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Arctic Inversion Strength in Climate Models
BRIAN MEDEIROS, CLARA DESER, ROBERT A. TOMAS, AND JENNIFER E. KAY
National Center for Atmospheric Research,* Boulder, Colorado(Manuscript received 2 August 2010, in final form 14 January 2011)
ABSTRACT
Recent work indicates that climate models have a positive bias in the strength of the wintertime low-level
temperature inversion over the high-latitude Northern Hemisphere. It has been argued this bias leads tounderestimates of the Arctic’s surface temperature response to anthropogenic forcing. Here the bias in inversion strength is revisited. The spatial distribution of low-level stability is found to be bimodal in climate
models and observational reanalysis products, with low-level inversions represented by a stable primary modeover the interior Arctic Ocean and adjacent continents, and a secondary unstable mode over the Atlantic
Ocean. Averaging over these differing conditions is detrimental to understanding the origins of the inversion
strength bias. While nearly all of the 21 models examined overestimate the area-average inversion strength,
conditionally sampling the two modes shows about half the models are biased because of the relative partitioning of the modes and half because of biases within the stable mode.

1. Introduction
Low-level temperature inversions are a noted featureof the Arctic winter climate (Serreze et al. 1992; Zhang
et al. 2011). The so-called Arctic inversion mediates thesurface energy balance and contributes to amplifying thehigh-latitude surface temperature response to anthropogenic increases in greenhouse gas (GHG) concentrations(Serreze and Barry 2005). The amplified warming over theArctic Ocean and surrounding continents in recent years
has been most pronounced in the lower troposphere andstably stratified PBL (Serreze et al. 2009; Screen andSimmonds 2010). As Arctic sea ice and high-latitude terrestrial snow cover diminish in response to increasing
GHG, the inversion is expected to weaken, with consequences for the rate of surface warming, cloud type andamount, and other effects (Pavelsky et al. 2010; Deseret al. 2010; Alexander et al. 2010; Kay and Gettelman2009). Boé et al. (2009) point out that climate models tend
to overestimate the Arctic inversion strength, some bymore than a factor of 2, and suggest that this bias leads to* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Brian Medeiros, NCAR/CGD,
P.O. Box 3000, Boulder, CO 80307-3000.
E-mail: brianpm@ucar.edu
DOI: 10.1175/2011JCLI3968.1
Ó 2011 American Meteorological Society

an excessive negative longwave radiative feedback andhence reduced climate sensitivity. Thus, the wintertime
low-level temperature inversion is of key interest forunderstanding high-latitude climate change.
The Arctic inversion that caps the PBL is influenced
by large-scale advection and local processes, particularly
surface fluxes and entrainment (Busch et al. 1982). The
inversion exhibits pronounced seasonality: elevated inversions are common during spring and summer when
low clouds are often present, while surface-based inversions are pervasive during autumn and winter (Tjernström
and Graversen 2009). Boé et al. (2009) and Pavelsky et al.
(2010) report winter inversion strengths ,3 K for theArctic as a whole (northward of 708 and 648N, respectively), based on several reanalysis and satellite products. This value is considerably smaller than the ’10-K
inversion strength obtained by Tjernström and Graversen(2009) from radiosonde observations over the central
Arctic Ocean from the Surface Heat Budget of the
Arctic Ocean (SHEBA) experiment.
Here we analyze the spatial distribution of the mean
wintertime inversion strength over high latitudes of theNorthern Hemisphere. We compare twentieth-century
integrations from 21 climate models (Table 1) in the Third
Coupled Model Intercomparison Project (CMIP3) archive(Meehl et al. 2007) with observational estimates fromthe 40-yr European Centre for Medium-Range Weather
Forecasts (ECMWF) Re-Analysis (ERA-40; used in Boé

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TABLE 1. List of CMIP3 models used here. Note that the MPI ECHAM twentieth-century integration continues to year 2100, but only
1860–2000 are used here. Two of the models are repeated, but using different resolutions, noted in the third column. Herein CM stands forclimate model.
Institution
Bjerknes Centre for Climate Research (BCCR)
Canadian Center for Climate Modelling and Analysis
CCCma
Center for Climate System Research (CCSR; The Universityof Tokyo)/National Institute for Environmental Studies(NIES)/Frontier Research Center for Global Change(FRCGC)
CCSR/NIES/FRCGC
Centre National de RecherchesMétéorologiques (CNRM)
Commonwealth Scientific andIndustrial Research Organisation
CSIRO
Geophysical Fluid Dynamics Laboratory (GFDL)
GFDLGoddard Institute for Space Studies (GISS)GISS
LASG/Institute of Atmospheric Physics (IAP)

Instituto Nazionale di Geofisica e Vulcanologia (INGV)
Institute for Numerical Mathematics (INM)
Institut Pierre Simon Laplace (IPSL)Max Planck Institute for Meteorology (MPI)
National Center for Atmospheric Research (NCAR)
NCAR
Hadley Centre for Climate Prediction
and Research/Met Office (UKMO)
UKMO

Model

VersionInterval

Bergen Climate Model (BCM)
Coupled General Circulation
Model (CGCM)
CGCM
Model for Interdisciplinary
Research on Climate (MIROC)

2.0
3.1 (T47)

1850–1999
1850–2000

3.1 (T63)
3.2 (T42 L20)

1850–2000
1850–2000

MIROC
CM

3.2 (T106 L56)
3

1900–2000
1860–1999

Mark

3.0

1871–2000

Mark
CM
CM
AOM
ModelE

3.5d
2.0
2.1
R
H
1.0_g

1871–2000
1861–2000
1861–2000
1850–2000
1880–1999
1880–2003
1850–1999

4.6
3.0
4_v1
5
1.1
3.0
3

1870–2000
1871–2000
1860–2000
1860–2000
1890–1999
1870–1999
1860–1999

1

1860–1999

Flexible Global Ocean–Atmosphere–Land
System Model (FGOALS)
ECHAM
CM
CM
ECHAM
Parallel Climate Model (PCM)
Community Climate System Model
Climate configuration of the Met
Office Unified Model
Hadley Centre Global
Environmental Modelet al. 2009; Pavelsky et al. 2010) and the updated ERAInterim Reanalysis (Simmons et al. 2007). For ERA-40,
we show results using the period with the highest-quality
remote sensing products in the assimilation (1987–2001;
Uppala et al. 2005), though using the full period (1957–
2001; not shown) supports all conclusions presented; for
ERA-Interim, we use the full record (1989–2009). For thereanalysis and CMIP3 models, monthly means are analyzed. Additional simulations with the National Center
for Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), are used to investigate
the temporal characteristics of the inversion.

2. Arctic inversion strength
The Arctic inversion strength can be estimated bydifferencing temperatures at a pressure level above theinversion and near the surface: DT [ Tatm 2 Tsfc. The
850-hPa level is chosen as the upper level, which is expected to be above the PBL (Serreze et al. 1992). The—

lower temperature is often taken at 1000 hPa, but wintertime surface pressure deviates enough from 1000 hPa that
surface temperature is preferred here; the main consequence of this choice is to slightly increase DT over theocean. Whatever definition is employed, this is a crude
estimate of the inversion strength, but is appropriate forcomparing the stability of the lower troposphere in climate models, which have coarse vertical resolution. A
strict definition would exclude DT , 0, since such unstable conditions do not represent an inversion, but theneglect of this criterion by many recent papers prompts
us to examine the consequences of including unstableconditions in the analysis.
Figure 1 shows the average DT for the 21 years of ERAInterim reanalysis and the ensemble of CMIP3 models(all years). Because the Arctic is data sparse, the reanalysis largely reflects the underlying model, but the inversion in the ERA-40 reanalysis compares reasonably wellto radiosonde observations (Tjernström and Graversen
2009; Zhang et al. 2011). Both reanalysis products are1 SEPTEMBER 2011

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FIG. 1. Average DT and sea ice concentration for (left) ERA-Interim (1989–2009) and (right) the CMIP3 ensemble
mean, both for November–February. To construct the ensemble mean maps, output from each model was interpolated to a 18 3 18 grid, which were then averaged; this interpolation step is excluded from all other analysis
presented.

interpolated to a 2.58 latitude–longitude grid, a resolution similar to the models, and only the winter season(November–February) is sampled. The ERA-Interim
map shows that the Arctic Ocean has a monthly-mean
DT of ;6 K, higher near North America and lower near
Asia; the multimodel mean shows a similar pattern, but
stronger DT. The North Atlantic, however, shows unstable conditions, exceeding 29 K off the Norwegiancoast, and the reanalysis has more unstable values thanthe models. Strong inversions are also evident over land,

especially Siberia, and the reanalysis and models agree toa large extent, except near Scandinavia. The unstableconditions over the ocean are associated with little to nosea ice, as can be seen from the corresponding sea ice
concentration distributions in the bottom panels of Fig. 1.
The black curves in Fig. 2 show histograms of DT inboth reanalysis products for ocean and land using thesame domain as in Fig. 1 [648–908N, which we loosely
define as the ‘‘Arctic’’ to be consistent with recent work;e.g., Pavelsky et al. (2010)]. The histograms are weighted

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FIG. 2. Distribution of inversion strength for the Arctic region(648–908N) for (black, solid) ERA-Interim, (black, dashed) ERA-40,
and (gray) the CMIP3 models. (top) Oceanic locations and (bottom)
land locations. The gray, dotted curve shows the multimodel average. Values are from monthly means and are binned in 1-K bins from
220 to 20 K. The FGOALS model is excluded as an outlier. Circles
show the maximum value in the model distributions.

by area and comprise all monthly-mean values at each grid
point in the domain. The oceanic distribution is bimodal,
with maxima around 6 and 29 K. Figure 1 suggests thatthe primary mode represents the Arctic Ocean while thesecondary mode corresponds to the North Atlantic. The
terrestrial histogram (excluding Greenland) is unimodal,
centered on stable values of DT around 7 K. There is a tailon the land distribution toward unstable conditions, andthese tend to be focused on Scandinavia, perhaps showing
the influence of North Atlantic conditions.
The CMIP3 models are also included in Fig. 2, basedon data availability (Table 1). We exclude the Instituteof Atmospheric Physics (IAP) Flexible Global Ocean–Atmosphere–Land System Model (FGOALS) from most
figures because its excessive sea ice makes it an outlier (cf.
Zhang and Walsh 2006). The models show the same bimodal pattern as the reanalysis products. The unstable,
North Atlantic mode tends to be less common, but peaks

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at a more unstable value. In the Arctic, the models show
a variety of solutions, but tend to overestimate the frequency of stable conditions compared with the reanalysis products. Eleven models have stable modes morestable than the reanalysis, 5 are within 1 K of the reanalysis
mode, and 5 have a less stable mode. Thus this analysis
confirms the finding that climate models overestimate
the Arctic inversion strength, but also demonstrates thatthe bias stems from different distributions among themodels. The land distributions show more agreement
between models and reanalysis, but most models still
exaggerate the stability and underestimate the unstable
portion of the reanalysis distributions.
Figure 3 shows the average oceanic value of DT for theregion poleward of 648N (gray). Dividing the distributions at DT 5 0 produces two well-separated ‘‘regimes,’’
showing stable (blue) and unstable (red) conditions.
The word ‘‘regime’’ is used here to distinguish two setsof behavior associated with differing surface conditions
and PBL structure, but not necessarily unique physicalmechanisms. All the models (except IAP FGOALS)
exhibit both stable and unstable regimes, with the stable
one being the larger contribution to the regional average. The ERA-Interim has slightly smaller regionallyaveraged DT than the ERA-40, and also within the stable part of the distribution. Most models place the Arctic
average value within 1s of either reanalysis product.
Figure 3 shows that the apparent agreement between
the models and reanalysis arises from compensating biases in the partitioning of the regimes and the magnitudeof DT within the regimes. Within the stable regime, about
half of the models have average DT more than 1s larger
than the reanalysis values. Agreement is better in theunstable regime, where almost all the models are within
the reanalysis range. This comparison shows that thepartitioning between regimes significantly impacts theaverage DT. Take for example the Institute of Numerical Mathematics Coupled Model, version 3 (INM CM3):
the regional average DT is close to the reanalysis value,
but its stable regime is more stable; the large area coveredby the unstable regime in this model reduces the regional
average DT to a value close to the reanalysis value. This iscorroborated by the model’s placement to the left side ofFig. 3, indicating it has relatively little sea ice.
Because the distribution of DT is bimodal, the meanvalue can be strongly biased and has limited utility. The
presence of extremes in either mode can further bias themean. To test for such bias, we have repeated the analysis
above using the area-weighted median (not shown). Doing so removes the impact of extreme values, but retains
the effects of different areal coverage of the regimes. The
Arctic area average (gray in Fig. 3) is consistently smaller
than the median because the stable regime covers a larger

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FIG. 3. Average inversion strength for the CMIP3 models, ERA-40, and ERA-Interim forthe (gray) Arctic region (648–908N), (red) unstable, and (blue) stable portion of the DT distribution. Markers show the mean value and whiskers show 61 standard deviation. The stableand unstable conditions are divided at DT 5 0. The pink and blue shading show the 61 standard
deviations of the ERA-Interim values for stable and unstable conditions, and horizontal dottedgray lines similarly show the Arctic region. Models are organized from left to right by increasing
Arctic average sea ice concentration in November–February; the exception is GISS-EH, forwhich sea ice concentration was unavailable, so that model was placed next to GISS-ER arbitrarily. The regional average (gray markers) is the sum of the two regimes weighted by theirrelative areas, providing information about the sizes of the regimes in each dataset.

area than the unstable regime in the winter. Within theindividual stable and unstable regimes, however, the median and mean agree well.
The regional-average DT is partly determined by the
relative sizes of the regimes, and the partitioning varies
across models. Therefore, care is required for interpreting regional averages. Figure 4 shows how the choice ofdomain affects the regional average. Including substantial
subpolar ocean area results in negative average DT. For
ERA-40, it is only north of 608N that average DT is
positive and the ice-covered Arctic overwhelms othersignals; for ERA-Interim the sign change occurs around
628N, while the CMIP ensemble average becomes positive at about 558N. Both ERA-40 and ERA-Interim
show a stable DT over land that increases as the domain
contracts northward until the land area becomes small(;708N).
The excessively strong Arctic inversion appears inmost of the models even for large domains (Fig. 4). Only
two models are consistently less stable than the reanalysis over the ocean. The models are more uniformly
distributed about the reanalysis over land, but the ensemble mean is more stable than either reanalysis. The
variation in the model spread with latitude reinforces thatsome conclusions could depend on one’s definition of the
Arctic region. Conditioning the analysis on regimes—
using DT as is done here or by using other criteria—removes such sensitivity; the averaging of Fig. 4 for the
stable regime demonstrates negligible latitude dependence because lower latitudes contribute little to thestable regime (not shown). Conditional sampling thus
provides more robust results than averaging over multiple regimes in a specified geographical area.
The importance of spatial sampling raises the questionof whether temporal sampling also matters. To explore
some issues of temporal sampling, we analyze a CAM3
integration forced by the observed evolution of SST and
sea ice concentration and thickness during 1980–2008.
Figure 5 shows the distributions of DT constructed from
4-times-daily instantaneous fields (black), 5-day averages of the instantaneous output (orange), and monthly
mean values (from all time steps, blue). For the oceanic
distributions, the 5-day average and the monthly mean
distributions are both bimodal as in Fig. 2, with the 5-day
averages having slightly larger variability. The distributions based on instantaneous data, however, containless evidence for bimodality. The interval between thestable and unstable modes near DT 5 0 appears to be

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FIG. 4. Area-average Arctic inversion strength for 21 CMIP3
models (gray), the multimodel mean (dashed gray), ERA-40(1987–2001, dashed black), and ERA-Interim (1989–2009, solid
black), all using November–February monthly-mean values andseparated by (top) ocean and (bottom) land. The horizontal axis
shows the southern latitude of the region used to calculate theaverage DT. The FGOALS model is excluded as an outlier.

populated in the instantaneous fields mostly at the expense of the stable regime, and there is also enhanced
frequency of very stable conditions. This additional variability represents transient weather conditions, whichappear to have a time scale of less than 5 days. Over land
areas, the instantaneous fields also show a broader distribution, and contrasting with the oceanic distributions,actually appear more bimodal than the lower-frequency
sampling.
Figure 5 also shows the distributions of monthly meansfrom the Community Climate System Model, version 3(CCSM3), and ERA-Interim from Fig. 2. The atmosphereonly and fully coupled results are similar to each other,
particularly over land (SST and sea ice conditions differ
between the integrations, precluding direct comparison).
In the stable mode, CAM3 is shifted toward slightly morestable oceanic conditions than CCSM3, and both are morestable than the reanalysis. The CCSM3 shows a sharp peak

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FIG. 5. Histograms as in Fig. 2, but for an integration of CAM3
using different temporal sampling (solid curves). The dashed curveshows the CCSM3 result and the shading shows the ERA-Interim
distribution, both as in Fig. 2. The horizontal axes are expanded toallow for additional variability, and the vertical axes are reduced
for clarity in comparison with Fig. 2.

near 29 K compared with the broader peak in CAM3
centered at about 215 K, but the area covered by theunstable regime is comparable. The overall similarity between CAM3 and CCSM3 suggests that for this model
atmospheric processes are probably responsible for thebias in DT compared with reanalysis. This view is also
supported by Fig. 3, which shows that the CCSM3 regional average is strongly influenced by its large DT in thestable regime.

3. Summary and discussion
Most of the CMIP3 climate models overestimate thestability of the lower troposphere over the Northern
Hemisphere high latitudes. This agrees with previous
studies and elaborates on them by exploring the spatial
distribution of DT. Differences in surface conditions andlarge-scale circulation lead to a bimodal distribution of

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DT over the ocean. The stable mode represents stableboundary layers over cold surfaces (e.g., sea ice and snow),
while the unstable mode is found over open water, mostly
in the North Atlantic, and represents more well-mixed
boundary layers. When these modes are treated as separate regimes the disagreement with reanalysis remains,though many models are within the variability of thereanalysis. In the stable regime, about half the models
overestimate the mean inversion strength by more than
the reanalysis 1s level.
This analysis makes clear that the reported bias in
Arctic inversion strength in CMIP3 models arises from two
different sources of error. First, the relative area coveredby stable versus unstable conditions. Second, the representation of the lower troposphere (i.e., the PBL) withinthose conditions, especially the representation of stableboundary layers. These are proximate factors, but determining the ultimate causes of the bias for each model
requires additional analysis. Investigating the sea ice distribution and large-scale atmospheric circulation patterns
may help explain the partitioning of stable and unstableconditions. Diagnosing problems in the representation of
stable boundary layers is likely impossible based on themonthly-mean CMIP3 archive. This is mainly becausemonthly time scales conflate many separate processes
affecting the PBL. For example, advection of warm air
over cold surfaces is often associated with transient conditions, and separating such transport factors from stableconditions driven by radiative cooling is not possible inmonthly averages. A secondary issue is that stable boundary layers are often very shallow, with complicated verticalstructure (Mahrt 1999), and climate models do not properly resolve such details. As such, detailed understandingof the stable boundary layer will require intricate knowledge of a model’s parameterizations and implementation,and so be model dependent. It is possible that futurecomparisons (e.g., CMIP5) that compile high-frequency
output will allow more nuanced understanding of theorigin of the Arctic inversion strength biases.
Differences between the ERA-Interim and ERA-40
products are relatively small, justifying use of either one
for comparison with the models. The ERA-40 reanalysis
has previously been favorably compared with observations (e.g., Tjernström and Graversen 2009; Zhang et al.
2011), and previous analyses suggest the present conclusions would stand with other reanalysis products (Boéet al. 2009). Several studies use Atmospheric InfraredSounder (AIRS) temperature profiles to investigate theArctic inversion (e.g., Kay and Gettelman 2009;
Devasthale et al. 2010); our analysis of AIRS inversion
strength (not shown) yields results similar to the comparison with reanlaysis (see also Pavelsky et al. 2010). We
find, however, that AIRS surface (and 1000 hPa)4739

temperature is biased warm compared with reanalysis; thisweakens DT and precludes quantitative comparison to the
CMIP3 models. We have investigated the impact of using
monthly averages by using 4-times-daily instantaneous
output from the CAM3. The high-frequency output contains weather noise at short time scales, but temporal
averages quickly converge toward the monthly mean
distribution. We have explored the sensitivity of theCMIP3 model results and the ERA-40 reanalysis to thetime period chosen for analysis and found little impact
on our conclusions with regard to the time-mean inversion strength.
These results provide some context for the exaggerated
Arctic inversion strength in climate models by demonstrating that some models overestimate local inversion
strength and others contain spatial biases. The central
Arctic in winter is completely ice covered, while the North
Atlantic is largely ice free. Over ice, stable boundary layers
and strong inversions are expected, as ice buffers ocean–
atmosphere heat exchange. In the North Atlantic, surface
fluxes can be large as warm water is advected northward tomeet cold, polar air. This drives a deeper, more well-mixed
PBL. The mean Arctic inversion strength is determinedby the partitioning between these PBL structures, whichchanges as sea ice decreases (Kay and Gettelman 2009;
Deser et al. 2010). Disagreement among climate models is
also affected by the representation of physical processes,
such as turbulent mixing, surface fluxes, and clouds.
Isolating regimes, using DT or other criteria, provides formore robust model evaluation and should help elucidate
the origin of biases. Here we find that about half themodels overestimate inversion strength even within stableconditions, suggesting deficiencies in representing stableboundary layers in those models. Some of the othermodels may better represent the inversion, but containspatial biases (possibly related to sea ice or large-scaleatmospheric circulation patterns) that impact the regional inversion strength. Focusing on the PBL provides
a link between the surface energy budget and inversion
strength and represents an approach to evaluating therepresentation of high-latitude boundary layer processes in models.

Acknowledgments. We thank the anonymous reviewers for their constructive comments. We thank D. Seidel,
Y. Zhang, C. Ao, J. C. Golaz, and S. Park for encouraging
this work, and PCMDI and the Working Group on Coupled Modelling for making available the WCRP CMIP3
dataset with support from the Office of Science, U.S. Department of Energy. C. Deser and R. Tomas acknowledge support from the NSF Office of Polar Programs.
B. Medeiros was supported by the Office of Science4740

JOURNAL OF CLIMATE(BER), U.S. DOE, Cooperative Agreement DE-FC0297ER62402. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
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