Online biogenic emissions

In the NMMB/BSC-CTM, biogenic emissions are computed online with the MEGANv2.04 (Guenther et al., 2006). MEGAN is a modelling system for estimating the net emission rate of gases and aerosols from terrestrial ecosystems into the above-canopy atmosphere at specific location and time. Driving variables include landcover and weather. Weather driving variables considered are temperature at 2m and short wave radiation. MEGAN canopy-scale emission factor differs from most other biogenic emissions models, which use a leaf-scale emission fac- tor. The emission-factor maps used in MEGAN are updated periodically and the algorithms are refined. MEGAN can be applied at regional or global scale with a horizontal resolution up to 1 km2. MEGAN estimates the emission of more than 130 non-methane volatile organic com- pounds (NMVOCs). All the MEGAN NMVOCs are speciated, following the CB05 chemical mechanism; thus, emissions for isoprene, lumped terpenes, methanol, nitrogen monoxide, ac- etaldehyde, ethanol, formaldehyde, higher aldehydes, toluene, carbon monoxide, ethane, ethene, paraffin carbon bond, and olefin carbon bond are considered within the chemical processes of the NMMB/BSC-CTM model. Biogenic emissions are computed every hour in order to account for evolving meteorological changes in solar radiation, surface temperature, moisture and pre- cipitation. Figure 2.10 (upper and bottom panels) shows the emission of isoprenes and terpenes (Tg/year) for January (left) and July (right) 2004 used in this model simulation.

Land cover, emission factors, and meteorological parameters are important driving variables of MEGAN, and the uncertainties of estimated biogenic volatile organic compounds (BVOCs) emissions and their impacts on surface ozone are hence associated with uncertainties in these inputs. Ashworth et al. (2010) evaluates the effect of varying the temporal resolution of the weather input data on isoprene emission estimates generated by the MEGAN. This study sug- gest that using daily or monthly data instead of hourly data a reduction of 3% and 7% is obtained. Moreover, the impact on a local scale can be more significant with reductions of up to 55% at some locations when using monthly average data compared with using hourly data. Another study, Marais et al. (2014), performs several sensitivity model runs to study the impact of differ- ent model input and model settings on isoprene estimates and resulted in differences of up to ± 17% of the reference isoprene total. In our study, weather inputs are based on previous day 24h averages and data of the hour of interest.

2.4. EMISSIONS

Figure 2.10: Biogenic emissions of isoprene (upper panel) and monoterpene (bottom panel), from the online model MEGANv2.04 for January (first column) and July (second column) 2004 used in this model simulation

Chapter 3

Inter-comparison of two ozone

stratospheric linear models within the

online global model

3.1

Introduction

Ozone is one of the dominant chemical species in the stratosphere. The majority of the ozone in the atmosphere, around 85 - 90%, is found in the stratosphere (Holton et al., 1995). The distribution of ozone in the stratosphere is a combination of chemical, dynamical and radiative processes. Ozone budgets in the upper troposphere and lower stratosphere (UTLS) are also controlled by Stratosphere-Troposphere Exchange (STE). Anthropogenic pollution increases in relation to STE events (Santer et al., 2003; Holton et al., 1995). Some regional air quality mod- elling results have shown a strong impact on ozone boundary conditions (obtained from global Chemical Transport Model (CTM)) in background ozone concentrations with the troposphere (Im et al., 2014a; Giordano et al., 2014). In this sense, proper STE treatment is demanded from global CTMs to properly model the ozone budget in the atmosphere.

A detailed description of the photochemistry of the ozone involves hundreds of chemical species and reactions. This significantly increases the complexity of numerical models a requiring large amount of computer time. In applications such as NWP or CTMs, with focus on the tropospheric chemistry, it is not feasible to implement full ozone photochemistry throughout the whole atmo- sphere. Consequently, a simple and faster ozone photochemistry approach for the stratosphere is preferable.

In the last two decades, a new generation of linear stratospheric photochemistry schemes and ozone distributions were developed using coefficients derived from a photochemical model with more detailed chemistry (Cariolle and Déqué, 1986; Cariolle and Teyssèdre, 2007; McLinden et al., 2000; McCormack et al., 2004, 2006; Monge-Sanz et al., 2011). These schemes are based on Cariolle and Déqué (1986), Cariolle v1.0, which uses a linear parameterization depending

3.1. INTRODUCTION

only on temperature and ozone amount in their coefficients. Cariolle v1.0 model lacks a hetero- geneous term to treat the ozone polar loss, however, in the later versions a heterogeneous term is included in the parametrization since the coefficients included only gas-phase chemistry (Car- iolle and Teyssèdre, 2007). However, a new linear ozone scheme COPCAT (Monge-Sanz et al., 2011) incorporates implicitly heterogeneous chemistry in their four coefficients and, hence, does not need an extra term accounting for the heterogeneous chemistry on its parametrization. Thus, both gas- phase and heterogeneous processes are included in a consistent way in the COPCAT linear approach providing a better representation of the stratospheric ozone in comparison with the current knowledge than in previous schemes (Monge-Sanz et al., 2011). Linear stratospheric photochemistry schemes have been implemented in many models (climate, CTM, NWP). The Cariolle and Déqué (1986) model has been introduced in the ARPEGE-Climate General Circu- lation Model and the European Centre for Medium-Range Weather Forecasts (ECMWF) model (Andersson et al. (2003)) for operational forecasts. It has also been used within 3D SLIMCAT stratospheric CTM for the study of ozone trends (Hadjinicolaou et al. (2005)). Parameterizations from Cariolle and Teyssèdre (2007) have been implemented within the MOCAGE CTM (Josse et al., 2004) and a 5 year simulation, 2000-2004, has been run with wind and temperature fields from the ECMWF operational analyses. Monge-Sanz et al. (2011) compare the new O3scheme,

COPCAT, within the same CTM used to calculate it, the TOMCAT/SLIMCAT (Chipperfield, 2006). COPCAT agrees mainly with TOMCAT/SLIMCAT full-chemistry. In addition, COP- CAT is compared with the current ECMWF scheme based on Cariolle and Teyssèdre (2007) for stratospheric O3and it is observed that COPCAT performs better the Antarctic ozone hole and

also at northern high latitudes (Monge-Sanz et al., 2011).

The principal motivation of this specific study is that a simple linear stratospheric ozone scheme is a good option to model the STE and gives a valuable alternative to the introduction of complex and computationally costly chemical schemes into a CTM that mainly focuses on tropospheric chemistry.

In this Ph.D. thesis, we implement and evaluate two different linear parameterisations for the stratospheric ozone within the global CTM model NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM). The two linear schemes evaluated are the last version of CD86, Cari- olle v2a (Cariolle and Teyssèdre, 2007) and the COPCAT scheme (Monge-Sanz et al., 2011). The ozone vertical structure is evaluated with ozonesondes, and Halogen Occultation Experi- ment (HALOE) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartogra- phY (SCIAMACHY) satellite retrievals. Both, the ozone vertical profile and the total ozone column are analysed. Special analysis on the stratosphere-troposphere ozone transition is pre- sented.

Chapter 2 summarises the main characteristics of the modelling system applied (NMMB/BSC- CTM), including a description of the ozone linear stratospheric models implemented (see Sec- tion 2.2.5). The model setup is described in Section 3.2. Observations used to evaluate the model are briefly described in Section 3.3. Results and conclusions of this work are given in Sections 3.4 and 3.5.