domain: sde sde.es spi

text:
Pierre Aumond
email: pierre.aumond@ifsttar.fr

Catherine Lavandier
email: catherine.lavandier@u-cergy.fr

Links between representation of outdoor soundscape, noise annoyance at home and outdoor acoustic measurement

Keywords:

come

INTRODUCTION

Noise impact on human health has been revealed by numerous studies, due to traffic in urban context, but also due to railway and aircraft industries. The annoyance is considered by the WHO as one of the health impact of this noise [START_REF] Brown | WHO Environmental Noise Guidelines for the European Region: A Systematic Review of Transport Noise Interventions and Their Impacts on Health[END_REF]. But pure silence has also a negative impact on urban life. So starting in the sixties with Murray Schafer, and developing in the nineties, a soundscape approach has been preferred to address the issue of the urban sound environment, in a more positive way, taking into account that some natural sounds such as bird singing or water streaming, can enhance the pleasantness of a urban situation [START_REF] Schafer | The Tuning of the World: Toward a Theory of Soundscape Design[END_REF].

In the annoyance approach, researchers tried to find a relationship between sound exposure, and long term annoyance. Noise annoyance is then considered as a one dimensional variable. At an individual level, it has been shown that the explained variance of the annoyance due to sound exposure is limited to about 30%. At a community level, dose response curves based on LDEN or LDN make it possible to predict the percentage of annoyed or highly annoyed people. These indicators characterizing the sound exposure levels derive from the long temporal weighted equivalent sound levels calculated over 24h. The short temporal variations of the sound level are not taken into account in these indicators.

In the soundscape approach, people are not anymore questioned on only one negative dimension about the impact of the sound environment, but on the different emotional dimensions which support their feelings, with all positive and negative aspects. Some of these dimensions, and especially the pleasantness/unpleasantness dimension, have been correlated not only with acoustic indicators but also with perceptual variables dedicated to sound sources such as their time of presence, their sound level, or their dominance. In this approach, the semantic dimension of sources is then very important.

In both approaches, researches tried to cross the sound measurements with the impacts on people, but this is quite difficult for different reasons. In the first approach, the importance of personal behaviors and social attitudes on long term annoyance is so important that the statistics need more than thousand individual data to be significant. Participants are contacted through mailings or telephonic campaigns. It is then impossible to measure the sound exposure in front of each house for a long duration. So calculations are used. In the second one, people are generally questioned in the streets. It is then easier to measure the acoustic indicators with simple monophonic microphones or with more sophisticated binaural or ambisonic ones, even with mobile microphones. Short temporal variations of the sound level can be captured, but as people are questioned one by one, it is difficult to collect a large amount of data in the same experiment.

In this paper, we present a first analysis of a large campaign of questionnaires distributed in the city of Lorient in the frame of the CENSE project, whose aim is to combine these two approaches. In the same city center, a network of sensors which store the third octave bands each second during about a year will make it possible to compare perceptual data and acoustic ones.

This paper presents the questionnaire that was sent to the inhabitants as well as a first preliminary analysis of the results (answers are currently still being received).

METHODOLOGY

Survey -General information

A questionnaire was sent to about 2293 households in a 1 km² study area in the city of Lorient in France during the second week of January 2019. Until 15 March 2019, it is possible for residents to return a paper version of the questionnaire or to fill it via a web platform. The survey lasts about 20-25 minutes and is composed of 5 parts detailed in the following section. About 100 people completed the paper form of the questionnaire and about 150 did the answers through Internet. Figure 1 presents some demographic information about the participants. In order to encourage participation, a draw will be held for 5 gift certificates of 50€. Figure 2 shows the study area and a heat map of the responses. The French version of the questionnaire can be consulted at http://www.cense.ifsttar.fr.

Survey in detail

In the first part, the respondent must assess the quality of the sound environment in his/her neighborhood and then in his/her street (when walking or cycling home). This section is composed of 5 bipolar semantic scales (7 levels) inspired on the Swedish protocol [START_REF] Axelsson | The Swedish soundscape-quality protocol[END_REF]. Table 1 presents the French semantic differential as well as a proposal for translation into English.

Then the respondents must inform a table on the time of presence ratio and on the volume of 13 sound sources that they can hear when they come in or out of their homes, on foot or by bike, on their streets, and during the year. The nomenclature had been previously established using information from sources on sites, and bibliographic work [START_REF] Aumond | Modeling Soundscape Pleasantness Using perceptual Assessments and Acoustic Measurements Along Paths in Urban Context[END_REF]. Table 2 presents the sources to be assessed. A free comment window closes this first section.

En inadéquation avec vos attentes

In inadequacy with your expectations

En adéquation avec vos attentes

In adequacy with your expectations

Ad

The second part focuses on the annoyance. Questions on the annoyance following the Guidelines from the noise Team of ICBEN are asked to residents [START_REF] Fields | Standardized General-Purpose Noise Reaction Questions For Community Noise Surveys: Research And A Recommendation[END_REF]. This part of the questionnaire can be summerized with this sentence: "Thinking about the last 12 months, when you are here

-

* Lorient is a harbor city with several complaints in the local press about the noise of gulls.

In part 3, four windows make a free expression possible for residents about the remarkable environments (pleasant, unpleasant, conducive to walking and conducive to rest) of their neighborhood.

In part 4, personal information is collected: noise sensitivity of the inhabitants based on the 6-item Weinstein's noise sensitivity scale (WNSS) [START_REF] Kishikawa | The development of Weinstein's noise sensitivity scale[END_REF], gender, age, socioprofessional category, membership (or not) to an association fighting against noise.

Finally, in the 5 th part, residents must provide information on where they live: the exact location, so that the questionnaires can be linked with the acoustic measurements made in the area and a set of questions on housing (Table 3). Finally, the respondents must give their level of satisfaction (5 levels) on 4 dimensions:  Acoustic insulation of their housing,  To what extent they are globally satisfied with their (home/street/neighborhood) as a place to live.

ANALYSES

Categorization of sound environments

Five categories of sound environments can be found on this study area. The perceived time of presence for each 13 sound source (see Table 2) is used in addition to the perceived overall loudness (Si) to perform the categorization. A k-means algorithm is used to cluster the observations into classes in which each observation belongs to the cluster with the nearest mean. Figure 3(a) presents the mean values of the perceived time of presence for each sound source, and for each cluster. Figure 3(b) shows the spatial distribution of these clusters using a spatial interpolation (a value at a point equals to the value of the nearest neighbor within a maximum radius of 80 meters).

-Cluster 1 is located throughout the area, It corresponds to very diverse areas where the perceived time of presence is important for different sources such as birds, wind, traffic or voices. -Cluster 2 is mainly situated along the main boulevards, and the perceived presence of time for road traffic and two-wheelers is particularly important. -Cluster 3 corresponds to the evaluations where the time of presence for railway is high. They are consequently located along the railway line. -Cluster 4 is located throughout the area. It corresponds to low noise areas where birds are often heard. -Cluster 5 is particularly situated in the hyper-center. It is the area where music from bars, many voices, etc. can be heard frequently.

Principal components analysis of soundscape dimensions

The six attributes of perceived affective quality of the inhabitant's streets presented in Table 2 were evaluated through correlations. The corresponding matrix was subjected to a PCA. Components 1 and 2 explained 51% and 25% of the variance in the data set, respectively. Component 3 explained 7 %. Thus, the results can be represented mainly in a two-dimensional plane. Figure 4 presents the component loadings of the six attribute scales along the circumplex. All the attributes are organized in the same and expected order along the circumplex: Calm, Pleasant close to the first component, Interesting/Exciting and Eventful close to the second component [START_REF] Axelsson | A principal components model of soundscape perception[END_REF]. The vector "adequation with the expectation" is correlated to the pleasantness of the street but slightly oriented to the vector Interesting/Exciting. The 5 previous clusters of sound environments are also projected on the PCA representation. As expected, cluster 4 is located in a pleasant area in the direction of the calm and silent vectors. Clusters 2 and 5 are located in an area perceived as unpleasant by residents. Cluster 1 is located in an area considered "interesting" and rather pleasant. In a subsequent more advanced analysis, this projection encourages us to propose different models of soundscape indicators (Pleasantness, Eventfulness) for each of the sound environment we came across in this town. For example, for cluster 3, the role of the sound source (rail traffic) will be much more important for the soundscape assessment than for the other clusters.

Models for annoyance and for affective dimension (all clusters included)

For this preliminary analysis, we will focus on modeling the following variables: pleasantness (in the street), noise annoyance (closed windows), noise annoyance (open windows, garden or balcony) and noise annoyance (in the street).

As independent variables, the dimensions that describe the external sound environment are first used as for the sound environment categorization (section 3.1). To select the models presented Table 4, the leaps v3.0 package in R is used. It allows selecting a subset of multiple linear regression (exhaustive search). Table 4 shows that 44% of the variance on pleasantness can be explained by a model based on the dimensions of the sound environment present in the street. In accordance with the literature, for all annoyance models, the variance explained by the external noise environment is around 30%. For the first three models, overall loudness is the variable that obtains the highest standardized coefficients. This variable is followed by the time of presence of two-wheelers (2Wh) or road traffic (Tra). However, all theses variables are significantly correlated and one of them could easily be replaced by another while maintaining a similar value for the adjusted R² (e. g. r=.74, p<.05) between the twowheelers and traffic and r=.37 (p<.05) between two-wheelers and the overall loudness (Si).

Expressive voices or music has a negative impact on the pleasantness or increase annoyance at home (open or closed windows). As before, the music (Mus) and Expressive Voice (ExV) variables are quite correlated (r=.56, p<.05) and one could be subtracted to the benefit of the other. This reflects the proximity of the leisure districts with the presence of many inhabitants of the hyper-central area in our corpus.

The presence of birds (Brd) appears in all models and reduces discomfort or increases pleasure. This positive effect can be directly related to the noise environment but also in the context of a reduction in the annoyance of the closed window, such as a proxy for the presence of vegetation near the dwelling.

Adding personal information

In this section, the age range variables (Age) (transformed into a continuous variable), Gender and noise sensitivity (NS) are added to the previous annoyance models. Table 5 shows that these variables allow a slight increase in the explained variance. According to the literature, noise sensitivity plays a significant role in linear regression. Age is also sometimes involved in the model but less significantly. However according to the literature, age does not behave always linearly with noise sensitivity and annoyance.

Adding housing information

In this section, all housing variables (part 4 of the questionnaire) are added to the noise annoyance models. Only models concerning annoyance inside home are therefore concerned. Table 6 shows that adding the housing variable allows an increase in the explained. One variable seems to have much more influence than the others. It is the possession or not of a quiet room in his dwelling.

CONCLUSIONS

All the results are not yet known (end of March 15) and only these preliminary results could be presented. However, the following conclusions can already be drawn: -The Swedish protocol once again shows its robustness and interest in describing the soundscape, -The study area contains a wide variety of sound environments that will allow for a rich analysis of the results, -The use of the perceived presence of time for different sound sources combined with the perception of global sound level seems to confirm that it is a robust and interesting way for predicting soundscape indicators. -In accordance with the literature, the addition of personal information (age, noise sensitivity) as well as housing information (quiet room) often explains a significant portion of the expressed annoyance.

A next step will be to cross all these variables with the acoustic variables measured by the sensors set in the area. As part of the CENSE project, it is also planned to work on the automatic recognition of sound sources from acoustical measurement. Models of the soundscape could then be proposed on the basis of physical measurements of the sound environment.

Figure 1 .

 1 Figure 1. Age and gender of the participants

Figure 2 .

 2 Figure 2. Heat map of the number of filled questionnaires in the selected area

  Figure 3. (a) Mean values for each sound environment dimensions (Time of presence of sound sources and perceived overall loudness) for each cluster. (b) localization of the clusters (nearest neighbor algorithm)

Figure 4

 4 Figure 4 Principal Component Analysis of the soundscape dimensions and projected clusters

Table 1 -

 1 Anchors of the bipolar scales. The last column correspond to their codification.

	Désagréable	Unpleasant	Agréable	Pleasant	Pl
	Inerte, Amorphe	Inert	Animé,	Eventful	Ev
			mouvementé
	Bruyant	Noisy	Silencieux	Silent	Si
	Ennuyeux,	Boring	Stimulant,	Exciting	Ex
	Inintéressant		Intéressant
	Agité, Chaotique	Chaotic	Calme, Tranquille	Calm	Ca

Table 2

 2 List of sources that must be assessed in the questionnaire.

	Road Traffic (Tra)	Sirens, alarms	Children's voices (schools,	Gulls*
		(Sir)	playgrounds)	(Gul)
			(ChV)
	2-wheel motor	Urban	Music from bars,	Sources from
	vehicles	maintenance	restaurants, shops...	neighboring dwellings
	(2Wh)	(cleaning,	(Mus)	(voices, steps, animals,
		garbage...)		crafts, music...)
		(UMa)		(Not used in this
				analysis)
	Rail traffic	Expressive voices,	Wind in the vegetation	Other ...
	(Rail)	festive voices,	(Wnd)
		laughter, shouts
		(ExV)
	Air traffic	Calm voices,	Small birds	Other ...
	(Air)	conversations…	(Brd)
		(CaV)

Table 3

 3 Questions about the housing of participants

	Tenant / Owner	Courtyard or garden area?	Has quiet room?
		(yes/no)	(yes /no)
	House/Apartment	Living space overlooking the	Double glazing?
		street? (yes/no)	(yes /no)
	Time of occupancy?	Living space with a view on	Insulation of the facade <10 years
	(<1 year, 1-3 year, >3 year)	natural elements?	ago?
		(no, a little, a lot)	(yes/no)

Table 4

 4 Selection of regression models for soundscape dimensions based on sound environment dimensions. Standarized coefficient, Adjusted R-squared and p-value (*** <.001, ** <.01, *.05, + .1)

		Pl	Annoyance (CW)	Annoyance (OW)	Annoyance (Street)
	Si	.45***	-.38***	-.39***	-.28***
	Tra	-.13*
	2Wh			.14*	.30***
	Air	-.14**	.14**
	Rail				.14*
	ExV	-.11*	.16*	.23***
	CaV			-.11 +
	Mus		.17**
	Brd	.27***	-.16**	-.13*	-.21***
	Radj²	44%	34%	35%	29%

Table 5

 5 Selection of regression models for soundscape dimensions based on sound environment dimensions. Standarized coefficient, Adjusted R-squared and p-value (*** <.001, ** <.01, *.05, + .1)

		Annoyance (CW) Annoyance (OW)	Annoyance (Street)
	Si	-.36***	-.36***	-.24***
	2Wh		.16*	.31***
	Air	.14*
	Rail			.15**
	ExV .12 +	.20**
	CaV		-.16**
	Mus .17**
	Brd	-.16**		-.16 **
	NS	.13*	.24***	.26***
	Age -.17**	-.10 +
	Radj² 37%	40%	36%

Table 6

 6 Selection of regression models for soundscape dimensions based on sound environment dimensions. Standarized coefficient for numeric variables, Adjusted R-squared and p-value (*** <.001, ** <.01, *.05, + .1)

		Annoyance (CW)	Annoyance (OW)
	(Intercept)	-.20**	-.15*
	Si	-.34***	-.32***
	2Wh		.13*
	ExV	.15*	.16*
	CaV		-.15**
	NS	.13*	.21***
	Age	-.17**	-.11*
	Quiet_Room
	-Yes
	-No	.62***	.47***
	Radj²	40 %	44%

ACKNOWLEDGEMENTS

The authors want to thank the city of Lorient and in particular J.C. Baron and P. Crépeaux from "le pôle cadre de vie et development durable" (the life and sustainable development center). This work was financed under the ANR project CENSE No. ANR-16-CE22-0012.


Amato T Evan
email: aevan@ucsd.edu

Stephanie Fiedler

Chun Zhao

Laurent Menut

Kerstin Schepanski

Cyrille Flamant

Owen Doherty

Derivation of an Observation-Based Map of North African Dust Emission

Changes in the emission, transport and deposition of aeolian dust have profound effects on regional climate, so that characterizing the lifecycle of dust in observations and improving the representation of dust in global climate models is necessary. A fundamental aspect of characterizing the dust cycle is quantifying surface dust fluxes, yet no spatially explicit estimates of this flux exist for the World's major source regions. Here we present a novel technique for creating a map of the annual mean emitted dust flux for North Africa based on retrievals of dust storm frequency from the Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the relationship between dust storm frequency and emitted mass flux derived from the output of five models that simulate dust. Our results suggest that 64(±16)% of all dust emitted from North Africa is from the Bodélé depression, and that 13(±3)% of the North African dust flux is from a depression lying in the lee of the Aïr and Hoggar Mountains, making this area the second most important region of emission within North Africa.

Introduction

By mass, aeolian dust is the most pervasive aerosol on the planet, and the largest fraction of all global dust emission is in North Africa (e.g. [START_REF] Engelstaedter | Western African dust emissions and transport[END_REF][START_REF] Cakmur | Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations[END_REF]. African dust emission and transport is both affected by-and affects-the climate. For example, previous work has shown there is an increase in dust emission and transport over the Atlantic during periods of Sahelian drought (Prospero and Lamb, 2003) due to a decrease in soil moisture over the Sahel [START_REF] Cowie | Are vegetation-related roughness changes the cause of the recent decrease in dust emission from the Sahel?[END_REF], increased surface wind speeds over the Sahara [START_REF] Ridley | What controls the recent changes in African mineral dust aerosol across the Atlantic?[END_REF], or some combination of the two [START_REF] Doherty | Transport of Saharan Mineral Dust in the Boreal Summer and its Controls by the Intertropical Convergence Zone[END_REF]. Once transported over the Atlantic, direct radiative forcing by dust both warms the atmosphere and cools the surface (Evan and Mukhopadhyay 2010), contributing interannual to decadal scale variability of tropical Atlantic sea surface temperatures (Evan et al. 2012) and exciting coupled modes of equatorial variability [START_REF] Evan | Influence of African dust on ocean-atmosphere variability in the tropical Atlantic[END_REF].

The influence of these aerosols on the climate system extends well beyond the direct radiative effect. Recent work has shown that African dust may influence clouds as far away as the west coast of the United States, providing ice nuclei required for precipitation in so-called atmospheric rivers [START_REF] Creamean | Dust and biological aerosols from the Sahara and Asia influence precipitation in the western US[END_REF]. Aeolian dust contains nitrogen, phosphorus and iron, all of which are required for primary productivity in oceanic and terrestrial ecosystems, and there is a large body of work demonstrating the importance of the atmospheric input of these elements via dust transported from western Africa [START_REF] Das | Contributions of long-distance dust transport to atmospheric P inputs in the Yucatan Peninsula[END_REF][START_REF] Okin | Impacts of atmospheric nutrient deposition on marine productivity: Roles of nitrogen, phosphorus, and iron[END_REF][START_REF] Mahowald | Observed 20th century desert dust variability: impact on climate and biogeochemistry[END_REF].

In order to improve understanding of dust-climate effects it is necessary to elucidate surface and atmospheric processes governing emission. However, the vast majority of North African dust emission occurs within largely uninhabited regions and thus there is a paucity of both meteorological and surface observations in these locations, particularly homogeneous measurements of each that span time scales of years to decades. As a result, numerical models play a crucial role in this field of study, yet there are relatively few observational data sets against which model output can be validated, and as a result model output is often validated against surface visibility observations and retrievals of aerosol optical depth from satellites and groundbased instrumentation-none of which are direct measures of dust-and surface concentrations from a limited number of sampling stations (e.g., [START_REF] Ginoux | Sources and distributions of dust aerosols simulated with the GOCART model[END_REF], Huneeus et al, 2011;[START_REF] Tegen | A general circulation model study on the interannual variability of soil dust aerosol[END_REF][START_REF] Todd | Quantifying uncertainty in estimates of mineral dust flux: An intercomparison of model performance over the Bodélé Depression, northern Chad[END_REF].

Given the lack of observations it is not surprising that recent studies have identified issues with the representation of the dust cycle in models. [START_REF] Kok | A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle[END_REF] evaluated the emitted size distribution of dust within several climate models, finding that all of the models underestimated the number of large particles emitted and thus the emitted mass flux, in agreement with earlier findings by [START_REF] Cakmur | Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations[END_REF]. [START_REF] Evan | An analysis of aeolian dust in climate models[END_REF] examined dust in Climate Model Intercomparison Project Phase 5 (CMIP5) models, corroborating the findings of [START_REF] Kok | A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle[END_REF] and finding that, when forced by observed sea surface temperatures, models cannot reproduce historical year-toyear variability in cross-Atlantic dust transport, as determined by satellite data and paleo-proxy data. Furthermore, considering soil characteristics, Kok (2014) found that a majority of models underestimate dust emissions sensitivity to the soil erodability, i.e. the ability of the soil to emit dust for a given above-threshold friction velocity (definition following [START_REF] Kok | An improved dust emission model with insights into the global dust cycle's climate sensitivity[END_REF][START_REF] Zender | Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology[END_REF]).

In the present paper we attempt to address the need for more observational records against which models can be evaluated by creating a spatially explicit map of annual dust emission using data from the Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The remainder of this paper is organized as follows. In Section 2 we describe the models and satellite data used in this study. In Section 3 we compare the spatial structure of emission amount and emission frequency among the models, and define a statistical relationship between the two. In Section 4 we use this statistical relationship to derive the new observational climatology of dust emission amounts. We conclude in Section 5 with a summary of the main results of the paper.

Models and Satellite Data

In this paper we examine dust emission amount and frequency of events using one year of output from four regional models centered over North Africa and one global climate model. A summary of the models considered here and some of their relevant features can be found in Table 1.

Satellite Data

15-minute IR dust index images calculated from brightness temperatures at 8.7µm, 10.8µm and 12.0µm observed by SEVIRI MSG satellite are used inferring dust source activation frequencies over North Africa for the period March 2006 to February 2010 [START_REF] Schepanski | A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels[END_REF][START_REF] Schepanski | Comparison of satellite based observations of Saharan dust source areas[END_REF]. As the images are available throughout day and night, dust source activation events were identified at sub-daily (hourly) resolution and geo-located by tracking back dust plumes individually to their point of origin, which is assumed to be the dust source and recorded on a 1°x1° map. It is noted that no dust sources can be spotted under optical thick clouds or dust plumes. Due to the 15-minute resolution of the native dust index images information on the diurnal cycle of dust source activation events can be retrieved, allowing for a relatively precise location of dust sources [START_REF] Schepanski | Comparison of satellite based observations of Saharan dust source areas[END_REF]. Compared to an automated detection of dust plumes (Ashpole & Washington 2013), the manual identification by [START_REF] Schepanski | Meteorological processes forcing Saharan dust emission inferred from MSG-SEVIRI observations of sub-daily source activation and numerical models[END_REF] is likely less prone to systematic errors. Furthermore, the results from Ashpole & Washington do not cover all of North Africa, which is required for this study. The Schepanski et al. data set has been used for mapping dust sources [START_REF] Schepanski | A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels[END_REF], identifying the diurnal cycle of dust emission onset over Western Africa and associated meteorological conditions driving dust uplift [START_REF] Schepanski | Meteorological processes forcing Saharan dust emission inferred from MSG-SEVIRI observations of sub-daily source activation and numerical models[END_REF]model validation (e.g., Johnson et al., 2011). The dust source activation data set compares well with the spatio-temporal distribution of dust sources identified from surface visibility observations at weather stations [START_REF] Laurent | A model study of Saharan dust emissions and distributions during the SAMUM-1 campaign[END_REF].

Models

In this study we examine the output from two dust simulations [START_REF] Zhao | The spatial distribution of mineral dust and its shortwave radiative forcing over Western Africa: modeling sensitivities to dust emissions and aerosol size treatments[END_REF][START_REF] Creamean | Dust and biological aerosols from the Sahara and Asia influence precipitation in the western US[END_REF] made with the Weather Research and Forecasting with Chemistry (WRF-Chem) model [START_REF] Grell | Fully coupled "online" chemistry within the WRF model[END_REF][START_REF] Skamarock | A time-split nonhydrostatic atmospheric model for weather research and forecasting applications[END_REF]. WRF-Chem simulates trace gases and particulates with the meteorological fields and simulates a variety of coupled physical and chemical processes such as transport, deposition, emission, chemical transformation, and radiation, and includes online coupling of chemistry and meteorology. WRF-Chem has been widely used to simulate the dust life cycle and climatic impact at the global scale (e.g., [START_REF] Zhao | Uncertainty in Modeling Dust Mass Balance and Radiative Forcing from Size Parameterization[END_REF]) and the regional scale over West Africa [START_REF] Zhao | The spatial distribution of mineral dust and its shortwave radiative forcing over Western Africa: modeling sensitivities to dust emissions and aerosol size treatments[END_REF][START_REF] Zhao | Radiative impact of mineral dust on monsoon precipitation variability over West Africa[END_REF], Saudi Arabia (Kalenderski et al., 2013), North America [START_REF] Zhao | Impact of the Desert dust on the summer monsoon system over Southwestern North America[END_REF], and East Asia [START_REF] Chen | Modeling the transport and radiative forcing of Taklimakan dust over the Tibetan Plateau: A case study in the summer of 2006[END_REF]. The simulations are conducted at one-degree horizontal resolution throughout the domain. Two dust emission schemes, one based on [START_REF] Ginoux | Sources and distributions of dust aerosols simulated with the GOCART model[END_REF] and the other on [START_REF] Kok | An improved dust emission model with insights into the global dust cycle's climate sensitivity[END_REF], both coupled with a modal aerosol model, are used in this study. The emission scheme from [START_REF] Ginoux | Sources and distributions of dust aerosols simulated with the GOCART model[END_REF] (hereafter referred to as WRF-GOCART) calculates the dust emission flux as a function of horizontal wind speed at 10 m, the threshold 10 m wind speed below which dust emission does not occur, and a prescribed source function that defines the potential dust source regions and comprises surface factors, such as vegetation and snow cover. The dust emission scheme developed by [START_REF] Kok | An improved dust emission model with insights into the global dust cycle's climate sensitivity[END_REF] (hereafter referred to as WRF-KOK) is derived from a physically based theory that uses the concept that dust emission is a threshold effect without an explicitly prescribed dust source function. This parameterization depends only on the wind friction speed, the threshold friction speed, and the soil clay content.

We use output from the regional CHIMERE chemistry transport model. In CHIMERE dust emission is calculated following Alfaro and Gomes (2011) and [START_REF] Menut | Sensitivity of the sandblasting fluxes calculations to the soil size distribution accuracy[END_REF]. The surface and soil properties are provided by USGS and STATSGO-FAO global databases, the aeolian roughness lengths are derived from ERS satellite data, as described in [START_REF] Menut | Relative impact of roughness and soil texture on mineral dust emission fluxes modeling[END_REF]. The wind speed is calculated using the WRFV3 regional model, forced by the NCEP global meteorological fields. All these data are projected onto a regular 1x1 degrees grid. In order to take into account the subgrid scale variability, a Weibull distribution is applied to the mean wind speed.

We also use output from the dust emission model of [START_REF] Tegen | Impact of vegetation and preferential source areas on global dust aerosols: Results from a model study[END_REF] following the setup of Fiedler et al. (2013a;2014). Model dust emission is forced by 3-hourly wind speeds and soil moisture from ERA-Interim forecasts [START_REF] Dee | The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[END_REF]. The horizontal resolution of the model is 1° throughout the domain. In this model, hereafter referred to as TEGEN, a grid box is a potential dust source when at least two dust source activation events have been observed by SEVIRI satellite imagery between March 2006 and February 2010 [START_REF] Schepanski | A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels[END_REF]2012).

In addition to these regional simulations, we examine dust emission from the global Community Earth System Model [START_REF] Hurrell | The Community Earth System Model: A Framework for Collaborative Research[END_REF] with prescribed SST (hereafter referred to as CESM). Dust emission in CESM is based on the Dust Entrainment And Deposition (DEAD) model [START_REF] Zender | Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology[END_REF][START_REF] Mahowald | Climate response and radiative forcing from mineral aerosols during the last glacial maximum, pre-industrial, current and doubled-carbon dioxide climates[END_REF], modified to include saltation [START_REF] Albani | Improved dust representation in the Community Atmosphere Model[END_REF], as suggested by Kok 2011. In this experiment CESM is run at a resolution of 0.94° x 1.24° globally, using fixed (AMIP) SST and climatological land and surface properties. Here SSTs were prescribed from the 1982 season, although there was little difference in the results of this analysis using simulations forced with SSTs from two other years.

In CESM dust emission is calculated within the land model, the Community Land Model (CLM), and then passed to the atmospheric model, the Community Atmospheric Model (CAM). However, once passed to CAM the emission fluxes are scaled by a so-called "source function," which is a non-dimensional global map of soil erodibility from [START_REF] Zender | Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology[END_REF]. Emission fluxes from CESM are actually the scaled CAM fluxes. In this paper we examine both the CESM/CAM fluxes and the CLM fluxes.

In Table 1 is a summary of the salient features of the different models examined in this paper.

Intercomparison

For all models we firstly scaled emission fluxes so that the annual total emission from North Africa was equal to 4500 Tg, which is an observation-based estimate of annual North African dust emission [START_REF] Evan | An analysis of aeolian dust in climate models[END_REF]. We next calculated the number of dust emission events in the models, where an event is defined as a 3hourly dust emission flux greater than or equal to 1 ߤ݃ ݉ ିଶ ݏ ିଵ [START_REF] Laurent | A model study of Saharan dust emissions and distributions during the SAMUM-1 campaign[END_REF][START_REF] Tegen | Comparing two years of Saharan dust source activation obtained by regional modelling and satellite observations[END_REF]. We note that emission data from the CESM model is 3-hourly averaged, whereas from the WRF, ECHAM and CHIMERE models this is an instantaneous field. The emission frequency is defined as the total number of events divided by the total number of 3-hourly time-steps in the model output. Small changes in the threshold for detecting an event had no effect on the results from this study. This is because the total mass flux is dominated by regions that are frequently emitting at rates well above this threshold, and thus for our statistical analysis we disregard regions with low emission frequencies, as is discussed further in Section 3.3. In addition, we repeated the analysis after scaling emission fluxes from the models so that the annual total North African emission was from 500-4500 Tg, in increments of 500 Tg, finding qualitatively identical results in all cases, also discussed in Section 3.3.

Results

We first examine differences in annual dust emission and mean emission frequency among the models (Sections 3.1 and 3.2), we then use these data to identify a functional relationship between the two (Section 3.3).

Dust Emission Frequency

We compare the spatial structure of dust emission frequency in the models and from SEVIRI (Fig 1). Of the six data sets examined, WRF-GOCART and CHIMERE have the highest average dust emission frequencies, and the CHIMERE and CESM models exhibit the least spatial uniformity in these frequencies (i.e., the standard deviation of the maps in Figure 1 are larger that that of the other three models). Since emissions are tuned to be equivalent amongst the models, on average WRF-GOCART and CHIMERE emit less dust per emission event than do the other models. Interestingly, WRF-KOK and WRF-GOCART are forced with identical wind fields (Table 1) and thus lack of agreement between them is due to differences in the dust emission schemes. All the models except CESM and CLM show a maximum in emission frequency in the Bodélé depression (approximately between 15°-20°E and 15°-20°N). CHIMERE shows an additional peak in emission at the same latitude of the Bodélé but near the Atlantic coast. TEGEN, the WRF models and CLM show peaks in emission along the western coast between 20°-30°N and in the area of 0°E and 30°N. None of the models produce dust between 8°N and 12°N. Beyond these highly active regions, all models agree that there are other active regions to the east of the Bodélé and within Northern Africa (near the Mediterranean coast). The emission frequency maps for CESM and CLM show far less spatial structure than do the other models.

The map of annual mean dust emission frequency from SEVIRI shows a distinct band spanning approximately 15°-25°N, also with a regional maximum over the Bodélé depression, but a secondary maxima immediately to the west of the Bodélé (0°-10°W, which is in the Lee of the Aïr and Hoggar Mountains), which has been previously identified as an important dust source region [START_REF] Kocha | The importance of the diurnal cycle of aerosol optical thickness in West Africa[END_REF]. Like the models SEVIRI does not show dust emission south of 12°N. We note that emission here likely occurs during the summertime in association with monsoon generated cold pools [START_REF] Knippertz | Mineral dust aerosols over the Sahara: Meteorological controls on emission and transport and implications for modeling[END_REF]. It is possible that these events are missed in the models due to lack of sufficient resolution to resolve such features, and in SEVIRI because these emission events are obscured under clouds. Qualitatively, the CHIMERE model exhibits the similarity to the spatial structure of the SEVIRI emission frequency map, although the magnitudes of the frequencies in SEVIRI are much smaller than those from CHIMERE, and are more similar to the WRF-KOK and TEGEN models. See [START_REF] Schepanski | Meteorological processes forcing Saharan dust emission inferred from MSG-SEVIRI observations of sub-daily source activation and numerical models[END_REF] for a detailed analysis of the spatial structure of the SEVIRI map of dust emission frequency.

Model differences in the spatial structures of emission frequency can also be elucidated by plots of the cumulative distribution functions (CDFs) of the annual mean dust emission frequency for each model. In order to facilitate intra-model comparison we linearly scaled the emission frequency for each model so that the maximum emission frequency is one. Only locations with a non-zero annual emission are included in the CDF calculations. The CDFs of the satellite data (SEVIRI), TEGEN, CHIMERE, WRF-KOK and CLM have an approximately logarithmic shape (Fig 2a), indicative of a small number of highly active regions (ref Fig 1). All of these models have CDF values greater than 80% at emission frequencies of 0.5 and above, meaning that 20% of the dust-emitting land areas have emission events more than 50% of the time. In contrast, the CDFs of WRF-GOCART and CESM increase slowly for small emission frequencies and then much more rapidly at high emission frequencies (Fig. 2a), reflecting a large number of locations that emit dust very frequently (compare Fig 1). Thus, for WRF-GOCART approximately 80% of the dustemitting land surface areas have emission events more than 50% of the time.

As there is no regional network of surface observations of emission frequency it is not possible to determine which of the CDFs in Figure 2a are more realistic, and the SEVIRI data can not be used conclusively as a validation data set since it possibly underestimates emission frequency along coast of West Africa and within the Saharan Heat Low region [START_REF] Brindley | A critical evaluation of the ability of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) thermal infrared red-green-blue rendering to identify dust events: Theoretical analysis[END_REF]. However, previous work has suggested that frequency maps should be characterized by a small number of highly active dust emission "hotspots" [START_REF] Prospero | Environmental characterization of global sources of atmospheric soil dust derived from the Nimbus 7 TOMS absorbing aerosol product, rev[END_REF][START_REF] Engelstaedter | Western African dust emissions and transport[END_REF]Ginoux et al. 2012;[START_REF] Schepanski | Comparison of satellite based observations of Saharan dust source areas[END_REF], and thus the SEVIRI data and those models exhibiting CDFs with a logarithmic shape (WRF-KOK, TEGEN, CHIMERE) may be more accurate than those with CDFs with cumulative frequencies that increase gradually for small emission frequencies (WRF-GOCART, CESM). We again note that we have scaled the models' annual cumulative North African dust emission to 4,500 Tg. However, we repeated the analysis after scaling the output to 1,000 Tg, obtaining similar results and identical conclusions about the model differences and agreement with SEVIRI.

Emitted Mass Flux

We next examine intra-model differences in the spatial structures of annual dust emission rates. For the purpose of an intra-model comparison of emission the value of the scaling factor has no effect on the results and their interpretation. All but the CESM model (Fig 3) show the Bodélé depression as having the highest annual emission rates within North Africa, which range from 1,200 (WRF-GOCART) to 7,000 (CHIMERE) g m -2 (Fig 3). WRF-KOK has two additional locations where emission is comparable to that of the Bodélé, in the lee of the Atlas Mountains (32°N & 5°W) and along the southernmost stretch of the western coast of the Red Sea (17°N & 37°E). In WRF-GOCART the region spanning 25°-30°N and 0°E and westward to the coastline, nominally characterized by the Saharan Heat Low during summer (Lavaysse et al. 2009), has an annual emission rate that is similar in magnitude to the Bodélé. The TEGEN and both WRF models show northeast Libya (30°N & 20°E) as being a region of high emission, but having annual emission rates that are still smaller than those for the Bodélé. We again note that differences in emission in the WRF models are due to dust emission schemes since the wind fields in each are identical.

Uniquely, the CESM model has an emission maximum in northeast Libya (30°N & 20°E), where the annual emission of approximately 4,000 g m -2 is nearly twice the value the model has within the Bodélé. In CLM there are local maxima in emission near the Northern Africa coast east of 10°E, but the regional maximum in emission is along the West African coastline between 25° and 30°N, and emission within the Bodélé is only a local maximum but is still far smaller than emission in the model hotspots to the north and west. Thus, while application of the source function in CESM produces a suspect dust maximum along the northern African coastline, it also increases the amount of dust emitted from the Bodélé, bring the CESM more in-line with the other models. It is interesting to note that the pattern of emission from CLM in the northwestern sector of the domain is very similar to the emission pattern from TEGEN, and that application of the source function moves this coastal emission further inland, which is more similar to the output from WRF-GOCART.

We also calculated CDFs of the models' annual emission (Fig 3) . The models' emission CDFs all have a logarithmic shape, indicating a small number of regions that dominate the total mass flux off of North Africa. Only WRF-GOCART shows a slightly distinct emission CDF, with a more gradual increase in the cumulative frequency than the other models. For example, for WRF-GOCART emission from 40% of the land surface area contributes 80% of the total dust flux, whereas for all the other models, 80% of the total dust flux is emitted from less than 10% of the North Africa land surface area.

Functional Relationship Between Frequency and Emission

We quantify the relationship between dust emission frequency and the emitted mass by estimating the dependency of emission on frequency using modeled annual mean emission frequency (Fig 1) and total annual emission (Fig 3). For most of the models the vast majority of the land surface area contributes very little to the total annual emission (Fig 3). To provide a better fit to the frequency and emission data we exclude grid cells having event frequencies that are less than 5% of each model's maximum North African event frequency; in the models these regions of low event frequency cumulatively contribute less than 1% to the total annual North African dust emission.

Scatterplots (Fig 4) of dust emission (E) as a function of emission frequency (݂)

suggest that the relationship between the two follows a power law,

ܧሺ݂ሻ ൌ ߙ݂ ఉ ,
(1 where ߙ and ߚ are coefficients of the power law fit. The r-squared (r 2 ) and root mean squared error (RMSE) of the fit is summarized for each model in Table 2, based on a annual emission amount of 4500 Tg. All of the r 2 values are statistically significant. The r 2 values for TEGEN, CHIMERE, WRF-GOCART and CLM are all greater than or equal to 0.60 (Table 2), while that for WRF-KOK is 0.42 and for CESM if 0.27. The RMSEs for the TEGEN, CLM and two WRF models are less than 10 Tg, the RMSE for CHIMERE is approximately 12 Tg, and that for the CESM is nearly a factor of three larger than that at 28 Tg. As the fit metrics for CLM are far better than for CESM we opt to continue the study only using dust emission data from CLM and do not provide further analysis of dust from CESM (CAM).

One feature of a power law relationship is scale invariance. As annual North African dust emission is the sum over all E, if we scale emission frequency by the constant ܿ,

∑ ܧሺ݂ܿሻ ൌ ܿ ఉ ∑ ܧሺ݂ሻ, or ∑ ܧሺ݂ܿሻ ן ∑ ߙ݂ ఉ .
Thus the functional dependency of dust emission upon emission frequency scales linearly with ߙ for different amounts of total emission, while ߚ remains unchanged. We examine scale invariance in the relationship between dust emission and emission frequency by calculating the coefficients in (1) after scaling the 3-hourly model emission data so that the total North African dust emission is 500 to 4500 Tg, in increments of 500 Tg. All of the models except WRF-GOCART exhibit power-law behavior in that ߙ increases linearly ( The coefficients (1) for WRF-GOCART do not exhibit power-law behavior in that ߙ is approximately constant (Fig 4a) and ߚ increases nearly linearly (Fig 5b) for increasing North African dust emission. Additional analysis did not elucidate the cause of the lack of a robust power law relationship, nor show a clear alternative functional relationship between dust emission and emission frequency for WRF-GOCART. As such, we do not consider WRF-GOCART further for the purposes of estimating dust emission via the SEVIRI emission frequency data.

The multi-model mean values, which do not include WRF-GOCART, of ߙ and ߚ are plotted as black lines in Figure 5. Given the scale invariance of the power law relation we report the coefficients in (1) and their 95% confidence intervals for each of the models at the arbitrarily chosen total emission amount of 2500 Tg (Table 3). We also report the multi model mean values for these coefficients and their uncertainty (Table 3), which in this case is defined as the standard deviation of the model coefficients divided by the square root of one minus the number of models used to calculate the means (Wilks, 2006).

Emission Estimate from SEVIRI

Having derived empirical relationships between dust emission frequency and dust emission we next estimate a total North African mass flux based on the observational SEVIRI estimates of dust emission event frequency (Fig 1). We calculate dust emission from the SEVIRI emission frequency data via (1) using the multi-model mean coefficient values in Table 3. The main value of these SEVIRIemission maps is to elucidate the spatial structure of emission across West Africa using observations. To emphasize this point we present the resultant SEVIRI emission data in units of percentage contribution to the total North African dust flux (Fig 6a). As such, these maps can be converted to an annual dust emission rate by multiplying the percentages by an estimate of the total annual North African dust emission. We also show the uncertainty in the SEVIRI emission estimates (Fig 6b ), which is based on the uncertainty in the multimodel mean coefficients (Table 3).

Not surprisingly, the pattern of emission from SEVIRI (Fig 6a) is similar in spatial structure to the SEVIRI emission frequency data (Fig 1), except that two important hotspots emerge in the emission maps. The strongest of these hotspots is the Bodélé depression, which we define as encompassing the region of 14°-21°N and 14°-24°E (easternmost green box in Figs 6ab). In both maps the Bodélé is the region of the highest dust emission amounts (percentages) within North Africa, consistent with a number of studies suggesting as much (e.g., [START_REF] Washington | Dust-storm source areas determined by the Total Ozone Monitoring Spectrometer and surface observations[END_REF]. The cumulative emission from the Bodélé is 64% (± 16%) of the total North Africa dust emission. Arguably the second most active region of dust emission is from a depression in the lee of the Aïr and Hoggar Mountains, which we define as encompassing the region of 16°-24°N and 2°-10°E (westernmost green box in Figs 6ab). The cumulative emission from the Aïr and Hoggar region is 13% (± 3%).

Based on the SEVIRI maps dust emission from the Bodélé and Aïr and Hoggar hotspots constitutes 77% (± 19%) of the total North African dust emission. Furthermore, from both maps the majority of all North African dust is emitted between the latitudes of 15°-20°N; the cumulative emission from the longitudinal segment spanning 15°-20°N is 82% (± 10%) of the total. Thus, according to the SEVIRI estimates, the overwhelming amount of all dust from North Africa is emitted within a 5° band of latitudes.

In comparison with emission from the models considered here (Fig 3) the SEVIRI emission estimates (Figs 6a) are most similar in spatial structure to that from the CHIMERE model. In both the SEVIRI and CHIMERE maps emission is dominated by the Bodélé. In the CHIMERE map there is a secondary maximum in emission to the west of the Bodélé that is similar is structure to the emission from the Aïr and Hoggar region. Additionally, the CHIMERE and the SEVIRI emission maps show little emission north of 25°N, whereas all of the other models exhibit substantial emission north of this latitude. However, other work has shown that the SEVIRI maps likely underestimate the frequency of emission across Northern Africa [START_REF] Brindley | A critical evaluation of the ability of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) thermal infrared red-green-blue rendering to identify dust events: Theoretical analysis[END_REF]. Thus the SEVIRI record may underestimate the emission frequency, which would in-turn result in a too strong an emphasis on emission equatorward of 20°N.

Discussion

One item not addressed thus far is the fundamental cause of the power law relationship between dust emission and dust emission frequency in the models (1). We speculate that the power law relation represents a good fit to the dust emission and emission frequency data because the sample populations (the models' distributions of emission and emission frequency) are approximately lognormal, or are characterized as having long tails.

Dust emission is though to be proportional to the cube of wind speed (e.g., [START_REF] Marticorena | Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme[END_REF]. Indeed, among the four model exhibiting a power law relationship between dust emission and emission frequency (TEGEN, WRF-KOK, CHIMERE, CLM) we find a positive and statistically significant correlation between annual mean emission and the cube of the annual mean 10m wind speed, where the correlation coefficients range from 0.49 to 0.52, with p-values all less than 0.01. In addition, histograms of the cube of annual mean 10m wind speeds for these models show that their occurrence distributions are lognormal (not shown). As such, it is not surprising that the probability distributions of annual emission are also characterized as having long tails (Fig 7a). One can also infer the lognormal probability distributions of emission in the models as the CDFs in While the cubed 10m wind speed distribution from WRF-GOCART is also lognormal (not shown) the emission frequency probability distribution for this model increases very slowly at low frequencies and then peaks at high frequencies (Fig 7b ), which is opposite in shape to the other four models that exhibit power law behavior. This unique and opposite characteristic of the WRF-GOCART distribution is also reflected in the emission frequency CDF (Fig 2a). We suspect this lack of a lognormal probability distribution in the WRF-GOCART emission frequency is the main reason why this model fails to show a power law relationship between emission and emission frequency.

Conclusions

Here we have performed a statistical analysis of North African dust emission in five models and one satellite-based data set. We firstly showed that these five models all agree that the Bodélé depression is one of the-if not the-most active dust source in West Africa, both in terms of emission amount and frequency (Figs 1 and3). Furthermore, none of the models show dust emission south of 12°N (Fig 3). Beyond these regions, the models show widespread disagreement in the relative importance of other regions with respect to emission and frequency of events.

We examined CDFs of the frequency maps (Fig 2a). The CDFs for two models (CESM and WRF-GOCART) increased slowly for small emission frequencies and then much more rapidly at high emission event frequencies, indicating that those two models have a large number of locations that emit dust very frequently. The other four models and SEVIRI had CDFs that were more logarithmic in shape, indicating that in these models most regions infrequently emit dust, and that there are a small number of very regions with a high emission frequency. We found better agreement in CDFs of model emission; all models had a logarithmic emission CDF, indicating that emission is dominated by a small number of very active regions (Fig 2b).

We demonstrated that there is a power law relationship (1) between modeled dust emission frequency and dust emission (Fig 4) in the TEGEN, WRF-KOK, CHIMERE and CLM models (Figs 5ab). We used (1) and the coefficients in Table 3 to estimate annual mean North African dust emission based on the SEVIRI emission frequency (Figs 6ab). To the best of our knowledge this SEVIRI dust emission map represents the first observationally based estimate of the North African dust emission. Here we presented the final maps in units of percentages of total North African dust emission in order to stress that the maps are scalable to any choice of total emission. From the observation-based map of emission we showed that the majority of dust emission from North Africa is from the Bodélé Depression and a depression in the lee of the Aïr and Hoggar Mountains (green boxes, Figs 6ab), and that 82% of all dust from North Africa is emitted between the latitudes of 15°-20°N. These results have implications for studying long-term variability of North Africa dust emission and transport in that, to first order, in order to understand the effect of the environment on regional dust emission it is particularly important to quantify change (in surface wind speeds, soil moisture, etc.) over this narrow band of latitudes.

This study is limited in that the SEVIRI dust emission frequency record likely underestimates emission in regions frequently obscured by either clouds or dense layers of dust. Thus, while we think this new map of emission (Fig 6a) is an important step forward in generating an observation-based estimate of North African dust emission, there is a need to improve our understanding of the spatial distribution of emission across the continent. As the majority of North Africa is uninhabited, the likely only way to achieve such a goal is the deployment of an automated ground-based dust observational network. 2a) are cumulative distribution functions (CDFs) for the annual mean emission frequencies from models and satellite data (Figure 1). Here all data has been linearly scaled to have a maximum frequency of 1 to facilitate model intercomparison. Also shown (2b) are the CDFs of emission for the six models, again where emission has been scaled to a maximum value of one. In both plots the identify line (black dashed) is plotted for reference. 3.

Tables

Model

  . The emission CDFs (Fig 2b) exhibit greater intermodel agreement than do the CDFs of event frequency (Fig 2a)

  Fig 5a) and ߚ approximately remains constant (Fig 5b) as emission amount increases. The small increases in ߚ for CHIMERE and CLM result from the fact that the power law relationship does not hold for all values of emission frequency (Fig 3) and is valid over a range of emission frequencies.

  Fig 2b increase steeply at low emission values and change little thereafter. Similarly, the probability distributions of the TEGEN, WRF-KOK and CHIMERE models also are characterized as having long tails (Fig 7b), which is consistent with their steep rate of increase at low emission frequencies in the CDFs (Fig 2a). Although the emission frequency CDF of the CLM model does not increase steeply until scaled frequencies of 0.4 (Fig 2a), the model's probability distribution of emission frequency does still have some characteristics consistent with a long tail (Fig 7b).

Figure 2 .

 2 Figure2. Dust emission frequency cumulative distribution functions and sensitivity of emission to frequency. Plotted (2a) are cumulative distribution functions (CDFs) for the annual mean emission frequencies from models and satellite data (Figure1). Here all data has been linearly scaled to have a maximum frequency of 1 to facilitate model intercomparison. Also shown (2b) are the CDFs of emission for the six models, again where emission has been scaled to a maximum value of one. In both plots the identify line (black dashed) is plotted for reference.

Figure 3 .Figure 4 .Figure 5 .

 345 Figure 3. Annual mean emission from models and satellite imagery. Shown are maps of emission (g m -2 per year) calculated from the 3-hourly output of the six models in Figure 1.

Figure 6 .Figure 7 .

 67 Figure 6. Estimate of annual emission rates from SEVIRI. Shown are SEVIRIderived maps of annual mean dust emission from (1) using the multimodel mean coefficients in Table 3 (left) and the uncertainty in the estimate based on the standard error on the coefficients (right). The data is in units of % of annual North Africa dust emission. The green boxes outline the major dust hotspots of the Bodélé Depression (easternmost box) and a depression in the lee of the Aïr and Hoggar Mountains (westernmost box).

Table 1 :

 1 Overview of model characteristics. Shown here are models' dust emission scheme types and number of bins, meteorological forcing at model boundaries, horizontal resolution, and model output data time resolution. All dust output fields are instantaneous.

		Dust emission	Boundaries	Horiz. Resolution	Output
	TEGEN	Offline, 3 bins (Tegen et al. 2002)	ERA-Interim forecasts (Dee et al. 2011)	Western Africa 1x1 degree res	3-Hourly
	WRF-GOCART WRF-Kok	Online, 3 mode (Zhao et al. 2013) Online, 3 mode (Kok et al. 2014)	Forced everywhere with NCAR/NCEP reanalysis	60°S to 70°N and all longitudes 1x1 degree res 60°S to 70°N and 1x1 degree res all longitudes	3-Hourly 3-Hourly
	CESM	Online, 3 bins (Mahowald et al. 2006)	N/A	Global 0.94° x 1.24° res	30-Minute
		Online, 9 bins (0.039
	CHIMERE	to 40 um), saltation & sandblasting, (Menut	N/A	Global 1x1 degree res	Hourly
		et al., 2013)

Table 2 :

 2 Emission and emission frequency fit statistics. Shown are the r-squared (r 2 ) and root mean squared error (RMSE) for each model from power law fit of dust emission as a function of emission event frequency (1). All r 2 values are statistically significant at the 95% level.

	Model	α	β
	TEGEN	82.3±8.0	1.30±0.05
	WRF-KOK	227.7±57.2	1.92±0.14
	CHIMERE	107.7±13.3	3.95±0.35
	CLM	74.2±6.6	3.16±0.14
	Mean	123.0±41.1	2.58±0.69

Table 3 :

 3 Power law coefficients. Shown are the coefficients and standard error from the power law fit of the emission and emission frequency data (Eqns 1, 2). These values represent the mean coefficient values from Figure5at a total North Africa emission amount of 2500 Tg. The bold numbers represent the multi-model mean values that are used to estimate dust emission from SEVIRI. The uncertainty ranges for the multi-model mean values are defined as the standard deviation of the models' regression coefficients divided by the square root of one minus the number of models (the standard error on the mean value estimates).

Acknowledgements

The authors would like to thank the two anonymous reviewers and the associate editor for their helpful and constructive comments on an earlier version of this manuscript. This work was supported by the french Agence Nationale de la Recherche (ANR) grant ANR-10-LABX-18-01 of the national Programme Investissements d'Avenir. Funding for this work was also provided by Laboratoire d'excellence Institute Pierre Simon Laplace (L-IPSL), a grant from the "Research in Paris" programme, and National Oceanographic and Atmospheric Administration Climate Program Office grant NA11OAR4310157. S. Fiedler acknowledges the research funding by the European Research Council grant 257543. C. Zhao acknowledges support by the U.S. DOE as part of the Regional and Global Climate Modeling program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830.


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  layer i in z for  ij Hellinger-Reissner formulation(Reissner, 1950)

15

  Ehrlacher A. 1998, JNC11, Arcachon, 3, 1389-1397 For pavements (Tran et al., RILEM CP2004) (Chabot et al., BLPC2005) etc PHD 2016) (Nasser and Chabot, 2018) 2D Problem  only 1D system to solve with matrix (12,12) Numerically  Newmark method Nasser H., Chabot A., 2018. A Half-analytical Elastic Solution for 2D Analysis of Cracked Pavements. Advances in Engineering Software, 117: 107-Boundary condition on the left and right at the point of crack and so changes are made in the system Layer 2 stresses around this node are zero X The material in the first layer is at more than 30°C at 10 Hz When the material in box is same as in layer, results match. 2 (x) = s 33 x,h 1 + ( ) = s 33 x,h 2 - ( ) M&S design needs: asphalt tRoads  eRoads M4with viscoelasticity model in (Chen,2016), here discontinuities are studied between the CU & material layer A better access to interface stress field plays for a better understanding of the PB  need to use alternative modeling Prospects  Use of M4-5n 3D new development with FreeFem++ code (Nasser et al., 2018)  Partial debonding boundary conditions  Lateral loading study according to (Hammoum et al., 2010)  Thermal gradient effect studies specially for the use of top layers made with CC materials as done by (Chabot et al., 2005) d'interface nuls sur la surface décollée Idem : dédoublement des noeuds sur contour de la surface décollée à l'interface chaussée-sol M4-Boussinesq results (Tran, 2004) Example of a cracked bi-layers pavement with thermic effects in the Layer 2

Chabot & DeepPaper N°186

Base course

Sub-base course


Isabelle Hoang

Maud Ranchet

Romain Derollepot

Fabien Moreau

Laurence Paire-Ficout

Cognitive load during a walking task: a pilot study

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

References Discussion

• In young adults, objective measure showed an increased of CL in dual task walking (SW) compared to the walking task alone (W)  subjective measure of CL showed similar results • In older adults, objective measure showed no significant differences between SW and W whereas they declared increased CL in dual task  it is possible that SW condition is too difficult for older adults and that the walking task alone may already requires cognitive ressources 5 • This pilot study highlights the importance of using both subjective and neurophysiological measures, to assess CL during a walking task, particularly in older adults

Objectives

• Test the feasibility of our protocol • Explore the cognitive load during a simple vs. dual walking task in young and older adults

•

  Cognitive load (CL) is the amount of mental effort required by the participant to perform a task 1 • Dual task paradigm showed that gait is affected when individuals are asked to walk and perform a cognitive task at the same time 2 • Older adults may be more affected by dual-task walking due to an increase of CL 3 • How can we measure the CL in a walking task ? Objective measure (physiological activity) / Subjective measure (felt effort) Participants : 15 young adults (27.3 ans ± 4.9) and 15 older adults (62.1 ans ± 5.6) Task : walking task, 4 conditions -Standing (B) -Walking (W) -Subtracting while standing (S) (Number -7) -Subtracting while walking (SW) Measurements : changes in hemoglobin oxygenated in the prefrontal cortex using functional Near Infra-Red Spectroscopy (fNIRS) + NASA-TLX questionaire after each condition (W, S, and SW) Methods Results • Non-invasive optical neuroimaging technique • Based on hemodynamic response • Oxygenated and deoxygenated hemoblogin absorb light at different spectra • Indirect measurement of brain activity • Applied to a walking task What is fNIRS ?


Beatrice Bechet

Laure Beaudet

Philippe Branchu

Patrice Cannavo

Cécile Delolme

Liliane Jean-Soro

Thierry Lebeau

Cécile Le Guern

Fabienne Marseille

Christophe Schwartz

Soils in the urban critical zone -Analyse of anthropogenic pressures and current proposals to preserve soil functions and ecosystem services

 ----------------------------------------------Soils in the urban critical zone -Analyses of anthropogenic pressures and current proposals to preserve soil functions and ecosystem services et al., 2017) is moving away from the description of urban soils to a broader understanding of this compartment of urban ecosystems, including the functions and the services they provide. This approach, which complements the naturalistic description of the soil, corresponds to the concept proposed by the Millennium Ecosystems Assessment (Morel et al., 2015;Walter et al., 2015). It is considered to be relatively anthropocentric and thus favors the integration of the soil in the urban socio-ecosystem.

Considering the soil by both its pedogenesis and functioning in ecosystems induces taking into account the dynamics of this system, but raises, with regard to the literature on urban soils, the existing lack to qualify and quantify the processes of genesis and evolution, especially in relation to ongoing climate change (Baveye et al., 2016). On the other hand, the description of soil ecosystem services (regulation, provisioning, cultural services) immediately reveals the interdependence of soil biophysicochemical processes with those occurring in the hydrosphere, the atmosphere and the biosphere (Adhikari and Hartemink, 2016). In this respect, the soil plays a role of interface, but is deeply disturbed in urban areas.

The objective of the communication will to review the status of urban soil in the "urban critical zone" concept. Through methodologies and results from projects implemented in French major cities that have enabled the development of databases, we will review the classification of these atypical soils and the changes in their properties and functions. Through the definition of the services they provide, we will propose a more integrated vision of this compartment of the urban ecosystem, by specifying the forcing caused by its interface position, but also the opportunities of improvement foreseen by the development of solutions for revegetation and de-sealing. We will see how the timeframe of soil evolution in urban zones can influence the data collection of soil parameters and mapping. Are you involved in projects on soil desealing?

Challenge

  : preservation of soil functions and 'wise' uses & practices of urban soils Projects developed at various scales : function/urban project/ urban planning  Propose a procedure for evaluating services provided by urban soils at the city scale • Quantify functions of open urban soils: e.g. organic carbon storage • Propose a Decision Support System to take better account of soil resource in urban projects and build cities with a high level of ecosystem services © Authors. All rights reserved Organic carbon contents in urban soils are equal or higher than in agricultural and forest soils • Assess the potential of soils on the basis Hartemink AE (2016) Linking soils to ecosystem services -A globalreview,Geoderma,262, 101-111 Baveye P.C, Baveye J., Gowdy J., 2016. Soil "ecosystem" services and natural capital : critical appraisal of research on uncertain ground, Frontiers in environmental science, vol.4, article 41. Levin M. J.; John Kim K-H; Morel J L; Burghardt W; Charzynski P; Shaw R K. (2017) Soils within Cities -Global approaches to their sustainable management -composition, properties, and functions of soils of the urban environment, Catena-Schweizerbart, 253 pages Morel JL, Chenu C, Lorenz K (2015) Ecosystem services provided by soils of urban, industrial, traffic, mining and military areas (SUITMAs). J Soils Sediments 15:1659-1666. Walter C., Bispo A., Chenu C., Langlais-Hesse A. et Schwartz C., 2015. Les services écosystémiques des sols : du concept à la valorisation. Cahier Demeter n°15 Agriculture et foncier. Some questions we would like to address 1. Do you address the issue of urban soil in your projects on the critical zone and how (naturalistic description, functioning, soil and society (nature in the city), soil and health)? 2. Urban soils are highly heterogeneous. How do you deal with this in terms of sampling and very high spatial heterogeneity? 3. Which typology of urban soils do you use (WRB typology or use of typologies more adapted to your needs (more operational or according to the scales of approach)? Do you develop databases on urban soils and associated mapping (which methodology for the definition of soil units?)? 4. What soil functions and/or ecosystem services provided by soils are you working on and at what scale? Examples : urban soil and climate change ; infiltration and storage of water; CO2 storage; soil support of vegetation; peri(urban)soil and healthy food Ref : 5. Do you observe a confrontation of functions with uses and practices by urban actors? (cf also chat SSF 8.2 May8th 16.15 MUSE project) What are soil surveys for? (SUITMA10 conference 2019, Marseille et al., Towards taking into account the multifunctionality of soils in French urban planning regulation -Perspective of territorial stakeholders) © Authors. All rights reserved 6. Do you deal with de-sealing and renaturation issues in your research project? On which aspects?

  B. Béchet 1 , L. Beaudet-Vidal 1 , L. Boithias 2 , P. Branchu 2 , P. Cannavo 1 , C. Delolme 3 , L. Jean-Soro 1 , T. Lebeau 1 , C. Le Guern 1 , F. Marseille 2 , C. Schwartz 4 1 IRSTV Nantes, F (corresponding authorbeatrice.bechet@univ-eiffel.fr), 2 CEREMA Paris -Lyon, F, 3 Univ.Gustave Eiffel Marne-la-Vallée, F,

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Univ. Lorraine-INRAE Nancy, F By 2017, the book "Soils within cities" (Levin

© Authors. All rights reserved