ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-14813-2018Impact of physical parameterizations and initial conditions on simulated
atmospheric transport and CO2 mole fractions in the US MidwestImpact of physical parameterizations and initial conditionsDíaz-IsaacLiza I.lzd120@psu.eduLauvauxThomashttps://orcid.org/0000-0002-7697-742XDavisKenneth J.https://orcid.org/0000-0002-1992-8381Department of Meteorology and Atmospheric Science, Pennsylvania
State University, University Park, PA 16803, USAnow at: Scripps Institution of Oceanography, University of
California, San Diego, CA 92093, USALiza I. Díaz-Isaac (lzd120@psu.edu)16October2018182014813148351February201816March201827July201813September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/14813/2018/acp-18-14813-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/14813/2018/acp-18-14813-2018.pdf
Atmospheric transport model errors are one of the main
contributors to the uncertainty affecting CO2 inverse flux estimates.
In this study, we determine the leading causes of transport errors over the
US upper Midwest with a large set of simulations generated with the Weather
Research and Forecasting (WRF) mesoscale model. The various WRF simulations
are performed using different meteorological driver datasets and physical
parameterizations including planetary boundary layer (PBL) schemes, land
surface models (LSMs), cumulus parameterizations and microphysics
parameterizations. All the different model configurations were coupled to
CO2 fluxes and lateral boundary conditions from the CarbonTracker
inversion system to simulate atmospheric CO2 mole fractions. PBL
height, wind speed, wind direction, and atmospheric CO2 mole fractions
are compared to observations during a month in the summer of 2008, and
statistical analyses were performed to evaluate the impact of both physics
parameterizations and meteorological datasets on these variables. All of the
physical parameterizations and the meteorological initial and boundary
conditions contribute 3 to 4 ppm to the model-to-model variability in
daytime PBL CO2 except for the microphysics parameterization which has
a smaller contribution. PBL height varies across ensemble members by 300 to
400 m, and this variability is controlled by the same physics
parameterizations. Daily PBL CO2 mole fraction errors are correlated
with errors in the PBL height. We show that specific model configurations
systematically overestimate or underestimate the PBL height averaged across
the region with biases closely correlated with the choice of LSM, PBL
scheme, and cumulus parameterization (CP). Domain average PBL wind speed is overestimated in nearly
every model configuration. Both planetary boundary layer height (PBLH) and PBL wind speed biases show coherent
spatial variations across the Midwest, with PBLH overestimated averaged
across configurations by 300–400 m in the west, and PBL winds overestimated
by about 1 m s-1
on average in the east. We find model configurations with
lower biases averaged across the domain, but no single configuration is
optimal across the entire region and for all meteorological variables. We
conclude that model ensembles that include multiple physics
parameterizations and meteorological initial conditions are likely to be
necessary to encompass the atmospheric conditions most important to the
transport of CO2 in the PBL, but that construction of such an ensemble
will be challenging due to ensemble biases that vary across the region.
Introduction
The increase in atmospheric carbon dioxide (CO2) mole fraction is a
primary factor that is changing the radiation budget and causing significant
changes in the Earth's climate (IPCC, 2013). Atmospheric mole fractions have
increased primarily due to fossil fuel combustion and land use change. Not
all CO2 emitted remains in the atmosphere because the terrestrial
biosphere absorbs about 30 % of the emissions (Le Queré et al., 2015).
Terrestrial ecosystems in the temperate northern latitudes are identified as
a substantial sink (Tans et al., 1990; Ciais et al., 1995; Gurney et al.,
2002; Sarmiento et al., 2010; Pan et al., 2011; Le Queré et al., 2015).
However, the specific magnitudes and distributions of terrestrial sources
and sinks are still uncertain. Accurate and precise quantification of these
fluxes is an important step towards a successful prediction of future
atmospheric CO2 and climate change mitigation.
One method used to estimate the terrestrial fluxes is the “top-down” or
atmospheric inverse method. Atmospheric inversions use simulations of
atmospheric CO2 to estimate carbon fluxes (i.e., prior fluxes) by
adjusting these fluxes so that simulated CO2 is optimally consistent
with observed CO2 mole fractions (e.g., Enting, 1993; Bousquet et al.,
2000; Chevallier et al., 2010). Uncertainties in the inverse method can be
caused by sparse atmospheric data (Gurney et al., 2002), uncertain prior
flux estimates (Huntzinger et al., 2012), limited spatial resolution in
biospheric and atmospheric models, and transport model errors (Stephen et
al., 2007; Gerbig et al., 2008; Pickett-Heaps et al., 2011; Díaz Isaac
et al., 2014). Despite progress in top-down methodologies, these sources of
uncertainty have hindered the accuracy and precision of inverse estimates of
sources and sinks from terrestrial ecosystems at continental scales (Le
Quéré et al., 2015).
Current atmospheric inversion systems are limited to the optimization of
surface fluxes. However, the model–data mismatches used to optimize the
fluxes contain the contributions of both flux and transport errors.
Therefore, the atmospheric inversions may attribute atmospheric CO2
model–data mismatches to surface fluxes. In a Bayesian framework, the
atmospheric inversion assumes (1) atmospheric transport model errors are
unbiased and (2) the random errors are known. Incorrectly prescribed errors
(i.e., random and systematic) will be propagated into the state space by the
optimization process, generating biased inverse (i.e., posterior) fluxes
(Tarantola, 2005). The atmospheric inverse system will be reliable only if
both the atmospheric transport random errors are quantified rigorously and
the transport model is unbiased.
To date, relatively few studies have focused on atmospheric transport
errors. The Atmospheric Tracer Transport Model Intercomparison Project
(TransCom) has been dedicated to quantifying atmospheric transport errors
and their impact on CO2 fluxes through model intercomparisons (Gurney
et al., 2002; Baker et al., 2006; Stephen et al., 2007; Patra et al., 2008;
Peylin et al., 2013). As intercomparison exercises, TransCom studies were
not always limited to varying atmospheric transport but at times also
varied the number of observations, the inverse methodologies, and the prior
fluxes that were used. Some of these studies have concluded that only an
atmospheric transport model capable of representing synoptic and mesoscale
atmospheric dynamics will be able to extract high-resolution information
from atmospheric observations (Law et al., 2008; Patra et al., 2008).
Following these recommendations, the spatial resolution of transport models
used to simulate atmospheric CO2 mole fractions has increased to
capture local-scale variability in continental observations (e.g., Ahmadov et
al., 2009). Díaz Isaac et al. (2014) showed significant differences in
the atmospheric CO2 model–data mismatches when comparing a
lower-resolution global transport model to a high-resolution regional
transport model, but using identical surface fluxes, suggesting that changes
in the transport model resolution could lead to large differences in inverse
surface flux estimates.
A critical problem in atmospheric transport resides in the representation of
vertical mixing, which significantly impacts the interpretation of
near-surface CO2 mole fractions and the resulting inverse CO2 flux
estimates (Denning et al., 1995; Stephens et al., 2007). As a result,
several studies have been dedicated to the evaluation of mixed layer (ML)
depth (Yi et al., 2004; Gerbig et al., 2008; Kretschmer et al., 2012). An
overestimation of the ML depth by an atmospheric model, for example, will
cause an overestimation of the CO2 surface flux magnitude. The
misrepresentation of vertical mixing by TransCom's atmospheric models shown
by Stephens et al. (2007) led Gerbig et al. (2008) to evaluate uncertainty
in ML depth using a regional model and find random errors on the order of
several ppm in ML CO2 mole fractions in summertime over western Europe.
Sarrat et al. (2007) used an intercomparison of five mesoscale models and
identified discrepancies in the ML depth that were potentially impacting the
atmospheric CO2 mole fractions. These studies have attributed the
differences between simulated and observed mixed ML height to flaws in
planetary boundary layer (PBL) schemes and land surface models (LSMs). The
accurate representation of the ML depth, however, is a necessary but most
likely insufficient step for accurate and precise simulation of CO2
mole fractions in the lower troposphere. Mixing between the ML and the rest
of the atmosphere is also an important factor in the relationship between
surface fluxes of CO2 and ML CO2 mole fractions. It is likely that
parameterizations other than the PBL and LSM will influence ML CO2 mole
fractions.
Intercomparison of physical parameterization schemes using the Weather
Research and Forecasting (WRF; Skamarock et al., 2008) mesoscale model has
been explored to understand the impact of physics parameterizations on the
CO2 mole fractions (Kretschmer et al., 2012; Yver et al., 2013; Lauvaux
and Davis, 2014; Feng et al., 2016). These studies have found that
parameterization choices can result in systematic errors of several ppm in
atmospheric PBL CO2 that can lead to biased surface flux estimates.
These studies performed pseudo-data experiments or used a small number of
observations, and focused mostly on the impact of different PBL physics
schemes. There is agreement among the studies that misrepresentation of
vertical mixing causes biases in ML CO2 mole fractions, and that these
biases directly affect inverse flux estimates. Vertical mixing, however, is
not solely affected by the PBL parameterization. Therefore, investigations
of vertical mixing of CO2 remain incomplete. Additional
parameterizations that impact the transport of air masses both horizontally
and vertically should be evaluated.
In this work, we study uncertainty in an atmospheric transport model using a
multi-physics approach not limited to the evaluation of the PBL schemes and
LSMs. This evaluation will include different LSMs, cumulus parameterizations
(CPs), microphysics parameterizations (MP), and initial and boundary
conditions used by the WRF model. We will evaluate model performance using
observations of atmospheric transport variables, PBL depth, wind speed, and
wind direction, expected to be most important to ML CO2 mole fractions.
We aim to quantify the uncertainty of the atmospheric transport model and
propagate these errors into the CO2 mole fractions. We will focus on
the following questions. How do different physical parameterization schemes
affect ML CO2 mole fractions? Are some physics parameterizations more
effective/accurate than others at simulating atmospheric conditions
important to interpreting CO2 mole fraction observations in the PBL?
What are the nature and magnitude of random and systematic errors in the WRF
model, and how does this depend on model configuration? We will address
these questions by exploring atmospheric transport model performance over a
large, densely instrumented region, the US Midwest, site of the
Mid-Continent Intensive (MCI) study (Ogle et al., 2006). Evaluating the
atmospheric transport during summer, the most biologically active time of
the year, is a first step toward a more rigorous and complete atmospheric
inversion that quantifies random transport errors more accurately and
minimizes transport biases. This work will expand our ability to assess,
understand, and reduce transport errors in future atmospheric inversions.
MethodsRegion
The region selected for our study is the Midwest region of the United States
(Fig. 1). The US Midwest was chosen because the first multiyear
(2007–2009) campaign with a high-density CO2 measurement network was
deployed in this region (Ogle et al., 2006; Miles et al., 2012). This field
campaign, part of the North American Carbon Program (NACP), was called the
MCI and encompassed the agricultural belt in the
north-central US. The MCI campaign is unique for its density of
well-calibrated (Richardson et al., 2012) atmospheric CO2 mole fraction
measurements intended to constrain the region's carbon budget. We describe
the operational rawinsonde and greenhouse gas (GHG) tower networks over the region in Sect. 2.7. These networks provided significant observational constraint on both
transport and GHG mole fractions, which allow us to evaluate and quantify
the atmospheric transport errors in this study.
Geographical domain used by the WRF-ChemCO2 physics ensemble.
The parent domain (d01) is resolved at 30 km in the horizontal; the inner
domain (d02) is at 10 km. The color shading represents modeled terrain height
in meters above sea level. The inner domain covers the study region and
includes the rawinsonde sites (red circles) and the CO2 tower locations (blue
triangles).
Atmospheric model setup
The atmospheric transport model used in this study to generate our 45-member
physics ensemble is the WRF model version 3.5.1 (Skamarock et al., 2008) and
a modified chemistry module for CO2 (called WRF-ChemCO2;
Lauvaux et al., 2012). The atmospheric column in each simulation is described
with 59 vertical levels, with 40 of them within the first 4 km of the
atmosphere. Two nested domains were used. The coarse domain (d01) uses a
horizontal grid spacing of 30 km and the nested or inner domain (d02) uses
10 km grid spacing (Fig. 1). Because of limited computational time and the
resolution of the CO2 surface fluxes described on Sect. 2.6, we
decided to keep our highest resolution of the model up to 10 km. The coarse
domain covers most of the United States and parts of Canada, and the nested
domain is centered over Iowa and covers the Midwest region of United States.
The nesting method employed is the “one-way” nesting in which the outer
domain constrains the inner domain through nudging of the boundary conditions
that drive the meteorology once the outer domain simulation has finished
(Soriano et al., 2002). No feedback from the inner domain to the coarse
domain was allowed. For our sensitivity study, only the inner domain (d02)
has been analyzed as it covers the area of interest.
Ensemble configuration
Similar to any domain-limited atmospheric model, transport errors arise from
initial and boundary conditions and the different physics parameterizations.
Therefore, we have built an ensemble of 45 members using different physical
parameterization schemes and large-scale initial and boundary conditions
from reanalysis products (see Table 1). WRF offers multiple options for the
LSM, PBL, cumulus, and microphysics schemes. The members in our
multi-physics ensemble all use the same radiation schemes (both longwave and
shortwave) but the land surface, surface layer, boundary layer, cumulus, and
microphysics schemes are varied for both the inner and the outer domain. In
addition, we have initialized the meteorological boundary and initial
conditions with different datasets. Table 2 shows the different options used
in this study.
Different model configurations used in this study.
The land surface models (LSMs), which ingest land surface properties, soil,
and surface conditions from driver data, simulate the conditions at the land
surface, including surface energy fluxes. The partitioning of these fluxes
affects the structure and depth of the PBL through the turbulence
parameterization, hence modifying the near-surface in situ CO2 mole
fractions. To evaluate the sensitivity of modeled mole fractions to the
surface conditions, three LSM schemes are chosen for this study: the five-layer
soil thermal diffusion model (Dudhia, 1996), the Noah land surface model
(Chen and Dudhia, 2001), and the Rapid Update Cycle (RUC) (Smirnova et al., 2000).
These LSMs differ in several aspects, from the description of soil
properties to the physical processes driving the land–surface interactions.
The thermal diffusion model uses a simple thermal diffusion equation to
transfer thermal energy from the ground to the atmosphere, describing the
belowground profile with five soil layers (Dudhia, 1996). This LSM also
includes snow-covered land and constant soil moisture values for a given
land use type and season. The Noah LSM scheme uses time-dependent soil
temperature and moisture for four soil layers, canopy conductance and
moisture, and snow cover prediction (Chen and Dudhia, 2001). The RUC LSM
scheme includes six soil layers and includes the effects of vegetation,
canopy water, and snow (Smirnova, 2000). This scheme also includes
parameterizations for snow and frozen soil (Smirnova, 2000).
WRF physical parameterizations included in the sensitivity
analysis.
ParameterOptionsLand surface modelNoah (Chen and Dudhia, 2001)Rapid Update Cycle (RUC; Smirnova, 2000)Five-layer thermal diffusion (Dudhia, 1996)Planetary boundary layerYonsei University (YSU; Hong et al., 2006)schemeMellor–Yamada–Janjic (MYJ; Janjic, 2002)Mellor–Yamada–Nakanishi–Niino Level 2.5 (MYNN2.5;Nakanishi and Niino, 2004)Surface layerMM5 similarity/YSU PBL schemeEta similarity/MYJ PBL schemeMYNN surface layer/MYNN PBL schemeCumulusKain–Fritsch (KF; Kain, 2004)Grell-3D (G3D; Grell and Devenyi, 2002)No cumulus parameterizationMicrophysicsWSM 5-class (Hong et al., 2004)Thompson et al. (2004)Shortwave/longwaveDudhia/Rapid Radiative Transfer Model (RRTM)radiation physicsInitial and boundary conditionsNorth America Regional Reanalysis (NARR)Global Final Analysis (FNL)Planetary boundary layer (PBL) schemes
The planetary boundary layer (PBL) is directly influenced by frictional
drag, sensible heat flux, and evapotranspiration, all of which are
responsible for generating turbulent eddies. The PBL schemes parameterize
turbulent vertical fluxes of heat, momentum, and moisture within the PBL and
throughout the atmosphere. The three PBL schemes used in this study are the
Yonsei University (YSU) (Hong et al., 2006) PBL scheme, the
Mellor–Yamada–Janjic (MYJ) (Janjic, 2002) PBL scheme, and the
Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (Nakanishi and Niino,
2004). These three PBL schemes differ in the treatment of turbulent
diffusion. The YSU scheme is a first-order scheme that includes non-local
eddy diffusivity coefficients to compute turbulent fluxes. The YSU scheme
explicitly calculates entrainment at the top of the PBL as a function of the
surface buoyancy flux. The MYJ and MYNN 2.5 PBL schemes are local closure
schemes that include a prognostic equation for turbulent kinetic energy
(TKE) and a level 2.5 turbulence closure approximation to determine eddy
transfer coefficients. The MYJ scheme implicitly calculates the entrainment
layer while the MYNN uses a more explicit representation of entrainment at
the top of the PBL (Román-Cascón et al., 2012). The MYNN 2.5 is a
variation of the MYJ PBL scheme that includes a non-local component of the
turbulent mixing that reduces potential cold biases and increases PBL
depths. The MYJ PBL scheme used in this study has been slightly modified to
allow for very low turbulence regimes (e.g., nocturnal stable conditions)
with a decreased minimum value for TKE.
Cumulus parameterizations
The cumulus parameterization (CP) schemes are used with the aim of
representing the vertical fluxes due to unresolved updraft and downdrafts
and compensating motion outside the clouds. In this study, we use two
different cumulus parameterization schemes, Kain–Fritsch (KF) (Kain, 2004)
and Grell-3D (G3D) (Grell and Devenyi, 2002). The KF scheme is a deep and shallow
convection subgrid scheme, which uses a simple cloud model that simulates
moist updrafts and downdrafts along with detrainment and entrainment
effects. The G3D cumulus scheme is based on the Grell (1993) scheme, and G3D
is a scheme for higher-resolution domains allowing for subsidence and
neighboring columns. The G3D uses a large ensemble of closure assumptions
and parameters that are used in numerical models and implements statistical
techniques to determine the optimal value for feedback to the entire model
(Pei et al., 2014). The cumulus parameterization is theoretically only valid
for coarse grid resolutions (e.g., greater than 10 km) and should not be
used when the model has a higher resolution (e.g., less than 5 km) and will
resolve cumulus convection (Skamarock et al., 2008). Therefore, we are in a
“grey zone” (5–10 km), where it is unclear if cumulus parameterization
should be used or not. For that reason, we also ran simulations that do not
use a cumulus parameterization scheme in the nested domain.
Microphysics parameterizations
Microphysics parameterizations (MPs) describe cloud and precipitation
processes. In this study, we use two MP schemes: the WRF single-moment
5-class (WSM5) scheme (Hong et al., 2004) and the Thompson scheme (Thompson
et al., 2004). The WSM5 scheme is a single-moment parameterization that
includes five species: water vapor, cloud water, cloud ice, rain, and snow,
which are all treated independently. The Thompson scheme is a double-moment
scheme, which predicts the mole fraction of five hydrometeors species, the
number concentration of ice phase hydrometeors, and rain.
Meteorological initial and boundary conditions
Two meteorological datasets provide the initial and lateral boundary
conditions for our regional model. For initialization, WRF interpolates the
coarse-resolution analysis products onto the model grid and calculates the
values of the parent domain lateral boundaries. The inner grid uses the
boundary conditions of the parent domain. In this study, we compare two
different meteorological datasets: the North America Regional Reanalysis
(NARR) (Mesinger et al., 2006) and the Final Operational Global Analysis
(FNL). The NARR dataset was developed at the Environmental Modeling Center
(EMS) of the National Centers for Environmental Prediction (NCEP). NARR uses
a high-resolution NCEP Eta model with a horizontal grid spacing of 32 km and
includes 45 vertical levels. NARR provides both initial and boundary
conditions at 3-hourly intervals. The NCEP FNL analysis data have a
horizontal grid spacing of 1∘×1∘ and are
prepared operationally every 6 h. The FNL is prepared with the same
model that NCEP uses in the Global Forecast System (GFS). The initial
conditions in the WRF simulations are reset every 5 days to avoid the growth
of model errors in the absence of data assimilation. The WRF model spin-up
takes about 18 h, so we use model results after 18 h of the first
day of each 5-day simulation segment. We compared model–model differences
over the 5 days and found no significant trend over the 5-day periods once
removing the first 18 h of spin-up.
CO2 surface fluxes
For this study, we used the summer 2008 posterior surface fluxes from the
data assimilation system CarbonTracker
CarbonTracker CT2009,
https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/ (last access: 17 January 2018)
version CT2009 (Peters et al.,
2007; with updates documented at https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/, last access: 17 January 2018). This system
produces CO2 flux estimates by integrating daily daytime averaged
CO2 mole fractions from continuous hourly observations and then
minimizing the differences between the observed and modeled atmospheric
CO2 mole fractions. The Transport Model 5 (TM5) offline atmospheric
tracer transport model (Krol et al., 2005), driven by the European Centre for
Medium-Range Weather Forecasts (ECMWF) operational forecast model,
propagates the surface fluxes to generate 3-D mole fractions of CO2
across the globe.
The CO2 surface fluxes are represented by different subcomponents,
which include fossil fuel emissions, biomass burning, terrestrial biosphere
exchange, and ocean–atmosphere exchanges. The annual fossil fuel emissions
used in CT2009 are from the Carbon Dioxide Information and Analysis Center
(CDIAC) (Boden et al., 2009). These fossil fuel fluxes are mapped onto a
1∘×1∘ grid and are then distributed into
country totals according to the spatial patterns from the EDGAR-4
inventories (Olivier et al., 2001). Biomass burning is based on the
Global Fire Emission Database version 2 (GFEDv2). The dataset consists of
1∘×1∘ gridded monthly burned areas, fuel
loads, combustion completeness, and fire emissions. Prior terrestrial
biosphere flux estimates come from the Carnegie–Ames–Stanford approach
(CASA) global biogeochemical model (van der Werf et al., 2006; Giglio et
al., 2006) with 3 h variability imposed by temperature and incoming
radiation (Olsen and Randerson, 2004). The CASA biosphere model produces net
primary production (NPP) and heterotrophic respiration fluxes with a monthly
time resolution at 0.5∘×0.5∘ spatial
resolution. The long-term ocean fluxes and uncertainties are derived from
inversions reported in Jacobson et al. (2007). Ocean inverse flux estimates
are composed of preindustrial (natural), anthropogenic flux inversions, and
an additional level of biogeochemical interpretations (Gloor et al., 2003;
Gruber et al., 1996). Similar to most CO2 inverse
systems, the fossil fuel and fire emissions are specified (i.e., remain
constant) and only the oceanic and terrestrial biosphere fluxes are
optimized.
Dataset
Our interest is to explore and quantify atmospheric transport errors over
the US Midwest using observations that we have over this region.
Therefore, we will evaluate the errors over the inner domain (d02) of our
models. Figure 1 shows the location of all the stations that provide
atmospheric CO2 mole fractions and the meteorological observation sites
that will be used. Meteorological data were obtained from the University of
Wyoming's online data archive (http://weather.uwyo.edu/upperair/sounding.html, last access: 20 July 2018) for the 14 rawinsonde
stations shown in Fig. 1. In situ atmospheric CO2 mole fraction data
are provided by gas analyzers operating continuously on seven communication
towers (Fig. 1) (Miles et al., 2012). Five of these towers were part of an
experimental network, deployed from 2007 to 2009 (Richardson et al., 2012;
Miles et al., 2012, 2013; 10.3334/ORNLDAAC/1202). The other two towers (Park
Falls – WLEF and West Branch – WBI) are part of the Earth System Research
Laboratory/Global Monitoring Division (ESRL/GMD) tall tower network (Andrews
et al., 2014; https://www.esrl.noaa.gov/gmd/ccgg/insitu/, last access: 20 July 2018). Each of these
towers sampled air at multiple heights, ranging from 11 to 396 m above ground level (a.g.l.).
Data selection
Most atmospheric inversions that use continental PBL observations only use
daytime CO2 mole fractions from continuous observations (Law et al.,
2003), with the exception of mountain sites whose nighttime data are thought
to sample free tropospheric conditions (Brooks et al., 2012). Only daytime
measurements are assimilated due to the difficulty in simulating strong
vertical gradients in the nocturnal boundary layer. Vertical gradients are
minimized during daytime under well-mixed boundary layer conditions (Bakwin
et al., 1998). Therefore, both models and observations will be evaluated
during daytime.
We analyzed CO2 mole fractions collected from sampling levels at or
above 100 m a.g.l., which is the highest observation level available across the
entire MCI network (Miles et al., 2012). This ensures that the observed mole
fractions reflect the influence of regional CO2 fluxes and are
minimally influenced by near-surface gradients of CO2 in the
atmospheric surface layer (ASL) due to local CO2 fluxes (Wang et al.,
2007). Both observed and simulated CO2 mole fractions are averaged from
18:00 to 22:00 UTC (12:00–16:00 LST), the daytime period when the boundary
layer should be convective and the CO2 profile well mixed (e.g., Davis
et al., 2003; Stull, 1988). This averaged mole fraction will be referred to
hereafter as the daily daytime average (DDA).
In this study, we will also evaluate the PBL wind speed (hereafter wind
speed), PBL wind direction (hereafter wind direction), and PBL height (PBLH)
from the different rawinsonde stations. Similar to the CO2 mole
fractions, we want our meteorological observations to be within the
well-mixed layer. Therefore, we use the wind speed and wind direction
observed approximately 300 m a.g.l. CO2 mole
fraction observations were sampled at about 100 m; however, the availability
of meteorological observations at this height is too low to collect a
sufficient amount of data for our statistical evaluation. The observed PBLH
was estimated using the virtual potential temperature gradient with a
threshold of 0.2 K m-1. We want our simulated meteorological variables to be
close to the observational level; therefore, we use wind speed and wind
direction from level 11 (∼350 m) of the model. The WRF model
provides an estimate of the PBLH, but the methodology used to diagnose these
values varied with the PBL scheme used in the simulation. To remain
consistent, we decided to calculate the PBLH in WRF with the same potential
temperature gradient method that is used for the rawinsonde data. Rawinsonde
stations across this region collect data at 12:00 and 00:00 UTC, but our
model–data evaluation will be done for daytime conditions only. Therefore,
both the modeled results and data will be evaluated in the late afternoon
(i.e., 00:00 UTC) corresponding to well-mixed conditions.
Evaluation methodology or analyses of the models
Comparisons to measurements of wind speed, wind direction, PBLH, and DDA
CO2 mole fractions are used to inform the performance of each model
configuration. Modeled data are extracted from the simulations using the
nearest grid points to the locations of our observations. Each model
configuration is evaluated from 18 June to 21 July 2008 for the
meteorological variables and from 26 June to 22 July 2008 for the CO2
mole fractions. Summer in the US Midwest corresponds to the peak of the
growing season for both crops and most non-agricultural ecosystems (except
grasslands). We focus here on the growing season because the large biogenic
fluxes make this period the most important time of year for understanding
the relationship between fluxes and CO2 mole fractions. We first
explore meteorological variables, and the sensitivity of CO2 to
atmospheric transport but without comparison to observations, to avoid
confounding the impact of transport with errors from CO2 surface fluxes
and CT2009 global CO2 mole fractions. Finally, we compare CO2
observations with the knowledge that the results include both transport and
CO2 flux errors.
Analyses of physics parameterization and reanalyses impact
The daily mean of root mean square difference (RMSD) among ensemble members
is used to isolate the atmospheric transport variability and evaluate the
impact of the physics parameterizations on both CO2 mole fractions and
PBL dynamics. The RMSD does not consider the observations as we take the
square root of the average difference between model configuration and the
ensemble mean:
RMSD‾=∑i=1N1n∑j=1npji-μi2N,
where pji is the predicted variable for ensemble member j and day i,
μi is the mean of the ensemble for day i, N is the total number of days,
and n is the number of members. The RMSD was estimated for the different
physics parameterization used (i.e., LSM, PBL schemes, CP, MP) and
reanalysis. A different set of ensembles were created for each of the
physics parameterization, where the model configuration remained identical
except for the tested physics parameterization, and the different set of
members was used to compute the ensemble mean. The RMSD of the simulated
CO2 mole fractions was used to explore if other physics
parameterizations have a significant impact on CO2 mole fractions
compared to the PBL parameterizations. To explore which parameterizations
impact the PBL dynamics, we applied the RMSD to the three selected
meteorological variables (i.e., PBLH, wind speed, and wind direction),
assuming these variables contribute the most to the representations of the
CO2 mole fraction distributions in the PBL. The RMSDs for the
meteorological variables were then averaged across all of the rawinsonde
sites. The RMSD of the CO2 mole fractions was estimated using the
simulated CO2 mole fraction at each communication tower and then
averaged across the tower sites to match the model–data residual.
Analyses of model–data residuals
A series of statistical analyses are used to assess the performance of the
different model configurations for the three meteorological variables' wind
speed, wind direction, and PBLH. The different metrics used include the root
mean square error (RMSE) and mean bias errors (MBE):
RMSE=1N∑i=1Npi-oi2,MBE=1N∑i=1Npi-oi,
where oi is the observed variable for day i, pi is the predicted
variable for day i, and N is the total number of days. The RMSE represents the
magnitude of the model error without regard to the long-term mean (Wilks,
2011). The MBE describes the model–observations difference averaged errors
over the entire period (Wilks, 2011), and identifies model bias. These two
metrics are critical to inverse flux estimates as biases can arise from
day-to-day (which we will refer as random) or longer-term (systematic)
errors in the transport model. We acknowledge that the propagation of
meteorological errors to mole fractions, and mole fraction errors to
surface fluxes, is complex, but these metrics provide valuable insight into
model performance. Each of these statistics (i.e., RMSE and MBE) was
estimated for each model and each rawinsonde site using the late afternoon
(00:00 UTC) soundings.
Finally, we compare modeled and simulated PBL CO2. We use our
different model configurations, which all share the exact same surface
fluxes and identical boundary conditions to explore the impact of the
transport errors on CO2 mole fractions. We present the impact of model
configurations on the DDA CO2 mole fraction model–data mismatches (or
residuals) with Taylor diagrams and correlation between model–data residuals
in meteorological variables and DDA CO2 mole fractions. The Taylor
diagram relies on three nondimensional statistics: the variance ratio (model
variance normalized by the observed variance), the correlation coefficient,
and the normalized centered root mean square (CRMS) difference (Taylor,
2001). The variance ratio or normalized standard deviation (NSD) indicates
the difference in amplitude between the model and the observation. If this
ratio is less than 1.0, then the model tends to underestimate the amplitude
compared to the observation. The correlation coefficient measures the
similarity in the temporal variations between the model and the observation,
regardless of the amplitude. This correlation coefficient has a range of
-1.0≤R≤1.0 and is insensitive to systematic errors. As R
approaches 1.0, the model approaches agreement with the observation. The
CRMS is normalized by the observed standard deviation and quantifies the
ratio of the amplitude of the variations between the model and the
observation. The CRMS is also insensitive to systematic errors. Temporal
correlations between the modeled–observed residual in meteorological
variables and CO2 mole fractions are used to determine the impact that
meteorological errors have on the PBL CO2 mole fractions. This
model–data correlation will be done between each CO2 observing site and
rawinsonde site; therefore, we will be able to observe if any correlation is
dependent on the distance between sites. The model–data residual includes
both flux and transport errors; therefore, these errors will not show the
accuracy of the transport model. Nevertheless, each simulation uses the same
CO2 flux and boundary conditions that allow us to use the model–data
residuals as an indicator of the differences between model configurations.
ResultsImpact of physics parameterizations on atmospheric CO2 mole
fractions
The daily mean of RMSD of the simulated CO2 mole fraction was used
to explore the sensitivity of CO2 mole fractions to model physics
parameterization and meteorological reanalysis. The RMSD was computed for
different parameterizations schemes (i.e., LSM, PBL, CP, and MP) and for two
reanalysis products (i.e., NARR and FNL). For each group of
parameterizations, the model configuration remained identical except for the
tested parameterization scheme. For example, to evaluate the impact of LSM
schemes on CO2 mole fractions, three LSM schemes were used while
preserving the exact same physical schemes for the PBL, CP, MP, and the
reanalysis data. Figure 2 shows the results of these experiments.
CO2 mole fraction RMSD is greatest for the LSM, followed by the PBL
scheme and CP. The microphysics parameterization has the least impact on
CO2 mole fractions. Only two microphysics parameterizations are
tested in this ensemble but additional tests using only two options for all
the different physic parameterizations produced similar results.
We also explore how much the variability in PBL winds and depth is
influenced by physics parameterizations. Figure 3 shows the RMSD of PBL wind
speed and direction, and PBLH over the entire simulation period. The results
for all three meteorological variables are similar to those for CO2
mole fractions. Reanalysis has a greater impact on wind speed (Fig. 3b)
and wind direction (Fig. 3c) than it does on PBLH (Fig. 3a). It is worth
noting that the PBLH RMSD (Fig. 3a) shows the same RMSD ranking (i.e.,
relative importance of the physics) as for CO2 mole fraction RMSD
(Fig. 2).
Sensitivity of CO2 mole fractions as a function of model
physics parameterizations, i.e., land surface model (LSM), planetary boundary
layer scheme (PBL), cumulus parameterization (CP), microphysics
parameterization (MP) and reanalyses. The root mean square difference (RMSD)
of the CO2 mole fractions simulated at each site and for each model
ensemble member was computed by varying only the type of physics
parameterization noted and keeping all other model elements constant. RMSD
was averaged across sites and across model ensembles.
Based on the evaluation of the CO2 mole fraction, wind speed, wind
direction, and PBLH RMSD, the LSM has the greatest impact on PBL CO2
transport, followed closely by the PBL scheme, CP, and reanalysis. All the
parameterization schemes, including the reanalysis data source, have a
significant impact on each of these variables. The RMSDs were significant
values compared to typical spatial and temporal differences (for PBL
CO2; see Miles et al., 2012) and for mean PBL properties (PBLH,
winds), confirming the importance of model parameterization for these
variables.
Root mean square difference (RMSD) of the PBLH (a), wind
speed (b), and wind direction (c) for the different physics parameterizations,
i.e., land surface model (LSM), planetary boundary layer scheme (PBL),
cumulus parameterization (CP), microphysics parameterization (MP) and
reanalyses. The RMSDs were computed in the same way for these variables as
for PBL CO2 in Fig. 2.
Meteorological day-to-day variability
Figure 4 shows a time series of the 00:00 UTC observed and simulated wind
speed (Fig. 4a), wind direction (Fig. 4b), and PBLH (Fig. 4c) from 18 June
to 21 July 2008 at the Chanhassen, Minnesota (MPX), rawinsonde site. Across the
study region, we found maximum monthly average model–data differences across
sites and configurations of 9 m s-1 for wind speed, 153∘ in wind
direction, and 2000 m for PBLH. These values confirm the large spread among
model results and sites over the simulation time period. Other sites have
similar characteristics to Fig. 4. The ensemble shows less variability
(i.e., relative spread of the ensemble compared to the observed variability)
for the wind speed and wind direction compared to the PBLH. The time series
at each rawinsonde site shows that for certain days, all ensemble are biased
(i.e., all the members either overestimate or underestimate) as compared to
observed wind speed and wind direction (e.g., DOYs 181 and 201, respectively).
The time series of the PBLH, however, shows that simulated PBLH can vary
significantly across the different physics configurations and that the
ensemble encompasses the observed PBLH over the time period.
Observed (black line) and simulated (colored lines; see
Table 1) PBL (300 m a.g.l.) wind speed (a), wind direction (b), and PBLH (c) at
00:00 UTC from day of the year (DOY) 169 to 203 of 2008 at the
Chanhassen, Minnesota (MPX), rawinsonde site.
Characterization of transport errorsRoot mean square error (RMSE)
Figure 5 shows the regionally and monthly averaged RMSE of wind speed
(Fig. 5a), wind direction (Fig. 5b), and PBLH (Fig. 5c) for the
different model configurations. For both wind speed and wind direction, we
found small to no differences in the regional RMSE as a function of model
configuration. Although the regional RMSEs for both wind speed and wind
direction are fairly constant, the two variables have the same two model
configurations with the highest RMSE. These two configurations share the
same LSM scheme (RUC) and the same PBL scheme (MYJ) (models 14 and 23; see
Fig. 5a, b and Table 1). Differences among configurations are larger in the
regional RMSE of the PBLH (Fig. 5c), with configuration RMSEs ranging from
680 to 1149 m. The model configurations that show the highest PBLH RMSEs
include the same LSM (RUC) and PBL parameterization scheme (YSU) (models 4,
13, 22, and 34; see Fig. 5c and Table 1). Although the configurations that
show the highest RMSEs are not always the same across the different
variables, these configurations share the same LSM (RUC). The two model
configurations that showed the lowest RMSE for both wind speed and wind
direction both used MYNN 2.5 as their PBL parameterization. Many
configurations show low RMSE for the PBLH and all the configurations with
low RMSE use either the MYJ scheme or the MYNN 2.5 scheme. However, no
single configuration performs best at the regional scale for all of the
meteorological variables.
Regional averages of the monthly average of wind speed (a), wind direction (b), and PBLH (c)
RMSE for the different models (see
Table 1 for model configurations).
Ensemble mean of monthly averaged RMSE of wind speed (a),
wind direction (b), and PBLH (c).
We computed the ensemble mean of the monthly averaged RMSE at each of the
rawinsonde sites for wind speed (Fig. 6a), wind direction (Fig. 6b), and
PBLH (Fig. 6c). We did not find any regional patterns in wind speed
(Fig. 6a) and wind direction (Fig. 6b). However, PBLH shows that the
highest RMSEs are located in the west of the domain, with an RMSE 400 m or
higher than the sites in the east.
Monthly average wind speed (a–c), wind direction (d–f), and
PBLH (g–i) RMSE for rawinsonde sites North Platte Regional Airport, Nebraska (LBF) (first row), MPX (second row), and
Gaylord, Michigan (APX) (third row). Models are sorted from the smallest to the highest RMSE.
Model configurations are ordered by RMSE and identified by color (see Table 1).
Figure 7 shows the monthly average RMSE of wind speed (Fig. 7a–c), wind
direction (Fig. 7d–f), and PBLH (Fig. 7g–i) for each model configuration
at specific rawinsonde sites. We computed the RMSE for all the different
sites (not shown), and we found the highest RMSE in the model configurations
that included RUC and thermal diffusion as the LSM and at some sites when
these LSMs were combined with YSU as a PBL scheme. Although the RMSE was
computed at each of the rawinsonde sites, we show only three sites located
in three different regions of the domain: North Platte Regional Airport, Nebraska (LBF), in the west (Fig. 7a, d, g),
MPX, which is close to the center of the domain (Fig. 7b, e, h), and Gaylord, Michigan (APX) (third row), in
the eastern part of the domain (Fig. 7c, f, i). Similar to the regional
RMSE (Fig. 5), both LBF and MPX show that the LSM RUC leads to the highest
RMSE for the three meteorological variables. However, this pattern is not
found at APX, where other configurations show the highest RMSE for wind
speed, wind direction, and PBL height. Across simulations and meteorological
variables, RMSEs vary, but no configuration shows a lower value across all
sites.
Mean bias error (MBE)
The average over- or underestimation of the model configurations is assessed
by computing the regional monthly average MBE for wind speed (Fig. 8a),
wind direction (Fig. 8b), and PBLH (Fig. 8c). In this study, a positive
MBE means the model configuration is systematically higher than the
observation. We found remarkable variations in the regional MBE both as a
functions of different model configurations and across the meteorological
variables. The regionally averaged PBL wind speed bias for any single
ensemble member ranges from -0.2 to 1.2 m s-1, relative to the mean regional
midday wind speed of 6.2 m s-1, showing that the bias of any single ensemble
member ranges from less than 5 % to nearly 20 % of the regional mean PBL
wind speed (Fig. 8a). All configurations that use YSU (e.g., models 1, 4,
7, 10; see Fig. 8a and Table 1) have greater regional wind speed biases
than the rest of the PBL schemes. The regional MBE for wind direction varies
according to model configuration. Models using YSU as PBL schemes tend to
show a systematic positive bias in the wind direction (e.g., models 1, 4, 7,
10; see Fig. 8b and Table 1), whereas models that use MYJ as PBL scheme
show a negative bias (e.g., models 2, 5, 8, and 11; Fig. 8b and Table 1).
Similar to the wind direction, the regional PBLH bias is correlated with
model configuration. Any model configuration that uses YSU shows a positive
bias, larger than the rest of the PBL schemes (e.g., models 1, 4, 7, 11; see
Fig. 8c and Table 1). The model configurations that do not include cumulus
parameterizations (white filled bars; Fig. 8c) also show positive biases,
with one exception, regardless of the choice of LSM or PBL scheme used. The
wind speed analysis shows that the two model configurations with the
smallest regional MBE (±0.1 m s-1) share the same LSM (thermal
diffusion) and PBL (MYNN 2.5) parameterization. For wind direction, two of
the three model configurations with the lowest MBE (±0.1∘) use
the same LSM (Noah) and PBL (YSU) parameterizations. All 15 model
configurations with the lowest MBE for PBLH (±100 m or less) share
the same PBL parameterizations (MYJ and MYNN 2.5). Although the
configurations that provide the lowest regional MBE are not the same across
all variables, we found that the lowest biases for the three variables were
produced by model 18 (see Table 1). This model configuration is driven by the
NARR reanalysis product and used thermal diffusion as the LSM, MYNN as the PBL
scheme, Grell-3D as the CP, and WSM 5-class as the MP.
Regional average of the monthly average of PBL wind speed (a),
PBL wind direction (b), and PBLH (c) bias for the different model
configurations (identified by number and color; see Table 1).
Ensemble mean of the mean bias error (MBE) for PBL wind
speed (a), PBL wind direction (b), and PBLH (c).
The spatial structures of the MBE over a month are evaluated by estimating
the ensemble mean of the MBE at each rawinsonde site (Fig. 9). The
ensemble mean of the MBE reveals a spatial pattern in the wind speed (Fig. 9a) and PBLH (Fig. 9c). The map of wind speed MBE (Fig. 9a) shows that
the ensemble is positively biased in the eastern region of the domain.
However, sites in the western region of the domain show that the ensemble
average has either negative or near-zero wind speed MBEs. The PBLH MBE map
(Fig. 9c) also shows a clear spatial pattern, with the highest values,
nearly all positive, at sites located in the western part of the domain,
whereas the sites in the eastern part of our domain show a smaller MBE and
no distinct regional sign. PBL wind direction does not show any spatial
pattern in the ensemble mean of the MBE (Fig. 9b). We found that our
ensemble of simulations can produce an MBE range from rawinsonde site to
rawinsonde site of ±1.5 m s-1 in wind speed, ±20∘ for
wind direction, and ±400 m for PBLH.
Monthly average wind speed (a–c), wind
direction (d–f), and PBLH (g–i) MBE for rawinsonde sites Aberdeen, South Dakota (ABR) (first row),
Davenport, Iowa (DVN) (second
row), and Nashville Airport, Tennessee (BNA) (third row). Models are sorted from negative to positive
bias. Model configurations are ordered by MBE and identified by color (see
Table 1).
The MBE analysis was performed for all the sites (not shown); for this
statistic, we found that all the model configurations show a positive wind
speed MBE (overestimation) for the majority of the rawinsonde sites,
whereas wind direction and PBLH show both positive and negative
(underestimation) MBEs across the different configurations at the different
rawinsonde sites. Some of the positive and negative biases are associated
with specific LSMs and PBL schemes. Figure 10 shows the MBEs of three sites that
are representative of regional patterns. The three sites shown are located
in three different regions of the domain: Aberdeen, South Dakota (ABR), in the west (Fig. 10a, d,
g), Davenport, Iowa (DVN), which is close to the center of the domain (Fig. 10b, e, h), and
Nashville Airport, Tennessee (BNA), in the eastern part of the domain (Fig. 10c, f, i). Most of the model
configurations show positive wind speed MBE (overestimation) for the
majority of the rawinsonde sites (e.g., Fig. 10b–c); however, one site
shows both positive and negative MBEs for the different model configurations
(e.g., Fig. 10a). Overall, we found that 10 out of the 14 rawinsonde sites
show all the model configurations with a positive wind speed bias; these
sites were located in the eastern and center areas of the domain. However,
the MBEs for wind direction (e.g., Fig. 10d–f) and PBLH (e.g., Fig. 10g–i)
are highly variable across the rawinsonde sites. At the majority of
the sites, the simulations had both positive and negative biases. Although
wind speed and wind direction do not show any of the simulations with a
systematic behavior across the sites, PBLH MBE showed some simulations with
systematic bias across the different sites. The highest positive biases were
found in configurations that use RUC as the LSM and YSU as the PBL scheme in
the western region of the domain (e.g., Fig. 10g, red bars). This is
unlike the eastern region of the domain, where the highest biases were
dominated by configurations that use thermal diffusion as the LSM and YSU as
the PBL scheme (e.g., Fig. 10i, white bar with green border). These
results indicate that wind speed MBEs are strongly impacted by other
components of the model (e.g., reanalysis dataset) or that the WRF
transport model carries a systematic bias that will show up regardless of
the configuration used. However, PBLH bias is highly controlled by two
components of the model, the LSM and the PBL parameterization scheme.
Overall, the spatial patterns show that no configurations can avoid spatial
biases across the region.
Observed (black stars) and simulated (colored lines) DDA
CO2 mole fraction (ppm) at Centerville (RCV) (a) and
WBI (b).
Sensitivity of CO2 mole fractions to model configuration
Figure 11 shows simulated and observed atmospheric DDA CO2 mole
fraction for Centerville (Fig. 11a) and Kewanee (Fig. 11b) from 26 June
to 21 July 2008. For this period, both sites show large residuals that are
not encompassed by the ensemble spread (RMSD) for several periods (e.g., DOYs
182–183 at Centerville or DOYs 185–186 at WBI). This result suggests that
transport model errors from our ensemble only represent a fraction of the
total uncertainty in our modeling system. In this study, we use
CarbonTracker fluxes which is a global inversion system and does not aim to
represent regional fluxes. Therefore, additional errors can be due to
incorrect CO2 surface fluxes and boundary conditions.
Over the region, most of the sites show that the ensemble generally
underestimates the atmospheric CO2 mole. We note here that this
ensemble has not been calibrated; therefore, the ensemble spread is unlikely
to serve as quantification of WRF transport errors or total error in
simulated PBL CO2, but this sensitivity test could have resulted in an
ensemble spread that is much larger than the model–data differences. Our
results suggest that the spread of this physics ensemble underestimates
total model–data error in PBL CO2.
Taylor diagram comparing observations versus simulations
at (a) Mead (RMM) and (b) West Branch (WBI), using DDA
CO2 mole fractions from 100 m a.g.l. Black dots at (1,
1) represent the observations.
To evaluate the performance of the different models over the month, we
computed the correlation coefficient, the NSD and the CRMS difference
(Taylor, 2001) for each of the in situ sites. These results are presented as
Taylor diagrams (Fig. 12) using the DDA observed and simulated CO2
mole fractions. Nearly all ensemble members overestimate the temporal
variability at in PBL CO2 (e.g., Fig. 12a) and at some sites all
members overestimate the temporal variability (e.g., Fig. 12b). The
correlation between simulated and observed CO2 mole fractions can vary
from 0.8 to 0.1, indicating a wide range of model performance at
site level. Interestingly, some of the models that show a high correlation
between the modeled and observed DDA CO2 mole fractions are the model
configurations with the highest PBLH bias (see Fig. 8c, models 4 and 22).
The correlation between meteorological and CO2 mole fraction model–data
differences is evaluated using the MBE for each model at the different
rawinsonde and CO2 tower sites. These correlations (Fig. 13) reveal
that, to first order, errors in simulated PBLH govern model–data differences
in PBL CO2 mole fraction. Both wind speed (Fig. 13a) and wind
direction (Fig. 13b) show low correlations, whereas PBLH (Fig. 13c)
shows consistently positive correlation with the CO2 mole fraction
errors across all sites. We did not find any relationship between error
correlation and distance (see Fig. A1 in Appendix). These results suggest
that the bias errors in the in situ CO2 mole fractions are directly
related to the MBE in PBLH. The sign of the correlation (overpredicted PBLH
correlated with overpredicted PBL CO2) is expected given net uptake of
CO2 by the regional biosphere.
Tower and rawinsonde site-specific spatial correlation
coefficients between ensemble mean MBE of (a) wind speed, (b) wind direction
and (c) PBLH and ensemble mean MBE of DDA CO2 mole
fractions. The abscissa shows the different CO2 tower
sites, while the ordinate shows rawinsonde sites.
Discussion
The evaluation of the RMSD of daytime PBL CO2 mole fractions shows that
all the physics parameterizations have a significant impact on the simulated
values, with only the microphysics parameterization showing a lesser impact
(Fig. 2). Previous research has focused on the potential impact of PBL
schemes on CO2 mole fractions (e.g., Kretschmer et al., 2012, 2014;
Lauvaux and Davis, 2014). Results from our study indicate that other physics
parameterizations including the LSM and CP generate errors of similar
magnitude in simulated daytime PBL CO2 mole fractions (Fig. 2). The
PBLH is also sensitive to all of these physical parameterizations (Fig. 3a), and there is a high correlation between PBLH errors and CO2 mole
fraction errors (see Fig. 13c). In this sense, our results agree with
previous research that assumes that the misrepresentation of the PBLH plays
an important role in PBL CO2 errors (Stephens et al., 2007; Gerbig et
al., 2008; Kretschmer et al., 2012). We show, however, that multiple
elements of the modeling system, not just the PBL parameterization,
influence PBLH. Further, although PBL wind speed (Fig. 13a) and wind
direction (Fig. 13b) errors are not clearly correlated with PBL CO2
errors, this does not imply that these errors are unimportant. Figure 2 also
shows that the reanalysis has an impact on atmospheric CO2 mole
fractions, which indicates that even if the wind speed and wind direction
errors do not show a high correlation with atmospheric CO2 errors
(Fig. 13a–b), these two variables can contribute to the errors in CO2
mole fractions. Indirectly, we demonstrated that the reanalysis directly
impacts CO2 mole fractions by changing wind speed and direction in WRF
(Fig. 3b–c), whereas PBLH errors are primarily driven by physical schemes
(surface and PBL schemes). The relationship between PBL winds and CO2
mole fraction is dependent on the local spatial distribution of CO2
surface fluxes and could easily show no clear correlation when averaged over
time and space. However, we know errors in these two variables can impact
the distribution and magnitude of the inverse CO2 fluxes over the
region (Deng et al., 2017; Lauvaux and Davis, 2014).
The square root of the model errors (RMSE) of wind speed and wind direction
shows similar magnitudes in the errors regardless of the model configuration
that we use. Additionally, we found over the region a systematic positive
bias (MBE; overestimation) of wind speed for the majority of the model and
sites. These results, especially the ones from wind speed, lead us to consider
other elements of the models as a contributor of the errors. Past literature
has stated and shown that the WRF mesoscale model has a tendency to produce
high wind speed over land (Cheng and Steenburgh, 2005; Roux et al., 2009;
Zhang et al., 2009; Yerramilli et al., 2010; Jimenez and Dudhia, 2012). Some
studies have attributed this high wind speed bias to the smoothed
representation of the topography (Jimenez and Dudhia, 2012; Santos-Alamillos
et al., 2013). Biases of approximately 3 m s-1 have been attributed to this
misrepresentation of topography. Fovell and Cao (2014) argue that the
misrepresentation of the terrain, or possibly the vegetation, can produce a
biased roughness length that can lead to wind speed biases of about 2 m s-1.
There are other factors that contribute to wind speed errors such as the
reanalysis product (Fig. 3b). We recommend further analysis of the WRF
model and its driver and input data to better understand PBL wind speed
random and systematic errors.
Monthly averaged PBLH MBE for rawinsonde sites (a–b) and
sensible heat MBE for eddy covariance sites (c–d). The two rawinsonde
sites, MPX (a) and Omaha/Valley, Nebraska (OAX) (b), are close to the eddy covariance sites
KUOM Turfgrass Field, Minnesota (USKUT) (c), and
Mead Rainfed Maize, Nebraska (USNe3) (d), respectively. Model configurations are identified by color (see
Table 1).
The PBLH biases across the region show that the YSU PBL scheme tends to
produce higher PBLH than the MYJ PBL scheme. This is consistent with previous
studies (Hu et al., 2010; Coniglio et al., 2013; Milovac et al., 2016) that
have found local PBL schemes (MYJ and MYNN) producing shallower PBLHs
compared to non-local PBL schemes (YSU). As explained in Coniglio et al. (2013), the MYJ scheme produces cool and moist conditions near the ground
and hence low vertical mixing, whereas the YSU scheme produces warm and dry
conditions in the PBL resulting in deep mixing. Since daytime PBLH is
closely linked to the surface energy balance, an additional analysis was
performed using the sensible heat fluxes observed at eddy covariance
stations from the AmeriFlux network (Boden et al., 2013;
http://ameriflux.lbl.gov, last access: 17 January 2018). The sensible heat flux was averaged from 12:00 to
23:00 UTC, and we computed the MBE of the sensible heat flux for the eddy
covariance stations close to the rawinsonde sites. The MBE was estimated at
all the eddy covariance stations available over the region (not shown), and
we found that the highest positive sensible heat MBEs were found on
simulations that used YSU as the PBL scheme and RUC or thermal diffusion as the LSM.
Figure 14 shows PBLH MBE of two rawinsonde sites (Fig. 14a, b) and the
sensible heat MBE of two eddy covariance stations (Fig. 14c, d) close to
each of these rawinsonde sites. We found that model configurations that use
YSU as the PBL scheme in combination with RUC (Fig. 14c, d, red bars) or
thermal diffusion (Fig. 14a, b, green bars) as the LSM have the highest
positive bias for sensible heat flux (Fig. 14c, d), consistent with the
positive biases in PBLH associated with these configurations.
We also found a spatial pattern in PBLH bias averaged over all model
configurations (Fig. 9c), where the west region of the domain shows a
large positive bias, with no persistent bias in the east part of the
domain. This spatial gradient may be associated with the representation of
warmer and drier areas in the west and cooler and moister areas in the
eastern portion of the domain (Molod et al., 2015). This
spatially structured PBLH bias could be associated with the choice of LSM
since high biases are dominated by members using the thermal diffusion scheme
(e.g., Fig. 10i, white bar with green border) in the east region of the
domain and by members using RUC (e.g., Fig. 10g, red bars) in the west.
Although both the RUC and thermal diffusion LSM tend to show a higher PBLH bias
when the configuration includes YSU as PBL scheme, ensemble mean biases are
larger in the west because RUC LSM produces higher positive bias compared to
the thermal diffusion LSM. Also, we showed in Fig. 14 how these two LSMs tends
to overestimate the sensible heat. These results suggest that the different
LSMs could misrepresent the surface energy budget in spatially coherent ways
over the region, causing spatially coherent biases in the PBLH. Cumulus
parameterization also played an important role in the PBLH, as the ensemble
members that did not include a cumulus parameterization produced high positive
biases. This could be explained by the lack of parameterized subgrid-scale
convection that could, in reality, limit PBL growth. While we were not able
to find an optimal configuration across all the sites, we did find, similar
to Coniglio et al. (2013) and Milovac et al. (2016), that the MYNN PBL
scheme produced the smallest PBLH biases averaged over the region.
This ensemble helped us to understand and evaluate atmospheric transport
errors due to physics parameterization and reanalysis, and to understand how
these transport errors are propagated into simulated PBL CO2 mole
fractions. However, it is important to note several limitations of this
study: (1) we explore fewer microphysics and reanalysis options (only two)
compared to the number of PBL, cumulus, and LSM with three options for each;
(2) this evaluation was performed over a limited period of time and
location; (3) the range of parameterizations available for this sensitivity
study is ad hoc and uncalibrated; and (4) the cumulus parameterizations
utilized do not include parameterized transport of CO2. We also note
that some of the parameterizations (i.e., RUC LSM) were only run using the
FNL reanalysis product, which may cause some underestimation of the
variability as this LSM contributes significantly to the errors of all the
meteorological variables. Also, as noticed in recent studies, the
Grell–Freitas convection scheme produced more reliable simulations of the
atmospheric dynamics (Gao et al., 2017; Gbode et al., 2018). Therefore, we
recommend the use of newly developed schemes for future studies as model
schemes are made available in new model versions. The impact of CP on
CO2 mole fractions requires more evaluation, because our convective
scheme is not coupled with the tracers (i.e., CO2 mole fractions);
however, we can still use the convective schemes to evaluate its impact on
wind fields and PBLH. Consequently, we cannot yet quantify the impact of the
lack of parameterized cumulus transport of CO2 transport on our
findings. We also note that models were compared only to rawinsonde data,
the only type of observation that had both the temporal and vertical
resolution needed to evaluate the models within the PBL. More observations
with higher temporal, spatial, and vertical resolution will be an asset for
future evaluation of transport models, focusing on intensive campaigns over
multiple seasons. Our meteorological results, however, are broadly
consistent with past literature. The biases found in this study are a
concern, since atmospheric inversions assume atmospheric transport errors
are unbiased. Since this bias exists across the different meteorological
variables studied here, future selection of a least biased model may need to
weight the impact of each meteorological variable on CO2 or to optimize
the atmospheric transport model through a data assimilation technique. A
calibrated transport ensemble may be the most efficient approach to
generating an unbiased representation of atmospheric transport and
associated errors.
Conclusions
In this study, we evaluated and quantified the atmospheric transport errors
across a highly instrumented area, the Mid-Continent Intensive region of the
US Midwest, for the period 18 June to 21 July 2008. Transport errors
were quantified independently of flux errors and propagated into CO2
mole fractions using a multi-physics and multi-reanalysis ensemble. Each
model configuration was coupled to the same surface fluxes from
CarbonTracker CT2009. We conclude that all physics parameterizations except
for microphysics have a significant impact on both CO2 mole fractions
and meteorological variables. We also found that PBLH and CO2 mole
fractions have similar sensitivities to the different physics schemes. The
relationship between the two variables is reinforced by the high
correlations between PBLH errors and CO2 mole fraction errors. Among
the multiple configurations evaluated here, we intended to find the
configuration best suited to represent the atmospheric transport over the
region. However, we show no single model configuration was free from bias
for every meteorological variable (PBLH, wind speed, and wind direction) and
these biases vary across the domain. Some of the physics parameterization
schemes tested in this study, such as the RUC LSM, YSU, and MYJ PBL schemes,
showed systematic biases over the entire region, whereas the MYNN PBL scheme
shows the most reasonable performance on average across the region.
The model configurations gave us additional insights into the magnitudes of
the atmospheric transport errors that can be encountered over this region.
However, multiple challenges remain. We showed that bias errors vary
spatially across the region. If these errors persist in the transport used
for a regional inversion, these biases will be propagated into the inverse
fluxes. Finally, no optimal model configuration was found for the entire
region. Therefore, we conclude that both random and systematic errors will
remain if any one model configuration is used. An ensemble approach,
possibly combined with data assimilation, could better minimize biases and
characterize the spatiotemporal structures of the atmospheric transport
errors for future regional inversion systems.
The code is accessible upon request by contacting the corresponding author
(lzd120@psu.edu).
Meteorological data were obtained from the University of Wyoming's online
data archive (http://weather.uwyo.edu/upperair/sounding.html, University of Wyoming, 2018)
for the 14 rawinsonde stations. The tower atmospheric CO2
concentration dataset is available online (https://daac.ornl.gov, Miles et al., 2013)
from Oak Ridge National Laboratory Distributed
Active Archive Center, Oak Ridge, Tennessee, USA 10.3334/ORNLDAAC/1202 (Miles et al., 2013). The other two towers (Park
Falls – WLEF and West Branch – WBI) are part of the Earth System Research
Laboratory/Global Monitoring Division (ESRL/GMD) tall tower network (Andrews
et al., 2014; https://www.esrl.noaa.gov/gmd/ccgg/insitu/, Carbon Cycle and Greenhouse Gases Group, 2018).
The WRF model
results are accessible upon request by contacting the corresponding author
(lzd120@psu.edu).
Tower and rawinsonde sites specific spatial correlation
coefficient between ensemble mean MBE of (a) wind speed, (b) wind
direction, and (c) PBLH and ensemble mean MBE of DDA CO2 mole
fractions versus their distance. The abscissa shows the distance between the
rawinsonde and tower sites, while the ordinate shows the spatial
correlation. A line of best fit is plotted in black.
LIDI performed the model simulations and the model–data
analysis. TL provided guidance with model simulations. TL
and KJD provided guidance with the model–data analysis. All authors
contributed to the design of the study and the preparation the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was supported by NASA's Terrestrial Ecosystem and Carbon
Cycle Program, grant NNX14AJ17G, NASA's Earth System Science Pathfinder
Program Office, Earth Venture Suborbital Program, grant NNX15AG76, NASA
Carbon Monitoring System, grant NNX13AP34G, and an Alfred P. Sloan Graduate
Fellowship. This document benefitted from the comments of Natasha Miles,
Chris E. Forest, and Andrew Carleton. Data were provided by
University of Wyoming's online meteorological data archive of NOAA NWS
rawinsondes, NOAA's Earth System Research Laboratory Global Monitoring
Division tall tower network, the AmeriFlux network, and Penn State's
contributions to the NACP Mid-Continent Intensive regional study.
CarbonTracker CT2009 results were provided by NOAA ESRL, Boulder, Colorado,
USA, from the website at https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/ (last access: 17 January 2018).
Edited by: Mathias Palm
Reviewed by: two anonymous referees
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