examining wrf’s sensitivity to contemporary land-use
TRANSCRIPT
Examining WRF’s Sensitivity to Contemporary Land-Use Datasets across theContiguous United States Using Dynamical Downscaling
MEGAN S. MALLARD, TANYA L. SPERO, AND STEPHANY M. TAYLOR
National Exposure Research Laboratory, Environmental Protection Agency, Research Triangle Park, North Carolina
(Manuscript received 21 November 2017, in final form 31 August 2018)
ABSTRACT
Land-use (LU) representation plays a critical role in simulating air–surface interactions that affect mete-
orological conditions and regional climate. In the Noah LSMwithin theWRFModel, LU categories are used
to set the radiative properties of the surface and to influence exchanges of heat, moisture, and momentum
between the air and land surface. Previous literature examined the sensitivity ofWRF simulations to LUusing
short-term meteorological modeling approaches. Here, the sensitivity to LU representation is studied using
continental-scale dynamical downscaling, which typically uses longer temporal and larger spatial scales. Two
LU datasets, the U.S. Geological Survey (USGS) dataset and the 2006 National Land Cover Dataset
(NLCD), are utilized in 3-yr dynamically downscaledWRF simulations over a historical period. Precipitation
and 2-m air temperature are evaluated against observation-based datasets for simulations covering the
contiguous United States. The WRF-NLCD simulation tends to produce lower precipitation than the
WRF-USGS run, with slightly warmer mean monthly temperatures. However, WRF-NLCD results in more
notable increases in the frequency of hot days [i.e., days with temperature.908F (32.28C)]. These changes areattributable to reductions in forest and agricultural area in the NLCD relative to USGS. There is also subtle
but important sensitivity to the method of interpolating LU data to theWRF grid in the model preprocessing.
In all cases, the sensitivity resulting from changes in the LU is smaller than model error. Although this
sensitivity is small, it persists across spatial and temporal scales.
1. Introduction
Accurate representation of air–surface exchanges of
heat, moisture, and momentum is critical for simulating
regional climate and meteorological conditions. In the
WRF Model, which is commonly used for both regional
climate and meteorological simulations, many of the phys-
ical processes that affect air–surface exchanges are a func-
tion of land use or land cover (hereinafter LU), which is a
prescribed field in WRF. Within each WRF grid cell, LU
affects radiative properties, roughness length, leaf area in-
dex (LAI), and near-surface processes that influence fluxes
of heat, moisture, and momentum between the air and
surface. The surface fluxes affect near-surface tempera-
tures, evaporation, PBL height, near-surface winds, and
precipitation. Thesemeteorological fields strongly influence
pollutant concentrations through atmospheric trans-
port and mixing, chemical reaction rates, and de-
position, all of which have implications for ecosystem
services and human health.
The sensitivity of WRF to LU changes has been ex-
amined previously with short-duration, high-resolution
meteorological case studies that focused on either specific
urban centers or regions where urbanization is significant.
These studies reinforced that increased urbanization
generally produces increased daytime and nighttime
temperatures in areas with ample rainfall, as shown by
Lopez-Espinoza et al. (2012) when simulating a 120-h
period over central Mexico and by Cheng et al. (2013)
when using various LU datasets to drive 3-km WRF
simulations of a 4-day period over Taiwan. The reduction
in vegetation from urbanization also promotes increased
sensible heat and decreased latent heat from the surface,
as shown in Li et al. (2013) when simulating a convective
event in the Baltimore, Maryland–Washington, D.C.,
area. Li et al. additionally concluded that WRF’s pre-
cipitation is comparably sensitive to various LUandurban
physics choices as it is to the microphysics parameteriza-
tion. LU data also affect wind speeds and circulation
patterns as roughness length increases (e.g., Li et al. 2013;
Kamal et al. 2015). The LU sensitivity studies cited above
often included LU datasets that are not available in publicCorresponding author: MeganMallard, [email protected]
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DOI: 10.1175/JAMC-D-17-0328.1
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versions of WRF and therefore are not easily accessible
or extendable to continental scales.
The meteorological studies assessingWRF’s sensitivity
to LU change were conducted at spatial and temporal
scales that are typically finer than those used in
continental-scale downscaling applications. Within con-
strained geographic areas, dynamical downscaling can be
more readily conducted at fine resolutions (i.e., 4–1km)
when simulations of atmospheric phenomena require the
use of those scales (e.g., Zhang et al. 2016; Wootten et al.
2016). Continental-scale dynamical downscaling at fine
spatial scales is computationally intensive and is limited
to research groups with access to preeminent computing
resources (e.g., Gao et al. 2012; Liu et al. 2017). In gen-
eral, dynamical downscaling does not use such fine hori-
zontal grid spacing because the simulations cover much
longer time periods and the computational requirements
are likely to be prohibitive for most groups for the fore-
seeable future (Wobus et al. 2017).
Consequently, regional-scale and continental-scale
dynamical downscaling is often conducted with 50–12-km
horizontal grid spacing for periods ranging from sea-
sons to decades (e.g., Otte et al. 2012; Casati et al. 2013;
Darmenova et al. 2013; Herwehe et al. 2014; Mallard
et al. 2014; Zhang et al. 2015; Bieniek et al. 2016; Spero
et al. 2016; Li et al. 2017; Bruyère et al. 2017). Down-
scaling simulations have been leveraged by collaborative
communities to produce regional climate ensembles, such
as in the North American Regional Climate Change
Assessment Program (NARCCAP; Mearns et al. 2012)
and the Coordinated Regional Climate Downscaling
Experiment (CORDEX; Giorgi et al. 2009), both of
which use ;50-km domains over North America.
Continental-scale downscaling simulations are critical
for examining potential impacts of climate change
across the Nation. For example, in the Third National
Climate Assessment, Walsh et al. (2014) incorporated
ensemble members from NARCCAP to project future
climatic conditions across the contiguous United States
(CONUS) between 2040 and 2070. Similarly, in the
Climate and Health Assessment, Fann et al. (2016) used
36-km dynamically downscaled projections from two
scenarios to drive air quality projections throughout
the CONUS at 2030. Furthermore, in a technical input
to the Fourth National Climate Assessment, EPA (2017)
used 36-km dynamically downscaled projections to un-
derstand potential implications of climate on air quality
following two scenarios at 2050 and 2090.
This study’s focus on downscaling adds a new perspec-
tive and could provide valuable guidance for continental-
scale applications. Dynamical downscaling presents a
unique challenge in representing the land surface, as
compared to modeling applications that use more limited
temporal and spatial scales. The differences between the
spatial scales of the LU data (ranging from 1km to 30m
for the LU sources used here) and the WRF grid are ex-
acerbated in a continental downscaling application, which
typically uses coarser grid spacing. This study contrasts
the use of two contemporary LU datasets in WRF for
continental-scale dynamical downscaling, where the sen-
sitivity due to different LU datasets can be expected to be
small relative to an evolution on longer multidecadal or
multicentury time scales. Yet the sensitivity of downscaled
simulations to the LU representation of the contemporary
period should be assessed and quantified. A better un-
derstanding of the sensitivity to LU datasets on regional
climate simulations over the contemporary period would
benefit future studies that focus on long-term trends in
anthropogenic LU changes, such as urbanization, agri-
cultural changes, deforestation, and reforestation. Al-
though this study quantifies the change in LUbetween the
two representations, the focus is not on the evolution of
LU over recent decades but rather on assessing the utility
of these LU datasets in current downscaling applications.
Here, WRF runs driven by the U.S. Geological Survey
(USGS) LU data and by the 2006 National Land Cover
Database (NLCD) are contrasted with 3-yr historical
downscaling simulations at 36-km horizontal grid spacing
over the CONUS. An additional simulation demonstrates
the sensitivity to the method used to interpolate LU from
its native resolution to the target model grid. In this study,
2-m air temperature and precipitation from 36-km WRF
simulations are validated against observation-based data
to illustrate the magnitude and pervasiveness of changes
resulting from differences between using the USGS and
NLCD LU datasets. The USGS LU dataset is chosen for
this study because of its longevity inWRF’s preprocessing
systems, as discussed further below. The NLCD LU is
often utilized for WRF-driven air quality modeling ap-
plications (e.g., Ran et al. 2015; Gan et al. 2015, 2016).
Therefore, understanding the effects of its use within
WRF can benefit future projections of pollutant concen-
trations as well as other modeling applications that aim to
protect ecosystem services and human health.
This paper is organized as follows. Section 2 describes
the WRF Model setup, the land-use data that are ex-
amined, and the datasets used for evaluation. Section 3
includes regional and CONUS-wide analyses of pre-
cipitation, 2-m temperature, and surface fluxes. In ad-
dition, section 3 contains a focused analysis of the
southeastern United States, where there are larger dif-
ferences between the previously analyzed fields. Section 3
further includes a brief illustration of the robustness of
these results by using an alternate configuration ofWRF
with different driving data. Section 4 contains our con-
clusions and a brief discussion of the results.
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2. Data and methods
a. WRF simulations
Simulations are conducted with WRF, version 3.8
(Skamarock and Klemp 2008), for 1 October 1987–
1 January 1991, where the first 3 months are a spinup
period and the remaining 3 yr are used for analysis.
Two-way-nested 108- and 36-km domains are used
(Fig. 1). Simulations are driven by the 2.58 3 2.58 R2
reanalysis (Kanamitsu et al. 2002), which serves as a
verifiable proxy for a GCM (e.g., Bowden et al. 2012;
Otte et al. 2012, Bowden et al. 2013; Bullock et al.
2014; Mallard et al. 2014). Here, spectral nudging
(Miguez-Macho et al. 2004) of potential temperature,
horizontal wind components, and geopotential is ap-
plied above the PBL at maximum wavenumber of
5 and 3 in the X and Y directions, respectively, on the
108-km domain and at wavenumbers 4 and 2 in the X
and Y directions on the 36-km domain with nudging
coefficients set to 3 3 1024 s21.
WRF is used with the Kain–Fritsch convective pa-
rameterization scheme (Kain 2004) with radiative ef-
fects of subgrid clouds included (Alapaty et al. 2012;
Herwehe et al. 2014). TheWRF single-moment six-class
microphysics scheme (Hong and Lim 2006) and the
Rapid Radiative Transfer Model for global climate
models (Iacono et al. 2008) were also employed. The
YonseiUniversity scheme (Hong et al. 2006) was used to
simulate processes in the PBL. The Noah land surface
model (LSM) (Chen and Dudhia 2001) was used, in
addition to the revised MM5 Monin–Obukhov surface
scheme (Jimenez et al. 2012), to simulate land-based
processes and air–surface interactions.
b. WRF land-use data
The 24-category USGS LU dataset in WRF is
derived from 1-kmAVHRR satellite observations taken
between April 1992 and March 1993 (Loveland et al.
2000; Sertel et al. 2010). USGS LU has been available
since the initial release of the WRF Preprocessing Sys-
tem (WPS) that accompanied WRF, version 2.2, and
was the only LU dataset available within the WRF
Standard Initialization software that preceded WPS
(NCAR 2002, 2006). USGS LU was the default option
until WRF, version 3.8 (NCAR 2017). The 30-m NLCD
2006 LU dataset used here was introduced in WRF,
version 3.5 (NCAR 2014). It was developed by the
Multi-Resolution Land Characteristics Consortium and
is based on observations from the Landsat-7 Enhanced
Thematic Mapper Plus and Landsat Thematic Mapper
(Fry et al. 2011). Although there are 40 categories
in WRF’s version of the NLCD dataset (called NLCD
and NLCD2006 in the WRF documentation), this
dataset uses the original 20 NLCD categories within the
CONUS andMODIS categories elsewhere (Fig. 2). The
process of merging NLCD and MODIS data from their
original resolutions (30m for NLCD and 1000m for
MODIS) is described by Ran et al. (2010).
Contrasts between the LU fields result from using
different methods to generate the original USGS and
FIG. 1. The WRF 108- and 36-km domains, with the inner domain outlined in black. The nine NCEI U.S. climate
regions are shown as they appear in the WRF-USGS simulation.
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FIG. 2. Dominant LU category within each grid cell of the 36-kmWRF domain for the (top) USGS, (middle)
NLCD, and (bottom) NLCDDEF LU representations.
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NLCD data, as well as differences in the temporal rep-
resentativeness of each dataset. The USGS data were
collected from 1992 to 1993, whereas the NLCD data
were collected in 2006. Because the simulations ana-
lyzed here are valid for 1988–90, the LU in the USGS
may be considered to be more appropriate for these
simulations than the NLCD. However, the focus here is
dynamical downscaling, in which strategies for repre-
senting underlying fields like LU must be practical over
multidecadal historical and future simulations. The LU
in the WRF Model is typically stationary in time—
a ubiquitous assumption where present-day LU data are
also often utilized for future climate simulations (e.g.,
Patricola and Cook 2010; Liang et al. 2012; He et al.
2013; Fann et al. 2015). Therefore, this study will de-
scribe the sensitivity to the LU change and the quality of
the WRF-driven output relative to observed meteoro-
logical conditions rather than evaluating the accuracy of
the LU sources over the simulated period.
In WPS, the ‘‘geogrid’’ program interpolates geo-
graphic data from their native resolution to the target
WRF grid. Several interpolation methods are available,
and the default interpolation method in WPS differs as a
function of the LU data source. WithinWPS, version 3.8,
the default interpolation scheme used with the USGS
data is a four-point bilinear interpolation (‘‘four_pt’’),
whereas a grid-cell averaging technique (‘‘average_
gcell’’) is the default choice for the NLCD. Regardless of
the interpolation scheme, after considering the land–
water mask, the largest fractional LU category in each
grid cell is assigned as the dominant LU type for theNoah
LSM. In this study, it was found that widespread changes
to the dominant LU can be attributed to using different
interpolation schemes. Therefore, in this study the same
interpolation scheme is used to interpolate both datasets
from their native resolutions to theWRF domains so that
the simulations (referred to as WRF-USGS and WRF-
NLCD) can be compared without influence from the in-
terpolation schemes (Fig. 2). The four-point scheme was
chosen because it has been the default option with USGS
LU for many years, while NLCD in WRF and its default
interpolation scheme are much newer and less tested. A
second NLCD-driven simulation is also run in which
NLCD’s default scheme, gridcell averaging, was used.
That simulation, WRF-NLCDDEF, is compared with
WRF-NLCD (which uses four-point interpolation) to
examine the impact on the resulting WRF fields of
changing the LU interpolation scheme (Fig. 2).
The differences between the LU fields are first exam-
ined so that the resulting changes in atmospheric fields
can then be linked to systematic differences in how the
land surface is represented. Direct comparison across all
categories is impossible because of differences in the
categorization systems. Instead, a unified set of consoli-
dated LU categories is constructed to aggregate USGS
and NLCD categories under common themes (Table 1).
The LU shown in Fig. 2 is aggregated to the consolidated
categories and is plotted in Fig. 3. As expected, the gen-
eral distribution and predominance of LU types across
the CONUS is consistent among data sources and in-
terpolationmethods. At 36km, forest LU types dominate
the eastern and northwestern United States, agricultural
LU covers the Midwest, and grass and shrubland extend
over much of the western United States. However, the
spatial transitions between LU types are sensitive to both
the source data and the interpolation scheme (cf. Figs. 2
and 3). WRF-NLCDDEF results in the smoothest ap-
pearance in Fig. 3, with more homogeneity across the
CONUS than WRF-NLCD and WRF-USGS. Overall,
WRF-NLCD is the most heterogeneous. Figure 3 high-
lights the influence of the interpolation scheme, as the
WRF-USGS and WRF-NLCD (which share a common
interpolation scheme) are more alike than WRF-NLCD
and WRF-NLCDDEF (which share the same source
data). The smoother appearance of WRF-NLCDDEF
results from the average_gcell interpolation, wherein, for
each grid box on the target WRF grid, LU from all of the
grid boxes in the source data that are closer to the target
grid cell than any other are averaged to produce the in-
terpolated value (NCAR 2017). By contrast, LU in each
grid cell in the WRF-NLCD and WRF-USGS is calcu-
lated while considering only four points from the source
data. Therefore, the gridcell-averaging technique gener-
ally produces a smoother appearance because averaging
occurs over a larger number of source grid cells.
Figure 4 shows the percentage of all grid cells in the
domain (normalized by the total number of land cells) in
each of the consolidated categories, as well as the dif-
ferences in the percentages. Overall, the largest contrasts
between the WRF-USGS and WRF-NLCD datasets are
in grassland/shrubland (7.0% more in NLCD than in
USGS) and forest (6.1% more in USGS than in NLCD).
The USGS also has more grid cells that are agricultural
land (2.5%) and barren/tundra (3.4%), while the NLCD
LU contains 3.8% more wetland grid cells than USGS.
On the regional scale, the increase in wetlands, grass/
shrubland, and urban grid cells in NLCD at the expense
of forest and agricultural LU is most apparent in the
South and Southeast (Fig. 3). Also, there are more urban
and wetlands areas in the Upper Midwest with NLCD
than in USGS. In addition, a large area of Mexico that
contains various forest types in the USGS is classified as
woody savannah in the NLCD and NLCDDEF. How-
ever, the percentage differences in forest and grass/
shrubland LU remain large relative to changes in the
other categories within the CONUS only (not shown).
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The magnitudes of the differences in the domain-wide
coverages of each of the consolidated categories between
NLCD and NLCDDEF are smaller than those between
NLCDandUSGS, except for changes in forest with 4.7%
more in WRF-NLCDDEF than WRF-NLCD.
c. Observation-based datasets
Model-simulated precipitation is compared with CPC
Unified Precipitation data. Daily CPC precipitation is
available at 0.258 resolution (Chen and Xie 2008). The
use of rain gauge analysis in this product and the
sparseness of gauge data in areas of mountainous terrain
in the western United States can lead to increased
sampling error in those areas (Cui et al. 2017). However,
its accuracy was sufficient to evaluate other global ana-
lyses in that study, and areas of complex terrain are not
emphasized in the present work. Here, the CPC pre-
cipitation totals and are interpolated to the 36-kmWRF
domain to facilitate comparisons using difference fields.
Because CPC originates on a comparable resolution
TABLE 1.Assignment ofUSGSandNLCDLUcategories to consolidatedLU categories. The category index usedwithinWRF is provided
in parentheses. Note that NLCD LU categories that compose ‘‘unclassified’’ are not present in the 36-km domain (Fig. 2).
Consolidated LU USGS NLCD
Urban Urban and built-up land (1) Urban and built up (13)
Developed open space (23)
Developed low intensity (24)
Developed medium intensity (25)
Developed high intensity (26)
Agricultural Dryland cropland and pasture (2) Croplands (12)
Irrigated cropland and pasture (3) Cropland/natural vegetation mosaic (14)
Mixed dryland/irrigated cropland and pasture (4) Pasture/hay (37)
Cropland/grassland mosaic (5) Cultivated crops (38)
Cropland/woodland mosaic (6)
Grass/shrubland Grassland (7) Closed shrublands (6)
Shrubland (8) Open shrublands (7)
Mixed shrubland/grassland (9) Woody savannas (8)
Savanna (10) Savannas (9)
Grasslands (10)
Shrub/scrub (32)
Grassland/herbaceous (33)
Forest Deciduous broadleaf forest (11) Evergreen needleleaf forest (1)
Deciduous needleleaf forest (12) Evergreen broadleaf forest (2)
Evergreen broadleaf (13) Deciduous needleleaf forest (3)
Evergreen needleleaf (14) Deciduous broadleaf forest (4)
Mixed forest (15) Mixed forest (5)
Deciduous forest (28)
Evergreen forest (29)
Mixed forest (30)
Wetlands Herbaceous wetland (17) Permanent wetlands (11)
Wooden wetland (18) Woody wetlands (39)
Emergent herbaceous wetlands (40)
Barren/tundra Barren or sparsely vegetated (19) Barren or sparsely vegetated (16)
Herbaceous tundra (20) Barren land (rock/sand/clay) (27)
Wooded tundra (21)
Mixed tundra (22)
Bare ground tundra (23)
Ice/snow Snow or ice (24) Permanent snow and ice (15)
Perennial ice/snow (22)
Ocean Water bodies (16) International Geosphere–Biosphere
Programme (IGBP) water (17)
Unclassified Unclassified (18)
Fill value (19)
Unclassified (20)
Open water (21)
Dwarf scrub (31)
Sedge/herbaceous (34)
Lichens (35)
Moss (36)
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FIG. 3. As in Fig. 2, but with coloring indicating the consolidated LU (as assigned in Table 1)
for each of the three LU representations.
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to the WRF simulations, the effects of interpolation to
the WRF domain should be minimal for the domain-
based and regional analysis conducted in this study.
Regional analysis of both temperature and precipitation
is conducted over the nine NCEI U.S. climate regions
(Karl and Koss 1984), as shown in Fig. 1. Simulated
monthly 2-m temperatures are compared with NOAA’s
Gridded Climate Divisional Dataset (nClimDiv), which
contains spatially averaged temperatures for each of the
NCEI regions, as well as the CONUS (Vose et al. 2014).
The nClimDiv data are derived from area-weighted
station data from the Global Historical Climatology
Network (Menne et al. 2012).
3. Results
Precipitation and 2-m air temperatures are two fields
that are important to understanding the potential effects
of climate change on ecosystems, pollutant concentra-
tions, and human health. This analysis focuses on the
sensitivity of precipitation, 2-m air temperature, and the
surface fluxes that influence those fields to the un-
derlying LU representation. Analysis is conducted on
the 36-km domain, and results are shown both across the
CONUS and within each region.
a. Precipitation
Themean bias in monthly precipitation relative to CPC
indicates a general underprediction of precipitation across
the CONUS in all runs (Table 2). This signal is regionally
variable, however, with a dry bias in the midwestern and
southern regions (Ohio Valley, Upper Midwest, South,
and Southeast), and a wet bias in the Northeast and
the four western regions (Northern Rockies and Plains,
Northwest, Southwest, and West). While the magnitudes
and signs of the biases in the WRF runs vary from region
to region, they are relatively consistent among WRF-
USGS, WRF-NLCD, and WRF-NLCDDEF within each
region. All three simulations also agree on the magnitude
of the RMSE over the CONUS and in each region
(not shown).
Time series of spatially averaged monthly observed
precipitation and model bias are shown in Fig. 5. Here, a
paired Student’s t test is used to compare differences be-
tween WRF-USGS and WRF-NLCD within the regions
and over the CONUS. It is assumed that autocorrelation is
small, as it is not considered in the Student’s t test.Although
data from consecutive months are not fully independent,
the regional averages are sufficiently independent in mid-
latitude climates to not invalidate tests of significance.
Decremer et al. (2014) concluded that more sophisticated
methods of significance testing (i.e., those that account for
autocorrelation) did not outperform the Student’s t test
when assessing the robustness of seasonal averages in cli-
matemodels. They also state that autocorrelation is less of a
concern when using a Student’s t test to examine statistical
significance across spatial averages, which is how it is ap-
plied in the present work.
A paired Student’s t test indicates that differences
between WRF-USGS and WRF-NLCD are significant
in the CONUS and all regions using a significance level
p 5 0.05 criterion (not shown). Here, differences be-
tween the two NLCD-based runs are also found to be
FIG. 4. (top) The percentage of LU (aggregated as listed under
the consolidated LU categories in Table 1) taken from the
dominant LU field within the 36-km domain for the WRF-USGS,
WRF-NLCD, and WRF-NLCDDEF runs. (middle) The differ-
ence in percentages, where positive values indicate larger per-
centages within the USGS and negative values indicate larger
percentages within the NLCD. (bottom) As in the middle panel,
but for NLCD and NLCDDEF.
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significant in the CONUS and in five out of the nine re-
gions (Northeast, Ohio Valley, Northwest, Southeast, and
West). During periods where the time series diverge in
Fig. 5, the USGS-driven simulation has consistently more
precipitation than the other two runs, while the WRF-
NLCD simulation is the driest. None of the runs is con-
sistently closer to the CONUS-wide, monthly averaged
precipitation from CPC. For example, during April–May
1988, WRF-USGS was ;5mm month21 wetter than the
NLCD runs, but all three simulations were substantially
wetter than CPC by 12–17mm month21. By contrast,
while WRF-USGS remains wetter than the NLCD runs
by ;5mm month21 during July–October 1989, all three
simulations are substantially drier than CPC by ;10–
20mm month21. The dry bias becomes even more pro-
nounced during July–October 1990, with monthly dry
biases exceeding 25mm month21 in September 1990 for
each of the WRF runs. There are also periods when the
WRF runs are generally unbiased across the CONUS,
such asDecember 1988–February 1989 and January–June
1990. Overall, the differences in bias that are attributable
to LU changes tend to be small compared to the total
model bias, which suggests that there are larger, physics-
based sources of error in this configuration of WRF.
Furthermore, the source of the LU data does not sys-
tematically adjust the magnitude of the biases in the re-
gional and continental-scale precipitation.
The USGS-driven simulation is often the wettest
within each region (Fig. 5). The largest differences
between the runs occur in the Southeast and Upper
Midwest, where forest and agricultural LU types are
prominent in all three runs. WRF-NLCD has more
wetland grid cells in the Upper Midwest and additional
wetlands and shrubland in the Southeast relative to
USGS (Fig. 3). The Northern Rockies and Plains,
Northwest, Ohio Valley, and South all show smaller but
persistent differences, and the WRF-USGS is generally
the wettest simulationwhileWRF-NLCDappears as the
driest. In the Southwest and West, where the LU is
dominated by shrubland and forest types, there are only
small differences in bias in monthly precipitation be-
tween the simulations. The Northeast monthly pre-
cipitation totals are unique in that theWRF-NLCDDEF
often shows the largest simulated precipitation
during periods where observed precipitation totals
are heavy (summers of 1988 and 1989) but does
not have the largest mean bias, as compared in
Table 2. While the WRF-NLCD often appears as the
driest of the three simulations, differences between
WRF-NLCD and WRF-NLCDDEF failed to test as
statistically significant in several central U.S. regions
(Northern Rockies and Plains, Upper Midwest, South,
and Southwest).
Precipitation is also analyzed using CDFs of regional
and CONUS-averaged daily precipitation during each
season. The distributions of summer (June–August)
rainfall are shown in Fig. 6; CDF comparisons for other
seasons yielded smaller contrasts between the runs (not
shown). The Perkins skill score (PSS; Perkins et al. 2007)
is used to assess how closely the PDF of simulated
rainfall matches that of the observed:
PSS5 �n
1
min (Zmodeled,
Zobserved
) , (1)
where n is the number of bins and the Zs represent the
frequency of values in each bin from the modeled and
observed distributions, respectively. The PSS is a metric
of how well the PDFs coincide. As the integral of any
PDF should sum to one, a PSS of 1 represents a per-
fect overlap of the two distributions. As seen in Fig. 6,
the WRF-USGS run shows the highest PSS across the
CONUS, but only in three of the regions (South,
Southwest, and Upper Midwest). Most of the remaining
TABLE 2. Mean bias in monthly accumulated precipitation (when compared with CPC; mm month21) and monthly averaged 2-m
temperature (when compared with nClimDiv; K) for WRF-USGS, WRF-NLCD, and WRF-NLCDDEF. Variables are calculated using
only land grid cells within each region.
Monthly accumulated precipitation Monthly averaged 2-m temperature
WRF-USGS WRF-NLCD WRF-NLCDDEF WRF-USGS WRF-NLCD WRF-NLCDDEF
CONUS 23.81 26.31 25.14 1.38 1.53 1.43
Northeast 15.16 13.07 14.99 21.18 21.14 21.28
Northern Rockies and Plains 9.01 6.53 6.92 0.42 0.48 0.49
Northwest 13.18 10.75 12.11 21.35 21.19 21.23
Ohio Valley 211.09 215.12 214.07 20.26 20.04 20.27
South 220.55 222.16 222.29 1.45 1.65 1.56
Southeast 26.17 211.78 27.50 0.34 0.61 0.26
Southwest 5.61 4.74 4.67 20.74 20.72 20.74
Upper Midwest 21.41 24.83 23.93 20.11 20.05 20.11
West 1.45 1.09 1.35 0.07 0.11 0.12
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FIG. 5. Monthly total observed precipitation (mmmonth21, shown on the left axis with gray shading) andmodel bias (mmmonth21, shown
on the right axis with colored lines), spatially averaged over the CONUS and in each of the nine NCEI U.S. climate regions from the
WRF-USGS, WRF-NLCD and WRF-NLCDDEF (blue, green, and orange lines, respectively). Note that all of the regional plots share
common axes for comparison. The x axes are labeled every other month beginning in January.
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FIG. 6. CDFs of daily precipitation totals (mmday21) for the summer season (June–August), averaged over the CONUS and NCEI
regions from the WRF-USGS, WRF-NLCD, and WRF-NLCDDEF simulations and CPC data (blue, green, orange, and gray curves,
respectively). The inset boxes list PSS values for each simulation.
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regions feature the highest PSS for WRF-NLCD
(Northeast, Northern Rockies and Plains, and South-
east), while WRF-NLCDDEF scores highest only in the
Ohio Valley. All simulated distributions show a rea-
sonable fit with observations, as PSS values vary be-
tween 0.71 and 0.99.
In summer, daily precipitation from theWRF-NLCD is
drier relative to theWRF-USGS within most regions and
across the CONUS. Consistent with Fig. 5, Fig. 6 shows
that WRF-USGS tends to be the wettest of the three
simulations (as indicated by a rightward shift in the dis-
tribution toward higher daily totals), while the WRF-
NLCD run is driest, in areas where the simulations diverge.
Meanwhile, WRF-NLCDDEF simulation tends to be nei-
ther the wettest nor driest of the three runs, except in the
Northeast with slightly larger daily totals in WRF-
NLCDDEF compared to the other two simulations.
Although there is regional variability in WRF’s sensi-
tivity to LU, the distributions taken over the CONUS, as
well as over several regions, show that the influence of
LU on daily precipitation is consistent across a range of
precipitation events. Low, moderate, and heavy rainfall
totals are all affected by the changes in land cover, which
results in a systematic shift of rainfall totals, often with
heavier totals in WRF-USGS. Again, the magnitude of
the model error (the difference between the PSS values
and a perfect score of 1) is generally larger than the
differences between the three simulations.
The hydrological budget (evaporation, surface and
groundwater runoff) is shown in Fig. 7 using monthly
totals averaged over the CONUS. The WRF-USGS run
features higher evaporation than the other two simula-
tions, especially during summer, while the WRF-NLCD
run has the lowest total evaporation. Both surface runoff
and groundwater are higher inWRF-NLCD, although it
has lower precipitation relative to the WRF-USGS
(Fig. 5). The WRF-NLCD run shows a tendency to
partition precipitation into surface runoff and ground-
water, while WRF-USGS has a greater tendency toward
evaporation. A similar contrast occurs between WRF-
NLCD and WRF-NLCDDEF, where the latter simula-
tion features higher evaporation amounts with lower
runoff, more closely resembling the water balance in the
WRF-USGS run. This is a somewhat surprising result,
given that WRF-NLCD and WRF-NLCDDEF only
differ in the method used to interpolate LU data.
FIG. 7. Monthly total (top left) surface evaporation (kgm22), (top right) surface runoff (mm), and (bottom) groundwater runoff (mm)
averaged across the CONUS from each of the WRF simulations, with colors and x axes as in Fig. 5.
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Regional values are generally consistent with the
CONUS-averaged results with surface runoff featuring
the most regional variability (not shown).
b. 2-m temperature
A time series of monthly averaged 2-m temperature
bias relative to nClimDiv is shown over the CONUS and
in all nine regions in Fig. 8, and mean bias is shown in
Table 2. All simulations show a warm bias over the
CONUS, with mixed results in the regions. Each simu-
lation has the largestmean bias in the South (;1.5–1.7K),
where temperatures are overestimated throughout the
simulated period. Meanwhile, temperatures in the
Northwest and Northeast show a consistent cool bias
throughout each run. However, most regions feature
biases that vary in sign throughout the period. Differ-
ences among the three simulations are generally smaller
than model error.
Average temperatures are slightly warmer across the
CONUS in WRF-NLCD (by 0.1–0.2K over most of the
period) when compared with WRF-USGS and WRF-
NLCDDEF (Fig. 8). Inmost regions,WRF-NLCDeither
has the largest warm biases or a minimized cool bias,
relative to the other simulations (Table 2). As seen in
the CONUS-average time series, the WRF-USGS run
is generally the coolest. However, this signal varies re-
gionally as some areas (the Northern Rockies and Plains,
Northwest, and South) consistently show the cooler
temperatures in WRF-USGS while other areas (such as
the Northeast and Southeast) favor cooler temperatures
in the WRF-NLCDDEF simulation (Fig. 8). Overall, the
regional time series in Fig. 8 show the most prominent
differences between the runs in the South, Southeast,
Northwest, Upper Midwest, and Ohio Valley regions
where forest and agricultural land is dominant, while the
more arid West and Southwest regions show less di-
vergence between the runs. Differences between the runs
are found to be statistically significant using a t test with
p5 0.05, except that differences between the twoNLCD-
based runs are not significant in the Northern Rockies
and Plains region (not shown). In general, as with pre-
cipitation, the sensitivity of near-surface temperatures to
LU representation is smaller than the model error.
Previous studies showed that changes in LU affect
projections of temperature extremes (e.g., Deo et al.
2009; Avila et al. 2012). In the WRF Model, many of the
values that are retrieved from lookup tables on the basis
of LU are maximum and minimum thresholds (for LAI
and stomatal resistance, among others) used to bound
model behavior, as well as values that define surface
moisture availability and heat capacity, which influence
the partitioning of latent and sensible heat fluxes from the
surface. While mean temperature differences between
the three simulations are relatively small, more contrast
between the runs occurs in extreme temperatures.
To isolate differences in the number of ‘‘hot’’ days, the
average number of days per year on which the maximum
2-m temperature meets or exceeds 908F (32.28C) (e.g.,
Karl et al. 2009; Horton et al. 2014) was computed at each
grid cell across the domain, and the difference fields are
plotted in Fig. 9. The WRF-NLCD run has ;10–40
more hot days per year than WRF-USGS throughout
much of the Southeast and into the South. In areas of the
Upper Midwest and Ohio Valley, WRF-NLCD has ;5–
20 more hot days per year than WRF-USGS in several
areas. By contrast, WRF-USGS has more hot days than
WRF-NLCD along the western coast of Mexico, and
a sparse area of ;5–20 more hot days in parts of the
California coast.
WRF-NLCD generally has more hot days than WRF-
NLCDDEF where there are differences between the
runs (Fig. 9). This difference is most apparent in the
Southeast, where there are ;5–40 more hot days per
year and there is a similar spatial pattern to the com-
parison of WRF-NLCD to WRF-USGS. By contrast,
there are ;10–30 fewer hot days along parts of the
Gulf Coast in WRF-NLCD than in WRF-NLCDDEF.
Overall, the sensitivity to the LU source data appears
larger than sensitivity due to changes in interpolation
scheme. Of the three simulations, WRF-NLCD has the
highest number of hot days while WRF-USGS has the
smallest number among all three runs, with the differ-
ences concentrated most in the Southeast. In general,
using NLCD LU with either interpolation scheme re-
sults in warmer 2-m air temperatures, which increases
the frequency of hot days in the South and Southeast.
c. Surface fluxes
Because the LU affects the atmosphere through air–
surface exchanges of heat, moisture, and momentum,
the changes in 2-m air temperature and precipitation can
be linked to the changing LU dataset by examining how
the composition of LU types affects air–surface in-
teractions. Figure 10 compares sensible and latent heat
fluxes averaged in each of the consolidated categories
listed in Table 1. The values shown in Fig. 10 are nor-
malized by the number of grid cells in each category.
WRF-NLCDDEF is not shown because it is similar
to WRF-NLCD when averaged over consolidated LU
categories in this way.
As expected, urban categories are most effective at
transferring sensible heat to the atmosphere with an
annual average of ;100Wm22 in both simulations.
Grassland/shrubland categories also tend to be large
producers of sensible heat at 56–70Wm22, making it the
second largest source inWRF-USGSand the third largest in
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FIG. 8. Bias of simulated monthly averaged 2-m air temperature (K) taken against nClimDiv for each of the
three runs, shown in the CONUS and NCEI regions as in Fig. 5.
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WRF-NLCD. In both runs, forest, agricultural, and wet-
lands also have large sensible heat values, with averages of
46–59Wm22. The simulations do not give similar values of
sensible heat in the barren/tundra consolidated category,
varying by over 30Wm22. This may be due to the limita-
tions of representing a variety of LU types with consoli-
dated categories, as USGS has a variety of tundra
categories (including herbaceous and woody varieties)
while the NLCD only contributes cells from a single
barren land category within the CONUS (Table 1). In
bothWRF-USGS andWRF-NLCD, forest LU types are
the most important conduits of latent heat to the atmo-
sphere, with average values of ;56Wm22. The wetland
and agricultural categories are the next largest producers
of latent heat in both runs with values of;46–50Wm22.
Agricultural and forest LU types contribute most of the
FIG. 9. The difference in the average number of days per year on which the maximum 2-m
temperature meets or exceeds 908F for (top) WRF-USGS minus WRF-NLCD and (bottom)
WRF-NLCD minus WRF-NLCDDEF.
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total monthly latent heat over the spring and early
summer, but the contribution from wetlands becomes
dominant over the autumn and winter as fluxes from ag-
ricultural LU categories decrease after the growing season.
The analysis of all three simulations in consolidated
LU categories shows that the most prominent LU types
are forest, grassland/shrubland, and agricultural land
(Figs. 3 and 4). The largest changes in consolidated LU
between the runs are the decrease in forest and agri-
cultural types and increase in grass/shrubland when
changing from the WRF-USGS to the WRF-NLCD run
(Fig. 4). Therefore, the most prolific producers of latent
heat (forest and agricultural LU) are reduced in area
in WRF-NLCD, while there are increases in grass/
shrubland, which is a significant producer of sensible
heat. Accordingly, it can be expected that the total
surface evaporation, as well as precipitation, would de-
crease while surface and near-surface temperatures
would increase when choosing NLCD instead of USGS
for LU data when both use the four-point interpolation
scheme. The sensitivity of precipitation and 2-m air
temperature to interpolation scheme is smaller between
WRF-NLCD and WRF-NLCDDEF, but WRF-NLCD
is still drier and warmer. However, WRF-NLCD also
contains fewer forest LU cells than WRF-NLCDDEF,
which would promote increased surface latent heating
(at the expense of sensible heating) and increased pre-
cipitation totals in the latter simulation. All other changes
in LU type due to the difference in interpolation scheme,
including those in agricultural and grassland/shrubland,
are relatively small.
d. Regional results: Focus on the Southeast
Downscaled simulations are used as input for air
quality modeling or hydrological modeling with human
health and ecosystem services endpoints. It would be
expected that the LU changes, while assessed here in a
domain-aggregated sense, would have important regional
and local implications. This section focuses on the
Southeast during summer using the WRF-USGS and
WRF-NLCD runs. This region is chosen because of the
increased hot days, and because it featured the largest
differences inmonthly 2-m temperature and precipitation
bias between each set of runs (see Table 2 and Fig. 9). It is
also a region where forest LU types are prominent and
changes in the extent of forest LU due to the driving data
FIG. 10. Annual cycle of monthly averaged (left) sensible and (right) latent heat fluxes (Wm22) averaged within each of the consoli-
dated LU categories for the (top)WRF-USGS and (bottom)WRF-NLCD simulations, colored according to the legend at the bottom. The
inset tables show averages taken over each simulation within each of the consolidated categories.
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or interpolation scheme would be expected to affect other
near-surface variables. Comparison of the consolidated
LU types (Fig. 3) shows that both runs feature forest and
agricultural types throughout the Southeast, although
there is more diversity of LU types in WRF-NLCD in
that region. While the agricultural land inWRF-USGS is
primarily both in Florida and just inland of the Atlantic
coast, the presence of agricultural land inWRF-NLCD is
replaced with wetlands in areas throughout the region.
The differences in hot days (Fig. 9) and mean monthly
2-m temperatures (Fig. 8) indicate increases in average
summertime daily maximum temperatures. Figure 11a
shows increases of ;0.5K in WRF-NLCD throughout
most of the region, with a large area in the central portion
near the Atlantic coast (eastern Georgia, South Carolina,
and into southern North Carolina) with temperature
increases of 1K (Fig. 11a) and localized increases of
more than 2K. Slight cooling (generally,0.5K) occurs in
areas of Virginia. Consistent with the warmer tempera-
tures inWRF-NLCD throughout the Southeast, the PBL
heights are increased relative toWRF-USGS. PBLheight
differences of 25–100m extend through most of the
Southeast (Fig. 11b). WRF-NLCD shows PBL heights
increased by 200–250m at the North Carolina–South
Carolina border near Charlotte, North Carolina, which
is an area interspersed with urban categories in that
simulation, while WRF-USGS shows no urban grid cells
in the area (Fig. 3). Both the temperature and PBLheight
results are of particular importance for air quality simu-
lations, as future projections of near-surface temperature
and PBL heights would strongly affect changes in ozone
concentrations (e.g., Dawson et al. 2007; Nolte et al. 2008;
Wu et al. 2008; Haman et al. 2014) as well as particulate
matter and its health effects (e.g., Ren and Tong 2006;
Tai et al. 2010).
Evaluation of air–surface interactions with a focus on
moisture would be more important for hydrology or
ecosystems services applications supported by down-
scaled simulations. Consistent with the greater precipi-
tation shown in Table 2 and Figs. 5 and 6, summertime
2-m mixing ratio values are also higher in WRF-USGS
relative to WRF-NLCD throughout most of the
FIG. 11. The difference (WRF-USGS minus WRF-NLCD) in the June–August averaged (a) daily maximum 2-m temperature (K),
(b) PBL height (m), (c) 2-m mixing ratio (g kg21), and (d) LAI (m2m22).
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Southeast (Fig. 11c). Mixing ratio increases of more
than 0.5 g kg21 in WRF-USGS occur along the Atlantic
coast, consistent with the location where large swaths
of agricultural land are present in that run. Contrasts in
LAI, which is an important driver of evaporation and
humidity, are more heterogeneous than the mixing
ratio differences (Fig. 11d). While regionally averaged
LAI shows larger summertime values in WRF-USGS
relative to WRF-NLCD (not shown), a notable intra-
regional variability is present in the Southeast, potentially
due to LU changes and the associated lookup-table
values. The increase in agricultural land in the eastern
part of the region for WRF-USGS is consistent with its
increased LAI values, relative to the WRF-NLCD LU,
which features more wetlands in the area. It is also no-
table that maximum LAI values for the wetlands cate-
gories are 5.8m2m22 for USGS but are reduced to
3.5m2m22 with NLCD. While the current study is
conducted in a framework appropriate for CONUS-
wide downscaling, these results highlight the variability
of LU differences on a local to regional scale, which may
have important implications for some applications that
rely on downscaled data.
e. Sensitivity of results
Additional simulations are conducted to illustrate the
robustness of the results. These additional simulations
use the identical model configuration except that the
runs are driven with the 0.758 3 0.758 ECMWF interim
reanalysis (ERA-Interim), a single 36-km domain is
used, version 3.9.1.1 of WRF is used with the hybrid
vertical coordinate system, and the simulations are
conducted for 1988. These simulations, WRF-USGS2
and WRF-NLCD2, use four-point interpolation to set
LU on the WRF grid.
Figure 12 summarizes the results of these runs over
the CONUS utilizing plots like Figs. 5, 8, and 9. Similar
to WRF-NLCD, the results of WRF-NLCD2 tend to
show lower monthly precipitation values and warmer
mean monthly 2-m temperatures over the CONUS rel-
ative toUSGS. These runs also show notable differences
in the number of hot days, with WRF-NLCD2 having a
FIG. 12. (top left) Monthly CPC precipitation and precipitation bias (mm month21) shown as in Fig. 5 and (top right) monthly mean
temperature bias (K) as in Fig. 8 for the CONUS during 1988 for the WRF-USGS2 andWRF-NLCD2 simulations (blue and green lines,
respectively). (bottom) The difference in the annual frequency of hot days for WRF-USGS2 minus WRF-NLCD2, as in Fig. 9.
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greater frequency of these events than WRF-USGS2.
As in Fig. 9, Fig. 12 shows an increased frequency con-
centrated in the South and Southeast with widespread
differences of over 10 days per year and several areas in
Florida incurring an additional 30 hot days per year
when NLCD is used. The comparison of WRF-USGS2
to WRF-NLCD2 corroborates conclusions drawn from
the comparisons ofWRF-USGS toWRF-NLCD using a
different configuration of WRF. As before, WRF’s
sensitivity to LU is generally less than the biases shown
in Fig. 12.
4. Summary and conclusions
The sensitivity to the choice and interpolation of LU
data is assessed using WRF for continental-scale dy-
namical downscaling. Simulations are performed over
the CONUS for a 3-yr period, utilizing the USGS and
2006 NLCD datasets where each was interpolated to the
WRF grid using the same scheme (four_pt, which is the
default method associated with the USGS). A third
simulation uses NLCD’s default interpolation scheme
(average_gcell). Near-surface temperature and pre-
cipitation are sensitive to both the LU source and the
interpolation method. However, the model error is
systematically larger than the LU sensitivity.
In general, the WRF-NLCD simulation produces
lower precipitation totals and slightly higher 2-m air
temperatures, with more pronounced differences in
maximum daily temperatures, as compared with WRF-
USGS. Differences in temperatures and precipitation
are linked to changes in the most dominant LU cate-
gories: forest, grassland/shrubland, and agricultural. In
NLCD relative to USGS, the reduction of forest and
agricultural types and the compensating increase in
grassland/shrubland and other categories tends to pro-
mote the release of sensible heat into the atmosphere at
the expense of latent heating. In addition, WRF-NLCD
has lower total surface evaporation but more surface
and groundwater runoff (despite having lower pre-
cipitation amounts) relative to WRF-USGS.
The method used to interpolate and assign LU to the
grid cell can significantly change the composition of the
LUdata on the targetWRF grid.WithNLCD, this alters
2-m temperatures and rainfall at daily and monthly
scales. WRF-NLCD (which adopted the method usually
applied for USGS) tends to be warmer and drier than
the simulation that applied NLCD to the grid using
the default method (WRF-NLCDDEF). Similarly, the
concentration of forest LU types is lower in WRF-
NLCD relative to WRF-NLCDDEF, which affects the
partitioning of the sensible and latent heat fluxes.
Although LU datasets within WRF were collected at
comparable spatial scales (1 km and below), they should
continue to be interpolated to the WRF grids using
different methods because of distinctions in the repre-
sentation of the LU among the source data. For datasets
that provide one dominant LU category per pixel (e.g.,
MODIS), the nearest_neighbor method is preferred and
is set as the default; however, bilinear interpolation or
averaging methods are preferred when the source
dataset provides fractional values of LU within each
pixel for every category, as is the case with the USGS
and NLCD datasets (M. Duda 2017, personal commu-
nication). The default for USGS is the bilinear four_pt
scheme, whereas gridcell averaging is the default
method for NLCD. The current work illustrates that the
interpolation method for LU is worth consideration, as
dramatic differences in the spatial heterogeneity and
composition of the LU field on the WRF grid can alter
the simulation of 2-m temperatures and precipitation.
This study shows that the sensitivity of near-surface
temperatures and precipitation to changes in LU rep-
resentation is smaller than the model error for those
fields, and the sensitivity to LU source is larger than that
attributable to interpolation method. Here, LU sensi-
tivity cannot account for the majority of model error.
Additional changes to the model setup, such as the use
of different physics schemes or finer resolution, could be
utilized to improve the representation of precipitation
and temperature over this historical period. However,
this analysis demonstrates that differences between
downscaled simulations due to LU sensitivity persist
across daily andmonthly temporal scales and occur both
in CONUS-wide averages and within several regions of
the CONUS. Monthly CONUS-averaged precipitation
shows small but consistent differences between the
simulations. LU affects daily precipitation across a
range of both low and high rainfall events, and those
effects systematically shift the distribution. Monthly
average 2-m temperatures between the runs diverge
more in the warm season than in other periods, and the
number of days that exceed 908F differs between the
runs, notably in the South and Southeast. This suggests
that LU could more strongly influence temperature and
precipitation extremes. In focusing on the Southeast, it is
found that PBL heights are increased and low-level
moisture is decreased with WRF-NLCD relative to
WRF-USGS, as the WRF-USGS features forest and
agricultural LU types throughout this region while the
WRF-NLCD contains a more heterogeneous landscape
with additional wetlands and other LU types.
Here, the sensitivity to LU representation is examined
in a dynamical downscaling application during a 3-yr
historical period (1988–90). This period includes a
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range of climate and weather conditions with several
extreme events (e.g., drought, regional freezing condi-
tions, landfalling tropical systems). Results with this
WRF configuration are expected to be robust because of
the range of conditions included in the historical period.
The model configuration and physics options used here
were vetted in prior continental-scale downscaling
studies (e.g., Bowden et al. 2012; Otte et al. 2012;
Bowden et al. 2013; Herwehe et al. 2014). In addition, an
alternate modeling configuration that uses an updated
model version and finer-resolution driving data corrob-
orates those conclusions. However, these results could
also to be sensitive to other aspects of the experimental
design that influence the parameterization of processes
at the surface and the transport of turbulent fluxes into
the overlying atmosphere, such as the LSM and PBL
scheme. While it is expected that sensitivities to model
physics choices (such as PBL or convection parameteri-
zations) would play a more dominant role in the overall
model statistics, this paper shows that sensitivities to the
representation of LU are subtle but important in some
areas. As discussed above, NLCD is commonly used for
retrospective air quality applications, where other LSM
and PBL schemes are used, along with nudging for soil
moisture and temperature (e.g., Pleim and Xiu 2003;
Pleim and Gilliam 2009). Such a constraint could affect
sensitivity to LU in retrospective air quality applications.
The treatment of LU, as well as the grid spacing, may
also affect the sensitivity to LU in a downscaling
framework. Here, dominant LU is used where the LU
category that is most prolific represents the entire 36-km
cell. Therefore, small changes in the composition of the
original LU data could change the dominant LU cate-
gory, which could cascade onto the model’s simulation
of 2-m temperature and precipitation. Other LSM op-
tions in WRF use a mosaic approach in which fractional
LU values are considered within each grid cell. There
may be less sensitivity to LU source data in WRF at
36 km with mosaic-style LSMs because the composition
of LU in different datasets may be more comparable
when taken at the subgrid scale. LU changes on a finer-
resolution (i.e., 4 km) WRF grid using dominant LU
may be more apparent than in the current experiment
because higher-resolution grids could reflect smaller-
scale changes to the land surface that occurred between
USGS and NLCD. A more detailed exploration of the
physical mechanisms that drove the differences in the
atmospheric response to the different LU representa-
tions would be beneficial.
Our findings demonstrate that using the 2006 NLCD
instead of USGS to provide LU information for WRF
does not considerably change the accuracy of downscal-
ing simulations and that there is no penalty for using this
newer LUdataset to drive simulations of regional climate.
Being a more contemporary and higher-resolution data-
set, the NLCD data are advantageous for use in down-
scaling applications, especially as increased computational
resources provide the opportunity to use finer grid spac-
ing. However, both the source of the LU data and the
method of interpolating those LU data to the domain
influence the composition of the land surface, which, in
turn, affects the simulation of air–surface interactions.
Acknowledgments. The views expressed in this article
are those of the authors and do not necessarily represent
the views or policies of the U.S. Environmental Pro-
tection Agency (EPA). The R2 and CPC U.S. Unified
Precipitation data were obtained from the NOAA/
OAR/ESRL PSD (http://www.esrl.noaa.gov/psd/). The
nClimDiv data were acquired from the NCEI and are
available online (https://www.nodc.noaa.gov/access/).
Author SMT was supported by an appointment to the
Research Participation Program at the U.S. EPA Office
of Research andDevelopment, administered by theOak
Ridge Institute for Science and Education (ORISE).
The authors thank Chris Nolte (EPA) for the R scripts
that were leveraged to create some of the figures in this
paper. The authors appreciate the technical reviews and
feedback on this manuscript fromChris Nolte and Limei
Ran (EPA). The authors also thank the anonymous
reviewers for constructive comments that strengthened
this paper.
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