Online Resource 2
This resource describes the methods used for WRF model downscaling and a
comparison of the WRF-derived atmospheric forcing with CCSM4 climate
simulations and with the NLDAS-2 forcing dataset. This resource also describes
an evaluation of the simulated daily minimum and daily maximum air temperature
for CCSM4-CLM4 and WRF-CLM4 for 46 U.S. states using 5,332 National
Climatic Data Center weather stations.
Interactions between Urbanization, Heat
Stress, and Climate Change
K.W. Oleson, A. Monaghan, O. Wilhelmi, M. Barlage, N. Brunsell, J. Feddema,
L. Hu, D.F. Steinhoff
K.W. Oleson – Corresponding Author
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307
303-497-1332
FAX: 303-497-1348
oleson@ucar.edu
A. Monaghan, O. Wilhelmi, M. Barlage, D.F. Steinhoff
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307
N. Brunsell, J. Feddema, L. Hu
University of Kansas, 1475 Jayhawk Blvd, Lawrence, KS 66045
1
1 WRF Model Downscaling
WRF (version 3.3 is used here) has multiple options and choices for physical
parameterizations (i.e., radiation, cloud physics, cumulus clouds, planetary
boundary layer turbulence) and is optimized for each application based on the
prevailing weather conditions, location, and goals of the project. WRF has been
used for numerous studies of potential climate change impacts, including in urban
areas (e.g., Jiang et al. 2008, Kusaka et al. 2012). The following
parameterizations are chosen for the simulations based on extensive testing and
evaluation of various WRF configurations over the U.S. by the Developmental
Testbed Center (http://www.dtcenter.org): WRF Single-Moment 5-class cloud
microphysics scheme (Hong et al. 2004); Rapid Radiative Transfer Model
(RRTM) longwave radiation scheme (Mlawer et al. 1997); Dudhia shortwave
radiation scheme (Dudhia 1989); MM5 similarity for the surface layer based on
Monin-Obukhov and a Carlson-Boland viscous sublayer (Carlson and Boland
1978); Yonsei University scheme for the planetary boundary layer (Hong et al.
2006); Grell-Devenyi for cumulus clouds (Grell and Devenyi 2002) (this
parameterization is not recommended by DTC but we find that simulated rainfall
is greatly improved along the Gulf Coast when using it). Additional
modifications are made to the RRTM parameterization so that CO2 is changed
from a time-invariant value of 360 ppm to monthly-varying values matching those
used in the boundary conditions from CCSM4 (described below). To realistically
simulate the two-way land surface interactions with the atmosphere, WRF is
coupled to the Noah Land Surface Model (Noah LSM; e.g., Chen and Dudhia,
2001). The Noah LSM provides WRF with fluxes of energy and water from the
land surface, while also maintaining stores of water and energy in four soil layers
to a depth of 2 m. Land use for each grid cell is defined from the 20-category 30arc-second MODIS dataset that is packaged with WRF.
A one-domain, 15-km WRF configuration over the U.S. and southern Canada
is employed with 30 vertical levels from the surface to 10 hPa, including 7 levels
in the lowest 1000 m. The initial and boundary conditions for present-day (PD)
and mid-century (MC) are provided by Ensemble Member #6 of the 20th century
and RCP8.5 CCSM4 simulations run as part of phase five of the Coupled Model
Intercomparison Project (CMIP5). CCSM4 is in part chosen to force WRF in
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order to be consistent with the use of the CLMU for the offline simulations that
are the topic of this paper. The particular ensemble member is chosen because it
is the only publically available CCSM4 member for which the full 3-dimensional
output is saved at sub-daily resolution, which is a requirement for forcing the
lateral boundaries of WRF. Four dimensional data assimilation is performed in
the upper six model levels to relax the WRF-simulated temperature, specific
humidity and wind fields toward the CCSM4 fields. This "grid nudging"
approach provides constraint of the large-scale upper level forcing in WRF and
has been found to improve long (i.e., more than a few days) simulations without
dampening the energy spectrum in the lower model levels (Bowden et al. 2012).
Each monthly simulation begins on the last day of the previous month to allow
24 hours for spin-up of the modeled fields. Sea-surface temperatures (SSTs) are
derived from CCSM4 skin temperatures and are updated every 6 hours at the
WRF lower boundary. Monthly mean snow depth (water equivalent), soil
temperature profiles, and soil moisture profiles from CCSM4 are used to initialize
the land surface and subsurface states for the Noah LSM in WRF at the beginning
of each simulation.
2 NLDAS
We use the primary forcing data for phase 2 of the North American Land Data
Assimilation System project (NLDAS-2; Xia et al. 2012, hereafter referred to as
NLDAS) to provide some confidence in the PD atmospheric fields downscaled by
WRF, noting that the WRF-derived fields will also be affected to some extent by
biases in the CCSM4 simulation. The subset of 15-km Mercator-projected WRF
output fields required to drive the offline CLMU simulations is bilinearly
interpolated onto the same 1/8th degree latitude/longitude coordinate system as
NLDAS. The NLDAS primary forcing data was originally developed to provide a
high-quality, long-term dataset to drive a suite of research-grade land-surface
models (Cosgrove et al. 2003, Mitchell et al. 2004), but has subsequently been
used for a variety of climate research applications over central North America.
Here we use the “FORA” version of NLDAS atmospheric forcing (Rui 2012) for
comparison with the WRF-derived atmospheric forcing. The non-precipitation
forcing fields are derived from analysis fields of the NCEP North American
Regional Reanalysis (NARR; Mesinger et al. 2006) with bias corrections for
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downward solar radiation using satellite data (GOES). A vertical adjustment is
applied to the fields of surface pressure, surface downwelling longwave radiation,
near-surface air temperature and near-surface specific humidity to account for the
vertical difference between the NARR and NLDAS topographic height. The
precipitation field is developed from a temporal disaggregation of a gauge-only
CPC analysis of daily precipitation, augmented with satellite- and radar-derived
precipitation estimates.
3 Comparison of WRF, CCSM4, and NLDAS
atmospheric fields
Figures S1 and S2 show a comparison of WRF, CCSM4, and WRF minus
NLDAS atmospheric forcing fields, and Table S1 presents a statistical comparison
of WRF and NLDAS fields. The comparisons for air temperature and specific
humidity are a bit problematic as they differ in height according to the model
(WRF: 30m; CCSM4: 60m; NLDAS: 2m) (Fig. S2). For temperature, the 2-m air
temperature from the PD MD and CCSM4 simulations are also shown, which are
diagnosed by CLM4 but influenced by the atmospheric temperature. However,
the spatial patterns in atmospheric air temperature and 2-m air temperature are
quite similar. In general, WRF compares well with CCSM4 except over the
Pacific northwest, the Great Lakes, northeast Canada, and the gulf coast of Texas,
regions where it compares less well with NLDAS also. WRF is more
cold/humid/cloudy/rainy in the Pacific northwest and northeast Canada compared
to CCSM4 and NLDAS. This also affects the WRF incoming longwave and solar
radiation, which are biased high and low, respectively, compared to NLDAS.
Over most of the remaining domain, WRF generally has a positive (negative)
shortwave (longwave) radiation bias, though not as severe. This "clear sky" bias,
on average about 10-40 W m-2, is a known issue in WRF that has been linked in
part to poor treatment of the water vapor continuum (Markovic et al. 2008) and to
the choice of cloud microphysics scheme (Lu and Kueppers 2012). The relative
humidity bias appears to be due to the cold bias compared to NLDAS (Fig. S2)
since the specific humidity is similar to NLDAS (not shown).
WRF is more humid over the Great Lakes region compared to CCSM4, which
results in more longwave radiation. However, there is a small negative bias in
humidity compared to NLDAS. WRF air temperature appears to be consistent
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with CCSM4 in the water and adjacent land areas of the Great Lakes region, it is
too warm compared to NLDAS, with biases of 1-2°C.
WRF has more precipitation over the gulf coast of Texas compared to CCSM4
and NLDAS, which sensitivity tests suggest is associated with the convective
parameterization [the bias is even higher when employing parameterizations other
than the Grell and Devenyi (2002) parameterization used here]. This is supported
in that WRF is less cloudy than NLDAS (negative downward LW bias), but still
gets less downward solar radiation suggesting that the WRF convective
parameterization is too active in this region, at least for portions of the day.
The statistics for air temperature (Table S1) compare favorably with those
from a study evaluating regional model/reanalysis output over a similar domain
(the North American Regional Climate Change Assessment Program
(NARCCAP); Mearns et al. 2012). Mearns et al. (2012) compared temperature
and precipitation from six regional climate models and three reanalysis to
observations for 1980-2004. They reported a spatial root mean square error
(RMSE) for air temperature of 1.7-3.6°C with a median of 2.2°C (Table 2; Mearns
et al. 2012). The spatial pattern correlation appears to be 0.93-0.97 (Fig. 6;
Mearns et al. 2012). This compares to a RMSE here of 2.0°C and 1.8°C and a
spatial pattern correlation of 0.93 and 0.94 for Tatm and T2m, respectively (Table
S1).
Precipitation simulated by WRF appears to be at the lower end of the results
reported by Mearns et al. (2012). They reported a RMSE of 0.60-1.53 mm day-1
with a median of 0.70 and a spatial correlation of 0.76-0.82. While the WRF
RMSE for precipitation of 1.3 mm day-1 (Table S1) is within the range of
NARCCAP models, it does not compare as well with the NARCCAP median, and
the spatial pattern correlation of 0.57 is poorer than any NARCCAP model,
mainly because of the regional biases noted above. It is also possible that WRF is
being penalized for its high comparative resolution to both NARCCAP (15-km
versus 50-km) and NLDAS (which is based on the 32-km NARR). This is a
classic issue in validating high resolution models -- lower resolution models
almost always do better compared to obs for RMSE/correlation/bias statistics due
to their smoother nature (e.g., White et al. 1999). In general however, the WRF
downscaled atmospheric forcing appears to be a reasonable representation of
CCSM4 climate at finer spatial resolution and compares favorably with NLDAS
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climate. A comparison of WRF downscaled forcing to CCSM4 climate for 20462065 yields similar conclusions in that air temperature, humidity, and radiation
from WRF more closely match CCSM4 than precipitation (not shown).
4 Evaluation of CCSM4-CLM4 and WRF-CLM4 daily
maximum and minimum temperature
In this section we compare CLM4 simulations of daily maximum and
minimum 2-m air temperature (Tmax and Tmin) from the coupled 0.9375o
latitude x 1.25o longitude CCSM4 simulations (“CCSM4-CLM4”) and the
uncoupled 1/8o latitude x 1/8o longitude CLM4 simulations driven by WRF
(“WRF-CLM4”), to observations to further evaluate the simulations for presentday and also investigate whether the simulation of these variables is improved by
the higher spatial resolution of the WRF-CLM4 simulations compared to CCSM4CLM4. Observed daily Tmax and Tmin are obtained from 5,332 network stations
of the quality controlled National Climatic Data Center (NCDC) US COOP, as
documented by Meehl et al. (2009). Gao et al. (2012) used this NCDC data to
evaluate the simulation of heatwaves by CCSM4 and WRF. Following their
methodology, we compared the NCDC observations of Tmax and Tmin to Tmax
and Tmin from CCSM4-CLM4 and WRF-CLM4 for June 1–August 31, 19862004 on a station by station basis by matching the NCDC station
latitude/longitude with the closest CCSM4-CLM4 and WRF-CLM4 gridcells.
Means for each of 46 states were then calculated. A Student’s t-test (a = 0.05)
was performed to assess the statistical significance of the improvement in WRFCLM4 over CCSM4-CLM4.
Figure S3 summarizes the comparison of biases of daily minimum and
maximum temperature in CCSM4-CLM4 and WRF-CLM4 with NCDC for JuneAugust 1986-2004, averaged by state. For Tmin, WRF-CLM4 has significantly
smaller biases in 15 states, larger biases in two states, and no significantly
different biases in 29 states (46 states were analyzed), compared to CCSM4CLM4 (Fig. S3a). For Tmax, the results are mixed. WRF-CLM4 has significantly
smaller biases in 13 states, larger biases in 17 states, and no significantly different
biases in 16 states (Fig. S3b). WRF-CLM4 has larger biases than CCSM4-CLM4
in the western states and in the southeastern U.S. (except Florida), and generally
smaller biases than CCSM4-CLM4 in the upper midwest and northeast. The areas
6
in the western U.S. where WRF-CLM4 has larger Tmax biases than CCSM4CLM4 coincide with areas where WRF has excessive humidity and rainfall in the
western/northwestern U.S., (Figs. S1 and S2) which lead to cold biases during the
daytime when Tmax occurs (Fig S3d). Conversely, the southeastern states where
WRF-CLM4 is worse than CCSM4-CLM4 have warm biases (Fig. S3d) that may
be related to deficient rainfall, humidity and long wave radiation in WRF (Figs.
S1 and S2).
In summary, WRF-CLM4 generally simulates daily minimum temperatures
with smaller biases than CCSM4-CLM4, whereas the results are mixed for daily
maximum temperatures as a result of regional downwelling long- and short wave
radiation biases in WRF. The latter finding supports the argument that higher
spatial resolution does not translate into better results for every case (e.g., Pielke
Sr and Wilby, 2012); adequately simulating processes at all spatial scales is at
least as important as increasing spatial resolution. Perhaps a noteworthy result
from the present analysis is that WRF-CLM4 has statistically-significantly smaller
Tmax biases for Texas, Arizona and New York compared to CCSM4-CLM4, and
smaller Tmin biases for Texas (there is no difference for Arizona and New York).
These are states where three-of-the-four cities are located in which future changes
in heat stress are analyzed (Houston, Phoenix and New York).
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Mearns LO, Arritt R, Biner S et al (2012) The North American Regional Climate Change
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Table S1
Spatial root mean square error (RMSE), spatial pattern correlation, and mean
bias from comparison of summer (June-August) 1986-2005 climatology between WRF and
NLDAS as shown in Figures S1 and S2 (bottom rows). Downward longwave radiation (LW; W
m-2), shortwave radiation (SW; W m-2), rain (mm day-1), atmospheric air temperature (T atm; °C), 2m height air temperature (T2m; °C), and atmospheric specific humidity (qatm; g kg-1). Tatm and qatm
from WRF are at a height of 30m, and a height of 2m for NLDAS. T 2m for WRF is from the PD
MD simulation.
LW
SW
Rain
Tatm
T2m
qatm
Spatial RSME
14.6
27.6
1.3
2.0
1.8
1.6
Spatial Correlation
0.95
0.68
0.57
0.93
0.94
0.93
Mean Bias
-6.3
11.2
-0.1
-0.4
-0.3
-0.9
10
Fig. S1 Summer (June-August) 1986-2005 climatology of downward longwave radiation,
downward solar radiation, and rain from WRF (top row), CCSM4 (middle row), and WRFNLDAS (bottom row). WRF and WRF-NLDAS are at 1/8° spatial resolution. CCSM4 is at a
resolution of 0.9375° latitude by 1.25° longitude.
11
Fig. S2 As in Fig. S1 but for atmospheric air temperature, 2-m height air temperature, and
atmospheric relative humidity. The atmospheric air temperature and relative humidity height for
WRF is 30m, for CCSM4 is about 60m, and for NLDAS is 2m. The 2-m air temperature for WRF
is taken from the PD MD simulation.
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a.
b.
c.
d.
Fig. S3 Evaluation of summer (June-August) 1986-2004 daily maximum and minimum
temperature from WRF-CLM4 and CCSM4-CLM4 simulations versus the NCDC observations
described in the text, with results averaged by state. (a-b) Percentage improvement of WRF-CLM4
bias versus CCSM4-CLM4 bias for Tmin and Tmax (red values indicate WRF-CLM4 has smaller
biases, whereas blue values indicate CCSM4-CLM4 has smaller biases). (c-d) Bias for WRFCLM4 Tmin and Tmax versus NCDC. For all 4 panels, white states indicate those in which there
is no statistically-significant difference in the biases between CCSM4-CLM4 and WRF-CLM4.
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