Sensitivity of aerosol concentrations and cloud properties to nucleation and secondary organic distribution in ECHAM5-HAM global circulation model

The global aerosol-climate model ECHAM5-HAM was modified to improve the representation of new particle formation in the boundary layer. Activation-type nucleation mechanism was introduced to produce observed nucleation rates in the lower troposphere. A simple and computationally efficient model for biogenic secondary organic aerosol (BSOA) formation was implemented. Here we study the sensitivity of the aerosol and cloud droplet number concentrations (CDNC) to these additions. Activation-type nucleation significantly increases aerosol number concentrations in the boundary layer. Increased particle number concentrations have a significant effect also on cloud droplet number concentrations and therefore on cloud properties. We performed calculations with activation nucleation coefficient values of 2×10⁻⁷s⁻¹, 2×10⁻⁶s⁻¹ and 2×10⁻⁵s⁻¹ to evaluate the sensitivity to this parameter. For BSOA we have used yields of 0.025, 0.07 and 0.15 to estimate the amount of monoterpene oxidation products available for condensation. The hybrid BSOA formation scheme induces large regional changes to size distribution of organic carbon, and therefore affects particle optical properties and cloud droplet number concentrations locally. Although activation-type nucleation improves modeled aerosol number concentrations in the boundary layer, the use of a global activation coefficient generally leads to overestimation of aerosol number. Overestimation can also arise from underestimation of primary emissions

SALSA - a sectional aerosol module for large scale applications

"The sectional aerosol module SALSA is introduced. The model has been designed to be implemented in large scale climate models, which require both accuracy and computational efficiency. We have used multiple methods to reduce the computational burden of different aerosol processes to optimize the model performance without losing physical features relevant to problematics of climate importance. The optimizations include limiting the chemical compounds and physical processes available in different size sections of aerosol particles; division of the size distribution into size sections using size sections of variable width depending on the sensitivity of microphysical processing to the particles sizes; the total amount of size sections to describe the size distribution is kept to the minimum; furthermore, only the relevant microphysical processes affecting each size section are calculated. The ability of the module to describe different microphysical processes was evaluated against explicit microphysical models and several microphysical models used in air quality models. The results from the current module show good consistency when compared to more explicit models. Also, the module was used to simulate a new particle formation event typical in highly polluted conditions with comparable results to more explicit model setup.""The sectional aerosol module SALSA is introduced. The model has been designed to be implemented in large scale climate models, which require both accuracy and computational efficiency. We have used multiple methods to reduce the computational burden of different aerosol processes to optimize the model performance without losing physical features relevant to problematics of climate importance. The optimizations include limiting the chemical compounds and physical processes available in different size sections of aerosol particles; division of the size distribution into size sections using size sections of variable width depending on the sensitivity of microphysical processing to the particles sizes; the total amount of size sections to describe the size distribution is kept to the minimum; furthermore, only the relevant microphysical processes affecting each size section are calculated. The ability of the module to describe different microphysical processes was evaluated against explicit microphysical models and several microphysical models used in air quality models. The results from the current module show good consistency when compared to more explicit models. Also, the module was used to simulate a new particle formation event typical in highly polluted conditions with comparable results to more explicit model setup.""The sectional aerosol module SALSA is introduced. The model has been designed to be implemented in large scale climate models, which require both accuracy and computational efficiency. We have used multiple methods to reduce the computational burden of different aerosol processes to optimize the model performance without losing physical features relevant to problematics of climate importance. The optimizations include limiting the chemical compounds and physical processes available in different size sections of aerosol particles; division of the size distribution into size sections using size sections of variable width depending on the sensitivity of microphysical processing to the particles sizes; the total amount of size sections to describe the size distribution is kept to the minimum; furthermore, only the relevant microphysical processes affecting each size section are calculated. The ability of the module to describe different microphysical processes was evaluated against explicit microphysical models and several microphysical models used in air quality models. The results from the current module show good consistency when compared to more explicit models. Also, the module was used to simulate a new particle formation event typical in highly polluted conditions with comparable results to more explicit model setup."Peer reviewe

Sensitivity of aerosol concentrations and cloud properties to nucleation and secondary organic distribution in ECHAM5-HAM global circulation model

The global aerosol-climate model ECHAM5-HAM was modified to improve the representation of new particle formation in the boundary layer. Activation-type nucleation mechanism was introduced to produce observed nucleation rates in the lower troposphere. A simple and computationally efficient model for biogenic secondary organic aerosol (BSOA) formation was implemented. Here we study the sensitivity of the aerosol and cloud droplet number concentrations (CDNC) to these additions. Activation-type nucleation significantly increases aerosol number concentrations in the boundary layer. Increased particle number concentrations have a significant effect also on cloud droplet number concentrations and therefore on cloud properties. We performed calculations with activation nucleation coefficient values of 2 x 10(-7) s(-1), 2 x 10(-6) s(-1) and 2 x 10(-5) s(-1) to evaluate the sensitivity to this parameter. For BSOA we have used yields of 0.025, 0.07 and 0.15 to estimate the amount of monoterpene oxidation products available for condensation. The hybrid BSOA formation scheme induces large regional changes to size distribution of organic carbon, and therefore affects particle optical properties and cloud droplet number concentrations locally. Although activation-type nucleation improves modeled aerosol number concentrations in the boundary layer, the use of a global activation coefficient generally leads to overestimation of aerosol number. Overestimation can also arise from underestimation of primary emissions.The global aerosol-climate model ECHAM5-HAM was modified to improve the representation of new particle formation in the boundary layer. Activation-type nucleation mechanism was introduced to produce observed nucleation rates in the lower troposphere. A simple and computationally efficient model for biogenic secondary organic aerosol (BSOA) formation was implemented. Here we study the sensitivity of the aerosol and cloud droplet number concentrations (CDNC) to these additions. Activation-type nucleation significantly increases aerosol number concentrations in the boundary layer. Increased particle number concentrations have a significant effect also on cloud droplet number concentrations and therefore on cloud properties. We performed calculations with activation nucleation coefficient values of 2 x 10(-7) s(-1), 2 x 10(-6) s(-1) and 2 x 10(-5) s(-1) to evaluate the sensitivity to this parameter. For BSOA we have used yields of 0.025, 0.07 and 0.15 to estimate the amount of monoterpene oxidation products available for condensation. The hybrid BSOA formation scheme induces large regional changes to size distribution of organic carbon, and therefore affects particle optical properties and cloud droplet number concentrations locally. Although activation-type nucleation improves modeled aerosol number concentrations in the boundary layer, the use of a global activation coefficient generally leads to overestimation of aerosol number. Overestimation can also arise from underestimation of primary emissions.The global aerosol-climate model ECHAM5-HAM was modified to improve the representation of new particle formation in the boundary layer. Activation-type nucleation mechanism was introduced to produce observed nucleation rates in the lower troposphere. A simple and computationally efficient model for biogenic secondary organic aerosol (BSOA) formation was implemented. Here we study the sensitivity of the aerosol and cloud droplet number concentrations (CDNC) to these additions. Activation-type nucleation significantly increases aerosol number concentrations in the boundary layer. Increased particle number concentrations have a significant effect also on cloud droplet number concentrations and therefore on cloud properties. We performed calculations with activation nucleation coefficient values of 2 x 10(-7) s(-1), 2 x 10(-6) s(-1) and 2 x 10(-5) s(-1) to evaluate the sensitivity to this parameter. For BSOA we have used yields of 0.025, 0.07 and 0.15 to estimate the amount of monoterpene oxidation products available for condensation. The hybrid BSOA formation scheme induces large regional changes to size distribution of organic carbon, and therefore affects particle optical properties and cloud droplet number concentrations locally. Although activation-type nucleation improves modeled aerosol number concentrations in the boundary layer, the use of a global activation coefficient generally leads to overestimation of aerosol number. Overestimation can also arise from underestimation of primary emissions.Peer reviewe

Technology enhanced assessment in complex collaborative settings

Building upon discussions by the Assessment Working Group at EDUsummIT 2013, this article reviews recent developments in technology enabled assessments of collaborative problem solving in order to point out where computerised assessments are particularly useful (and where non-computerised assessments need to be retained or developed) while assuring that the purposes and designs are transparent and empowering for teachers and learners. Technology enabled assessments of higher order critical thinking in a collaborative social context can provide data about the actions, communications and products created by a learner in a designed task space. Principled assessment design is required in order for such a space to provide trustworthy evidence of learning, and the design must incorporate and take account of the engagement of the audiences for the assessment as well as vary with the purposes and contexts of the assessment. Technology enhanced assessment enables in-depth unobtrusive documentation or ‘quiet assessment’ of the many layers and dynamics of authentic performance and allows greater flexibility and dynamic interactions in and among the design features. Most important for assessment FOR learning, are interactive features that allow the learner to turn up or down the intensity, amount and sharpness of the information needed for self-absorption and adoption of the feedback. Most important in assessment OF learning, are features that compare the learner with external standards of performance. Most important in assessment AS learning, are features that allow multiple performances and a wide array of affordances for authentic action, communication and the production of artefacts

Regulation of emotions in socially challenging learning situations: An instrument to measure the adaptive and social nature of the regulation process

Self-regulated learning (SRL) research has conventionally relied on measures, which treat SRL as an aptitude. To study self-regulation and motivation in learning contexts as an ongoing adaptive process, situation-specific methods are needed in addition to static measures. This article presents an 18Adaptive Instrument for Regulation of Emotions 19 aimed at accessing students 19 experiences of individual and socially shared regulation of emotions in a socially challenging learning situation. The instrument, grounded in self-regulated and socially regulated learning theory, comprises four interrelated components: the socio-emotional challenges experienced in a collaborative learning situation; individual and group-level attempts to regulate the immediate emotions evoked by the challenges; the personal goals; and goal attainment pursued in that situation. The theoretical foundation of the instrument and its components are outlined and some reliability issues illustrated. The limitations but also educational potential of the instrument to understand regulation of emotions in socially challenging learning situations are discussed

Socially shared regulation of motivation and emotions in collaborative learning

Abstract This study focuses on motivation and emotions in collaborative learning. The aim is to investigate the kinds of socio-emotional challenges learners experience during learning processes, and to examine how motivation and emotions are regulated during challenging situations, in order to develop appropriate methods of identifying socially shared regulation of emotions from situated, real life data. The study includes the development and implementation of an instrument that collects data regarding learners’ situation-specific interpretations of their socio-emotional experiences, as well as analyses of the data derived from two different data collections. The first empirical data set is composed of elementary school students’ interviews and video-observations. The second data set includes higher education students’ self-reports, video-observations, and interviews. The analyses combine different data sources and qualitative and descriptive quantitative methods in order to create a comprehensive understanding of the regulation of motivation and emotions in collaborative learning situations. A lack of instruments that gather data of learners’ situation-specific, real-life experiences has been evident in motivation and self-regulated learning research, where static, general self-report measures have been dominant. In this study, the results from the first empirical data collection are implemented in the development of an AIRE (Adaptive Instrument for Regulation of Emotions) instrument. The AIRE collects situation-specific data dealing with learners. experienced socio-emotional challenges and their regulation within a group. The second empirical data collection of this study employs the AIRE instrument as a method. In social learning situations, learners’ can experience a variety of emotions that influence learning. The results of this study show that students can regulate their emotions in order to maintain a goal-oriented learning process. Furthermore, the results indicate that group members can regulate emotions and motivation together within the group. This socially shared regulation is distinct from self-regulation as well as from co-regulation, where development of self-regulation is supported by others, or where group members regulate their own learning processes in parallel with each other.Tiivistelmä Tämä tutkimus tarkastelee motivaation ja emootion ilmentymistä yhteisöllisessä oppimisessa. Tavoitteena on selvittää, millaisia sosio-emotionaalisia haasteita oppijat kohtaavat oppimisprosessin aikana ja miten motivaatiota ja emotionaalisia tuntemuksia säädellään näissä tilanteissa. Lisäksi tavoitteena on löytää ja kehittää tilannekohtaisia analysointimenetelmiä erityisesti sosiaalisesti jaetun emootion säätelyn tutkimiseksi. Tutkimus koostuu oppijoiden tilannesidonnaisia sosio-emotionaalisia tulkintoja keräävän instrumentin kehittelystä sekä kahdesta empiirisestä tutkimusaineistosta. Ensimmäinen tutkimusaineisto koostuu peruskoulun oppilaiden haastatteluista ja videoidusta työskentelystä. Toinen tutkimusaineisto sisältää korkeakouluopiskelijoiden kyselyaineistoa, videoitua työskentelyä ja haastatteluita. Kokonaisvaltaisen ymmärryksen luomiseksi aineiston analyysissä yhdistetään näitä erityyppisiä aineistoja ja kuvailevaa kvantitatiivista analyysiä käytetään tukemaan kvalitatiivisia tulkintoja. Itsesäädellyn oppimisen tutkimuksessa on ollut nähtävillä tarve löytää metodisia ratkaisuja, joiden avulla voidaan kerätä aineistoa yksilöiden vaihtelevista kokemuksista todellisissa oppimistilanteissa. Aikaisemmin pääpaino on ollut staattisissa, yksilöiden yleisiä käsityksiä mittaavien aineistojen analyysissä. Tässä tutkimuksessa ensimmäisen tutkimusaineiston tuloksia hyödynnetään AIRE (Adaptive Instrument for Regulation of Emotions) -instrumentin kehittelyssä. AIRE kerää tilannekohtaista tietoa sosio-emotionaalisten haasteiden kokemuksista ja näihin liittyvästä ryhmässä tapahtuvasta emootioiden säätelystä. AIRE:a käytetään toisen tutkimusaineiston yhtenä keruuvälineenä. Sosiaalisten oppimistilanteiden aikana oppijoissa herää erilaisia tuntemuksia, jotka vaikuttavat oppimistilanteeseen. Tämän tutkimuksen tulokset osoittavat, että oppijat voivat säädellä emotionaalisia tuntemuksia ylläpitääkseen tavoitesuuntautunutta opiskelua. Tulosten perusteella voidaan todeta, että yhteisöllisen oppimisen tilanteissa ryhmän jäsenet voivat yhdessä kontrolloida motivationaalisia ja sosio-emotionaalisia haasteita. Tämä sosiaalisesti jaettu emootioiden säätely (socially shared regulation) eroaa itsesäätelystä sekä yhdessä säätelemisestä (co-regulation), jossa tuetaan yksilön kehittymistä itsesäätöiseksi oppijaksi tai jossa ryhmän jäsenet säätelevät kukin rinnakkain omaa toimintaansa

Research on motivation in collaborative learning: Moving beyond the cognitive-situative divide and combining individual and social processes

In this article we propose that in order to advance our understanding of motivation in collaborative learning we should move beyond the cognitive-situative epistemological divide and combine individual and social processes. Our claim is that although recent research has recognized the importance of social aspects in emerging and sustained motivation in collaborative learning activities, the social is mainly conceived as a unidirectional source of influence on individual motivation. In the article we examine the significance of motivation in research on collaborative learning. We discuss two characterizations of the role of the social in conceptualizations of motivation, namely, social influence and social construction, and outline our case for moving beyond the cognitive-situative divide and combining individual and social processes in research on motivation. Finally, we present illustrations from recent research on motivation in collaborative learning that has attempted to bridge the cognitive-situative divide across theoretical perspectives or using different methods

Investigating the relation of higher education students’ situational self-efficacy beliefs to participation in group level regulation of learning during a collaborative task

Abstract Understanding the role individual beliefs play when the group faces challenge is key in understanding the shared regulation processes and participation that lead to collaborative learning success. As of now, there is not much research focusing on how self-efficacy plays a role in regulation taking place in collaborative group settings. Therefore, the aim of this study is to explore how situational self-efficacy beliefs relate to students’ participation in group level regulation during a collaborative task. The study involved 18 university students working in groups on a computer-based collaborative task. Repeated self-reports measuring group members’ self-efficacy were related to performance feedback from the task as well as participation in group level regulation identified from videotaped collaborative working. The results showed that self-efficacy varied depending on the nature of performance feedback. In addition, the way students participated in regulation was connected with their level of self-efficacy: low self-efficacy was associated with taking a passive role in regulation whereas high self-efficacy was associated with taking an active role. The study suggests that situational self-efficacy beliefs are associated with the participation roles during group level regulation, thus being of practical concern for educators seeking to support learners’ self-efficacy and active participation in collaborative learning

Socio-emotional interaction in collaborative learning:combining individual emotional experiences and group-level emotion regulation

Abstract This study explores how groups’ negative socio-emotional interactions and related emotion regulation during a collaborative physics task are interconnected with 12-year-old primary school students’ (N = 37) situated individual emotional experiences. To accomplish this, the study relates group-level video data analysis with students’ self-reported emotional experiences. The results indicate that students’ negative emotional experiences related to the task prior to collaborative working increase the group’s emotion regulation during the collaboration and that negative group interactions negatively affect students’ emotional experiences after the task. The study also shows that even though group-level regulation is more likely to change the valence of the group’s interaction from negative to positive, regulation does not always succeed in making a difference to the students’ overall emotional experiences