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Climatological Characteristics of Arctic and Antarctic Surface-Based
Inversions*YEHUI ZHANG AND DIAN J. SEIDELNOAA/Air Resources Laboratory, Silver Spring, Maryland

JEAN-CHRISTOPHE GOLAZ
NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

CLARA DESER AND ROBERT A. TOMASNCAR Climate and Global Dynamics Division, Boulder, Colorado(Manuscript received 16 August 2010, in final form 20 May 2011)
ABSTRACT
Surface-based inversions (SBIs) are frequent features of the Arctic and Antarctic atmospheric boundary
layer. They influence vertical mixing of energy, moisture and pollutants, cloud formation, and surface ozone
destruction. Their climatic variability is related to that of sea ice and planetary albedo, important factors inclimate feedback mechanisms. However, climatological polar SBI properties have not been fully characterized nor have climate model simulations of SBIs been compared comprehensively to observations. Using 20
years of twice-daily observations from 39 Arctic and 6 Antarctic radiosonde stations, this study examines thespatial and temporal variability of three SBI characteristic—frequency of occurrence, depth (from the surface
to the inversion top), and intensity (temperature difference over the SBI depth)—and relationships among
them. In both polar regions, SBIs are more frequent, deeper, and stronger in winter and autumn than insummer and spring. In the Arctic, these tendencies increase from the Norwegian Sea eastward toward theEast Siberian Sea, associated both with (seasonal and diurnal) variations in solar elevation angle at thestandard radiosonde observation times and with differences between continental and maritime climates. Twostate-of-the-art climate models and one reanalysis dataset show similar seasonal patterns and spatial distributions of SBI properties as the radiosonde observations, but with biases in their magnitudes that differ
among the models and that are smaller in winter and autumn than in spring and summer. SBI frequency,
depth, and intensity are positively correlated, both spatially and temporally, and all three are anticorrelatedwith surface temperature.

1. Introduction
Surface-based inversions (SBIs), where atmospherictemperature increases with height from the surface, area frequent feature of the atmospheric boundary layer inthe Arctic and Antarctic. They are found in more than* Supplemental information related to this paper is available
at the Journals Online Web site: http://dx.doi.org/10.1175/
2011JCLI4004.s1.

Corresponding author address: Dr. Dian Seidel, NOAA Air
Resources Laboratory (R/ARL), 1315 East West Highway, Silver
Spring, MD 20910.
E-mail: dian.seidel@noaa.gov
DOI: 10.1175/2011JCLI4004.1
Ó 2011 American Meteorological Society;40% of nighttime radiosonde observations and morethan ;20% of daytime observations at stations poleward of 60 degrees latitude and occur more often in
winter than summer (Seidel et al. 2010).
Two mechanisms dominate the formation of low-leveland surface-based inversions (Busch et al. 1982 andreferences therein; Bradley et al. 1992). One is imbalance between outgoing longwave radiation from thesurface and downwelling solar and infrared radiation,
leading to surface cooling, common in winter and at night.
The other mechanism is advection of warm air over acooler surface layer, which can occur at any time. Surfacebased and low-level inversions in polar regions may also
be affected by subsidence processes, radiative propertiesof clouds and precipitating ice crystals, turbulent mixing,

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surface fluxes of heat and moisture, etc. (Busch et al. 1982;
Curry 1983; Kahl 1990; Kahl et al. 1992; Serreze et al.
1992), and they, in turn, may influence vertical mixing ofenergy, moisture and pollutants, cloud formation, andsurface destruction of ozone (Andreas 1980; Andreas
and Murphy 1986; Bowling 1986; Barrie et al. 1988; Kahl
1990; Boé et al. 2009). Recent studies (Kay and Gettelman
2009; Deser et al. 2010; Mernild and Liston 2010; Pavelsky
et al. 2011) have linked SBIs and low-level inversions inboth hemispheres to variability of sea ice and thus planetary albedo, an important factor in climate feedback
mechanisms and therefore in determining climate sensitivity to changes in radiative forcing.
This study uses radiosonde observations to investigate
the climatology of SBIs in the polar regions. It builds
upon early work by Hosler (1961), who examined theclimatology of low-level inversions [based below 500 feet(;150 m)] over the contiguous United States. Later studies
also employed radiosonde data to study low-level andsurface-based inversions over portions of the Arctic(Kahl 1990; Kahl et al. 1992; Serreze et al. 1992; Bradley
et al. 1992, 1993; Walden et al. 1996; Kadygrov et al. 1999;
Bourne et al. 2010) and Antarctic (Phillpot and Zillman
1970; Stone and Kahl 1991; Connolley 1996; Mahesh et al.
1997; Andreas et al. 2000). However, either by design or
because of data limitations, these analyses have restrictedspatial and temporal scope and generally have not addressed diurnal variations of inversion characteristics (butsee Malingowski et al. 2010 on diurnal SBI development
over Fairbanks).
Several recent studies have examined the climate variability of the Arctic boundary layer using satellite observations (Liu and Key 2003; Kay and Gettelman 2009;
Devasthale et al. 2010; Pavelsky et al. 2011), but thesehave limitations for detailed examination of SBIs. Moderate Resolution Imaging Spectroradiometer (MODIS)
data are limited to clear-sky conditions to identify SBIs,
and inversions are defined based on brightness temperature differences between two channels (Liu and Key2003) rather than from the surface to the inversion top.
Similarly, Atmospheric Infrared Sounder (AIRS) data
have limited vertical resolution compared with radiosonde
observations, and investigators have defined inversions
using temperature differences between fixed pressurelevels (925 and 1000 hPa, Kay and Gettelman 2009; 850
and 1000 hPa, Pavelsky et al. 2011). Here, we use the fullvertical resolution of radiosonde observations to more
precisely identify the SBI layer and quantify its structure.
The main goals of this paper are 1) to develop a comprehensive climatology of SBI characteristics, including
seasonal, diurnal and spatial variations, for both polar
regions and 2) for the first time, to compare SBI properties derived from two state-of-the-art climate models

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and one reanalysis dataset with the observational results.
Section 2 describes the datasets and methodology anddiscusses sources of uncertainty in quantitative estimatesof SBI properties and their climatological statistics. Section 3 presents the observational characteristics of SBIs
and comparison with the reanalysis and climate models,
for both the Arctic and (to a lesser extent, due to data
limitations) the Antarctic. Some results pertinent to sections 2 and 3 are provided as supplemental material. The
final section 4 summarizes our main findings. To facilitatecomparison of our results with other climate model simulations and observational data, the complete SBI datasets
used in this analysis are also provided as supplemental
material (http://dx.doi.org/10.1175/2011JCLI4004.s1).

2. Data and methods
a. Radiosonde observations
Radiosonde data from an enhanced version of theIntegrated Global Radiosonde Archive (IGRA; Durreet al. 2006; Durre and Yin 2008) for the 20-yr period
1990–2009 form the basis of this analysis. The IGRA isa quality-controlled compilation of radiosonde observations from the global network of more than 1500 stations
and includes the observed temperature, geopotential
height, humidity, wind direction, and wind speed atstandard (mandatory) levels (including the surface leveland 1000, 925, 850, 700, 500 hPa, etc.) and significant
levels at which the sounding deviates from linearity (inlogarithm of pressure) between two standard levels. To
ensure a representative climatology, we require at least
50% of the expected number of soundings at a given
observation time (0000 or 1200 UTC) for Arctic stations.
For Antarctic stations, where data records are lesscomplete, we relax the threshold to 30%, although morethan half of Antarctic stations have reporting frequencies exceeding 50%.

b. Climate models
The observations are compared with simulations fromthe following two climate models: the new National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory Atmosphere Modelversion 3 (GFDL AM3; Donner et al. 2011) and the
National Center for Atmospheric Research (NCAR)
Community Atmosphere Model version 3 (CAM3; Collins
et al. 2006) coupled to the Community Land Model version 3 (CLM3; Oleson et al. 2004). The spatial resolution of
the GFDL AM3 is 28 latitude 3 2.58 longitude, with 48 vertical levels from the surface to the stratosphere, 15 below
500 hPa and 9 in the lowest 1.5 km. The NCAR CAM3/
CLM3 has approximately 1.48 horizontal resolution

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Meteorological Service 25-yr reanalysis (JRA-25) (Onogi
et al. 2007) and the National Centers for Environmental
Prediction–National Center for Atmospheric Research(NCEP–NCAR) reanalysis (Kalnay et al. 1996), we employ ERA because of its smaller temperature errors(Graversen et al. 2008) and reasonable representation ofobserved inversion properties (Tjernström and Graversen2009). In the course of this research, we chose ERAInterim over ERA-40, in large part because we found
ERA-Interim to be in better agreement with the radiosonde depiction of SBIs (particularly their depth)
in regions of good radiosonde coverage. Elsewhere, it
is likely more strongly influenced by the assimilation
model.

d. Surface-based inversion definition and analysis
methods
FIG. 1. Sample temperature profile from Alert, Canada (828N,
628W), at 1200 UTC 14 Feb 2009 showing a surface-based inversion
and its depth Dz and intensity DT. Thin embedded noninversion
layers (temperature decrease with height) are indicated, and Dhvalues are their thicknesses.

and 26 vertical levels (Collins et al. 2006), 8 below
500 hPa and about 4 in the lowest 1.5 km. Both models
use s (i.e., p/ps, where p is pressure, and ps is surfacepressure) as a vertical coordinate. The lowest atmosphericlevel is at s 5 0.9961 in GFDL AM3 and at s 5 0.9925
for NCAR CAM3. Unlike previous studies of climate
model representations of SBIs based on monthly-mean
model output (Boé et al. 2009; Deser et al. 2010), SBI
statistics in this study are based on instantaneous profiles
saved every 6 h (0000, 0600, 1200, 1800 UTC) from bothmodels.

c. Reanalysis data
Because of the irregular and sparse coverage of theradiosonde network, especially over the Arctic Ocean
and the interior of the Antarctic continent, we augment
the radiosonde observations with reanalysis data fromthe European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-Interim; Simmons et al.
2007). ERA-Interim is an updated and improved versionof the earlier 40-yr ECMWF Re-Analysis (ERA-40;
Uppala et al. 2005), with finer vertical resolution (37versus 23 levels from 1000 to 1 hPa) and finer horizontal
resolution (1.58 versus 2.58). ERA-Interim data have 16
levels below 500 hPa and 6-hourly sampling (0000, 0600,
1200, 1800 UTC). The surface level in the ERA-Interim
data is at 2 m, comparable to the radiosonde surface observation height (instrument shelter height). Althoughother reanalyses are now available, including the Japanese

For all four datasets, SBIs are identified from temperature profile data using Kahl’s (1990) algorithm, whichscans from the surface upward to 500 hPa to find caseswith temperature increasing with altitude. The inversion
top is defined as the bottom of the first layer in which
temperature decreases with altitude, but thin (,100 m)
noninversion layers, with temperature decreasing withheight, are ignored if they are embedded within a deeper
inversion layer. Soundings are considered to be unsuitable for analysis if the surface level is missing, there arefewer than 10 upper-air data levels from surface to 500 hPa,or the temperature difference across the inversion exceeds 40 K (Serreze et al. 1992). This definition represents a true inversion layer (from the surface to theinversion top), which is different from defining inversion
strength as a temperature difference between two prespecified levels or heights in the boundary layer (Hudson
and Brandt 2005; Kay and Gettelman 2009; Pavelsky
et al. 2011).
Two parameters give a quantitative measure of each
SBI: SBI depth [altitude above ground level (AGL) of
the SBI top Dz] and SBI intensity (temperature difference across the inversion layer DT). An example, using
the 1200 UTC 14 February 2009 sounding from Alert, on
the Canadian Arctic archipelago, is shown in Fig. 1.
Using the definitions above, we find the SBI top at
1259 m AGL, with SBI depth Dz and intensity DT of
1259 m and 13.8 K, respectively. This profile also reveals
several thin (7, 30, 32, 8, and 9 m) noninversion layers
embedded within the deep SBI. Above 1259 m, the186-m-thick noninversion layer (exceeding the 100-m
threshold) marks the top of the SBI.
For each grid point or radiosonde station, we compute
frequency of SBI occurrence ( f ) and average SBI depth
and intensity for the overall dataset and separately foreach season, for each month, and for 0000 and 1200 UTC

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FIG. 2. Time series of monthly (a) mean number of reported data levels (surface to 500 hPa)and (b) surface-based inversion frequency, (c) mean SBI depth, and (d) mean SBI intensity at
Jan Mayen (718N, 98W) for 1963–2009. Vertical lines show statistically detected changepoints
in the number of levels.

(61 h to account for the 1–2-h sounding duration).
Following Kahl (1990) and Serreze et al. (1992), threemonth seasons are defined as January–March (Arctic
winter, Antarctic summer), July–September (Arctic
summer and Antarctic winter), etc.
In all comparisons of the various datasets, the models
and reanalysis are sampled at 0000 and 1200 UTC to
mimic the radiosondes. Comparisons of radiosonde data
and the two climate models are for 1990–2007. Comparisons for radiosonde data and ERA-Interim, andanalysis of radiosonde data alone, are for 1990–2009.

e. Continuity and temporal homogeneity ofradiosonde data
Although radiosonde data are available in IGRA for
113 Arctic and 19 Antarctic stations, some spanning morethan 50 yr, this study employs only 39 and 6 of these,
respectively, and these only for 1990–2009. Many of thestation records do not have continuous records. Manyothers are plagued by temporal inhomogeneities due tochanges in radiosonde instruments and observing practices (including changes in temperature sensor, radiation
corrections, balloon type, observation time, and station
location) that introduce artificial changes in temperaturerecords (Gaffen 1994).
Improvements in data-recording systems (from manual
to mechanical to increasingly powerful computer-based
systems) have led to steplike increases in the verticalresolution of sounding data. Walden et al. (1996) showedthat the SBI time series of Bradley et al. (1992) contained
temporal inhomogeneities associated with increases in
the number of reported vertical levels in the soundings.

Our examination of long-term (1958–2009) Arctic andAntarctic SBI time series confirms this result (shown in
Fig. 2). We employed a nonparametric statistical method(Lanzante 1996) to detect changepoints in station timeseries of the number of reported data levels below
500 hPa. The detected changepoints and station history
metadata were then used in visual inspection of timeseries of SBI characteristics (frequency, depth, and intensity) to identify possible changepoints in SBI characteristics associated with detected changepoints invertical resolution.
Data from Jan Mayen (718N, 98W) are a good example of the effect of increasing vertical resolution on SBI
characteristics (Fig. 2). The time series of mean monthly
number of reported levels (surface to 500 hPa) has two
statistically detected changepoints—one in 1983 when
the average increased from 11.3 to 15.8 and the second in2000 when it increased to 17.0 levels per sounding. The
1983 increase is associated with a simultaneous 9% increase in f (from 16% to 25%) and even more obvious
decreases in Dz (of 325 m, from 456 to 131 m) and DT (of
1.3 K, from 2.5 to 1.2 K). The smaller 2000 increase invertical resolution is associated with smaller changes
in f, Dz, and DT (of 2%, 60 m, and 0.3 K, respectively). In
this case, we consider the f data homogeneous for theperiod following 1983 and the Dz and DT following 2000.
Examining all the Arctic and Antarctic radiosonde
station SBI time series, and attempting to balance spatialsampling, data continuity, and homogeneity concerns, welimited our analysis to the 20-yr period 1990–2009. A listof stations with homogeneous SBI frequency data for thisperiod is given in Tables S1 and S2 in the supplemental

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material, which also includes more detailed information
about data availability at these stations and at the stations
excluded from the analysis. Most Russian Arctic station
records have gaps, and most Canadian, Greenland, andEurasian Arctic stations are plagued by data inhomogeneities, particularly in Dz and DT time series,
associated with changes in sounding resolution.
The greater sensitivity of SBI depth and intensity (ascompared with SBI frequency) to vertical resolutionchanges is probably a result of our general requirement ofa minimum of 10 levels per sounding. The existence of
SBIs is likely to be captured in all such soundings, in accord with World Meteorological Organization reporting
requirements (WMO 2008, section 12.10.1). However,the SBI top is likely to be found at a lower level as thenumber of levels increases, so that Dz and DT are lessrobust to changes in resolution. Moreover, the impact issystematic—increasing of number of levels increases theresolution of small-scale temperature fluctuations in the
profile, resulting in SBI detection at lower heights withsmaller temperature differences. But because the shifts in
Dz and DT are small relative to their mean values, section
3 includes climatological Dz and DT results at all thestations with homogeneous f time series, even those withinhomogeneous Dz and DT time series.

f. Sensitivity of surface-based inversion statistics todefinition and analysis methods
Because the SBI definition and analysis method described in section 2d involve some choices that may
appear somewhat arbitrary, this section explores thesensitivity of SBI climatology statistics to these choices.
Below we address the following: inclusion versus omission of surface-level observations, the threshold thickness of noninversion layers allowed within the SBI, andthe focus on SBIs rather than all low-level inversions(and the related issue of using instantaneous rather thantime-averaged vertical profile data).
Superficially, it would seem obvious, even requisite, toinclude surface observations to analyze surface-based
inversions. However, we questioned this, particularly for
analysis of radiosonde data, which generally combine
a surface observation from instruments in shelters withupper-air observations from radiosonde instrument
packages. Because the shelter may not be collocatedwith, or at the same elevation as, the sounding launchsite, it is possible to imagine temperature differences between the surface observation and the first level of radiosonde observation showing (or not showing) an SBI
that is not representative of the rest of the sounding or of
the conditions at the sounding location.
The sensitivity of climatological Arctic SBI parameters to inclusion or omission of surface data is shown in

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the supplemental material (Fig. S1). The spatial patternsof SBI frequency, depth, and intensity are very similar inboth cases, but there are nonzero differences in magnitudes. The frequency differences (with surface minus
without surface) range from 29% to 23%, with a median absolute difference of 4% [and interquartile range(IQR) of 10%). SBI depth differences range from 2104
to 71 m, with a median absolute difference of 18 m (and56-m IQR). SBI intensity differences range from 20.7 to
2.7 K, with a median absolute difference of 1.0 K (and1.0-K IQR). The mainly positive SBI intensity differences (larger DT when surface data are included) are
due to the additional near-surface region of inversion
thickness contributing to the overall DT. We include
surface observations in the climatological analysis below, but we recognize that this results in more intense
SBIs than if we had omitted them and typical structural
uncertainties of about 4%, 18 m, and 1 K for SBI frequency, depth, and intensity, respectively.
We also examined the sensitivity of climatological SBI
statistics to the tolerated thickness of noninversion layers
embedded within the SBIs by testing thickness values of
1, 50, 100, 150, 200, and 300 m. As shown in the supplemental material (Fig. S2), for thickness thresholds of 1,
50, and 100 m, climatological f, Dz, and DT values arealmost identical, with differences typically ,0.3%, 20 m,
and 0.1 K, respectively. When thicker noninversion layers
are allowed, we obtain higher SBI Dz and DT values, as
expected. For example, when the embedded layer is allowed to reach 200 m, Dz and DT differences (comparedwith 100 m embedded layers) can exceed 70 m and 0.3 K,
respectively. Given median Arctic SBI depths of about
350 m (section 3), the lack of sensitivity of f, Dz and DT
values to thickness choices of 100 m or less, and theprecedent in literature for 100-m thickness (Kahl 1990),
our analysis allows noninversion layers of ,100-m thickness within a deeper SBI.
Lastly, we explain our focus on SBIs. Some previous
studies (Kahl 1990; Kahl et al. 1992; Serreze et al. 1992;
Andreas et al. 2000) of the polar boundary layer addressed all low-level temperature inversions, including
both SBIs and elevated inversion layers, and found thatmost profiles revealed elevated inversion layers rather
than SBIs. The supplemental material (Fig. S3) shows
climatological frequencies of SBI and elevated inversion
occurrence in the Arctic, with the latter occurring much
more often than the former. The combined low-level
inversion (SBI and elevated inversion) frequency is near
100%. This is also the case for the Antarctic (not shown),
and for the rest of the globe. Seidel et al. (2010) found that
only 13% of soundings in a global (505 station) analysis of
10 years of radiosonde data showed neither SBI nor elevated inversions at or below 500 hPa. Thus, except for5172

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FIG. 3. Maps of mean annual SBI (left) frequency, (middle) depth, and (right) intensity over the Arctic from 39 radiosonde stations withhomogeneous records for 1990–2009. Squares denote stations with inhomogeneous SBI depth and intensity time series.

regions of frequent cases of deep vertical convection,
analysis of low-level inversions is, for the most part,
analysis of the planetary boundary layer in general. This
study isolates SBI cases because of their prominence inpolar climates and their important role in limiting verticalmixing.
In addition, Walden et al. (1996) recommended theuse of monthly or seasonal average profiles instead ofindividual profiles in inversion studies. Such an approach is a de facto combination of SBI and elevatedinversion cases (as well as any cases without inversions)and so is not appropriate to analysis of SBIs. This study,
therefore, relies on individual soundings rather thanmonthly average profile data to isolate profiles with
SBIs.

3. Results
This section presents the climatology of SBI, first for
the Arctic then for the Antarctic. For both regions, results from radiosonde observations are shown separately
before being compared with reanalysis and climate
model simulations.

a. Observed Arctic SBI climatology
The maps in Fig. 3 show mean SBI frequency, depth,
and intensity over the Arctic. The depth and intensity
averages are based on all soundings exhibiting SBIs
during the period 1990–2009. The frequency results areshown only for 39 stations with temporally homogeneous records (section 2e). Depth and intensity results
are presented for the same stations, including some withinhomogeneous records, but the inhomogeneities do notstrongly affect the climatological results, as suggested by
the similarity of results from nearby stations with homogeneous records. Corresponding maps of standard

deviations of frequency, depth, and intensity are provided as supplemental material (Fig. S4). The results forstations in Alaska are in very good agreement with thoseof Bourne et al. (2010), who also used IGRA data to
study SBI characteristics in that region of the Arctic,
which instills confidence in the reproducibility of theseand other climatological statistics presented here.
Median (over all Arctic stations) f, Dz, and DT values
are 46%, 356 m, and 6.1 K, respectively. Given that radiative cooling of the surface is one of the key processes
responsible for SBI formation, it is perhaps not surprising that stations closer to the North Pole, particularly
in Greenland, Alaska, Canada, and Siberian Russia, experience more frequent (f . 50%), deeper (Dz approaching 1 km), and more intense (DT . 5 K) SBIs thanlower-latitude stations. The highest frequency, 67%, is at
Eureka, Canada (808N, 868W). The most intense SBIs,
with climatological temperature differences ;14 6 2 K(mean 6 one standard deviation), occur at the Siberian
stations, most of which are in valleys (the Yana Rivervalley), where stronger inversions are expected (Anquetin
et al. 1998). The deepest and strongest SBIs are found at
Verkhoyansk, Russia (688N, 1338E), where the annual SBI
depth and intensity are 839 m and 14 K, respectively. In
contrast, the lowest SBI frequency found among these
homogeneous Arctic stations, 26%, is at Jan Mayen, in
the Norwegian Sea (718N, 98W). This region also has theshallowest and weakest SBIs, a feature that is likely related to the relatively high surface temperatures associated with warm North Atlantic Ocean currents.
Complementing the annual data in Fig. 3, Fig. 4 shows
seasonal SBI frequency—seasonal depth and intensity
maps are provided in supplementary material (Figs. S5 andS6). In general, Arctic SBIs are more frequent, deeper, andstronger in winter and, to a lesser extent, in autumn, than inspring and summer. Over Siberia, Canada, and Greenland,1 OCTOBER 2011

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FIG. 4. Seasonal variation of Arctic SBI frequency from 39 radiosonde stations during 1990–2009.

SBIs are found in .80% of soundings, whereas springand summer SBI frequencies are 30%–40% at most stations, with minimum frequency around 20% in all seasons.
The distributions of SBI depth and intensity are similar.
SBIs are deepest (.800 m) and most intense (.15 K) over
Siberia in winter and autumn. Depth and intensity inspring and summer are relatively small, ,500 m and 7 K,
respectively, over the entire domain. Minimum SBI
depth and intensity are at island stations in the Norwegian Sea in all four seasons.
Standard deviations of annual values (Fig. S4) of f, Dz,
and DT are of order one-tenth of 20-yr means. For SBI
frequency, this interannual variability is smaller than theseasonal variations (Fig. 4). But for SBI Dz and DT, interannual and seasonal variability are comparable (Figs.
S5 and S6). Interestingly, the interannual standard deviations are similar in magnitude to the structural uncertainties discussed in section 2f.
Although not well sampled by the twice-daily radiosonde schedule, a possible diurnal variation (0000 versus

1200 UTC contrast) in SBI frequency is discernible (Fig. 5).
SBIs are more frequent in the Western Hemisphere at
1200 UTC and in the Eastern Hemisphere at 0000 UTC.
The maps of SBI depth and intensity for the two standard observation times do not show systematic patterns.
As will be shown in the next section, the frequency patterns have a strong seasonal structure, and the diurnal
differences in SBI frequency are mainly in summer, with
SBI frequency decreasing with increasing solar elevation
angle.
Clear gradients in frequency, depth, and intensity arefound in the Eastern Hemisphere, increasing fromlow values over the Norwegian Sea eastward toward
the East Siberian Sea (Fig. 3). Similar patterns wereidentified by Kahl et al. (1992) and Serreze et al.
(1992), who attribute the frequent, strong SBIs over
Siberia to cold-air drainage at valley locations combined with subsidence associated with the strong wintertime Siberian anticyclone, while less frequent, weaker
SBIs around Northern Europe and the Norwegian

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FIG. 5. Maps of mean Arctic SBI (top) frequency, (middle) depth, and (bottom) intensity from (left) 37 radiosonde
stations at 1200 UTC and (right) 34 stations at 0000 UTC during 1990–2009. Squares denote stations with inhomogeneous SBI depth and intensity records.

Sea are caused by the proximity of open water, cyclonic activity, and the effects of cloud cover on the
longwave flux. The eastward gradients in frequency(Fig. 3) are more pronounced in autumn and winterthan in spring and summer (Fig. 4), which supports thisinterpretation.
To examine covariations among these three SBI
properties, Table 1 and Figs. 6 and 7 show the results of

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TABLE 1. Spatial pattern correlations among SBI frequency ( f ),depth (Dz), and intensity (DT). All correlation coefficients are
based on paired values of long-term (monthly or annual) meansfrom 39 Arctic stations. Results in square brackets are not statistically significant at the 95% confidence level.

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annualr( f, Dz)r( f, DT)r(Dz, DT)

0.44
0.52
0.55
0.57
[0.13][20.06][20.21][20.19][0.27]
0.40
0.50
0.43
0.44

0.49
0.53
0.52
0.53
0.50
0.59
0.59
[0.34][0.24]
0.46
0.49
0.47
0.47

0.94
0.93
0.84
0.83
0.55
[0.30][0.24][0.21]
0.53
0.79
0.94
0.92
0.84

correlation analyses. Spatial correlations (Table 1)
based on monthly and annual climatological station
values of SBI frequency, depth, and intensity are almost
all positive. Correlations based on annual values all

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exceed 0.4, with annual r(Dz, DT) 5 0.84. Thus in regionswith more frequent SBIs, they tend to be deeper andstronger. Spatial correlations between depth and intensity, r(Dz, DT), are higher than those involving frequency, r( f, Dz) and r( f, DT), as expected from thesimilar patterns of SBI depth and intensity in Figs. 3, 5,
S5, and S6. There is an obvious seasonal variation in thecorrelations, with higher values in winter and autumn
months than the rest of the year, particularly for r(Dz,DT) and r(f, Dz). Again, this supports the notion thatdeep, intense SBIs form in winter in the regions where
they are more frequent.
Temporal correlation coefficients are presented in Figs.
6 and 7, which also show the correlation between surface
temperature (sfc) and SBI properties. Most correlation
coefficients for monthly SBI properties (Fig. 6) are close
to 11, indicating that all three SBI properties exhibit
similar annual cycles at most stations, except stations over
the Norwegian Sea. This result is also consistent with theresults shown in Figs. 4, S5, and S6. In contrast, mostcorrelation coefficients between surface temperature and
SBI properties are near 21. Higher surface temperatures

FIG. 6. Maps of temporal (annual cycle) correlation coefficients computed from paired series of 12 monthly-mean values of SBI
frequency ( f ), depth (Dz), intensity (DT), and surface temperature (sfc) at each Arctic station. Results with squares are not statisticallysignificant at the 95% confidence level. Red stars identify stations whose mean annual cycles are shown in Fig. 12.

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FIG. 7. Maps of interannual correlation coefficients computed from paired series of 20 annual-mean values (for 1990–2009) of SBI
frequency ( f ), depth (Dz), intensity (DT), and surface temperature (sfc) at 39 Arctic stations. Results with squares are not statisticallysignificant at the 95% confidence level.

(in summer) are associated with less frequent, shallower,
and weaker SBIs, which is again consistent with the seasonal variations of SBI properties.
Correlations based on annual time series (Fig. 7) show
a similar pattern as in Fig. 6, but with smaller r values. At
most stations, r(Dz, DT) . 0.8, but r( f, Dz) and r(f, DT)
are lower and not statistically significant at many stations.
Correlations between surface temperature and SBI properties are generally negative but weaker than for theseasonal cycle (Fig. 6), and r(sfc, f ) are close to 0. Thus,
years with higher than normal surface temperature haveshallower and weaker SBIs, although SBI frequency shows
little association with surface temperature.

b. Arctic SBI climatology in climate models andreanalysis data
Figures 8, 9, 10, and 11 show comparisons between theradiosonde-based Arctic SBI climatology and thosederived from the NCAR CAM3 and NOAA GFDL
AM3 models and from the ERA-Interim reanalysis.
Supplemental material (Figs. S7 and S8) provides additional comparisons.

The dominant seasonal patterns of SBI properties
derived from the models and reanalysis are similar to theobservational results (Figs. 8 and S7)—all show more
frequent, deeper, and stronger SBIs in winter and autumn. However, there are notable differences in magnitudes. For example, Fig. 9 shows wintertime SBI depth
and intensity. (Wintertime frequency is shown in Fig. 8
first row.) The ERA-Interim is in better accord with theobservations for SBI frequency, which the two models
underestimate over much of the domain, in all four
seasons (Fig. 8). It is not surprising that the reanalysis,
which assimilates observations, performs better than theunconstrained climate models. However, it is not clear
which is more accurate in regions where radiosondes are
not available, such as over the Arctic and North Atlantic
Oceans.
The depths of SBIs in NCAR CAM3, GFDL AM3,and ERA-Interim are deeper, thinner, and similar compared with the radiosonde observations, respectively(Figs. 9 and S7). The deeper SBIs in NCAR CAM3 areconsistent with that model’s lower vertical resolution
than the other datasets (section 2b). For SBI intensity,

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FIG. 8. Maps of mean seasonal SBI frequency over the Arctic from IGRA radiosonde observations (colored dots) and from the (left)
GFDL AM3 model, (middle) NCAR CAM3 model, and (right) ERA-Interim reanalysis. Data periods are 1990–2007 for the climate
models and 1990–2009 for ERA-Interim.

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FIG. 9. Maps of winter Arctic SBI (top) depth and (bottom) intensity compared with observations (colored dots) in the (left) GFDL-AM3
and (middle) NCAR-CAM3 (middle) models, and in (right) ERA-Interim reanalysis data.

both models and the reanalysis show patterns andmagnitudes that are quite consistent with the observations (Figs. 9 and S7), except the GFDL AM3, which
simulates lower SBI intensities than observed. Over Siberia, this may be related to stations’ valley locations not
being well represented in the gridded model output.
Model depictions of diurnal variations of SBI properties are presented in Figs. 10, 11, and S8, where wehave sampled the models at 0000 and 1200 UTC for
direct comparison with the observations, despite theavailability of 0600 and 1800 UTC model results. Climatological SBI frequency shows strong diurnal patterns, as seen by comparison of 0000 and 1200 averages(Fig. 10, which includes the radiosonde results shown in
Fig. 5) and in maps of the 1200 minus 0000 UTC differences (Fig. 11, top row). The reanalysis is in better
accord with the radiosondes than either climate model.
However, although the GFDL AM3 and NCAR CAM3
underestimate the frequency over much of the Arctic
domain at both observation times (Fig. 10), their depictionof the diurnal difference agrees with the radiosonde observations. Large frequency differences (;650%) havecoherent spatial patterns, with more frequent SBIs at
1200 UTC within about 908 longitude of the international
date line, and more frequent 0000 UTC SBIs within 958

longitude east (but not west) of the Greenwich Meridian,on an annual mean basis (Fig. 11, top row). However, thispattern is driven by summertime conditions, with results
for June (Fig. 11, middle row) showing even larger differences than the annual values, and December showing
near-zero differences (Fig. 11, bottom row). In Arctic summer (day), solar elevations angles are higher at 0000 UTC
near the date line, and solar heating reduces the tendency
for SBI formation. The larger differences east of the dateline and east of Greenwich can be understood in termsof the sun’s path in the sky; those regions have alreadyseen the sun’s highest elevation by the time of the 0000
or 1200 UTC observations, compared with comparable
regions west of those longitudes, which will see the sun’s
highest elevation after observation times. In contrast to
the frequency patterns (Figs. 10 and 11), SBI depth andintensity at the two observation times are fairly similar, as
seen both in the observations (Fig. 5) and in the models
and reanalysis (Fig. S8).
The analysis above has focused on gross spatial andtemporal patterns, but careful examination of the mapped results reveals local variations that merit our attention, particularly because of the strong local
topographic and microclimatological controls on SBI
features (as compared with the free troposphere above

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FIG. 10. Maps of mean annual SBI frequency at (left) 1200 and (right) 0000 UTC from radiosonde observations(colored dots) and from the (top) GFDL AM3, (middle) NCAR CAM3, and (bottom) ERA-Interim reanalysis. Data
periods are 1990–2007 for the climate models and 1990–2009 for ERA-Interim.

the boundary layer). The mean annual cycles of SBI
properties, at both 0000 and 1200 UTC, at four specificstations are shown in Fig. 12. The climate model andreanalysis results for the grid point closest to the station

are also shown. The stations are as follows, in order ofdecreasing latitude: Eureka, on the Canadian Arctic
archipelago; Bjornoya, Norway, an island in the Svalbard archipelago of the western Barents Sea; Fairbanks,

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FIG. 11. Differences between 1200 and 0000 UTC Arctic SBI frequency based on (top) annual (all months) data, (middle) June data, and(bottom) December data as observed by radiosondes (colored dots) and as simulated by the (left) GFDL AM3, (middle) NCAR-CAM3,
and (right) ERA-Interim reanalysis.

Alaska, an inland valley station; and Seimchan, Russia,
by the Seimchan River. See Fig. 6 for station locations.
The main feature of Fig. 12 is the spatial variability,
both of the observed seasonal cycles and of the model
simulations. For example, the observed seasonal variation of SBI frequency shows wintertime maximum andsummertime minimum at all four stations (Fig. 12, firstcolumn on left), but the amplitude is much larger at
Eureka, Fairbanks, and Seimchan than at Byjornoya.
The correlation results in Fig. 6 are consistent with theobserved annual cycles shown in Fig. 12. The models andreanalysis capture the seasonality at Eureka fairly well,

but they are much less successful at the other threestations and some even have an out-of-phase annual
cycle in SBI frequency. Discrepancies between the radiosonde results (black lines) and the reanalysis (bluelines) suggest that other assimilated data and/or model
physics sometimes override the assimilated radiosonde
observations in ERA-Interim’s depiction of SBIs.
Biases among the four datasets are also evident in
Fig. 12. The NCAR CAM3 (green lines) simulates deeper
SBIs than the other three datasets at all four stations. The
GFDL AM3 (red lines) tends to produce shallower andless intense SBIs than NCAR CAM3 and ERA-Interim,1 OCTOBER 2011

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FIG. 12. Monthly variation of SBI (left) frequency, (middle) depth, and (right) intensity at four stations: Eureka, Bjornoya, Fairbanks,and Seimchan. Station locations are shown in Fig. 6; coordinates are given in Table S1. Results from four datasets are represented by fourcolors: IGRA (black), GFDL AM3 (red), NCAR CAM3 (green), and ERA-Interim (blue). Solid lines denote 1200 UTC and dotted lines
denote 0000 UTC.

but none of the three consistently outperforms the others
in its consistency with the observations. The poor simulation of the observed seasonal cycle of SBI frequency at
Bjornoya may be related to the spatial resolution of themodels and reanalysis. Bjornoya is near a strong gradient in sea surface temperature associated with a narrow
warm North Atlantic current, which likely inhibits thedevelopment of SBIs. This feature may not be well represented in the models and may explain the seasonal variation of SBI depth and intensity at Bjornoya, which isdifferent from the other three stations.
The biases among the datasets have some systematic
structure, as seen in Fig. 13, which compares the models to

the radiosonde results using scatterplots. In each plot of
seasonal values of one of the three SBI characteristics(frequency, depth, and intensity), the three models aredifferentiated by color, and results from five Arctic regions(Canada, Europe, Greenland, Russia, and the UnitedStates) are differentiated by symbol shape. The tendency
for ERA-Interim (blue symbols) to overestimate SBI
frequency is clear, as is the NCAR CAM3 (green) and
GFDL AM3 (red) tendencies to underestimate it. In
winter and autumn, when SBIs are more frequent,
deeper, and more intense, and when there is a larger
range of values among the stations, the model biases are
relatively smaller. For inversion depth, the NCAR CAM3

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FIG. 13. Scatterplot comparisons of mean seasonal Arctic SBI (top) frequency, (middle) depth, and (bottom) intensity as observed byradiosondes (IGRA) and as depicted in the GFDL AM3 (red), NCAR CAM3 (green), and ERA-Interim reanalysis (blue). Stations aregrouped by the following regions: Canada (asterisk), Europe (3), Greenland (diamond), Russia (dot), and the United States (plus). The
thick solid lines show linear regression results, and dotted lines are 1:1 slopes.

overestimates at most stations and ERA-Interim results
are close to observed, while GFDL AM3 SBIs are shallower, especially at Russian stations. This may be related
to the prescribed sea surface temperatures (SSTs) in the
GFDL AM3 constraining the results at coastal stations
more than at inland Russian stations, and the model’slarge 2-m temperature warm bias over the northern latitude land areas.

c. SBI climatology for Antarctica
The radiosonde network provides a much less detailed
picture of the Antarctic atmosphere than the Arctic,
with less than one-quarter the number of stations, mostalong the continent’s coasts, many taking only one observation per day and some operating only part of theyear. Of the two inland Antarctic stations in IGRA, one(Amundsen-Scott, 908S, 08) has inhomogeneous records
and the other (Vostok, 788S, 1078E) has very incomplete
records. After considering data homogeneity and continuity issues, we retain only six coastal stations (Table
S2), which severely compromises our ability to describe
the SBI climatology in the south Polar region.
Radiosonde observations indicate that, comparedwith the Arctic, SBIs over the Antarctic are generally
more frequent, shallower, and weaker (Fig. S10). Median Antarctic SBI f, Dz, and DT are 58%, 197 m, and
2.6 K, respectively. However, both regions have similarseasonal patterns. As in the Arctic, SBIs over Antarctica
are more frequent, deeper, and stronger in winter andautumn than in spring and summer (Fig. S9). Figure 14
compares the three model results with the radiosonde
observations for winter, and Fig. 15 show scatterplots for1 OCTOBER 2011

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FIG. 14. Maps of winter Antarctic SBI (top) frequency, (middle) depth, and (bottom) intensity compared with observations (colored dots)in the (left) GFDL AM3 and (middle) NCAR-CAM3 models and in (right) ERA-Interim reanalysis data.

all four seasons. The two climate models and the reanalysis show SBIs in the Antarctic interior are morecommon than at the coasts, with seasonal frequencies
close to 100% in winter and autumn, in accord withprevious analyses (Phillpot and Zillman 1970; Connolley1996). Compared with the coast, the interior is colder,
drier, and less windy, all features that are conducive to
SBI formation.
There is more seasonal variation in SBIs along thecoasts, with summer and spring frequencies ,50%. While
ERA-Interim captures this seasonality, the two climate
models tend to underestimate the observed frequency(Fig. 15). As seen in Fig. 14, this may be related to thestrong model gradient in SBI frequency between the
Antarctic continent and the surrounding ocean. The
climate models simulate coastal conditions more similar
to the oceanic than the continental climatology.
At the East Antarctic coastal stations, SBIs are;200 m deep throughout the year, while along theWeddell Sea coast Dz ’ 300 m all year. The GFDL AM3
and ERA-Interim reanalysis capture observed SBI
depth better than the NCAR CAM3, which overestimates Dz, especially in autumn and winter (Figs. 14
and 15). However, the better agreement between the
GFDL AM3 results and the observations is due toa more complex spatial pattern (Figs. 14 and S9).

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FIG. 15. As in Fig. 13, but for Antarctica, and the symbols are for East Antarctic coast (3) and Weddell Sea coast (d).

At most East Antarctic coastal stations DT is around
3 K, but at the Weddell Sea coast in winter and autumn
DT ’ 10 K (Figs. 15 and S9). The models capture thesepatterns fairly well but underestimate the SBI intensity
along the Weddell Sea coast (Fig. 15).
Comparing Figs. 13 and 15 for the Arctic and Antarctic
suggests better model/observation agreement in the
Arctic, where there is less scatter in the data. However,this may be due to the much better sampling in the Arctic
and the fact that most of the Antarctic stations are in thetransitional coastal region, where land–sea surface differences and topographic gradients must influence SBI
formation in the atmosphere and in the models.

4. Conclusions
Analysis of Arctic and Antarctic surface-based inversions (SBIs) in a carefully selected set of radiosondedata, two climate models, and one reanalysis dataset hasrevealed climatological patterns in SBI frequency ofoccurrence, depth (from the surface to the SBI top), andintensity (temperature difference within the SBI). The
main findings are as follows.
1) Changes in the vertical resolution of archived radiosonde data lead to inhomogeneities in estimates of SBI
intensity and depth and, to a lesser extent, estimates of
SBI frequency. Such inhomogeneities, as well as large
data gaps, make many Arctic and Antarctic station
records not suitable for climatological analysis.
2) Although we have not attempted a complete analysisof structural uncertainty in SBI statistics, some uncertainties are comparable in magnitude to the interannual variability about the 20-yr climatologicalmean values of SBI frequency, depth, and intensity.
3) SBIs are generally more frequent, deeper, and more
intense in winter and autumn (polar night) than inspring and summer (polar day) over both polar regions.
4) In the Arctic, SBI frequency, depth, and intensity
appear to increase eastward from the Norwegian Sea

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toward the East Siberian Sea in winter and autumn,
possibly in association with increasing continentality.
5) In summer, solar elevation differences in local radiosonde launch times at different longitudes explain geographic patterns of SBI frequency differences between
the two observation times, 0000 and 1200 UTC. Such
differences are not observed in winter, polar night.
6) The radiosonde network is much sparser in the
Antarctic than in the Arctic, and most observations
are along the Antarctic coast. SBIs along the Weddell
Sea coast are deeper and more intense than those at
East Antarctic coast.
7) Compared with the radiosonde observations, theNCAR CAM3 and NOAA GFDL AM3 climate
models and the ERA-Interim reanalysis data show
generally similar seasonal patterns and spatial distributions of the three SBI characteristics examined.
However, biases among the datasets are found, andthere are spatial differences in the bias patterns. SBI
intensities derived from the four datasets are in a good
agreement. The GFDL AM3 has a larger bias in SBI
depth (with shallower SBIs than observed) at mostRussian stations and Antarctic stations. The NCAR
CAM3 shows deeper SBIs at most Arctic and Antarctic
stations (perhaps due to its lower vertical resolution),while ERA-Interim results are close to observed. Despite its assimilation of radiosonde observations, ERAInterim reanalysis overestimates SBI frequency at moststations, while the climate models underestimate it at
most stations. These biases are smaller in winter andautumn than in summer and spring.
8) Over most of the Arctic (but not over the North
Atlantic) the annual cycles of SBI frequency, depth,
and intensity are positively correlated, and these
SBI characteristics are negatively correlated withsurface temperature. Interannual variability shows
less association among SBI characteristics, although
SBI intensity is positively correlated with SBI depth
and negatively correlated with surface temperature
on an interannual basis.
This study is the first comprehensive analysis of SBI
features in the polar regions in observations and models.
Our analysis was limited to two models, and we hope theobservational data will be used in future investigations
involving other models. The radiosonde, reanalysis, andmodel datasets used herein are available as supplemental
material. A companion study (Zhang and Seidel 2011)
will examine long-term (multidecadal) variations andtrends of Arctic SBI characteristics.
Acknowledgments. We thank Journal of Climate Editor
Robert Wood and two anonymous referees, who provided
thorough and thoughtful reviews of this paper. Jennifer

Kay, Brian Medeiros, and Sungsu Park (NCAR), Chi Ao(Jet Propulsion Laboratory), and Julian Wang (NOAA)
offered insightful comments on this study. Y. Zhang is
a National Research Council Postdoctoral Associate at
NOAA’s Air Resources Laboratory. NCAR is sponsoredby the National Science Foundation. C. Deser andR. Tomas gratefully acknowledge funding from the NSF
Office of Polar Programs.
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