GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L10709, doi:10.1029/2012GL051598, 2012

Local and remote controls on observed Arctic warming
J. A. Screen,1 C. Deser,2 and I. Simmonds1
Received 4 March 2012; revised 17 April 2012; accepted 22 April 2012; published 30 May 2012.

[1] The Arctic is warming two to four times faster than the
global average. Debate continues on the relative roles of local
factors, such as sea ice reductions, versus remote factors indriving, or amplifying, Arctic warming. This study examines
the vertical profile and seasonality of observed troposphericwarming, and addresses its causes using atmospheric generalcirculation model simulations. The simulations enable the isolation and quantification of the role of three controlling factorsof Arctic warming: 1) observed Arctic sea ice concentration(SIC) and sea surface temperature (SST) changes; 2) observed
remote SST changes; and 3) direct radiative forcing (DRF) due
to observed changes in greenhouse gases, ozone, aerosols, andsolar output. Local SIC and SST changes explain a large portionof the observed Arctic near-surface warming, whereas remote
SST changes explain the majority of observed warming aloft.
DRF has primarily contributed to Arctic tropospheric warmingin summer. Citation: Screen, J. A., C. Deser, and I. Simmonds(2012), Local and remote controls on observed Arctic warming,
Geophys. Res. Lett., 39, L10709, doi:10.1029/2012GL051598.

1. Introduction[2] Surface temperatures are rising faster in the Arctic thanover the globe as a whole [Intergovernmental Panel on
Climate Change, 2007], a phenomenon commonly known
as Arctic amplification (AA). There has been much discussion about the causes of observed AA [e.g., Serreze andFrancis, 2006; Graversen et al., 2008; Serreze et al., 2009;
Screen and Simmonds, 2010a, 2010b; Serreze and Barry,
2011; Bintanja et al., 2011].
[3] One key aspect of this debate has been the verticalprofile of Arctic warming and its interpretation in the context
of the causes of AA. It has been shown that the verticalstructure of Arctic warming can provide insight into its
underlying causes, because different driving mechanisms
may have a different “fingerprint” in the atmospheric column. Sea ice concentration (SIC) or sea surface temperature(SST) changes are understood to induce a temperature
response that is strongest in the lowermost atmosphere[Serreze et al., 2009; Deser et al., 2010; Kumar et al., 2010;
Screen and Simmonds, 2010a, 2010b; Sedlác̆ek et al., 2012;
Orsolini et al., 2012] whereas changes in atmosphere energy
transport may cause a maximum thermal response in the1
School of Earth Sciences, University of Melbourne, Melbourne,
Victoria, Australia.
2
Global and Climate Dynamics, National Center for AtmosphericResearch, Boulder, Colorado, USA.

Corresponding author: J. A. Screen, School of Earth Sciences,
University of Melbourne, Melbourne, Vic 3010, Australia.
(screenj@unimelb.edu.au)
Copyright 2012 by the American Geophysical Union.
0094-8276/12/2012GL051598

mid-troposphere [Alexeev et al., 2005; Graversen et al.,2008; Yang et al., 2010; Chung and Räisänen, 2011].
[4] A number of studies have examined the vertical profile
of recent Arctic warming based on atmospheric reanalyses[Graversen et al., 2008; Screen and Simmonds, 2010a,
2010b; Alexeev et al., 2012]. On the basis of the ERA-40
reanalysis, Graversen et al. [2008] found greater warming
aloft than at the surface over the period 1979–2001, which
led them to conclude that atmospheric poleward energy
transport, not Arctic sea ice loss, was the main driver of AA.
In contrast, alternative reanalysis data sets analysed bySerreze et al. [2009] and Screen and Simmonds [2010a]
showed strongest AA at the surface, consistent with changes in the surface energy budget due to sea ice loss [Screen
and Simmonds , 2010b]. This discrepancy can be explainedby two compounding factors: errors in ERA-40 that result in
exaggerated warming in the mid- to lower-troposphere[Screen and Simmonds , 2011; Alexeev et al., 2012], and anincrease in the rate of sea ice decline [Comiso et al., 2008],
that has strengthened surface-based AA over the past decade[Serreze et al., 2009; Screen and Simmonds, 2010a].
[5] Despite differing perspectives on the relative role ofArctic sea ice loss in AA, both Graversen et al. [2008] andScreen and Simmonds [2010a] found mid-tropospheric
Arctic warming in all seasons, which, as mentioned above, ishighly suggestive of horizontal energy transport changes as
opposed to surface energy budget changes. Thus, it appears
very likely that multiple processes are important for
observed AA (see review by Serreze and Barry [2011]).
Furthermore, these multiple mechanisms of AA are likely to
be inter-connected, which makes them hard to disentangle
using observations (or reanalyses) alone. For example,
increased poleward heat transport would likely enhance sea
ice melt and therefore, trigger surface-based AA [Graversen
et al., 2011; Chung and Räisänen, 2011].
[6] The seasonality of Arctic warming may also provide
clues as to relative importance of the multiple driving processes [Lu and Cai, 2009]. The air temperature response to
Arctic sea ice loss is understood to be largest in autumn andearly winter [Deser et al., 2010]. The strong temperature
inversion amplifies near-surface polar warming primarily in
winter [Deser et al., 2010; Bintanja et al., 2011]. Chung and
Räisänen [2011] argue that the strength and vertical structure of summertime Arctic warming is a good indicator of
how much climate forcing from lower latitudes contributes
to Arctic warming.
[7] In this study we revisit the vertical profile and seasonality of observed Arctic warming and seek to quantify
the forcings that are responsible for it. In this context, theterminology “forcings” is used to describe both external
forcings, such as changes in greenhouse gases or aerosols,
and atmospheric lower boundary forcings, such as changes
in SST and SIC. We utilise atmospheric general circulation
model (AGCM) hind-casts to isolate and quantify the role ofL107091 of 5

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Figure 1. Vertical and seasonal structure of Arctic-meantemperature trends (1979–2008) in the (a) JRA25, (b) ERA-I,(c) MERRA and (d) CFSR reanalyses, respectively. Black
dots show trends that are statistically significant at the 95%
level (p < 0.05) based on a two-tailed student’s t-test.
three forcing factors to recent Arctic warming: 1) observed
Arctic SIC and SST changes; 2) observed remote SSTchanges; and 3) direct radiative forcing (DRF) due toobserved changes in greenhouse gases, ozone, sulphate andvolcanic aerosols, and solar output. These simulations add
new insight into the physical processes underlying theobserved Arctic warming. A major advance in our study isthe use of specially-designed model simulations to quantify
the relative roles of local and remote forcing of observed
Arctic warming, expanding on previous studies that haveshown qualitative differences in the vertical profile andseasonality of Arctic warming in response to these factors.

2. Data and Methods
2.1. Model Simulations[8] We utilize two independent AGCMs: the NCAR
Community Atmosphere Model version 3 (CAM3) and the
UK-Australian Unified Model version 7.3 (UM7.3). CAM3
is the atmospheric component of the NCAR Community
Climate System Model version 3 (CCSM3), which participated in the third Coupled Model Intercomparison Project(CMIP). The version used here has 26 vertical levels and aspectral resolution of T42, roughly equivalent to 2.8 degrees
of latitude and longitude. UM7.3 has been developed by the
UK Meteorological Office Hadley Centre and is the atmospheric model used in their Global Environmental Modelversion 2 (HadGEM2) and in the Australian Community
Climate and Earth System Simulator (ACCESS) version 1.0.
Both HadGEM2 and ACCESS are participating models in
the fifth CMIP. The configuration of UM7.3 used here has
38 vertical levels with a horizontal resolution of 1.25 degrees
of latitude by 1.875 degrees of longitude. For further details
the reader is directed to Collins et al. [2006] and Martin
et al. [2011], for CAM3 and UM7.3 respectively.
[9] Two distinct experiments have been performed identically with each model. In both experiments, the AGCMs
were coupled to a land surface model and the land surface

L10709boundary conditions were free to evolve. The oceanic surface boundary conditions were prescribed. In the firstexperiment, the models were forced with the observed evolution of monthly SIC and SST globally for the period 1979–
2008, from the Hurrell et al. [2008] data set. Hereafter wewill refer to this as the GLB experiment.
[10] In the second experiment, the models were forced
with the observed evolution of monthly Arctic SIC. SSTs
were allowed to vary from year-to-year only in regions ofArctic sea ice change, to account for local SST modifications
related directly to sea ice change, but were specified as theclimatological annual cycle elsewhere. Hereafter we willrefer to this as the ARC experiment. In both GLB and ARC,
greenhouse gas concentrations, ozone, sulphate and volcanic
aerosols, and solar output were all held constant.
[11] A third experiment has been performed in CAM3
only. In this experiment the oceanic surface boundary conditions were prescribed as in GLB, but additionally themodel was forced with the observed evolution of greenhousegases, ozone, sulphate and volcanic aerosols, and solar output. These forcings were taken from Meehl et al. [2006] andare based on observations until the year 2000 and the Special
Report on Emissions Scenarios (SRES) A1B scenario from2000 to 2008. As explained by Deser and Phillips [2009]and originally by Folland et al. [1998], this approach doesnot “double count” the atmospheric radiative forcing because
only the direct effect is specified, whereas the indirect effect(via changes in SST and SIC) is included in the prescribedsurface forcing. Hereafter we will refer to this as the ALLexperiment.
[12] The simulations cover the 30-year period from 1979
to 2008. To quantify the effects of atmospheric intrinsic
variability (AIV), all three experiments were conducted
multiple times starting from different atmospheric initialconditions. Both GLB and ARC were run five times in
CAM3 and eight times in UM7.3, giving an ensemble ofthirteen simulations. ALL was performed five times using
CAM3. The thermal responses in GLB and ARC werehighly consistent between the two independent AGCMs andtherefore we only present the ensemble-mean of all thirteen
simulations in what follows. We analyse temperature fields
at the surface and on seven standard pressure levels (925,
850, 700, 600, 500, 400, 300 hPa).
2.2. Reanalyses[13] We use temperature fields from four of the latest
atmospheric reanalyses: the Japanese 25-year reanalysis(JRA25) [Onogi et al., 2007], ECMWF ERA-Interim
reanalysis (ERA-I) [Dee et al., 2011], NASA Modern-Era
Retrospective analysis for Research and Applications(MERRA) [Rienecker et al., 2011] and the NCEP Climate
Forecast System Reanalysis (CFSR) [Saha et al., 2010]. For
the sake of comparison (and to facilitate averaging across thereanalyses), all reanalysis temperature fields were re-gridded
on to a 2.5 degree latitude-longitude grid, at the surface andon the same seven vertical levels as the model output.
[14] Figure 1 shows the vertical and seasonal structure of
Arctic-mean temperature trends over the 30-year period
from 1979 to 2008 in the four reanalyses. Here and in what
follows the Arctic is defined as the region poleward of 67 N(results were comparable for other reasonable definitions of
the Arctic, e.g., >60, 65, 70 N), which comprises of approximately 65% ocean and 35% land by area. The “seasons” are

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Figure 2. Vertical profile of Arctic-mean temperature
trends (1979–2008) for (a) Jan–Feb, (b) Mar–Apr, (c) May–
Jun, (d) Jul–Aug, (e) Sep–Oct, and (f) Nov–Dec. The blacklines denote the reanalysis ensemble-mean trends and thewhiskers show the reanalysis ensemble spread. The blue lines
denote the model ensemble-mean trends from the GLB
experiment and the shaded envelopes show the model ensemble spread.
defined as January–February (JF), March–April (MA), May–
June (MJ), July–August (JA), September–October (SO), andNovember–December (ND). We have chosen to conduct ouranalyses on these six two-month “seasons” rather than theconventional four (following Deser et al. [2010]) because thethermal response to Arctic sea ice loss displays rapid monthly
changes, which tend to be blurred out if three-month averages
are employed.
[15] Although the Arctic-mean temperature trends are not
identical, the four reanalyses do, however, depict a consistent vertical and seasonal pattern of warming (Figure 1). The
strong agreement between the reanalyses, in spite of differences in the observations assimilated, data assimilation
techniques and underpinning reanalysis model physics,
lends credence to the veracity of the trends. Further, Screen
and Simmonds [2011] showed that, barring one clearlyerroneous reanalysis not used here (ERA-40), the current
breed of reanalyses can accurately (to within the 95% confidence level) reproduce Arctic-mean upper-air temperature
trends measured by radiosondes and satellites.

3. Analysis and Results[16] Our first consideration is to determine whether themodels depict a realistic climatological-mean seasonal cycle
and vertical profile of Arctic-mean temperature. Figure S1 in
the auxiliary material shows that the models represent well
the main aspects of the seasonal and vertical structure of
Arctic temperature found in the reanalyses.1 Importantly, themodels capture the climatological-mean near-surface temperature inversion in the winter months (November–April).
However, both UM7.3 and CAM3 simulate overly strong
1
Auxiliary materials are available in the HTML. doi:10.1029/
2012GL051598.

L10709wintertime temperature inversions. This problem is common
to many climate models [Medeiros et al., 2011; Zhang et al.,2011]. The implications of these model biases will be discussed later and we are mindful of this issue as we proceed.
[17] Next we address whether the AGCM simulations cancapture the seasonal and vertical structure of Arctic warming
found in the reanalyses. Figure 2 shows, for each season, thevertical profile of Arctic temperature trends averaged acrossthe four reanalyses (hereafter termed OBS) in black and the
GLB ensemble-mean in blue. The spread of the four reanalyses (the range between minimum and maximum; whiskers
in Figure 2) allows a quantitative assessment of uncertainty
in Arctic temperature trends due to differences in thereanalysis process and different background atmosphericstates. The model ensemble spread (the range between
minimum and maximum of the thirteen individual simulations; shaded envelope in Figure 2) provides an estimate of
the uncertainty due to AIV and, to a lesser extent, model
differences. The model ensemble spread is generally larger
than the reanalysis ensemble spread.
[18] The vertical profiles of the temperature trends are inbroad agreement between OBS and the GLB ensemblemean. Of course, perfect agreement should not be expected
since the OBS trends will contain the imprint of AIV, whichis averaged out, at least partially, in the GLB ensemblemean. In most seasons and at the majority of heights in theatmosphere, the OBS trends lie within the spread of theindividual GLB ensemble members. When the reanalysis
uncertainty is also taken into account, there are only two
trends that differ significantly between GLB and OBS (in thesense that there is no overlap between the model ensemble
spread and reanalysis ensemble spread): the GLB simulations underestimate the surface and 925 hPa warming during
MA (Figure 2b). This discrepancy is most pronounced over
the terrestrial portion of our Arctic domain (not shown). The
observed springtime terrestrial Arctic warming has been
attributed in part to decreases in snow cover extent [Déryand Brown, 2007], which are not specified in our simulations and therefore, may in part explain the stronger land
surface warming in OBS compared to GLB. In ND, the GLB
ensemble-mean shows weaker warming at 925 and 850 hPa
than OBS. A possible explanation for this discrepancy is thatthe climatological near-surface temperature inversion isstronger in the models than it is in the reanalyses. Bothmodels overestimate the strength of the climatological-meantemperature inversion in ND, JF and MA (Figure S1), which
may excessively confine warming to the near-surface layers
in the simulations in these seasons. These differences aside,
the GLB simulations represent well the seasonality andvertical profile of Arctic warming in OBS.
[19] Figures 3a and 3b show the vertical and seasonal
structure of Arctic temperature trends in OBS and the GLB
ensemble mean, respectively. Both depict warming throughout the troposphere and in all seasons. However, the warming
is far from uniform with respect to season or altitude. In both
OBS and GLB, there is more pronounced warming in
September–February than in May–August. The warming isstrongly surface-intensified in SO and ND whereas there iscomparatively vertically-uniform warming in MJ and JA.
The most obvious discrepancies between OBS and GLB are
the larger near-surface warming during MA found in OBS
and the larger lower-tropospheric (925–700 hPa) warming
during ND in OBS than in GLB. As discussed above, these

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Figure 3. (a) Vertical and seasonal structure of the reanalysis ensemble-mean (OBS) Arctic-mean temperature trends(1979–2008). (b–d) As in Figure 3a, but for the model ensemble-mean trends in the GLB and ARC experiments, and theirdifference (REM), respectively. Black dots show trends thatare statistically significant at the 95% level (p < 0.05).
differences can be partially, but not fully, accounted for by
AIV, and may additionally reflect model biases in climatological-mean temperature inversion strength and land surfacechanges that have not been specified.
[20] We now consider the relative contributions of Arctic
SIC and SST changes versus non-Arctic SST changes toobserved Arctic warming. The former is obtained from ARC
ensemble-mean temperature trends (Figure 3c) whereas thelatter is estimated by subtracting the ensemble-mean temperature trends in ARC from those in GLB (Figure 3d),referred to hereafter as REM. The warming in ARC occurs
almost entirely below 700 hPa and is strongly surfaceintensified. This is consistent with the thermal response to
Arctic sea ice loss found in other AGCM studies [e.g., Deseret al., 2010; Kumar et al., 2010; Orsolini et al., 2012]. One
potential caveat is that the overly-strong surface temperature
inversions in the models (Figure S1) may result in ice-loss
induced warming that is too shallow compared to reality.
[21] In stark contrast to ARC, the warming in REM islarger aloft than at the surface, with maximum warming
located in the mid-troposphere (500–600 hPa). This verticaltrend profile closely resembles the temperature anomalies
correlated with increased atmospheric poleward heat transport [Alexeev et al., 2005; Graversen et al., 2008; Yanget al., 2010]. ARC and REM show broadly similar seasonal trend patterns, with strongest warming in SO. The
weakest warming in ARC is in JA, whereas in REM thesmallest trends are found in MA and MJ.
[22] In terms of the maximum trend magnitude at any particular altitude, ARC displays stronger warming trends than
REM: maximum Arctic-mean warming rates of 0.5–0.6 C per
decade (at the surface in SO) compared to 0.3–0.4 C per
decade (in the mid-troposphere in SO). Although the warmingin REM is not surface-intensified, Arctic-mean warming trendsof greater than 0.1 C per decade are found at the surface during
September–February. These are only, however, roughly one

L10709third of the magnitude of the surface warming in ARC during
the same seasons.
[23] The annual-mean surface warming in ARC is 0.30 C
per decade compared to 0.42 in GLB and 0.60 C per decadein OBS. Thus, Arctic SIC and SST changes account forroughly three quarters of the simulated annual-mean Arctic
surface warming and one half of the observed annual-mean
Arctic surface warming. The difference between ARC and
GLB implies that the remote SST change account for
approximately one quarter of the simulated annual-meansurface Arctic warming and one fifth of the observed
annual-mean Arctic surface warming. Higher in the atmospheric column, at 500 hPa, the annual-mean Arctic warming is 0.02, 0.26 and 0.25 C per decade in ARC, GLB and
OBS, respectively. Thus, remote SST changes account for
approximately all the simulated and observed annual-mean
mid-tropospheric Arctic warming, although there are differences between the OBS, GLB and REM trends for individual seasons.
[24] Lastly, we turn to the contribution of DRF. Figure 4ashows the Arctic-mean temperature trends in ALL, which
are in close correspondence with those in GLB (Figure 3b),but for larger tropospheric warming in ALL during JA. Thisdifference is clearer when the trends in GLB are subtracted
from those in ALL, providing an estimate of the componentof the trends solely due to DRF (Figure 4b). In JA, DRF is
responsible for vertically-uniform Arctic-mean warming of
0.2–0.3 C per decade throughout the free troposphere (i.e, at
all levels except the surface). The contribution of DRF issmall outside of JA (less than 0.2 C per decade).

4. Discussion and Conclusions[25] We have quantified the relative contributions ofArctic SIC and SST changes, remote SST changes and DRF
to recent Arctic warming. All three forcing factors areimportant for explaining the observed vertical profile andseasonality of Arctic warming. However, their relative
importance differs considerably with altitude and season.
Arctic SIC and SST changes are the main driver of nearsurface temperature trends, but have had a negligible influence on temperature trends above 700 hPa. By contrast,
remote SST changes, and presumably consequent increased
poleward energy transport, have been central to the observed
warming aloft. Remote SST changes have made a much

Figure 4. (a) Vertical and seasonal structure of the ensemblemean Arctic-mean temperature trends (1979–2008) in the ALLexperiment. (b) As in Figure 4a, but for difference between the
ALL and GLB experiments. Black dots show trends that are
statistically significant at the 95% level (p < 0.05).

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smaller, but non-negligible, contribution to observed nearsurface warming trends. DRF has primarily contributed to
Arctic tropospheric warming in summer.
[26] Our results demonstrate that the vertical profile ofArctic warming provides insight into the relative importanceof the driving mechanisms. Our simulations support the conclusions of Serreze et al. [2009] and Screen and Simmonds[2010a], that the surface-intensification of recent Arctic
warming is evidence of AA due to Arctic sea ice loss (andassociated local SST change). Equally, they also provide
support for the stance of Graversen et al. [2008] and others,
that warming aloft is driven by, and therefore indicative of,changes in poleward heat transport.
[27] We close by commenting that this study has not
attempted to separate the components of Arctic warming due
to anthropogenic forcing (e.g., greenhouse gases, ozone,
sulphate aerosols), natural forcing (e.g., solar output, volcanic aerosols) or internal climate (oceanic) variability. On this
topic, Gillett et al. [2008] concluded that “observed changes
in Arctic [and Antarctic] temperatures are not consistent
with internal climate variability or natural climate drivers
alone, and are directly attributable to human influence”.
Based on proxy temperature records (mostly for summer),
Kaufmann et al. [2009] showed that the 10-year period
ending in 2008 was the warmest decade of the past
2000 years in the Arctic. The Arctic is currently in an
extraordinary period of warmth.
[28] Acknowledgments. The authors thank JMA, ECMWF, NASAand NCEP for providing on-line access to their reanalysis data sets.
J. Screen and I. Simmonds acknowledge support from the Australian
Research Council. The UM7.3 simulations were supported by an award
under the Merit Allocation Scheme on the Australian National Computational Infrastructure. NCAR is sponsored by the National Science Foundation. The CAM3 simulations were kindly provided by R. Tomas and
A. Phillips in support of a grant from the NSF Office of Polar Programs
awarded to C. Deser. Two anonymous reviewers are thanked for their timeand advice.
[29] The editor thanks Chul Chung and an anonymous reviewer forassisting with the evaluation of this paper.

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