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ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 30, NO. 5, 2013, 1373–1386
Effect of Implementing Ecosystem Functional Type Data
in a Mesoscale Climate Model
Seung-Jae LEE
∗ 1
,E.HugoBERBERY
2
, and Domingo ALCARAZ-SEGURA
3
1
Complex Systems Science Laboratory, Department of Landscape Architecture and
Rural Systems Engineering, Seoul National University, Seoul, Korea
2
Department of Atmospheric and Oceanic Science, University of Maryland, College Park,
Maryland,U.S.A.
3
Department of Botany, University of Granada, Granada, Spain
(Received 7 July 2012; revised 7 December 2012; accepted 10 December 2012)
ABSTRACT
In this paper, we introduce a new concept of land-surface state representation for southern South Amer-
ica, which is based on “functional” attributes of vegetation, and implement a new land-cover (Ecosystem
Functional Type, hereafter EFT) dataset in the Weather and Research Forecasting (WRF) model. We found
that the EFT data enabled us to deal with functional attributes of vegetation and time-variant features more
easily than the default land-cover data in the WRF. In order to explore the usefulness of the EFT data in
simulations of surface and atmospheric variables, numerical simulations of the WRF model, using both the
US Geological Survey (USGS) and the EFT data, were conducted over the La Plata Basin in South America
for the austral spring of 1998 and compared with observations. Results showed that the model simulations
were sensitive to the lower boundary conditions and that the use of the EFT data improved the climate sim-
ulation of 2-m temperature and precipitation, implying the need for this type of information to be included
in numerical climate models.
Key words: Ecosystem Functional Type, WRF, land cover, climate simulation
Citation: Lee, S.-J., E. H. Berbery, and D. Alcaraz-Segura, 2013: Effect of implementing ecosystem func-
tional type data in a mesoscale climate model. Adv. Atmos. Sci., 30(5), 1373–1386, doi: 10.1007/s00376-
012-2143-3.
1. Introduction
In land–atmosphere interactions that involve ex-
change of the heat, moisture, and momentum between
them, vegetation provides important information to
establish the partitioning of surface-sensible and la-
tent heat fluxes, and affects the near-surface mass and
wind variables. Values of 2-m temperature, 2-m spe-
cific humidity, and 10-m wind are related to vegeta-
tion characteristics such as albedo, roughness length,
leaf area index, vegetation fraction, and others. Influ-
ences of vegetation on the near-surface atmosphere are
transferred to the planetary boundary layer and even
to higher levels in the free atmosphere through tur-
bulence processes and large-scale atmospheric circu-
lations. Hence, inaccurate information on vegetation
can give rise to biases in a model simulation, particu-
larly for near-surface, boundary layer, and eventually
the entire atmosphere. Therefore, it is hypothesized
that the realistic specification of land-surface charac-
teristics is important for the optimal performance of
the numerical model.
Several efforts have been made to replace the exist-
ing land-cover data, which were defined for relatively
short periods, with recent satellite-derived land-cover
data in numerical models. Those efforts were carried
out in state-of-the-art numerical models such as the
Weather and Research Forecasting (WRF) model. For
example, the WRF model uses 17-category Moderate
Resolution Imaging Spectroradiometer (MODIS) land-
∗
Corresponding author: Seung-Jae LEE, seungjaelee@snu.ac.kr
© China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric
Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2013
1374 USING ECOSYSTEM FUNCTIONAL TYPE DATA IN REGIONAL CLIMATE MODELING VOL. 30
cover data in addition to the traditional 24-category
US Geological Survey (USGS) land-cover data.
However, both USGS and MODIS datasets are
based on a land-cover classification mainly dictated
by “structural” attributes of vegetation, and have a
high inertia to rapid environmental changes, for ex-
ample, changes in land cover due to forest fires or
in vegetation health due to insect infestations and
droughts. To overcome this drawback, Paruelo et al.
(2001) and Alcaraz-Segura et al. (2013) proposed a
new land-cover classification based on ecosystem pro-
cesses, called ecosystem functional types (EFT). The
EFTs are patches of the land surface with similar car-
bon gain dynamics and, hence, are entirely based on
“functional” attributes of vegetation describing the ex-
change of energy and matter between the land surface
and the overlying atmosphere.Since EFTs can be up-
dated yearly, they allow to deal more effectively with
the time-variant features of the land cover, which oth-
erwise cannot be detected using the time-fixed USGS
land-cover types.
Because the EFT dataset has not yet been incorpo-
rated in any numerical weather or climate models, the
primary objectives of this study were to implement it
in the WRF model as a new terrestrial boundary con-
dition and to estimate its impact on the climate and
hydrology of the region. To fulfill these objectives,
numerical simulations of the WRF model using both
USGS and EFT data, were carried out for the aus-
tral spring of 1998 and compared with observational
data. As a first step, the EFTs were defined regionally,
not globally. Therefore, rather than providing a new
global land-cover map, the present study focused on
southern South America region that has been subject
to significant land-cover changes since the European
settlement, particularly in recent decades. The final
objective of this work was to investigate the physical
mechanisms by which regional land-cover changes gave
rise to changes in regional precipitation.
In section 2, the WRF model is introduced and
discussed briefly, along with a description of the EFT
data. Section 3 presents results of model simulations
and their analysis for surface prognostic and diagnos-
tic variables. In section 4, the model simulations are
compared with observational data, and section 5 sum-
marizes the work of this paper and presents the con-
clusions.
2. Model and data
2.1 The WRF model configuration
The WRF modeling system (Advanced Research
WRF) is a nonhydrostatic and primitive-equation
model with state-of-the-art physics options to parame-
terize subgrid-scale processes and multiple nesting ca-
pabilities, in order to increase the resolution over an
area of interest (Michalakes et al., 2001). Since its de-
velopment, this model has been widely used as a com-
munity numerical model and applied in many fields in-
cluding weather forecasting, climate simulations, and
air pollution studies. It is suitable for use in a broad
range of applications across scales ranging from meters
to thousands of kilometers. The Advanced Research
WRF, version 3.1.1, was used in this study. Figure
1 shows the configurations of the model domain and
the mother domain, which cover the southern South
America and the southwestern Atlantic Ocean. The
Andes mountain range is located along the west coast
and its average height is about 4 km. The Brazilian
Highlands along the central east coast and relatively
low lands were formed between the two high terrain
features. The boundaries of the La Plata Basin (LPB)
are also indicated in the figure.
The grid interval for the mother (nested) domain
was 36 km (12 km), with 27 vertical levels from the
surface to a height of 10 hPa. National Centers for
Environmental Prediction (NCEP) / National Center
for Atmospheric Research (NCAR) Reanalysis data
(Kalnay et al., 1996) were used for initial and 6-hourly
boundary conditions. We used a 180-s time step on
the coarse-grid domain, with intervals of progressively
shorter time steps on the inner grid.
In the WRF model, numerical simulation of precip-
itation is very sensitive to the choice of model physical
parameterizations. Lee (2010) conducted diverse nu-
merical experiments and suggested an optimal combi-
Fig. 1. Domain configuration of the WRF model used in
this study. Mother and nested domains have horizontal
resolutions of 30 and 10 km, respectively. Contour inter-
vals for topography are indicated at the bottom (units:
km).
NO. 5 LEE ET AL. 1375
nation of the model physical processes over southern
South America. In our experiment, we adopted that
combination, which consisted of the Dudhia short-
wave radiation scheme (Dudhia, 1989), the RRTM
longwave schemes (Mlawer et al., 1997), the Noah
land-surface model (Chen and Dudhia, 2001), the
Mellor–Yamada–Janji´c boundary layer scheme (Janji´c,
1990, 1996, 2002), the Monin–Obukhov–Janji´c surface
layer scheme (Janji´c, 1996, 2002), the Betts–Miller–
Janji´ccumulus scheme (Janji´c, 1994, 2000), and the
WRF Single Moment six-class microphysics (Hong and
Lim, 2006).
2.2 Land-cover data and experimental design
The WRF model has two default land-cover
datasets. The first one is based on the USGS global
1-km land-cover map (Anderson et al., 1976) produced
from the National Oceanic and Atmospheric Admin-
istration (NOAA)’s Advanced Very High Resolution
Radiometer (AVHRR) measurements (Loveland and
Belward, 1997) from 1992 to 1993. Surface properties
such as vegetation and soil moisture data were pre-
scribed following 24 unique USGS land-use categories
with different physical properties, including surface
albedo, moisture availability, emissivity, and rough-
ness values assigned to each category. The second
one is based on data from the National Aeronautics
and Space Administration (NASA)’s MODIS measure-
ments and was implemented by Yucel (2006). The
MODIS land-cover dataset had 17 USGS land-cover
types, which were translated from the International
Geosphere–Biosphere Program (IGBP) classes. Both
the AVHRR and the MODIS datasets were main based
on the “structural” attributes of vegetation.
Alcaraz-Segura et al. (2013) described the concept
of EFTs based on “functional” attributes of vegetation
related to the carbon gains dynamics. In this work, we
implemented the EFTs in the WRF model for southern
South America, to show that they can be used as a new
classification of land cover. Sixty-four EFTs were iden-
tified using three descriptors of carbon gain dynamics
derived from the seasonal curves of Normalized Differ-
ence Vegetation Index (NDVI), a surrogate of primary
production. These three descriptors of carbon gain dy-
namics were annual mean (estimator of primary pro-
duction), seasonal coefficient of variation (indicator of
seasonality), and date of maximum NDVI (descriptor
of phenology) (Alcaraz-Segura et al., 2013). For this,
Fig. 2. Land-use/land-cover maps used for CNTL. Left panels show the mother and nested domains.
1376 USING ECOSYSTEM FUNCTIONAL TYPE DATA IN REGIONAL CLIMATE MODELING VOL. 30
we used the 1982–1999 AVHRR-LTDR NDVI dateset
following the approach described in Alvaraz-Segura et
al. (2013). The 64 EFT types were not translated into
the existing USGS categories; rather, the surface pa-
rameters were mapped to each EFT category for the
period 1992–93 and converted to the format required
in the WRF model. EFTs change from year to year,
and so do their estimated surface parameters; inclu-
sion of this fact was the main difference between this
study and some previous studies (Kurkowski et al.,
2003; Yucel, 2006).
Figure 2 displays the USGS land-cover map derived
from the AVHRR data, for the 30-and 10-km model
domains. The USGS terrestrial datasets with resolu-
tions of 10
and 5
were used for the coarse and fine
domains, respectively. In the figure, out of the 24 land-
cover types, about 10 types can be seen in both do-
mains; somewhat detailed land-cover distribution can
be seen at a finer resolution. Figure 3 displays the me-
dian EFT map for the 1982–1999 period, derived from
the LTDR-NDVI data, in the model domains. Unlike
Fig. 2, all 64 EFT categories are seen in both domains,
with larger (smaller) values indicating higher (lower)
productivity of the surface vegetation.
WRF simulations were performed using two kinds
of land-cover datasets. All simulations were conducted
from 0000 UTC 1 September 1998 to 0000 UTC 1 De-
cember 1998, with NCEP/NCAR reanalysis data pro-
viding initial and 6-hourly boundary conditions. Ex-
perimental results for precipitation and 2-m air tem-
perature were compared with the observational data.
The control run (CNTL) represented the model simu-
lation with the default USGS land-cover map (Fig. 2),
which included various kinds of human-originated veg-
etation types(dry cropland, irrigated cropland, and
their mixture) as well as natural vegetation (Savanna,
evergreen broadleaf forest, and grasslands in the up-
per, middle, and lower parts of LPB, respectively).
In the experimental run (EFT), the 24 USGS land-
cover types were replaced by the 64 EFTs shown in
Fig. 3. In order to understand the changes to the mech-
anisms that induced precipitation, we examined the
surface fluxes, near-surface temperature and winds,
convective instability, and moisture transports feeding
the region.
2.3 Observational data
To evaluate the model’s performance, independent
measurements of precipitation were obtained from
satellite estimates, specifically from the Tropical Rain-
fall Measurement Mission (TRMM; Kummerow et al.,
1998) rainfall data. TRMM has a 3-hour time interval
and a horizontal resolution of 0.25
◦
×0.25
◦
.
Given the emphasis of this research on surface pro-
Fig. 3. The same as Fig. 2, but for EFT maps. Each of
the 64 EFTs was assigned a code based on two letters
and a number, i.e., 1: Aa1; 2: Aa2; 3: Aa3; 4: Aa4;
5: Ab1; ... ; 63: Dd3; and 64: Dd4. The first letter of
the code (capital) corresponds to the NDVI mean level,
ranging from A to D for low to high (increasing) pro-
ductivity. The second letter (small) shows the seasonal
coefficient of variation, ranging from a to d for high to
low (decreasing) seasonality. The numbers indicate the
season of maximum NDVI (1-4: spring, summer, autumn
and winter).
cesses, there was also interest in assessing the perfor-
mance of the model’s 2-m temperature. The monthly
surface temperature dataset, at a horizontal resolution
of 0.5
◦
×0.5
◦
(Brohan et al., 2006), from the Climate
Research Unit (CRU) of the University of East An-
glia was employed for this study. It is known that the
region has a sparse observational network, and con-
sequently the quality of this product remains to be
NO. 5 LEE ET AL. 1377
further assessed.
3. Effects of new land-cover data on the nu-
merical model simulation
3.1 Surface physical properties
Figure 4 displays the surface albedo and roughness
length fields in CNTL and their changes when using
EFTs (EFT minus CNTL). Surface albedo was found
to be low over evergreen broadleaf forest regions in the
central LPB and the Amazonia (Fig. 4a). The albedo
over the Andes mountains was 25% higher than that
over the central LPB and Amazonia, with its maxi-
mum being located at a latitude of to 45
◦
–50
◦
S. In
general, the characteristics of the surface roughness
length were found to be opposite to those of albedo.
The value of roughness over the evergreen broadleaf
forest regions was observed to be higher than that over
the other regions (Fig. 4c).
Hereafter, the analysis focused on the interior of
the LPB, which is the study interest area. To sim-
plify the analysis, two rectangle boxes were drawn in
the panels: the upper box (28
◦
–17
◦
S, 66
◦
–46
◦
W) was
called the “northern LPB” and the lower one (37
◦
–
28
◦
S, 65
◦
–51
◦
W) the “southern LPB”. The latitude
28
◦
S was properly located so that the northern LPB
contained areas in the central eastern LPB with in-
creased albedo and decreased roughness length, while
the southern LPB encompassed areas with decreased
albedo and increased roughness length. We did not
shrink the upper box to include the increased albedo
and decreased roughness only, because we wanted the
northern LPB to be covered as much as possible by the
rectangular box, so that all areas of the basin could be
mentioned. The same two boxes had also been used
in the works of Lee and Berbery (2012). It can be
seen that the maximum increase (decrease) in surface
albedo (surface roughness) occurred in the northern
LPB, while the maximum decrease (increase) in sur-
face albedo (surface roughness) occurred in the south-
ern LPB (Figs. 4b and d, respectively).
Fig. 4. Three-month (SON 1998) averaged (a) CNTL and (b) EFT minus CNTL fields for surface
albedo (%). Parts (c) and (d) represent the same fields for surface roughness length (units: cm).
1378 USING ECOSYSTEM FUNCTIONAL TYPE DATA IN REGIONAL CLIMATE MODELING VOL. 30
3.2 Surface heat fluxes and near-surface at-
mospheric variables: 5-day running aver-
age
Figures 5a and b present the time series of the dif-
ferences (EFT minus CNTL) in the area-averaged sur-
face heat fluxes over the two regions. In the northern
LPB, the replacement of the USGS land-cover map by
the EFTs led to a decrease in sensible heat fluxes, due
to the increased surface albedo, and an increase in la-
tent heat fluxes. The southern LPB exhibited opposite
behavior in the sensible and latent heat fluxes time se-
ries, and the signal was relatively weak compared with
the northern LPB.
Changes in the surface heat fluxes are expected
to alter near-surface atmospheric variables. This can
be seen in Figs. 5c and d, which represent the two
area-averaged time series of the difference (EFT mi-
nus CNTL) in 2-m temperature and 2-m specific hu-
midity. The decreased (increased) sensible heat fluxes
Fig. 5. Time series of (a) sensible heat fluxes (W m
−2
), (b) latent heat fluxes
(W m
−2
), (c) 2-m temperature (
◦
C), (d) 2-m water vapor mixing ratio (g kg
−1
),
and (e) 10-m wind speed (m s
−1
). All are five-day running averaged, and the red
(blue) colored line in parts (c) and (d) denotes the northern (southern) LPB.
NO. 5 LEE ET AL. 1379
in the northern (southern) LPB gave rise to a cooling
of up to 0.5
◦
C (a warming of below 0.8
◦
C) near the
surface (Fig. 5c). On the other hand, the increase in
latent heat fluxes in the northern LPB was associated
with an increase in specific humidity near the surface
(Fig. 5d). In the southern LPB, the latent heat fluxes
were reduced, but the corresponding 2-m specific hu-
midity showed almost neutral changes because of its
dependency on near-surface temperature and winds,
in addition to latent heat fluxes.
Roughness length was reported to play a crucial
role in determining near-surface wind direction and
speed. The reduction of the roughness length in the
northern LPB region gave rise to a reduction of 10-
m wind speed in both the northern and the southern
LPB regions (Fig. 5e). The maximum increase in sur-
face roughness occurred in the southern LPB region; as
a consequence a larger decrease in near-surface winds
was reported in that region. The difference in wind
speed was noted not to exceed 1 m s
−1
in magnitude
in an original time series without 5-day moving aver-
age, but the effect was consistent and lasted during
most of the model integration process. The 3-month
mean 10-m winds (Figs. 6a, c) showed strong easterly
components in the CNTL simulation, especially along
the eastern boundary of the basin. However, westerly-
component winds were created by the use of EFTs,
especially in the southern LPB (Figs. 6b, d). While
the changes in near-surface winds were observed at a
height of 10 m, they could modify, although slightly,
the higher-level wind patterns.
3.3 Local thermodynamic forcing
The convective available potential energy (CAPE)
and convective inhibition (CIN) are two important pa-
rameters that are widely used to analyze the local pro-
cesses that account for the development of convective
precipitation. CAPE (CIN) can be regarded as the
Fig. 6. Three-month (SON 1998) averaged (a) CNTL and (b) EFT minus CNTL for 10-m
wind vector fields (m s
−1
), and (c) CNTL and (d) EFT minus CNTL for 10-m wind speed;
contour intervals in (d) are 0.1 m s
−1
.
1380 USING ECOSYSTEM FUNCTIONAL TYPE DATA IN REGIONAL CLIMATE MODELING VOL. 30
thermodynamic forcing facilitating (inhibiting) local
convection and precipitation (Bluestein, 1993; Barlow
et al., 1998). In this study, CAPE (CIN) was defined
as the amount of positively (negatively) buoyant en-
ergy in the vertical sounding of temperature, so both
have positive signs. To simplify the analysis, the max-
imum values from the vertical profile of CAPE and
CIN at each horizontal point were used, which were
named as MCAPE and MCIN, respectively.
Figure 7 displays the simulated 3-month average of
the MCAPE and MCIN fields. It can be seen from
Figs. 7a and b that in general there were relatively
large MCAPE and MCIN over Paraguay in the CNTL.
The increase in MCAPE in the northern LPB region
was associated with an increase in the latent heat flux,
as shown in Fig. 4b. The difference field (EFT minus
CNTL) showed that use of the new land-cover map
would increase MCAPE in the northern LPB region,
including Paraguay, while decrease it slightly in the
southern LPB (Fig. 7c). As shown in Fig. 7d, use of
the new landscape data decreased MCIN in the south-
ern LPB but had neutral impact in the northern LPB.
Because large values of CAPE and small values of
CIN are favorable conditions for local convection and
precipitation, the increased CAPE over the northern
LPB implies a higher possibility of the development of
mesoscale convective systems in the region, especially
around Paraguay.
3.4 Large-scale horizontal moisture flows
The local thermodynamic instability was not the
only factor affecting precipitation in the LPB region.
The change in precipitation was also a consequence
of changes in moisture transport into the LPB. Such
Fig. 7. Three-month (SON 1998) averaged CNTL for (a) maximum CAPE (MCAPE) and (b) max-
imum CIN (MCIN) (units: J kg
−1
). Parts (c) and (d) represent EFT minus CNTL for MCAPE
and MCIN, and contour intervals of 10 and 2 J kg
−1
, respectively.
NO. 5 LEE ET AL. 1381
changes can be inferred based on the discussions in the
previous section (Fig. 6) that the reduction of near-
surface winds was expected to influence higher-level
moisture flows and distributions.
Figure 8 displays the 3-month averaged moisture
flux, vertically integrated from 1000 to 300 hPa, and
the corresponding convergence and divergence fields.
The CNTL (Fig. 8a) showed features that were consis-
tent with previous climatologies derived from global
reanalyses (Labraga et al., 2000; Berbery and Barros,
2002; Doyle and Barros, 2002; Marengo et al., 2004),
and with month- or season-long simulations (Collini
et al., 2008; Lee and Berbery, 2012). The northwest-
ern LPB, including Bolivia and Paraguay, exhibited a
large southeastward moisture transport (Fig. 8b), sup-
plying moisture into the LPB from the Amazon basin.
As can be seen from Figs. 8c and d, use of the
new land-cover map modified regional moisture trans-
ports. From Fig. 8c, it is clear that westerly moisture
flows were produced in the southern LPB and most
of them turned toward north in the northern LPB.
Some of the westerly moisture maintained an east-
ward flow and created relatively small perturbations
in moisture fluxes over the South Atlantic Ocean. The
Brazilian Highlands seemed to play an important role
in the splitting of the westerly moisture flow. The
westerly (southerly) moisture fluxes in the southern
(northern) LPB produced convergence of moisture flux
in the southern LPB (Fig. 8d). In the northern LPB,
both moisture flux convergence and moisture flux di-
vergence occurred, but on average moisture flux con-
vergence was slightly larger, with its maximum be-
ing located in northern Paraguay. All these features
suggest that precipitation in the southern LPB would
have been supported mainly by the large-scale mois-
ture flux convergence rather than by local thermody-
namic forcing.
3.5 Precipitation
Figures 9a and b display the 3-month averaged to-
tal precipitation in CNTL and the difference (EFT
minus CNTL). The CNTL precipitation field showed
Fig. 8. Three-month (SON 1998) averaged CNTL for (a) vertically integrated moisture fluxes in
kg (m s)
−1
and (b) their convergence (mm d
−1
). Parts (c) and (d) represent, respectively, the fluxes
and their convergence for EFT minus CNTL. Shaded areas in (a) and (c) are model terrain height
(km), and the moisture flux convergence (divergence) is positive (negative) in (b) and (d).
1382 USING ECOSYSTEM FUNCTIONAL TYPE DATA IN REGIONAL CLIMATE MODELING VOL. 30
overall consistency with the vertically integrated mois-
ture flux convergence and divergence fields (Fig. 8b) in
both the northern and the southern LPB regions. The
regions of increased (decrease) precipitation were col-
located with vertically integrated moisture flux conver-
gence (divergence). The difference field (Fig. 9b) also
corresponded well to the moisture flux convergence
field (Fig. 8d). On the other hand, similar compar-
isons in case of the MCAPE difference field (Fig. 7c)
showed a good correspondence over the northern LPB
but not for the southern LPB portion.
The difference in total precipitation (Fig. 9b) be-
tween CNTL and EFT can be understood as the com-
bined effects of both local (subsection 3.3) and large-
scale (subsection 3.4) forcing induced by the use of
the new land-cover map. Figures 9c and d display
area-averaged time series of the difference (EFT minus
CNTL) in convective and nonconvective precipitations
over the northern and southern LPB regions. The pre-
cipitation type in northern LPB was clearly different
from that in the southern LPB. The northern LPB ex-
hibited increases in both convective and nonconvective
precipitations with time. The southern LPB showed
an increase in nonconvective precipitation similar to
that in case of the northern LPB, but almost no change
in convective precipitation. This means that, the use
of new land-cover map increased both local thermo-
dynamic and large-scale dynamic forcing in the north-
ern LPB, while only a large-scale dynamic forcing in-
creased in the southern LPB.
Fig. 9. Three-month (SON 1998) averaged (a) CNTL and (b) EFT minus CNTL for surface
precipitation (mm d
−1
). Time series of 3-month averaged fields of the difference (EFT minus
CNTL) in (c) convective precipitation and (d) nonconvective precipitation (units: mm d
−1
).
All are 5-day running averaged, and the red (blue) colored line in (c) and (d) denotes the
northern (southern) LPB.
NO. 5 LEE ET AL. 1383
4. Comparison with observations
Figure 10 shows the time series of simulated minus
observed total accumulated precipitation over the LPB
during the austral spring of 1998. Overall, the CNTL
showed smaller-than-observed precipitations, except in
September. The difference between CNTL and ob-
served precipitations became clearer with time, reach-
ing about 20–30 mm by the end of November. How-
ever, use of the EFT data in the model increased accu-
mulated precipitation, and the gap between EFT and
CNTL data increased with time. This implies that
use of the new land-cover map can reduce biases in
precipitation, which are relevant for the warmer sea-
son, with the potential improvement of summer season
precipitation.
Figure 11 displays the 1-month averaged precipita-
Fig. 10. Time series of observed, CNTL, and EFT accu-
mulated precipitations (mm).
Fig. 11. Averaged precipitation (mm d
−1
) in November 1998 for (a) observation, (b) CNTL, (c)
EFT, and (d) EFT minus CNTL.
1384 USING ECOSYSTEM FUNCTIONAL TYPE DATA IN REGIONAL CLIMATE MODELING VOL. 30
tion for November 1998. First, with the introduction
of the new land-cover data, the areas of total precip-
itation increased by over 1 mm in the interior LPB.
Also, the horizontal distribution of precipitation was
improved substantially and the magnitude of precipi-
tation was closer to the TRMM observation data.
Figures 12a and b display 3-month averaged field
of observed and simulated 2-m temperature, respec-
tively. The observations showed maximum temper-
atures around 18
◦
S in latitude and gradients in the
northeast–southwest directions (Fig. 12a). The CNTL
simulation reproduced such patterns well and, in gen-
eral, showed reasonable consistency with the spatial
distribution of observed temperature. Figure 12c dis-
plays the difference between CNTL and observation
data. The CNTL simulation tended to have cold biases
along the boundary of the LPB, while having warm
biases in southern Paraguay and north of Paraguay
(Fig. 12c). As can be seen from Fig. 12d, the warm
bias over the southern Paraguay and the cold bias over
Uruguay were reduced with the use of the new land-
cover map.
Temperature-measuring stations are even sparser
than the precipitation-measuring ones, and this
dataset might have unreliable values over large un-
gauged regions; thus, the evaluation should not be
more than qualitative. In order to see how monthly
mean error of the 2-m temperature evolves with time,
biases in 2-m temperature were calculated in the rect-
angular box denoting low-altitude regions within the
Fig. 12. Three-month (SON 1998) averaged 2-m air temperature (
◦
C) for (a) observation, (b)
CNTL, (c) CNTL minus observation, and (d) EFT minus observation.
NO. 5 LEE ET AL. 1385
Fig. 13. Biases in the 2-m temperature (
◦
C) over the
box area in Fig. 12d for each month during SON 1998.
LPB, which were considered to exhibit better-quality
observations than the mountainous regions within the
basin. Figure 13 shows the difference between sim-
ulated and observed mean 2-m temperatures over the
low-altitude region for each month in the austral spring
of 1998. All the 3 months had biases ranging from
about −0.7
◦
Cto0.4
◦
C, with October showing the
largest difference among them. The CNTL run had
negative differences in the first two months and posi-
tive difference afterward. The biases of the EFT run
were of the same sign as, but smaller amplitude than,
those of CNTL in all the 3 months. Use of EFT data
instead of USGS data in the model reduced biases
in monthly mean 2-m temperature over the LPB by
54% (an average of 89%, 14%, and 60% for the three
months) in spring 1998. However, as these results were
based on single simulations, the statistical significance
could not be determined, and thus they should be con-
sidered preliminary.
5. Summary and conclusions
Accurate specification of the land-surface states is
important in numerical weather and climate predic-
tion, and simulation studies in general. Recent efforts
have focused on substituting the existing ground-based
land-cover data, which are estimated by a multiyear
climatology, with satellite-based land-cover data in nu-
merical models. Those efforts were realized in state-
of-the-art numerical models. Typically, ground-based
and satellite-derived datasets use structural classifica-
tions of vegetation, and are insensitive to rapid and
complex environmental changes.
In this paper, a new concept of a functional land-
cover classification (EFTs) was introduced in a numer-
ical mesoscale model. The EFTs are patches of the
land surface with similar carbon gain dynamics and
consist of 64 functional categories of vegetation. A
surface physical parameter set, linked with each EFT,
was incorporated into the model without mapping to
existing structural categories. The effect of this incor-
poration on model simulations was investigated at the
LPB region in South America for the austral spring of
1998.
Compared with the existing USGS land-cover
dataset, the use of the new dataset showed substan-
tial changes in surface albedo and roughness length
fields. Such changes were found to modify surface
fluxes and near-surface atmospheric variables, result-
ing in changes in local thermodynamic forcing and
large-scale moisture flow patterns.
Through comparisons with observations, it was
found that the use of the new data improved model
performance for precipitation and 2-m temperature
simulations in terms of magnitude and spatial distri-
bution. These encouraging first results indicate the
importance of the EFT information and the need for
weather and climate models to incorporate, in some
form, the functional changes in vegetation properties
to increase the accuracy of forecasts and simulations.
The results reported here are based on limited sim-
ulations to present alternative ways of representing
the surface parameters, but more work needs to be
done to confirm the robustness of the results. As pre-
sented, these results should be viewed as a proof of
concept rather than as definitive conclusions. It re-
mains for subsequent studies to investigate whether
this improvement for the austral spring of 1998 is true
for other seasons and years.
Acknowledgements. We are grateful to two anony-
mous reviewers for many valuable comments. This research
was supported by the Korea Meteorological Administration
Research and Development Program under Grant CATER
2012–3030. This research was supported by NASA Grant
NNX08AE50G, NOAA Grant NA09OAR4310189, and the
Inter American Institute for Global Change Research (IAI)
through the Cooperative Research Network (CRN)-2094.
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