Clim Dyn
DOI 10.1007/s00382-014-2371-6
Quantitative decomposition of radiative and non‑radiative
contributions to temperature anomalies related to siberian high
variability
Tae‑Won Park · Jee‑Hoon Jeong · Yi Deng ·
Renjun Zhou · Ming Cai Received: 23 May 2014 / Accepted: 9 October 2014
© Springer-Verlag Berlin Heidelberg 2014
Abstract In this study, we carried out an attribution analysis that quantitatively assessed relative contributions to the
observed temperature anomalies associated with strong and
weak Siberian High (SH). Relative contributions of radiative and non-radiative processes to the variation of surface
temperature, in terms of both amplitude and spatial pattern,
were analyzed. The strong SH activity leads to the continental-scale cold temperature anomalies covering eastern Siberia, Mongolia, East China, and Korea (i.e., SH domain). The
decomposition of the observed temperature anomalies associated with the SH variation was achieved with the Coupled
atmosphere–surface climate Feedback-Responses Analysis
Method, in which the energy balance in the atmosphere–surface column and linearization of radiative energy perturbation
are formulated. For the mean amplitude of −3.13 K of cold
temperature anomaly over the SH domain, sensible heat flux
is tightly connected with a cooling of −1.26 K. Atmospheric
dynamics adds another −1.13 K through the large-scale cold
advection originated from the high latitudes. The longwave
T. Park (*) · Y. Deng School of Earth and Atmospheric Sciences, Georgia Institute
of Technology, Atlanta, GA, USA
e-mail: [email protected]; [email protected]
J. Jeong Department of Oceanography, Chonnam National University,
Gwangju, South Korea
R. Zhou CAS Key Laboratory of Geospace Environment, School of Earth
and Space Sciences, University of Science and Technology
of China, Hefei, Anhui, China
M. Cai Department of Earth, Ocean, and Atmospheric Science,
Florida State University, Tallahassee, FL, USA
effects of cloud and water vapor account for the remaining
cold anomalies of −1.00 and −0.60 K, respectively, while
surface dynamics (0.71 K) and latent heat flux (0.26 K) help
to mitigate the cold temperature anomalies. Influences of
ozone and albedo processes are found to be relatively weak.
Keywords Siberian High-related temerature change ·
Temperature decomposition · CFRAM · Radiative and
non-radiative processess
1 Introduction
The Siberian High (SH) is a conspicuous surface highpressure system observed over the Eurasian continent during boreal winter. It is recognized as a semi-permanent or
quasi-stationary atmospheric center of action associated
with the coldest and densest air masses found in the Northern Hemisphere (Lydolf 1977). The SH is climatologically
located over northern Mongolia but often amplifies and
expands to a large part of East Asia, causing enormous
influences on weather and climate in Northern Eurasia,
East Asia, and even further in South Asia (Cohen et al.
2001). The influences of the SH activity range from synoptic to interannual, and even to interdecadal time-scales.
The amplification and southeastward expansion of the SH
on synoptic to intraseasonal time-scales is a key factor in
causing severe cold surge outbreak in East Asia (Boyle
1986; Zhang et al. 1997). The intraseasonal to interannual
variability of the SH is closely connected with the strengths
of East Asian winter monsoon (Jhun and Lee 2004), Arctic
Oscillation (Jeong and Ho 2005; Park et al. 2011), stratospheric circulation (Jeong et al. 2006), and El-Nino Southern Oscillation (Chen et al. 2004). In addition, interdecadal
variation of the SH is accompanied by long-term changes
13
T.-W. Park et al.
in temperatures and large-scale atmospheric circulations in
East Asia (Panagiotopoulos et al. 2005; Jeong et al. 2011).
Fundamental characteristics of the SH are very cold
temperatures near surface and associated with strong stability in the lower troposphere, which are sustained by
cold and dry continental air masses accumulated in the SH
domain. For the causes of cold temperatures, previous literatures reported the radiative and non-radiative dynamical
processes associated with the SH (Ding 1990). A strong
radiative cooling above the snow-covered surface over
Eurasian continent is most responsible for the formation of
a shallow cold-core pressure system confined to the lower
troposphere over northern Mongolia in early winter (Panagiotopoulos et al. 2005). Ding and Krishnamurti (1987)
emphasized the role of thermal advection of cold air in the
amplification of the cold high, where dynamical process
resulting from tropospheric horizontal convergence is primarily responsible for the initial pressure rise. Outside the
SH domain, the cloud feedback produces extremely cold
and dry conditions over large parts of central and eastern
Siberia through cloud cover reduction and the subsequent
longwave radiation loss (Lydolf 1977).
Despite efforts in evaluating the linkage of processes
with SH-related cold temperatures, a complete and quantitative account of the relative importance of various radiative and dynamical processes is still missing in terms of
their contributions to the cold temperature. Recent studies
suggested that the intraseasonal to interdecadal variation of
SH intensity and accompanying cold surge outbreaks over
East Asia are strongly influenced by radiative processes
associated with surface conditions, e.g., snow cover (Jeong
et al. 2011) and large-scale atmospheric conditions like the
Arctic Oscillation (Gong et al. 2001; Jeong and Ho 2005)
and Madden-Julian Oscillation (Jeong et al. 2005; Wang
et al. 2012). It becomes increasingly more important to
quantify the relative contributions of these processes on SH
amplification for accurate weather prediction and interpretation of long-term climate change over the eastern Eurasia.
The purposes of this study are to fill the knowledge gap
and to provide a comprehensive assessment utilizing a
recently developed feedback analysis method, called by the
Coupled atmosphere–surface climate Feedback Responses
Analysis Method (CFRAM) (Cai and Lu 2009; Lu and Cai
2009). Deng et al. (2012) adopted the CFRAM to understand the relative roles of radiative and dynamical processes in contributing to warming sea surface temperature
(SST) anomalies over the eastern equatorial Pacific associated with the El Ninõ–Southern Oscillation (ENSO). Most
recently, the CFRAM was used to quantify contributions of
climate processes to the structure of zonal-mean temperature changes driven by increased atmospheric CO2 concentration (Taylor et al. 2013) and to the annual-mean surface
temperature biases (Park et al. 2014) in model simulations.
13
Here, we use the CFRAM to assess quantitative contributions of individual radiative and dynamical processes (e.g.,
albedo, cloud, water vapor, sensible/latent heat flux, surface
and atmospheric dynamics) to the SH-related temperature
changes that are defined as differences between strong and
weak SH winters.
This paper is organized as follows. Section 2 describes
the application of CFRAM to a decomposition analysis of
the SH-related cold temperature, including description of
the data used, the mathematical formulation of CFRAM,
and detailed procedures of decomposition. The main results
are provided in Sect. 3, which contains two subsections.
The first subsection reports the partial temperature anomalies due to radiative and dynamical processes, and the second subsection discusses the possible causes. The final section includes an overview of the decomposition results and
some discussion.
2 Methodology
For input variables, the CFRAM analysis requires solar
insolation at the top of the atmosphere, atmospheric/surface
temperature, specific humidity, ozone mixing ratio, cloud
amount, cloud liquid/ice water content, and surface albedo.
Solar insolation at the top of the atmosphere (I) is calculated by the following equation:
I=
R02 S0
[H sin ϕ sin δ + cos ϕ cos δ cos H]
RE2 π
(1)
where S0 is solar constant of which values is 1,360 W m−2
approximately according to measurements from the Total
Irradiance Monitor (TIM) on NASA’s Solar Radiation and
Climate Experiment (SORCE) and a series of new radiometric laboratory tests (Kopp and Lean 2011). ϕ is latitude, and δ is solar declination angle. RE and R0 indicate
the distance of the Earth from the sun and its annual mean,
respectively; for simplicity, we assume that they are same.
H is length of day, which is defined as
H = cos−1 [− tan ϕ tan δ].
(2)
Only the solar insolation is calculated from the equation, other variables are taken from the European Centre for Medium-range Weather Forecasts (ECMWF)
Re-Analysis Interim (ERA-Interim) data set (Dee et al.
2011). ERA-Interim is the latest ECMWF global atmospheric reanalysis, which covers the period 1979 to present and has a horizontal resolution of 1.5° × 1.5° with
37 isobaric levels. Though it is used as an observation
in the present study, the ERA-Interim is a data assimilation product where real observations blend with a model.
Thus, variables in ERA-Interim have biases with respect
Quantitative decomposition of radiative and non-radiative contributions
(b)
(a)
Fig.  1 a Climatological sea level pressure for December–January–February (units: hPa) and b normalized Siberian High Index. The box in (a)
indicates the domain used for the Siberian High Index
to actual observations, particularly cloud-related variables and surface heat flux estimates. The accuracy of the
ERA-Interim has been discussed in a lot of recent literatures. For example, in Decker et al. (2012), the fidelity of
the ERA-Interim surface fluxes is evaluated with global
flux tower observations. Jakobson et al. (2012) examined
standard variables in the Arctic from multi-reanalysis
products and showed that the best overall performance
came from the ERA-Interim. Through a comparison to
satellite retrievals, the reliability of the ERA-Interim is
validated for the Arctic (Zygmuntowska et al. 2012), the
Antarctic Ocean (Tastula et al. 2013), and Antarctica
(Bracegirdle and Marshall 2012; Rodrigo et al. 2013). In
Wang and Zeng (2012) and Bao and Zhang (2013), biases
of the ERA-Interim over the Tibetan Plateau were shown
to be smaller compared to other reanalysis datasets. The
ERA-Interim is also highly correlated with ground-based
observations in Ireland (Mooney et al. 2011) and France
(Szczypta et al. 2011). Jeong et al. (2011) showed that
the ERA-Interim well reproduces the recent change of the
Siberian High intensity found from observational datasets.
Here we adopt the ERA-Interim as the observation given
its reasonable quality and comprehensive list of available
variables for the CFRAM diagnostics.
The continental-scale temperature variation related to
the SH is derived from a composited difference of temperature anomalies between strong and weak SH winters.
The SH intensity (SHI) is defined as the winter (December–January–February, or DJF, when the SH tends to be
the most active) mean sea level pressure (SLP) averaged
over 80°–120°E and 40°–65°N where the center of the SH
in DJF-mean climatological SLP is observed (Fig. 1a), following Jeong et al. (2011). For 31 winters over the period
1979/1980 to 2009/2010, the SHI is calculated and its normalized value is plotted in Fig. 1b. In constructing the composites, we identify the strong (weak) SH winters when the
normalized SHI is greater (less) than +1.0 (−1.0). Given
the above definitions and procedures, a total of four strong
and five weak SH winters are selected and used in the composite analysis.
Lu and Cai (2009) developed the CFRAM based on
the difference of the total energy balance in a multi-layer
atmosphere–surface column between two time-mean climate states written as
∆
−
→
∂E
−
→
−
→
−
→
= ∆ S − ∆ R + ∆ Q non−radiative ,
∂t
(3)
−
→
−
→
where ∆ ∂∂tE is the difference in energy storage, and ∆ S
−
→
and ∆ R are the differences in the divergence of the shortwave radiation flux and the convergence of longwave
−
→
radiation flux, respectively. ∆ Q non−radiative represents
the difference in the convergence of total energy flux due
to non-radiative, dynamical processes. If the two climate
states are steady, we can ignore the difference in energy
storage. Assuming negligible impacts due to interactions
among various radiative and non-radiative processes, we
can then linearize the right hand side (RHS) of Eq. (3) and
express them as the sum of partial energy differences due to
changes in individual radiative and non-radiative processes
as follows:
−
→
−
→
−
→
−
→
−
→
∆ S ≈ ∆ S (α) + ∆ S (w) + ∆ S (c) + ∆ S (O3 ) ,
(4)
−
→
∂R −
−
→
−
→
→
−
→
−
→
∆ R ≈ ∆ R (w) + ∆ R (c) + ∆ R (O3 ) + −
→∆ T ,
∂T
(5)
−
→
−
→
−
→
−
→
∆ Q non−radiative = ∆ Q (SH) + ∆ Q (LH) + ∆ Q (sfc_dyn)
(6)
−
→
+ ∆ Q (atmos_dyn) ,
where superscripts α, w, c, and O3 stand for surface albedo,
−
→
water vapor, cloud, and ozone,
∆ T is the
respectively.
−
→
R
total temperature difference. ∂ −
→ is the Planck feedback
∂T
−
→
−
→
matrix. In Eq. (6), ∆ Q (SH) and ∆ Q (LH), indicating energy
13
T.-W. Park et al.
differences due to changes in sensible heat flux and latent
heat flux, respectively, have non-zero values in all layers
except at the surface and the lowest atmospheric layer. At
−
→
−
→
the surface, ∆ Q (SH) and ∆ Q (LH) are changes in sensible
heat flux and latent heat flux, respectively, leaving from the
surface to the atmosphere. In the lowest atmospheric layer,
−
→
−
→
∆ Q (SH) and ∆ Q (LH) are the changes in energy convergence into the lowest-atmospheric layer from the surface
due to surface latent and sensible heat fluxes, respectively.
−
→
∆ Q (sfc_dyn) is energy difference due to changes in sur−
→
face dynamics change. ∆ Q (sfc_dyn) has non-zero values in
the surface layer and zero values in the atmospheric lay−
→
ers above. ∆ Q (atmos_dyn) represents energy differences
driven by turbulent, convective and large-scale atmospheric
motions; it has zero values in the surface layer and nonzero values in all the atmospheric layers above.
Substituting Eqs. (4)–(6) into Eq. (3), rearranging terms,
and multiplying both sides of the resultant equation by
−
→ −1
∂R
, we obtain
−
→
∂T
−
→
∆T ≈
−
→ −1 ∂R
−
→
−
→ −
−
→ −
→
→
∆ S (α) + ∆( S − R )(w) + ∆( S − R )(c)
−
→
∂T
−
→ −
−
→
−
→
→
+∆( S − R )(O3 ) + ∆ Q (SH) + ∆ Q (LH)
−
→
−
→
(7)
+∆ Q (sfc_dyn) + ∆ Q (atmos_dyn)
Equation (7) states that local temperature differences
−
→
between two climate states (∆ T ) can be decomposed into
eight partial temperature differences due to (from left to right)
changes in surface albedo, water vapor, cloud, ozone, sensible
heat flux, latent heat flux, surface dynamics, and atmospheric
dynamics. For more detail of the CFRAM formulation, readers are referred to Lu and Cai (2009) and Park et al. (2012).
To quantify the relative contributions of the eight processes on the RHS of Eq. (7) to continental-scale cold
temperature anomalies related to the SHI, the two climate
states being considered are the strong and weak SH winters. The radiative energy differences (i.e., the first four
terms in the bracket on the RHS of Eq. (7)) and the Planck
feedback matrix are derived by off-line radiative transfer
calculations using the Fu-Liou radiative transfer model
(Fu and Liou 1992, 1993). The radiative transfer code is
applied to the composited values of all the input variables
for the strong and weak SH winters. When computing
cloud-induced radiative energy differences, the use of time
and domain mean clouds can produce significant errors in
radiative fluxes due to the unresolved sub-grid variability
of cloudy atmosphere (Pincus and Klein 2000). To reduce
such errors, Taylor et al. (2011, 2013) suggested the Monte
Carlo Independent Column Approximation (MCICA) technique utilizing a maximum-random overlap cloud generator (Raisanen et al. 2004). In the MCICA method, each
13
grid box is subdivided into 100 sub-grid boxes, and cloud
profiles for each sub-grid box are stochastically generated
following the maximum-random overlap rule (Geleyn and
Hollingsworth 1979). In our study, we essentially followed
the same approach to deal with cloud-related uncertainties
in radiative flux calculations. The total energy differences
due to changes in surface sensible and latent heat fluxes are
estimated directly using the surface heat flux data in the
ERA-Interim data set. The last two terms in the brackets on
the RHS of Eq. (7), i.e., the energy differences due to surface and atmospheric dynamics, are estimated as the residuals following Eq. (3), with changes in the heat storage
neglected, because the land and snow surface does not have
a large specific heat capacity or a deep layer over which the
energy can be stored in contrast to ocean.
3 Results
3.1 Decomposition of siberian high‑related temperature
The composite surface air temperature differences between
strong and weak SH winters, including SH-related cold
temperatures, are shown in Fig. 2a. In general, the influences of the SH over land are much more than over the
ocean. To focus on the temperature differences over land,
we set the temperature values over the ocean as “missing
values.” Based on the standard two-sample t test, statistically significant regions at the 99 % confidence level are
observed in the SH domain including eastern Siberia, Mongolia, East China, and Korea, indicating that the cold temperature anomalies are closely associated with the interannual variability of the SH over much of Eurasian continent.
Interestingly, another statistically significant warm temperature change occurs over the Mediterranean-Sahara under
the influence of strong SH activity. Hong et al. (2008)
reported that the atmospheric teleconnection pattern associated with the North Atlantic Oscillation (NAO) represents
the dipole of SLP anomalies between eastern Siberia and
the Mediterranean-Sahara. This dipole pattern seems to be
associated with the cold temperature around the SH region
and with the warm temperature over the MediterraneanSahara. In addition, cold temperature changes are found
over Europe though they are much weaker and insignificant
compared to the above-mentioned signals. Figure 2b shows
the corresponding distribution of the temperature changes
(sum of the eight partial temperature changes) obtained
through the CFRAM analysis. In the CFRAM framework, since the partial temperature changes are obtained
through linearizing radiative energy perturbations, to a
certain degree the accuracy of the CFRAM can be evaluated by comparing the temperature differences directly
calculated from the original data with the sum of the
Quantitative decomposition of radiative and non-radiative contributions
(a)
(b)
Fig.  2 a Surface temperature difference (units: K) between strong
and weak Siberian High winters. Dotted regions indicate the statistically significant grid cells at 99 % confidence level. b Sum of partial
temperature changes (units: K) derived from the CFRAM according
to Eq. (7). The box indicates the domain used for the Siberian High
Index
partial temperature changes (Lu and Cai 2009). Comparing
Fig. 2b with Fig. 2a, the CFRAM does a reasonably good
job in reproducing the SH-related anomalous temperature
field directly derived from the ERA-interim data. It demonstrates that the linearization of radiative energy perturbation in the CFRAM calculation is a sound approximation.
Figure 3 shows the partial temperature changes due
to the eight radiative and dynamical processes obtained
through the CFRAM. Over the SH domain, cold temperature changes related to the sensible heat flux contribute significantly to the total temperature change (Fig. 3a). Overall,
the spatial distribution of the partial temperature changes
due to sensible heat flux resembles that of the total temperature changes shown in Fig. 2, in particular, for cooling and
warming over Europe and the Mediterranean-Sahara. This
indicates the sensible heat flux is a primary driver of the
SH-related temperature pattern. The contribution of sensible heat flux to the cold temperature over the SH domain is
counteracted partly by a negative contribution (i.e., warming) from the latent heat flux (Fig. 3b). On the other hand,
water vapor gives a positive contribution to enhance the
total temperature changes (Fig. 3c). In Fig. 3d, the partial
temperature changes due to cloud process are mainly over
the region north of 50°N and account for the warming over
Europe and cooling over Russia. The contributions of cloud
process is confined mainly to significant cooling anomalies
around the northern boundary of the SH domain. Figure 3e
shows that atmospheric dynamics is another primary driver
of cold temperatures over the SH domain whose contribution is comparable with that of the sensible heat flux. This
agrees with the previous studies that emphasized sensible
heat transfer and cold advection as key factors to drive the
cold temperatures (Ding and Krishnamurti 1987). The contribution of surface dynamics, from energy changes due to
mostly horizontal heat diffusion in the soil and transport
of energy by river run-off, provides localized temperature
anomalies of warming and cooling over the SH domain
(Fig. 3f). The magnitude of contribution related to ozone
change is minimal throughout the whole domain (Fig. 3g).
Contribution of the albedo process is also weak, except
over isolated regions with intra-winter variability of snow
cover, a key factor to control albedo (Fig. 3h).
To quantify the relative contributions of individual processes to both the spatial pattern and mean amplitude of
the total temperature changes at a given region, beyond
the grid-by-grid attribution as done in Fig. 3, we calculate
a pattern-amplitude projection (PAP) coefficient following
Park et al. (2014). The PAP coefficient is defined as
A−1
−1
PAPi = A
A
a2 ∆Ti ∆T cos ϕddϕ
A
2
a ∆T cos ϕddϕ ·
A−1
a2 (∆T )2 cos ϕddϕ
(8)
A
where ϕ and λ are latitude and longitude, respectively; a is the
mean radius of the Earth, and A is the area of the region under
consideration. ΔT and ΔTi are respectively the total and partial temperature changes associated with the ith radiative/
dynamical process at an individual grid-point. Instead of spatial average, the PAP is used in the present study in order to
better estimate area-averaged contribution from each process
to the total temperature changes when temperature changes of
opposite signs co-exist and cancel each other over the regions
under consideration. For example, if the partial temperature
changes due to a specific process have the mostly uniform
sign over the region under consideration (e.g., sensible heat
flux, water vapor, and atmospheric dynamics), the quantification to use weighted spatial average would be fine. However,
in a case that partial temperature changes have both negative and positive signs (e.g., surface dynamics), area average
will lead to significant cancellation of the signals. To avoid
this problem, the PAP is essentially defined as the weighted
area average of the observed temperature changes multiplied
by a “pattern projection coefficient”. The sum of the pattern
projection coefficients calculated for all radiative/dynamical
13
T.-W. Park et al.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Fig. 3 Partial temperature changes (units: K) related to the Siberian
High activity due to (a) sensible heat flux change, (b) latent heat flux
change, (c) water vapor change, (d) cloud change, (e) atmospheric
dynamics, (f) surface dynamics, (g) ozone feedback, and (h) surface
albedo change. Dotted regions are same as those in Fig. 2a
processes gives 1 exactly. This ensures that the sum of all the
PAPs due to various processes for a given region equals to the
area average of the total temperature changes over this region.
The PAP thus provides a balanced measure of relative contributions from individual radiative and dynamical processes
when considering both the spatial pattern and amplitude of
temperature differences.
Here, we define the domain of SH influence as a given
region where statistically significant cold temperature
differences are found over the Eurasian domain shown
in Fig. 2a. The estimated PAP coefficients for the eight
individual processes are shown in Fig. 4. Sensible heat
flux, atmospheric dynamics and cloud process are found
to be the major contributors to cold temperatures over the
SH domain, whereas surface dynamics and latent heat
flux partly cancel out the cold temperature. As expected
from Fig. 3, the impacts of ozone and albedo process are
weak. Dynamical forcing due to sensible heat flux changes
contributes −1.26 K to the regional mean amplitude of
−3.13 K. Atmospheric dynamics and cloud process add
−1.13 and −1.00 K, respectively, to the total cooling.
Providing the negative contribution to the above cooling,
13
Quantitative decomposition of radiative and non-radiative contributions
(a)
Fig. 4 Pattern-amplitude projections (PAPs) of the eight partial temperature changes (units: K) onto statistically significant cold temperature regions over the SH domain shown in Fig. 2a
(b)
surface dynamics produces a warming of 0.71 K. Latent
heat flux also gives a warming of 0.26 K, which weakens
the total cold temperature change.
3.2 Possible causes of the partial temperature changes
Next, we examine the possible causes of the SH-related
partial temperature changes reported above. Note that
within the scope of the current analysis, an explicit assessment of detailed energy perturbations by surface and
atmospheric dynamics is not feasible due to the ‘‘off-line’’
approach where the energy perturbations related to dynamics are treated as the residuals. Instead, the energy perturbation by atmospheric dynamics is estimated implicitly by
diagnosing horizontal temperature advection by large-scale
atmospheric motions.
The energy perturbation by atmospheric dynamics
is mostly linked to large-scale atmospheric motions. To
assess this impact on the SH-induced temperature changes
implicitly, we investigate the differences of temperature
advection (Fig. 5a) and wind vector (Fig. 5b) at 850 hPa
between strong and weak SH winters. Over the SH domain,
lower-tropospheric cold advection originated from high
latitudes, in particular the Chukchi Sea through eastern
Russia (Fig. 5b), accounts for the cooling associated with
the SH by the atmospheric dynamics (Fig. 3e). Figure 5c
indicates that strong cold advection prevails throughout the
entire troposphere with two maxima near the surface and
in the upper troposphere. Takaya and Nakamura (2005a, b)
pointed out that near-surface cold advection by anomalous
wind across climatological temperature gradient modulates
the amplification of the cold SH. The upper-tropospheric
cold advection is also a favorable condition for the amplification of cold SH (Ding and Krishnamurti 1987).
Figure 6 shows the differences of surface sensible and
latent heat fluxes between strong and weak SH winters
directly calculated from the ERA-Interim data. In Fig. 6a,
(c)
Fig. 5 Differences of (a) 850-hPa temperature advection (units:
K m s−1), (b) 850-hPa wind, (c) vertical profile of temperature
advection averaged over the Siberian High domain (units: K m s−1)
between strong and weak Siberian High winters
the sign of the differences is opposite to the partial temperature change due to sensible heat flux (shown in Fig. 3a),
because positive (negative) sensible heat flux change means
more (less) conductive heat release from the surface to the
atmosphere, resulting in cooling (warming) at the surface.
As shown in Fig. 5c, for the SH domain, cold advection by
atmospheric dynamics is stronger in the lower troposphere
around 900 hPa than at the surface. This induces a sharp
negative temperature gradient with altitude which leads to
enhanced sensible heat flux from the surface to the lower
atmosphere. Thus, a strong SHI is associated with excessive sensible heat flux over the SH domain, indicating a
critical role of heat release from the surface in determining SH-induced cold temperatures at the surface (Ding
and Krishnamurti 1987). The cooling over the northern
Europe is also contributed by the excessive sensible heat
flux, whereas the strong SHI through reduced sensible heat
flux is responsible for the warming over the MediterraneanSahara. Figure 6b plots the differences of latent heat flux
13
T.-W. Park et al.
(a)
(b)
Fig. 6 Differences of (a) surface sensible heat flux (units: W m−2) and (b) surface latent heat flux (units: W m−2) between strong and weak
Siberian High winters
(a)
(c)
(b)
(d)
Fig. 7 Differences of (a) surface specific humidity (units: kg/kg), (b) surface heating rate (units: W m−2), (c) surface shortwave heating rate
(units: W m−2), and (d) surface longwave heating rate (units: W m−2) between strong and weak Siberian High winters
between strong and weak SH winters. Following a similar
argument, the change of latent heat flux has opposite signs
to the corresponding partial temperature changes shown in
Fig. 3b. Latent heat flux is the heat flux from the surface to
the atmosphere that is associated with evaporation or transpiration of water at the land surface. Therefore, an increase
(decrease) in latent heat flux causes a decrease (increase) in
temperature at the surface through more (less) evaporation
or transpiration. In boreal winter, the ground over the SH
domain is frozen and covered mostly with snow, which significantly limits the water availability and latent heat flux
at the surface. Under a colder condition during a strong SH
winter, the ground may be frozen more deeply and covered
with more snow, leading to the reduction of latent heat flux
13
at the surface. As a result, reduced latent heat flux over the
SH domain represents less heat being extracted from the
surface, which tends to slightly mitigate the surface cooling
by further sensible heat flux.
The differences of near-surface specific humidity
between strong and weak SH winters are shown Fig. 7a,
and its spatial distribution well matches that of the partial
temperature change due to water vapor (Fig. 3c). There
is also a general agreement between the surface humidity change and the surface radiative heating rate change
(Fig. 7b) in that a positive (negative) humidity change leads
to positive (negative) surface heating change. This confirms
that surface temperature change related to water vapor is
largely dominated by the longwave (i.e., greenhouse) effect
Quantitative decomposition of radiative and non-radiative contributions
(a)
(b)
(c)
(d)
Fig. 8 Differences of (a) vertically integrated cloud water content (units: kg/kg), (b) surface cloud forcing (units: W m−2), (c) surface shortwave
cloud forcing (units: W m−2), and (d) surface longwave cloud forcing (units: W m−2) between strong and weak Siberian High winters
of water vapor (Fig. 7d) while the shortwave effect of water
vapor (Fig. 7c) tends to partially counteract the longwave
effect. Over mid- and high-latitudes in winter, the incident
shortwave radiation is limited and the effect of water vapor
on shortwave heating rate is minimal. The total heating
rate is controlled mostly by the longwave effect. Because
the SH during the wintertime is usually covered by snow,
which has a very large emissivity, the longwave radiative
cooling is very effective. As a result, reduced water vapor
in the SH domain and over Europe is responsible for partial cold temperature change shown in Fig. 3c, indicating a
positive feedback to the total cold temperature change. The
partial warm temperature change over the MediterraneanSahara is related to the positive water vapor change.
The partial temperature change related to the cloud process is attributable to changes in cloud water content and
the subsequent cloud forcing. In Fig. 8a, we present the difference of cloud water content vertically integrated for the
whole atmosphere. The widely spreading minimal clouds
associated with the SH are observed over the central and
eastern Siberian, which has a good agreement with the
finding by Lydolf (1977). Figure 8b shows a change in surface radiative heating associated with cloud water content
change (i.e., cloud forcing change), whose spatial distribution closely resembles that of the cloud-induced partial
temperature changes shown in Fig. 3d. Comparing Fig. 8a
and b, however, it is found that the cloud forcing change in
low and mid latitudes (south of 45°N) is very small though
there are considerable changes in cloud water content
associated with the SH. This indicates the impact of shortwave cloud forcing (SWCF), blocking effect of solar insolation reaching the surface, is mostly offset by greenhouse
effect due to longwave cloud forcing (LWCF) over these
regions (Fig. 8c, d). Over high-latitude regions (north of
45°N), on the other hand, negative (positive) cloud water
content changes lead to negative (positive) cloud forcing at the surface, consistent with the presence of strong
LWCF (Fig. 8d) and the absence of SWCF (Fig. 8c). Due
to the dominant role of LWCF seen in Fig. 8d over central and eastern Siberia, decreases in cloud water content
are mostly responsible for the cloud-related partial cooling through longwave radiation loss (Lydolf 1977). Warming over Western Europe is, on the contrary, contributed
by increased cloud water content and associated warming
effect by the longwave radiation.
4 Summary and discussion
This study applies a new framework of the CFRAM to isolate quantitative contributions from individual radiative and
dynamical processes to the observed surface temperature
changes associated with the SH in boreal winter. Through
the CFRAM technique, the temperature change associated
with the SH is decomposed into eight partial temperature
changes associated with changes in surface albedo, water
vapor, cloud, ozone, sensible heat flux, latent heat flux,
surface dynamics, and atmospheric dynamics. It is found
13
T.-W. Park et al.
that over the SH domain the sensible heat flux provides the
largest cooling effect on the surface temperature anomalies
associated with the strong SH. Contributions of atmospheric dynamics, cloud change and water vapor change
are the second, third and fourth important contributors to
the SH-related cold temperature change, respectively. On
the other hand, surface dynamics and latent heat flux act
to reduce the cold temperature anomalies. Possible causes
of individual processes to the SH-related cold temperature
change are investigated. During strong SH winters, there is
an excessive release of sensible heat flux from the surface
to the atmosphere that leads to the majority of cold temperature anomalies. The atmospheric dynamics supports
the cooling over the SH domain by a strong cold advection
associated with large-scale northerly wind. The enhanced
vertical temperature gradient in the lower troposphere supports the sensible heat flux as well. On the other hand,
more frozen and snow-covered ground condition related to
the strong SH winters reduces the latent heat flux from the
surface that counteracts the cold temperature anomalies.
The negative specific humidity at the surface contributes
to the cold surface temperature changes in the SH domain
through the longwave (i.e., greenhouse) effect of water
vapor. The cloud water content also gives cold surface temperature changes through the dominant LWCF, in particular, over central and eastern Siberia.
In this study, we assess separately the isolated contributions of individual processes to the total SH-related cold
temperature change. However, various processes, in particular, the four dominant processes (i.e., sensible heat flux,
atmospheric dynamics, water vapor, and cloud), which provide positive contribution to the cold temperature change,
are linked together. The amplified high pressure in strong
SH winters brings anomalous northerly wind accompanying cold and dry air originated from high latitudes.
Under condition of positive vertical temperature gradient
associated with the lower atmospheric cold advection, an
increased sensible heat flux over the snow-covered surface in Eurasia continent during boreal winter modulates
the amplification of the cold SH (Ding and Krishnamurti
1987). Reduced specific humidity by the intrusion of dry
air gives an additional cooling to the SH domain by a
weakened greenhouse effect. The high-pressure system and
dry air condition are related to the decrease of cloud cover
and the subsequent negative LWCF anomaly, adding cloudinduced cooling to the total cold temperature change.
Despite the successful decomposition shown in Fig. 2,
the limitation of CFRAM analysis should be noted carefully. For instance, the diagnosis is highly dependent on
the overall quality of the ERA-Interim data. Although
the ERA-Interim is a blend of real observations with the
most updated model physics/dynamics (Dee et al. 2011),
it still has large discrepancies with respect to observations
13
(e.g., ground-based observations, flux tower data, and radiosonde data), in particular in terms of cloud-related variables and surface heat flux estimates. The discrepancies,
particularly those related to clouds, have effects on the partitioning of energy perturbations between cloud change and
atmospheric–surface dynamics in the CFRAM.
This study demonstrates that the CFRAM is an efficient “off-line” diagnostic tool that aids our quantitative
understanding of the relative contributions from various
radiative and non-radiative/dynamical processes to the
temperature anomalies associated with the SH variation.
The results of quantitative attribution obtained through
the CFRAM give diagnostic assessment for understanding
which process is dominant or negligible to modulate the
SH and its surface temperature variability. Furthermore,
the diagnostic information can be applied to predict the
surface temperature changes associated with the SH activity through monitoring the individual processes. For the
statistical prediction model, the present study helps us to
determine processes to be weighted as predictors. In terms
of dynamical prediction, the implication of this analysis is applicable to provide critical information for model
developers to effectively trace origins of model defects and
initiate targeted effort for model improvement. For an indepth study beyond the interannual variability of the SH,
our ongoing investigations explore the use of CFRAM
analysis in evaluating daily evolution of SH activity, which
includes a further separation of the atmospheric dynamics into components related to large-scale and convectivescale motions. We also plan to perform a diagnosis of the
surface dynamics using “on-line” approach based on the
model outputs.
Acknowledgments The ERA Interim data used in this study were
provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Yi Deng and Tae-Won Park were supported by
the DOE’s Office of Science Regional and Global Climate Modeling (RGCM) Program (DE-SC0005596) and by NSF grants AGS1147601 and AGS-1354402. Jee-Hoon Jeong was supported by the
Korea Polar Research Institute (PE14010).
References
Bao XH, Zhang FQ (2013) Evaluation of NCEP-CFSR, NCEPNCAR, ERA-Interim, and ERA-40 reanalysis datasets against
independent sounding observations over the Tibetan Plateau. J
Climate 26(1):206–214. doi:10.1175/JCLI-D-12-00056.1
Boyle JS (1986) Comparison of the synoptic conditions in midlatitudes accompanying cold surges over Eastern Asia for the months
of December 1974 and 1978. 1. Monthly mean fields and individual events. Mon Weather Rev 114(5):903–918
Bracegirdle TJ, Marshall GJ (2012) The reliability of antarctic tropospheric pressure and temperature in the latest global reanalyses. J
Climate 25(20):7138–7146. doi:10.1175/JCLI-D-11-00685.1
Cai M, Lu JH (2009) A new framework for isolating individual feedback processes in coupled general circulation climate models.
Quantitative decomposition of radiative and non-radiative contributions
Part II: method demonstrations and comparisons. Clim Dynam
32(6):887–900. doi:10.1007/s00382-008-0424-4
Chen TC, Huang WR, Yoon J (2004) Interannual variation of the east
Asian cold surge activity. J Climate 17(2):401–413
Cohen J, Saito K, Entekhabi D (2001) The role of the Siberian high
in Northern Hemisphere climate variability. Geophys Res Lett
28(2):299–302
Decker M, Brunke MA, Wang Z, Sakaguchi K, Zeng XB, Bosilovich
MG (2012) Evaluation of the reanalysis products from GSFC,
NCEP, and ECMWF using flux tower observations. J Climate
25(6):1916–1944. doi:10.1175/JCLI-D-11-00004.1
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S,
Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H,
Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally
AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay
P, Tavolato C, Thepaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system.
Q J Roy Meteor Soc 137(656):553–597. doi:10.1002/Qj.828
Deng Y, Park T-W, Cai M (2012) Process-based decomposition of the
global surface temperature response to El Niño in Boreal Winter.
J Atmos Sci 69(5):1706–1712. doi:10.1175/JAS-D-12-023.1
Ding YH (1990) Buildup, air-mass transformation and propagation of
siberian high and its relations to cold surge in East-Asia. Meteorol Atmos Phys 44(1–4):281–292
Ding Y, Krishnamurti TN (1987) Heat-budget of the siberian high and
the winter monsoon. Mon Weather Rev 115(10):2428–2449
Fu Q, Liou KN (1992) On the correlated K-distribution method for
radiative-transfer in nonhomogeneous atmospheres. J Atmos Sci
49(22):2139–2156
Fu Q, Liou KN (1993) Parameterization of the radiative properties of
cirrus clouds. J Atmos Sci 50(13):2008–2025
Geleyn J-F, Hollingsworth A (1979) An economical, analytical
method for the computation of the interaction between scattering
and line absorption of radiation. Contribut Atmos Phys 52:1–16
Gong DY, Wang SW, Zhu JH (2001) East Asian winter monsoon and
Arctic Oscillation. Geophys Res Lett 28(10):2073–2076
Hong CC, Hsu HH, Chia HH, Wu CY (2008) Decadal relationship
between the North Atlantic Oscillation and cold surge frequency
in Taiwan. Geophys Res Lett 35(24). doi:10.1029/2008gl034766
Jakobson E, Vihma T, Palo T, Jakobson L, Keernik H, Jaagus J
(2012) Validation of atmospheric reanalyses over the central
Arctic Ocean. Geophys Res Lett 39:L10802. doi:10.1029/201
2GL051591
Jeong J-H, Ho C-H (2005) Changes in occurrence of cold surges over
east Asia in association with Arctic Oscillation. Geophys Res Lett
32:L14704. doi:10.1029/2005GL023024
Jeong J-H, Ho C-H, Kim B-M, Kwon W-T (2005) Influence of the
Madden-Julian Oscillation on wintertime surface air temperature
and cold surges in east Asia. J Geophys Res Atmos 110:D11104.
doi:10.1029/2004jd005408
Jeong J-H, Kim B-M, Ho C-H, Chen D, Lim G-H (2006) Stratospheric origin of cold surge occurrence in East Asia. Geophys
Res Lett 33:L14710. doi:10.1029/2006GL026607
Jeong JH, Ou TH, Linderholm HW, Kim BM, Kim SJ, Kug JS, Chen
DL (2011) Recent recovery of the Siberian High intensity. J Geophys Res Atmos 116. doi:10.1029/2011jd015904
Jhun JG, Lee EJ (2004) A new East Asian winter monsoon index
and associated characteristics of the winter monsoon. J Climate
17(4):711–726
Kopp G, Lean JL (2011) A new, lower value of total solar irradiance: evidence and climate significance. Geophys Res Lett
38(1):L01706. doi:10.1029/2010gl045777
Lu JH, Cai M (2009) A new framework for isolating individual feedback processes in coupled general circulation climate models.
Part I: formulation. Clim Dynam 32(6):873–885. doi:10.1007/
S00382-008-0425-3
Lydolf PE (1977) Climates of the Soviet Union. Elsevier, Amsterdam
Mooney PA, Mulligan FJ, Fealy R (2011) Comparison of ERA-40,
ERA-Interim and NCEP/NCAR reanalysis data with observed
surface air temperatures over Ireland. Int J Climatol 31(4):545–
557. doi:10.1002/JOC.2098
Panagiotopoulos F, Shahgedanova M, Hannachi A, Stephenson DB
(2005) Observed trends and teleconnections of the Siberian high:
a recently declining center of action. J Climate 18(9):1411–1422
Park T-W, Ho C-H, Yang S (2011) Relationship between the Arctic
Oscillation and cold surges over East Asia. J Climate 24(1):68–
83. doi:10.1175/2010jcli3529.1
Park T-W, Deng Y, Cai M (2012) Feedback attribution of the El NiñoSouthern Oscillation-related atmospheric and surface temperature
anomalies. J Geophys Res 117(D23):D23101. doi:10.1029/201
2JD018468
Park T-W, Deng Y, Cai M, Jeong J-H, Zhou R (2014) A dissection of
the surface temperature biases in the Community Earth System
Model. Clim Dynam 1–17. doi:10.1007/s00382-013-2029-9
Pincus R, Klein SA (2000) Unresolved spatial variability and microphysical process rates in large-scale models. J Geophys ResAtmos 105(D22):27059–27065. doi:10.1029/2000JD900504
Raisanen P, Barker HW, Khairoutdinov MF, Li JN, Randall DA
(2004) Stochastic generation of subgrid-scale cloudy columns for
large-scale models. Q J Roy Meteor Soc 130(601):2047–2067.
doi:10.1256/QJ.03.99
Rodrigo JS, Buchlin JM, van Beeck J, Lenaerts JTM, van den Broeke
MR (2013) Evaluation of the antarctic surface wind climate
from ERA reanalyses and RACMO2/ANT simulations based
on automatic weather stations. Clim Dynam 40(1–2):353–376.
doi:10.1007/s00382-012-1396-y
Szczypta C, Calvet JC, Albergel C, Balsamo G, Boussetta S, Carrer
D, Lafont S, Meurey C (2011) Verification of the new ECMWF
ERA-Interim reanalysis over France. Hydrol Earth Syst Sci
15(2):647–666. doi:10.5194/hess-15-647-2011
Takaya K, Nakamura H (2005a) Mechanisms of intraseasonal amplification of the cold Siberian high. J Atmos Sci 62(12):4423–4440
Takaya K, Nakamura H (2005b) Geographical dependence of upperlevel blocking formation associated with intraseasonal amplification of the Siberian high. J Atmos Sci 62(12):4441–4449
Tastula EM, Vihma T, Andreas EL, Galperin B (2013) Validation of
the diurnal cycles in atmospheric reanalyses over Antarctic sea
ice. J Geophys Res-Atmos 118(10):4194–4204. doi:10.1002/J
GRD.50336
Taylor PC, Ellingson RG, Cai M (2011) Geographical distribution of climate feedbacks in the NCAR CCSM3.0. J Climate
24(11):2737–2753. doi:10.1175/2010JCLI3788.1
Taylor PC, Cai M, Hu AX, Meehl J, Washington W, Zhang
GJ (2013) A decomposition of feedback contributions to
polar warming amplification. J Climate 26(18):7023–7043.
doi:10.1175/JCLI-D-12-00696.1
Wang AH, Zeng XB (2012) Evaluation of multireanalysis products
with in situ observations over the Tibetan Plateau. J Geophys Res
Atmos 117:D05102. doi:10.1029/2011JD016553
Wang L, Kodera K, Chen W (2012) Observed triggering of tropical
convection by a cold surge: implications for MJO initiation. Q J
Roy Meteor Soc 138(668):1740–1750. doi:10.1002/Qj.1905
Zhang Y, Sperber KR, Boyle JS (1997) Climatology and interannual
variation of the East Asian winter monsoon: results from the 197995 NCEP/NCAR reanalysis. Mon Weather Rev 125(10):2605–2619
Zygmuntowska M, Mauritsen T, Quaas J, Kaleschke L (2012) Arctic clouds and surface radiation—a critical comparison of satellite retrievals and the ERA-Interim reanalysis. Atmos Chem Phys
12(14):6667–6677. doi:10.5194/ACP-12-6667-2012
13