Can North Atlantic Sea Ice Anomalies Account for Dansgaard–OeschgerClimate Signals?*

CAMILLE LI

Bjerknes Centre for Climate Research, and Department of Earth Science, University of Bergen, Bergen, Norway

DAVID S. BATTISTI

Department of Atmospheric Sciences, University of Washington, Seattle, Washington, and Geophysical Institute,

University of Bergen, Bergen, Norway

CECILIA M. BITZ

Department of Atmospheric Sciences, University of Washington, Seattle, Washington

(Manuscript received 2 September 2009, in final form 17 May 2010)

ABSTRACT

North Atlantic sea ice anomalies are thought to play an important role in the abrupt Dansgaard–Oeschger

(D–O) cycles of the last glacial period. This model study investigates the impacts of changes in North Atlantic

sea ice extent in glacial climates to help provide geographical constraints on their involvement in D–O cycles.

Based on a coupled climate model simulation of the Last Glacial Maximum (21 ka), the Nordic seas and

western North Atlantic (broadly, south of Greenland) are identified as two plausible regions for large and

persistent displacements of the sea ice edge in the glacial North Atlantic. Sea ice retreat scenarios targeting

these regions are designed to represent ice cover changes associated with the cold-to-warm (stadial-to-

interstadial) transitions of D–O cycles. The atmospheric responses to sea ice retreat in the Nordic seas and in

the western North Atlantic are tested individually and together using an atmospheric general circulation

model. The Nordic seas ice retreat causes 108C of winter warming and a 50% increase in snow accumulation

at Greenland Summit; concomitant ice retreat in the western North Atlantic has little additional effect. The

results suggest that displacements of the winter sea ice edge in the Nordic seas are important for creating the

observed climate signals associated with D–O cycles in the Greenland ice cores.

1. Introduction

Sea ice is an important element in the glacial climate

system because of its pivotal role in the surface heat,

moisture, and momentum budgets of the polar regions.

The presence of sea ice lowers surface temperature by

insulating the atmosphere from the ocean heat reservoir

and by increasing surface albedo. Sea ice also affects the

vertical structure of the upper ocean, alters deep-water

formation sites, and regulates the availability of moisture

for building continental ice sheets (Hebbeln et al. 1994;

Rind et al. 1995).

Sea ice is thought to be a principal player in Dansgaard–

Oeschger (D–O) cycles, the millennial-scale climate cy-

cles that are prominent features of North Atlantic (NAtl)

proxy records throughout much of the last ice age

(Dansgaard et al. 1993; Bond et al. 1993; Taylor et al.

1993; Grootes and Stuiver 1997; Sachs and Lehman

1999; Andersen et al. 2004). The interstadial phase of the

D–O cycle is a warm period in the mid- to high-latitude

Atlantic region, whereas the stadial phase is a cold pe-

riod. A remarkable feature of the D–O cycle is the abrupt

transition between stadial (cold) and insterstadial (warm)

conditions, which manifest in the Greenland ice cores

by an annual mean warming of up to 168C in a matter of

decades (Lang et al. 1999; Landais et al. 2004a,b; Huber

et al. 2006; Sanchez Goni et al. 2008) and a 50%–100%

* Bjerknes Centre for Climate Research Publication Number

A292.

Corresponding author address: Camille Li, Bjerknes Centre for

Climate Research, Allegaten 55, 5007 Bergen, Norway.

E-mail: camille@uib.no

15 OCTOBER 2010 L I E T A L . 5457

DOI: 10.1175/2010JCLI3409.1

� 2010 American Meteorological Society

increase in snowfall (Cuffey and Clow 1997; Andersen

et al. 2006).

Displacements of the sea ice edge have been proposed

as a key ingredient of D–O cycles (Broecker 2000; Gildor

and Tziperman 2003) because of their strong influence

on regional temperature and their potential to occur rap-

idly in response to relatively weak forcing via processes

such as the ice–albedo feedback (Curry et al. 1995). Re-

constructing past sea ice variability is difficult, but some

observational evidence exists to support the idea that

changing sea ice extent plays an important role in D–O-

related abrupt climate changes. For example, deuterium

excess measurements in the Greenland ice cores exhibit

rapid transitions associated with D–O cycles, and these

transitions have been interpreted as north/south shifts of

the moisture source for Greenland snow because of the

retreat or advance of North Atlantic sea ice (Masson-

Delmotte et al. 2005; Jouzel et al. 2007). Furthermore, a

benthic d18O record from a high-resolution marine core

on the Norwegian slope indicates more sea ice forma-

tion during cold stadial phases of D–O cycles than dur-

ing warm interstadials (Dokken et al. 2010, manuscript

submitted to Nat. Geosci., hereafter DOK).

The exact nature of sea ice’s involvement in D–O

cycles is the subject of much current research. One im-

portant question is what mechanisms drive these dis-

placements of the sea ice edge. Wind- and ocean-driven

Arctic sea ice variability in today’s climate exists on times

scales from weeks to decades (e.g., Fang and Wallace

1994; Venegas and Mysak 2000; Deser et al. 2002), but

the millennial pacing of D–O cycles requires long-lived

changes in sea ice cover. One hypothesis attributes the

cycles to an internal oscillation of the ocean thermoha-

line circulation (OTC) that causes abrupt shifts in ocean

heat transport into the North Atlantic, thereby altering

sea ice and regional climate (Broecker et al. 1985). A

recent multiproxy analysis of a high-resolution marine

core on the Norwegian slope suggests an alternative hy-

pothesis: a series of interactions between the ocean and

the marine and terrestrial cryosphere alternately build

and erode a fresh surface halocline in the Nordic seas

(NS) over millennial times scales, promoting the pres-

ence or absence of sea ice cover and a toggling of climate

between cold stadials and warm interstadials (DOK).

During stadials, the ocean continues to supply heat from

more southerly latitudes; however, this heat is stored

beneath the surface halocline, where it remains inacces-

sible to the atmosphere. The subsurface ocean expe-

riences a warming, also seen in other North Atlantic

marine cores and in climate models under certain fresh-

water hosing scenarios (Knutti et al. 2004; Rasmussen

and Thomsen 2004; Ruhlemann et al. 2004; Cheng et al.

2007; Mignot et al. 2007). The halocline is maintained

throughout the stadial owing in large part to high rates

of sea ice formation, a process that rejects brine in dense,

sinking plumes and encourages stratification (DOK).

Eventually, the subsurface becomes warm enough to

destabilize the water column. The surface halocline is

eroded, and the release of stored heat in combination

with other hypothesized changes in heat flux conver-

gence in the high latitudes produce warm, ice-free ocean

conditions through the duration of the interstadial.

We aim to assess what type of sea ice anomalies can

create the signatures of cold stadials and warm inter-

stadials recorded in the paleoclimate archives. Previous

modeling experiments using atmospheric general circu-

lation models (GCMs) suggest that winter ice edge dis-

placements produce temperature and accumulation

changes consistent with observations from the Green-

land ice cores, whereas summer ice edge displacements

produce too much of an accumulation effect and not

enough of a temperature effect (Li et al. 2005). The Li

et al. (2005) study supports other observational and

modeling studies that identify abrupt North Atlantic

climate events as primarily a wintertime phenomenon

(Renssen and Isarin 2001; Denton et al. 2005), but sev-

eral caveats concerning the modeling results must be

mentioned. First, the ocean boundary conditions used in

the study were based on a glacial sea surface tem-

perature (SST) and sea ice reconstruction [the revised

Climate: Long-Range Investigation, Mapping and Pre-

diction (CLIMAP) reconstruction; Crowley 2000] that is

now known to contain substantial errors, particularly in

regions such as the tropical Pacific and the North Atlantic

(see synthesis in Li and Battisti 2008). Second, the pre-

scribed sea ice edge displacement patterns were some-

what arbitrary and highly simplified.

Li et al. (2005) established seasonality constraints on

D–O-related displacements of the sea ice edge, pointing

to large winter changes and small summer changes as

a necessary condition. The goal of the present work is to

develop some intuition for the geographical nature of

these sea ice anomalies. Because there are still very few

reconstructions of past sea ice conditions, we adopt a

modeling approach using a combination of coupled and

atmosphere-only simulations to extend the work of Li

et al. (2005). Coupled model simulations are examined

to establish robust characteristics of the North Atlantic

sea ice cover in glacial times. Sea ice retreat scenarios

are created based on these glacial sea ice distributions,

making the results of this study more applicable to D–O

events than those of Li et al. (2005), in which the retreat

scenarios were defined in a somewhat ad hoc fashion.

Although geographically more realistic, the scenarios re-

main idealized in the sense that, where sea ice retreats,

the sea surface temperatures are set to the freezing point,

5458 J O U R N A L O F C L I M A T E VOLUME 23

and sea surface conditions elsewhere (outside of the North

Atlantic) are not altered. We focus on two issues in par-

ticular: 1) What are the characteristics of the North Atlantic

sea ice cover in glacial climates, and in which regions is it

susceptible to exhibiting large anomalies? 2) Is it possible to

place geographical constraints on D–O-related displace-

ments of the ice edge in these regions? In answering the

second question, we wish to evaluate where sea ice

anomalies must occur to have a substantial climate impact

consistent with the D–O signals recorded in Greenland

rather than to establish a precise D–O retreat scenario

that produces an exact match to the ice core records.

Section 2 contains a description of the simulations and

model datasets. In section 3, we analyze coupled model

simulations of the Last Glacial Maximum (LGM) to

identify the Nordic seas and western North Atlantic

(WAtl) as regions of interest for North Atlantic win-

ter sea ice variability. Section 4 examines surface cli-

mate and circulation changes associated with sea ice

perturbations in these two regions using a series of

atmosphere-only experiments. Section 5 presents a dis-

cussion of pending issues related to the response of the

atmosphere to North Atlantic sea ice perturbations and

implications for D–O cycles. Finally, concluding re-

marks are presented in section 6.

2. Data and methods

We use sea ice data from coupled climate model sim-

ulations of LGM (21 ka) climate from the second phase

of the Paleoclimate Modelling Intercomparison Project

phase 2 (PMIP2). The models included in the analysis

are listed in Table 1. For the LGM, the models are forced

in compliance with PMIP2 protocol: 21-ka insolation,

atmospheric greenhouse gas concentrations based on ice

core measurements (Fluckiger et al. 1999; Dallenbach

et al. 2000; Monnin et al. 2001), atmospheric aerosols

at preindustrial (PI) values, and land ice and coastlines

from the ICE-5G reconstruction (Peltier 2004; see data-

set online at http://www.atmosp.physics.utoronto.ca/

;peltier/data.php) corresponding to 120-m sea level

depression at LGM. The PMIP2 control simulations for

PI climate are also used for comparison. General features

of the simulated climates are presented in Braconnot

et al. (2007). The LGM simulations exhibit good agree-

ment, with key changes indicated by proxy data such as

a weakening of the monsoons, cooling of the tropics, cooling

of the North Atlantic region and Eurasian continent, and

a southeastward extension of the North Atlantic storm

track (Kageyama et al. 2006; Braconnot et al. 2007; Laıne

et al. 2009; Otto-Bliesner et al. 2009), although cooling in

polar regions is underestimated (Masson-Delmotte et al.

2006). Complete documentation of the models and ex-

perimental setup is available at the PMIP2 Web site

(available online at http://pmip2.lsce.ipsl.fr).

One hundred years of monthly mean sea ice data

from one of the models, the Community Climate Sys-

tem Model, version 3 (CCSM3), developed at the Na-

tional Center for Atmospheric Research (NCAR), are

used to better understand the distribution and natural

variability of North Atlantic sea ice at the LGM. The

model and its performance in a present-day setup is

documented in Collins et al. (2006a), and the LGM sim-

ulation is described in Otto-Bliesner et al. (2006). Briefly,

the relevant configuration of CCSM3 comprises the

primitive equation Community Atmosphere Model, ver-

sion 3 (CAM3), at T42 horizontal resolution with 26 hy-

brid coordinate vertical levels (Collins et al. 2006b); a land

model with land cover and plant functional types, prog-

nostic soil and snow temperature, and a river-routing

scheme (Dickinson et al. 2006); the NCAR implementa-

tion of the Parallel Ocean Program (POP) on a 320 3 384

displaced pole grid (nominal horizontal resolution of 18)

with 40 vertical levels (Smith and Gent 2002); and a

dynamic–thermodynamic sea ice model on the same grid

as the ocean model (Briegleb et al. 2004). The model has

TABLE 1. PMIP2 coupled models. Please note the following for sea ice: elastic–viscous–plastic dynamics (EVP); free drift dynamics

(FD; neglects internal ice force); ice thickness distribution (ITD); viscous–plastic dynamics (VP); the number of layers refers to tem-

perature layers in ice/snow; and heat reservoir for solar radiation (Semtner 1976) or brine pockets (Bitz and Lipscomb 1999). The

sponsoring institutions of the models are the following: CCSM3, National Center for Atmospheric Research, United States; CNRM-

CM33, Meteo-France/Centre National de Recherches Meteorologiques, France; ECBILTCLIO, Royal Netherlands Meteorological

Institute (KNMI), Netherlands; HadCM3M2, UK Met Office, United Kingdom; IPSL CM4, Institut Pierre Simon Laplace, France; and

MIROC3.2, Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies, and Frontier

Research Center for Global Change (JAMSTEC), Japan.

Model Atmosphere Ocean Sea ice

CCSM3 T42 (;2.88 3 2.88) 3 L18 0.38–18 3 18 3 L40 ITD, EVP, 4 layers, brine pockets

CNRM-CM33 T63 (;1.98 3 1.98) 3 L45 0.58–28 3 28 3 L31 ITD, EVP, 4 layers

ECBILTCLIO T21 (;5.68 3 5.68) 3 L3 38 3 38 3 L20 VP, 3 layers

HadCM3M2 2.58 3 3.758 3 L19 1.258 3 1.258 3 L20 FD, leads, 0 layers

IPSL CM4 2.58 3 3.758 3 L19 28 3 28 3 L31 VP, 3 layers, heat reservoir

MIROC3.2 T42 (;2.88 3 2.88) 3 L20 0.58–1.48 3 1.48 3 L43 EVP, 0 layers

15 OCTOBER 2010 L I E T A L . 5459

also been used in a range of paleoclimate studies, in

particular to examine the climate response to freshwater

hosing in glacial and deglacial climates compared to the

present climate (Cheng et al. 2007; Bitz et al. 2007; Liu

et al. 2009; Otto-Bliesner and Brady 2010).

The climatological sea ice cover in the CCSM3 LGM

simulation compares well with reconstructions from this

time period (Li and Battisti 2008), and so it is used as

the starting point in designing sea ice scenarios repre-

senting the stadial and interstadial phases of the D–O

cycle. CAM3, the atmospheric component of CCSM3, is

forced with these scenarios to investigate the climate

response to sea ice perturbations in key regions of the

North Atlantic. The CAM3 experiments are initialized

in January with output from the CCSM3 LGM simula-

tion, and results are based on 30 yr of monthly mean

output after discarding 2 yr of spinup. The National Snow

and Ice Data Center’s sea ice index (detrended) gener-

ated from passive microwave satellite data (Fetterer et al.

2009) is used to calculate Northern Hemisphere (NH)

sea ice extent and variability in the 1978–2008 observing

period for comparison with model simulations.

3. Coupled model simulations of NorthernHemisphere LGM sea ice

The objective of this section is to identify the regions

where we expect large excursions of the winter sea ice

edge in glacial climates. The analysis is based mainly on

CCSM3 coupled model output because there are very

few long, proxy-based reconstructions of glacial sea ice

extent. We first provide an overview of Northern Hemi-

sphere sea ice in all available LGM simulations from

PMIP2, and then we focus on a more detailed analysis

of the CCSM3 simulation.

a. PMIP2 model intercomparison

The PMIP2 model participants listed in Table 1 sim-

ulate a range of sea ice thickness and concentration dis-

tributions in the LGM climate (Fig. 1). In the Northern

Hemisphere, the higher-resolution climate models pro-

duce annual mean ice areas from over 6.4 3 1012 m2 to

10.4 3 1012 m2, with mean Arctic ice thicknesses from

5.2 to 15.7 m (Table 2). The two models with the greatest

mean ice thickness—the Centre National de Recherches

Meteorologiques Coupled Global Climate Model, ver-

sion 3.3 (CNRM-CM33) and ECBILTCLIO, the latter

of which is the only low-resolution intermediate com-

plexity model of the group—produce on the order of

double the volume of Northern Hemisphere sea ice

compared to the other models and exhibit relatively

weak seasonal cycles in ice area and volume (Fig. 1g and

Table 2).

Because the reconstruction of past sea ice extent re-

mains a difficult and unresolved challenge (Kucera et al.

2005), it is unclear how best to evaluate the reliability of

these simulation results. All the higher-resolution models

perform quite well in simulating the mean Arctic sea ice

cover over the 1981–2000 satellite observing period

(Arzel et al. 2006); compared to the National Snow and

Ice Data Center dataset (Comiso 2002), the errors in

March and September sea ice extent are ,10%, with

the exception of certain cases such as CCSM3 in March

(116%) and the Met Office Hadley Centre Coupled

Model, version 3, with the MOSES2 land surface scheme

(HadCM3M2) in September (218%). Northern Hemi-

sphere sea ice area in the PMIP2 control (PI) simulations

ranges from 7.1 to 14.7 3 1012 m2 (Table 2) and also

compares reasonably well with estimates from the 1978–

2008 observing period (approximately 12 3 1012 m2, not

detrended). However, good validation with observations

does not necessarily indicate that these models have

a better representation of sea ice processes, or that they

will produce a more realistic evolution of sea ice in past or

future scenarios.

There are several observational constraints that

help to discriminate among the LGM simulations of

these models. CNRM-CM33 and ECBILTCLIO ap-

pear to be the least reasonable models: they have ex-

tremely thick ice in the Arctic (15.7 and 9.5 m annual

mean) and perennial sea ice in the Nordic seas. The

latter is not in agreement with marine proxy data,

which suggest the presence of at least a seasonally open

ocean in the Nordic seas at the LGM (Meland et al. 2005;

de Vernal et al. 2005; Kucera et al. 2005). Consistent with

the proxy data, the summer Nordic seas are entirely ice

free in two of the models, CCSM3 and HadCM3M2,

whereas L’Institut Pierre-Simon Laplace Coupled Model,

version 4 (IPSL CM4) and the Model for Interdisci-

plinary Research on Climate 3.2 (MIROC3.2) show

a strip of open ocean confined to the Norwegian coast.

All the models simulate sea ice concentrations close

to 100% year-round in the Arctic Basin, but they

show considerable differences in the low-concentration

(,50%) regions of the western North Atlantic and

western North Pacific. Consistent with proxy recon-

structions, CCSM3 and HadCM3M2 exhibit the most

winter sea ice in these regions, particularly in the

Atlantic off the coast of North America, where one

would expect favorable conditions for sea ice formation

because of freshwater input and cold-air advection off

the margin of the Laurentide ice sheet. [Note, however,

that a model bias could also be contributing to the

western North Atlantic ice cover in CCSM3, which ex-

hibits overly extensive sea ice in the Labrador Sea for

the present climate (Jochum et al. 2008).] Hence, we

5460 J O U R N A L O F C L I M A T E VOLUME 23

accept the CCSM3 and HadCM3M2 as plausible rep-

resentations of the sea ice extent at LGM, and we fur-

ther explore the former model to help guide the design

of sea ice retreat scenarios that might be expected dur-

ing glacial times.

b. Maintenance and variability of North Atlantic seaice in CCSM3

We turn our attention to North Atlantic sea ice as

simulated by CCSM3 to gain insight into its glacial dis-

tribution, and to identify regions where it can be sensitive

to forcing and hence likely to undergo large excursions.

The processes controlling the hypothesized D–O sea

ice anomalies are obviously not captured in this type of

equilibrium coupled simulation, but we use the simu-

lated climatology to guide the design of the sea ice per-

turbation experiments in the next section. These analyses

also serve to illustrate that the North Atlantic sea ice

cover can exhibit substantially altered behavior in a dif-

ferent climate.

Annual mean sea ice thickness and concentration dis-

tributions are shown in Fig. 1a. Of particular interest is

FIG. 1. Sea ice thickness and extent at LGM simulated by PMIP2 models. (a)–(f) Color shading shows annual mean sea ice thickness (m).

The thick black lines show March ice extent, and the thin black lines show September ice extent (both 50% ice concentration). Model

descriptions appear in Table 1. (g) Seasonal cycle of North Atlantic sea ice area (1012 m2) for each model. (left to right) For each

month, the bars represent models (a)–(f) and are color coded to match the model names in the map panels.

15 OCTOBER 2010 L I E T A L . 5461

the seasonal sea ice cover in the Nordic seas and western

North Atlantic. In winter, both regions exhibit extensive

sea ice; in summer, ice retreats back toward the east

coasts of Greenland and North America, leaving the

Nordic seas and western North Atlantic ice free.

Figure 2 shows the movement, growth, and melt of

North Atlantic sea ice as January–March (JFM) seasonal

averages. These months were chosen to represent the

heart of the Northern Hemisphere winter growth sea-

son: sea ice growth peaks in February, whereas the net

gain in sea ice volume peaks in December/January. The

growth term includes congelation (basal) and frazil (sur-

face ocean layer) ice growth and snow ice formation by

flooding of snow-covered ice. The melt term includes

basal and lateral melt, determined largely by the ocean–

sea ice heat flux, and top melt (negligible during winter) is

determined largely by atmosphere–sea ice heat flux.

As in today’s climate, growth occurs in the Arctic

Basin and along high-latitude coastlines, where persis-

tent offshore winds drive sea ice away from the land to

produce polynya-like conditions. High growth rates in

the Nordic seas are consistent with the presence of win-

ter sea ice cover there, and thermodynamic processes

account for over 90% of the ice volume tendency over

the JFM season. Dynamics plays a more important role

for the western North Atlantic ice tongue extending off

the coast of North America. Growth rates are relatively

small in the interior of the ice tongue (Labrador Sea and

central North Atlantic), and melt dominates at the

southern and eastern edges. Considerable ice growth is

seen in Baffin Bay and Davis Strait, and ice velocities

indicate advection out from Davis Strait and south with

the East Greenland Current to feed the ice tongue. In

fact, the ice volume tendency due to thermodynamic

processes is on average negative over the JFM season,

and it is the large positive dynamical tendency that main-

tains the sea ice cover in the western North Atlantic.

Winter melt takes place along the ice edge and in

several locations in the interior of the Labrador Sea and

the Nordic seas (Fig. 2, bottom panel). This melt occurs

primarily at the base of the sea ice, although there is a

substantial contribution from lateral melt along the south-

ern portion of the East Greenland Current and at the

southeastern edge of the ice tongue (not shown). Basal

and lateral melt are controlled by ocean–sea ice heat flux,

hence winter melt is attributed mainly to ocean heat

transport at the LGM, much as was found for the present

climate in an earlier version (CCSM2) of the NCAR cou-

pled model (Bitz et al. 2005). However, as noted by Bitz

et al. (2005), large offshore ice growth and ice advection are

important influences on the position of the ice edge in

certain regions (such as the glacial western North Atlantic).

In addition to the sea ice cover being more extensive,

other seasonal aspects are altered in the LGM simulation

compared to the present day. For example, interannual

variability in sea ice extent is mostly a wintertime phe-

nomenon in the LGM simulation (Fig. 3, bottom),

whereas both ice area and sea ice volume exhibit sub-

stantial summer variability in observations (Deser et al.

2000) and in the simulations of present-day climate (Fig. 3,

top). The interannual sea ice variability shown here arises

from internal atmosphere–ocean processes in the coupled

model, and it is unlikely that these are the same processes

controlling the long-lived ice edge displacements in the

proposed D–O mechanism. It is nevertheless informa-

tive that shifts in the seasonality of variability are possi-

ble in different climates; and despite the disparate time

scales, the dominance of winter variability is consistent with

TABLE 2. PMIP2 coupled model control (preindustrial) and LGM sea ice conditions for the NH, NAtl, and Arctic (defined as poleward of

808N). The annual mean and seasonal range are shown for each variable. Model descriptions appear in Table 1.

NH area (1012 m2) NH volume (1013 m3) NAtl area (1012 m2) Arctic thickness (m)

Model Mean Range Mean Range Mean Range Mean Range

Control

CCSM3 13.4 7.9–18.5 4.7 3.7–5.5 3.7 1.9–5.3 4.5 4.1–5.2

CNRM-CM33 10.7 8.7–12.8 2.2 1.5–2.8 2.1 1.0–3.0 2.1 1.7–2.6

ECBILTCLIO 14.7 10.4–18.0 4.4 3.8–4.9 4.5 2.8–5.6 3.5 3.0–4.2

HadCM3M2 10.2 3.6–16.4 2.1 0.8–3.5 2.4 0.5–4.4 2.2 1.5–2.7

IPSL CM4 10.2 5.8–13.7 3.0 2.2–3.7 3.0 1.2–4.7 3.3 2.5–4.5

MIROC3.2 7.1 4.3–10.0 1.3 0.7–2.0 1.9 0.7–3.1 2.1 1.5–2.8

LGM

CCSM3 10.4 6.7–15.1 4.3 3.9–4.8 4.4 2.4–6.7 5.5 5.3–6.0

CNRM-CM33 10.0 8.2–13.2 12.3 11.9–12.6 3.8 3.6–3.9 15.7 15.6–15.9

ECBILTCLIO 13.3 11.2–15.6 8.9 8.6–9.0 6.8 5.9–7.3 9.5 9.5–9.5

HadCM3M2 9.4 6.4–14.2 4.2 3.4–5.2 3.7 2.2–5.8 5.6 5.2–6.0

IPSL CM4 8.1 5.6–10.3 4.8 4.4–5.1 2.9 1.4–4.1 6.9 6.0–8.7

MIROC3.2 6.4 4.9–8.0 2.6 2.3–3.0 2.1 1.5–2.6 5.2 4.8–5.5

5462 J O U R N A L O F C L I M A T E VOLUME 23

paleoclimate evidence and modeling results (Renssen

and Isarin 2001; Denton et al. 2005; Li et al. 2005), in-

dicating that winter season ice anomalies were instru-

mental in producing abrupt climate change in the North

Atlantic region during the last glaciation and the early

Holocene.

Geometry is a chief cause for this shift in seasonality.

The maps in Fig. 4 show that variations in sea ice con-

centration occur in the marginal ice zones, the part of the

ice cover that is affected by ocean processes such as swells

and waves. Variability is small close to the pole, where

relatively thick ice is more or less constantly present. If the

sea ice edge is long (e.g., located far from the coast with

excursions into open ocean), the marginal ice zone is ex-

tensive and there is more opportunity for differences in ice

extent from one year to the next. The LGM simulation has

very little marginal ice zone in summer compared to

winter and consequently very little summer variability in

sea ice area. In today’s climate, summer sea ice is re-

stricted mostly to the Arctic Ocean, but there is a consid-

erable amount of marginal ice because much of the ice

cover at the periphery of the Arctic Basin is relatively thin.

Like seasonality, the spatial signature of sea ice vari-

ability can also change in different climates. Today, sea

ice variability has a dipole pattern with opposing centers

of action in the Davis Strait–Labrador Sea region and

the Nordic seas from weekly (Fang and Wallace 1994) to

seasonal (Walsh and Johnson 1979) time scales. The

temporal variability of this dipole pattern is coupled to

the North Atlantic Oscillation (NAO), the leading mode

of Northern Hemisphere sea level pressure variability

(Rogers and van Loon 1979; Fang and Wallace 1994). In

the LGM simulation, sea ice variability in the Nordic

seas and western North Atlantic is not strongly related.

The simultaneous correlation of winter sea ice area in

the Nordic seas and western North Atlantic regions is

less than 0.3 (,10% shared variance)—similar corre-

lation values between the Nordic seas and western

North Atlantic are found with the latter leading by up to

2 yr. Figure 4 shows composites of JFM sea ice con-

centration for the 25 yr (i.e., 25% of the total 100 yr)

with the least (category: 2) and most (category: 1) sea

ice cover in the two regions. The bar codes indicate that,

FIG. 2. Sea ice velocity, growth rate, and melt rate in CCSM3

LGM simulation: (top) speed (cm s21) in color shading, with ice

flow ($2 cm s21) by arrows; (middle) growth rate (cm day21) in-

cluding congelation and frazil growth, and snow–ice formation;

and (bottom) melt rate (cm day21) including basal, lateral, and top

melt. In all panels, the black contour shows JFM sea ice extent

(15% ice concentration).

15 OCTOBER 2010 L I E T A L . 5463

one-third of the time, the regions experience anoma-

lously low or high sea ice cover simultaneously; the other

two-thirds of the time, the anomalously low or high ice

conditions in the regions share no particular relationship.

The maps in Fig. 3 reinforce the point, with the NS2 and

NS1 composites showing weak concentration anoma-

lies in the western North Atlantic, and the WAtl2 and

WAtl1 composites showing weak concentration

anomalies in the Nordic seas.

In summary, there are two main masses of sea ice in

the glacial North Atlantic. The Nordic seas ice distri-

bution is governed primarily by in situ growth and melt

in the CCSM3 simulation of LGM climate, whereas the

western North Atlantic ice tongue owes its existence in

large part to the import of ice formed in Baffin Bay and

Davis Strait and is advected southeastward into the open

ocean. Variability in the LGM sea ice extent is mainly a

wintertime phenomenon, and it is concentrated in these

two regions, albeit uncorrelated in time.

4. Climate impacts of North Atlantic sea iceperturbations

In this section, we construct scenarios for hypothetical

sea ice anomalies associated with D–O cycles and examine

their climate impacts in CAM3, the atmospheric compo-

nent of CCSM3. The scenarios are based on CCSM3’s

simulated LGM sea ice cover, with alterations to the two

regions that are the most likely to exhibit large displace-

ments of the ice edge—the Nordic seas and western North

FIG. 3. Seasonal cycle and variability of Northern Hemisphere sea ice area in CCSM3 (top) present day (PD) and (bottom) LGM

simulations. Plots show the seasonal cycle of simulated Northern Hemisphere ice area, normalized by and expressed as a fraction of the

annual mean (purple) and of the marginal ice zone (15%–50% ice concentration) area (1013 m2; dashed blue). Shading indicates the in-

terdecile range (10th–90th percentiles; light green) and interquartile range (25th–75th percentiles; dark green) of ice area, normalized by and

expressed as a fraction of the monthly mean. The PD panel also shows observational estimates of Northern Hemisphere ice area (thick gray)

and its interdecile range (thin gray) from the National Snow and Ice Data Center’s sea ice index 1978–2008 (detrended), both standardized as

described earlier. Maps show the variance of seasonally averaged sea ice concentration in JFM and July–September (JAS) (units of sea ice

fraction squared). Black contours mark the 15% (thin) and 50% (thick) ice concentrations that define the marginal ice zone.

5464 J O U R N A L O F C L I M A T E VOLUME 23

Atlantic—during winter months to represent possible

stadial and interstadial conditions. The use of prescribed

SST and sea ice conditions is a common modeling strategy

for testing the response of the atmosphere to changes in

the surface ocean. In this case, the changes (sea ice only)

are attributed to a mechanism for D–O cycles (DOK) in-

volving ocean processes that are not represented by the

model (i.e., halocline formation, brine rejection, and

freshwater input from land ice). Thus, our experiments

provide insight into the climate impacts of sea ice anom-

alies in two regions of the North Atlantic but not into what

creates and maintains the sea ice anomalies themselves.

We first construct a stadial (cold) glacial state with

extensive sea ice in the North Atlantic and all other

boundary conditions (insolation, greenhouse gases, ice

sheets, and land–ocean geometry) corresponding to 21 ka,

as specified in PMIP2. The stadial simulation (Fig. 5a)

has identical sea surface conditions to the CCSM3 simu-

lation in the western North Atlantic ice tongue region

year-round but higher winter sea ice concentrations in and

south of the Nordic seas. We then perturb this state by

removing sea ice in the Nordic seas and the western North

Atlantic during the winter (December–May) months such

that there is a smooth transition from the November ice

cover to the April ice cover (Fig. 5e). Sea surface con-

ditions for the scenarios are shown in Fig. 5: scenario

lowiceNS represents a retreat of sea ice in the Nordic seas,

scenario lowiceWAtl represents a retreat of sea ice in the

western North Atlantic, and scenario lowice represents

a retreat of sea ice in both regions.

Before examining the temperature response to the sea

ice scenarios, we note that the CCSM3’s standard LGM

FIG. 4. Composites of winters (JFM average) with restricted and extensive sea ice cover in WAtl and NS in CCSM3

LGM simulation. Maps show averages of the 25 yr (i.e., 25% of the total 100 yr) with the least (WAtl2 and NS2) and

most (WAtl1 and NS1) JFM sea ice area in each region, expressed as concentration anomalies (%) from the

seasonal average. Black contours show the climatological JFM sea ice extent (15% ice concentration). The bar codes

provide a schematic indication of the specific years that are included in the (top) 25th and (bottom) 75th percentiles.

The vertical axis represents time through the 100-yr simulation, with time running from bottom to top. Gray bars

mark years used in the (left side of bar code) WAtl composites and (right side) NS composites; common years appear

as long black bars.

15 OCTOBER 2010 L I E T A L . 5465

simulation exhibits approximately half the observed an-

nual mean LGM-to-present-day temperature difference

at Greenland Summit. In fact, this weak LGM Summit

cooling is a general feature of the PMIP2 models. In

a five-model intercomparison study, the model-to-data

discrepancy in Greenland cooling was found to range

from 68 to 168C, with the CCSM3 performing better than

all but HadCM3M2 (Masson-Delmotte et al. 2006).

Masson-Delmotte et al. (2006) examine some possible

explanations for the model-to-data discrepancy and

find that the lowering of the Summit because of topo-

graphic smoothing (e.g., in CCSM3, the model Summit

at LGM is approximately 250 m lower than today’s Sum-

mit elevation of 3200 m) and shifts in the seasonality

of precipitation produce small—and, in some cases,

unhelpful—adjustments to the simulated Summit

temperatures. They speculate that the discrepancy could

be linked to missing factors such as the lack of dust

changes, vegetation feedbacks, soil feedbacks, and the

associated impacts on atmospheric heat transport to

Greenland.

The model’s response to sea ice retreat will be com-

pared to the Greenland ice core records of D–O warm-

ings. A comprehensive review of ice core parameters

that vary in concert with D–O cycles can be found in

Wolff et al. (2010). The three proxy records of particular

interest here are the following: 1) abrupt annual mean

temperature changes derived from fractionated gases

trapped in the ice (Severinghaus et al. 1998), 2) annual

accumulation changes derived from ice layer thickness

corrected by ice flow models (Alley et al. 1993), and

3) an accumulation-weighted temperature signal derived

FIG. 5. Sea ice and SST boundary conditions for CAM3 sea ice experiments. These scenarios are based on the SST

and sea ice from the coupled CCSM3 LGM run but with some additional fabricated alterations to North Atlantic sea

ice extent in the winter months as described in the text. (a)–(d) Colors show annual mean SSTs, the thick black lines

show March ice extent, and the thin black lines show September ice extent (both 50% ice concentration). (e) Seasonal

cycle of North Atlantic sea ice area (1012 m2) for each scenario. (left to right) For each month, the bars represent

scenarios (a)–(d) and are color coded to match the scenario names in the map panels.

5466 J O U R N A L O F C L I M A T E VOLUME 23

from the isotopic composition of the ice itself (Jouzel

et al. 1997). This last dataset is one of the best known ice

core proxies, but also perhaps one of the most difficult to

interpret, as it is affected by a host of other factors such

as changes in moisture source, moisture transport path,

seasonality of snowfall, and the strength of the atmo-

spheric inversion (Cuffey et al. 1994; Johnsen et al. 1995;

Masson-Delmotte et al. 2005). As noted in the introduc-

tion, the goal of these scenario experiments is to de-

termine where sea ice anomalies must occur to have a

substantial climate impact that is consistent with these

ice core records; it is not to establish one definitive retreat

scenario that produces an exact match to the ice core

records.

We focus on the large-scale features of the atmo-

spheric response to sea ice retreat with some attention

given to the response at Greenland Summit, defined as

the two highest model grid boxes in Greenland (with

topographic smoothing, the mean elevation is 2957 m

above sea level compared to today’s elevation of 3200 m).

Comparisons are also made at several other locations on

Greenland (see details in Table 3 caption), where there

is a clear signature of D–O cycles in existing ice cores.

While the D–O-related isotope signal at these sites am-

plifies from south to north, differences in moisture source

are thought to be partly responsible, and thus the spatial

pattern of the associated temperature signal remains

unclear (Johnsen et al. 2001; Wolff et al. 2010).

In the sea ice retreat scenarios, there is in general an

increase in mean surface temperature and a reduction in

interannual variability over the North Atlantic (Figs. 6b–d).

The warming is quite localized to the region where sea ice

is removed, is relatively shallow (up to about 700 hPa),

and decays from south to north over Greenland (Table 3).

Temperature is most sensitive to sea ice changes in the

Nordic seas. Removing sea ice in this region alone

(scenario lowiceNS) causes over 108C of wintertime

warming and 58C of annual mean warming in the Summit

region (Table 3)—sea ice retreat in the western North

Atlantic alone (scenario lowiceWAtl) has less than half

the effect on Summit temperature and accumulation

as sea ice retreat in the Nordic seas alone (scenario

lowiceNS). If the Nordic seas are already ice free, then

the additional retreat of western Atlantic sea ice has

little effect, producing a statistically significant but small

warming, amounting to less than half a degree Celsius

even in the winter season (cf. scenarios lowiceNS and

lowice).

Sea ice retreat also produces an increase in snow ac-

cumulation in Greenland (Table 3). Net accumulation

(accumulation minus ablation) is calculated from the

snow hydrology component of the land model (Figs. 6e–h).

A proper assessment of the ice sheet mass balance

requires that the atmospheric fields be downscaled to

adequately high spatial resolutions and then used to

force an ice sheet model that includes a good repre-

sentation of ice dynamics. However, the accumulation

field can give a sense of the expected mass balance

changes when sea ice retreats. As with temperature, ac-

cumulation is more affected by retreat of sea ice in the

Nordic seas alone (50% increase in the annual mean in

the Summit region) than in the western North Atlantic

alone (11% increase in the annual mean in the Summit

region); and once the Nordic seas ice cover has been

removed (scenario lowiceNS), the further removal of

western North Atlantic sea ice (scenario lowice) produces

no additional accumulation increase (Table 3). The ac-

cumulation changes associated with the Nordic seas re-

treat are on the low end of observational estimates, which

show a 50%–100% increase at Summit and the North

Greenland Ice Core Project (NorthGRIP) site during

D–O warmings (Cuffey and Clow 1997; Andersen et al.

2006).

TABLE 3. Temperature and net accumulation on Greenland in

CAM3 sea ice experiments. Winter season temperature TDJF (8C),

annual mean temperature TANN (8C), and net annual accumulation

accANN (mm yr21) averaged over two model grid points near the

core sites of Greenland Summit (mean coordinates 71.28N, 38.58W,

2957 m), Dye 3 (mean coordinates 65.68N, 43.68W, 2333 m),

NorthGRIP (mean coordinates 75.38N, 42.28W, 2612 m), and

Camp Century (mean coordinates 76.78N, 61.98W, 815 m). The

columns marked D show the difference between each scenario and

the stadial case. The 95% confidence interval is #0.028C for tem-

perature and #0.8 mm yr21 for accumulation, making all differ-

ences significant at the 0.05 level.

TDJF TANN accANN

Scenario (8C)

D

(8C) (8C)

D

(8C) (mm yr21)

D

(%)

Greenland Summit

Stadial 252.26 237.65 71.8

LowiceWAtl 247.78 4.47 235.53 2.12 80.0 11

LowiceNS 241.87 10.38 232.61 5.04 107.7 50

Lowice 241.44 10.81 232.51 5.14 108.6 51

Dye 3

Stadial 243.65 228.39 293.9

LowiceWAtl 240.29 3.36 226.84 1.54 337.0 15

LowiceNS 229.03 14.63 221.58 6.81 430.8 47

Lowice 228.84 14.82 221.50 6.89 441.1 50

NorthGRIP

Stadial 252.60 238.17 46.5

LowiceWAtl 249.91 2.68 236.78 1.39 51.6 11

LowiceNS 243.45 9.15 233.53 4.65 59.0 27

Lowice 243.04 9.56 233.36 4.81 58.1 25

Camp Century

Stadial 252.18 234.80 80.5

LowiceWAtl 250.21 1.98 233.86 0.94 88.9 10

LowiceNS 246.76 5.43 232.08 2.72 96.7 20

Lowice 246.98 5.20 232.02 2.80 94.0 17

15 OCTOBER 2010 L I E T A L . 5467

The Greenland warming in these sea ice retreat sce-

narios is somewhat less than estimates of up to 168C

from proxy data (Lang et al. 1999; Landais et al. 2004a,b;

Huber et al. 2006; Sanchez Goni et al. 2008) and up to

78C in the previous model study of Li et al. (2005). We

note, however, that the 168C warming pertains to one of

the largest warmings recorded in a D–O cycle (event 19),

and that the range of abrupt warmings spans from 78 to

168C (see Wolff et al. 2010, and references therein). As

for the comparison to Li et al. (2005), there are several

key differences in experimental setup that contribute to

the larger warming response in that study. First, Li et al.

(2005) used an old version of the NCAR Community

Climate Model, version 3 (CCM3) that allows for only

100% ice concentration or 0% ice concentration in each

grid box, whereas the new version (CAM3) allows for

partial sea ice cover over a grid box such that sea ice

retreats from non-100% cover in most places. Second, the

old experiments included increases in SSTs at locations

where sea ice is removed, whereas the current experi-

ments keep SSTs at the freezing point. If we increase

SSTs above the freezing point where sea ice is removed,

more atmospheric warming is achieved (not shown).

Practical limits on how much SSTs may rise are ultimately

set by the ocean’s heat supply to the region. In the pro-

posed D–O mechanism, the stadial-to-interstadial change

in sea ice is part of a coupled, transient response of the

climate system associated with the disappearance of the

Nordic seas halocline and the opening of the subsurface

ocean heat reservoir to the atmosphere. Benthic fora-

minifera data show evidence of subsurface heat storage

during stadials (possible warming of up to 58–68C in sev-

eral hundred meters of the upper ocean), although there

remains some debate over the exact interpretation of

the benthics (DOK). The existence of subsurface warm-

ing when there is extensive sea ice in the North Atlantic

is also demonstrated in coupled climate model simula-

tions, which produce a maximum warming of several

FIG. 6. Surface temperature and net accumulation in CAM3 sea ice experiments. (a) 2-m temperature for the stadial (cold) simulation

and (b)–(d) temperature change in the sea ice retreat scenarios. (a)–(d) Annual mean temperature at Greenland Summit is indicated at

the bottom left. (e) Net accumulation over the ice sheets for the stadial simulation and (f)–(h) the net accumulation change with sea ice

retreat. The thick black contour shows March sea ice extent, the thin black contour shows September sea ice extent (50% ice concen-

tration), and the location of Greenland Summit (71.168N, 38.458W, 2957 m) is marked by a dot.

5468 J O U R N A L O F C L I M A T E VOLUME 23

degrees to 88C at depths of up to 1500 m in the Nordic

seas, with an additional weaker warming extending be-

low the surface cooling into the tropical and south trop-

ical Atlantic (Knutti et al. 2004; Cheng et al. 2007; Mignot

et al. 2007).

The weaker response upon removal of sea ice in the

western North Atlantic compared to the Nordic seas can

be viewed in terms of the circulation changes and local

diabatic changes produced in each case. Sea ice retreat

in the Nordic seas (scenario lowiceNS) causes a decrease

in sea level pressure along the northern flank of the

Icelandic low, implying a strengthening of the easterly

flow over the Nordic seas (Fig. 7). This is consistent with

Byrkjedal et al. (2006), who tested slightly different

glacial ocean boundary conditions and find an increase

in warm easterly flows at Summit when the Nordic seas

are ice free. These sea level pressure changes are ac-

companied by a local increase in tropospheric eddy ki-

netic energy consistent with more synoptic activity (not

shown). When sea ice retreats in the western North

Atlantic (scenario lowiceWAtl) only the western flank

of the Icelandic low deepens, producing low-level cir-

culation changes that do not greatly impact the Summit.

Modeling studies investigating the atmospheric re-

sponse to sea ice variability in today’s climate and to

future Arctic sea ice decline have found a large-scale at-

mospheric response that projects strongly onto the NAO,

the leading pattern of Northern Hemisphere atmospheric

variability (Alexander et al. 2004; Magnusdottir et al.

2004; Seierstad and Bader 2009; Honda et al. 2009). At

the LGM, the leading pattern of atmospheric vari-

ability exhibits an NAO-like dipole structure and ap-

pears to be governed by the same physics as its modern

counterpart, but its spatial signature is distorted and

shifted (Pausata et al. 2009). The sea level pressure re-

sponse to sea ice retreat in the Nordic seas (scenario

lowiceNS) projects onto this altered leading pattern of

variability [cf. Fig. 2 of Pausata et al. (2009) with Fig. 7b].

Sea ice retreat in the western North Atlantic (scenario

lowiceWAtl) has a weaker sea level pressure response

signature (Fig. 7a) that bears no similarity to the leading

pattern of variability. Despite differences in the mean

climate states, these results are consistent with the study

of Magnusdottir et al. (2004), who find that atmospheric

circulation is more sensitive to sea ice anomalies in the

Barents Sea than those in the Labrador Sea in today’s

climate. Aloft, both glacial retreat scenarios produce

wavelike patterns in geopotential height, reminiscent

of Fig. 11 in Byrkjedal et al. (2006), suggesting a linear

response to midlatitude thermal anomalies on a sphere

(Hoskins and Karoly 1981).

In terms of local diabatic changes, the effect of sea ice

retreat at Greenland Summit is related to (i) whether

the atmosphere feels the influence of a cold ice-covered

surface, warm ice-free ocean, or something in between

(partially ice free) at its bottom boundary, and (ii) how

the atmosphere distributes the (changed) heat flux when

the bottom boundary changes. First, much of the west-

ern North Atlantic ice cover exists in low concentrations

(indicated by the area between the 15% and 50% ice

concentration contours in Fig. 8), allowing some heat ex-

change between the ocean and atmosphere even when sea

ice is present. Hence, the change in ocean–atmosphere

heat flux from the stadial scenario to the retreat scenarios

is moderate in the case of western North Atlantic retreat

compared to Nordic seas retreat. Second, temperature

advection is largest where the mean near-surface winds

have a large component perpendicular to the sea ice

edge (i.e., perpendicular to the strongest temperature

gradients). These locations are primarily associated with

the Nordic seas rather than the western North Atlantic

(Fig. 9, top). When sea ice retreats, the convergence of

heat due to changes in temperature advection increases

over southeastern Greenland (Fig. 9, bottom), and this

occurs whether the western North Atlantic ice tongue

remains or retreats (Figs. 9d,f).

FIG. 7. Sea level pressure in CAM3 sea ice experiments. DJF sea

level pressure (5-hPa contours, black #1030 hPa, gray $1035 hPa)

for sea ice retreat scenarios (a) lowiceWAtl and (b) lowiceNS.

Color shading shows the difference compared to sea level pressure

for stadial sea ice conditions.

15 OCTOBER 2010 L I E T A L . 5469

To summarize, the scenario experiments presented

here indicate that temperature in the vicinity of the

Greenland Summit region is more sensitive to sea ice re-

treat in the Nordic seas than in the western North Atlantic.

5. Discussion

It is well known that variability in the large-scale at-

mospheric circulation causes sea ice variability over a

wide range of time scales in observational studies (Fang

and Wallace 1994). For example, a weak negative feed-

back of winter sea ice on the NAO has been detected in

modeling experiments (Magnusdottir et al. 2004) and

observations (Strong et al. 2009), operating on lags of up

to several weeks. The question of whether sea ice has

a similar restoring negative feedback or rather an am-

plifying positive feedback on the atmosphere in glacial

climates could be crucial to evaluating the role of sea ice

in D–O cycles.

As we have already stated, sea ice anomalies in the

D–O mechanism proposed by DOK are maintained by

interactions between different components of the glacial

climate system, most importantly the ocean and the ma-

rine and terrestrial cryosphere. There are a number of

ways in which the atmosphere might reinforce the North

Atlantic climate response to stadial or interstadial sea

ice conditions. Modeling studies suggest that extratropical

thermal forcing produces atmosphere–ocean changes that

extend to the tropics. Specifically, high-latitude cooling—

either due to Atlantic meridional overturning circulation

(AMOC) shutdown (Vellinga and Wood 2002; Zhang

and Delworth 2005; Broccoli et al. 2006) or forced ex-

clusively in the high latitudes by the presence of sea ice

or land ice sheets (Chiang et al. 2003; Chiang and Bitz

2005)—has been shown to induce a southward shift of

the Atlantic intertropical convergence zone (ITCZ), which

in turn can strengthen the winter Hadley cell as well as the

(thermally driven) subtropical component of the jet (Lee

and Kim 2003). The result would be a strong, steady, and

relatively southward-lying jet that transports less heat into

the North Atlantic region. Conversely, upon retreat of sea

ice, there would be a northward migration of the ITCZ,

a weakening of the Hadley cell, and a shift toward a more

poleward-lying eddy-driven jet. The boost in atmospheric

heat transport and mechanical stirring would further

break up the North Atlantic ice cover and thus provide

a positive feedback to the ice retreat in the Nordic seas.

Such processes are beyond the scope of the model-

ing strategy adopted here, in which atmosphere–ocean

feedbacks are not represented. However, the uncoupled

FIG. 8. Surface heat flux in CAM3 sea ice experiments. Annual mean ocean–atmosphere heat flux (W m22) for

(a) stadial sea ice conditions, (b) sea ice retreat scenario lowice, and (c) the stadial–lowice difference. Black contours

show the 15% (thin) and 50% (thick) annual mean sea ice concentration.

5470 J O U R N A L O F C L I M A T E VOLUME 23

atmospheric GCM experiments we have performed in-

dicate that, in glacial climates, sea ice retreat produces

a more variable jet while sea ice advance produces a

steadier jet. For example, in retreat scenario lowice, there

is an increase in the interannual variability of 300-hPa

winds of about 110% in winter [December–February

(DJF)] despite the fact that the climatological mean wind

field is not affected much (Fig. 10, top panels). The pat-

tern of jet variability, with maxima straddling the jet axis,

indicates more frequent north–south excursions of the jet

over the Atlantic sector when it is ice free. In addition, the

leading mode of interannual jet variability from EOF

analysis of 300-hPa wind explains slightly more of the total

variance and has larger amplitude (Fig. 10, bottom panels).

This result is consistent with the modeling study of

Byrkjedal et al. (2006), who find that reducing sea ice

in the glacial Nordic seas leads to increased sea level

pressure variability.

6. Concluding remarks

The climate impacts of sea ice anomalies in the glacial

North Atlantic have been investigated using coupled

climate model simulations of Last Glacial Maximum

(LGM) climate as well as a series of sea ice perturba-

tion experiments performed with an atmospheric gen-

eral circulation model. The main findings of this work

are summarized as follows:

d Coupled climate models simulate a range of sea ice

distributions for the LGM climate (Fig. 1 and Table 2).

Of the six PMIP2 models examined here, all exhibit

a perennially ice-covered Arctic. Only CCSM3 and

HadCM3M2 simulate the seasonally open Nordic seas

inferred from paleoclimate proxy data (Meland et al.

2005; de Vernal et al. 2005; Kucera et al. 2005).d In the CCSM3 simulation of LGM climate, two main

masses of sea ice exist: one in the Nordic seas, where

the ice distribution is governed primarily by in situ

growth and melt, and one in the western North At-

lantic, where the ice distribution owes its existence in

large part to the import of ice formed in Baffin Bay

and Davis Strait (Fig. 2). Other seasonal and spatial

aspects of the sea ice cover are altered relative to the

present-day CCSM3 simulation, such as the appear-

ance of a pronounced wintertime maximum in inter-

annual variability (Fig. 3).d Surface climate signals recorded at Greenland Sum-

mit are influenced more strongly by Nordic seas ice

anomalies than by western North Atlantic ice anom-

alies (Fig. 6 and Table 3).

Collectively, our experiments suggest that the abrupt

retreat of Nordic seas ice is an effective way to produce

large, abrupt warmings and accumulation increases on

Greenland, consistent with those observed during tran-

sitions from the stadial to intersadial phase of D–O cy-

cles (although the simulated response is not as strong as

the largest transitions in the proxy records). Our results

are also likely to be relevant for understanding a different

class of the near-global abrupt climate changes known as

Heinrich events. Heinrich events involve massive iceberg

FIG. 9. Temperature advection by surface winds for (left) stadial sea ice conditions, (middle) sea ice retreat scenario lowice, and (right)

sea ice retreat scenario lowiceNS. (a),(c),(e) DJF advection of 2-m temperature Ts by surface winds us with 308C day21 reference length

shown by (top right) the bold horizontal line. (b),(d),(f) Convergence of DJF surface temperature advection field. Contours are drawn at

0.58C day21 m21 intervals with black denoting convergence and blue denoting divergence; the zero contour has been omitted. The thick

purple contours show March sea ice extent, and the thin purple contours show September sea ice extent (50% ice concentration) for the

scenario.

15 OCTOBER 2010 L I E T A L . 5471

discharges from the North American ice sheet, and they

produce prominent and systematic climate changes in-

cluding a cooling of the Northern Hemisphere Atlantic

region, a strengthening of winter monsoon winds, and

shifts in ocean circulation (see Hemming 2004, and ref-

erences therein). Heinrich events occur during the stadial

phases of certain selected D–O cycles, but these stadials

are not distinguishable as more extreme than others

in the Greenland ice cores. An explanation for this

discrepancy could be that the icebergs associated with

Heinrich events primarily affect the North Atlantic ice-

rafting zone, a belt of latitudes (408–558N) situated well

south of the Nordic seas. If Greenland is indeed most

sensitive to the Nordic seas, it is possible that surface

ocean variability in other regions of the North Atlantic

could impact global climate despite remaining largely

undetected in Greenland ice cores.

Acknowledgments. We wish to thank T. Dokken,

S. Hemming, and K. Nisancioglu for insightful discus-

sions; M. Michelsen and J.-Y. Peterschmitt for assistance

with model simulations and data; and N. G. Kvamstø,

B. Tremblay, and three anonymous reviewers for help-

ful comments on the manuscript. We acknowledge the

international modeling groups and the Laboratoire des

Sciences du Climat et de l’Environnement (LSCE) for

providing and archiving the PMIP2 model data. The

PMIP2/MOTIF Data Archive is supported by CEA,

CNRS, the EU MOTIF project (EVK2-CT-2002-00153)

and the Programme National d’Etude de la Dynamique

du Climat (PNEDC). This research is part of the Nor-

wegian Research Council ARCTREC project and was

funded by the Comer Fellowship Program (CL, DSB)

and National Science Foundation Grant ATM-0502204

(DSB, CMB).

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