Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
A record-breaking low ice cover over the Great Lakesduring winter 2011/2012: combined effects of a strong positiveNAO and La Nina
Xuezhi Bai • Jia Wang • Jay Austin • David J. Schwab • Raymond Assel • Anne Clites •
John F. Bratton • Marie Colton • John Lenters • Brent Lofgren • Trudy Wohlleben •
Sean Helfrich • Henry Vanderploeg • Lin Luo • George Leshkevich
Received: 27 September 2013 / Accepted: 18 June 2014
� Springer-Verlag (outside the USA) 2014
Abstract A record-breaking low ice cover occurred in
the North American Great Lakes during winter 2011/2012,
in conjunction with a strong positive Arctic Oscillation/
North Atlantic Oscillation (?AO/NAO) and a La Nina
event. Large-scale atmosphere circulation in the Pacific/
North America (PNA) region reflected a combined signal
of La Nina and ?NAO. Surface heat flux analysis shows
that sensible heat flux contributed most to the net surface
heat flux anomaly. Surface air temperature is the dominant
factor governing the interannual variability of Great Lakes
ice cover. Neither La Nina nor ?NAO alone can be
responsible for the extreme warmth; the typical mid-
latitude response to La Nina events is a negative PNA
pattern, which does not have a significant impact on Great
Lakes winter climate; the positive phase of NAO is usually
associated with moderate warming. When the two occurred
simultaneously, the combined effects of La Nina and
?NAO resulted in a negative East Pacific pattern with a
negative center over Alaska/Western Canada, a positive
center in the eastern North Pacific (north of Hawaii), and
an enhanced positive center over the eastern and southern
United States. The overall pattern prohibited the movement
of the Arctic air mass into mid-latitudes and enhanced
southerly flow and warm advection from the Gulf of
Mexico over the eastern United States and Great Lakes
region, leading to the record-breaking low ice cover. It is
another climatic pattern that can induce extreme warming
in the Great Lakes region in addition to strong El Nino
events. A very similar event occurred in the winter of
1999/2000. This extreme warm winter and spring in 2012
had significant impacts on the physical environment, as
well as counterintuitive effects on phytoplankton
abundance.
Keywords Great Lakes ice cover � Ice growth � Surface
heat budget � NAO � La Nina � ENSO
1 Introduction
During winter 2011/2012 (hereafter winter 2012), while
there was a cold spell in Europe and Asia with extreme low
temperatures in northern and central Asia, North America
experienced a very mild winter. The December–March
mean temperature in the contiguous United States was
4.7 �C, which is 2.8 �C above the 1901–2000 long-term
average and the warmest winter in a record that began in
X. Bai � R. Assel � L. Luo
Cooperative Institute for Limnology and Ecosystems Research,
University of Michigan, 4840 S. State Road,
Ann Arbor, MI 48108, USA
J. Wang (&) � D. J. Schwab � A. Clites �J. F. Bratton � M. Colton � B. Lofgren � H. Vanderploeg �G. Leshkevich
NOAA Great Lakes Environmental Research Laboratory,
4840 S. State Road, Ann Arbor, MI 48108, USA
e-mail: [email protected]
J. Austin
Large Lakes Observatory, University of Minnesota Duluth,
Duluth, MN 55812, USA
J. Lenters
School of Natural Resources, University of Nebraska-Lincoln,
3310 Holdrege Street, Lincoln, NE 68583, USA
T. Wohlleben
Canadian Ice Service, Environment Canada, Ottawa,
ON, Canada
S. Helfrich
NOAA National Ice Center, Washington, DC, USA
123
Clim Dyn
DOI 10.1007/s00382-014-2225-2
1896. March 2012 was the warmest March on record. In
terms of the December–February mean, winter 2012 was
ranked the fourth warmest winter since 1896 (NOAA
National Climate Data Center at http://www.ncdc.noaa.
gov/temp-and-precip/time-series/). Positioned near the
warming center, the Great Lakes had the lowest ice cover
since systematic ice observations began in the 1960s.
Great Lakes ice cover exhibits variations ranging from
interannual to interdecadal to long-term trends, in response
to global warming and large-scale climatic drivers (Mag-
nuson et al. 2000; Assel et al. 2003; Assel 2005; Wang
et al. 2012b). Variability in ice cover over the Great Lakes
and other small lakes in North America is often associated
with atmospheric teleconnections, such as El Nino-South-
ern Oscillation (ENSO) and Arctic Oscillation/North
Atlantic Oscillation (AO/NAO) (Assel et al. 1985, 2000;
Assel and Rodionov 1998; Rodionov and Assel 2000,
2003; Robertson et al. 2000; Bonsal et al. 2006; Bai et al.
2010, 2011, 2012; Wang et al. 2010b; Mishra et al. 2011;
Bai and Wang 2012). The North Atlantic Oscillation refers
to a redistribution of atmospheric mass between the Arctic
and the subtropical Atlantic. It is usually measured by the
difference between the sea level pressure over Iceland and
Azores (or Lisbon). The NAO swings from one phase to
another produce large changes in the mean wind speed and
direction over the Atlantic (Hurrell et al. 2003).
It is rather obvious that no single climate index is suf-
ficient to summarize the state of the atmospheric circula-
tion in North America. Wang et al. (1994) investigated the
sea-ice anomalies in Hudson Bay, Baffin Bay and the
Labrador Sea and their relationship to the NAO and the
Southern Oscillation. Quadrelli and Wallace (2002) found
that the structure of the AO is shown to be significantly
different during warm and cold winters of the ENSO cycle.
They suggest that the impacts of the AO upon regional
climate may prove to be even stronger than reported by
Thompson and Wallace (2001) if systematic changes in its
structure that occur in response to variations in the basic
state are taken into account. Bond and Harrison (2006)
investigated the joint effect of ENSO and AO on winter
(November–February) conditions in the vicinity of Alaska.
The Great Lakes have a unique position in terms of
large-scale teleconnection patterns. Namely, they are close
to the nodal point of the Pacific/North America pattern
(PNA, Wallace and Gutzler 1981), between the Alberta
high and the southeastern U.S. low (Fig. 1a), and they are
located at the western edge of the Icelandic Low and Az-
ores High, the action centers of the AO/NAO (Fig. 1b).
Any distortion of the patterns and shift of the centers may
result in different responses in the seasonal mean position
of the jet stream, and hence surface air temperature and ice
cover (Wang et al. 2010b). Thus, both ENSO (via the PNA
or Tropical Northern Hemisphere pattern; Mo and Livezey
1986) and NAO are found to have impacts on Great Lakes
ice cover (Assel et al. 1985, 2000; Rodionov et al. 2001),
but neither of them dominates. Characterizing the joint
forcing of ENSO and AO/NAO is essential to under-
standing the interannual variability of Great Lakes winter
climate and ice cover (Bai et al. 2011, 2012).
Bai et al. (2012) investigated the general relationship of
lake ice with individual NAO and ENSO events and with
the combined NAO and ENSO events. Wang et al. (2010b)
examined an unexpected, anomalously severe ice cover
during winter 2008/2009 due to a southwest displacement
of the Icelandic Low. Bai et al. (2011) examined a below-
normal ice cover in the Great Lakes during winter
2009/2010 due to a strong El Nino event in comparison
with a severe sea ice cover in the Bohai Sea, China. The
important findings include (1) Great Lakes ice cover
responds linearly to NAO events, while nonlinearly to
ENSO events, and asymmetrically to El Nino and La Nina
events. The asymmetric response to ENSO is mainly due to
the phase shift of the teleconnection patterns during the
opposite phases of ENSO, and the NAO may also con-
tribute to the asymmetric response (Bai et al. 2012); (2)
The combined effects of these two teleconnection forcing
possesses higher prediction skill than the individual ones;
and (3) Strong El Nino events alone can explain 50 % of
the minimal ice years in the Great Lakes, leaving the other
50 % unexplained. Since the 2012 winter belongs to the
50 % unexplained, the dynamic and thermodynamic
mechanisms causing such a record low ice cover cannot be
obtained from the previous mentioned studies. Therefore,
the purposes of this study that differs from the previous
studies is to reveal: (1) What atmospheric circulation pat-
terns are associated with the warmth conditions in the
Great Lakes region over the 2011/2012 ice season? (2) Can
the strong ?NAO and La Nina events, which occurred
simultaneously during winter 2012, explain the record low
ice cover for winter 2012 and the other unexplained min-
imal winters? (3) How did the Great Lakes respond to such
extreme event?
2 Data and methods
2.1 Ice cover data
Systematic lake-scale observations of Great Lakes ice
cover by federal agencies in the United States (U.S. Army
Corps of Engineers and U.S. Coast Guard) and Canada
(Atmospheric Environment Service, Canadian Coast
Guard) began in the 1960s (Assel and Rodionov 1998).
Two datasets were used in this study: one from the Cana-
dian Ice Service (CIS) and the other from the NOAA
National Ice Center (NIC). The CIS data are from 1973 to
X. Bai et al.
123
2000. These agencies have coordinated their data since
1989. During the ice year, each agency has at least one
chart per week; more frequently during freeze-up and
break-up periods to aid navigation. Ice charts depicting ice
concentration and ice extent were constructed from satellite
imagery, side-looking airborne radar imagery, and visual
aerial ice reconnaissance. The accuracy and precision of
the original charts is not known with certainty. Partington
et al. (2003) cites ±5 to ±10 % as the accuracy of ice
concentration estimates. Updated data can be obtained
from the NOAA Great Lakes Environmental Research
Laboratory (GLERL, Wang et al. 2012a) (http://www.glerl.
noaa.gov/data/pgs/glice/glice.html).
Annual-averaged ice cover (AAIC) is defined as the
average of 24 weekly ice charts from the week of
December 3 to the week of May 14 for each ice season
(Fig. 2). In this analysis, ice seasons with AAIC less than
or equal to 10 % were identified as minimal ice cover ice
seasons. Annual maximum ice coverage (AMIC) for each
winter is defined as the greatest percentage of the surface
area of a lake covered by ice on a single day (i.e., a
snapshot). The time series of AMIC is from 1963 to 2012
(Fig. 2b) and is longer than the AAIC time series
(1973–2012) due to the insufficient number of ice charts
needed to calculate AAIC prior to 1973. AAIC and AMIC
has a high correlation (0.94).
2.2 Water data
Daily average surface water temperature data for the Great
Lakes from 1994 to 2012 were obtained from the NOAA/
GLERL Coastwatch program (http://coastwatch.glerl.noaa.
gov/statistic/statistic.html). The data set is called Great
Lakes Surface Environment Analysis (GLSEA), which is
from the satellite Advanced Very High Resolution Radi-
ometer (AVHRR) (Schwab et al. 1992). In GLSEA, only
surface water temperature is analyzed, leaving ice cover
pixels as missing data. The missing data is then filled by
interpolation. In this study, we use the daily ice cover to
mask the ice-covered grids. When a grid is ice covered, the
water temperature of that grid is set 0.2 �C. The daily ice
cover is obtained from the original weekly or twice per
week ice charts by interpolating.
Satellite-retrieved chlorophyll concentration data were
derived from MODIS images (Moderate Resolution
Imaging Spectroradiometer, http://oceandata.sci.gsfc.nasa.
gov/MODIST/).
2.3 Atmosphere data
Daily air temperature data (maximum, minimum, mean)
from surface meteorological stations were obtained from
NOAA’s National Climatic Data Center (NCDC).
The National Centers for Environmental Prediction/
National Center for Atmospheric Research (NCEP/NCAR)
re-analysis dataset (Kalnay et al. 1996) was used to provide
estimates of monthly surface air temperature (SAT), sur-
face and 500-hPa level winds, and 500-hPa geopotential
height for the period 1948–2012. The resolution is 2.5�latitude 9 2.5� longitude. The climatology of the period
from 1948 to 2011 was calculated and subtracted from the
individual months to obtain monthly anomalies. Average
winter anomalies (December–January–February–March;
hereafter DJFM) were calculated for each year. Although
NCEP Reanalysis 2 is an improved version of the NCEP
Reanalysis 1 model that fixed errors and updated
(a) (b)
Fig. 1 Positive phase of the a PNA and b NAO pattern (interval: 10 m). The pattern was obtained by regressing the PNA and NAO index upon
the winter mean 500-hPa geopotential height anomaly for the period 1951–2010 (Bai et al. 2012)
Combined effects of a strong positive NAO and La Nina
123
parameterizations of physical processes (Kanamitsu et al.
2002), we chose NCEP 1 because it has a longer time
coverage (1948-present) than NCEP2 (1979-present).
2.4 Surface heat fluxes
Daily surface heat fluxes over ice and water from 1979 to
2012 were calculated using the formulas described in
‘‘Appendix’’. Three datasets are used: one is daily ice
cover, which is obtained from the original weekly or twice
per week ice charts by interpolating; second is daily lake
surface temperature from 1995 to 2012, for the years prior
to 1995, daily climatological lake surface temperature is
used instead, because there are no satellite data available;
the third is daily atmosphere variables (2-m surface air
temperature, 2-m specific humidity, 10-m wind speed, total
(a)
(b)
Fig. 2 a AAIC of all five lakes
and the whole Great Lakes for
the period 1973–2012 (bars),
with solid lines depicting the
long-term means; b time series
of AMIC (black line with circle,
1963–2012), AAIC (red line
with solid circle, 1973–2012)
and DJFM mean over-lake
surface air temperature (green
line with cross, 1980–2012) for
the whole Great Lakes. The
correlation coefficient for the
period 1973–2012 is 0.94. Solid
lines depict the long-term means
X. Bai et al.
123
cloud cover, and incoming solar radiation), which are from
North American Regional Reanalysis with a higher spatial
resolution of 32 km (Mesinger et al. 2006). Note that wind
changes its direction frequently in short time scales. Daily
mean wind speed, which is calculated from wind velocity,
could be under-estimated. The sensible and latent heat
fluxes over lakes also might be under-estimated to some
degree by using the daily mean wind speed.
Monthly surface heat fluxes for years from 1979 to 2012
are obtained by averaging daily ones. The climatology for
the period from 1979 to 2012 was calculated and subtracted
from the individual months to obtain monthly anomalies.
2.5 Climate indices
Monthly Nino 3.4 indices for the years 1950–2012 were
taken from NOAA’s Climate Prediction Center (http://
www.cpc.ncep.noaa.gov/products/analysis_monitoring/
ensostuff/ONI_change.shtml). The Nino 3.4 index is
defined as the 3-month running mean sea surface temper-
ature (SST) anomalies in the Nino 3.4 region (5�N–5�S,
120�–170�W), based on centered 30-year base periods
updated every 5 years. The index measures the warm and
cold events occurred in the central eastern Tropical Pacific
Ocean. Warm and cold episodes are based on a threshold of
±0.5 �C for the Nino 3.4 index. Cold and warm episodes
are defined when the threshold is met for a minimum of
five consecutive overlapping seasons. The monthly AO
indices for the years 2010–2012 were obtained from
NOAA’s Climate Prediction Center.
The winter principal component (PC)-based indices of
the NAO from 1899 to 2012 (Hurrell et al. 2003) were
provided by the Climate Analysis Section, NCAR, Boul-
der, USA (http://climatedataguide.ucar.edu/guidance/hur
rell-north-atlantic-oscillation-nao-index-pc-based). The
indices are the time series of the leading empirical
orthogonal function (EOF) of DJFM Sea Level Pressure
(SLP) anomalies over the Atlantic sector, 20�–80�N,
90�W–40�E. A winter is defined as a positive (negative)
NAO phase when the DJFM mean index exceeds ?0.5
(-0.5), otherwise a winter is defined as NAO-neutral.
Table 1 Lists La Nina, ?NAO, and La Nina/?NAO years
since 1950.
Similarities and dissimilarities between the NAO and
AO are still in debate (e.g. Itoh 2008). Some studies argue
that the NAO and AO are synonyms—they are different
names for the same variability, not different patterns of
variability (Wallace 2000). The difference between the two
lies in whether the variability is interpreted as a regional
pattern controlled by Atlantic sector processes or as an
annular mode whose strongest teleconnections lie in the
Atlantic sector. The NAO index highly correlates with the
AO index, with some remaining differences (Wang et al.
2010a; Itoh 2008). The correlation of the DJFM mean
NAO and AO time series is 0.94 for the period 1951–2012.
As the Great Lake close to the Atlantic Ocean, the AAIC
in the Great Lakes has a higher correlation with NAO
(-0.33) than AO (-0.27). Note that similar patterns and
results will be obtained if the AO index is used.
2.6 Methods
The main methods used in this study are correlation analysis
and composite analysis. Composite analysis is a common
method to present the responses associated with a certain
climatic event (such as ENSO and NAO) by averaging the
data over the years when the event occurred. To account for
the small samples available in this study, Student’s-t distri-
bution was used to determine the statistical significance
between means of two sets of samples. The difference
between La Nina (or NAO) and climatology was computed,
and shading on subsequent composite maps indicates
instances in which the significance level exceeds 5 and 1 %.
3 Ice and water conditions during the 2011–2012 ice
season
During winter 2012, the Great Lakes experienced very mild
temperatures and had the some of the lowest ice coverage
Table 1 Statistics of DJFM mean surface heat fluxes (Wm-2) and atmospheric variables over the Great Lakes for 1979–2012
Qnet Hs Hl Hlw Hsw Ta LST Shum Wind Cloud I0
Climatology -96.6 -55.3 -49 -75.5 83.0 -1.5 2.7 2.6 5.0 0.72 107.0
Std 16.8 7.4 3.7 4.7 7.8 1.8 2.0 0.3 0.46 0.04 4.0
Std/clim. (%) 17 13 7.6 6 9 120 76 12 9 5 3.7
DJFM 2012 -56.0 -37.4 -43.4 -67.6 92.3 3.24 3.8 3.7 5.7 0.7 105
2012 anom. 40.6 18. 5.6 7.9 9.3 4.7 1.7 1.1 0.7 -0.2 2
Qnet: net surface heat flux; Hs: sensible heat flux; Hl: latent heat flux; Hlw: Net long wave radiation, Hsw: absorbed solar radiation. Unit: Wm-2
Ta: air temperature (�C); LST: lake surface temperature (�C); shum: specific humidity (kg/kg); wind: wind speed (ms-1); I0: incoming solar
radiation (Wm-2)
Combined effects of a strong positive NAO and La Nina
123
since systematic ice observations began (Fig. 2a, b).
December–March mean air temperatures for four stations
around the Great Lakes were 4.2 �C above the 1981–2010
normal during winter 2012 (Fig. 3a). The AAICs in Lakes
Superior, Michigan, Huron, Erie, and Ontario were 1.8, 3.1,
5.2, 0.8, and 0.4 %, respectively. The AAIC for the whole
Great Lakes was 2.7 %, which was a record low. The
AMIC in Lakes Superior, Michigan, Huron, Erie, and
Ontario were 8.2, 16.7, 23.1, 13.9, and 1.9 %, respectively.
The AMIC for the whole Great Lakes was 12.9 % on
January 22, 2012 (Fig. 3b), which was the third lowest
since 1963, after 2002 (9.5 %) and 1998 (11.5 %). The
AMIC during winter 2012 (12.9 %) is remarkably lower
than the 1963–2012 normal (52.8 %) (Fig. 2b). On January
22, ice cover was limited primarily to areas along the lake
shore (Fig. 3c). Lake Erie was nearly ice-free by February
22 (Fig. 3b). Compared with the climatology (Table 4), ice
cover over the Great Lakes was greatly reduced during
winter 2012.
The date of AMIC during winter 2012 is unusually
early. The AMIC for the whole Great Lakes usually occurs
in mid-late winter (February and March). From 1973 to
2012, 21 AMICs occurred in February, 17 occurred in
March, and only 2 in January (one on January 22, 2012, the
other on January 15, 1998 in association with a strong El
Nino event).
(c)
(b)(a)
(d)
Fig. 3 a Daily maximum air temperature averaged across four
stations (Buffalo, New York; Detroit, Michigan; Sault Ste. Marie,
Michigan; and Duluth, Minnesota) for the period November 1, 2011
to July 31, 2012 (red line) and the long-term climatology of
1981–2010 (black line); b daily lake-averaged percent ice cover
during winter 2012; c Ice cover (grey shaded) and lake surface
temperature (color shaded) on January 22, 2012 when the maximum
ice cover occurred; d daily mean Great Lakes basin-averaged water
surface temperature for the period January 1, 2011 to mid-May 2012
(red line) and the long-term climatology of 1995–2011 (black line)
X. Bai et al.
123
The mean lake surface water temperature (averaged over
the entire Great Lakes) shows a warming spike in mid-late
March (Fig. 3d), with the average surface water tempera-
ture exceeding 4 �C on March 17 and peaking on March
25. Since fresh water has its maximum density at approx-
imately 4 �C, the lake is generally stratified once spring
surface temperatures exceed 4 �C. Typically, the surface
water temperature then increases more rapidly after
reaching 4 �C, since stratification limits the transfer of
surface heat into the deeper layers. The early stratification
observed in 2012 is unprecedented and likely has large
ramifications for chemical and biological processes
occurring within the Great Lakes.
The warming spike during March was a direct response
to unusual heat that occurred east of the Rocky Mountains
during 12–23 March, with a warming center extending over
Great Lakes region. The unusual heat, as reported by
NCDC, included over 7000 daily record high temperatures
within the U.S. that were broken during the period 1–27
March 2012. For example, eight of nine consecutive record
high temperatures in Chicago from 14–22 March exceeded
26.7 �C, which does not normally occur until summer
(NOAA Earth System Research Laboratory; ESRL; http://
www.esrl.noaa.gov/psd/csi/events/2012/marchheatwave/
index.html). In an evolving research assessment, a group
from NOAA ESRL led by Marty Hoerling suggests that
strong poleward heat transport in the region east of the
Rocky Mountains during 12–23 March 2012 was respon-
sible for the unusual heat.
As an example of the response of lake surface temper-
atures to the anomalous warmth, directly measured near-
surface water temperatures (Fig. 4a) from 2006 to 2012
(excluding 2010, due to instrument failure) from a site in
the western portion of Lake Superior are shown. Surface
water temperatures are significantly greater in 2006 and
2012 than in other years, with temperatures never dipping
below 1.5 �C in 2012. Both 2006 and 2012 had an anom-
alously low AAIC (Fig. 2). The lack of ice in these years
was associated with a less rapid decline in January–March
water temperatures and a slightly earlier heating of the
water column by the first week of March. This is likely due,
in part, to the additional absorption of solar radiation that
results from the significant difference in albedo between
ice and open water, which can lead to a positive ice/water
albedo feedback as further ice melt occurs (Wang et al.
2005; Austin and Colman 2007). In addition, high tem-
peratures in late winter/early spring led to lake surface
water temperatures reaching 4 �C significantly earlier than
normal. The date of positive stratification onset (Fig. 4b)
was estimated from NOAA National Data Buoy Center
(NDBC) data from three sites in Lake Superior, and the
observations show a distinct trend toward earlier onset,
with stratification occurring nearly a month earlier in 2012
than the 1980–2012 average. Other studies have demon-
strated a strong relationship between ice cover and the date
of stratification onset, and by extension, summer water
temperatures (Austin and Colman 2007).
4 Ice growth and surface heat budget analysis
4.1 Ice growth
New ice growth occurs whenever the surface layer in the
lake is at the freezing temperature and the fluxes would
draw additional heat out of the lake. During the freezing
phase, within the two-level ice thickness approximation,
change in ice concentration A (in tenth, 0–1) results from
ice grown on open water (Hilber 1979) (see ‘‘Appendix’’
for details),
Fig. 4 a Directly measured near-surface temperatures from 2006 to
2012 (excluding 2010, due to instrument failure) from a buoy in the
western portion of Lake Superior. b Date of onset of positive
stratification using NOAA NDBC buoy data from three sites in
western, central, and eastern Lake Superior from 1979 to 2012
Combined effects of a strong positive NAO and La Nina
123
oA
ot¼ fg 0ð Þ
H0
1� Að Þ ð1Þ
The equation has a solution:
A tð Þ ¼ e�R t
0a0dt
Z t
0
a0e
R t
0a0dt þ C
2
4
3
5 ð2Þ
with a0 ¼ fg 0ð Þ=H0, and C is an arbitrary constant.
If a0 does not change with time, with the initial condi-
tion A(0) = A0 (in tenth, 0–1) for t = 0, the solution
becomes
A tð Þ ¼ 1:� ð1:� A0Þe�a0t ð3Þ
Given the net surface heat flux, ice coverage can be
quantitatively estimated by using the Eq. (3). For example,
if we set initial ice concentrate A0 = 0, and new ice
thickness h0 = 0.3 m, with a continuous 100 Wm-2 net
surface heat loss from the lake, the maximum ice coverage
will be 55 % after 2 months and 70 % after 3 months. In
the Great Lakes, the maximum ice coverage usually occurs
in February. If we set surface air temperature, wind speed,
cloud cover, specific humidity and incoming solar radiation
as -10 �C, 10 ms-1, 0.7, 2.6 kg/kg, 107 Wm-2, respec-
tively, the calculated net heat loss from water at the
freezing temperature is 232 Wm-2. If the surface temper-
ature decreases by 1 �C, while keep others unchanged, the
calculated net heat loss is 254 Wm-2. As a result, 2 %
more ice will be produced after 10 days. Note that the
amount of additional ice formation also depends on the
initial ice concentration, according to Eq. (3).
4.2 Surface heat budget analysis
Ice growth depends on the net surface heat flux, which is
controlled by various atmospheric variables: surface air
temperature, specific humidity, wind speed, cloud cover
and incoming solar radiation. Heat exchanges between the
atmosphere and lake occur over three different surfaces:
ice, lead (open water between ices), and water.
Ice cover usually cuts off most of the heat transfer,
especially the turbulence heat flux (sensible and latent heat
flux) and solar radiation (ice has a high albedo of about
0.65) (Fig. 5), while lead and water remains the major area
of surface heat loss. Figure 6a shows the basin-wide
averaged daily net surface heat flux (NSHF) over the three
different surface types for winter 2012. It is clearly seen
that the heat losses over water and lead are much larger
than those over ice, especially during the synoptic storm
events when the higher wind speed and lower air temper-
ature results in more heat loss (Fig. 6a, c). The total net
surface heat flux (net surface heat flux averaged over all
grids, regardless of surface type) (Fig. 6b) is almost the
same as the NSHF over water (Fig. 6a), because of very
little ice cover during winter 2012.
For winter 2012, daily surface temperature has the
largest significant correlation with the total NSHF (0.84),
while specific humidity is second (0.8). The daily wind
speed and cloud cover have smaller correlations with the
total NSHF (-0.44 and -0.5, respectively). While higher
wind speed increases surface cooling, it also enhances
water mixing in the upper layer. The former is favorable
for ice formation, while the latter is not. Before ice for-
mation, surface cooling leads to inverse temperature
stratification: a surface layer of low-density water of colder
than 4 �C, but warmer than 0 �C, forms above a well mixed
water column around 4 �C (Bai et al. 2013). The weakened
mixing associated with the inverse stratification is favor-
able for further surface cooling and ice formation. Higher
wind speed would enhance vertical mixing in the water
column and even destroy the inverse stratification, which
would bring up the warmer water from lower layer, delay
the surface cooling, and reduce ice formation. Therefore,
wind-induced mixing is a very important, small-scale
process on shorter (hourly to daily) time scales; neverthe-
less, it can contribute to ice formation and variability on
long-term (seasonal) time scales. This topic should be
further investigated using wintertime in situ measurements
and coupled ice-lake models (Wang et al. 2010c; Fujisaki
et al. 2012, 2013).
The ice cover in the Great Lakes is seasonal and changes
day by day. To represent the total heat exchanges between
the atmosphere and the Great Lakes, the DJFM mean heat
fluxes are calculated by averaging the daily surface heat
fluxes over the three different surfaces.
Figure 7a, b show DJFM mean net surface heat flux in
the Great Lakes during winter 2011/2012 and climatology
(1980–2012), respectively. Compared to the climatology,
the net heat loss to the atmosphere was remarkably reduced
during winter 2012. The basin-wide averaged DJFM NSHF
for winter 2012 was -56.1 Wm-2, which is significantly
less than the 1980–2012 climatology of -96.6 Wm-2
(Table 1). It is almost the same as winter 1998 (-55.9
Wm-2), which is the lowest since winter 1980 (Fig. 7a).
Among the four components of the surface heat flux,
sensible heat flux contributed about 45 % to the anomaly
during winter 2012 (18/40.7). Absorbed solar radiation
contributed about 23 % (9.3/40.7), while the other two
contributed less: 19 % for net long wave radiation and
13.5 % for latent heat flux (Table 1). The large sensible
heat anomaly is obviously from the large over-lake air
temperature anomaly of 4.7 �C (Table 1). The absorbed
solar radiation anomaly results from changes both in cloud
and water area-water has a much smaller albedo (0.1) than
ice (0.65).
X. Bai et al.
123
The DJFM mean sensible heat flux has the largest cor-
relation with the total NSHF from 1980 to 2012 (0.97)
(Table 1; Fig. 8), implying that sensible heat flux explains
most of the variances of the total NSHF. Although the
latent heat flux has a comparable magnitude with the sen-
sible heat flux, and the net long-wave radiation even larger
than the sensible heat flux, they both have a smaller stan-
dard deviation (Table 1, the third row; Fig. 8), thus they
have smaller correlations, and explain less variance of the
total NSHF (Table 2).
Besides the sensible heat flux, surface air temperature is
also involved in the calculation of latent heat flux and net long
wave radiation (‘‘Appendix’’). It has a closer relationship with
the total NSHF than the other atmospheric variables (such as
humidity, wind speed and cloud cover) (Fig. 9; Table 2). Its
correlation with the total heat flux is 0.88 (Table 2).
Surface air temperature has high correlations with
AAIC and AMIC (-0.85 and -0.79, Table 2), which are
consistent with previous studies (Quinn et al. 1978; Bai
et al. 2011). Specific humidity also has a high correlation
with AAIC and AMIC. However, this may be because of
the close relationship between air humidity and temper-
ature in nature (warm air will hold more moisture than
cold air). Latent heat flux has a poor correlation with
AAIC (0.04), while sensible heat has a high correlation
with AAIC (-0.54). Both wind speed and cloud cover
have no significant correlation with AAIC and AMIC
(Table 2). Unlike on the synoptic scale, wind speed has a
smaller variation on the inter-annual timescale, thus, it
explains less variance of the AAIC/AMIC than surface air
temperature does.
Using the method of least squares, linear regression
models were derived linking the basin-averaged DJFM
mean over-lake surface air temperature and AAIC and
AMIC as follows:
AAIC ¼ 9:4� 4:0� Ta ð4ÞAMIC ¼ 34:8� 9:7� Ta ð5Þ
where Ta is the DJFM mean air temperature. The corre-
lations between observations and estimates by the linear
model are 0.78 and 0.85, respectively. Based on this
relationship, it is estimated that one-degree winter mean
air temperature drop will result in an additional 10 %
annual maximum ice coverage and 4 % annual mean ice
coverage.
In addition to winter atmospheric conditions, atmo-
spheric and lake conditions during autumn may have
effects on winter ice cover due to heat storage in the large
body of water. The correlation analysis shows that surface
air temperature, specific humidity, cloud cover, net surface
heat flux in November has significant correlations with
AAIC and AMIC, though the correlations are much smaller
than the winter ones (Tables 2, 3). Wind speed in
November does not have significant correlations with
AAIC and AMIC (Table 3). Note that surface air temper-
ature in November has a high correlation with winter sur-
face air temperature (0.52), implying the persistency of the
atmosphere conditions.
(a)
(b)
(c)
Fig. 5 Basin-wide averaged
daily over-ice a net surface heat
flux (black line), surface air
temperature (green line),
b sensible (red line), latent heat
flux (blue line), net long-wave
radiation (black line), and
c absorbed solar radiation (black
line) for the winter 2012
Combined effects of a strong positive NAO and La Nina
123
Meanwhile, the lake surface temperature in November
does not have a significant correlation with AAIC/
AMIC. Figure 3d shows that lake surface temperature in
autumn 2011 was close to the climatology. The low
correlation may due to the ‘‘autumn overturn’’ in the
Great Lakes. The observations in Lake Michigan
(Church 1942) and modeling of the whole Great Lakes
(Bai et al. 2013) show this phenomenon from October to
December. Fresh water has a maximum density at 4 �C.
When water temperature approaches 4 �C caused by
surface cooling during autumn, the water in lakes will
sink and the whole water column tends to be well mixed.
Shallow Lake Erie is already well mixed in October. In
November, most of the water column is well mixed with
a weak stratification remaining in the bottom layers. In
December, all the Great Lakes have a well-mixed water
column with a temperature around 5–6 �C (Bai et al.
2013). The surface temperature represents a large part of
the heat capacity, although we do not have enough
observations in depths to estimate the heat capacity. The
autumn overturn will eliminate most of the heat memory,
and the lake is mostly controlled by the current atmo-
spheric conditions.
From the above analysis, it is clear that sensible heat
flux contributed most to the net surface heat flux anomaly
and surface air temperature during wintertime is a domi-
nating factor governing the ice coverage in the Great
Lakes.
(a)
(b)
(c)
(d)
Fig. 6 Basin-wide averaged
daily a net surface heat flux over
ice (black), lead (light blue) and
open water (red) (negative
means upward), b total net heat
flux (black line), c surface air
temperature (black line),
specific humidity (green line),
d 10 m wind speed (red line)
and total cloud (blue line) for
the winter 2012
X. Bai et al.
123
(a)
(b)
Fig. 7 DJFM averaged ice
cover (shaded, in percent) and
net surface heat fluxes (contour,
Wm-2) in the Great Lakes for
a the winter 2012 and b their
climatology (1980–2012)
(negative means upward)
Combined effects of a strong positive NAO and La Nina
123
5 Positive NAO, La Nina, and Great Lakes ice cover
Winter 2012 had a strong positive AO/NAO in the extra-
tropics, and a La Nina event in the tropical Pacific
(Fig. 10). A positive AO began in September 2011 and
persisted through December, when the AO index reached
its peak of ?2.2. After December, the monthly mean AO
indices in January and February became weakly negative.
In the Atlantic sector, a positive phase of NAO existed
during the period from September 2011 to April 2012 with
DJFM mean index being ?1.65. The NAO peaked in
December 2011 with an index of ?2.65, which was the
strongest in December since 1899. In the tropical Pacific
Ocean, a La Nina event developed in the summer of 2010
and lingered into the spring of 2012, with its peak in
December 2011. The winter event of 2010/2011 was a
strong one, with a DJF mean Nino 3.4 index of -1.5 �C. It
decayed during the summer of 2011 and re-developed in
the fall, reaching a peak in December with a minimum
Nino 3.4 index of -1.08 �C.
The overall climate patterns during winter 2012 reflect
the combination of a La Nina influence and a positive AO/
NAO pattern (Fig. 11a). Positive height anomalies pre-
vailed over the mid-latitudes and negative height anomalies
prevailed over the high latitudes. This condition pushed the
jet stream northward and kept cold, Arctic air masses from
intruding southward. In the Pacific sector, a negative phase
of the East Pacific (EP) pattern (Barnston and Livezey
1987) dominated, with negative height anomalies over
Alaska/Western Canada, and positive height anomalies
over the central and eastern North Pacific (e.g., north of
Hawaii). The climatological ridge over Alaska/Western
Canada was reduced, while a deeper than normal trough
was located in the vicinity of the Gulf of Alaska/Western
North America. Figure 11a shows a typical positive NAO
pattern in the Atlantic sector; above-normal heights over
the eastern U.S. and the mid-latitude Atlantic Ocean, with
centers over the eastern North Atlantic and the Great Lakes
region, and below-normal heights over Greenland. The
height anomaly over the eastern Atlantic was as high as
120 m. The positive anomalies in mid-latitudes were
associated with ridges over the eastern North Pacific and
eastern North America, which resulted in a deep trough
over the Southwest. The warm advection downstream of
(a)
(b)
(c)
Fig. 8 Time series of basin-
wide averaged DJFM mean
a surface air temperature (green
line, �C), net surface heat flux
(black line), b sensible heat flux
(black line), latent heat flux
(green line), net long-wave
radiation and c absorbed solar
radiation (black line) (Wm-2)
from 1980 to 2012
Table 2 Correlations between DJFM mean atmospheric variables
and Great Lakes ice cover for 1980–2012
Ta Wind Shum Cloud LST Qnet
AAIC -0.85 -0.22 -0.79 0.22 -0.69
AMIC -0.79 -0.14 -0.75 0.17 -0.71
Qnet 0.88 0.33 0.87 0.02 0.37 1
Hs 0.81 0.35 0.81 0.02 0.25 0.97
Hl 0.19 -0.18 0.26 0.1 -0.26 0.6
Hlw 0.55 0.63 0.60 0.14 -0.05 0.65
Hsw 0.69 0.07 0.62 0.64 0.63 0.54
The symbols are the same as Table 1. Boldface indicates correlation
coefficients that are significant at the 95 % level
X. Bai et al.
123
the trough led to above-normal temperatures in the eastern
U.S. and Canada, including the Great Lakes region. Sur-
face air temperature (SAT) anomalies in the Great Lakes
region ranged from 3 to 5 �C, with the greatest warming
occurring near Lake Superior (Fig. 11b). Compared to the
climatology, the pattern during winter 2012 was spring-
like, and the onset of spring was much earlier than the
1981–2010 climatological normal.
Because winter 2012 had a La Nina event and a ?NAO
simultaneously, and because the ENSO and NAO both
have impacts on Great Lakes winter climate and ice cover
(Bai et al. 2011, 2012), it is natural to investigate these two
forcings as possible causes for the observed minimal ice
cover during winter 2012. The relationship between inter-
annual variability of Great Lakes ice cover and ENSO has
been extensively studied (Assel and Rodionov 1998; Ro-
dionov and Assel 2000, 2003; Wang et al. 2010b; Bai et al.
2010, 2011, 2012; Bai and Wang 2012). Strong El Nino
events are usually associated with minimal ice cover in
spite of other factors, while the linkage of maximal ice
cover to La Nina events is not significant. Correlation
analysis shows that none of the five lakes have a significant
correlation with the Nino 3.4 index, though the ENSO
(a)
(b)
(c)
Fig. 9 Time series of basin-
wide averaged DJFM a surface
air temperature (black line),
specific humidity (green line),
b 10 m wind speed (red line)
and lake surface temperature,
c incoming solar radiation
(black line) and total cloud
(light blue line) from 1980 to
2012
Table 3 List of La Nina, ?NAO, and La Nina/?NAO years since
1950
La Nina 1950 1951 1955 1956 1957 1963 1965 1968 1971 1972
1974 1975 1976 1985 1989 1996 1999 2000 2001
2006 2008 2011 2012 (24 cases)
?NAO 1950 1961 1967 1973 1975 1976 1983 1989 1990 1991
1992 1993 1994 1995 1997 1999 2000 2002 2007
2008 2012 (21 cases)
La Nina/
NAO
1950 1975 1976 1989 1999 2000 2008 2012 (8 cases)
Fig. 10 Monthly mean AO
(black line), NAO (red line with
solid circle) and Nino34 (blue
line with open circle) indices
from June 2010 to June 2012
Combined effects of a strong positive NAO and La Nina
123
signal does exist in the ice record. However, every lake has
a significant correlation with the square of the Nino 3.4
index (Table 4), which has been discussed in depth by Bai
et al. (2012). The AAIC on each of the Great Lakes has a
significant negative correlation with the NAO index, which
indicates that the Great Lakes tend to have lower (higher)
ice cover during the positive (negative) NAO.
Since 1973, there have been 10 winters in which the
whole Great Lakes AAIC was less than 10 %: 1982/1983,
1986/1987, 1994/1995, 1997/1998, 1998/1999, 1999/2000,
2001/2002, 2005/2006, 2009/2010, and 2011/2012
(Fig. 2b). Five of these winters coincided with strong El
Nino events (82/83, 86/87, 94/95, 97/98, and 09/10), while
the causes of the other five other ice minima remain
(a)
(b)
Fig. 11 a DJFM mean 500-hPa height and anomalies (in color) during winter 2012, b DJFM mean surface air temperature (solid line),
anomalies (color shaded), and wind anomalies (vectors) over North America during winter 2012
X. Bai et al.
123
unexplained (Fig. 12). The two types of El Nino events
(dateline and conventional, Larkin and Harrison 2005)
have similar effects on the Great Lakes Region: except for
1977/1978, dateline El Nino years (Wang and Wang 2013)
(1963/64, 1968/69, 1986/87, 1994/1995, 2004/05, 2009/10)
all have light ice cover, which is similar to the conventional
El Nino years.
It is natural to expect the positive NAO to be a pos-
sible cause of ice minima since it is usually associated
with warming in the eastern U.S., including the Great
Lakes. There are 18 ?NAO cases since 1973 (Table 3),
and the mean AAIC for all these cases is 12.4 %, which
is lower than, but not significantly different from, the
climatology (at the 90 % significance level; Table 5).
The correlation between the NAO index and AAIC is
significant for all five lakes (Table 5). Below-normal ice
cover is usually expected on the Great Lakes when a
?NAO occurs. The record also shows that 15 out of 18
?NAO winters were associated with below-normal ice
since 1973 (Fig. 12). However, the association between
?NAO and minimal ice cover is not robust: there are
only 6 out of 18 ?NAO winters with minimal ice cover
(Fig. 12). Among the six ?NAO cases with minimal ice
cover, two coincide with a strong El Nino (1983 and
1995) and three coincide with a La Nina event (1999,
2000, 2012) (Fig. 12).
The averaged DJFM 500-hPa height anomalies for 18
?NAO winters (Fig. 13a) show a well known pattern with
a negative anomaly over the Arctic (including Iceland), and
a positive anomaly over the mid-latitude Atlantic Ocean.
The Great Lakes were covered by weak positive anomalies
of 5–10 m. The temperature anomalies associated with
?NAO show a warming center in the southeastern U.S.,
and the Great Lakes are on the edge of the warming center,
with slight warming of around 0.6–0.9 �C (Fig. 13b). The
regression of SAT onto the NAO index conducted by
Hurrell et al. (2003) shows that the changes in DJFM mean
surface temperatures in the Great Lakes region corre-
sponding to a unit deviation of the NAO index range from
0.2 to 0.4 �C. An index of 1.65, therefore, is expected to
produce above-normal temperatures of approximately 0.33
to 0.66 �C in the Great Lakes.
Table 4 Long-term mean AAIC and its standard deviation (in parenthesis), composite AAIC, and Student T-valuesa (in parenthesis) for 14 La
Nina, 16 ?NAO, and 7 La Nina/?NAO cases for each lake and the whole Great Lakes (GL) for the period 1973–2012
Superior Michigan Huron Erie Ontario GL
Long term mean 17.4 (12.3) 11 (6.6) 20.9 (10.3) 26.5 (14.3) 4.7 (3.7) 16.8 (9.5)
La Nina 14.7 (-0.62) 9.1 (-0.88) 17.9 (-0.79) 24.6 (-0.37) 3.1 (-1.35) 14.3 (-0.72)
?NAO 12.6 (-1.28) 8.0 (-1.54) 16.2 (-1.4) 19.3 (-1.75) 3.0 (-1.4) 12.4 (-1.5)
La Nina/?NAO 9.78 (-1.5) 7.4 (-1.3) 13.7 (-1.6) 18.5 (-1.4) 1.7 (-2.0) 10.4 (-1.6)
a The 80, 90, and 95 % significance Student’s t critical values are 1.3, 1.67, and 2.0, respectively for degrees of freedom between 40 and 60.
Bold font indicates significance at the 90 % level
Fig. 12 The plane scatterplot of
below-normal ice cover winters
(small light blue solid circle)
and minimal ice cover winters
(AAIC B 10 %, large red solid
circle with year) with the
Nino3.4 index as the x axis and
the NAO index as the y axis
Combined effects of a strong positive NAO and La Nina
123
The composite map of DJFM mean total net surface heat
flux anomaly for ?NAO winters since 1979 (Fig. 13c)
shows less than 10 Wm-2 positive anomalies in the wes-
tern and southern parts and negligible anomalies in the
eastern and northern parts of the lakes, which is consistent
with the northwest-southeast-oriented surface air tempera-
ture anomaly shown in Fig. 13b. The pattern indicates that
a ?NAO has larger impacts on Lakes Michigan, Erie and
Ontario than it does on Lakes Superior and Huron. The
AAIC in Lake Michigan has the largest negative correla-
tion with NAO, while Lake Erie is the second. Lake
Superior has the smallest correlation with NAO (Table 5).
The composite AAICs and Student’s T-values for each lake
are consistent with correlation analysis (Table 4).
The composite analysis shows that La Nina events do
not have a significant impact on Great Lakes ice cover
(Fig. 14; Table 4). The average AAIC for 14 La Nina cases
since 1973 is 14.3 % for the whole Great Lakes, which is
slightly lower than, but not significantly different from, the
climatology (16.4 %) (Table 4). The DJFM 500-hPa height
anomalies associated with La Nina events display a typical
negative PNA pattern, with the Great Lakes close to the
nodal line (Fig. 14a). This pattern is associated with a more
zonally oriented 500-hPa height field (Wallace and Gutzler
1981), and the ridge-trough system over North America
shifts westward, with a ridge over the central eastern North
Pacific and a trough over central North America. The air
temperature anomalies associated with La Nina events
show cooling along the west coast and northwestern North
America and warming in the southeastern U.S., with the
Great Lakes being in between (Fig. 14b). The Great Lakes
may experience a slight warming or cooling depending on
the strength of a La Nina event. For example, a strong
(weak) La Nina event is likely to be associated with a slight
warming (cooling) in the Great Lakes, as the positive cell
of the PNA pattern over the southeastern U.S. is enhanced
(reduced) during strong (weak) La Nina events (Bai et al.
2012). The effect of La Nina events on Great Lakes ice
cover is much less than that of the ?NAOs. The compos-
ited total net surface heat flux in the Great Lakes for the La
Nina years ranges within ±10 Wm-2 (Fig. 14c). The effect
of La Nina events on Great Lakes ice cover is much less
than that of the ?NAOs.
6 Combined effects of 1NAO and La Nina
The above evidence suggests that neither La Nina nor
?NAO alone explain the record low ice during winter
2012. Rather, our hypothesis is that the combined effect of
a La Nina and ?NAO caused the significant warming in
the Great Lakes region during this particularly anomalous
winter.
The above analysis and other studies (e.g., Hoerling
et al. 1997; Straus and Shukla 2002) suggest that the
typical mid-latitude response to La Nina events is a
negative PNA pattern, which is comprised of four cen-
ters; one (negative) is located near Hawaii, a second
(positive) is over the North Pacific, a third (negative) is
over Alberta, and a fourth (positive) is over the Gulf
Coast region of the United States (Fig. 15a; see also
Wallace and Gutzler 1981). The ?NAO has a dipole
pattern with a negative center over Iceland and positive
anomalies in a broad east–west belt centered at 40� N,
extending from the east coast of the United States to the
Mediterranean (Fig. 15b; see also Walker and Bliss 1932;
Wallace and Gutzler 1981).
The combined effect of the two teleconnection patterns
is shown in Fig. 15c, which is the sum of Fig. 15a, b. The
whole pattern is very similar to that observed during winter
2012 (Fig. 11a), implying that the prevailing 2012 winter
atmospheric pattern resulted from the combined effect of
La Nina and ?NAO. Specifically, in the Pacific sector,
positive anomalies over the North Pacific associated with
La Nina events and negative anomalies over the Arctic
associated with a ?AO/NAO result in a negative East
Pacific pattern (Barnston and Livezey 1987). The pattern
has a negative center over Alaska/Western Canada and a
positive center in the central and eastern North Pacific
(e.g., north of Hawaii). The positive anomalies over the
southeastern United States are enhanced, because both
negative PNA and ?NAO have positive height anomalies.
The negative EP pattern is associated with a weakened
blocking over the North Pacific/Alaska and strong
westerlies over the west coast of the United States. At the
same time, the enhanced positive center over the south-
eastern U.S. is accompanied with enhanced southerly flow
and warm advection from the Gulf coast region over the
eastern United States.
Since 1973, there have been seven winters with La Nina/
?NAO: 1974/75, 1975/76, 1988/89, 1998/99, 1999/2000,
2007/08, and 2011/12. Six of them had below-normal ice
cover, three of them (1998/99, 1999/2000, and 2011/12)
had AAIC less than 10 %, and 1975, 1976, and 2008 had
AAICs of 10.4, 13.9, and 14.3 % respectively. One winter
(1988/1989) had above-normal ice cover (18.9 %). The
average AAIC for these seven winters is 10.4 %, which is
less than the mean of all ?NAO years (12.4 %) and all La
Table 5 Correlation coefficients between the AAIC of each lake and
various climatic indices
Superior Michigan Huron Erie Ontario Gls
Nino34 -0.16 -0.075 -0.091 -0.14 0.04 -0.13
Nino342 -0.36 -0.38 -0.42 -0.45 -0.38 -0.41
NAO -0.26 -0.41 -0.30 -0.39 -0.35 20.33
Boldface indicates correlation coefficients that are significant at the
95 % level
X. Bai et al.
123
(a)
(b)
(c)
Fig. 13 Composite maps of
a DJFM mean 500-hpa height
(black line) and anomalies (blue
and red contours), b SAT
anomalies (red and blue
contours), mean surface winds
(black vectors, m/s), and
anomalies (green vectors), and
c total net surface heat flux
(Wm-2) for winters of ?NAO.
The intervals for 500-hPa height
and anomalies, SAT anomalies
are 80, 5 m, and 0.3 �C,
respectively. The gray and dark
gray shaded regions indicate the
95 and 99 % significant levels,
respectively. The significance
applies to the 500 hPa height
and SAT anomalies fields
Combined effects of a strong positive NAO and La Nina
123
(a)
(b)
(c)
Fig. 14 Same as Fig. 13, but
for La Nina
X. Bai et al.
123
Nina years (14.3 %), and much less than the long-term
mean (16.4 %, see Table 4).
A schematic diagram was constructed from the com-
posite maps for the eight La Nina/?NAO winters (the 3rd
row in Table 3) by drawing only the 10 and -10 m lines
(Fig. 15d). The whole pattern is quite similar to the linear
sum of the individual La Nina (Fig. 15a) and individual
?NAO (Fig. 15b) effects shown in Fig. 15c. The detailed
composite map for La Nina/?NAO winters (Fig. 16)
shows greater positive height anomalies in mid-latitudes
than ?NAO (Fig. 13a) or La Nina (Fig. 14a) alone, with
centers over the eastern North Pacific, western Atlantic,
and eastern Atlantic (Fig. 16a). In particular, the greater
positive anomalies over the southeastern U.S. indicate that
the Azores High was enhanced, which induces anoma-
lously strong southerly winds on the western periphery,
bringing warm air from the Gulf of Mexico and leading to
warmer-than-normal temperatures over the Great Lakes
(Fig. 16b). The composite map of total NSHF anomaly for
five La Nina/NAO winters since 1979 (Fig. 16c) shows
much larger positive anomalies in all five lakes than
?NAO or La Nina alone.
Except for 1950 and 1989, all other La Nina/?NAO
winters show a similar pattern—namely, a negative EP
pattern in the North Pacific and a positive NAO in the
North Atlantic (Fig. 17). During the winters of 1949/1950
and 1988/1989, the positive center over the North Pacific
was very strong, extending over Alaska and western Can-
ada. Thus, atmospheric blocking over Alaska was stronger
than normal, leading to cold, Arctic air extending into mid-
latitudes and restricting the warming associated with the
?NAO to south of the Great Lakes (Fig. 18). Consistent
with the SAT anomaly, during winter 1988/1989, Lakes
Ontario and Erie had below-normal AAICs (1.7 and
22.5 %, respectively), Lakes Michigan and Huron had
close to normal AAICs (10.2 and 21 %, respectively), and
Lake Superior had much-above normal AAIC (25.2 %;
Fig. 2a).
The circulation pattern observed during winter 2012
shows a close resemblance to the winter of 1999/2000.
(c) (d)
(a) (b)
Fig. 15 Schematic Diagram of a La Nina, b NAO, c combined La
Nina and ?NAO effects, and d composite map of eight La Nina/
?NAO winters. The diagram was drawn from the composite maps of
500-hPa height and surface air temperature anomaly for La Nina and
?NAO, the sum of the two, and composite maps for La Nina/?NAO
winters by drawing the -20, -10, 10 and 20 m lines. Blue (red)
shaded areas indicate cold (warm) area with anomalies greater than
0.6 �C
Combined effects of a strong positive NAO and La Nina
123
(a)
(b)
(c)
Fig. 16 Same as Fig. 13, but
for La Nina/?NAO winters
X. Bai et al.
123
2012 1950
1975 1976
1989 1999
2000 2008
Fig. 17 The DJFM 500-hPa
height (contours) and anomalies
(shaded) for individual winters
with La Nina/?NAO
Combined effects of a strong positive NAO and La Nina
123
2012 1950
1975 1976
2008
19991989
2000
Fig. 18 The DJFM SAT (contours), anomalies (shaded, and surface wind anomalies (vectors) for individual winters with La Nina/?NAO
X. Bai et al.
123
During 1999/2000, the U.S. had its warmest winter on
record (in terms of DJF mean SATs), while the winter of
2011/2012 ranked as the fourth warmest. The whole Great
Lakes AAIC for winter 1999/2000 was 6.83 %, which is
the eighth lowest ice cover, while the AAIC for winter
2012 was 2.3 %, which is the lowest. As shown in Fig. 18,
the warming center during 1999/2000 is located west of the
Great Lakes region. Hoerling et al. (2001) and Hoerling
and Kumar (2003) attributed the mid-latitude warming
during 1998–2002 to the ocean. Maps of global sea surface
temperature anomalies (not shown) indicate similar pat-
terns during the two winters.
7 Conclusions and discussion
During winter 2012, in conjunction with a strong positive
AO/NAO and a La Nina event, the Great Lakes experi-
enced very mild air temperatures and record-breaking low
ice cover. An anomalously warm lake surface temperature
was observed. The 4 �C water was captured by
*1.5 months earlier than the climatological mean. The
onset of positive stratification in Lake Superior was
reached in spring of 2012, which had not happened since
1979. The cause of this anomalous event has been inves-
tigated here from the perspective of joint NAO and ENSO
forcing, coincident with one of the warmest events in
history since 1973. Based on the above investigations, the
following conclusions can be drawn.
1. Surface heat flux analysis shows that sensible heat flux
contributed most to the net surface heat flux anomaly.
Surface air temperature is the dominant factor govern-
ing the interannual variability of Great Lakes ice
cover.
2. Composite analysis shows that neither La Nina nor
?NAO alone was responsible for the extreme warmth
in the Great Lakes region during 2012. The typical
mid-latitude response to La Nina events is a negative
PNA pattern, which does not have a significant impact
on the Great Lakes winter climate, since the Great
Lakes are located around the nodal line separating the
negative center over Alberta and the positive center
over the Gulf Coast region of the United States.
Furthermore, the positive phase of the NAO is usually
associated with only moderate warming in the Great
Lakes region.
3. When the two teleconnection patterns occur simulta-
neously, the positive ENSO center over the North
Pacific associated with La Nina events, and the
negative anomalies over the Arctic associated with
?AO/NAO result in a negative East Pacific (EP)
pattern. This negative EP pattern has a negative center
over Alaska/Western Canada and a positive center in
the central and eastern North Pacific. The positive
center over the southeastern United States is also
enhanced, because both a negative PNA and ?NAO
have positive height anomalies in this region. Such was
the case in winter 2012; the overall pattern prevented
cold, arctic air from intruding into mid-latitudes, as
enhanced southerly winds and warm-air advection
from the Gulf of Mexico over the eastern United States
and Great Lakes region led to extreme warmth and
record-breaking low ice cover.
4. Previous studies suggest that strong El Nino events are
usually associated with minimal Great Lakes ice cover.
Strong El Nino events explained about 50 % (5 out of
10) of the warm events (Assel 1998; Bai et al. 2012),
while the five other events remain unexplained. A new
finding of this study is that since 1973, three out of
10 years with minimal ice cover occurred during the
simultaneous La Nina and ?NAO winters, which is a
significant climatic pattern that can induce extreme
warming in the Great Lakes region.
While ENSO can be predicted with good skill for lead
times of up to 6 months (Jin et al. 2008), the NAO lacks
similar reliable seasonal prediction because of its chaotic
dynamical nature and weak persistence (Kushnir et al.
2006). As such, the low seasonal ice cover anomalies in the
Great Lakes were not accurately predicted for the winter of
2012. For example, on October 20, 2011, NOAA’s Climate
Prediction Center (CPC) released their first outlook for the
2011–12 winter (http://www.cpc.ncep.noaa.gov/products/
predictions/90day/). The temperature forecast strongly
reflected typical La Nina temperature anomalies through-
out the United States. This included higher probabilities of
warmer-than-normal conditions in the southeast and
colder-than-normal temperatures along the west coast, the
northwest, and over much of the northern tier of the U.S.
The Great Lakes were therefore expected to be colder and
wetter than average, which turned out to be the opposite of
what actually occurred. NOAA noted at the time that La
Nina was not the only climate factor at work, and that the
‘wild card’ was the less predictable Arctic Oscillation,
which can produce dramatic short-term swings in winter
temperatures (e.g., see http://www.noaanews.noaa.gov/
stories2011/20111020_winteroutlook.html).
The extreme warmth, record low ice cover, and early
spring during 2012 could have significant impacts on Great
Lakes ecosystems including food web dynamics and
changes in water level due to enhanced evaporation (Wang
et al. 2010b), especially given the drought conditions that
also occurred during the spring and summer of 2012.
Generally speaking, early loss of ice cover and rapid
warm-up could lead to an earlier and stronger spring
Combined effects of a strong positive NAO and La Nina
123
phytoplankton bloom in the epilimnion (the layer of water
above the thermocline), since it is usually assumed that ice
cover limits light reaching the water column, and that
stability of the water column provided by rapid warm-up
allows for the development and rapid growth of phyto-
plankton at the surface (e.g. Sommer 1989; Gerten and
Adrian 2000). However, it is also recognized that there can
be differences in the strength of such a pattern due to such
factors as grazing, light, and the abundance of overwin-
tering populations of both phytoplankton and zooplankton
(e.g. Berger et al. 2010; Gaedke et al. 2010).
The limitation of these generalities can be appreciated
from examination of mixed layer chlorophyll a (Chl-a) and
suspended solids concentrations from satellite imagery in
2012, as well as seasonal Chl-a concentrations in 2012
relative to the long-term average for Lake Erie (Fig. 19),
the most shallow and nutrient rich of all the Great Lakes.
On average, Lake Erie Chl-a concentrations are much
higher than those of the other Great Lakes (Fig. 19b), and
during spring and early summer of 2012 (February to June)
the concentrations were generally similar to the long-term
mean (Fig. 19a, b, d). Later in the summer season, how-
ever, Chl-a concentrations rose to values that were con-
siderably above the climatological mean (e.g., *30 %
higher than average by July and August; Fig. 19d).
We can hypothesize a number of reasons for the
‘‘below-normal’’ spring phytoplankton pattern, despite the
warm temperatures. The most likely mechanism for the
2012 spring was that water column stability and light are
important factors affecting phytoplankton growth during
spring (Vanderploeg et al. 2007). Under isothermal con-
ditions, downward mixing of phytoplankton can remove
them from surface layers and out of the photic zone,
thereby limiting growth. Light available for photosynthesis
(as well as depth of the photic zone) decreases exponen-
tially with the extinction coefficient of light, which is
directly related to turbidity and the concentration of sus-
pended solids (e.g., Vanderploeg et al. 2007). Although
there was rapid warm-up and incipient stratification
implied by warm surface temperatures across Lake Erie,
there was extreme turbidity (Fig. 19c)—presumably asso-
ciated with strong wind mixing under nearly ice-free open
water—particularly in the shallow western basin (mean
depth = 7.3 m) and central basin (mean depth = 18.3 m).
(c)(d)
(a) (b)
Fig. 19 a Satellite-measured surface chlorophyll-a concentration
(mg/m3) during March 2012, and b the long-term climatology for
March (2000–2012); c Satellite-measured water color (i.e., turbidity)
in Lake Erie on March 15, 2012; d the time series of domain-averaged
chl-a concentration in Lake Erie for both 2012 (solid line) and the
long-term climatology (dashed line)
X. Bai et al.
123
In Lake Michigan, turbidity associated with wind mixing
resulted in rapid and large drops in Chl-a concentrations in
shallow water (Vanderploeg et al. 2007). It is possible that
turbid conditions associated with winter–spring wind
mixing under the nearly ice-free conditions resulted in
lower light availability and reduced primary production
during spring.
Acknowledgments NCEP/NCAR reanalysis data were provided by
the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, at http://
www.cdc.noaa.gov/. This study was supported by NOAA/GLERL
and the EPA/NOAA Great Lakes Restoration Initiative. We thank
Cathy Darnell for her editorial assistance. In situ Lake Superior
observations were supported by the National Science Foundation
Geosciences directorate Grant 0825633. We thank Cathy Darnell for
her editorial assistance. We sincerely thank the two anonymous
reviewers for their constructive comments of the first draft, which
helped significantly improve the quality of the paper. This is GLERL
Contribution No. 1717.
Appendix: Sea ice thermodynamic equations
Typically, an ice cover will contain a variety of ice
thickness. To approximately parameterize this variable
thickness ice cover, two thickness levels are used in the sea
ice model: thick and thin. Two quantities are used to
describe an ice cover in any grid cell–equivalent thickness
h and concentration A (0–1), which is defined as the
fraction covered by thick ice. The rest of the cell is covered
by thin ice, which is always taken to be of zero thickness
(i.e. open water or lead) (Hibler 1979).
Within the two-level approximation, ice thickness and
concentration growth is given by (Hibler 1979)
Sh � oh=ot ¼ fg h=Að ÞAþ 1� Að Þfgð0Þ
Sa � oA=ot ¼ fg 0ð Þ=H0
� �1� Að Þd1 þ A=2hð ÞShd2
d1 ¼ 1if fg 0ð Þ[ 0; d1 ¼ 0if fg 0ð Þ\0
d2 ¼ 1if Sh\0; d2 ¼ 0 if Sh\0
with fg(h) the growth rate of ice of thickness h, and H0 a
fixed demarcation thickness between thin and thick ice
(0.5 m is often used in Arctic). The growth rate is deter-
mined by the net heat losses over ice or water
fgðhÞ ¼ �Qnet=qiLv
with Qnet the net surface heat flux over ice or water at the
freezing temperature, qi = 900 kgm-3 the density of ice,
Lv = 2.4 9 106 Jkg-1 is the latent heat of fusion of ice.
Heat budget on the upper ice surface
The net heat flux on the ice surface is
Qai ¼ Hsw þ Hlw þ Hs þ Hl
where short wave radiation Hsw, net long wave radiation
Hlw, the sensible heat flux Hs, the latent heat flux Hl, net
long wave radiation, and conductive flux Fc are calculated
by the formulations:
Hsw ¼ I0ð1� aiÞ
Hlw ¼ �ed 1� kcCð Þ 0:254� 4:95� 10�5ea
� �Ta þ 4ðTi � TaÞ
� �T3
a
Hs ¼ qaCsCp Vaj j Ta � Tið Þ
Hl ¼ qaClle Vaj j qa � qið Þ
where Cp (=1,005 J kg-1K-1) is the specific heat of air and
le (=2.5 9 106 J/kg) is the latent heat of evaporation. Ta and
Ti are the surface air temperature and the surface tempera-
ture of ice, respectively. qa and qi are the specific humidity
of the surface air and ice surface, respectively. The heat
transfer coefficient Cs and Cl, which depend on wind speed,
are set based on Large and Pond (1981; 1982). For example,
at a given wind of 5 m/s, the Cs is 1.06 9 10-3
(0.6 9 10-3) for an unstable (stable) condition, and Cl is
1.1 9 10-3. e (=0.95) is the emissivity of sea surface rel-
ative to a black body. r (=5.67 9 10-8) is Stefan-Boltz-
mann constant. kc (=0.7) is the cloud factor, and C is the
cloud fraction. qa (=1.3 kg m-3) and qw (=1.025 9 103
kg m-3) is the air and the seawater density, respectively.
Inside the ice, heat conduction flux Fc exists because of
a nonuniform temperature distribution:
Fc ¼ �kiðTi � Tf Þ=hi
where ki = 2.04 J (kg K)-1 is the ice thermal conductivity,
Tf is the ice bottom temperature, Ti is the sea ice surface
temperature, and hi is the sea ice thickness.
The sea ice surface temperature Ti is an unknown, and is
calculated using the following heat equilibrium equation at
the air-ice interface:
Qai � Fc ¼ 0
Solving this equation, we can obtain Ti by an iteration
method, and, in turn, we can calculate the heat fluxes over
the upper ice surface.
Heat budget over lead and water
Sensible and latent heat flux over lead and water are cal-
culated using the bulk aerodynamic method suggested by
Large and Pond (1982). The drag coefficient, Stanton
number and Dalton number are functions of height and
stability and commonly evaluated in the equivalent neutral
case at 10 m. For brevity’s sake, the calculation process is
not copied here. The net long wave radiation is calculated
as the same method as over ice.
Combined effects of a strong positive NAO and La Nina
123
References
Vanderploeg HA et al (2007) Anatomy of the recurrent coastal
sediment plume in Lake Michigan and its impacts on light
climate, nutrients, and plankton. J Geophys Res 112:C03S90.
doi:10.1029/2004JC002379
Assel RA (2005) Classification of annual Great Lakes ice cycles:
winters of 1973–2002. J Clim 18:4895–4905
Assel RA, Rodionov S (1998) Atmospheric teleconnections for
annual maximal ice cover on the Laurentian Great Lakes. Int J
Climatol 18:425–442
Assel RA, Snider CR, Lawrence R (1985) Comparison of 1983 Great
Lakes winter weather and ice conditions with previous years.
Mon Weather Rev 113:291–303
Assel RA, Janowiak JE, Boyce D, O’Connors C, Quinn FH, Norton
DC (2000) Laurentian Great Lakes ice and weather conditions
for the 1998 El Nino winter. Bull Am Meteorol Soc 81:703–717
Assel RA, Cronk K, Norton DC (2003) Recent trends in Laurentian
Great Lakes ice cover. Clim Change 57:185–204
Austin JA, Colman S (2007) Lake Superior summer water temper-
atures are increasing more rapidly than regional air temperatures:
A positive ice-albedo feedback. Geophys Res Lett 34. doi:10.
1029/2006GL029021
Bai X, Wang J (2012) Atmospheric teleconnection patterns associated
with severe and mild ice cover on the Great Lakes, 1963–2011.
Water Qual Res J Can 47(3–4):421–435
Bai X, Wang J, Sellinger CE, Clites AH, Assel RA (2010) The
impacts of ENSO and AO/NAO on the interannual variability of
Great Lakes ice cover. NOAA Technical Memorandum GLERL-
152. NOAA, Great Lakes Environmental Research Laboratory,
Ann Arbor, MI, p 44
Bai X, Wang J, Liu Q, Wang D, Liu Y (2011) Severe ice conditions in
the Bohai Sea, China and mild ice conditions in the Great Lakes
during the 2009/2010 winter: links to El Nino and a strong
negative Arctic Oscillation. J Appl Meteorol Climatol
50:1922–1935. doi:10.1175/2011JAMC2675.1
Bai X, Wang J, Sellinger CE, Clites AH, Assel RA (2012) Interannual
variability of Great Lakes ice cover and its relationship to NAO
and ENSO. J Geophys Res 117(C03002):25. doi:10.1029/
2010JC006932
Bai X, Wang J, Schwab DJ, Yang Y, Luo L, Leshkevich GA, Liu S
(2013) Modeling 1993–2008 climatology of seasonal general
circulation and thermal structure in the Great Lakes using
FVCOM. Ocean Model 65:40–63
Barnston AG, Livezey RE (1987) Classification, seasonality and
persistence of low-frequency atmospheric circulation patterns.
Mon Weather Rev 115:1083–1126. doi:10.1175/1520-
0493(1987)115\1083:CSAPOL[2.0.CO;2
Berger SA, Diehl S, Stibor H, Trommer G, Ruhenstroth M (2010)
Water temperature and stratification depth independently shift
cardinal events during plankton spring succession. Glob Change
Biol 16:1954–1965
Bond NA, Harrison DE (2006) ENSO’s effect on Alaska during
opposite phases of the arctic oscillation. Int J Climatol
26:1821–1841. doi:10.1002/joc.1339
Bonsal BR, Prowse TD, Duguay CR, Lacroix MP (2006) Impacts of
large-scale teleconnections on freshwater-ice duration over
Canada. J Hydrol 330:340–353
Church PE (1942) The annual temperature cycle of Lake Michigan, I.Cooling from Late Autumn to the terminal point, 1941–1942. A
publication of the Institute of Meteorology of the University of
Chicago, Miscellaneous Report No. 4, The University of
Chicago Press, Chicago
Fujisaki A, Wang J, Hu H, Schwab D, Hawley N, Yerubandi R (2012)
A modeling study of ice-water processes for Lake Erie using
coupled ice-circulation models. J Great Lakes Res
38(4):585–599. doi:10.1016/j.jglr.2012.09.021
Fujisaki A, Wang J, Bai X, Leshkevich G, Lofgren B (2013) Model-
simulated interannual variability of Lake Erie ice cover,
circulation, and thermal structure in response to atmospheric
forcing, 2003–2012. J Geophys Res Oceans 118(9):4286–4304
Gaedke U, Ruhenstroth-Bauer M, Wiegand I, Tirok K, Aberle N,
Breithaupt P, Lengfellner K, Wohlers J, Sommer U (2010) Biotic
interactions may overrule direct climate effects on spring
phytoplankton dynamics. Glob Change Biol 16:1122–1136
Gerten D, Adrian R (2000) Climate-driven changes in spring plankton
dynamics and the sensitivity of shallow polymictic lakes to the
North Atlantic Oscillation. Limnol Oceanogr 45:1058–1066
Hibler WDIII (1979) A dynamic thermodynamic sea ice model.
J Phys Oceanogr 9(4):815–846
Hoerling MP, Kumar A (2003) The perfect ocean for drought. Science
299:691–694
Hoerling MP, Kumar A, Zhong M (1997) El Nino, La Nina, and the
nonlinearity of their teleconnections. J Clim 10:1769–1786.
doi:10.1175/1520-0442(1997)010\1769:ENOLNA[2.0.CO;2
Hoerling MP, Kumar A, Whitaker JS, Wang W (2001) The midlatitude
warming during 1998–2000. Geophys Res Lett 28:755–758
Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) An overview
of the North Atlantic Oscillation. In: Hurrell JW, Kushnir Y,
Ottersen G, Visbeck M (eds) The North Atlantic Oscillation:
climate significance and environmental impact. Geophysical
Monograph Series, vol 134, 279 pp
Itoh H (2008) Reconsideration of the true versus apparent Arctic
oscillation. J Clim 21:2047–2062
Jin EK, Kinter JL III, Wang B, Park C-K, Kang I-S, Kirtman BP, Kug
J-S, Kumar A, Luo J–J, Schemm J, Shukla J, Yamagata T (2008)
Current status of ENSO prediction skill in coupled ocean–
atmosphere models. Clim Dyn 31:647–664. doi:10.1007/s00382-
008-0397-3
Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project.
Bull Am Meteorol Soc 77:437–471. doi:10.1175/1520-
0477077\0437:TNYRP[2.0.CO;2
Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino
M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull
Am Meteorol Soc 83:1631–1643
Kushnir Y, Robinson W, Chang P, Robertson A (2006) The physical
basis for predicting Atlantic sector seasonal to interannual
climate variability. J Clim 19:5949–5970
Large WG, Pond S (1981) Open ocean momentum flux measurements
in moderate to strong winds. J Phys Oceanogr 11:324–336
Large WG, Pond S (1982) Sensible and latent heat flux measurements
over the ocean. J Phys Oceanogr 12:464–482
Larkin NK, Harrison DE (2005) On the definition of El Nino and
associated seasonal average U.S. weather anomalies. Geophys
Res Lett 32:L13705. doi:10.1029/2005GL022738
Magnuson JJ et al (2000) Historical trends in lake and river ice cover
in the Northern Hemisphere. Science 289:1743–1746. doi:10.
1126/science.289.5485.1743
Mesinger F et al (2006) North American regional reanalysis. Bull Am
Meteorol Soc 87:343–360. doi:10.1175/BAMS-87-3-343
Mishra V, Cherkauer KA, Bowling LC, Huber M (2011) Lake ice
phenology of small lakes: impacts of climate variability in the
Great Lakes region. Glob Planet Change 76:168–185. doi:10.
1016/j.gloplacha.2011.01.004
Mo KC, Livezey RE (1986) Tropical-extratropical geopotential
height teleconnections during the Northern Hemisphere winter.
Mon Weather Rev 114:2488–2515
Partington et al (2003) Late twentieth century Northern Hemisphere
sea-ice record from U.S. National Ice Center ice charts.
J Geophys Res 108(C11):3343. doi:10.1029/2002JC001623
X. Bai et al.
123
Quadrelli R, Wallace JM (2002) Dependence of the structure of the
Northern Hemisphere annular mode on the polarity of ENSO.
Geophys Res Lett 29(23):2132. doi:10.1029/2002GL015807
Quinn FH, Assel RA, Boyce DE, Leshkevich GA, Snider CR,
Weisnet D (1978) Summary of Great Lakes weather and ice
conditions, winter 1976–77. NOAA Technical Memorandum
ERL GLERL-20, Great Lakes Environmental Research Labora-
tory, Ann Arbor, MI (PB-292-613/7GA) 141 pp
Robertson DM, Wynne RH, Chang WYB (2000) Influence of El Nino
on lake and river ice cover in the Northern Hemisphere from
1900 to 1995. Verhandlungen Internationale Vereinigung fur
Theoretische und Angewandte Limnologie 27:2784–2788
Rodionov S, Assel RA (2000) Atmospheric teleconnection patterns
and severity of winters in the Laurentian Great Lakes basin.
Atmos Ocean XXXVIII(4):601–635
Rodionov S, Assel RA (2003) Winter severity in the Great Lakes
region: a tale of two oscillations. Clim Res 24:19–31
Rodionov S, Assel RA, Herche LR (2001) Tree-structured modeling
of the relationship between Great Lakes ice cover and atmo-
spheric circulation patterns. J Great Lakes Res 27(4):486–502
Schwab DJ, Leshkevich GA, Muhr GC (1992) Satellite measurements
of surface water temperature in the Great Lakes: Great Lakes
Coast Watch. J Great Lakes Res 18(2):247–258
Sommer U (1989) Plankton ecology: succession in plankton com-
munities. Brock/Springer Series in contemporary Bioscience.
Springer, Berlin
Straus DM, Shukla J (2002) Does ENSO force the PNA? J Clim
15:2340–2358. doi:10.1175/1520-0442(2002)015\2340:
DEFTP[2.0.CO;2
Thompson DWJ, Wallace JM (2001) Regional climate impacts of the
Northern Hemisphere annular mode. Science 293:85–89
Walker GT, Bliss EW (1932) World weather V. Mem R Meteorol Soc
4:53–84
Wallace JM (2000) North Atlantic oscillation/annular mode: two
paradigms—one phenomenon. Q J R Meteorol Soc 126:791–805
Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential
height field during the Northern Hemisphere winter. Mon
Weather Rev 109:784–812. doi:10.1175/1520-0493(1981)109
\0784:TITGHF[2.0.CO;2
Wang C, Wang X (2013) Classifying El Nino Modoki I and II by
different impacts on rainfall in Southern China and Typhoon
tracks. J Clim 26:1322–1338. doi:10.1175/JCLI-D-12-00107.1
Wang J, Mysak LA, Ingram RG (1994) Interannual variability of sea-
ice cover in Hudson Bay, Baffin Bay and the Labrador Sea.
Atmos Ocean 32(2):421–447
Wang J, Ikeda M, Zhang S, Gerdes R (2005) Linking the northern
hemisphere sea ice reduction trend and the quasi-decadal Arctic
Sea Ice Oscillation. Clim Dyn 24:115–130. doi:10.1007/s00382-
004-0454-5
Wang C, Liu H, Lee S-K (2010a) The record-breaking cold
temperatures during the winter of 2009/10 in the Northern
Hemisphere. Atmos Sci Lett 11:161–168. doi:10.1002/asl.278
Wang J, Bai X, Leshkevich GA, Colton MC, Clites AH, Lofgren BM
(2010b) Severe ice cover on Great Lakes during winter
2008–2009. EOS Trans 91(5):41–42
Wang J, Hu H, Schwab D, Leshkevich G, Beletsky D, Hawley N,
Clites A (2010c) Development of the Great Lakes ice-circulation
model (GLIM): application to Lake Erie in 2003–2004. J Great
Lakes Res 36:425–436. doi:10.1016/j.jglr.2010.04.002
Wang J, Assel RA, Walterscheid S, Clites A, Bai X (2012a) Great
lakes ice climatology update: winter 2006–2011 description of
the digital ice cover data set, NOAA Technical Memorandum
GLERL-155, 37 pp
Wang J, Bai X, Hu H, Clites AH, Colton MC, Lofgren BM (2012b)
Temporal and spatial variability of Great Lakes ice cover,
1973–2010. J Clim 25(4):1318–1329. doi:10.1175/2011JCLI4066.1
Combined effects of a strong positive NAO and La Nina
123