GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-11-4603-2018Global simulation of tropospheric chemistry at 12.5 km resolution: performance
and evaluation of the GEOS-Chem chemical module (v10-1) within the NASA GEOS
Earth system model (GEOS-5 ESM)GEOS-5 Nature Run with GEOS-Chem chemistryHuLulu.hu@mso.umt.eduhttps://orcid.org/0000-0002-4892-454XKellerChristoph A.LongMichael S.SherwenTomáshttps://orcid.org/0000-0002-3006-3876AuerBenjaminDa SilvaArlindoNielsenJon E.PawsonStevenThompsonMatthew A.https://orcid.org/0000-0001-6222-6863TrayanovAtanas L.TravisKatherine R.https://orcid.org/0000-0003-1628-0353GrangeStuart K.https://orcid.org/0000-0003-4093-3596EvansMat J.https://orcid.org/0000-0003-4775-032XJacobDaniel J.Department of Chemistry and Biochemistry, University of Montana, Missoula, MT, USAJohn A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA, USAGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USAUniversities Space Research Association, Columbia, MD, USANational Centre for Atmospheric Science, University of York, York, UKWolfson Atmospheric Chemistry Laboratory, University of York, York, UKScience Systems and Applications, Inc., Lanham, MD, USADepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USALu Hu (lu.hu@mso.umt.edu)16November201811114603462023April20189May201816October201818October2018This 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://gmd.copernicus.org/articles/11/4603/2018/gmd-11-4603-2018.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/11/4603/2018/gmd-11-4603-2018.pdf
We present a full-year online global simulation of tropospheric chemistry
(158 coupled species) at cubed-sphere c720 (∼12.5×12.5km2) resolution in the NASA Goddard Earth
Observing System Model version 5 Earth system model (GEOS-5 ESM) with
GEOS-Chem as a chemical module (G5NR-chem). The GEOS-Chem module within GEOS
uses the exact same code as the offline GEOS-Chem chemical transport model
(CTM) developed by a large atmospheric chemistry research community. In this
way, continual updates to the GEOS-Chem CTM by that community can be
seamlessly passed on to the GEOS chemical module, which remains state of the
science and referenceable to the
latest version of GEOS-Chem. The 1-year G5NR-chem simulation was conducted to
serve as the Nature Run for observing system simulation experiments (OSSEs)
in support of the future geostationary satellite constellation for
tropospheric chemistry. It required 31 wall-time days on 4707 compute cores
with only 24 % of the time spent on the GEOS-Chem chemical module. Results
from the GEOS-5 Nature Run with GEOS-Chem chemistry were shown to be
consistent to the offline GEOS-Chem CTM and were further compared to global
and regional observations. The simulation shows no significant global bias
for tropospheric ozone relative to the Ozone Monitoring Instrument (OMI)
satellite and is highly correlated with observations spatially and
seasonally. It successfully captures the ozone vertical distributions
measured by ozonesondes over different regions of the world, as well as
observations for ozone and its precursors from the August–September 2013
Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by
Regional Surveys (SEAC4RS) aircraft campaign over the southeast US. It
systematically overestimates surface ozone concentrations by
10ppbv at sites in the US and Europe, a problem currently being
addressed by the GEOS-Chem CTM community and from which the GEOS ESM will
benefit through the seamless update of the online code.
Introduction
Integration of atmospheric chemistry into Earth system models
(ESMs) has been identified as a next frontier for ESM development
and is a priority science area for atmospheric chemistry
research . Atmospheric chemistry drives climate forcing and
feedbacks, is an essential component of global biogeochemical cycling, and is
key to air quality applications. A growing ensemble of atmospheric chemistry
observations from space needs to be integrated into ESM-based data
assimilation systems. Models of atmospheric chemistry are rapidly evolving,
and an atmospheric chemistry module within an ESM must be able to readily
update to the state of the science. We have developed such a capability by
integrating the Goddard Earth
Observing System with chemistry (GEOS-Chem) chemical transport model (CTM) as
a comprehensive and seamlessly updatable atmospheric chemistry module in the
NASA GEOS ESM . Here, we present the first application and
evaluation of this GEOS-Chem capability within GEOS version 5 (GEOS-5) for a
full-year global simulation of tropospheric ozone chemistry at cubed-sphere
c720 (∼12.5×12.5km2) resolution. This simulation is
now serving as the Nature Run (pseudo-atmosphere) for observing system
simulation experiments (OSSEs) in support of the near-future geostationary
satellite constellation for tropospheric chemistry .
GEOS-Chem (http://geos-chem.org) is an open-source global 3-D Eulerian
model of atmospheric chemistry driven by GEOS-5 assimilated meteorological
data. It includes state-of-the-science capabilities for tropospheric and
stratospheric gas–aerosol chemistry , with
additional capabilities for aerosol microphysics
. It is used by over a hundred active
research groups in 25 countries around the world for a wide range of
atmospheric chemistry applications, providing a continual stream of
innovation . Strong version control and benchmarking maintain
the integrity and referenceability of the model. The code is freely available
through an open license
(http://acmg.seas.harvard.edu/geos/geos_licensing.html, last access:
14 September 2018).
GEOS-Chem as used by the atmospheric chemistry community operates in an
“offline” CTM mode, without explicit simulation of meteorology.
Meteorological data are inputted to
the model to simulate chemical transport and other processes. The offline
approach makes the model simple to use and facilitates community development
of the core chemical module that describes local chemical sources and sinks
from emissions, reactions, thermodynamics, and deposition. Implementing the
GEOS-Chem chemical module into ESMs offers a state-of-the-science and referenceable representation of
atmospheric chemistry, but it is essential that the module be able to
automatically incorporate new updates as the offline model evolves.
Otherwise, it would become quickly dated and unsupported.
Schematic of GEOS-Chem chemical module used either offline as a
chemical transport model (CTM) or online in an Earth system model (ESM), with
interfaces managed through the Earth system modeling framework (ESMF). The
module is grid independent, with individual computations done on atmospheric
columns at user-selected grid points. It computes local changes in
concentrations with time (dn/dt) as a result of emissions
(E), chemical production (P), chemical loss (L), and deposition (D).
Emissions are handled through the Harvard-NASA EMission COmponent (HEMCO) to
provide ESM users the option of only integrating emissions.
From a GEOS-Chem atmospheric chemistry user perspective, there are a number
of reasons why an online simulation capability is of interest. Users working
on both climate and atmospheric chemistry modeling can use the same module
for both, thus improving the consistency of their approach. Some atmospheric
chemistry problems involving fast coupling between chemistry and dynamics,
such as aerosol–cloud interactions, require online coupling. As the
resolution of ESMs increases, use of archived meteorological data becomes
more difficult and incurs increased error , so online
simulation can become more attractive. Finally, online simulations can take
advantage of vast computational resources available at climate modeling
centers to achieve very high resolution as illustrated in this paper. Such
high-resolution model outputs are particularly important for air quality and
human health applications and for OSSEs to support the
design of satellite missions
. A future
modus operandi for the GEOS-Chem community might involve development
in the offline model at coarse resolution, and online simulations when high
resolution is required.
We have developed the capability to efficiently integrate the GEOS-Chem
chemical module into ESMs in a way that enables seamless updating to the
latest standard version of GEOS-Chem. This involved transformation of
GEOS-Chem into a grid-independent and Earth system modeling framework
(ESMF)-compliant model . The exact same code is now used in
stand-alone offline mode (CTM) by the GEOS-Chem community and as an online
chemical module within GEOS-5 (Fig. 1). Using the exact same code for both
applications ensures that the chemical module in the ESM keeps current with
the latest well-documented standard version of GEOS-Chem. An important
component is the Harvard-NASA EMission COmponent
HEMCO;, which allows GEOS-Chem users to build
customized layers of emission inventories on any grid and with no editing of
the GEOS-Chem source code. HEMCO is now the standard emission module in
GEOS-Chem and is used in GEOS-5 as an independent module for applications
when full chemistry is not needed.
Here, we apply the GEOS-Chem chemical module within GEOS-5 in a
very-high-resolution (VHR) simulation of global tropospheric ozone chemistry.
The simulation is conducted for 1 full year (2013–2014) at c720
cubed-sphere resolution (∼12.5×12.5km2)
with 158 coupled chemical species. To the best of our knowledge, such a
resolution in a state-of-the-science global simulation of tropospheric
chemistry is unprecedented. Resolution not only increases the quality of
local information, e.g., for air quality, but it also provides better
representation of chemical non-linearities. We compare the model outputs with
coarse offline GEOS-Chem CTM results and with independent observations for
tropospheric ozone and precursors as a test of fidelity and increased power.
We refer to this VHR simulation as the GEOS-5 Nature Run with GEOS-Chem
chemistry, or G5NR-chem.
The 500 hPa ozone distribution on 1 August 2013 at 00:00 Z
simulated by GEOS-5 with the GEOS-Chem chemical module at cubed-sphere c720
(∼12.5×12.5km2) resolution.
GEOS-Chem as an atmospheric chemistry module in the GEOS ESM
An Eulerian (fixed frame of reference) CTM such as GEOS-Chem solves the
system of coupled continuity equations for an ensemble of m species with
number density vector n=n1,…,nmT:
∂ni∂t=-∇⋅(niU)+(Pi-Li)n+Ei-Dii∈1,m,
where U is the wind vector including subgrid components to be
parameterized as turbulent diffusion or convection.
(Pi-Li)n, Ei, and Di are the local
chemical production and loss, emission, and deposition rates, respectively,
of species i. Coupling across species is through the chemical term
Pi-Li. In GEOS-Chem, as in all 3-D CTMs, Eq. ()
is solved by operator splitting to separate the transport and local
components over finite time steps. The local operator,
dnidt=(Pi-Li)n+Ei-Dii∈1,m,
includes no transport terms (no spatial coupling) and thus reduces to a
system of coupled ordinary differential equations (ODEs). It is commonly
called the chemical operator even though emission and deposition terms are
included. The transport operator,
∂ni∂t=-∇⋅(niU)i∈1,m,
does not involve coupling between chemical species.
Use of GEOS-Chem as chemical module in an ESM requires only the code that
updates concentrations over a given time step for local production and loss
as given by Eq. () (Fig. ). The CTM has its own
transport modules to solve Eq. () using archived meteorological
inputs (offline), but these are not needed in the ESM where transport is
computed as part of atmospheric dynamics (online).
Middle tropospheric ozone distribution at 700–400 hPa from the
GEOS-5 Nature Run with GEOS-Chem chemistry (a), Ozone Monitoring
Instrument (OMI) satellite observations (b), and the offline
GEOS-Chem CTM (c) for four seasons covering July 2013–June 2014.
The data are on a 2∘×2.5∘ grid. All data are
smoothed by the OMI averaging kernels, using a single fixed a priori profile
so that variability is solely driven by observations. The OMI observations
have been further corrected for a global mean positive bias of 3.6 ppbv
. Both models are sampled along the OMI tracks. Numbers in the
left and right columns are the mean model bias ± standard deviation. Gray
shading indicates regions where OMI data are unreliable and not used
(poleward of 45∘ in winter–spring and poleward of 60∘ year
round; see ).
The ESM chemical module is tasked with updating chemical concentrations by
integration of Eq. () on the ESM grid and time step. Exchange of
information between the chemical module and other ESM modules can be done by
various couplers such as ESMF . Our guiding principle is that
the CTM and the ESM chemical module share the exact same code. This required
restructuring GEOS-Chem to a grid-independent form and making the code
compliant with the ESMF Modeling Analysis and Prediction Layer (MAPL) coupler
used by GEOS . The 1-D vertical columns are the smallest
efficient unit of computation for the chemical module because several
operations are vertically coupled, including radiative transfer, vertically
distributed emissions, wet scavenging, and particle settling. In the now
grid-independent GEOS-Chem code, horizontal grid points are selected at
runtime through the ESMF interface. The chemical and emission modules proceed
to solve Eq. () on 1-D columns for the specified horizontal grid
points (Fig. ). We managed to carry out this major software
transformation in GEOS-Chem in a way that was completely transparent to CTM
users . The exact same ESMF-compliant, grid-independent
GEOS-Chem code is now used both in the stand-alone CTM and within GEOS. This
enables seamless integration of future new GEOS-Chem scientific developments
into the GEOS chemical module, which thus always remains current and
referenced to the latest standard version of GEOS-Chem.
An important step in transforming GEOS-Chem to a grid-independent structure
was to reconfigure the emission module. The emission module consists of
multiple layers of databases and algorithms describing emissions for
different species and regions, with scaling factors defining
diurnal/weekly/seasonal/secular trends or dependences on environmental
variables. The databases are on different grids and time stamps, and may add
to or supersede each other as controlled by the user. The HEMCO module allows
users to choose any combination of emission inventories in NetCDF format, on
any grid and with any scaling factors, and apply them to any model grid
specified at runtime . HEMCO provides a complete functional
separation of emissions from transport, deposition, and chemistry in
GEOS-Chem. The purpose of this separation is that the ESM may need emissions
independently from atmospheric chemistry, for example, to simulate species
such as CO2 and methane. HEMCO is thus configured as a
stand-alone component in the ESM, accessed separately through the ESMF
interface (Fig. ).
Some care is needed when
interfacing the GEOS-Chem chemical module with fast vertical transport
processes in GEOS-5 involving boundary layer mixing and deep convection with wet scavenging. Here, we apply
boundary layer mixing at every time step in GEOS-5 with emissions and dry
deposition updates from GEOS-Chem but before applying chemistry. This avoids
anomalies in the lowest model layer when the timescale for boundary layer
mixing is shorter than the time step for emissions. Deep convective transport
of chemical species including scavenging in the updrafts is performed by the
GEOS-Chem convection scheme driven by instantaneous diagnostic variables from
the GEOS-5 convection component . This takes advantage of
the gas and aerosol scavenging capability of the GEOS-Chem scheme
. Radon-222 tracer simulation tests within GEOS-5
show that the GEOS-Chem convection scheme closely reproduces the GEOS-5
convective transport . As convection becomes increasingly
resolved at higher model resolution, the GEOS-5 subgrid convection
parameterization is invoked less frequently. As a
consequence, an increasing fraction of the washout in GEOS-Chem becomes
characterized as large scale, as opposed to convective. No attempts were made
to offset the possible increase in washout efficiency that may
arise from this.
GEOS-5 Nature Run with GEOS-Chem chemistry
We perform a year-long (1 July 2013 to 1 July 2014) GEOS-5 simulation with
GEOS-Chem at cubed-sphere c720 (∼12.5×12.5km2)
horizontal resolution and 72 vertical levels
extending up to 0.01hPa. For initialization, we use 12 months at
c48 resolution (∼200×200km2) followed by
6 months at c720 resolution. Figure 2 shows a snapshot of the simulated
500hPa ozone field, illustrating the fine detail enabled by the
very high resolution.
General description
The GEOS-5 Nature Run with GEOS-Chem chemistry is performed with the Heracles
version of GEOS-5 (tag “M2R12K-3_0_GCC”). The finite-volume dynamics is
run in a non-hydrostatic mode with a heartbeat time of 300 s applied
to the physics, chemical, and dynamics components. The simulation is forced by
downscaled meteorological data from the lower-resolution Modern-Era Retrospective Analysis
for Research and Applications version 2 (MERRA-2) reanalysis
. The downscaling is performed by using the
replay capability in GEOS, which adds a forcing term to the model equations,
constraining them to a specific trajectory to simulate the 2013–2014
meteorological year . Downscaling applications filter the
replay increments so that only the larger scales of the flow are constrained,
allowing scales finer than the analysis to evolve freely. In this simulation,
a wave number of 60 was chosen as the cutoff. The simulation is performed in
two segments, with the first with GEOS-Chem turned off (“regular replay”). The
analysis increment produced during the first run segment is saved and reused
in the subsequent run with GEOS-Chem turned on (“exact increment”). This
two-segment process is computationally more efficient, as it avoids rewinding
and checkpointing the model with full chemistry during the regular replay stage.
The chemical module is GEOS-Chem version v10-01 in tropospheric-only mode. It
is run “passively” in G5NR-chem; thus, aerosols and trace gases do not
influence the meteorology. It includes detailed
HOx-NOx–VOC–ozone–BrOx–aerosol
tropospheric chemistry with 158 species and 412 reactions, following Jet Propulsion Laboratory (JPL) and
International Union of Pure and Applied Chemistry (IUPAC) recommendations for chemical kinetics and updates
for BrOx and isoprene chemistry .
The default GEOS-Chem bulk aerosol scheme is used to simulate major
components for dust, sea salt, black carbon, organic carbon, sulfate,
nitrate, and ammonium aerosols
. The Fast-JX scheme
with approximate randomized cloud overlap method and taking aerosol loading
into account is used to calculate photolysis frequencies , as
implemented by . Linearized stratospheric chemistry is used
. The dry deposition calculation is based on a
resistance-in-series model , as implemented by
. Wet scavenging of aerosols and gases is as described by
and .
Emissions are calculated through HEMCO v1.1.008 . They are
the default 2013–2014 emissions for GEOS-Chem see Table 1
in, with a few modifications. Open fire emissions are from the
Quick Fire Emissions Dataset (QFED) version 2.4-r6 . US
NOx emissions follow . Parameterizations for
lightning NOx and mineral dust aerosol
emissions have large dependencies on grid resolution and
are scaled globally following general GEOS-Chem practice to annual totals of
6.5TgN for lightning and 840TgC for dust. No
adjustments are made to emission of biogenic volatile organic compounds
(VOCs) MEGANv2.1; and sea salt aerosol
, both of which agree with GEOS-Chem emissions within 15 %.
GEOS-Chem uses in-plume chemistry of ship emissions (PARANOx) to account for
the excessive dilution of ship exhaust plumes at coarse model resolution
; this was disabled in the VHR simulation given that the
Nature Run resolution is fine enough to resolve the non-linear chemistry
associated with ship plume emissions. A summary of the various emission
sources used for the simulation is given in Table .
a. b Calculated with a
chemical tropopause as the 150 ppbv ozone
isopleth. c Interquartile range of 50 models summarized in
; limited observational estimates fall within that
range. d. e Global mean air-mass-weighted OH
concentration. f From 16 model results summarized in .
Comparison of GEOS-5 Nature Run with GEOS-Chem chemistry to OMI
700–400 hPa ozone measurements for four seasons in July 2013–June 2014,
colored by the latitude of the observations. Each point represents the
seasonal mean for a 2∘×2.5∘ grid cell. Black dashed lines
show the best fit (reduced major axis regression) with regression parameters
given in the inset. Numbers on the bottom right are the global mean model bias
± standard deviation. The 1:1 line is shown in red.
Annual mean ozonesonde profiles in July 2013–June 2014 for
representative global regions . Results from the GEOS-5
Nature Run with GEOS-Chem chemistry (red) are compared to observations
(black) and to the GEOS-Chem CTM (blue; 2∘×2.5∘
version in ). The models are sampled at the ozonesonde launch
times and locations.
Mean vertical profiles of trace gas concentrations over the
southeast US during the NASA Studies of Emissions, Atmospheric Composition, Clouds
and Climate Coupling by Regional Surveys (SEAC4RS) aircraft campaign
(August–September 2013; ). Results from the GEOS-5 Nature Run with GEOS-Chem
chemistry are compared to observations for ozone and NOx, CO,
peroxyacetyl nitrate (PAN), and formaldehyde (HCHO), and to the GEOS-Chem CTM
(nested 0.25∘×0.3125∘ version in ).
Model results are sampled along the flight tracks at the time of flights.
Computational environment and cost
The computations were carried out on the Discover supercomputing cluster of
the NASA Center for Climate Simulation. Overall, 1 day of simulation took
approximately 2 wall-time hours, using 4705 compute cores with 45 % spent
on dynamics and physics, 24 % on chemistry, and 31 % on input/output
(I/O) (Table ). The large I/O wall time is due to
bottlenecks in the MAPL software version used for the simulation, with
excessive reading and remapping of the hourly high-resolution emission
fields. This issue has been addressed in newer versions of MAPL. As first
shown by , the chemical module has excellent scalability even
when running with thousands of cores. The percentage of the wall time spent
on chemistry in G5NR-chem (24 %) is much lower than in coarse-resolution
simulations that are typically done with only a small number of cores
. The computational cost of chemistry relative to
dynamics/transport decreases as grid resolution increases; thus, it is no
longer the computing bottleneck in ESM simulations.
Model evaluationObservational datasets and offline CTM
The GEOS-5 Nature Run with GEOS-Chem chemistry simulation is intended to
support geostationary constellation OSSEs focused on tropospheric ozone and
related satellite measurements , and ozone is therefore
our evaluation focus. We use 2013–2014 observations that were previously
compared to the GEOS-Chem CTM including (1) global ozonesondes and Ozone
Monitoring Instrument (OMI) satellite data , (2) aircraft data
for ozone and precursors from the NASA Studies of Emissions, Atmospheric
Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS)
campaign over the southeast US , and (3) surface ozone
monitoring data over Europe and the US . An
important goal of the evaluation here is to examine consistency between the
GEOS-Chem chemical module within the GEOS ESM c720 environment and the
offline GEOS-Chem CTM. Although the GEOS-Chem simulation is at coarser
resolution and offline transport may incur errors , it is
extensively diagnosed by the GEOS-Chem user community, including recently by
for global tropospheric ozone. Two GEOS-Chem CTM v10-01
simulations are used for comparison to G5NR-chem: a global simulation with
2∘×2.5∘ resolution, and a nested simulation for North
America with 0.25∘×0.3125∘ resolution. Both are driven
by GEOS-5 Forward Processing (GEOS-FP) (GEOS-5.7.2 and later versions)
assimilated meteorological data. Some differences with G5NR-chem are to be
expected because of differences in the transport modules, resolution,
distribution of natural sources computed online such as lightning
NOx, and meteorological data from different versions of the
GEOS system (MERRA-2 vs. GEOS-FP). All comparisons to observations use model
output sampled at the location and time of observations.
Monthly median afternoon (12:00–16:00 LT) ozone concentrations
(ppbv) for 2013–2014 at surface sites in the US and Europe, with
interquartile range shaded. Surface monitoring sites are grouped according to
Fig. . Also shown are the GEOS-5 Nature Run
with GEOS-Chem chemistry (red) and the offline GEOS-Chem CTM (blue). Hourly
model outputs are sampled for the locations and time of observations at the
surface (lowest) level.
Observational datasets are described in the above references. Briefly, global
ozonesonde observations are extracted from the WOUDC (World Ozone and
Ultraviolet Radiation Data Centre; http://www.woudc.org, last access:
14 September 2018) and NOAA ESRL-GMD (Earth System Research Laboratory –
Global Monitoring Division; ftp://ftp.cmdl.noaa.gov/ozwv/Ozonesonde/,
last access: 14 September 2018). Ozonesonde stations are grouped into
coherent regions for model evaluation . OMI middle
tropospheric ozone data at 700–400hPa are from the Smithsonian
Astrophysical Observatory (SAO TROPOZ) retrieval
and are regridded to 2∘×2.5∘ resolution to reduce
retrieval noise. The NASA SEAC4RS dataset for southeast US described by
is filtered following to remove open fire
plumes (CH3CN> 200 pptv), stratospheric air
(O3/CO> 1.25 molmol-1), and urban plumes
(NO2> 4 ppbv). Hourly surface observations for ozone
are taken from the European Environment Agency database (complied by
) and the US Environmental Protection Agency Air Quality
System
(http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html#Raw,
last access: 14 September 2018). Only “background” sites are considered in
the analysis: for the US, this includes sites defined by the EPA as
“suburban” and “rural”; for Europe, this includes sites categorized as
“urban background”, “background”, and “rural” (see
Fig. ).
Global burdens
Standard global metrics for evaluation of tropospheric chemistry models
include the global burdens of tropospheric ozone, CO, and OH
(Table ). The global annual average ozone burden in
G5NR-chem amounts to 348Tg, consistent with the GEOS-Chem CTM and
the Tropospheric Ozone Assessment Report . The global
burden of tropospheric CO of 294Tg is consistent with the
GEOS-Chem CTM and on the low end of the observationally based estimate of
. The global mean OH concentration is lower than in the
GEOS-Chem CTM and more consistent with observational constraints
. The differences appear to be mainly driven by
differences in the meteorological data.
Figure compares the GEOS-5 Nature Run with GEOS-Chem chemistry
to OMI midtropospheric ozone. GEOS-Chem CTM results from are
also shown. OMI data have been reprocessed with a single fixed a priori
profile (so that variability is solely due to observations), corrected for
their global mean bias relative to ozonesondes, and filtered for high
latitudes because of large biases . Model outputs are sampled
along the OMI tracks and smoothed with the OMI averaging kernels. G5NR-chem captures well-known major features of the ozone distribution, such
as ozone enhancements at northern midlatitudes during MAM and JJA, and
downwind of South America and Africa during SON. It shows no significant
global bias relative to OMI and relative to the offline GEOS-Chem CTM. The
global mean seasonal biases are less than 2.7±3.2ppbv. Spatial
correlations for the four seasons on the 2∘×2.5∘ grid scale
are high and show no significant latitudinal bias (R=0.81-0.93; Fig. ).
Figure further evaluates the simulated vertical distribution
of ozone in comparison to ozonesonde data. There are differences between G5NR-chem and the GEOS-Chem CTM that could be due to a number of factors
including differences in tropopause altitude and the distribution of
lightning. For example, although the annual total lightning NOx
emission in G5NR-chem is scaled to that in GEOS-Chem CTM, the lightning
location is not constrained by satellite lightning flash data as the CTM
is. Annual mean ozone biases in G5NR-chem are generally less than
6ppbv in the lower troposphere. There are some larger biases in
the upper troposphere including differences with the GEOS-Chem CTM that could
be due to the spatial distribution of lightning. Overall, G5NR-chem tends
to improve the simulation of ozone vertical profiles compared to the
GEOS-Chem CTM, most dramatically at high southern latitudes.
Figure compares the model to mean vertical profiles of ozone
and precursors measured over the southeast US during the SEAC4RS aircraft
campaign. Here, the GEOS-Chem CTM results are from a nested
0.25∘×0.3125∘ simulation by . The lower
ozone in the northern midlatitude upper troposphere in G5NR-chem
appears to be due to a weaker lightning NOx source. G5NR-chem overestimates ozone in the lower troposphere by 10ppbv,
while such bias is reduced in the GEOS-Chem CTM, even through both show
almost identical NOx levels. The lower HCHO over the southeast
US is due to weaker isoprene emission because of lower temperatures. Both
models underestimate CO in the free troposphere but the bias is more apparent
in the CTM, likely due to differences in fire emissions, global OH fields, or
transport error in offline simulations .
Evaluation with surface observations over the US and Europe
Figure shows monthly median surface ozone concentrations
grouped by regions in the US and Europe. Here, hourly data between 12:00 and
16:00 LT are used to remove the known issue that models typically
underestimate the ozone nighttime depletion at surface sites
e.g.,. G5NR-chem systematically overestimates
surface ozone in almost all months by about 10ppbv in all regions,
while the GEOS-Chem CTM has no or small bias in winter and spring, but shows
similar overestimates as in G5NR-chem during summer and fall. In general,
the GEOS-5 Nature Run with GEOS-Chem chemistry better captures the observed
seasonality. Expanding the analysis to all hourly data does not affect the
systematic bias in G5NR-chem significantly but tends to increases the
summer–fall bias in the CTM (Fig. ). Part
of the systematic bias is due to the subgrid vertical gradient between the
lowest model level and the measurement altitude
60 m above ground vs. 10 m;. Recent model developments such as improved halogen
chemistry, aromatic chemistry, and ozone dry deposition are expected to
reduce the surface high bias . These updates are being incorporated into
the GEOS-Chem version currently under development and will be passed on to
the GEOS-5 simulation as they become available.
Conclusions
We presented a 1-year global simulation of tropospheric
chemistry within the NASA GEOS ESM version 5 (GEOS-5) at cubed-sphere c720
(∼12.5×12.5km2) resolution. This demonstrated the
success of implementing the GEOS-Chem chemical module within an ESM for
online simulations with detailed chemistry. The GEOS-Chem chemical module
online within GEOS and offline as the GEOS-Chem CTM uses exactly the same
code. In this way, the continual stream of chemical updates from the large
GEOS-Chem CTM community can be seamlessly incorporated as updates to the
online model, which always remains state of the science and referenceable to
the latest version of the GEOS-Chem. This 1-year simulation addressed an
immediate need to generate the Nature Run for OSSEs in support of the
geostationary satellite constellation for tropospheric chemistry. More
broadly, implementation of the GEOS-Chem capability opens up a new capability
for GEOS to address aerosol–chemistry–climate interactions, to assimilate
satellite data of atmospheric composition, and to develop global air quality
forecasts.
The 1-year GEOS-5 simulation at c720 resolution required 31 days of wall time on
4705 cores. Overall, 45 % of the wall time was spent on model dynamics and physics,
31 % on input/output, and 24 % on chemistry. Chemistry has near-perfect
scalability in massively parallel architectures because it operates on
individual grid columns; thus, it is no longer a computing bottleneck in ESM
simulations. Transporting the large number of species involved in atmospheric
chemistry simulations may be a greater challenge.
We evaluated the GEOS-5 Nature Run with GEOS-Chem chemistry for consistency
with the offline GEOS-Chem CTM at coarser resolution
(2∘×2.5∘ global and 0.25∘×0.3125∘ nested
over North America) as well as an ensemble of global observations for
tropospheric ozone and aircraft observations of ozone precursors over the
southeast US. The model shows no significant global bias relative to OMI
midtropospheric ozone data and the offline GEOS-Chem CTM. Evaluations with
ozonesondes show reduced model biases for high-latitude ozone. The GEOS-5
Nature Run with GEOS-Chem chemistry systematically overestimates surface
ozone concentrations by 10ppbv all year round in the US and
Europe but is able to capture the observed seasonality, while the offline
GEOS-Chem CTM reproduces observed surface ozone levels in winter and spring
but has similar biases in summer and fall in all regions. Resolving this
surface bias is presently a focus of attention in the GEOS-Chem CTM community
and future model updates to address that bias can then be readily implemented
into GEOS-5.
GEOS-Chem CTM is available at http://geos-chem.org/
(last access: 14 September 2018). GEOS-5 is available at
https://geos5.org/wiki/index.php?title=GEOS-5_public_AGCM_Documentation_and_Access
(last access: 14 September 2018).
All model outputs are available for download at
https://portal.nccs.nasa.gov/datashare/G5NR-Chem/Heracles/12.5km/DATA
(last access: 14 September 2018) or can be accessed through the OpenDAP
framework at the portal
https://opendap.nccs.nasa.gov/dods/OSSE/G5NR-Chem/Heracles/12.5km (last
access: 14 September 2018).
Same as Fig. 5 but for western US, equatorial South America, and
Atlantic/equatorial Africa.
Probability distribution of afternoon (12:00–16:00 LT) ozone
concentrations (ppbv) for 2013–2014 at surface sites in the US and Europe.
Monthly median ozone concentrations (ppbv; all 24 h data) for
2013–2014 at surface sites in the US and Europe, with interquartile range
shaded. Surface monitoring sites are grouped according to Fig. .
Also shown are the GEOS-5 Nature Run with
GEOS-Chem chemistry (red) and the offline GEOS-Chem CTM (blue). Hourly model
outputs are sampled for the locations and time of observations at the surface
(lowest) level.
Probability distribution of ozone concentrations (ppbv; all 24 h
data) for 2013–2014 at surface sites in the US and Europe.
Computing resources for the GEOS-5 Nature Run with GEOS-Chem
chemistrya.
a The simulation is from 1 July 2013 to 1 July 2014 in
GEOS ESM with GEOS-Chem as a chemical module at
12.5×12.5km2. The computation was carried out at NASA
Discover supercomputing cluster. b The simulation uses 337 14-core
2.6 GHz Intel Xeon Haswell compute nodes with 128 GB of memory per node and
an Infiniband FDR interconnect using the Intel compiler suite (v. 15.0.0.090)
and MPT v. 2.11. c 158 species are simulated and transported by the
GEOS ESM; among them, 29 species are saved out as hourly outputs. Data are
available at
https://portal.nccs.nasa.gov/datashare/G5NR-Chem/Heracles/12.5km/DATA/
(last access: 14 September 2018).
Emissions in the GEOS-5 Nature Run with GEOS-Chem
chemistry.
SpeciesAnthropogenicaAircraftShipVolcanoesbBiomassBiogenicSoil andLightningdOceanic/Dust fAnnual emissionburningagriculturecsea spraye(Tg yr-1 orTgC yr-1)gEDGARRETRO iXiao et al.NEI 2011jMIXkAEIClHTAPhQFEDmMEGAN nHTAP v2hNOXXXXXXX117COXXXXXX958SO2XXXXXX129SO4XXX2.93Organic carbonXXXXXX72.1Black carbonXXXXX10.3NH3XXX60.5≥C4 alkanesXXXX32.1AcetoneXXXXXX77.6MEKXXX4.09CH3CHOXXXXX20.9C3H6XXXXX31.3C3H8XXXX6.33CH2OXXXX4.58IsopreneX345C2H6XX15.3CHBr3X0.429CH2Br2X0.0621Br2X0.63Sea salt A (0.1–0.5 µg)X65.9Sea salt C (0.5–4.0 µg)X3990DMSX35.2Dust1 (0.1–1.0 µg)X103Dust2 (1.0–1.8 µg)X212Dust3 (1.8–3.0 µg)X271Dust4 (3.0–6.0 µg)X254
a Excluding aircraft and ships, which are listed separately.
bhttp://aerocom.met.no/download/emissions/AEROCOM_HC/volc/
(last access: 14 September 2018). c. d. e and .
f. g TgC yr-1 for
≥C4 alkanes, acetone, methyl ethyl ketone (MEK), CH3CHO,
C3H6, C3H8, isoprene, and C2H6. Tg yr-1 for
the rest of the species. hhttp://edgar.jrc.ec.europa.eu/htap_v2/index.php?SECURE=123 (last
access: 14 September 2018). i RETRO monthly global inventory for
the year 2000 implemented as described by
. j US EPA National Emission Inventory 2008
(http://www.epa.gov/ttn/chief/net/2008report.pdf; last access:
14 September 2018) and scaled to 2013 (https://www3.epa.gov/airtrends/;
last access: 14 September 2018). k Asian anthropogenic emissions
. l. m Quick Fire
Emissions Dataset (QFED) version 2.4-r6 . n
MEGANv2.1 implemented in GEOS-Chem as described by
.
SP and DJJ provided project
oversight and top-level design. CAK, MSL, BA, ADS, JEN, MAT, and ALT
performed code development and tests. KRT, SKG, and MJE provided additional
data for model evaluation. LH, CAK, and TS performed evaluation analysis. LH,
CAK, and DJJ wrote the manuscript. All authors contributed to manuscript
editing and revisions.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the NASA Modeling, Analysis, and Prediction
Program (MAP). Resources supporting the GEOS-5 simulations were provided by
the NASA Center for Climate Simulation at Goddard Space Flight Center. Lu Hu
acknowledges high-performance computing support from NCAR's
, sponsored by the National Science Foundation. Mat
J. Evans and Tomás Sherwen acknowledge funding for the computational
resource to perform the GEOS-Chem CTM model run from UK Natural Environment
Research Council (NERC, BACCHUS project NE/L01291X/1). We also acknowledge
all contributors to the ozonesonde data retrieved from the World Ozone and
Ultraviolet Radiation Data Centre (WOUDC) website
(http://www.woudc.org, last access: 14 September 2018).
Edited by: Fiona O'Connor
Reviewed by: two anonymous referees
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