Post on 24-Feb-2020
Subseasonal and Interannual Temperature Variability in Relation to ExtremeTemperature Occurrence over East Asia*
HIROYUKI ITO
Department of Meteorology, School of Ocean and Earth Science and Technology, University of Hawaii at Manoa, Honolulu, Hawaii
NATHANIEL C. JOHNSON
International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawaii at Manoa,
Honolulu, Hawaii
SHANG-PING XIE
Department of Meteorology, and International Pacific Research Center, School of Ocean and Earth Science and Technology, University
of Hawaii at Manoa, Honolulu, Hawaii, and Institution of Oceanography, University of California, San Diego, La Jolla, California
(Manuscript received 17 September 2012, in final form 6 May 2013)
ABSTRACT
This study investigates interannual variability in the frequency of occurrence of daily surface air temper-
ature (SAT) extremes over East Asia in summer and winter between 1979 and 2009. In particular, this study
examines the dominant seasonal SAT patterns, as obtained through empirical orthogonal function (EOF)
analysis, and the associated variability in SAT extreme occurrence. Overall, the authors find that changes in
extreme temperature occurrence associated with these dominant patterns are impacted by both shifts and
narrowing/broadening of the subseasonal SAT probability distribution functions (PDFs). In summer, the
leading pattern features large SAT anomalies in midlatitude East Asia centered over Mongolia. Over this
center of action, positive SAT anomalies are accompanied by decreased precipitation and soil moisture, which
increases the ratio of sensible to latent heat flux. Consequently, subseasonal SAT variance increases, resulting
in an enhanced occurrence of positive SAT extremes relative to a simple SATPDF shift. In winter, the leading
pattern, which is highly correlated with the Arctic Oscillation, features large loadings in high-latitude Siberia
that decay southward. In contrast with summer, large-scale dynamics play a larger role in the leading pattern:
positive SAT anomalies are accompanied by a weakened and northward-shifted storm track, reduced sub-
seasonal SAT variance, and amore pronounced decrease of cold extreme occurrence relative to a simple PDF
shift. Finally, a brief look at the secular trends suggests that both shifts and narrowing/broadening of the PDF
may also impact long-term trends in SAT extreme occurrence over some regions of East Asia.
1. Introduction
Extreme weather and climate events, such as heat
waves, drought, and cold surges, are of great concern for
society because of their severe impacts on life, property,
and economy. The number of such extremes, and the
associated economic losses, has increased worldwide
due primarily to higher population density in hazardous
areas (Munich Re 2003; Bouwer 2011), but climate
change likely has contributed as well (Munich Re 2010;
Peterson et al. 2012). Various communities have rec-
ognized the value of studying temperature extremes to
reduce the socioeconomic risk (Beniston and Stephenson
2004). In East Asia, as in many parts of the world, the
impacts of climate extremes have been remarkable; for
example, the severe heat wave in summer 2010 caused
several hundred fatalities in Japan alone. The occur-
rence of abnormal warm spells has dramatically changed
in the past few decades, at least partly in association with
* International Pacific Research Center Contribution Number
994 and School of Ocean and Earth Science and Technology
Contribution Number 8963.
Corresponding author address: Nathaniel Johnson, IPRC, Uni-
versity of Hawaii at Manoa, 1680 East–West Road, 401 POST
Building, Honolulu, HI 96822.
E-mail: natj@hawaii.edu
9026 JOURNAL OF CL IMATE VOLUME 26
DOI: 10.1175/JCLI-D-12-00676.1
� 2013 American Meteorological Society
global warming (Christidis et al. 2005; Hansen et al.
2013; Wen et al. 2013). Although the relationship of
extreme occurrence to both seasonal-mean climate and
large-scale atmospheric circulation patterns has been
studied on the global and regional scales (Scaife et al.
2008; Higgins et al. 2002; Kenyon and Hegerl 2008), few
studies target interannual variability in extreme tem-
perature occurrence in East Asia.
Changes in extreme occurrence may be due both to
shifts in the probability distribution without change in
shape (seasonal-mean shift) and to changes in the prob-
ability distribution shape, such as that owing to a change
in subseasonal standard deviation. Substantial changes
in the frequency of rare events can result from a rela-
tively small shift or change in the probability distribu-
tion function (PDF) of climate variables. Under the
assumption that daily-mean surface air temperature
(SAT) follows a Gaussian distribution with fixed shape,
an increase in the frequency of extreme hot days is
generally accompanied by a decline in the opposite ex-
tremes (Fig. 1a). However, changes in the standard de-
viation of the distribution might be more effective in
changing the number of extremes than the simple dis-
tribution shift (Fig. 1b) (Katz and Brown 1992). In re-
ality, both the shift and shape-change effects are at work
and will complicate the simple pictures above (Fig. 1c).
While a previous study shows that the distribution of
seasonal-mean temperature has shifted in the positive
direction and its variation has increased in the last few
decades (Hansen et al. 2013), the present study focuses
on interannual variability of the temperature distribu-
tions that affect the frequency of SAT extremes.
There is an extensive body of literature on interannual
and interdecadal variations in the seasonal-mean cli-
mate of East Asia. East Asia’s climate is controlled in
large part by the variability of the East Asia monsoon
(Chang et al. 2006; Webster 2006). In summer, seasonal-
mean atmospheric circulations in East Asia and the
western North Pacific respond to both local forcing from
underlying sea surface temperature (SST) anomalies,
particularly during early summer (Wu et al. 2010), and
from remote forcing through El Ni~no–Southern Oscil-
lation (ENSO) and associated Indian Ocean SST vari-
ations (Wu et al. 2009; Xie et al. 2009, 2010; Kosaka and
Nakamura 2010; Chowdary et al. 2011; Hu et al. 2011;
Zhou et al. 2011). Also, snowfall over the Tibetan Pla-
teau and the phase of the Arctic Oscillation (AO) in
spring leads circulation anomalies over East Asia in
summer (Seol and Hong 2009; Zhou el al. 2011). Im-
portant factors for the summer climate of East Asia are
two atmospheric teleconnections, the Silk Road pattern
along the midlatitude Asian jet and the Pacific–Japan
FIG. 1. Schematic showing the effect on extreme temperatures when (a) the mean temperature increases, (b) the std
dev of temperature increases, and (c) both the mean and std dev of temperature increase for a normal temperature
distribution. The dashed lines are the mean climate distribution and solid lines are perturbed climate distributions.
15 NOVEMBER 2013 I TO ET AL . 9027
pattern from the tropical western North Pacific
(Wakabayashi and Kawamura 2004; Nitta 1987; Kosaka
and Nakamura 2006; Yasunaka and Hanawa 2006;
Kosaka et al. 2012). In addition, blocking episodes as-
sociated with variability of the Okhotsk high strongly
impact summertime temperatures in East Asia, particu-
larly Japan (Nakamura and Fukamachi 2004). On inter-
decadal time scales, the East Asian summermonsoon has
undergone a secular weakening since the late 1970s,
which has resulted in a ‘‘southern China flood and north-
ern China drought’’ rainfall trend pattern (Zhou et al.
2009; Zhou et al. 2011).
In winter, the northerly monsoonal circulations vary
in response to ENSO (Tomita and Yasunari 1996;
Miyazaki and Yasunari 2008; Chan and Li 2004) and the
AO (Xie et al. 1999; Gong and Ho 2004; Thompson and
Wallace 2000; Wu and Wang 2002). Anomalous circu-
lation patterns modulate intense midlatitude cyclones that
develop over East Asia where a strong lower-tropospheric
baroclinicity and intensified jet aloft exist.
Cold surges are often associated with the transient
eddies, hence contributing to subseasonal SAT varia-
tions (Nakamura et al. 2002; Wang et al. 2010).
The present study investigates interannual variations
in SAT extremes in relation to the seasonal mean and
variance of the SAT distribution over East Asia. We
further investigate relevant physical mechanisms for
anomalies of extreme temperature occurrence in an ef-
fort to promote better understanding of the predictability
of SAT extremes. Important questions to be addressed
include how extreme temperature occurrences respond
to changes in both the seasonal-mean SAT and sub-
seasonal SAT variance.What are the physical mechanisms
for shifts and shape changes in the SAT distribution on
interannual time scales?We relate PDF changes in SAT to
the leading patterns of climate variability in East Asia.We
focus on SAT rather than precipitation variability because
temperature tends to be better organized in space than
precipitation. Both summer and winter are examined.
The rest of the paper is organized as follows. Section 2
describes the data andmethods. Sections 3 and 4 present
results for summer and winter, respectively, including
the climatology, interannual variability, extreme SAT
occurrences, and subseasonal SAT probability distri-
butions. Section 5 is a summary with discussion.
2. Data and methods
a. Data sources
We define East Asia as the region from 208 to 608Nand 908 to 1608E. This region includes the eastern half of
the Tibetan Plateau and Gobi Desert (Fig. 2a). The
daily- and monthly-mean surface and upper-level cli-
mate variables, on a 1.18 latitude–longitude grid, are
obtained from the Japanese 25-yr Reanalysis Project
(JRA-25; Onogi et al. 2007). In the analysis of surface
shortwave, longwave, sensible heat, and latent heat flux
variability, we acknowledge substantial uncertainty in
the reanalysis-derived products, but we focus on the
most robust variability supported by additional analysis.
We also use the monthly model-calculated soil moisture
of 0.58 latitude–longitude resolution from the Climate
Prediction Center (CPC); the monthly gridded data for
air temperature and precipitation from the University
of Delaware (UD) product (http://www.esrl.noaa.gov/
psd); the National Oceanic and Atmospheric Adminis-
tration (NOAA) extended reconstructed SST, version
3b (ERSST.v3b; Smith et al. 2008), with 28 latitude–
longitude resolution; and station data from the National
Climatic Data Center (NCDC). All of the daily and
monthly data cover a 31-yr period from 1979 to 2009.
Here, 29 February in leap years was removed so that
each winter season has the same number of calendar
days.
The seasonal cycle is defined by calendar day means
that are smoothed by a centered 21-day moving aver-
age. The daily-mean SAT anomalies are obtained by
removing the seasonal cycle. We then calculate the
number of cold and warm extreme days, which, fol-
lowing the model of Kenyon and Hegerl (2008), are
defined as those days below the 10th and above the 90th
percentiles of the climatological SAT distribution at
each grid point or station, respectively. The JRA-25 has
been shown to perform reasonably well in capturing
temperature extremes over China (Mao et al. 2010),
and we perform comparisons with station data to en-
sure the robustness of our results. The seasonal-mean
and subseasonal standard deviation of SAT are ob-
tained by calculating the mean and standard deviation
of the daily-mean SAT anomalies for each season, re-
spectively. The linear trend is removed from the sea-
sonal time series, though we provide some discussion of
the long-term trend in section 5.
To identify the influence of the circulation patterns
associated with patterns of large-scale climate vari-
ability, we examine relationships with climate indices
from three dominant modes. We use the monthly-mean
AO index; the projection time series for the leading
empirical orthogonal function (EOF) of monthly-mean
Northern Hemisphere sea level pressure for all months
during 1979–2000 (Thompson andWallace 2000); and the
Southern Oscillation index (SOI), the normalized time
series of the difference in monthly-mean sea level pres-
sure anomalies between Tahiti and Darwin (Ropelewski
and Jones 1987) from NOAA/CPC. We also use the
9028 JOURNAL OF CL IMATE VOLUME 26
Pacific decadal oscillation (PDO) index obtained from
the Joint Institute for the Study of the Atmosphere and
Ocean (JISAO) at the University of Washington website
(http://jisao.washington.edu/pdo). The PDO index is de-
fined as the projection time series for the leading EOF of
North Pacific monthly SST variability, poleward of 208Nfor the 1900–93 period (Mantua et al. 1997). All indices
are normalized by subtracting the mean and dividing by
the standard deviation for the 1979–2009 period. Positive
(negative) years are defined when an index is greater
(less) than or equal to 0.5 (20.5) and neutral years are
defined when an index is within 60.5.
b. Analysis of interannual variability of temperatureextremes
To examine the effect of subseasonal SAT variance
change on extreme temperature occurrence, we first
extract the leading EOFs of seasonal-mean SAT vari-
ability for both summer and winter. The EOF analysis is
applied to detrended seasonal-mean SAT anomalies,
where the anomalies are weighted by the square root of
cosine latitude to ensure correct geographical weighting.
We next regress the number of cold and warm extreme
days onto the corresponding principal components
(PC), and then we sum the regression coefficients to
define a quantity we call the ‘‘asymmetric occurrence
between warm and cold extremes’’ corresponding to
that EOF pattern. The reasoning for this calculation is as
follows. For a symmetric temperature distribution that
shifts right or left without a change in shape with each
leading PC, the sum of warm and cold extreme occur-
rence regression coefficients would be zero, as changes
in warm extremes for one EOF phase balance the
changes in cold extremes for the opposite phase (Fig.
1a). A nonzero sum, however, provides one indication
that changes in temperature distribution shape over
some regions have a substantial impact on extreme
temperature occurrence. In the following sections, we
examine this asymmetric occurrence between warm and
cold extremes in conjunction with changes in subseasonal
temperature variance to illustrate regions where broad-
ening or narrowing of the subseasonal temperature dis-
tributions impacts extreme temperature occurrence (as
depicted in Fig. 1c).
c. Mechanisms of subseasonal temperature variancechange
To elucidate the mechanisms of subseasonal tem-
perature variance change, we examine the relation-
ships between the leading summer and winter East
FIG. 2. (a)Major geographic features in East Asia. Elevations greater than 2500m are shaded in gray. The summer
(JJA) climatology of (b) the seasonal-mean SAT (contours; 8C), std dev of subseasonal SAT (color shading; 8C) with925-hPa winds (arrows), and (c) the UD precipitation (color shading; cmmonth21) with the 500-hPa EKE (contours;
m2 s22).
15 NOVEMBER 2013 I TO ET AL . 9029
Asian temperature EOFs and the dominant terms of
the subseasonal potential temperature variance bud-
get. To derive the potential temperature variance
budget, we begin with the potential temperature ten-
dency equation
›u
›t1 uj
›u
›xj5
Q
Cp
�p0p
�K
, (1)
where u is the potential temperature, uj 5 (u, y, w) is the
surface wind component, xj5 (x, y, z) is spatial position,
Q is the diabatic heating rate per unit mass, Cp is the
specific heat capacity of air at constant pressure, p0 is
a reference pressure of 1000 hPa, p is the surface pres-
sure, and K is R/Cp 5 0.286. We then decompose u, u,
and Q into the seasonal mean, denoted by an overbar,
and a deviation from the seasonal mean, denoted by
a prime. Thus, (1) becomes
›(u 1 u0)›t
1 (uj 1 u0j)›(u 1 u0)
›xj5(Q 1 Q0)
Cp
�p0p
�K
.
(2)
With the knowledge that the time scale of individual
weather systems is much smaller than a season, we apply
Reynolds averaging to yield
›u
›t1 uj
›u
›xj1
›u0ju0
›xj5
Q
Cp
�p0p
�K
. (3)
Subtracting (3) from (2), multiplying by 2u0, and then
Reynolds averaging yields the budget equation for
subseasonal potential temperature variance:
›u02
›t5 2uj
›u02
›xj2 2u0ju
0 ›u›xj
2›u0ju
02
›xj1
2
Cp
�p0p
�K
Q0u0.
(4)
Here, (4) states that four terms contribute to the time
rate of change of subseasonal potential temperature
variance: 1) mean advection, 2) the product of eddy heat
fluxes with the mean potential temperature gradient,
which we shall term ‘‘storm-track production,’’ 3) eddy
transport, and 4) a diabatic heating covariance term,
that is, a term driven by the covariance between poten-
tial temperature and diabatic heating deviations from the
seasonal mean. In the following analysis, we discuss the
dominant terms that contribute to subseasonal tempera-
ture variance changes in association with the leading
patterns of variability, which then impact the occurrence
of temperature extremes over East Asia.
3. Results for summer
a. Climatology
We first examine the summer [June–August (JJA)]
climatology in East Asia. The cool moist Okhotsk and
warmmoistOgasawara (Bonin) airmasses create a large
SAT gradient over the Kuroshio–Oyahio extension re-
gion (Fig. 2b). Over the Asian continent, a strong SAT
gradient exists between the wet eastern and dry western
China, which also differentiates wet Siberia from the dry
Gobi Desert. Subseasonal SAT variance is larger relative
to surroundings over Mongolia and the western North
Pacific to the east of northern Japan, where the SAT
gradient is also large. Amajor axis of eddy kinetic energy
[EKE5½(u021 y02), where u0 and y0 in this case are high-pass-filtered zonal and meridional wind, respectively,
with a cutoff period of 9 days] at 500hPa broadens
through northeastern China and Mongolia, and a minor
ridge extends into southeastern China in association with
the meiyu–baiu rainband (Fig. 2c) (Sampe and Xie
2010). A region of large subseasonal SAT variance to
the east of Japan seems to be influenced by storm ac-
tivity and the associated transport of heat over the sea
surface across the climatological SST front.
Over the Mongolian region, the southerly monsoonal
flow and northerlies from Siberia converge, placing
a local maximum of subseasonal SAT variance (Fig. 2b).
This maximum of subseasonal variability is related to
a strong mean SAT gradient to the north of the Gobi
Desert, suggestive of the advection by storms along the
East Asian subtropical westerly jet (Zhang et al. 2006).
In addition, soil moisture probably also plays a role.
There is a positive feedback between soil conditions and
rainfall in Mongolia: increased rainfall keeps soil wet
and wet soil helps increase rainfall (Iwasaki et al. 2008).
Given that the lowmoisture and heat capacity of the dry
soil generally cause energy to be converted into sensible
heat, interactive soil moisture strongly amplifies skin
and surface temperature variability over southern
Siberia–northern Mongolia and the region from north-
eastern to central China (Zhang et al. 2011). This in-
teraction generates the large variability in daily-mean
SAT during the dry season. Thus, soil moisture, rainfall,
and advection by storm activity are likely significant
modifiers of subseasonal SAT variability in the summer
climatology.
b. Leading pattern of interannual variability
The leading EOF of detrended summertime (JJA)
East Asian SAT anomalies, which accounts for 27% of
the total SAT variance, is presented in Fig. 3. The
present study uses the standardized PC of this EOF as
a climate index for the leading pattern of interannual
9030 JOURNAL OF CL IMATE VOLUME 26
SAT variability in East Asia [denoted hereafter as PC1
(JJA), and shown in Fig. 3a; the spatial pattern shall be
designated as EOF1 (JJA)]. This pattern shows large
loadings aroundMongolia and in a zonal band extending
through northern Japan (Fig. 3b). The corresponding
PC exhibits both pronounced interannual and inter-
decadal variability and is correlated with the SOI in the
preceding winter [November–February (NDJF)] at 0.42
and with the concurrent PDO (JJA) index at 20.56.
Both correlations are significant at the 5% level. PC1
(JJA), however, is not significantly correlated with in-
dices of dominant planetary-scale wave trains, that is,
with neither the circumglobal teleconnection (CGT)
(Ding and Wang, 2005; r 5 0.15) nor the Silk Road
pattern (Kosaka et al. 2009; r 5 0.26).
The SST regression with PC1 (JJA) (Fig. 3c) shows an
elongated warming in the western North Pacific, con-
sistent with the SST pattern identified to be associated
with warming in northeastern China (Wu et al. 2010).
The SST pattern is also suggestive of a relationship with
the negative phase of the PDO (Schneider and Cornuelle
2005), as supported by the significant correlation noted
above, with a particularly strong connection to the
positive SST anomalies in the Kuroshio extension re-
gions. SST anomalies in the North Indian Ocean point
toward the ocean basin’s role in relaying the ENSO ef-
fect on the western North Pacific climate via an atmo-
spheric Kelvin wave–induced Ekman divergence
mechanism (Xie et al. 2009). The delayed warming of
the tropical Indian Ocean (TIO) in the summer fol-
lowing El Ni~no induces tropical convection anomalies
that emanate an equatorial Kelvin wave. The resulting
northeasterly surface winds and low-level divergence
over the western North Pacific suppress convection,
which promotes the development of an anomalous an-
ticyclone through a positive feedback mechanism. The
opposite response is expected in the summers following
La Ni~na. Thus, the TIO response contributes to a pro-
nounced development of atmospheric anomalies over the
western North Pacific and East Asia during the summer
of an ENSO decay year (Yang et al. 2007; Wu et al. 2009,
2010; Xie et al. 2010; Chowdary et al. 2010). Indeed, it has
been recognized that the dipole pattern of SAT in south
and northeast China is a signature of the Indian Ocean
FIG. 3. (a) Three-year running-mean time series of the leading PC [PC1 (JJA); black line], the SOI in the preceding
winter (NDJF; orange), and the PDO (JJA) index (green). (b) Regression of SAT (color shading; 8C) on PC1 (JJA)
and the climatology of SAT (contours; 8C). The dashed box is the region for area mean analysis described in the text.
(c) Regression of SST (color shading; 8C) on PC1 (JJA) and the climatology of SST contoured at intervals of 48C. Thebox represents the East Asia domain. Color shading is applied to values that are statistically significant at the 10%
level.
15 NOVEMBER 2013 I TO ET AL . 9031
SST effect (Fig. 3b) (Hu et al. 2011). Thus, ENSO, PDO,
and Indian Ocean SST are considered as contributing
factors for the leading pattern of East Asian summer
SAT variability, although the fairly modest ENSO and
PDO correlations also suggest the importance of in-
ternal atmospheric variability.
This leading EOF appears to be closely related to the
second EOF of upper-tropospheric (200–500 hPa) tem-
perature identified by Zhang and Zhou (2012). Both of
these two EOF patterns are significantly correlated with
ENSO in the preceding winter, which suggests that both
patterns are closely tied to the decaying phase of ENSO.
Zhang and Zhou (2012) argue that the second EOF of
upper-tropospheric temperature results, in part, from
both remote ENSO forcing and local moist processes, as
discussed above.
c. Extreme SAT occurrence
Next, we focus on the relationship between PC1 (JJA)
and extreme temperature occurrence. Figure 4a shows
FIG. 4. Regressions of (a) 500-hPa geopotential height (color shading; m) and wind (arrows), (b) subseasonal SAT
std dev (color shading; 8C) and SAT (contours at intervals of 0.28C), (c) cold extreme occurrence (color shading;
days season21), (d) warm extreme occurrence (color shading; days season21), and (e) the asymmetric occurrence
between cold andwarm extremes [i.e., sum of (c) and (d); color shading; days season21] on PC1 (JJA). Line contours in
(c),(d),(e) indicate regressions of subseasonal SAT std dev on PC1 (JJA) at intervals of 0.18C. Locations with an ‘‘x’’ in(b),(c),(d) indicate statistically significant regressions of subseasonal SAT std dev, cold extreme occurrence, and warm
extreme occurrence, respectively. In (e), locations with an ‘‘o’’ indicate a statistically significant subseasonal SAT std
dev regression [as in (b)], and locations with a ‘‘*’’ indicate statistically significant differences in the magnitude of cold
and warm extreme occurrence regression coefficients. All significance tests are conducted at the 10% level.
9032 JOURNAL OF CL IMATE VOLUME 26
that the leading EOF of summertime SAT is associated
with positive geopotential height anomalies in the
midtroposphere, most pronounced over the region of
maximum SAT anomalies (Fig. 3b). In addition, the
subseasonal SAT standard deviation also increases over
this region of maximum warmth centered on Mongolia
(Fig. 4b). Associated with this broad area of warmth, the
number of cold extreme days is significantly reduced in a
swath from Siberia to the western North Pacific (Fig. 4c).
In contrast, the number of warm extreme days increases in
a similar region to that of cold extreme decreases, except
for the northern Philippine Sea (Fig. 4d). These mirrored
spatial patterns are suggestive of the effect of a shift in the
SAT distribution by the change in seasonal-mean SAT.
However, the spatial pattern of the asymmetric occurrence
between cold and warm extremes related to the leading
mode shows large amplitudes of 2–4 days season21 over
Mongolia and northeastern China, the same region
with largest increases in the subseasonal SAT standard
deviation, and of 4–8 days season21 in the northern
Philippine Sea (Fig. 4e). Thus, the results of Fig. 4 sug-
gest that the broadening of the subseasonal temperature
distribution contributes to an enhancement of extreme
temperature occurrence over Mongolia and northeast-
ern China (Fig. 4e) during the positive phase of EOF1.
Next, we turn our attention toward possible mecha-
nisms that result in enhanced subseasonal temperature
variance and extreme temperature occurrence over the
central part of the East Asian domain. Figure 5a illus-
trates the regression of the seasonal net surface energy
imbalance (QS 2QL 2QH 2QE) on PC1 (JJA), where
QS, QL, QH, and QE are the solar shortwave, outgoing
longwave, sensible heat, and latent heat fluxes, respec-
tively. The net surface energy flux regressions are rather
modest, suggesting that the total surface flux anomalies
are not driving the increase in mean and extreme tem-
peratures. However, what may be more important than
the net surface flux is the partitioning between sensible
and latent heat fluxes. For example, a significant con-
tributor of the severe European heat wave in the sum-
mer of 2003 was an increase in sensible heat fluxes at the
expense of latent heat fluxes in response to increasingly
negative soil moisture anomalies (Black et al. 2004;
Ferranti and Viterbo 2006; Fischer et al. 2007). To ex-
pound further, consider a typical summertime convec-
tive boundary layer in which the dominant energy
FIG. 5. Regressions of the (a) net surface energy imbalance (QS 2 QL 2 QH 2 QE; color shading; Wm22),
(b) covariance between subseasonal potential temperature and sensible heat flux (color shading; WKm22), (c) co-
variance between subseasonal potential temperature and latent heat flux (color shading; WKm22), (d) soil moisture
(color shading; cm month21), and precipitation (line contours at intervals of 0.4 cm month21) on PC1 (JJA). Line
contours in (a)–(c) represent regressions of subseasonal SAT std dev on PC1 (JJA) at intervals of 0.18C. Locations withan ‘‘x’’ in (a)–(d) represent statistically significant regressions for variables represented in color at the 10% level.
15 NOVEMBER 2013 I TO ET AL . 9033
balance is between sensible heat flux convergence and
turbulent heat transport. In this case, the primary means
by which the mean boundary layer temperature changes
is through the sensible heat flux. In this case, the fourth
term on the rhs of (4) suggests that an increasing co-
variance between subseasonal temperature and sub-
seasonal surface sensible heat flux would contribute to
a broadening of the subseasonal temperature distribu-
tion, thus increasing the occurrence of positive SAT
extremes. Furthermore, Fig. 5b supports this interpre-
tation, as the enhancement of the subseasonal temper-
ature variance that exacerbates extreme temperature
occurrence over Mongolia is generally associated with
the increased covariance between subseasonal potential
temperature and sensible heat flux, as captured in term
four of (4).
As expected, the increased covariance between po-
tential temperature and sensible heat flux is accompa-
nied by the decreased covariance between potential
temperature and latent heat flux (Fig. 5c), which sug-
gests that the positive SAT extremes and the broaden-
ing of the subseasonal temperature distribution over
Mongolia are associated with an increased Bowen ratio
(RB 5QH/QE). This increase in the sensible heat flux at
the expense of latent heat flux is apparently related to
negative precipitation and soil moisture anomalies over
the region (Fig. 5d), drawing a similarity to the extreme
European heat wave of 2003 (Ferranti andViterbo 2006;
Fischer et al. 2007). This point is further supported by
Fig. 6, which focuses on the region of maximum SAT
anomalies in the positive phase of EOF (JJA) but for all
summer periods, not just those periods associated with
high projections onto EOF (JJA). Even with this ex-
panded scope, we see a rather strong relationship be-
tween the seasonal-mean temperature anomaly and the
subseasonal temperature standard deviation (Fig. 6a;
r 5 0.63). Consistent with the discussion above, these
increases in the SAT mean and subseasonal standard
deviation are associated with reduced precipitation and
soil moisture (Fig. 6b). The correlation between the
seasonal-mean SAT (subseasonal SAT standard de-
viation) and soil moisture over this region is 20.56
(20.60), whereas the correlation between the Bowen
ratio and soil moisture anomalies is 20.52.
We can examine the changes in PDF shape in further
detail by focusing on the SAT anomaly distributions for
the positive and negative phases of PC1 (JJA) at Ulan
Bator, Mongolia (Fig. 7), a station in the region of the
largest loadings on EOF1 (JJA). The PDF estimates in
Fig. 7 are smoothed with a normal kernel function.
In years of the positive phase, the SAT distribution
shifts to the right and the standard deviation becomes
larger, hence broadening the distribution (Fig. 8a).
This broadening results in a substantially greater increase
in warm extreme days than a decrease in cold extreme
days. Distributions based on the station data are similar
to those of the reanalysis data in terms of mean shift and
narrowing/broadening of the distribution (Fig. 7b). These
results are in good agreement with the linear regression
results presented in the preceding section. Therefore,
both distribution shifts and narrowing/broadening are
influential on extreme SAT occurrence in particular
regions such as Mongolia.
4. Results for winter
a. Climatology
The surface winter [December–February (DJF)] cli-
matology features dominant northwesterly monsoonal
flows between the Siberian high and Aleutian low (Fig.
8a). The SAT gradient is larger in south- and north-
eastern China and along to the Kuroshio–Oyashio ex-
tension. Subseasonal SAT variability is large along two
zonal bands of large mean SAT gradients, north- and
southeast of the Tibetan Plateau, respectively. There,
storm activity is high as measured by the meridional
eddy heat flux standard deviation in the lower tropo-
sphere [i.e., s(y0T 0) at 850 hPa, where primes indicate
the deviation from seasonal mean] and EKE in the
midtroposphere (Fig. 8b). These two major storm-track
FIG. 6. Time series of the following variables, calculated as
anomalies, averaged over the region shown as a dashed box in Figs.
3b and 5: (a) seasonal-mean SAT (solid; 8C), subseasonal SAT std
dev (dashed; 8C), (b) soil moisture (orange), precipitation (green),
and the Bowen ratio (black). The y axis of (b) represents variation
of both soil moisture/precipitation (cm month21) and the Bowen
ratio (unitless).
9034 JOURNAL OF CL IMATE VOLUME 26
axes are associated with the polar and subtropical jets.
Large eddy heat flux is consistent with the storm tracks
as well as subseasonal SAT variations. Large sub-
seasonal SAT variability in the Sea of Okhotsk is most
likely due to sea ice variability (Ohshima et al. 2006).
b. Leading pattern of interannual variability
Similar to the summer analysis, EOF analysis is ap-
plied to the detrended winter (DJF) seasonal-mean
SAT. The leading EOF [EOF1 (DJF)], explaining 45%
of the total variance, is a monopole pattern with large
loadings over Siberia that gradually decreases south-
ward toward eastern China, the Korean Peninsula, and
Japan (Fig. 9a). This mode is strongly related to the AO,
with a correlation of 0.72 that is statistically significant at the
1% level. The normalized PC time series of this AO-like
mode [PC1 (DJF)] is used as an index for the leading pat-
tern of interannual SAT variability in East Asian winter.
We examine surface and upper-level climate variables
by linear regression analysis with the leading PC.
Anomalous low pressure in the polar region, intensified
westerlies between 508 and 708N, and suppressed me-
ridional polar air intrusion into midlatitudes are prom-
inent features characteristic of the positive phase of the
AO (Figs. 9a,c) (Thompson andWallace 2000; Xie et al.
1999). Advection of the climatological temperature by
FIG. 7. PDF estimates of daily-mean SAT anomalies at Ulan Bator, Mongolia, based on the phase of PC1 (JJA)
with (a) reanalysis and (b) station data. Each panel shows distributions for climatology (black), positive years (red),
and negative years (blue). Panels are based on the raw anomalies (top) or the anomalies with the seasonal-mean
temperature subtracted (bottom). The 10th and 90th percentiles of the climatological distribution are shown as
a triangle in each panel.
FIG. 8. (a) Winter (DJF) climatology of the seasonal-mean SAT (contours; 8C), subseasonal SAT std dev (color
shading; 8C), and 925-hPa wind (arrows). (b) Winter climatology of the std dev of subseasonal meridional eddy heat
flux (i.e., y0T 0) at 850 hPa (color shading; Kms21), and the 500-hPa EKE (contours; m2 s22).
15 NOVEMBER 2013 I TO ET AL . 9035
anomalous wind in the lower troposphere seems to be an
important mechanism for generating the seasonal SAT
anomalies, particularly over Siberia (Figs. 9a,b).
c. Extreme SAT occurrence
The mechanisms for winter subseasonal SAT vari-
ability are somewhat different from those of summer. In
contrast with summer, when patterns of warming are
associated with increased subseasonal SAT variance
over a broad region, EOF1 (DJF) is associated with
reduced subseasonal SAT variance over a broad region
of warming. In particular, the subseasonal SAT standard
deviation decreases in two banded regions, one from
southern Siberia to northern China, and the other along
the south China coast, respectively (Fig. 10a). Associ-
ated with the positive phase of EOF1 (DJF), the number
of cold extreme days decreases throughout the domain
whereas that of warm extreme days increases over the
regions to the north of 358N (Figs. 10b,c). As in the
summertime analysis, the asymmetric occurrence be-
tween the number of cold and warm extreme days
through linear regression is similar in distribution to the
subseasonal SAT standard deviation anomaly (Fig. 10d),
though of opposite sign in this case. Thus, the reduced
subseasonal variance (narrowing of the temperature
distribution) that accompanies the warming results in a
greater decrease in the number of cold extreme days than
the increase of the warm extreme days.
Given the contrasting behavior between summer and
winter subseasonal temperature variance, the dominant
mechanisms regulating variance changes must also be
different. One would suspect that large-scale dynamics
would play a greater role in winter, given the increased
baroclinicity, increased teleconnection pattern vari-
ability, and decreased solar heating in winter. Returning
to the subseasonal potential temperature variance bud-
get, we see that the second term on the rhs of (4) poten-
tially captures important large-scale dynamical processes,
as this storm-track production term entails a product of
eddy heat fluxes with the seasonal-mean potential tem-
perature gradient. Figure 11a confirms that the broad
region of subseasonal temperature variance decreases
associatedwithEOF1 (DJF) correspondwith a significant
reduction of this storm-track variance production term.
Overall, the regions of strongest subseasonal temperature
variance reductions in the northwest and northeast part of
FIG. 9. Regressions of (a) SAT (color shading; 8C) and 925-hPa wind (arrows), (b) 850-hPa horizontal temperature
advection (color shading; 1025K s21) and 850-hPa wind (arrows), and (c) 500-hPa geopotential height (color shading; m)
on PC1 (DJF). In (a), the SAT climatology is contoured at intervals of 48C. In (b), the climatology of 850-hPa
potential temperature is contoured at intervals of 5K, and elevation greater than 2000m is shaded in gray. In (c), the
500-hPa height climatology is contoured at intervals of 100m, and the box represents the East Asia domain. Color
shading is applied to values that are statistically significant above the 10% level.
9036 JOURNAL OF CL IMATE VOLUME 26
the domain (Fig. 10a) are generally associated with some
of the strongest negative storm-track production re-
gressions (Fig. 11a). We further isolate the effects of eddy
heat flux and temperature gradient changes by perform-
ing the storm-track production regressions with the eddy
heat fluxes held constant (Fig. 11b) and with the seasonal-
mean temperature gradient held constant (Fig. 11c). These
calculations suggest that both temperature gradient de-
creases (Fig. 11b) and eddy heat flux decreases (Fig. 11c)
contribute to the decrease in subseasonal temperature
variance, although the eddy heat flux decreases con-
tribute more strongly to regional variations.
As in the summer analysis, we also examine PDF es-
timates of daily-mean SAT anomalies related to PC1
(DJF) at Ulan Bator because of the large loadings in
EOF1 (DJF) over East Asia (Fig. 9a). In the regression
analysis (Fig. 10), cold days decrease by 7 andwarm days
increase by 5, yielding an asymmetry of 2 days. In
the reanalysis data, the seasonal-mean SAT shifts to
the right and the standard deviation decreases during the
positive phase (Fig. 12a). As a result, the decrease in the
number of cold extreme days is greater than the increase
in the number of warm extreme days. Both the shift
and shape change in the SAT distribution evidently
contribute to changes in the number of extreme SAT
occurrences. The shift of the distribution is consistent
between reanalysis and station data, although the
change in standard deviation is less pronounced in sta-
tion data than in the reanalysis (Fig. 12b). Overall, the
examination of the PDFs supports the linear regression
analysis of Fig. 10.
5. Summary and discussion
We analyze interannual variability of seasonal mean
and subseasonal variance of SAT in East Asia in order
to examinemechanisms associated with the frequency of
extreme SAT occurrence. Our results show that in as-
sociation with the dominant patterns of interannual
variability, the distribution shifts because of seasonal-
mean temperature change are most influential on the
number of extreme days. In addition, changes in the
distribution shape, namely those associated with sub-
seasonal temperature variance change, have some re-
gional effect on the number of extreme days.
We examine physical mechanisms for the distribution
shifts and shape changes. In summer, the leading pattern
of interannual SAT variability in East Asia features
FIG. 10. Regressions of (a) subseasonal SAT std dev (color shading; 8C), (b) cold extreme occurrence (color
shading; days season21), (c) warm extreme occurrence (color shading; days season21), and (d) the asymmetric oc-
currence between cold and warm extremes [i.e., sum of (b) and (c); color shading; days season21] on PC1 (DJF). Line
contours at intervals of 0.58C in (a) are the regression of seasonal-mean SAT, and the line contours at intervals of
0.18C in (b)–(d) are the regressions of subseasonal SAT std dev on PC1 (DJF). Locations with an ‘‘x,’’ ‘‘o,’’ or ‘‘*’’
indicate regressions that are statistically significant at the 10% level, with the same conventions as in Fig. 4.
15 NOVEMBER 2013 I TO ET AL . 9037
large loadings overMongolia, and the corresponding PC
is significantly correlated with the preceding winter
ENSO and concurrent PDO. The region of greatest
warming during the positive phase of this leading mode
also corresponds with a broadening of the subseasonal
SAT distribution, which exacerbates the occurrence of
warm temperature extremes. This PDF broadening ap-
pears to be closely related to the increasing covariance
between subseasonal temperature and sensible heat flux
deviations. This finding suggests that changes in the
Bowen ratio owing to variations in precipitation and soil
moisture are important modifiers of summertime tempera-
ture distributions and extreme temperature occurrence over
some regions such as Mongolia and northeastern China.
In winter, the leading mode of interannual SAT var-
iability is highly correlated with the AO. The seasonal-
mean anomalies of SAT are largely due to advection
of the climatological SAT by anomalous winds in the
lower troposphere, with the largest amplitudes over the
Siberia andMongolia. In contrast with summer, positive
temperature anomalies associated with the leading
mode accompany a reduction in subseasonal tempera-
ture variance, which results in a pronounced decrease in
cold temperature extremes, relative to a simple PDF
shift. This reduction in subseasonal temperature vari-
ance appears to be closely linked with large-scale dy-
namical processes, namely the combined influence of
reduced eddy heat fluxes and a reduced seasonal-mean
temperature gradient.
Our results for both summer and winter suggest that
changes in subseasonal variance associated with the
dominant patterns of SAT variability substantially im-
pact the frequency of SAT extremes in some regions,
particularly over the interior region centered on
Mongolia. Our analyses of the asymmetric occurrence
between warm and cold extremes are supported by the
examination of probability distribution function esti-
mates of daily SAT anomalies at Ulan Bator, a repre-
sentative location that exhibits pronounced interannual
SAT variability, in both atmospheric reanalysis and
station data. In both data sources, the leading patterns
of variability produce both substantial shifts and shape
changes of the temperature distribution. We made
similar PDF estimate comparisons at other stations in-
cludingHarbin, Shanghai, andChengdu inChina; Irkutsk
in Russia; and Sapporo and Tokyo in Japan, and the
results show good agreement (not presented).
This study has implications for the predictability of
extreme SAT occurrence in East Asia, given that modes
of interannual climate variability are important for
extreme occurrence. In the case of ENSO, numerical
weather prediction models overall are successful in
predicting western North Pacific atmospheric anomalies
summer several months in advance (Chowdary et al.
2010), which is identified as influential on seasonal-
mean SAT variability in East Asia via atmospheric
teleconnections (Hu et al. 2011). This would suggest
promise for some extreme temperature predictability
FIG. 11. (a) Regression of the storm-track potential tempera-
ture variance production term on PC1 (DJF) (color shading;
1025K2 s21). (b)As in (a), but for after holding the eddy heat fluxes
constant at the time mean. (c) As in (a), but for after holding the
potential temperature gradient constant at the time mean. Loca-
tions with an ‘‘x’’ in (a) designate statistically significant regressions
at the 10% level.
9038 JOURNAL OF CL IMATE VOLUME 26
on seasonal time scales during summer. In the case of
winter, however, when variability that is dominated by the
AO, the seasonal prediction is more challenging because
the AO is dominated by internal atmospheric variability
(Feldstein 2000) and its coupling with the ocean is weak.
Finally, we comment on the trends, which we have
excluded from our analysis so far. In summer, positive
temperature trends dominate almost the entire region,
with a particularly large warming trend [38–58C (31 yr)21]
centered over Mongolia (Fig. 13a). Several previous
FIG. 12. As in Fig. 7, but for PC1 (DJF) at Ulan Bator.
FIG. 13. The following summer (JJA) linear trends for the 1979–2009 period: (a) seasonal-mean SAT [8C (31 yr)21],
(b) std dev of subseasonal SAT [8C (31 yr)21], and the number of (c) cold and (d) warm extreme occurrences [days
(31 yr)21].
15 NOVEMBER 2013 I TO ET AL . 9039
studies identified notable increases of warm extremes
because of the large positive trend in mean temperature
in northern, northeastern, and southeastern China (Qian
and Lin 2004; Qian et al. 2010; Xu et al. 2011; You et al.
2010), a significant part of which is likely attributable to
anthropogenic forcing (Wen et al. 2013). Weak trends of
increasing cold extremes in central China have been
recognized while the rest of the regions show decreasing
trends of cold extreme days (Gong et al. 2004). In addi-
tion, Fig. 13b also indicates an increasing trend of the
SAT subseasonal standard deviation [18–28C (31 yr)21]
over Mongolia, which generally coincides with negative
precipitation trends (not shown). Both of these warming
and distribution-broadening trends are associated with
a greater increase in the number of warm extreme days
[15–25 days (31 yr)21] than a decrease in the number of
cold extreme days [5–15 days (31yr)21; Figs. 13c,d]. The
similarity between the analysis of interannual variability
and long-term trends suggests that the interactions among
soil moisture, sensible heat fluxes, temperature distribu-
tions, and extreme temperatures also are important on
longer time scales. In support of this perspective, Sch€ar
et al. (2004) perform a regional modeling study to show
that summer temperature distributions over Europe are
projected to broaden substantially in the 21st century un-
der global warming, apparently in relation to reductions
in precipitation and soil moisture, which would greatly
exacerbate extreme temperature occurrence. The mod-
eling study of Brabson et al. (2005) shows similar pro-
jections and suggests similar reasoning for Britain. Based
on the evidence presented here, we speculate that the
region centered on Mongolia also is particularly suscep-
tible to similar temperature distribution broadening and
extreme temperature increases under global warming in
response to feedbacks with soil moisture.
In winter, the linear trend of seasonal-mean SAT
shows strong warming in the Sea of Okhotsk and cooling
on the northern flank of the Tibetan Plateau, whereas the
rest of the domain haswarmed from18 to 28Cover 31 years
(Fig. 14a). There is an increasing trend in subseasonal SAT
variability from northern to southeastern China and a de-
creasing trend at higher latitudes through Japan (Fig. 14b)
(Gong and Ho 2004). Overall, a broad region in the
northern half of the domain has experienced both a
warming trend and a reduction in subseasonal temperature
variance, which bears some similarity to the analysis of
interannual variability. Consequently, the reduction in cold
extremes in regions of mean warming generally exceeds
any increase in warm extremes (Figs. 14c,d). However, the
large internal variability of the winter climate makes it
difficult to detect significant changes in extreme tem-
perature trends over a 31-yr period (Wen et al. 2013).
FIG. 14. As in Fig. 13, but for winter (DJF).
9040 JOURNAL OF CL IMATE VOLUME 26
Overall, our results suggest that different mechanisms
relating to land surface/atmosphere interactions and
large-scale dynamics may alter the shape of local tem-
perature distributions, which can affect the occurrence
of extreme temperatures. Future work may refine the
attribution of such changes to local temperature distri-
butions and extreme temperatures over East Asia. For
example, Wen et al. (2013) recently detected a signifi-
cant influence of greenhouse gases and land use changes
on extreme temperature occurrence over China, but
questions remain about how these two factors separately
affect extreme temperatures. Future work with multi-
model ensembles and additional observational analysis
is warranted. In any case, the analysis presented here
suggests that the interannual variability of subseasonal
temperature distributions and extreme temperature
occurrence may provide important clues about relevant
mechanisms on longer time scales.
Acknowledgments. We thank Drs. Kevin Hamilton
and Yi-Leng Chen for helpful comments that improved
the quality of this work. We also thank two anonymous
reviewers for comments and suggestions that greatly
benefited this study. This work is supported by the Japan
Agency for Marine-Earth Science and Technology, and
the U.S. National Science Foundation.
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