ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-4191-2016Stratospheric ozone changes under solar geoengineering: implications for UV exposure and air qualityNowackPeer Johannespjn35@cam.ac.ukhttps://orcid.org/0000-0003-4588-7832AbrahamNathan Lukehttps://orcid.org/0000-0003-3750-3544BraesickePeterPyleJohn AdrianCentre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, UKNational Centre for Atmospheric Science, University of Cambridge, Cambridge, UKKarlsruhe Institute of Technology, IMK-ASF, 76344 Eggenstein-Leopoldshafen, GermanyPeer Johannes Nowack (pjn35@cam.ac.uk)31March20161664191420312October201513November201520February201618March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/4191/2016/acp-16-4191-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/4191/2016/acp-16-4191-2016.pdf
Various forms of geoengineering have been proposed to counter anthropogenic
climate change. Methods which aim to modify the Earth's energy balance by
reducing insolation are often subsumed under the term solar radiation
management (SRM). Here, we present results of a standard SRM modelling
experiment in which the incoming solar irradiance is reduced to offset the
global mean warming induced by a quadrupling of atmospheric carbon dioxide.
For the first time in an atmosphere–ocean coupled climate model, we include
atmospheric composition feedbacks for this experiment. While the SRM scheme
considered here could offset greenhouse gas induced global mean surface
warming, it leads to important changes in atmospheric composition. We find
large stratospheric ozone increases that induce significant reductions in
surface UV-B irradiance, which would have implications for vitamin D
production. In addition, the higher stratospheric ozone levels lead to
decreased ozone photolysis in the troposphere. In combination with lower
atmospheric specific humidity under SRM, this results in overall surface
ozone concentration increases in the idealized G1 experiment. Both UV-B and
surface ozone changes are important for human health. We therefore highlight
that both stratospheric and tropospheric ozone changes must be considered in
the assessment of any SRM scheme, due to their important roles in regulating
UV exposure and air quality.
Introduction
The scientific consensus (Stocker et al., 2013) is that
man-made climate change caused by anthropogenic emissions of greenhouse
gases such as carbon dioxide is taking place. It is recognized that reducing
greenhouse gas emissions is difficult so that, under these circumstances,
there is discussion on alternative measures to counteract the effects of
climate change (e.g. Govindasamy and
Caldeira, 2000; Cicerone, 2006; Crutzen, 2006). Such interventions are
commonly referred to as geoengineering, “the intentional large-scale
manipulation of the environment that is intended to reduce undesired
anthropogenic climate change” (Keith, 2000).
Here, we use an atmosphere–ocean chemistry-climate model to study
atmospheric composition changes for one of the most common geoengineering
modelling experiments: the reflection of solar energy before it can enter
the Earth's atmosphere, an idea often depicted by the use of space mirrors
(Early, 1989; Seifritz, 1989). This idealized geoengineering
experiment belongs to methods subsumed under the term solar radiation
management (SRM). SRM methods aim to offset the additional radiative forcing
due to increases in atmospheric greenhouse gas concentrations by reflecting
solar radiation before it can reach the Earth's surface. A major issue with
any SRM scheme is that they are not designed to directly address the cause
of change, namely the elevated levels of carbon dioxide and other greenhouse
gases in the Earth system. Instead they affect other processes whose changes
counteract those due to the greenhouse gases (Shepherd, 2009). This has been
demonstrated in numerous SRM modelling studies
(e.g. Govindasamy and Caldeira, 2000; Govindasamy et al., 2002, 2003; Bala et al., 2008; Jones et al., 2011;
Kravitz et al., 2012, 2013b; Lunt et al., 2008; Matthews and Caldeira, 2007;
Niemeier et al., 2013; Ricke et al., 2010; Robock et al., 2008; Schmidt et al., 2012; Tilmes et
al., 2013).
Atmospheric composition changes under SRM have received much attention in
the context of stratospheric particle injection schemes
(Budyko, 1977; Crutzen, 2006) as increased particle
loadings could enhance the heterogeneous catalysis of reactions that
eventually lead to ozone depletion (e.g. Heckendorn et al., 2011; Pitari et al., 2014; Pope et al., 2012; Rasch et
al., 2008; Tilmes et al., 2008, 2009, 2012; Weisenstein and Keith, 2015).
This would have important implications for human health since stratospheric
ozone is the major absorber of solar UV-B radiation. UV-B radiation
interacts with the human DNA and has been connected to many acute and
chronic illnesses of the eye, immune system, and skin and, inter alia, to various forms
of skin cancer (e.g. Norval et al., 2011; Slaper et al., 1996).
However, UV-B radiation is also needed in beneficial biological processes
such as in the photobiological production of vitamin D (Holick,
1981). Consequently, a large future increase in the total column amount of
ozone, and thus decreased surface UV-B radiation, could itself have severe
adverse effects on life on Earth (McKenzie et
al., 2009). Vitamin D deficiency, for example, has been related to an
increased likelihood of occurrence of internal cancers, autoimmune diseases,
mental illnesses and lower bone density (e.g. Mora
et al., 2008; Norval et al., 2011; Ross et al., 2011; Williamson et al.,
2014). Other organisms in the biosphere also depend on UV radiation
including certain types of plants whose defence mechanisms against pests and
pathogenic micro-organisms are regulated by UV-B radiation (Williamson et al., 2014).
Surface ozone is a pollutant, which has been associated both with diseases
of the respiratory system and crop damage (Avnery
et al., 2011; Silva et al., 2013). Many countries have introduced emission
controls aimed at reducing emissions of tropospheric ozone precursors.
However, tropospheric surface ozone depends not just on in situ emissions
but also on processes in the stratosphere. For example, changes in
stratospheric ozone will impact tropospheric chemistry by altering the
photolysis environment in the troposphere (Madronich et al., 2015). Similarly,
the transport of ozone from the stratosphere is an important component of
the tropospheric ozone budget (e.g. Holton et al., 1995; Neu et al., 2014). Any SRM scheme which affects the
stratosphere could therefore also impact tropospheric composition.
In contrast to the case of particle injection schemes, stratospheric
composition changes and their potential tropospheric impacts in a
“space-mirror” geoengineered climate have not yet been included in a 3D
atmosphere–ocean modelling study. We investigate changes in ozone, and
consequently in biologically active ultraviolet surface radiation (in
particular UV-B), contrasting our results with composition changes under
pure greenhouse gas forcing. Changes in UV-B fluxes by changes in clouds and
surface albedo are also considered. Finally, we briefly discuss potential
surface ozone, and thus air quality changes as a result of SRM.
This paper is organized as follows: Sects. 2.1 and 2.2 introduce the model
used to run the simulations and the experimental setup. Section 3.1
introduces the global and regional surface temperature response. Changes in
atmospheric composition and their impact on surface UV and air quality are
explained in Sects. 3.2. to 3.4. Finally, Sect. 4 puts our results into
context, also regarding other SRM schemes and health implications.
Experimental setupModel description
A version of the recently developed atmosphere–ocean coupled configuration
of the Hadley Centre Global Environment Model version 3, additionally
coupled to an atmospheric chemistry scheme, has been employed here
(Hewitt et al., 2011; Nowack et al., 2015).
For the atmosphere, the UK Met Office's Unified Model (MetUM) version 7.3 is
used (Hewitt et al., 2011). The configuration is
based on a regular grid with a horizontal resolution of 3.75∘
longitude by 2.5∘ latitude and comprises 60 vertical levels up to
a height of ∼ 84 km, and so includes a full representation of
the stratosphere. Its dynamical core is non-hydrostatic and employs a
semi-Lagrangian advection scheme. The radiation scheme by
Edwards and Slingo (1996) is used in the
MetUM, with nine bands in the longwave and six bands in the shortwave part of the
spectrum, extended by the k distribution method by Cusack (1999).
Subgridscale features such as clouds and gravity waves are parameterized.
For ocean dynamics and thermodynamics an updated version of the OPA
component (Hewitt et al., 2011;
Madec et al., 1998) of the Nucleus for European Modelling of the Ocean (NEMO)
framework version 3.0, coupled to the Los Alamos sea ice model CICE
version 4.0 (Hunke and Lipscomb, 2008) is used. It contains
31 vertical levels reaching down to a depth of 5 km. The NEMO configuration
used in this study deploys a tripolar, locally anisotropic grid which has
2∘ resolution in longitude everywhere, but an increased
latitudinal resolution in certain regions with up to 0.5∘ in the tropics.
Atmospheric chemistry is represented by the United Kingdom Chemistry and
Aerosols (UKCA) model in an updated version of the stratospheric chemistry
configuration (Morgenstern et al., 2009) which is
coupled to the MetUM. The stratospheric chemistry scheme is comprehensive. A
relatively simple tropospheric chemistry scheme that simulates hydrocarbon
oxidation is also included, which provides for emissions of three chemical
species (NO (surface, lightning), CO (surface), HCHO (surface)). In
addition, surface mixing ratios of four further species (N2O, CH3Br,
H2, CH4) are constrained by calculating the effective emission
required to maintain their surface mixing ratios, e.g. for nitrous oxide
280 ppbv and for methane 790 ppbv. This keeps their tropospheric mixing ratios
approximately constant at pre-industrial levels in all simulations. Nitrogen
oxide emissions from lightning are parameterized according to
Price and Rind (1992, 1994). Ozone, nitrous oxide, and methane are fully interactive
in the model so that any changes in these species are seen by the radiation
module and thus affect the modelled climate. Changes in photolysis
rates in the troposphere and the stratosphere are calculated interactively
using the Fast-JX photolysis scheme (Bian
and Prather, 2002; Neu et al., 2007; Telford et al., 2013; Wild et al.,
2000). Photolysis in FastJX responds, inter alia, to ozone and solar flux as well as
to multiple layers of clouds of varying degrees of thickness.
Our simulations follow standards set for the G1 experiment (see Table 1), which
was defined as part of the Geoengineering Model Intercomparison Project (GeoMIP)
(Kravitz et al., 2011, 2013a). In the G1 experiment the effect of an abrupt quadrupling of
atmospheric carbon dioxide (CO2) on the global mean surface temperature
is approximately offset by reducing the model's solar constant. This can be
thought of as an experiment in which space-mirrors reflect sunlight before
it enters the Earth's atmosphere (Early, 1989; Seifritz,
1989). Starting from approximately pre-industrial concentrations with
atmospheric CO2 at ∼ 285 ppmv (piControl), we thus carried out,
firstly, an abrupt 4 × CO2 experiment, in which atmospheric CO2 is
instantaneously quadrupled to ∼ 1140 ppmv and, secondly, a
G1 type experiment in which the global warming caused by 4 × CO2 was offset
by a solar irradiance reduction of 49.0 Wm-2 (∼ 3.6 %).
This value lies well within the range found in previous G1 modelling studies
(e.g. Schmidt et al., 2012). It
was obtained by iterating the radiative imbalance at the top of the
atmosphere and the global mean surface temperature response to various
values of solar dimming, thereby optimizing the latter towards a zero offset
from the pre-industrial simulation. The radiative forcing in the 4 × CO2 experiment
roughly matches the levels attained by the end of the 21st century under the
transient RCP8.5 forcing scenario defined for the Coupled Model
Intercomparison Project phase 5 (Moss
et al., 2010; Taylor et al., 2012). Both experiments were run for 75 years
after the CO2 and solar forcings were imposed. For analysis, we use the
last 50 years of each experiment in the following. By design, the
G1 experimental set-up does not include pre-defined changes in surface
emissions of ozone-depleting substances from anthropogenic sources (e.g. CFCs
whose abundance is equal to zero in this set-up), or tropospheric ozone precursors.
Temporal evolution of the annual and global mean surface
temperature anomalies. The anomalies (∘C) are shown relative to
the average temperature of the pre-industrial experiment. The piControl and G1
experiment are highlighted in the inset panel with the straight lines
marking the average temperature anomalies. The grey and red shading give the
±2σ temperature interval for piControl and G1 respectively.
The highly idealized nature and theoretical simplicity of the G1 experiment
allows us to discuss possible unintended consequences of solar
geoengineering in an intuitive way. Our stratospheric chemistry scheme
allows a detailed analysis of possible changes in UV penetration into the
troposphere as well as of stratosphere–troposphere exchange of ozone. Our
tropospheric chemistry scheme, while simplified, then allows a simple,
first-order quantification of the impact of these on tropospheric
composition. While the exact impact of any changes would be strongly
dependent on both forcing scenario and SRM scheme, this study aims to
demonstrate why changes in these metrics are to be expected for any SRM scheme.
Annual mean surface temperature differences. The
differences are based on the average temperatures of the last 50 years of
each experiment. (a) 4 × CO2 relative to preindustrial conditions. (b) G1 relative to
pre-industrial conditions. Note the non-linear colour scale. Non-significant
changes (using a two-tailed Student's t test at the 95 % confidence level)
are marked by stippling.
ResultsSurface temperature response
The temporal evolution of the global mean surface temperature for all
simulations is shown in Fig. 1. As expected, a rapid warming is found in
4 × CO2 relative to piControl in response to the abrupt forcing whereas G1 remains (by design)
at effectively the same average surface temperature (Table 2). Although
surface temperatures are offset globally, there are important regional
differences between 4 × CO2 and G1. As shown in Fig. 2, the model yields the
characteristic distribution of overcooling in the tropics and warming at
high latitudes in G1 (Kravitz et al., 2013b), an effect which can be explained
by the proportionally larger impact of reducing insolation on the tropics
than on high latitudes (Govindasamy and Caldeira, 2000; Lunt et al., 2008).
Stratospheric ozone and temperature changes
Figure 3a to d show latitude-height cross sections of changes in zonal
mean ozone mass mixing ratio and zonal mean temperature. We find large
increases in ozone in the middle-upper stratosphere (∼ 30–50 km altitude,
Fig. 3a and b) under both 4 × CO2 and G1, a ubiquitous feature in
chemistry-climate modelling studies (e.g. Oman et al., 2010) with a cooler
stratosphere (Fig. 3c) under increased atmospheric CO2 concentrations
(Fels et al., 1980). Note that this
cooling effect largely persists in G1; the stratosphere is warmer in some
areas than in 4 × CO2, but remains much colder than in piControl (compare Fig. 3c and d).
CO2-driven ozone increases in the middle-upper stratosphere are well
understood and are mainly caused by a slowing of temperature-dependent
catalytic ozone (O3) loss reactions
X+O3→XO+O2XO+O→X+O2Net:O+O3→2O2
under cooler stratospheric conditions (Haigh and Pyle, 1982),
with the radical species X typically being NO, OH, Cl or Br. The cooling
also shifts the thermal partitioning between atomic oxygen and ozone towards
the latter, which further slows down the rate-determining step (Reaction R2) in the
catalytic cycles (Jonsson et al., 2004). As already
mentioned, the stratospheric cooling due to increased CO2 persists in
G1. In fact, the solar irradiance reduction would, as a single effect, be
expected to further cool the stratosphere (Govindasamy et al., 2003; Braesicke et
al., 2011). However, some regions in the stratosphere are actually warmer in
G1 than in 4 × CO2 (Fig. 3d). Increased shortwave heating by higher ozone levels,
local tropopause height shifts, and changes in dynamical heating certainly
contribute to this, and importantly so does less longwave cooling as a
result of the much lower stratospheric water vapour concentrations
(Maycock et al., 2011) in G1, as discussed below.
Differences in zonal and annual mean ozone mass mixing
ratio and temperature. (a, b) Percentage differences in ozone as labelled.
(c, d) Temperature differences (K) as labelled. Note that (d) shows the difference
between G1 and 4 × CO2, i.e. not the changes relative to piControl, in contrast to (a)–(c), and
that a different colour scale is used than in (c). The ozone changes are given
in percentages to highlight in terms of absolute mass mixing ratios the much
smaller changes in the ozone-poor troposphere as compared to the larger
absolute changes in the stratosphere, which in turn occur on much higher
background ozone levels. The colour scale for ozone is adapted to changes in
the middle-upper stratosphere; for the whole extent of the changes in the
tropical upper troposphere and lower stratosphere under 4 × CO2, see
Nowack et al. (2015). Differences are calculated on
altitude levels, the pressure axis gives approximate values for
pre-industrial conditions. Coloured lines in (a, b) mark the zonal and annual
mean tropopause heights for each experiment. Non-significant differences
(using a two-tailed Student's t test at the 95 % confidence level) are crossed out.
The ozone increases in the upper stratosphere are larger in G1 than under
4 × CO2 (compare Fig. 3a and b), see also Jackman and Fleming (2014).
In our simulations, there are two main drivers behind this
additional ozone increase. Firstly, less ozone is photolysed (O3+hν-> O2+ O) as a consequence of the reduced
insolation in G1, which happens at the expense of atomic oxygen abundances: in
G1 both ground state O(3P) and excited state O(1D) at a given
atmospheric pressure are ∼ 3–8 % less abundant than in
4 × CO2 (not shown). Less abundant atomic oxygen in turn implies a slowing of
Reaction (R2) and thus further reduced ozone loss. Secondly, we find a
significant decrease in stratospheric specific humidity in G1, which reduces
HOx (OH, HO2, H) formation and therefore ozone loss via, for
example, Reactions (R1) and (R2). Specifically, the stratosphere is
∼ 10–20 % drier in G1 than in piControl. This is related to a weaker hydrological cycle
under SRM (e.g. Bala et al., 2008; Govindasamy et al., 2003; Kravitz et al., 2013b; Lunt et al.,
2008; Matthews and Caldeira, 2007; Ricke et al., 2010; Schmidt et al., 2012;
Tilmes et al., 2009, 2013), which gives rise to characteristic reductions in
global mean precipitation (Table 2) and evaporation. In contrast, the more
humid stratosphere found under 4 × CO2 (∼ 30 % wetter than
pre-industrial) results in greater production of HOx species, which is
additionally coupled to the above-mentioned changes in O(1D) via the
HOx-producing reaction H2O + O(1D) -> 2 OH. As
O(1D) concentrations are lower in G1 than in 4 × CO2, this further enhances the
differences in HOx; overall the abundance of OH and HO2 is
∼ 15–25 % smaller in the middle-upper stratosphere in G1.
Finally, higher levels of nitrogen oxides (NOx= NO, NO2;
∼, 5–13 %) in the upper stratosphere under 4 × CO2 will also
contribute to the differences in ozone. They are mainly driven by changes in
stratospheric temperature, photolysis, transport of the NOx precursor
nitrous oxide as well as its reaction with O(1D); a discussion of
various factors involved is for example given in Revell
et al. (2012). Changes in other radical species play secondary roles in this
experiment (Jackman and Fleming, 2014).
Global annual mean quantities. For piControl and corresponding
differences under 4 × CO2 and G1 (highlighted in bold). The clear-sky, unpolluted UV
index at noon is calculated using the formula by Madronich (2007), including only
changes by column ozone and by the solar irradiance reduction. Standard
deviations for the annual mean data are given in brackets, with the
exception of the mean UVI indices, which were calculated from climatological
ozone fields without inter-annual variation.
In the tropical lower stratosphere, we find ozone decreases under 4 × CO2, which is
characteristic for an acceleration of the Brewer-Dobson circulation under
CO2-driven tropospheric warming (Shepherd and McLandress, 2011; Nowack et
al., 2015). In response to solar geoengineering, the residual circulation
(not shown) and thus ozone (Fig. 3b) in the tropical lower stratosphere is
almost brought back to pre-industrial levels. The remaining ozone decreases
mainly result from an effect often referred to as “inverse self-healing”
of the ozone column (e.g. Haigh and Pyle,
1982; Jonsson et al., 2004; Portmann and Solomon, 2007), in which the
increased ozone concentrations in the upper stratosphere allow less
shortwave radiation to propagate to lower altitudes. Relative to
pre-industrial conditions, this mechanism acts in concert with the (by
design) reduced insolation to leave fewer photons of relevant wavelengths to
produce ozone in the lower stratosphere. However, these effects are partly
compensated by coincident decreases in ozone losses in G1, mainly due to the
lower temperatures and lower HOx concentrations than in piControl. Overall, the
significant changes in stratospheric ozone have important implications for
UV fluxes into the troposphere and to the surface, as discussed in Sects. 3.3 and 3.4.
Column ozone differences and their impact on the UV index.
Relative to piControl: left for 4 × CO2, right for G1. Top row panels: annual mean Δcolumn
ozone (colours, %). Non-significant changes (using a two-tailed Student's
t test at the 95 % confidence level) are marked by stippling. Middle row panels:
seasonal cycle of the column ozone changes as longitudinal and monthly
means. Bottom row panels: seasonal cycle of the column ozone induced changes in the
UV index, and in (f) additionally by the solar constant reduction, at noon.
Polar night regions in (e, f) are crossed out; both daily (solar declination)
and monthly changes (ozone) are considered, giving rise to a less smooth
appearance. Contour lines show pre-industrial column ozone in Dobson Units (DU)
in the upper two rows and pre-industrial UV indices in the last row.
The effect of column ozone and cloud changes on surface UV-B
UV-B surface fluxes can change for a variety of reasons (Bais
et al., 2015; McKenzie et al., 2011). Changes in column ozone have the
potential to provide particularly strong contributions since ozone is the
only major absorber of UV-B radiation in the atmosphere. As discussed above,
SRM could lead to changes in column ozone; in G1, we find that relative to
piControl the global mean column ozone increased by ∼ 8 % compared to
only ∼ 4 % under 4 × CO2 (Fig. 4 and Table 2).
Differences in the cloud modification factor and their
impact on the UV index. (a) Annual mean ΔCMF (colours) under 4 × CO2 and
(b) under G1 relative to piControl (contour lines). Non-significant changes (using a
two-tailed Student's t test at the 95 % confidence level) are marked by
stippling. Zonal mean percentage changes in the UV index at noon induced by
ΔCMF are shown for (c) 4 × CO2 and (d) G1 according to the formulas by
den Outer et al. (2005) and Staiger et al. (2008). Polar night regions in (c, d) are crossed out; both daily (solar
declination) and monthly changes (ozone) are considered, giving rise to a
less smooth appearance.
The harmful effect of UV exposure on human skin is commonly measured using
the UV index (UVI), starting at 0 and with higher UVI equalling greater
skin-damaging potential (WHO, 2002). Here, we use the approximate formula of
Madronich (2007) to estimate UVI changes in response to the changes in column
ozone in 4 × CO2 and G1 under clear-sky, unpolluted conditions:
UVI∼12.5μ2.42(Ω/300)-1.23,
where μ is the cosine of the solar zenith angle and Ω the total
vertical ozone column in Dobson Units (DU). As a further approximation, we
use monthly and zonal mean values for column ozone, but have updated the
solar zenith angle on a daily basis according to the changing solar
declination. The resulting UVI is therefore both a function of the changing
angle of incidence of the Sun's radiation to the Earth's surface and the
seasonally varying column ozone (Fig. 4c and d) at a given location. The
UVI found for piControl at noon and relative changes (ΔUVI) for G1 and 4 × CO2 in
percentages, are shown in Fig. 4e and f (see Table 2 for global mean
differences). In G1, the UVI decreases everywhere during the whole year due to
both changes in column ozone and the 3.6 % reduced intensity of the solar
radiation. However, the effect of the changes in ozone generally dominates.
In particular, during Northern Hemisphere (NH) spring and summer average
decreases of 10–20 % are found at NH mid- and high latitudes in G1. We
caution that although the percentage changes at high latitudes may be
larger, they are relative to much lower background UVI levels. In addition,
Eq. (1) is expected to perform less well in areas of high surface
albedo, as well as in regions with widespread occurrences of sea and
land ice (Madronich, 2007). Nevertheless, a further reduction in UV irradiance in already light-poor
seasons and regions could aggravate medical conditions connected to vitamin D
deficiency. We note that vitamin D production exhibits a slightly
different sensitivity to certain wavelengths of solar radiation than is
assumed in the calculation of the UVI (Fioletov
et al., 2009; McKenzie et al., 2009) so that our calculations should be
considered as qualitative.
Column ozone changes are not the only factor with the potential to change
surface UV as a result of climate engineering. Changes in clouds, surface
reflectivity (due to surface albedo changes), or aerosols could all
significantly affect UV transmission, reflection, and scattering. Here, we
focus just on the impact of ozone and cloud changes, assuming that other
changes are small under pre-industrial background conditions. The residual
high-latitude warming in G1 (Fig. 2b) implies that albedo changes could play a
role, e.g. due to decreases in snow and sea ice. However, in our model, the
higher temperatures do not suffice to trigger statistically significant ice
or snow loss under SRM, in agreement with multi-model studies of the G1
experiment (Kravitz et al., 2013b; Moore et al., 2014).
A common way to estimate the average effect of clouds on shortwave (SW)
surface radiation is the cloud modification factor (CMFSW). The
CMFSW is the total solar irradiance (Wm-2) reaching the Earth's
surface at any point (all-sky) divided by its idealized clear-sky value in
which any cloud effects are ignored (den Outer et
al., 2005). A CMFSW of 1 thus implies that the net cloud effect on
surface SW radiation is zero, values larger than 1 imply SW amplification by
clouds, values smaller than 1 net reflection of SW radiation by clouds.
Figure 5a and b show differences in the CMFSW for 4 × CO2 and G1 relative to
piControl. Under 4 × CO2, the overall pattern of CMFSW changes is in agreement with
previous (chemistry-)climate modelling results (Bais
et al., 2011, 2015) under greenhouse gas forcing. In G1 (Fig. 5b), the
CMFSW is predicted to increase in many regions while decreases are
virtually non-existent. Similar cloud changes have been found in previous
G1 modelling studies and have been attributed to reductions in the highly
reflective cloud cover at low altitudes (Kravitz
et al., 2013b; Schmidt et al., 2012). Consequently, an increase in surface
SW radiation from cloud changes is expected in G1, in contrast to the decrease
in UVI which would follow the column ozone changes.
In order to compare the UV effects of changes in the CMFSW and changes
in ozone, we use an empirical relationship established by den Outer et
al. (2005) and modified by Staiger et al. (2008) to estimate the effect of the CMFSW changes in terms of
the UVI at noon. The results are presented in Fig. 5c and d. In G1, the UVI
changes by clouds are overall positive. As expected, this is the opposite
sign response to the UVI changes induced by ozone. However, the cloud effect
is much smaller with percentage increases of only ∼ 1–2 %
for most latitudes and times compared with the much higher values for the
ozone-induced changes (Fig. 4f). Only during NH summer, between around
40–60∘ N, are the cloud-induced UVI increases of comparable size
(∼ 5 %) to the decreases driven by changes in the ozone
column. Our calculations show that cloud effects are generally small and do
not offset ozone-induced UV changes in light-poor seasons, which are the
times when major problems connected to vitamin D deficiency primarily occur.
In summary, our results indicate that changes in column ozone and hence
surface UV fluxes represent an important change to the climate system, which
could arise following an SRM scheme and which is of potential importance for
human health. These changes would need to be taken into account when
evaluating benefits and risks of any possible geoengineering scheme in which
elevated atmospheric CO2 concentrations persist.
Tropospheric ozone changes
As mentioned in Sect. 1, tropospheric ozone affects air quality, human
health, and ecology. Ozone concentrations in the troposphere are controlled
by a variety of processes which could be affected by SRM. These include
Photochemical processes influenced by changing UV-B 280–315 nm) and
UV-A (315–400 nm) fluxes into the troposphere (Madronich et al., 2015;
Williamson et al., 2014). High-energy photons needed to produce ozone from
molecular oxygen (λ< 240 nm) are absorbed at higher
altitudes and tropospheric ozone levels are determined by other production
and loss processes. For example, under clean environmental background
conditions, ozone loss and production of the hydroxyl radical OH via
O3+hν(λ<328nm)→O2+O1DO1D+H2O→2OHNet:O3+H2O+hν→O2+2OHis of prime importance. This reaction pathway is non-linearly dependent on
stratospheric ozone changes due to the photons needed in Reaction (R3)
(McKenzie et al., 2011).
Changes in tropospheric concentrations of chemical species involved in the
formation of ozone or its depletion, for example due to changes in
atmospheric humidity and thus in concentrations of a key reactant in loss
reactions such as Reaction (R5).
Changes in stratosphere–troposphere exchange (STE) (Holton
et al., 1995; Lin et al., 2014, 2015; Morgenstern et al., 2009; Neu et al.,
2014; Zeng et al., 2010), i.e. due to changes in the transport of ozone from
the ozone-rich stratosphere into the troposphere. Such changes are strongly
coupled to changes in atmospheric dynamics.
In our simulations, there is a global mean surface ozone increase in G1 (+5.0 %)
and a decrease in 4 × CO2 (-4.2 %), see Table 2. The surface ozone
differences between the runs are to first order determined by processes i and ii. Firstly,
UV fluxes into the troposphere decrease in G1 due both to the solar irradiance
reduction and the increase in stratospheric ozone concentrations. The UV
reduction in G1 relative to piControl leads to a ∼ 5–10 % reduction in
the flux through Reaction (R3) in the tropical troposphere (and
∼ 15 % reduction at higher latitudes). These results
contrast with the changes between 4 × CO2 and piControl where the reaction flux increases in
the tropical troposphere by ∼ 15 %. It is clear that changes
in the stratosphere under both increased greenhouse gases, or under SRM, would have important consequences for the UV fluxes
into the troposphere and, hence, for surface irradiation and tropospheric
chemistry. SRM does not avoid changes to the stratosphere (and hence to the
troposphere) that increased CO2 would lead to.
Secondly, the tropospheric humidity changes under SRM contrast significantly
with those found under 4 × CO2. In the latter case, tropospheric humidity increases
while for G1 we find, in common with many other studies mentioned above, a
weakening of the hydrological cycle and reduced specific humidity. In our
calculations, tropospheric humidity is up to 20% lower in G1 under SRM than
in piControl. Consequently, Reaction (R4) slows down by ∼ 10–20 % in the
lower–middle troposphere and by up to ∼ 25–30 % in the upper troposphere in G1.
Changes in STE (iii) have a negligible effect on the global mean surface ozone
change in G1 (Table 2). Nonetheless, STE can be regionally and seasonally
important under 4 × CO2, where surface ozone increases at mid- and high latitudes
in the Northern Hemisphere and Southern Hemisphere (Fig. 6a). These annual
mean changes result from increases during the respective winter and spring
seasons when STE increases (by ∼ 38 % on the annual mean).
Annual mean surface ozone changes. Absolute values (ppbv).
Difference between (a) 4 × CO2 and piControl,
(b) G1 and piControl. Non-significant changes (using a two-tailed
Student's t test at the 95 % confidence level) are marked by stippling.
We emphasize that the effect of SRM on tropospheric chemistry is expected to
be strongly dependent on the scenario, reference state, and geoengineering
method used. Here, we assume pre-industrial conditions by following the G1
scenario, which only allows for low, natural background pollution. Under
different forcing scenarios other aspects of tropospheric chemistry could
change the surface ozone response. For example, different chemical
mechanisms could be more important for SRM under more polluted conditions
(e.g. Morgenstern et al., 2013; Squire et al., 2014; Tang et al., 2011).
Nevertheless, changes in humidity and photolysis as described here are
robust modelling features that could occur under a range of geoengineering
scenarios. These mechanisms will be key to tropospheric chemistry
considerations under geoengineering in general. Consequently, our results
demonstrate the potential for substantial changes in tropospheric chemistry
in the different climate state created by SRM. Here, we find a particularly
strong effect in the tropics, where model surface ozone increases under G1 and
decreases under 4 × CO2, amounting to annual mean differences of around 5 ppbv
between these two simulations in some regions, compare Fig. 6a and b. As
with the surface ozone response under a range of RCP scenarios (which can
differ in sign, O'Connor et al., 2014; Young et al., 2013), there is clearly a need to study surface
ozone changes for a range of geoengineering forcing scenarios.
Discussion and conclusions
Using a coupled atmosphere–ocean chemistry-climate model, we have carried
out an idealized SRM experiment in which we offset the effect of quadrupling
atmospheric carbon dioxide on the global mean surface temperature by
reducing the incoming solar radiation. Although the global mean surface
temperature is, by design, unchanged in this geoengineering experiment,
other environmental factors change considerably. In particular, we find
large increases in stratospheric ozone, with an ∼ 8 %
increase in global mean column ozone. Solar radiation management under G1
fails to offset the cooling of the stratosphere resulting from increased
CO2, which leads to higher ozone concentrations there. The reduction in
solar flux intensity in G1 also plays a role in reducing ozone loss. In
consequence, the stratospheric ozone optical depth increases and leads to a
reduction in tropospheric UV, with regional and seasonal reductions of up to
∼ 20 % in local UV-indices at the surface. This reduced
surface UV could have adverse effects on medical conditions connected to
vitamin D deficiency. In contrast, the general decrease in UV radiation is
also expected to have beneficial effects such as a reduced likelihood in
populations of developing skin cancer. We find that cloud-induced UV changes
play a minor role compared with the change in ozone column.
A further unintended consequence of the SRM scheme considered here would be
a change in tropospheric composition. The main drivers of change are
decreases in tropospheric specific humidity as well as a reduced flux of
UV-B and UV-A radiation into the troposphere. Relative to the pre-industrial
control run, surface ozone increases in G1 by about 5 % (and decreases in
4 × CO2). Such an increase is qualitatively consistent with calculations, with
detailed tropospheric chemistry schemes, of tropospheric ozone changes
following an increase in stratospheric ozone
(e.g. Banerjee et al., 2016). A major challenge in the 21st century will be
to prevent large changes in tropospheric ozone, which would follow increased
emissions of NOx and volatile organic compounds. It is important that
geoengineering schemes do not make this challenge even more difficult. We
note that the increase in ozone found here could also lead to a change in
the lifetime of the greenhouse gas methane in a geoengineered climate (Holmes et
al., 2013; Morgenstern et al., 2013) and thus in the amount of solar
geoengineering needed to offset the anthropogenic greenhouse gas forcing.
It is important to stress again that our modelled changes in atmospheric
composition are strongly scenario- and SRM scheme-dependent. Important
factors in other scenarios that would affect composition include the
reduction in ozone-depleting substances by the Montreal Protocol, not
considered here, or more detailed changes in tropospheric ozone precursors
(Squire et al., 2015; Young et al., 2013). For stratospheric particle injection
schemes, stratospheric ozone depletion would be a major concern
(e.g. Pope et al., 2012), especially in the
near future. In addition, UV considerations for aerosol schemes are further
complicated by UV scattering and absorption by the aerosol particles
(Tilmes et al., 2012) as well as aerosol indirect effects (Kuebbeler et al., 2012). Aerosol
geoengineering might also affect the stratospheric circulation
(Ferraro et al., 2015) with likely changes in STE different
than found here for the G1 experiment. Finally, it is also unclear how
long-term injections of aerosols into the atmosphere would affect air
quality at the surface due to potentially much increased particle pollution.
In conclusion, increases in CO2 will increase the stratospheric ozone
column and solar radiation management schemes will not offset this increase.
In the G1 experiment considered here, large increases in stratospheric ozone
are calculated leading to decreases in tropospheric UV. That surface UV and
surface ozone would change under solar geoengineering is a robust modelling
result and their effects on human health and ecology could be considerable.
Just as with continued ocean acidification (Caldeira and Wickett,
2003) and changes in the hydrological cycle under SRM, ozone changes and
their effect on surface UV and air quality would have to be expected in a
solar geoengineered world. Consequently, we highlight this issue as an
important factor to be accounted for in future discussions and evaluations
of all SRM methods.
Acknowledgements
We thank the European Research Council for funding through the ACCI project,
project number 267760. In particular, we thank Jonathan M. Gregory (UK Met
Office, University of Reading), Manoj M. Joshi (University of East Anglia)
and Annette Osprey (University of Reading) for model development as part of
the QUEST-ESM project supported by the UK Natural Environment Research
Council (NERC) under contract numbers RH/H10/19 and R8/H12/124. We
acknowledge use of the MONSooN system, a collaborative facility supplied
under the Joint Weather and Climate Research Programme, which is a strategic
partnership between the UK Met Office and NERC. For plotting, we used
Matplotlib, a 2-D graphics environment for the Python programming language
developed by Hunter (2007). We are grateful for advice of P. Telford during
the model development stage of this project and thank the
UKCA team at the UK Met Office for help and support.
Edited by: J.-U. Grooß
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