Introduction
The hydroxyl radical (OH) is essential to atmospheric chemistry as the
leading oxidizing agent. It acts as a “detergent”, reacting with
numerous, mostly organic pollutants and controls the lifetimes of many trace gases
containing carbon–hydrogen bonds, particularly methane (CH4), because
reaction with OH is their dominant removal mechanism. It is also
responsible for oxidizing atmospheric trace gases such as carbon monoxide
(CO), non-methane volatile organic compounds (NMVOCs), and also some
ozone-depleting substances such as hydrochlorofluorocarbons (HCFCs)
. Therefore, the oxidizing capacity of the atmosphere is
largely defined by the abundance of OH. Tropospheric ozone (O3), an
air pollutant and greenhouse gas (GHG), is the primary source of OH in the
troposphere. Although it only accounts for 10 % of the total atmospheric
O3 abundance, it plays an essential role in photochemical processes
controlling tropospheric composition. It forms OH via O3
photolysis yielding excited oxygen (O(1D)) and a subsequent reaction
of O(1D) with water vapour (H2O). CH4 and CO
oxidation by OH, and other oxidation processes involving NMVOCs, lead
to formation of tropospheric O3 in the presence of NOx
. In low-NOx
atmospheric environments, such as in much of the SH, downward transport of
O3 from the stratosphere is the main source of tropospheric
O3, followed by O3 transport from other regions where it is
chemically produced . Stratospheric O3 also plays an
important role through its impact on the O3 photolysis rate
jO1D which is affected by the overhead O3 column. For
instance, stratospheric O3 depletion produces increased UV
penetration to the troposphere. This affects the production of tropospheric
OH.
The most widely used method for field measurements of OH is the
Fluorescence Assay by Gas Expansion (FAGE) technique and is based on the
measurement of OH and other species concentrations through
ultra-violet (UV) laser-induced fluorescence spectroscopy. OH
measurements using the FAGE technique have been conducted in a large variety
of atmospheric environments, ranging from polluted to clean atmospheres. However,
due to its very short lifetime the global lifetime is estimated to be
∼ 1 s, and large variability, such in
situ measurements of OH do not sufficiently capture its global
abundance, which makes it difficult to sufficiently constrain global
OH abundances with in situ measurements . For that
reason, modelling is an essential tool to study global OH. OH
is routinely included in global models of tropospheric chemistry, but the
complexity of the tropospheric chemical system and the sensitivity of
OH to a variety of environmental factors mean that there is
considerable disagreement among global chemistry-transport and
chemistry–climate models regarding the global OH abundance; this is
often expressed in terms of the CH4 lifetime
e.g.. Several model
studies have examined changes in OH abundance and the CH4
lifetime since pre-industrial times. Chemistry-transport models (which use
off-line, precalculated meteorology) generally simulate decreases in
OH and increases in the CH4 lifetime, ranging from 6 to
25 % during the 21st century .
These results differ from those produced by chemistry–climate models which
account for changes in both emissions and climate . All of them project a
reduction in the CH4 lifetime and an increase in OH. In
particular, and obtain a ∼10 %
decrease in the CH4 lifetime using different emission scenarios in
their simulations. More recent and comprehensive studies compare present-day
and future results for OH and the CH4 lifetime among models
participating in the Atmospheric Chemistry and Climate Model Intercomparison
Project ACCMIP,.
analyse the evolution of the CH4 lifetime and OH in ACCMIP
models since pre-industrial times (1850–2000). They point out large
variations in the sign and magnitude of OH changes (from -12.7%
to 14.6%) amongst ACCMIP models, reflecting uncertainties in natural
CO, NOx, and NMVOC emissions as well as roles of the diverse
chemical mechanisms included in the models. For present-day (year 2000)
simulations of OH and the CH4 lifetime,
suggest that diversity in photolysis schemes and
NMVOC emissions might cause large variations in simulated OH and the
CH4 lifetime. Trends in OH between 2000 and 2100 are mainly
attributed to stratospheric O3 changes and trends in modelled
temperature fields.
A useful indirect method for constraining global OH is based on
tracking the abundance of long-lived, well mixed chemicals for which
oxidation by OH is the dominant sink and which have a well quantified,
industrial source. The most widely used such species is methyl chloroform
(CH3CCl3) .
use CH3CCl3 measurements to infer only a small
interannual variability in OH for 1998–2007. The global multi-model mean
OH inferred from the ACCMIP ensemble increases
slightly (3.5±2.2%) over the period 1980–2000. This result largely
agrees with and with other models , but disagree with other studies of
CH3CCl3 observations that find a decrease in OH over that
period . For the year 2000,
underestimate the CH3CCl3 lifetime (and thus overestimate OH)
by 5 to 10 % relative to observations. CH3CCl3 is controlled
under the Montreal Protocol, meaning its abundance in the atmosphere is
approaching the detection limit and it will no longer be a useful constraint
on OH in decades to come.
A further indirect method to address OH is to measure 14CO.
find some considerable variability but no long-term
trend using this method. According to , this method is
considerably more sensitive to high-latitude than low-latitude OH, in
contrast to the CH3CCl3 method which is mostly sensitive to tropical
OH.
Therefore, a step forward in addressing the uncertainty in modelling
OH in global models
is to quantitatively assess the contributions of biases in long-lived species that are central to OH. This sometimes involves juxtaposing
global models to local-scale (box or single-column) models constrained as
much as possible by observations and incorporating only fast photochemical
processes. For example, develop a box
model to assess the sensitivity of OH and HO2 to biases in
long-lived species, and compare the model results to observations. However,
their analyses only pertain to polluted environments not representative of
much of the global atmosphere and only take in episodic and surface
measurements. Single-column models have been applied to modelling the
atmospheric boundary layer , diabatic
processes , clouds and aerosols
, the impacts of GHGs on
climate change , and the chemistry of halogen compounds
. Tropospheric OH chemistry of the remote
atmosphere has not been assessed in a single-column model framework before.
In the present paper, we introduce and evaluate a single-column model (SCM)
constrained with available long-term observations at Lauder, New Zealand
(45∘ S, 170∘ E, 370 m above sea level), to investigate how
chemistry–climate model biases in long-lived chemical
species and temperature affect OH. Lauder is known for its clean air and large diversity of available measurements
it is part of the Network for the Detection of Atmospheric Composition Change (NDACC),.
Observations made at Lauder include UV radiation and surface, profile, and/or total columns of O3 and several other species.
The O3, H2O, and temperature records produced by ozone sondes
cover 1986 to the present. Lauder therefore is ideal for this kind of
study. The SCM is built around a medium-complexity stratosphere–troposphere
chemistry scheme. The model is forced with Lauder observations and/or output
from a chemistry–climate model that uses the same scheme (see below). In
Sect. , we describe the set-up of the SCM, the construction of time
series of key species and meteorological parameters that drive the SCM, and
the simulations. In Sect. , we present results of simulated OH
concentrations and trends from the SCM and analyse the sensitivity of OH to
various forcings. Conclusions are gathered in Sect. .
Simulations performed with the SCM to assess the contribution of
changes in the key forcings to OH chemistry at Lauder under clear-sky
conditions. The table includes the type of measurement/dataset used to
prescribe the key forcings. The time period of simulation is between 1994 and
2010.
Forcings
Data used
O3
1. Kinetics effect: O3 changes → ozone sondes (0–25 km) + MOPI1 (26–84 km)
NIWA–UKCA data for other species and temperature
2. Photolysis effect: jO1D changes according to O3 changes
NIWA–UKCA data for all species and temperature
3. Kinetics + photolysis effects: O3 changes → ozone sondes + MOPI1
jO1D changes according to O3 changes
NIWA–UKCA data for other species and temperature
H2O
1. Changes in H2O → radiosondes (0–8 km) + NIWA–UKCA H2O (9–84 km)
NIWA–UKCA data for other species and temperature
2. Changes in H2O → ERAI (0–8 km) + NIWA–UKCA H2O (9–84 km)
NIWA–UKCA data for other species and temperature.
CH4
Changes in CH4 → rescaled NIWA–UKCA CH4 to Cape Grim surface CH4
NIWA–UKCA data for other species and temperature.
CO
Changes in CO → rescaled NIWA–UKCA CO profiles to FTIR CO
NIWA–UKCA data for other species and temperature
radiosondes (surface–25 km) +
T
1. Kinetics effect: temperature changes → NCEP/NCAR reanalyses (26–50 km) +
lidar climatology (50–84 km)
NIWA–UKCA data for all species
2. Photolysis effect: jO1D changes according to temperature changes
NIWA–UKCA data for all species and temperature
radiosondes (surface–25 km) +
3. Kinetics + photolysis effects: temperature changes → NCEP/NCAR (26–50 km) +
lidar climatology (50–84 km)
jO1D changes according to temperature changes
NIWA–UKCA data for all species
O3, H2O,
Changes in O3,H2O,CH4,CO, and temperature using observations mentioned above.
CH4,CO, T
For H2O, radiosonde (0–8 km) + NIWA–UKCA (9–84 km) data are used.
Reference
NIWA–UKCA data for all species and temperature
(a) Time series of O3 profiles constructed by
ozone sonde measurements spliced with MOPI1 measurements. (b) Time
series of H2O profiles constructed by radiosonde measurements spliced
with NIWA–UKCA H2O (time series of ERAI – NIWA–UKCA H2O is
not displayed here). (c) Time series of CH4 profiles
constructed by rescaling the NIWA–UKCA CH4 to surface CH4
measurements from Cape Grim (Tasmania). (d) Time series of CO
profiles constructed by rescaling the NIWA–UKCA CO to CO
measurements from the FTIR spectrometer. (e) Time series of
temperature profiles constructed by radiosonde measurements (up to 25 km)
merged with NCEP/NCAR reanalyses up to the stratopause (50 km) and a
mesospheric climatology based on local lidar measurements. Above 25 km these
data are as used in the retrieval of O3 from MOPI1 measurements. The
areas within black boxes were filled using a Fourier series gap filling
method.
Models and simulations
The single-column photochemical model (SCM)
The single-column photochemical model is a stand-alone version of the
stratosphere–troposphere chemistry mechanism used by the National Institute
of Water and Atmospheric Research (NIWA) and United Kingdom Chemistry and Aerosol
(UKCA) model (NIWA–UKCA), which comprises gas-phase photochemical reactions relevant
to the troposphere and stratosphere . For consistency with NIWA–UKCA,
the SCM uses the same chemical mechanism. Had we used a more complex
mechanism (which the SCM approach lends itself to), then a direct comparison
with the NIWA–UKCA output would no longer be possible, and also the results
would be less relevant to other global CCMs characterized by relatively
simple chemical mechanisms. The 60 vertical levels of the SCM are the same as
in NIWA–UKCA, extending to 84 km. We do not use horizontal interpolation and
take profiles of atmospheric properties from the grid point closest to Lauder
(45∘ S, 168.75∘ E). Unlike NIWA–UKCA, the SCM excludes
all non-chemistry processes, such as transport, dynamics, the boundary-layer
scheme, radiation, emissions, etc. This means the model is only suitable for
assessing fast photochemistry. Forcing data for the SCM are mostly
interpolated from 10-daily instantaneous outputs from a NIWA–UKCA simulation
(see below), except for those fields for which observational data are used.
and describe the chemistry scheme
included in the SCM. The SCM includes an isoprene oxidation scheme
not included in the NIWA–UKCA
model version used by . In addition to CH4 and
CO, the model includes a number of primary NMVOC source gases, i.e. ethane (C2H6), propane
(C3H6), acetone (CH3COCH3), formaldehyde (HCHO),
acetaldehyde (CH3CHO), and isoprene (C5H8). As noted above,
emission and deposition of species are not considered in the SCM. The SCM
includes a comprehensive formulation of stratospheric chemistry
comprising bromine and chlorine chemistry and
heterogeneous processes on liquid sulfate aerosols. Overall, the model
represents 86 chemical species and 291 reactions including 59 photolysis and
5 heterogeneous reactions. The FAST-JX interactive photolysis scheme
has been implemented in the SCM; this scheme
solves a radiative transfer equation accounting for absorption by ozone. The
chemical integration is organized through a self-contained atmospheric
chemistry package , and the differential equations
describing chemical kinetics are solved using a Newton–Raphson solver
.
Construction of vertical profiles of forcing species and meteorological parameters
Time series of O3,H2O,CO, and temperature profiles are produced
using mainly long-term measurements from Lauder, supplemented with NIWA–UKCA
results as detailed below. Lauder is a member of several international
networks, including the NDACC (http://www.ndsc.ncep.noaa.gov), the
Global Reference Upper Air Network (GRUAN; http://www.gruan.org), and
Global Atmosphere Watch (GAW;
http://www.wmo.int/pages/prog/arep/gaw/gaw_home_en.html), where these
data are archived and made available. The networks coordinate long-term
observations of O3, various other constituents, and meteorological
parameters. Here we briefly describe the procedure of constructing forcing
data, using Lauder observations, to be used to constrain the SCM. The
resulting profiles are shown in Fig. .
O3 profiles used here are a combination of ozone sonde time series
from the surface to 25 km, combined with the
Microwave Ozone Profiler Instrument 1 (MOPI1) time series for altitudes above
25 km , covering 1994 to 2010
(Fig. a). The ozone sondes have been launched approximately
weekly; this defines the temporal coverage of the forcing data used in the
SCM calculations. Microwave measurements used here come as several profiles a
day at a variable spacing; we interpolate them to the ozone sonde launch
times. Any missing data (during the two periods when the microwave instrument
was out of service) are filled using a Fourier series gap-filling method. We
compare the two datasets in the height region usefully covered by both (20 to
30 km). The differences between the two measurements range between -2
and +6%, and a mean bias that is less than 5%. O3 profiles
are linearly interpolated onto the SCM's grid. Total column ozone calculated
by integrating the observed O3 profiles is also compared to
total-column O3 measured by the Lauder Dobson instrument; the
difference is about 5 % on average . Lauder ozone
measurements have been used in various World Meteorological Organization
(WMO) ozone assessments e.g..
H2O profiles have been constructed using the weekly radiosonde
measurements of H2O vapour below 8 km (the same soundings that also
measure ozone) and NIWA–UKCA model output data above. For validation, we use
the monthly National Atmospheric and Oceanic Administration
(NOAA) Frost Point Hygrometer (FPH) H2O vapour
measurements , which start in 2003. FPHs are more
accurate compared to radiosonde hygrometers, particularly for stratospheric
conditions. However, due to the later start of the FPH time series and the
lower measurement frequency, radiosonde measurements are used in this study.
The comparison of FPH and radiosonde H2O reveals differences that are
generally less than ± 5% in the lower and middle troposphere but
generally increase in and above the tropopause region ∼ 11
km,. The radiosonde hygrometers have some known problems with
measuring low humidity . This is reflected in the
large differences observed particularly at these altitudes (up to 30 %)
and, to a lesser degree, below them (Fig. a). In a comparison of
NIWA–UKCA output with FPH H2O, larger discrepancies are found
throughout the whole troposphere and tropopause region (Fig. b),
as can be expected from a low-resolution model unconstrained by observations
and subject to problems with modelling H2O. Given the consistency of
FPH and radiosonde H2O below 8 km found before, here we use
radiosonde data up to 8 km of altitude merged, in the absence of a more
suitable dataset, with NIWA–UKCA output above that.
(a) Multi-annual and monthly mean percentage differences between
radiosonde and FPH H2O measurements. (b) Multi-annual and
monthly-mean percentage differences between NIWA–UKCA output and FPH
H2O.
Multi-annual and monthly-mean OH responses to O3
biases between observations and the reference simulation. (a)
Difference in O3 (%) between ozone sonde and NIWA–UKCA ozone,
relative to NIWA–UKCA ozone as prescribed in the reference simulation.
(b) OH difference (%) relative to the reference simulation
accounting only for the kinetics effects of O3 differences (e.g. with
jO1D unchanged). (c) Difference in jO1D
(%) relative to the reference simulation. (d) OH difference
(%) relative to the reference simulation accounting only for
jO1D differences (e.g. with O3 unchanged). (e)
OH differences relative to the reference simulation considering the
combined kinetics and photolysis effects. (f) Sum of (b)
and (d).
We use surface in situ measurements from Cape Grim, Tasmania
to rescale NIWA–UKCA model profiles, producing
CH4 profiles that coincide with the ozone sonde launches. The
NIWA–UKCA model simulation had been constrained with historical global-mean
surface CH4 values, resulting in an overestimation relative to the
Cape Grim data by ∼2% (not shown), and both data show a ∼5%
increase in CH4 at the surface over the period between 1994 and 2010.
Cape Grim CH4 is a good surrogate for the Lauder measurements because
CH4 is a long-lived, well-mixed atmospheric constituent.
The time series of CO profile over the period of 1994–2010 has been
constructed using the NIWA–UKCA CO profiles, rescaled such that the total
columns match those obtained from the mid-infrared Fourier Transform
Spectrometer (FTS) at Lauder .
Gaps in the total-column FTS series, such as the period between 1994 and 1996
when the FTS measurements had not started yet, are filled using a regression
fit accounting for the mean annual cycle (modelled as a 6-term harmonic
series) and the linear trend.
The time series of temperature profiles are constructed following the same
procedure as used in the construction of O3 profiles, comprising the
radiosonde temperature profiles (from the surface to 25 km) merged with
NCEP/NCAR reanalyses temperatures used in the retrieval
of MOPI1 ozone (above 25 km) for the period of 1994–2010. From near the
stratopause upwards the NCEP/NCAR temperatures are merged with a mesospheric
climatology based on local lidar measurements. There are some warm anomalies
occurring in the data at 40–60 km during winter months (e.g. in 1996); these
may reflect planetary wave breaking in the upper stratosphere.
Simulations
We perform SCM simulations covering the period of 1994–2010, summarized in
Table . The forcing data needed by the SCM consist of profile
series of temperature, pressure, optional cloud liquid and ice mass mixing
ratios, and the mixing ratios of 86 chemical compounds. With the exceptions
detailed below, these fields and species are taken from a NIWA–UKCA
simulation for the period of 1994–2010 interpolated to the times of the
ozone sonde launches. The CCM simulation used here consists of the last 17
years of a NIWA–UKCA “REF-C1” simulation conducted for the Chemistry–Climate
Model Initiative CCMI;. REF-C1 is a hindcast simulation
for the period of 1960–2010, using prescribed Hadley Centre Sea Ice and Sea
Surface Temperature (HadISST) fields . The surface
emissions of primary species are as described in ,
ozone-depleting substances (ODSs) follow the A1 scenario of the WMO Report , and surface (or
bulk, in the case of CO2) abundances of GHGs
follow the “historical” Intergovernmental Panel on Climate Change
scenario of global-mean GHG mixing ratios .
In a “reference” simulation of the SCM all forcings are taken from this
REF-C1 simulation of NIWA–UKCA. Alternatively, in sensitivity simulations
O3,H2O,CH4,CO, and temperature, or all of these simultaneously,
are replaced with the time series of the profiles that are constructed using
long-term observational data as described above. For species other than those
five fields, in all cases we use NIWA–UKCA REF-C1 forcings. We evaluate the SCM
only for those times, spaced roughly weekly, for which ozone sonde data are
available. With the exceptions of those simulations assessing cloud
influences, simulations are conducted assuming clear-sky conditions.
OH sensitivity to correcting chemistry–climate model biases
In this section, we present sensitivity studies to assess the contribution of
biases in known factors (O3,H2O,CH4,CO, and temperature)
affecting OH photochemistry at Lauder. The response of OH to
changes in each forcing is assessed individually and also in combination.
OH sensitivity to O3 biases
Three sensitivity simulations are conducted to quantify the impact of
O3 biases (defined as differences between observed O3 and
NIWA–UKCA simulated O3) on OH at Lauder.
As discussed above, the rate of production P of HOx via
O(1D)+H2O can be expressed as
P(HOx)≈2k1jO1D[O3][H2O]k2[O2]+k3[N2]+k1[H2O],
where k1 is the rate coefficient for O(1D)+H2O,
jO1D is the rate of O3 photolysis producing O(1D),
and k2 and k3 are the rate coefficients of quenching of
O(1D) with O2 and N2, respectively . Accordingly, P(HOx)
is affected by ozone changes principally in two different ways: Either
locally through a change in [O3] or non-locally through a change in
jO1D caused by changes in the overhead total-column ozone (TCO). To
separate the effects, we conduct three simulations with the SCM: The first
simulation targets the local kinetics effect by applying changes in
O3 concentrations but keeping all photolysis rates unchanged vs.
the reference simulation. A second simulation involves applying changes in
jO1D according to changes in O3 (keeping the rest of
photolysis rates unchanged), but considering a fixed O3
concentration, i.e. using the O3 concentrations of the reference
simulation. The jO1D calculation consistently takes into account
absorption and scattering by stratospheric and tropospheric O3. A
third simulation includes both effects simultaneously.
The results of these three sensitivity runs are displayed in
Fig. . As expected, the pattern of O3 differences between
observed O3 and modelled O3 (Fig. a) is reflected
in the pattern of OH differences produced by the SCM, considering only the
“kinetics” effect and assuming no changes in the photolysis rates
(Fig. b), with increases of ozone in spring and decreases in
autumn, relative to the reference simulation, resulting in changes of the
same sign in OH. However, there is a height dependence to this
relationship.
In summer and autumn, O3 biases range between -5 and -45%,
meaning that the reference simulation overestimates the observations. Such a
bias in O3 results in up to 12 % reductions in OH for
these seasons when the bias is corrected. In spring between 2 and 6 km,
observed O3 is larger than in the reference simulation by up to
10 % at 4 km in October. Consequently, this results in an increase of
OH at around the same altitudes and times of up to 5 %.
Regarding the sensitivity simulation considering the photolysis effects,
jO1D exhibits differences relative to the reference simulation
ranging from ∼14 to ∼30%. The corrections are positive
everywhere, in accordance with the overestimation of TCO in the NIWA–UKCA
model with respect to observations . In
accordance with eq. , such an intensification of jO1D
causes OH to increase (Fig. c). The relative OH
response is approximately 50% of the jO1D relative difference.
However, Fig. c and d suggest that the magnitudes of the
kinetics and the photolysis effects, for the O3 bias found at Lauder,
are comparable, but the seasonalities differ. For example, the kinetics
effect maximizes in spring at 5 % and minimizes in summer/early autumn at
-15% (in the upper troposphere) whereas the photolysis effect on
OH maximizes in summer at 16 to 20 % and minimizes in spring
(Fig. b and d).
OH resulting from the combined kinetics and photolysis effects is
displayed in Fig. e. OH responds approximately linearly to the two
effects combined, compared to the sum of their individual impacts
(Fig. f), despite some small differences between Fig. e
and f.
Next, we examine the relationship between the slant column of O3 (SCO),
jO1D, and OH. Figure a shows that there is
an approximately exponential relationship between jO1D and the SCO
at 6 km of altitude (this effect also exists at other altitudes). The small
curvature may be the result of inaccurately diagnosing the SCO (ignoring the
curvature of the Earth). Another reason could be that the cross section of
O3 is wavelength-dependent, and consequently the actinic flux
spectrum moves towards longer wavelengths with increasing SCO. Under
Lambert–Beer's Law, a perfectly exponential relationship would be expected
for a monochromatic UV light source and an isothermal atmosphere.
jO1D and the OH concentration exhibit an approximately
linear relationship (Eq. , Fig. b). Combining
these results, we derive an approximately exponential relationship between
the SCO and the OH concentration (Fig. c). The fit
parameters are stated in Fig. . Due to the compact
relationship between jO1D and the SCO, and to account for the
curvature, we fit a quadratic relationship between the SCO and
log(jO1D).
(a) Scatter plot of jO1D with the slant column of
O3 (SCO) at 6 km of altitude. (b) Same, but for
jO1D and OH. (c) Same, but for the SCO and
OH. The results shown in this figure are those obtained from the
combined simulation (kinetics and photolysis effects). Red lines denote
least-squares fits between the variable pairs. The best fits are stated in
the panels, with [OH] in units of 106 mol cm-3,
jO1D in units of 10-5 s-1, and the SCO in Dobson
Units.
Sensitivity coefficients Ai′′ between OH and each perturbation
variable: In the calculation, multi-annual mean relative differences in
OH and in the forcing are ratioed. (a) Sensitivity of
OH to changes in O3 levels (kinetics effect) denoted by A1
(solid line) and to changes in jO1D due to changes in O3 (photolysis effect) denoted by A1′′
(dashed line); (b) sensitivity of OH to changes in radiosonde
– NIWA–UKCA CCM H2O (A2 solid line) and to changes in ERAI –
NIWA–UKCA H2O (A3 dashed line); (c) sensitivity of
OH to changes in CH4 (A4); (d) sensitivity of
OH to changes in CO (A5); (e) sensitivity of
OH to changes in temperature (kinetics effect) denoted by A6;
(f) sensitivity of OH to changes in
jO1D due to changes in
temperature (photolysis effect) denoted by A6′′.
Multi-annual and monthly-mean OH responses to H2O
between perturbation simulations and the reference simulation. (a)
Radiosonde – NIWA–UKCA H2O (%) relative to the reference
simulation. (b) ERAI – NIWA–UKCA H2O (%) relative to the
reference simulation. (c) OH difference (%) relative to the
reference simulation between simulations using radiosonde and NIWA–UKCA
H2O (a). (d) OH differences (%) relative to
the reference simulation between simulations using ERAI and NIWA–UKCA CCM
H2O (b). (e) Ratio of relative OH changes
(c) to relative changes in H2O (a). (f)
Ratio of relative OH changes (d) to changes in H2O
(b). Above 8 km NIWA–UKCA H2O was used in both cases.
Therefore, differences with respect to the reference simulation are close to
0.
To determine a simple coefficient that describes the quantitative
contribution of O3 to OH, a linear regression between
differences in OH and O3 relative to the reference was
conducted through the following expression (note that this equation is also
used to derive the linear contributions of the other key species to OH
chemistry at Lauder):
Δ[OH][OH]ref=AiΔXiXi,ref,
where Xi is the perturbation variable (in this case [O3]), Ai
is the slope of the linear regression, Δ[OH] is the absolute
difference between the OH concentrations in the reference and
perturbation simulations, ΔX is the absolute difference in
concentrations of the perturbation variable X between the observations and
the reference, [OH]ref is the OH concentration
obtained from the reference simulation, and Xi,ref is the value
of Xi in the reference simulation. The regression coefficients Ai
represent the sensitivity of OH to changes in each individual variable
for the troposphere at Lauder. The regression coefficients are depicted in
Fig. . Reverting to infinitesimal notation, we note that
Ai=∂ln[OH]∂lnXi.
The sensitivity coefficients of OH to the kinetics and photolysis
effects of O3 are shown in Fig. a. Coefficient
A1, which represent the kinetics effect, ranges from 0 to 0.25
(meaning the relative response of OH is up to a quarter of the
relative difference in O3). The sensitivity to photolysis (A1′′)
is >0.5 throughout much of the troposphere (meaning the relative response
in OH is over half the relative change in jO1D).
Multi-annual and monthly-mean OH responses to CH4
and CO biases between observations and the reference simulation.
(a) Difference in CH4 (%) relative to the reference
simulation. (b) Difference in CO (%) relative to the
reference simulation. (c) OH difference (%) relative to the
reference simulation caused by the CH4 change (a).
(d) OH difference (%) relative to the reference simulation
caused by the CO change (b). (d) Ratio of relative
OH changes (c) to relative changes in CH4
(a). (f) Ratio of relative OH changes (d)
to relative changes in CO (b).
OH sensitivity to H2O biases
A perturbation simulation was performed using combined radiosonde and
NIWA–UKCA H2O (Sect. ). The OH response to
correcting H2O biases (Fig. ) shows an approximately
linear response with respect to the relative changes in H2O, i.e.
decreases in H2O generally lead to a reduction of OH
concentrations (Eq. ). Note that NIWA–UKCA substantially
overestimates the radiosonde-observed H2O vapour by up to 60 %
between 2 and 6 km (Fig. a); this translates into an
overestimation of OH by up to ∼40% in the reference
simulation (Fig. c) in the same region. The sensitivity of OH to
changes in H2O (Eq. ) range from 5 to 0.5 in the
troposphere (Figs. e and b coefficient
A2), with high sensitivity in the lower and free troposphere and reduced
sensitivity in the tropopause region.
It is known that large uncertainties are associated with H2O vapour
measurements. To illustrate this, we repeat the above simulation but now
using European Centre for Medium-Range Weather Forecasts (ECMWF)
ERA-Interim reanalysis (hereafter ERAI) H2O .
Irrespectively of the large differences and the opposite signs in H2O
biases between Lauder radiosonde and ERAI data, the OH response to biases in
H2O shows approximately the same linear relationship in both cases
(Fig. ). Likewise, the sensitivity of OH to changes in
H2O using ERAI data (Figs. f and b,
coefficient A3) resembles the sensitivity simulation using radiosonde
H2O.
OH sensitivity to CH4 and CO biases
The effect of CH4 changes on OH is displayed in
Fig. a, c, e. The CH4 biases are generally small, up
to only ∼2%, and are assumed to be vertically uniform, with some
seasonal variations. Decreases in CH4 lead to increases in OH
due to reduced loss of HOx by CH4+OH. The response of
OH to CH4 changes maximizes at 0.6 % around 2 km, and
decreases at higher altitudes. The seasonal variation of the OH
response to CH4 biases maximizes in March/April
(Fig. c), which coincides with the maximum absolute bias in
CH4 (Fig. a) in the same months. The sensitivity
coefficient describing the dependence of OH on CH4 changes
(denoted as A4 in Fig. c) ranges from -0.17 at the
surface to -0.32 at ∼ 2 km of altitude, and then decreases to
-0.15 at 10 km.
Multi-annual and monthly-mean OH responses to temperature
biases between observations (radiosonde and NCEP/NCAR temperature) and the
reference simulation. (a) Difference in radiosonde and NCEP/NCAR
temperature (K) relative to the reference temperature. (b) OH
difference (%) relative to the reference simulation accounting only for the
kinetics effects of temperature differences (e.g. with jO1D
unchanged). (c) Difference in jO1D (%) relative to the
reference simulation. (d) OH difference (%) relative to the
reference simulation accounting only for jO1D differences (i.e. with temperature unchanged). (e)
OH differences relative to the reference simulation considering the
combined kinetics and photolysis effects. (f) Sum of (b)
and (d).
The CO bias and the resulting differences in OH are displayed
in Fig. b, d, f. The relative difference of OH with
respect to the reference simulation is less than ±5% for all seasons
(Fig. d), showing that decreases in CO generally lead
to increases in OH through the reduced loss of OH through
OH+CO. Note that during austral spring NIWA–UKCA overestimates
CO, presumably due to exaggerated tropical biomass burning in the
model which causes CO biases of up to 10%
(Fig. b). The sensitivity of OH to changes in CO
(∂ln[OH]/∂ln[CO]) shown in
Fig. f varies from -0.3 to -0.5 and in absolute terms
increases with altitude (the white band shown in October is the result of
CO differences being close to zero).
The sensitivities of OH to CH4 and CO show comparable values
at the surface, but the OH sensitivity to CO increases with height whereas
its sensitivity to CH4 decreases. Note that the CH4+OH
reaction rate is strongly temperature-dependent, which may contribute to the
lower sensitivity of OH to CH4 changes at altitude than to
CO. However, further investigation will need to investigate how these
ratios change in different chemical regimes, and to assess whether the
relative sensitivity of OH to CO and to CH4 are specific to the clean
SH environment.
OH sensitivity to temperature biases
To assess the effects of changes in temperature on OH, we apply the
same procedure as for O3, for which the effects of temperature have
been decomposed into kinetics and photolysis effects. We perform three
simulations: In the first simulation, we only apply temperature changes to
chemical kinetics, keeping all photolysis rates fixed (noting that most uni-,
bi-, and termolecular reaction rates are temperature-dependent). In the
second simulation, we only consider the photolysis effect, which arises
mainly because the cross section of O3, the primary UV absorber, is
temperature-dependent. The impact of temperature on OH via ozone
photolysis again occurs via two different mechanisms: firstly, the changes in
jO1D caused by changes in the actinic flux which relates to changes
in the atmospheric transmissivity in the UV (caused by a temperature
dependence of the cross section of overhead ozone), and the local changes of
jO1D, due to the local temperature dependence of the ozone cross
section. Here, we only evaluate the combined photolysis effect in the second
simulation. Finally, we perform a third simulation by applying both the
kinetics and the photolysis effects simultaneously.
At Lauder, the reference simulation is generally cold-biased (i.e. the
temperature correction is positive; Fig. a). This is particularly
the case in the lowest 2 km and throughout the troposphere in the
autumn–winter season. The kinetics effect leads to a reduction of OH
by up to 2% (Fig. b). O(1D)+H2O and the
quenching reactions (Eq. ) are not, or are weakly, temperature-dependent,
making CH4 + OH (which is much more sensitive to temperature) the
leading factor in causing this small OH reduction. The rate
coefficient for this reaction in NIWA–UKCA and the SCM is
kOH+CH4=1.85×10-12exp(-1690K/T); at 290 K
the sensitivity of kOH+CH4 to temperature changes evaluates
to about 2 % K-1. However, OH is well buffered by other
reactions, so its sensitivity is considerably smaller than that. The
photolysis effect is often somewhat larger than the kinetics effect but peaks
in spring (Fig. c). This translates into a slight OH
reduction comparable in magnitude to the kinetics effect (Fig. d).
Both effects add nearly linearly in the combined simulation
(Fig. e, f).
We calculate sensitivity coefficients A6 and A6′′ that define the
OH responses to both effects (Fig. e, f).
Coefficient A6 represents the kinetics effect and varies from 0 to -1.75
(i.e. in absolute terms, the relative OH response can be larger than
the relative difference in T). The sensitivity coefficient that describes
the sensitivity of OH to changes in photolysis (A6′′) ranges from 0.6 at
the surface to 0 at 10 km of altitude. Figure e and f
show sensitivity coefficients for both effects (A6 and A6′′).
OH changes due to both effects are small (up to 2.5%) and
comparable in magnitude.
Several sensitivity studies have been conducted previously to elucidate the
impact of temperature on OH . None of these studies separately assessed the impacts of the
kinetics and photolysis effects of temperature on OH.
applied a globally uniform temperature rise of 5 K that led to a larger
OH abundance and an around 10% decrease in the CH4
lifetime. showed a small impact on global OH
abundances due to temperature biases; this may be because either the
temperature biases in their model were both positive and negative, in
different regions, leading to some cancellation of the impact on global
OH, or to low OH sensitivity to temperature biases. Here,
bias-correcting temperature is shown to also have only a small impact on
OH abundance (Fig. e); this result broadly corroborates that
of .
Linearity of OH sensitivity to biases in all forcings
Here, we assess the effect of changing all forcings (O3, H2O, CH4,
CO, and temperature) simultaneously on OH at Lauder.
Figure a shows the responses of OH to changing all forcings.
A comparison with Fig. suggests that H2O changes
dominate the total response of OH to changes in these forcings. At
Lauder, NIWA–UKCA is too moist (relative to radiosonde water vapour); this
translates into a large OH overestimation of up to ∼40% in
the reference simulation (Fig. a). This is consistent with the
underestimated CH4 lifetime by the NIWA–UKCA model
, assuming that the NIWA–UKCA model is also
too moist in other regions. (In the NIWA–UKCA reference simulation used
here, the global CH4 lifetime, disregarding dry deposition, is 7.2
years, whereas a recent best estimate is 9.8 years, with an uncertainty range
of 7.6–14 years; .) In general, in the SCM OH
responds approximately linearly to the combined changes in major forcings
that play an important role in OH chemistry (Fig. ).
(a) Multi-annual and monthly-mean percentage difference
in OH between a simulation with bias correction applied to all five
fields and the reference simulation. Radiosonde H2O is assumed below
8 km. (b) Summation of all the single forcing contributions as
expressed by the right hand side of Eq. (). Radiosonde
H2O is assumed below 8 km. (c) Scatter plot of the response
of OH to the combination of all forcings (vertical axis, denoted as
Δ[OH]) vs. the summation of the OH response to
individual forcings (horizontal axis) as expressed by the right hand side of
Eq. () (denoted by ∑iΔ[OHi]). The
red solid line denotes an orthogonal fit. The black dashed line is the
diagonal.
Variability and trends of the OH anomalies at different
altitudes: (a) 0–2.5, (b) 2.5–5, (c)
5–7.5, and (d) 7.5–10 km. The red solid line is the time
series of the reference simulation and the blue solid line is the combined
forcings simulation considering radiosonde – NIWA–UKCA H2O.
Variability and trend of the OH column anomaly. The red solid
line is the time series of the reference simulation and the blue solid line
is the combined forcings simulation considering radiosonde – NIWA–UKCA
H2O.
To examine the linearity of OH responses to simultaneous changes in
key forcings defined in this study, the combination of all individual
contributions, i.e. O3 (kinetics and photolysis effects),
H2O, CH4, CO, and temperature (kinetics and photolysis
effects) to OH, was compared to the OH response to all forcings
combined simulation in the SCM through Eq. ():
Δ[OH][OH]ref≈A1′′Δ[O3][O3]ref+A1′′ΔjO1DO3jO1Dref+A2Δ[H2O][H2O]ref+A4Δ[CH4][CH4]ref+A5Δ[CO][CO]ref+A6ΔTTref+A6′′ΔjO1DTjO1Dref,
where Δ[OH]/[OH]ref is the relative difference
in the OH concentration obtained with the SCM with respect to the
reference simulation, using all forcings combined. The forcings comprise the
kinetics and photolysis effects of O3 (A1 and A1′′), radiosonde
H2O (A2), CH4 (A4), CO (A5), and the kinetics
and photolysis effects of temperature (A6 and A6′′).
Equation () expresses a working hypothesis that the model
responds linearly to the applied forcings; we will test this assumption in
the following paragraph.
Figure a and b indicate that the model responds approximately
linearly to the combinations of all forcings, with OH responses in the
all-forcings simulation correlating at 0.9 with the sum of the OH responses
in the individual-forcing simulations. Figure c however also
suggests that there is some notable non-linearity in the chemistry of the
troposphere at Lauder. Chemical feedbacks between the impacts of correcting
water vapour and ozone may contribute to this non-linearity; for example, a
change in the water vapour abundance may impact the sensitivity of OH
to changing O3.
Multi-annual and monthly-mean OH responses to the presence
of clouds. Multi-annual and monthly mean (a) ice content
(10-5 kg kg-1). (b) liquid water content
(10-5 kg kg-1). (c) Response of jO1D (%) to
the presence of ICs relative to the cloud-free reference simulation.
(d) response of jO1D (%) to the presence of LWCs relative
to the cloud-free reference simulation. (e) response of OH
(%) to the presence of ICs relative to the cloud-free reference simulation.
(f) Response of OH (%) to the presence of LWCs relative to
the cloud-free reference simulation. (g) response of OH (%)
to the presence of both LWCs and ICs relative to the cloud-free reference
simulation. (h) Sum of (e) and (f).
Trends in OH
We examine variability and trends in OH using the SCM simulation
including all key forcings separately for different altitude bins. The
results (Fig. ) indicate that there are no significant
long-term trends in OH throughout the troposphere for the period of
the simulation (1994–2010) We find trends of -2.1±4.8% at
0–2.5 km, 0.9±2.3% at 2.5–5 km, 2.6±3.5% at 5–7.5 km, and
3.6±4.1% at 7.5–10 km over the period of 1994–2010), but there is
evidence of interannual variations at all altitudes
e.g..
In addition, we explore variability and trends in the OH column at
Lauder to be compared with other estimates of global OH. As expected
from the results of OH trends at different altitude bins, we find no
significant long-term trend in the OH column (0.5±1.3%)
(Fig. ). However, there is evidence of short-term
variations (5–10%), in agreement with other studies that used
observations to infer global OH concentrations
e.g..
OH sensitivity to the presence of clouds
We have assessed the OH sensitivity to correcting biases in key forcings
assuming clear skies. Here we explore the impact of simulated clouds on
OH, recognizing that this process is associated with large
uncertainties due to difficulties with representing clouds in models.
Measurements of cloud profiles do not exist at Lauder, hence a bias
correction like that performed with the composition and temperature fields is
not possible. Therefore, here we only examine the impact of clouds simulated
by NIWA–UKCA on jO1D and OH at Lauder, relative to the
clear-sky reference simulation used before. The impacts of liquid water
clouds (LWCs) and ice clouds (ICs) were assessed separately and in
combination. Three simulations are defined here, i.e. (1) including only
ICs, (2) including only LWCs, and (3) considering both combined (LICs).
Figure a, c and e show the response of jO1D and OH
to the presence of the ICs. jO1D and OH are generally reduced below
the ICs, relative to the cloud-free situation. The maximum reduction in OH is
10 to 15% in winter below 2 km, coinciding with the maximum reduction
in jO1D. There are increases in both fields (up to ∼8%)
above the ICs in austral spring, associated with the seasonal peak in IC
occurrence at the same time. In general, jO1D and OH impacts
vary strongly with season, with the maximum reduction occurring in winter
close to the surface, and the maximum increase in spring above the ICs.
LWCs are mostly present between 1 and 4 km with the seasonal peak in austral
spring (Fig. b). Similarly to ICs, jO1D and OH
are enhanced above and throughout much of the cloud layer, and reduced in the
lowest 1 km above the surface (Fig. e, g). The enhancement in
jO1D and OH peaks at 12% between 2 and 4 km of
altitude, coinciding with the spring maximum in liquid water content at
1–2 km. Conversely, the reduction in jO1D and OH with
respect to the clear-sky condition is ∼10% and is produced below
the clouds.
The simulation with the combined effect of ICs and LWCs (LICs) produces a
reduction in jO1D and OH that ranges between 0 and
20% below the transition of ICs to LWCs at around 2 km, since LWCs are
as much as twice as optically dense as ICs (Fig. g). An
enhancement is produced above this altitude of up to 18%. The magnitudes
of changes in jO1D and OH are similar when either ICs or
LWCs are considered in the SCM. Furthermore, their effects add up slightly
less than linearly when both are present in the simulations
(Fig. h).
The results shown here indicate that lower clouds generally produce an
enhancement in jO1D (Fig. d), but higher clouds
generally produce a reduction in jO1D in the free troposphere
Fig. b;. Furthermore, the
vertically and seasonally averaged enhancement and reduction in
jO1D are about 2 and 6% respectively for the LWCs,
similar to the response for the ICs' condition; this suggests that the cloud
vertical distribution has a bigger effect on photolysis than the change in
cloud water content .
Conclusions
The sensitivity of the OH abundance at Lauder to NIWA–UKCA model
biases in key forcing variables (O3, H2O, CH4, CO,
and temperature) have been quantified for clear-sky conditions, using a
single-column model. Only fast photochemistry is represented in the
SCM; slow chemistry (i.e. timescales similar to or longer than the 1 h
chemical timestep), transport, and other physical processes are thus not
considered. The bias-corrected profiles of the key forcing variables have
been constructed largely using long-term Lauder measurements, combined with
NIWA–UKCA output. A few other sources of data (Cape Grim methane
measurements, ERA-Interim water vapour) have also been used.
The results show that OH responds approximately linearly to correcting
biases in O3, H2O, CH4, CO, and temperature.
We have decomposed the OH response to O3 changes into the
kinetic effect (i.e. local impacts on the chemical steady state of changing
[O3]) and the photolysis effect (as mediated by changes in the
overhead O3 column affecting photolysis rates). We find that the
kinetic effect of correcting positive biases in modelled O3 causes a
reduction in OH during austral summer and autumn (by up to 20% at
7 km), and an increase in the free troposphere in austral spring (of
>5% in October at 3 km); such changes in OH are nearly linearly
related to the corresponding ozone biases. NIWA–UKCA generally overestimates
the ozone column. Correcting this bias causes jO1D to increase by
15–30% below 10 km, causing general OH increases which maximize
at around 16 % between 2 and 6 km in summer. The model responds
approximately linearly to the combined effects of photolysis and kinetics.
NIWA–UKCA considerably overestimates the H2O vapour concentration by
up to ∼ 50 % compared to radiosonde measurements. Correcting this moist
bias leads to >34% reductions in OH in the free troposphere during
the austral summer. The sensitivity coefficient of OH to biases in
H2O vapour is relatively large in the lower troposphere but decreases
with altitude. Assuming this moist bias is not restricted to Lauder (which we
do not assess here), this is thus a leading explanation for NIWA–UKCA to
produce an underestimated CH4 lifetime , relative to literature estimates .
The bias in modelled CH4 is small since surface CH4 in the
SCM reference simulation is constrained to follow globally averaged surface
observations. The Southern Hemisphere generally has a slightly smaller
CH4 burden than the Northern Hemisphere. Correcting the resulting positive bias at
Lauder causes increases in OH throughout the troposphere, with a
seasonal peak in March/April. OH is most sensitive to CH4
changes in winter, though. In the analysis of the OH sensitivity to
CH4, the impact of subsequent changes in CH4 oxidation
products which also affect OH could not be addressed within the
constraints of an SCM. Inclusion of this effect could change the sensitivity
coefficient for CH4 .
Except for October–December, NIWA–UKCA has a tendency to underestimate
CO. As with CH4, the sensitivity of OH to changes in
CO is negative throughout the troposphere, reflecting that CO + OH is an important sink for OH.
We show that OH responds linearly to temperature biases. These effects
cause a reduction in OH due to the strong dependence of OH + CH4 on temperature (Eq. ). However, the impact of this reaction
on OH is buffered by other less temperature-dependent reactions,
causing only a small sensitivity of OH to temperature. This is in
agreement with .
The results of the simulation considering simultaneous changes in all the key
forcings indicate that OH responds approximately linearly to all the
major forcings that contribute to the oxidizing capacity of the atmosphere.
We find that biases in O3, H2O, CH4, CO, and
temperature all affect the oxidizing capacity of the atmosphere at Lauder,
with H2O and O3 biases dominating. We find no significant
trend in OH over Lauder over the period 1994–2010.
The SCM approach can be applied to other parts of the globe where reliable
long-term observations of O3 and H2O exist. In situ
observations of CH4 and CO are not that critical; CH4
can be estimated from non-local measurements, and relatively reliable
satellite measurements of total-column CO exist
e.g.. However, in polluted regions, such
as in much of the Northern Hemisphere, NOx and NMVOC levels are
elevated relative to Lauder and affect in situ ozone production. This means
that these constituents might need to be bias-corrected if the SCM is applied
in such regions. This might affect the suitability of our approach under
these conditions.
Having determined the contributions of the major forcings to the chemistry of
OH at Lauder under clear-sky conditions, a step forward would be to
assess the impact of clouds on photolysis and thus OH, which could be
substantial. Due to a lack of suitable observations to constrain the SCM
model with cloud profiles at Lauder, we only assessed how the presence of
modelled cloud affects OH, relative to the clear-sky situation. The results
show that OH response to cloud strongly depends on the vertical distribution
of the clouds, not just the total amount. Both liquid and ice clouds lead to
increases in OH above and to some extent inside the cloud,
particularly in the spring season when this effect maximizes. Considering
that clouds are amongst the most difficult aspects of the climate system to
model adequately, we stipulate that observational profiles of cloud
properties would be highly desirable to use for a future continuation of this
line of research.
In summary, we conclude that at Lauder, OH modelled in NIWA–UKCA is
most sensitive to issues with representing water vapour and ozone. This
points to the need to improve representations of the hydrological cycle and
of tropospheric and stratospheric ozone chemistry in NIWA–UKCA and possibly
other, similar chemistry–climate models. Water vapour is coupled to clouds in
NIWA–UKCA; it is well known that clouds are difficult to represent adequately
in global low-resolution climate models. The biases in ozone may well be
partly caused by the moist bias in NIWA–UKCA; this is a subject of ongoing
research.
Progress with the simulation of the hydrological cycle in present-generation
Earth system models should improve the simulated water vapour product.
Simulating an accurate hydrological cycle has been a long-standing issue in
climate models, and progress has been slow. If errors in the simulation of
moisture cannot be avoided, perhaps their impact on OH can be
corrected for using an approach similar to that which we have presented but
using global water vapour measurements. Such a “correction” of modelled
OH might result in a reduction in the inter-model spread of the
OH abundance and consequently a more accurate quantification of the
methane lifetime. For this, tropical radiosonde data would be particularly
valuable – most OH is located in the tropics . A
similar approach could be used to account for the influence of errors in
ozone, although tropospheric in situ ozone measurements may be too sparse to
allow for a sufficient characterization of the error in models.