Simulated rainfall in a GCM with retrieved root-zone soil moisture
from surface soil moisture estimated with TRMM/PR over the Tropics
Yukiko Hirabayashi1, Shinta Seto1, Taikan Oki1, Shinjiro Kanae1, Katumi Musiake1
1 IIS, University of Tokyo
ABSTRACT
The purpose of this study is an application of satellite-based soil moisture estimates to numerical climate
simulations. The research consists of two parts. Firstly, a method estimating root-zone soil moisture from
surface soil moisture has developed using global soil moisture data set with a land surface model under the
global soil wetness project (GSWP). Analyses of the data set has showed that the relationship between
surface and root-zone soil moisture could be linearly approximated with the correction of the time lag, and
thus the root-zone soil moisture can be estimated from surface soil moisture. This method was applied to the
satellite-based surface soil moisture estimated by microwave remote sensing observed by the Precipitation
Radar on the Tropical Rainfall Measuring Mission (TRMM/PR) and a new soil moisture was created.
Secondary, boreal summer (June-August) simulated rainfall over the tropics by an atmospheric GCM with
different soil moisture distributions as for boundary condition were compared. Results showed that simulated
precipitation error was larger when soil moisture estimated by TRMM/PR was directly used since the soil
moisture estimates by satellite did not represent deeper soil states. The simulated precipitation has improved
with the new soil moisture of TRMM/PR by our method compared to the case of the simulation of direct use
of satellite observations.
1. Introduction
estimate deeper soil moisture from is highly desired.
Soil moisture plays an important role in the exchange
In this study, a simple approach to estimate root-zone
of water and energy between land surface and
soil moisture from surface moisture was developed with
atmosphere. Numerical model studies have shown that
the data estimated by the Simple Biosphere Model
giving proper soil moisture data as initial and/or
implemented
boundary conditions should improve the precipitation in
(JMA/SiB) under the Global Soil Wetness Project
model simulations (Dirmeyer, 1999). However, since
(GSWP: IGPO, 1995). With use of this new technique, a
direct observation of soil moisture is limited in their
soil moisture obtained by TRMM/PR was applied for
spatial extent and temporal frequency, there have been
numerical experiments with the CCSR/NIES (Center for
no observed data set sufficient to apply global climate
Climate System Research, University of Tokyo/National
simulations.
Institute for Environmental Studies) atmospheric general
Recently, passive and active microwave remote
at
Japan
Meteorological
Agency
circulation model (AGCM).
sensors are expected to provide quantitative information
2. Global soil moisture data
about soil moisture on a large scale, however, microwave
(1) JMA/SiB GSWP
can detect the information of soil moisture only in very
In this study, a global soil moisture distribution, which
near-surface soil layers. Since soil moisture in root-zone
was calculated with a land surface model (LSM) with
is relevant for climatic and hydrological studies in areas
observed forcing data, was used in order to obtain a
with vegetation cover, developing an algorithm that can
transfer function between moisture in surface and deeper
soil layers.
JMA/SiB is a SiB type LSM that was used in previous
Under the GSWP, the deca-day (three times per
JMA-GCM with some modification concerning snow
month) temporal-mean values of 2-year (1987-1988) 1°
melting process. Hydrological process in JMA/SiB
×1°global data set of soil moisture were produced by
scheme considers water pumping from deeper soil layer
integrating one-way uncoupled LSMs with climatic
by vegetation, evapotranspiration from leaf stomata and
forcing. The forcing given to the LSMs was ISLSCP
interception loss. Soil is divided into three layers in this
Initiative I CD-ROM data. In this research, JMA/SiB
scheme where the first layer denotes 5cm deep from top
GSWP was selected for a global soil moisture data,
surface, from 5cm deep to root depth in terms of
because JMA/SiB was the only data set providing both
vegetation type for the second layer, and more deeper
surface soil moisture and total soil moisture at the
soil is classified as the third layer. All the data employed
redistributed
in this study was the ensemble mean value of 1987 and
GSWP
data
center
at
that
time
(http://www.tkl.iis.u-okyo.ac.jp:8080/DV/gswp/index.html).
1988 in order to eliminate the bias in a specific year. Ws
(2) Satellite observation by TRMM/PR
was the value in the first layer and Wa was the integrated
Soil moisture distribution by TRMM/PR active
value of the first and second layer in SiB model.
microwave satellite sensor was obtained by Seto et al.
(2001). Monthly mean 1 ° × 1 ° soil moisture
distribution in the tropics ranged from 36°N to 36°S
are available in 1998. However, similar to the other
microwave remote sensing, this data reflects only the
physical states between the surface and at most a few
centimeters deep. As the microwave penetration depth
Fig. 1 One-to-one relationship between Ws and Wa of
into the ground is less than its wavelength, information
JMA/SiB GSWP, averaged value in 1987 and 1988, in
obtained by TRMM/PR represents approximately 2cm
Thailand.
depth of the soil with its 13.5 GHz frequency.
In this study, all soil moisture values are expressed in
Figure 1-a and 1-b show average value of Ws and Wa
in 95-110°E, 10-20°N, where corresponds to Thailand.
unit of soil wetness index (SWI). Definition of the value
Figure 1-a is the time series in a year. The shapes of the
of “soil wetness index” is indicated as
SWI = (Sm - Wlp) / (Fc - Wlp)
(1)
curves of Ws and Wa indicate that their values seem to be
correlated although their ranges and the phases are
where Sm is the volumetric soil moisture of the grid, Wlp
different. Figure 1-b is the one-to-one relationship of Ws
is the wilting point, and Fc is the field capacity. SWI
ranges from 0 (wilting point) to 1 (field capacity), and
and Wa where dots express average value of 36 deca-day
in a year and a black star indicates the first deca-day
the larger value indicates wetter state. SWI could be
average of the year (from 1st to 10th in January). It is
greater than 1, or negative in certain instances.
clearly seen in Figure 1-b that the relationship between
3. Derivation of total soil moisture from surface
moisture
Relationship between surface soil moisture (Ws) and
total integrated soil moisture (Wa) was investigated from
Ws and Wa draws a loop within a year. This annual loop
is due to the phase difference of the time series between
Ws and Wa seen in Figure 1-a.
In order to define the phase difference between Ws and
JMA/SiB GSWP soil moisture data set.
Wa, we introduced a new index called “maximum and
minimum peak index” in each 1°×1°pixel in the way
as follows: Firstly, smoothing procedure was adopted in
both spatial and temporal distribution. Each grid was
averaged with 8 surrounding grids as spatial smoothing.
As for temporal smoothing, 5 deca-day moving average
was adopted. At the end, the maximum and the minimum
values at each grid box were picked up. The maximum
and the minimum peak index were equal to the deca-day
number (1-36) of those peak values.
With this maximum and minimum peak index, time
lag between Ws and Wa were calculated at each grid by
subtracting peak deca-day number of Wa from those of
Ws as
LAGmax/min = (deca-day of Wa – deca-day of Ws)max/min (2)
Fig.2 LAG of maximum peak (a) and minimum peak
index (b). Negative value is circled by solid line.
(Noted that if the LAG in (2) becomes less than –18, 36
regions (not shown) is almost constant and its fluctuation
is added to LAG. If LAG exceeds 18, 36 is subtracted
from LAG, so that LAG becomes between –18 and 18.)
range is very small due to high moisture supplement
from snow and lower evaporation ratio. We conclude that
In equation (2), LAG indicates the time lag. It should be
due to such characters of soil moisture, the simple
noted that this method could not catch accurate time lag
algorithm to detect the phase lags can not be applied to
between Ws and Wa when their variation difference
those regions.
exceeds six months (deca-day number of -18 or 18).
It is also seen in Figure 2 that LAG values are larger at
Figure 2 shows the global maps of the LAG for
minimum peak (b) than at maximum peak (a). This
maximum (a) and minimum (b) peak index. Globally,
difference of LAG may be explained by the difference of
most part shows positive value indicating that the Ws
water movement of wet and dry periods. Figure 2
reaches its maximum (minimum) peak earlier than Wa. In
suggests that in wet period, when soil moisture is
most regions, LAG values are from zero to less than 3
reaching its maximum peak in a year, the effects of
deca-days (30 days), or, at most, a couple of months
atmospheric forcing on upper surface are transmitted
(30-90days). Some areas show negative (circled by solid
faster to deeper soil, comparing to the case of the dry
line) or too long positive lags. These areas are basically
period when soil is drying and reaching its minimum.
placed in high latitude such as Canada, Scandinavian
This difference of vertical transmission speed of soil
Peninsula, northern part of China and Siberia, or in
moisture in the two periods may be due to the difference
high-elevated area such as in Rocky Mountains in USA,
of the characteristics of the infiltration and the
European Alps, and Tibetan Plateau. Soil moisture in
evapotranspiration.
these regions is strongly affected by snow. Time
Using this time lag information, Ws data was shifted in
variations of Ws in these regions (not shown) have
several peaks corresponding to their rainy season,
time. At first, adjusting time for Ws to fit the time of Wa
was
snowfall, and snow melting within a year. Wa in these
calculated.
Shifting
amount
of
maximum
Before applying the transfer function above to the
TRMM/PR data, it should be noted that soil wetness
obtained from different LSMs under same climatic
forcing do not show the same value. In this context, the
transfer function obtained from Wa of JMA/SiB can not
be directly applied to soil moisture for CCSR/NIES
bucket.
Fig.3 One-to-one relationship between time-shifted
Ws and Wa by JMA/SiB GSWP, averaged value in
1987 and 1988 in Thailand (a), East China (b),
India (c), and Mississippi (d).
(minimum) peak is obtained as a difference of maximum
(minimum) peak deca-day between Ws and Wa. Shifting
length of the data between peaks are simply calculated
linearly. At last, original Ws data was shifted as a
function of varying time by liner interpolation (expressed
Fig. 4 One-to-one relationship between Ws of
JMA/SiB
GSWP
and
CCSR-NIES/buckets
GSWP, averaged value in 1987 and 1988 in Thai
as Ws’).
Figure 3 illustrates the same figure of Figure 1-b, but
(a), East China (b), India (c), and Mississippi (d).
with Ws’. Average values in Thailand (same area as
Fortunately, as shown in Figure 4, comparisons of total
Fig.1-b), East China (110-125°E, 20-30°N), India
soil moisture by JMA/SiB GSWP (Wa) and by
(75-85 ° E, 10-20 ° N), and Mississippi (60-75 ° W,
CCCSR/NIES bucket GSWP (Waccsr) showed that the
0-10°S) are shown. It is clearly seen from these figures
difference due to models could be corrected by a unique
that after our phase-shift corrections, relationships
function globally. Although this approximation was
between Ws‘ and Wa can be approximated to linear
worse in high-vegetated area like Thailand, the
regression lines. Assuming that neither this time lag nor
regression lines seemed almost the same at anyplace.
regression line change inter-annually, transfer function of
This liner function adjustment was applied to the
deeper soil moisture from surface soil moisture value
converted TRMM/PR data before they were adapted to
was defined. This transfer function obtained from
CCSR/NIES AGCM. All these procedures transferring
JMA/SiB GSWP was applied to a satellite observation
N98 from T98 are schematically shown in Figure 5,
data set by TRMM/PR in the next section.
where f(x,y,t) indicates the phase shift and liner
4.
Application
of
soil
moisture
data
TRMM/PR for summer rainfall simulations
by
approximation and g indicates the unique liner function
between JMA/SiB and CCSR/NIES bucket.
TRMM/PR
few centimeter
T98
JMA/SiB
GSWP
Ws Ws’ Wa
f(x,y,t)
CCSR/AGCM
total root-depth
CCSR/NIES
bucket GSWP
Waccsr
N98
g
ｇ ｆ（ｘ,y,t）
Figure 5 Schematic representation of the soil moisture
transfer method
Fig. 6 Error of JJA total precipitation in 1998
(simulation-observation). Each dot indicates
The AGCM employed in this study is CCSR/NIES
land point of GCM grid.
AGCM (Numaguti et al., 1997) at a spectral resolution
NCEP/NCAR was used as the boundary condition over
of T42 (approximately 2.8 degrees) and 20 vertical levels.
the ocean.
The LSM coupled to the GCM is CCSR/NIES bucket
Figure 6 shows the comparison of errors in simulated
model. Experiment period is three months of boreal
three months precipitation of these two experiments.
summer (June, July, and August) in 1998. In these three
Precipitation error is calculated by subtracting observed
months, snow melting effects in northern continents are
precipitation data by Global Precipitation Climatology
smallest in a year and many regions have a rainy season.
Center (GPCP, WCRP, 1990) from their simulation
Integration is conducted in ensembles of nine runs,
results. From this figure, it is clear that precipitation
which is initialized at 0000 UTC on 23-31 May, and
error is less in N98 than T98.
integrated forward through the end of 31 August.
Figure 7 is a map indicating which experiment is
Two sets of ensembles with specified soil moisture
better in terms of error range of simulated precipitation.
were conducted. One ensemble used original monthly
Light gray indicates the places where precipitation error
TRMM/PR soil moisture (Seto et al. 2001) as for the
of N98 is less than that of T98 and dark gray indicates
boundary condition, as if SWI of this was representative
places where T98 showed better result. Regions with less
to deeper soil state. This ensemble is referred to as T98
precipitation error than 50mm in the both two ensembles
in this paper. In another ensemble, integrated total soil
are masked in the map. Decreasing of the precipitation
moisture transferred from TRMM/PR data with the new
error in N98 was obvious over the land in tropical area
algorithm was specified. The latter ensemble with
where transferred soil moisture was given, such as most
converted satellite soil moisture is referred to as N98.
part of South America and Africa. However, precipitation
In both experiments, CCSR/NIES GSWP two-year
error in N98 was larger than T98 in wide area in
ensemble mean was used for boundary soil moisture in
Southeast Asia. One possible reason of this could be that
high latitudes where TRMM/PR does not cover.
the precipitation process in these regions is mainly
Other all conditions were same between two
governed by large circulation induced by sea surface
ensembles, where climatic condition was initialized with
conditions,
and
the
effect
of
land
surface
is
National Centers for Environmental Prediction/National
comparatively lower.
Center for Atmospheric Research (NCEP/NCAR) daily
Figure8 indicates averaged soil moisture difference in
reanalysis
and
observed
monthly
SST
data
by
three months used as the boundary conditions in the two
with direct use of satellite-based soil moisture especially
over the tropical land where transferred soil moisture
information was given. In South America and the
southern part in Africa, regions where simulated
precipitation errors have decreased in N98 and the places
of large soil moisture difference were corresponded.
Fig.7 Comparison of three months (JJA) precipitation
In this research, soil moisture by TRMM/PR was
error where error was over 50mm. Light gray
applied to GCM seasonal simulation as a boundary
indicates regions where the error in N98 is lower.
condition. Utilization of such kind of data as for initial
Dark color indicates where error in T98 is lower.
condition of seasonal predictions and application for 4
dimensional data assimilation are highly expected as
future studies.
References
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Fig.8 Average soil moisture difference in three months
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367-385
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IGPO (1995): Global soil wetness project. Technical
experiments. Large differences are seen in south part in
report, International GEWEX Project Office, Silver
Africa, most part in South America, India, and northeast
Spring, MD 20910.
part in Australia. In some regions, precipitation
Numaguti A., S. Sugata, M.Takahashi, T. Nakajima ans
improvements seen in Figure 7 corresponded to large
A. Sumi, 1997: Study on the Climate System and
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Mass Transport by a Climate Model, CCSR’s
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5. Conclusion
Simple Biosphere model (SiB) for use within general
Soil moisture in deeper soil was estimated from
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surface soil moisture with phase correction and a linear
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