regression models derived for each of the eight regions and for the entire ice sheet

August beta values


(SW)Region 2

(SW)Region 3

(W)Region 4

(NW)Region 5

(NE)Region 6

(E)Region 7

(E)Region 8

(SE) All

1 NAO 0�18 0�44 0�49 0�35 0�19 Ð ÿ0�17 0�24 0�522 WP Ð Ð 0�18 0�17 ÿ0�46 ÿ0�41 ÿ0�30 Ð Ð3 SCA±EA Jet Ð Ð 0�22 20 0�31 ÿ0�26 Ð ÿ0�15 Ð4 EAWR±SCA ÿ0�06 Ð ÿ0�14 ÿ0�26 ÿ0�40 Ð Ð Ð Ð5 EA±EAWR 0�11 0�25 Ð 0�22 Ð Ð Ð 0�18 0�13

Table III. Standardized partial regression coef®cients (beta values) for the ®rst ®ve principal components during May. Thevalues are from nine regression models derived for each of the eight regions and for the entire ice sheet

May beta values


(SW)Region 2

(SW)Region 3

(W)Region 4

(NW)Region 5

(NE)Region 6

(E)Region 7

(E)Region 8

(SE) All

1 NAO 0�42 0�43 0�60 0�54 0�15 Ð 0�21 0�45 0�532 WP Ð 0�13 Ð 0�16 Ð ÿ0�16 ÿ0�35 ÿ0�16 Ð3 SCA±EA Jet Ð Ð 0�12 Ð 0�40 Ð ÿ0�25 ÿ0�18 Ð4 EAWR±SCA Ð ÿ0�10 ÿ0�16 ÿ0�29 ÿ0�26 ÿ0�26 Ð Ð5 EA±EAWR 0�49 0�37 Ð ÿ0�12 Ð 0�18 Ð 0�21 0�26



ACKNOWLEDGEMENTS

This work was partially funded by NASA grant NAGW-1266 awarded to Mark Anderson, Clint Rowe and Karl

Kuivinen at the University of Nebraska-Lincoln, and NASA grants NGT-30127 and NAGW-4573 awarded to the

author. I gratefully acknowledge the assistance of Mike Palecki and Dan Leathers for their assistance in this

research and the comments of two anonymous reviewers.

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