Erdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37
https://doi.org/10.1186/s40645-018-0195-4

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessRole of soil moisture-atmosphere feedback
during high temperature events in 2002
over Northeast Eurasia
Enkhbat Erdenebat*

and Tomonori Sato

Abstract
In summer 2002, abnormally high-temperature events and associated low soil moisture conditions were observedin Northeast Eurasia. In this study, two regional climate model experiments, one with and the other without soil
moisture–atmosphere interaction, were conducted to investigate the role of soil moisture in surface air temperatureand precipitation in Northeast Eurasia utilizing the Weather Research and Forecasting (WRF) model. In the experiment
without interaction, satellite-based soil moisture was prescribed. Under the persistent mid-tropospheric ridge patternwith prolonged clear skies, both experiments captured the magnitude of the extreme surface air temperature events. A
comparison of the model experiments demonstrated that the mid-tropospheric ridge pattern was intensified by dry
soil moisture conditions that further increase the surface air temperature. The temporal variability of surface
air temperature for the experiments with and without soil moisture–atmosphere interaction reveals the strong
coupling of soil moisture to surface air temperature in June–August, raising the possibility that extreme hottemperature events in Northeast Eurasia were strengthened by the soil moisture anomaly. These results also
indicate that the impact of soil moisture–atmosphere coupling on surface air temperature varies spatially andtemporally, having higher importance for the predictability of extreme high-temperature events in Northeast
Eurasia during mid-summer. This also suggests that the performance of the land surface model in simulating
appropriate land–atmosphere coupling intensity is a key to evaluating the impact of climate change on
extreme heat events in this region.
Keywords: Extreme temperature, Soil moisture, Land–atmosphere interaction, Northeast Eurasia

Introduction
The global mean surface air temperature (SAT) has been
increasing steadily since the middle of the 1950s (Hartmann
et al. 2013). It is widely known that the spatiotemporal variation in mean and extreme temperature increase is not uniform. Thus, several hot spots where warming rate is greaterthan the surrounding areas have been identified across theworld (Alexander et al. 2006; Della-Marta et al. 2007; Seneviratne et al. 2012). In America, Europe, Australia, andAsia, the number of extreme-temperature-related weather
events has increased as mean SAT has increased since 1950
(Seneviratne et al. 2012). Record-breaking extreme
high-temperature events that occurred recently in Europe
and North America have focused attention on the* Correspondence: enkhbat@ees.hokudai.ac.jp
Graduate School of Environmental Science, Hokkaido University, Kita-10,
Nishi-5, Sapporo 060-0810, Japan

interannual variation in extreme high-temperature eventsand the physical mechanisms responsible for them, such asatmospheric circulation and the possible contribution of
the land surface (Black et al. 2004; Meehl and Tebaldi 2004;
Schär et al. 2004; Ferranti and Viterbo 2006; Fischer et al.
2007; Lau and Nath 2012).
A persistent anticyclonic circulation and blocking are
the important atmospheric forcing conditions that cause
hot weather events such as heat waves (HWs) in the
mid-latitude regions (Dole et al. 2011; Schubert et al.
2014). In the 2000s, the mid-tropospheric ridge pattern
was dominant during June–July–August (JJA) in Northeast Eurasia, providing favorable conditions for HWs(Erdenebat and Sato 2016). Soil moisture (SM) in JJA
had declined in this region due to the lack of precipitation, and this possibly intensified the land–atmosphereinteraction (Erdenebat and Sato 2016).

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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Erdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37

Water availability in the soil regulates evapotranspirationthat alters air temperature and humidity in the lower atmosphere. Pre-existing dry soil conditions enhance surface
sensible heat flux and limit evapotranspiration, leading toa rapid increase of SAT (Zaitchik et al. 2006). Observations
and reanalysis data suggested a possible contribution of
SM to the recent increasing number of HWs (Beniston
2004; Weisheimer et al. 2011; Erdenebat and Sato 2016).
However, analysis of observations and reanalysis data has
limitations for examining the role of SM in HWs because
it is difficult to distinguish SM-to-HW and HW-to-SM
contributions as both processes occur simultaneously
during the hot events.
Several numerical studies have been conducted to determine the importance of SM conditions on precipitation
variability (Koster et al. 2004; Su and Dickinson 2017) andHWs (Fischer et al. 2007; Seneviratne et al. 2006). The SM
deficit was found to have a role in intensifying the European HW in 2003 (e.g., Fischer et al. 2007). Fischer et al.
(2007) concluded that the warm summer temperature
anomalies in 2003 would have been reduced by 40% if theSM had been at the level of the climatological mean. In
North America, the SM anomaly in April tends to persist
until July as a result of local positive feedback processes between SM and the atmosphere, as the surface heat flux
anomaly in July induces a wave pattern in the upper troposphere (Koster et al. 2014). This suggests the influence of
SM–atmosphere interaction on the predictability of hot
events. Berg et al. (2015) found that the co-variability oftemperature and precipitation is controlled by SM throughsurface heat flux partitioning. Thus, a reliable hydrologicalcycle in the model may reduce uncertainty in future projections. Although the effect of global SM–atmosphere interactions on the intensity of severe HWs has been studied,
HW events in Northeast Eurasia have not been studied intensively, especially from the viewpoint of SM–atmosphereinteraction during a hot summer.
Summer 2002 was the hottest summer in Northeast Eurasia over the period 1981–2010. In Mongolia, the accumulated summer duration of the strong mid-troposphericridge in 2002 was the longest over the period 1981–2010(Erdenebat and Sato 2016). In addition, the SM averaged
over 42° N–52° N, 90° E–120° E (black box in Fig. 1a) derived from ERA-Interim at surface layer (0–7 cm depth)
was 0.213, 0.215, and 0.205 m3 m−3 for June, July, and August 2002, respectively, lower than the JJA mean SM for
1981–2010 (0.223 m3 m−3). The significantly low SM in
2002 may have played a role in further intensifying HWs.
In Mongolia, the SM affects the predictability of interannual variation in precipitation (Sato and Xue 2013). However, the contribution of SM to HW in Northeast Eurasia
is not known. Therefore, this study aims to investigate therole of SM–atmosphere interactions during extreme HWevents in summer 2002 over Northeast Eurasia.

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Methods/Experimental
Data

ERA-Interim (Dee et al. 2011) reanalysis data at 0.75° ×
0.75° resolution from the European Centre for
Medium-range Weather Forecasts (ECMWF) were used to
examine atmospheric circulation, geopotential height, SM,and temperature variations from June to August 2002. The
ERA-Interim has been shown to be the most reliable reanalysis dataset in terms of extreme temperature variation(Gross et al. 2017). ERA-Interim data were used as initialand boundary conditions for the regional model experiments and to validate the model results over a large area.
For SM validation and analysis, the shallowest level(0–7 cm depth) of ERA-Interim data was used at timeintervals of 1 day. Daily maximum SAT and precipitation
at 70 meteorological stations in Mongolia provided by the
National Agency for Meteorology and Environment
Monitoring of Mongolia were also used. Daily gridded precipitation data from Asian Precipitation Highly Resolved
Observational Data Integration Towards Evaluation(APHRODITE; Yatagai et al. 2012) were used to evaluate
precipitation variation. Anomalies in observations and reanalysis data are calculated with respect to the corresponding 30-year (1981–2010) mean. We used the European
Space Agency-Climate Change Initiative (ESA-CCI; Liu etal. 2011) version 2.2 multi-satellite SM product as an initial
SM condition for model experiments and to constrain the
SM in the model. This SM product has a spatial resolutionof 0.25° × 0.25° and represents the volumetric soil watercontent (m3 m−3) in the uppermost 10 cm of the soil layer.
Model setup

To evaluate the role of SM in HW events, numerical experiments were conducted with the non-hydrostatic Weather Research and Forecasting (WRF) model version 3.6(Skamarock et al. 2005) using the Advanced Research
WRF (ARW) solver. The physics schemes are mostly as in
Sato and Xue (2013), who assessed the model performance over Northeast Eurasia: the Eta (Ferrier) microphysics scheme (Rogers et al. 2001), the Kain–Fritsch cumulus
parameterization scheme (Kain 2004), the rapid radiativetransfer model longwave radiation scheme (Mlawer et al.
1997), the Dudhia shortwave radiation scheme (Dudhia
1989), and the Unified Noah land-surface model (LSM; Ek
et al. 2003). The Noah LSM considers four layers in the
ground (10, 30, 60, and 100 cm depth below the groundsurface). The model domain has 340 × 260 grid points
with a resolution of 20 km covering most of Northeast
Eurasia centered around Mongolia (Fig. 1a). Initial andboundary conditions are obtained from ERA-Interim data
(Dee et al. 2011) at 6-hour intervals. To consider the
nonlinear atmospheric response to SM, we conducted fiveensemble experiments with slightly different initial dates
starting at 0000 UTC on 13, 14, 15, 16, and 17 June 2002.

Erdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37

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Fig. 1 a Domain of the numerical experiment. Color shading represents the SM ratio between ESA-CCI and ERA-Interim (ESA-CCI divided by ERAInterim) for the surface layer during 11–20 June 2002. The black box presents the sub-domain used for area averaging. b Initial SM condition(m3 m−3) at 0–10 cm for the WRF experiment initialized at 0000 UTC 13 June 2002

All experiments ended at 0000 UTC on 1 September
2002. The ensemble-mean model output between 20 Juneand 31 August 2002 (referred to as JJA in the model
analysis) is used in the analysis unless indicated otherwise.
Experimental design

To evaluate the impact of SM on HW, two numericalexperiments were conducted, one with and one without
SM–atmosphere interaction. In the experiment with
SM–atmosphere interaction, SM varies according to thehydrological cycle simulated by two-way coupling
between land and atmospheric processes (hereinafter,
the CSM experiment). In the experiment without SM–
atmosphere interaction (hereinafter, the PSM experiment),
SM was prescribed using the original SM dataset as

described below and thus varies independently of the atmospheric conditions.
To obtain a realistic spatial distribution of initial SM forboth experiments in 2002, we created a new SM dataset
based on ERA-Interim after applying a bias correction
using the ESA-CCI satellite-based product. Firstly, theclimatological mean (1981–2010) field of SM was createdfrom the ESA-CCI for the period 11–20 June to use as areference field to fill missing grid points. Then, pentad
interval SM was created using ESA-CCI during 11–20
June 2002. To interpolate the missing grid points, weapplied a ratio of pentad climatological SM between the
missing grid point and available neighboring grid points
onto the pentad SM field in 2002. Secondly, pentad interval SM data were created using the ERA-Interim in theErdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37

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same period for each layer. We calculated the ratio of
ESA-CCI (0–10 cm depth) to ERA-Interim (shallowest
level, 0–7 cm) SM defined for each pentad during 11–20
June 2002 (Fig. 1a). The ratio below 1.0 indicates that
ERA-Interim SM is wetter than ESA-CCI SM. Although
there is a small difference in the thickness of the layer ineach case, we consider that both datasets reflect surface
SM conditions. Then, the ratio was multiplied by the
ERA-Interim SM time series in four layers (0–7, 7–28,
28–100, 100–255 cm), assuming that the ratio is the samefor all layers. Finally, SM in the four soil layers (0–10, 10–40, 40–100, 100–200 cm) of the Noah LSM was obtainedby linear interpolation (Fig. 1b). In the following analysis,
we used the SM in the 0–10 cm depth layer. In the PSM
experiment, the SM of all ground layers was prescribed athourly intervals. To create hourly SM data, we preparedpentad SM fields from 11 June to 1 September 2002 as
described above for the SM fields for initial conditions.
Then, the pentad SM was linearly interpolated to thehourly interval. The same initial condition of SM was used

for the CSM and PSM experiments if their initialization
dates are the same. Each ensemble member uses different
initial SM since they are initialized at different date.
In the PSM experiment, the variation in SM is independent of the precipitation predicted in the WRF
model. By comparing the CSM and PSM experiments,
we will discuss the impact of SM–atmosphere interaction on HW events in summer 2002.

Results and discussion
Atmospheric circulation and observed temperature in JJA
2002

In this study, the occurrence of HW was judged foreach day and for each station in summer 2002 basedon the definition of Erdenebat and Sato (2016) which
is summarized as follows. A HW was assigned if thedaily maximum temperature exceeded the 30-year
average (1981–2010) of daily maximum temperatureby 5 °C, and remained at this level for at least fourFig. 2 a SAT anomalies at stations observing HW in Mongolia with respect to their daily climatology. The number of stations observing
HW is indicated by gray bars. b Daily precipitation averaged for 70 stations in Mongolia. The black bars on the top of a and b indicate
the duration of HW

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consecutive days. Events with a 1-day interruption
were also regarded as HW.
In Mongolia, there were three distinct HW events insummer 2002. The average daily maximum SAT anomaly for stations with HW relative to the 30-year averageof each calendar day remained high (Fig. 2a). In 2002,
the number of stations experiencing a HW increased toward the end of summer, suggesting that the HW spreadover a wider area (Fig. 2a). Summer of 2002 (JJA) was
the driest summer during 1981–2010 in Mongolia (Erdenebat and Sato 2016). The daily precipitation in JJA 2002
has decreased gradually toward the end of the summer in
Mongolia (Fig. 2b). In general, an anomalous planetary
wave is a key factor in triggering abnormal SAT events inthe mid-latitudes (e.g., Black et al. 2004; Meehl and Tebaldi2004). The mid-tropospheric atmospheric circulation wasinvestigated during JJA of 2002 using reanalysis datasets.
Figure 3a shows the SAT and 500 hPa wind and geopotential height anomalies in JJA of 2002 relative to theclimatology. Note that SAT is examined at 0600 UTC(1400 LT) and all the other parameters are daily means. In
the JJA mean wind field at 500 hPa geopotential height, aweak southwesterly over Northeast Europe and Northeast
Asia and a northwesterly over the west Siberian plain wereapparent, corresponding to positive and negative geopotential height anomalies, respectively. There was a pair of positive anomalies in geopotential height over Northeast
Europe and Asia, corresponding to regions with warm
SAT. The anomalous geopotential height in JJA 2002 wasvery similar to the decadal mean for 2001–2010 whenmany intense HWs occurred (Erdenebat and Sato 2016).
This suggests that the summer of 2002 is a characteristic
period representing recent hot summers around the study
area.
There occurred three massive HW events during summer 2002: from 25 June to 10 July, from 16 July to 02
August, and from 07 August to 27 August (hereafter, thefirst, second, and third HW events, respectively). The first

Fig. 3 Distribution of SAT (°C, shaded), geopotential height (m, contours), and wind (m s−1, arrows) at 500 hPa, in a JJA 2002, b first HW (25 June
to 10 July), c second HW (16 July to 2 August), and d third HW (7 August to 27 August). Variables in b–d are expressed as anomalies with respect
to the 30-year average (1981–2010) of the corresponding period, but as the mean absolute value for wind at the 500 hPa level. The red line in adelineates the study domain by model. Negative contours are shown by dashed lines

Erdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37

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HW event in late June (Fig. 3b) had a different wave
propagation pattern from the subsequent two events(Fig. 3c, d). Figure 3b shows a positive anomaly of 500 hPa
geopotential height over Northeast Eurasia associated witha negative anomaly over Scandinavia and the west Siberian
plain. In the second HW, the height anomaly during
mid-July to early August was highest (+ 80 m) over
Mongolia. Similarly, high positive anomalies (+ 60 m) wereobserved for the first and third HW in Mongolia. The
third HW event (7–27 August) had the largest spatial extent. The persistent positive anomalous field at 500 hPa
geopotential height suggests that the prevailing ridge pattern in Northeast Eurasia was associated with the atmospheric circulation in regions upstream of the dominantwesterly winds. For aforementioned three events, themid-latitude wave trains seem to be a main trigger of
HWs over Northeast Eurasia although each event hasslightly different wave propagation patterns.
To clarify the local intensification of HWs and the originof the warm air, a Hovmöller diagram of SAT is depicted
for the meridional average between 42° N and 52° N(Fig. 4a). There are two regions of anomalously high SAT:
Europe around 40° E–60° E and East Asia around 90° E–

120° E. The warm SAT anomaly relative to the 30-year dailymean (1981–2010) propagating from Europe to Asia was
weak in early summer (1 June to 15 July 2002). This weak
SAT anomaly before the first HW event is due to its different wave propagation pathway at 500 hPa (Fig. 3b) which is
not captured by the Hovmöller diagram. There was a cold
anomaly of daily SAT (− 2.2 °C, averaged over 1 June to 15
July 2002) between 60° E and 80° E (Fig. 4b), but this tended
to weaken toward the end of summer. There was eastward
propagation of SAT anomalies from Europe to Asia as indicated by the mean warm anomaly of 0.4 °C over 60° E–80°
E from 16 July to 31 August 2002 (Fig. 4b). Eastward
propagation of the warm SAT anomaly is intensified around
Northeast Eurasia between 90° E and 120° E.
The temporal variations in local SAT and SM were investigated over Mongolia (Fig. 4c). There is a pronounced negative relationship between SM and SAT.
The correlation coefficients between daily mean SM and
SAT from ERA-Interim (at 1400 LT) averaged around
Mongolia (black box in Fig. 1a) were – 0.632, − 0.634,
and – 0.625 for June, July, and August, respectively. The
correlation coefficients are enhanced during HW periods, with values of − 0.872, − 0.885, and – 0.836 for the

Fig. 4 The Hovmöller diagram (averaged between 42° N and 52° N) of a SAT in 2002 summer and b its anomaly from the 30-year mean daily
SAT in ERA-Interim. c Time series of SAT (red line) and SM (black line) in ERA-Interim averaged over Mongolia (black box in Fig. 1a). Red straightlines in a and b represent Mongolia. The gray shadings in c represent the durations of HW events

Erdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37

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first (25 June to 10 July), second (16 July to 2 August),
and third (7 to 27 August) HW events, respectively. The
high negative correlation (95% significance) during HWperiods strongly suggests that SM–SAT interaction was
crucial during the HW events. In the following section,
the relationship between SM and HW events is investigated by numerical experiment to elucidate the role ofland–atmosphere interaction.

Most of the cold and warm biases appear between 90° E
and 120° E. Figure 5c, e shows the area-averaged deviationof SAT in the CSM and PSM with respect to
ERA-Interim, respectively. The JJA SAT variation in the
PSM experiment is statistically different (p = 0.10) from
ERA-Interim, whereas that in the CSM experiment is not.
On a subseasonal timescale, the CSM and PSM experiments underestimate SAT compared with ERA-Interim,
especially during late July and August. The CSM experiment underestimates the JJA mean SAT by 0.6 °C, with aroot mean square error (RMSE) of daily SAT for JJA of
1.9 °C. In the PSM experiment, the mean bias and RMSE
are − 0.7 and 2.0 °C for JJA, respectively. In both experiments, in comparison with ERA-Interim, the RMSE ofarea-averaged SAT around Mongolia was low for the firstand second HWs, i.e., 1.4 and 1.2 °C in the CSM and 1.5
and 1.6 °C in the PSM, respectively. In the first HW, SATsof the CSM and PSM experiments are not significantly
different from ERA-Interim, which indicates that themodel agrees well with reanalysis data. During the second
HW, temporal- and area-averaged SAT in the CSM isclose to ERA-Interim, whereas PSM underestimates SAT
by 0.93 °C (p = 0.10). The RMSE of SAT is highest in
the third HW (7–27 August), with 1.9 °C in the CSM(p = 0.05) and 2.4 °C in PSM (p = 0.01). Those worsening of the SAT estimations in the PSM experimentconfirm the importance of correct SM variation to
SAT prediction in the model. Further analysis is carried out how SM impact on the heat flux partitioning
among the experiments.

Experiment on SM–atmosphere interaction

WRF experiments were conducted to examine the roleof SM–atmosphere interaction during the HW event in2002. The ensemble mean SM (0–10 cm depth) variations averaged around Mongolia (black box in Fig. 1a) in
the CSM and PSM experiments are shown in Fig. 5a.
There is a gap in SM between the CSM and PSM
throughout the summer. Although the CSM and PSM
have the same initial SM, the CSM gets wetter during
the spin up duration. As indicated by the wetter SM in
ERA-interim than the satellite SM (Fig. 1a), it is reasonable to speculate that hydrological cycle in ERA-interim
may be stronger than the real atmosphere, and thereby,
our WRF experiments have weak biases in precipitationand SM (Table 1), allowing the SM gap between two experiments throughout the summer (Fig. 5a). The fluctuations of SM are larger in the CSM than in the PSM. The
Hovmöller diagram of simulated SAT (at 1400 LT) averaged between 42° N and 52° N is depicted in Figs. 5b, d toevaluate the performance of the CSM and PSM experiments in simulating zonal and temporal SAT variations.

Fig. 5 a Time series of ensemble mean SM over Mongolia (black box in Fig. 1a) in the CSM (black line) and PSM (red line) experiment. b As in
Fig. 4b but for the deviation of SAT in the CSM experiment relative to ERA-Interim. d As in b but for PSM. c, e Time series of area-averaged (blackbox in Fig. 1a) SAT in the CSM and PSM (red lines), respectively, referred to ERA-Interim (black lines). The gray shadings in a,c and e indicate theduration of HWs

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Table 1 lists the area-averaged (box in Fig. 1a) surface variables for the CSM, PSM, and reference datasets of ERA-Interim. Note that heat fluxes, SAT, andradiation are examined at 0600 UTC (1400 LT). As
mentioned above, the SATs in the two experiments
are similar to ERA-Interim, with the highest SAT during the second HW. Although the event-to-eventchanges of surface variables in WRF experiments are
similar with ERA-Interim, some differences arepresent. The JJA-mean SM is pronouncedly wetter in
ERA-Interim than the WRF model. Thus, in the WRF
runs, less latent heat and more sensible heat fluxes
are found in comparison to ERA-Interim. In particular, sensible heat flux in the WRF runs is much larger
than that in ERA-Interim. Since simulated SM in the
CSM is more tightly constrained by ERA-Interim
fields than in PSM, the CSM shows greater precipitation and SM than the PSM. As a result, the latent(sensible) heat flux in PSM is smaller (larger) than in
the CSM for the JJA-mean and also for each HW.
The downward shortwave radiation during first andsecond HWs is far exceeding the JJA average, indicating less cloud cover, while it is relatively low in third
HW because of the seasonal cycle of solar radiation.
The downward radiations in CSM and PSM are comparable which suggests SM’s influence on cloud coveris negligible for the JJA average although CSM tends
to have larger downward radiation during HW events.
Since the analysis was limited to the particular region

and for three HW events, spatial distribution of the
WRF’s skill and its sensitivity to SM for simulating
sub-seasonal SAT and precipitation variations were investigated as follows.
To investigate the impact of SM–atmosphere interaction on the geographical distribution of subseasonal variation, the temporal correlations of pentad
mean time series between simulated and observed
variables (SAT and precipitation) are shown in Fig. 6.
In both the CSM and PSM experiments, a high correlation of SAT is obtained near the northern and westernlateral boundaries where the atmosphere is less influenced
by land surface conditions (Fig. 6a, c). The correlation of
SAT deteriorates toward the center of the study domain,with the lowest correlation around southern China andthe Far Eastern region of Russia. The southeastward decreasing tendency of the correlation coefficient of SAT
suggests that the dependence of SAT on land surface conditions increases southeastward. The CSM outperforms
the PSM for most of the center of the study domain(Fig. 6e), which means land–atmosphere interaction improves the temporal variation in SAT for continental regions where land surface processes play a crucial role in
influencing SAT. On the other hand, the correlation was
higher in the PSM than the CSM around the east coast of
China. This suggests that having the correct SM in the
PSM is more important than having SM–atmosphereinteraction for the subseasonal variation in SAT in thisarea.

Table 1 Area-averaged (see Fig. 1a) mean surface variables in the CSM and PSM experiments and ERA-interim for corresponded
periods. Note that heat fluxes, surface air temperature, and radiation are examined at 0600 UTC (1400 LT)
Surface air temperature (°C)

Soil moisture (m3 m−3)−2Latent heat (W m )Sensible heat (W m−2)

−1

Precipitation (mm day )Downward shortwave radiation (W m−2)

JJA

1st HW

2nd HW

3rd HW

CSM

24.4

25.2

26.1

24.5

PSM

24.2

25.3

25.5

24.2

ERA-interim

25.0

25.3

26.4

26.2

CSM

0.173

0.180

0.172

0.164

PSM

0.146

0.152

0.150

0.135

ERA-interim

0.210

0.211

0.215

0.201

CSM

128.6

140.3

140.3

117.0

PSM

126.7

131.9

138.8

119.8

ERA-interim

141.2

152.6

162.9

128.0

CSM

245.7

257.7

248.1

230.3

PSM

255.0

270.5

252.7

236.0

ERA-interim

193.5

200.6

188.1

189.6

CSM

1.4

1.6

1.6

0.7

PSM

1.3

1.4

1.4

0.8

APHRODITE

1.1

1.3

1.1

0.5

CSM

823.7

871.6

846.9

783.8

PSM

823.8

867.5

837.3

782.1

ERA-interim

723.8

751.3

741.1

720.8

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Fig. 6 Spatial distribution of temporal correlation for pentad-mean SAT (a, c and e) and precipitation (b, d and f) during JJA between the WRF
experiment and reference dataset. ERA-Interim and APHRODITE were used as reference datasets for SAT and precipitation, respectively. (top) CSM,
(middle) PSM, and (bottom) the difference in correlation (CSM minus PSM). The black box in a and b presents the sub-domain used for area averaging

The correlation of precipitation is also analyzed at eachgrid point in Fig. 6b, d, and f. In both experiments, however, the correlation for pentad mean precipitation timeseries between APHRODITE and WRF is low (RCSM =0.24 and RPSM = 0.17 over the study domain). There is no
significant difference in area-averaged (black box in
Fig. 6b) daily precipitation between CSM and PSM during
JJA (Student’s t test, 95% confidence level). This unclear
response of precipitation to SM indicates that the influence of SM on precipitation is complicated in space andtime or that the expected impact is weak. Since thecurrent study focused on high-temperature events inwhich the atmosphere is stable under high pressure andhence precipitation is low, additional analysis of years withmore precipitation events is needed to understand thepossible influences of SM on precipitation.
We have examined the performance of the CSM andPSM experiments against observations and reanalysis. In
the following part of this study, further analysis was used
to clarify the SM–atmosphere interaction, focusing on the
SM difference between the CSM and PSM experiments.

The difference in SM between the CSM and PSM is
inhomogeneously distributed (Fig. 7). It also varies on asubseasonal timescale, especially during the first and third
HWs (Fig. 7b, d) over Mongolia by about 0.03 m3 m−3. In
contrast, the area-averaged (black box in Fig. 7a) SM difference is small (0.02 m3 m−3) for the second HW (Fig. 7c).
The horizontal distribution of anomalous latent and sensible heat flux is consistent with that of the anomalous SM(not shown), with more latent heat (less sensible heat) flux
as SM increases. We will later discuss the SM’s impact on
middle tropospheric circulation through a comparison
with SM patterns in Fig. 7.
Figure 8 depicts the difference in 500 hPa geopotential
height fields between the CSM and the PSM. The mean
JJA geopotential field at 500 hPa has a weak ridge over
Mongolia. The strength of the ridge varies for each HW,
and the strongest ridge occurred in the second HW. In
the JJA mean field, the difference in geopotential height
between the CSM and PSM was small (Fig. 8a). The
greater differences occurred for the first (Fig. 8b) HWevents in regions with lower SM in the CSM experiment

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Fig. 7 Horizontal distribution of SM difference between the CSM and PSM experiments for a JJA, b first HW (25 June to 10 July), c second HW(16 July to 2 August), and d third HW (7 August to 27 August). The black box in a presents the sub-domain used for area averaging(Fig. 7b). The atmospheric response to SM is strong in thefirst and second HW events, in which the 500 hPa geopotential height increased by more than 25 m (Fig. 8b) and
20 m (Fig. 8c) in the PSM relative to the CSM experiment.
On the other hand, the influence of SM to the middle
troposphere tends to be weak in the third HW (Fig. 8d)
when SM is much wetter in the CSM (Fig. 7d). This suggests that the impact of SM on the mid-tropospheric circulation differs among three HW events. Kanae et al.
(2001) suggested that the impact of land surface anomaly
is likely to present during the period when prevailing wind
weakens. To investigate the relationship between the environmental winds and impact of SM anomaly,
upper-level wind speeds were compared among HWevents. The areal mean (black box in Fig. 8a) daily windspeeds in the CSM at 500 hPa were 6.2, 7.9, and 8.1 m s−1
for the first, second, and third HWs, respectively. Underthe relatively strong westerly wind during the third HWevent, the impact of the SM anomaly might propagate
weakly upward. It is speculated that the mid-troposphereis more sensitive to a SM anomaly when Northeast
Eurasia is covered by a strong ridge and weak westerly

wind, and less sensitive when strong westerly wind dominates. Further studies that consider different environmental conditions will be needed to verify this speculation.
Here, we discuss the strength of the coupling between
land and atmosphere. To investigate the spatial distributionof temperature and SM interaction in the model experiments, a coupling strength is estimated by using the output
from the CSM and PSM experiments. We followed themethodology proposed by Seneviratne et al. (2006), which
considers the standard deviation of temporal variability(hereinafter, coupling strength) of particular variables derived from the CSM and PSM experiments. In this study,
coupling strength is estimated by (σCSM2 − σPSM2)/(σCSM2)
where σCSM and σPSM represent the standard deviations ofdaily SAT (1400 LT) or precipitation during JJA in the
CSM and PSM outputs, respectively. The couplingstrength for SAT is highest in Siberia and east China(Fig. 9a). Positive coupling strength implies that SAT variability in the CSM experiment is larger than in the PSM
experiment. The spatiotemporal distribution of the coupling strength for all JJA is not uniform. A high couplingstrength is widely distributed over Siberia during the firstErdenebat and Sato Progress in Earth and Planetary Science (2018) 5:37

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Fig. 8 a–d Same as Fig. 7, but for 500 hPa geopotential height (Z500hPa) difference (shading). Contours represent 500 hPa geopotential heightin the CSM experiment

HW event (Fig. 9b), which remains in JJA average (Fig. 9a).
Since low SM (Fig. 7b) and high geopotential (Fig. 8b) areco-locating over Siberia in the CSM run, it is speculated
that positive feedback in local land–atmosphere interaction
is intensified during the first HW event. The area-averaged(black box in Fig. 9a) monthly based coupling strengths are
0.02, 0.17, and 0.04 for June, July, and August, respectively.
The coupling strength during the first HW is high in thewestern Mongolia, which is likely to be caused bytemperature advection rather than local SM–SAT coupling.
A weaker surface westerly in the CSM results in weaker
cold advection in the western Mongolia during the decaying period of first HW, leading to higher SAT variance in
the CSM (Fig. 5b). The highest coupling strength appears
in central China around the Yellow River basin during thethird HW (0.68), suggesting that the impact of SM on SAT
is strongest in this region (Fig. 9d). A negative couplingstrength, namely higher variance of SAT in the PSM thanthe CSM, occurs when the availability of SM in the PSM islower than in the CSM. In general, the coupling strength
between SM and SAT was found to be low over the
Mongolia. Here, we raise two possible reasons. Firstly, thesign of SM difference between CSM and PSM varies spatiotemporally. This allows the SAT difference being both
positive and negative signs, and consequently, the difference in SAT variance between two runs becomes small,leading to low coupling strength. Secondly, the study domain covers various hydrological regimes from dry tohumid regions, which may result in diverse atmospheric
response against SM anomaly as indicated by Guo and Dirmeyer (2013).
The coupling strength for precipitation does not show
meaningful signals in the studied area (Fig. 10). This result
is consistent with other studies (Koster et al. 2004; Seneviratne et al. 2010; Zhang et al. 2011), which found weakcoupling strength for SM and precipitation in multiple
GCM experiments in this region. Guo and Dirmeyer(2013) found strong SM-to-precipitation coupling dominates over Northeast Asia in dry years. Since the targeting
year was dry in our studied area, we expected a strong
coupling in the current study. Although positive couplingstrength in eastern Mongolia (Fig. 10a) agrees with Guo
and Dirmeyer (2013), the coupling strength over the surrounding region seems to be randomly distributed. This

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Fig. 9 Horizontal distribution of coupling strength for SAT for a JJA, b first HW (25 June to 10 July), c second HW (16 July to 2 August), and dthird HW (7 August to 27 August)may be due to different analysis period and different physics schemes in the model. Since this study is limited for
typical hot summer in 2002, further investigation isneeded by taking account of large number of cases for the
analysis of SM-to-precipitation coupling.

Conclusions
In this study, the role of SM in the intensification of HWevents in Northeast Eurasia has been investigated with observational datasets and numerical experiments for the extremely hot summer of 2002. A mid-troposphericcirculation analysis using the ERA-Interim reanalysis
shows that HWs over Northeast Eurasia were triggered by
mid-latitude wave trains. The Hovmöller diagram for SAT(Fig. 4b) demonstrates that HWs are concentrated around
Mongolia associated with a low SM anomaly that can potentially enhance sensible heat flux and the mid-tropospheric ridge. Numerical experiments with (CSM) andwithout (PSM) SM–atmosphere interaction were conducted to study the importance of land–atmosphere interaction during the strong HW events in 2002. The
correlations of pentad SAT and precipitation are high inNortheast Eurasia and become lower toward the southeastof the study domain, suggesting that the contribution of
SM to SAT is stronger in Northeast Eurasia. Dry (wet) SM
conditions in the PSM experiment increase (decrease) SAT
by enhancing (limiting) the sensible heat flux in comparison with the CSM experiment. The dry (wet) SM in the
CSM experiment can intensify (weaken) the positive
anomaly at 500 hPa geopotential height. The couplingstrength index reveals that there is higher couplingstrength in Northeast Eurasia in this study domain andthat land–atmosphere interaction varies on a sub-seasonal
timescale. The CSM experiment resembles the variation in
SAT seen in the reanalysis more closely than does thePSM, suggesting that land–atmosphere interaction is important in this region. On the other hand, in the PSM experiment, the model performance in simulating the
temporal variation in SAT is better than the CSM over theeast coast of China. Thus, correction of SM variation in
the model using the satellite-based SM dataset has the potential to improve the sub-seasonal variability in SAT byconstraining the internal variability in atmospheric circulation through land–atmosphere interaction. The correct

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Fig. 10 a–d Same as Fig. 9 but for precipitation

land–atmosphere coupling in a model is expected to regulate the relationship between SM and SAT. The intensityof SM–atmosphere interaction varies spatially and temporally, i.e., there is weak interaction under strong westerly
wind (during the third HW) and strong interaction under
weak westerlies in the mid-troposphere, as occurred insummer 2002 over Northeast Eurasia. The CSM and PSM
experiments suggest the importance of SM in regulating
SM–atmosphere coupling that should eventually improve
the representation of subseasonal variability in SAT during
HW events.
Finally, we must point out the limitations of this study.
In comparison to SM–SAT coupling, the SM–precipitation coupling strength is heterogeneously distributed. Although this might represent a complicated nonlinear
interaction between SM and precipitation in mid-latitude,
five ensemble experiments in this study may be too few todetect SM’s impact on precipitation. A comprehensive
analysis by using a large number of ensemble experimentis demanded in the future study. The SM dataset for theprescribed SM experiment also requires improvements.
Recently, satellite-based and model-based SM products

become available for such a purpose. However, there is a
gap in seasonal mean SM values between the SM products
and SM derived from atmospheric reanalysis. In such acase, simulated hydrological cycle in a regional climate
model is likely to be biased that may hinder a proper
evaluation of SM–precipitation interaction. With respect
to future climate change, summer precipitation changearound the studied region yet contains large uncertainty(Collins et al. 2013). Since SM–atmosphere interaction
plays a key role in attributing the future change in summer precipitation (Sato et al. 2007), further studies areneeded to enhance our understanding in land–atmosphere interactions over Northeast Eurasia.
Abbreviations
APHRODITE: Asian Precipitation Highly Resolved Observational Data
Integration Towards Evaluation; ARW: Advanced Research WRF;
CSM: Coupled soil moisture; ECMWF: European Centre for Medium-range
Weather Forecasts; ERA: ECMWF reanalysis; ESA-CCI: European Space AgencyClimate Change Initiative; HW: Heat wave; JJA: June–July–August; LSM: Landsurface model; LT: Local time; PSM: Prescribed soil moisture; RMSE: Rootmean square error; SAT: Surface air temperature; SM: Soil moisture;
UTC: Coordinated Universal Time; WRF: Weather Research and Forecasting;
Z500hPa: Geopotential height at 500 hectopascal

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Acknowledgements
The authors acknowledge the Arctic Challenge for Sustainability Project andthe Social Implementation Program on Climate Change Adaptation
Technology (SI-CAT) project funded by the Ministry of Education, Culture,
Sports, Science and Technology (MEXT) of Japan, and Grant-in-Aid for Young
Scientists A (Grant No. 15H05464) funded by the Japan Society for the Promotion of Sciences (JSPS). The authors also would like to thank the two anonymous reviewers for their comments and suggestions to improve thequality of the paper.

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Funding
This study was funded by the Arctic Challenge for Sustainability (ArCS)
Project and Social Implementation Program on Climate Change Adaptation
Technology (SI-CAT) by MEXT, Japan, and also the Grant-in-Aid for Young Scientists A (Grant No. 15H05464) by the Japan Society for the Promotion of
Sciences (JSPS).
Availability of data and materials
The datasets used, generated, and analyzed during the current study areavailable from the corresponding author on reasonable request.
Authors’ contributions
EE and TS proposed the topic. EE conceived the study and analyzed thedata. TS designed the experiment and interpreted the results of theexperiment. EE wrote the first draft, and TS revised the manuscript. Bothauthors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Received: 10 July 2017 Accepted: 6 July 2018

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