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We evaluate the capability of the global atmospheric transport model TM5 to simulate the boundary layer dynamics and associated variability of trace gases close to the surface, using radon (222Rn). Focusing on the European scale, we compare the boundary layer height (BLH) in the TM5 model with observations from the National Oceanic and Atmospheric Admnistration (NOAA) Integrated Global Radiosonde Archive (IGRA) and also with ceilometer and lidar (light detection and ranging) BLH retrievals at two stations. Furthermore, we compare TM5 simulations of 222Rn activity concentrations, using a novel, process-based 222Rn flux map over Europe (Karstens et al., 2015), with harmonised 222Rn measurements at 10 stations. The TM5 model reproduces relatively well the daytime BLH (within 10–20 % for most of the stations), except for coastal sites, for which differences are usually larger due to model representation errors. During night, however, TM5 overestimates the shallow nocturnal BLHs, especially for the very low observed BLHs ( The 222Rn activity concentration simulations based on the new 222Rn flux map show significant improvements especially regarding the average seasonal variability, compared to simulations using constant 222Rn fluxes. Nevertheless, the (relative) differences between simulated and observed daytime minimum 222Rn activity concentrations are larger for several stations (on the order of 50 %) than the (relative) differences between simulated and observed BLH at noon. Although the nocturnal BLH is often higher in the model than observed, simulated 222Rn nighttime maxima are actually larger at several continental stations. This counterintuitive behaviour points to potential deficiencies of TM5 to correctly simulate the vertical gradients within the nocturnal boundary layer, limitations of the 222Rn flux map, or issues related to the definition of the nocturnal BLH. At several stations the simulated decrease of 222Rn activity concentrations in the morning is faster than observed. In addition, simulated vertical 222Rn activity concentration gradients at Cabauw decrease faster than observations during the morning transition period, and are in general lower than observed gradients during daytime. Although these effects may be partially due to the slow response time of the radon detectors, they clearly point to too fast vertical mixing in the TM5 boundary layer during daytime. Furthermore, the capability of the TM5 model to simulate the diurnal BLH cycle is limited by the current coarse temporal resolution (3 h/6 h) of the TM5 input meteorology.

The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts in these distal areas. In our approach, we use real-life aircraft in situ observations, measured in the northwestern part of Germany during the 2010 Eyjafjallajökull eruption, in an ensemble-based data assimilation system combined with a volcanic ash transport model to investigate the potential improvement on the forecast accuracy with regard to the distal volcanic ash plume. We show that the error of the analyzed volcanic ash state can be significantly reduced through assimilating real-life in situ measurements. After a continuous assimilation, it is shown that the aviation advice for Germany, the Netherlands and Luxembourg can be significantly improved. We suggest that with suitable aircrafts measuring once per day across the distal volcanic ash plume, the description and prediction of volcanic ash clouds in these areas can be greatly improved.

It has been proposed that an implicit learning deficit explains the difficulties with grammar commonly observed in children with Developmental Language Disorder (DLD). The present study further investigates this link in two ways. Firstly, we investigate whether kindergartners with DLD have more difficulties with preposition understanding and production as compared to their typically developing peers because they have not (yet) learned to weigh implicit structural information (word order) over more explicit semantic information (noun animacy; Study 1). Secondly, we investigate whether kindergartners with DLD learn to comprehend and produce locative prepositions from an implicit learning context (Study 2). In Study 1 we observed that Dutch kindergartners with DLD (n = 32) made more errors in preposition comprehension (picture-matching task) and preposition production (semi-spontaneous production task) as compared to their typically developing peers (n = 30). We have no evidence that these differences can be explained by a difference in cue weighing or implicit learning ability (serial reaction time task). In Study 2 we observed that the storytelling context led to an increase in preposition production in children with DLD, but we found no evidence that it also led to a better understanding and more semantically accurate productions of the prepositions. We conclude that Dutch kindergartners with DLD have difficulties with locative prepositions but cannot conclude that differences in cue-weighing or implicit learning play a role herein.

This article explores the impact of collaboration on target text quality in translator training. By comparing team translations with those by individual peers, and analysing the highest and lowest scoring teams, the authors aimed to understand the impact of collaboration on quality. The comparison indicates that translations in a skills lab setting marginally outperform individual translations, but that carelessness is more likely to be observed in the intermediate individual target texts produced in a collaborative setting.

Background: Word-to-text integration (WTI) can be challenging for second-language (L2) learners, although it can positively contribute to reading comprehension. The present study examined the role of WTI, after controlling for decoding, vocabulary and morphosyntactic awareness, in predicting English as an L2 reading comprehension development in 441 Dutch seventh-grade students. Methods: At the beginning (Time 1 [T1]) and the end (Time 2 [T2]) of the school year, students were tested on their English decoding, vocabulary, morphological and syntactic awareness and reading comprehension with paper-and-pencil tasks and on WTI with a self-paced reading task with reading times being compared on passages with unknown versus known words and passages with and without anomalies. Results: Mediation analyses showed small indirect effects of processing argument overlap and anomalies on T2 reading comprehension, via T1 reading comprehension. Conclusions: WTI explained unique variance in reading comprehension at T2 via reading comprehension at T1, suggesting that it moderately impacts initial stages of reading comprehension in English as a second language (ESL).

This study investigated the impact of second language (L2) lexical quality on L2 reading comprehension in bilingual primary education. The participating children were taught in the L2 English for 30-50% of the time and the remaining time in their L1 Dutch. Seventy-three pupils from four different schools were assessed in the L2 on vocabulary, lexical specificity and phonological awareness in kindergarten, vocabulary, lexical specificity and decoding in grade 1, and reading comprehension in grade 3. L1 reading comprehension was tested in grade 2. Results showed that lexical specificity in kindergarten had a direct effect on L2 reading comprehension in grade 3, and phonological awareness in kindergarten had an indirect effect on L2 reading comprehension via L2 decoding skills in grade 1. These effects remained significant after taking L1 reading comprehension skills in grade 2 into account. The lexical specificity task, measuring specificity of phonological representations, had a more profound impact on reading comprehension than vocabulary. This highlights the importance of examining the specificity of phonological representations when it comes to studying L2 reading comprehension proficiency and implies that teachers should focus on L2 lexical specificity in bilingual kindergarten to build the foundation for L2 reading comprehension in grade 3.

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 μg m−3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30–50 μg m−3. Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM10 for winter 2013–1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 μg m−3. The ensemble median fractional gross error is larger for PM10 (~ 0.52) than for ozone and the correlation is lower (~ 0.35 for PM10 and ~ 0.54 for ozone). This is related to a larger spread of the seven model scores for PM10 than for ozone linked to different levels of complexity of aerosol representation in the individual models. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat underestimated in the southern part of the birch habitat. In the northern part of Europe, a record-low pollen season was strongly overestimated by all models. The median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most models this was the first experience of pollen forecasting.

At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH4) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH4 retrievals can be compared during the period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH4, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH4. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH4 emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH4 emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH4 fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH4 retrievals available from the Total Carbon Column Observing Network (TCCON).

European CH4 and N2O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived emissions, which is particularly important for verifying bottom-up emission inventories. We use continuous observations from 10 European stations (including 5 tall towers) for CH4 and 9 continuous stations for N2O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH4 and N2O emissions from north-western and eastern Europe. The inversions are strongly driven by the observations and the derived total emissions of larger countries show little dependence on the emission inventories used a priori. Three inverse models yield 26–56% higher total CH4 emissions from north-western and eastern Europe compared to bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N2O emissions are in general close to the UNFCCC values, with the overall range from all models being much smaller than the UNFCCC uncertainty range for most countries. Our analysis suggests that the reported uncertainties for CH4 emissions might be underestimated, while those for N2O emissions are likely overestimated.

In this study the sensitivity of the model performance of the chemistry transport model (CTM) LOTOS-EUROS to the description of the temporal variability of emissions was investigated. Currently the temporal release of anthropogenic emissions is described by European average diurnal, weekly and seasonal time profiles per sector. These default time profiles largely neglect the variation of emission strength with activity patterns, region, species, emission process and meteorology. The three sources dealt with in this study are combustion in energy and transformation industries (SNAP1), nonindustrial combustion (SNAP2) and road transport (SNAP7). First of all, the impact of neglecting the temporal emission profiles for these SNAP categories on simulated concentrations was explored. In a second step, we constructed more detailed emission time profiles for the three categories and quantified their impact on the model performance both separately as well as combined. The performance in comparison to observations for Germany was quantified for the pollutants NO2, SO2 and PM10 and compared to a simulation using the default LOTOS-EUROS emission time profiles. The LOTOS-EUROS simulations were performed for the year 2006 with a temporal resolution of 1 h and a horizontal resolution of approximately 25 × 25km2. In general the largest impact on the model performance was found when neglecting the default time profiles for the three categories. The daily average correlation coefficient for instance decreased by 0.04 (NO2), 0.11 (SO2) and 0.01 (PM10) at German urban background stations compared to the default simulation. A systematic increase in the correlation coefficient is found when using the new time profiles. The size of the increase depends on the source category, component and station. Using national profiles for road transport showed important improvements in the explained variability over the weekdays as well as the diurnal cycle for NO2. The largest impact of the SNAP1 and 2 profiles were found for SO2. When using all new time profiles simultaneously in one simulation, the daily average correlation coefficient increased by 0.05 (NO2), 0.07 (SO2) and 0.03 (PM10) at urban background stations in Germany. This exercise showed that to improve the performance of a CTM, a better representation of the distribution of anthropogenic emission in time is recommendable. This can be done by developing a dynamical emission model that takes into account regional specific factors and meteorology.

The present study aims to develop, validate, and cross-validate an instrument measuring three proactive social informal learning activities, namely feedback seeking, help seeking and information seeking. Prior research mainly focused on detecting or measuring the frequency of these seeking behaviours and did not consider whether the information, help or feedback that has been sought was also used. Furthermore, although these seeking behaviours were studied extensively in different research fields, the current study takes a learning perspective and interprets feedback, help and information seeking as learning activities. The questionnaire was completed by a convenient sample of 650 professionals working in higher education, consultancy, retail and food production. We performed an exploratory factor analysis (N = 230 educators) to explore the structure of the questionnaire. This was followed by a confirmatory factor analysis (N = 230 educators) to confirm the structure. Finally, we cross-validated the questionnaire in a sample of business professionals (N = 190) by testing for measurement invariance. The final questionnaire resulted in a five-factor structure measuring the (1) Use of Feedback Seeking, (2) Frequency of Help and Information Seeking, (3) Use of Help seeking, (4) Use of Information Seeking and (5) Frequency of Feedback Seeking. The current study presents a preliminary, yet promising instrument that taps into proactive social informal learning.

This study is conducted in the framework of the Air Quality Modelling Evaluation International Initiative (AQMEII) and aims at the operational evaluation of an ensemble of 12 regional-scale chemical transport models used to predict air quality over the North American (NA) and European (EU) continents for 2006. The modelled concentrations of ozone and CO, along with the meteorological fields of wind speed (WS) and direction (WD), temperature (T), and relative humidity (RH), are compared against high-quality in-flight measurements collected by instrumented commercial aircraft as part of the Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by Airbus In-service airCraft (MOZAIC) programme. The evaluation is carried out for five model domains positioned around four major airports in NA (Portland, Philadelphia, Atlanta, and Dallas) and one in Europe (Frankfurt), from the surface to 8.5 km. We compare mean vertical profiles of modelled and measured variables for all airports to compute error and variability statistics, perform analysis of altitudinal error correlation, and examine the seasonal error distribution for ozone, including an estimation of the bias introduced by the lateral boundary conditions (BCs). The results indicate that model performance is highly dependent on the variable, location, season, and height (e.g. surface, planetary boundary layer (PBL) or free troposphere) being analysed. While model performance for T is satisfactory at all sites (correlation coefficient in excess of 0.90 and fractional bias ≤ 0.01 K), WS is not replicated as well within the PBL (exhibiting a positive bias in the first 100 m and also underestimating observed variability), while above 1000 m, the model performance improves (correlation coefficient often above 0.9). The WD at NA airports is found to be biased in the PBL, primarily due to an overestimation of westerly winds. RH is modelled well within the PBL, but in the free troposphere large discrepancies among models are observed, especially in EU. CO mixing ratios show the largest range of modelled-to-observed standard deviations of all the examined species at all heights and for all airports. Correlation coefficients for CO are typically below 0.6 for all sites and heights, and large errors are present at all heights, particularly in the first 250 m. Model performance for ozone in the PBL is generally good, with both bias and error within 20%. Profiles of ozone mixing ratios depend strongly on surface processes, revealed by the sharp gradient in the first 2 km (10 to 20 ppb km−1). Modelled ozone in winter is biased low at all locations in the NA, primarily due to an underestimation of ozone from the BCs. Most of the model error in the PBL is due to surface processes (emissions, transport, photochemistry), while errors originating aloft appear to have relatively limited impact on model performance at the surface. Suggestions for future work include interpretation of the model-to-model variability and common sources of model bias, and linking CO and ozone bias to the bias in the meteorological fields. Based on the results from this study, we suggest possible in-depth, process-oriented and diagnostic investigations to be carried out next.

Organic acids attract increasing attention as contributors to atmospheric acidity, secondary organic aerosol mass and aerosol hygroscopicity. Oxalic acid is globally the most abundant dicarboxylic acid, formed via chemical oxidation of gas-phase precursors in the aqueous phase of aerosols and droplets. Its lifecycle and atmospheric global distribution remain highly uncertain and are the focus of this study. The first global spatial and temporal distribution of oxalate, simulated using a state-of-the-art aqueous-phase chemical scheme embedded within the global 3-dimensional chemistry/transport model TM4-ECPL, is here presented. The model accounts for comprehensive gas-phase chemistry and its coupling with major aerosol constituents (including secondary organic aerosol). Model results are consistent with ambient observations of oxalate at rural and remote locations (slope = 1.16 ± 0.14, r2 = 0.36, N = 114) and suggest that aqueous-phase chemistry contributes significantly to the global atmospheric burden of secondary organic aerosol. In TM4-ECPL most oxalate is formed in-cloud and less than 5 % is produced in aerosol water. About 62 % of the oxalate is removed via wet deposition, 30 % by in-cloud reaction with hydroxyl radical, 4 % by in-cloud reaction with nitrate radical and 4 % by dry deposition. The in-cloud global oxalate net chemical production is calculated to be about 21–37 Tg yr−1 with almost 79 % originating from biogenic hydrocarbons, mainly isoprene. This condensed phase net source of oxalate in conjunction with a global mean turnover time against deposition of about 5 days, maintain oxalate's global tropospheric burden of 0.2–0.3 Tg, i.e. 0.05–0.1 Tg-C that is about 5–9 % of model-calculated water soluble organic carbon burden.

We present a comprehensive description and benchmark evaluation of the tropospheric chemistry version of the global chemistry transport model TM5 (Tracer Model 5, version TM5-chem-v3.0). A full description is given concerning the photochemical mechanism, the interaction with aerosol, the treatment of the stratosphere, the wet and dry deposition parameterizations, and the applied emissions. We evaluate the model against a suite of ground-based, satellite, and aircraft measurements of components critical for understanding global photochemistry for the year 2006. The model exhibits a realistic oxidative capacity at a global scale. The methane lifetime is ~8.9 years with an associated lifetime of methyl chloroform of 5.86 years, which is similar to that derived using an optimized hydroxyl radical field. The seasonal cycle in observed carbon monoxide (CO) is well simulated at different regions across the globe. In the Northern Hemisphere CO concentrations are underestimated by about 20 ppbv in spring and 10 ppbv in summer, which is related to missing chemistry and underestimated emissions from higher hydrocarbons, as well as to uncertainties in the seasonal variation of CO emissions. The model also captures the spatial and seasonal variation in formaldehyde tropospheric columns as observed by SCIAMACHY. Positive model biases over the Amazon and eastern United States point to uncertainties in the isoprene emissions as well as its chemical breakdown. Simulated tropospheric nitrogen dioxide columns correspond well to observations from the Ozone Monitoring Instrument in terms of its seasonal and spatial variability (with a global spatial correlation coefficient of 0.89), but TM5 fields are lower by 25–40%. This is consistent with earlier studies pointing to a high bias of 0–30% in the OMI retrievals, but uncertainties in the emission inventories have probably also contributed to the discrepancy. TM5 tropospheric nitrogen dioxide profiles are in good agreement (within ~0.1 ppbv) with in situ aircraft observations from the INTEX-B campaign over (the Gulf of) Mexico. The model reproduces the spatial and seasonal variation in background surface ozone concentrations and tropospheric ozone profiles from the World Ozone and Ultraviolet Radiation Data Centre to within 10 ppbv, but at several tropical stations the model tends to underestimate ozone in the free troposphere. The presented model results benchmark the TM5 tropospheric chemistry version, which is currently in use in several international cooperation activities, and upon which future model improvements will take place.

Three global Chemistry Transport Models – MOZART, MOCAGE, and TM5 – as well as MOZART coupled to the IFS meteorological model including assimilation of ozone (O3) and carbon monoxide (CO) satellite column retrievals, have been compared to surface measurements and MOZAIC vertical profiles in the troposphere over Western/Central Europe for summer 2003. The models reproduce the meteorological features and enhancement of pollution during the period 2–14 August, but not fully the ozone and CO mixing ratios measured during that episode. Modified normalised mean biases are around −25% (except ~5% for MOCAGE) in the case of ozone and from −80% to −30% for CO in the boundary layer above Frankfurt. The coupling and assimilation of CO columns from MOPITT overcomes some of the deficiencies in the treatment of transport, chemistry and emissions in MOZART, reducing the negative biases to around 20%. The high reactivity and small dry deposition velocities in MOCAGE seem to be responsible for the overestimation of O3 in this model. Results from sensitivity simulations indicate that an increase of the horizontal resolution to around 1°×1° and potential uncertainties in European anthropogenic emissions or in long-range transport of pollution cannot completely account for the underestimation of CO and O3 found for most models. A process-oriented TM5 sensitivity simulation where soil wetness was reduced results in a decrease in dry deposition fluxes and a subsequent ozone increase larger than the ozone changes due to the previous sensitivity runs. However this latest simulation still underestimates ozone during the heat wave and overestimates it outside that period. Most probably, a combination of the mentioned factors together with underrepresented biogenic emissions in the models, uncertainties in the modelling of vertical/horizontal transport processes in the proximity of the boundary layer as well as limitations of the chemistry schemes are responsible for the underestimation of ozone (overestimation in the case of MOCAGE) and CO found in the models during this extreme pollution event.