Here we present a description of the UKCA StratTrop chemical mechanism, which is used in the UKESM1 Earth system model for CMIP6. The StratTrop chemical mechanism is a merger of previously well-evaluated tropospheric and stratospheric mechanisms, and we provide results from a series of bespoke integrations to assess the overall performance of the model.
We find that the StratTrop scheme performs well when compared to a wide
array of observations. The analysis we present here focuses on key
components of atmospheric composition, namely the performance of the model
to simulate ozone in the stratosphere and troposphere and constituents that
are important for ozone in these regions. We find that the results obtained
for tropospheric ozone and its budget terms from the use of the StratTrop
mechanism are sensitive to the host model; simulations with the same
chemical mechanism run in an earlier version of the MetUM host model show a
range of sensitivity to emissions that the current model does not fall
within.
Whilst the general model performance is suitable for use in the UKESM1 CMIP6 integrations, we note some shortcomings in the scheme that future targeted studies will address.
Introduction
The ability to model the composition of the atmosphere is vital for a wide
range of applications relevant to society at large. Atmospheric composition
modelling can broadly be subdivided into two sub-disciplines: (1) aerosol
processes and microphysics and (2) atmospheric chemistry. Coupling these
processes in climate models is paramount for being able to simulate
atmospheric composition at the global scale. The most societally important
questions revolve around understanding how the composition of the atmosphere
has changed over the past, attributing this change, understanding how this
system is likely to change into the future, and what the impacts of these
changes are on the Earth system and on human health. It is these pressing
issues that have led to the development of the new UK Earth system model,
UKESM1 (Sellar et al., 2019), which uses the UK Chemistry and Aerosol model
(UKCA) (O'Connor et al., 2014; Morgenstern et al., 2009; Mulcahy et al.,
2018) as its key component to simulate atmospheric composition in the Earth
system. The key challenge UKCA is applied to is understanding and predicting
how the concentrations of a range of trace gases, especially the greenhouse
gases methane (CH4), ozone (O3) and nitrous oxide (N2O), and
aerosol species will evolve in the Earth system under a range of different
forcings. UKCA simulates the processes that control the formation and
destruction of these species. Here we describe and document the performance
of the version of UKCA used in UKESM1, which includes a representation of
combined stratospheric and tropospheric chemistry that enhances the
capability of UKCA beyond the version used in the Atmospheric Chemistry and
Climate Model Intercomparison Project (ACCMIP; Young et al., 2013; O'Connor
et al., 2014) and the recent Chemistry–Climate Model Initiative (CCMI)
intercomparison (Bednarz et al., 2018; Hardiman et al., 2017; Morgenstern et
al., 2017). There have been a number of previous versions of UKCA with
defined scopes, but we denote the version used in UKESM1 and described here
as UKCA StratTrop to signify its purpose of the holistic treatment of
composition processes in the troposphere and stratosphere.
As a result of the Chemistry–Climate Model Validation Activity (CCMVal), it
was recommended that models which are aimed at simulating the coupled
ozone–climate problem should include processes to enable interactive ozone
in the troposphere and stratosphere (Morgenstern et al., 2010).
Chemistry–climate models (CCMs) use schemes to describe the reactions that
chemical compounds undergo. These chemistry schemes can be constructed to
explicitly model a specific chemical reaction system (e.g. Aumont et al.,
2005), but in most applications the chemistry schemes are heavily simplified.
Until recently, models of atmospheric chemistry tended to focus on chemistry
schemes formulated for limited regions of the atmosphere; detailed schemes
have been constructed to examine phenomena such as stratospheric ozone
depletion or tropospheric air pollution. Examples of this using the UKCA
model framework are two studies of the effects of the eruption of Mt.
Pinatubo, for which Telford et al. (2009) used the stratospheric scheme of
Morgenstern et al. (2009) to study the effects of the eruption on
stratospheric ozone, whereas Telford et al. (2010) used the tropospheric
scheme of O'Connor et al. (2014) to examine the effects on tropospheric
oxidising capacity. Whilst the chemical schemes described in O'Connor et al. (2014) (hereafter OC14) and Morgenstern et al. (2009) (hereafter MO09) have
some overlap (for example the use of some common reactions) the schemes were
developed with specific applications in scope. The reason for partitioning
chemical complexity like this is to reduce the computational resources
required. Moreover, simulations with these process limitations were found to
be able to capture the phenomena of interest.
However, increases in computational power and a drive to answer a greater
number of questions from model simulations have allowed models that simulate
both the stratosphere and troposphere to be developed which are now
widely used (e.g. Pitari et al., 2002; Jöckel et al., 2006; Lamarque et al.,
2008; Morgenstern et al., 2012). The removal of the need for prescribed
upper boundary conditions (for the stratosphere) and a more comprehensive
chemistry scheme make their increased cost worth bearing. In this work, we
describe the implementation of a combined chemistry scheme suitable for
simulating the stratosphere and the troposphere within the UKCA model as
used in UKESM1 (Sellar et al., 2019). This scheme, UKCA StratTrop, builds
on and combines the existing stratospheric (MO09) and tropospheric schemes
(OC14). In various configurations of UKCA (under the names HadGEM3-ES,
UMUKCA-UCAM, NIWA-UKCA, ACCESS), this combined chemical scheme has already
been used to study stratospheric ozone and its sensitivity to changes in
bromine (Yang et al., 2014), subsequent circulation changes (Braesicke et
al., 2013) and how it may be impacted by certain forms of geoengineering
(Tang et al., 2014); the role of ozone radiative feedback on temperature and
humidity biases at the tropical tropopause layer (TTL) (Hardiman et al.,
2015); the effects on tropospheric and stratospheric ozone changes under
climate and emissions changes following the Representative Concentration
Pathways (RCPs) (Banerjee et al., 2016; Dhomse et al., 2018);
climate-induced changes in lightning (Banerjee et al., 2014); and changes in
methane chemistry between the present day and the last interglacial (Quiquet
et al., 2015). The scheme has been included in model simulations as part of
the CCMI project (Eyring et al., 2013; Hardiman et al., 2017; Morgenstern et
al., 2017; Dhomse et al., 2018) as well as all future Earth system modelling
studies using the UKESM1 model (Sellar et al., 2019).
This paper is organised in the following sections: in Sect. 2, we present
a thorough description of UKCA StratTrop, including the physical model and
details of the chemistry scheme, followed by a detailed description of the
emissions used and some notes on the historical development of the scheme.
In Sect. 3, we describe two 15-year simulations we have performed with
UKCA StratTrop in an atmosphere-only configuration of UKESM1. In Sect. 4,
we use these simulations to review the performance of UKCA StratTrop,
focusing on the model's ability to simulate key features of tropospheric and
stratospheric chemistry as simulated by other models or observed using in
situ and remote sensing measurements. Finally, in Sect. 5, we discuss the
performance of the model and make some recommendations for further targeted
studies.
Model description
In this section, we present a thorough description of UKCA StratTrop, from
the host physical model to the detailed process representation of the
StratTrop chemistry scheme.
Physical model
The physical model to which the UKCA StratTrop chemistry scheme has been
coupled is the Global Atmosphere 7.1/Global Land 7.0 (GA7.1/GL7.0; Walters
et al., 2019) configuration of the Hadley Centre Global Environment Model
version 3 (HadGEM3; Hewitt et al., 2011).
The coupling between the UKCA StratTrop chemistry scheme and the GA7.1/GL7.0
configuration of HadGEM3 is based on the Met Office's Unified Model (MetUM;
Brown et al., 2012). As a result, UKCA uses aspects of MetUM for the
large-scale advection, convective transport and boundary layer mixing of
its tracers. The large-scale advection makes use of the semi-implicit
semi-Lagrangian formulation of the ENDGame dynamical core (Wood et al.,
2014) to solve the non-hydrostatic, fully compressible deep-atmosphere
equations of motion. These are discretised onto a regular
latitude–longitude grid, with Arakawa C-grid staggering (Arakawa and Lamb,
1977). The discretisation in the vertical uses Charney–Phillips staggering
(Charney and Phillips, 1953) with terrain-following hybrid height
coordinates. Although GA7.1/GL7.0 can be run at a variety of resolutions, as
detailed in Walters et al. (2019), the resolution here is N96L85
(1.875∘×1.25∘ longitude–latitude), i.e. approximately
135 km resolution in the horizontal and with 85 terrain-following levels
spanning the altitude range from the surface to 85 km. Of the 85 model
levels, 50 lie below 18 km and 35 levels are above 18 km (Walters et al.,
2019). Mass conservation of UKCA tracers is achieved with the optimised
conservative filter (OCF) scheme (Zerroukat and Allen, 2015); use of this
scheme for virtual dry potential temperature resulted in reducing the warm
bias at the TTL (Hardiman et al., 2015; Walters et al., 2019). This
conservation scheme is also used for moist prognostics (e.g. water vapour
mass mixing ratio and prognostic cloud fields). Although this makes the
conservation scheme for moist prognostics consistent with the treatment of
UKCA tracers and virtual dry potential temperature, Walters et al. (2019)
found that it had little impact on moisture biases in the lower
stratosphere.
The convective transport of UKCA tracers is treated within the MetUM
convection scheme. It is essentially the mass flux scheme of Gregory and
Rowntree (1990) but with updates for downdrafts (Gregory and Allen, 1991),
convective momentum transport (Gregory et al., 1997) and convective
available potential energy closure. The scheme involves diagnosis of
possible convection from the boundary layer, followed by a call to shallow
or deep convection on selected grid points based on the diagnosis from step
one, and then a call to the mid-level convection scheme at all points. One
key difference between the convective treatment of UKCA chemical and aerosol
tracers is that convective scavenging of aerosols (simulated with
GLOMAP-mode) is coupled with the convective transport following Kipling et
al. (2013), whereas for chemical tracers, convective transport and
scavenging are treated independently. Further details on the convection
scheme in GA7.1 can be found in Walters et al. (2019). Finally, mixing over
the full depth of the troposphere is carried out by the so-called
“boundary layer” scheme in GA7.1; this scheme is that of Lock et al. (2000) but with updates from Lock (2001) and Brown et al. (2008).
The GA7.1/GL7.0 configuration described in Walters et al. (2019) already
includes the two-moment GLOMAP-mode aerosol scheme from UKCA (Mann et al.,
2010; Mulcahy et al., 2018, 2020), in which sulfate and
secondary organic aerosol (SOA) formation is driven by prescribed oxidant
fields. In the UKCA–StratTrop configuration described here, the oxidants
driving secondary aerosol formation are fully interactive; this coupling
between UKCA chemistry and GLOMAP-mode is fully described in Mulcahy et al. (2020). Together with dynamic vegetation and a terrestrial carbon and nitrogen
scheme (Sellar et al., 2019), GA7.1/GL7.0 and UKCA StratTrop make up the
atmospheric and land components of the UK Earth system model, UKESM1 (Sellar
et al., 2019), which forms part of the UK contribution to the Sixth
Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016).
Chemistry scheme
The UKCA StratTrop scheme is based on a merger between the stratospheric
scheme of MO09 and the tropospheric “TropIsop” scheme of OC14. StratTrop
simulates the Ox, HOx and NOx chemical cycles and the
oxidation of carbon monoxide, ethane, propane, and isoprene in addition to
chlorine and bromine chemistry, including heterogeneous processes on polar
stratospheric clouds (PSCs) and liquid sulfate aerosols (SAs). The level of
detail of the VOC oxidation is far from the complexity of explicit
representations (e.g. Aumont et al., 2005), but the VOCs simulated are
treated as discrete species.
Wet deposition is parameterised using the approach of Giannakopoulos et al. (1999). Dry deposition is parameterised employing a resistance type model
(Wesely, 1989) using the implementation described in OC14, updated to
account for advancements in the Joint UK Land Environment Simulator (JULES;
Best et al., 2011), in particular a significant increase in land surface
types (an increase from 9 to 27; see below for more details). Interactive
photolysis is represented with the Fast-JX scheme (Neu et al., 2007), as
implemented in Telford et al. (2013). Fast-JX covers the wavelength range of
177 to 750 nm. For shorter wavelengths, effective above 60 km of altitude, a
correction is applied to the photolysis rates following the formulation of
Lary and Pyle (1991).
The StratTrop scheme includes emissions of 12 chemical species: nitrogen
oxide (NO), carbon monoxide (CO), formaldehyde (HCHO), ethane
(C2H6), propane (C3H8), acetaldehyde (CH3CHO),
acetone ((CH3)2CO), methanol (CH3OH) and isoprene
(C5H8) in addition to trace-gas aerosol precursor emissions
(dimethyl sulfide (DMS), sulfur dioxide (SO2) and monoterpenes). For
the implementation used in UKESM1, emissions may be prescribed or
interactive and are described in more detail in Sect. 2.6.1 to 2.6.3. A
further seven long-lived species (N2O, CF2Cl2, CFCl3,
CH3Br, COS, H2 and CH4) are constrained by lower boundary
conditions; for more details see Sect. 2.6.4.
List of chemical species in UKCA StratTrop. Species in italics are
not advected tracers but are calculated using a steady-state approximation.
Species in bold are set as constant mixing ratios throughout the atmosphere.
NameFormulaDry depositedWet depositedEmitted or LBCO(3P)O(3P)NoNoNoO(1D)O(1D)NoNoNoO3O3YesNoNoNNNoNoNoNONOYesNoEmittedNO3NO3YesYesNoNO2NO2YesNoNoN2O5N2O5YesYesNoHO2NO2HO2NO2YesYesNoHONO2HONO2YesYesNoH2O2H2O2YesYesNoCH4CH4NoNoLBCCOCOYesNoEmittedHCHOHCHOYesYesEmittedMeOOCH3OONoYesNoMeOOHCH3OOHYesYesNoHHNoNoNoH2OH2ONoNoNoOHOHNoNoNoHO2HO2NoYesNoClClNoNoNoCl2O2Cl2O2NoNoNoClOClONoNoNoOClOOClONoNoNoBrBrNoNoNoBrOBrONoNoNoBrClBrClNoNoNoBrONO2BrONO2NoYesNoN2ON2ONoNoLBCHClHClYesYesNoHOClHOClYesYesNoHBrHBrYesYesNoHOBrHOBrYesYesNoClONO2ClONO2NoYesNoCFCl3CFCl3NoNoLBCCF2Cl2CF2Cl2NoNoLBCMeBrCH3BrNoNoLBCHONOHONOYesYesNoC2H6C2H6NoNoEmittedEtOOC2H5OONoNoNoEtOOHC2H5OOHYesYesNoMeCHOCH3CHOYesNoEmittedMeCO3CH3C(O)OONoNoNoPANPANYesNoNoC3H8C3H8NoNoEmittedn-PrOOC3H7OONoNoNoi-PrOOCH3CH(OO)CH3NoNoNon-PrOOHC3H7OOHYesYesNoi-PrOOHCH3CH(OOH)CH3YesYesNoEtCHOC2H5CHOYesNoNoEtCO3C2H5C(O)OONoNoNoMe2COCH3C(O)CH3NoNoEmittedMeCOCH2OOCH3C(O)CH2OONoNoNoMeCOCH2OOHCH3C(O)CH2OOHYesYesNo
Continued.
NameFormulaDry depositedWet depositedEmitted or LBCPPANPPANYesNoNoMeONO2MeONO2NoNoNoC5H8C5H8NoNoEmittedISO2HOC5H8OONoNoNoISOOHHOC5H8OOHYesYesNoISONISONYesYesNoMACRC4H6OYesNoNoMACRO2C4H6O(OO)NoNoNoMACROOHC4H6O(OOH)YesYesNoMPANMPANYesNoNoHACETCH3C(O)CH2OHYesYesNoMGLYCH3COCHHOYesYesNoNALDNALDYesNoNoHCOOHHC(O)OHYesYesNoMeCO3HCH3C(O)OOHYesYesNoMeCO2HCH3C(O)OHYesYesNoH2H2NoNoLBCMeOHCH3OHYesYesEmittedCO2CO2NoNoNoO2O2NoNoNoN2N2NoNoNoDMSCH3SCH3NoNoEmittedSO2SO2YesYesEmittedH2SO4H2SO4YesNoNoMSAMSANoNoNoDMSODMSOYesYesNoCOSCOSNoNoEmittedSO3SO3NoNoNoMonoterpC10H16YesNoEmittedSec_Org*Sec_OrgYesYesNo
*The molecular mass of Sec_Org is set to 150 g mol-1.
UKCA StratTrop was developed by starting with the stratospheric chemistry
scheme (MO09) and adding aspects of chemistry unique to the tropospheric
scheme (OC14). In most cases the formulation and reaction coefficients are
taken from reference evaluations (JPL and IUPAC) or the Master Chemical
Mechanism, as detailed in OC14. Table 1 provides a list of the chemical
tracers included in the StratTrop configuration used in UKESM1. In total the
model employs 84 species and represents the chemistry of 81 of these.
O2, N2 and CO2 are not treated as chemically active species.
Note that the scheme has a simplified treatment of stratospheric halocarbons
and lumps all chlorine and bromine source gases into CFC-11, CFC-12 and
CH3Br. This chemistry scheme accounts for 199 bimolecular reactions
(Table S1), 25 unimolecular and termolecular reactions (Table S2), 59 photolytic
reactions (Table S3), 5 heterogeneous reactions (Table S4) and 3 aqueous-phase reactions for the sulfur cycle (Table S5). Hence, UKCA–StratTrop
describes the oxidation of organic compounds – e.g. methane, ethane,
propane and isoprene and their oxidation products – coupled to the
inorganic chemistry of Ox, NOx, HOx, ClOx and BrOx
using a continuous set of equations with no artificial boundaries imposed on
where to stop performing chemistry. Except for water vapour, at the top two
levels, the mixing ratios of all species are held identical to those at the
third-highest level. The time-dependent chemical reactions are integrated
forward in time using an implicit backward Euler solver with Newton–Raphson
iteration (Wild and Prather, 2000). This solver has a relative convergence
criterion of 10-4 with a time step of 60 min throughout the
atmosphere. An extensive discussion of the solver used here is presented in
Esentürk et al. (2018).
The treatment of polar stratospheric cloud (PSC) has been recently expanded
in UKCA (Dennison et al., 2019), but these improvements did not make it into
the UKESM1 version of UKCA discussed here, which remains unmodified from the
original Morgenstern et al. (2009) scheme. The abundance of nitric acid
trihydrate (NAT) and mixed NAT–ice polar stratospheric clouds is calculated
following Chipperfield (1999) assuming thermodynamic equilibrium with
gas-phase HNO3 and water vapour; the treatment of reactions on liquid
sulfate aerosol also follows Chipperfield (1999). Sedimentation of PSCs is
included in the model, whilst dehydration is handled as part of the model's
hydrological cycle. Denitrification is prescribed in the same way as in
Chipperfield (1999) with two different sedimentation velocities. We refer
the reader to Morgenstern et al. (2009) and Dennison et al. (2019) for
further details.
The stratospheric sulfate aerosol optical depth, used in the radiation
scheme of MetUM, is modified to be consistent with the aerosols used in the
heterogeneous chemistry which, by default, are taken from a surface area
density climatology prepared for the CMIP6 model intercomparison (Beiping Luo,
personal communication, 2016). The surface aerosol density is converted to a mass
mixing ratio using a climatology of particle size (Thomason and Peter,
2006) and assuming a density of 1700 kg m-3.
Photolysis
The most significant new development relative to MO09 and OC14 in the
UKCA–StratTrop scheme used in UKESM1 is the interactive Fast-JX photolysis
scheme, which is applied to derive photolysis rates between 177 and 750 nm
(Neu et al., 2007) as described in Telford et al. (2013). This is an
important new addition as it enables interactive treatment of photolysis
rates (key drivers for the photochemistry of the atmosphere) under changing
climate and atmospheric composition. For shorter wavelengths relevant above
60 km, a correction is added to account for photolysis occurring between
112 and 177 nm, following Lary and Pyle (1991).
In older versions of UKCA (i.e. MO09 and OC14) precalculated photolysis
frequencies were applied in the model. Sellar et al. (2019) show a
comparison of these and we note here that the switch from precalculated to
online interactive photolysis calculations has had a significant effect on
shortening the model-simulated methane lifetime and increasing the
tropospheric mean [OH] (Telford et al., 2013; O'Connor et al., 2014;
Voulgarakis et al., 2009), as shown in Fig. 4.
Dry deposition
In UKCA the representation of dry deposition follows the
resistance-in-series model as described by Wesely (1989) in which the
removal of material at the surface is described by three resistances:
ra, rb and rc. The deposition velocity vd (m s-1)
is then a function of these three resistance terms according to
vd=1ra+rb+rc,
where ra denotes the aerodynamic resistance to dry deposition, rb
is the quasi-laminar resistance term and rc represents the resistance
to uptake at the surface. Of these three terms rc tends to be the most
complex because it encompasses a variety of exchange fluxes, such as
stomatal and cuticular uptake and assimilation by soil microbes. The
uptake at the surface also depends strongly on the presence of dew, rain or
snow, which can interrupt the deposition process altogether.
Surface dry deposition is calculated interactively at every time step for a
number of atmospheric gas-phase species (see Table 1 for a list of
deposited species). The aerodynamic resistance ra is given by
ra=lnzz0-Ψk×u*,
where z0 is the roughness length, Ψ denotes the Businger
dimensionless stability function, k is the von Karman constant and
u* is the friction velocity; ra represents the resistance to
turbulent mixing in the boundary layer and therefore depends crucially on
the stability of the boundary layer. It is independent of the chemical
species that is deposited.
The quasi-laminar resistance rb, on the other hand, depends on the
chemical and physical properties of the deposited species. It describes the
transport through the thin, laminar layer of air closest to the surface.
Transport through this layer is diffusive due to the absence of turbulent
mixing.
The third resistance term rc depends on both the physico-chemical
properties of the deposited species and the properties and condition of the
respective surface to which deposition occurs. The surface can be anything
from bare soil or rock to vegetation and even urban environments. Surface
uptake varies with season, time of day and current meteorological
conditions. The largest individual surface type is water in the form of the
world's oceans. In this latter case solubility clearly plays the key role
(Hardacre et al., 2015; Luhar et al., 2017).
A particularly important surface uptake process is the deposition flux to
the terrestrial vegetation. In this case a number of pathways exist which
are commonly integrated into the so-called “big-leaf” model (Smith et al.,
2000; Seinfeld and Pandis, 2006). Of all the deposition pathways manifesting
in vegetated regions, for most species the most important is uptake through
the stomata. Through these tiny pores in the leaf surface plants take up
carbon dioxide from the atmosphere and exchange water vapour and oxygen with
it. This exchange also includes all other species that make up the ambient
air, including pollutants such as ozone. For this, the specific
type of vegetation is crucial. Ozone deposition fluxes, for instance, vary
widely between forests and grasslands.
The calculation of the surface resistance term and land surface type
information provided by the dynamic vegetation model JULES (Best et al.,
2011; Clark et al., 2011) is used in UKCA. JULES forms part of UKESM1 and is
thus coupled with UKCA. Within JULES, various land surface type
configurations may be selected. In the most simple configuration, which was
also used in the UKESM1 predecessor model HadGEM2-ES, any land-based grid
box at the surface can be subdivided into variable-sized fractions assigned
to any of nine different surface types: broadleaf trees, needleleaf trees, C3
grasses, C4 grasses, shrubs, bare soil, rivers and lakes, urban
environments, and ice. Non-land grid boxes are treated separately.
Since then, the number of land surface types in JULES has increased
substantially (see Harper et al., 2018). Apart from the original 9-tile
version (five vegetation and four non-vegetation types), 13-, 17- and also 27-tile
configurations are now included. The upgrade to the 13-tile configuration
increases the number of vegetation types by introducing three broadleaf plant
functional types (PFTs), two needleleaf PFTS and two shrub PFTS; the number of
grass-related PFTs as well as the number of non-vegetation types remains the
same in this configuration. The 17-tile configuration further extends the
number of PFTs by introducing four cropland types, two C3-grass-related and two
C4-grass-related PFTs; again, the number of non-vegetation types remains the
same. Finally, the 27-tile land surface configuration, corresponding to the
UKESM1 release configurations and the configurations used for this paper, introduces a substantial number of additional land ice tiles.
Each of these land surface and PFT tiles offers a specific resistance to dry
deposition of atmospheric gas-phase species.
For dry deposition of aerosols a slightly different treatment is taken to
that described above, and we direct the reader to Mulcahy et al. (2020) and
references therein for more details.
Wet deposition
The wet deposition scheme employed in UKCA for the removal of tropospheric
gas-phase species through convective and stratiform precipitation is the
same as that described in O'Connor et al. (2014). The original scheme was
implemented from the TOMCAT chemistry transport model (CTM) where it
previously had been validated by Giannakopoulos (1998) and Giannakopoulos et
al. (1999). In this paper we provide a brief description of the scheme but
will not present an evaluation because there have been no changes since the
last published version. For an in-depth performance evaluation in UKCA we
refer to Sect. 3.4 in O'Connor et al. (2014).
Following a scheme originally developed by Walton et al. (1988) wet
deposition is parameterised as a first-order loss process which is
calculated as a function of the three-dimensional convective and stratiform
precipitation. The climate model provides the required precipitation
activity to UKCA. The wet scavenging rate r is calculated at every grid
box and time step according to
r=Sj×pj(l),
where Sj is the wet scavenging coefficient for precipitation type
j (cm-1) and pj(l) is the precipitation rate for type j
(convective or stratiform), provided at model level l (cm h-1).
Scavenging coefficients for nitric acid (HNO3) of 2.4 and 4.7 cm-1 for stratiform and convective precipitation, respectively, are
applied (see Penner et al., 1991). These parameters are scaled down for
individual species using the fraction of each species in the aqueous phase,
faq, calculated by
faq=L×Heff×R×T1+L×Heff×R×T,
where L represents the liquid water content, R is the universal gas
constant, T denotes ambient temperature and Heff is the effective
Henry's law constant for each species. Heff includes the effects of
solubility, dissociation and complex formation. Tables S6, S7 and S8 (in
the Supplement) summarise the parameters used in the UKCA wet deposition
scheme for each soluble species included in the StratTrop chemical
mechanism.
Furthermore, in the scheme precipitation only occurs over a fraction of the
grid box. This fraction is assumed to be 1.0 and 0.3 for stratiform and
convective precipitation, respectively. These fractions are applied in the
calculation of the grid-box mean wet scavenging rate for both precipitation
types after which point the two rates are added together.
Emissions
This section describes the implementation of tropospheric ozone precursor
emissions used in the UKCA StratTrop scheme in detail. The scheme includes
the emissions of nine chemical species: nitric oxide (NO), carbon monoxide
(CO), formaldehyde (HCHO), ethane (C2H6), propane
(C3H8), acetaldehyde (MeCHO), acetone (Me2CO), isoprene
(C5H8) and methanol (MeOH). Emissions to UKCA can be broadly
classified into two categories: offline, where pre-computed fluxes are read from input files, and online, where fluxes are computed in real time during the simulation by making use of online meteorological variables from the MetUM. The implementation of offline emissions will be described in Sect. 2.6.1. Examples of online emissions currently in UKCA StratTrop are biogenic volatile organic compound (BVOC) emissions (Sect. 2.6.2) and lightning NOx (Sect. 2.6.3).
All emissions, including offline emissions, have interannual variability
over the time period of the model simulations.
When UKCA StratTrop is coupled to the UKCA aerosol scheme, GLOMAP-mode (Mann
et al., 2010) as here, there are additional trace-gas aerosol precursor
emissions for dimethyl sulfide (DMS), sulfur dioxide (SO2) and
monoterpenes (C10H16). These emissions will be discussed in the
context of the UKESM1 aerosol performance in Mulcahy et al. (2020); the
focus here will solely be on the tropospheric ozone precursor emissions.
Table S10 and Figs. S7–S8 summarise the mean global annual emissions
totals for the time period considered here (2005–2014) and their global and
seasonal distributions.
Offline anthropogenic and natural emissions
Offline tropospheric ozone precursor emissions are either injected into the model's lowest layer or, in the case of aircraft emissions and some biomass burning
emissions, injected into a number of model levels. The emissions are added
to the appropriate UKCA tracers (see Table 1) and mixed simultaneously by
the boundary layer mixing scheme (Sect. 2.1). While boreal and temperate
forest and deforestation emissions (van Marle et al., 2017) of black carbon
(BC) and organic carbon (OC) are considered “high level” (Mulcahy et al.,
2020) and are spread uniformly up to level 20 (∼ 3 km in L85),
all gas-phase biomass burning emissions are added to the surface layer.
For anthropogenic emissions, we make use of historical (1750–2014) annual
emissions of reactive gases from the Community Emissions Data System (CEDS;
Hoesly et al., 2018) that were prepared for use in CMIP6. The CEDS emissions
are generally greater than those of other emission datasets (e.g. Lamarque
et al., 2010) for the years that are used in the simulations evaluated here
(i.e. 2005–2014). Biomass burning emissions are taken from van Marle et al. (2017). They combined satellite observations from 1997 with various proxies
and output from six fire models participating in the Fire Model
Intercomparison Project (FireMIP; Rabin et al., 2017) to provide a complete
dataset of biomass burning emissions from 1750 to 2014 for use in CMIP6. As
was the case for anthropogenic emissions, emissions from the years 2005–2014
are used here. For both anthropogenic and biomass burning, the emissions
were re-gridded from their native resolution to N96L85 while conserving
global annual totals and seasonal cycles. Emissions of all C2 and
C3 VOCs are included as ethane and propane, respectively.
For natural emissions which are not simulated, offline emissions are prescribed through the provision of pre-computed fluxes. For example, oceanic emissions of CO, ethane (including ethene – C2H4) and propane (including
propene – C3H6) are taken from the POET (Granier et al., 2005)
inventory for the year 1990, which contains one annual cycle with 12 monthly
fluxes. These fluxes are applied perpetually to all years of the time
series. Biogenic emissions of acetaldehyde (MeCHO) make use of combined
emissions of MeCHO and other aldehydes from the MACCity-MEGAN emissions
inventory (Sindelarova et al., 2014); biogenic emissions of CO, HCHO, MeOH
and propane (including C3H6) are also taken from this inventory.
For biogenic acetone emissions, emissions of acetone and other ketones from the
MACCity-MEGAN emissions inventory (Sindelarova et al., 2014) are combined.
Based on the years 2001–2010, a monthly mean climatology is derived and
applied to all years (see Sect. 3 for the implementation of the emission
in the model). Finally, soil emissions of NOx are distributed according
to Yienger and Levy (1995) and scaled to give a global annual total of 12.0 Tg NO yr-1, again perpetually applied to all years.
Biogenic VOC emissions
In the standard configuration of UKCA StratTrop in UKESM1, emissions of
organic compounds from the natural environment (BVOC) are added to UKCA
interactively (Sellar et al., 2019). Specifically, emissions of isoprene
(C5H8) and (mono)terpenes are online, the latter represented by a
lumped compound in UKCA with the formula C10H16 and a
corresponding molecular weight of 136 g mol-1, and calculated by the
interactive biogenic VOC (iBVOC) emission model (Pacifico et al., 2011).
Emission fluxes are passed to UKCA at every model time step.
Tropical distribution of the LIS-observed (Lightning Imaging Sensor) climatological annual
mean lightning flash density over the period 1999–2013 (a) in comparison
with the modelled annual mean climatology from the period 2005–2014 (b).
The corresponding standard deviation of the observed and modelled
climatologies are shown in panels (c) and (d), respectively.
In iBVOC the emissions of isoprene are coupled to the gross primary
productivity of the terrestrial vegetation (Arneth et al., 2007; Pacifico et
al., 2011). The biogenic emission of all other organic compounds included in
the iBVOC model, i.e. (mono)terpenes, methanol and acetone, follows the
original model described in Guenther et al. (1995). Note that the current
configuration of UKCA used in UKESM1 does not make use of the interactive
emissions of methanol or acetone; these are offline as discussed in Sect. 2.6.1.
To the best of our knowledge, in the case of the non-isoprene biogenic VOCs
there is no equivalent process-based formulation for an interactive BVOC
emission model applicable to Earth system models (ESMs).
For present-day conditions total global annual emissions of isoprene amount
to 495.9(±13.6) Tg C yr-1. This number represents the 10-year
average annual total emission strength and the uncertainty quantified by the
standard deviation over the 10-year period between 2005 and 2014 taken from
a historic run with UKESM1 (Sellar et al., 2019). This is in good agreement
with estimates reported for other emission models (e.g. Arneth et al., 2008;
Guenther et al., 2012; Messina et al., 2016; Müller et
al., 2008; Sindelarova et al., 2014; Stavrakou et al., 2009; Young et al.,
2009). For the global annual total (mono)terpene emissions, iBVOC
calculates 115.1(±1.6) Tg C yr-1 over the same period of model
simulation. This model estimate is in reasonably good agreement with the
literature (e.g. Folberth et al., 2006; Lathière et al., 2006; Arneth et
al., 2007, 2011; Acosta Navarro et al., 2014; Sindelarova et al., 2014;
Bauwens et al., 2016; Messina et al., 2016).
In the configuration of UKCA StratTrop used in UKESM1, isoprene is included
in the gas-phase chemistry but does not contribute to the formation of
secondary organic aerosol (SOA). Emissions of (mono)terpenes are oxidised
using a fixed yield approach (e.g. Kelly et al., 2018) to form SOA in the
GLOMAP-mode aerosol scheme – see Table S1 and Mulcahy et al. (2020) for a
detailed description and evaluation.
Emissions of NOx from lightning
The lightning NOx emissions scheme in UKCA StratTrop is based on the
cloud-top parameterisation proposed by Price and Rind (1992). Based on
satellite data and storm measurements, the lightning flash density is
parameterised as
5Fl=3.44×10-5H4.9,6Fo=6.2×10-4H1.3,
where F is the flash density (flash min-1), H is the cloud-top height (km), and the “l” and “o” subscripts are used to represent the land and ocean, respectively, and to distinguish between the updraft velocities experienced over the two surfaces. The scheme also differentiates between cloud-to-cloud and cloud-to-ground flashes based on the grid cell latitude (Price and Rind,
1993) and is resolution-independent by the implementation of a spatial
calibration factor (Price and Rind, 1994). A minimum cloud depth of 5 km is
required for NOx emissions to be activated and is diagnosed on a time-step basis from the physical model's convection scheme. For NOx production, the parameterisation assumes that the production efficiency per unit of energy discharged is 25 ×1016 molec NO J-1, with the energy discharged from cloud-to-ground flashes (3.0 ×109 J flash-1) being approximately 3 times greater than that for cloud-to-cloud (0.9 ×109 J flash-1) flashes (Schumann and Huntrieser, 2007).
This implementation is identical to that implemented in HadGEM2-ES (Collins
et al., 2011) by O'Connor et al. (2014) except that NOx emissions are
now distributed linearly in log(pressure) rather than linearly
in pressure. Whereas global annual lightning emissions in HadGEM2-ES were
inadvertently too low (O'Connor et al., 2014; Young et al., 2013), here the
emissions have been scaled to give an average global annual emission rate of
5.93 and 5.98 Tg N yr-1 over the period 2005 to 2014 in the
free-running and nudged simulations, respectively. When compared with
anthropogenic, biomass burning and natural emissions, lightning contributes
approximately 10 % to the global annual NOx emission rate, consistent with estimates from Schumann and Huntrieser (2007).
Scatter plot of the modelled versus the LIS-observed multi-annual
mean lightning flash density (a) and the standard deviation (b).
Figure 1 shows tropical distributions of decadal mean annual flash density
as observed by the Lightning Imaging Sensor (LIS) onboard the Tropical
Rainfall Measuring Mission (TRMM) satellite (Theon, 1994) in
comparison with the free-running simulation being evaluated here (see
Sect. 3 for details). It demonstrates that UKCA is capable of capturing
the broad features of the observed climatology, with peak densities over South
America, Africa and East Asia; the spatial coefficient of determination
(R2) between the modelled and observed climatology is 0.65 and 0.69 in
the free-running and nudged (not shown) simulations, respectively. However,
the model tends to be biased low in regions of low flash density (e.g. over
the oceans and towards the extratropics) compared to the observations
(Fig. 2), consistent with the assessment of Finney et al. (2014). In considering the variability, the spatial R2 between the modelled and
observed standard deviation is 0.57 and 0.59 in the free-running and nudged
simulations, respectively. The variability from UKCA is comparable in
magnitude to that observed over Africa, albeit displaced geographically.
Over the Maritime Continent and South America, for example, UKCA overestimates
the variability relative to the LIS observations.
Whilst the skill of the cloud-top parameterisation is good relative to other
parameterisations (Finney et al., 2014), and the performance here in the
free-running and nudged model simulations is consistent with that
assessment, raising the diagnosed cloud-top height over land to the power of
4.9 makes the cloud-top parameterisation susceptible to model biases in
cloud-top height, as noted by Allen and Pickering (2002) and Tost et al. (2007). Lightning is potentially a key chemistry–climate interaction in
Earth system models, but the sensitivity to how it is represented (i.e. using
cloud-top height (Banerjee et al., 2014) or ice-flux-based parameterisations; Finney et al., 2018) warrants further investigation. Indeed, Hakim et al. (2019) recently identified uncertainty in modelled lightning NOx in the
Indian subcontinent as being an important source of uncertainty in model
simulations of tropospheric ozone in that region.
Lower boundary conditions
Lower boundary conditions are provided at the surface for the chemical
species CH4, N2O, CFC-11 (CFCl3), CFC-12 (CF2Cl2), CH3Br, H2 and COS. Values for H2 and COS are fixed at 500 ppb and 482.8 ppt, respectively (invariant with time). Values for the remaining species are specified using time series data provided for the 5th Coupled Model Intercomparison Project (CMIP5) for the greenhouse gas concentrations (RCP Database, 2020). The values provided are valid on 1 July for each
year specified and are linearly interpolated in time to give daily values
if data for more than one time point are defined. CFC-11, CFC-12 and
CH3Br also contain contributions from other Cl- and Br-containing source
gases which are not explicitly treated in the model to ensure that there is
the correct stratospheric chlorine and bromine loading, with these
contributing species given in Table 2. These values are converted into a
two-dimensional “effective emission” field at each time step that is used
to fix the surface concentrations of these species.
List of halocarbons (not explicitly treated in the model)
contributing to the lower boundary conditions of CFC-11, CFC-12 and
CH3Br. Note that H-1211 contributes to both CFC-11 and CH3Br as it
contains both Cl and Br. Contributions are included by moles of Cl or Br.
CFC-11CFC-12CH3BrCCl4CFC-113H-1211CH3CCl3CFC-114H-1202HCFC-141bCFC-115H-1301HCFC-142bHCFC-22H-2402H-1211CH3ClCoupling with other Earth system components
Secondary aerosol formation of sulfate and organic carbon in UKESM1 (Sellar
et al., 2019) is determined by oxidants (OH, O3, H2O2,
NO3) modelled interactively by the UKCA StratTrop chemistry scheme. For
further details on the oxidation of sulfate and SOA precursors,
chemistry–aerosol coupling, and the scientific performance of the aerosol
scheme (GLOMAP-mode; Mann et al., 2010) in UKCA and UKESM1, the reader is
referred to Mulcahy et al. (2020).
In the HadGEM2-ES model (Collins et al., 2011) used for CMIP5, radiative
feedbacks between UKCA-modelled methane and tropospheric ozone
concentrations were active (OC14); stratospheric ozone was prescribed and
combined with the modelled interactive tropospheric concentrations. In
UKESM1 (Sellar et al., 2019), however, the coupling between the UKCA-modelled radiatively active trace gases and the radiation scheme has been
extended to include N2O and stratospheric ozone (in addition to methane
and tropospheric ozone). Although chlorofluorocarbons (CFCs) and
hydrochlorofluorocarbons (HCFCs) are modelled in UKCA StratTrop, the
radiation scheme cannot handle the speciation. Therefore, separate lumped
species (CFC12-eq and HFC134a-eq) are prescribed in the radiation scheme
(see Sect. 2.6.4 on how the lumping and mapping is done).
Heterogeneous chemistry couplings
In UKCA StratTrop as implemented in UKESM1, five different heterogeneous
reactions are included (see Table S4). These reactions occur on the modelled
soluble aerosol surface area, which in the troposphere is calculated
interactively using GLOMAP-mode by summing over all soluble aerosol modes.
In the stratosphere (defined here as being 12 km above the surface) the
aerosol surface area comes from the stratospheric sulfate surface area
density input climatology, discussed in Sellar et al. (2020). The combining
of the stratospheric aerosol surface area density from the climatology and
the interactive components of GLOMAP-mode is calculated at each UKCA time
step, and only the soluble aerosol modes simulated by GLOMAP are included in
the calculation.
Heterogeneous reactions are extremely important for simulating composition
change in the stratosphere (Keeble et al., 2014), and there is increasing
attention to the simulation of these processes in the troposphere (e.g.
Jacob et al., 2000; Lowe et al., 2015). One of the most important
tropospheric heterogeneous reactions is that of N2O5 on aerosol
surfaces (Jacob et al., 2000). This reaction is complicated because of the
dependence of the uptake parameter (γ) on the composition of the
aerosol as well as on temperature and relative humidity (Bertram and
Thornton, 2009). Macintyre and Evans (2010) suggest that models that use high
values of γN2O5 (∼ 0.1) overestimate the impact of
changing aerosol loadings on tropospheric composition through heterogeneous
uptake. In UKCA StratTrop, γN2O5 is set at this higher
value, 0.1, throughout the atmosphere. In part this compensates for the fact
that there is an important missing aerosol surface in UKESM1 in the
troposphere in the form of nitrate aerosol. The lack of nitrate aerosol is
an issue for UKESM1 simulations of particulate matter, particularly in
regions with high levels of ammonia emissions. An improved understanding of
γN2O5 is needed to understand both the current composition and the combined impact of changing gas- and aerosol-phase composition.
Whilst more sophisticated treatments of γN2O5 are
available (e.g. Bertram and Thornton, 2009) and have been included in
versions of UKCA, further work is required to improve this aspect of the
mechanism for UKCA in UKESM1.
Multi-annual mean zonal-mean production of H2O from the UKCA
StratTrop mechanism in UKESM1. Panel (a) shows the production in moles per second and
panel (b) in parts per billion per day, highlighting the larger relative source of water from
chemical processes in the upper atmosphere.
Chemical production of H2O
There are many chemical reactions which consume or produce water vapour in
the troposphere and stratosphere. For example, reactions between the
hydroxyl radical (OH) and VOCs usually result in the production of a water
molecule.
OH+VOC→H2O+organic radical
In the troposphere the chemical source of water vapour is negligible
compared with that from the oceans and evapotranspiration from the Earth's
land surface, but given the low temperatures around the tropopause,
chemically produced water is very important in the lower stratosphere.
Furthermore, the main source of chemical water in the middle to upper
stratosphere comes from the oxidation of CH4. Complete oxidation of
CH4 to CO2 can result in the net production of two water
molecules.
In previous versions of UKCA, such as that used in HadGEM2-ES, the oxidation
of CH4 to produce chemical water was neglected. Instead, stratospheric
water vapour was simulated using the following simple relationship:
2×[CH4]+[H2O]=3.75(ppm),
where UKCA was used to calculate [CH4]. In UKCA StratTrop as
implemented in UKESM1 we now include interactive H2O production from
all chemical reactions in the mechanism. In this way UKCA now passes the
water vapour field after the chemistry step back to the main climate model
where it is used in other routines. The annual mean zonal-mean chemical
production of H2O as simulated by UKESM1 is shown in Fig. 3. There
are two clear regions which dominate where H2O chemical production
takes place: in the tropical lower troposphere and the tropical upper
stratosphere. In both regions the primary source of chemical water is the
oxidation of CH4. Figure 3 compares the absolute production of chemical
water (panel a) and the production of chemical water as expressed in mixing
ratio units (panel b). In this sense, panel (b) shows that the relative
production of chemical water is greatest in the upper stratosphere. The
contribution of this source of stratospheric H2O to the present-day
forcing of climate relative to the pre-industrial period will be assessed in
O'Connor et al. (2019).
Future couplings
Although UKESM1 (Sellar et al., 2019) represents a significant enhancement
in the representation of atmospheric chemistry and Earth system
interactions, a number of key interactions are not included. For example,
the coupling of aerosols with Fast-JX is omitted despite the impact of
aerosols on the tropospheric photochemical production of ozone (e.g. Xing et
al., 2017; Wang et al., 2019). This development is currently underway and
will be included in future versions of UKCA and UKESM. Ozone damage to
natural and managed ecosystems (e.g. Ashmore, 2005) has an important impact
on the strength of carbon uptake by vegetation (Sitch et al., 2007; Oliver
et al., 2018) and has yet to be implemented. In addition, although the
terrestrial carbon cycle considers nitrogen availability and limitation,
nitrogen deposition rates are prescribed in UKESM1; future work will include
implementing a nitrate aerosol scheme in GLOMAP-mode and coupling the deposition
of both oxidised and reduced nitrogen from the atmosphere to the terrestrial
biosphere.
Historic development of the chemistry scheme
During the development of the StratTrop chemistry scheme, several
simulations were run to test the scheme and its sensitivity to different (a) rate coefficients (updating the JPL and IUPAC recommendations), (b) reactions (by looking at the sensitivity to specific reactions associated
with isoprene oxidation (Archibald et al., 2011) and the reaction between
HO2 and NO; Butkovskaya et al., 2005, 2007, 2009), (c) treatment of
photolysis, (d) emissions and (e) deposition parameters. These one-at-a-time
simulations are outlined in Table S9 in the Supplement. It should be noted
that these simulations provide an ensemble of opportunity; they were not
designed to probe model sensitivity in a targeted way. However, they result
in some useful information which helped the development of the StratTrop
mechanism. These simulations made use of an older version of the MetUM and an
earlier atmosphere-only version of UKCA, which is now deprecated. That
version of UKCA ran at a lower resolution than the version discussed in this
paper and used in UKESM1 (about half the resolution). The results from these
simulations are shown in Fig. 4 where they are compared against results
from model intercomparison studies (further analysis of the model
sensitivity tests is presented in Figs. S1–S6 in the Supplement). Figure 4
focuses on a subset of the full range of experiments performed but
contextualises these by comparing to results from the ACCENT simulations
discussed in Stevenson et al. (2006) (black dots) and the ACCMIP simulations
discussed in Young et al. (2013) (orange dots). In addition to the early
sensitivity tests (the blue dots in Fig. 4), we also show the results from
the simulations presented here, labelled UKESM1 (red triangle in Fig. 4).
The figure focuses on the relationship between methane lifetime and ozone
chemical loss, important metrics for representing key sources and sinks of
tropospheric OH (Wild, 2007). Both metrics are calculated by masking out the
stratosphere. The methane lifetime is calculated by dividing the burden of
methane in the model by the reaction flux between methane and OH in the
troposphere, so it represents the lifetime with respect to OH in the
troposphere. The ozone loss is calculated by summing the reaction fluxes
which are key for O3 loss in the troposphere (reactions of O3 with HOx species and the reaction between O(1D) and H2O). The
experiments outlined in Table S9 and shown in Fig. 4 emphasise that the
range in O3 loss and CH4 lifetime spanned by changing aspects of
the UKCA model span a range as wide as that covered by the ACCMIP models
(Young et al., 2013). In other words, the ensemble of opportunity from the early tests of the UKCA
StratTrop scheme span as wide a range in the metrics presented as the
structurally different ACCMIP and ACCENT models. Interestingly, the UKESM1
simulations discussed in this paper in detail lie close to the ACCENT
ensemble (black dots), yet the early test simulations using the same
chemical mechanism but an earlier version of the MetUM model do not (the
blue cluster of dots). This highlights that structural changes in the
underlying meteorological model can substantially influence key metrics of
atmospheric composition through changes in the distribution of clouds, water
vapour and other key variables.
Comparison of early tests of the StratTrop scheme running in an
older version of UKCA (blue dots) with the scheme applied in UKESM1 (red
triangle; free-running simulation), other CCMs which took part in the
ACCMIP intercomparison (orange dots) and CTMs which took part in the ACCENT
intercomparison (black dots). The letters in the legend (i.e. B–A) refer to
the experiments outlined in Table S9.
These sensitivity studies highlight some important points. Simulations using
kinetic data recommendations from IUPAC and JPL updated from 2005 to 2011
led to a decrease in model methane lifetime and an increase in ozone
chemical loss flux (grey arrow), indicating increased photochemical
activity. The attribution of which rate coefficients were dominant in this
behaviour is outside the scope of this work. Similarly, we note that the
metrics analysed are sensitive to lightning NOx (Banerjee et al., 2014); decreasing the lightning NOx emissions by 50 % (to ∼ 3 Tg yr-1) results in an increased methane lifetime of ∼ 1 year (purple arrow). Figure 4 also highlights a non-linear
response in the simulations to changes in isoprene emissions; scaling them
by a factor of 2 (100 % increase and 50 % decrease; green arrows)
leads to a highly non-linear response in the metrics analysed. Finally, we
note that the change which had the biggest impact on the metrics was
switching to the FAST-JX photolysis scheme (Telford et al., 2013) from
precalculated photolysis rates and a lookup table (pink arrow). The main
reason for this is that the precalculated photolysis rates had
underestimated rates for the photolysis of O3 to O(1D). This
behaviour has been documented previously (Voulgarakis et al., 2009; Telford
et al., 2013).
In addition to the tests described above we found during the testing of the
StratTrop scheme that inclusion of the termolecular reaction
HO2+NO+M→HONO2+M,
which has been shown to exhibit both pressure and water vapour dependence
(Butkovskaya et al., 2005, 2007, 2009), led to large changes in the metrics
analysed in Fig. 4 (see Sect. S1.2 of the Supplement for further
details). Previous modelling work highlighted that this could have an
important impact on the simulation of ozone (Cariolle et al., 2008).
However, owing to uncertainty in its recommendation between the recent
evaluations by JPL and IUPAC we have omitted it from the StratTrop scheme
used in UKESM1.
Model simulations to evaluate UKCA StratTrop in UKESM1
In this section, we discuss a series of simulations that have been performed
to evaluate the performance of the UKCA StratTrop scheme in UKESM1. These
simulations link closely to the UKESM1 historical and AMIP simulations by
using similar inputs, e.g. emissions, and crucially the version of UKCA
StratTrop is identical to that used in UKESM1 (Sellar et al., 2019).
Simulations analysed in this paper have been carried out with an
atmosphere-only configuration of UKESM1 (Sellar et al., 2019). The sea
surface temperatures and sea ice cover used to drive the model are those
specified for the historical period by the Sixth Coupled Model Intercomparison
Project (CMIP6 project; Durack et al., 2016). Land cover fraction,
vegetation canopy height and leaf area index (LAI) have been provided as
multi-annual monthly mean climatologies derived from a historical
simulation of UKESM1, which includes the dynamic vegetation model TRIFFID
(Cox, 2001). Anthropogenic and biomass burning emissions of ozone precursors
are prescribed on a monthly basis using a 2005–2014 time series from Hoesly
et al. (2018) (see Sect. 2.6) and van Marle et al. (2017), respectively.
Land-based biogenic emissions not simulated within the JULES model (e.g. CO)
are provided as monthly climatologies for the period 2001–2010 from the
MEGAN-MACC dataset (Sindelarova et al., 2014), supplemented by soil NOx emissions based on Yienger and Levy (1995) and oceanic emissions from POET. Greenhouse gas concentrations for CFC-12, CH4, CO2, HFC-134 and N2O are derived from the dataset generated by Meinshausen et al. (2017) for CMIP6. Concentrations of other CFCs seen only by UKCA are derived from
the same dataset but described in more detail in the section “Lower boundary
conditions” (Sect. 2.6.4). The model is initialised using output after
nearly 150 years of the UKESM1 coupled historical simulation. The land
surface setup used in this paper is based on a 27-sub-grid-tile
configuration including 13 plant functional types (three broadleaf tree
tiles, two needleleaf tree tiles, three C3 grass tiles including crops,
three C4 grass tiles including crops and two tiles representing shrubs),
one water tile (to represent lakes), one tile for bare soil, one urban tile
and 11 land ice tiles.
Two simulations have been carried out using the atmosphere-only
configuration, covering January 1999 to December 2014. The first is a
free-running (FR) simulation wherein the meteorology is allowed to evolve independently
based on the influence of the aforementioned forcing agents. The second is a
nudged (ND) simulation wherein the meteorology, though under the same forcings as
the FR simulation, is in addition relaxed toward the ECMWF's ERA-Interim
reanalysis (Dee et al., 2011) using the nudging functionality in the MetUM
(Telford et al., 2008). Nudging is applied to model temperature and winds
from about 1.2 km (to be generally free of the boundary layer) to 65 km
(maximum height of ERA data) using an e-folding relaxation timescale of 6 h. In the following section, output from the ND simulation will mainly
be used for the comparison of modelled fields with observations, unless
otherwise stated, in order to reduce biases. On the other hand, the FR
simulation will be useful to document some key performance indicators such
as the tropospheric oxidising capacity (OH concentrations and methane
lifetime) or the middle atmosphere age of air.
For both simulations, output from the first 6 years is considered spin-up,
and analysis from the years 2005–2014 inclusive is presented in this paper.
Model fields used in the analysis have been output mainly as monthly means.
In addition, some aerosol-related fields were produced at daily and 6-hourly
intervals, while ozone, nitric acid and nitrogen dioxide at the surface were
produced at hourly intervals.
Table S10 provides a summary of the sectors contributing to the emissions of
the nine tropospheric ozone precursor species treated in UKCA StratTrop and
their corresponding global annual totals, averaged (mean) over the 2005–2014
time period covered by the two simulations. Figures S7 and S8 show the
multi-annual global annual mean distributions and the seasonal cycle for
different emission sectors and regions for NO and CO, respectively.
Evaluation of model fields
We start our evaluation of UKCA StratTrop in UKESM1 by assessing the
performance of the model in the troposphere against surface observations
and build up the evaluation to focus on tropospheric integrated quantities
and stratospheric quantities before concluding with an analysis of transport
in the model. The evaluation presented here is mainly targeted at model
fields which are relevant to document the model's ability to reproduce
tropospheric and stratospheric ozone. Some additional evaluation of
H2O2, important for the oxidation of SO2 in the aqueous
phase, is presented in Sect. S2.2 of the Supplement.
Simulated (ND) seasonal mean surface O3 concentrations in (a) December–January–February (DJF) and (c) June–July–August (JJA) over the
2005–2014 period. Difference between simulated and observed surface O3 from the gridded TOAR database in (b) DJF and (d) JJA.
Normalised mean bias factors (NMBFs) calculated for annual and
seasonal means by comparing modelled concentrations of surface O3 in
the UKESM1 ND simulations to gridded observations from the TOAR database
across each region over the 2005–2014 period. The solid line shows the
median value for the region, the boxes show the 25th and 75th percentile
values (Q1 and Q3), with the error bars extending from the boxes to cover
the main body of the data from (Q1 - 1.5 IQR) to (Q3 + 1.5 IQR) (IQR: interquartile range), and crosses represent outliers (values > 1.5 × interquartile range). The total number of sites used for each region
is shown in parenthesis. Comparisons on annual (grey), DJF (blue), MAM
(green), JJA (red) and SON (orange) timescales are shown.
Simulated (ND) and observed daily and monthly mean surface O3
over the period 2005–2014 at four individual monitoring locations of (a) Utqiagvik (formerly Barrow) (b) Mace Head, (c) Cape Grim and (d) the South Pole.
Evaluation of surface ozone against TOAR observations
The surface O3 concentrations in the ND simulation with UKCA StratTrop in UKESM1 for December–January–February (DJF) and June–July–August (JJA)
(seasonal means calculated from monthly means over the 2005–2014 period)
show elevated values across the tropics in both seasons as well as in the
northern mid-latitudes in JJA (Fig. 5a and c). Maximum surface O3
concentrations of more than 60 ppb are simulated across the Middle East,
northern Africa and South Asia in JJA due to large anthropogenic and
biogenic sources of O3 precursors. In DJF, surface O3
concentrations are lower over the continental northern mid-latitudes due to
slow O3 production and an enhanced O3 removal from elevated
NOx emissions. Meanwhile, surface O3 concentrations are slightly higher over oceanic areas (North Atlantic and north-west Pacific) than over land in DJF, probably due to transport from the stratosphere and a reduced
chemical sink from weaker photolysis of O3 (Banerjee et al., 2016).
Surface O3 concentrations are slightly higher over some oceanic areas
in JJA, indicating long-range transport from polluted continental areas.
Surface O3 concentrations simulated in the nudged configuration of
UKESM1 have been evaluated over the period 2005–2014 by comparing to the
gridded monthly mean rural observations in the TOAR database over the same
time period (Schultz et al., 2017). These data provide a global perspective
on surface O3 and are by far the most comprehensive surface O3
database for use in the evaluation of global models. However, the TOAR database
does not provide globally uniform coverage and as such the evaluation of the
model performance for surface O3 over key regions, such as South Asia
(Hakim et al., 2019), will be analysed in more specific follow-up studies
making use of bespoke datasets. Figure 5b and d show that the model
underpredicts surface O3 concentrations in DJF and overpredicts O3
in JJA across the northern mid-latitudes, in a similar way as other global
models (Young et al., 2018). Potential reasons for these discrepancies could
be the coarse model resolution, associated errors in the emissions
inventories, errors in the vertical injection of the emissions (for example,
we inject most of the NOx near the surface, which will titrate O3), representation of VOCs in the chemistry scheme and uncertainties in O3 loss processes (dry deposition).
Each grid point containing observations has been evaluated against the
corresponding model values by calculating a normalised mean bias factor
(NMBF; Yu et al., 2006). Figure 6 shows the distribution of NMBFs within a
particular region for different seasons. Over northern mid-latitudes (Europe,
North America and East Asia) the model clearly underrepresents surface
O3 in DJF (by a factor of 1.5 to 2), suggesting excessive O3
titration by NOx. The model agrees better with observations in other
seasons across these regions, with a slight overprediction in JJA. The
limited number of available observations in other regions (< 10 grid points)
makes it difficult to draw firm conclusions but suggests that UKCA StratTrop
in UKESM1 tends to overpredict surface O3 across the oceanic and
Southern Hemisphere sites. The model consistently underpredicts observed
surface O3 at sites located in Antarctica, implying a lack of transport
and a modelled O3 lifetime in this region that is too low, particularly in
March–April–May (MAM) and JJA.
Simulated daily and monthly mean surface O3 concentrations over the
period 2005–2014 from UKESM1 have been interpolated and compared to four
individual measurement locations from the TOAR database with daily and
monthly mean observations (Fig. 7). UKESM1 is able to reproduce the
seasonal cycle of surface O3 observed at Cape Grim (r2=0.74
NMBF =-0.08) and the South Pole (r2=0.79, NMBF =-0.81), although
it underestimates the magnitude in JJA at Cape Grim and in all seasons at
the South Pole. There is reasonably good model–observation agreement in
JJA at the two Northern Hemisphere sites (Utqiagvik, which was called Barrow, AK, until 2016, and Mace Head) (albeit with
some disagreement in the phase), although in DJF the model underpredicts
surface O3 at both sites. The surface model evaluation of UKESM1 at
selected individual measurement locations exhibits a similar performance to
that of HadGEM2-ES in O'Connor et al. (2014).
Multi-annual average monthly mean O3 dry deposition fluxes
for the global domain (a) and three latitudinal sections (b–d) for 15 models participating in the HTAP
model intercomparison: northern extratropics (NET; 90–30∘ N; b), tropics (TR; 30∘ N–30∘ S; c) and southern extratropics (SET; 30–90∘ S; d). The multi-model ensemble average (solid black line and
filled circles) and single-model monthly means (grey solid lines) were
provided by Hardacre et al. (2015). Error bars indicate ±1σ
in the single-model monthly means. The solid red line shows UKCA ND StratTrop
multi-annual average (2005–2014) monthly mean O3 dry deposition fluxes (figure based on Hardacre et al., 2015).
Dry deposition of ozone – comparison with HTAP models and observations
A total of 1030 Tg (O3), around 20 % to 25 % of the gross chemical ozone
production in the troposphere, is removed from the atmosphere in the ND
simulation through dry deposition at the surface (Stevenson et al., 2006;
Wild, 2007; Young et al., 2013; Hardacre et al., 2015). Uptake by
terrestrial vegetation plays a crucial role; however, Hardacre et al. (2015)
demonstrated that the oceans also represent a very important sink. Much
uncertainty still remains about the exact magnitude and many of the
processes around ozone removal at the surface (e.g. Hardacre et al. 2015;
Luhar et al., 2017). A thorough evaluation and, if necessary, recalibration
of ozone dry deposition models are thus critical in developing robust
models of atmospheric composition.
Ozone surface dry deposition measurement sites (reproduced from
Hardacre et al., 2015).
SamplingSamplingSite nameGrid referenceLand coverheight (m)PeriodRef.Long-term sitesAuchencorth Moss55∘47′ N, 3∘14′ EMoorland0.3–3.0Oct 1995–Dec 20001Blodgett Forest38∘53′ N, 120∘37′ WPine plantation12.5Jan 2001–Dec 20072Citrus Orchard36∘21′ N, 119∘5′ WCitrus orchard1.0–9.2Oct 2009–Nov 20104Castel Porziano41∘44′ N, 12∘24′ EHolm oak35Jan–Dec 20135Harvard Forest42∘32′ N, 72∘11′ WMixed deciduous forest30Jan 1992–Dec 20017Hyytiälä61∘51′ N, 24∘17′ EScots pine forest23Jan 2002–Dec 20038Ulborg56∘17′ N, 8∘25′ EMixed coniferous18, 36Oct 1995–Dec 200010Short-term sitesBorneo OP345∘8′ N, 117∘51′ ETropical forest75Apr, Jul 20081Burriana39∘55′ N, 0∘03′ WCitrus orchard1016–29 Jul 1995328 Apr–3 May 1996La Cape Sud44∘24′ N, 0∘38′ EMaize crop3.4, 3.7, 6.4Jul–Oct 20076Klippeneck48∘10′ N, 8∘45′ EGrass2, 81–22 Sep 19923Le Dezert44∘05′ N, 0∘43′ EPine forest3716–18 Apr 19973San Pietro Capofiume44∘39′ N, 11∘37′ EBeet crop815–22 Jun 19933South-western Amazon3∘00′ S, 60∘00′ WTropical forest53May 19999Sep–Oct 1999Viols en Levant43∘41′ N, 3∘47′ EMediterranean shrub3716–24 Jul 19983Voghera45∘01′ N, 9∘00′ EOnion field2.5May–Jul 200311
(1) Fowler et al. (2001); (2) Fares et al. (2010); (3) Cieslik (2004); (4) Fares et al. (2012); (5) Fares et al. (2014); (6) Stella et al. (2011); (7) Munger et al. (1996); (8) Rannik et al. (2012); (9) Fan et al. (1990); (10) Mikkelsen et al. (2004, 2000); (11) Gerosa et al. (2007).
Comparison with the HTAP multi-model ensemble ozone deposition fluxes
Figure 8 shows a comparison of multi-annual average monthly mean ozone
deposition modelled by UKCA StratTrop in UKESM1 with a multi-model ensemble
of 15 HTAP atmospheric composition models (Hardacre et al., 2015). The
StratTrop model data here are taken from the ND simulation. Monthly mean
ozone deposition is depicted for the entire global domain (Fig. 8a) and
split into the northern extratropics, the tropics and the southern
extratropics, respectively, each representing a distinctly different
deposition regime (Fig. 8b–d). The solid black line and filled circles
represent ensemble average monthly mean ozone deposition, with the error bars
indicating ±1σ in the single-model monthly mean ensemble; the
solid grey lines represent single-model monthly means from the HTAP models,
indicating the spread in the multi-model ensemble. The multi-annual average
(10 years) monthly mean ozone dry deposition flux modelled by UKESM1-UKCA is
shown as the red solid line.
In general, ozone dry deposition from UKCA StratTrop in UKESM1 compares
favourably with the HTAP multi-model ensemble, nearly always falling within
the 1σ range of the HTAP multi-model average. UKCA StratTrop also
correlates well with the multi-model average seasonal cycle for each of the
depicted regions; however, a systematic low bias is evident, particularly in
the global and tropical domains (panels a and c in Fig. 8). Most of the
low bias occurs in the tropical region. Since the tropics are dominated by
both a large ocean surface area and the most productive portion of the
Earth's terrestrial vegetation in the form of the tropical rainforests of
South America, equatorial Africa and the Maritime Continent, the tropical
low bias in the model could be due to an underestimation of O3
concentration, the stomatal ozone uptake by tropical rainforests or a
similar underestimation of O3 removal at the ocean's air–sea interface.
The latter seems less likely in view of the relatively good performance in
the southern extratropics, which are also dominated by a large ocean surface.
Comparison with observations of ozone deposition fluxes
Measurements of ozone dry deposition fluxes collected over extended periods
of time are still very sparse; however, a number of long-term datasets
exist. Hardacre et al. (2015) compiled a comprehensive dataset from
available long-term and short-term observations. This comprehensive dataset
has been adopted for our evaluation of O3 dry deposition in UKCA
StratTrop in UKESM1. Table 3 summarises the locations of all the measurement
sites included in this comparison. A comparison of the dry deposition fluxes
of ozone with observations at these 16 sites is presented in Fig. 9. Some
sites cover the seasonal cycle over several years (e.g. Castel Porziano,
Harvard Forest, Ulborg) and others only offer data spanning less than
1 month (e.g. Klippeneck, Le Dezert, Viols en Levant).
Due to its removal via stomatal exchange and relative insolubility in water,
O3 dry deposition depends strongly on the underlying land surface type.
Therefore, a reliable representation of ozone dry deposition in models
requires not only the composition model to perform well. A robust model of
the land surface including dynamic vegetation is also indispensable. The
land surface representation in UKCA StratTrop in UKESM1 relies on JULES
(Best et al., 2011; Clark et al., 2011). Thus, a comparison of ozone dry
deposition (or any dry deposition process for that matter) reflects on the
broader Earth system framework than just the atmospheric composition
component alone.
Comparison of observed and modelled monthly mean ozone dry
deposition fluxes. Grey circles indicate monthly mean ozone deposition
fluxes at measurement sites (see Table 3 for site details); error bars
denote standard errors. Solid red lines represent modelled multi-annual
average monthly mean O3 deposition fluxes extracted from UKCA StratTrop
ND in UKESM1 at the site locations by interpolation of the nearest grid
boxes (averaged over all surface tiles in these grid boxes). Ozone dry
deposition fluxes are given in 10-10 kg m-2 s-1, and measurement data are from Hardacre et al. (2015) and references therein.
Overall, Fig. 9 demonstrates that the UKCA(StratTrop)/JULES/UKESM1 framework
shows a reasonably good performance, albeit with some substantial
model-to-observation deviations evident. At the Castel
Porziano, La Cape Sud and Harvard Forest sites the model reproduces
both magnitude and seasonal cycle of ozone dry deposition well. To a somewhat
lesser degree the model performance is also good at the California Citrus
Orchard and Hyytiälä sites. At both locations the model captures most of the
seasonal cycle well but fails to reproduce the magnitude of the flux fully.
Interestingly, there is no systematic bias in the model-to-observation deviations
with respect to magnitude and land cover type.
Further locations with good model-to-observation agreement include the densely
forested OP3 site in Borneo and the Klippeneck site in Germany. However, these
sites only provide campaign data for a limited period of time. The model
shows very low skill in reproducing either the magnitude or seasonal cycle at
three sites with long-term observational records, namely Auchencorth Moss
(Scotland, UK), Blodgett Forest (California, USA) and Ulborg (Denmark).
In all three cases the model severely underestimates O3 dry deposition fluxes. The model also shows a fairly low skill in reproducing the seasonal
cycle at these three sites. Potential reasons for the low model skill at
these long-term observation sites include modelled surface ozone levels,
deposition velocities and the appropriateness of the vegetation type, but
more detailed analysis is required to explore these further. However, by and
large, the model performance appears reasonable when compared to both
observations and other models, although with an overall negative bias.
Model-simulated methane and OH
Here we discuss the performance of UKCA StratTrop modelled methane and tropospheric OH
distributions. OH is the primary oxidising agent in the
troposphere and is the key determinant on the burden of methane in the
troposphere (Monks et al., 2015).
A commonly cited indicator of tropospheric oxidising capacity, the
tropospheric lifetime of methane with respect to OH, has been calculated for
the FR simulation, averaged over the entire length of the run. The modelled
average tropospheric mean methane lifetime with respect to OH oxidation is
calculated to be 8.5 years (with a standard deviation of 0.1 years). This
value is in good agreement with the ACCMIP ensemble average of 9.7±1.5 years (Naik et al., 2013) (i.e. falling within 1 standard deviation of
the ACCMIP ensemble). We note that the methane lifetime for UKESM1 is much
shorter than the methane lifetime for HadGEM2-ES. Figure 4 shows this is
largely down to improvement in the treatment of photolysis since HadGEM2-ES
(Telford et al., 2013).
We further focus our analysis on comparing the climatological distribution
of OH as a function of latitude and altitude (Fig. 10).
Evaluation of the UKCA StratTrop zonal distribution of
tropospheric [OH] (×105 molecules cm-3) in the FR simulation. Values plotted in black refer to the UKCA StratTrop multi-annual mean [OH]
in each region of latitude and pressure range, with the values following
being ±1 standard deviation around the mean.
The FR UKCA StratTrop simulation results in a global mean tropospheric [OH]
of 1.22×106 molecules cm-3, averaged over the period 2005–2014.
As with the methane lifetime, this value is slightly higher than the ACCMIP
ensemble mean (11.1±1.6×105 molecules cm-3) but sits
within the standard deviation of the ACCMIP ensemble mean (Naik et al.,
2013). Figure 10 shows how the distribution of [OH] varies throughout the
troposphere relative to the ACCMIP multi-model mean, the HadGEM2-ES model
and the data from Spivakovsky et al. (2000), who pioneered the development of
[OH] climatologies in the troposphere. Compared against these data, UKCA
StratTrop in UKESM1 performs well: the global tropospheric mean [OH] is
within 10 % of the ACCMIP ensemble mean. The model captures the
latitudinal and vertical profiles found in the other datasets and agrees on
the magnitude of [OH] in 10 of the 12 regions analysed (when considering the
model uncertainty).
The [OH] is higher in UKCA StratTrop than in HadGEM2-ES, partly because of
different emissions used in the HadGEM2-ES study, but also in part owing to
the change in photolysis scheme (as discussed previously). UKCA StratTrop
agrees better with the ACCMIP multi-model mean than Spivakovsky or
HadGEM2-ES, but the tropics from 1000 to 750 hPa are regions where the model
consistently disagrees with the other datasets, simulating higher levels of
OH in these regions. These regions of the troposphere are the regions where
most CH4 is oxidised, so high biases in the model here will tend to lead to lower CH4 lifetimes than in observation-derived estimates.
Vertical profiles of the mean tropical (±10∘ N)
modelled methane from multi-annual mean output from an
atmosphere-only free-running simulation of HadGEM2-ES (OC14; blue), an
atmosphere-only free-running FR (green) and nudged (ND; red) simulations of
UKCA StratTrop in UKESM1 (this study). The shading represents ±1
standard deviation about the multi-annual mean.
Multi-annual (2005–2014) monthly zonal mean methane (ppm) from
the UKESM1 FR simulation in (a) January and (b) July, with scatter plots of
modelled versus observed concentrations for January and July in panels (c)
and (d), respectively. The coloured contours in (a) and (b) are from UKCA and
the black contours are the HALOE/CLAES climatology. The scatter plots also
include a 1 : 1 line as well as the root mean square error (RMSE), the slope of a
least squares linear fit and the correlation coefficient (r).
In the previous configurations of UKCA (MO09 and OC14), methane
concentrations fell off too quickly with height above the tropopause; this
was attributed to the stratospheric transport timescale being too long in
the respective physical model. Comparisons of methane columns from the
HadGEM2-UKCA coupled model with SCIAMACHY, for example, were too low and
required modelled methane above 300 hPa to be overwritten with Halogen
Occultation Experiment (HALOE; Russell et al., 1993) and Atmospheric
Chemistry Experiment (ACE; Bernath et al., 2005) assimilated output from
TOMCAT (Hayman et al., 2014). Figure 11 shows that the fall-off of methane
with height in both the FR and ND simulations of UKESM1 is less rapid than
in HadGEM2 and is consistent with the age of air in the model being
comparable to that inferred from observations (Sect. 4.6). As comparisons
with surface observations and SCIAMACHY (with its strong sensitivity to
surface concentrations) are not appropriate here because surface methane
concentrations are relaxed to LBCs (Sect. 2.6.4), only comparisons with
stratospheric observations are shown.
Figure 12 shows multi-annual zonal mean comparisons for January and July of
modelled methane from the free-running (FR) simulation against the
HALOE/Cyrogenic Limb Array Etalon Spectrometer (CLAES) climatology (Kumer et
al., 1993). It indicates that UKCA StratTrop in UKESM1 is capable of
simulating the absolute concentrations and the morphology of the
observed distribution. The only exception to this is the tongue of
methane-depleted air descending from the mesosphere over the SH high
latitudes in July, which was also evident in MO09. Nevertheless, UKESM1 is
able to capture the observed vertical fall-off with height. There is an
excellent 1 : 1 correspondence between the model and observations: the slopes
of the least squares fits for January and July are within 0.05 of unity, the
correlation coefficients are greater than 0.98, and the root mean square
errors between UKESM1 and the HALOE/CLAES climatology are less than 0.1 ppm
for the free-running (Fig. 12) and nudged (not shown) simulations.
Comparison of total ozone column
Here we discuss the modelled total ozone column through an analysis of the data
from the FR simulation averaged over the 2005–2014 period. We note here that
there is little difference between the ND and FR total column, so for
simplicity we focus on the FR data.
Figure 13a shows the multi-annual average total ozone column in
Dobson units as a function of latitude and time. As with most
chemistry–climate models (Dhomse et al., 2018), UKCA simulates the main
features of the total column well, with a minimum in the tropics and maxima
at high latitudes during the hemispheric spring seasons. When compared with
the total-column ozone in older versions of UKCA (M09, Fig. 9) the current
model configuration simulates similar biases at high latitudes but a
pronounced positive bias in the tropics. Figure 13b highlights that
the tropical column is biased high by 30–40 DU when compared to the Bodeker
climatology (Hassler et al., 2008), and the Antarctic ozone hole extends for
too long in the model, leading to low biases in the austral summer. The high
biases in total-column ozone in the tropics are very likely
driven in part by high biases of around 15 DU in the tropical tropospheric ozone
column (see Sect. 4.5 below). The extratropical biases may well be
related to this bias through the transport of ozone-rich air in the upper troposphere–lower stratosphere (UTLS)
into this region, but further work is needed to resolve the causes of the
bias in the total ozone column.
Evaluation of the FR UKCA total ozone climatology. Panel (a)
shows the FR simulation multi-annual mean total-column ozone climatology (in
Dobson units – DU). Panel (b) shows the difference between the FR ozone climatology and the Bodeker ozone climatology v2.8.
Comparisons with satellite retrievals of tropospheric columns of
O3, CO and NO2
Here we compare the results from the UKCA StratTrop runs against satellite
data with a focus on assessing performance in the troposphere. In all cases,
the run analysed is the nudged dynamics (ND) run discussed in Sect. 3.
Nudging enables a more robust comparison against the satellite observations
as it reduces biases caused by circulation errors in the free-running model,
although we note that it does not completely remove these biases (Orbe et
al., 2018; Chrysanthou et al., 2019). As well as nudging, the model output
is sampled instantaneously every 3 h to allow for time and space
sampling to the satellite data locations. The comparison between the model
and the observations is made using OMI-MLS for the tropospheric column of
O3, MOPITT for the tropospheric column of CO and OMI for the tropospheric
column of NO2.
In the following analysis, the stratosphere has been removed by screening
out regions where the monthly mean ozone exceeded 125 ppb, the ozonopause;
columns are calculated by summing variables from the surface to the height
at which the ozonopause starts. The model ozone data presented here have not
been corrected to account for optically thick clouds in the troposphere,
which may affect retrieved ozone profiles (Ziemke et al., 2006) since
averaging kernel (AK) information is not available for the OMI-MLS dataset.
As satellite measurement errors were not available, we have used 2 times the standard deviation of the retrievals to estimate when the
differences between modelled and observed ozone are significant. This
implies that the stippling area in the plots, corresponding to grid cells
in which model bias> satellite error, could be
reduced (i.e. better agreement with the observations) if the satellite error
is added to the 2×SD. The plots therefore show a “worst-case
scenario”.
Comparison of observed and modelled tropospheric ozone columns
(DU) from the ND simulations. Plots show seasonal means and differences for
the period 2005–2014. (a) OMI-MLS tropospheric column (DJF); (b) difference
between the model nudged simulations and OMI-MLS tropospheric column (DJF); (c) OMI-MLS (JJA) tropospheric column; (d) difference between the model and OMI-MLS
tropospheric column (JJA). Stippling indicates grid points for which bias>2×SD of the observations.
The model fields have been co-located in time and space with the
observations to reduce representation errors. For each satellite retrieval,
the nearest model grid box is subsampled within 3 h of the observation
and the model profile interpolated onto the satellite pressure grid. The
satellite AKs (where available) are then applied to the model profile to
account for the vertical sensitivity of the instrument. Then the model
sub-columns are calculated and summed between the surface and the tropopause
to determine the co-located model tropospheric column. The equations used to
apply the OMI NO2 and MOPITT CO AKs to the model profiles are
10y=A⋅x,11y=10(A(log10(x)-log10(xa))+log10(xa)),
where x is the co-located model profile interpolated onto the satellite
pressure grid, A is the satellite averaging kernel, xa is the satellite a priori
and y is the modified model profile. Here x for NO2 is in sub-columns (units: 1015 molecules cm-2), while x for CO has units of volume mixing ratio (vmr)
before conversion into a ratio of sub-columns to the tropospheric column. Tropopause height
information was provided by the OMI NO2 files, but for MOPITT-derived
tropospheric column CO we use the climatological tropopause described by
Monks et al. (2017).
The modelled tropospheric ozone column (TC_O3) is evaluated
against the OMI-MLS tropospheric ozone column (Ziemke et al., 2006). The
general agreement between UKCA StratTrop and OMI-MLS is good and in line
with many other CCMs (Young et al., 2013). A general feature of the model is
a small underestimation in the tropospheric ozone column in the Southern
Hemisphere extratropics, generally good agreement in the Northern Hemisphere
extratropics and significant positive biases of 15 DU in the tropics (Fig. 14). The underestimation in tropospheric ozone in the southern mid-latitudes
is worse in the late summer and early autumn when OMI-MLS shows a seasonal
maximum in the Southern Hemisphere that the model fails to reproduce (Fig. 15c).
For the northern mid-latitudes, Fig. 14b shows that in DJF the
model overestimates tropospheric ozone over large parts of the North
Atlantic Ocean while underestimating it over northern Russia and large parts
of the North Pacific Ocean. These two biases counteract each other in the
time series plot (Fig. 15a) to give good net agreement overall.
It is worth noting that the time series plots show that there are very small,
if any, trends in tropospheric column ozone when averaging across these
large domains. Figures 14d and 15a show that in JJA the
model biases in the northern mid-latitudes are generally very small, and the
amplitude and phase of the modelled seasonal cycle are in good agreement with
the OMI-MLS data. In the tropics the differences shown in Fig. 14b and d are around 25 %–50 %. There are potentially several causes for this
including (a) the representation of chemistry in this region, (b) the
underlying emission inventories, (c) the deposition rates (which are on the
low end compared with other models) and (d) the emissions of ozone
precursors. The pattern of the bias strongly resembles patterns in the
emissions of NOx from lightning. It has been noted before that the modelled tropospheric ozone is extremely sensitive to the average global NOx emitted by lightning, which is mainly centred around the tropics. The model bias in the tropics might be a result of the simplified parameterisation of lightning NOx emissions, and further work will focus on reducing this bias.
Tropospheric column O3 (DU) zonal time series
(30–60∘ N – a, 30∘ N–30∘ S – b, 30–60∘ S – c) for the ND simulation (red) and
OMI-MLS (black). Dashed lines represent the satellite uncertainty range
(±2×SD).
Comparison of observed and modelled tropospheric CO columns (DU)
from the ND simulations. Plots show seasonal means and differences for the
period 2005–2014. (a) MOPITT tropospheric column (DJF); (b) difference between the
model and MOPITT tropospheric column (DJF); (c) MOPITT (JJA) tropospheric
column; (d) difference between the model and MOPITT tropospheric column (JJA).
Stippling indicates grid points for which bias> satellite error.
Tropospheric column CO (DU) zonal time series (30–60∘ N
– a, 30∘ N–30∘ S – b,
30–60∘ S – c) for the ND simulation (red) and MOPITT
(black). Dashed lines represent the satellite uncertainty range.
Figure 16 shows a comparison of the tropospheric column of CO in the UKCA
StratTrop nudged dynamics runs with retrievals from the MOPITT instrument onboard Terra (Emmons et al., 2004). The MOPITT data reveal that the
tropospheric column CO (TC_CO) is highest over anthropogenic
and biomass burning emission regions and lowest over the remote oceans.
There is a strong north–south gradient which is set up from the short
lifetime of CO (∼ 30 d) and the timescales for
interhemispheric mixing. (NB Fig. 16a highlights strong emissions of CO
in DJF in the northern mid-latitudes). The general feature evident from
Fig. 16 is that the model significantly underestimates TC_CO in the Northern Hemisphere (NH) in both winter and summer. The negative
bias in TC_CO is especially large at high northern latitudes,
consistent with surface CO biases in this region (e.g. Shindell et al.,
2006). Whilst the NH shows a negative bias, there is a strong positive bias
in CO in regions associated with agricultural (Indo-Gangetic Plains) and
forest burning (central Africa and northern South America).
There are a number of reasons for the model–satellite biases in
TC_CO, including (1) CO emissions in the NH being underestimated
(Miyazaki et al., 2015), (2) insufficient secondary production of CO from
non-methane VOC oxidation (e.g. Grant et al., 2010), (3) excess biomass
burning emissions in the Southern Hemisphere (SH) during DJF (potentially
the same cause in central Africa in JJA) and (4) strong loss through OH in the
NH in both seasons. We note that these types of biases are not unique to
UKCA StratTrop and that further work is required to ameliorate them
(Shindell et al., 2006).
As shown in Fig. 17, there is no clear trend in modelled and observed
TC_CO over time. However, both datasets show seasonal cycles
in TC_CO in the NH and SH with a very muted seasonal cycle in
the tropics. The model simulations again underestimate (∼ 10–20 DU) TC_CO in the NH mid-latitudes but successfully
capture the amplitude and phase of the seasonal cycle (albeit with a
slightly smaller amplitude) and the magnitude of interannual variability
well. In the Southern Hemisphere, the model is doing very well in capturing
the absolute concentration, seasonal cycle and interannual variability, although
it underestimates the peaks during the austral winter. There is also an
underestimation of CO in the tropics despite the positive bias over biomass
burning areas.
Comparison of observed and ND modelled UKCA StratTrop in UKESM1
tropospheric NO2 columns (molecules cm-2). Plots show seasonal
means and differences for the period 2005–2014. (a) OMI tropospheric column
(DJF); (b) difference between the model and OMI tropospheric column (DJF); (c) OMI
(JJA) tropospheric column; (d) difference between the model and OMI tropospheric
column (JJA). Stippling indicates grid points for which bias> satellite error.
Tropospheric column NO2 (1015 molecules cm-2)
zonal time series (30–60∘ N – a, 30∘ N–30∘ S – b, 30–60∘ S – c) for the
ND simulation (red) and MOPITT (black). Dashed lines represent the satellite
uncertainty range.
Finally, we focus on the comparison of modelled and observed tropospheric
NO2 columns. The observed tropospheric NO2 column
(TC_NO2) data come from the OMI instrument onboard AURA
(Boersma et al., 2011). The observed NO2 column is highly heterogeneous
and localised to the major industrialised regions, where anthropogenic
emissions are highest, and major biomass burning zones (Fig. 18). The
figure highlights strong seasonal differences in the observations, with
TC_NO2 being larger in winter (panel a) than in summer (panel c), most likely as a result of higher emissions and a longer NO2 lifetime
than in the former season. Averaged across the whole troposphere, the model
compares well with OMI TC_NO2 spatially (Fig. 18b, d).
However, there are very significant positive biases over the main
anthropogenic emission regions (i.e. South Asia, eastern Europe, East Asia
and outflow from the US eastern seaboard), particularly in the boreal
winter. These biases in TC_NO2 are only weakly correlated
with the biases in TC_O3 in these regions, suggesting
different causes, and they are dominant in different regions of the
atmosphere (boundary layer vs. free troposphere). A high bias in
TC_NO2 extends out from the North China Plain region, across
the Sea of Japan and into the Pacific Ocean, suggesting either errors in the
underlying emission inventory or in the modelled NO2 lifetime.
Zonal annual, seasonal annual, and multi-annual mean nitric oxide (NO) volume
mixing ratio in parts per billion. Top row: UKCA StratTrop, February 2004 to February 2013. Bottom row: bias versus the ACE-FTS v3.5 climatology, with the same units and period. The
climatology represents the average of sunrise and sunset
measurements, while the model values are averaged over all local times. The
model data analysed are from the FR simulation.
Same as Fig. 22 but for odd nitrogen (NOy), defined here as NOy=NO+NO2+HNO3 (ppb). For ACE-FTS, NO and
NO2 fields are the average of sunrise and sunset
measurements. The model values are averaged over all local times.
Over biomass burning regions, there is evidence for low biases over central
Africa and South America (mainly in JJA). This may well be a vertical
sensitivity issue in the comparison of the datasets. As OMI has peak
sensitivity in the middle to upper troposphere, OMI detects enhanced NO2
values over biomass burning regions due to the buoyant fire plumes. In
UKESM1, the gas-phase anthropogenic and biomass burning emissions are added
to the surface level, so most of the NOx will be trapped in the boundary layer where OMI is less sensitive. Therefore, the satellite AKs
will give this sub-column less weighting and a negative bias occurs.
Figure 19 highlights that in both the model simulation and satellite data,
the average Southern Hemisphere extratropical TC_NO2 is
lower than in the Northern Hemisphere due to fewer emission sources.
However, in the model there is a significant low bias in this region,
∼ 50 %. This bias is largest over the oceans and may be
connected with biases in the representation of NOy species (i.e. PAN), which are large contributors to NOx in this region.
In the northern extratropics, the model-simulated TC_NO2 is
within the observational uncertainty but with too large a seasonal cycle,
the simulated mean annual minima and maxima being much lower and higher, respectively, than the
observed mean annual minima and maxima.
Evaluation of zonal mean stratospheric composition
Sellar et al. (2019) provide an overview of the simulation of total-column
ozone. Their results and ours (see Fig. 13) indicate that UKESM1 produces
relatively realistic ozone fields, albeit with some remaining issues. Among
these is a tendency for the Antarctic ozone hole to be too persistent,
insufficiently variable and on average too deep. This is linked to a
stratospheric cold bias noted before (Dennison et al., 2019).
In the analyses below, UKCA StratTrop seasonal and zonal mean composition
fields from the FR simulation are compared to selected species from the
Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS)
climatology version 3.5. ACE-FTS is an ongoing satellite mission sponsored
by the Canadian Space Agency; it uses solar occultation to observe a
substantial number of species with a coverage extending in some cases into
the mesosphere. The climatologies used here cover the period of February 2004 to February 2013 (http://www.ace.uwaterloo.ca/climatology_3.5.php, last access: September 2017). Here we
focus on NO, NOy (defined here as NO+NO2+HNO3), CO, H2O and O3. Climatologies of N2O5 and ClONO2
measurements by ACE-FTS are also available but are not included in the
NOy calculation presented here because of their more restricted
coverages than the NO, NO2 and HNO3 climatologies. Both would
contribute relatively minor amounts to NOy compared to the large biases
discussed below.
Same as Fig. 20 but for carbon monoxide (CO; ppm).
Same as Fig. 20 but for water vapour (H2O; ppm).
NO is underestimated throughout the model domain (Fig. 20). In the
troposphere and much of the stratosphere, NO is subject to a large diurnal
cycle. When exposed to sunlight it is maintained by photolysis but converts
to NO2 at night by reacting with O3. However, near and above the top of the region covered by NO2 measurements, at ∼ 50 km, this conversion becomes slow and NO is also the dominant form of nitrogen in
the ACE-FTS measurements at night. This implies that the large
underestimation of NO seen above 50 km, which reaches about 1 ppm, is not a
sampling problem associated with imperfect spatio-temporal matching of
satellite and model data. Rather, it reflects a model shortcoming. To
illustrate the consequences of this issue for stratospheric composition, we
compare NOy (Fig. 21). This diagnostic reveals tongues of
nitrogen-depleted air descending in the polar vortices of both hemispheres
in the model, which in the ACE-FTS measurements are, however, relatively
nitrogen-rich. This discrepancy lasts into southern spring when NOy is
underestimated by up to 12 ppb at around 70∘ S. The depletion of
HNO3 due to denitrification in the lower Antarctic polar vortex appears
to be well reproduced in winter but is perhaps overestimated in spring, in
line with the generally excessively long lifetime of the polar vortex in the
model (Sellar et al., 2019; not shown).
The model gets the shape of the distribution of CO about right but
substantially underestimates the amount of CO in the mesosphere (Fig. 22).
A variant simulation with a modified top boundary condition (TBC), whereby
the top two levels are not overwritten with the third-highest level, reveals
that with this variant TBC CO would now be overestimated. Essentially, CO
production is due to CO2 photolysis, which is extremely height-sensitive. The simulation shows that mesospheric air reaches the lower polar
vortex in Antarctic spring; this process is relatively well simulated in the
model.
In much of the stratosphere, H2O is overestimated by 0.3 to 2 ppm,
suggesting that perhaps the tropical tropopause cold point is still slightly
too warm (Fig. 23). This has been a persistent problem in the MetUM
coupled to UKCA (Morgenstern et al., 2009), and a significant amount of work
identified remedies to this issue in earlier versions of UKCA StratTrop
(Hardiman et al., 2015). One cause highlighted by Hardiman et al. (2015) was
the role of ozone in the upper troposphere–lower stratosphere (UTLS)
region. Biases in ozone here are important to this issue of stratospheric
moistening. In addition, a new development in UKCA StratTrop has been the
interactive simulation of H2O from CH4 oxidation in the
stratosphere, so biases in CH4 or the transport of CH4 into the
stratosphere may also play a role. Further work will focus on understanding
the causes of this H2O bias. In the mesosphere and in the polar
vortices, however, H2O is underestimated by several parts per million in many
locations. Unlike all other gas-phase chemical species, H2O is not
subject to the overwriting of the top two levels. It photolyses at similarly
short wavelengths as CO2 (see above); an overestimation of its
photolysis may explain a large amount of the mesospheric bias.
Same as Fig. 20 but for ozone (O3; ppm).
Figure 24 highlights a generally good simulation of stratospheric ozone in
UKCA StratTrop. In the lower stratosphere, ozone is mostly overestimated (by
around 0.2 to 1 ppm), whereas in the upper stratosphere it is underestimated
by similar amounts. Larger underestimations exist in Antarctic winter. In
the mesosphere, ozone is generally overestimated.
Taken together, these disagreements indicate some progress with the
simulation of odd nitrogen compounds, albeit with substantial remaining
problems. HNO3 is now in better agreement with observations than
documented by Morgenstern et al. (2009). However, this appears to be mostly
the case because ACE-FTS finds considerably more HNO3 in the
stratosphere than the older Upper Atmosphere Research Satellite (UARS) data
used there (Randel et al., 1998). The substantial deficit of NO in the
mesosphere is most likely the result of missing model physics: energetic
particle precipitation (EPP) is well documented to cause the break-up of
nitrogen molecules and the formation of NOx (for a review see e.g. Sinnhuber et al., 2012), but this process is not represented in UKCA
StratTrop. This model deficiency results in a misrepresentation of odd
nitrogen descending in the polar vortices towards the ozone layer. This
might explain the NOy deficit in winter–spring over both poles,
although further studies are needed to confirm this. This problem is
receiving much more attention here than e.g. in the earlier investigation by
Morgenstern et al. (2009) because the newer ACE-FTS satellite data offer
much better coverage of high latitudes and altitudes than the observational
references used by Morgenstern et al. (2009).
Morgenstern et al. (2009) had to artificially reduce water vapour at the
tropical tropopause; the reasonable agreement found here is achieved without
such an intervention. H2O loss and CO production are both the result of the photolysis of molecules (CO2, H2O) in the mesosphere where the
photolysis rate increases sharply with height and may be sensitive to
assumptions about the residual ozone column above the model top. In
combination, these findings suggest that this residual ozone column (which
is a parameter in the photolysis scheme) may be too small or that making
this a simple universal constant in the model may be inadequate.
Analysis of zonal asymmetry of ozone
Stratospheric ozone is often validated against zonal mean satellite data
(e.g. see above). As the simulation of ozone improves in models, attention
turns to higher-order diagnostics. A recent analysis by Dennison et al. (2017) revealed that zonal asymmetries of the stratospheric polar vortex, in simulations by a model closely related to UKESM1, were strongly
underestimated; the vortex was generally too circular and its centre too
close to the South Pole, when in reality the southern polar vortex is often
distorted and displaced towards the Indian Ocean sector. Dennison et al.
found a westward progression of this displacement, which their model failed
to reproduce. The climate impacts of ozone depletion are also often thought
of in zonal mean terms (e.g. Kang et al., 2011); any effort to attribute
regional climate change beyond the zonal mean to ozone depletion might well
be impeded by such model behaviour. Hence, here we briefly assess how UKCA
StratTrop handles zonal asymmetries of the Antarctic polar vortex. We
focus on the CMIP6 coupled historical UKESM1 simulations (Sellar et al., 2019), which use
the same version of UKCA StratTrop documented here, rather than the
experiments discussed in Sect. 3.
The analysis consists of expanding total column ozone (TCO) in a Fourier series:
O3=ZMO3+Acos(λ+b)+higher-order terms(ignored here).
Here O3 is monthly mean total-column ozone meridionally averaged over 60 to 70∘ S, ZMO3 is its zonal mean, A>=0 is the amplitude of the zonal asymmetry, λ is longitude and b is the phase shift; b=0 would
correspond to an ozone maximum occurring at the Greenwich Meridian and a
minimum occurring at the Date Line. Positive values for b correspond to a
westward displacement of these features.
Amplitude A [DU] of the zonal asymmetry in total-column ozone at
60–70∘ S in October. Green: NIWA–Bodeker Scientific total-column ozone
climatology vn 3.4. Orange: the model used by Dennison et al. (2017),
NIWA-UKCA. The data represent the average of five CCMI REF-C2 simulations by
their model. Black: UKESM1. The data represent the average of two CMIP6
“historical” simulations (Sellar et al., 2019). Straight lines are linear
regression fits. The numbers represent mean trends and associated 95 %
confidence intervals (DU yr-1).
Figure 25 displays A for October (when the ozone hole is typically deepest). The NIWA–Bodeker Scientific total-column ozone climatology
(http://www.bodekerscientific.com/data/total-column-ozone, last access: March 2019;
green) indicates that the zonal asymmetry is typically about 40 to
120 DU in size, and on average there is a positive trend, with the ozone
asymmetry increasing significantly by nearly 40 DU between 1979 and 2014.
UKESM1 (black) reproduces the magnitude and variability of the ozone
asymmetry, a big advance over the model used by Dennison et al. (2017)
(orange). The difference in the trend is not statistically significant at
the 95 % confidence level. For the phase b (Fig. 26) we find that the
model produces an ozone peak usually around 60–70∘ E (i.e. in the Indian Ocean sector), whereas in the NIWA–Bodeker Scientific climatology this maximum occurs further west, on average around 20–30∘ E. The mean eastward trend simulated by UKESM1 is outside the range of possibilities for the observations (which indicate a westward trend), but the uncertainty
intervals overlap.
Evaluation of transport and long-lived tracer–tracer correlation
Our final aspect of model evaluation focuses on the comparison of the
large-scale transport in the modelled middle atmosphere, analysed through
a comparison of the modelled age-of-air profiles against the age of air determined
using observations of SF6 made by the MIPAS instrument (Stiller et al.,
2008) and through a comparison of observed (ACE-FTS) and modelled
tracer–tracer correlations. The model data analysed here are from the FR
simulation.
A simple but powerful way to test the representation of stratospheric
chemistry in a model is to analyse the correlations between long-lived trace
gases (e.g. chap. 6, SPARC 2006). Long-lived tracers are known to exhibit
compact correlations with each other (Plumb and Ko, 1992), and comparisons of
modelled and observed correlations can test aspects of the model chemistry
independent of dynamics. This is particularly useful when comparing complex
3-D climate models such as UKESM1 with observations made by a range of
platforms at different spatial resolution and coverage, as well as under different
meteorological conditions.
Same as Fig. 25 but for the phase b, in degrees.
Figure 27 shows the correlations of CH4 vs. N2O, CH4 vs. H2O and NOy vs. N2O from a present-day UKESM1 simulation
(2005–2010) as well as from ACE and MIPAS satellite data. The ACE V4
(2004–2018) data were obtained from http://www.ace.uwaterloo.ca/data.php (last access: April 2019), and monthly mean zonal-mean values at
5∘ latitude bins were created by averaging all profiles with retrieval errors less than 100 %. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) V1.4 data used here are an update of those used in the CCMVal-2010 report (SPARC, 2010) (see http://eodg.atm.ox.ac.uk/MIPAS/, last access: April 2019). Co-located profiles of H2O,
CH4, N2O, NO2 and HNO3 are retrieved simultaneously for
both day and night-time profiles and are available for the mission period
(2002–2012). MIPAS data were obtained at:
ftp://ftp.ceda.ac.uk/neodc/mipas-oxford/data/ (last access: April 2019).
CH4 and N2O are two chemically independent but long-lived
tracers with significant stratospheric sinks. Accordingly, they are expected
to show compact correlations in the stratosphere (Plumb and Ko, 1992).
Overall, UKESM1 seems to show very good agreement with the recent
satellite-observed relationships, suggesting that the relative loss of
CH4 and N2O in the stratosphere is well represented. However, the model and the satellite observations differ slightly from the older ER-2
in situ lower stratospheric observations, possibly due to different relative
changes in CH4 and N2O in recent years. Note also that the model simulation covers the period 2000–2004, while ACE data cover 2004–2018; hence, even after applying the quality flag, ACE CH4 and N2O values in the troposphere are larger than model values.
More noticeable model–observation differences are found for the
CH4 : H2O correlation. These two long-lived tracers are chemically linked in the stratosphere: CH4 oxidation leads to the production of nearly two molecules of H2O (with a small yield of H2). As the maximum observed upper stratosphere H2O mixing ratio is typically around 7 ppm, and CH4 is the primary source of stratospheric H2O, the H2O vs. CH4 relationship is expected to be close to H2O+2×CH4=7 ppm, which is included in the plots as a
reference. The ACE observations show a slightly weaker relationship
(H2O+1.75×CH4=6.8), while MIPAS data show a
stronger slope, which is larger than 2 (H2O+2.4×CH4=8.0). There will be some uncertainty in the satellite data but it is
clear that UKESM1 has a significantly different relationship. The upper
stratospheric H2O values are reasonable, but the lower stratosphere
seems to be much wetter compared to observations (see Sect. 4.6). For
example, near 90 hPa most of the ACE profiles show H2O values close to 3 ppm, whereas modelled values hardly go below 5 ppm, suggesting that water
vapour entry mixing ratios near the tropical tropopause layer are not well
constrained in the model. However, in UKESM1 CH4 oxidation appears to
yield only 1 H2O per CH4 oxidised, which allows the model to
achieve realistic upper stratospheric H2O values. Further detailed
studies are required to verify the cause of this model discrepancy. We have
noted that there is a missing H2O product in the reaction HO2+MeOO (listed in Table S1). However, we calculate that this reaction only accounts for 2.3 % of the fate of MeOO in the stratosphere (which is dominated by reaction with NO), so it appears unlikely that this is the source of the bias.
Finally, we compare the NOy vs. N2O tracers, which are also chemically linked. N2O is the main source of stratospheric NOy, with a yield
of about 6 % via reaction of O(1D) (see Eq. 6.2b in SPARC,
2010). ACE NOy values are calculated simply by adding the observations of HNO3, NO, NO2, 2N2O5 and ClONO2. For MIPAS, zonal
mean (5∘ latitude bin) monthly mean profiles were calculated by
averaging all the measurements with standard errors less than 100 %. For
NOy : N2O plots, only night-time profiles are selected (SZA > 95) and NOy is calculated as HNO3+NO2+2N2O5+ClONO2. For large values of N2O, the UKESM1 correlation is less compact than the observations, although the modelled slope indicates a
realistic 6.7 % yield of NOy. The model also produces a reasonable peak NOy mixing ratio of around 17 ppb, although this is slightly smaller than observations, in particular from ACE. The model also tends to simulate larger occurrences of low NOy values for a given N2O, which may be an
indication of strong polar denitrification.
Correlations between selected long-lived chemical species
(monthly mean zonal-mean values for 60∘ S–60∘ N) from FR UKESM1 (a, d, g), ACE V4 data (b, e, h) and MIPAS data (c, f, i). The coloured
legend shows the corresponding pressure level (hPa) of the data points. The
linear regression fits to the model, ACE and MIPAS data are shown in the
respective panels along with the equations of the lines. The MIPAS data are
the same as those used in Figs. 6.12, 6.13 and 6.14 in the CCMVal-2 report
(SPARC, 2010). ACE NOy values are calculated as NOy=NO+NO2+HNO3+2N2O5+ClONO2. (a–c)CH4 vs. N2O. The linear fit is calculated for N2O values ranging from 100 to 300 ppb. The dashed line shows the estimated fit from ER-2 data (N2O (ppb) =261.8×CH4 (ppm) -131; see Kawa et al., 1993).
(d–f)CH4 vs. H2O. The linear fit is calculated for
CH4 values ranging from 0.5 to 1.5 ppm. The dashed line represents
H2O+2CH4=7 ppm. (g–i)NOy vs. N2O. The linear
fit is calculated for N2O values ranging from 100 to 300 ppb, and the
dashed line shows the equation NOy (ppb) =20.0-0.0625×N2O (ppb) based on mid-latitude balloon profiles and ER-2 data (see
Kondo et al., 1996).
Figure 28 compares data from the FR simulation and observations. The FR run
is shown here as this allows for a more robust comparison of the model data
where it is not constrained by the reanalysis meteorology. Figure 28 shows
the modelled multi-annual mean age-of-air profile in the stratosphere against
observations of SF6 from 2002 to 2010 used to calculate the age of air
from the MIPAS instrument (Stiller et al., 2008). The model includes a
diagnostic to quantify the age of air. This is effectively a “species” in
the model that is emitted at the model surface continually and undergoes
full tracer advection and diffusion. Whilst below the modelled tropopause
(based on a merger of the 380 K and 2 PVU surfaces) the tracer is set to
have an age of zero, above the tropopause the tracer has its age increased
every model time step that it stays above the tropopause.
Comparison of multi-annual mean FR modelled (red) age of air with
values derived from MIPAS observations of SF6 (black) (Stiller et al.,
2008). (a) Tropical profile. (b) NH mid-latitude profile. (c) Mid-latitude tropics profile. (d) Mean age at 23 km (∼50 hPa).
Figure 28a shows the modelled mean tropical (±10∘) age
profile as a function of altitude and that there is very good agreement
between the model and the values derived from MIPAS observations, with an
increase in the age of air as both profiles increase in altitude and a
maximum age of around 5 years. The modelled Northern Hemisphere mid-latitude
(35–45∘ N) age profile (panel b) agrees very well
with the observations from 16 to about 24 km, but the model tends to
simulate an age of air which is younger than the observations above 24 km
(up to a year difference younger). Panel (c) shows the difference between
the mid-latitude and tropical profiles and further emphasises the good agreement
of the model with the observations below 23 km but divergence above this
altitude. However, the zonal cross section at 23 km (∼ 50 hPa)
(panel d) shows that the model generally falls within the observational
uncertainty (1 standard deviation of the multi-annual observations) at all
latitudes.
Discussion and conclusions
In this paper we have documented the species and reactions that make up the
UKCA StratTrop mechanism for the first time and performed an evaluation of
the model output for the recent past. UKCA is the module for simulating
chemical and aerosol processes in the UKESM1 Earth system model (Sellar et
al., 2019), and UKCA StratTrop enables a holistic representation of
gas-phase chemistry in the troposphere and stratosphere, which is important for
understanding short-lived climate forcers.
Our focus here has been to document the performance of the chemical fields
simulated by UKCA StratTrop as implemented in UKESM1; the aerosol schemes,
processes and performance are discussed in detail in Mulchay et al. (2020).
Further studies are planned which will assess the role of
composition–climate Earth system couplings in the UKESM1 framework. Hence,
we present simulations which have enabled a more focused assessment of key
performance indicators of the UKCA StratTrop scheme. We have analysed data
from two model runs; the first was a free-running (FR) simulation wherein the meteorology
was allowed to evolve independently based on the influence of the prescribed
forcing agents (sea surface temperatures, greenhouse gases and sea ice), and the second was a nudged (ND)
simulation wherein the meteorology was relaxed toward the ERA-Interim reanalysis.
In general, and focusing on the gas phase as we have here, we find that the
performance of UKCA StratTrop in UKESM1 is in line with the range of models
that are applied to simulating the coupled chemistry–climate system (Young
et al., 2013, 2018).
Our key performance indicators have included the following.
An assessment of the magnitude and spatial distribution of lightning
NOx:
we note here that whilst the model simulates a global annual total lightning
NOx emissions magnitude that is in the middle of the range quoted in
the literature based on observational constraints (∼ 6 Tg yr-1),
and the spatial distribution in lightning flash frequency matches well with
observations from satellites, the variability in lightning flash frequency
is not in good agreement with the observations (Fig. 2). The UKESM1 model
predicts too much lightning activity in the tropics at the expense of the
extratropics, something which could be resolved by moving to an ice-flux-based scheme (Finney et al., 2018). Moreover, the vertical profile of
lightning NOx may have a significant impact on modelled O3. Hakim et al. (2019) have shown that across India the vertical profile in simulated
lightning NOx is very model-dependent. We suggest that further work be
performed to better understand the impacts of both the spatial distribution
of lightning NOx and the impacts of lightning NOx on the
tropospheric column biases in O3 in the model.
Surface ozone correlations and mean bias against TOAR observations:
TOAR (Schultz et al., 2018) provides the chemistry modelling community with
an unprecedented dataset to evaluate surface O3. In our analysis of the FR and ND runs presented here, we show that the annual mean bias is very
low, but this hides biases in summer and wintertime (Young et al., 2018).
However, we suggest that further work be performed to understand the cause
of the low and high biases in surface O3, especially with regards to
how these may impact studies that use UKESM1 surface O3 in health
assessment studies.
The tropospheric oxidising capacity:
a key component to determine the lifetime of emitted reactive gases in the
troposphere is the oxidising capacity. Whilst this has to be inferred from
observations (i.e. through the inferred lifetime of methane) it is an
important metric to evaluate the model against. In this study we found that
the methane lifetime in the troposphere with respect to OH was 8.5 years, which is
within the ACCMIP multi-model range but slightly low compared to
observational analyses (Naik et al., 2013). When compared against other
model estimates of the zonal mean distribution of OH, UKESM1 performs well
in 10 out of 12 regions analysed, with a significant high bias in the
tropical boundary layer. This is a region where the majority of methane
oxidation takes place and may explain the slightly low modelled methane
lifetime. With the recent development of aircraft OH datasets appropriate
for global model evaluation (Prather et al., 2017) we intend to extend this
analysis further and interrogate the model with these data to confirm if the
bias is indeed large compared with direct observations.
Tropospheric columns of reactive gases (CO, NO2 and O3):
the analysis of the model ND runs highlighted some success and failure in
the model's representation of tropospheric columns of CO, NO2 and
O3. The best performance was found for O3 (Figs. 14–15),
although we note that there is a significant positive bias in the tropics
(which has been shown to have an effect on modelled tropospheric photolysis
rates; Hall et al., 2018). In part we believe this bias is connected with
the vertical profile and magnitude of lightning NOx, and further work
will focus specifically on this area. The modelled tropospheric column of CO
shows significant negative biases in the Northern Hemisphere (Fig. 16). In
part this is believed to relate to biases in the representation of higher
hydrocarbons that could contribute significantly to secondary CO production
(Grant et al., 2010), but high OH could also be a contributing factor. The
performance of modelled NO2 tropospheric columns was found to be
generally acceptable in northern mid-latitudes (Fig. 19), but there are
large biases in regions of high emissions (such as the North China Plain; Fig. 18). One hypothesis is that the model simulates too little OH in
the regions of high NO2 emissions owing to a lack of reactive VOC
emissions and titration of O3, which extends the lifetime of NO2
in these regions. Further studies are required to evaluate the modelled
NO2 lifetime and its response to changes in emissions of NOx.
Biases in stratospheric composition:
by examining selected climatologies of observations from satellites (Figs. 20–24) we have been able to show here that the simulation of stratospheric
composition has improved significantly in StratTrop compared with the older
“stratosphere”-focused scheme of MO09. In part this is largely due to
improvements in the dynamical model (MetUM) and reductions in biases in
modelled water vapour (Hardiman et al., 2015). Key questions remain about
the fidelity of the upper stratospheric–mesospheric photolysis rates and the
upper boundary conditions. Given the generally poorer performance of NO and
NOy it would be useful to investigate the implementation of
parameterised EPP to see if this ameliorates the problems. Further work is
also required to understand the cause of the disagreement between the
CH4 : H2O correlation in the stratosphere, which suggests that too little H2O is produced from methane oxidation in the model.
Middle atmosphere age of air: the modelled middle atmosphere circulation has been evaluated against the age of
air derived from observations of SF6 and through the use of
tracer–tracer correlations. These tracer–tracer correlations further
motivate the need for a more detailed investigation of modelled
stratospheric NOy and its budget (production and loss). The comparison of
the age of air in the model generally looks acceptable in the middle
stratosphere but tends to deviate at higher altitudes. In part there is more
uncertainty in observations at higher altitudes (owing to loss processes of
SF6), but further studies are required to understand if these biases are
dependent on the resolution of the model. To understand this, a high-top
(> 120 km) version of the model is in preparation, as are
simulations of UKESM1 at much higher horizontal resolution (∼ 25 km).
In summary, UKCA StratTrop represents a substantial step forward compared to
previous versions of UKCA. We have shown here that it is well suited to the
challenges of representing interactions in a coupled Earth system model (key
for CMIP6 and beyond), and we have identified key areas and components for
future development that will further improve the model.
Code and data availability
Due to intellectual property rights restrictions, we cannot provide either the source code or documentation papers for the UM (including UKCA) or JULES.
Obtaining the UM (including UKCA). The Met Office Unified Model (MetUM) is available for use under licence. A
number of research organisations and national meteorological services use
the UM in collaboration with the Met Office to undertake basic atmospheric
process research, produce forecasts, develop the UM code, and build and
evaluate Earth system models. For further information on how to apply for a
licence, see
http://www.metoffice.gov.uk/research/modelling-systems/unified-model (last access: 14 August 2019).
Obtaining JULES. JULES is available under licence, free of charge. For further information
on how to gain permission to use JULES for research purposes, see
http://jules-lsm.github.io/access_req/JULES_access.html (last access: 14 August 2019).
Details of the simulations performed. UM and JULES simulations are compiled and run in suites developed using the
Rose suite engine (http://metomi.github.io/rose/doc/html/index.html, last access: 14 August 2019) and scheduled using the Cylc workflow engine
(https://cylc.github.io/cylc/, last access: 14 August 2019). Both Rose and
Cylc are available under version 3 of the GNU General Public License (GPL).
In this framework, the suite contains the information required to extract
and build the code as well as configure and run the simulations. Each suite
is labelled with a unique identifier and is held in the same
revision-controlled repository service in which we hold and develop the
model's code. This means that these suites are available to any licensed
user of both the UM and JULES.
All code related to the offline emissions is freely available on GitHub at:
https://github.com/acsis-project/emissions (last access: 22 October 2018) and the release is available on Zenodo: 10.5281/zenodo.3693799 (Köhler, 2020); the data for
biogenic emissions are available for free download from: http://eccad.sedoo.fr/ (last access: 31 March 2017, Darras et al., 2018). The model–satellite evaluation codes are available
on request. We acknowledge the use of the TEMIS OMI NO2 (DOMINO vn2.0; http://www.temis.nl/airpollution/no2.html, last access: July 2019, Boersma et al., 2011) data and NASA's MOPITT CO (vn7.0;
https://search.earthdata.nasa.gov/, last access: July 2019, MOPITT Algorithm Development Team, 2017) data. The observations used to evaluate the
age of air were the IMK/IAA-generated MIPAS-ENVISAT datasets developed at
KIT and available from: http://www.imk-asf.kit.edu/english/308.php (last access: July 2019, Haenel et al., 2015). To access MIPAS-ENVISAT data users have to register with IMK/IAA here: http://www.imk-asf.kit.edu/english/1500.php (last access: 4 March 2020).
Author contributions
ATA and NLA led the initial development of StratTrop,
and ATA and FMO'C led the writing of the paper. ATA, NLA, FMO’C, JPM, AJH, GAF, MD, PTG, JMK, MRR and ST contributed during model development, data analysis and paper preparation. Simulation design, setup and execution was performed by FMO'C
and MD. CH and OW provided the ozone data for the O3 dry deposition
evaluation. All co-authors contributed to writing sections of the
paper, performing evaluation and reviewing drafts of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge the international community of UKCA users for all their efforts in developing and applying the model. In particular, we would like to acknowledge Prof. John A. Pyle, who pioneered the development of the UKCA project. We would especially like to thank the atmospheric chemistry observational community, who have developed numerous datasets used in this paper to help evaluate the model. This work used JASMIN, the UK collaborative data analysis facility. We would like to thank Bodeker Scientific for providing the combined NIWA–BS total column ozone database. We thank the Global Emission Initiative (GEIA) for providing access to emissions data via the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) archive.
The development of UKCA for inclusion in UKESM1 was facilitated by the use of the Monsoon2/NEXCS system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council.
Financial support
This research has been supported by the NERC–Met Office Joint Weather and Climate Research Programme (UKCA); NERC (grant nos. NCAS, NCEO, NE/K001272/1, NE/M00273X/1, NE/P016383/1); the BEIS/Defra Met Office Hadley Centre Climate Programme (grant no. GA01101) and the UK–China Research and Innovation Partnership Fund through CSSP China, part of the Newton Fund; the Horizon 2020 Framework Programme (CRESCENDO, grant no. 779366); FP7 (project StratoClim, grant no. 603557); the Spanish Ministerio de Economía y Competitividad (RYC-2014-15036) and the New Zealand Government under its Strategic Science Investment Fund (Deep South National Science Challenge).
Review statement
This paper was edited by Slimane Bekki and reviewed by two anonymous referees.
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