Subseasonal and Interannual Temperature Variability in Relation...

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: [email protected]

9026 JOURNAL OF CL IMATE VOLUME 26

DOI: 10.1175/JCLI-D-12-00676.1

� 2013 American Meteorological Society

mailto:[email protected]

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


http://www.esrl.noaa.gov/psd

http://www.esrl.noaa.gov/psd

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).


http://jisao.washington.edu/pdo

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


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.


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.


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.


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).


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).


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.


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.


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.


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].


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).


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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