a record-breaking low ice cover over the great lakes during winter...

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

http://www.ncdc.noaa.gov/temp-and-precip/time-series/

http://www.ncdc.noaa.gov/temp-and-precip/time-series/

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

http://www.glerl.noaa.gov/data/pgs/glice/glice.html

http://www.glerl.noaa.gov/data/pgs/glice/glice.html

http://coastwatch.glerl.noaa.gov/statistic/statistic.html

http://coastwatch.glerl.noaa.gov/statistic/statistic.html

http://oceandata.sci.gsfc.nasa.gov/MODIST/

http://oceandata.sci.gsfc.nasa.gov/MODIST/

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)


123

http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_change.shtml



http://climatedataguide.ucar.edu/guidance/hurrell-north-atlantic-oscillation-nao-index-pc-based

http://climatedataguide.ucar.edu/guidance/hurrell-north-atlantic-oscillation-nao-index-pc-based

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


123

http://www.esrl.noaa.gov/psd/csi/events/2012/marchheatwave/index.html



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


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)


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


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


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


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


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


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


123

http://www.cpc.ncep.noaa.gov/products/predictions/90day/

http://www.cpc.ncep.noaa.gov/products/predictions/90day/

http://www.noaanews.noaa.gov/stories2011/20111020_winteroutlook.html

http://www.noaanews.noaa.gov/stories2011/20111020_winteroutlook.html

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.


123

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http://www.cdc.noaa.gov/

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a record-breaking low ice cover over the great lakes during winter...

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