The Impacts of El Nino on Mid-Atlantic Winter Snowfall with
Regards to North American Climate Variability
Christopher Castellano
The University at Albany, SUNY
ATM 522: Climate Variability & Predictability
Professor Vuille
December 10, 2010
Abstract
The impacts of ENSO and North American climate modes on seasonal snowfall in the
Mid-Atlantic region are investigated. Particular emphasis is placed on the convergent
patterns of El Nino and negative NAO during Northern Hemisphere winter. This study is
motivated by the highly anomalous events associated with the 2009-2010 winter season,
as well as by a general interest in mid-latitude synoptic variability. Principal sources of
Eastern U.S. wintertime climate variability (ENSO, NAO, and PNA) are examined for
potential effects on large-scale flow, cyclone activity, temperature and precipitation
patterns, and regional snowfall. These findings are used to qualitatively infer the
combined influence of El Nino and –NAO within the specified region of interest. A
supplementary statistical analysis is conducted to evaluate the hypothesis and provide a
quantitative assessment of changes in seasonal snowfall at selected stations.
Evaluation of the three climate modes reveals that warm ENSO and –NAO (+PNA)
regimes create synoptic environments and temperature anomalies favorable for increased
East Coast cyclone activity and winter snowfall. Moreover, results from the statistical
analysis indicate that seasonal snowfall in the Mid-Atlantic region is significantly
different (higher) during warm ENSO/-NAO winters, as compared to the long-term
average (1950-2010).
1. Introduction
The year of 2010 has featured abundant extremes across the globe, including recordbreaking snowfall in the Eastern United States, incredible summer heat in Eastern Europe and
Siberia, and catastrophic flooding in Pakistan. Over recent decades, climate scientists have
learned that such seasonal anomalies are often related to the variability of climate modes which
operate on intra-seasonal to multi-decadal time scales. The convergence of different climate
modes can generate the most extraordinary events, especially in the mid-latitudes, where weather
patterns exhibit sensitivity to both tropical and extratropical forcings. One phenomenon of
particular interest is the anomalous 2009-2010 winter weather which greatly impacted millions
of people in the Mid-Atlantic States. Between December and March, a remarkable number of
storms wreaked havoc on the East Coast, leaving behind incredible snowfall amounts in major
cities such as Philadelphia, Baltimore, and Washington, D.C. (Seager et al 2010). Large-scale
dynamics over the Eastern U.S. were clearly altered by one or more components of climate
variability with North American teleconnections. Given the Atlantic corridor’s high population
density, complex infrastructure, and economic importance, significant snowstorms can have
crippling effects on major industries and daily human activities.
Recent papers have discussed the influence of certain oceanic and atmospheric
oscillations (with both tropical and extratropical origins) on North American winter weather.
Specifically, the North Atlantic Oscillation and Pacific North American pattern (extratropical
forcing) are frequently cited as the most important drivers of wintertime temperature and
precipitation regimes over the Northeastern U.S. These perspectives have been substantiated by
Notaro et al. (2006) and Archambault et al. (2008). However, additional research has also
focused on the implications of the El Nino Southern Oscillation (tropical forcing) with regards to
cyclone activity and regional snowfall. As Seager et al. (2010) suggest, this past winter’s record
snowfall likely resulted from the combined influence of El Nino and Northern Hemispheric
climate modes (in particular, a highly negative NAO). Though topics such as extratropical
cyclone variability and winter weather regimes have been thoroughly investigated, it would be
prudent to integrate previous studies and qualitatively evaluate the impacts of an El Nino event
occurring simultaneously with an enhanced NAO and/or PNA pattern. However, since the PNA
does not exhibit complete independence from ENSO (the PNA pattern is often modulated during
stronger ENSO events), this paper will concentrate primarily on the combined effects of ENSO
and the NAO.
2. Methodology
The ultimate goal of this paper is to evaluate the hypothesis that the convergence of El
Nino and a persistently negative NAO produces anomalously high snowfall in the Mid-Atlantic
region. Supporting evidence for any conclusions will be drawn from existing literature and
historical records. Literature reviews will build a foundation for understanding these sources of
climate variability and recognizing how they can determine winter weather regimes and largescale dynamical features. This involves gaining insights to the individual and collective
influence of El Nino, NAO, and PNA on wintertime synoptic patterns throughout the Eastern
U.S. Furthermore, historical records (station-based seasonal snowfall) will provide a means for
quantitatively examining the effects on winter snowfall within the region of interest. These
records contain information that will either substantiate or reject the initial hypothesis, and
should reflect consistency with conclusions in the literature. Statistical analyses will provide an
assessment of how seasonal snowfall has varied under specific conditions and ultimately
compares to the long-term mean. Results which support the hypothesis should reveal statistically
significant differences between scenarios, indicating that seasonal snowfall is enhanced when El
Nino converges with a negative NAO.
a. Literature Review
Hirsch et al. (2001) and Eichler and Higgins (2005) discussed the relationship between
cyclone activity (frequency, track, and intensity) and ENSO variability in order to determine
whether particular phases of ENSO are correlated with anomalous winter behavior. Eichler and
Higgins (2005) considered ENSO-related impacts throughout the entire continental U.S.,
utilizing both NCEP-NCAR (1950-2002) and ECMWF (1971-2000) reanalysis data. Hirsch et
al. (2001) focused solely on the East Coast, using NCEP-NCAR reanalysis data (1948, 1951-97)
to establish certain criteria (spatial domain and intensity) for defining East Coast winter storms.
Hirsch et al. (2001) identified a subset of ENSO events consistent with Trenberth (1997),
whereas Eichler and Higgins (2005) defined events based on Kousky and Higgins’ ENSO
intensity scale (EIS).
Smith and O’Brien (2001) and Patten et al. (2003) were primarily concerned with
regional snowfall variability and its dependence on different ENSO phases. Smith and O’Brien
(2001) examined regional snowfall patterns (statistical distributions) during warm, cold and
neutral ENSO seasons, utilizing daily snowfall data from NCDC’s FSOD dataset (1950-1994).
Patten et al. (2003) categorized snowfall events by intensity (light, moderate, and heavy), then
applied statistical tests to determine whether or not a particular region experiences significant
increases or decreases in the occurrence of each category. The authors used daily snowfall data
from 442 stations in the U.S. Historical Climate Network (1900-1997). Both papers classified
warm, cold, and neutral ENSO years based on the Japanese Meteorological Society (JMA) SST
index. In addition, Smith and O’Brien (2001) proposed the idea of creating qualitative regional
forecasts for winter snowfall based on our current knowledge of ENSO activity.
Notaro et al. (2006) and Archambault et al. (2008) assessed the impact of large-scale
circulation regimes on temperature, precipitation, and synoptic-dynamic characteristics in the
Northeast. Both papers evaluated the individual and collective influence of NAO and PNA
modes on winter weather patterns. Notaro et al (2006) employed observational data (1958-2000)
from NCDC and GHCN to highlight the effects of various NAO and PNA regimes on
temperature and precipitation. The authors also performed simulations (SUNY Albany Regional
Climate Model) of the ten Decembers with the five strongest +PNA and –PNA indices (1980s1990s), and compared temperature and precipitation responses with actual observations.
Archambault et al (2008) obtained daily NAO and PNA indices (based on 500-hPa geopotential
heights) from NCEP-NCAR reanalysis (1948-2003), identified eight large-scale flow regimes,
and studied the associated cool-season precipitation responses (precipitation data from NCEP
Unified Precipitation Dataset). Moreover, they examined a subset of major 24-hr precipitation
events, hoping to gain insights as to how different NAO and PNA regimes determine the
dynamical processes associated with extreme events. As previously mentioned, ENSO activity
can modulate the PNA, making it difficult to isolate the two climate modes and view the PNA as
completely independent from ENSO. The influence of ENSO on the PNA remains unclear in the
present day, with some studies demonstrating significant in-phase behavior (Yu, 2007), and
others finding evidence that the PNA phase is generally unrelated to ENSO (Straus and Shukla,
2002). A compromising viewpoint suggests that although the PNA is an intrinsic mode of
variability in the Northern Hemisphere with distinct spatial characteristics, its pattern may be
modified during ENSO events via tropical-extratropical connections. In addition, +PNA (-PNA)
patterns exhibit a tendency to occur during warm (cool) ENSO phases (Yu, 2007).
Finally, Seager et al. (2010) employed a unique approach to determine whether or not this
past winter’s anomalous snowfall could be attributed to the convergence of El Nino and a
persistently negative NAO. First, the authors evaluated the individual relationships between
seasonal snowfall (NCDC, 1950-1999) and ENSO and NAO indices. Later, they derived a new
NINO-NAO index, based on the difference between the standardized anomalies of the Nino 3
and NAO indices. In addition, they performed correlations and regressions for other fields,
including 850-hPa temperatures and 300-hPa transient eddy meridional velocity variance
(NCEP-NCAR reanalysis, 1951-2009). Lastly, the authors applied the results of their statistical
analyses and created a “hindcast” for the 2009-2010 winter season. Many of the ideas and
motivations expressed in Seager et al. (2010) resonate with those in this paper.
The results of these individual studies will be discussed in greater detail throughout
Sections 3, 4, and 5. Sections 3 & 4, respectively, will examine the influence of El Nino and
Northern Hemisphere climate modes on winter patterns in the Eastern U.S. Section 5 will
synthesize prior research with background meteorological knowledge, in order to objectively
evaluate the combined effects of El Nino and negative NAO. Specifically, this section will
address synoptic-dynamic features, cyclone activity, temperature and precipitation patterns, and
emergent snowfall characteristics in the region of interest.
b. Statistical Analysis
A supplementary statistical analysis of historical records (NCDC local climatological
data) will serve as a practical approach to determining the seasonal response of snowfall in the
Mid-Atlantic region. These records contain seasonal snowfall data (July-June) which will enable
a quantitative assessment of snowfall tendency for various stations. Seven stations have been
chosen based on their geographic location and high sensitivity to synoptic weather patterns and
extratropical cyclone variability during winter. Standardized anomalies of ENSO and the NAO
will be employed in order to define certain scenarios of seasonal climate variability. ENSO
winters will be identified according to the latest CPC definition (Nino 3.4 index), whereas –NAO
winters will be identified using the monthly PC NAO index. Since the CPC values are
unavailable before 1950, the relevant time range of data has been limited to 60 years, beginning
with the 1950-51 winter, and ending with the 2009-2010 winter. Of particular interest is the
detection of anomalous behavior under convergent patterns of El Nino and persistently negative
NAO. Due to the relatively small sample size and inherently large annual variations of seasonal
snowfall, a statistical test is required to measure the actual significance of any observed
differences.
3. Influence of ENSO on Winter Patterns in the Eastern U.S.
a. Synoptic Characteristics
As Eichler and Higgins (2005) suggest, ENSO-related changes in tropical convection
patterns and SST anomalies can drastically alter mid-latitude dynamics over North America. In
particular, enhanced meridional thermal gradients during El Nino events support an intensified
subtropical jet, whereas reduced thermal gradients during La Nina events favor a prominent polar
jet. These teleconnection patterns propagate eastward and have major impacts on cyclone
activity throughout the continental U.S.
Eichler and Higgins (2005) explored the relationship between ENSO and regional storm
track frequencies. Using reanalysis and ERA-40 data to identify individual storms, the authors
investigated changes in cyclone behavior under warm and cold ENSO episodes. Figure 1
illustrates the mean JFM storm-track frequency for various ENSO conditions (EIS > 3, 0 < EIS <
3, EIS = 0, -3 < EIS < 0, and EIS < -3). These composites indicate that the greatest cyclone
frequency occurs near the Mid-Atlantic and New England coasts during El Nino winters. As the
ENSO index becomes more negative, the regional maximum off the East Coast shifts
northeastward. La Nina winters feature enhanced cyclone frequency over the Great Lakes region
and southeastern Canada (Canadian Maritimes). Figure 2 displays the mean difference in JFM
storm-track frequency for strong El Nino (EIS > 3) and strong La Nina (EIS < -3) episodes, with
respect to neutral winters. Both reanalysis and ERA-40 data demonstrate a definitive increase in
extratropical cyclones near the Gulf of Mexico and along the East Coast during strong El Nino
events. Moreover, El Nino conditions suppress cyclone activity in southern Canada, the northern
U.S., and the Central Plains. Since cyclogenesis is preferred on the cyclonic shear side of upper-
level jet cores, El Nino favors cyclone formation along the Southeast and Mid-Atlantic coasts,
rather than downstream of the Rocky Mountains (lee cyclones). Strong La Nina episodes depict
a nearly opposite scenario, with decreased storm frequency in the Southeast and Mid-Atlantic
region, and a noticeable increase throughout the Midwest and Great Lakes region.
Hirsch et al. (2001) performed a statistical analysis of East Coast Winter Storm
frequencies with respect to ENSO polarity over the Oct-Apr (active mid-latitude storm season)
and Dec-Feb (winter season) periods. The authors compared warm and cold ENSO seasons
against neutral seasons, as well as identified different classifications of ECWS based on their
tracks (southern, northern, and full-coast storms). Both periods (Oct-Apr and Dec-Feb) revealed
statistically significant increases in total ECWS during El Nino episodes. Hirsch et al. (2001)
found a 25% increase in ECWS (p = 0.02) for the Oct-April period, and an impressive 44%
increase in ECWS (p = 0.01) for the Dec-Feb period. Moreover, the greatest effects are
manifested in storms traversing the entire East Coast (p = 0.036), whose frequency increases by a
remarkable 75%. When considering five unusually strong El Nino events, increases in total and
full-coast ECWS remained significant at p = 0.10. With the exception of southern storms,
comparatively weaker and less coherent correlations exist between La Nina and ECWS.
Nevertheless, Hirsch et al. (2001) identified a consistent decrease in southern ECWS, especially
during the Dec-Feb period (p = 0.028). Strong La Nina conditions also favor enhanced northern
storm activity for the Oct-Apr period (p = 0.06). Figure 3 illustrates the categorical changes in
ECWS frequency and accompanying statistical values (Dec-Feb period).
These two studies draw similar conclusions concerning the teleconnections between
ENSO and extratropical cyclones over the Eastern U.S. First and foremost, El Nino conditions
are associated with greater cyclone activity along the East Coast, primarily within the Southeast
and Mid-Atlantic sectors. La Nina conditions inhibit cyclogenesis near the Southeast coast, with
the highest concentration of storm tracks observed in northern regions (Great Lakes, northern
New England, and Canadian Maritimes). Such changes in cyclone behavior may be largely
explained by the ENSO-induced modulation of large-scale circulation patterns and upper-level
jet characteristics. El Nino episodes support an intensified and northward-displaced subtropical
jet extending into the Southeast. Moreover, stronger El Nino events can theoretically modify the
PNA pattern by encouraging negative height anomalies and higher-amplitude troughs in the
Eastern U.S (more positive PNA index). La Nina episodes generally feature an enhanced and
northward-displaced polar jet, along with the absence of a well-defined subtropical jet. As
previously mentioned, cyclogenesis typically occurs on the cyclonic shear side of these jet cores
(right-entrance or left-exit region). Since cyclone frequencies through the U.S. are influenced by
ENSO behavior, and ENSO operates on interannual time scales, one may infer that ENSO
explains some of the interannual variability in ECWS. Hirsch et al. (2001) performed a spectral
analysis to determine the preferred periodicity of ECWS and found the greatest variance at
frequencies of 2.3, 2.8, 3.4, 4.8, and 10.2 years. This evidence suggests a meaningful connection
between East Coast cyclones and the interannual variability of ENSO.
b. Regional Snowfall Impacts
Past studies have established the notion that ENSO variability can influence winter
snowfall patterns in specific regions of North America. Smith and O’Brien (2001) and Patten et
al (2003) investigated the relationship between ENSO phase and seasonal snowfall
characteristics within the continental U.S. Both papers demonstrated that snowfall in the MidAtlantic and Northeast exhibits sensitivity to ENSO, particularly during El Nino episodes.
Smith and O’Brien (2001) analyzed ENSO-related changes in statistical snowfall
distributions for the early-winter (Oct-Jan), mid-winter (Dec-Feb), and late-winter (Feb-Apr)
periods. The authors created “scaled” snowfall quartiles by comparing the warm, cold, and
neutral phase quartiles with those calculated from the 1950-1994 climatology. Afterward, these
scaled quartiles were used to create statistical composites of regions with internally similar
snowfall distributions. For each season (Oct-Dec, Dec-Feb, Feb-Apr), they applied a two-sample
median test to determine statistically significant differences in median snowfall. Smith and
O’Brien (2001) revealed that the greatest snowfall discrepancies in the Mid-Atlantic and
Northeast occur during mid-winter. Specifically, the region most affected extends from southcentral Virginia to northeastern New York, and encompasses much of the I-95 corridor between
Washington, D.C. and New York City. Dec-Feb warm phase snowfall substantially exceeds
climatology (1950-1994), neutral phase snowfall, and cold phase snowfall at the 25th, 50th, and
75th percentiles. For instance, when considering warm versus neutral events, New York City’s
mid-winter snowfall is 14 cm (18 cm) greater at the 50th (75th) percentile. Despite these
differences, the warm phase increases did not reflect significance at the 95% confidence level.
Smith and O’Brien (2001) acknowledged that mid-winter snowfall exhibits rather large
variability during El Nino years, a factor likely responsible for the failed significance test. The
authors also found insignificant, yet consistent decreases at all quartiles during La Nina episodes.
Hoping to offer a qualitative explanation for the observed snowfall distributions, Smith
and O’Brien (2001) employed a schematic representation of the 300-hPa jet stream (1947-1993
NCAR analyses) and associated temperature anomalies (Sittel, 1994). As Figure 4 suggests, La
Nina conditions feature a prominent polar jet extending from the Pacific Northwest, through the
Northern Plains, and into the Mid-Atlantic region. Northerly storm tracks and positive
temperature anomalies may collectively inhibit snowfall by placing the Mid-Atlantic on the
warm, anticyclonic shear side of many cyclones. During El Nino, an intensified subtropical jet
dominates and shifts poleward over the northern Gulf of Mexico and Southeast coast.
Additionally, the polar jet weakens due to an amplified ridge-trough pattern between the
Northwest and Eastern U.S. The resulting southward displacement in storm tracks places the
Mid-Atlantic and Northeast sectors on the colder, cyclonic shear side of cyclones, thereby
increasing the potential for snowfall. However, as Smith and O’Brien (2001) emphasize, other
sources of extratropical climate variability (NAO) can influence the large-scale dynamics which
govern cyclone activity, and thus modulate storm tracks during any given El Nino episode.
Because the type of precipitation (frozen versus liquid) is extremely sensitive to East Coast storm
tracks, the interaction of ENSO and Northern Hemisphere climate modes likely contributes to
increased snowfall variability in warm ENSO years.
Motivated by the prior research in Smith and O’Brien (2001), Patten et al. (2003)
examined the impacts of ENSO on the frequency of snow events by intensity (light, moderate,
and heavy). The authors considered 442 USCHN stations which met certain criteria regarding
data completeness (≤ 1% missing daily data) and cool-season frequency of total snow days (at
least 20 seasons with greater than 15 snow days). Unfortunately, these restrictions excluded the
southern portion of the Mid-Atlantic region (Virginia, Maryland, and Delaware) from their
study. Average frequencies of light, moderate, and heavy snow events (measured as the number
of days within the Nov-Mar period) were calculated for warm, cold, and neutral ENSO phases.
Independent significance tests (individual stations) and field-significance tests (regional
sampling of these stations) were used to detect statistical differences between warm/cold ENSO
events and neutral ENSO events. Patten et al. (2003) found that La Nina episodes have little
influence on light snow events, but are associated with decreased occurrences of moderate and
heavy snow events throughout the Northeast Corridor (including eastern Pennsylvania, northern
New Jersey, and southeastern New York). Overall, 82% of qualifying “Northeast” stations
experienced fewer heavy snow days, and 47% have statistically significant decreases (90%
confidence). El Nino conditions suppress both light and moderate snowfall events, yet
encourage generally higher frequencies of heavy snow days. Though statistically insignificant,
55% of qualifying Northeast and New England stations revealed an increase in heavy snow
events (see Figure 5), suggesting that as much as 1.25 additional heavy snow days may be
reasonably expected during warm ENSO periods.
These rather complex results encouraged Patten et al. (2003) to discuss the mechanisms
responsible for observed changes. In particular, the authors noted that coastal storms play a
crucial role in determining both precipitation type and intensity. Since El Nino triggers
increased cyclone activity along the East Coast, the Mid-Atlantic and Northeast sectors are more
likely to experience heavy precipitation events (Hirsch et al., 2001). Depending on the exact
storm track, the most significant snowfall will either occur inland or adjacent to the coast.
Additionally, cold ENSO phases show a preference for zonal storm tracks throughout the
northern U.S., placing the Mid-Atlantic region within the warm sector, and minimizing the
diabatic influence of the Gulf of Mexico and Atlantic Ocean (moisture supply, thermal
gradients). Eichler and Higgins (2005) created composites of JFM total precipitation and JFM
precipitation close to storm tracks over the continental U.S. Their results illustrated that the
heaviest precipitation occurs over the Southeast and along the East Coast during El Nino, with
East Coast cyclones accounting for the largest fraction of near-storm precipitation. As the ENSO
polarity becomes more representative of La Nina, the area of heaviest precipitation migrates
away from the East Coast, indicative of decreased cyclone activity.
4. Influence of PNA/NAO on Winter Patterns in the Eastern U.S.
a. Synoptic Characteristics
Notaro el al. (2006) utilized observational data (1958-2000) to describe PNA and NAOrelated impacts on large-scale circulation and jet characteristics during early winter (December).
Figure 6 illustrates the mean December mid-tropospheric flow for the 10 most negative and 10
most positive PNA and NAO indices (derived from the 500-hPa wind field). This diagram
implies that the PNA (NAO) index is correlated with positive (negative) height anomalies
throughout the Eastern U.S. Moreover, the upper-level height field exhibits much greater
sensitivity to changes in the PNA index, with a highly amplified ridge-trough pattern during
+PNA winters. One may also deduce that +PNA/-NAO regimes generate the deepest troughs,
whereas –PNA/+NAO regimes encourage more zonal flow. Striking similarities exist between
the December PNA pattern and Smith and O’Brien’s (2001) representation of the 300-hPa polar
jet (by ENSO phase). Positive (negative) PNA patterns resemble warm (cold) ENSO episodes,
thus suggesting a physical connection between ENSO and the PNA (in-phase variability).
Notaro et al. (2006) found significant negative correlations between 1) the PNA index and polar
jet latitude and 2) the NAO index and polar jet strength. Positive PNA Decembers featured a
southeastward-displaced 250-hPa jet core, while negative NAO Decembers revealed an
intensified 250-hPa jet core. As expected, the most pronounced patterns emerged during +PNA/NAO regimes. Mean 250-hPa scalar winds increased from 35 ms-1 to 50 ms-1 (+PNA/+NAO vs.
+PNA/-NAO), with the jet maximum shifted to the southeast (off the Mid-Atlantic coast).
Archambault et al. (2008) also investigated cool-season (November-March) PNA and
NAO teleconnection patterns within the Eastern U.S. The authors sought to identify important
synoptic-dynamic features associated with major 24-hr precipitation events during various largescale flow regimes (+PNA,-PNA,+NAO, and –NAO). Contrary to Notaro et al. (2006),
Archambault et al. (2008) used daily PNA and NAO indices to classify these precipitation
events. Qualitative results are illustrated in Figure 7, with an emphasis on 500-hPa height
anomalies, 300-hPa jet locations, and 850-hPa LLJ orientations. These composites reveal
negative height anomalies (amplified troughs) throughout the Eastern U.S. during both +PNA
and –NAO regimes. Additionally, the upper-level jet core is typically situated over the
Southeastern U.S., creating an environment favorable for cyclogenesis along the Mid-Atlantic
coast (left-exit region). During –PNA and +NAO regimes, positive height anomalies appear
over much of the western Atlantic Ocean. Reduced ridge-trough patterns also encourage
northern, zonal storm tracks, while inhibiting cyclonic activity along the Southeast and MidAtlantic coasts. A comparison of 300-hPa jet locations revealed the most dramatic differences
between +NAO and –NAO regimes. The prominent +NAO jet extended from northern New
England to the Canadian Maritimes, implying a significant poleward shift in cyclone activity
within the Eastern U.S.
b. Temperature and Precipitation Patterns
Based on the premise that large-scale circulation patterns can influence regional climate
variability, Notaro et al. (2006) evaluated the effects on temperature and precipitation in the
Northeast. As shown by Figure 8, the authors determined correlations between December statemean temperatures and both indices (1958-2000 period). The PNA (NAO) index was negatively
(positively) correlated with state-mean temperature throughout the entire region. Correlations
were significant at 95% confidence and differed slightly between the PNA and NAO. For the
Mid-Atlantic states (New York, New Jersey, Pennsylvania, Delaware, Maryland, and Virginia), r
≤ -0.54 and r ≥ 0.45, respectively. These correlations suggest that +PNA/-NAO regimes
generate the coldest anomalies, whereas –PNA/+NAO regimes produce the warmest anomalies.
Notaro et al. (2006) also examined changes in maximum temperatures for 768 individual stations
(NCDC and GHCN data), and found generally similar relationships with the PNA and NAO.
One may speculate that these temperature patterns have profound implications for the type of
precipitation (frozen versus liquid) falling in the Mid-Atlantic region during early winter.
Substantially weaker correlations existed between December precipitation and the PNA/NAO
indices, especially in the southeastern Mid-Atlantic region. Although +PNA/-NAO winters are
often characterized by anomalously cold and dry conditions over the Northeast (amplified upperlevel trough), the large-scale circulation may actually permit increased cyclone activity near the
Mid-Atlantic coast.
Archambault et al. (2008) focused primarily on cool-season precipitation characteristics
during various PNA/NAO regimes. Figure 9 demonstrates the changes in cool-season
precipitation amounts (standard anomalies based on 32 Northeast stations) for all eight regimes.
Though differences were relatively small, this diagram reveals that –PNA (+PNA) patterns are
associated with positive (negative) precipitation anomalies, with the greatest correlations in late
winter (February and March). Moreover, PNA-related positive (negative) anomalies are
enhanced during +NAO (-NAO) regimes (significance at the 99% level). Considering the subset
of major 24-hr precipitation events, the authors identified 26 (27) positive (negative) PNA
regimes and 30 (21) positive (negative) NAO regimes. Significant snowfall accounted for 23%
(26%) of the +PNA (-PNA) events and 17% (33%) of the +NAO (–NAO) events. This suggests
that the potential for major Northeast snowstorms depends largely on the prevailing NAO
pattern. Despite the negative precipitation anomalies associated with +PNA/-NAO regimes,
colder temperatures increase the probability for snowfall (versus rain). Archambault et al.
(2008) discussed various mechanisms linked to enhanced vertical motion in various PNA/NAO
patterns. Such factors include cyclonic vorticity advection, ageostrophic transverse circulations,
and the low-level jet. Cyclonic vorticity advection encourages ascent during +PNA, +NAO, and
–NAO events, but plays an insignificant role in –PNA events. Thermally direct circulations
force ascent in +NAO events (surface cyclone near right entrance region), whereas thermally
indirect circulations force in –NAO events (surface cyclone near left-exit region). +PNA and
–PNA events exhibit a more complicated, coupled-jet structure, which supports vertical motion
via both thermally direct and indirect circulations. Near the East Coast, the LLJ is important for
meridional moisture transport during cool-season cyclones. For +PNA and –NAO events, an
extended LLJ over the Atlantic Ocean enhances both diabetic heating and the low-level
advection of warm, moist air. Considering these mechanisms in conjunction with previously
determined temperature patterns and jet characteristics, +PNA/-NAO regimes would
theoretically create the most favorable conditions for significant snowfall in the Mid-Atlantic
region.
5. Convergence of ENSO and NAO
Throughout the previous two sections, the impacts of ENSO and Northern Hemisphere
climate modes were discussed independently. In reality, different sources of climate variability
may interact to modulate mid-latitude teleconnections and even generate extreme events.
Characterized by El Nino conditions, an enhanced +PNA/-NAO regime, and unprecedented
snowfall, the 2009-2010 winter season exemplifies this notion. This section will combine prior
research with conventional knowledge, and evaluate the collective influence of El Nino and
negative NAO on Eastern U.S. winter patterns. Due to significant uncertainty about the role of
ENSO in modifying the PNA pattern, the PNA is given no further consideration.
a. Large-Scale Flow and Synoptic Features
Past studies (Hirsch et al., 2001; Smith and O’Brien, 2001; Eichler and Higgins, 2005;
Notaro et al., 2006; Archambault et al., 2008) have examined the influence of ENSO and the
NAO on large-scale circulation patterns and synoptic-dynamic features over the Eastern U.S. El
Nino winters often feature an enhanced subtropical jet and weakened northerly jet. Negative
NAO regimes are associated with a deeper trough in the Eastern U.S., thereby restricting cyclone
development further south. Figure 10 illustrates ENSO and NAO-related changes in the upperlevel height field. Seasonal (Dec-Mar) regressions of 250 mb (300 mb) geopotential height with
the Nino 3.4 (NAO) index are expressed in meters, using 1949-2006 NCEP/NCAR reanalysis
data. El Nino episodes induce small negative height anomalies (> 15 m for Nino 3.4 = +1.0)
throughout the southern U.S., whereas –NAO regimes cause larger negative height anomalies (>
40 m for NAO = -1.0) in the Mid-Atlantic, Northeast, and northern Ohio Valley. Based on
observed jet characteristics and storm track behavior, the convergence of El Nino and –NAO
suggests greater cyclone activity along the Southeast and Mid-Atlantic coasts. Figure 11 depicts
seasonal composites (Dec-Mar) of the 300 mb scalar wind speed, calculated from NCEP/NCAR
reanalysis. One composite represents +ENSO/-NAO winters (defined in the next section), while
the other reflects the 1951-2010 climatology. These composites reveal significant changes in the
location and strength of the 300-hPa jet core during +ENSO/-NAO winters. In particular, the
mean position of the jet streak shifts from southeast of Long Island (climatology) to east of Cape
Hatteras, while wind speeds increase from roughly 42 ms-1 to nearly 45 ms-1. This synoptic
setting places the Mid-Atlantic states on the cyclonic-shear side of upper-level jet streaks and in
the cold region of northeasterly tracking surface cyclones.
b. Potential Impacts on Mid-Atlantic Snowfall
Considering the changes in large-scale circulation, dynamical features, and temperature
patterns, one may reasonably infer that convergent ENSO and NAO regimes have profound
implications for winter snowfall in the Eastern U.S. Motivated by the 2009-2010 snowfall
anomalies, Seager et al. (2010) produced correlation and regression maps based on the Dec-Mar
mean Nino 3 index, NAO index, and NINO-NAO index. They found significant negative
correlations (p = 0.01) between Dec-Mar snowfall and the NAO index in the Mid-Atlantic
region, and positive correlations (though less coherent) with the Nino 3 index along much of the
East Coast. Moreover, the regression of Dec-Mar snowfall on the NINO-NAO index revealed
large positive values concentrated within the coastal Mid-Atlantic sector. These results (see
Figure 12) emphasize that positive snowfall anomalies are generally favored by more positive
ENSO and more negative NAO conditions. Seager et al. (2010) also constructed regressions of
Dec-Mar mean 850 mb temperature on all three indices. Although ENSO polarity has little or no
influence on temperature patterns, the NAO regression suggests significant cold anomalies
during –NAO regimes. The combination of cold temperature anomalies and more southerly
storm tracks thus encourages increased snowfall over the Mid-Atlantic region, particularly near
the Atlantic Coast. Returning to the composites in Figure 11, changes in the location of the 300
mb jet core support cyclogenesis near the Southeast coast, with significant snowfall preferred on
the northwest side of northeasterly tracking cyclones. In addition, the location of the left-exit
region implies a thermally indirect circulation near the Mid-Atlantic coast and low-level
transport of warm, moist air over the Atlantic. Both factors enhance vertical motion and
precipitation in East Coast cyclones, and theoretically, encourage major snowfall events in the
Mid-Atlantic region.
6. Statistical Analysis
Conclusions from previous studies provide a solid theoretical basis for increased MidAtlantic snowfall during warm ENSO and negative NAO winters. However, if one desires to
evaluate the physical significance of these relationships, one must conduct a statistical analysis.
One approach (Seager et al., 2010) involves determining statistical correlations and regressions
between seasonal snowfall and the combined ENSO/NAO pattern. A simple alternative, which
will be further explored in this section, uses certain criteria to define ENSO and NAO regimes,
and then compares mean snowfall for select winters against background climatology.
a. Procedure
The second approach requires a consistent philosophy for identifying seasons
representative of specific ENSO and NAO regimes. In this case, we are primarily concerned
with winters exhibiting El Nino conditions and negative NAO persistence. El Nino events were
identified based on the NOAA/CPC definition, which requires standardized SST anomalies ≥ 0.5
C (3-month running mean) in the Nino 3.4 region for 5 consecutive “seasons”. Because the CPC
ONI (Oceanic Nino Index) values are unavailable before 1950, the analysis is restricted to the
1950-2010 period (60 winters). Between 1950 and 2010, 19 winters met the above criteria for El
Nino. Negative NAO regimes were defined as winters in which the monthly PC NAO index ≤
-0.5 for at least three months between December and March. Though slightly different from
conventional methods which use seasonal means, this technique provides an assessment of
intraseasonal persistence in an otherwise low-frequency oscillation. Sixteen winters met the
–NAO criteria, 8 of which occurred during El Nino episodes (1958, 1964, 1969, 1970, 1977,
1987, 2005, and 2010).
After determining the eight +ENSO/-NAO seasons, the next step involved a comparison
of mean snowfall during these winters against the 60-year climatology. Seven stations were
chosen based on similarities in geographic location and seasonal snowfall variability; KNYC
(New York, NY), KPHL (Philadelphia, PA), KMDT (Harrisburg, PA), KILG (Wilmington, DE),
KBWI (Baltimore, MD), KDCA (Washington, D.C.), and KRIC (Richmond, VA). The selection
of these particular stations also highlights the emphasis this study places on the densely
populated I-95 corridor. A one-sample mean Student’s t-test was performed to assess the
statistical significance of any differences between the eight-season sample and 60-year
climatology. Since the hypothesis predicted increased snowfall during +ENSO/-NAO winters,
this analysis considered only one-sided significance values.
b. Results
Table 1 presents the mean snowfall (eight +ENSO/-NAO seasons versus climatology),
percent change, t-statistic, and one-sided significance level (7 degrees of freedom) by station.
Every station revealed definitive increases (greater than 25%) in snowfall during +ENSO/-NAO
winters. The section of the I-95 corridor between Philadelphia, PA and Washington, D.C. is
especially sensitive, with snowfall increases exceeding 53% at KPHL, KILG, KBWI, and
KDCA. Moreover, the snowfall increases for all seven stations were significant at the 90% level
or higher (97.5% for KNYC and 95% for KILG). These results exhibit consistency with past
studies and provide physical evidence that supports the initial hypothesis. However, one must
recognize several caveats and exercise caution before making bold statements about any given
winter. First, because these results are based on rather small sample and population sizes, they
lack power in terms of reproducibility. Second, although these stations are fairly representative
of Mid-Atlantic, their general proximity to the Atlantic coast may fail to capture the entire scope
of snowfall changes throughout the region. Moreover, the mean snowfall values themselves give
no indication of how subtle differences in storm tracks can yield remarkable discrepancies
between individual stations (snowfall at each station may respond differently during any one
month or season). Third, seasonal snowfall during the eight +ENSO/-NAO winters exhibits very
large variability, particularly between Washington, D.C. and Philadelphia, PA. Standard
deviations for the eight-winter sample exceeded 17.0 at KDCA, KBWI, KILG, and KPHL.
Finally, the analysis does not offer any insights concerning the dependence of seasonal snowfall
on the magnitude of ENSO and NAO events. Snowfall patterns during particularly enhanced
ENSO/NAO regimes (2009-2010 winter) may be drastically different from those in winters
defined by weaker ENSO/NAO regimes.
7. Discussion
Sources of North American winter climate variability have been discussed, with an
emphasis on large-scale dynamics, temperature patterns, and potential snowfall responses. Prior
research and individual efforts were synthesized to objectively determine the separate and
combined impacts of ENSO, the NAO, and the PNA. These findings suggest that ENSO-related
variability is primarily manifested in mid-latitude jet behavior and cyclone activity throughout
the Eastern U.S. The PNA and NAO modes are connected to the upper-level ridge-trough
pattern, jet streak dynamics, temperature anomalies, and to a lesser extent, precipitation
characteristics. Based on these individual relationships, one may reasonably conclude that El
Nino, +PNA, and –NAO regimes create environments which favor increased snowfall within the
Mid-Atlantic region. In particular, a convergent El Nino and +PNA/-NAO pattern suggests the
greatest potential for anomalously high snowfall amounts. Though not included in the statistical
analysis, it may be of future interest to further investigate the role of the PNA, especially if new
research offers convincing evidence of PNA-ENSO independence.
The Mid-Atlantic region is quite vulnerable to shifts in large-scale flow regimes during
winter. Thus, a more precise understanding of the interactions between ENSO, NAO, and PNA
variability is necessary to 1) diagnose intraseasonal weather patterns and 2) issue qualitative
long-range forecasts for cyclone activity, temperature, and precipitation (especially snowfall).
Future research could be highly beneficial for the Mid-Atlantic region, where transportation and
are profoundly affected by winter climate variability.
References
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Figures
Fig 1. Eichler and Higgins (2005). JFM storm-track frequency binned into 5° latitude × 5°
longitude boxes composited by ENSO phase. Shading denotes average storm frequency for each
winter with a contour interval of two storms. Composites are done for (a) EIS ≥ 3, (b) 0 < EIS <
3, (c) EIS = 0, (d) −3 < EIS < 0, and (e) EIS ≤ −3.
Fig 2. Eichler and Higgins (2005). Average JFM storm-track frequency difference for strong El
Niño (EIS ≥ 3) minus neutral for (a) reanalysis data and (b) ERA-40 data, and strong La Niña
(EIS ≤ −3) minus neutral for (c) reanalysis and (d) ERA-40 data. Positive (negative) values are
indicated by dark (light) shading and are drawn for a contour interval of 0.3
Fig 3. Hirsch et al (2001). Test statistics, t, comparing the average frequency anomalies of
ECWS during ENSO months to those during neutral months over the Dec-Feb period. Positive
(negative) values indicate a(n) increase (decrease) in frequency anomalies. The 5% level of
significance based on a two-tailed hypothesis test using the normal distribution is denoted for
reference. The p values obtained using resampling appear on selected bars.
Fig 4. Smith and O’Brien (2001). Schematic representation of midwinter surface temperature
anomalies (1°C contours with respect to neutral years) and mean 300-hPa jet stream positions
during (a) cold and (b) warm ENSO phases. Dominant jets are noted by thick arrows and weaker
jets with thin arrows (adapted from Smith et al. 1998). Temperature anomaly patterns are from
Sittel (1994).
Fig 5. Patten et al (2003). Percentage of stations, within each of the 10 geographic regions, that
show increases ≥0.25 sd/w or decreases ≤−0.25 sd/w for heavy snowfall during (a) cold- and (b)
warm-ENSO-phase winters relative to neutral winters. Shading denotes percentage of stations for
which the equivalence of the extreme vs. neutral-phase snowfall frequencies are rejected at the
95% (dark gray) and 90% (light gray) levels.
Fig 6. Notaro et al (2006). Schematic of the typical December midtropospheric flow pattern
during positive and negative phases of both the PNA and NAO patterns, based on 500-hPa winds
from the NCEP–NCAR reanalysis data (monthly winds computed from 6-hourly data). The 10
Decembers with the most positive PNA (NAO) index are compared with the 10 with the most
negative PNA (NAO) index for 1958–2000. The flow pattern was estimated based on mean
streamlines and isotachs.
Fig 7. Archambault et al (2008). Synoptic-scale patterns for the onset of major 24-h cool-season
Northeast precipitation events occurring during (a) positive NAO, (b), negative NAO, (c)
positive PNA, (d) negative PNA regimes. Shading indicates mean locations of 500-hPa
geopotential height departures from climatology found to be statistically significant at the 99%
confidence level, with red (blue) shading denoting positive (negative) geopotential height
anomalies. The “L” symbol indicates the mean position of the surface cyclone center. The solid
black line denotes the mean position of the 552-dam geopotential height contour. Hatching
indicates the mean locations of 300-hPa jet streaks, whereas the line segment denotes the mean
position of the PW axis and 850-hPa LLJ.
Fig 9. Notaro et al (2006). Correlation coefficients for (a) PNA index and (b) NAO index vs.
NCDC observed state-mean temperature during December 1958–2000.
Fig 8. Archambault et al (2008). Cool-season precipitation anomalies for eight large-scale flow
regimes relative to the cool-season climatology. Shaded bars indicate statistical significance at
the 99% confidence interval as determine by a two-sided Student’s t test.
Fig 10. Seasonal regression of (a) 250 mb geopotential height with Nino 3.4 index and (b) 300
mb geopotential height with NAO index. Mean Dec-Mar values are plotted in meters. Based on
1949-2006 NCEP/NCAR Reanalysis data.
Fig 11. Mean Dec-Mar 300 mb scalar wind speed (m/s) for (a) eight +ENSO/-NAO winters and
(b) all winters for the 1950-2010 period. Based on NCEP/NCAR Reanalysis data.
Fig 12. Seager et al (2010). The correlation of snowfall with the NINO3 index (top left), the
NAO index (bottom left) and the standardized NINO3 minus standardized NAO (NINO-NAO)
index and the regression of snowfall on the NINO-NAO index (bottom right). All indices and the
snowfall are for the winter (December to March) mean. Units for the regression are inches.
Tables
STATION
Snowfall (in)
2
Normal (in)
3
% Change
4
T-Statistic
5
Sig. Level
1
1
Table 1: Seasonal Snowfall for Mid-Atlantic Stations
KYNC KPHL KMDT KILG
KBWI
35.65
34.03
44.55
35.16
34.46
25.11
22.22
34.07
21.17
21.10
42.0%
53.2%
30.8%
66.1%
63.3%
2.702
1.710
1.585
2.039
1.707
97.5%
90%
90%
95%
90%
KDCA
25.99
16.68
55.8%
1.546
90%
KRIC
16.79
12.96
29.6%
1.457
90%
Mean seasonal snowfall for eight +ENSO/-NAO winters (1958, 1965, 1969, 1970, 1977, 1987,
2005, and 2010).
2
Mean seasonal snowfall during the 1950-2010 period.
3
Percent change with respect to the climatological mean snowfall.
4
Calculated using a one sample mean test with 7 degrees of freedom.
5
Refers to one-sided t-test significance.