Evaluation of the new UKCA climate-composition model – Part 2: The Troposphere

F. M. O'Connor; C. E. Johnson; O. Morgenstern; N. L. Abraham; P. Braesicke; M. Dalvi; G. A. Folberth; M. G. Sanderson; P. J. Telford; P. J. Young; G. Zeng; W. J. Collins; J. A. Pyle

Geosci. Model Dev. Discuss., 6, 1743-1857, 2013 www.geosci-model-dev-discuss.net/6/1743/2013/ doi:10.5194/gmdd-6-1743-2013 © Author(s) 2013. CC Attribution 3.0 License.

Geoscientific Model Development

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This discussion paper is/has been under review for the journal Geoscientific Model Development (GMD). Please refer to the corresponding final paper in GMD if available.

Evaluation of the new UKCA climate-composition model - Part 2: The Troposphere

F. M. O'Connor1, C. E. Johnson1, O. Morgenstern2 *, N. L. Abraham2, P. Braesicke2, M. Dalvi1, G. A. Folberth1, M. G. Sanderson1, P. J. Telford2, P. J. Young2 *, G. Zeng2 *, W. J. Collins1***, and J. A. Pyle2

1Met Office Hadley Centre, Exeter, UK 2NCAS-Climate-Chemistry, Cambridge University, UK

now at: National Institute of Water and Atmospheric Research, Lauder, New Zealand **now at: Lancaster Environment Centre, Lancaster University, UK ***now at: Reading University, Reading, UK

Received: 1 February 2013 - Accepted: 14 February 2013 - Published: 6 March 2013

Correspondence to: F. M. O'Connor (fiona.oconnor@metoffice.gov.uk)

Published by Copernicus Publications on behalf of the European Geosciences Union.

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Abstract

In this paper, we present a description of the tropospheric chemistry component of the UK Chemistry and Aerosols (UKCA) model which has been coupled to the Met Office Hadley Centre's HadGEM family of climate models. We assess the model's transport and scavenging processes, in particular focussing on convective transport, boundary layer mixing, wet scavenging and inter-hemispheric exchange. Simulations with UKCA of the short-lived radon tracer suggest that modelled distributions are comparable to those of other models and the comparison with observations indicate that apart from a few locations, boundary layer mixing and convective transport are effective in the model as a means of vertically re-distributing surface emissions of radon. Comparisons of modelled lead tracer concentrations with observations suggest that UKCA captures surface concentrations in both hemispheres very well although there is a tendency to underestimate the observed geographical and interannual variability in the Northern Hemisphere. In particular, UKCA replicates the shape and absolute concentrations of observed lead profiles, a key test in the evaluation of a model's wet scavenging scheme. The timescale for inter-hemispheric transport, calculated in the model using a simple krypton tracer experiment, does appear to be long relative to other models and could indicate deficiencies in tropical deep convection and/or insufficient boundary layer mixing. We also describe the main components of the tropospheric chemistry and evaluate it against observations and other tropospheric chemistry models. In particular, from a climate forcing perspective, present-day observed surface methane concentrations and tropospheric ozone concentrations are reproduced very well by the model, thereby making it suitable for long centennial integrations as well as studies of bio-geochemical feedbacks. Results from both historical and future simulations with UKCA tropospheric chemistry are presented. Future projections of tropospheric ozone vary with the Representative Concentration Pathway (RCP). In RCP2.6, for example, tropospheric ozone increases up to 2010 and then declines by 13% of its year-2000 global mean by the end of the century. In RCP8.5, tropospheric ozone continues to steadily

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rise throughout the 21st century, with methane being the main driving factor. Finally, we highlight aspects of the UKCA model which are undergoing and/or have undergone recent developments and are suitable for inclusion in a next-generation Earth System Model.

1 Introduction

Atmospheric chemistry, aerosols, and the biosphere are important features of the Earth System (ES). The atmospheric abundance and lifetime of many greenhouse gases, such as methane (CH4) and nitrous oxide (N2O), depend on atmospheric chemistry. Likewise, heterogeneous chemistry involving reactive chlorine from chlorofluorocar-bons (CFCs) causes stratospheric ozone (O3) depletion with implications for surface ultraviolet radiation (Madronich et al., 1998) and stratospheric dynamics (e.g. Butchart et al., 2003). Of significant importance in the troposphere are reactive gases such as carbon monoxide (CO), nitrogen oxides (NOx = NO + NO2), and volatile organic compounds (VOCs) which do not have a direct radiative effect but still influence the climate indirectly by affecting the abundance of greenhouse gases such as CH4 and tropo-spheric O3. Atmospheric chemistry in the ES also influences climate through the formation of inorganic and organic aerosols (e.g. Seinfeld and Pandis, 2006). Together, tropospheric O3, NOx, sulphur dioxide (SO2), and aerosols contribute to poor air quality and have detrimental impacts on human health (e.g. WHO, 2003) and/or natural and managed ecosystems (e.g. Ashmore, 2005).

The role of feedbacks, such as chemistry-climate and chemistry-biosphere feedbacks, is also being recognised (e.g. Johnson et al., 2001; Sitch et al., 2007) and ES models (ESMs) increasingly include aspects of atmospheric chemistry. For example, climate change influences the timescale of stratospheric O3 recovery (e.g. Waugh et al., 2009) and may affect future surface O3 concentrations through changes in vegetation and biogenic VOC emissions (e.g. Sanderson et al., 2003). As a result, there is a need to implement stratospheric, tropospheric, and troposphere-stratosphere

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chemistry and aerosol schemes into climate models to understand and quantify interactions between climate, atmospheric composition, and other Earth-System components such as vegetation and hydrology.

To address this increasing need, the objective of the UK Chemistry and Aerosols (UKCA) project is to develop a state-of-the-art chemistry and aerosol model, capable of simulating atmospheric composition from the troposphere to the upper stratosphere. The various configurations of UKCA are described and evaluated in a series of papers. Part 1 of this series, by Morgenstern et al. (2009), provided a detailed description and evaluation of the stratospheric configuration of UKCA. It was used to explore the impact of the Montreal Protocol on climate by Morgenstern et al. (2008). Furthermore, the stratospheric configuration participated in the recent Stratospheric Processes and their Role in Climate (SPARC) Report on the Evaluation of Chemistry-Climate Models (Eyring et al., 2010a) and was used in the World Meteorological Organization (WMO) ozone assessment (WMO, 2011). Part 3 of the series, by Mann et al. (2013), will focus on the aerosol component of UKCA, called GLOMAP-mode. Part 4 will discuss an extension to the tropospheric chemistry scheme with enhanced coupling between the atmosphere and biosphere (Folberth et al., 2013). This paper is Part 2 of the series and will focus on the gas-phase tropospheric chemistry configuration of UKCA in a chemistry-climate model. The climate model has already been used by O'Connor et al. (2009) to evaluate the interactions between climate model biases and tropospheric chemistry. Telford et al. (2010) used this configuration to explore the impact of climate-induced changes by the Pinatubo volcanic eruption on biogenic emissions and atmospheric composition. More recently, Archibald et al. (2011) explored the impacts of odd hydrogen regeneration and recycling in the oxidation of isoprene for past, present, and future atmospheres using this configuration. It has also been used in all centennial simulations of the Hadley Centre's ESM HadGEM2-ES (Collins et al., 2011) for the fifth Coupled Model Intercomparison Project (CMIP5) and in the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; Lamarque et al., 2012). In this paper, we provide a more detailed description and evaluation of the tropospheric

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chemistry configuration of the UKCA model. In Sect. 2, we will introduce the climate models to which UKCA has been coupled. We will then assess aspects of the tracer transport which form the basis of UKCA in Sect. 3. In Sect. 4, we will describe the two tropospheric chemistry schemes in this configuration of UKCA and in Sect. 5, we will compare UKCA to both observations and other chemistry models. Results from the transient CMIP5 integrations using the HadGEM2-ES ESM which included the tropospheric chemistry configuration of UKCA are presented in Sect. 6. Finally, in Sect. 7, we discuss the results, draw some conclusions about UKCA's current performance, and provide a summary of ongoing developments which will be included in future versions of the UKCA model.

2 The climate models, HadGEMI and HadGEM2

The UK Chemistry and Aerosol (UKCA) model has been successfully coupled to a number of Met Office Hadley Centre climate models, HadGEMI (Johns et al., 2006; Martin et al., 2006) and HadGEM2 (The HadGEM2 Development Team: Martin et al., 2011; Collins et al., 2011), all of which are based on the UK Met Office's Unified Model (MetUM). The configuration used here is an atmosphere-only version of HadGEM2 with sea surface temperature and sea ice datasets taken from the second Atmosphere Model Intercomparison Project (www-pcmdi.llnl.gov/projects/amip); the resolution was N96L38. For a detailed description and evaluation of the N96L38 configuration of HadGEM2, and how it differs from HadGEM1, the reader is referred to the paper by The HadGEM2 Development Team: Martin et al. (2011).

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3 Tracer transport and physical processing

The coupling between the UKCA chemistry-aerosol model and the HadGEM models is based on MetUM. UKCA uses components of the Unified Model for the large-scale advection, convective transport, and boundary layer mixing of its chemical and aerosol

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tracers. The large-scale transport is based on the new dynamical core implemented in MetUM by Davies et al. (2005) with semi-Lagrangian advection with conservative and monotone treatment of tracers (Priestley, 1993). Tracers in the boundary layer are mixed using the boundary layer scheme from Lock et al. (2000) which includes a representation of non-local mixing in unstable layers and an explicit entrainment parameterization. Convective transport is treated according to the mass-flux scheme of Gregory and Rowntree (1990) with extensions described in Martin et al. (2006).

In the development phase of chemistry-climate models and chemistry transport models, simple tracer experiments (e.g. Rind and Lerner, 1996; Jacob et al., 1997; Rasch et al., 2000) are useful as a means of assessing some of the dynamical processes of the base models. Here, we perform a number of experiments to evaluate the convec-tive transport, wet scavenging, inter-hemispheric transport, and boundary layer mixing in the HadGEM2-UKCA coupled model. Furthermore, an inert tracer is used to evaluate the degree to which global tracer mass is conserved. Before we describe and evaluate the tracer transport experiments, a brief description of the wet and dry deposition schemes that are employed in the model are outlined in the following sections.

3.1 Dry deposition

The process by which chemical species are transferred to the surface and removed from the atmosphere in the absence of precipitation is represented in the tropospheric chemistry configuration of UKCA by one of two dry deposition schemes. The first scheme uses prescribed deposition velocities at 1 m above the ground (Ganzeveld and Lelieveld, 1995; Sander and Crutzen, 1996; Giannakopoulos, 1998, and references therein), which are then extrapolated to the mid-point of the lowest model level according to Sorteberg and Hov (1996). This scheme was used in the tracer experiment assessing wet scavenging in Sect. 3.4 and matches what was evaluated in the TOMCAT chemical transport model (CTM) by Giannakopoulos (1998) and Giannakopoulos et al. (1999).

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The preferred dry deposition scheme in UKCA is analogous to an electrical circuit, in which the transport of material to the surface is governed by three resistances, ra,

rb, and rc in series and the deposition velocity Vd (ms 1) is related to the resistances as follows (Wesely, 1989):

ra + rb + rc

A full description of this representation of dry deposition is given by Seinfeld and Pandis (2006) and Smith et al. (2000) and is only briefly described here. The first resistance, ra, the so-called aerodynamic resistance, represents the resistance to transport of material through the boundary layer to a thin layer of air just above the surface. The second resistance term, rb, called the quasi-laminar resistance, refers to the resistance to transport through the thin layer of air close to the surface. Finally, the third resistance, rc, is called the surface or canopy resistance and represents the resistance to uptake at the surface itself. It is dependent on the chemical and physical properties of the species as well as the absorbing surface. Under ideal conditions, rc can be related to surface conditions, time of day, and season. It is often represented by further application of the electrical resistance analogy and is called the "Big-leaf" model (Smith et al., 2000; Seinfeld and Pandis, 2006). This is illustrated schematically in Fig. 1. The multiple resistances are calculated for the 9 surface types considered within a grid box and are then combined to give a grid-box mean deposition velocity and a first order loss rate. However, not all the terms will be non-zero at a particular grid box and a particular time. In vegetated areas, for example, plant stomata open during daylight hours and deposition to the interior of the plant via the stomata can occur. This acts as a major daytime sink for O3. At night, the stomata are closed and the major loss is to the cuticles and underlying soil. An important difference between the two dry deposition schemes is that the dry deposition loss rates are only applied in the bottom model level with the first scheme but applied in all model levels within the boundary layer with the second scheme. This second scheme has been implemented in UKCA in a similar way to its

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implementation in the STOCHEM model by Sanderson et al. (2006) and Sanderson et al. (2007) and is the scheme of choice. Therefore, the chemistry model integrations being evaluated here use the multiple resistance approach to dry deposition.

3.2 Wet deposition

5 The wet deposition scheme in UKCA for the tropospheric gas-phase species is the same scheme as was implemented and validated in the TOMCAT CTM by Giannakopoulos (1998) and Giannakopoulos et al. (1999). It is parameterized as a first order loss rate, calculated as a function of the climate model's three-dimensional con-vective and large-scale precipitation. It follows a scheme originally developed by Walton io et al. (1988). At each grid box, the scavenging rate r can be written as follows:

r = Sj x pj(l)

where Sj is the scavenging co-efficient for precipitation type j (cm-1) and Pj(l) is the precipitation rate for type j (convective or large-scale) from model level l (cmh-1). 15 The scheme uses the scavenging co-efficient values for HNO3 proposed by Penner et al. (1991) i.e. 2.4cm-1 and 4.7cm-1 for large-scale and convective precipitation, respectively, but which are scaled down for the individual species by the fraction of each species in the aqueous phase. The fraction in the aqueous phase, faq, from Henry's Law is as follows:

L x Heff x R x T 'aq = 1 + L x Heff x R x T

where L is the liquid water content, R is the gas constant, T is temperature, and Heff is the effective Henry's co-efficient. Heff not only depends on the solubility of a species, but also includes the effects of dissociation and complex formation. Tables 1 and 2 show the parameters required to calculate the fraction of each soluble species in the aqueous phase for the two tropospheric chemistry schemes that will be described here.

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It is also assumed that precipitation type j only occurs over a fraction of the grid box. This fraction is taken to be 1.0 and 0.3 for large-scale and convective precipitation, respectively, and is taken into account when calculating a grid-box mean scavenging rate for both types of precipitation which are then added together. The scheme is evaluated

fully using a lead-210 ( Pb) tracer experiment in Sect. 3.4. 3.3 Convective transport and boundary layer mixing

We assess the dynamical performance of HadGEM2 by separating the dynamics of

the model from the chemistry by using an inert species. The short-lived radioisotope 222 222 radon-222 ( Rn) is such a tracer and simulations of Rn have been extensively used

to evaluate convective and synoptic-scale processes in global models (e.g. Rind and

Lerner, 1996; Jacob et al., 1997; Stevenson et al., 1998; Collins et al., 2002; Hauglus-

taine et al., 2004) and to a lesser extent in regional models (e.g. Lin et al., 1996;

Chevillard et al., 2002). It is emitted from non-frozen soils due to radioactive decay of 226 222 radium-226 ( Ra). Although emissions of Rn depend on soil and meteorological

conditions, a very simple emission scenario is used here. Surface Rn emissions are prescribed as 1.0 atom cm-2 s-1 over all land between 60° S and 60° N except Greenland and emitted into the lowest model layer. This prescribed emission rate matches

the best mean estimate of 1.2 atom cm s from a compilation of radon flux measurements by Turekian et al. (1977). Emissions between 60° and 70° N are specified as 0.5 atom cm-2 s-1 (to account for soil freezing) with no emissions poleward of 60° S and 70° N. Emissions from oceanic regions are approximately two orders of magnitude lower than land emissions (Broecker et al., 1967) and are neglected here, giving

a global annual total emission of 15kgyr . These emissions match those used in

the intercomparison study of Rasch et al. (2000). Due to its lack of solubility in water,

loss of Rn from the atmosphere is solely by radioactive decay (through a number of

short-lived daughters) to Pb with a half-life of 3.8 days.

Two additional tracers were included in the simulation. Both represent the daughter 222 210 210 product of Rn, Pb. The first Pb tracer accumulates in the atmosphere to test

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global mass conservation (Sect. 3.5). The second 210Pb tracer is removed from the atmosphere through wet scavenging processes (Sect. 3.4) and dry deposition (Sect. 3.1). All the tracers were initialised to zero and allowed to spin up and the simulation was

then run for 5yr. An additional experiment was also run for 5yr, in which the mixing

of Rn by the boundary layer scheme was switched off, thereby enabling the relative

importance of boundary layer mixing and convective transport to be assessed.

For ease of comparison with other models, units of 10 mol mol , equivalent to

1.52 pCi m at standard temperature and pressure (STP), are used. Figure 2 shows

the multi-annual zonal mean distribution of Rn from the two experiments for the

June-July-August (JJA) period. It shows that for the control experiment, the zonal mean

distribution from UKCA is similar to the models that participated in the World Climate

Research Program (WCRP) intercomparison (Jacob et al., 1997) and other models

(e.g. Dentener et al., 1999; Hauglustaine et al., 2004). Surface concentrations show

a maximum in the northern mid-latitudes of 20-30 x10 mol mol , in close agreement with Jacob et al. (1997). From the UKCA distribution, there is evidence of deep convection in the tropics, mid-level convection at northern mid-latitudes, and meridional gradients in the lower and mid troposphere. In the model intercomparison of Jacob et al. (1997), the GISS model showed a secondary maximum in the tropical upper troposphere due to frequent deep convection; a similar feature is evident in UKCA albeit less pronounced. When boundary layer mixing was switched off (Fig. 2b), concentrations at the surface exceed 50 x 10 - 2 mol mol - 1 and tropical upper tropospheric

concentrations only reach a maximum of 2 x 10 mol mol . In the study by Stockwell

et al. (1998), they found that if the planetary boundary layer was not well mixed, this

affected the amount of Rn which could be lifted aloft in convective updrafts. This experiment with UKCA clearly indicates the role of boundary layer mixing in determining

the vertical re-distribution of Rn from its source regions.

Figure 3 shows a comparison of climatological surface Rn observations with UKCA modelled concentrations at a number of different surface sites, spanning the latitude range 53° N to 67° S, and including coastal (e.g. Mace Head), continental (e.g.

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Socorro), and remote marine (e.g. Amsterdam Island) sites. There are some stations where the model performs well (e.g. Amsterdam Island, Bermuda). At the remote site of Amsterdam Island, local emissions do not influence the observation site and these measurements can be considered truly representative of long-range transport from Africa (Dentener et al., 1999). In this case, UKCA matches the observed concentrations quite well. Bermuda, on the other hand, is a surface site where concentrations are sensitive to planetary boundary layer ventilation (Allen et al., 1996). The observed seasonality and the absolute monthly mean concentrations are captured by UKCA particularly well. At the continental site of Socorro, there is an observed autumn to winter increase associated with seasonality in emissions and/or mixing by Allen et al. (1996). UKCA captures the wintertime observations quite well. However, it fails to capture the correct seasonal cycle or the observed autumn to winter increase but no seasonality in emissions was taken into account. At Bombay, UKCA significantly overestimates surface concentrations by a factor of 2 during winter, suggesting that there is insufficient ventilation out of the boundary layer in that region. UKCA also doesn't capture the correct seasonal cycle at Dumont d'Urville although it manages to simulate concentrations comparable to the observed concentrations during JJA. Other models also do poorly at this location (e.g. Hauglustaine et al., 2004) and Josse et al. (2004) have attributed this in the MOCAGE model to the lack of penetration of polar storms onto the continent due to sea-ice extent. Some studies have also indicated that the use of a northwards-decreasing emission source between 30 and 70° N could improve simulations in the Northern Hemisphere (Conen and Robertson, 2002; Gupta et al., 2004), particularly north of 50° N (Robertson et al., 2005). This may well improve the simulations from UKCA at Chester and Socorro but is likely to make comparisons at Mace Head worse. Overall, the performance of the UKCA simulations suggest that there may be insufficient boundary layer ventilation at some locations in the Northern Hemisphere.

A comparison of modelled and measured vertical profiles of Rn at two different locations can be seen in Fig. 4. The observations from Moffett Field, California (37.4° N, 122.0° W) were taken onboard 11 flights in the June-August 1994 time period (Kritz

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et al., 1998). Of the 11 flights, only 1 took place during July and the modelled profiles

were taken from June and August only. UKCA overestimates Rn concentrations at the surface over Moffett Field; this may be attributed to the limited horizontal resolution of the model when comparing with measurements taken at a coastal site where strong horizontal gradients exist (Stockwell et al., 1998). Above the boundary layer, the UKCA model is in very close agreement with the observed concentrations between p and 10 km. However, it fails to capture the enhanced concentrations at 11-13 km, associated with long-range transport of Asian air following deep convection (Stockwell et al., 1998). This discrepancy has been found in other models (e.g. Stockwell et al., 1998; Gupta et al., 2004) and could be related to a lack of deep convection over Asia, insufficient long-range transport from Asia, and/or an underestimate in the Asian emissions in the simple emissions scenario used here (Jacob et al., 1997; Denteneret al., 1999).

The Nova Scotia measurements were taken during 9 North Atlantic Regional Experiment (NARE) flights over the North Atlantic Ocean and close to Nova Scotia and New Brunswick, Canada, between 16 and 31 August 1993 (Zaucker et al., 1996). For comparison purposes, the modelled profiles are multi-annual means for August only. The UKCA model replicates the observed shape of the vertical profile and the magnitude of the observed concentrations in the 2-5 km region very well. It underestimates concentrations in the lowest 1 km, although the observations show a great deal of vari-ablity there. Enhanced concentrations were observed at 5-6 km but UKCA indicates

a minimum at 6 km and place the enhancement aloft. However, on the whole, the per-

formance of the UKCA model in relation to the vertical profiles of Rn is very good.

Further evaluation of tropical deep convection in MetUM in comparision with observations and other models has been carried out by Russo et al. (2011) and Hoyle et al. (2011).

3.4 Wet scavenging

As was mentioned in Sect. 3.3, the Rn experiment above included the daughter product, 210Pb, which was subject to wet and dry deposition. Dry deposition velocities

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at 1 m were prescribed as follows: 0.2 cms-1 overland and 0.05 cms- over the ocean (Balkanski et al., 1993) and extrapolated to the mid-point of the lowest model level

according to Sorteberg and Hov (1996) assuming neutral stability conditions. For wet

deposition, Pb is assumed to attach itself to soluble sub-micron aerosols and scavenging was treated as a first-order loss process as described in Sect. 3.2. For the scavenging co-efficients, we used values of 2.4 cm-1 for dynamic precipitation and 4.7 cm-1 for convective precipitation, which are appropriate for highly soluble gaseous species — such as HNO3 (Penner et al., 1991). Radioactive decay of 210Pb (half-life of 22.3yr) D

was not taken into account. s

In order to assess the modelled surface Pb concentrations, climatological observations were obtained at 20 global sites spanning latitudes 90° S-77° N from the Surface Air Sampling Program database of the Environmental Measurements Laboratory (EML) (http://www.nbl.doe.gov/htm/EML_Legacy_Website/databases.htm). Fig-

ures 5 and 6 show comparisons of multi-annual surface Pb concentrations from

UKCA with climatological observed surface Pb concentrations in the Northern and Southern Hemispheres, respectively. They show that the UKCA model is in excellent agreement with the measurements in terms of the seasonal cycle and absolute concentrations at some sites (e.g. Moosonee, Beaverton, Chester, and Cape Grim). At Barrow, for example, UKCA correctly simulates the wintertime maximum associated with long-range boundary layer transport of Eurasian air which gives rise to the Arctic haze phenomenon (Barrie, 1986). At Kap Tobin, UKCA does not simulate the seasonal cycle indicated by the observations. However, the measurement record at this site is

quite short (approximately 2yr) relative to the other surface sites. The UKCA model

also tends to underestimate surface Pb concentrations and fails to capture the seasonal cycle at the South Pole; this was also evident in the 15 models that participated in the WCRP workshop of 1995 (Rasch et al., 2000).

A further assessment of UKCA's wet scavenging is carried out by comparing mod-

elled multi-annual mean surface Pb deposition fluxes with the global gridded dataset of Preiss and Genthon (1997). This dataset is based on the previously compiled

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database by Preiss et al. (1996) except that it has been reduced and aggregated

onto a 5°x 5° grid, thus making it more convenient for model-observation comparisons.

Figure 7 shows a scatter plot of UKCA modelled multi-annual mean surface Pb deposition fluxes and observed fluxes. The correlation co-efficient between modelled and measured deposition fluxes is fairly high at 0.69. However, there is considerable scatter about the 1:1 line, indicating some large regional discrepancies (e.g. Northern Hemisphere tropics and mid-latitudes). Some of these could be related to precipitation

biases in the climate model (Martin et al., 2006, 2010). However, they could also be

related to regional variability in Rn emissions not represented in the UKCA model simulation. Figure 8 shows the global distribution of the modelled global multi-annual mean deposition terms from each of the deposition processes in addition to the total deposition obtained from the 5-yr simulation. The contributions from the various processes were found to be as follows: dry deposition 9.5%, dynamic scavenging 17.8%, and convective scavenging 72.7%, and are reasonably consistent with other estimates (e.g. 14, 12, and 74% for dry deposition, dynamic scavenging, and convective scavenging, respectively, from Balkanski et al., 1993).

Guelle et al. (1998a) concluded that it is necessary to compare with vertical profiles of 210Pb to discriminate between wet deposition schemes. As a result, we make use of 210Pb measurements taken onboard 16 flights of the NASA DC-8 aircraft during the Global Tropospheric Experiment/Pacific Exploratory Mission-West B (GTE/PEM-West B) campaign which took place during February and March 1994 (Dibb et al., 1997). Following Dibb et al. (1996), the measurements from PEM-West B were separated into two geographic regions: "remote Pacific" and "near Asia". For the purposes of comparing with the observations, multi-annual February and March monthly mean concentrations from UKCA were averaged over the regions: 10-30° N, 140-150° E and 20-45° N, 120145° E. Figure 9 shows the comparison of UKCA with the observed profiles over these regions. Given that UKCA is not running with the specific meteorology associated with PEM West-B, the comparison is only intended to be a general comparison of the calculated profiles. However, it is expected that the measured profiles lie within the variability

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(spatial and temporal) of the modelled profiles. For the "remote Pacific", UKCA matches the absolute mean 210Pb concentrations and the shape of the observed profile particularly well. In particular, scavenging during PEM-West B was dominated by dynamic scavenging (Guelle et al., 1998a) and the results suggest that the climate model's dynamic precipitation and UKCA's wet scavenging scheme are performing well. For the "near Asia" comparison, the modelled concentrations and the shape of the modelled profile from UKCA again closely match those of the observations. These comparisons, albeit limited, suggest that the wet scavenging in UKCA by dynamic and convective precipitation is reasonable. Guelle et al. (1998a) concluded that the scheme used by Balkanski et al. (1993) and extended by Guelle et al. (1998b) to take account of size-distributed aerosols was better than that of Walton et al. (1988). Although we haven't tested multiple scavenging schemes within UKCA, these comparisons suggest that the performance of the Walton et al. (1988) scheme in UKCA is satisfactory and comparable to that from other models (e.g. Guelle et al., 1998a; Hauglustaine et al., 2004).

3.5 Global mass conservation

By examining the timeseries of the accumulated burden of the second (non-deposited) 210 222

Pb tracer and comparing it with the accumulated Rn emissions, the global mass

conservation of a tracer can be assessed. It was found that the rate of increase of this

inert tracer was less than 0.008% per year compared to the accumulated emissions,

indicating that the model drift due to global non-conservation of tracers is quite small.

3.6 Inter-hemispheric transport

With the majority of anthropogenic emissions originating north of the equator, inter-hemispheric transport plays an important role in re-distributing pollutants from the Northern to the Southern Hemisphere. A model's ability to simulate inter-hemispheric transport accurately and with the correct timescale has implications for modelling meridional gradients of long-lived tracers and the accuracy of source estimates by

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inverse modelling (e.g. Houweling et al., 1999; Chen and Prinn, 2006). Meridional surface observations of long-lived tracers of Northern Hemisphere origin have been used to estimate the timescale for inter-hemispheric exchange (e.g. Weiss et al., 1983). However, estimates from three-dimensional fields tend to be more accurate (Jacob et al., 1987; Levin and Hesshaimer, 1996; Denning et al., 1999). The timescale for inter-hemispheric transport from a number of model studies has been estimated to be of the order of one year (Prather et al., 1987; Jacob et al., 1987; Rind and Lerner, 1996; Levin and Hesshaimer, 1996) but intercomparisons indicate that models reproduce this timescale with varying degrees of success (e.g. 0.55-1.26yr from three-dimensional fields in Denning et al., 1999). A number of different long-lived tracers, such as CFCs, sulphur hexafluoride (SF6),

and Krypton-85 ( Kr), have been used to assess inter-hemispheric transport, Here, we

use the classical Kr experiment, as discussed by Jacob et al. (1987), to estimate the

timescale for inter-hemispheric exchange in the UKCA model. Kr, produced during reprocessing of nuclear fuel, was emitted from seven known point sources and one estimated point source in the Northern Hemisphere during the period 1978-1983. The integration itself was started on 1 September 1978 with initial mass mixing ratios taken from Rind and Lerner (1996) and scaled to give a global burden in kg of M = 166.2 + 5.10(T -1980.0), where T is the initialisation time in yr. In this way, the initial global

burden agreed with that estimated by Jacob et al. (1987). The only sink for Kr was

radioactive decay to Rubidium-85 ( Rb) with a half-life of 10.76 yr.

Before inter-hemispheric transport can be assessed, the model simulation itself was verified against that of Jacob et al. (1987). For example, it was found that the rate of

change of the global burden of Kr during the 1978-1983 period was comparable to that of Jacob et al. (1987). UKCA model results show that in the Northern Hemisphere winter, there are extensive areas north of 45° N with surface concentrations in excess

of 23 pCi m at STP. By contrast, in the Northern Hemisphere summer, modelled con-

centrations greater than 23 pCi m at STP were only found over a limited region within Europe. These wintertime maxima can largely be attributed to the lack of convective

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activity in the Northern Hemisphere winter, resulting in high Kr plumes being vertically confined to the lower troposphere. This winter/summer contrast was also evident

in the Kr model results of Jacob et al. (1987) and Hauglustaine et al. (2004) and in other modelling studies (e.g. Prather et al., 1987; Wang and Shallcross, 2000).

The UKCA model results can be further verified by comparison with observations.

Nine meridional profiles of Kr were measured during ship cruises in the Atlantic Ocean between 1980 and 1987 by W. Weiss and reported by Jacob et al. (1987). Model results are shown for two longitude bands (7.5 and 30° W) which bracket the measurements. The comparison in March (Fig. 10a) shows that UKCA captures the homogeneous meridional profile in the Southern Hemisphere and the more spatially variable profile in the Northern Hemisphere indicated by the observations. The model's meridional gradient seems quite robust in all model years and occurs at approximately 0° N. This matches quite well with the observed tropical gradient. The comparison for October (Fig. 10b) shows that there is a good correspondence between modelled and measured concentrations for October 1980, suggesting that the initial mass mixing ratio field used was reasonable. However, the tropical gradient in the model is shifted equa-torward by about 10° in comparison with the observations. This was also the case in the evaluation of the LMDz-INCA model by Hauglustaine et al. (2004), and was attributed to a reduced seasonal cycle in the Intertropical Convergence Zone (ITCZ) location. For October 1983, modelled concentrations in the Southern Hemisphere appear too high, the tropical gradient is again shifted equatorward, and the low observed concentrations at 40° N are not well simulated. Nevertheless, the modelled inter-hemispheric difference in the surface concentrations matches the observed inter-hemispheric difference and as a result, the modelled three-dimensional concentrations can be used to evaluate a timescale for inter-hemispheric exchange.

In an idealised two-box model, the inter-hemispheric difference in mean concentration, Cns, evolves with time according to Jacob et al. (1987):

dCns dt

- Cns x (2t-1 + t-1)

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where Cns = Cn - Cs is the difference between the mean northern and southern hemi— 3 — 1

spheric concentrations (in pCim at STP), E is the emission rate (in MCiyr ), te is the inter-hemispheric transport timescale (in yr), td is the e-folding lifetime (in yr) and the 1.96 is a conversion factor to relate a mean hemispheric concentration in pCim (STP) to a hemispheric inventory in MCi. If Cns reaches a steady state, then the inter-hemispheric transport timescale can be computed from the steady state value of Cns as follows:

te 1.3.92 x Cn

In this experiment, Cns was reasonably stable throughout the integration i.e. the slope

of a least squares linear fit to the timeseries of Cn

was -0.02pCim-3 (STP)yr-1

a mean value for Cns of 2.01 pCi m 3 (STP) during the years 1979-1983. Taking a mean emission rate of 6.33 MCi yr-1 for the years 1978-1983, the inter-hemispheric transport timescale was estimated to be 1.3yr. This UKCA-derived timescale is larger than the value of 1.1 yr estimated by Jacob et al. (1987) and at the upper end of the range of

estimates from Denning et al. (1999) but close to that estimated from CFC and Kr experiments in the Goddard Institude for Space Studies (GISS) model by Rind and Lerner (1996).

Prather et al. (1987) found that cross-equatorial transport occurs primarily in the tropical upper troposphere and is associated with deep convection. The mechanistic analysis by Hartley and Black (1995) also found that convective outflow rather than Rossby waves was the dominant mechanism for cross-equatorial transport and is supported by the more recent study of Lintner et al. (2004). However, the model intercomparison of SF6 simulations by Denning et al. (1999) found that models which compare favourably with observations in the remote marine boundary layer tended to overestimate observed concentrations at Northern Hemisphere continental sites. They deduced that boundary layer mixing rather than meridional transport may be the dominant control on simulating the meridional gradient of long-lived tracers of Northern Hemisphere

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origin. Given this sensitivity to boundary layer mixing, Law and Corbin (2009), using an atmosphere-only version of HadGEM2 at N48L38, tested the sensitivity of carbon dioxide and SF6 transport to the stability dependence of the vertical transport in the stable boundary layer. Irrespective of their choice of function for parameterizing mixing within a stable boundary layer, the inter-hemispheric difference in SF6 was still too large and they inferred that inter-hemispheric transport in the N48 version of HadGEM2 was too

slow. A simulation of Kr with UKCA at a horizontal resolution of N48 gave idential values to those computed for N96 above and confirms their conclusions. Moreover, Martin et al. (2006) found that in HadGAM1, meridional winds, particularly at upper levels, were worse and there was less boundary layer mixing at a given stability than in its predecessor, HadCM3 (Gordon et al., 2000). Other contributing factors to the slow inter-hemispheric transport in UKCA may be insufficient mixing within unstable boundary layers and/or deficiencies in tropical deep convection.

4 Tropospheric chemistry description 4.1 Chemistry schemes

Included in this description of the tropospheric configuration of the UKCA model are two chemistry schemes. The first chemistry scheme, called StdTrop, includes a relatively thorough description of inorganic odd oxygen (Ox), nitrogen (NOy), hydrogen (HOx), and carbon monoxide (CO) chemistry with near-explicit treatment of methane (CH4), ethane (C2H6), propane (C3H8), and acetone (CH3COCH3) degradation (including formaldehyde (HCHO), acetaldehyde (CH3CHO), peroxy acetyl nitrate (PAN), and peroxy propionyl nitrate (PPN)). It makes use of 25 tracers and represents 41 species, as indicated in Table 3. The atmospheric constituents H2, CO2, O2, and N2 have fixed global concentrations. We ignore chemical production and loss of water vapour; instead, it is taken from the climate model on a timestep basis. All remaining

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non-advected species are treated in steady state and their concentrations are retained from one timestep to the next.

This chemistry scheme accounts for 25 photolytic reactions (Table 4), 83 bimolec-ular reactions (Table 5), and 13 uni- and termolecular reactions (Table 6). Reaction rate co-efficients are taken from the 2000-2005 recommendations provided by the International Union of Pure and Applied Chemistry (IUPAC) Subcommittee for Gas Kinetic Data Evaluation (http://www.iupac-kinetic.ch.cam.ac.uk/), versions 2 and 3.1 of the Master Chemical Mechanism (MCM) (Jenkin et al., 1997, 2003; Saunders et al., 1997, 2003; Bloss et al., 2005; Tyndall et al., 2001; Ravishankara et al., 2002). This scheme has previously been used in both the TOMCAT CTM (e.g. Law et al., 1998; Savage et al., 2004) and chemistry-climate integrations (e.g. Zeng and Pyle, 2003, 2005). The non-methane VOCs (NMVOCs) included in StdTrop were found by Saunders et al. (2003) to have low photochemical ozone creation potential (POCP) values (Derwent and Jenkin, 1991) (i.e. in the range of 7.5-55.0) and, as a result, the StdTrop scheme is more representative of the background troposphere rather than a polluted boundary layer.

Alkenes/dienes have the highest POCP values and isoprene (C5H8), for example, has a POCP value of 101.6 (Saunders et al., 2003) although its impact depends on the environment. Wang et al. (1998) has also shown that C5H8 alone accounts for half of PAN formation and half of the effects of all NMVOCs on NOx, hydroxyl (OH), and O3. Emissions of C5H8 are comparable in magnitude to methane emissions (Lamb et al., 1993). As a result, the second chemistry scheme implemented in the tropo-spheric configuration of UKCA, called TropIsop, is an extension of StdTrop to include the extra species and reactions of the lumped C5H8 oxidation scheme from the Mainz Isoprene Mechanism (MIM) described by Poschl et al. (2000). It includes an additional 14 organic species (Table 7), 12 of which are treated as tracers with the remainder treated in steady state. The additional 10 photolytic, 31 bimolecular, and 2 uni-and termolecular reactions can be found in Tables 8-10. Reaction rate co-efficients are taken from the 2005 recommendations provided by the International Union of

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Pure and Applied Chemistry (IUPAC) Subcommittee for Gas Kinetic Data Evaluation (http://www.iupac-kinetic.ch.cam.ac.uk/), version 3 of the MCM (Saunders et al., 2003; Jenkin et al., 2003), and Poschl et al. (2000) and references therein. Most of the reactions from the MIM are combinations of the major MCM reaction pathways and the additional species represent different classes of compounds as described in Table 7. This scheme has previously been used in both the p-TOMCAT CTM (Voulgarakis et al., 2009, 2011a) and in chemistry-climate integrations (e.g. Zeng et al., 2008; Young et al., 2009).

In the Poschl et al. (2000) study, the MIM was found to perform well in comparison with MCMv2 (Jenkin et al., 1997; Saunders et al., 1997); the two mechanisms agreed to within 10% for O3, OH, CO, methyl hydroperoxide (MeOOH), and PAN. The TropIsop chemistry scheme, as implemented in UKCA, was also included in a chemical mechanism intercomparison by Emmerson and Evans (2009); the scheme (referred to as TOMCAT in Emmerson and Evans, 2009) was found to show good agreement with MCMv3.1 in simulating both O3 and OH concentrations, even though all the carbon not represented explicitly in the scheme was lumped into C3H8. More recently, Archibald et al. (2010) carried out an intercomparison of C5H8 oxidation mechanisms, and found that all existing mechanisms, including MCMv3.1, cannot generate/recycle HOx at the rates needed to match reported radical observations from high C5H8/low NOx environments (Lelieveld et al., 2008; Butler et al., 2008). In addition to insufficient HOx recycling, other uncertainties in the treatment of C5H8 in chemistry models include the strength of the global emission source (e.g. Arneth et al., 2008), chemical lumping (Archibald et al., 2010), treatment of isoprene nitrates (Poschl et al., 2000; von Kuhlmann et al., 2004), and deposition of intermediate products (von Kuhlmann et al., 2004). Although not considered here, this chemistry scheme has also been extended to include sulphur oxidation and a parameterization for monoterpene oxidation, thus enabling coupling to the aerosol component of the UKCA model, GLOMAP-mode. A description and evaluation of GLOMAP-mode, as implemented in a CTM, can be found in Mann et al. (2010). Part 3 of this series (Mann et al., 2013) will discuss the

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implementation and evaluation of GLOMAP-mode in a global climate model configuration.

The two schemes above, StdTrop and Troplsop, do not include higher NMVOCs than C3H8 and C5H8. However, details of an extension to Troplsop, called ExtTC, which considers the degradation pathways of C2 and C3 alkenes, C4+ alkanes, terpenes, and aromatics for use in chemistry-climate integrations can be found in Part 4 of the UKCA series by Folberth et al. (2013). Furthermore, a chemistry mechanism which considers some higher NMVOCs than C3H8 and C5H8 has been implemented in a regional configuration of UKCA for air quality purposes and is discussed in a separate publication (Savage et al., 2012).

Model studies suggest that the heterogeneous loss of the nitrate radical and N2O5 can substantially reduce the tropospheric NOx burden (Dentener and Crutzen, 1993; Evans and Jacob, 2005), and that heterogeneous loss of HO2 can substantially impact tropospheric HOx (Mao et al., 2010). However, Shindell et al. (2009) argued that these heterogeneous loss processes only have a very small impact on the radiatively active species O3 and CH4. However, development work to introduce a parameterization for the uptake of both N2O5 and HO2 onto aerosols from the GLOMAP-mode scheme (Macintyre and Evans, 2010, 2011) has been completed and will be implemented in a future UKCA chemistry scheme.

The chemical equations in UKCA can be solved using one of two chemical solvers. The first one is an explicit iterative backward Euler approach (Hertel et al., 1993) with a chemical timestep of 5min as is used in the STOCHEM model (e.g. Johnson et al., 1999; Collins et al., 2002). Alternatively, the chemistry can be treated in a more flexible way using the ASAD framework (Carver et al., 1997), built around a symbolic non-families sparse-matrix Newton-Raphson solver derived from Wild and Prather (2000); it is described in Morgenstern et al. (2009). In this paper, and in the sensitivity study of climate model biases by O'Connor et al. (2009), all model results were derived from the former approach.

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4.2 Photolysis

The tropospheric chemistry schemes can make use of three options in relation to photolysis. In the simplest set up, photolysis rates are calculated offline in the Cambridge 2-D model (Law and Pyle, 1993) using the two-stream approach of Hough (1988). They are read in by UKCA on the first time step of the model integration and interpolated in time and space at each model grid box. Although this treatment gives realistic diurnal and seasonal variations in photolysis rates, there is no variability associated with cloud cover, surface albedo and/or aerosols; the 2-D rates were calculated with a climatolog-ical cloud cover and prescribed surface albedo and aerosol loading. One alternative option available in UKCA is the use of Fast-J (Wild et al., 2000) which provides a flexible and accurate scheme for the online calculation of photolysis rates in the presence of both cloud and aerosol layers. It solves the full radiative transfer equations with a reduced expansion of the scattering phase function over a relatively small number of wavelength bins. As well as assessing the accuracy of the calculated photolysis rates from Fast-J relative to clear-sky photolysis rates, Wild et al. (2000) also noted that the upper troposphere is a key region for climate forcing by O3 and hence, the accurate treatment of photolysis in this region of the atmosphere is critical in chemistry-climate models.

Figure 11 shows the zonal mean photolysis rate for the NO2 ^ NO + O(3P) reaction for January and July from both the offline 2-D photolysis scheme and Fast-J. Also included are scatter plots comparing the two. Both schemes capture the seasonal, latitudinal, and altitude dependence of J(NO2). The figure also clearly shows that although there are differences in photolysis rates from the two schemes due to differences in formulation, cloud and aerosol distributions, they are within the spread of photolysis rates from a recent intercomparison of chemistry-climate models (Eyring et al., 2010a). Offline photolysis has been used within UKCA in all the Met Office Hadley Centre's centennial integrations for Phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

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simulations using the Earth System version of the HadGEM2 model (Collins et al., 2011; Jones et al., 2011) and is also used in the model integrations being evaluated here. However, Fast-J is used routinely in the context of air quality forecasts from UKCA which are discussed in Savage et al. (2012).

More recently, there has been work to add Fast-JX (Neu et al., 2007) to UKCA. This is an improved version of the original Fast-J scheme, with scattering calculations extended up to the stratosphere and lower mesosphere as well as updates to the treatment of optically thick clouds and in the solar flux and cross-section datasets used. The implementation and use of Fast-JX in comparison with the offline photolysis scheme is evaluated in Telford et al. (2013), in which discrepancies in J(O1D) between the offline scheme and Fast-JX are discussed.

4.3 Emissions

The land-based anthropogenic, biomass burning, and shipping emissions used in the UKCA simulations being evaluated here make use of year-2000 emissions supplied as part of a new dataset of gridded emissions covering the historical period 1850-2000 (Lamarque et al., 2010). A brief summary of the emissions will be given here. However, for a more complete discussion on the full set of aerosol and O3 precursor emissions and their application over the historical period in two chemistry-climate models, the reader is referred to Lamarque et al. (2010).

Land-based anthropogenic emissions take account of the following sectors: energy production and distribution, industry, land transport, residential and commercial sources, solvent use, agriculture, agricultural waste burning, waste and waste distribution and were based on a combination of regional (Vestreng et al. (2006) inventory for Europe) and global inventories (e.g. EDGARv32 FT2000 dataset from van Aardenne et al., 2005). Biomass burning emissions consider fires in forests, savanna, and grasslands. They are taken as the mean emissions from the 1997-2006 period from version 2 of the Global Fire Emissions Database (GFEDv2) inventory (van der Werf et al., 2006). Emissions from maritime transport are also considered. Fleet information and

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fuel consumption are taken from Eyring et al. (2010b) and non-CO2 emission totals for the year 2000 are taken as the mean of previous studies (Corbett and Koehler, 2003; Eyring et al., 2005; Endresen et al., 2003, 2007). They are then distributed globally and monthly using the International Comprehensive Ocean-Atmosphere Data Set (ICOADS; Wang et al., 2008).

The standard tropospheric chemistry scheme, StdTrop, takes account of surface emissions of C2H6, C3H8, CH4, CO, HCHO, NOx, CH3CHO, and CH3COCH3. A summary is provided in Table 11. For C2H6 and C3H8, it was decided to lump all C2 (C2H6, ethene (C2H4), and ethyne (C2H2)) and C3 (propane and propene (C3H6)) species together and treat as emissions of C2H6 and C3H8, respectively. As well as emissions from land-based anthropogenic sources, biomass burning and shipping (Lamarque et al., 2010), natural emissions of CH4 are also included. Those from termites, wetlands, swamps, bogs, and tundra are from Fung et al. (1991) while oceanic and hydrate emissions are distributed temporally and spatially using distributions from Global Emissions Inventory Activity (GEIA; http://www.geiacenter.org/inventories/present.html).

For CO, emissions from Lamarque et al. (2010) are combined with oceanic emissions of CO, whose spatial and temporal distribution were provided by the Global Emissions Inventory Activity (GEIA; http://www.geiacenter.org/inventories/present.html) and are based on distributions of oceanic VOC emissions from Guenther et al. (1995). In the absence of a C5H8 degradation scheme, an additional 354TgCOyr-1 was added, based on a global mean CO yield of 30% from C5H8 from a study by Pfister et al. (2008) and a global C5H8 emission source of 503TgCyr-1 (Guenther et al., 1995). It is distributed spatially and temporally based on C5H8 emissions from Guenther et al. (1995). The global annual mean emissions for CO are shown in Fig. 12. For HCHO emissions, land-based anthropogenic sources and biomass burning contribute to the global annual total emissions of 9.0 Tg HCHO yr-1.

For NOx surface emissions, contributions from land-based anthropogenic sources, biomass burning, and shipping from Lamarque et al. (2010) are used. Added to these are a contribution from natural soil emissions, based on a global and monthly

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distribution provided by GEIA (http://www.geiacenter.org/inventories/present.html), and based on the global empirical model of soil-biogenic NOx emissions from Yienger and Levy (1995). For CH3CHO, the NMVOC biomass burning emissions for the period 1997-2006 from Lamarque et al. (2010) are used. Using emission factors from An-dreae and Merlet (2001) for grass fires, tropical forest fires, and extra-tropical forest fires, emissions of NMVOCs are converted into emissions of CH3CHO, giving a global annual total of 8.7TgCH3CHOyr-1. Finally, emissions of CH3COCH3 are added from land-based anthropogenic sources and biomass burning from Lamarque et al. (2010), giving a global annual emission total of 5.7TgCH3COCH3yr-1. The dominant source of CH3COCH3, however, is from vegetation through direct emissions, production from decomposing plant matter as well as production from terpene oxidation. A global emission strength of 40.0TgCH3COCH3yr-1 is used here following von Kuhlmann et al. (2003) which are distributed spatially and temporally based on distributions from Guen-ther et al. (1995). For the Troplsop chemistry scheme, the emissions are identical to those used by StdTrop except that the additional CO emissions from C5H8 oxidation are omitted; instead, a global annual C5H8 emission source of 506 Tg C yr-1 (Guenther et al., 1995) is prescribed, onto which a diurnal cycle is imposed based on local solar zenith angle. The use of interactive biogenic emissions in UKCA will be discussed in Folberth et al. (2013).

In addition to surface emissions, three-dimensional emissions of NOx from aircraft and lightning are also taken into account. Aircraft emissions are taken from the historical dataset compiled by Lamarque et al. (2010) and are concentrated in the Northern Hemisphere and are strongest in the upper troposphere/lower stratosphere (UTLS) region. Lightning emissions of NOx are also added to UKCA, based on the distribution and frequency of lightning flashes following Price and Rind (1992), Price and Rind (1993), and Price and Rind (1994), where the flash frequency is related to the height of the climate model's convective cloud top. Emissions are added from the surface up to 500 hPa for cloud-to-ground (CG) flashes and from 500 hPa up to the convective cloud top for intracloud (IC) flashes and are related to the discharge energy, with the energy

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from a CG flash being 10 times higher than that from an IC flash (Price et al., 1997). A scaling factor was also applied to give a global annual emission total of 5 Tg N yr-1. However, this scaling had been evaluated at an early stage of the model development, leading to emissions that were inadvertently too low in this model configuration i.e. < 2TgNyr-1 (Young etal., 2012).

4.4 Boundary conditions

In the tropospheric configuration of UKCA using the L38 climate model, the transport and chemistry are calculated interactively up to the model lid at 39 km. Nevertheless, boundary conditions are applied at the top of the model's domain to take account of missing stratospheric processes, such as chlorine and bromine chemistry. The location of a combined isentropic-dynamical tropopause (Hoerling et al., 1993) is diagnosed on a timestep-by-timestep basis. Then, modelled O3 from UKCA is overwritten in all model levels which are 3-4 km above the tropopause using the time-varying O3 field that is normally used to drive the climate model's radiation scheme in the absence of chemistry. In the case of the chemistry-climate simulations here, stratospheric O3 was that recommended for CMIP5 (Cionni et al., 2011). For NOy, a fixed O3 to HNO3 ratio of 1.0/1000.0 kg N/kg O3 from Murphy and Fahey (1994) is applied to HNO3 on the same model levels where O3 is being overwritten. For CH4, an explicit loss term, scaled to give a global annual loss of approximately 40 Tg CH4 yr-1, is applied in the 3 uppermost levels to account for stratospheric removal.

In addition to running with CH4 fully interactive, UKCA also has the option of running with a prescribed global surface CH4 concentration; this can be a time-invariant or time-varying concentration, depending on the type of model integration being carried out. In the tropospheric UKCA model simulations being evaluated here, CH4 was fully interactive.

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5 Model evaluation

Although the UKCA configuration being described here supports two chemistry schemes, StdTrop and TropIsop, the model evaluation here will focus mainly on StdTrop. This is the chemistry scheme that formed part of the Met Office Hadley Centre's ESM HadGEM2-ES (Collins et al., 2011) and was used in all the HadGEM2-ES centennial simulations submitted to CMIP5 (Jones et al., 2011). A total of 10yr has been simulated using the StdTrop chemistry scheme, with initial concentrations taken from previous integrations of UKCA. Surface and aircraft emissions for the year 2000 were used repeatedly, and CH4 reached steady state within 2yr. All 10 yr are used here in the model evaluation. Although the UKCA model simulates over 40 species, the discussion here focuses on key species such as CH4, CO, NOx, OH, and O3, which directly govern the oxidising capacity of the troposphere. For a comparison of TropIsop in UKCA with the G-PUCCINI model and O3 and CO measurements from the Tropo-spheric Emission Spectrometer (TES) onboard the Aura satellite, the reader is referred to Voulgarakis et al. (2011b) and Telford et al. (2013). Furthermore, the TropIsop chemistry scheme has been combined with the stratospheric chemistry from Morgenstern et al. (2009) and will be evaluated fully in Archibald et al. (2013).

5.1 Methane (CH4)

Multi-annual annual mean surface and zonal mean plots of CH4 from the StdTrop chemistry scheme can be seen in Fig. 13a and b. They indicate that surface concentrations of CH4 are very homogeneous in the Southern Hemisphere, with concentrations typically in the 1700-1750 ppbv range, except for higher concentrations in Africa and South America associated with anthropogenic sources, mainly from the agricultural sector, and natural emissions. In the Northern Hemisphere, variability is high over the continental regions. Over Canada and Alaska, for example, enhanced concentrations can be attributed principally to wetland emissions. Over North America, on the other hand, the dominant source of CH4 emissions are of anthropogenic origin (agriculture,

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energy production, and waste treatment) and to a lesser extent, natural emissions from swamps, bogs, and tundra. The largest enchancements in surface concentration above the global mean are over India and South East Asia; emissions from these regions are of anthropogenic origin (agriculture, energy, waste treatment, and domestic sectors). Less variability is evident over the Northern Hemisphere oceans, away from continental emissions sources and where CH4 can be well mixed. The global annual mean surface concentration from the StdTrop UKCA simulation is 1797 ppbv, within less than 3% of the globally and annually averaged observed concentration for the year 2000 of 1751 ppbv (Dlugokencky et al., 2003).

The multi-annual zonal annual mean distribution, shown in Fig. 13b, is characterized by high CH4 concentrations near the surface in the Northern Hemisphere mid and high latitudes, with decreasing concentrations aloft. In the Southern Hemisphere, the entire troposphere is very well mixed with concentrations similar to surface concentrations (i.e. in the 1700-1750 ppbv range). In the tropics, there is evidence of efficient upward transport from the surface to the upper troposphere due to convection as well as some indication of inter-hemispheric transport. There is a strong vertical gradient above the tropopause although it appears to be too strong in comparison to the zonal mean distribution from the MOZART model (Hauglustaine et al., 1998; Park et al., 2004) and from concentrations derived from Halogen Occultation Experiment (HALOE) satellite observations (Park et al., 2004). Results from a comparison between UKCA and SCIA-MACHY also support this (Hayman et al., 2013). This steep decline of CH4 was also evident in the stratospheric configuration of UKCA (Morgenstern et al., 2009), particularly in the tropics, and was attributed to slow ascent in the tropical pipe. In the tro-pospheric configuration of UKCA, it could also be partly due to the entire stratospheric removal of CH4 occurring at too low an altitude in comparison to interactive removal in a model with an extended top.

The total CH4 burden, calculated as a multi-annual annual mean from the 10yr of model integration, is found to be 4722TgCH4, reasonably comparable to the total burden of 4850Tg CH4 for the year 1998 calculated by Prather et al. (2001). Of the total

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model burden of 4722TgCH4, 3984TgCH4 of it resides below the diagnosed model tropopause with the remaining 738TgCH4 aloft. The only source of atmospheric CH4 in the model is from surface emissions (Table 11), totalling 526 Tg CH4 yr-1 for the year 2000. It is removed from the atmosphere by tropospheric oxidation by OH, which in the model accounts for 420 ± 3.4 Tg CH4 yr-1. The atmospheric lifetime associated with the tropospheric CH4 burden and its loss by OH is 9.96 yr, which is in excellent agreement with the estimate of 9.6 yr from the Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report (TAR) by Prather et al. (2001). It also agrees with a more recent intercomparison of chemistry models by Stevenson et al. (2006), in which the lifetime against OH loss was calculated to be 9.6 ± 0.4yr. In UKCA, the stratospheric removal and soil sink account for approximately 68 and 50TgCH4yr-1. These are larger than estimated by Prather et al. (2001) although there is significant inter-annual variability, particularly in the strength of the stratospheric loss (i.e. ± 10TgCH4yr-1).

A comparison between the modelled surface CH4 concentrations from the StdTrop chemistry integration and observations from the Earth System Research Laboratory's Global Monitoring Division of the National Oceanic and Atmospheric Administration (http://www.esrl.noaa.gov/gmd/) can be seen in Fig. 14. The particular stations used were chosen on the basis of their long measurement records (for CH4 in addition to O3 and CO) although only observations from the 1999-2006 time period are used here. This is because CH4 observations suggest that atmospheric levels were fairly constant during this period apart from a small increase in 2002 to 2003 (Dlugokencky et al., 2009). The comparison indicates that the StdTrop chemistry scheme in UKCA captures the seasonal cycle and absolute concentrations of surface CH4 at most stations very well (e.g. Niwot Ridge, Bermuda). The most notable exception to this is at Barrow (71° N, 157° W), where modelled surface concentrations are overestimated by approximately 10% during the Northern Hemisphere summer and autumn. A similar positive bias, albeit reduced, is also evident in the comparison at Mace Head. A study by Wang et al. (2004) found that the significiant summer/autumn positive bias in a priori modelled surface concentrations from the GEOS-CHEM model simulation at Barrow was greatly

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reduced by their inversion and was specifically due to decreases in the animal, fossil fuel, waste, bogs, and tundra emission sources located in the Northern Hemisphere middle and high latitudes. More recent work by Hayman et al. (2013) also supports the hypothesis that the strength of the Fung et al. (1991) wetland emissions in the Northern Hemisphere (as used in this study) is too high.

Latitudinal profiles of surface concentrations of CH4 from GLOBALVIEW-CH4 (2009) and the UKCA simulation can be found in Fig. 15. It indicates a clear inter-hemispheric difference with maximum modelled concentrations in the northern mid-latitudes and minimum concentrations in the Southern Hemisphere. There is also some variability in the modelled concentrations in the Northern Hemisphere which is not evident in the observations. By contrast, UKCA and the observations are consistent in terms of variability in the Southern Hemisphere. Modelled concentrations of surface CH4 are overestimated in the Northern Hemisphere by about 3% and underestimated in the Southern Hemisphere by approximately 1 %, resulting in a modelled inter-hemispheric difference that is greater than that observed. These overestimates and underestimates can have implications for the accuracy of source estimates by inverse modelling (e.g. Houweling et al., 1999; Chen and Prinn, 2006) and may reflect the long inter-hemispheric transport timescale in UKCA relative to other models, as indicated in Sect. 3.6. However, the absolute concentrations of the model are reasonably comparable with the observations, given that the uncertainty in the strength of the global CH4 source and sink is of the order of ± 15 % (Prather et al., 2001; Denman et al., 2007). These results clearly demonstrate the ability of the UKCA model to model CH4 interactively and therefore, has the potential to become a very useful tool in the investigation of feedbacks involving CH4 in the ES (e.g. O'Connor et al., 2010).

5.2 Carbon monoxide (CO)

Carbon monoxide (CO), together with CH4, is a primary sink for OH and CO is an important tropospheric O3 precursor, thereby playing a significant role in tropospheric chemistry. The dominant source of CO to the atmosphere is at the surface from

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incomplete combustion associated with both fossil fuel and biomass burning. However, CO is also produced by oxidation of CH4 and NMVOCs. Multi-annual annual mean surface and zonal mean plots of CO from the StdTrop chemistry scheme can be seen in Fig. 16a and b. The surface plot shows a very pronounced hemispheric asymmetry, with maximum concentrations over the continental regions of the Northern Hemisphere and minimum concentrations over the Southern Hemisphere, particularly over the oceans and Antarctica. The highest concentrations are seen over south and east Asia (over 300ppbv), Africa (over 300ppbv), South America (over 300ppbv), North America (over 200ppbv), and Europe (over 200ppbv). In South America and Africa, the dominant sources of CO are due to biomass burning and the BVOC surrogate emissions, with a smaller contribution from anthropogenic sources. Over North America, Europe, and south and east Asia, the high modelled surface concentrations are largely attributable to anthropogenic emissions, although there is also a contribution from the surrogate BVOC emissions over the south east United States and south east Asia. The spatial heterogeneity, even within the Northern Hemisphere, evident from the UKCA StdTrop surface distribution is also supported by other model studies (Hauglus-taine et al., 2004). Minimum concentrations of less than 60ppbv are evident over the oceanic regions of the Southern Hemisphere and are consistent with other models (e.g. Lawrence et al., 1999; Sudo et al., 2002).

The multi-annual zonal annual mean distribution in Fig. 16b indicates that maximum concentrations occur at the surface in the tropics and mid-latitudes of the Northern Hemisphere, with the maximum being more pronounced in the Northern Hemisphere winter than in the Northern Hemisphere summer. This seasonality in the maximum concentrations was also evident in other models (e.g. Muller and Brasseur, 1995; Hauglus-taine et al., 1998) and is attributed to the seasonality of the efficiency of the CO + OH reaction. CO concentrations decrease above the surface in the Northern Hemisphere and the vertical gradient is stronger during the Northern Hemisphere winter than in summer. By contrast, the troposphere in the mid and high latitudes of the Southern Hemisphere is well mixed with typical concentrations of 50-60 ppbv in the annual

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mean. At the tropopause, there is a strong vertical gradient, particularly in the Northern Hemisphere, with minimum concentrations (below 20 ppbv) above 18 and 10 km in the tropics and extra-tropics, respectively. This can be attributed to the relatively higher vertical resolution in HadGEM2 in comparison with other models (e.g. Lawrence et al., 1999). As was the case for CH4, there is evidence of convective uplift, with a secondary maximum in the tropical upper troposphere. There is also a relative minimum in the CO concentration in the lower troposphere at 20° N. Both of these features are evident in other model simulations (e.g. Muller and Brasseur, 1995; Hauglustaine et al., 1998) and their existence is supported by aircraft observations taken during vertical profiles along meridional cross-sections from 70° N-60° S (Marenco et al., 1995).

Figure 17 shows a comparison between modelled surface CO concentrations from the StdTrop UKCA chemistry scheme with climatological observations from selected sites from NOAA's Earth Systems Research Laboratory's Global Monitoring Division (http://www.esrl.noaa.gov/gmd/). On the whole, the model captures both the seasonal cycle and the absolute surface concentrations reasonably well. At Barrow, however, the model fails to capture the enhanced surface concentrations observed during the Northern Hemisphere winter and spring. There is also some evidence of a similar winter-spring underestimate at other Northern Hemisphere surface sites (e.g. Mace Head, Bermuda). Using identical anthropogenic emissions, both the G-PUCCINI (Shin-dell et al., 2006) and the CAM-Chem (Lamarque et al., 2008; Gent et al., 2010) models in the recent Lamarque et al. (2010) study indicate a negative bias and a reduced seasonal cycle in modelled surface CO concentrations relative to observations at Mace Head and at most Northern Hemisphere stations, with a larger negative bias in winter. They hypothesised that biomass burning emissions from Russia in the latter part of the year may be too low to provide the wintertime Northern Hemisphere maximum but acknowledge that further investigations are necessary. Biases between UKCA-modelled surface CO concentrations and observations are much smaller in the Southern Hemisphere and this was also the case with the G-PUCCINI and CAM-Chem model simulations (Lamarque et al., 2010).

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Comparisons of vertical profiles of modelled and observed CO from various regions and aircraft campaigns can be found in Fig. 18. In particular, regional profiles are compared from 4 different aircraft campaigns. The Pacific Exploratory Mission in the Tropical Pacific (PEM-Tropics B) campaign took place during the wet season of the southern tropics (March-April 1999) and was characterised by very clean air over the South Pacific with some influence from pollution over the tropical North Pacific (Raper et al., 2001). More specifically, measurements span the latitude and longitude range of 36° S-38° N and 148° W-76° E, respectively. The second aircraft campaign considered here, the Pacific Exploratory Mission West A (PEM West A) aircraft campaign, involved the NASA DC-8 aircraft, and took place in September-October 1991 over the northwestern Pacific, in which the lower tropospheric airflow was dominated by flow from mid-Pacific regions (Hoell et al., 1996). Emmons et al. (2000) split the PEM West A aircraft measurements into 5 regional profiles, namely, China Coast, Hawaii, Japan, Philippine Sea, and the western Tropical Pacific. The third set of aircraft campaign measurement data used here are from the Subsonic Assessment Ozone and Nitrogen Oxide Experiment (SONEX) and Pollution From Aircraft Emissions in the North Atlantic Flight Corridor (POLINAT 2) campaigns which took place during September to November 1997 and provides full longitudinal coverage over the Atlantic between 30 and 60° N (Thompson et al., 2000). In particular, the regional profiles cover three regions: Ireland, East Atlantic, and Newfoundland. Finally, the comparisons include observations from the Transport and Atmospheric Chemistry near the Equator - Atlantic (TRACE A) field campaign (Fishman et al., 1996). TRACE-Atook place over the tropical South Atlantic Ocean and the adjacent South American and African continents, with the specific objectives of investigating enhanced ozone concentrations over the tropical Atlantic Ocean and to quantify the role of ozone precursors associated with biomass burning in South America and southern Africa. It took place during September-October 1992 and was part of NASA's Global Tropospheric Experiment (GTE).

These aircraft measurements are not climatological in the sense that they represent long-term averages (Emmons et al., 2000). Instead, they provide snapshot distributions

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of trace species in different regions, at different times of the year, and are influenced by different meteorological conditions and anthropogenic and natural emissions. Therefore, given the disparities in meteorology and emissions, the comparison at best is expected to be qualitative. Some profiles compare extremely well (e.g. west African coast from TRACE A and over the Philippine Sea during PEM-West A). Others indicate that the model is biased high (e.g. PEM-Tropics B) or low (e.g. SONEX). The high and low biases from UKCA StdTrop are not unique to this model or chemistry scheme. A comparison between an ensemble of 26 different chemistry models and CO observations from MOPITT (Shindell et al., 2006) finds that the range in modelled CO concentrations for the annual cycle of global mean CO at 500 hPa, for example, is as large as 40 ppbv. This suggests that the performance of UKCA relative to the regional CO profiles is reasonable, given the inconsistencies in meteorology and emissions.

Comparisons of the TropIsop chemistry scheme with global CO observations from the TES instrument on NASA's Aura satellite can be found in Voulgarakis et al. (2011b) and Telford et al. (2013). Surface CO comparisons of the UKCA TropIsop scheme against observations can be found in Archibald et al. (2011). Finally, an idealised CO tracer, initialised from MOPITT, was used by Hoyle et al. (2011) to evaluate tropical deep convection in UKCA and other models.

5.3 Nitrogen oxides (NOx)

Nitrogen oxides, NOx (= NO + NO2), play a critical role in troposheric O3 production and in determining the HO2/OH ratio and CH4 lifetime against OH in the troposphere. Figure 19 shows the modelled annual mean surface and zonal mean NOx distributions from the multi-annual simulation of the UKCA StdTrop chemistry integration. Due to the conversion of NOx to HNO3 at the surface and in the lower troposphere on a timescale of 1 day, the surface plot indicates that enhanced concentrations of NOx are strongly confined to the continental source regions such as north eastern USA, Europe, and eastern Asia. These regions are associated with anthropogenic emissions. Secondary maxima are evident over continental regions such as South America, Africa,

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and southern Asia, and are associated with biomass burning. The modelled global NOx distribution is also clearly influenced by ship emissions, with the main routes across the northern and southern Atlantic Oceans, the north Pacific and the Indian Ocean. Away from the source regions, NOx concentrations are less than 10pptv and are consistent with other modelling studies (e.g. Lawrence et al., 1999; Jockel et al., 2006). However, maximum concentrations can be greater than 10ppbv at the surface over the continental emission regions. These high concentrations are inconsistent with both observations (e.g. Horowitz et al., 1998) and other models (e.g. Hauglustaine et al., 2004), suggesting that the lifetime of surface NOx in UKCA is too long due to the absence of heterogeneous chemistry (Dentener and Crutzen, 1993). Alternatively, it may

be due to insufficient boundary layer mixing, as was the case at some locations with

the Rn simulations in Sect. 3.3. Sensitivity tests in which NOx emissions were added over a number of model layers reduce these maximum concentrations, bringing UKCA modelled concentrations more in line with those from other models. Emissions are also handled in this way in the regional air quality configuration of UKCA (Savage et al., 2012), which was found to be important for high-ozone episodes.

The zonal annual mean distribution of NOx from the UKCA StdTrop multi-annual simulation can be seen in Fig. 19b. It indicates that NOx concentrations vary by two orders of magnitude between the Northern Hemisphere boundary layer and the tropical troposphere and again, between the tropical troposphere and the stratosphere. Maximum concentrations in the annual mean are simulated in the Northern Hemisphere boundary layer between 20 and 60° N associated with anthropogenic emissions and in the stratosphere due to photolysis of HNO3. The latitudinal extent of the Northern Hemisphere boundary layer maximum varies with season and extends further poleward during the Northern Hemisphere winter. Similarly, in the annual mean, enhanced concentrations are evident in the Northern Hemisphere troposphere, the vertical extent of which varies with season and is dependent on NOx lifetime. A secondary maximum is also evident in the tropical boundary layer associated with biomass burning. The vertical extent of the enhanced concentrations in the tropics is limited to approximately

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1-2 km depending on season, and reflects the efficient conversion of NOx to HNO3 in that region in all seasons as well as lightning emissions being too low. Nevertheless, the UKCA modelled zonal mean distribution is reasonably comparable to that of other models (e.g. Collins et al., 1997; Lawrence et al., 1999). One difference between the models is related to the latitudinal extent of the enhanced concentrations in the Northern Hemisphere. In both UKCA and the MATCH-MPIC (Lawrence et al., 1999) models, concentrations of 100pptv only extend up to 70° N whereas in the STOCHEM (Collins et al., 1997) model, simulated concentrations poleward of 70° N are of the order of 1 ppbv during the Northern Hemisphere winter.

Comparisons of modelled NOx concentrations with regional observations derived from a variety of aircraft campaigns (Emmons et al., 2000) can be found in Fig. 20. Given the short lifetime of NOx, it does not provide the integrated view of regional emissions and transport that CO provides (Shindell et al., 2006). Nevertheless, it does give some indication of the NOx emissions being used in the model. There is considerable variability in the observed concentrations in both time and space. However, in some locations (e.g. East Brazil Coast during TRACE A), the model captures the observed "C-shaped" profiles, with high concentrations in the polluted boundary layer, decreasing concentrations in the lowest troposphere, and enhanced concentrations aloft due to both lightning emissions and stratospheric input. In other locations, however, the model fails to capture the high concentrations in the mid and upper troposphere. Part of this bias may be related to lightning emissions being too low in the model (Young et al., 2012) although it may also be related to the emissions being added to the lowest model layer, insufficient boundary layer mixing and/or lack of convective uplift. The comparison shows that this aspect of the model performs satisfactorily but that ventilation of the boundary through mixing and/or convection may need improving.

5.4 Hydroxy! radical (OH)

The zonal and diurnal mean distribution of OH for January and July from the UKCA StdTrop chemistry integration can be found in Fig. 21. The latitudinal and seasonal

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variations in OH can be attributed to the distributions of ultraviolet (UV) radiation and water vapour which together provide the primary source of OH through the O(1D) + H2O reaction (Levy, 1971). Concentrations are typically higher during the summer in the Northern Hemisphere, reflecting the role of O3 and NOx in enhancing OH production. Maximum OH concentrations are found in the boundary layer, as is the case in the ECHAM/MESSyl model (Jockel et al., 2006) and was attributed by them to a lack of liquid-phase OH chemistry, HOx uptake by aerosols, and secondary organic aerosol (SOA) formation in the boundary layer. In the StdTrop chemistry configuration of UKCA, there is also a lack of NMVOC chemistry, which is known to reduce OH in the boundary layer, particularly over forested continents (Houweling et al., 1998; Wang et al., 1998; Roelofs and Lelieveld, 2000). Minimum concentrations are modelled in the winter mid and high latitudes as well as in the summer UTLS.

The global annual mean OH concentration from the UKCA StdTrop simulation is 0.8 ± 0.03 x106 molecules cm-3 and 1.03 ± 0.03 x106 molecules cm-3 based on weighting factors of air mass and reaction rate with CH4, respectively (Lawrence et al., 2001). In both cases, the standard deviation represents the variability in the global mean concentration using different vertical domains (i.e. region below modelled climatological tropopause, region below 100 hPa, region below 200 hPa, and region where O3 is less than or equal to 100ppbv), confirming the weak dependence of the global mean OH concentration on the choice of domain for these particular weightings (Lawrence et al., 2001). The air mass weighted OH concentration from the TropIsop

chemistry scheme with the offline photolysis scheme is 0.78 x10 molecules cm (Telford et al., 2013), comparable to that from StdTrop. The air mass weighted OH concentration from StdTrop is, however, on the low side of the chemical models that participated in the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; Lamarque et al., 2012), in which the multi-model mean concentration

is 1.11 ± 0.18 x10 moleculescm (Voulgarakis et al., 2012). However, lightning emissions of NOx were inadvertently too low (Young et al., 2012), resulting in reduced efficiency of OH recycling in these model runs. As indicated in Sect. 5.1, the atmospheric

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lifetime associated with the tropospheric CH4 burden and its loss by OH, calculated following Lawrence et al. (2001) is 9.96 yr, which is in excellent agreement with the estimates from Prather et al. (2001), Stevenson et al. (2006) and the multi-model mean estimate from Voulgarakis et al. (2012). Note that Voulgarakis et al. (2012) quote the lifetime for the full atmosphere, rather than the tropospheric values used here.

Further comparisons of modelled OH from the UKCA TropIsop chemistry scheme against surface measurements from a south east Asian tropical forest can be found in Archibald et al. (2011).

5.5 Ozone (O3)

In Fig. 22a and b, the UKCA modelled global distribution of surface O3 concentrations for January and July are shown, respectively. In January, the UKCA model simulates maximum surface concentrations over central Africa (greater than 60 ppbv) and south Asia (50-60 ppbv) associated with O3 precursor emissions from biomass burning and industrial sources, respectively. High surface concentrations are also simulated over the north Pacific (typically over 40 ppbv) and Atlantic (35-40 ppbv) Oceans, providing evidence for the longer O3 lifetime and transport from the stratosphere during the Northern Hemisphere winter. The calculated stratospheric ozone contribution to the modelled surface O3 abundance in UKCA is of the order of 40-50 % in these regions in January (not shown) and is similar to that calculated by Sudo et al. (2002). By contrast, concentrations are less than 20 ppbv over the US and the Eurasian continental regions. At the low HOx concentrations found in the Northern Hemisphere winter, O3 production is slow and in the boundary layer, O3 is removed directly through titration by NO. In the Southern Hemisphere, higher surface concentrations are found over the continental regions (typically 20-30 ppbv) than over the remote oceans (less than 20 ppbv). In July, O3 concentrations are generally much higher than in January, with maximum concentrations over southern Europe, the middle East, central Asia, and the continental US. High concentrations are also evident over western Africa associated with biomass burning and there is some evidence of outflow from the continental regions

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to the oceanic regions. In the absence of biogenic NMVOC emissions, surface concentrations are underestimated over the eastern US, for example. However, the main features of the surface distribution, both in January and July, is consistent with other modelling studies (e.g. Hauglustaine et al., 2004; Zeng et al., 2008).

The multi-annual zonal mean O3 distribution for January and July can be found in Fig. 23. It indicates that tropospheric ozone concentrations typically increase with height and are higher in the Northern Hemisphere than in the Southern Hemisphere in both seasons. Northern Hemisphere concentrations in the mid-troposphere are typically 40-60 ppbv whereas Southern Hemisphere concentrations are closer to 3040 ppbv. Maximum concentrations are found in the upper troposphere of the Northern Hemisphere during July (greater than 60 ppbv) with minimum concentrations near the surface in the Southern Hemisphere in January. In the tropics, there is evidence of a secondary minimum in the mid- and upper troposphere due to advection and con-vective transport of ozone-poor air from the surface. The influence of the stratosphere on the tropospheric concentrations is also clearly evident in both hemispheres, due to transport of high-O3 air across the sub-tropical jetstreams. In the Northern Hemisphere mid-troposphere (45° N, 5 km), stratospheric O3 contributes 50-60% to the modelled tropospheric O3 distribution in January which reduces to 35-40 % in July. In the Southern Hemisphere, on the other hand, stratospheric O3 contributes 40-50 % and 50-60 % to the mid-troposphere modelled concentrations in January and July, respectively, and is similar to that of other models (e.g. Hauglustaine et al., 2004). A comparison of the zonal annual mean with the ACCENT (Atmospheric Composition Change: the European Network of excellence) multi-model ensemble by Stevenson et al. (2006) indicates that modelled O3 from UKCA sits within ± 1 standard deviation of the ensemble mean in most regions and altitudes. The most notable exception is in the tropical mid-and upper troposphere, where the absence of an isoprene mechanism in StdTrop may account for some of the discrepancy (Poschl et al., 2000; von Kuhlmann et al., 2004). However, the role of lightning emissions cannot be ruled out and is explored later in this section.

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The chemistry scheme being evaluated here, due to its lack of NMVOC chemistry, is appropriate for the remote troposphere and is not expected to reproduce observed surface O3 in regions influenced by NMVOC emissions from vegetation and industry. Therefore, a comparison between modelled and observed surface concentrations of O3 (Fig. 24) is indicative only. The model, however, captures both the seasonal cycle and the absolute concentrations of surface O3 at northern mid-latitudes (e.g. Mace Head, Bermuda, Niwot Ridge). The springtime maximum at the remote sites of Mace Head and Bermuda is represented very well along with the summertime maximum at the polluted site of Niwot Ridge. In the tropics, the seasonal cycle of the surface observations is reproduced well (Mauna Loa, Barbados, and Samoa) but the model overestimates concentrations by up to 10ppbv. A similar disagreement in the tropics between modelled and observed surface concentrations was also evident in the LMDz-INCA model (Hauglustaine et al., 2004). The model performs less well at the northern high latitude site of Barrow. Although the summertime concentrations are captured reasonably well, the model fails to reproduce the autumn-winter maximum; modelled concentrations are

underestimated by 50 %. The cause of the discrepancy is unclear but modelled surface

Pb concentrations at Barrow in Sect. 3.4 suggest that the model is representing the long-range boundary layer transport into the Arctic well. Other possibilities for the discrepancy could be biases in O3-precursor emissions (as seen in CO in Sect. 5.2), lack of gas-phase and heterogeneous bromine chemistry (e.g. Yang et al., 2005), excessive titration of O3 by NO, in addition to the difficulty that a coarse-grid model has in representing the uniqueness of the observational site at Barrow.

The radiative forcing efficiency on a per molecule basis is greatest for O3 changes near the tropopause (Lacis et al., 1990), thus making upper levels more important than the surface for climate forcing by O3. As a result, in Fig. 25, a comparison between UKCA and ozonesonde observations (Logan et al., 1999; Thompson et al., 2003a,b) is presented. Also included in the comparison is the ACCENT multi-model ensemble from Stevenson et al. (2006). UKCA performs exceptionally well in the northern extra-tropics at all pressure levels in capturing both the seasonal cycle and the absolute O3

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concentrations. In particular, the ACCENT ensemble underestimates the amplitude of the seasonal cycle and overestimates Northern Hemisphere wintertime O3 by approximately 10 ppbv (Stevenson et al., 2006) whereas UKCA reproduces both the amplitude of the seaonal cycle and the observed wintertime O3 at 750 and 500 hPa very well. Similarly, the comparison of UKCA with observations at 850, 600, and 400 hPa in the northern extratropics is very good. The model, however, tends to underestimate the observed O3 concentrations at 250 hPa with a slight overestimate at 100 hPa. This overestimate at 100 hPa is also evident in the tropics and results in a warming of the tropical tropopause and a moistening of the stratosphere when O3 from UKCA is passed to the climate model's radiation scheme in place of a prescribed O3 climatology (O'Connor et al., 2009). An underestimate of modelled O3 from UKCA at 250 hPa in comparison with observations is also evident in the tropics and places UKCA at the lower end of the ACCENT multi-model ensemble. At 500 hPa, UKCA is again at the lower end of the model ensemble and is biased low in comparison with the observations in the 030° S latitude band. At the same pressure surface in the northern tropics, however, UKCA is in better agreement with the observations than the ensemble mean but the number of observational sites (4) covering this region is limited. In the southern extratropics, UKCA matches the observations very well at 850 and 750 hPa, with also a good agreement at 750 hPa with the ensemble mean. Aloft, UKCA performs well in comparison with both the observations and the multi-model ensemble but tends to underestimate O3 during the Southern Hemisphere winter. The model intercomparison by Stevenson et al. (2006) indicated that the greatest variability in modelled O3 is in the tropics as a result of model differences in the treatments of convection, lightning NOx emissions, biogenic isoprene emissions and biomass burning emissions. Indeed, it was found that the low bias from UKCA relative to observations and the other ACCENT models was due to the lightning NOx emissions in the model being inadvertently lower (i.e. < 2TgNyr-1) than the anticipated level of 5TgNyr-1 (Young et al., 2012). A sensitivity study in which the lightning emissions were increased results in a reduction in the UKCA tropical O3 bias. Stevenson et al. (2006) also noted that the

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mid-latitudes are less affected by convection, lightning, biomass burning and biogenic emissions and the performance of UKCA is better in the extratropics than in the tropics. In terms of the overall performance of the model, a root-mean-square-error (RMSE) for UKCA was calculated using modelled and observed monthly mean O3 concentrations at 750, 500, and 250 hPa and excluding the extratropics at 250 hPa; UKCA has a RMSE of 12.4 ppbv, well within the range in RMSE across the ACCENT models of 5.1-18.0 ppbv (Stevenson et al., 2006). For a comparison of the more recent ACCMIP multi-model ensemble, including UKCA StdTrop in HadGEM2, with the ACCENT models (Stevenson et al., 2006) and ozonesonde and satellite observations, the reader is referred to Young et al. (2012).

An additional comparison, making use of the Fortuin and Kelder (1998) O3 climatology which is based on ozonesonde and satellite measurements made between 1980 and 1991, is shown in Fig. 26. This observational climatology was used to evaluate some of the models which participated in the ACCENT model intercomparison (Gauss et al., 2006). In the tropical troposphere (20°S-20°N, 0-12km), they found that all models except one agreed with the observed O3 climatology to within ± 40%. This is also the case with UKCA, with maximum deviations occurring in the 8-12 km altitude range. The study by Gauss et al. (2006) also indicated that near-surface O3 at 30° N in the ACCENT models was higher than the climatology and extended to other latitudes and higher altitudes to varying degrees. Modelled O3 from UKCA also shows a positive anomaly in comparison with the Fortuin and Kelder (1998) climatology at the surface between 15 and 30° N which extends upwards to 3 km at 25° N. Thirdly, for the Northern Hemisphere extra-tropical troposphere (45-90° N, 0-8 km), O3 was simulated to within ± 30% by two of the ACCENT models and to within ± 10-20% by the other models. UKCA appears to be in better agreement with the climatology in this region than in the tropics, with anomalies generally less than 20%. The most notable exceptions to this are negative anomalies in excess of 25 % near the surface between 45 and 75° N and in the upper troposphere/lower stratosphere. The former discrepany could be related to the lack of NMVOC chemistry in StdTrop whereas the latter discrepancy can be most

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likely attributed to a mismatch in tropopause location between UKCA and the O3 climatology. Gauss et al. (2006) argue that an agreement between modelled and observed O3 concentrations of better than 20-40 % cannot be expected due to the mismatch in meteorology between the observations and that used by the models, the time lag between the model simulations and the observations, and the lack of interannual variability in the models' prescribed emissions. From a UKCA perspective, although these caveats also apply, the key point to note is that the performance of UKCA relative to the climatology is quantitatively comparable to that for the other models.

Further comparisons of StdTrop and TropIsop relative to global O3 observations from the TES instrument onboard NASA's Aura satellite can be found in Young et al. (2012), Voulgarakis et al. (2011b), and more recently, in Telford et al. (2013). Archibald et al. (2011) also compare O3 from TropIsop with Stevenson et al. (2006) which looks broadly similar to the StdTrop comparison in Fig. 25 here.

6 Historical and future simulations

One of the main objectives of StdTrop was the ability to represent CH4, tropospheric O3, and oxidant fields for sulphate aerosol formation in a climate model for use in centennial climate projections. Given the performance of StdTrop relative to present-day observations, it was included in all the centennial simulations carried out with the Met Office Hadley Centre's climate model, HadGEM2-ES (Collins et al., 2011), for CMIP5 along with a few other climate modelling centres. For details on the historical and future O3-precursor emissions used in the CMIP5 simulations, the reader is referred to Lamarque et al. (2010) and Lamarque et al. (2011); details on all the other HadGEM2-ES forcings can be found in Jones et al. (2011). Figure 27 shows the global mean surface CH4 and O3 concentrations, the tropospheric CH4 lifetime against OH, and the tropospheric O3 burden for the historical period and out to 2100 for each of the Representative Concentration Pathways (RCPs) - RCP2.6 (van Vuuren et al., 2011), RCP4.5 (Thomson et al., 2011), RCP6.0 (Masui et al., 2011), and RCP8.5 (Riahi et al., 2011).

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In the historical and future runs, global mean surface CH4 concentrations were used to prescribe the surface CH4 concentration in UKCA. The CH4 abundance aloft, however, evolved interactively along with the other gas-phase constituents.

Figure 27b, in particular, shows how the tropospheric CH4 lifetime against OH varies over the historical and future periods relative to a pre-industrial control experiment. In the control, the CH4 lifetime is of the order of 10.1 yr. There is also some evidence of interannual and multidecadal variability in the lifetime. In the historical simulation, the CH4 lifetime remains similar to that in the pre-industrial control until about 1950. Thereafter, the lifetime increases, reaching a maximum value of 10.6 yr before decreasing until the year 2000. This can be attributed to the SSTs in the coupled model being biased cold in the tropical oceans, thereby affecting atmospheric temperatures and humidity. In RCP2.6, the lifetime continues to decrease, reaching a minimum value of 8.4yr at 2050. Thereafter, the lifetime increases until 2100. Although Shindell et al. (2012) didn't include a historical study, their time evolution of CH4 lifetime against OH from 2000-2100 is similar to that in this study. However, there are substantial differences in RCP8.5, for example. In the GISS model, Shindell et al. (2012) found that the lifetime was longer throughout the 21st century than at the year 2000 whereas in HadGEM2 with UKCA StdTrop, CH4 lifetime reduces between 2000 and 2050, then increases before decreasing to a lifetime of 9.6 yr at 2100. Simulations of present-day and future OH and CH4 lifetime from the ACCMIP multi-model ensemble are discussed further in Voulgarakis et al. (2012).

Tropospheric O3 increased during the historical simulation, giving rise to an increase in the global mean surface concentration of almost 9 ppbv and in the tropospheric O3 burden of 7.3 Dobson Units (DU) i.e. from 220± 1.7Tg O3 (20.5±0.16 DU) in the 1860 s to 300 ± 1.6 Tg O3 (27.9 ± 0.15 DU) for the 2000s. This increase in tropospheric O3 burden is similar in sign and magnitude to that from Cionni et al. (2011) and is at the lower end of the estimates from the ACCMIP models i.e. 98 ± 17Tg O3 (Young et al., 2012). This can be most likely attributed to the lack of NMVOCs in StdTrop (Young et al., 2012). The time evolution of the increase, however, shows a slow increase from

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1860 up to 1940, a more rapid increase between 1940 and 1980, followed by a slower

increase between 1980 and the 2000s, reasonably consistent with that from Kawase

et al. (2011). In the case of UKCA StdTrop, this increase in tropospheric O3 burden

gives rise to a global mean radiative forcing of 0.24 Wm for the 2000s relative to the

—2 — 1

1860s based on a global mean normalised radiative forcing of 0.033 W m DU by Gauss et al. (2006) and is consistent with that from Cionni et al. (2011). This estimate, however, does not take account that forcing by O3 varies strongly with region (Shin-dell et al., 2003) and it is lower than previous estimates (cf. 0.32 ± 0.07 Wm from Gauss et al., 2006). Cionni et al. (2011) argue that it is primarily the result of 18502000 changes in biomass burning emissions being smaller than in the models used by Gauss et al. (2006). However, more recently, Stevenson et al. (2012) calculated radiative forcing from all the ACCMIP models using a number of different radiative transfer models and different tropopause definitions. They calculated an estimate of 0.3 Wm-2 for UKCA StdTrop in HadGEM2, at the lower end of the ACCMIP ensemble mean (0.36 ± 0.06 Wm-2).

The simulations from year 2000 out to 2100 indicate that tropospheric O3 burden projections vary with RCP. In RCP2.6, the O3 burden continues to increase from 2000 until 2010, followed by a decrease until the end of the century. In RCP4.5, it reaches a maximum of 310TgO3 around 2040, and then declines to 285TgO3 at 2100. In RCP6.0, there is little change in the tropospheric O3 burden until around 2050-2055, it then starts to decline, reaching a value of 283TgO3 at 2100. In contrast, RCP8.5 sees a continual increase in tropospheric O3 throughout the 21st century, reaching a maximum of 355TgO3 at 2100, with CH4 being the main driving factor. This time evolution is quite consistent with that from the CHASER CTM (Kawase et al., 2011), with reasonable agreement for the 2090s-2000s tropospheric O3 changes i.e. -3.7 DU (RCP2.6), -1.2 DU (RCP4.5), -1.2 DU (RCP6.0), and +4.9 DU (RCP8.5). Cionni et al. (2011), likewise, calculated global annual mean tropospheric O3 column changes of -5, -2, -2, and +5 DU between the 2090s and 2000s for RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The corresponding radiative forcing estimates from Stevenson

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et al. (2012) for StdTrop in HadGEM2 are -0.15, -0.03, and +0.27 Wm-2 for RCP2.6, RCP4.5, and RCP8.5. In particular, there is a clear consensus amongst the ACCMIP models on the sign and magnitude of the troposheric O3 changes and the corresponding radiative forcing for each RCP.

7 Conclusions

This paper is Part 2 of a series of papers, which aim to describe and evaluate the new UK Chemistry and Aerosol (UKCA) model. In particular, in this part, we evaluate aspects of dynamical processes which are important for tropospheric composition.

222 85 210

A number of simple tracer experiments were set up, including Rn, Kr, and Pb,

to assess boundary layer mixing, convective transport, inter-hemispheric exchange,

and wet scavenging. In relation to boundary layer mixing and convective transport, the

performance of the UKCA model was comparable to that of other models. It showed

excellent comparison with surface observations at some sites but poorer performance

222 210

at other sites. It reproduced the vertical profile of Rn and Pb well, suggesting that boundary layer mixing and convective transport are well represented in the base dynamical model. The 8 Kr simulation indicated that the timescale for inter-hemispheric exchange was long relative to other model estimates and could have implications for meridional gradients of long-lived tracers, such as CH4. Wet scavenging was also evaluated by means of a 210Pb tracer and results suggest that the UKCA model simulates the shape and absolute concentrations of observed profiles very well. Finally, global mass conservation was assessed and found to give a drift of less than 0.01 % per year.

The climate model, HadGEM2, can be run with two tropospheric chemistry schemes. The first scheme, called StdTrop, is a fairly simple chemistry scheme but includes Ox-NOy-HOx-CO chemistry with near-explicit treatment of methane, ethane, propane, and acetone degradation (including formaldehyde, acetaldehyde, peroxy acetyl nitrate, and peroxy propionyl nitrate). The second chemistry scheme, called TropIsop, is an extension to StdTrop to include the species and reactions from the Mainz Isoprene

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Mechanism. Both chemistry schemes can be run with a choice of chemical solver (Backward Euler or Newton-Raphson), photolysis scheme (Offline Hough scheme, Fast-J and Fast-JX) and dry deposition (Prescribed deposition velocities or Resistance approach), all of which are described. Descriptions of wet scavenging, upper boundary conditions, and emissions are also included.

The evaluation of the troposheric configuration of UKCA focussed on the performance of StdTrop, particularly because the StdTrop chemistry scheme was included in all centennial model simulations with the HadGEM2-ES Earth System Model (Collins et al., 2011) by the Met Office Hadley Centre for the fifth Coupled Model Intercom-parison Project (CMIP5). It was also included in the multi-model ensemble of the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). In summary, it was found that the StdTrop chemistry scheme performs well in comparison with other models in simulating global and zonal mean distributions of methane, ozone, carbon monoxide, nitrogen oxides, and hydroxyl radical. Surface comparisons of methane and carbon monoxide were particularly good but the model did less well in reproducing observed surface concentrations of ozone. This can be attributed to the lack of non-methane hydrocarbon chemistry included in the chemical scheme, making it unsuitable for air quality purposes. However, from a climate forcing perspective, the UKCA StdTrop chemistry scheme compares well with ozonesonde observations and other models in the mid- and upper-troposphere. The global annual mean hydroxyl concentration is 0.8 ± 0.03 x106 molecules cm-3, resulting in a tropospheric methane lifetime against hydroxyl of 9.96 yr, in line with other published estimates.

Further developments on the UKCA model are already underway and/or completed. In particular, the aerosol component of the UKCA model, GLOMAP-mode, has been coupled to the StdTrop and TropIsop chemistry schemes (with extensions for sulphur chemistry, for example) will be described and evaluated in Part 3 of the UKCA paper series (Mann et al., 2013). In addition, an extended tropospheric chemistry scheme, ExtTC, has also been implemented in UKCA with enhanced coupling between the atmosphere and the land surface; this will be discussed in Part 4 of the series (Folberth

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et al., 2013). Furthermore, the TropIsop chemistry scheme has been combined with the stratospheric chemistry (Morgenstern et al., 2009) and will be discussed in a forthcoming paper by Archibald et al. (2013). As with other chemistry models, UKCA is under continual development; these developments will continue to be published in future papers.

Acknowledgements. The development of the UKCA model (www.ukca.ac.uk) and this work was supported by the Joint DECC/Defra Hadley Centre Climate Programme (GA01101) and the Natural Environment Research Council (NERC) through the National Centre for Atmospheric Science (NCAS) initiative. The authors would also like to thank all those who participated in the model intercomparison as part of the European Union project Atmospheric Composition Change: the European Network of excellence (ACCENT; http://www.accent-network.org), the results from which were used in Fig. 25.

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von Kuhlmann, R., Lawrence, M., Crutzen, P., and Rasch, P.: A model for studies of tropospheric ozone and nonmethane hydrocarbons: Model description and ozone results, J. Geophys. Res., 108, 4294, doi:10.1029/2002JD002893, 2003. 1768 von Kuhlmann, R., Lawrence, M. G., Poschl, U., and Crutzen, P. J.: Sensitivities in global scale modeling of isoprene, Atmos. Chem. Phys., 4, 1-17, doi:10.5194/acp-4-1-2004, 2004.1763, 1782

Voulgarakis, A., Savage, N. H., Wild, O., Braesicke, P., Young, P. J., Carver, G. D., and Pyle, J. A.: Interannual variability of tropospheric composition: the influence of changes in emissions, meteorology and clouds, Atmos. Chem. Phys., 10, 2491-2506, doi:10.5194/acp-10-2491-2010, 2010.1763 Voulgarakis, A., Telford, P. J., Aghedo, A. M., Braesicke, P., Faluvegi, G., Abraham, N. L., Bowman, K. W., Pyle, J. A., and Shindell, D. T.: Global multi-year O3-CO correlation patterns from models and TES satellite observations, Atmos. Chem. Phys., 11, 5819-5838, doi:10.5194/acp-11-5819-2011, 2011a. 1763 Voulgarakis, A., Telford, P. J., Aghedo, A. M., Braesicke, P., Faluvegi, G., Abraham, N. L., Bowman, K. W., Pyle, J. A., and Shindell, D. T.: Global multi-year O3-CO correlation patterns from models and TES satellite observations, Atmos. Chem. Phys., 11, 5819-5838, doi:10.5194/acp-11-5819-2011, 2011b. 1770, 1777, 1786 Voulgarakis, A., Naik, V., Lamarque, J.-F., Shindell, D. T., Young, P. J., Prather, M. J., Wild, O., Field, R. D., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsoren, S. B., Doherty, R. M., Eyring, V., Faluvegi, G., Folberth, G. A., Horowitz, L. W., Josse, B., McKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Stevenson, D. S., Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Analysis of present day and future OH and methane lifetime in the ACCMIP simulations, Atmos. Chem. Phys. Discuss., 12, 2294523005, doi:10.5194/acpd-12-22945-2012, 2012. 1780, 1781, 1787 Walton, J., MacCracken, M., and Ghan, S.: A global-scale lagrangian trace species model of transport, transformation, and removal processes, J. Geophys. Res., 93, 8339-8354, doi:10.1029/JD093iD07p08339, 1988. 1750, 1757 Wang, C., Corbett, J., and Firestone, J.: Improving spatial representation of global ship emissions inventories, Environ. Sci. Tech., 42, 193-199, doi:10.1021/es0700799, 2008. 1767 Wang, J., Logan, J., McElroy, M., Duncan, B., Megretskaia, I., and Yantosca, R.: A 3-D model analysis of the slowdown and interannual variability in the methane growth rate from 1988 to 1997, Global Biogeochem. Cy., 18, doi:10.1029/2003GB002180, 2004. 1772

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Wang, K.-Y. and Shallcross, D. E.: A modelling study of tropospheric distributions of the trace gases CFCl3 and CH3CCl3 in the 1980s, Ann. Geophys., 18, 972-986, doi:10.1007/s00585-000-0972-3, 2000.1759 Wang, Y. H., Jacob, D. J., and Logan, J. A.: Global simulation of tropospheric O3-NOx-hydrocarbon chemistry 3. Origin of tropospheric ozone and effects of nonmethane hydrocarbons, J. Geophys. Res., 103, 10757-10767, doi:10.1029/98JD00156, 1998. 1762, 1780 Waugh, D. W., Oman, L., Kawa, S. R., Stolarski, R. S., Pawson, S., Douglass, A. R., Newman, P. A., and Nielsen, J. E.: Impacts of climate change on stratospheric ozone recovery, Geophys. Res. Lett., 36, 3857-3860, doi:10.1029/2008GL036223, 2009. 1745 Weiss, W., Sittkus, A., Stockburger, H., Sartorius, H., and Munnich, K.: Large-scale atmospheric mixing derived from meridional profiles of KR-85, J. Geophys. Res., 88, 8574-8578, doi:10.1029/JC088iC13p08574, 1983. 1758, 1840 Wesely, M.: Parameterization of surface resistances to gaseous dry deposition in regional-scale

numerical-models, Atmos. Environ., 23, 1293-1304, 1989. 1749 WHO: Health Aspects of Air Pollution with Particulate Matter, Ozone, and Nitrogen Dioxide, Report of a WHO Working Group EUR/03/5042688, World Health Organization European Centre for Environment and Health, Bonn, Germany, 2003. 1745 Wild, O. and Prather, M.: Excitation of the primary tropospheric chemical mode in a global three-dimensional model, J. Geophys. Res., 105, 24647-24660, doi:10.1029/2000JD900399, 2000. 1764

Wild, O., Zhu, X., and Prather, M.: Fast-j: Accurate simulation of in- and below-cloud photolysis

in tropospheric chemical models, J. Atmos. Chem., 37, 245-282, 2000. 1765 WMO: Scientific Assessment of Ozone Depletion: 2010, World Meteorological Organization, Geneva, Switzerland, global Ozone Research and Monitoring Project - Report No. 52, 516 pp., 2011. 1746

Yang, X., Cox, R. A., Warwick, N. J., Pyle, J. A., Carver, G. D., O'Connor, F. M., and Savage, N. H.: Tropospheric bromine chemistry and its impacts on ozone: a model study, J. Geophys. Res., 110, D23311, doi:10.1029/2005JD006244, 2005. 1783 Yienger, J. and Levy, H.: Empirical-model of global soil-biogenic NOx emissions, J. Geophys.

Res., 100, 11447-11464, doi:10.1029/95JD00370, 1995. 1768 Young, P. J., Arneth, A., Schurgers, G., Zeng, G., and Pyle, J. A.: The CO2 inhibition of terrestrial isoprene emission significantly affects future ozone projections, Atmos. Chem. Phys., 9, 2793-2803, doi:10.5194/acp-9-2793-2009, 2009.1763

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Young, P. J., Archibald, A. T., Bowman, K. W., Lamarque, J.-F., Naik, V., Stevenson, D. S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsoren, S. B., Doherty, R. M., Eyring, V., Faluvegi, G., Horowitz, L. W., Josse, B., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Skeie, R. B., Shindell, D. T., Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys. Discuss., 12, 21615-21677, doi:10.5194/acpd-12-21615-2012, 2012. 1769, 1779, 1780, 1784, 1785, 1786, 1787 Zaucker, F., Daum, P., Wetterauer, U., Berkowitz, C., Kromer, B., and Broecker, W.: Atmospheric Rn-222 measurements during the 1993 NARE intensive, J. Geophys. Res., 101, 2914929164, doi:10.1029/96JD02029, 1996. 1754, 1834 Zeng, G. and Pyle, J. A.: Changes in tropospheric ozone between 2000 and 2100 modeled in a chemistry-climate model, Geophys. Res. Lett., 30, 1392m doi:10.1029/2002GL016708, 2003. 1762

Zeng, G. and Pyle, J. A.: Influence of El Nino Southern Oscillation on stratosphere/troposphere exchange and the global tropospheric ozone budget, Geophys. Res. Lett., 32, L01814, doi:10.1029/2004GL021353, 2005. 1762 Zeng, G., Pyle, J. A., and Young, P. J.: Impact of climate change on tropospheric ozone and its global budgets, Atmos. Chem. Phys., 8, 369-387, doi:10.5194/acp-8-369-2008, 2008.1763, 1782

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Table 1. Values required to calculate the effective Henry's Law co-efficient for the soluble species included in the UKCA standard tropospheric (StdTrop) scheme, where Me = CH3, Et = C2H5, and Pr = C3H7. References: 1. Jacob (2000); 2. Regimbal and Mozurkewich (1997); 3. CRC (2003); 4. Lelieveld and Crutzen (1991); 5. Hanson et al. (1992); 6. O'Sullivan et al. (1996); 7. Staudinger and Roberts (2001); and 8. Becker et al. (1996).

Species

Henry's Law Data Dissociation Data KH(298K) -AH/R Ka (298 K) -AH/R

Reference

NO3 2.0E+00 2000.0 0.0E+00 0.0 1

N2O5 2.1E+05 8700.0 2.0E+01 0.0 As for HONO2

HO2NO2 1.3E+04 6900.0 1.0E-05 0.0 2; 3

hono2 2.1E+05 8700.0 2.0E+01 0.0 4

HO2 4.0E+03 5900.0 2.0E-05 0.0 5; 1

H2O2 8.3E+04 7400.0 2.4E-12 -3730.0 6

HCHO 3.3E+03 6500.0 0.0E+00 0.0 7

MeOO 2.0E+03 6600.0 0.0E+00 0.0 4

MeOOH 3.1E+02 5000.0 0.0E+00 0.0 7

HONO 5.0E+01 4900.0 5.6E-04 -1260.0 8

EtOOH 3.4E+02 5700.0 0.0E+00 0.0 7

rt-PrOOH 3.4E+02 5700.0 0.0E+00 0.0 As for EtOOH

i-PrOOH 3.4E+02 5700.0 0.0E+00 0.0 As for EtOOH

MeCOCH2OOH 3.4E+02 5700.0 0.0E+00 0.0 As for EtOOH

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Table 2. Values required to calculate the effective Henry's Law co-efficient for the additional soluble species included in the UKCA standard tropospheric scheme with isoprene ox-

idation (TropIsop), where Me = CH3, IS:

C5 H7,

ISON = Lumped peroxy alkyl nitrates (from

C5H8 + NO3) and hydroxy alkyl nitrates (from ISO2 + NO), MACR = Lumped species consisting of methacrolein, methyl vinyl ketone and other C4 carbonyls from isoprene oxidation, HACET = hydroxyacetone and other C3 ketones, MGLY = methylglyoxal and other C3 carbonyls, NALD = nitrooxy acetaldehyde, and Prpe = C3H5. References: 1. Staudinger and Roberts (2001); and 2. CRC (2003).

Species Henry's Law Data ^H(298K) -AH/R Dissociation Data Ka (298 K) -AH/R Reference

ISOOH 1.7E+06 9700.0 0.0E+00 0.0 As for PrpeOOH from 1

ISON 3.0E+03 7400.0 0.0E+00 0.0 1

MACROOH 1.7E+06 9700.0 0.0E+00 0.0 As for PrpeOOH from 1

HACET 1.4E+02 7200.0 0.0E+00 0.0 1

MGLY 3.5E+03 7200.0 0.0E+00 0.0 1

HCOOH 6.9E+03 5600.0 1.8E-04 -1510.0 1; 2

MeCO3H 7.5E+02 5300.0 6.3E-09 0.0 1

MeCO2H 4.7E+03 6000.0 1.8E-05 0.0 1; 2

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Table 3. Tracers/species in the UKCA standard tropospheric (StdTrop) scheme, where

Me = CH3, Et = trate.

: C3H7,

PAN = peroxy acetyl nitrate, and PPAN = peroxy propionyl ni-

Species Name Treatment

H2O Constant Field

CO2 Constant Tracer

H2 Constant Tracer

N2 Constant Tracer

O2 Constant Tracer

O(3P) Steady State

O(1D) Steady State

OH Steady State

HO2 Steady State

MeOO Steady State

EtOO Steady State

MeCO3 Steady State

л-PrOO Steady State

i-PrOO Steady State

EtCO3 Steady State

MeCOCH2OO Steady State

O3 Transported Tracer

NO Transported Tracer

NO3 Transported Tracer

NO2 Transported Tracer

N2O5 Transported Tracer

HO2NO2 Transported Tracer

hono2 Transported Tracer

H2O2 Transported Tracer

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Species Name Treatment

CH4 Transported Tracer

CO Transported Tracer

HCHO Transported Tracer

MeOOH Transported Tracer

HONO Transported Tracer

C2H6 Transported Tracer

EtOOH Transported Tracer

MeCHO Transported Tracer

PAN Transported Tracer

C3H8 Transported Tracer

/7-PrOOH Transported Tracer

/-PrOOH Transported Tracer

EtCHO Transported Tracer

Me2CO Transported Tracer

MeCOCH2OOH Transported Tracer

PPAN Transported Tracer

Me0N02 Transported Tracer

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Table 4. Photolytic reactions in the UKCA standard tropospheric (StdTrop) scheme, where Me = CH3, Et = C2H5, Pr = C3H7, PAN = peroxy acetyl nitrate, and PPAN = peroxy propionyl nitrate.

Label Reaction

10 11 12

20 21 22

MeCHO + HO2 + OH OH + OH

HO2 + HO2 + CO H2 + CO -» HO2 + no2

OH + NO2

EtOOH + hv-H2O2 + hv — HCHO + hv-HCHO + hv-HO2NO2 + hv-HONO2 + hv — MeCHO + hv — MeOO + HO2 + CO MeCHO + hv — CH4 + CO MeOOH + hv — HO2 + HCHO + OH ► NO3 + NO2 NO + O(3P) NO + O2

N2O5 + hv NO2 + hv -NO3 + hv -NO3 + hv -

02 + hv —

03 + hv — O3 + hv —

NO2 + O(3P)

O(3P) + O(3P) O2 + O(1D) O2 + O(3P) PAN + hv — MeCO3 + NO2 HONO + hv — OH + NO EtCHO + hv — EtOO + HO2 + CO Me2CO + hv — MeCO3 + MeOO л-PrOOH + hv i-PrOOH + hv-

EtCHO + HO2 + OH Me2CO + HO2 + OH

MeCOCH2OOH + hv

MeCO3 + HCHO + OH

PPAN + hv-

EtCO3 + NO2

MeONO2 + hv —

HO2 + HCHO + NO2

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Table 5. Bimolecular reactions in the UKCA standard tropospheric (StdTrop) scheme, where

Me = CH3, Et = C2H5, Pr = C3H7, PAN = peroxy acetyl nitrate, PPAN = peroxy propionyl ni-

trate, and MGLY = methylglyoxal. The rate co-efficient of a bimolecular reaction is given by k = A (300)aexp ^R^) , where A is the pre-exponential factor, T is the temperature, R is the

gas constant, a is the exponent for the (3T0) temperature dependence, and Ea is the Arrhenius activation energy. References: 1. Atkinson et al. (2000), 2. Atkinson et al. (2003), 3. Atkinson et al. (2004), 4. Atkinson et al. (2005), 5. Jenkin et al. (1997); Saunders et al. (1997), 6. Bloss et al. (2005), 7. Tyndall et al. (2001), and 8. Ravishankara et al. (2002).

Label Reaction A a Ea R Ref.

1 HO2 + NO ^ OH + NO2 3.60E-12 0.00 -270.0 2

2 HO2 + NO3 ^ OH + NO2 4.00E-12 0.00 0.0 2

3 HO2 + O3 ^ OH + O2 2.03E-16 4.57 -693.0 2

4 HO2 + HO2 ^ H2O2 2.20E-13 0.00 -600.0 2

5 HO2 + MeOO ^ MeOOH 3.80E-13 0.00 -780.0 4

6 HO2 + MeOO ^ HCHO 3.80E-13 0.00 -780.0 4

7 HO2 + EtOO ^ EtOOH 3.80E-13 0.00 -900.0 1

8 HO2 + MeCO3 ^ MeCO3H 2.08E-13 0.00 -980.0 4

9 HO2 + MeCO3 ^ MeCO2H + O3 1.04E-13 0.00 -980.0 4

10 HO2 + MeCO3 ^ OH + MeOO 2.08E-13 0.00 -980.0 4

11 HO2 + ^-PrOO ^ ^-PrOOH 1.51E-13 0.00 -1300.0 5

12 HO2 + i-PrOO ^ i-PrOOH 1.51E-13 0.00 -1300.0 5

13 HO2 + EtCO3 ^ O2 + EtCO3H 3.05E-13 0.00 -1040.0 5

14 HO2 + EtCO3 ^ O3 + EtCO2H 1.25E-13 0.00 -1040.0 5

15 HO2 + MeCOCH2OO ^ MeCOCH2OOH 1.36E-13 0.00 -1250.0 5

16 MeOO + NO ^ HO2 + HCHO + NO2 2.95E-12 0.00 -285.0 2

17 MeOO + NO ^ MeONO2 2.95E-15 0.00 -285.0 2

18 MeOO + NO3 ^ HO2 + HCHO + NO2 1.30E-12 0.00 0.0 2

19 MeOO + MeOO ^ MeOH + HCHO 1.03E-13 0.00 -365.0 2

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Label Reaction A a Ea R Ref.

20 MeOO + MeOO ^ HO2 + HO2 + HCHO + HCHO 1.03E-13 0.00 -365.0 2

21 MeOO + MeCO3 ^ HO2 + HCHO + MeOO 1.80E-12 0.00 -500.0 1

22 MeOO + MeCO3 ^ MeCO2H + HCHO 2.00E-13 0.00 -500.0 1

23 EtOO + NO ^ MeCHO + HO2 + NO2 2.60E-12 0.00 -380.0 2

24 EtOO + NO3 ^ MeCHO + HO2 + NO2 2.30E-12 0.00 0.0 1

25 EtOO + MeCO3 ^ MeCHO + HO2 + MeOO 4.40E-13 0.00 -1070.0 2

26 MeCO3 + NO ^ MeOO + CO2 + NO2 7.50E-12 0.00 -290.0 2

27 MeCO3 + NO3 ^ MeOO + CO2 + NO2 4.00E-12 0.00 0.0 5

28 ^-PrOO + NO ^ EtCHO + HO2 + NO2 2.90E-12 0.00 -350.0 2

29 ^-PrOO + NO3 ^ EtCHO + HO2 + NO2 2.50E-12 0.00 0.0 5

30 i-PrOO + NO ^ Me2CO + HO2 + NO2 2.70E-12 0.00 -360.0 2

31 i-PrOO + NO3 ^ Me2CO + HO2 + NO2 2.50E-12 0.00 0.0 5

32 EtCO3 + NO ^ EtOO + CO2 + NO2 6.70E-12 0.00 -340.0 2

33 EtCO3 + NO3 ^ EtOO + CO2 + NO2 4.00E-12 0.00 0.0 5

34 MeCOCH2OO + NO ^ MeCO3 + HCHO + NO2 2.80E-12 0.00 -300.0 7

35 MeCOCH2OO + NO3 ^ MeCO3 + HCHO + NO2 2.50E-12 0.00 0.0 5

36 NO + NO3 ^ NO2 + NO2 1.80E-11 0.00 -110.0 2

37 NO + O3 ^ NO2 1.40E-12 0.00 1310.0 2

38 NO2 + O3 ^ NO3 1.40E-13 0.00 2470.0 1

39 NO3 + HCHO ^ HONO2 + HO2 + CO 2.00E-12 0.00 2440.0 2

40 NO3 + MeCHO ^ HONO2 + MeCO3 1.40E-12 0.00 1860.0 2

41 NO3 + EtCHO ^ HONO2 + EtCO3 3.46E-12 0.00 1862.0 6

42 NO3 + Me2CO ^ HONO2 + MeCOCH2OO 3.00E-17 0.00 0.0 2

43 N2O5 + H2O ^ HONO2 + hono2 2.50E-22 0.00 0.0 2

44 O(3P) + O3 ^ O2 + O2 8.00E-12 0.00 2060.0 1

45 O(1D) + CH4 ^ OH + MeOO 1.05E-10 0.00 0.0 2

46 O(1D) + CH4 ^ HCHO + H2 7.50E-12 0.00 0.0 2

47 O(1D) + CH4 ^ HCHO + HO2 + HO2 3.45E-11 0.00 0.0 2

48 O(1D) + H2O ^ OH + OH 2.20E-10 0.00 0.0 1

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Label Reaction A a Ea R Ref.

49 O(1D) + N2 ^ O(3P) + N2 2.10E-11 0.00 -115.0 8

50 O(1D) + O2 ^ O(3P) + O2 3.20E-11 0.00 -67.0 1

51 OH + CH4 ^ H2O + MeOO 1.85E-12 0.00 1690.0 2

52 OH + C2H6 ^ H2O + EtOO 6.90E-12 0.00 1000.0 2

53 OH + C3H8 ^ ^-PrOO + H2O 7.60E-12 0.00 585.0 2

54 OH + C3H8 ^ i-PrOO + H2O 7.60E-12 0.00 585.0 2

55 OH + CO ^ HO2 1.44E-13 0.00 0.0 4

56 OH + EtCHO ^ H2O + EtCO3 5.10E-12 0.00 -405.0 2

57 OH + EtOOH ^ H2O + MeCHO + OH 8.01E-12 0.00 0.0 6

58 OH + EtOOH ^ H2O + EtOO 1.90E-12 0.00 -190.0 5

59 OH + H2 ^ H2O + HO2 7.70E-12 0.00 2100.0 2

60 OH + H2O2 ^ H2O + HO2 2.90E-12 0.00 160.0 1

61 OH + HCHO ^ H2O + HO2 + CO 5.40E-12 0.00 -135.0 3

62 OH + HO2 ^ H2O 4.80E-11 0.00 -250.0 1

63 OH + HO2NO2 ^ H2O + no2 1.90E-12 0.00 -270.0 2

64 OH + HONO2 ^ H2O + NO3 1.50E-13 0.00 0.0 2

65 OH + HONO ^ H2O + NO2 2.50E-12 0.00 -260.0 2

66 OH + MeOOH ^ H2O + HCHO + OH 1.02E-12 0.00 -190.0 2

67 OH + MeOOH ^ H2O + MeOO 1.89E-12 0.00 -190.0 2

68 OH + MeONO2 ^ HCHO + NO2 + H2O 4.00E-13 0.00 845.0 1

69 OH + Me2CO ^ H2O + MeCOCH2OO 8.80E-12 0.00 1320.0 2

70 OH + Me2CO ^ H2O + MeCOCH2OO 1.70E-14 0.00 -420.0 2

71 OH + MeCOCH2OOH ^ H2O + MeCOCH2OO 1.90E-12 0.00 -190.0 6

72 OH + MeCOCH2OOH ^ OH + MGLY 8.39E-12 0.00 0.0 6

73 OH + MeCHO ^ H2O + MeCO3 4.40E-12 0.00 -365.0 6

74 OH + NO3 ^ HO2 + NO2 2.00E-11 0.00 0.0 1

75 OH + O3 ^ HO2 + O2 1.70E-12 0.00 940.0 2

76 OH + OH ^ H2O + O(3P) 6.31E-14 2.60 -945.0 2

77 OH + PAN ^ HCHO + NO2 + H2O 3.00E-14 0.00 0.0 1

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Table 6. Un/'- and termolecular reactions in the UKCA standard tropospheric (StdTrop) scheme,

where Me = CH3, Et:

PAN = peroxy acetyl nitrate, and PPAN = peroxy propionyl ni-

UKCA: The troposphere

trate. Rate co-efficients for termolecular reactions are calculated using a modified Lindemann- a

Hinshelwood mechanism i.e. k =

, where ko and kra are the low and high-

pressure limits calculated in the same manner as bimolecular rate co-efficients, and [M] is the

number density in molecules cm-(2003).

References: 1. Atkinson et al. (2000), 2. Atkinson et al.

Label Reaction

Low pressure limit, ko A a

High pressure limit, k„ A a

1 HO2 + HO2 + M ^ H2O2 + O2 + M 0.00 1.90E-33 0.00 -980.0 0.00E+00 0.00 0.0 1

2 HO2 + NO2 + M ^ HO2NO2 + M 0.60 1.80E-31 -3.20 0.0 4.70E-12 0.00 0.0 1

3 HO2NO2 + M ^ HO2 + NO2 + M 0.60 4.10E-05 0.00 10 650.0 4.80E+15 0.00 11 170.0 2

4 MeCO3 + NO2 + M ^ PAN + M 0.30 2.70E-28 -7.10 0.0 1.20E-11 -0.90 0.0 1

5 PAN + M ^ MeCO3 + NO2 + M 0.30 4.90E-03 0.00 12100.0 5.40E+16 0.00 13 830.0 1

6 N2O5 + M ^ NO2 + NO3 + M 0.35 1.30E-03 -3.50 11 000.0 9.70E+14 0.10 11 080.0 2

7 NO2 + NO3 + M ^ N2O5 + M 0.35 3.60E-30 -4.10 0.0 1.90E-12 0.20 0.0 2

8 O(3P) + O2 + M ^ O3 + M 0.00 5.70E-34 -2.60 0.0 0.00E+00 0.00 0.0 2

9 OH + NO + M ^ HONO + M 1420.00 7.40E-31 -2.40 0.0 3.30E-11 -0.30 0.0 2

10 OH + NO2 + M ^ HONO2 + M 0.40 3.30E-30 -3.00 0.0 4.10E-11 0.00 0.0 2

11 OH + OH + M ^ H2O2 + M 0.50 6.90E-31 -0.80 0.0 2.60E-11 0.00 0.0 1

12 EtCO3 + NO2 + M ^ PPAN + M 0.30 2.70E-28 -7.10 0.0 1.20E-11 -0.90 0.0 2

13 PPAN + M ^ EtCO3 + NO2 + M 0.30 1.70E-03 0.00 12100.0 5.40E+16 0.00 13 830.0 1

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Species Name

Treatment

ISO2 Steady State

macro2 Steady State

C5H8 Transported Tracer

ISOOH Transported Tracer

ISON Transported Tracer

MACR Transported Tracer

MACROOH Transported Tracer

MPAN Transported Tracer

HACET Transported Tracer

MGLY Transported Tracer

NALD Transported Tracer

HCOOH Transported Tracer

MeCO3H Transported Tracer

MeCO2H Transported Tracer

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Table 7. Additional tracers/species associated with the Mainz Isoprene Mechanism in the UKCA TropIsop chemistry scheme, where Me = CH3, IS = C5H7, ISON = Lumped peroxy alkyl

nitrates (from C5H8+NO3) and hydroxy alkyl nitrates (from ISO2 + NO), MACR = Lumped — species consisting of methacrolein, methyl vinyl ketone and other C4 carbonyls from isoprene

oxidation, MPAN = peroxy methacrylic nitric anhydride (and other higher PANs from isoprene s

chemistry), HACET = hydroxyacetone and other C3 ketones, MGLY = methylglyoxal and other 2

C3 carbonyls, and NALD = nitrooxy acetaldehyde. i

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Table 8. Additional photolytic reactions associated with the Mainz Isoprene Mechanism in the UKCA TropIsop chemistry scheme, where IS = C5H7, ISON = Lumped peroxy alkyl nitrates (from C5H8+NO3) and hydroxy alkyl nitrates (from ISO2 + NO), MACR = Lumped species consisting of methacrolein, methyl vinyl ketone and other C4 carbonyls from isoprene chemistry, MPAN = peroxy methacrylic nitric anhydride (and other higher PANs from isoprene chemistry), HACET = hydroxyacetone and other C3 ketones, MGLY = methylglyoxal nd other C3 carbonyls, and NALD = nitrooxy acetaldehyde.

Label Reaction

■ OH + MACR + HCHO + HO2

NO2 + MACR + HCHO + HO2 -» MeCO3 + HCHO + CO + HO2

ISOOH + hv-ISON + hv -MACR + hv-MPAN + hv — MACRO2 + NO2 MACROOH + hv — OH + OH + HO2 + HO2 MACROOH + hv — HACET + CO + MGLY + HCHO HACET + hv-MGLY + hv

- MeCO3 + HCHO + HO2 • MeCO3 + CO + HO2 HCHO + CO + NO2 + HO2

NALD + hv-MeCO3H + hv — MeOO + OH

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Table 9. Additional bimolecular reactions associated with the Mainz Isoprene Mechanism in the UKCA TropIsop chemistry scheme, where IS = C5H7, ISON = Lumped peroxy alkyl nitrates (from C5H8+NO3) and hydroxy alkyl nitrates (from ISO2 + NO), MACR = Lumped species consisting of methacrolein, methyl vinyl ketone and other C4 carbonyls from isoprene chemistry, MPAN = peroxy methacrylic nitric anhydride (and other higher PANs from isoprene chemistry), HACET = hydroxyacetone, MGLY = methylglyoxal, and NALD = nitrooxy acetaldehyde. The rate co-efficient of a bimolecular reaction is given by k = A (3O0)a exp ^, where A is the pre-

exponential factor, T is the temperature, R is the gas constant, a is the exponent for the (3O0) temperature dependence, and Ea is the Arrhenius activation energy. References: 1. Atkinson et al. (2005), 2. Saunders et al. (2003), 3. Poschl et al. (2000) and references therein.

Label Reaction A a Ea R

84 OH + C5H8 — ISO2 2.70E-11 0.00 -390.0

85 O3 + C5H8 — 1.95MACR + 1.74HCHO + 0.3MACRÖ2 + 0.3MeCÖ3 3.47E-15 0.00 1995.0

86 O3 + C5H8 — 0.24MeOO + 0.84HCOOH + 0.42CO + 0.27H2O2 3.47E-15 0.00 1995.0

87 O3 + C5H8 — 0.75HO2 + 0.75OH 3.47E-15 0.00 1995.0

88 NO3 + C5H8 — ISON 3.15e-12 0.0 450.0

89 NO + ISO2 — NO2 + MACR + HCHO + HO2 2.43E-12 0.00 -360.0

90 NO + ISO2 — ISON 1.12E-13 0.00 -360.0

91 HO2 + ISO2 — ISOOH 2.05E-13 0.00 -1300.0

92 ISO2 + ISO2 — 2MACR + HCHO + HO2 2.00E-12 0.00 0.0

93 OH + ISOOH — MACR + OH 1.00E-10 0.00 0.0

94 OH + ISON — HACET + NALD 1.30E-11 0.00 0.0

95 OH + MACR — MACRO2 1.30E-12 0.00 -610.0

96 OH + MACR — MACRO2 4.00E-12 0.00 -380.0

97 O3 + MACR — 1.80MGLY + 0.90HCOOH + 0.64HO2 + 0.44CO 2.13E-16 0.00 1520.0

98 O3 + MACR — 0.38OH + 0.20MeCO3 2.13E-16 0.00 1520.0

99 O3 + MACR — 1.80MGLY + 0.90HCOOH + 0.64HO2 + 0.44CO 3.50E-16 0.00 2100.0

100 O3 + MACR — 0.38OH + 0.20MeCO3 3.50E-16 0.00 2100.0

101 NO + MACRO2 — 2.0NO2 + 0.5MeCO3 + 0.5HACET + 0.5CO 1.27E-12 0.00 -360.0

102 NO + MACRO2 — 1.0MGLY + 1.5HCHO + 1.5HO2 1.27E-12 0.00 -360.0

103 HO2 + MACRO2 — MACROOH 1.82E-13 0.00 -1300.0

104 MACRO2 + MACRO2 — 2HACET + 2MGLY + HCHO + CO 1.00E-12 0.00 0.0

105 MACRO2 + MACRO2 — HO2 + HO2 1.00E-12 0.00 0.0

106 OH + MPAN — HACET + NO2 2.90E-11 0.00 0.0

107 OH + MACROOH — MACRO2 3.00E-11 0.00 0.0

108 OH + HACET — MGLY + HO2 3.00E-12 0.00 0.0

109 OH + MGLY — MeCO3 + CO 1.50E-11 0.00 0.0

110 NO3 + MGLY — MeCO3 + CO + HONO2 3.46E-12 0.00 1860.0

111 OH + NALD — HCHO + CO + NO2 4.40E-12 0.00 -365.0

112 OH + MeCO3H — MeCO3 3.70E-12 0.00 0.0

113 OH + MeCO2H — MeOO 4.00E-13 0.00 -200.0

114 OH + HCOOH — HO2 4.50E-13 0.00 0.0

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Table 10. Additional uni- and termolecular reactions associated with the Mainz Isoprene Mechanism in the UKCA TropIsop chemistry scheme, where MACR = Lumped species consisting of methacrolein, methyl vinyl ketone and other C4 carbonyls from isoprene chemistry, and MPAN = peroxy methacrylic nitric anhydride (and other higher PANs from isoprene chemistry). Rate co-efficients for termolecular reactions are calculated using a modified Lindemann-

Hinshelwood mechanism i.e. k ■■

where ko and kra are the low and high-

pressure limits calculated in the same manner as bimolecular rate co-efficients, and [M] is the

number density in molecules cm 3. References: 1. Poschl et al. (2000) and references therein.

Label Reaction Fc Low pressure limit, ko High pressure limit, kœ Ref.

A a R A a R

14 MACRO2 + NO2 ^ MPAN 0.30 2.70E-28 -7.10 0.0 1.20E-11 -0.90 0.0 1

15 MPAN ^ MACRO2 + NO2 0.30 4.90E-03 0.00 12100.0 5.40E+16 0.00 13830.0 1

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Table 11. Summary of the global annual emissions of trace gases used with the two chemistry schemes, StdTrop and TropIsop, in UKCA. * In the case of the StdTrop chemistry scheme, an additional 354TgCOyr-1 are added as a surrogate for C5H8 emissions. When running with the TropIsop chemistry scheme, these additional emissions are neglected but C5H8 emissions with

a global annual emission total of 506TgCyr 1 are added instead.

Species Sector

Emissions

Anthropogenic (C2H6) Anthropogenic (C2H4) Anthropogenic (C2H2) Biomass Burning (C2H6) Biomass Burning (C2H4) Biomass Burning (C2H2) Shipping (C2H6) Shipping (C2H4) Shipping (C2H2)

Anthropogenic (C3H8) Anthropogenic (C3H6) Biomass Burning (C3H8) Biomass Burning (C3H6) Shipping (C3H8) Shipping (C3H6)

Anthropogenic Biomass Burning Shipping

3.2 Tg 8.0 Tg 3.8 Tg 3.2 Tg 6.8 Tg 1.7 Tg 0.2 Tg 0.2 Tg 0.0 Tg 27.1 Tg

C2H6Vr" C2H6Vr" C2H6 yr-C2H6 yr-C2H6 yr-C2H6 yr-C2H6 yr-C2H6yr-C2H6yr-C2H6yr-

3.4 TgC3H8yr-3.3 TgC3H8yr-

2.0 TgC3H8yr-

4.1 TgC3H8yr-0.4 TgC3H8yr-0.3 TgC3H8yr-

13.5 TgC3H8yr-

274.1 TgCH4yr-25.5 TgCH4yr-0.4 TgCH4yr-

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Termites 20.0 TgCH4yr

Oceans 15.0 TgCH4yr

Hydrates 10.0 TgCH4yr

Wetlands 108.7 TgCH4yr

Bogs 30.0 TgCH4yr

Swamps 39.1 TgCH4yr

Tundra 3.2 TgCH4yr

Total 526.0 TgCH4yr

CO Anthropogenic 607.5 TgCOyr

Biomass Burning 459.1 TgCOyr

Shipping 1.2 TgCOyr

Oceans 45.0 TgCOyr

* Vegetation 354.0 TgCOyr

Total 1466.8 TgCOyr

HCHO Anthropogenic 3.2 TgHCHOyr

Biomass Burning 5.8 Tg HCHOyr

Total 9.0 TgHCHOyr

NOx Anthropogenic 26.5 TgNyr

Biomass Burning 4.5 Tg Nyr

Shipping 5.4 Tg Nyr

Soil 5.6 TgNyr

Aircraft 0.8 TgNyr

Lightning 5.0 TgNyr

Total 47.8 TgNyr

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ch3cho Biomass Burning 8.7TgMeCHOyr

Total 8.7 Tg MeCHOyr"

ch3coch3 Anthropogenic 0.7TgMe2COyr"

Biomass Burning 5.0 Tg Me2CO yr"

Vegetation 40.0 Tg Me2CO yr"

Total 45.7 TgMe2COyr"

* C5H8 Vegetation 506.0 TgCyr"

Total 506.0 TgCyr"

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Fig. 1. Schematic of the terms used to calculate the overall surface or canopy resistance term, rc, for dry deposition. Adapted from Sanderson et al. (2007).

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Fig. 2. Multi-annual zonal mean radon concentrations in units of 10-21 mol mol-1 for (a) the control experiment and (b) an experiment with no boundary layer mixing for the June-July-August (JJA) time period.

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i. 60 I 40

•L 30

Mace Head (53°N, 10°W)

X Observations

— UKCA

Socorro (39°N, 107°W)

OF T T T T T T T T T T "

Bermuda (32°N, 65°W)

JFMAMJJASOND month

Mauna Loa (19°N, 156°W)

Cape Grim (40°S, 144°E)

JFMAMJJASOND month

E 200 E 150

Chester (41 °N,75°W)

üvermore (37°N, 122°W)

Bombay (19°N, 72°E)

1 60 5 <o

JFMAMJJASOND

Amsterdam Island {37°s, 77°E)

Dumont d'Urvllle (67°S. 140°E)

JFMAMJJASOND

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Fig. 3. Comparison of modelled surface radon concentrations in units of 10-21 mol mol-1 with surface observations for a number of sites spanning the latitude range 53° N-67° S. Observations are shown as black crosses and the UKCA model is shown in red. Measurements are taken from Hauglustaine et al. (2004) and references therein.

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Moffett Field , Jun and Aug

Nova Scotia, Aug

X Observations — UKCA

Observations — UKCA

Radon, 1.0x10 mol/mol

Fig. 4. Comparison of modelled profiles of radon in units; of 10"21 mol mol"' with observations at two different sites. Observations are shown as black crosses and the UKCA model is shown in red. Measurements from Moffett Field and Nova Scotia are taken from Kritz et al. (1998) and Zaucker et al. (1996), respectively.

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Thüle (77°N, 67°W)

Kap Tobin (70°N, 22°W)

s jf >|c

Beaverton (45"N, 1 23°W) X Observations

Chester (41 °N, 75°W)

Miami (26°N, 80°W)

arrow (71 "N, 1 56°W)

Moosonee (51°N, 80°W)

Rexburg (44°N, 67°W)

Montgomery (32°N 85°W) X Observations

Mauna Loa (19°N, 156°W)

Fig. 5. Comparison of modelled surface lead concentrations in units of nBq m-3 (STP) with surface observations for a number of sites in the Northern Hemisphere. Observations are shown as black crosses and the UKCA model is shown in red. Measurements are taken from the Surface Air Sampling Program database of the Environmental Measurements Laboratory (EML) (http://www.nbl.doe.gov/ht m/EML_Legacy-Website/databases .htm).

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Guayaquil (2°S, 80°W)

X Observation — UKCA I

J F M A M J JASOND month La Reunion (21°S, 56DE)

X Observation ' - UKCA liiii^ litifcj

Perth (32°5, 115°E)

Chatham Is. (44°S, 176°W)

Falklands (51 °S, 58°W)

X Observation

— UKCA 1

Chacaltay (16°S, 68°W)

X Observations_I

' — UKCA A 1 \

jjJn 1- Î

Norfolk Is. (29°S, 168°E)

Cape Grim (40aS, 144°E)

Invercargill (46°S, 1 68°E)

South Pole (9Q°S, 0°E)

ïHiiitij

Fig. 6. Comparison of modelled surface lead concentrations in units of nBq m-3 (STP) with surface observations for a number of sites in the Southern Hemisphere. Observations are shown as black crosses and the UKCA model is shown in red. Measurements are taken from the Surface Air Sampling Program database of the Environmental Measurements Laboratory (EML) (http://www.nbl.doe.gov/ht m/EML_Legacy-Website/databases .htm).

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100 200 300 400

Measured 210Pb deposition flux / Bq m"2 yr"1

Fig. 7. Scatter plot of the modelled and measured

of Bqm 2yr 1, where 1.0 Bq m 2yr

Pb annual deposition fluxes in units Northern Hemi-

22 2 1 is equivalent to 8.87 x10 kgm yr

sphere points are shown in blue and Southern Hemisphere points are shown in red. The vertical whiskers rep resent ± 1 standard deviation of tin e modelled annual mean deposition flu xes at each location. Measurements are taken from Preiss and Genthon (1997).

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10 20 50 50 100 200 500 500

50 50 100 200 500 500

10 20 50 50 100 200 500 500

Fig. 8. Global multi-annual mean distribution of 210Pb deposition fluxes from UKCA: Dry deposition, convective scavenging, dynamic scavenging, and total deposition flux. Units are in Bqm-2yr-1.

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0 100 200 300 400 500 600 210Pb concentration / /j,Bq (STP)

0 200 400 600 800 1000 1200 210Pb concentration / /j,Bq (STP)

Fig. 9. Comparison between otu^^rveïd vertical profiies of Pb concentrations and modelled concentrations from UKCA (red) over (a) the remote Pacific and (b) over near Asia. Observations are taken from Dibb et ai. (1997).

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980 at 30°W

981 at30°W

983at30°w 980 at 7.5DW at 7.5°W 982 at 7.5"W 9S3 at 7.5°W

Obseivatlon8 from October 19fl0 Obseivatlon8 from October 1983

Fig. 10. Comparison between observed meridional profiles of 85Kr concentrations with modelled concentrations from UKCA over the Atlantic ocean (a) in March and (b) in October. Observations are taken from Weiss et al. (1983) and UKCA was sampled at two longitudes (7.5 and 30° W) to straddle the area sampled by the observations.

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a) 2D Offline: zonal mean j(N02) in January/s"1

d) 2D Offline: zonal mean j(NOz) in July / s"

1a-4 25&4 0.001 0.002 0.005 0.007 0.01

1e-4 2e-4 5e-4 0.001 0.002 0005 0.007 0.01

I 0.0,2

I 0.006

1 0.003

c) jiNOJ rates in January

I 0-006

I 0-003 0.000

f) ¡(NOJ rates in July

Level 1

âlÊlkéAÊËÊr^^ Level 5

Level 10

Level 15

Level 20

¿¿i»^® r*= 0.99 Level 30

0.006 0.009

2D phototysla rate/a'1

0.006 0.009

2D photolyals rats / 81

Fig. 11. Comparison lb etwee n zonal mean J(N02) rates in January and in July from the offline 2-D photolysis scheme in (a) and (d) and from the online; Fast-J scheme in (b) and (e). Scatter plots of the J(NO2) photolysis rate from both schemes are shown in (c) and (f).

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a) Year 2000 aritirapogark: emissions / kg(CO) mV

Year 2000 Momaas bumhg amtealofis I kg(CO) mV

c) Year 2000 ahlp amlaalons I kg(CO) mV

e) Yaar 2000 surrogate BVOC emEatora / kg(CO) mV

d) Year 2000 oceanic amlsalofia / kg(CD) mV

Fig. 12. Annual mean surface CO emissions used in the UKCA StdTrop tropospheric chemistry scheme from (a) land-based anthropogenic sources, (It»)) biomass burning, (c) shipping, (d) natural ocean source, and (e) surrogate isoprene emissions. Land-basend anthropogenic emissions take account of the following sectors: energy production and distribution, industry, land transport, residential and commercial sources, solvent use, agriculture, agricultural waste burning, waste and waste distribution. Note that the scales for (c) and (d) are two orders and one order of magnitude lower than the others, respectively. Emissions are for the year 2000.

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a) Annual mean surface CH4 / ppbv

1700 1750 1000

1900 1960

b) Annual zonal mean CH4 / ppbv

600 1000 1200 1400 1600 1700 1750 1800 1850 1900

Fig. 13. Multi-annual annual mean (a) surface and (b) zonal mean CH4 distributions from the StdTrop chemistry scheme in the tropospheric configuration of the UKCA model. The thick dashed line in (b) marks the position of the modelled tropopause (Hoerling et al., 1993).

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Barrow {71 °N, 157°W)

Mace Head (53°N, 10°W)

J F M A M J JASOND

Niwot Ridge (40°N, 106°W)

J F M A M J J A S O N D

Bermuda (32°N, 65°W)

J F M A M J J A S O N D

Mauna Loa (20°N, 156°W)

JFMAMJJASOND

{13°N, 59°W)

J F M A M J JASOND

(14°S, 171°W)

JFMAMJJASOND

Cape Grim (41 °S, 145°E)

JFMAMJJASOND

Syowa (69°S, 39° E)

J F M A M J JASOND

South Pole (90°S, 0°E)

JFMAMJJASOND

J F M A M J JASOND

Fig. 14. Comparison between multi-annual seasonal cycles of modelled and observed surface CH4 concentrations from the "1999-2006 time period. Th e surface sites in clude Barrow (71° N, 157° W), Mace Head (53° N, 10° W), Niwot Ridge (40° N, 106° W), Bermuda (32° N, 65° W), Mauna Loa (20° N, 156° W), Barbados (13° N, 59° W), Samoa (14° S, 171° W), Cape Grim (41° S, 145° E), Syowa (69° S, 39° E), and the South Pole (90° S, 0° E).

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É Cl Cl

Latitude/ N

Fig. 15. Comparison between multi-annual latitudinal profiles of observed and modelled surface CH4 concentrations with surface observations from GLOBALVIEW-CH4 (2009). Every second model latitude is omitted from the plot for the purpose of clarity.

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a) Annual mean surface CO / ppbv

70 60 90 100 120 160 200 300

b) Annual zonal mean CO I ppbv

20 30 40

100 120 160

Fig. 16. Multi-annual annual mean (a) surface and (b) zonal mean CO distributions from the StdTrop chemistry scheme in the tropospheric configuration of the UKCA model. The thick dashed line in (b) marks the position of the modelled tropopause (Hoerling et al., 1993).

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Barrow (71°N, 157°W)

Measurements

• • • • . --, »

JFMAMJJASOND

Niwot Ridge (40°N, 106°W)

JFMAMJJASOND

Mauna Loa (20°N, 156°W)

JFMAMJJASOND

Samoa (14°S, 171°W)

JFMAMJJASOND

Syowa (69°S, 39°E)

JFMAMJJASOND

s Head (53°N, 10°W)

JFMAMJJASOND

Bermuda (32°N, 65°W)

JFMAMJJASOND

Cape Grim (41 °S,145°E)

JFMAMJJASOND

South Pole (90°S, 0°E)

JFMAMJJASOND

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UKCA: The troposphere

F. M. O'Connor et al.

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Fig. 17. Comparison between multi-annual mont hly mean modelled surface CO concentrations with surface observations from th e 1999-2006 time period. The surface sites includ e Barrow (71° N, 157° W), Mace Head (53° N, 10° W), Niwot Ridge (40° N, 106° W), Bermuda (32° N, 65° W), Mauna Loa (20° N, 156° W), Barbados (13° N, 59° W), Samoa (14° S, 171° W), Cape Grim (41° S, 145° E), Syowa (69° S, 39° E), and the South Pole (90° S, 0° E).

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PEM-Trop-B: Tahiti

0 20 40 60 80 100

CO/ppbv

PEM-West-A: Hawaii

50 100 150 200 CO / ppbv

SON EX: EAtiantic

50 100 150 200 CO/ppbv

TRACE-A: E-Brazil

100 200 300 400 CO / ppbv

PEM-Trop-B: Hawaii

0 50 100 150 200 CO/ppbv

PEM-West-A: Japan

50 100 150 200 CO / ppbv

SONEX: Ireland

100 150 200 CO/ppbv

TRACE-A: S-Africa

100 200 300 400 500 CO / ppbv

PEM-Trop-B: Chr-lsland

0 50 100 150 200 CO / ppbv

PEM-West-A: Phil-Sea

50 100 150 200 CO / ppbv

SONEX: Newfoundland

100 1 50 200 CO/ppbv

TRACE-A: S-Atlantic

100 150 200 CO/ppbv

PEM-West-A: China-Coast

0 100 200 300 400 CO/ppbv

PEM-West-A: WTrop-Pac

20 40 60 80 100 CO/ppbv

TRACE-A: E-Brazil-Coast

100 1 50 200 CO /ppbv

TRACE-A: W-Afr-Coast

100 1 50 200 CO/ppbv

Fig. 18. Comparison between regional vertical profiles of modelled (red) and observed (black) CO from various aircraft campaigns compiled inter data composites by Emmons et al. (2000). In each case, the mean concentration in each altitude bin is plotted and the horizontal lines represent the mean ± 1 standard deviation.

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UKCA: The troposphere

F. M. O'Connor et al.

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a) Annual mean surface NOJ pptv

80 SOW 0 90E 180

10 20 50 100 200 500 1000 2000 S0C b) Annual zonal mean NOx/ pptv » ie+4

10 20 50 100

1000 2000

Fig. 19. Multi-annual annual mean (a) surface and (b) zonal mean NOx distributions from the StdTrop chemistry scheme in the tropospheric configuration of the UKCA model. The thick dashed line in (b) marks the position of the modelled tropopause (Hoerling et al., 1993).

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UKCA: The troposphere

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PEM-Trop-B: Tahiti

NOx / pptv

PEM-West-A: Hawaii

1 10 100 1000 NO,/pptv

TRACE-A: E-Brazil-Coast

10 100 1000 NOx / pptv

TRACE-A: W-Afr-Coast

10 100 1 000 NOx / pptv

PEM-Trop-B: Hawaii

10 100 1000 NOx / pptv

PEM-West-A: Japan

NO,/pptv

TRACE-A: E-Brazil

100 100010000 NOx / pptv

PEM-Trop-B: Chr-Island

NOx / pptv

PEM-West-A: Phil-Sea

NO,/pptv

TRACE-A: S-Africa

10 100 100010000 NOx / pptv

PEM-West-A: China-Coast

10 100 1000 NO,/pptv

PEM-West-A: WTrop-Pac

NO,/pptv

TRACE-A: S-Atlantic

10 100 1000 NOx / pptv

Fig. 20. C omparison betwe en region ail vertica I profiles of mode lied (red) and observed (black) NOx from various aircraft campaigns compiled into) data composites by Emmons et al. (2000). In each case, the mean concentration in each altitude bin is plotted and the horizontal lines represent the mean ± 1 standard deviation.

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UKCA: The troposphere

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a) Zonal mean OH in JAN / 10s molec cm"3

a) Zonal mean OH in JUL/10 molec cm

Fig. 21. Multi-annual zonal and diurnal mean OH distributions from the StdTrop chemistry scheme in the tropospheric configuration of the UKCA model in (a) January and (b) July. The thick dashed lines mark the position of the modelled tropopause (Hoerling et al., 1993).

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UKCA: The troposphere

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a) Surface 03 in JAN / ppbv

40 45 50 55 60

a) Surface 03 in JUL / ppbv

50 55 60

Fig. 22. Multi-annual monthly mean distribution of surface O3 from the StdTrop chemistry scheme in the tropospheric configuration of the UKCA model in (a) January and (b) July.

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UKCA: The troposphere

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a) Zonal mean 03 in JAN / ppbv

35 40 50

80 100 500 1000

b) Zonal mean 03 in JUL/ppbv

40 50 60 80 100 500 1000

Fig. 23. Multi-annual zonail and diurnal mean O3 distributions from the StdTrop chemistry scheme in the tropospheric configuration of the UKCA model in (a) January and (b) July. The thick dashed lines mark the position of the modelled tropopause (Hoerling et al., 1993).

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UKCA: The troposphere

F. M. O'Connor et al.

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Barrow (71 °N, 157°W)

J F M A M J JASOND

Niwot Ridge (40°N, 106°W)

J F M A M J JASOND

Mauna Loa (20°N, 156°W)

':ÍTímttíffl

J F M A M J JASOND

Samoa (14°S, 171°W)

rrTiiTÏÏTîîl

J F M A M J JASOND

Syowa (69°S, 39°E)

J F M A M J JASOND

Mace Head (53°N, 10°W)

J F M A M J JASOND

Bermuda (32°N, 65°W)

J F M A M J JASOND

Barbados (13°N, 59°W)

J F M A M J JASOND

Cape Grim (41°S, 145°E)

J F M A M J JASOND

South Pole {90 S, 0 E)

J F M A M J JASOND

Fig. 24. Comparison between m ulti-annual mo nth ly mean modelled surface O3 concentrations with surface observations. The surface sites include Barrow ("^1 ° 157° W)i Mace Head (53° N, 10° W), Niwot Ridge (40° N, 106° W), Bermuda (32° N, 65° W), Mauna Loa (20° N, 156° W), Barbados (13° N, 59° W), Samoa (14° S, 171° W), Cape Grim (41° S, 145° E), Syowa (69° S, 39° E), and the South Pole (90° S, 0° E).

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UKCA: The troposphere

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90S—30S 100 hPa n= 5 30S-EQ 100 hPa *2.0 n = 15 EQ-30N 100 hPa *2.0 n= 4 fTrrr+tTTTTl 30N-90N 100 hPa n=10 ttttWH

90S—30S 250 hPa n= 5- ■30S-EQ 250 hPa *2.0 n = 15- ■ EQ-30N 250 hPa *2.0 n= 4-

90S—30S 400 hPa n= 5" ' 30S—EQ 400 hPa n = 15" tiiiiiiiiiii 'EQ-30N 400 hPa n= 4" M+-WÜ4

90S—30S 500 hPa n= 5" ' 30S—EQ 500 hPa n = 15" ■ EQ-30N 500 hPa n=

90S—30S 600 hPa n= 5" ■30S-EQ 600 hPa n = 15" uiüÄ: ■EQ-30N 600 hPa n= 4" ■30N-90N 600 hPa n=18

90S—30S 750 hPa n= 5- ■30S-EQ 750 hPa n = 15- • EQ-30N 750 hPa n=

90S-30S 850 hPa n= 5-■JFMAMJJAS0ND- ■30S-EQ B50 hPa n = 1Z ■J FMAMJJAS0ND- ■EQ-30N 850 hPa n= 4-■J FMAMJ J AS0ND- ■30N-90N 850 hPa n=17 ■JFMAMJJAS0ND-

Fig. 25. Comparison of the annual cycle of O3 observations (black) with UKCA (blue), the ACCENT ensemble of models (g(ey) and thee ACCENT multi-model ensemble mean (red). Observations are taken from Logan et al. (1999), Thompson et al. (2003a) and Thompson et al. (2003b). The modelled and observed O3 concentrations at 100 and 250 hPa in the latitude bands 30° S-EQ and EQ-30° N have been multiplied by a factor of 2.

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UKCA: The troposphere

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<8 10000

Fig. 2(5. Percentage difference in zonal annual mean O3 between UKCA and the Fortuin and Kelder (1998) climatology i.e. 100(model-climatology)/climatology. For the purposes of the comparison, the stratosphere (taken as O3 > 150ppbv) was masked.

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UKCA: The troposphere

F. M. O'Connor et al.

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£ 4000 a) Historical CL : Control

b) — Historical Control

- RCP2.6 RCP4.5

RCP6.0 RCP8.5

1850 1900 1950 2000 2050 2100 Year

- Historical — RCP2.6 Control RCP4.5

1850 1900 1950 2000 2050 2100 Year

200 I".......

1850 1900

1950 2000 2050 2100 Year

Fig. 27. Timeseries of (a) global mean surface CH4 concentration (b) CH4 lifetime against OH, (c) global mean surface O3 concentration and (d) tropospheric O3 burden in the HadGEM2-eS simulations carried out for CMIP5. Simulations include a pre-industrial cnnt-ol, an historical simulation, and 4 future? simulations using RCP2.6, RCP4.5, RCP6.0, and RCP8.5.

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UKCA: The troposphere

F. M. O'Connor et al.

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Evaluation of the new UKCA climate-composition model – Part 2: The Troposphere Academic research paper on "Earth and related environmental sciences"

Similar topics of scientific paper in Earth and related environmental sciences , author of scholarly article — F. M. O'Connor, C. E. Johnson, O. Morgenstern, N. L. Abraham, P. Braesicke, et al.

Academic research paper on topic "Evaluation of the new UKCA climate-composition model – Part 2: The Troposphere"