Seeking for the rational basis of the median model: the optimal combination of multi-model ensemble results

International audienceIn this paper we present an approach for the statistical analysis of multi-model ensemble results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides. We first introduce the theoretical basis (with its roots sinking into the Bayes theorem) and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called "median model", originally introduced in Galmarini et al. (2004a, b). This approach also provides a way to systematically reduce (and quantify) model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner

Modeling the impact of sub-grid scale emission variability on upper-air concentration

International audienceThe long standing issue of sub-grid emission heterogeneity and its influence to upper air concentration is addressed here and a subgrid model proposed. The founding concept of the approach is the assumption that average emission act as source terms of average concentration, emission fluctuations are source for the concentration variance. The model is based on the derivation of the sub-grid contribution of emission and the use of the concentration variance equation to transport it in the atmospheric boundary layer. The model has been implemented in an existing mesoscale model and the results compared with Large-Eddy Simulation data for ad-hoc simulation devised to test specifically the parametrization. The results show an excellent agreement of the models. For the first time a time evolving error bar reproducing the sub-grid scale heterogeneity of the emissions and the way in which it affects the concentration has been shown. The concentration variance is presented as an extra attribute to better define the mean concentrations in a Reynolds-average model. The model has applications from meso to global scale and that go beyond air quality

Tax autonomy, lobbying, and welfare

What degree of tax autonomy should be granted to a taxing authority? Although the policy maker aims at maximizing social welfare, her tax policy may be distorted by the lobbying activity of taxpayers. In this political environment we characterize the conditions under which social welfare can be increased by restricting the set of tax instruments available to the policy maker, i.e. the degree of tax autonomy. We show that full tax autonomy is more costly, in terms both of welfare distortions and lobbying effort, when the lobbies are asymmetric in size, while minimal tax autonomy is more costly when the tax bases are asymmetric across different groups

The runaway taxpayer

In order to analyse the determinants of tax evasion, the existing literature on individual tax compliance typically takes a prior-to-audit point of view. This paper focuses on a post-audit, post-detection -so far unexplored- framework, by investigating what happens after tax evasion has been discovered and noncompliant taxpayers are asked to pay their debts. We fi rst develop a two-period dynamic model of individual choice, considering an individual that has been already audited and detected as tax evader, who knows that Tax Authorities are looking for her to cash the due amount. We derive the optimal decision of running away in order to avoid paying the bill, and show that the experience of a prior tax notice reduces the probability to behave as a scofflaw. We then exploit information on post-audit, post-detection tax compliance provided by an Italian collection agency for the period 2004-2007 to empirically test the effectiveness of the prior notice against scofflaws. The evidence from alternative logit model speci cations supports our theoretical prediction: experiencing a tax notice reduces the probability of running away by about 10%. However, this may prove to be insufficient to discourage some individuals to runaway in order to avoid paying their dues

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Examination of the Community Multiscale Air Quality (CMAQ) model performance over the North American and European domains

Copyright 2011 Elsevier B.V., All rights reserved.The CMAQ modeling system has been used to simulate the air quality for North America and Europe for the entire year of 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII). The operational model performance of tropospheric ozone (O), fine particulate matter (PM) and total particulate matter (PM) for the two continents has been assessed. The model underestimates daytime (8am-8pm LST) O mixing ratios by 13% in the winter for North America, primarily due to an underestimation of daytime O mixing ratios in the middle and lower troposphere from the lateral boundary conditions. The model overestimates winter daytime O mixing ratios in Europe by an average of 8.4%. The model underestimates daytime O by 4-5% in the spring for both continents, while in the summer daytime O is overestimated by 9.8% for North America and slightly underestimated by 1.6% for Europe. The model overestimates daytime O in the fall for both continents, grossly overestimating daytime O by over 30% for Europe. The performance for PM varies both seasonally and geographically for the two continents. For North American, PM is overestimated in the winter and fall, with an average Normalized Mean Bias (NMB) greater than -30%, while performance in the summer is relatively good, with an average NMB of -4.6%. For Europe, PM is underestimated throughout the entire year, with the NMB ranging from -24% in the fall to -55% in the winter. PM is underestimated throughout the year for both North America and Europe, with remarkably similar performance for both continents. The domain average NMB for PM ranges between -45% and -65% for the two continents, with the largest underestimation occurring in the summer for North American and the winter for Europe.Peer reviewedSubmitted Versio

Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII Phase-2

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).This study reviews the top ranked meteorology and chemistry interactions in online coupled models recommended by an experts’ survey conducted in COST Action EuMetChem and examines the sensitivity of those interactions during two pollution episodes: the Russian forest fires 25 Jul -15 Aug 2010 and a Saharan dust transport event from 1 Oct -31 Oct 2010 as a part of the AQMEII phase-2 exercise. Three WRF-Chem model simulations were performed for the forest fire case for a baseline without any aerosol feedback on meteorology, a simulation with aerosol direct effects only and a simulation including both direct and indirect effects. For the dust case study, eight WRF-Chem and one WRF-CMAQ simulations were selected from the set of simulations conducted in the framework of AQMEII. Of these two simulations considered no feedbacks, two included direct effects only and five simulations included both direct and indirect effects. The results from both episodes demonstrate that it is important to include the meteorology and chemistry interactions in online-coupled models. Model evaluations using routine observations collected in AQMEII phase-2 and observations from a station in Moscow show that for the fire case the simulation including only aerosol direct effects has better performance than the simulations with no aerosol feedbacks or including both direct and indirect effects. The normalized mean biases are significantly reduced by 10-20% for PM10 when including aerosol direct effects. The analysis for the dust case confirms that models perform better when including aerosol direct effects, but worse when including both aerosol direct and indirect effects, which suggests that the representation of aerosol indirect effects needs to be improved in the model.Peer reviewedFinal Published versio

Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. (C) 2012 Elsevier Ltd. All rights reserved.Peer reviewe