An altered Atlantic atmospheric circulation regime duringLast Glacial Maximum: 1. Evidence from a coupled climate model

Camille Li†∗

David S. Battisti

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

†Current affiliation: Bjerknes Centre for Climate Research,Bergen, Norway∗Corresponding author address: Camille Li, Bjerknes Centre for Climate Research, Allégaten 55, 5007 Bergen, Norway.

E-mail: camille@uib.no

Abstract

The Last Glacial Maximum (LGM), 21 thousand years before present, was the time of max-

imum land ice extent during the last ice age. A recent simulation of LGM climate by a state-of-

the-art fully coupled global climate model is shown to exhibit strong, steady atmospheric jets and

weak transient eddy activity in the Atlantic sector compared to today’s climate. In contrast, pre-

vious work based on uncoupled atmospheric model simulations has shown that the LGM jets and

eddy activity in the Atlantic sector are similar to those observed today, with the main difference

being a northeastward extension of their maxima. The coupled model simulation is shown to agree

better with paleoclimate proxy records, and thus, is taken as the more reliable representation of

LGM climate. The existence of this altered atmospheric circulation state during LGM in the model

has implications for our understanding of the stability of glacial climates, for the possibility of

multiple atmospheric circulation regimes, and for the interpretation of paleoclimate proxy records.

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1. Introduction

The Last Glacial Maximum was a cold period approximately 21 thousand years before present

(21 ka BP), when massive ice sheets covered much of the Northern Hemisphere continents. Paleo-

climate proxy records provide valuable information on the climate forcings and climate state at the

Last Glacial Maximum (LGM), but they are limited in spatial and temporal resolution and can be

difficult to interpret, especially if one wishes to deduce circulation features such as flow fields or

mass fluxes. Climate models have proved to be increasingly useful as a tool for tackling some of

the questions that cannot be answered using proxy data alone. Of interest in this study is the large-

scale circulation of the atmosphere during glacial times, and how it compares to the circulation

observed in the present day climate. In particular, we direct our attention towards the North At-

lantic sector, a region of deep water formation that experienced large, abrupt climate events during

the last glacial period.

In the 1990s, the Paleoclimate Model Intercomparison Project was undertaken to evaluate past

climates using a collection of climate models. The first phase of the project (PMIP-1) comprised,

for the most part, uncoupled atmospheric general circulation models forced with LGM boundary

conditions: a sea surface temperature and sea ice cover reconstruction from the Climate: Long

range Investigation, Mapping, and Prediction (CLIMAP) project (CLIMAP 1981) and the ICE-

4G land ice reconstruction (Peltier 1994). The resulting simluations offered a glimpse into how

atmospheric circulation may have been during LGM. Subsequent studies focused on describing

and understanding specific features such as atmospheric heat transport, transient activity and storm

tracks (Hall et al. 1996; Kageyama et al. 1999; Kageyama and Valdes 2000). Their findings have

been used to endorse the idea that the glacial world was a stormier world thanks to stronger equator-

to-pole temperature gradients and enhanced production of baroclinic eddies. Revisiting the original

studies, however, discloses some often-overlooked details.

Synthesizing results from all the European models in PMIP-1, Kageyama et al. (1999) report

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a northeastward extension of both Northern Hemisphere storm tracks across the board, and in six

of seven models, an elongation of the Atlantic storm track. They note “no systematic increase or

decrease in the storminess from the present climate to the last glacial maximum”, although the

figures in the paper do seem to indicate an increase in peak lowlevel transient eddy activity for

most of the models (see Kageyama et al. 1999, Figures 1–2, 6–7). In the specific case of the

U.K. Universities’ Global Atmospheric Modelling Project (UGAMP) simulation, the increase in

Atlantic eddy activity is limited to low levels of the atmosphere and is associated with enhanced

low level baroclinicity, thus suggesting the presence of stronger but shallower synoptic waves (Hall

et al. 1996). Furthermore, it was found that changes in normal modes largely account for changes

in the position and dominant wavenumber of the storms, but not the amplitude of the actual storm

tracks (Kageyama and Valdes 2000).

In summary, the PMIP-1 simulations show a northeastward extension of the Atlantic storm

track, with either slight increases or no change in the strength of eddy activity at low levels during

LGM. Discussion of the time-mean atmospheric flow has been cursory, but Kageyama et al. (1999)

indicate that the Northern Hemisphere jets seem to change ina similar fashion as the storm tracks

from the present day climate to the LGM, exhibiting a northeastward extension and little to slight

increases in strength.

More recently, models that include some form of ocean coupling have been used to simulate

the LGM climate. Among these, there is some support for the PMIP-1 results. For example, an

atmosphere model coupled to a slab ocean (Dong and Valdes 1998) and an intermediate com-

plexity model (Justino et al. 2005) have both shown evidencefor increased storminess at low to

middle levels of the atmosphere in the Atlantic sector when run with the same ICE-4G land ice

configuration (Peltier 1994) as used in PMIP-1. Whether morecomplex, fully coupled models also

produce a more stormy glacial world is unclear. The majorityof the literature documenting these

simulations are more concerned with issues such as atmosphere-ocean processes in the tropics and

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subtropics, where the CLIMAP reconstruction is known to have problems (Bush and Philander

1998; Broccoli 2000; Kitoh and Murakami 2002; Timmermann etal. 2004); the general features

of the LGM climate (Kitoh and Murakami 2001; Kim et al. 2003; Shin et al. 2003; Otto-Bliesner

et al. 2006); and the role of ocean dynamics in the maintenance of this climate state (Dong and

Valdes 1998; Hewitt et al. 2003).

In this study, we examine the atmospheric circulation at Last Glacial Maximum in a simula-

tion from a state-of-the-art, fully coupled climate model,the Community Climate System Model

(CCSM3) developed at the National Center for Atmospheric Research (NCAR). The simulation

was set up and performed by Otto-Bliesner et al. (2006) in order to contrast pre-industrial, mid-

Holocene and LGM climate. Our goal is to characterize the mean state and variability of atmo-

spheric circulation in the Atlantic sector during the LGM byexamining the jet and transient eddies,

with an emphasis on identifying differences between the LGMand present day climates. Interest-

ingly, the model produces a a strong, stable LGM Atlantic jetand enhanced low level baroclinicity,

but diminished wintertime eddy activity at all levels of theatmosphere compared to the present

day. These results appear to be at odds with the atmosphere-only simulations from PMIP-1, and

furthermore suggest the existence of an altered atmospheric circulation regime during LGM. A

companion paper identifies aspects of the land ice topography and North Atlantic sea surface con-

ditions that play key roles in establishing this circulation regime (Li and Battisti 2007).

The paper is organized as follows. A brief description of themodel and methods is contained

in section 2. Section 3 presents the model results indicating that, during Last Glacial Maximum,

the atmosphere exhibited a strong, steady mean circulationwith decreased eddy activity. Section 4

discusses some possible mechanisms for the suppression of eddy activity. Section 5 points out

some important differences between the coupled CCSM3 simulation and the PMIP-1 simulations

of LGM climate, and evaluates the simulations against the observational record, concluding in the

end that the coupled model produces a more realistic representation of the LGM. Finally, the main

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results of this work are summarized in section 6.

2. Model description and methods

We investigate changes between present day (PD) and Last Glacial Maximum (LGM) climates

as simulated by the Community Climate System Model 3 (CCSM3;Collins et al. 2006a), a global

coupled atmosphere-ocean-sea ice-land surface climate model developed at the National Center

for Atmospheric Research (NCAR). The setup of and results from these model simulations are

documented in detail in Collins et al. (2006a) and Otto-Bliesner et al. (2006). Briefly, the CCSM3

comprises the primitive equation Community Atmosphere Model version 3 (CAM3) at T42 hori-

zontal resolution with 26 hybrid coordinate vertical levels (Collins et al. 2006b); a land model with

land cover and plant functional types, prognostic soil and snow temperature and a river routing

scheme (Dickinson et al. 2006); the NCAR implementation of the Parallel Ocean Program (POP)

on a 320×384 dipole grid (nominal horizontal resolution of 1◦) with 40 vertical levels (Smith and

Gent 2002); and a dynamic-thermodynamic sea ice model on thesame grid as the ocean model

(Briegleb et al. 2004).

The forcings for the LGM simulation are in accordance with the protocols established by

PMIP-2 (http://www-lsce.cea.fr/pmip2): 21 ka BP insolation; atmospheric greenhouse gas con-

centrations based on ice core measurements (Flückiger et al. 1999; Dällenbach et al. 2000; Monnin

et al. 2001); atmospheric aerosols at preindustrial values; land ice and coastlines corresponding to

120 m sea level depression from the ICE-5G reconstruction (Peltier 2004).

We use monthly mean output from 50 years of the simulations. Daily output for transient eddy

analyses are taken from 25 year branch runs. A boostrap method was used to determine that 25 year

samples are adequate for generating stable eddy statistics. As an additional check, we repeated the

eddy analysis on 45 years of daily output from the PD simulation and found no change to the

main results of the study. Eddy fields were filtered with a sixth order high-pass Butterworth filter

5

to emphasize variability at periods less than 8 days. Such filters are often used in the study of

storm tracks, with the high-pass cutoff varying between 6–10 days (for example Nakamura 1992;

Trenberth 1991; Yin 2002). The exact choice of filter is relatively unimportant since baroclinic

waves dominate the eddy statistics at these synoptic time scales.

3. Last Glacial Maximum climate in the coupled model (CCSM3)

Of interest here is the large scale atmospheric circulationin the model’s simulations of the Last

Glacial Maximum (LGM) and present day (PD) climates. We willfocus on the Northern Hemi-

sphere Atlantic sector, where differences in forcings between the LGM and PD, and consequently

circulation features, are most dramatic. All differences discussed are significant at the 95% level

unless otherwise noted.

a. Circulation and heat transport

Upper level zonal wind and geopotential height provide a useful broad-brush picture of large

scale flow characteristics of the atmosphere. Figure 1 showswintertime maps of these two fields

for the LGM and PD simulations. Under the LGM forcings described in the previous section,

we observe an enhanced stationary wave associated with the Laurentide ice sheet covering most

of North America, and a stronger, more zonal jet in the Atlantic sector downstream of the ice

sheet. The stronger winds during LGM are consistent with thestronger equator-to-pole surface

temperature gradient. Changes in the Pacific are more subtle, showing a slight equatorward shift

of the jet and the development of a split flow over Siberia.

The change in atmospheric circulation over the Atlantic sector during LGM is particularly

striking. There is an inverse relationship between maximumjet strength and jet width in January

for the PD simulation (red circles in Figure 2), where jet strength uATL is the maximum zonal wind

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in the sector, and jet width is the latitude range over which uATL decreases to half its maximum

value. The relationship is not evident in the LGM climate (blue triangles), which furthermore

inhabits a sector of width-strength space separate from thePD climate. The LGM jet is 30%

stronger on average, with 35% less variability in maximum speeds than the PD jet; it is also 30%

narrower, with almost six times less variability in width. The results are robust to the choice of

month(s) used to define winter, and to the exact longitude range used to define the Atlantic jet.

Figure 3 shows implied annual meridional energy transportscalculated from the model output.

First, the total required heat transport RT by the climate system is determined by integrating the

top-of-atmosphere (TOA) radiation imbalanceRTOA over all longitudesθ from the North Pole to

each latitudeφ:

RT(φ) = R 2E

∫

2π

0

∫ π/2

φRTOA(φ

′, θ′) cos φ′dφ′dθ′ (1)

where RE is the radius of the Earth. Next, we sum the shortwave, longwave, latent heat and

sensible heat fluxes from the model to get the annual mean surface heat fluxRsfc. IntegratingRsfc

over all ocean points gives the implied ocean heat transportOT:

OT(φ) = R 2E

∫

2π

0

∫ π/2

φδocn(φ

′, θ′)Rsfc(φ′, θ′) cos φ′dφ′dθ′ , (2)

where

δocn(φ, θ) =

1 if (φ, θ) is ocean

0 if (φ, θ) is land.

Finally, the atmospheric heat transport AT is calculated asa residual:

AT = RT − OT . (3)

We find that the amount of heat transported towards the poles is remarkably similar in the PD

and LGM simulations (compare the black curves and grey curves in Figure 3b). The discrepancy

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between the model’s PD total heat transport (black line) andthe satellite-derived radiatively re-

quired total heat transport from Trenberth and Caron (2001)(filled grey area) is less than 0.5 PW,

or 10% of the maximum heat transport. Between the two model simulations, the LGM does indeed

have slightly more transport (Figure 3b), with the atmosphere helping to increase the peak North-

ern Hemisphere (NH) value to 6.28± 0.09 PW, about 0.3 PW greater than in PD (Figure 3c). The

increase is, however, relatively modest. All else being equal, a back-of-the-envelope calculation

tells us that a 0.3 PW boost in heat transport at 35◦N translates to a 3 W m−2 boost in heating rate

north of this latitude circle. Assuming a mid-range climatesensitivity of 0.5◦C per W m−2, this

is equivalent to a 1.5◦C warming of the mid-to-high latitude regions. Clearly, allelse is not equal,

and the actual surface temperature difference between the two climates poleward of 35◦N is closer

to 10◦C.1 The robustness of the total heat transport curve found across the CCSM3’s simulations

of PD and LGM climates is corroborated by other coupled climate models (Hewitt et al. 2003; Shin

et al. 2003) and from uncoupled general circulation models using a prescribed sea surface temper-

ature forcing (Hall et al. 1996), with the magnitude of the LGM increase varying from 0.2–1.5 PW

for peak NH values.

Upon closer inspection, there are interesting differencesin the partitioning of these heat fluxes

in the PD and LGM simulations. During LGM, the ocean accountsfor slightly less of the transport

in the NH and the atmosphere transports slightly more through the midlatitudes. Within the atmo-

sphere itself, the pronounced ridging forced by the Laurentide ice sheet boosts the dry stationary

wave heat transport contributionv∗ T ∗ in the LGM compared to PD (Figure 3c). Between 40–

1Technically, the 0.5◦C per W m−2 value is a global climate sensitivity, and should not be usedto estimate the response of the

polar cap to increased heat flux from the lower latitudes. However, the polar cap as defined in our calculation (35◦N to the North Pole)

is quite a large region, and one that sees a net TOA radiation loss to space. Furthermore, our use of this global climate sensitivity is

supported by an experiment in Seager et al. (2002) in which ocean heat transport was turned off, leading to a 1.3 PW reduction in heat

moved across 35◦N, a 13 W m−2 decrease in heating rate north of this latitude circle, and a6◦C cooling of the mid-to-high latitude

regions.

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65◦N, the LGM shows total atmospheric heat transport comparable to that in the PD, but greater

stationary wave heat transport. By inference, the transient heat transport term must be diminished

in this region. In a global view, these results are in fact in agreement with the energy budget anal-

ysis of the UGAMP LGM simulation by Hall et al. (1996). Although they observe an increase in

transient heat transport at low levels, there is a compensating decrease aloft such that the column-

integrated transient eddy transport is lower during LGM.

b. Transient eddy activity

We can diagnose the eddy activity responsible for this transient heat transport by calculating

high-pass filtered quantities such as low level temperatureflux (v′T ′), upper level momentum flux

(u′v′) and upper level eddy kinetic energy ((u′2 + v′2)/2). In addition to presenting maps of these

eddy statistics, we will use several metrics to quantify thesteadiness of the atmospheric circulation

at low levels and aloft during the winter DJFM season. The first metric is the maximum zonal wind

at 200 mb in the Atlantic sector (15◦N–65◦N, 90◦W–0◦). The second is a kinetic energy index of

monthly departures of the 200 mb flow from the climatologicalmean averaged over the Atlantic

sector,

KEIATL =1

2AATL

∫

AATL

(u200 − u200)2 + (v200 − v200)2 dA , (4)

whereu andv are monthly mean fields,A represents area, and overbars indicate climatological

means. The final metric is the northward eddy heat flux at 850 mbaveraged over the Atlantic

sector,

vTATL =1

AATL

∫

AATL

v′850

T ′850

dA , (5)

where primes indicate daily fields that have been high-pass filtered to retain variability at periods

less than 8 days.

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Concentrating on boreal winter in the NH, the eddy fields reveal an LGM climate that is more

quiescent than the PD (Figure 4). In the Atlantic sector, thereduction in eddy activity from PD to

LGM is observed at both low levels (15% decrease in sector-averagedv′T ′) and upper levels (30%

decrease in sector-averaged(u′2 + v′2)/2). Compared to PD, the LGM jets are strong and narrow

(Figure 5), and the eddy fluxes occupy a narrower latitudinalband hugging the axis of the jet core

rather than a broad band perched on the poleward flank of the jet (Figure 4a–d). The differences

between the two climates can also be seen in Figure 2, in whichJanuary poleward heat fluxes in the

LGM simulation (blue triangles) span a narrow range of smaller values than in the PD simulation

(red circles). Although we will not discuss the Pacific sector, it too exhibits changes in jet structure

and eddy fluxes.

The measures of atmospheric flow in Table 1 provide another way to compare the Atlantic

sector in today’s climate and in glacial climates. The weak eddy activity in the LGM simulation

coexists with a stronger, narrower Atlantic jet, as we saw inFigure 2. From this plot, we inferred

a less variable upper level flow field during LGM compared to PD. The winter season flow metrics

now provide additional and more direct ways to evaluate the steadiness of the LGM jet. Looking

at Table 1, we see a 35% decrease in monthly departures of kinetic energy from its climatological

mean state (KEIATL in column three). Together, these results are consistent with the picture of

global heat transport in Figure 3, in which a slight overall increase in meridional heat flux during

LGM is achieved by a greatly enhanced stationary wave, with the implication that the contribution

from transient eddies must be weaker.

As a final remark on this topic, we note that the gross structure of the Atlantic jet and ed-

dies described here for the PD simulation is consistent withobservational data from the National

Centers for Environmental Prediction NCEP-NCAR reanalysis (Kalnay et al. 1996) and the Euro-

pean Medium-Range Weather Forecasts (ECMWF) Reanalysis ERA-40 (Uppala et al. 2005). Both

NCEP (not shown) and ERA-40 (Figure 6) show a broad wintertime jet with a SW-NE tilt, and eddy

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activity peaking poleward of the jet, just as we have seen forthe PD simulation (Figure 4). One

feature that does not compare well is the absolute jet strength, which the model overestimates in

each winter month (as seen for January by comparing Figures 2and 7) such that the DJFM winter

season average of uATL is 25% too strong (Table 1). However, the variability in the large-scale

flow field is comparable in the PD climate across the two reanalyses and the model; furthermore,

this variability is substantially different from that in the LGM simulation. For example, uATL spans

a range of 25 m s−1 in both the observations (Figure 7) and in 50 years of the PD simulation (Fig-

ure 2), while the range in jet strengths in the LGM simulationis approximately half this value.

Also, the variability as measured by the poleward heat flux vTATL and kinetic energy KEIATL in-

dices (Table 1) appear to show characteristic values for thePD climate (3.42–3.56 K m s−1, 31–34

m2 s−2) that are distinct from those of the LGM climate (2.93 K m s−1, 22 m2 s−2).

c. Baroclinicity of the atmosphere

The result of diminished meridional heat transport in a climate with sharper temperature gradi-

ents and stronger jets is somewhat surprising. To express this apparent paradox in more quantitative

terms, we use the Eady growth rate parameter introduced by Lindzen and Farrell (1980) as a means

of predicting transient behavior from the time mean flow. Theparameter is the growth rate of the

fastest growing Eady mode, and is given by

σ = 0.31f

N

∂uh∂z

, (6)

wheref is the Coriolis parameter,N is the Brunt-Väisälä buoyancy frequency and∂uh/∂z is the

vertical shear of the horizontal wind component. We calculate this growth rate for the 850–700 mb

layer (Figure 8).

For each climate, regions of high Eady growth rate correspond by and large to regions of

enhanced eddy activity; across different climate states, however, regions where Eady growth rates

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increase are regions where eddy amplitudes decrease. For example, there is a general increase in

midlatitude growth rates during LGM relative to PD, especially over the Atlantic, which suggests

that the glacial climate should be stormier than today’s climate. That the eddy diagnostics indicate

decreased eddy activity during LGM despite more baroclinicconditions (largerσ) points to the

complicated nature of the relationship between transient activity and the mean flow.

4. Suppression of eddy activity

The LGM climate as simulated by the CCSM3 coupled model exhibits stronger jets but weaker

storms. This quiescent glacial climate runs counter to the intuitive idea that a more vigorously

circulating atmosphere should produce a more tempestuous world. It also runs counter to more

theoretically based indicators such as the stronger meridional temperature gradient and increased

low level baroclinicity. However, a range of factors not captured by the Eady growth rate parameter

also affect the lifecycle of eddies. These factors range from diabatic heating effects (Hoskins and

Valdes 1990) to “governors” that limit the ability of eddiesto tap into the available baroclinicity

(James 1987; Lee and Kim 2003; Harnik and Chang 2004). In thissection, we discuss whether

such factors may play a role in reducing the eddy activity in the coupled model LGM simulation.

A pre-requisite to this discussion is an assessment of the structure of the eddies.

a. Eddy strucutre

To study the structure of eddies, we use one-point regression analysis (Lim and Wallace 1991;

Chang 1993) in which a regression coefficientb(i) is calculated for a specific pressure, latitude,

longitude locationi by linearly regressing the input variable of interestyt(i) against a reference

time seriesxt:

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b(i) =

N∑

t=1y′t(i)x

′t

( N∑

t=1x′t

2)1/2

. (7)

Here,N is the number of time samples and primes denote daily fields which have been high-pass

filtered to retain variability at periods less than 8 days.

We examine regression structures at 200 mb and 850 mb in the CCSM3 simulations of PD and

LGM climates (Figure 9). The reference time series against which variables are linearly regressed

is the 200 mb meridional wind fieldv′200 at 43◦N, 28◦W for the PD climate, and at 38◦N, 28◦W

for the LGM climate. These locations were selected based on the location of maximum variance

in the DJFMv′200 field. We normalize theb(i) coefficients such that the regression maps show

amplitude, expressed in the units of the variable of interest, per standard deviation of the reference

time series. Structures calculated using reference time series at different locations, reference time

series based on different fields, and alternate definitions of the winter season are similar to these,

and are not shown here.

The eddy structure in both the PD and LGM climates resemble localized wave trains. The LGM

eddy amplitudes peak just south of 40◦N, the latitude marked by the solid grey line in Figure 9,

while the PD eddy amplitudes peak just north of this latitude. Although meridional wind variations

v′ are much stronger aloft (Figure 9a,d) than at low levels (Figure 9b,e) in both simulations (note

the different contour intervals), there is a greater discrepancy in the LGM. Thus, eddies in glacial

times appear to be trapped closer to the tropopause, with weaker low-level disturbance amplitudes.

Furthermore, though the LGM has slightly weaker eddy amplitudes, the eddy structures are cor-

related over greater distances and are more persistent, as seen in lag regression analyses in which

the time seriesyt(i) is shifted by a certain number of days relative toxt (not shown). These results

suggest that differences in eddy structure may be an important part of why the LGM simulation

exhibits such weak eddy activity compared to the PD simulation. There is a large body of work de-

voted to understanding why eddy activity might be suppressed in conditions that appear conducive

13

to eddy growth, and we now turn our attention to some of these studies for inspiration.

b. Discussion

One established idea in the literature is the barotropic governor mechanism, in which hori-

zontal shear is thought to limit baroclinic instability, and hence, storm growth (James 1987). The

sharpening of the Atlantic jet in the LGM simulation (Figure5) makes this an attractive candidate,

but some telltale signs of the barotropic governor are absent. If the barotropic governor were in

operation, we would expect the eddies to bend and elongate into a boomerang or banana shape as

they are deformed by the strongly sheared mean flow; this characteristic shape is not evident in the

LGM simulation (Figure 9).

Another explanation is inspired by the existence of a modernanalogue to the strong jet/weak

storms dichotomy, namely the Pacific midwinter suppressionof storms (Nakamura 1992) which

occurs every January when the jet is at its strongest (Figure10). According to Yin (2002), this

phenomenon has to do with the southward migration of tropical convection in the western Pacific,

and the related strengthening of the subtropical jet, during boreal winter. More specifically, Yin

(2002) diagnosed that these changes increase upper level static stability and lower the tropopause

height in a manner that discourages the growth of storms. In the modern climate, the climatological

latitude of the Atlantic jet is 2–3◦ north of the climatological latitude of the Pacific jet in winter,

perhaps positioning it too far poleward to be affected by changes in tropical heating. In the LGM

simulation, however, the more zonal orientation of the Atlantic jet pulls it towards the equator, in

closer proximity to the tropical convection sites. Preliminary inspection reveals little difference in

tropical heating or precipitation between the LGM and PD simulations in the Atlantic sector, but

subtle changes in the tropics can have a large impact, so a more thorough investigation should be

carried out before this mechanism for jet stability can be rejected.

In a related vein, Lee and Kim (2003) used idealized numerical experiments to demonstrate

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flow regimes involving strong (weak) subtropical jets with weaker (stronger) eddy activity located

just (far) poleward of the jet core. In a series of experiments, the strength of the tropical convective

heating and of the high latitude cooling were varied in orderto strengthen or weaken the subtropical

jet (Lee and Kim 2003; Son and Lee 2005). A key result was the observation of more isotropic

eddies and weaker barotropic conversion in the strong subtropical jet regime. The LGM simulation

exhibits several features in common with their strong subtropical jet case, including relatively weak

eddy fluxes located at roughly the same latitude as the tropospheric jet maximum (Figure 4) and a

slightly more isotropic eddy structure (Figure 9). Rather than requiring changes in tropical heating,

this idea allows for the possibility of a strong jet regime being set up some other way, for example,

by topographic effects from the Laurentide or additional high latitude cooling due to extensive sea

ice cover in the North Atlantic.

To advance our understanding of this problem, we are currently analyzing the energy budget,

as presented by the Lorenz energy cycle (Lorenz 1955), in thePD and LGM simulations. The

terms in the energy budget equation describe how energy is exchanged between the mean flow and

the transient eddies as the eddy disturbances grow and decay. Different suppression mechanisms

affect different parts of the eddy life cycle, and thus should have different signatures on these

budget terms. Although the energy budget will not provide conclusive evidence to single out one

mechanism, it should support some candidates and help to eliminate others.

5. Evaluation of the CCSM3 simulation

We have established that the PMIP-1 simulations of LGM climate share certain atmospheric

flow characteristics that are very similar to those in the present day climate, while the coupled

model simulation of LGM exhibits fundamental differences from the present day climate. In the

PMIP-1 models, the amount of storminess in the Atlantic sector is comparable in the PD and

LGM climates, with slight increases in peak low level transient eddy activity during the LGM

15

(Kageyama et al. 1999). Furthermore, in the PMIP-1 LGM simulations, the Atlantic jet shows the

same northeastward extension as the Atlantic storm track, with little change to slight increases in

strength (Kageyama et al. 1999; Li and Battisti 2007). Conversely, the CCSM3 LGM simulation

exhibits a strong, steady jet with a zonal orientation, and weaker transient eddies at all levels of the

atmosphere (Figure 4).

We have reproduced the PMIP-1 results using the atmosphericcomponent of the CCSM3 (Fig-

ure 11) to illustrate more clearly the differences between the circulation simulated by the coupled

model and the generic result obtained by the suite of uncoupled PMIP-1 models. In other words,

we performed an uncoupled simulation with the same atmosphere model as that used for the cou-

pled simulations, but now with sea surface temperature (SST), sea ice and land ice prescribed

exactly as specified in the PMIP-1 experimental setup. Figure 11e and 11f show the wintertime

jet and storm statistics of this PMIP-1-based simulation (labelled PMIPa where the “a” stands for

“atmosphere-only”). Compared to the coupled model LGM simulation (Figure 11c,d) the Atlantic

is stormier and the jet is weaker, exhibiting a marked SW-to-NE tilt across the North Atlantic. In-

deed, the eddy heat transport and jet orientation in PMIPa bear a closer resemblance to the coupled

PD simulation (Figure 11a,b) than to the coupled LGM simulation (Figure 11c,d).

What is the source of the difference between the LGM climatessimulated by the uncoupled

models and the CCSM3 coupled model? Recall that the uncoupled PMIP-1 models experience

or “see” one set of prescribed land ice and SST/sea ice boundary conditions. The coupled model

experiences a somewhat different set of land ice boundary conditions, those of the improved ICE-

5G (Peltier 2004) reconstruction. In addition, the coupledmodel has interactive ocean and sea

ice components that create their own SST/sea ice fields. We show in the companion paper (Li

and Battisti 2007) that it is these land and sea surface forcings, rather than a disparity in models

or model physics, that are key for creating the different LGMclimates (Li 2007). Assuming that

this is the case, we must then evaluate whether the uncoupledPMIP-1 models or the coupled

16

CCSM3 model produces a more reliable representation of the Last Glacial Maximum climate. In

the following sections, we compare these boundary conditions and model results to records of past

climate conditions extracted from paleoclimate archives.Overall, we find that the SST and sea ice

simulated by the coupled model is in better agreement with reconstructions from the proxy data

than with the boundary conditions used to force the uncoupled models in the PMIP-1 experiments.

a. Land ice

The ICE-4G (Peltier 1994) and ICE-5G (Peltier 2004) reconstructions of deglaciation history

are based in part on the theory of glacial isostatic adjustment, a process by which the external

surface load of the continental ice sheets is compensated bychanges in the surface of the Earth.

ICE-5G is widely considered to be an improvement over ICE-4G: its land ice reconstruction ac-

counts for new refinements in the reconstruction of global sea level, and it also corrects many of

the regional shortcomings of ICE-4G (see Peltier 2004, and references therein). As a result, at the

time of the LGM, ICE-5G has more land-based ice than did ICE-4G (approximately 12 m sea level

equivalent). In ICE-5G, there is much less land ice on Greenland and the Eurasian continent, while

the Laurentide ice sheet complex over North America is significantly larger in volume compared

to ICE-4G (by 15 m sea level equivalent, or 25% of the Laurentide ice volume), with the bulk

of the extra ice forming the Keewatin Dome west of Hudson Bay (Figure 12). The addition of

this Dome reconciles the original ICE-4G results with more recent observations, namely the large

crustal uplift rates near Yellowknife, Canada (Argus et al.1999) and gravity measurements south

and west of Hudson Bay (Lambert et al. 2001).

17

b. SST and sea ice

The CCSM3 coupled model provides a self-consistent simulation of Last Glacial Maximum

climate. It produces a climatological SST distribution andsea ice coverage that differ from those

determined in CLIMAP and used as boundary conditions in PMIP-1 (CLIMAP 1981). At first

glance, one might expect the observation-based CLIMAP dataset to provide a more trustworthy

picture of the glacial ocean than a model. CLIMAP produced the first SST maps of the glacial

ocean by using transfer functions to translate population distributions of fossil plankton species

found in ice age marine sediments (CLIMAP 1981) into sea surface temperatures. The trans-

fer functions (Imbrie and Kipp 1971) were derived from knowledge of how these same plankton

species are distributed in today’s ocean. In the intervening decades, however, the proliferation of

sediment core data and the advent of new statistical and geochemical methodologies for recon-

structing past SSTs have challenged some of the assumptionsand results of the CLIMAP method.

Moreover, modelling studies have shown that the consequences of such SST errors on the atmo-

spheric circulation are potentially dramatic, whether theerrors themselves are in the tropics or high

latitudes (Yin and Battisti 2001; Toracinta et al. 2003; Hostetler et al. 2006).

Though the data still fall short of providing reliable SST and sea ice distributions in the glacial

ocean, in regions where there is growing consensus, the CCSM3 simulation shows a clear improve-

ment over the CLIMAP reconstruction. Otto-Bliesner et al. (2006) present a comparison of sea ice

cover in the simulation and in proxy observations to this effect, but their SST comparison (their

Figure 9), done in a basin-wide average, is not conclusive. Following is a more regional assessment

of the differences between the model results, CLIMAP and theupdated proxy record, in terms of

absolute temperature changes and of patterns of sea ice and SST.

18

1) TROPICS AND SUBTROPICS.

Among the more problematic areas of the CLIMAP reconstruction is the lack of cooling in

tropical and subtropical regions. In the annual mean, CLIMAP estimates glacial maximum sea

surface temperatures at less than 1◦C cooler than present day over the global tropics (20◦S–20◦N)

and in the Atlantic subtropical gyres, and slightly warmer than present day in the Pacific subtropical

gyres. The CCSM3 simulation, on the other hand, yields annual mean tropical SSTs that are

approximately 1◦C colder than those in CLIMAP (Otto-Bliesner et al. 2006). These colder tropical

waters are in agreement with results from updated foraminifera-based analyses, including modified

transfer functions (Mix et al. 1999; Niebler et al. 2003), modern analogue matching (Trend-Staid

and Prell 2002; Pflaumann et al. 2003), and multi-technique approaches (Chen et al. 2005; Kucera

et al. 2005), which distinctly portray the tropics as coolerduring LGM than they are now, with

differences of 2–6◦C in the Atlantic (Mix et al. 1999; Trend-Staid and Prell 2002; Niebler et al.

2003; Pflaumann et al. 2003; Kucera et al. 2005), 3–5◦C in the eastern equatorial Pacific (Mix et al.

1999; Trend-Staid and Prell 2002; Kucera et al. 2005), and approximately 1◦C in the Pacific warm

pool (Trend-Staid and Prell 2002; Chen et al. 2005; Kucera etal. 2005).

There is further support for tropical cooling from other proxy data. In the Atlantic, LGM

cooling estimates are 2–3◦C fromδ18O of planktic foraminifera off the coast of Brazil (Wolff et al.

1998), and from Mg/Ca measurements (Hastings et al. 1998) and alkenones (Ruhlemann et al.

1999) in the Caribbean Sea, in agreement with the CCSM3 simulation. Sr/Ca andδ18O analyses

of corals near Barbados indicate even greater cooling of up to 5–6◦C (Guilderson et al. 1994).

In the eastern equatorial Pacific, the estimates from proxy data span a wider range. Most

records are consistent with a substantial amount of cooling, from 2–3◦C from foraminifera Mg/Ca

ratios (Lea et al. 2000) and alkenones (Kienast et al. 2006) to 3–5◦C from radiolarian assemblages

(Pisias and Mix 1997). However, just north of the equator, Mg/Ca ratios in sea-floor sediments

near the Galápagos Islands indicate only 1◦C of cooling (Koutavas et al. 2002). The slight cooling

19

of the Pacific warm pool in the CCSM3 simulation is less than the 2–3◦C estimate from alkenones

(Lea et al. 2000) and Mg/Ca ratios (Rosenthal et al. 2003) on the Ontong Java Plateau, and from

alkenones (Pelejero et al. 1999) and Mg/Ca ratios (Lea et al.2000; Stott et al. 2002; Visser et al.

2003; Rosenthal et al. 2003) in the western Pacific marginal seas.

In the subtropics, the CCSM3 simulation is again consistentwith updated foraminiferal recon-

structions whereas CLIMAP is much too warm. The updated reconstructions indicate relatively

stable gyres, but with definite cooling in the Atlantic (Mix et al. 1999; Trend-Staid and Prell 2002;

Niebler et al. 2003; Pflaumann et al. 2003) and no evidence of the Pacific warming that CLIMAP

suggests (Trend-Staid and Prell 2002; Kucera et al. 2005). Reconstructions using alkenones from

the Bermuda Rise (Sachs and Lehman 1999), andδ18O (Lee and Slowey 1999) and alkenones

(Rosell-Melé et al. 1998) from the northern subtropical Pacific are also consistent with the moder-

ate cooling simulated by the CCSM3.

More relevant to us than changes in the absolute temperaturefield are changes in the distri-

bution of SSTs and SST gradients in the tropics. Evaluating whether CLIMAP or the CCSM3

simulation does a better job in this respect is difficult given the uncertainties in the SST reconstruc-

tions and the differences between them. However, several interesting features are robust enough in

the simulation and the records to merit mention. At Last Glacial Maximum, a contracted Pacific

warm pool is evident in the CCSM3 simulation and is also suggested by the foraminifera census

data (Trend-Staid and Prell 2002; Kucera et al. 2005), whilethe CLIMAP warm pool is extensive

and separated into northern and southern lobes. Several studies point to stronger zonal gradients

across the tropical Pacific (Lea et al. 2000; Trend-Staid andPrell 2002; Kucera et al. 2005), but the

reconstructed SST patterns themselves are quite dissimilar. Nevertheless, the CCSM3 simulation

does exhibit strong tropical SST gradients – both zonal and meridional – compared to CLIMAP.

In the northern subtropics, the reconstruction of Trend-Staid and Prell (2002) also resembles the

CCSM3 simulation more so than CLIMAP, with the isotherms indicating more zonal flow patterns

20

in the Kuroshio and Gulf Stream currents.

2) NORTH ATLANTIC

Another region where there are significant differences between CLIMAP and CCSM3-simulated

SSTs is the North Atlantic (Figure 14). Compared to CLIMAP, the simulated winter SSTs are

3–5◦C warmer along the west coast of Norway and 1–3◦C warmer in a broad swath of the Atlantic

Ocean just south of the Greenland-Scotland ridge. These areas remain ice free in winter in CCSM3,

while in CLIMAP, perennial sea ice cover extends south of 50◦N. The presence of open ocean and

relatively warm temperatures in portions of the eastern North Atlantic and, at least seasonally, in

the Nordic Seas is supported by a large body of proxy data including estimates of SST and sea ice

extent based on coccoliths (Hebbeln et al. 1994), biomarkerpigments (Rosell-Melé and Koç 1997),

alkenones (Rosell-Melé and Comes 1999), dinoflagellates (de Vernal and Hillaire-Marcel 2000)

and foraminifera (Pflaumann et al. 2003; Sarnthein et al. 2003; Meland et al. 2005). The simulated

North Atlantic conditions in CCSM3 are further corroborated by other coupled atmosphere-ocean

models (Hewitt et al. 2003; Shin et al. 2003), and by inferrednorth Atlantic circulation patterns

from foraminiferal assemblages (Lassen et al. 1999).

6. Concluding remarks

We have seen evidence for an altered atmospheric circulation regime in the Atlantic sector dur-

ing Last Glacial Maximum from a global coupled climate model. This LGM Atlantic circulation

is characterized by a stronger, more zonally oriented Atlantic jet, and yet reduced storminess. In

other words, the coupled model produces a glacial climate that is quiescent compared to today’s

climate. Analysis of the eddy structures suggests that the barotropic governor is unlikely to play

a role in suprressing the storms; candidates still in contention and currently being examined in-

21

clude reduced seeding and increased static stability over the expanded sea ice cover in the western

Atlantic.

The LGM climate simulated by the coupled model is remarkablydifferent from the LGM cli-

mate simulated by atmosphere models forced with PMIP-1 boundary conditions. Whereas the

coupled simulation exhibits a strong, zonal jet with reduced eddies, the uncoupled simulations

feature a North Atlantic circulation regime that is similarto the one in today’s climate. We have

found that this discrepancy can be traced to differences in the ice sheet topography and sea surface

conditions, and not to differences in the atmosphere modelsand model physics (Li 2007). Among

the boundary conditions that matter, the most important is the LGM land ice, which was updated

from the ICE-4G reconstruction in PMIP-1 to the improved ICE-5G reconstruction in the coupled

simulation (Li 2007; Li and Battisti 2007). In addition, theSST and sea ice distributions simulated

by the coupled model are more consistent with the proxy data than are the prescribed SST and sea

ice boundary conditions used in PMIP-1. Thus, to the extent that changes in atmospheric circula-

tion are linked to changes in the sea surface conditions, theCCSM3 coupled model simulation can

be regarded as a better estimate of the LGM climate.

The existence of a different Atlantic atmospheric circulation regime during LGM has implica-

tions for our understanding of global heat transport and thestability of glacial climates. In partic-

ular, as explored in a companion paper (Li and Battisti 2007), it offers new outlooks on modes of

climate variability that appear to be unique to glacial climates, such as the abrupt D-O warming

events recorded in Greenland ice cores.

Acknowledgments.

We wish to thank Joe Barsugli, Kerim Nisancioglu, Richard Seager, Mike Wallace, Justin

Wettstein and Jeff Yin for their helpful suggestions, and Cecilia Bitz and Marc Michelson for

assistance with the model simulations. This research was supported by the Comer Fellowship

22

Program.

23

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32

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33

List of Figures

1 Wintertime atmospheric circulation in CCSM3 simulations. Zonal wind at 250 mb

from the (a) PD and (b) LGM simulations (10 m s−1 contours). The Atlantic jet is

stronger and more zonal while the Pacific jet is slightly morezonal but largely un-

changed. Geopotential height at 500 mb from the (c) PD and (d)LGM simulations

(120 m contours with an offset of -5400 m). The Laurentide icesheet over North

America forces a strong stationary wave that intensifies theflow downstream. The

thick solid lines in all the maps denote the zero contour. . . .. . . . . . . . . . . . 40

2 Atlantic jet and eddy characteristics in CCSM3 coupled simulations of PD and

LGM climate. These plots show the relationship between the width and strength of

zonal mean zonal winds and northward eddy heat flux vTATL (K m s−1) at 850 mb

over the Atlantic sector (90◦W–0◦). The top panel shows the monthly mean jet

width versus jet strength for 50 Januarys in the PD (red) and LGM (blue) simula-

tions; the bottom panels show the strength of the eddy flux versus jet strength for

25 Januarys. The horizontal line is the mean vTATL for each simulation, with the

vertical dash marking the 95% confidence limits. The jet strength is the maximum

zonal wind speed in the sector; the jet width is the latitude range over which the

jet speed decreases to half its maximum value. The results are robust to the choice

of month(s) used to define winter, and to whether the jet and eddy strengths are

taken as the maximum in the zonal mean of different longituderanges straddling

the jet/storm track core, or as the area-weighted mean of thelargest 30–50 values

at the jet/storm track core. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 41

34

3 Meridional heat transport from observations and simulations. (a) Recent estimates

(Trenberth and Caron 2001) of total (entire shaded area), atmosphere (dark grey

shading) and ocean (light grey shading) using data from the Earth Radiation Bud-

get Experiment (ERBE) and reanalysis products from the European Centre for

Medium-Range Weather Forecasts (ECMWF). The sum of the darkand light grey

areas represents the total heat transport by the climate system. (b) Comparison of

heat transports from observations and in the PD and LGM simulations by CCSM3.

The shaded curves are the same observational estimates shown in the top panel.

Maximum uncertainties in the model simulations are

5 Wintertime zonal wind and temperature profiles by ocean sector. Zonal wind (con-

tours) and temperature (colors) for DJFM averaged over (top) the Atlantic sector

(90◦W–0◦) and (bottom) the Pacific sector (150◦E–27◦W). The contour interval is

10 m s−1 and the thick solid lines denote the zero contour. The rightmost panels

show the horizontal zonal wind shear in a 10 degree latitude band, marked on each

panel by horizontal bars, equatorward of the jet core (red isPD, blue is LGM). The

sharp midlatitude temperature front and strong jet in the LGM Atlantic are robust

features no matter how the ocean sector is defined. . . . . . . . . .. . . . . . . . 44

6 Wintertime jet position and eddy diagnostics from ERA-40 reanalysis data (1958–

2001). Colors show (a) eddy temperature fluxv′T ′ (K m s−1) at 850 mb, (b) eddy

kinetic energy(u′2 + v′2)/2 (m2 s−2) at 200 mb and (c) zonal momentum flux

u′v′ (m2 s−2) at 200 mb, all for DJFM. Black contours show zonal wind at 250mb

(10 ms−1 contours starting at 30 m s−1). All eddy fields are calculated from daily

data which have been high-pass filtered to retain variability at frequencies greater

than 8 days. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

7 Atlantic jet and eddy characteristics from reanalysis data. These plots show the

relationship between the width and strength of zonal mean zonal winds and north-

ward eddy heat flux vTATL (K m s−1) at 850 mb over the Atlantic sector (90◦W–0◦)

for 44 Januarys in the period 1958–2001. See details in Figure 2 caption. . . . . . . 46

8 Wintertime baroclinicity in CCSM3 simulations. The Eady growth rate parameter,

σ = 0.31f0N−1(∂uh/∂z), in the 850–700 mb layer for DJFM (0.2 day−1 contours

with values greater than 0.8 day−1 shaded). . . . . . . . . . . . . . . . . . . . . . 47

36

9 1-point regression analysis of wintertime eddy structurein CCSM3 simulations.

DJFM regression maps for the present day (left) and LGM (right) climates: (a,d)

v′200 (2 m s−1) meridional wind at 200 mb; (b,e)v′850 (1 m s

−1) meridional wind at

850 mb; (c,f)v′850T′850 (5 K m s

−1) meridional heat flux at 850 mb. Black contours

shows positive correlations, grey contours show negative correlations and the zero

contour is omitted. The black crosses mark the locations of the reference time

series, which is thev′200 wind at 43◦N, 28◦W for the PD simulation, and at 38◦N,

28◦W for the LGM simulation. The solid grey line marks 40◦N. All eddy fields are

calculated from daily data which have been high-pass filtered to retain variability

at periods less than 8 days. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 48

10 Midwinter suppression of the Pacific storm track. This is an update of the figure

from Nakamura (1992) showing the midwinter suppression of the Pacific storm

track from NCEP reanalysis data 1948-2002. The top panel shows the seasonal

cycle of eddy temperature fluxv′T ′ at 850 mb (2.5 K m s−1 contours) and surface

temperature (colors) in the Pacific sector (160E-180E). Thebottom panel shows

the maximumv′T ′ (dark red line with 95% confidence limits shown in pink); su-

perimposed are the seasonal cycles of maximum zonal wind at 250 mb (solid blue

line) and∆T between 20–70N at 850 mb (dashed blue line), both arbitrarily scaled

to fit on the plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

37

11 Wintertime jet position and eddy diagnostics in CCSM3 simulations compared

to simulation PMIPa reproducing the PMIP-1 results. Colorsshow (a,c,e) eddy

temperature fluxv′T ′ (K m s−1) at 850 mb and (b,d,f) zonal momentum fluxu′v′

(m2 s−2) at 200 mb, all for DJFM. Black contours show zonal wind at 250mb

(10 m s−1 contours starting at 30 m s−1). All eddy fields are calculated from daily

data which have been high-pass filtered to retain variability at periods less than 8

days. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

12 Reconstructions of land ice topography over North America at Last Glacial Maxi-

mum. The left panel shows profiles of the Laurentide ice sheetcomplex at 21 ka BP

across latitude 55◦N from the ICE-4G (Peltier 1994) and ICE-5G (Peltier 2004)

deglaciation history data sets. The right panel shows topography and bathymetry

in the North American sector from the ICE-5G data set, with the thick white con-

tour marking the extent of the ice sheet and the black line (A–A’) marking the

latitude of the profiles in the left panel. . . . . . . . . . . . . . . . .. . . . . . . . 51

13 Sea surface conditions during Last Glacial Maximum from (a) the CLIMAP re-

construction and (b) as simulated by the coupled model CCSM3when forced by

ICE-5G, and 21 ka BP insolation and greenhouse gases. Contours show annual

mean sea surface temperature (4◦C), and colors show the difference relative to

present day observations. The white contours mark the 50% sea ice concentration

line for August (dashed) and February (solid). . . . . . . . . . . .. . . . . . . . . 52

38

14 Sea surface conditions in the North Atlantic during Last Glacial Maximum from

reconstructions and as simulated by CCSM3. The reconstructions are from the

CLIMAP project (CLIMAP 1981), the Glacial Atlantic Ocean Mapping (GLAMAP)

project (Sarnthein et al. 2003) and Meland et al. (2005). Colors show sea surface

temperature (◦C), with the 50% sea ice concentration line marked by the thick

white contour, for August (top) and February (bottom). . . . .. . . . . . . . . . . 53

39

DJFM

aPRESENT DAY

JJA

DJFM

bLGM

JJA

DJFM

c

JJA

DJFM

d

JJA

FIG. 1. Wintertime atmospheric circulation in CCSM3 simulations. Zonal wind at 250 mb from the (a) PD

and (b) LGM simulations (10 m s−1 contours). The Atlantic jet is stronger and more zonal whilethe Pacific

jet is slightly more zonal but largely unchanged. Geopotential height at 500 mb from the (c) PD and (d) LGM

simulations (120 m contours with an offset of -5400 m). The Laurentide ice sheet over North America forces

a strong stationary wave that intensifies the flow downstream. The thick solid lines in all the maps denote the

zero contour.

40

jet w

idth

(la

titud

e)

20

30

40

50

60

jet strength (m/s)

vTA

TL

(Km

s−1 )

35 45 55 65

2

3

4

5

LGMPD

FIG. 2. Atlantic jet and eddy characteristics in CCSM3 coupled simulations of PD and LGM climate. These

plots show the relationship between the width and strength of zonal mean zonal winds and northward eddy heat

flux vTATL (K m s−1) at 850 mb over the Atlantic sector (90◦W–0◦). The top panel shows the monthly mean

jet width versus jet strength for 50 Januarys in the PD (red) and LGM (blue) simulations; the bottom panels

show the strength of the eddy flux versus jet strength for 25 Januarys. The horizontal line is the mean vTATL for

each simulation, with the vertical dash marking the 95% confidence limits. The jet strength is the maximum

zonal wind speed in the sector; the jet width is the latitude range over which the jet speed decreases to half its

maximum value. The results are robust to the choice of month(s) used to define winter, and to whether the jet

and eddy strengths are taken as the maximum in the zonal mean of different longitude ranges straddling the

jet/storm track core, or as the area-weighted mean of the largest 30–50 values at the jet/storm track core.

41

−6

−3

0

3

6

PW

aECMWF atmosECMWF ocean

PW

b

−6

−3

0

3

6 PD totalLGM totalPD oceanLGM ocean

−90 −60 −30 0 30 60 90−6

−3

0

3

6

latitude

PW

transient eddytransport

stationarywave transport

cPD eddyLGM eddyPD swLGM sw

FIG. 3. Meridional heat transport from observations and simulations. (a) Recent estimates (Trenberth and

Caron 2001) of total (entire shaded area), atmosphere (darkgrey shading) and ocean (light grey shading) using

data from the Earth Radiation Budget Experiment (ERBE) and reanalysis products from the European Centre

for Medium-Range Weather Forecasts (ECMWF). The sum of the dark and light grey areas represents the total

heat transport by the climate system. (b) Comparison of heattransports from observations and in the PD and

LGM simulations by CCSM3. The shaded curves are the same observational estimates shown in the top panel.

Maximum uncertainties in the model simulations are

e f

0

20

40

c d

0

48

96

aPRESENT DAY

bLGM

0

10

20

FIG. 4. Wintertime jet position and eddy diagnostics in CCSM3 simulations. Colors show (a–b) eddy temper-

ature fluxv′T ′ (K m s−1) at 850 mb, (c–d) eddy kinetic energy(u′2 + v′2)/2 (m2 s−2) at 200 mb and (e–f)

zonal momentum fluxu′v′ (m2 s−1) at 200 mb, all for DJFM. Black contours show zonal wind at 250mb (10

m s−1 contours starting at 30 m s−1). All eddy fields are calculated from daily data which have been high-pass

filtered to retain variability at periods less than 8 days.

43

ATL

PRESENT DAYp(

mb)

200

400

600

800

1000ATL

LGM

latitude

p (m

b)

PAC30 45 60

200

400

600

800

1000

latitude

PAC30 45 60 0 2 4

m/s/deg

K190 212 234 256 278 300

FIG. 5. Wintertime zonal wind and temperature profiles by ocean sector. Zonal wind (contours) and tem-

perature (colors) for DJFM averaged over (top) the Atlanticsector (90◦W–0◦) and (bottom) the Pacific sector

(150◦E–27◦W). The contour interval is 10 m s−1 and the thick solid lines denote the zero contour. The right-

most panels show the horizontal zonal wind shear in a 10 degree latitude band, marked on each panel by

horizontal bars, equatorward of the jet core (red is PD, blueis LGM). The sharp midlatitude temperature front

and strong jet in the LGM Atlantic are robust features no matter how the ocean sector is defined.

44

c

0

20

40

b

0

48

96

a

0

10

20

FIG. 6. Wintertime jet position and eddy diagnostics from ERA-40 reanalysis data (1958–2001). Colors show

(a) eddy temperature fluxv′T ′ (K m s−1) at 850 mb, (b) eddy kinetic energy(u′2 + v′2)/2 (m2 s−2) at 200 mb

and (c) zonal momentum fluxu′v′ (m2 s−2) at 200 mb, all for DJFM. Black contours show zonal wind at

250 mb (10 ms−1 contours starting at 30 m s−1). All eddy fields are calculated from daily data which have

been high-pass filtered to retain variability at frequencies greater than 8 days.

45

jet w

idth

(la

titud

e)

20

30

40

50

60

jet strength (m/s)

vTA

TL

(Km

s−1 )

35 45 55 65

2

3

4

5

NCEPECMWF

FIG. 7. Atlantic jet and eddy characteristics from reanalysis data. These plots show the relationship between

the width and strength of zonal mean zonal winds and northward eddy heat flux vTATL (K m s−1) at 850 mb

over the Atlantic sector (90◦W–0◦) for 44 Januarys in the period 1958–2001. See details in Figure 2 caption.

46

PRESENT DAY LGM

FIG. 8. Wintertime baroclinicity in CCSM3 simulations. The Eady growth rate parameter,

σ = 0.31f0N−1(∂uh/∂z), in the 850–700 mb layer for DJFM (0.2 day−1 contours with values greater than

0.8 day−1 shaded).

47

15

30

45

60

v200

(2 ms−1)

a

latit

ude

PRESENT DAY

15

30

45

60

v200

(2 ms−1)

d

LGM

15

30

45

60

v850

(1 ms−1)

b

latit

ude

15

30

45

60

v850

(1 ms−1)

e

−90 −60 −30 0 30

15

30

45

60

vT850

(5 Kms−1)

c

latit

ude

longitude−90 −60 −30 0 30

15

30

45

60

vT850

(5 Kms−1)

f

longitude

FIG. 9. 1-point regression analysis of wintertime eddy structure in CCSM3 simulations. DJFM regression

maps for the present day (left) and LGM (right) climates: (a,d) v′200 (2 m s−1) meridional wind at 200 mb; (b,e)

v′850 (1 m s−1) meridional wind at 850 mb; (c,f)v′850T

′850 (5 K m s

−1) meridional heat flux at 850 mb. Black

contours shows positive correlations, grey contours show negative correlations and the zero contour is omitted.

The black crosses mark the locations of the reference time series, which is thev′200 wind at 43◦N, 28◦W for the

PD simulation, and at 38◦N, 28◦W for the LGM simulation. The solid grey line marks 40◦N. All eddy fields are

calculated from daily data which have been high-pass filtered to retain variability at periods less than 8 days.

48

latit

ude

JAN APR JUL OCT JAN APR

15

45

75

eddy

hea

t flu

x 85

0mb

[Km

/s]

JAN APR JUL OCT JAN APR

5

10

15

20

eddy heat fluxu 250mb∆T(E−P) 850mb

FIG. 10. Midwinter suppression of the Pacific storm track. This is an update of the figure from Nakamura

(1992) showing the midwinter suppression of the Pacific storm track from NCEP reanalysis data 1948-2002.

The top panel shows the seasonal cycle of eddy temperature flux v′T ′ at 850 mb (2.5 K m s−1 contours) and

surface temperature (colors) in the Pacific sector (160E-180E). The bottom panel shows the maximumv′T ′

(dark red line with 95% confidence limits shown in pink); superimposed are the seasonal cycles of maximum

zonal wind at 250 mb (solid blue line) and∆T between 20–70N at 850 mb (dashed blue line), both arbitrarily

scaled to fit on the plot.

49

a PD v′ T′ at 850 mb

c LGM

e PMIPa

0 10 20

b PD u′ v′ at 200 mb

d LGM

f PMIPa

0 20 40

FIG. 11. Wintertime jet position and eddy diagnostics in CCSM3 simulations compared to simulation PMIPa

reproducing the PMIP-1 results. Colors show (a,c,e) eddy temperature fluxv′T ′ (K m s−1) at 850 mb and

(b,d,f) zonal momentum fluxu′v′ (m2 s−2) at 200 mb, all for DJFM. Black contours show zonal wind at

250 mb (10 m s−1 contours starting at 30 m s−1). All eddy fields are calculated from daily data which have

been high-pass filtered to retain variability at periods less than 8 days.

50

ò�

ò�

ò�

ò�

0

�

�

�

90W ��>���>

3 km

��RT

1 km

��RT

A’

A A’

A

ICE-5G

ICE-4G

km

FIG. 12. Reconstructions of land ice topography over North America at Last Glacial Maximum. The left

panel shows profiles of the Laurentide ice sheet complex at 21ka BP across latitude 55◦N from the ICE-4G

(Peltier 1994) and ICE-5G (Peltier 2004) deglaciation history data sets. The right panel shows topography and

bathymetry in the North American sector from the ICE-5G dataset, with the thick white contour marking the

extent of the ice sheet and the black line (A–A’) marking the latitude of the profiles in the left panel.

51

CLIMAP

60° S

30° S

0°

30° N

60° N

CCSM3 LGM

180° W 120° W 60° W 0° 60° E 120° E 180° E

60° S

30° S

0°

30° N

60° N

−15−10 −8 −6 −3 −2 −1−0.5 0 0.5 1 1.5

FIG. 13. Sea surface conditions during Last Glacial Maximum from (a) the CLIMAP reconstruction and (b)

as simulated by the coupled model CCSM3 when forced by ICE-5G, and 21 ka BP insolation and greenhouse

gases. Contours show annual mean sea surface temperature (4◦C), and colors show the difference relative to

present day observations. The white contours mark the 50% sea ice concentration line for August (dashed) and

February (solid).

52

M e l a n d e t a l . 2 0 0 5C L I M A P G L A M A P C C S M 3

ï�ï� 0 � � 3 4 5 6 7 8 9 ��FIG. 14. Sea surface conditions in the North Atlantic during Last Glacial Maximum from reconstructions and

as simulated by CCSM3. The reconstructions are from the CLIMAP project (CLIMAP 1981), the Glacial

Atlantic Ocean Mapping (GLAMAP) project (Sarnthein et al. 2003) and Meland et al. (2005). Colors show

sea surface temperature (◦C), with the 50% sea ice concentration line marked by the thick white contour, for

August (top) and February (bottom).

53

List of Tables

1 Jet and eddy characteristics in the Atlantic sector (15◦N–65◦N, 90◦W–0◦) for

DJFM winter. uATL is the maximum zonal wind at 200 mb in the sector;σu is the

standard deviation of uATL; KEIATL is the sector-mean monthly departure from

the climatological mean of the kinetic energy of the horizontal wind at 200 mb;

and vTATL is the sector-mean northward eddy heat flux at 850 mb. The 95% con-

fidence intervals were determined using a Student’s t-test for the means, and a

chi-squared test for the standard deviations. . . . . . . . . . . .. . . . . . . . . . 55

54

TABLE 1. Jet and eddy characteristics in the Atlantic sector (15◦N–65◦N, 90◦W–0◦) for DJFM winter. uATL is

the maximum zonal wind at 200 mb in the sector;σu is the standard deviation of uATL; KEIATL is the sector-

mean monthly departure from the climatological mean of the kinetic energy of the horizontal wind at 200 mb;

and vTATL is the sector-mean northward eddy heat flux at 850 mb. The 95% confidence intervals were deter-

mined using a Student’s t-test for the means, and a chi-squared test for the standard deviations.

uATL σu KEIATL vTATL

m s−1 m s−1 m2 s−2 K m s−1

ERA-40 38.5± 1.0 3± 2 31± 2 3.56± 0.07

NCEP 38.2± 0.9 3± 2 31± 3 3.49± 0.08

PD 47.3± 0.9 3± 2 34± 3 3.42± 0.19

LGM 58.7± 0.5 2± 1 22± 1 2.93± 0.13

PMIPa 39.1± 1.0 4± 2 24± 2 3.63± 0.22

55