Jérémie Ginzburg

Annie Moulin

Lesly Fornoni

Francesca Talamini

Barbara Tillmann

Anne Caclin

Development of auditory cognition in 5-to 10-year-old children: focus on musical and verbal short-term-memory

Keywords: working memory, auditory perception, music, speech, recognition task, speech-in-noise

We thank all the children who took part in the study, along with their parents, and their welcoming teachers. We also thank Nathalie Bedoin and Emmanuel Ferragne for their help

INTRODUCTION

Auditory Short-Term Memory (STM) allows for encoding, storage, and retrieval of auditory information during a short amount of time (within several seconds, [START_REF] Baddeley | Working memory. The psychology of learning and motivation[END_REF][START_REF] Cowan | Chapter 20 What are the differences between long-term, short-term, and working memory[END_REF] 1 . Along with auditory scene analysis and auditory attention, it is a key component of central auditory processing that subtends auditory cognition and allows making sense of the ever-changing acoustic environment. Numerous relationships have been described between central auditory processing disorders (CAPD) and learning disabilities in children [START_REF] Goswami | A temporal sampling framework for developmental dyslexia[END_REF][START_REF] Iliadou | Contribution of psychoacoustics and neuroaudiology in revealing correlation of mental disorders with central auditory processing disorders[END_REF][START_REF] Medwetsky | Spoken Language Processing Model : Bridging Auditory and Language Processing to Guide Assessment and Intervention[END_REF][START_REF] Moore | Nature of Auditory Processing Disorder in Children[END_REF]. Recent research suggests a link between verbal STM deficits and learning disorders [START_REF] Männel | Working-memory endophenotype and dyslexia-associated genetic variant predict dyslexia phenotype[END_REF][START_REF] Nithart | Reading Disabilities in SLI and Dyslexia Result From Distinct Phonological Impairments[END_REF][START_REF] Perez | Evidence for a Specific Impairment of Serial Order Short-term Memory in Dyslexic Children : Serial Order STM Deficits in Dyslexia[END_REF] and a link between impaired speech-in-noise processing and learning disorders [START_REF] Bradlow | Speaking Clearly for Children With Learning DisabilitiesSentence Perception in Noise[END_REF][START_REF] Sperling | Deficits in perceptual noise exclusion in developmental dyslexia[END_REF][START_REF] Ziegler | Speech-perception-innoise deficits in dyslexia[END_REF][START_REF] Ziegler | Noise on, voicing off : Speech perception deficits in children with specific language impairment[END_REF]. Specifically, verbal STM impairment has been observed in dyslexic children with reduced digit spans or poor non-word repetition in recall tasks [START_REF] Adlard | Speech Perception in Children with Specific Reading Difficulties (Dyslexia)[END_REF][START_REF] Kramer | Verbal Memory Impairments in Dyslexia[END_REF][START_REF] Majerus | The Nature of Verbal Short-Term Impairment in Dyslexia : The Importance of Serial Order[END_REF][START_REF] Nithart | Reading Disabilities in SLI and Dyslexia Result From Distinct Phonological Impairments[END_REF][START_REF] Plaza | Oral Language Deficits in Dyslexic Children : Weaknesses in Working Memory and Verbal Planning[END_REF][START_REF] Roodenrys | Serial recall and nonword repetition in reading disabled children[END_REF][START_REF] Tijms | Verbal memory and phonological processing in dyslexia[END_REF]. The link between impaired verbal STM or working memory (WM) and learning disorders has also been reported for specific language impairment (SLI, [START_REF] Nithart | Reading Disabilities in SLI and Dyslexia Result From Distinct Phonological Impairments[END_REF] and dyscalculia [START_REF] Attout | Working memory deficits in developmental dyscalculia : The importance of serial order[END_REF]. It appears that both verbal STM and speech-in-noise perception are consistently reported to be impaired in learning disorders and CAPD [START_REF] Moore | Nature of Auditory Processing Disorder in Children[END_REF][START_REF] Perez | Evidence for a Specific Impairment of Serial Order Short-term Memory in Dyslexic Children : Serial Order STM Deficits in Dyslexia[END_REF][START_REF] Ziegler | Noise on, voicing off : Speech perception deficits in children with specific language impairment[END_REF].

Similarly to verbal STM deficits observed in several language-related disorders [START_REF] Adlard | Speech Perception in Children with Specific Reading Difficulties (Dyslexia)[END_REF][START_REF] Kramer | Verbal Memory Impairments in Dyslexia[END_REF][START_REF] Majerus | The Nature of Verbal Short-Term Impairment in Dyslexia : The Importance of Serial Order[END_REF][START_REF] Nithart | Reading Disabilities in SLI and Dyslexia Result From Distinct Phonological Impairments[END_REF][START_REF] Plaza | Oral Language Deficits in Dyslexic Children : Weaknesses in Working Memory and Verbal Planning[END_REF][START_REF] Roodenrys | Serial recall and nonword repetition in reading disabled children[END_REF][START_REF] Tijms | Verbal memory and phonological processing in dyslexia[END_REF], musical STM deficits have also been reported in children with neurodevelopmental learning disorders. Dyslexic children display lower performance than typicallydeveloping children in recognition STM tasks for pitch [START_REF] Ziegler | Global and local pitch perception in children with developmental dyslexia[END_REF] and in tonal recognition tasks from the Primary Measure of Music Audiation (PMMA, [START_REF] Gordon | Manual for the Primary Measures of Music Audiation and the Intermediate Measures of Music Audiation[END_REF] as shown by [START_REF] Atterbury | Musical Differences in Learning-Disabled and Normal-Achieving Readers, Aged Seven, Eight and Nine[END_REF] and [START_REF] Forgeard | The relation between music and phonological processing in normal-reading children and children with dyslexia[END_REF]. Furthermore, a sizeable comorbidity between dyslexia and congenital amusia has been observed in adults and children [START_REF] Couvignou | Comorbidity and cognitive overlap between developmental dyslexia and congenital amusia[END_REF][START_REF] Couvignou | Comorbidity and cognitive overlap between developmental dyslexia and congenital amusia in children[END_REF]. Congenital amusia is characterized by a deficit in music processing and in particular musical STM [START_REF] Tillmann | Congenital amusia : A short-term memory deficit for non-verbal, but not verbal sounds[END_REF][START_REF] Tillmann | Impaired short-term memory for pitch in congenital amusia[END_REF]. These findings stress the importance of characterizing the development of central auditory processing in typically developing children, in particular to improve diagnosis and rehabilitation of central auditory processing deficits associated with learning disorders.

It has been well documented that verbal short-term storage capacity increases during childhood [START_REF] Alloway | Verbal and Visuospatial Short-Term and Working Memory in Children : Are They Separable?[END_REF][START_REF] Chuah | Verbal and Spatial Short-Term Memory : Common Sources of Developmental Change[END_REF][START_REF] Cowan | The Role of Attention in the Development of Short-Term Memory : Age Differences in the Verbal Span of Apprehension[END_REF][START_REF] Dempster | Memory Span : Sources of Individual and Developmental Differences[END_REF][START_REF] Gathercole | Cognitive approaches to the development of short-term memory[END_REF][START_REF] Gathercole | The Structure of Working Memory From 4 to 15 Years of Age[END_REF][START_REF] Orsini | Verbal and spatial immediate memory span : Normative data from 1355 adults and 1112 children[END_REF]. [START_REF] Gathercole | Cognitive approaches to the development of short-term memory[END_REF] considers age as the most powerful factor influencing verbal STM capacity. These verbal STM abilities, tested with digit, word, and nonword spans (i.e., recall tasks), increase linearly with age between 4 to 14 years of age and appear to level-off by the age of 14-15 years [START_REF] Gathercole | The Structure of Working Memory From 4 to 15 Years of Age[END_REF]. The development of STM over childhood was thus mostly investigated using serial recall paradigms that are reliant on verbal production, hence specific to verbal and/or phonological material [START_REF] Gathercole | Cognitive approaches to the development of short-term memory[END_REF]. Only a few studies have investigated children's STM for other types of auditory information (e.g., pitch, timbre or rhythm and temporal processing), but not yet over development nor in comparison to verbal material. Reviewing previous research reveals that only few studies investigated musical STM over children development.

Pitch memory for single tones arise as early as 6 months of age [START_REF] Plantinga | Infants' Memory for Isolated Tones and the Effects of Interference[END_REF]. Later in development, STM for tone sequences appears to mature from early childhood (six years old) to preteenage years (thirteen years old, [START_REF] Clark | Tone series and the nature of working memory capacity development[END_REF]. Indeed, [START_REF] Clark | Tone series and the nature of working memory capacity development[END_REF] found a developmental increase in the memory capacity for single tones as well as tone sequences similar to the developmental trajectory of visuo-spatial memory in children from 6-to 13-years of age, using an adapted part-set cueing task. In addition to an increase of capacity, [START_REF] Keller | Developmental Increase in the Duration of Memory for Tone Pitch[END_REF] found a decrease in STM trace decay for pitch over time in children from 4-to-12 years old using a 2-tone comparison task. This latter study found an increase in the persistence of memory for pitch between the ages of 6 to 7 years. To our knowledge, no study has compared directly the precise development of musical and verbal STM. Our study aimed at doing so systematically in children from 5-to 10-years old by using a paradigm that allows for direct comparison between musical and verbal STM.

Musical and verbal STM are difficult to compare if standard recall paradigms (most frequently used to evaluate verbal STM) are used. Even if some studies compared musical and verbal STM using mixed recall/reconstruction paradigms [START_REF] Gorin | Domain-Generality of Timing-Based Serial Order Processes in Short-Term Memory : New Insights from Musical and Verbal Domains[END_REF][START_REF] Gorin | A comparison of serial order short-term memory effects across verbal and musical domains[END_REF][START_REF] Williamson | Musicians' and nonmusicians' short-term memory for verbal and musical sequences : Comparing phonological similarity and pitch proximity[END_REF], they nonetheless required production processes that are difficult to adapt for children. The classical delayed matchingto-sample task (DMST) allows us to circumvent the need of oral or motor production that is required in a recall paradigm. In a DMST, participants have to memorize a first (S1) sequence of sounds (or an isolated sound). After a delay, a second stimulus is presented (S2) and participants have to report whether S1 and S2 are identical or different. This task has the advantage of allowing for the use of different kinds of materials (verbal, musical, environmental sound…, e.g., Talamini et al., 2021) and entails the three memorization steps of encoding (during S1), retention (during the delay), and retrieval (during S2) without relying on a production phase (as required in recall tasks). Hence, the DMST appears to be a well-suited paradigm to assess the development of auditory STM (for both musical and verbal materials) in children.

In adults, it has been suggested that auditory STM, rather than being a unitary phenomenon, could be based on partly separate subsystems for different types of material, in particular musical and verbal information (for a review, see Caclin & Tillmann, 2018). [START_REF] Berz | Working Memory in Music : A Theoretical Model[END_REF] and [START_REF] Pechmann | Interference in memory for tonal pitch : Implications for a working-memory model[END_REF] proposed a musical/tonal loop that would account for a storage component specific to the representational features of tonal information, based on [START_REF] Baddeley | Working memory. The psychology of learning and motivation[END_REF] multicomponent model of WM. [START_REF] Ockelford | A Music Module in Working Memory? Evidence from the Performance of a Prodigious Musical Savant[END_REF], based on the same multicomponent model, has also suggested to add a musical central executive component that would entail attentional processes specific to musical information. These models might predict different maturation patterns for each material type, leading to domain-specific patterns of developmental trajectory (i.e. different patterns of recency effects and absence of correlations between musical and verbal STM). In another line of research, other models have postulated more general attentional processes involved in the maintaining of information in a short-term storage and more specific item-related processes concerning the encoding of information [START_REF] Barrouillet | The time-based resource-sharing model of working memory[END_REF][START_REF] Cowan | Attention and Memory : An Integrated Framework[END_REF]. These latter models would predict a greater involvement of domain-general attentional processes in STM for both materials and would probably predict a similar developmental trajectory for musical and verbal STM as well as similitudes of domain-general processes between them (similar recency effects and correlations between both materials).

In addition, to further our understanding of the distinct mechanisms between musical and verbal STM, our study aimed at testing the development of auditory cognition beyond STM. It has been proposed that STM and speech-in-noise perception might share encoding processes [START_REF] Murphy | Comparing the effects of aging and background noise on short-term memory performance[END_REF][START_REF] Pichora-Fuller | How young and old adults listen to and remember speech in noise[END_REF][START_REF] Sarampalis | Objective Measures of Listening Effort : Effects of Background Noise and Noise Reduction[END_REF], and both are deficient in learning disorders [START_REF] Perez | Evidence for a Specific Impairment of Serial Order Short-term Memory in Dyslexic Children : Serial Order STM Deficits in Dyslexia[END_REF][START_REF] Ziegler | Speech-perception-innoise deficits in dyslexia[END_REF]. In order to confirm that STM and speech-in-noise perception share encoding processes and to shed light on material-specific perceptual processes shared by auditory STM and speech-in-noise, we compared musical and verbal STM with speech-in-noise perception abilities.

The two types of WM models described in the previous paragraph would both predict different encoding processes for musical and verbal material. Consequently, if STM and speech-in-noise share encoding processes, we predict differential links between musical and verbal STM, and speech-in-noise (e.g. only musical STM is linked to speech-in-noise perception or only verbal STM). If only musical STM shares processes with speech-in-noise, these results would be in line with the already observed reliance of sound segregation on pitch processing [START_REF] Oxenham | Pitch Perception and Auditory Stream Segregation : Implications for Hearing Loss and Cochlear Implants[END_REF] and bring evidence for the observed musician-advantage for speech-in-noise processing [START_REF] Chandrasekaran | Music, Noise-Exclusion, and Learning[END_REF][START_REF] Parbery-Clark | Musician Enhancement for Speech-In-Noise[END_REF][START_REF] Strait | Musical training during early childhood enhances the neural encoding of speech in noise[END_REF][START_REF] Zendel | The Impact of Musicianship on the Cortical Mechanisms Related to Separating Speech from Background Noise[END_REF]. If, however, only verbal STM is linked to speech-in-noise, the evidence would be in favor of top-down lexical influences or fine temporal structure encoding shared by speech-in-noise perception and verbal STM [START_REF] Zekveld | The effects of working memory capacity and semantic cues on the intelligibility of speech in noise[END_REF].

The aims of the present work are thus threefold. First, this study aims at describing the developmental trajectory of musical and verbal STM as, until now, only the trajectory of verbal STM using recall tasks has been described. The use of a DMST provides the potential to investigate specific patterns of development that could be task-related and/or material-specific, as it allows us to directly compare musical and verbal STM. Second, the present study aims at bringing insights about the shared and distinct mechanisms between musical and verbal STM, as domain-general and domain-specific processes have been described for musical and verbal STM in adults [START_REF] Gorin | Domain-Generality of Timing-Based Serial Order Processes in Short-Term Memory : New Insights from Musical and Verbal Domains[END_REF][START_REF] Gorin | A comparison of serial order short-term memory effects across verbal and musical domains[END_REF]. Third, in order to go beyond auditory STM and to scrutinize domain-specific processes in auditory STM, dependences between auditory STM and speech-in-noise perception were explored using cocktail party noise. As speech-in-noise and auditory STM would share encoding processes [START_REF] Murphy | Comparing the effects of aging and background noise on short-term memory performance[END_REF][START_REF] Pichora-Fuller | How young and old adults listen to and remember speech in noise[END_REF][START_REF] Sarampalis | Objective Measures of Listening Effort : Effects of Background Noise and Noise Reduction[END_REF], finding out if both materials are linked to speech-in-noise or only one of them would bring insights into the existence of shared (e.g. both materials linked to speech-in-noise) or distinct (e.g. one of the materials linked to speech-in-noise) encoding processes.

In the present study, we created a child-adapted DMST with 4-tone sequences for the musical material and with sequences of four consonant-vowel syllables for the verbal material. We implemented two levels of difficulty and tested 100 children ranging from 5 to 10 years of age (from kindergarten to 5 th grade). The same 100 children were tested with a 4-alternative forced-choice speech-in-noise perception task.

METHODS

Participants

One hundred children (mean age = 7.6 y.o ; min = 54 month-old, max = 127 month-old, 6 left-handed), attending a public primary school in South-East of France, participated in this study. The children were tested during school hours. Participation in the study was proposed to a total of 114 children and they were included in the study only if both parents or legal tutors provided written informed consent. The study was approved by the relevant services of the French public education services (Inspection de l'Education Nationale (IEN) and Direction Académique des Services de l'Education Nationale (DASEN) of the Isere department). Children from Kindergarten (n=12), 1 st (n=17), 2 nd (n=18), 3 rd (n=19), 4 th (n=16), and 5 th (n=18) grade underwent the experiment (Table 1). Among the 100 children, parents' responses to questionnaires revealed that 5 children had a diagnosed learning disability (dyslexia, dysphasia, dyscalculia, dysorthographia, or dysgraphia), 30 had seen at least once a speech-therapist and 6 had already worn grommets. Nine out of the hundred children had specific musical training for more than 2 months (mean = 2.01 years, SD = 1.29), other children had never had any musical training apart from normal school curriculum. As we aimed here to explore the cognitive abilities of children among a representative set of the population, we present the results including all children.

Twelve adults (mean age = 26.5 years, SD = 9.6, one left-handed) were also included in the study. None reported any neurological or psychiatric troubles. Six of them had had a few years of musical education (mean = 4 years, SD = 2.8) but none of them were practicing any instrument at the time of the experiment and this for at least the last 10 years. Their level of education, along with children's parental level of education, are shown in Figure 1.

Stimuli construction and task design

Short-Term Memory task

As shown in Figure 2, each trial of the STM recognition task consisted in listening to a 4-item auditory sequence (S1), then after a silent retention delay of 2000 ms, to another 4-item sequence (S2) that could be identical or different. When S2 was different, a new item could appear equiprobably at the 2 nd , 3 rd , or 4 th position of the sequence. Each item lasted 500 ms, the silent inter-stimulus interval (ISI) between two items lasted 100 ms so overall there was a 600 ms stimulus onset asynchrony (SOA), leading to a duration of 6600 ms for S1 and S2 sequences. Children were given unlimited time to give a response. The next trial started after a 1500 ms delay. Presentation® software (Version 18.0,Neurobehavioral Systems,Inc.,Berkeley,CA,www.neurobs.com) was used to present stimuli and record responses in the STM task. Based on previous data-points in the literature [START_REF] Gathercole | Dissociable Lexical and Phonological Influences on Serial Recognition and Serial Recall[END_REF]Jarrold, Thorn, et Stephens 2009;[START_REF] Majerus | Relations between vocabulary development and verbal short-term memory : The relative importance of short-term memory for serial order and item information[END_REF]Majerus et al. , 2007) ) we found that the use of 4-item sequences was an optimal trade-off for younger children to be able to do the task and for older children not being at ceiling. There were two material conditions for the STM task: musical piano tones (music condition) or syllables (verbal condition).

For the musical STM task, six musical tones (piano timbre, Cubase database, Steinberg) belonging to the C major scale were used (C2, D2, E2, F2, G2, A2) with frequencies ranging from 131 to 220 Hz (thus encompassing the fundamental frequency range of the vowel recordings: 202-212 Hz). A total of 48 four-item sequences were generated, all tones were different within a given sequence and they all contained at least one ascending interval and one descending interval (to avoid simple, constantly rising or falling patterns that could facilitate memorization). Twenty-four S1 sequences were used as S2 sequences for the "same" trials, half for the easy condition and half for the difficult one. There was no difference between the difficulty conditions for the "same" trials. Twenty-four S2 sequences were generated for the "different" trials, which can be of two types depending on whether the change violated contour or not. Previous research has shown that a contour-violation leads to better performance in melody discrimination tasks than contour preservation [START_REF] Dyson | A figural approach to the role of melodic contour in melody recognition[END_REF][START_REF] Monahan | The effect of melodic and temporal contour on recognition memory for pitch change[END_REF][START_REF] Peretz | The role of contour and intervals in the recognition of melody parts : Evidence from cerebral asymmetries in musicians[END_REF][START_REF] Ziegler | Global and local pitch perception in children with developmental dyslexia[END_REF]. For the easy condition, twelve S2 sequences were generated with the new item in S2 changing the contour of the sequence (the contour is the up-and-down scheme of a melody,). So, if S1 had an up-down-up contour (e.g. E-A-D-F), S2 could have a down-up-up contour (e.g. E-C-D-F, Supplementary Material, Sound 1). In the difficult condition, twelve S2 sequences were generated, the new item in S2 did not change the contour (Supplementary Materials, Sound 2). It should be noted that here, greater difficulty of the musical memory task is induced by not only the absence of contour change but also by smaller pitch differences between the new item and the original one in the difficult condition compared to the easy condition.

For the verbal STM task, the items were Consonant-Vowel syllables. To avoid difficulties due to children's phonological skills, the consonant-vowel stimuli were selected to show the greatest perceptual distance with each other, within a S1 (or S2) sequence Six consonants and six vowels were thus selected: /f/ /t/ /z/ /g/ /m/ /l/ and /i/ /e/ /a/ /y/ /ø/ /u/ resulting into 36 syllables that were then recorded by a professional mezzo-soprano singer (for details about syllables construction, see Supplementary Figure S1). A total of 48 four-item sequences were generated to be used as S1. All vowels and all consonants were different in a given sequence. Twenty-four S1 sequences were used as S2 sequences for the "same" trials, half for the easy condition and half for the difficult one. For the other twenty-four S1 sequences, twenty-four S2 sequences were created for the "different" trials. For the easy condition, twelve S2 sequences were created with the new item in S2 differing from the item in S1 by both consonant and vowel (e.g., /lu/ instead of /mi/, Supplementary Material, Sound 3). For the difficult condition, twelve S2 sequences were created with the new item in S2 differing by the vowel only (e.g., /lu/ instead of /la/, Supplementary Material, Sound 4). When S2 was different, no substitution was made between /i/, /e/, and /y/ because of their shorter distance on the vowel triangle compared to other possible substitutions.

Speech-in-noise task

The speech-in-noise task was specifically designed for children. It was a French language adaptation [START_REF] Moulin | French "Audimots" : Development of a French version of the Four Alternative Auditory Features Test. 11[END_REF] of [START_REF] Foster | The Four Alternative Auditory Feature test (FAAF)-Linguistic and psychometric properties of the material with normative data in noise[END_REF]'s British Four Alternative Auditory Feature test. The adaptation for children [START_REF] Bourgeois-Vionnet | A case of verbal and emotional prosody processing dissociation after a right temporal venous infarct[END_REF][START_REF] Ginzburg | Neurodevelopmental aspects of hearing-in-noise in 5 to 11 year-old children. Oral communication at the 14th congress of the European Federation of Audiological Societies, 22-25 may 2019, Lisbon (Portugal)[END_REF] used pictures instead of written words and was implemented on a touch-tablet (iPad). For each trial, children had to match an aurally-presented word with its corresponding image among four pictures, by tapping on the corresponding picture on the tablet. The material consisted of 24 spoken words selected as a function of their concreteness, their frequency of occurrence in the French language and their age of acquisition [START_REF] New | Lexique 2 : A new French lexical database[END_REF]. The 24 selected words were recorded several times by a French-native female speaker in order to have at least nine different sound exemplars of each word. All the exemplars were equalized in RMS amplitude and two listeners chose independently the best sounding exemplars for each word that was included in the final test. From this list of 24 words, six four-images arrays were created so that each array contained images for which the words denominating them were phonological neighbors (difficult condition, e.g. [floeʁ][boe:ʀ][loeʁ][koeʁ]: fleur, beurre, l'heure, coeur -flower, butter, hour, heart). With the same words, six four-images arrays were created with phonologically distant words that had at least two different phonemes (easy condition, e.g.

[floeʁ][mɛ][nø]

[dɑ]: fleur, main, noeud, dent -flower, hand, node, tooth). For each trial, an array of four-images was displayed on the touchpad's screen followed by the sound stimuli: a target word denominating one of the four images after a 600ms delay. The child had to tap with its finger on the matching image as quickly as possible but without any time limit. The next trial was triggered by the child's click on the screen. Each word was presented twice: once in the difficult condition (phonological neighbors) and once in the easy condition (phonologically distant words) so that a total of 48 sets of four-images-array were presented. During the task, a continuous Cocktail-Party noise, made of 16 unintelligible French male and female voices, was presented binaurally via headphones. The targetword was systematically presented at 66 dB SPL. The Cocktail-party noise was presented at either 63 dB SPL (signal/noise ratio = +3 dB), or 69 dB SPL (signal/noise ratio = -3 dB).

Procedure

Before testing, parents filled a questionnaire about their child's level of education and their own level of education, the child's laterality, possible vision or auditory impairments, musical activities, bilingualism, learning disabilities, and eleven questions adapted from adults' musical listening questionnaires [START_REF] Lévêque | Musical emotions in congenital amusia : Impaired recognition, but preserved emotional intensity[END_REF][START_REF] Tillmann | Musical familiarity in congenital amusia : Evidence from a gating paradigm[END_REF].

Children were tested by groups of five or six in the gym of their school. 3 rd to 5 th graders were tested first and KG to 2 nd graders were tested a week after. Before testing, each child sat in front of a desk, listening to the experimenter's instructions. Before the STM tasks, the experimenter told a story about the elephant-professor and his two pupils, corresponding to the visual stimuli displayed on the computer screen during the task. These stimuli and the overall cover story for the child-friendly STM implementation were adapted from the melodic and rhythmic discrimination tasks of [START_REF] Ireland | Rhythm and Melody Tasks for School-Aged Children With and Without Musical Training : Age-Equivalent Scores and Reliability[END_REF] and [START_REF] Wieland | Evidence for a rhythm perception deficit in children who stutter[END_REF]. After the six children were settled in front of their table, the experimenter sat in front of them and started telling the cover-story for the task instructions using cardboard panels on which the task visual stimuli were printed. During the first sequence, a cartoon picturing an elephant teacher would appear on the computer screen and during the second sequence, a cartoon of a nice baby-elephant would appear on the left and a cartoon of a grimacing baby-monkey would appear on the right. After the second sequence, a question mark would appear on the screen between the two cartoons. Children were given the instruction that the baby-elephant was always repeating correctly the sequence produced by the elephant-professor and that the grimacing-monkey was always repeating incorrectly. They had to give their answer by clicking on the left button of the laptop trackpad for the baby-elephant ("same") and on the right button for the grimacing-monkey ("different"). After the STM task, the experimenter explained the speech-in-noise task with cartoons printed on cardboard panels: children saw 4 images appear on the touchpad and at the same time, they heard a word through their headphones. They had to find the spoken word in one of the four images and tap on it as quickly as possible. They had to ignore the people talking in the background (cocktail-party noise). A training block of 4 trials was given first. The entire testing session lasted around 30 minutes, including instructions and breaks.

All children underwent:

-The Short-Term Memory (STM) recognition task with both material (verbal/musical) and difficulty levels (easy/difficult). To avoid having a testing session of more than 30 minutes, younger children (KG, 1 st , and 2 nd grade) were not tested with the music difficult block since they were slower than the older children and the results of older children tested during the first day revealed that this condition was the most difficult. Auditory stimuli were presented with AKG-142-HD headphones and visual stimuli via laptop computer screens.

For each material, the STM task was divided into four blocks of 24 trials (two materials and two conditions of difficulty). In each block, half of the trials were "same" trials (identical S1 and S2) and the other half were "different" trials. These 24 trials were pseudorandomly presented during a block, with the constraint that no more than three "same" or three "different" trials could appear consecutively.

Children always began with the two easy blocks. Half of the children began with the verbal material and the other half with the musical material. Each of the four STM blocks lasted around 4 minutes and was preceded by a training with corresponding material and difficulty conditions. The training consisted of 2 "same" and 2 "different" trials with a smiley-shaped error feedback for each trial. At the end of the training blocks and the test blocks, a feedback specified the number of correct answers. For the test blocks, no trial-based error feedback was given. Overall, the STM tasks lasted around 20 minutes.

-The speech-in-noise test with two levels of noise (-3 dB SNR and +3 dB SNR) and two levels of phonological proximity [START_REF] Bourgeois-Vionnet | A case of verbal and emotional prosody processing dissociation after a right temporal venous infarct[END_REF][START_REF] Ginzburg | Neurodevelopmental aspects of hearing-in-noise in 5 to 11 year-old children. Oral communication at the 14th congress of the European Federation of Audiological Societies, 22-25 may 2019, Lisbon (Portugal)[END_REF]. Auditory stimuli were displayed through Sennheiser HD-250-Pro headphones and the task was performed on touchpads.

Children underwent a training comprising eight sets of images with a signal/noise ratio (SNR) of +3 dB, then a 48-trial block (24 words in each of the two phonological conditions, in pseudo-random order, so that the same word was not presented one after the other) with a SNR of +3 dB and finally a second 48-trial block with a SNR of -3 dB. Each block lasted around 3 minutes, so the speech-in-noise test lasted around 10 minutes overall with the instructions.

Adult participants were tested in the lab with the four STM tests, as for the older children.

STM data preprocessing

To assess whether instructions were well understood, we computed the STM task's answers at inappropriate time points, the correct response would be any answer given after S2. Responses during the S2 sequence were coded as "anticipation", responses between the child's response and the next trial were coded as "responses between trials" and responses during S1 or the retention delay between S1 and S2 as "within-trial error". Distribution of responses between trials and anticipations showed no particular information about a possible indicator of misunderstanding and/or problematic impulsivity of children during the task. However, within-trial errors displayed interesting information about children's ability to understand the task. A high rate of errors during the S1 sequence corresponded to the experimenters' observation of children's inability to understand the task correctly during the experimental session. On the account that they displayed more than 5 within-trial errors out of 24 trials during at least one block, we excluded 8 children among the 100 from the analysis (two of them were in kindergarten, three were in 1 st grade, one in 2 nd grade, one in 4 th grade and one in 5 th grade).

Data analyses

In all analyses, the developmental aspects of children's performance 2 were tested as a function of children's school grades, allowing for homogeneous groups with children being equally scholarly educated regarding reading and other abilities.

For the STM task, measures of d' and bias (c parameter) were obtained according to Signal Detection Theory (SDT) for each material (verbal/musical), for each difficulty level (easy/difficult) and for each participant (Macmillan & Creelman, 2004). Hit corresponded to a correct answer for different trials.

False alarm corresponded to an incorrect answer for same trials. d' and criterion (c) were calculated using Dominique Makowski's "dprime" R function from the psycho package [START_REF] Makowski | The psycho Package : An Efficient and Publishing-Oriented Workflow for Psychological Science[END_REF]. d', or sensitivity, was calculated as the z-score of False Alarms subtracted from the z-score of Hits. The criterion, c, is calculated as the mean z-score of Hits and False-alarm rates multiplied by minus one and reflects an observer's bias to say yes (in our case "different") or no ("same"), an unbiased observer having a value around 0. A liberal bias (tendency to say "different") results in a negative c, a conservative one results in positive c. Correction of extreme values are made following the recommendation of [START_REF] Hautus | Corrections for extreme proportions and their biasing effects on estimated values ofd′[END_REF]. Furthermore, we analyzed the response times of participants after 2 it should be noted that from this point on, the term "performance" refers to the sensitivity measure (d') described in this section the end of S2, on correct trials. Recency effects were examined by analyzing the percentage of correct responses as a function of the position of the new item in 'different' trials (2 nd , 3 rd , or 4 th position).

For the speech-in-noise task, we obtained percentages of correct answers for each child, for each condition of phonological proximity (close/distant) and for each signal/noise ratio (-3 dB and +3 dB).

Statistical analyses were performed on R (3.5.5 version). Mixed-design ANOVAs were performed to analyze data from the STM tasks using the rstatix R package [START_REF] Kassambara | rstatix : Pipe-Friendly Framework for Basic Statistical Tests[END_REF]. Due to the unbalanced experimental design (younger children did not undergo the difficult musical STM task), three sets of analyses were performed to assess all aspects of the data (factors for each analysis are detailed in the results section). Greenhouse-Geisser correction was applied when the sphericity assumption was violated, assessed by Mauchly's test. Significant effects and interactions were analyzed using pairwise t-tests as post-hoc tests adjusted with the Tukey method. We also performed one-sampled t-tests comparing c values to 0, the value of an unbiased observer. Spearman correlations were performed between age and performance in the DMST and the speech-in-noise task. Spearman partial correlation analyses with age as control variable were performed between performance in the STM task and the speech-in-noise task using the ppcor R package [START_REF] Kim | ppcor : An R Package for a Fast Calculation to Semi-partial Correlation Coefficients[END_REF].

Two ANOVAs are presented in the results section: the first one concerns children of all six grades (KG to 5 th grade) on the easy conditions of the STM task in order to examine the developmental trajectory of STM for both materials. The second one concerns older children (from 3 rd grade to 5 th grade) and examines the differential effects of material and its interaction with the task difficulty. A third ANOVA has been performed with children of all six grades on the verbal conditions in order to assess difficulty effects and is provided as a supplementary figure (see supplementary Figure S2 and Table S1).

RESULTS

Musical and verbal STM: easy conditions

We computed two 6x2 mixed-design analyses of variance (ANOVA) for the easy conditions on d' (Figure 3) and c (Table 2) with Grade as a between-subjects factor (kindergarten to 5 th grade) and with Material as within-subject factor (musical/verbal), as well as a 6x2x2 mixed-design ANOVA for response times analysis (Figure 3) with the aforementioned factors and with type of trial as within-factor (same/different). We also performed a 6x2x3 ANOVA on the percentage of correct responses in different trials to assess recency effects, with Grade as between-subject factor, and Material and Position (2 nd , 3 rd , and 4 th position) as within-subject factors. The position factor corresponded to the position of the item that changed during the S2 of different trials. Complete ANOVA results are shown in Table 3, and we present significant effects and interactions below. d'. The main effect of Grade was significant, F(5,86) = 3.030, p = .014, ηp 2 = .257, revealing better performance for older children. Post-hoc tests showed a significantly lower performance of kindergarten children compared to all other grades (all p-values < .02) except 1 st grade and 2 nd grade (both p-values > .13). The main effect of Material was significant F(1,86) = 32.33, p < .001, ηp 2 = .273, showing better performance with the verbal material than with musical material. The interaction between grade and material was not significant (p = .644) even though the observation of the results suggests that 3 rd graders performed better for the musical material than 2 nd graders whereas for the verbal material 2 nd graders seemed to reach a similar performance level as did 3 rd graders (Figure 3).

Correct response times. There were no significant effects in the response time analysis. We expected a grade effect which was marginally significant, F(5,85) = 2.238, p = .058, ηp 2 = .116.

Bias. Comparison of the mean c parameter per grade and condition to zero showed that 2 nd , 3 rd , 4 th and 5 th grade children displayed a positive response bias c that was significantly above zero in the easy musical condition (Table 2). The ANOVA revealed a significant effect of Material F(1,86) = 40.89, p < .001, ηp 2 = .322, with a higher c for the musical material than the verbal material.

Recency effects. Only the main effect of Material was significant, F(1,87) = 54.850, p < .001, ηp 2 = .387, with better performance for verbal than musical material, thus mirroring the d' analysis.

When children were gathered in three groups of age (KG-1 st , 2 nd -3 rd , 4 th -5 th ), the ANOVA on recency effect showed a main effect of Grade, albeit with a small effect size F(2,89) = 3.591, p = .032, ηp 2 = .075.

Musical and verbal STM: effect of difficulty and material

Two 3x2x2 mixed-design analyses of variance (ANOVA) were performed on d' (Figure 4) and c (Table 4) with Grade as a between-subjects factor (3 rd to 5 th grade), and Material (verbal/musical) and Difficulty (easy/difficult) as within-subject factors. A 3x2x2x2 mixed-design ANOVA was performed on response times (Figure 4) with the aforementioned factors and with type of trial as within-subject factor (same/different). A 3x2x2x3 mixed design ANOVA was performed on the percentage of correct response for different trials, adding the Position factor (2 nd , 3 rd and 4 th position) as within-subject factor. These analyses were only possible with data from children between 3 rd and 5 th grade (n = 50). d'. No significant effect of Grade was observed in these groups spanning a more restricted age range, F(2,47) = 0.325, p = .7, ηp 2 = .038. We observed a main effect of Material, F(1,47) = 94.8, p < .001, ηp 2 = .787, with lower performance for musical material than for verbal material. The main effect of Difficulty was significant, F(1,47) = 31.24, p < .001, ηp 2 = .544 and interacted with Material, F(1,47) = 42.4, p < .001, ηp 2 = .474: performance was lower in the difficult condition than in the easy condition for the musical material (p < .001), but not for the verbal material (p = .96).

Correct response times. We found a significant effect of Material F(1,47) = 22.373, p < .001, ηp 2 = .428 with longer response times for the musical material than for the verbal material. The material effect interacted significantly with the type of trial factor F(1,47) = 7.874, p = .007, ηp 2 = .143 with a difference between musical and verbal materials only for different trials (p < .001).

Bias. Comparison of the mean c parameter per grade and condition to zero showed that all children from 3 rd to 5 th grade displayed positive response bias c that was significantly above 0 in the difficult musical condition (Table 2). The ANOVA revealed a significant effect of material F(1,47) = 92.585, p < .001, ηp 2 = .734 with a higher positive c for the musical material than the verbal material. The main effect of difficulty was significant, F(1,47) = 40.558, p < .001, ηp 2 = .432, children displaying a higher c for the difficult condition. The material and difficulty interaction was significant F(1,47) = 23.118, p < .001, ηp 2 = .330. Post-hoc tests revealed a significantly higher c in the difficult condition compared to the easy condition, only for the musical material (p < .001) but not for the verbal material (p = 0.46).

Recency effects. We found a Position effect F(2,144) = 32.381 p < .001, ηp 2 = .403 with performance on the 2 nd position lower than for 3 rd position (p = .035) and 4 th position (p < .001) and 3 rd position lower than 4 th (p = .028). The Position, Material, and Difficulty interaction F(2,144) = 38.196, p < .001, ηp 2 = .442 showed a lower performance on the 2 nd position compared to the 3 rd and 4 th and between 3 rd and 4 th only for the difficult musical material (p < .001).

Partial correlations between STM and speech-in-noise performance

For the speech-in-noise task, as expected, effects of age, phonological proximity, and noise intensity were observed [START_REF] Ginzburg | Neurodevelopmental aspects of hearing-in-noise in 5 to 11 year-old children. Oral communication at the 14th congress of the European Federation of Audiological Societies, 22-25 may 2019, Lisbon (Portugal)[END_REF]. The correlation analysis shown here-after include 87 children: data from five children were excluded because of technical difficulties when recording speech-in-noise data.

We first performed correlations between musical and verbal STM performance (d', in the easy condition only, as data for the difficult condition was not available for children from KG to 2 nd grade) and the age of children (in months). Both conditions were significantly correlated with age r(85) = .30, p = .0056 for the musical material and r(85) = .24, p = .028 for the verbal material and these two correlation coefficient were not significantly different (p = .80). We then performed partial correlation accounting for the age variable between musical and verbal STM performance (d'), in the easy condition only, as data for the difficult condition was not available for children from KG to 2 nd grade.

Musical and verbal STM were significantly correlated even after factoring out the effect of age, r(85) = .26, p = .014. Next, we performed partial correlations between performance in the STM tasks (easy conditions) and in the speech-in-noise tasks accounting for the age variable. The analysis revealed that only the correlation between music STM performance with performance in the phonologically distant condition with a -3 dB SNR in the speech-in-noise task was significant, r(85) = 0.32, p = 0.025, after Bonferroni correction for multiple comparisons (Figure 6). No other significant correlations were observed between performance in STM tasks and speech-in-noise tasks when age was considered (p > 0.53).

Effect of learning disorders or musical training

Five children, out of the 92 included in the analyses above, had various learning disorders diagnosis (dyslexia, dysphasia, dyscalculia, dysorthographia, dysgraphia), according to parental reports. No statistical analysis was conducted on their performance because of their small number. Nonetheless, for the STM task, we compared each child's performance in every condition to the corresponding median and their placement in the quartiles of performance of other children of the same grade and in the same condition. This analysis revealed that in the musical easy condition, all five children with learning disorders scored below the median. Among those five children, four of them performed in the first quartile. In the other conditions, at most two out of these five children performed above the median. All the ANOVAs described before were performed excluding these five children and, aside from a small decrease of statistical power, the effects remained consistent.

For the speech-in-noise task, we compared these children's performance in each condition of phonological difficulty (easy/hard) and noise intensity (SNR-3/SNR+3) to the median of the other children for the corresponding grade, phonological difficulty, and noise intensity. Noticeably in the easy condition (phonologically distant) with a +3 dB SNR, all of the five children with learning disorders performed below the median, in the lowest quartile. In both difficulty conditions for the -3 dB SNR, at most one child performed above the median, but never the same child. In the difficult condition for +3 dB SNR, only two children performed below the median. Furthermore, nine children reported musical training of at least one year (conservatory or communal music school). Given their small number, we did not perform any statistical analysis but when we removed them from the analysis, the effects remained consistent. Seven out of these nine children performed above the median and five out of these seven performed in the upper quartile in the music easy condition and in the verbal difficult condition. Five out of these nine children underwent the musical difficult condition (the others being too young) and all of them performed above the median for this condition. For the speech-in-noise task, a majority of children with musical training (at least seven out of nine in each condition) scored above the median in all conditions.

Comparison between children and adults

To compare performance of the oldest children with performance of adults, three 2x2x2 mixed-design ANOVA on d', RTs and c were performed with Group (adults (n = 12) and children (5 th graders, n = 17)) as between-subject factor, and Material (verbal/musical) and Difficulty (easy/difficult) as withinsubject factors and type of trials as within-subject factor (same/different) for the response times analysis. A 2x2x2x3 mixed-design ANOVA was performed on the percentage of correct responses for different trials adding the Position factor (2 nd , 3 rd and 4 th position) as within-subject factor. Complete results are shown in Table 5. d'. The main effect of Group was significant, F(1,27) = 13.25 p = .00114, ηp 2 = .743 with children having poorer performance than adults. A significant Group and Material interaction was found F(1,27) = 4.981, p = .0341, ηp 2 = .269. Post-hoc tests showed a significant difference between all comparisons (p < .003) except for a marginally significant difference between children and adults for the verbal material (p = .061). Adults displayed a significantly smaller difference between musical and verbal material than children (p = .02, t-test comparing the subtraction of musical and verbal performance between adults and children). All other effects mirrored the former d' analysis on children's data.

Correct response times

The main effect of Group was significant F(1,27) = 4.825, p = .037 ηp 2 = . 152 with longer response times for children than for adults. The main effect of Material was significant F(1,27) = 9.911, p = .004 ηp 2 = . 269 with longer response times for the musical material. There was also a significant interaction between Material and Type of trial F(1,27) = 8.625, p = .007, ηp 2 = . 242 with a difference between musical and verbal material only in different trials (p < .001).

Bias. Comparison of the mean c parameter per condition to 0 showed that adults displayed a positive bias parameter c for all conditions and all of them except the musical easy condition were significantly different from zero (Table 2). No effect of group was found in the ANOVA.

Recency effects.

We observed an effect of the Group factor F(1,84) = 10.99, p = .00254, ηp 2 = . 177 with lower performance for children. None of the other significant effects or interactions involved the Group or the Position factor (Figure 5).

We emphasize the fact that ceiling performance (e.g. 100% correct responses) was observed for the majority of adults in the easy and difficult conditions for the verbal material and for the easy musical condition. However less than half of 5 th graders reached ceiling in all conditions.

DISCUSSION

The aim of this study was to investigate the development of auditory STM in 5 to 10-year-old children for musical and verbal material. A DMST was created in a child-friendly version in highly comparable ways for both materials, each one with two levels of difficulty. Results showed that overall, younger children (KG and 1 st grade, see Table 1 for age equivalences) displayed poorer memory performance than older children (2 nd , 3 rd , 4 th and 5 th grade). This was observed for both materials: children's performance increased from KG to 2 nd grade, and then remained stable until 5 th grade. Overall, children's performance was lower for musical material (sequences of 4 tones) than verbal material (sequences of 4 syllables). Adults showed the same pattern of performance, but with ceiling performance for verbal material. For both musical and verbal materials, children did not reach the same level of performance as adults, thus revealing the immaturity of auditory STM even in 10-year old children. Response times analysis showed the same pattern as performance analysis: younger children were slower than older children thus confirming the absence of an impulsivity issue or a potential difference in speed-accuracy trade-off between age-groups. An effect of difficulty level was found for the musical material, revealing a facilitatory effect of contour violation and large interval changes, in both children and adults. Manipulating the difficulty in the verbal material (i.e., changing only a vowel in the second sequence in the difficult task instead of both consonant and vowel in the easy version) elicited only minor decrease in performance, if any. As for the recency effects expected in that kind of task, they were more apparent in the difficult conditions. Children displayed a recency effect for both materials, but older children and adults mostly for the difficult musical material.

Developmental trajectory of auditory STM

The present study, based on a recognition paradigm with a fixed number of items, suggests the following pattern of development for musical and verbal STM: increasing performance from KG to 2 nd grade (5 to 7 years-old) followed by a standstill until 5 th Grade (10 years old). However, neither Gathercole et al. (2004b) nor [START_REF] Alloway | Verbal and Visuospatial Short-Term and Working Memory in Children : Are They Separable?[END_REF] observed such a pattern for verbal STM (for a comparison between the former two studies and the present one, see Figure 7), they rather observed a linear increase in capacity. Regarding musical STM, [START_REF] Clark | Tone series and the nature of working memory capacity development[END_REF] found a developmental increase in children from 6 to 13 years-old similar to the one of visuo-spatial STM, with a linear increase in storage capacity as observed in former studies [START_REF] Alloway | Verbal and Visuospatial Short-Term and Working Memory in Children : Are They Separable?[END_REF][START_REF] Gathercole | The Structure of Working Memory From 4 to 15 Years of Age[END_REF].

The difference of developmental pattern observed between our study and the former ones may rely on the difference of cognitive processes used in recall tasks and DMST. Recall tasks might rely more strongly on memory capacity and long-term knowledge (if it is used with digits or words), whereas the DMST task might involve different procedural processes as it involves a comparison process between S1 and S2 and rely more on sensory memory traces as they can be used in stimulus recognition [START_REF] Cowan | On Short and Long Auditory Stores[END_REF]. Regarding the weight of long-term knowledge, lexical knowledge has indeed been found to have a significant influence on verbal STM in children when recall tasks are used [START_REF] Gathercole | Dissociable Lexical and Phonological Influences on Serial Recognition and Serial Recall[END_REF][START_REF] Messer | Growth of verbal short-term memory of nonwords varying in phonotactic probability : A longitudinal study with monolingual and bilingual children[END_REF] and also when serial-order STM reconstruction tasks are used [START_REF] Gorin | A comparison of serial order short-term memory effects across verbal and musical domains[END_REF][START_REF] Leclercq | Serial-order short-term memory predicts vocabulary development : Evidence from a longitudinal study[END_REF][START_REF] Majerus | Relations between vocabulary development and verbal short-term memory : The relative importance of short-term memory for serial order and item information[END_REF]. The reconstruction task is supposed to minimize phonological and lexical demands, however these studies are using verbal materials (like animal names in Leclercq & Majerus, 2010 for example). Forward recall tasks also rely on phonological production and thus present constraints linked to the phonological loop and specific mechanisms of motor production.

Several factors have been proposed to account for the increase of performance in studies investigating the development of STM: the decrease of memory trace decay throughout childhood, the increase of memory capacity, the increasing involvement of executive functions as they mature, and the influence of long-term knowledge that would support short-term storage [START_REF] Messer | Growth of verbal short-term memory of nonwords varying in phonotactic probability : A longitudinal study with monolingual and bilingual children[END_REF]. The developmental trajectory observed in the present study could thus arise from the use of a DMST that reduces lexical demands and necessitates different executive demands during the comparison between the two sequences. Furthermore, DMST has the significant advantage of allowing the same implementation for different auditory material (verbal, musical, timbre), which allows direct comparisons.

Development of musical and verbal STM: shared and distinct mechanisms

As mentioned in the introduction, several WM model and in particular their account for short-term storage could be divided into two groups regarding their prediction for the developmental trajectory of musical and verbal STM. On one hand, theoretical framework based on [START_REF] Baddeley | Working memory. The psychology of learning and motivation[END_REF] WM model postulate the existence of a separate short-term storage for musical material [START_REF] Pechmann | Interference in memory for tonal pitch : Implications for a working-memory model[END_REF] and even a specific musical central executive [START_REF] Ockelford | A Music Module in Working Memory? Evidence from the Performance of a Prodigious Musical Savant[END_REF]. These frameworks might predict two different developmental trajectories for musical and verbal STM as they would rely on different attentional processes. On the other hand, other models consider that attentional processes involved in the maintenance of items in STM do not present such a modular structure [START_REF] Barrouillet | The time-based resource-sharing model of working memory[END_REF][START_REF] Cowan | Two Separate Verbal Processing Rates Contributing to Short-Term Memory Span[END_REF]. A common consideration between these models is that maintenance processes involving attentional resources used to refresh memory traces in shortterm storage are non-modular and can be allocated to different materials. Such models account for the distinction between domain-general and domain-specific processes in musical and verbal STM [START_REF] Gorin | A comparison of serial order short-term memory effects across verbal and musical domains[END_REF] and they would probably account for a similar developmental trajectory between musical and verbal STM as it was observed in this study. In addition to a similar trajectory, we found recency effects for both materials. Some models posit that recency effect would represent behavioral signatures of serial order constructs in recall and recognition tasks [START_REF] Hurlstone | Memory for serial order across domains : An overview of the literature and directions for future research[END_REF][START_REF] Lewandowsky | Short-Term Memory : New Data and a Model[END_REF]. Indeed, it has been argued that musical and verbal STM systems may rely on similar sequential processes. These similar sequential processes were characterized in adults by [START_REF] Gorin | A comparison of serial order short-term memory effects across verbal and musical domains[END_REF] who found similar selective sensitivity to time-based interference in musical and verbal STM (with a mixed recall/recognition paradigm) and similar transposition gradients. They also found similar error patterns, sequence length effects, and recency and primacy effects for both materials as well as similar limited capacity and an effect of pitch proximity, comparable to phonological proximity [START_REF] Williamson | Musicians' and nonmusicians' short-term memory for verbal and musical sequences : Comparing phonological similarity and pitch proximity[END_REF]. Another finding in favor of shared mechanisms between the two materials is the significant correlation between musical and verbal STM performance, even when the effect of age was factored out.

We observed similarities in the developmental trajectory for both materials, but we also observed better performance for the verbal material, despite using the same number of items for both tasks, both for children and adults (Figure 4). Although this latter finding could be related to discriminability differences for musical and verbal items used in the DMST, we cannot rule out the possibility that modality-specific STM systems could treat the two materials differently, as domain-specific systems have been suggested for encoding, storage, and maintenance of musical and verbal information [START_REF] Gorin | A comparison of serial order short-term memory effects across verbal and musical domains[END_REF][START_REF] Schulze | Working memory for pitch, timbre, and words[END_REF]. In addition, the fact that only musical STM performance correlated with the speech-in-noise performance in the present study suggests differentiated encoding processes between musical and verbal STM. A study involving comparison between STM for words, tones, and timbres using a recognition paradigm in adults, suggested similar storage of musical and verbal information and different internal sensorimotor codes used to maintain musical and verbal information [START_REF] Schulze | Working memory for pitch, timbre, and words[END_REF]. In our present study, the similarity of the developmental trajectory for both materials could be the consequence of the involvement of shared domain-general systems in serial order coding, given in particular the finding of a recency effect for both materials, shared maintenance processes, and similar rehearsal mechanisms. Conversely, the difference of performance that we found in children between materials, might arise from different sensorimotor codes used for musical and verbal information.

Development of auditory cognition

The secondary aim of this study was to investigate auditory cognition beyond auditory STM. Indeed, the links between CAPDs and learning disorder, as well as the advantage of musicians in speech-innoise abilities [START_REF] Parbery-Clark | Musician Enhancement for Speech-In-Noise[END_REF][START_REF] Slater | Music training improves speech-in-noise perception : Longitudinal evidence from a community-based music program[END_REF][START_REF] Zendel | The Impact of Musicianship on the Cortical Mechanisms Related to Separating Speech from Background Noise[END_REF], suggest that the joint investigation of speech-in-noise and verbal and non-verbal STM would be informative. Here, children underwent a speech-in-noise task [START_REF] Ginzburg | Neurodevelopmental aspects of hearing-in-noise in 5 to 11 year-old children. Oral communication at the 14th congress of the European Federation of Audiological Societies, 22-25 may 2019, Lisbon (Portugal)[END_REF] and it was found that only performance in the musical DMST correlated with speech-in-noise performance, specifically in the phonologically easy condition with a -3 dB SNR (Figure 5). This finding suggests that children might rely on perceptual processes used in pitch encoding to process speech in cocktail-party noise, these processes being shared with musical STM but not with verbal STM. These results verify the already observed link between sound segregation and pitch information processing [START_REF] Oxenham | Pitch Perception and Auditory Stream Segregation : Implications for Hearing Loss and Cochlear Implants[END_REF]. Overall, these results seem to corroborate the hypothesis that musical and verbal STM have distinct mechanisms regarding item-information processing. Indeed, we showed that domain-general procedural and attentional processing seem to be involved in both musical and verbal STM given their similar developmental trajectory. The distinct mechanisms involved in musical and verbal STM appears lie in item-specific encoding processes that only speech-in-noise and musical STM share in the present study.

Auditory cognition and learning disorders

These two components of auditory cognition (STM and speech-in-noise) might thus share processes that are of particular interest to understand the underpinnings of central auditory processing. We found, in the present study, that children who presented learning disorders all performed below the median in the musical easy condition of the DMST and in the easy condition at +3 dB SNR of the speech-in-noise task. These results indicate that the musical easy condition in the STM task and the easy +3 dB SNR condition in the speech-in-noise task might allow discriminating children with learning disorders. These results are in agreement with the hypothesis of a general deficit in auditory perception in learning disorders [START_REF] Nithart | Reading Disabilities in SLI and Dyslexia Result From Distinct Phonological Impairments[END_REF] encompassing verbal STM deficits [START_REF] Perez | Evidence for a Specific Impairment of Serial Order Short-term Memory in Dyslexic Children : Serial Order STM Deficits in Dyslexia[END_REF] and speech-in-noise deficits [START_REF] Ziegler | Deficits in speech perception predict language learning impairment[END_REF]. Therefore, it seems that STM for pitch and speech in cocktail party noise could be of great interest to identify early CAPD and, as suggested by these studies, early learning disorders. As comorbidity between CAPD and learning disorders has been reported, the use of early identification of CAPD would facilitate the adaptation of a child's school and home environment earlier in development. Indeed, the diagnosis of learning disorders is currently highly reliant on reading abilities and thus cannot be done before reading acquisition. The use of auditory child-adapted tasks, such as the ones used here, allows overriding the reading acquisition problematic as no reading abilities are required. Furthermore, remediation and clinical treatment could benefit from these insights as pitch encoding seems to be closely related to learning disorders. Indeed, clinical population could take advantage of the indirect effect of pitch encoding enhancement that arise from musical training [START_REF] Forgeard | The relation between music and phonological processing in normal-reading children and children with dyslexia[END_REF].

CONCLUSION

This study investigated the developmental trajectory of auditory STM in 5 to 10 years-old children for musical and verbal material. Results suggest that auditory STM is developing throughout childhood and is still under maturation at the age of 10. With the use of a recognition task, we observed that STM performance increases until 2 nd grade and levels-off until 5 th grade for musical and verbal material.

Using the DMST allowed discussing shared processes between the two materials and the different processes at stake in recognition tasks compared to forward recall tasks. Children, as well as adults, also showed poorer performance for the musical material compared to the verbal material, providing some evidence for specific mechanisms for the processing of the two materials in STM. We also observed that similar processes might be at stake between musical STM and speech-in-noise perception revealing the relevance of investigating the specific processes involved in musical information processing. Future studies should further investigate the development of auditory cognition in a systematic way that relies less on verbal production and LTM knowledge as serial recall tasks do. Future investigation should also assess jointly short-term memory and speech-in-noise processing in children with typical development and learning disorders, to pave the way for new diagnosis and rehabilitation tools of central auditory processing deficits. In each trial, the child hears the first sequence S1 and after a 2000 ms delay, the second sequence S2. In a block, in half of the trials, the S2 sequence was identical to S1, in the other half S2 was different. A new item could equiprobably appear in the 2 nd , 3 rd or 4 th position of S2. We represent here only the easy condition (Supplementary material, Sounds 1 & 3). In the difficult musical condition, the new item in the S2 sequence did not violate the contour of the melody (Supplementary material, Sound 2); in the difficult verbal condition, the new item in the S2 sequence changed only by the vowel (Supplementary material, Sound 4). Visual stimuli were part of the playful story children were told to understand the task, and were inspired by melodic and rhythmic discrimination task from [START_REF] Ireland | Rhythm and Melody Tasks for School-Aged Children With and Without Musical Training : Age-Equivalent Scores and Reliability[END_REF] and [START_REF] Wieland | Evidence for a rhythm perception deficit in children who stutter[END_REF]. b: Speech-in-noise paradigm. Children heard a word and 4 pictures appeared simultaneously on the touchpad. They had to tap on the corresponding picture as fast as possible. A cocktail-party noise was presented at either -3 or +3 dB SNR in the background and two conditions of phonological proximity were possible between the words corresponding to the four images: phonologically distant (e.g. fleur-main-noeud-dent, \floeʁ\ \mɛ\ \nø\ \dɑ̃\, as shown in the figure) or phonologically close (fleur-coeur-beurre-l'heure, \floeʁ\ \koeʁ\ \boeʁ\ \loeʁ\) Fig. 3. Performance for the easy conditions as a function of material (musical or verbal) for all children's grades (KG: kindergarten, G: grade) and for adults. a: mean and standard error of children and adult's sensitivity (calculated as the d'). b: mean and standard error of children and adult's correct response times (time in millisecond that subjects spent after the end of S2 before giving a "same" or "different" answer).

Fig.4. Performance for all conditions of Material (musical or verbal) and Difficulty (easy or difficult)

for children of 3 rd , 4 th and 5 th grades and for adults. a: mean and standard error of children and adult's sensitivity (calculated as the d'). b: mean and standard error of children and adult's response time (time in millisecond that subjects spent before giving a "same" or "different" answer). Percentage of correct response for identical S1-S2 are also represented for comparison. These effects were similar for the other younger children. The lower right panel correlation was the only correlation between STM performance and Speechin-Noise performance that showed significance when a partial correlation was computed with age as a control variable. Fig. 7. Left and middle panels: z-scores of mean performance in verbal forward recall tasks with digits, words, and nonwords in children from 4 to 10 years of age, adapted from [START_REF] Gathercole | The Structure of Working Memory From 4 to 15 Years of Age[END_REF] and [START_REF] Alloway | Verbal and Visuospatial Short-Term and Working Memory in Children : Are They Separable?[END_REF] respectively. Right panel: z-scores of mean musical and verbal recognition tasks of the present study in the easy condition.

Fig.6. Top panels: Spearman correlation between children's age (in number of months) and the performance of children for the STM task (calculated as the d') in the musical easy condition (top left), the verbal easy condition (top middle) and the performance of children in the speech-in-noise task (in percentage of correct response) for the easy condition and a -3 dB SNR (top right). Lower left panel: correlation between the musical easy condition and the verbal easy condition in

Table 2: Mean c values and standard deviation as a function of grade (columns) and condition (rows). As the mean value of the c parameter of an unbiased observer is 0, we performed one-sample t-tests (Bonferroni corrected per condition) comparing c mean values per grade and condition to 0. All mean c values (except 1 st grades in the verbal difficult condition) were numerically higher than 0 (conservative bias) as is usually observed in DMST tasks. Asterisks indicate a c value significantly different from 0.

Condition

FiguresFig. 1 .

 1 FiguresFig.1. Distribution of children's parents and adult participants' level of education. Level of education categories correspond to the number of years spent in scholar institutions, from 1 st grade on. A Pearson's Chi-squared test between all children's grades and the number of parents per category of level of education revealed that the proportion of parental level of education did notsignificantly differ by grade X 2 (25, N = 181) = 25.97, p = 0.41. A Chi-squared was also performed between adult's distribution of level of education and the mean proportion of children's parental level of education. The proportion of parental level of education differed by group (parents/adults) X 2 (5, N = 195) = 25.6, p = 0.00011 3 .

Fig. 5 .

 5 Fig.5. Recency effects for the older children (5 th grade, n = 17) and adults (% of correct responses for different trials). Results are presented as a function of the material 'musical/verbal), difficulty (easy/difficult) and the position of the item change in the S2 sequence (2 nd , 3 rd or 4 th position).Percentage of correct response for identical S1-S2 are also represented for comparison. These effects were similar for the other younger children.

  Fig.6. Top panels: Spearman correlation between children's age (in number of months) and the performance of children for the STM task (calculated as the d') in the musical easy condition (top left), the verbal easy condition (top middle) and the performance of children in the speech-in-noise task (in percentage of correct response) for the easy condition and a -3 dB SNR (top right). Lower left panel: correlation between the musical easy condition and the verbal easy condition in the STMtask. Lower right panel: partial correlation between the musical easy condition in the STM task and the speech-in-noise task in the easy phonological condition and -3 dB SNR. RSpearman: Spearman correlation coefficient and its corresponding p-value. Rpartial: partial correlation coefficient after factoring out the effect of age and its corresponding p-value. Partial correlations were Bonferroni corrected for eight comparisons (two STM conditions and four speech-in-noise conditions): the pvalues were multiplied by eight.

Table 3 : For the easy conditions, results of the repeated measures ANOVAs on each measure (d', c, Response times) with Grade as between-subject factor (6 levels: KG, 1 st , 2 nd , 3 rd , 4 th , 5 th grade) and Material (2 levels: verbal, musical) as within-subject factor. For the response times analysis, the Type of trial was taken into account as within-subject factor (same/different). The analysis of Recency effects was done on the percentage of correct responses for different trials with Position (3 levels: 2 nd , 3 rd , 4 th position) and Grade as between-subject factors and Material as within-subject factor.

 3

	Significant effects are in bold font. df: degrees of freedom, ηp 2 : partial eta-squared, W: Mauchly's
	statistic. An Asterisk indicates a significant W. If so, corrected degrees of freedom are reported in
	parenthesis in the df1 and df2 columns and the corresponding statistics are corrected with ε:

Table 4 : With older children, results of the repeated measures ANOVAs on each measure (d', c, Response times) with Grade as between-subject factor (3 levels: 3 rd , 4 th , 5 th grade) and Material (2 levels: verbal, musical) and Difficulty (2 levels: easy, difficult) as within-subject factors. For the response times analysis, the Type of trial was taken into account as within-subject factor (same/different). The analysis of Recency effects was done on the percentage of correct responses for different trials with Position (3 levels: 2 nd , 3 rd , 4 th position) and Grade as between-subject factors and Material and Difficulty as within-subject factors.

 4 Significant effects are in bold font. df: degrees of freedom, ηp 2 : partial eta-squared. W: Mauchly's statistic. An Asterisk indicates a significant W. If so, corrected degrees of freedom are reported in parenthesis in the df1 and df2 columns and the corresponding statistics are corrected with ε: Greenhouse-Geisser estimates of sphericity.

	Measure Factor(s)	df1	df2	F-value p-value	ηp 2	W	ε
	d' Grade	2	47	0.174	0.841	0.029	-	-
	Material	1	47	94.803 7.52e -13 0.787	-	-
	Difficulty	1	47	31.237 1.12e -06 0.544	-	-
	Grade : Material	2	47	0.462	0.633	0.035	-	-
	Grade : Difficulty	2	47	0.872	0.425	0.062	-	-
	Material : Difficulty	1	47	42.426 4.48e -08 0.474	-	-
	Grade : Material : Difficulty	2	47	0.125	0.883	0.005	-	-
	c Grade	2	47	0.616	0.545	0.043	-	-
	Material	1	47	92.585 1.09e -12 0.734	-	-
	Difficulty	1	47	40.

558 7.45e -08 0.432 - -

		Grade : Material	2	47	2.509	0.0922	0.130	-	-
		Grade : Difficulty	2	47	1.064	0.353	0.038	-	-
		Material : Difficulty	1	47	23.118	1.6e -05 0.330	-	-
		Grade : Material : Difficulty	2	47	1.775	0.181	0.070	-	-
	Response times	Grade	2	47	0.760	0.473	0.031	-	-
		Material	1	47	27.73	3.4e -06 0.371	-	-
		Difficulty	1	47	3.381	0.072	0.067	-	-
		Type of trial	1	47	1.331	0.254	0.028
		Grade : Material	2	47	0.314	0.732	0.013	-	-

Table 5 : Comparison of children and adult data, results of the repeated measures ANOVAs on each measure (d', c, Response times) with Group as between-subject factor (2 levels: 5 th grade and adults) and Material (2 levels : verbal, musical) and Difficulty (2 levels : easy, difficult) as within-subject factors. For the response times analysis, the Type of trial was taken into account as within-subject factor (same/different). The analysis of Recency effects was done on the percentage of correct responses for different trials with Position (3 levels: 2 nd , 3 rd , 4 th position) and Group as between- subject factors and Material and Difficulty as within-subject factors.

 5 Significant effects are in bold font. df: degrees of freedom, ηp 2 : partial eta-squared. W: Mauchly's statistic. An Asterisk indicates a significant W: Mauchly's statistic. An Asterisk indicates a significant W. If so, corrected degrees of freedom are reported in parenthesis in the df1 and df2 columns and the corresponding statistics are corrected with ε: Greenhouse-Geisser estimates of sphericity.

	Measure	Factor(s)	df1 df2 F-value p-value	ηp 2	W	ε
	d' Group	1	27	13.25	0.00114	0.743	-	-
		Material	1	27	89.526 4.59e -10	0.869	-	-
		Difficulty	1	27	78.887	1.7e -09	0.693	-	-
		Group:	1	27	4.981	0.0341	0.269	-	-
		Material
		Group :	1	27	0.142	0.709	0.004	-	-
		Difficulty
		Material :	1	27	52.691	8.3e -08	0.661	-	-
		Difficulty
		Group:	1	27	0.631	0.434	0.023	-	-
		Material :
		Difficulty
	c Group	1	27	0.003	0.957	0	-	-
		Material	1	27	29.615 9.31e -06	0.719	-	-
		Difficulty	1	27	28.635 1.19e -05	0.454	-	-
		Group:	1	27	1.752	0.197	0.131	-	-
		Material
		Group:	1	27	0.322	0.575	0.009	-	-
		Difficulty
		Material :	1	27	14.159	0.000826	0.344	-	-
		Difficulty
		Group:	1	27	1.752	0.197	0.061	-	-
		Material :
		Difficulty
	Response	Group	1	27	4.825	0.037	0.152	-	-
	times

Note that "Working memory" is sometimes used in a larger sense encompassing this STM definition. Here we will refer to working memory only when the paradigm entails a manipulation of information.

We aimed at reaching homogeneity between children's parental level of education and adult participants' level of education but recruitment of adult participants fell during the Covid-19 pandemic, preventing us to fully achieve that goal. In the sample tested, all adult participants had very high level of performance irrespective of their education level.

This work was conducted within the framework of the LabEx CeLyA (''Centre Lyonnais d'Acoustique", ANR-10-LABX-0060) and of the LabEx Cortex (''Construction, Cognitive Function and Rehabilitation of the Cortex", ANR-11-LABX-0042) of Université de Lyon, within the program ''Investissements d'avenir" (ANR-16-IDEX-0005) operated by the French National Research Agency (ANR). This work was funded by a Pack Ambition Recherche (COGAUDYS project) from the Auvergne-Rhône-Alpes Region to AC, AM, and BT.

RESEARCH HIGHLIGHTS

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

SUPPLEMENTARY MATERIALS

Supplementary Figure S1 The Suppinfo_FigureS1_CV.pdf file describes how consonants and vowels were selected to build the syllables used in the DMST and how the sound files were pre-processed to build the four-item sequences.

Supplementary Figure S2 and Table S1 The Suppinfo_FigureS2_TableS1.pdf file contains a short description of the additional ANOVA that was performed on all six grades for the verbal material of the STM task. It also includes the graphical representation of performance (d') of children as a function of grade and difficulty of the task. The Table S1 describes in detail the ANOVA.

Supplementary Sound 1 The Suppinfo_Sound1_musicSTM_easy.wav file is an example of a S1-S2 "different" trial during the easy musical STM block: two sequences of four tones separated by a twoseconds delay. The second tone in S1, A2, is replaced at the same position in S2 by a tone, C2, that changes the contour of the melody. S1 contour: up-down-up (E2-A2-D2-F2). S2 contour: down-up-up (E2-C2-D2-F2).

Supplementary Sound 2 The Suppinfo_Sound2_musicSTM_difficult.wav file is an example of a S1-S2 "different" trial during the difficult musical STM block: two sequences of four tones separated by a two-seconds delay. The second tone in S1, G2, is replaced at the same position in S2 by a tone, A2, that does not change the contour of the melody. S1 contour: up-down-up (E2-G2-D2-F2). S2 contour: up-down-up (E2-A2-D2-F2).

Supplementary Sound 3 The Suppinfo_Sound3_verbalSTM_easy.wav file is an example of a S1-S2 "different" trial during the easy verbal STM block: two sequences of four syllables separated by a two-seconds delay. The second syllable in S1, /mi/, is replaced at the same position in S2 by a syllable that changes by its consonant and its vowel, /lu/.

Supplementary Sound 4 The Suppinfo_Sound4_verbalSTM_difficult.wav file is an example of a S1-S2 "different" trial during the difficult verbal STM block: two sequences of four syllables separated by a two-seconds delay. The fourth syllable in S1, /la/, is replaced at the same position in S2 by a syllable that changes only by its vowel, /lu/.


Clément Lavollée

Vincent Guidard

Emanuele Emili

Mohammad El Aabaribaoune

Mohammad El

Aabaribaoune Is

Is it worth assimilating all channels in the IASI ozone band to improve ozone analysis in MOCAGE ?

2-DFS and Ranking of Channels

Using the DFS as a figure of merit, we worked on 4873 profiles evenly distributed over the globe. We started by selecting the channel having the largest individual DFS Tot in average over the whole profiles. Then we selected a second channel, which, together with the first selected, had the largest DFS Tot . The process was iterated to rank all channels.

Fig. 1 shows the evolution of DFS: after a quick increase, the growth of DFS slows down by adding more channels. DFS for skin temperature reaches almost 1 with very few channels. 70% and 80% of the DFS Tot are reached with 34 and 76 channels rp. Selections with 90 and 120 channels will also be assessed. Fig. 2 shows the selected channels.

Is it worth assimilating all channels in the IASI ozone band to improve ozone analysis in MOCAGE?

3-Assimilation of Selections :

We will evaluate the 34, 76, 90 and 120 channel selections and the full channel list (called full channels), over the month of July 2021, in the assimilation of our chemistry transport model MOCAGE. An hourly 3D-VAR assimilation is used.

The hourly ozone analyses are validated against ozone-sondes from the WOUDC collection and MLS retrieved ozone profiles. Biases, standard deviation and root mean squared differences are computed. Fig. 3 shows the vertical profiles of scores. The fewer channels in the selection, the better the scores. The vertical integrations of the scores are given in the table below. We can notice that the number of iterations needed within the assimilation process decreases when the number of channels decreases.

4-Observation error estimation: Desroziers Diagnostic

The Desroziers method can estimate, after an assimilation, a new observationerror covariance matrix R, using the formula R Des

= E [ d o a (d o b ) T ] with d o a = y o -(x 𝓗 a ), d o b = y o -(x 𝓗 b
). y o is the observation, x a the analysis vector and x b the background vector. The process can be iterated We used initially an R matrix from El Aabaribaoune et al. ( 2021). Then we apply Desroziers method on the assimilation of the 34 channel selection, starting with a R diagonal matrix (=0,7). The diagnosed matrix is called R1A. After one iteration (using R1A in the assimilation), we obtain the diagnosed R2A matrix. Fig. 4 shows the correlation parts and Fig. 5 shows the standard deviations.

5-Impact of R estimation on the assimilation

Assimilation experiments over one month and using 34 channels have been carried out. Three different settings have bee used for the observation error covariance matrix R: the initial matrix, the diagnosed matrix R1A and the diagnosed matrix R2A.

Same scores as for Fig. 3 are shown in Fig. 6. A synthesis is given in the table below. Using a R1A drastically lowers the iteration number (from 57 to 21) and scores are slightly better than with the initial matrix.

There is no significant improvement between R1A and R2A.

We can conclude that applying the Desroziers diagnostic once is enough to improve the assimilation results. 2021) used 284 channels in the long-wave ozone band their assimilation in the Chemistry Transport Model MOCAGE to analyse ozone and surface temperature. Information brought by these channels can be redundant. The aim of this study is to evaluate the information content of various channel selections among this 284-channel set and evaluate their impact on the quality and the convergence of the assimilation in MOCAGE. Then, some refinements of the assimilation settings are explored.

1-Degrees of Freedom for Signal (DFS)

In our study, the DFS is used as a figure of merit. DFS = Tr(Id -(Id + BH T R -1 H) -1 ) , with Id the identity matrix B the background error covariance matrix R the observation error covariance matrix H the Jacobian matrix. In our case, control variables are ozone (Oz) and skin temperature (Tf).

Clément Lavollée 1 , Vincent Guidard 1,* , Emanuele Emili 2 , Mohammad El Aabaribaoune 2 1 CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France 2 CECI, Université de Toulouse, CERFACS, CNRS, Toulouse, France * corresponding author: vincent.guidard@meteo.fr

Conclusion

From 284 channels we extracted a shorter selection of 34 channels. We showed that results of this selection are better, and assimilation is faster. Then applying the Desroziers method to this selection improves again the quality of results and the assimilation speed.

Perspectives

In this study, we used a list that contains channels sensitive to Ozone and skin temperature. Some channels can also be sensitive to other parameters, such as humidity. It could be interesting to add other variable to the control parameters, starting with humidity.

Fig 3 :

 3 Fig 3: Vertical profiles of scores wrt WOUDC (left) and MLS (right), for biases (top) and standard deviations (bottom).

Fig 4 :

 4 Fig 4: Initial and diagnosed observation error correlations, differences and relative differences

Fig 5 :

 5 Fig 5: Initial and diagnosed sigma-o

Fig2

  Fig2: Channel selections

Fig 6 :

 6 Fig 6: Vertical profiles of scores wrt WOUDC (left) and MLS (right), for biases (top) and standard deviations (bottom).

Acknowledgements

This work has been done within the frame of Clément Lavollée's master stay, which was funded by CNES under the programme IASI IASI-NG.


Francesca Vittorioso
email: francesca.vittorioso@meteo.fr

Nadia Fourrié

Vincent Guidard

Marine Claeyman

Jérôme Vidot

Contri- Bution

I. Introduction

Air quality can deeply affect population health and environment. This is associated with atmospheric composition near the surface. It is influenced by local contributions (emissions), chemistry and small-scale and large-scale transport. Its modelling is complex because it involves several physical and chemical processes. Weather conditions, and especially winds, play an important role in dispersion or accumulation of chemical compounds and fine particles. The observation system of the atmospheric chemical composition is mainly based on "in situ" measurements and observations from polar orbiting satellites. However, in situ measurements are few and very localized. Measurements from polar satellites, on the other hand, although nowadays very valid, are limited by the scanning of the satellite footprint. This often limits the study of the transport of a specific species. For this reason, having exploitable data from sounders based on geostationary satellites would represent an interesting improvement. Three-dimensional model that covers the global scale (GLOB11), down to a smaller scales (e.g. MACC01), using two-way nested grids.

Contribution

Non-uniform vertical resolution: 47 vertical altitude-pressure levels from the surface up to 5 hPa. A 60 hybrid levels version is actually used in research mode: from surface, up to 0.1 hPa. MOCAGE simulates gases, primary aerosols and secondary inorganic aerosols.

Meteorological forecasts (wind, pressure, temperature, specific humidity, precipitation) are used as input (e.g ARPEGE or ECMWF meteorological forecast from IFS).

III. Meteosat Third Generation (MTG) -Infrared Sounder (IRS)

The Infrared Sounder (IRS), which will be launched in 2023 on board the Meteosat Third Generation (MTG) geostationary series, will be able to provide ~6 km resolution data (over Europe), with a temporal sample of less than one hour. Nature Run (NR):

IV. GOALS

reference atmospheric state for the entire OSSE, providing the "truth". Usually produced with a good-quality, realistic model in a freerunning mode without data assimilation.

  of the MTG/IRS radiance assimilation in the characterization of the atmospheric chemical composition II. Modèle de Chimie Atmosphérique à Grande Echelle (MOCAGE) Météo-France takes part to the operational air quality forecasting system with the MOCAGE chemistry-transport model (developed since 2000).

  for new data, i.e. data obtained by hypothetical observing systems that do not yet exist [Masutani et al. (2010)].

Data

  period June-July 2019 with settings:  Assimilation of 34 IASI channels in Ozone sensitive band (selected during another project)  Bias correction + full R matrix diagnosed trough Desroziers method -𝑅𝑈𝑁0 𝑖 ) 𝑁𝑅 𝑖 = daily average of the considered field from Nature Run 𝑅𝑈𝑁0 𝑖 = daily average of the considered field from Run 0 Min = -0.2, Max = 3.1, Mean = 1.5 Data Min = -0.6, Max = 1.0, Mean = 0.7 World Ozone and Ultraviolet Radiation Data Centre (WOUDC) location VIII. Validation through Ozone Sondes IX. Next steps  Completing Nature Run in the present configuration + evaluating other configurations (amount of IASI channels to assimilate, error matrices, etc). NR1, NR2, NR3 choice  Finalizing the evaluation of the Control Run  Completing the IRS and IRS*2 observation simulations  Assimilation Run  Evaluation of the IRS and IRS*~0.603 cm -1 (LWIR) ~0.604 cm -1 (MWIR) LWIR: ~679.70 -1210.44 cm -1 (881 channels) MWIR: ~1600.00 -2250.20 cm -1 (1079 channels) Spectral sampling: 0.3251 cm -1 LWIR: 680.00 -1210.24 cm -1 (1632 channels) MWIR: 1600.00 -2250.20 cm -1 (2001 channels)

1960 channels TOT: 3633 channels

	IRS characteristics
	can be essential in deducing information about atmospheric pollution.	While the IRS is being build, a new version with increased spectral resolution is
	It will cover two bands over the	already being considered.
	Infrared spectrum.

TOT:

Acknowledgements


Olivier Audouin

Nadia Fourrié

Vincent Guidard

Assimilation of GOES16 ABI radiances in ARPEGE model

Introduction

A large part of assimilated observations in the global ARPEGE model of Météo-France comes from infrared radiances. Even if the majority of these data are provided by hyperspectral instruments such as IASI, CrIS and AIRS, geostationary imagers provide in clear sky conditions frequent data over the same area. Up to now, observations from geostationary infrared sounders were assimilated in the global model as Clear Sky Radiances which represent an average of clear radiances present over a spatial grid. Here we presents a study on the assimilation of GOES-16 ABI radiances in the global model ARPEGE.

Experimental set up

Only 3 channels sensitive to upper-tropospheric water vapour and temperature (640 nm, 860 nm and 1380 nm) have been considered for the assimilation. These 3 channels have not been used over sea ice.

Sea Land Channel clear sky low clouds clear sky only

2 -6150nm 1.2 K 1.2 K 1.8 K 3 -7000nm 1.2 K 1.3 K 1.8 K 4 -7400nm 1.2 K - -
Inter-channel observation error correlations (obtained from a Desroziers diagnostic) were considered and specified in the following correlation matrix of observation errors :

2 3 4 C R =     
1.00 0.92 0.59 0.92 1.00 0.76 0.59 0.76 1.00

    

Contact Information

Email: olivier.audouin@meteo.fr

Biais correction

VarBC is used to compute the biais correction. It performs well with a strong spatial dependency.

Impact on analysis

The assimilation of GOES16 ABI radiances allows for better analysis. For example (Figure 2), the standard deviation of the first guess departure of ATMS over tropics is improved, especially for humidity channels. Same results are obtained for AM-SUA or MHS and over extratropical regions.

Impact on forecast scores

The assimilation of GOES16 ABI channels 2, 3 and 4 leads to an improvement of the forecasts on each of the three main domains (Tropics, and extra-tropical regions of the Northern and Southern hemispheres).

The most significant improvement is in mid-troposphere temperature forecasts in the tropics at all forecast ranges. For each of the three main domains, the forecast of the temperature in the stratosphere is significantly improved. Degradations of the temperature forecast in the very low layers remain. Work is in progress to understand the origin of this degradation and to deal with it.

The results on the moisture fields show an improvement on the three main domains up to 40 hours of forecast in mid-troposphere and upper troposphere. The same degradation than in temperature fields is noticed in the very low layers. A degradation of the humidity near the tropopause is shown and could be related to numerical aspects.

Figure 3: Relative difference of Root Mean Square Error with respect to the ECMWF analysis between a control experiment and an experiment assmilating GOES16 ABI channels 2,3 and 4 over a period from September 10, 2020 to December 14, 2020. Blue isolines stand for improvement compared to the control experiment. Yellow areas represent a significant difference.

Conclusions

The assimilation of GOES16 ABI channels sensitive to upper-tropospheric water vapour and temperature leads to improvement of the forecast (especially over tropics) and will be put in operations in a near future. Future studies will be conducted to evaluated GOES16 ABI radiances against former ABI products.

  each channel, observation errors are specified considering surface and cloud conditions and are given in the following table.

  Figure 1 shows an example of bias correction for channel 2 (left) on November 10, 2020. Same patterns are observed throughout the duration of the experiment.The bias correction also shows a strong dependence on the assimilation base time. This should be taken into account in future work. Figure1shows the evolution of the bias correction throughout September 2020.

Figure 1 :

 1 Figure 1: Biais correction (K) for channel 2 on November 10, 2020 (left). Evolution of Biais correction (K) for channel 2 throughout November 2020 for the 4 analysis times (right)

Figure 2 :

 2 Figure 2: Difference of the standard deviation of the first guess departure (solid lines) between a control experiment and an experiment assimilating ABI radiances for ATMS over tropics over a period from September 10, 2020 to December 10, 2020. Horizontal blue lines represent the confidence interval.


Mohammad El Aabaribaoune

Emanuele Emili

Vincent Guidard

Impact of the updated observation error covariance matrix on the ozone analysis