Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate matter

Highlights

• Seventeen modeling groups from EU and NA simulated PM for 2010 under AQMEII phase 2.

• A general model underestimation of surface PM over both continents up to 80%.

• Natural PM emissions may lead to large underestimations in simulated PM₁₀.

• Dry deposition can introduce large differences among models.

Abstract

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together seventeen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America using common emissions and boundary conditions. The simulated annual, seasonal, continental and sub-regional particulate matter (PM) surface concentrations for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. The results show a systematic underestimation for all models in almost all seasons and sub-regions, with the largest underestimations for the Mediterranean region. The rural PM₁₀ concentrations over Europe are underestimated by all models by up to 66% while the underestimations are much larger for the urban PM₁₀ concentrations (up to 75%). On the other hand, there are overestimations in PM_2.5 levels suggesting that the large underestimations in the PM₁₀ levels can be attributed to the natural dust emissions. Over North America, there is a general underestimation in PM₁₀ in all seasons and sub-regions by up to ∼90% due mainly to the underpredictions in soil dust. SO₄²⁻ levels over EU are underestimated by majority of the models while NO₃⁻ levels are largely overestimated, particularly in east and south Europe. NH₄⁺ levels are also underestimated largely in south Europe. SO₄ levels over North America are particularly overestimated over the western US that is characterized by large anthropogenic emissions while the eastern USA is characterized by underestimated SO₄ levels by the majority of the models. Daytime AOD levels at 555 nm is simulated within the 50% error range over both continents with differences attributed to differences in concentrations of the relevant species as well as in approaches in estimating the AOD. Results show that the simulated dry deposition can lead to substantial differences among the models. Overall, the results show that representation of dust and sea-salt emissions can largely impact the simulated PM concentrations and that there are still major challenges and uncertainties in simulating the PM levels.

Introduction

Particulate matter (PM) is related to respiratory and cardiovascular diseases as well as to mortality (Schwartz et al., 1996, Bernard et al., 2001). PM has direct and indirect effects on climate (IPCC, 2007) and in turn, climate may have a significant impact on PM levels and composition (Jacob and Winner, 2009). PM has both anthropogenic and natural sources and is emitted as primary aerosols or is chemically formed from gaseous precursors in the atmosphere. PM levels are still a concern, particularly in the urban areas and its adverse effects on climate and health are expected to persist (Winker et al., 2013). Due to the greater potential of PM_2.5 (PM with an aerodynamic diameter smaller than 2.5 μm) to cause adverse effects on public health compared to PM₁₀ (PM with an aerodynamic diameter below 10 μm), PM_2.5 attracted more scientific attention that led to air quality model (AQM) development to focus more on this size of PM and its composition. PM can lead to reductions in radiation reaching the earth and therefore impact the temperature, wind speed and humidity, and it can also modify cloud droplet size and number (Baklanov et al., 2014, Brunner et al., 2015). On-line coupled AQMs can simulate the aerosol feedbacks on meteorology that can be important on a wide range of temporal and spatial scales (Zhang, 2008, Grell and Baklanov, 2011).

The Air Quality Model Evaluation International Initiative (AQMEII) is designed to promote policy-relevant research on regional air quality model evaluation across the atmospheric modeling communities in Europe (EU) and North America (NA) through the exchange of information on current practices and the identification of research priorities (Galmarini and Rao, 2011). Standardized observations and model outputs were made available through the ENSEMBLE web-based system (http://ensemble2.jrc.ec.europa.eu/public/) that is hosted at the Joint Research Centre (JRC; Bianconi et al., 2004, Galmarini et al., 2012). The first phase of AQMEII focused on the evaluation of off-line atmospheric modeling systems against large sets of monitoring observations over Europe and North America for the year 2006 (Solazzo et al., 2012a, Solazzo et al., 2012b, Solazzo et al., 2013, Vautard et al., 2012, Hogrefe et al., 2014). The results from this first phase demonstrated a large underestimation by all models throughout the year and a large variability among models in representing emissions, deposition and concentrations of PM and their composition (Solazzo et al., 2012b).

The second phase of AQMEII extends this model assessment to on-line air quality models. In this study, we analyze PM₁₀ and PM_2.5 mass concentrations simulated by eight on-line-coupled models, which have been run by seventeen independent groups from Europe and North America (a companion study is devoted to the analyses of ozone, Im et al., 2015). The surface PM levels simulated by the individual models as well as their ensemble mean and median are compared with the observational data provided by the ENSEMBLE system. As multi-model ensemble analyses is not the scope of this paper, further analyses have been performed by Kioutsioukis et al. (2014) for the EU case using the multi-model data presented in the present paper. The aim of the study is to evaluate the performances of widely used operational on-line coupled models in EU and NA in simulating PM and its chemical components on a sub-regional and seasonal basis employing an experimental set up with common anthropogenic emission and boundary conditions and thus, to identify areas of model improvements and the links to policy applications.