Examining WRF’s Sensitivity to Contemporary Land-Use

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]

NOVEMBER 2018 MALLARD ET AL . 2561

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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.

2562 JOURNAL OF APPL IED METEOROLOGY AND CL IMATOLOGY VOLUME 57


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.



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.



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).



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)



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.



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.



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



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.



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.



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.



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



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.



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.



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.



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).



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.



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



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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