The Specified Dynamics version of the Whole Atmosphere Community Climate
Model (SD-WACCM) and the Goddard Space Flight Center two-dimensional (GSFC
2-D) models are used to investigate the effect of galactic cosmic rays (GCRs)
on the atmosphere over the 1960–2010 time period. The Nowcast of Atmospheric
Ionizing Radiation for Aviation Safety (NAIRAS) computation of the GCR-caused
ionization rates are used in these simulations. GCR-caused maximum NO
Galactic cosmic rays (GCRs) from outside the solar system are comprised of highly energetic charged particles and are believed to be the result of supernova events and other high-energy astrophysical processes. GCRs contain a wide range of energetic particles, which are also influenced by the Earth's magnetosphere. High-energy GCRs not only penetrate further into the atmosphere, but can also cause atmospheric effects outside the polar cap regions. The flux of GCRs is larger during solar minimum, when the reduced solar magnetic field less effectively shields the solar system from the particles.
The influence of GCRs on the middle atmosphere has been studied since the 1970s (e.g., Warneck, 1972; Ruderman and Chamberlain, 1975; Nicolet, 1975; Jackman et al., 1980, 1987, 1996; Thorne, 1980; Garcia et al., 1984; Legrand et al., 1989; Jackman, 1991, 1993; Müller and Crutzen, 1993; Vitt and Jackman, 1996; Krivolutsky et al., 1999, 2001, 2002; Vitt et al., 2000; Semeniuk et al., 2011; Calisto et al., 2011). These previous studies made use of GCR-produced ionization rates (GPIR) in computing atmospheric chemistry impacts. The GPIR were deduced primarily in a couple of different methodologies.
For example, Nicolet (1975) made use of balloon soundings and ionization chambers to compute the GPIR. Several of the other earlier studies roughly followed the Nicolet (1975) methodology for inclusion of GPIR in atmospheric analyses. A more recent study by Calisto et al. (2011) primarily relied on the computations of the Cosmic Ray induced Cascade: Application for Cosmic Ray Induced Ionization (CRAC : CRII) of Usoskin et al. (2010) to deduce the GPIR. Another method of computing GPIR has been developed by the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) team at NASA Langley Research Center (see Mertens et al., 2013). The NAIRAS-deduced GPIR has been computed over the years 1960–2010. The solar cycle shows substantial variation over this 51-year time period, which is reflected in the GPIR.
GCRs also affect the atmosphere through the production of the important
constituent families of NO
This paper is divided into six primary sections, including the Introduction.
The NAIRAS GCR ionization rate computation is discussed in Sect. 2 and the
GCR-induced production of HO
The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS)
team at NASA Langley Research Center (see
In the NAIRAS model, GCRs travel from outside the heliosphere to 1 AU by the Badhwar and O'Neill (1992, 1994, 1996) and O'Neill (2010) NASA model, with the solar modulation potential determined from measurements of ground-based neutron monitor count rates. The GCR spectral flux at 1 AU travel through the magnetosphere by means of a transmission factor determined by the vertical geomagnetic cutoff rigidity computed in the International Geomagnetic Reference Field model (Finlay et al., 2010). The vertical cutoff rigidities are determined by numerical solutions of charged particle trajectories in the IGRF field using the techniques advanced by Smart and Shea (1994, 2005). After transmission through the magnetosphere, the GCR spectral flux travels through the neutral atmosphere using the NASA HZETRN deterministic transport code (Mertens et al., 2012). The global distribution of atmospheric mass density is obtained from NCAR/NCEP Reanalysis 1 data at pressure levels larger than 10 hPa (Kalnay et al., 1996) and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter model atmosphere data at pressure levels less than 10 hPa (Picone et al., 2002).
The NAIRAS model has been used to compute the annual average GCR-produced
ionization rates (GPIR) for the 1960–2010 time periods. For these time
periods, measurements from the Thule and Izmiran neutron monitor stations
were used to determine the solar modulation potential. GPIR in the NAIRAS
model are computed by multiplying the dose rate in air by the atmospheric
density, divided by 35 eV per ion pair. The annual average GPIR from the
NAIRAS model for 2 years, 2002 and 2009, are presented in Fig. 1. This
shows the inverse relationship between GPIR and solar activity. Year 2002 is
very close to solar maximum and shows a smaller GPIR with maximum ionization
rates of nearly 15 cm
The Mertens et al. (2013) GPIR are about a factor of two smaller than those
presented in Usoskin et al. (2010), and the altitude of the maximum in the
GPIR is lower in the NAIRAS results as well. A comparison of these two
computations of GCR ion rates at 90
NAIRAS model computed GCR annual average ionization rates for years
2002 (left) and 2009 (right). Contour intervals are 1, 2, 5, 10, 15, 20, 25,
and 30 (#cm
NAIRAS model computed galactic cosmic ray annual average ionization
rates at 90
NAIRAS model computed galactic cosmic ray annual average ionization rates (Mertens et al., 2013) compared to those given in Usoskin et al. (2010) for solar minimum (1965, top plot) and solar maximum (1960, bottom plot).
Description of model simulations.
Besides ionization, GCRs also produce the important constituent families of
NO
The latest version of the NCAR Community Earth System Model, version 1
(CESM1) Specified Dynamics – Whole Atmosphere Community Climate Model
(SD-WACCM) was used to predict the impact of GCRs on the atmosphere. SD-WACCM
is a global model with 88 vertical levels from the surface to
The chemical module of SD-WACCM is based upon the 3-D chemical transport
Model of Ozone and Related Tracers, Version 3 (MOZART) (Kinnison et al.,
2007). It includes a detailed representation of the chemical and physical
processes from the troposphere through the lower thermosphere. The species
included within this mechanism are contained within the O
For this work, the SPARC Chemistry Climate Model Initiative (CCMI), REFC1 scenario was used (see Eyring et al., 2013). This scenario included observed time-dependent evolution of greenhouse gases (GHGs); ozone depleting substances (ODSs); sea surface temperatures and sea ice concentrations (SSTs/SICs); stratospheric sulfate surface area densities (SADs); and 11-year solar cycle variability, which includes spectrally resolved solar irradiances.
The most recent version of the Goddard Space Flight Center (GSFC)
two-dimensional (2-D) atmospheric model was used to predict the impact of
GCRs on the atmosphere. This model was first discussed over 25 years ago
(Douglass et al., 1989; Jackman et al., 1990) and has undergone extensive
improvements over the years (e.g., Considine et al., 1994; Jackman et al.,
1996; Fleming et al., 1999, 2007, 2011, 2015). The vertical range of the
model, equally spaced in log pressure, is from the ground to approximately
92 km (0.0024 hPa) with about a 1 km grid spacing. The model has a
4
The specified transport version of the model is used for this study. Here, the model transport fields are derived using daily average global winds and temperatures from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis project for years 1960–1978 (Kalnay et al., 1996; Kistler et al., 2001) and the MERRA meteorological analyses for years 1979–2010. Thirty-day running averages of the residual circulation, eddy diffusion, zonal mean wind, and zonal mean temperature are computed using the methodology detailed in Fleming et al. (2007). For use in some of the simulations a climatological average was constructed of the transport over these years and applied it over the simulated periods. The averaged transport fields change daily, but repeat yearly.
The ground boundary conditions in the GSFC 2-D model for the ozone depleting substances are taken from WMO (2014) for years 1960–2010. The model uses a chemical solver described in Jackman et al. (2005) and Fleming et al. (2007, 2011). For these computations, the photochemical gas and heterogeneous reaction rates and photolysis cross sections have been updated to the Jet Propulsion Laboratory recommendations (Sander et al., 2011) with further updates based on SPARC (2013).
The model tropospheric chemistry scheme has also been updated to include the
following species: CH
We conducted 14 model simulations with the two models, which are all
briefly described in Table 1. SD-WACCM was used for two simulations, both
over the period 2000–2010. One of the SD-WACCM simulations did not include
GCRs (simulation
The GSFC 2-D model was used for 12 simulations, all over the 51-year
period 1960–2010. The transport was specified for all simulations, either
interannually varying with NCEP-NCAR data for years 1960–1978 and with MERRA
data for years 1979–2010 or with a climatological average of those data over
the 1960–2010 time period. Five of the simulations (labeled
SD-WACCM and GSFC 2-D model simulations were compared to delineate the
GCR-caused changes under different atmospheric conditions. Model simulations
were compared for the year 2009 (solar minimum, GCR maximum) to determine the
GCR impact on several constituents in Sect. 5.1. The influence of GCRs over
the solar cycle is also shown in Sect. 5.1 (comparing year 2009 to year
2002). Changing atmospheric conditions over the years 1960–2010 and their
impact on the GCR atmospheric influence are shown in Sect. 5.2. In
particular, GCR-caused global total ozone changes in the different regions of
the atmosphere (1000–100, 100–1, and 1000–1 hPa) are discussed in
Sect. 5.2 as well as the global total ozone changes caused by GCRs with
different imposed atmospheric conditions. Finally, the GCR-caused NO
Annual average percentage change for year 2009 in zonal mean
NO
The GCR-caused NO
Annual average percentage change for year 2009 in zonal mean
NO
The GCR-caused ozone impact is shown in Fig. 4 (bottom) for SD-WACCM and in
Fig. 5 (bottom) for the GSFC 2-D model. Ozone is mostly enhanced in the
troposphere and lowest part of the stratosphere with largest increases of
1–2 % from GCRs in the south polar troposphere in 2009. The GCR-caused
ozone increase is due to two processes: (1) the NO reacting with CH
For example,
Ozone is decreased in most of the stratosphere due to the NO
Annual average percentage change for year 2009 in zonal mean
HO
The computed impact of GCRs on HO
Annual average percentage change from year 2002 (solar maximum) to
2009 (solar minimum) in zonal mean NO
The SD-WACCM computations can also be used to address the question of the
change in GCR influence over a solar cycle. The focus in this section has
been on year 2009 since that was near solar minimum resulting in the maximum
atmospheric influences caused by GCRs. The last previous solar maximum or GCR
minimum occured in year 2002. Since the background atmosphere changes
significantly from year 2002 to year 2009, it would be confusing to directly
compare atmospheric changes between the 2 years to derive any GCR-caused
change. Instead, the annual average percentage change from GCRs was computed
for years 2002 and 2009 separately and then differenced from each other to
illustrate the GCR-caused change over the solar cycle. The results are given
in Fig. 7 for NO
GSFC 2-D model GCR-computed impacts of AAGTO between 1000 and 100 hPa (dotted black),
between 100 and 1 hPa (dashed black), and for the entire troposphere and stratosphere, 1000 to 1 hPa, (solid black)
over the 1960–2010 time period. The top plot shows the comparison of
simulation
The GSFC 2-D model gives fairly similar results to SD-WACCM (compare Figs. 4
and 5) and is significantly faster computationally to use for longer-term
simulations. Thus, the GSFC 2-D model was used in several sensitivity study
simulations described in Table 1 (and Sect. 4.2) to investigate the longer
term GCR-caused changes, particularly focusing on annual average global total
ozone (AAGTO) as well as global column ozone in the two regions between 1000
and 100 hPa and between 100 and 1 hPa. The GCR-caused change in ozone in
those two regions, separately, and for the entire troposphere and
stratosphere (1000–1 hPa) is computed for two pairs of scenarios:
(1) Fig. 8 (top) shows a comparison of the first pair (
GSFC 2-D model GCR-computed impacts of annual average polar total
ozone (AAPTO) between 1000 and 100 hPa (dotted black), between 100 and
1 hPa (dashed black), and for the entire troposphere and stratosphere, 1000
to 1 hPa, (solid black) over the 1960–2010 time period. The top plot shows
the comparison of simulation
First, focus on the results intercomparing scenarios
Second, intercompare the more complete simulations
The GCR-caused atmospheric changes are larger at higher latitudes, thus we
also compute the annual average polar total ozone (AAPTO). The AAPTO is
calculated using the model output only at polar latitudes (60–90
Forcing used in the GSFC 2-D model over the 1960–2010 time period.
These include:
The impact of five simultaneous atmospheric changes are responsible for the
GCR-caused variations in AAGTO observed in Fig. 8 (bottom). These changes
are (1) background total chlorine; (2) sulfate aerosol surface area;
(3) solar cycle photon flux variation; (4) solar cycle GCR variation; and
(5) interannual transport variability. Background total chlorine increases
dramatically from 0.7 to 3.5 ppbv over the 1960–2010 period (Fig. 10a,
Equator, 1 hPa). Volcanoes can add substantially to the aerosol surface area
during certain years (especially 1963, 1982, and 1991, see Fig. 10b). The
photon flux varies over the solar cycle and is especially important to the
stratosphere at ultraviolet wavelengths. The solar flux variation at 200 nm
(up to about 8.5 % from solar minimum to maximum) is important for ozone
production and is shown in Fig. 10c. The GCRs vary over the solar cycle as
well and the GCR-caused ion pair production is given in Fig. 10d at 200 hPa
and 90
The smoothest change over the 1960–2010 time period occurred with the amount
of background total chlorine. The AAGTO has been computed for the six
scenarios (
First, this is partly a reflection of the role that chlorine, through the
ClO
Second, this is also a reflection of the interference of the NO
Both of these processes are ongoing in the atmosphere and are reflected in Fig. 11a, which illustrates most clearly the correlation between the GCR-caused change in ozone and background total chlorine amount.
Figure 11b shows the results of the AAGTO computed in
GSFC 2-D model GCR-computed AAGTO impacts (black lines) over the
1960–2010 time period. The Cl
Figure 11c illustrates the results of a comparison of the AAGTO computed in
Finally, Fig. 11d illustrates the results of a comparison of the AAGTO
computed in
GSFC 2-D model GCR-computed AAGTO impacts (black line) over the
1960–2010 time period (simulation
The aerosol surface area varies dramatically over the 1960–2010 time period.
Volcanoes in years 1963, 1982, and 1991 caused large increases in the aerosol
surface area. Enhanced aerosol surface area results in an increase in
heterogeneous reactions on the sulfate aerosols. In particular, the reaction
The sun not only influences the GCR flux over a solar cycle, but it also shows a significant variation in solar photons and solar particles (electrons, protons and other particles). The photon flux variation and its impact on the GCR effect will be addressed here. However, it is outside the scope of this paper to discuss the influence of solar energetic particles (e.g., protons, other ions, and electrons) on the GCR-caused atmospheric influence.
The solar cycle variation led to changes in the photon flux, especially at
the X-ray, extreme ultraviolet, and ultraviolet wavelengths. In particular,
the stratosphere is greatly influenced by photons at ultraviolet wavelengths
(e.g., 200 nm photons are important in producing ozone) and a variation of
up to about 8.5 % from solar minimum to maximum was shown in Fig. 10c. A
comparison of the AAGTO computed in
The GCRs vary from year-to-year, influenced primarily by the strength of the
solar magnetic field. The GCR variation (given in ion pair production) can be
as large as a factor of two at the poles (see Fig. 10d). Most of the impact
from GCRs is in the polar lower stratosphere/upper troposphere, since the GCR
caused ionization rates peak there (see Fig. 1). The residence time for
constituents in the lower stratosphere is long (
The interannual transport variation drives changes in the impact of the GCRs
on the AAGTO. This interannual behavior is best observed in Fig. 11b, where
the mean GCRs are imposed continuously over the entire 51-year time period.
Variations of up to
The total NO
GSFC 2-D model GCR-computed AAGTO change (black line) over the
1960–2010 time period caused by the interannual GCR variation. The global
total ozone change shown in Fig. 11c is differenced from that shown in
Fig. 11a. A 2-year boxcar (running) average of the GCR ion pair production
(in cm
Two global models, SD-WACCM and GSFC 2-D, were used to study the atmospheric
impact of GCRs over the 1960–2010 time period. The largest atmospheric
impacts occurred in the NO
Charles H. Jackman, Daniel R. Marsh, Douglas E. Kinnison, Christopher J. Mertens, and Eric L. Fleming thank the NASA Headquarters Living With a Star Targeted Research and Technology Program for support during the time that this manuscript was written. Charles H. Jackman and Eric L. Fleming were also supported by the NASA Headquarters Atmospheric Composition Modeling and Analysis Program. The National Center for Atmospheric Research (NCAR) is sponsored by the US National Science Foundation. WACCM is a component of the Community Earth System Model (CESM), which is supported by the National Science Foundation (NSF) and the Office of Science of the US Department of Energy. Computing resources were provided by NCAR's Climate Simulation Laboratory, sponsored by NSF and other agencies. This research was enabled by the computational and storage resources of NCAR's Computational and Information System Laboratory (CISL). Edited by: M. Palm